Parameter estimation of an aeroelastic aircraft using neural networks

Indian Academy of Sciences (India)

Many proposed model reduction procedures rely on numerical techniques andaor ... The capacity to act as general function approximator presents FFNNs as an alternative tool ... This paper investigates the aerodynamic modelling of an aeroelastic aircraft using ... the learning (training) process ± backpropagation of error.

Longitudinal control of aircraft dynamics based on optimization of PID parameters

Science.gov (United States)

Deepa, S. N.; Sudha, G.

2016-03-01

Recent years many flight control systems and industries are employing PID controllers to improve the dynamic behavior of the characteristics. In this paper, PID controller is developed to improve the stability and performance of general aviation aircraft system. Designing the optimum PID controller parameters for a pitch control aircraft is important in expanding the flight safety envelope. Mathematical model is developed to describe the longitudinal pitch control of an aircraft. The PID controller is designed based on the dynamic modeling of an aircraft system. Different tuning methods namely Zeigler-Nichols method (ZN), Modified Zeigler-Nichols method, Tyreus-Luyben tuning, Astrom-Hagglund tuning methods are employed. The time domain specifications of different tuning methods are compared to obtain the optimum parameters value. The results prove that PID controller tuned by Zeigler-Nichols for aircraft pitch control dynamics is better in stability and performance in all conditions. Future research work of obtaining optimum PID controller parameters using artificial intelligence techniques should be carried out.

The Accuracy of Parameter Estimation in System Identification of Noisy Aircraft Load Measurement. Ph.D. Thesis

Science.gov (United States)

Kong, Jeffrey

1994-01-01

This thesis focuses on the subject of the accuracy of parameter estimation and system identification techniques. Motivated by a complicated load measurement from NASA Dryden Flight Research Center, advanced system identification techniques are needed. The objective of this problem is to accurately predict the load experienced by the aircraft wing structure during flight determined from a set of calibrated load and gage response relationship. We can then model the problem as a black box input-output system identification from which the system parameter has to be estimated. Traditional LS (Least Square) techniques and the issues of noisy data and model accuracy are addressed. A statistical bound reflecting the change in residual is derived in order to understand the effects of the perturbations on the data. Due to the intrinsic nature of the LS problem, LS solution faces the dilemma of the trade off between model accuracy and noise sensitivity. A method of conflicting performance indices is presented, thus allowing us to improve the noise sensitivity while at the same time configuring the degredation of the model accuracy. SVD techniques for data reduction are studied and the equivalence of the Correspondence Analysis (CA) and Total Least Squares Criteria are proved. We also looked at nonlinear LS problems with NASA F-111 data set as an example. Conventional methods are neither easily applicable nor suitable for the specific load problem since the exact model of the system is unknown. Neural Network (NN) does not require prior information on the model of the system. This robustness motivated us to apply the NN techniques on our load problem. Simulation results for the NN methods used in both the single load and the 'warning signal' problems are both useful and encouraging. The performance of the NN (for single load estimate) is better than the LS approach, whereas no conventional approach was tried for the 'warning signals' problems. The NN design methodology is also

Parameter Estimation

DEFF Research Database (Denmark)

Sales-Cruz, Mauricio; Heitzig, Martina; Cameron, Ian

2011-01-01

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......In this chapter the importance of parameter estimation in model development is illustrated through various applications related to reaction systems. In particular, rate constants in a reaction system are obtained through parameter estimation methods. These approaches often require the application...... of algebraic equations as the basis for parameter estimation.These approaches are illustrated using estimations of kinetic constants from reaction system models....

Method of test signal design for estimating the aircraft aerodynamic parameters

Science.gov (United States)

Belokon', S. A.; Zolotukhin, Yu. N.; Filippov, M. N.

2017-07-01

A method of test signal design is proposed for studying the aircraft aerodynamic characteristics with the use of the technology of dynamically scaled free-flight models. Simultaneous excitation of all input channels in a prescribed frequency band by a set of mutually orthogonal signals is applied to increase the efficiency. A modified method of calculating the set of mutually orthogonal sinusoidal signals with a small normalized peak factor is presented. Results of simulating the aircraft motion in the MATLAB/Simulink environment with the use of the developed method of test signal design are reported.

Real Time Predictive Flutter Analysis and Continuous Parameter Identification of Accelerating Aircraft

National Research Council Canada - National Science Library

Farhat, Charles

1998-01-01

... Parameter Identification of Accelerating Aircraft. Flutter clearance, which is part of any new aircraft or fighter weapon system development, is a lengthy and tedious process from both computational and flight testing viewpoint...

Residual life estimation of cracked aircraft structural components

OpenAIRE

Maksimović, Mirko S.; Vasović, Ivana V.; Maksimović, Katarina S.; Trišović, Nataša; Maksimović, Stevan M.

2018-01-01

The subject of this investigation is focused on developing computation procedure for strength analysis of damaged aircraft structural components with respect to fatigue and fracture mechanics. For that purpose, here will be defined computation procedures for residual life estimation of aircraft structural components such as wing skin and attachment lugs under cyclic loads of constant amplitude and load spectrum. A special aspect of this investigation is based on using of the Strain Energy Den...

State Estimation for Landing Maneuver on High Performance Aircraft

Science.gov (United States)

Suresh, P. S.; Sura, Niranjan K.; Shankar, K.

2018-01-01

State estimation methods are popular means for validating aerodynamic database on aircraft flight maneuver performance characteristics. In this work, the state estimation method during landing maneuver is explored for the first of its kind, using upper diagonal adaptive extended Kalman filter (UD-AEKF) with fuzzy based adaptive tunning of process noise matrix. The mathematical model for symmetrical landing maneuver consists of non-linear flight mechanics equation representing Aircraft longitudinal dynamics. The UD-AEKF algorithm is implemented in MATLAB environment and the states with bias is considered to be the initial conditions just prior to the flare. The measurement data is obtained from a non-linear 6 DOF pilot in loop simulation using FORTRAN. These simulated measurement data is additively mixed with process and measurement noises, which are used as an input for UD-AEKF. Then, the governing states that dictate the landing loads at the instant of touch down are compared. The method is verified using flight data wherein, the vertical acceleration at the aircraft center of gravity (CG) is compared. Two possible outcome of purely relying on the aircraft measured data is highlighted. It is observed that, with the implementation of adaptive fuzzy logic based extended Kalman filter tuned to adapt for aircraft landing dynamics, the methodology improves the data quality of the states that are sourced from noisy measurements.

Parameterized Flight Mission for Secondary Power Requirement Estimations of Commercial Transport Aircraft

OpenAIRE

Lampl, Thomas; Muschkorgel, Sandra; Hornung, Mirko;

2018-01-01

The trend towards More-Electric Aircraft (MEA) and the introduction of new system technologies lead to considerable changes at the system level of commercial transport aircraft. Because the number of systems and power requirements are increasing, the consideration and integration of aircraft systems in early aircraft design phases is important. The objective of this contribution is to develop a characteristic flight mission with modelled aircraft systems to estimate the secondary power requir...

Support Resources Demand Parameters - Aircraft. Revision A

Science.gov (United States)

1980-01-15

Maintenance Squadron A- ST Advance Medium STOL Transport APU Auxillary Power Unit ASSY Assembly ATC Air Training Command AVG Average BAC Boeing Aerospace...entire study that will result in an organized and prioritized body of decision criteria and parameters that may be used by logistics managers, supervisors...technicians, and other decision makers in the process of predicting resource demand rates for operational and new emerging aircraft weapon systems

Development of a probabilistic safety assessment framework for an interim dry storage facility subjected to an aircraft crash using best-estimate structural analysis

International Nuclear Information System (INIS)

Almomani, Belal; Jang, Dong Chan; Lee, Sang Hoon; Kang, Hyun Gook

2017-01-01

Using a probabilistic safety assessment, a risk evaluation framework for an aircraft crash into an interim spent fuel storage facility is presented. Damage evaluation of a detailed generic cask model in a simplified building structure under an aircraft impact is discussed through a numerical structural analysis and an analytical fragility assessment. Sequences of the impact scenario are shown in a developed event tree, with uncertainties considered in the impact analysis and failure probabilities calculated. To evaluate the influence of parameters relevant to design safety, risks are estimated for three specification levels of cask and storage facility structures. The proposed assessment procedure includes the determination of the loading parameters, reference impact scenario, structural response analyses of facility walls, cask containment, and fuel assemblies, and a radiological consequence analysis with dose–risk estimation. The risk results for the proposed scenario in this study are expected to be small relative to those of design basis accidents for best-estimated conservative values. The importance of this framework is seen in its flexibility to evaluate the capability of the facility to withstand an aircraft impact and in its ability to anticipate potential realistic risks; the framework also provides insight into epistemic uncertainty in the available data and into the sensitivity of the design parameters for future research

Development of a Probabilistic Safety Assessment Framework for an Interim Dry Storage Facility Subjected to an Aircraft Crash Using Best-Estimate Structural Analysis

Directory of Open Access Journals (Sweden)

Belal Almomani

2017-03-01

Full Text Available Using a probabilistic safety assessment, a risk evaluation framework for an aircraft crash into an interim spent fuel storage facility is presented. Damage evaluation of a detailed generic cask model in a simplified building structure under an aircraft impact is discussed through a numerical structural analysis and an analytical fragility assessment. Sequences of the impact scenario are shown in a developed event tree, with uncertainties considered in the impact analysis and failure probabilities calculated. To evaluate the influence of parameters relevant to design safety, risks are estimated for three specification levels of cask and storage facility structures. The proposed assessment procedure includes the determination of the loading parameters, reference impact scenario, structural response analyses of facility walls, cask containment, and fuel assemblies, and a radiological consequence analysis with dose–risk estimation. The risk results for the proposed scenario in this study are expected to be small relative to those of design basis accidents for best-estimated conservative values. The importance of this framework is seen in its flexibility to evaluate the capability of the facility to withstand an aircraft impact and in its ability to anticipate potential realistic risks; the framework also provides insight into epistemic uncertainty in the available data and into the sensitivity of the design parameters for future research.

Development of a probabilistic safety assessment framework for an interim dry storage facility subjected to an aircraft crash using best-estimate structural analysis

Energy Technology Data Exchange (ETDEWEB)

Almomani, Belal; Jang, Dong Chan [Dept. of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of); Lee, Sang Hoon [Dept. of Mechanical and Automotive Engineering, Keimyung University, Daegu (Korea, Republic of); Kang, Hyun Gook [Dept. of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy (United States)

2017-03-15

Using a probabilistic safety assessment, a risk evaluation framework for an aircraft crash into an interim spent fuel storage facility is presented. Damage evaluation of a detailed generic cask model in a simplified building structure under an aircraft impact is discussed through a numerical structural analysis and an analytical fragility assessment. Sequences of the impact scenario are shown in a developed event tree, with uncertainties considered in the impact analysis and failure probabilities calculated. To evaluate the influence of parameters relevant to design safety, risks are estimated for three specification levels of cask and storage facility structures. The proposed assessment procedure includes the determination of the loading parameters, reference impact scenario, structural response analyses of facility walls, cask containment, and fuel assemblies, and a radiological consequence analysis with dose–risk estimation. The risk results for the proposed scenario in this study are expected to be small relative to those of design basis accidents for best-estimated conservative values. The importance of this framework is seen in its flexibility to evaluate the capability of the facility to withstand an aircraft impact and in its ability to anticipate potential realistic risks; the framework also provides insight into epistemic uncertainty in the available data and into the sensitivity of the design parameters for future research.

Real-Time Radar-Based Tracking and State Estimation of Multiple Non-Conformant Aircraft

Science.gov (United States)

Cook, Brandon; Arnett, Timothy; Macmann, Owen; Kumar, Manish

2017-01-01

In this study, a novel solution for automated tracking of multiple unknown aircraft is proposed. Many current methods use transponders to self-report state information and augment track identification. While conformant aircraft typically report transponder information to alert surrounding aircraft of its state, vehicles may exist in the airspace that are non-compliant and need to be accurately tracked using alternative methods. In this study, a multi-agent tracking solution is presented that solely utilizes primary surveillance radar data to estimate aircraft state information. Main research challenges include state estimation, track management, data association, and establishing persistent track validity. In an effort to realize these challenges, techniques such as Maximum a Posteriori estimation, Kalman filtering, degree of membership data association, and Nearest Neighbor Spanning Tree clustering are implemented for this application.

Optomechanical parameter estimation

International Nuclear Information System (INIS)

Ang, Shan Zheng; Tsang, Mankei; Harris, Glen I; Bowen, Warwick P

2013-01-01

We propose a statistical framework for the problem of parameter estimation from a noisy optomechanical system. The Cramér–Rao lower bound on the estimation errors in the long-time limit is derived and compared with the errors of radiometer and expectation–maximization (EM) algorithms in the estimation of the force noise power. When applied to experimental data, the EM estimator is found to have the lowest error and follow the Cramér–Rao bound most closely. Our analytic results are envisioned to be valuable to optomechanical experiment design, while the EM algorithm, with its ability to estimate most of the system parameters, is envisioned to be useful for optomechanical sensing, atomic magnetometry and fundamental tests of quantum mechanics. (paper)

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

Science.gov (United States)

Simon, Donald L.; Rinehart, Aidan W.

2016-01-01

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Real-Time Aerodynamic Parameter Estimation without Air Flow Angle Measurements

Science.gov (United States)

Morelli, Eugene A.

2010-01-01

A technique for estimating aerodynamic parameters in real time from flight data without air flow angle measurements is described and demonstrated. The method is applied to simulated F-16 data, and to flight data from a subscale jet transport aircraft. Modeling results obtained with the new approach using flight data without air flow angle measurements were compared to modeling results computed conventionally using flight data that included air flow angle measurements. Comparisons demonstrated that the new technique can provide accurate aerodynamic modeling results without air flow angle measurements, which are often difficult and expensive to obtain. Implications for efficient flight testing and flight safety are discussed.

The potential performance of microwave remote sensing for the estimation of stratospheric aircraft effect on ozone layer

Energy Technology Data Exchange (ETDEWEB)

Kadygrov, E.; Sorokin, M.; Troitsky, A. [Central Aerological Observatory, Moscow (Russian Federation)

1997-12-31

A remote sensing capability is described for measurement of temperature fluctuation and some important gas species concentration at the wake vortex and wake dispersion regimes behind the supersonic aircraft at cruise altitude. The proposed new method of observation is based on the measurement of radio-brightness contrast between the ambient atmosphere and perturbed area behind the aircraft by using millimeter or submillimeter wave scanning spectroradiometers with specially selected spectral parameters. The qualitative estimation of the sensitivity of measurement to temperature fluctuation, changing concentration of ozone, water vapour, nitrogen oxide and sulfur dioxide were calculated. The preliminary test of a new equipment were conducted from high-altitude balloon (temperature profiles and fluctuation and ozone concentrations) and from the ground (sulfur dioxide relative concentration) measurement. (author) 9 refs.

The potential performance of microwave remote sensing for the estimation of stratospheric aircraft effect on ozone layer

Energy Technology Data Exchange (ETDEWEB)

Kadygrov, E; Sorokin, M; Troitsky, A [Central Aerological Observatory, Moscow (Russian Federation)

1998-12-31

A remote sensing capability is described for measurement of temperature fluctuation and some important gas species concentration at the wake vortex and wake dispersion regimes behind the supersonic aircraft at cruise altitude. The proposed new method of observation is based on the measurement of radio-brightness contrast between the ambient atmosphere and perturbed area behind the aircraft by using millimeter or submillimeter wave scanning spectroradiometers with specially selected spectral parameters. The qualitative estimation of the sensitivity of measurement to temperature fluctuation, changing concentration of ozone, water vapour, nitrogen oxide and sulfur dioxide were calculated. The preliminary test of a new equipment were conducted from high-altitude balloon (temperature profiles and fluctuation and ozone concentrations) and from the ground (sulfur dioxide relative concentration) measurement. (author) 9 refs.

Estimation of dynamic rotor loads for the rotor systems research aircraft: Methodology development and validation

Science.gov (United States)

Duval, R. W.; Bahrami, M.

1985-01-01

The Rotor Systems Research Aircraft uses load cells to isolate the rotor/transmission systm from the fuselage. A mathematical model relating applied rotor loads and inertial loads of the rotor/transmission system to the load cell response is required to allow the load cells to be used to estimate rotor loads from flight data. Such a model is derived analytically by applying a force and moment balance to the isolated rotor/transmission system. The model is tested by comparing its estimated values of applied rotor loads with measured values obtained from a ground based shake test. Discrepancies in the comparison are used to isolate sources of unmodeled external loads. Once the structure of the mathematical model has been validated by comparison with experimental data, the parameters must be identified. Since the parameters may vary with flight condition it is desirable to identify the parameters directly from the flight data. A Maximum Likelihood identification algorithm is derived for this purpose and tested using a computer simulation of load cell data. The identification is found to converge within 10 samples. The rapid convergence facilitates tracking of time varying parameters of the load cell model in flight.

Aircraft nonlinear stability analysis and multidimensional stability region estimation under icing conditions

Directory of Open Access Journals (Sweden)

Liang QU

2017-06-01

Full Text Available Icing is one of the crucial factors that could pose great threat to flight safety, and thus research on stability and stability region of aircraft safety under icing conditions is significant for control and flight. Nonlinear dynamical equations and models of aerodynamic coefficients of an aircraft are set up in this paper to study the stability and stability region of the aircraft under an icing condition. Firstly, the equilibrium points of the iced aircraft system are calculated and analyzed based on the theory of differential equation stability. Secondly, according to the correlation theory about equilibrium points and the stability region, this paper estimates the multidimensional stability region of the aircraft, based on which the stability regions before and after icing are compared. Finally, the results are confirmed by the time history analysis. The results can give a reference for stability analysis and envelope protection of the nonlinear system of an iced aircraft.

Applied parameter estimation for chemical engineers

CERN Document Server

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

Improved Estimates of Thermodynamic Parameters

Science.gov (United States)

Lawson, D. D.

1982-01-01

Techniques refined for estimating heat of vaporization and other parameters from molecular structure. Using parabolic equation with three adjustable parameters, heat of vaporization can be used to estimate boiling point, and vice versa. Boiling points and vapor pressures for some nonpolar liquids were estimated by improved method and compared with previously reported values. Technique for estimating thermodynamic parameters should make it easier for engineers to choose among candidate heat-exchange fluids for thermochemical cycles.

An Adaptive Nonlinear Aircraft Maneuvering Envelope Estimation Approach for Online Applications

Science.gov (United States)

Schuet, Stefan R.; Lombaerts, Thomas Jan; Acosta, Diana; Wheeler, Kevin; Kaneshige, John

2014-01-01

A nonlinear aircraft model is presented and used to develop an overall unified robust and adaptive approach to passive trim and maneuverability envelope estimation with uncertainty quantification. The concept of time scale separation makes this method suitable for the online characterization of altered safe maneuvering limitations after impairment. The results can be used to provide pilot feedback and/or be combined with flight planning, trajectory generation, and guidance algorithms to help maintain safe aircraft operations in both nominal and off-nominal scenarios.

Aircraft Aerodynamic Parameter Detection Using Micro Hot-Film Flow Sensor Array and BP Neural Network Identification

Directory of Open Access Journals (Sweden)

Ruiyi Que

2012-08-01

Full Text Available Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft. For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed. A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters. Two different sensor arrangements are tested in wind tunnel experiments and dependence of the system performance on the sensor arrangement is analyzed.

The High Level Mathematical Models in Calculating Aircraft Gas Turbine Engine Parameters

Directory of Open Access Journals (Sweden)

Yu. A. Ezrokhi

2017-01-01

Full Text Available The article describes high-level mathematical models developed to solve special problems arising at later stages of design with regard to calculation of the aircraft gas turbine engine (GTE under real operating conditions. The use of blade row mathematics models, as well as mathematical models of a higher level, including 2D and 3D description of the working process in the engine units and components, makes it possible to determine parameters and characteristics of the aircraft engine under conditions significantly different from the calculated ones.The paper considers application of mathematical modelling methods (MMM for solving a wide range of practical problems, such as forcing the engine by injection of water into the flowing part, estimate of the thermal instability effect on the GTE characteristics, simulation of engine start-up and windmill starting condition, etc. It shows that the MMM use, when optimizing the laws of the compressor stator control, as well as supplying cooling air to the hot turbine components in the motor system, can significantly improve the integral traction and economic characteristics of the engine in terms of its gas-dynamic stability, reliability and resource.It ought to bear in mind that blade row mathematical models of the engine are designed to solve purely "motor" problems and do not replace the existing models of various complexity levels used in calculation and design of compressors and turbines, because in “quality” a description of the working processes in these units is inevitably inferior to such specialized models.It is shown that the choice of the mathematical modelling level of an aircraft engine for solving a particular problem arising in its designing and computational study is to a large extent a compromise problem. Despite the significantly higher "resolution" and information ability the motor mathematical models containing 2D and 3D approaches to the calculation of flow in blade machine

Parameter estimation in plasmonic QED

Science.gov (United States)

Jahromi, H. Rangani

2018-03-01

We address the problem of parameter estimation in the presence of plasmonic modes manipulating emitted light via the localized surface plasmons in a plasmonic waveguide at the nanoscale. The emitter that we discuss is the nitrogen vacancy centre (NVC) in diamond modelled as a qubit. Our goal is to estimate the β factor measuring the fraction of emitted energy captured by waveguide surface plasmons. The best strategy to obtain the most accurate estimation of the parameter, in terms of the initial state of the probes and different control parameters, is investigated. In particular, for two-qubit estimation, it is found although we may achieve the best estimation at initial instants by using the maximally entangled initial states, at long times, the optimal estimation occurs when the initial state of the probes is a product one. We also find that decreasing the interqubit distance or increasing the propagation length of the plasmons improve the precision of the estimation. Moreover, decrease of spontaneous emission rate of the NVCs retards the quantum Fisher information (QFI) reduction and therefore the vanishing of the QFI, measuring the precision of the estimation, is delayed. In addition, if the phase parameter of the initial state of the two NVCs is equal to πrad, the best estimation with the two-qubit system is achieved when initially the NVCs are maximally entangled. Besides, the one-qubit estimation has been also analysed in detail. Especially, we show that, using a two-qubit probe, at any arbitrary time, enhances considerably the precision of estimation in comparison with one-qubit estimation.

Parameter Estimation in Continuous Time Domain

Directory of Open Access Journals (Sweden)

Gabriela M. ATANASIU

2016-12-01

Full Text Available This paper will aim to presents the applications of a continuous-time parameter estimation method for estimating structural parameters of a real bridge structure. For the purpose of illustrating this method two case studies of a bridge pile located in a highly seismic risk area are considered, for which the structural parameters for the mass, damping and stiffness are estimated. The estimation process is followed by the validation of the analytical results and comparison with them to the measurement data. Further benefits and applications for the continuous-time parameter estimation method in civil engineering are presented in the final part of this paper.

In-flight dose estimates for aircraft crew and pregnant female crew members in military transport missions

International Nuclear Information System (INIS)

Alves, J. G.; Mairos, J. C.

2007-01-01

Aircraft fighter pilots may experience risks other than the exposure to cosmic radiation due to the characteristics of a typical fighter flight. The combined risks for fighter pilots due to the G-forces, hypobaric hypoxia, cosmic radiation exposure, etc. have determined that pregnant female pilots should remain on ground. However, several military transport missions can be considered an ordinary civil aircraft flight and the question arises whether a pregnant female crew member could still be part of the aircraft crew. The cosmic radiation dose received was estimated for transport missions carried out on the Hercules C-130 type of aircraft by a single air squad in 1 month. The flights departed from Lisboa to areas such as: the Azores, several countries in central and southern Africa, the eastern coast of the USA and the Balkans, and an estimate of the cosmic radiation dose received on each flight was carried out. A monthly average cosmic radiation dose to the aircraft crew was determined and the dose values obtained were discussed in relation to the limits established by the European Union Council Directive 96/29/Euratom. The cosmic radiation dose estimates were performed using the EPCARD v3.2 and the CARI-6 computing codes. EPCARD v3.2 was kindly made available by GSF-National Research Centre for Environment and Health, Inst. of Radiation Protection (Neuherberg (Germany)). CARI-6 (version July 7, 2004) was downloaded from the web site of the Civil Aerospace Medical Inst., Federal Aviation Administration (USA). In this study an estimate of the cosmic radiation dose received by military aircraft crew on typical transport missions is made. (authors)

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

Estimating Bird / Aircraft Collision Probabilities and Risk Utilizing Spatial Poisson Processes

Science.gov (United States)

2012-06-10

ESTIMATING BIRD/AIRCRAFT COLLISION PROBABILITIES AND RISK UTILIZING SPATIAL POISSON PROCESSES GRADUATE...AND RISK UTILIZING SPATIAL POISSON PROCESSES GRADUATE RESEARCH PAPER Presented to the Faculty Department of Operational Sciences...COLLISION PROBABILITIES AND RISK UTILIZING SPATIAL POISSON PROCESSES Brady J. Vaira, BS, MS Major, USAF Approved

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.

THE FEATURES OF AIRCRAFT FUNCTIONAL SYSTEMS PERFORMANCE MONITORING

Directory of Open Access Journals (Sweden)

Stanislav Alexandrovich Krotov

2017-01-01

Full Text Available The key steps of aircraft essential parameters and events monitoring during its operation are considered in the arti- cle. Conditions for specific risk monitoring are also presented.The notion of fail-safe feature of aircraft functional systems is analysed, and the necessity of continuous process of safety flight level estimate is shown. The method of quantitative assessment of key events and risks probabilities with the use of modern software is proposed. This method contains 5 basic stages: The monitoring parameters setting - this stage is initial and begins with the consideration of organization safety cul- ture, the main purposes and problems determination, the basic parameters and characteristics forming which are to be monitored. The event monitoring in operation - on this stage continuous process of key events searching and monitoring which are a thing of importance within the framework of the established problems takes place. This process is closely relat- ed to parameters monitoring set on the first stage. The event and risk estimate - this stage begins directly after the event has been discovered. The estimate pro- cess is as long as it is required to identify the event gravity. It also contains the preliminary risk estimate for using in priori- tization of initial expanded estimate and in the working out of plan for activities realization. The working out of plan for activities - on this stage correction data is determined that will make changes to aero- technics working out, operation, maintenance and to staff training directly in linkage to the problem event identified earlier. The activity carrying-out - the realization of actions according to the activity plan. This stage concludes priori- tization, planning and problem carrying-out. The dependence set between the probability of failure situations and the degree of their danger is shown. The key factors which are subject to be estimated while aircraft operating and which aim with

Velocity-Aided Attitude Estimation for Helicopter Aircraft Using Microelectromechanical System Inertial-Measurement Units

Directory of Open Access Journals (Sweden)

Sang Cheol Lee

2016-12-01

Full Text Available This paper presents an algorithm for velocity-aided attitude estimation for helicopter aircraft using a microelectromechanical system inertial-measurement unit. In general, high- performance gyroscopes are used for estimating the attitude of a helicopter, but this type of sensor is very expensive. When designing a cost-effective attitude system, attitude can be estimated by fusing a low cost accelerometer and a gyro, but the disadvantage of this method is its relatively low accuracy. The accelerometer output includes a component that occurs primarily as the aircraft turns, as well as the gravitational acceleration. When estimating attitude, the accelerometer measurement terms other than gravitational ones can be considered as disturbances. Therefore, errors increase in accordance with the flight dynamics. The proposed algorithm is designed for using velocity as an aid for high accuracy at low cost. It effectively eliminates the disturbances of accelerometer measurements using the airspeed. The algorithm was verified using helicopter experimental data. The algorithm performance was confirmed through a comparison with an attitude estimate obtained from an attitude heading reference system based on a high accuracy optic gyro, which was employed as core attitude equipment in the helicopter.

Velocity-Aided Attitude Estimation for Helicopter Aircraft Using Microelectromechanical System Inertial-Measurement Units

Science.gov (United States)

Lee, Sang Cheol; Hong, Sung Kyung

2016-01-01

This paper presents an algorithm for velocity-aided attitude estimation for helicopter aircraft using a microelectromechanical system inertial-measurement unit. In general, high- performance gyroscopes are used for estimating the attitude of a helicopter, but this type of sensor is very expensive. When designing a cost-effective attitude system, attitude can be estimated by fusing a low cost accelerometer and a gyro, but the disadvantage of this method is its relatively low accuracy. The accelerometer output includes a component that occurs primarily as the aircraft turns, as well as the gravitational acceleration. When estimating attitude, the accelerometer measurement terms other than gravitational ones can be considered as disturbances. Therefore, errors increase in accordance with the flight dynamics. The proposed algorithm is designed for using velocity as an aid for high accuracy at low cost. It effectively eliminates the disturbances of accelerometer measurements using the airspeed. The algorithm was verified using helicopter experimental data. The algorithm performance was confirmed through a comparison with an attitude estimate obtained from an attitude heading reference system based on a high accuracy optic gyro, which was employed as core attitude equipment in the helicopter. PMID:27973429

Estimation of Aerodynamic Parameters in Conditions of Measurement

Directory of Open Access Journals (Sweden)

Htang Om Moung

2017-01-01

Full Text Available The paper discusses the problem of aircraft parameter identification in conditions of measurement noises. It is assumed that all the signals involved into the process of identification are subjects to measurement noises, that is measurement random errors normally distributed. The results of simulation are presented which show the relation between the noises standard deviations and the accuracy of identification.

Estimate of aircraft crash hit frequencies on to facilities at the Lawrence Livermore National Laboratory (LLNL) Site 200

International Nuclear Information System (INIS)

Kimura, C.Y.

1997-01-01

Department of Energy (DOE) nuclear facilities are required by DOE Order 5480.23, Section 8.b.(3)(k) to consider external events as initiating events to accidents within the scope of their Safety Analysis Reports (SAR). One of the external initiating events which should be considered within the scope of a SAR is an aircraft accident, i.e., an aircraft crashing into the nuclear facility with the related impact and fire leading to penetration of the facility and to the release of radioactive and/or hazardous materials. This report presents the results of an Aircraft Crash Frequency analysis performed for the Materials Management Area (MMA), and the National Ignition Facility (NIF) at the Lawrence Livermore National Laboratory (LLNL) Site 200. The analysis estimates only the aircraft crash hit frequency on to the analyzed facilities. No initial aircraft crash hit frequency screening structural response calculations of the facilities to the aircraft impact, or consequence analysis of radioactive/hazardous materials released following the aircraft impact are performed. The method used to estimate the aircraft crash hit frequencies on to facilities at the Lawrence Livermore National Laboratory (LLNL) generally follows the procedure given by the DOE Standard 3014-96 on Aircraft Crash Analysis. However, certain adjustments were made to the DOE Standard procedure because of the site specific fight environment or because of facility specific characteristics

Multi-objective optimization in quantum parameter estimation

Science.gov (United States)

Gong, BeiLi; Cui, Wei

2018-04-01

We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.

Linear Parameter Varying Versus Linear Time Invariant Reduced Order Controller Design of Turboprop Aircraft Dynamics

Directory of Open Access Journals (Sweden)

Widowati

2012-07-01

Full Text Available The applicability of parameter varying reduced order controllers to aircraft model is proposed. The generalization of the balanced singular perturbation method of linear time invariant (LTI system is used to reduce the order of linear parameter varying (LPV system. Based on the reduced order model the low-order LPV controller is designed by using synthesis technique. The performance of the reduced order controller is examined by applying it to lateral-directional control of aircraft model having 20th order. Furthermore, the time responses of the closed loop system with reduced order LPV controllers and reduced order LTI controller is compared. From the simulation results, the 8th order LPV controller can maintain stability and to provide the same level of closed-loop systems performance as the full-order LPV controller. It is different with the reduced-order LTI controller that cannot maintain stability and performance for all allowable parameter trajectories.

Estimating Parameters for the Earth-Ionosphere Waveguide Using VLF Narrowband Transmitters

Science.gov (United States)

Gross, N. C.; Cohen, M.

2017-12-01

Estimating the D-region (60 to 90 km altitude) ionospheric electron density profile has always been a challenge. The D-region's altitude is too high for aircraft and balloons to reach but is too low for satellites to orbit at. Sounding rocket measurements have been a useful tool for directly measuring the ionosphere, however, these types of measurements are infrequent and costly. A more sustainable type of measurement, for characterizing the D-region, is remote sensing with very low frequency (VLF) waves. Both the lower ionosphere and Earth's ground strongly reflect VLF waves. These two spherical reflectors form what is known as the Earth-ionosphere waveguide. As VLF waves propagate within the waveguide, they interact with the D-region ionosphere, causing amplitude and phase changes that are polarization dependent. These changes can be monitored with a spatially distributed array of receivers and D-region properties can be inferred from these measurements. Researchers have previously used VLF remote sensing techniques, from either narrowband transmitters or sferics, to estimate the density profile, but these estimations are typically during a short time frame and over a narrow propagation region. We report on an effort to improve the understanding of VLF wave propagation by estimating the commonly known h' and beta two parameter exponential electron density profile. Measurements from multiple narrowband transmitters at multiple receivers are taken, concurrently, and input into an algorithm. The cornerstone of the algorithm is an artificial neural network (ANN), where input values are the received narrowband amplitude and phase and the outputs are the estimated h' and beta parameters. Training data for the ANN is generated using the Navy's Long-Wavelength Propagation Capability (LWPC) model. Emphasis is placed on profiling the daytime ionosphere, which has a more stable and predictable profile than the nighttime. Daytime ionospheric disturbances, from high solar

Impact of Various Parameters on the Performance of Inter-aircraft Optical Wireless Communication Link

Science.gov (United States)

Singh, Mehtab

2017-12-01

Optical wireless communication (OWC) systems also known as Free space optics (FSO) are capable of providing high channel bandwidth, high data transmission rates, low power consumption, and high security. OWC links are being considered in different applications such as inter-satellite links, terrestrial links, and inter-aircraft communication links. This paper investigates the impact of different system parameters such as transmission power level, operating wavelength, transmitter pointing error angle, bit transmission rate, atmospheric attenuation, antenna aperture diameter, geometric losses, the responsivity of the photodetector, and link range on the performance of inter-aircraft optical wireless communication link.

Dynamic systems models new methods of parameter and state estimation

CERN Document Server

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...

On parameter estimation in deformable models

DEFF Research Database (Denmark)

Fisker, Rune; Carstensen, Jens Michael

1998-01-01

Deformable templates have been intensively studied in image analysis through the last decade, but despite its significance the estimation of model parameters has received little attention. We present a method for supervised and unsupervised model parameter estimation using a general Bayesian form...

Aircraft to aircraft intercomparison during SEMAPHORE

Science.gov (United States)

Lambert, Dominique; Durand, Pierre

1998-10-01

During the Structure des Echanges Mer-Atmosphère, Propriétés des Hétérogénéités Océaniques: Recherche Expérimentale (SEMAPHORE) experiment, performed in the Azores region in 1993, two French research aircraft were simultaneously used for in situ measurements in the atmospheric boundary layer. We present the results obtained from one intercomparison flight between the two aircraft. The mean parameters generally agree well, although the temperature has to be slightly shifted in order to be in agreement for the two aircraft. A detailed comparison of the turbulence parameters revealed no bias. The agreement is good for variances and is satisfactory for fluxes and skewness. A thorough study of the errors involved in flux computation revealed that the greatest accuracy is obtained for latent heat flux. Errors in sensible heat flux are considerably greater, and the worst results are obtained for momentum flux. The latter parameter, however, is more accurate than expected from previous parameterizations.

Dependence of Dynamic Modeling Accuracy on Sensor Measurements, Mass Properties, and Aircraft Geometry

Science.gov (United States)

Grauer, Jared A.; Morelli, Eugene A.

2013-01-01

The NASA Generic Transport Model (GTM) nonlinear simulation was used to investigate the effects of errors in sensor measurements, mass properties, and aircraft geometry on the accuracy of identified parameters in mathematical models describing the flight dynamics and determined from flight data. Measurements from a typical flight condition and system identification maneuver were systematically and progressively deteriorated by introducing noise, resolution errors, and bias errors. The data were then used to estimate nondimensional stability and control derivatives within a Monte Carlo simulation. Based on these results, recommendations are provided for maximum allowable errors in sensor measurements, mass properties, and aircraft geometry to achieve desired levels of dynamic modeling accuracy. Results using additional flight conditions and parameter estimation methods, as well as a nonlinear flight simulation of the General Dynamics F-16 aircraft, were compared with these recommendations

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.

Parameter Estimation of Partial Differential Equation Models.

Science.gov (United States)

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.

Quantitative analysis of aircraft multispectral-scanner data and mapping of water-quality parameters in the James River in Virginia

Science.gov (United States)

Johnson, R. W.; Bahn, G. S.

1977-01-01

Statistical analysis techniques were applied to develop quantitative relationships between in situ river measurements and the remotely sensed data that were obtained over the James River in Virginia on 28 May 1974. The remotely sensed data were collected with a multispectral scanner and with photographs taken from an aircraft platform. Concentration differences among water quality parameters such as suspended sediment, chlorophyll a, and nutrients indicated significant spectral variations. Calibrated equations from the multiple regression analysis were used to develop maps that indicated the quantitative distributions of water quality parameters and the dispersion characteristics of a pollutant plume entering the turbid river system. Results from further analyses that use only three preselected multispectral scanner bands of data indicated that regression coefficients and standard errors of estimate were not appreciably degraded compared with results from the 10-band analysis.

Precision Parameter Estimation and Machine Learning

Science.gov (United States)

Wandelt, Benjamin D.

2008-12-01

I discuss the strategy of ``Acceleration by Parallel Precomputation and Learning'' (AP-PLe) that can vastly accelerate parameter estimation in high-dimensional parameter spaces and costly likelihood functions, using trivially parallel computing to speed up sequential exploration of parameter space. This strategy combines the power of distributed computing with machine learning and Markov-Chain Monte Carlo techniques efficiently to explore a likelihood function, posterior distribution or χ2-surface. This strategy is particularly successful in cases where computing the likelihood is costly and the number of parameters is moderate or large. We apply this technique to two central problems in cosmology: the solution of the cosmological parameter estimation problem with sufficient accuracy for the Planck data using PICo; and the detailed calculation of cosmological helium and hydrogen recombination with RICO. Since the APPLe approach is designed to be able to use massively parallel resources to speed up problems that are inherently serial, we can bring the power of distributed computing to bear on parameter estimation problems. We have demonstrated this with the CosmologyatHome project.

Data-driven fault detection, isolation and estimation of aircraft gas turbine engine actuator and sensors

Science.gov (United States)

Naderi, E.; Khorasani, K.

2018-02-01

In this work, a data-driven fault detection, isolation, and estimation (FDI&E) methodology is proposed and developed specifically for monitoring the aircraft gas turbine engine actuator and sensors. The proposed FDI&E filters are directly constructed by using only the available system I/O data at each operating point of the engine. The healthy gas turbine engine is stimulated by a sinusoidal input containing a limited number of frequencies. First, the associated system Markov parameters are estimated by using the FFT of the input and output signals to obtain the frequency response of the gas turbine engine. These data are then used for direct design and realization of the fault detection, isolation and estimation filters. Our proposed scheme therefore does not require any a priori knowledge of the system linear model or its number of poles and zeros at each operating point. We have investigated the effects of the size of the frequency response data on the performance of our proposed schemes. We have shown through comprehensive case studies simulations that desirable fault detection, isolation and estimation performance metrics defined in terms of the confusion matrix criterion can be achieved by having access to only the frequency response of the system at only a limited number of frequencies.

Reionization history and CMB parameter estimation

International Nuclear Information System (INIS)

Dizgah, Azadeh Moradinezhad; Kinney, William H.; Gnedin, Nickolay Y.

2013-01-01

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

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.

Parameter Estimation of Partial Differential Equation Models

KAUST Repository

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.

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.

A new Bayesian recursive technique for parameter estimation

Science.gov (United States)

Kaheil, Yasir H.; Gill, M. Kashif; McKee, Mac; Bastidas, Luis

2006-08-01

The performance of any model depends on how well its associated parameters are estimated. In the current application, a localized Bayesian recursive estimation (LOBARE) approach is devised for parameter estimation. The LOBARE methodology is an extension of the Bayesian recursive estimation (BARE) method. It is applied in this paper on two different types of models: an artificial intelligence (AI) model in the form of a support vector machine (SVM) application for forecasting soil moisture and a conceptual rainfall-runoff (CRR) model represented by the Sacramento soil moisture accounting (SAC-SMA) model. Support vector machines, based on statistical learning theory (SLT), represent the modeling task as a quadratic optimization problem and have already been used in various applications in hydrology. They require estimation of three parameters. SAC-SMA is a very well known model that estimates runoff. It has a 13-dimensional parameter space. In the LOBARE approach presented here, Bayesian inference is used in an iterative fashion to estimate the parameter space that will most likely enclose a best parameter set. This is done by narrowing the sampling space through updating the "parent" bounds based on their fitness. These bounds are actually the parameter sets that were selected by BARE runs on subspaces of the initial parameter space. The new approach results in faster convergence toward the optimal parameter set using minimum training/calibration data and fewer sets of parameter values. The efficacy of the localized methodology is also compared with the previously used BARE algorithm.

Ranking as parameter estimation

Czech Academy of Sciences Publication Activity Database

Kárný, Miroslav; Guy, Tatiana Valentine

2009-01-01

Roč. 4, č. 2 (2009), s. 142-158 ISSN 1745-7645 R&D Projects: GA MŠk 2C06001; GA AV ČR 1ET100750401; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : ranking * Bayesian estimation * negotiation * modelling Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2009/AS/karny- ranking as parameter estimation.pdf

Dynamic Modeling Accuracy Dependence on Errors in Sensor Measurements, Mass Properties, and Aircraft Geometry

Science.gov (United States)

Grauer, Jared A.; Morelli, Eugene A.

2013-01-01

A nonlinear simulation of the NASA Generic Transport Model was used to investigate the effects of errors in sensor measurements, mass properties, and aircraft geometry on the accuracy of dynamic models identified from flight data. Measurements from a typical system identification maneuver were systematically and progressively deteriorated and then used to estimate stability and control derivatives within a Monte Carlo analysis. Based on the results, recommendations were provided for maximum allowable errors in sensor measurements, mass properties, and aircraft geometry to achieve desired levels of dynamic modeling accuracy. Results using other flight conditions, parameter estimation methods, and a full-scale F-16 nonlinear aircraft simulation were compared with these recommendations.

Cosmological parameter estimation using Particle Swarm Optimization

Science.gov (United States)

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.

Cosmological parameter estimation using Particle Swarm Optimization

International Nuclear Information System (INIS)

Prasad, J; Souradeep, T

2014-01-01

Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite

Statistics of Parameter Estimates: A Concrete Example

KAUST Repository

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.

Estimating Soil Hydraulic Parameters using Gradient Based Approach

Science.gov (United States)

Rai, P. K.; Tripathi, S.

2017-12-01

The conventional way of estimating parameters of a differential equation is to minimize the error between the observations and their estimates. The estimates are produced from forward solution (numerical or analytical) of differential equation assuming a set of parameters. Parameter estimation using the conventional approach requires high computational cost, setting-up of initial and boundary conditions, and formation of difference equations in case the forward solution is obtained numerically. Gaussian process based approaches like Gaussian Process Ordinary Differential Equation (GPODE) and Adaptive Gradient Matching (AGM) have been developed to estimate the parameters of Ordinary Differential Equations without explicitly solving them. Claims have been made that these approaches can straightforwardly be extended to Partial Differential Equations; however, it has been never demonstrated. This study extends AGM approach to PDEs and applies it for estimating parameters of Richards equation. Unlike the conventional approach, the AGM approach does not require setting-up of initial and boundary conditions explicitly, which is often difficult in real world application of Richards equation. The developed methodology was applied to synthetic soil moisture data. It was seen that the proposed methodology can estimate the soil hydraulic parameters correctly and can be a potential alternative to the conventional method.

Estimating the impact of investment tax credits on aircraft demand

OpenAIRE

Mackay, Daniel

2011-01-01

This paper uses exogenous price changes from the shifting tax policies of the 1980’s to identify the parameters of a nested-logit discrete choice model of the aircraft market. The federal Investment Tax Credit (ITC) was a tax credit of 6-10% of a firm's new capital investment that was removed by the Tax Reform Act of 1986 (TRA86). Such tax credits continue to be proposed as tools to spur investment, and they are still utlized in many states and select industries. This research adds to the ...

State Estimation-based Transmission line parameter identification

Directory of Open Access Journals (Sweden)

Fredy Andrés Olarte Dussán

2010-01-01

Full Text Available This article presents two state-estimation-based algorithms for identifying transmission line parameters. The identification technique used simultaneous state-parameter estimation on an artificial power system composed of several copies of the same transmission line, using measurements at different points in time. The first algorithm used active and reactive power measurements at both ends of the line. The second method used synchronised phasor voltage and current measurements at both ends. The algorithms were tested in simulated conditions on the 30-node IEEE test system. All line parameters for this system were estimated with errors below 1%.

Kinetic parameter estimation from attenuated SPECT projection measurements

International Nuclear Information System (INIS)

Reutter, B.W.; Gullberg, G.T.

1998-01-01

Conventional analysis of dynamically acquired nuclear medicine data involves fitting kinetic models to time-activity curves generated from regions of interest defined on a temporal sequence of reconstructed images. However, images reconstructed from the inconsistent projections of a time-varying distribution of radiopharmaceutical acquired by a rotating SPECT system can contain artifacts that lead to biases in the estimated kinetic parameters. To overcome this problem the authors investigated the estimation of kinetic parameters directly from projection data by modeling the data acquisition process. To accomplish this it was necessary to parametrize the spatial and temporal distribution of the radiopharmaceutical within the SPECT field of view. In a simulated transverse slice, kinetic parameters were estimated for simple one compartment models for three myocardial regions of interest, as well as for the liver. Myocardial uptake and washout parameters estimated by conventional analysis of noiseless simulated data had biases ranging between 1--63%. Parameters estimated directly from the noiseless projection data were unbiased as expected, since the model used for fitting was faithful to the simulation. Predicted uncertainties (standard deviations) of the parameters obtained for 500,000 detected events ranged between 2--31% for the myocardial uptake parameters and 2--23% for the myocardial washout parameters

Robust Parameter and Signal Estimation in Induction Motors

DEFF Research Database (Denmark)

Børsting, H.

This thesis deals with theories and methods for robust parameter and signal estimation in induction motors. The project originates in industrial interests concerning sensor-less control of electrical drives. During the work, some general problems concerning estimation of signals and parameters...... in nonlinear systems, have been exposed. The main objectives of this project are: - analysis and application of theories and methods for robust estimation of parameters in a model structure, obtained from knowledge of the physics of the induction motor. - analysis and application of theories and methods...... for robust estimation of the rotor speed and driving torque of the induction motor based only on measurements of stator voltages and currents. Only contimuous-time models have been used, which means that physical related signals and parameters are estimated directly and not indirectly by some discrete...

Standard Errors of Estimated Latent Variable Scores with Estimated Structural Parameters

Science.gov (United States)

Hoshino, Takahiro; Shigemasu, Kazuo

2008-01-01

The authors propose a concise formula to evaluate the standard error of the estimated latent variable score when the true values of the structural parameters are not known and must be estimated. The formula can be applied to factor scores in factor analysis or ability parameters in item response theory, without bootstrap or Markov chain Monte…

Extraction of Lateral-Directional Stability and Control Derivatives for the Basic F-18 Aircraft at High Angles of Attack

Science.gov (United States)

Iliff, Kenneth W.; Wang, Kon-Sheng Charles

1997-01-01

The results of parameter identification to determine the lateral-directional stability and control derivatives of an F-18 research aircraft in its basic hardware and software configuration are presented. The derivatives are estimated from dynamic flight data using a specialized identification program developed at NASA Dryden Flight Research Center. The formulation uses the linearized aircraft equations of motions in their continuous/discrete form and a maximum likelihood estimator that accounts for both state and measurement noise. State noise is used to model the uncommanded forcing function caused by unsteady aerodynamics, such as separated and vortical flows, over the aircraft. The derivatives are plotted as functions of angle of attack between 3 deg and 47 deg and compared with wind-tunnel predictions. The quality of the derivative estimates obtained by parameter identification is somewhat degraded because the maneuvers were flown with the aircraft's control augmentation system engaged, which introduced relatively high correlations between the control variables and response variables as a result of control motions from the feedback control system.

Industrial point source CO2 emission strength estimation with aircraft measurements and dispersion modelling.

Science.gov (United States)

Carotenuto, Federico; Gualtieri, Giovanni; Miglietta, Franco; Riccio, Angelo; Toscano, Piero; Wohlfahrt, Georg; Gioli, Beniamino

2018-02-22

CO 2 remains the greenhouse gas that contributes most to anthropogenic global warming, and the evaluation of its emissions is of major interest to both research and regulatory purposes. Emission inventories generally provide quite reliable estimates of CO 2 emissions. However, because of intrinsic uncertainties associated with these estimates, it is of great importance to validate emission inventories against independent estimates. This paper describes an integrated approach combining aircraft measurements and a puff dispersion modelling framework by considering a CO 2 industrial point source, located in Biganos, France. CO 2 density measurements were obtained by applying the mass balance method, while CO 2 emission estimates were derived by implementing the CALMET/CALPUFF model chain. For the latter, three meteorological initializations were used: (i) WRF-modelled outputs initialized by ECMWF reanalyses; (ii) WRF-modelled outputs initialized by CFSR reanalyses and (iii) local in situ observations. Governmental inventorial data were used as reference for all applications. The strengths and weaknesses of the different approaches and how they affect emission estimation uncertainty were investigated. The mass balance based on aircraft measurements was quite succesful in capturing the point source emission strength (at worst with a 16% bias), while the accuracy of the dispersion modelling, markedly when using ECMWF initialization through the WRF model, was only slightly lower (estimation with an 18% bias). The analysis will help in highlighting some methodological best practices that can be used as guidelines for future experiments.

Parameter estimation and inverse problems

CERN Document Server

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...

Pollen parameters estimates of genetic variability among newly ...

African Journals Online (AJOL)

Pollen parameters estimates of genetic variability among newly selected Nigerian roselle (Hibiscus sabdariffa L.) genotypes. ... Estimates of some pollen parameters where used to assess the genetic diversity among ... HOW TO USE AJOL.

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.

Multi-Parameter Estimation for Orthorhombic Media

KAUST Repository

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.

Multi-Parameter Estimation for Orthorhombic Media

KAUST Repository

Masmoudi, Nabil; Alkhalifah, Tariq Ali

2015-01-01

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 and State Estimator for State Space Models

Directory of Open Access Journals (Sweden)

Ruifeng Ding

2014-01-01

Full Text Available This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.

Parameter Estimation in Stochastic Grey-Box Models

DEFF Research Database (Denmark)

Kristensen, Niels Rode; Madsen, Henrik; Jørgensen, Sten Bay

2004-01-01

An efficient and flexible parameter estimation scheme for grey-box models in the sense of discretely, partially observed Ito stochastic differential equations with measurement noise is presented along with a corresponding software implementation. The estimation scheme is based on the extended...... Kalman filter and features maximum likelihood as well as maximum a posteriori estimation on multiple independent data sets, including irregularly sampled data sets and data sets with occasional outliers and missing observations. The software implementation is compared to an existing software tool...... and proves to have better performance both in terms of quality of estimates for nonlinear systems with significant diffusion and in terms of reproducibility. In particular, the new tool provides more accurate and more consistent estimates of the parameters of the diffusion term....

Kinetic parameter estimation from SPECT cone-beam projection measurements

International Nuclear Information System (INIS)

Huesman, Ronald H.; Reutter, Bryan W.; Zeng, G. Larry; Gullberg, Grant T.

1998-01-01

Kinetic parameters are commonly estimated from dynamically acquired nuclear medicine data by first reconstructing a dynamic sequence of images and subsequently fitting the parameters to time-activity curves generated from regions of interest overlaid upon the image sequence. Biased estimates can result from images reconstructed using inconsistent projections of a time-varying distribution of radiopharmaceutical acquired by a rotating SPECT system. If the SPECT data are acquired using cone-beam collimators wherein the gantry rotates so that the focal point of the collimators always remains in a plane, additional biases can arise from images reconstructed using insufficient, as well as truncated, projection samples. To overcome these problems we have investigated the estimation of kinetic parameters directly from SPECT cone-beam projection data by modelling the data acquisition process. To accomplish this it was necessary to parametrize the spatial and temporal distribution of the radiopharmaceutical within the SPECT field of view. In a simulated chest image volume, kinetic parameters were estimated for simple one-compartment models for four myocardial regions of interest. Myocardial uptake and washout parameters estimated by conventional analysis of noiseless simulated cone-beam data had biases ranging between 3-26% and 0-28%, respectively. Parameters estimated directly from the noiseless projection data were unbiased as expected, since the model used for fitting was faithful to the simulation. Statistical uncertainties of parameter estimates for 10 000 000 events ranged between 0.2-9% for the uptake parameters and between 0.3-6% for the washout parameters. (author)

Traveltime approximations and parameter estimation for orthorhombic media

KAUST Repository

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 of Nonlinear Models in Forestry.

OpenAIRE

Fekedulegn, Desta; Mac Siúrtáin, Máirtín Pádraig; Colbert, Jim J.

1999-01-01

Partial derivatives of the negative exponential, monomolecular, Mitcherlich, Gompertz, logistic, Chapman-Richards, von Bertalanffy, Weibull and the Richard’s nonlinear growth models are presented. The application of these partial derivatives in estimating the model parameters is illustrated. The parameters are estimated using the Marquardt iterative method of nonlinear regression relating top height to age of Norway spruce (Picea abies L.) from the Bowmont Norway Spruce Thinnin...

Nonlinear Parameter Estimation in Microbiological Degradation Systems and Statistic Test for Common Estimation

DEFF Research Database (Denmark)

Sommer, Helle Mølgaard; Holst, Helle; Spliid, Henrik

1995-01-01

Three identical microbiological experiments were carried out and analysed in order to examine the variability of the parameter estimates. The microbiological system consisted of a substrate (toluene) and a biomass (pure culture) mixed together in an aquifer medium. The degradation of the substrate...... and the growth of the biomass are described by the Monod model consisting of two nonlinear coupled first-order differential equations. The objective of this study was to estimate the kinetic parameters in the Monod model and to test whether the parameters from the three identical experiments have the same values....... Estimation of the parameters was obtained using an iterative maximum likelihood method and the test used was an approximative likelihood ratio test. The test showed that the three sets of parameters were identical only on a 4% alpha level....

Parameter estimation in X-ray astronomy

International Nuclear Information System (INIS)

Lampton, M.; Margon, B.; Bowyer, S.

1976-01-01

The problems of model classification and parameter estimation are examined, with the objective of establishing the statistical reliability of inferences drawn from X-ray observations. For testing the validities of classes of models, the procedure based on minimizing the chi 2 statistic is recommended; it provides a rejection criterion at any desired significance level. Once a class of models has been accepted, a related procedure based on the increase of chi 2 gives a confidence region for the values of the model's adjustable parameters. The procedure allows the confidence level to be chosen exactly, even for highly nonlinear models. Numerical experiments confirm the validity of the prescribed technique.The chi 2 /sub min/+1 error estimation method is evaluated and found unsuitable when several parameter ranges are to be derived, because it substantially underestimates their joint errors. The ratio of variances method, while formally correct, gives parameter confidence regions which are more variable than necessary

A Novel Nonlinear Parameter Estimation Method of Soft Tissues

Directory of Open Access Journals (Sweden)

Qianqian Tong

2017-12-01

Full Text Available The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values. To provide highly precise data for estimating nonlinear parameters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM. Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young’s modulus and Poisson’s ratio to avoid solving complicated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg–Marquardt (LM algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM_SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.

Estimates for the parameters of the heavy quark expansion

Energy Technology Data Exchange (ETDEWEB)

Heinonen, Johannes; Mannel, Thomas [Universitaet Siegen (Germany)

2015-07-01

We give improved estimates for the non-perturbative parameters appearing in the heavy quark expansion for inclusive decays. While the parameters appearing in low orders of this expansion can be extracted from data, the number of parameters in higher orders proliferates strongly, making a determination of these parameters from data impossible. Thus, one has to rely on theoretical estimates which may be obtained from an insertion of intermediate states. We refine this method and attempt to estimate the uncertainties of this approach.

On robust parameter estimation in brain-computer interfacing

Science.gov (United States)

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.

A simulation of water pollution model parameter estimation

Science.gov (United States)

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.

Numerical Investigation of Effect of Parameters on Hovering Efficiency of an Annular Lift Fan Aircraft

Directory of Open Access Journals (Sweden)

Yun Jiang

2016-10-01

Full Text Available The effects of various parameters on the hovering performance of an annular lift fan aircraft are investigated by using numerical scheme. The pitch angle, thickness, aspect ratio (chord length, number of blades, and radius of duct inlet lip are explored to optimize the figure of merit. The annular lift fan is also compared with a conventional circular lift fan of the same features with the same disc loading and similar geometry. The simulation results show that the pitch angle of 27°, the thickness of 4% chord length, the aspect ratio of 3.5~4.0, 32 blades, and the radius of inlet lip of 4.7% generate the maximum figure of merit of 0.733. The optimized configuration can be used for further studies of the annular lift fan aircraft.

Estimation of Poisson-Dirichlet Parameters with Monotone Missing Data

Directory of Open Access Journals (Sweden)

Xueqin Zhou

2017-01-01

Full Text Available This article considers the estimation of the unknown numerical parameters and the density of the base measure in a Poisson-Dirichlet process prior with grouped monotone missing data. The numerical parameters are estimated by the method of maximum likelihood estimates and the density function is estimated by kernel method. A set of simulations was conducted, which shows that the estimates perform well.

ANALISIS OF THE INFLUENCE OF THE MAIN PARAMETERS AND CONDITION OF WORK OF AIRCRAFT CESN ON THE EFFICENCY OF THEIR USE METHODS OF MATHEMATICAL MODELING

Directory of Open Access Journals (Sweden)

Vladimir I. Troitsky

2018-01-01

Full Text Available The article describes the results of the numerical experiments on the mathematical model of the correlation-extreme navigation system (CESN of the aircraft (LA using microwave radiation of the earth's cover. The aim of the numerical experiments was the analysis of the influence of the main parameters (characteristics of the radiometer and antenna, a means of reviewing the space, parameters of the current image and the reference image, methods for image processing (algorithms for image correlation, conditions of the equipment operation (the speed and altitude of aircraft, the evolution of media on the efficiency of CESN. The experiments were carried out with the fields of the underlying surface of three types-with an artificially synthesized map (CLAIM containing several objects of different thermal contrast; with a homogeneous random field (OSP, with fragments of a digital map object structure (TSKOS of real surface area of the earth. As a result of numerical experiments the author studied the influence on exactness characteristics of CESN navigation parameters (bank angles, pitch, yaw, flight altitude and speed, the noise of the radiometer, the pattern width, the width of the review sector, mis-scaling and angular misalignment of the current and reference images. Comparison of different methods of surface scanning was made based on the simulation results. During the experiments, the variation of one of the parameters with respect to the base variants of the parameters was carried out and the values and variances of errors of the CESN were estimated. All three main methods of beam scanning (longitudinal with a multi-beam radiometer, conical and transverse were considered. The operation of the maximum search was made up of two procedures: searching for the global maximum area of the correlation matrix by enumerating all matrix entries and refining the location of the true maximum point by quadratic interpolation of the function. The

SIMPLIFIED MATHEMATICAL MODEL OF SMALL SIZED UNMANNED AIRCRAFT VEHICLE LAYOUT

Directory of Open Access Journals (Sweden)

2016-01-01

Full Text Available Strong reduction of new aircraft design period using new technology based on artificial intelligence is the key problem mentioned in forecasts of leading aerospace industry research centers. This article covers the approach to devel- opment of quick aerodynamic design methods based on artificial intelligence neural system. The problem is being solved for the classical scheme of small sized unmanned aircraft vehicle (UAV. The principal parts of the method are the mathe- matical model of layout, layout generator of this type of aircraft is built on aircraft neural networks, automatic selection module for cleaning variety of layouts generated in automatic mode, robust direct computational fluid dynamics method, aerodynamic characteristics approximators on artificial neural networks.Methods based on artificial neural networks have intermediate position between computational fluid dynamics methods or experiments and simplified engineering approaches. The use of ANN for estimating aerodynamic characteris-tics put limitations on input data. For this task the layout must be presented as a vector with dimension not exceeding sev-eral hundred. Vector components must include all main parameters conventionally used for layouts description and com- pletely replicate the most important aerodynamics and structural properties.The first stage of the work is presented in the paper. Simplified mathematical model of small sized UAV was developed. To estimate the range of geometrical parameters of layouts the review of existing vehicle was done. The result of the work is the algorithm and computer software for generating the layouts based on ANN technolo-gy. 10000 samples were generated and the dataset containig geometrical and aerodynamic characteristics of layoutwas created.

Parameter estimation in stochastic differential equations

CERN Document Server

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.

How to fool cosmic microwave background parameter estimation

International Nuclear Information System (INIS)

Kinney, William H.

2001-01-01

With the release of the data from the Boomerang and MAXIMA-1 balloon flights, estimates of cosmological parameters based on the cosmic microwave background (CMB) have reached unprecedented precision. In this paper I show that it is possible for these estimates to be substantially biased by features in the primordial density power spectrum. I construct primordial power spectra which mimic to within cosmic variance errors the effect of changing parameters such as the baryon density and neutrino mass, meaning that even an ideal measurement would be unable to resolve the degeneracy. Complementary measurements are necessary to resolve this ambiguity in parameter estimation efforts based on CMB temperature fluctuations alone

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models.

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.

Multi-objective optimization of aircraft design for emission and cost reductions

Directory of Open Access Journals (Sweden)

Wang Yu

2014-02-01

Full Text Available Pollutant gases emitted from the civil jet are doing more and more harm to the environment with the rapid development of the global commercial aviation transport. Low environmental impact has become a new requirement for aircraft design. In this paper, estimation method for emission in aircraft conceptual design stage is improved based on the International Civil Aviation Organization (ICAO aircraft engine emissions databank and the polynomial curve fitting methods. The greenhouse gas emission (CO2 equivalent per seat per kilometer is proposed to measure the emissions. An approximate sensitive analysis and a multi-objective optimization of aircraft design for tradeoff between greenhouse effect and direct operating cost (DOC are performed with five geometry variables of wing configuration and two flight operational parameters. The results indicate that reducing the cruise altitude and Mach number may result in a decrease of the greenhouse effect but an increase of DOC. And the two flight operational parameters have more effects on the emissions than the wing configuration. The Pareto-optimal front shows that a decrease of 29.8% in DOC is attained at the expense of an increase of 10.8% in greenhouse gases.

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.

Estimation of light transport parameters in biological media using ...

Indian Academy of Sciences (India)

Estimation of light transport parameters in biological media using coherent backscattering ... backscattered light for estimating the light transport parameters of biological media has been investigated. ... Pramana – Journal of Physics | News.

Statistics of Parameter Estimates: A Concrete Example

KAUST Repository

Aguilar, Oscar; Allmaras, Moritz; Bangerth, Wolfgang; Tenorio, Luis

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

Parameter Estimation of Partial Differential Equation Models

KAUST Repository

Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Maity, Arnab; Carroll, Raymond J.

2013-01-01

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

Individual aircraft life monitoring: An engineering approach for fatigue damage evaluation

Directory of Open Access Journals (Sweden)

Rui JIAO

2018-04-01

Full Text Available Individual aircraft life monitoring is required to ensure safety and economy of aircraft structure, and fatigue damage evaluation based on collected operational data of aircraft is an integral part of it. To improve the accuracy and facilitate the application, this paper proposes an engineering approach to evaluate fatigue damage and predict fatigue life for critical structures in fatigue monitoring. In this approach, traditional nominal stress method is applied to back calculate the S-N curve parameters of the realistic structure details based on full-scale fatigue test data. Then the S-N curve and Miner’s rule are adopted in damage estimation and fatigue life analysis for critical locations under individual load spectra. The relationship between relative small crack length and fatigue life can also be predicted with this approach. Specimens of 7B04-T74 aluminum alloy and TA15M titanium alloy are fatigue tested under two types of load spectra, and there is a good agreement between the experimental results and analysis results. Furthermore, the issue concerning scatter factor in individual aircraft damage estimation is also discussed. Keywords: Fatigue damage, Fatigue monitoring, Fatigue test, Scatter factor, S-N curve

Kalman filter data assimilation: targeting observations and parameter estimation.

Science.gov (United States)

Bellsky, Thomas; Kostelich, Eric J; Mahalov, Alex

2014-06-01

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.

Kalman filter data assimilation: Targeting observations and parameter estimation

International Nuclear Information System (INIS)

Bellsky, Thomas; Kostelich, Eric J.; Mahalov, Alex

2014-01-01

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

Modeling and Parameter Estimation of a Small Wind Generation System

Directory of Open Access Journals (Sweden)

Carlos A. Ramírez Gómez

2013-11-01

Full Text Available The modeling and parameter estimation of a small wind generation system is presented in this paper. The system consists of a wind turbine, a permanent magnet synchronous generator, a three phase rectifier, and a direct current load. In order to estimate the parameters wind speed data are registered in a weather station located in the Fraternidad Campus at ITM. Wind speed data were applied to a reference model programed with PSIM software. From that simulation, variables were registered to estimate the parameters. The wind generation system model together with the estimated parameters is an excellent representation of the detailed model, but the estimated model offers a higher flexibility than the programed model in PSIM software.

Aircraft ground damage and the use of predictive models to estimate costs

Science.gov (United States)

Kromphardt, Benjamin D.

Aircraft are frequently involved in ground damage incidents, and repair costs are often accepted as part of doing business. The Flight Safety Foundation (FSF) estimates ground damage to cost operators $5-10 billion annually. Incident reports, documents from manufacturers or regulatory agencies, and other resources were examined to better understand the problem of ground damage in aviation. Major contributing factors were explained, and two versions of a computer-based model were developed to project costs and show what is possible. One objective was to determine if the models could match the FSF's estimate. Another objective was to better understand cost savings that could be realized by efforts to further mitigate the occurrence of ground incidents. Model effectiveness was limited by access to official data, and assumptions were used if data was not available. However, the models were determined to sufficiently estimate the costs of ground incidents.

Estimation of parameter sensitivities for stochastic reaction networks

KAUST Repository

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.

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 m...

A variational approach to parameter estimation in ordinary differential equations

Directory of Open Access Journals (Sweden)

Kaschek Daniel

2012-08-01

Full Text Available Abstract Background 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. Results 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. Conclusions 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.

A variational approach to parameter estimation in ordinary differential equations.

Science.gov (United States)

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.

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...

Approximate effect of parameter pseudonoise intensity on rate of convergence for EKF parameter estimators. [Extended Kalman Filter

Science.gov (United States)

Hill, Bryon K.; Walker, Bruce K.

1991-01-01

When using parameter estimation methods based on extended Kalman filter (EKF) theory, it is common practice to assume that the unknown parameter values behave like a random process, such as a random walk, in order to guarantee their identifiability by the filter. The present work is the result of an ongoing effort to quantitatively describe the effect that the assumption of a fictitious noise (called pseudonoise) driving the unknown parameter values has on the parameter estimate convergence rate in filter-based parameter estimators. The initial approach is to examine a first-order system described by one state variable with one parameter to be estimated. The intent is to derive analytical results for this simple system that might offer insight into the effect of the pseudonoise assumption for more complex systems. Such results would make it possible to predict the estimator error convergence behavior as a function of the assumed pseudonoise intensity, and this leads to the natural application of the results to the design of filter-based parameter estimators. The results obtained show that the analytical description of the convergence behavior is very difficult.

Lift/cruise fan V/STOL technology aircraft design definition study. Volume 3: Development program and budgetary estimates

Science.gov (United States)

Obrien, W. J.

1976-01-01

The aircraft development program, budgetary estimates in CY 1976 dollars, and cost reduction program variants are presented. Detailed cost matrices are also provided for the mechanical transmission system, turbotip transmission system, and the thrust vector hoods and yaw doors.

Neglect Of Parameter Estimation Uncertainty Can Significantly Overestimate Structural Reliability

Directory of Open Access Journals (Sweden)

Rózsás Árpád

2015-12-01

Full Text Available Parameter estimation uncertainty is often neglected in reliability studies, i.e. point estimates of distribution parameters are used for representative fractiles, and in probabilistic models. A numerical example examines the effect of this uncertainty on structural reliability using Bayesian statistics. The study reveals that the neglect of parameter estimation uncertainty might lead to an order of magnitude underestimation of failure probability.

Parameter estimation of variable-parameter nonlinear Muskingum model using excel solver

Science.gov (United States)

Kang, Ling; Zhou, Liwei

2018-02-01

Abstract . The Muskingum model is an effective flood routing technology in hydrology and water resources Engineering. With the development of optimization technology, more and more variable-parameter Muskingum models were presented to improve effectiveness of the Muskingum model in recent decades. A variable-parameter nonlinear Muskingum model (NVPNLMM) was proposed in this paper. According to the results of two real and frequently-used case studies by various models, the NVPNLMM could obtain better values of evaluation criteria, which are used to describe the superiority of the estimated outflows and compare the accuracies of flood routing using various models, and the optimal estimated outflows by the NVPNLMM were closer to the observed outflows than the ones by other models.

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.)

Exploratory Study for Continuous-time Parameter Estimation of Ankle Dynamics

Science.gov (United States)

Kukreja, Sunil L.; Boyle, Richard D.

2014-01-01

Recently, a parallel pathway model to describe ankle dynamics was proposed. This model provides a relationship between ankle angle and net ankle torque as the sum of a linear and nonlinear contribution. A technique to identify parameters of this model in discrete-time has been developed. However, these parameters are a nonlinear combination of the continuous-time physiology, making insight into the underlying physiology impossible. The stable and accurate estimation of continuous-time parameters is critical for accurate disease modeling, clinical diagnosis, robotic control strategies, development of optimal exercise protocols for longterm space exploration, sports medicine, etc. This paper explores the development of a system identification technique to estimate the continuous-time parameters of ankle dynamics. The effectiveness of this approach is assessed via simulation of a continuous-time model of ankle dynamics with typical parameters found in clinical studies. The results show that although this technique improves estimates, it does not provide robust estimates of continuous-time parameters of ankle dynamics. Due to this we conclude that alternative modeling strategies and more advanced estimation techniques be considered for future work.

A parameter tree approach to estimating system sensitivities to parameter sets

International Nuclear Information System (INIS)

Jarzemba, M.S.; Sagar, B.

2000-01-01

A post-processing technique for determining relative system sensitivity to groups of parameters and system components is presented. It is assumed that an appropriate parametric model is used to simulate system behavior using Monte Carlo techniques and that a set of realizations of system output(s) is available. The objective of our technique is to analyze the input vectors and the corresponding output vectors (that is, post-process the results) to estimate the relative sensitivity of the output to input parameters (taken singly and as a group) and thereby rank them. This technique is different from the design of experimental techniques in that a partitioning of the parameter space is not required before the simulation. A tree structure (which looks similar to an event tree) is developed to better explain the technique. Each limb of the tree represents a particular combination of parameters or a combination of system components. For convenience and to distinguish it from the event tree, we call it the parameter tree. To construct the parameter tree, the samples of input parameter values are treated as either a '+' or a '-' based on whether or not the sampled parameter value is greater than or less than a specified branching criterion (e.g., mean, median, percentile of the population). The corresponding system outputs are also segregated into similar bins. Partitioning the first parameter into a '+' or a '-' bin creates the first level of the tree containing two branches. At the next level, realizations associated with each first-level branch are further partitioned into two bins using the branching criteria on the second parameter and so on until the tree is fully populated. Relative sensitivities are then inferred from the number of samples associated with each branch of the tree. The parameter tree approach is illustrated by applying it to a number of preliminary simulations of the proposed high-level radioactive waste repository at Yucca Mountain, NV. Using a

Online State Space Model Parameter Estimation in Synchronous Machines

Directory of Open Access Journals (Sweden)

Z. Gallehdari

2014-06-01

The suggested approach is evaluated for a sample synchronous machine model. Estimated parameters are tested for different inputs at different operating conditions. The effect of noise is also considered in this study. Simulation results show that the proposed approach provides good accuracy for parameter estimation.

Unifying parameter estimation and the Deutsch-Jozsa algorithm for continuous variables

International Nuclear Information System (INIS)

Zwierz, Marcin; Perez-Delgado, Carlos A.; Kok, Pieter

2010-01-01

We reveal a close relationship between quantum metrology and the Deutsch-Jozsa algorithm on continuous-variable quantum systems. We develop a general procedure, characterized by two parameters, that unifies parameter estimation and the Deutsch-Jozsa algorithm. Depending on which parameter we keep constant, the procedure implements either the parameter-estimation protocol or the Deutsch-Jozsa algorithm. The parameter-estimation part of the procedure attains the Heisenberg limit and is therefore optimal. Due to the use of approximate normalizable continuous-variable eigenstates, the Deutsch-Jozsa algorithm is probabilistic. The procedure estimates a value of an unknown parameter and solves the Deutsch-Jozsa problem without the use of any entanglement.

Impacts of Different Types of Measurements on Estimating Unsaturatedflow Parameters

Science.gov (United States)

Shi, L.

2015-12-01

This study evaluates the value of different types of measurements for estimating soil hydraulic parameters. A numerical method based on ensemble Kalman filter (EnKF) is presented to solely or jointly assimilate point-scale soil water head data, point-scale soil water content data, surface soil water content data and groundwater level data. This study investigates the performance of EnKF under different types of data, the potential worth contained in these data, and the factors that may affect estimation accuracy. Results show that for all types of data, smaller measurements errors lead to faster convergence to the true values. Higher accuracy measurements are required to improve the parameter estimation if a large number of unknown parameters need to be identified simultaneously. The data worth implied by the surface soil water content data and groundwater level data is prone to corruption by a deviated initial guess. Surface soil moisture data are capable of identifying soil hydraulic parameters for the top layers, but exert less or no influence on deeper layers especially when estimating multiple parameters simultaneously. Groundwater level is one type of valuable information to infer the soil hydraulic parameters. However, based on the approach used in this study, the estimates from groundwater level data may suffer severe degradation if a large number of parameters must be identified. Combined use of two or more types of data is helpful to improve the parameter estimation.

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

Postprocessing MPEG based on estimated quantization parameters

DEFF Research Database (Denmark)

Forchhammer, Søren

2009-01-01

the case where the coded stream is not accessible, or from an architectural point of view not desirable to use, and instead estimate some of the MPEG stream parameters based on the decoded sequence. The I-frames are detected and the quantization parameters are estimated from the coded stream and used...... in the postprocessing. We focus on deringing and present a scheme which aims at suppressing ringing artifacts, while maintaining the sharpness of the texture. The goal is to improve the visual quality, so perceptual blur and ringing metrics are used in addition to PSNR evaluation. The performance of the new `pure......' 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....

Identifyability measures to select the parameters to be estimated in a solid-state fermentation distributed parameter model.

Science.gov (United States)

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.

Composite likelihood estimation of demographic parameters

Directory of Open Access Journals (Sweden)

Garrigan Daniel

2009-11-01

Full Text Available Abstract Background Most existing likelihood-based methods for fitting historical demographic models to DNA sequence polymorphism data to do not scale feasibly up to the level of whole-genome data sets. Computational economies can be achieved by incorporating two forms of pseudo-likelihood: composite and approximate likelihood methods. Composite likelihood enables scaling up to large data sets because it takes the product of marginal likelihoods as an estimator of the likelihood of the complete data set. This approach is especially useful when a large number of genomic regions constitutes the data set. Additionally, approximate likelihood methods can reduce the dimensionality of the data by summarizing the information in the original data by either a sufficient statistic, or a set of statistics. Both composite and approximate likelihood methods hold promise for analyzing large data sets or for use in situations where the underlying demographic model is complex and has many parameters. This paper considers a simple demographic model of allopatric divergence between two populations, in which one of the population is hypothesized to have experienced a founder event, or population bottleneck. A large resequencing data set from human populations is summarized by the joint frequency spectrum, which is a matrix of the genomic frequency spectrum of derived base frequencies in two populations. A Bayesian Metropolis-coupled Markov chain Monte Carlo (MCMCMC method for parameter estimation is developed that uses both composite and likelihood methods and is applied to the three different pairwise combinations of the human population resequence data. The accuracy of the method is also tested on data sets sampled from a simulated population model with known parameters. Results The Bayesian MCMCMC method also estimates the ratio of effective population size for the X chromosome versus that of the autosomes. The method is shown to estimate, with reasonable

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.

Validation of Cloud Optical Parameters from Passive Remote Sensing in the Arctic by using the Aircraft Measurements

Science.gov (United States)

Chen, H.; Schmidt, S.; Coddington, O.; Wind, G.; Bucholtz, A.; Segal-Rosenhaimer, M.; LeBlanc, S. E.

2017-12-01

Cloud Optical Parameters (COPs: e.g., cloud optical thickness and cloud effective radius) and surface albedo are the most important inputs for determining the Cloud Radiative Effect (CRE) at the surface. In the Arctic, the COPs derived from passive remote sensing such as from the Moderate Resolution Imaging Spectroradiometer (MODIS) are difficult to obtain with adequate accuracy owing mainly to insufficient knowledge about the snow/ice surface, but also because of the low solar zenith angle. This study aims to validate COPs derived from passive remote sensing in the Arctic by using aircraft measurements collected during two field campaigns based in Fairbanks, Alaska. During both experiments, ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) and ARISE (Arctic Radiation-IceBridge Sea and Ice Experiment), the Solar Spectral Flux Radiometer (SSFR) measured upwelling and downwelling shortwave spectral irradiances, which can be used to derive surface and cloud albedo, as well as the irradiance transmitted by clouds. We assess the variability of the Arctic sea ice/snow surfaces albedo through these aircraft measurements and incorporate this variability into cloud retrievals for SSFR. We then compare COPs as derived from SSFR and MODIS for all suitable aircraft underpasses of the satellites. Finally, the sensitivities of the COPs to surface albedo and solar zenith angle are investigated.

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.

Assumptions of the primordial spectrum and cosmological parameter estimation

International Nuclear Information System (INIS)

Shafieloo, Arman; Souradeep, Tarun

2011-01-01

The observables of the perturbed universe, cosmic microwave background (CMB) anisotropy and large structures depend on a set of cosmological parameters, as well as the assumed nature of primordial perturbations. In particular, the shape of the primordial power spectrum (PPS) is, at best, a well-motivated assumption. It is known that the assumed functional form of the PPS in cosmological parameter estimation can affect the best-fit-parameters and their relative confidence limits. In this paper, we demonstrate that a specific assumed form actually drives the best-fit parameters into distinct basins of likelihood in the space of cosmological parameters where the likelihood resists improvement via modifications to the PPS. The regions where considerably better likelihoods are obtained allowing free-form PPS lie outside these basins. In the absence of a preferred model of inflation, this raises a concern that current cosmological parameter estimates are strongly prejudiced by the assumed form of PPS. Our results strongly motivate approaches toward simultaneous estimation of the cosmological parameters and the shape of the primordial spectrum from upcoming cosmological data. It is equally important for theorists to keep an open mind towards early universe scenarios that produce features in the PPS. (paper)

Bayesian estimation of Weibull distribution parameters

International Nuclear Information System (INIS)

Bacha, M.; Celeux, G.; Idee, E.; Lannoy, A.; Vasseur, D.

1994-11-01

In this paper, we expose SEM (Stochastic Expectation Maximization) and WLB-SIR (Weighted Likelihood Bootstrap - Sampling Importance Re-sampling) methods which are used to estimate Weibull distribution parameters when data are very censored. The second method is based on Bayesian inference and allow to take into account available prior informations on parameters. An application of this method, with real data provided by nuclear power plants operation feedback analysis has been realized. (authors). 8 refs., 2 figs., 2 tabs

Tropical cyclones-Pacific Asian Research Campaign for Improvement of Intensity estimations/forecasts (T-PARCII): A research plan of typhoon aircraft observations in Japan

Science.gov (United States)

Tsuboki, Kazuhisa

2017-04-01

Typhoons are the most devastating weather system occurring in the western North Pacific and the South China Sea. Violent wind and heavy rainfall associated with a typhoon cause huge disaster in East Asia including Japan. In 2013, Supertyphoon Haiyan struck the Philippines caused a very high storm surge and more than 7000 people were killed. In 2015, two typhoons approached the main islands of Japan and severe flood occurred in the northern Kanto region. Typhoons are still the largest cause of natural disaster in East Asia. Moreover, many researches have projected increase of typhoon intensity with the climate change. This suggests that a typhoon risk is increasing in East Asia. However, the historical data of typhoon include large uncertainty. In particular, intensity data of the most intense typhoon category have larger error after the US aircraft reconnaissance of typhoon was terminated in 1987.The main objective of the present study is improvements of typhoon intensity estimations and of forecasts of intensity and track. We will perform aircraft observation of typhoon and the observed data are assimilated to numerical models to improve intensity estimation. Using radars and balloons, observations of thermodynamical and cloud-microphysical processes of typhoons will be also performed to improve physical processes of numerical model. In typhoon seasons (mostly in August and September), we will perform aircraft observations of typhoons. Using dropsondes from the aircraft, temperature, humidity, pressure, and wind are measured in surroundings of the typhoon inner core region. The dropsonde data are assimilated to a cloud-resolving model which has been developed in Nagoya University and named the Cloud Resolving Storm Simulator (CReSS). Then, more accurate estimations and forecasts of the typhoon intensity will be made as well as typhoon tracks. Furthermore, we will utilize a ground-based balloon with microscope camera, X-band precipitation radar, Ka-band cloud radar

A Modified Penalty Parameter Approach for Optimal Estimation of UH with Simultaneous Estimation of Infiltration Parameters

Science.gov (United States)

Bhattacharjya, Rajib Kumar

2018-05-01

The unit hydrograph and the infiltration parameters of a watershed can be obtained from observed rainfall-runoff data by using inverse optimization technique. This is a two-stage optimization problem. In the first stage, the infiltration parameters are obtained and the unit hydrograph ordinates are estimated in the second stage. In order to combine this two-stage method into a single stage one, a modified penalty parameter approach is proposed for converting the constrained optimization problem to an unconstrained one. The proposed approach is designed in such a way that the model initially obtains the infiltration parameters and then searches the optimal unit hydrograph ordinates. The optimization model is solved using Genetic Algorithms. A reduction factor is used in the penalty parameter approach so that the obtained optimal infiltration parameters are not destroyed during subsequent generation of genetic algorithms, required for searching optimal unit hydrograph ordinates. The performance of the proposed methodology is evaluated by using two example problems. The evaluation shows that the model is superior, simple in concept and also has the potential for field application.

Parameter Estimation Analysis for Hybrid Adaptive Fault Tolerant Control

Science.gov (United States)

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

Advanced Composite Air Frame Life Cycle Cost Estimating

Science.gov (United States)

2014-06-19

the ACCA based on the cost . This cost analysis takes into account the increased performance parameters of the new airframe structure. This research...20 Advanced Composite Cargo Aircraft ( ACCA ) ..........................................................23 viii Cost Estimation...establishing the procurement strategies and life cycle cost (LCC) model cost estimations. The current LCC models do not take into account the potential cost

SCoPE: an efficient method of Cosmological Parameter Estimation

International Nuclear Information System (INIS)

Das, Santanu; Souradeep, Tarun

2014-01-01

Markov Chain Monte Carlo (MCMC) sampler is widely used for cosmological parameter estimation from CMB and other data. However, due to the intrinsic serial nature of the MCMC sampler, convergence is often very slow. Here we present a fast and independently written Monte Carlo method for cosmological parameter estimation named as Slick Cosmological Parameter Estimator (SCoPE), that employs delayed rejection to increase the acceptance rate of a chain, and pre-fetching that helps an individual chain to run on parallel CPUs. An inter-chain covariance update is also incorporated to prevent clustering of the chains allowing faster and better mixing of the chains. We use an adaptive method for covariance calculation to calculate and update the covariance automatically as the chains progress. Our analysis shows that the acceptance probability of each step in SCoPE is more than 95% and the convergence of the chains are faster. Using SCoPE, we carry out some cosmological parameter estimations with different cosmological models using WMAP-9 and Planck results. One of the current research interests in cosmology is quantifying the nature of dark energy. We analyze the cosmological parameters from two illustrative commonly used parameterisations of dark energy models. We also asses primordial helium fraction in the universe can be constrained by the present CMB data from WMAP-9 and Planck. The results from our MCMC analysis on the one hand helps us to understand the workability of the SCoPE better, on the other hand it provides a completely independent estimation of cosmological parameters from WMAP-9 and Planck data

Bayesian parameter estimation in probabilistic risk assessment

International Nuclear Information System (INIS)

Siu, Nathan O.; Kelly, Dana L.

1998-01-01

Bayesian statistical methods are widely used in probabilistic risk assessment (PRA) because of their ability to provide useful estimates of model parameters when data are sparse and because the subjective probability framework, from which these methods are derived, is a natural framework to address the decision problems motivating PRA. This paper presents a tutorial on Bayesian parameter estimation especially relevant to PRA. It summarizes the philosophy behind these methods, approaches for constructing likelihood functions and prior distributions, some simple but realistic examples, and a variety of cautions and lessons regarding practical applications. References are also provided for more in-depth coverage of various topics

Methodology to estimate particulate matter emissions from certified commercial aircraft engines.

Science.gov (United States)

Wayson, Roger L; Fleming, Gregg G; Lovinelli, Ralph

2009-01-01

Today, about one-fourth of U.S. commercial service airports, including 41 of the busiest 50, are either in nonattainment or maintenance areas per the National Ambient Air Quality Standards. U.S. aviation activity is forecasted to triple by 2025, while at the same time, the U.S. Environmental Protection Agency (EPA) is evaluating stricter particulate matter (PM) standards on the basis of documented human health and welfare impacts. Stricter federal standards are expected to impede capacity and limit aviation growth if regulatory mandated emission reductions occur as for other non-aviation sources (i.e., automobiles, power plants, etc.). In addition, strong interest exists as to the role aviation emissions play in air quality and climate change issues. These reasons underpin the need to quantify and understand PM emissions from certified commercial aircraft engines, which has led to the need for a methodology to predict these emissions. Standardized sampling techniques to measure volatile and nonvolatile PM emissions from aircraft engines do not exist. As such, a first-order approximation (FOA) was derived to fill this need based on available information. FOA1.0 only allowed prediction of nonvolatile PM. FOA2.0 was a change to include volatile PM emissions on the basis of the ratio of nonvolatile to volatile emissions. Recent collaborative efforts by industry (manufacturers and airlines), research establishments, and regulators have begun to provide further insight into the estimation of the PM emissions. The resultant PM measurement datasets are being analyzed to refine sampling techniques and progress towards standardized PM measurements. These preliminary measurement datasets also support the continued refinement of the FOA methodology. FOA3.0 disaggregated the prediction techniques to allow for independent prediction of nonvolatile and volatile emissions on a more theoretical basis. The Committee for Aviation Environmental Protection of the International Civil

Iterative methods for distributed parameter estimation in parabolic PDE

Energy Technology Data Exchange (ETDEWEB)

Vogel, C.R. [Montana State Univ., Bozeman, MT (United States); Wade, J.G. [Bowling Green State Univ., OH (United States)

1994-12-31

The goal of the work presented is the development of effective iterative techniques for large-scale inverse or parameter estimation problems. In this extended abstract, a detailed description of the mathematical framework in which the authors view these problem is presented, followed by an outline of the ideas and algorithms developed. Distributed parameter estimation problems often arise in mathematical modeling with partial differential equations. They can be viewed as inverse problems; the `forward problem` is that of using the fully specified model to predict the behavior of the system. The inverse or parameter estimation problem is: given the form of the model and some observed data from the system being modeled, determine the unknown parameters of the model. These problems are of great practical and mathematical interest, and the development of efficient computational algorithms is an active area of study.

A distributed approach for parameters estimation in System Biology models

International Nuclear Information System (INIS)

Mosca, E.; Merelli, I.; Alfieri, R.; Milanesi, L.

2009-01-01

Due to the lack of experimental measurements, biological variability and experimental errors, the value of many parameters of the systems biology mathematical models is yet unknown or uncertain. A possible computational solution is the parameter estimation, that is the identification of the parameter values that determine the best model fitting respect to experimental data. We have developed an environment to distribute each run of the parameter estimation algorithm on a different computational resource. The key feature of the implementation is a relational database that allows the user to swap the candidate solutions among the working nodes during the computations. The comparison of the distributed implementation with the parallel one showed that the presented approach enables a faster and better parameter estimation of systems biology models.

Estimation of pharmacokinetic parameters from non-compartmental variables using Microsoft Excel.

Science.gov (United States)

Dansirikul, Chantaratsamon; Choi, Malcolm; Duffull, Stephen B

2005-06-01

This study was conducted to develop a method, termed 'back analysis (BA)', for converting non-compartmental variables to compartment model dependent pharmacokinetic parameters for both one- and two-compartment models. A Microsoft Excel spreadsheet was implemented with the use of Solver and visual basic functions. The performance of the BA method in estimating pharmacokinetic parameter values was evaluated by comparing the parameter values obtained to a standard modelling software program, NONMEM, using simulated data. The results show that the BA method was reasonably precise and provided low bias in estimating fixed and random effect parameters for both one- and two-compartment models. The pharmacokinetic parameters estimated from the BA method were similar to those of NONMEM estimation.

Estimation of aircraft aerodynamic derivatives using Extended Kalman Filter

OpenAIRE

Curvo, M.

2000-01-01

Design of flight control laws, verification of performance predictions, and the implementation of flight simulations are tasks that require a mathematical model of the aircraft dynamics. The dynamical models are characterized by coefficients (aerodynamic derivatives) whose values must be determined from flight tests. This work outlines the use of the Extended Kalman Filter (EKF) in obtaining the aerodynamic derivatives of an aircraft. The EKF shows several advantages over the more traditional...

Parameter Estimates in Differential Equation Models for Chemical Kinetics

Science.gov (United States)

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…

Estimating physiological skin parameters from hyperspectral signatures

Science.gov (United States)

Vyas, Saurabh; Banerjee, Amit; Burlina, Philippe

2013-05-01

We describe an approach for estimating human skin parameters, such as melanosome concentration, collagen concentration, oxygen saturation, and blood volume, using hyperspectral radiometric measurements (signatures) obtained from in vivo skin. We use a computational model based on Kubelka-Munk theory and the Fresnel equations. This model forward maps the skin parameters to a corresponding multiband reflectance spectra. Machine-learning-based regression is used to generate the inverse map, and hence estimate skin parameters from hyperspectral signatures. We test our methods using synthetic and in vivo skin signatures obtained in the visible through the short wave infrared domains from 24 patients of both genders and Caucasian, Asian, and African American ethnicities. Performance validation shows promising results: good agreement with the ground truth and well-established physiological precepts. These methods have potential use in the characterization of skin abnormalities and in minimally-invasive prescreening of malignant skin cancers.

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.

Nonlinear adaptive control system design with asymptotically stable parameter estimation error

Science.gov (United States)

Mishkov, Rumen; Darmonski, Stanislav

2018-01-01

The paper presents a new general method for nonlinear adaptive system design with asymptotic stability of the parameter estimation error. The advantages of the approach include asymptotic unknown parameter estimation without persistent excitation and capability to directly control the estimates transient response time. The method proposed modifies the basic parameter estimation dynamics designed via a known nonlinear adaptive control approach. The modification is based on the generalised prediction error, a priori constraints with a hierarchical parameter projection algorithm, and the stable data accumulation concepts. The data accumulation principle is the main tool for achieving asymptotic unknown parameter estimation. It relies on the parametric identifiability system property introduced. Necessary and sufficient conditions for exponential stability of the data accumulation dynamics are derived. The approach is applied in a nonlinear adaptive speed tracking vector control of a three-phase induction motor.

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.

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.

Application of isotopic information for estimating parameters in Philip infiltration model

Directory of Open Access Journals (Sweden)

Tao Wang

2016-10-01

Full Text Available Minimizing parameter uncertainty is crucial in the application of hydrologic models. Isotopic information in various hydrologic components of the water cycle can expand our knowledge of the dynamics of water flow in the system, provide additional information for parameter estimation, and improve parameter identifiability. This study combined the Philip infiltration model with an isotopic mixing model using an isotopic mass balance approach for estimating parameters in the Philip infiltration model. Two approaches to parameter estimation were compared: (a using isotopic information to determine the soil water transmission and then hydrologic information to estimate the soil sorptivity, and (b using hydrologic information to determine the soil water transmission and the soil sorptivity. Results of parameter estimation were verified through a rainfall infiltration experiment in a laboratory under rainfall with constant isotopic compositions and uniform initial soil water content conditions. Experimental results showed that approach (a, using isotopic and hydrologic information, estimated the soil water transmission in the Philip infiltration model in a manner that matched measured values well. The results of parameter estimation of approach (a were better than those of approach (b. It was also found that the analytical precision of hydrogen and oxygen stable isotopes had a significant effect on parameter estimation using isotopic information.

A Novel Methodology for Estimating State-Of-Charge of Li-Ion Batteries Using Advanced Parameters Estimation

Directory of Open Access Journals (Sweden)

Ibrahim M. Safwat

2017-11-01

Full Text Available State-of-charge (SOC estimations of Li-ion batteries have been the focus of many research studies in previous years. Many articles discussed the dynamic model’s parameters estimation of the Li-ion battery, where the fixed forgetting factor recursive least square estimation methodology is employed. However, the change rate of each parameter to reach the true value is not taken into consideration, which may tend to poor estimation. This article discusses this issue, and proposes two solutions to solve it. The first solution is the usage of a variable forgetting factor instead of a fixed one, while the second solution is defining a vector of forgetting factors, which means one factor for each parameter. After parameters estimation, a new idea is proposed to estimate state-of-charge (SOC of the Li-ion battery based on Newton’s method. Also, the error percentage and computational cost are discussed and compared with that of nonlinear Kalman filters. This methodology is applied on a 36 V 30 A Li-ion pack to validate this idea.

Numerical Investigation of Effect of Parameters on Hovering Efficiency of an Annular Lift Fan Aircraft

OpenAIRE

Yun Jiang; Bo Zhang

2016-01-01

The effects of various parameters on the hovering performance of an annular lift fan aircraft are investigated by using numerical scheme. The pitch angle, thickness, aspect ratio (chord length), number of blades, and radius of duct inlet lip are explored to optimize the figure of merit. The annular lift fan is also compared with a conventional circular lift fan of the same features with the same disc loading and similar geometry. The simulation results show that the pitch angle of 27°, the th...

On the estimation of water pure compound parameters in association theories

DEFF Research Database (Denmark)

Grenner, Andreas; Kontogeorgis, Georgios; Michelsen, Michael Locht

2007-01-01

Determination of the appropriate number of association sites and estimation of parameters for association (SAFT-type) theories is not a trivial matter. Building further on a recently published manuscript by Clark et al., this work investigates aspects of the parameter estimation for water using t...... different association theories. Their performance for various properties as well as against the results presented earlier is demonstrated.......Determination of the appropriate number of association sites and estimation of parameters for association (SAFT-type) theories is not a trivial matter. Building further on a recently published manuscript by Clark et al., this work investigates aspects of the parameter estimation for water using two...

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...

Modular Estimation Strategy of Vehicle Dynamic Parameters for Motion Control Applications

Directory of Open Access Journals (Sweden)

Rawash Mustafa

2018-01-01

Full Text Available The presence of motion control or active safety systems in vehicles have become increasingly important for improving vehicle performance and handling and negotiating dangerous driving situations. The performance of such systems would be improved if combined with knowledge of vehicle dynamic parameters. Since some of these parameters are difficult to measure, due to technical or economic reasons, estimation of those parameters might be the only practical alternative. In this paper, an estimation strategy of important vehicle dynamic parameters, pertaining to motion control applications, is presented. The estimation strategy is of a modular structure such that each module is concerned with estimating a single vehicle parameter. Parameters estimated include: longitudinal, lateral, and vertical tire forces – longitudinal velocity – vehicle mass. The advantage of this strategy is its independence of tire parameters or wear, road surface condition, and vehicle mass variation. Also, because of its modular structure, each module could be later updated or exchanged for a more effective one. Results from simulations on a 14-DOF vehicle model are provided here to validate the strategy and show its robustness and accuracy.

Estimation of object motion parameters from noisy images.

Science.gov (United States)

Broida, T J; Chellappa, R

1986-01-01

An approach is presented for the estimation of object motion parameters based on a sequence of noisy images. The problem considered is that of a rigid body undergoing unknown rotational and translational motion. The measurement data consists of a sequence of noisy image coordinates of two or more object correspondence points. By modeling the object dynamics as a function of time, estimates of the model parameters (including motion parameters) can be extracted from the data using recursive and/or batch techniques. This permits a desired degree of smoothing to be achieved through the use of an arbitrarily large number of images. Some assumptions regarding object structure are presently made. Results are presented for a recursive estimation procedure: the case considered here is that of a sequence of one dimensional images of a two dimensional object. Thus, the object moves in one transverse dimension, and in depth, preserving the fundamental ambiguity of the central projection image model (loss of depth information). An iterated extended Kalman filter is used for the recursive solution. Noise levels of 5-10 percent of the object image size are used. Approximate Cramer-Rao lower bounds are derived for the model parameter estimates as a function of object trajectory and noise level. This approach may be of use in situations where it is difficult to resolve large numbers of object match points, but relatively long sequences of images (10 to 20 or more) are available.

State and parameter estimation in biotechnical batch reactors

NARCIS (Netherlands)

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

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.

Global parameter estimation for thermodynamic models of transcriptional regulation.

Science.gov (United States)

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.

Uncertainty estimation of core safety parameters using cross-correlations of covariance matrix

International Nuclear Information System (INIS)

Yamamoto, A.; Yasue, Y.; Endo, T.; Kodama, Y.; Ohoka, Y.; Tatsumi, M.

2012-01-01

An uncertainty estimation method for core safety parameters, for which measurement values are not obtained, is proposed. We empirically recognize the correlations among the prediction errors among core safety parameters, e.g., a correlation between the control rod worth and assembly relative power of corresponding position. Correlations of uncertainties among core safety parameters are theoretically estimated using the covariance of cross sections and sensitivity coefficients for core parameters. The estimated correlations among core safety parameters are verified through the direct Monte-Carlo sampling method. Once the correlation of uncertainties among core safety parameters is known, we can estimate the uncertainty of a safety parameter for which measurement value is not obtained. Furthermore, the correlations can be also used for the reduction of uncertainties of core safety parameters. (authors)

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...

Combining tracer flux ratio methodology with low-flying aircraft measurements to estimate dairy farm CH4 emissions

Science.gov (United States)

Daube, C.; Conley, S.; Faloona, I. C.; Yacovitch, T. I.; Roscioli, J. R.; Morris, M.; Curry, J.; Arndt, C.; Herndon, S. C.

2017-12-01

Livestock activity, enteric fermentation of feed and anaerobic digestion of waste, contributes significantly to the methane budget of the United States (EPA, 2016). Studies question the reported magnitude of these methane sources (Miller et. al., 2013), calling for more detailed research of agricultural animals (Hristov, 2014). Tracer flux ratio is an attractive experimental method to bring to this problem because it does not rely on estimates of atmospheric dispersion. Collection of data occurred during one week at two dairy farms in central California (June, 2016). Each farm varied in size, layout, head count, and general operation. The tracer flux ratio method involves releasing ethane on-site with a known flow rate to serve as a tracer gas. Downwind mixed enhancements in ethane (from the tracer) and methane (from the dairy) were measured, and their ratio used to infer the unknown methane emission rate from the farm. An instrumented van drove transects downwind of each farm on public roads while tracer gases were released on-site, employing the tracer flux ratio methodology to assess simultaneous methane and tracer gas plumes. Flying circles around each farm, a small instrumented aircraft made measurements to perform a mass balance evaluation of methane gas. In the course of these two different methane quantification techniques, we were able to validate yet a third method: tracer flux ratio measured via aircraft. Ground-based tracer release rates were applied to the aircraft-observed methane-to-ethane ratios, yielding whole-site methane emission rates. Never before has the tracer flux ratio method been executed with aircraft measurements. Estimates from this new application closely resemble results from the standard ground-based technique to within their respective uncertainties. Incorporating this new dimension to the tracer flux ratio methodology provides additional context for local plume dynamics and validation of both ground and flight-based data.

Bias-Corrected Estimation of Noncentrality Parameters of Covariance Structure Models

Science.gov (United States)

Raykov, Tenko

2005-01-01

A bias-corrected estimator of noncentrality parameters of covariance structure models is discussed. The approach represents an application of the bootstrap methodology for purposes of bias correction, and utilizes the relation between average of resample conventional noncentrality parameter estimates and their sample counterpart. The…

Optimal design criteria - prediction vs. parameter estimation

Science.gov (United States)

Waldl, Helmut

2014-05-01

G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.

Maximum profile likelihood estimation of differential equation parameters through model based smoothing state estimates.

Science.gov (United States)

Campbell, D A; Chkrebtii, O

2013-12-01

Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm

International Nuclear Information System (INIS)

Lazzús, Juan A.; Rivera, Marco; López-Caraballo, Carlos H.

2016-01-01

A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.

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.

Maximum-likelihood estimation of the hyperbolic parameters from grouped observations

DEFF Research Database (Denmark)

Jensen, Jens Ledet

1988-01-01

a least-squares problem. The second procedure Hypesti first approaches the maximum-likelihood estimate by iterating in the profile-log likelihood function for the scale parameter. Close to the maximum of the likelihood function, the estimation is brought to an end by iteration, using all four parameters...

Identification of integrated airframe: Propulsion effects on an F-15 aircraft for application to drag minimization

Science.gov (United States)

Schkolnik, Gerard S.

1993-01-01

The application of an adaptive real-time measurement-based performance optimization technique is being explored for a future flight research program. The key technical challenge of the approach is parameter identification, which uses a perturbation-search technique to identify changes in performance caused by forced oscillations of the controls. The controls on the NASA F-15 highly integrated digital electronic control (HIDEC) aircraft were perturbed using inlet cowl rotation steps at various subsonic and supersonic flight conditions to determine the effect on aircraft performance. The feasibility of the perturbation-search technique for identifying integrated airframe-propulsion system performance effects was successfully shown through flight experiments and postflight data analysis. Aircraft response and control data were analyzed postflight to identify gradients and to determine the minimum drag point. Changes in longitudinal acceleration as small as 0.004 g were measured, and absolute resolution was estimated to be 0.002 g or approximately 50 lbf of drag. Two techniques for identifying performance gradients were compared: a least-squares estimation algorithm and a modified maximum likelihood estimator algorithm. A complementary filter algorithm was used with the least squares estimator.

Identification of integrated airframe-propulsion effects on an F-15 aircraft for application to drag minimization

Science.gov (United States)

Schkolnik, Gerald S.

1993-01-01

The application of an adaptive real-time measurement-based performance optimization technique is being explored for a future flight research program. The key technical challenge of the approach is parameter identification, which uses a perturbation-search technique to identify changes in performance caused by forced oscillations of the controls. The controls on the NASA F-15 highly integrated digital electronic control (HIDEC) aircraft were perturbed using inlet cowl rotation steps at various subsonic and supersonic flight conditions to determine the effect on aircraft performance. The feasibility of the perturbation-search technique for identifying integrated airframe-propulsion system performance effects was successfully shown through flight experiments and postflight data analysis. Aircraft response and control data were analyzed postflight to identify gradients and to determine the minimum drag point. Changes in longitudinal acceleration as small as 0.004 g were measured, and absolute resolution was estimated to be 0.002 g or approximately 50 lbf of drag. Two techniques for identifying performance gradients were compared: a least-squares estimation algorithm and a modified maximum likelihood estimator algorithm. A complementary filter algorithm was used with the least squares estimator.

Dual ant colony operational modal analysis parameter estimation method

Science.gov (United States)

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.

Analysis of Loss of Control Parameters for Aircraft Maneuvering in General Aviation

Directory of Open Access Journals (Sweden)

Sameer Ud-Din

2018-01-01

Full Text Available A rapid increase in the occurrence of loss of control in general aviation has raised concern in recent years. Loss of control (LOC pertains to unique characteristics in which external and internal events act in conjunction. The Federal Aviation Administration (FAA has approved an Integrated Safety Assessment Model (ISAM for evaluating safety in the National Airspace System (NAS. ISAM consists of an event sequence diagram (ESD with fault trees containing numerous parameters, which is recognized as casual risk model. In this paper, we outline an integrated risk assessment framework to model maneuvering through cross-examining external and internal events. The maneuvering is in the critical flight phase with a high number of LOC occurrences in general aviation, where highly trained and qualified pilots failed to maintain aircraft control irrespective of the preventive nature of the events. Various metrics have been presented for evaluating the significance of these parameters to identify the most important ones. The proposed sensitivity analysis considers the accident, fatality, and risk reduction frequencies that assist in the decision-making process and foresees future risks from a general aviation perspective.

Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

Science.gov (United States)

Jorgensen, Charles C.

1997-01-01

A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

Cosmological parameter estimation using particle swarm optimization

Science.gov (United States)

Prasad, Jayanti; Souradeep, Tarun

2012-06-01

Constraining theoretical models, which are represented by a set of parameters, using observational data is an important exercise in cosmology. In Bayesian framework this is done by finding the probability distribution of parameters which best fits to the observational data using sampling based methods like Markov chain Monte Carlo (MCMC). It has been argued that MCMC may not be the best option in certain problems in which the target function (likelihood) poses local maxima or have very high dimensionality. Apart from this, there may be examples in which we are mainly interested to find the point in the parameter space at which the probability distribution has the largest value. In this situation the problem of parameter estimation becomes an optimization problem. In the present work we show that particle swarm optimization (PSO), which is an artificial intelligence inspired population based search procedure, can also be used for cosmological parameter estimation. Using PSO we were able to recover the best-fit Λ cold dark matter (LCDM) model parameters from the WMAP seven year data without using any prior guess value or any other property of the probability distribution of parameters like standard deviation, as is common in MCMC. We also report the results of an exercise in which we consider a binned primordial power spectrum (to increase the dimensionality of problem) and find that a power spectrum with features gives lower chi square than the standard power law. Since PSO does not sample the likelihood surface in a fair way, we follow a fitting procedure to find the spread of likelihood function around the best-fit point.

Hull loss accident model for narrow body commercial aircraft

Directory of Open Access Journals (Sweden)

Somchanok Tiabtiamrat

2010-10-01

Full Text Available Accidents with narrow body aircraft were statistically evaluated covering six families of commercial aircraft includingBoeing B737, Airbus A320, McDonnell Douglas MD80, Tupolev TU134/TU154 and Antonov AN124. A risk indicator for eachflight phase was developed based on motion characteristics, duration time, and the presence of adverse weather conditions.The estimated risk levels based on these risk indicators then developed from the risk indicator. Regression analysis indicatedvery good agreement between the estimated risk level and the accident ratio of hull loss cases per number of delivered aircraft.The effect of time on the hull loss accident ratio per delivered aircraft was assessed for B737, A320 and MD80. Equationsrepresenting the effect of time on hull loss accident ratio per delivered aircraft were proposed for B737, A320, and MD80,while average values of hull loss accident ratio per delivered aircraft were found for TU134, TU154, and AN 124. Accidentprobability equations were then developed for each family of aircraft that the probability of an aircraft in a hull loss accidentcould be estimated for any aircraft family, flight phase, presence of adverse weather factor, hour of day, day of week, monthof year, pilot age, and pilot flight hour experience. A simplified relationship between estimated hull loss accident probabilityand unsafe acts by human was proposed. Numerical investigation of the relationship between unsafe acts by human andfatality ratio suggested that the fatality ratio in hull loss accident was dominated primarily by the flight phase media.

Estimating RASATI scores using acoustical parameters

International Nuclear Information System (INIS)

Agüero, P D; Tulli, J C; Moscardi, G; Gonzalez, E L; Uriz, A J

2011-01-01

Acoustical analysis of speech using computers has reached an important development in the latest years. The subjective evaluation of a clinician is complemented with an objective measure of relevant parameters of voice. Praat, MDVP (Multi Dimensional Voice Program) and SAV (Software for Voice Analysis) are some examples of software for speech analysis. This paper describes an approach to estimate the subjective characteristics of RASATI scale given objective acoustical parameters. Two approaches were used: linear regression with non-negativity constraints, and neural networks. The experiments show that such approach gives correct evaluations with ±1 error in 80% of the cases.

Simultaneous Parameters Identifiability and Estimation of an E. coli Metabolic Network Model

Directory of Open Access Journals (Sweden)

Kese Pontes Freitas Alberton

2015-01-01

Full Text Available This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of the Escherichia coli K-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available.

Hydrogen Storage for Aircraft Applications Overview

Science.gov (United States)

Colozza, Anthony J.; Kohout, Lisa (Technical Monitor)

2002-01-01

Advances in fuel cell technology have brought about their consideration as sources of power for aircraft. This power can be utilized to run aircraft systems or even provide propulsion power. One of the key obstacles to utilizing fuel cells on aircraft is the storage of hydrogen. An overview of the potential methods of hydrogen storage was compiled. This overview identifies various methods of hydrogen storage and points out their advantages and disadvantages relative to aircraft applications. Minimizing weight and volume are the key aspects to storing hydrogen within an aircraft. An analysis was performed to show how changes in certain parameters of a given storage system affect its mass and volume.

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.

Iterative importance sampling algorithms for parameter estimation

OpenAIRE

Morzfeld, Matthias; Day, Marcus S.; Grout, Ray W.; Pau, George Shu Heng; Finsterle, Stefan A.; Bell, John B.

2016-01-01

In parameter estimation problems one computes a posterior distribution over uncertain parameters defined jointly by a prior distribution, a model, and noisy data. Markov Chain Monte Carlo (MCMC) is often used for the numerical solution of such problems. An alternative to MCMC is importance sampling, which can exhibit near perfect scaling with the number of cores on high performance computing systems because samples are drawn independently. However, finding a suitable proposal distribution is ...

Uncertainties in the Item Parameter Estimates and Robust Automated Test Assembly

Science.gov (United States)

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,…

Parameter estimation in nonlinear models for pesticide degradation

International Nuclear Information System (INIS)

Richter, O.; Pestemer, W.; Bunte, D.; Diekkrueger, B.

1991-01-01

A wide class of environmental transfer models is formulated as ordinary or partial differential equations. With the availability of fast computers, the numerical solution of large systems became feasible. The main difficulty in performing a realistic and convincing simulation of the fate of a substance in the biosphere is not the implementation of numerical techniques but rather the incomplete data basis for parameter estimation. Parameter estimation is a synonym for statistical and numerical procedures to derive reasonable numerical values for model parameters from data. The classical method is the familiar linear regression technique which dates back to the 18th century. Because it is easy to handle, linear regression has long been established as a convenient tool for analysing relationships. However, the wide use of linear regression has led to an overemphasis of linear relationships. In nature, most relationships are nonlinear and linearization often gives a poor approximation of reality. Furthermore, pure regression models are not capable to map the dynamics of a process. Therefore, realistic models involve the evolution in time (and space). This leads in a natural way to the formulation of differential equations. To establish the link between data and dynamical models, numerical advanced parameter identification methods have been developed in recent years. This paper demonstrates the application of these techniques to estimation problems in the field of pesticide dynamics. (7 refs., 5 figs., 2 tabs.)

Simple method for quick estimation of aquifer hydrogeological parameters

Science.gov (United States)

Ma, C.; Li, Y. Y.

2017-08-01

Development of simple and accurate methods to determine the aquifer hydrogeological parameters was of importance for groundwater resources assessment and management. Aiming at the present issue of estimating aquifer parameters based on some data of the unsteady pumping test, a fitting function of Theis well function was proposed using fitting optimization method and then a unitary linear regression equation was established. The aquifer parameters could be obtained by solving coefficients of the regression equation. The application of the proposed method was illustrated, using two published data sets. By the error statistics and analysis on the pumping drawdown, it showed that the method proposed in this paper yielded quick and accurate estimates of the aquifer parameters. The proposed method could reliably identify the aquifer parameters from long distance observed drawdowns and early drawdowns. It was hoped that the proposed method in this paper would be helpful for practicing hydrogeologists and hydrologists.

Compressive Parameter Estimation for Sparse Translation-Invariant Signals Using Polar Interpolation

DEFF Research Database (Denmark)

Fyhn, Karsten; Duarte, Marco F.; Jensen, Søren Holdt

2015-01-01

We propose new compressive parameter estimation algorithms that make use of polar interpolation to improve the estimator precision. Our work extends previous approaches involving polar interpolation for compressive parameter estimation in two aspects: (i) we extend the formulation from real non...... to attain good estimation precision and keep the computational complexity low. Our numerical experiments show that the proposed algorithms outperform existing approaches that either leverage polynomial interpolation or are based on a conversion to a frequency-estimation problem followed by a super...... interpolation increases the estimation precision....

Estimation of Parameters in Mean-Reverting Stochastic Systems

Directory of Open Access Journals (Sweden)

Tianhai Tian

2014-01-01

Full Text Available Stochastic differential equation (SDE is a very important mathematical tool to describe complex systems in which noise plays an important role. SDE models have been widely used to study the dynamic properties of various nonlinear systems in biology, engineering, finance, and economics, as well as physical sciences. Since a SDE can generate unlimited numbers of trajectories, it is difficult to estimate model parameters based on experimental observations which may represent only one trajectory of the stochastic model. Although substantial research efforts have been made to develop effective methods, it is still a challenge to infer unknown parameters in SDE models from observations that may have large variations. Using an interest rate model as a test problem, in this work we use the Bayesian inference and Markov Chain Monte Carlo method to estimate unknown parameters in SDE models.

Conceptual design of high speed supersonic aircraft: A brief review on SR-71 (Blackbird) aircraft

Science.gov (United States)

Xue, Hui; Khawaja, H.; Moatamedi, M.

2014-12-01

The paper presents the conceptual design of high-speed supersonic aircraft. The study focuses on SR-71 (Blackbird) aircraft. The input to the conceptual design is a mission profile. Mission profile is a flight profile of the aircraft defined by the customer. This paper gives the SR-71 aircraft mission profile specified by US air force. Mission profile helps in defining the attributes the aircraft such as wing profile, vertical tail configuration, propulsion system, etc. Wing profile and vertical tail configurations have direct impact on lift, drag, stability, performance and maneuverability of the aircraft. A propulsion system directly influences the performance of the aircraft. By combining the wing profile and the propulsion system, two important parameters, known as wing loading and thrust to weight ratio can be calculated. In this work, conceptual design procedure given by D. P. Raymer (AIAA Educational Series) is applied to calculate wing loading and thrust to weight ratio. The calculated values are compared against the actual values of the SR-71 aircraft. Results indicates that the values are in agreement with the trend of developments in aviation.

Statistical distributions applications and parameter estimates

CERN Document Server

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 ...

Uncertainty estimation of core safety parameters using cross-correlations of covariance matrix

International Nuclear Information System (INIS)

Yamamoto, Akio; Yasue, Yoshihiro; Endo, Tomohiro; Kodama, Yasuhiro; Ohoka, Yasunori; Tatsumi, Masahiro

2013-01-01

An uncertainty reduction method for core safety parameters, for which measurement values are not obtained, is proposed. We empirically recognize that there exist some correlations among the prediction errors of core safety parameters, e.g., a correlation between the control rod worth and the assembly relative power at corresponding position. Correlations of errors among core safety parameters are theoretically estimated using the covariance of cross sections and sensitivity coefficients of core parameters. The estimated correlations of errors among core safety parameters are verified through the direct Monte Carlo sampling method. Once the correlation of errors among core safety parameters is known, we can estimate the uncertainty of a safety parameter for which measurement value is not obtained. (author)

Adaptive distributed parameter and input estimation in linear parabolic PDEs

KAUST Repository

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.

Robust estimation of hydrological model parameters

Directory of Open Access Journals (Sweden)

A. Bárdossy

2008-11-01

Full Text Available The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

Targeted estimation of nuisance parameters to obtain valid statistical inference.

Science.gov (United States)

van der Laan, Mark J

2014-01-01

In order to obtain concrete results, we focus on estimation of the treatment specific mean, controlling for all measured baseline covariates, based on observing independent and identically distributed copies of a random variable consisting of baseline covariates, a subsequently assigned binary treatment, and a final outcome. The statistical model only assumes possible restrictions on the conditional distribution of treatment, given the covariates, the so-called propensity score. Estimators of the treatment specific mean involve estimation of the propensity score and/or estimation of the conditional mean of the outcome, given the treatment and covariates. In order to make these estimators asymptotically unbiased at any data distribution in the statistical model, it is essential to use data-adaptive estimators of these nuisance parameters such as ensemble learning, and specifically super-learning. Because such estimators involve optimal trade-off of bias and variance w.r.t. the infinite dimensional nuisance parameter itself, they result in a sub-optimal bias/variance trade-off for the resulting real-valued estimator of the estimand. We demonstrate that additional targeting of the estimators of these nuisance parameters guarantees that this bias for the estimand is second order and thereby allows us to prove theorems that establish asymptotic linearity of the estimator of the treatment specific mean under regularity conditions. These insights result in novel targeted minimum loss-based estimators (TMLEs) that use ensemble learning with additional targeted bias reduction to construct estimators of the nuisance parameters. In particular, we construct collaborative TMLEs (C-TMLEs) with known influence curve allowing for statistical inference, even though these C-TMLEs involve variable selection for the propensity score based on a criterion that measures how effective the resulting fit of the propensity score is in removing bias for the estimand. As a particular special

minimum variance estimation of yield parameters of rubber tree

African Journals Online (AJOL)

2013-03-01

Mar 1, 2013 ... It is our opinion that Kalman filter is a robust estimator of the ... Kalman filter, parameter estimation, rubber clones, Chow failure test, autocorrelation, STAMP, data ...... Mills, T.C. Modelling Current Temperature Trends.

Parameter Estimation of Multiple Frequency-Hopping Signals with Two Sensors

Directory of Open Access Journals (Sweden)

Le Zuo

2018-04-01

Full Text Available This paper essentially focuses on parameter estimation of multiple wideband emitting sources with time-varying frequencies, such as two-dimensional (2-D direction of arrival (DOA and signal sorting, with a low-cost circular synthetic array (CSA consisting of only two rotating sensors. Our basic idea is to decompose the received data, which is a superimposition of phase measurements from multiple sources into separated groups and separately estimate the DOA associated with each source. Motivated by joint parameter estimation, we propose to adopt the expectation maximization (EM algorithm in this paper; our method involves two steps, namely, the expectation-step (E-step and the maximization (M-step. In the E-step, the correspondence of each signal with its emitting source is found. Then, in the M-step, the maximum-likelihood (ML estimates of the DOA parameters are obtained. These two steps are iteratively and alternatively executed to jointly determine the DOAs and sort multiple signals. Closed-form DOA estimation formulae are developed by ML estimation based on phase data, which also realize an optimal estimation. Directional ambiguity is also addressed by another ML estimation method based on received complex responses. The Cramer-Rao lower bound is derived for understanding the estimation accuracy and performance comparison. The verification of the proposed method is demonstrated with simulations.

Automatic smoothing parameter selection in GAMLSS with an application to centile estimation.

Science.gov (United States)

Rigby, Robert A; Stasinopoulos, Dimitrios M

2014-08-01

A method for automatic selection of the smoothing parameters in a generalised additive model for location, scale and shape (GAMLSS) model is introduced. The method uses a P-spline representation of the smoothing terms to express them as random effect terms with an internal (or local) maximum likelihood estimation on the predictor scale of each distribution parameter to estimate its smoothing parameters. This provides a fast method for estimating multiple smoothing parameters. The method is applied to centile estimation where all four parameters of a distribution for the response variable are modelled as smooth functions of a transformed explanatory variable x This allows smooth modelling of the location, scale, skewness and kurtosis parameters of the response variable distribution as functions of x. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Estimation of G-renewal process parameters as an ill-posed inverse problem

International Nuclear Information System (INIS)

Krivtsov, V.; Yevkin, O.

2013-01-01

Statistical estimation of G-renewal process parameters is an important estimation problem, which has been considered by many authors. We view this problem from the standpoint of a mathematically ill-posed, inverse problem (the solution is not unique and/or is sensitive to statistical error) and propose a regularization approach specifically suited to the G-renewal process. Regardless of the estimation method, the respective objective function usually involves parameters of the underlying life-time distribution and simultaneously the restoration parameter. In this paper, we propose to regularize the problem by decoupling the estimation of the aforementioned parameters. Using a simulation study, we show that the resulting estimation/extrapolation accuracy of the proposed method is considerably higher than that of the existing methods

Joint Multi-Fiber NODDI Parameter Estimation and Tractography using the Unscented Information Filter

Directory of Open Access Journals (Sweden)

Yogesh eRathi

2016-04-01

Full Text Available Tracing white matter fiber bundles is an integral part of analyzing brain connectivity. An accurate estimate of the underlying tissue parameters is also paramount in several neuroscience applications. In this work, we propose to use a joint fiber model estimation and tractography algorithm that uses the NODDI (neurite orientation dispersion diffusion imaging model to estimate fiber orientation dispersion consistently and smoothly along the fiber tracts along with estimating the intracellular and extracellular volume fractions from the diffusion signal. While the NODDI model has been used in earlier works to estimate the microstructural parameters at each voxel independently, for the first time, we propose to integrate it into a tractography framework. We extend this framework to estimate the NODDI parameters for two crossing fibers, which is imperative to trace fiber bundles through crossings as well as to estimate the microstructural parameters for each fiber bundle separately. We propose to use the unscented information filter (UIF to accurately estimate the model parameters and perform tractography. The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF. Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters, which can be quite useful to measure model complexity. Results from in-vivo human brain data sets demonstrate the ability of our algorithm to trace through crossing fiber regions, while estimating orientation dispersion and other biophysical model parameters in a consistent manner along the tracts.

Estimation of octanol/water partition coefficients using LSER parameters

Science.gov (United States)

Luehrs, Dean C.; Hickey, James P.; Godbole, Kalpana A.; Rogers, Tony N.

1998-01-01

The logarithms of octanol/water partition coefficients, logKow, were regressed against the linear solvation energy relationship (LSER) parameters for a training set of 981 diverse organic chemicals. The standard deviation for logKow was 0.49. The regression equation was then used to estimate logKow for a test of 146 chemicals which included pesticides and other diverse polyfunctional compounds. Thus the octanol/water partition coefficient may be estimated by LSER parameters without elaborate software but only moderate accuracy should be expected.

MCMC for parameters estimation by bayesian approach

International Nuclear Information System (INIS)

Ait Saadi, H.; Ykhlef, F.; Guessoum, A.

2011-01-01

This article discusses the parameter estimation for dynamic system by a Bayesian approach associated with Markov Chain Monte Carlo methods (MCMC). The MCMC methods are powerful for approximating complex integrals, simulating joint distributions, and the estimation of marginal posterior distributions, or posterior means. The MetropolisHastings algorithm has been widely used in Bayesian inference to approximate posterior densities. Calibrating the proposal distribution is one of the main issues of MCMC simulation in order to accelerate the convergence.

Estimating model parameters in nonautonomous chaotic systems using synchronization

International Nuclear Information System (INIS)

Yang, Xiaoli; Xu, Wei; Sun, Zhongkui

2007-01-01

In this Letter, a technique is addressed for estimating unknown model parameters of multivariate, in particular, nonautonomous chaotic systems from time series of state variables. This technique uses an adaptive strategy for tracking unknown parameters in addition to a linear feedback coupling for synchronizing systems, and then some general conditions, by means of the periodic version of the LaSalle invariance principle for differential equations, are analytically derived to ensure precise evaluation of unknown parameters and identical synchronization between the concerned experimental system and its corresponding receiver one. Exemplifies are presented by employing a parametrically excited 4D new oscillator and an additionally excited Ueda oscillator. The results of computer simulations reveal that the technique not only can quickly track the desired parameter values but also can rapidly respond to changes in operating parameters. In addition, the technique can be favorably robust against the effect of noise when the experimental system is corrupted by bounded disturbance and the normalized absolute error of parameter estimation grows almost linearly with the cutoff value of noise strength in simulation

Surface BRDF estimation from an aircraft compared to MODIS and ground estimates at the Southern Great Plains site

Energy Technology Data Exchange (ETDEWEB)

Knobelspiesse, Kirk D.; Cairns, Brian; Schmid, Beat; Roman, Miguel O.; Schaaf, Crystal B.

2008-10-21

The surface spectral albedo is an important component of climate models since it determines the amount of incident solar radiation that is absorbed by the ground. The albedo can be highly heterogeneous, both in space and time, and thus adequate measurement and modeling is challenging. One source of measurements that constrain the surface albedo are satellite instruments that observe the Earth, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). Satellites estimate the surface bidirectional reflectance distribution function (BRDF) by correcting top of the atmosphere (TOA) radiances for atmospheric effects and accumulating observations at a variety of viewing geometries. The BRDF can then be used to determine the albedo that is required in climate modeling. Other measurements that provide a more direct constraint on surface albedo are those made by upward and downward looking radiometers at the ground. One product in particular, the Best Estimate Radiation Flux (BEFLUX) value added product of the Department of Energy’s Atmospheric Radiation Measurement (ARM) Program at the Southern Great Plains Central Facility (SGP CF) in central Oklahoma, has been used to evaluate the quality of the albedo products derived from MODIS BRDF estimates. These comparisons have highlighted discrepancies between the energy absorbed at the surface that is calculated from the BEFLUX products and that is predicted from the MODIS BRDF product. This paper attempts to investigate these discrepancies by using data from an airborne scanning radiometer, the Research Scanning Polarimeter (RSP) that was flown at low altitude in the vicinity of the SGP CF site during the Aerosol Lidar Validation Experiment (ALIVE) in September of 2005. The RSP is a polarimeter that scans in the direction of the aircraft ground track, and can thus estimate the BRDF in a period of seconds, rather than the days required by MODIS to accumulate enough viewing angles. Atmospheric correction is aided by the

Adaptive distributed parameter and input estimation in linear parabolic PDEs

KAUST Repository

Mechhoud, Sarra

2016-01-01

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.

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.

Estimation of delays and other parameters in nonlinear functional differential equations

Science.gov (United States)

Banks, H. T.; Lamm, P. K. D.

1983-01-01

A spline-based approximation scheme for nonlinear nonautonomous delay differential equations is discussed. Convergence results (using dissipative type estimates on the underlying nonlinear operators) are given in the context of parameter estimation problems which include estimation of multiple delays and initial data as well as the usual coefficient-type parameters. A brief summary of some of the related numerical findings is also given.

Nonparametric estimation of location and scale parameters

KAUST Repository

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.

Bayesian estimation of parameters in a regional hydrological model

Directory of Open Access Journals (Sweden)

K. Engeland

2002-01-01

Full Text Available This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR(1 process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties. Keywords: regional hydrological model, model uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis

Synthesis of Algorithm for Range Measurement Equipment to Track Maneuvering Aircraft Using Data on Its Dynamic and Kinematic Parameters

Science.gov (United States)

Pudovkin, A. P.; Panasyuk, Yu N.; Danilov, S. N.; Moskvitin, S. P.

2018-05-01

The problem of improving automated air traffic control systems is considered through the example of the operation algorithm synthesis for a range measurement channel to track the aircraft, using its kinematic and dynamic parameters. The choice of the state and observation models has been justified, the computer simulations have been performed and the results of the investigated algorithms have been obtained.

Estimation of common cause failure parameters with periodic tests

Energy Technology Data Exchange (ETDEWEB)

Barros, Anne [Institut Charles Delaunay - Universite de technologie de Troyes - FRE CNRS 2848, 12, rue Marie Curie - BP 2060 -10010 Troyes cedex (France)], E-mail: anne.barros@utt.fr; Grall, Antoine [Institut Charles Delaunay - Universite de technologie de Troyes - FRE CNRS 2848, 12, rue Marie Curie - BP 2060 -10010 Troyes cedex (France); Vasseur, Dominique [Electricite de France, EDF R and D - Industrial Risk Management Department 1, av. du General de Gaulle- 92141 Clamart (France)

2009-04-15

In the specific case of safety systems, CCF parameters estimators for standby components depend on the periodic test schemes. Classically, the testing schemes are either staggered (alternation of tests on redundant components) or non-staggered (all components are tested at the same time). In reality, periodic tests schemes performed on safety components are more complex and combine staggered tests, when the plant is in operation, to non-staggered tests during maintenance and refueling outage periods of the installation. Moreover, the CCF parameters estimators described in the US literature are derived in a consistent way with US Technical Specifications constraints that do not apply on the French Nuclear Power Plants for staggered tests on standby components. Given these issues, the evaluation of CCF parameters from the operating feedback data available within EDF implies the development of methodologies that integrate the testing schemes specificities. This paper aims to formally propose a solution for the estimation of CCF parameters given two distinct difficulties respectively related to a mixed testing scheme and to the consistency with EDF's specific practices inducing systematic non-simultaneity of the observed failures in a staggered testing scheme.

Consistent Parameter and Transfer Function Estimation using Context Free Grammars

Science.gov (United States)

Klotz, Daniel; Herrnegger, Mathew; Schulz, Karsten

2017-04-01

This contribution presents a method for the inference of transfer functions for rainfall-runoff models. Here, transfer functions are defined as parametrized (functional) relationships between a set of spatial predictors (e.g. elevation, slope or soil texture) and model parameters. They are ultimately used for estimation of consistent, spatially distributed model parameters from a limited amount of lumped global parameters. Additionally, they provide a straightforward method for parameter extrapolation from one set of basins to another and can even be used to derive parameterizations for multi-scale models [see: Samaniego et al., 2010]. Yet, currently an actual knowledge of the transfer functions is often implicitly assumed. As a matter of fact, for most cases these hypothesized transfer functions can rarely be measured and often remain unknown. Therefore, this contribution presents a general method for the concurrent estimation of the structure of transfer functions and their respective (global) parameters. Note, that by consequence an estimation of the distributed parameters of the rainfall-runoff model is also undertaken. The method combines two steps to achieve this. The first generates different possible transfer functions. The second then estimates the respective global transfer function parameters. The structural estimation of the transfer functions is based on the context free grammar concept. Chomsky first introduced context free grammars in linguistics [Chomsky, 1956]. Since then, they have been widely applied in computer science. But, to the knowledge of the authors, they have so far not been used in hydrology. Therefore, the contribution gives an introduction to context free grammars and shows how they can be constructed and used for the structural inference of transfer functions. This is enabled by new methods from evolutionary computation, such as grammatical evolution [O'Neill, 2001], which make it possible to exploit the constructed grammar as a

Parameter and state estimation in nonlinear dynamical systems

Science.gov (United States)

Creveling, Daniel R.

This thesis is concerned with the problem of state and parameter estimation in nonlinear systems. The need to evaluate unknown parameters in models of nonlinear physical, biophysical and engineering systems occurs throughout the development of phenomenological or reduced models of dynamics. When verifying and validating these models, it is important to incorporate information from observations in an efficient manner. Using the idea of synchronization of nonlinear dynamical systems, this thesis develops a framework for presenting data to a candidate model of a physical process in a way that makes efficient use of the measured data while allowing for estimation of the unknown parameters in the model. The approach presented here builds on existing work that uses synchronization as a tool for parameter estimation. Some critical issues of stability in that work are addressed and a practical framework is developed for overcoming these difficulties. The central issue is the choice of coupling strength between the model and data. If the coupling is too strong, the model will reproduce the measured data regardless of the adequacy of the model or correctness of the parameters. If the coupling is too weak, nonlinearities in the dynamics could lead to complex dynamics rendering any cost function comparing the model to the data inadequate for the determination of model parameters. Two methods are introduced which seek to balance the need for coupling with the desire to allow the model to evolve in its natural manner without coupling. One method, 'balanced' synchronization, adds to the synchronization cost function a requirement that the conditional Lyapunov exponents of the model system, conditioned on being driven by the data, remain negative but small in magnitude. Another method allows the coupling between the data and the model to vary in time according to a specific form of differential equation. The coupling dynamics is damped to allow for a tendency toward zero coupling

Parameter estimation for lithium ion batteries

Science.gov (United States)

Santhanagopalan, Shriram

With an increase in the demand for lithium based batteries at the rate of about 7% per year, the amount of effort put into improving the performance of these batteries from both experimental and theoretical perspectives is increasing. There exist a number of mathematical models ranging from simple empirical models to complicated physics-based models to describe the processes leading to failure of these cells. The literature is also rife with experimental studies that characterize the various properties of the system in an attempt to improve the performance of lithium ion cells. However, very little has been done to quantify the experimental observations and relate these results to the existing mathematical models. In fact, the best of the physics based models in the literature show as much as 20% discrepancy when compared to experimental data. The reasons for such a big difference include, but are not limited to, numerical complexities involved in extracting parameters from experimental data and inconsistencies in interpreting directly measured values for the parameters. In this work, an attempt has been made to implement simplified models to extract parameter values that accurately characterize the performance of lithium ion cells. The validity of these models under a variety of experimental conditions is verified using a model discrimination procedure. Transport and kinetic properties are estimated using a non-linear estimation procedure. The initial state of charge inside each electrode is also maintained as an unknown parameter, since this value plays a significant role in accurately matching experimental charge/discharge curves with model predictions and is not readily known from experimental data. The second part of the dissertation focuses on parameters that change rapidly with time. For example, in the case of lithium ion batteries used in Hybrid Electric Vehicle (HEV) applications, the prediction of the State of Charge (SOC) of the cell under a variety of

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)

Accuracy and sensitivity analysis on seismic anisotropy parameter estimation

Science.gov (United States)

Yan, Fuyong; Han, De-Hua

2018-04-01

There is significant uncertainty in measuring the Thomsen’s parameter δ in laboratory even though the dimensions and orientations of the rock samples are known. It is expected that more challenges will be encountered in the estimating of the seismic anisotropy parameters from field seismic data. Based on Monte Carlo simulation of vertical transversely isotropic layer cake model using the database of laboratory anisotropy measurement from the literature, we apply the commonly used quartic non-hyperbolic reflection moveout equation to estimate the seismic anisotropy parameters and test its accuracy and sensitivities to the source-receive offset, vertical interval velocity error and time picking error. The testing results show that the methodology works perfectly for noise-free synthetic data with short spread length. However, this method is extremely sensitive to the time picking error caused by mild random noises, and it requires the spread length to be greater than the depth of the reflection event. The uncertainties increase rapidly for the deeper layers and the estimated anisotropy parameters can be very unreliable for a layer with more than five overlain layers. It is possible that an isotropic formation can be misinterpreted as a strong anisotropic formation. The sensitivity analysis should provide useful guidance on how to group the reflection events and build a suitable geological model for anisotropy parameter inversion.

Estimation of Compaction Parameters Based on Soil Classification

Science.gov (United States)

Lubis, A. S.; Muis, Z. A.; Hastuty, I. P.; Siregar, I. M.

2018-02-01

Factors that must be considered in compaction of the soil works were the type of soil material, field control, maintenance and availability of funds. Those problems then raised the idea of how to estimate the density of the soil with a proper implementation system, fast, and economical. This study aims to estimate the compaction parameter i.e. the maximum dry unit weight (γ dmax) and optimum water content (Wopt) based on soil classification. Each of 30 samples were being tested for its properties index and compaction test. All of the data’s from the laboratory test results, were used to estimate the compaction parameter values by using linear regression and Goswami Model. From the research result, the soil types were A4, A-6, and A-7 according to AASHTO and SC, SC-SM, and CL based on USCS. By linear regression, the equation for estimation of the maximum dry unit weight (γdmax *)=1,862-0,005*FINES- 0,003*LL and estimation of the optimum water content (wopt *)=- 0,607+0,362*FINES+0,161*LL. By Goswami Model (with equation Y=mLogG+k), for estimation of the maximum dry unit weight (γdmax *) with m=-0,376 and k=2,482, for estimation of the optimum water content (wopt *) with m=21,265 and k=-32,421. For both of these equations a 95% confidence interval was obtained.

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.

Estimating 3D Object Parameters from 2D Grey-Level Images

NARCIS (Netherlands)

Houkes, Z.

2000-01-01

This thesis describes a general framework for parameter estimation, which is suitable for computer vision applications. The approach described combines 3D modelling, animation and estimation tools to determine parameters of objects in a scene from 2D grey-level images. The animation tool predicts

Estimations of parameters in Pareto reliability model in the presence of masked data

International Nuclear Information System (INIS)

Sarhan, Ammar M.

2003-01-01

Estimations of parameters included in the individual distributions of the life times of system components in a series system are considered in this paper based on masked system life test data. We consider a series system of two independent components each has a Pareto distributed lifetime. The maximum likelihood and Bayes estimators for the parameters and the values of the reliability of the system's components at a specific time are obtained. Symmetrical triangular prior distributions are assumed for the unknown parameters to be estimated in obtaining the Bayes estimators of these parameters. Large simulation studies are done in order: (i) explain how one can utilize the theoretical results obtained; (ii) compare the maximum likelihood and Bayes estimates obtained of the underlying parameters; and (iii) study the influence of the masking level and the sample size on the accuracy of the estimates obtained

Comparisons of cloud ice mass content retrieved from the radar-infrared radiometer method with aircraft data during the second international satellite cloud climatology project regional experiment (FIRE-II)

Energy Technology Data Exchange (ETDEWEB)

Matrosov, S.Y. [Univ. of Colorado, Boulder, CO (United States)]|[National Oceanic and Atmospheric Administration Environmental Technology Lab., Boulder, CO (United States); Heymsfield, A.J. [National Center for Atmospheric Research, Boulder, CO (United States); Kropfli, R.A.; Snider, J.B. [National Oceanic and Atmospheric Administration Environmental Technology Lab., Boulder, CO (United States)

1996-04-01

Comparisons of remotely sensed meteorological parameters with in situ direct measurements always present a challenge. Matching sampling volumes is one of the main problems for such comparisons. Aircraft usually collect data when flying along a horizontal leg at a speed of about 100 m/sec (or even greater). The usual sampling time of 5 seconds provides an average horizontal resolution of the order of 500 m. Estimations of vertical profiles of cloud microphysical parameters from aircraft measurements are hampered by sampling a cloud at various altitudes at different times. This paper describes the accuracy of aircraft horizontal and vertical coordinates relative to the location of the ground-based instruments.

Quasi-Newton methods for parameter estimation in functional differential equations

Science.gov (United States)

Brewer, Dennis W.

1988-01-01

A state-space approach to parameter estimation in linear functional differential equations is developed using the theory of linear evolution equations. A locally convergent quasi-Newton type algorithm is applied to distributed systems with particular emphasis on parameters that induce unbounded perturbations of the state. The algorithm is computationally implemented on several functional differential equations, including coefficient and delay estimation in linear delay-differential equations.

Statistical aspects of carbon fiber risk assessment modeling. [fire accidents involving aircraft

Science.gov (United States)

Gross, D.; Miller, D. R.; Soland, R. M.

1980-01-01

The probabilistic and statistical aspects of the carbon fiber risk assessment modeling of fire accidents involving commercial aircraft are examined. Three major sources of uncertainty in the modeling effort are identified. These are: (1) imprecise knowledge in establishing the model; (2) parameter estimation; and (3)Monte Carlo sampling error. All three sources of uncertainty are treated and statistical procedures are utilized and/or developed to control them wherever possible.

Estimating Arrhenius parameters using temperature programmed molecular dynamics

International Nuclear Information System (INIS)

Imandi, Venkataramana; Chatterjee, Abhijit

2016-01-01

Kinetic rates at different temperatures and the associated Arrhenius parameters, whenever Arrhenius law is obeyed, are efficiently estimated by applying maximum likelihood analysis to waiting times collected using the temperature programmed molecular dynamics method. When transitions involving many activated pathways are available in the dataset, their rates may be calculated using the same collection of waiting times. Arrhenius behaviour is ascertained by comparing rates at the sampled temperatures with ones from the Arrhenius expression. Three prototype systems with corrugated energy landscapes, namely, solvated alanine dipeptide, diffusion at the metal-solvent interphase, and lithium diffusion in silicon, are studied to highlight various aspects of the method. The method becomes particularly appealing when the Arrhenius parameters can be used to find rates at low temperatures where transitions are rare. Systematic coarse-graining of states can further extend the time scales accessible to the method. Good estimates for the rate parameters are obtained with 500-1000 waiting times.

Estimating Arrhenius parameters using temperature programmed molecular dynamics

Energy Technology Data Exchange (ETDEWEB)

Imandi, Venkataramana; Chatterjee, Abhijit, E-mail: abhijit@che.iitb.ac.in [Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076 (India)

2016-07-21

Kinetic rates at different temperatures and the associated Arrhenius parameters, whenever Arrhenius law is obeyed, are efficiently estimated by applying maximum likelihood analysis to waiting times collected using the temperature programmed molecular dynamics method. When transitions involving many activated pathways are available in the dataset, their rates may be calculated using the same collection of waiting times. Arrhenius behaviour is ascertained by comparing rates at the sampled temperatures with ones from the Arrhenius expression. Three prototype systems with corrugated energy landscapes, namely, solvated alanine dipeptide, diffusion at the metal-solvent interphase, and lithium diffusion in silicon, are studied to highlight various aspects of the method. The method becomes particularly appealing when the Arrhenius parameters can be used to find rates at low temperatures where transitions are rare. Systematic coarse-graining of states can further extend the time scales accessible to the method. Good estimates for the rate parameters are obtained with 500-1000 waiting times.

Using linear time-invariant system theory to estimate kinetic parameters directly from projection measurements

International Nuclear Information System (INIS)

Zeng, G.L.; Gullberg, G.T.

1995-01-01

It is common practice to estimate kinetic parameters from dynamically acquired tomographic data by first reconstructing a dynamic sequence of three-dimensional reconstructions and then fitting the parameters to time activity curves generated from the time-varying reconstructed images. However, in SPECT, the pharmaceutical distribution can change during the acquisition of a complete tomographic data set, which can bias the estimated kinetic parameters. It is hypothesized that more accurate estimates of the kinetic parameters can be obtained by fitting to the projection measurements instead of the reconstructed time sequence. Estimation from projections requires the knowledge of their relationship between the tissue regions of interest or voxels with particular kinetic parameters and the project measurements, which results in a complicated nonlinear estimation problem with a series of exponential factors with multiplicative coefficients. A technique is presented in this paper where the exponential decay parameters are estimated separately using linear time-invariant system theory. Once the exponential factors are known, the coefficients of the exponentials can be estimated using linear estimation techniques. Computer simulations demonstrate that estimation of the kinetic parameters directly from the projections is more accurate than the estimation from the reconstructed images

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.

Improved Battery Parameter Estimation Method Considering Operating Scenarios for HEV/EV Applications

Directory of Open Access Journals (Sweden)

Jufeng Yang

2016-12-01

Full Text Available This paper presents an improved battery parameter estimation method based on typical operating scenarios in hybrid electric vehicles and pure electric vehicles. Compared with the conventional estimation methods, the proposed method takes both the constant-current charging and the dynamic driving scenarios into account, and two separate sets of model parameters are estimated through different parts of the pulse-rest test. The model parameters for the constant-charging scenario are estimated from the data in the pulse-charging periods, while the model parameters for the dynamic driving scenario are estimated from the data in the rest periods, and the length of the fitted dataset is determined by the spectrum analysis of the load current. In addition, the unsaturated phenomenon caused by the long-term resistor-capacitor (RC network is analyzed, and the initial voltage expressions of the RC networks in the fitting functions are improved to ensure a higher model fidelity. Simulation and experiment results validated the feasibility of the developed estimation method.

RETRACTED ARTICLE: Validation of mean and turbulent parameters measured from the aircraft in the marine atmospheric boundary layer

Science.gov (United States)

Kwon, Byung Hyuk; Lee, Gyuwon

2010-11-01

The SEMAPHORE (Structure des Echanges Mer-Atmosphère, Propriétés Océaniques/ Recherche Expérimentale) experiment, which took place between 04 Oct. and 17 Nov. 1993, was conducted over the oceanic Azores current located in the Azores basin. The SST (Sea Surface Temperature) field was characterized in the SEMAPHORE area (31°-38°N; 21°-28°W) by a large meander with a SST gradient of about 1°C per 100 km. In order to study the evolution of the MABL (Marine Atmospheric Boundary Layer) over the ocean, the mean and the turbulent data were evaluated by the measurement with two aircraft and a ship in different meteorological conditions. Three cases of low pressure and three cases of high pressure are mainly presented here. For the six cases, the satellite images (NOAA) did not show any relation between the SST field and the cloud cover. At each flight level, the decrease of the SST with the altitude due to the divergence of the infrared radiation flux from the ocean is 0.25°C per 100 m. For the comparison between the two aircraft, the mean thermodynamic and dynamic parameters show a good agreement except for the temperature. The dispersion of the sensible heat flux is larger than that of the latent heat flux due to the weak sensible heat flux over the ocean both in the intercomparison between two aircraft and in the comparison between the aircraft and the ship.

Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

Science.gov (United States)

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.

Circuit realization, chaos synchronization and estimation of parameters of a hyperchaotic system with unknown parameters

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.

Comparison of sampling techniques for Bayesian parameter estimation

Science.gov (United States)

Allison, Rupert; Dunkley, Joanna

2014-02-01

The posterior probability distribution for a set of model parameters encodes all that the data have to tell us in the context of a given model; it is the fundamental quantity for Bayesian parameter estimation. In order to infer the posterior probability distribution we have to decide how to explore parameter space. Here we compare three prescriptions for how parameter space is navigated, discussing their relative merits. We consider Metropolis-Hasting sampling, nested sampling and affine-invariant ensemble Markov chain Monte Carlo (MCMC) sampling. We focus on their performance on toy-model Gaussian likelihoods and on a real-world cosmological data set. We outline the sampling algorithms themselves and elaborate on performance diagnostics such as convergence time, scope for parallelization, dimensional scaling, requisite tunings and suitability for non-Gaussian distributions. We find that nested sampling delivers high-fidelity estimates for posterior statistics at low computational cost, and should be adopted in favour of Metropolis-Hastings in many cases. Affine-invariant MCMC is competitive when computing clusters can be utilized for massive parallelization. Affine-invariant MCMC and existing extensions to nested sampling naturally probe multimodal and curving distributions.

PARAMETER ESTIMATION AND MODEL SELECTION FOR INDOOR ENVIRONMENTS BASED ON SPARSE OBSERVATIONS

Directory of Open Access Journals (Sweden)

Y. Dehbi

2017-09-01

Full Text Available This paper presents a novel method for the parameter estimation and model selection for the reconstruction of indoor environments based on sparse observations. While most approaches for the reconstruction of indoor models rely on dense observations, we predict scenes of the interior with high accuracy in the absence of indoor measurements. We use a model-based top-down approach and incorporate strong but profound prior knowledge. The latter includes probability density functions for model parameters and sparse observations such as room areas and the building footprint. The floorplan model is characterized by linear and bi-linear relations with discrete and continuous parameters. We focus on the stochastic estimation of model parameters based on a topological model derived by combinatorial reasoning in a first step. A Gauss-Markov model is applied for estimation and simulation of the model parameters. Symmetries are represented and exploited during the estimation process. Background knowledge as well as observations are incorporated in a maximum likelihood estimation and model selection is performed with AIC/BIC. The likelihood is also used for the detection and correction of potential errors in the topological model. Estimation results are presented and discussed.

Parameter Estimation and Model Selection for Indoor Environments Based on Sparse Observations

Science.gov (United States)

Dehbi, Y.; Loch-Dehbi, S.; Plümer, L.

2017-09-01

This paper presents a novel method for the parameter estimation and model selection for the reconstruction of indoor environments based on sparse observations. While most approaches for the reconstruction of indoor models rely on dense observations, we predict scenes of the interior with high accuracy in the absence of indoor measurements. We use a model-based top-down approach and incorporate strong but profound prior knowledge. The latter includes probability density functions for model parameters and sparse observations such as room areas and the building footprint. The floorplan model is characterized by linear and bi-linear relations with discrete and continuous parameters. We focus on the stochastic estimation of model parameters based on a topological model derived by combinatorial reasoning in a first step. A Gauss-Markov model is applied for estimation and simulation of the model parameters. Symmetries are represented and exploited during the estimation process. Background knowledge as well as observations are incorporated in a maximum likelihood estimation and model selection is performed with AIC/BIC. The likelihood is also used for the detection and correction of potential errors in the topological model. Estimation results are presented and discussed.

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

Mathematical models have long been used for prediction of dynamics in biological systems. Recently, several efforts have been made to render these models patient specific. One way to do so is to employ techniques to estimate parameters that enable model based prediction of observed quantities....... 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...... a subset of model parameters reducing the complexity of the problem. In this study, we compare three methods that allow identification of parameter subsets that can be estimated given a model and a set of data. These methods will be used to estimate patient specific parameters in a model predicting...

Models for estimating photosynthesis parameters from in situ production profiles

Science.gov (United States)

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

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

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...... parameters, which are strongly correlated with each other. State-of-the-art methodologies such as nonlinear regression (using progress curves) or graphical analysis (using initial rate data, for example, the Lineweaver-Burke plot, Hanes plot or Dixon plot) often incorporate errors in the estimates and rarely...

Systematic analysis of aircraft separation requirements

Science.gov (United States)

Ennis, Rachelle Lea

2005-12-01

Minimum separation standards are necessary for safety in the air traffic control system. At the same time, minimum separation standards constrain the flow of air traffic and cause delays that translate to millions of dollars in fuel costs. Two necessary separation standards are defined. Then, practical methods for calculating the minimum required size of these separation standards are presented. First, the protected zone is considered. The protected zone represents a region around a given aircraft that no other aircraft should penetrate for the safety of both aircraft. It defines minimum separation requirements. Three major components of the protected zone and their interplays are identified: a vortex region, a safety buffer region, and a state-uncertainty region. A systematic procedure is devised for the analysis of the state-uncertainty region. In particular, models of trajectory controls are developed that can be used to represent different modes of pilot and/or autopilot controls, such as path feedback and non-path feedback. Composite protected zones under various conditions are estimated, and effective ways to reduce sizes of protected zones for advanced air traffic management are examined. In order to maintain minimum separation standards between two aircraft, proper avoidance maneuvers must be initiated before their relative separation reaches the minimum separation due to aircraft dynamics, controller and pilot response delays, etc. The concept of the required action threshold is presented. It is defined as the advanced time for which the conflict resolution process must begin in order to maintain minimum separation requirements. Five main segments in the process of conflict resolution are identified, discussed, and modeled: state information acquisition, comprehension and decision, communication, pilot response, and aircraft maneuver. Each of the five segments is modeled via a time constant. Time estimates for the first four segments are obtained from

Tsunami Prediction and Earthquake Parameters Estimation in the Red Sea

KAUST Repository

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.

Estimating Parameters in Physical Models through Bayesian Inversion: A Complete Example

KAUST Repository

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.

Mathematical Modeling of the Braking System of Wheeled Mainline Aircraft

Directory of Open Access Journals (Sweden)

I. S. Shumilov

2016-01-01

Full Text Available The braking system of the landing gear wheels of a mainline aircraft has to meet mandatory requirements laid out in the Aviation Regulations AP-25 (Para 25.735. «Brakes and brake systems". These requirements are essential when creating the landing gear wheel brake control system (WBCS and are used as main initial data in its mathematical modeling. The WBCS is one of the most important systems to ensure the safe completion of the flight. It is a complex of devices, i.e. units (hydraulic, electrical, and mechanical, connected through piping, wiring, mechanical constraints. This complex should allow optimizing the braking process when a large number of parameters change. The most important of them are the following: runway friction coefficient (RFC, lifting force, weight and of the aircraft, etc. The main structural elements involved in braking the aircraft are: aircraft wheels with pneumatics (air tires and brake discs, WBCS, and cooling system of gear wheels when braking.To consider the aircraft deceleration on the landing run is of essence at the stage of design, development, and improvement of brakes and braking systems. Based on analysis of equation of the aircraft motion and energy balance can be determined energy loading and its basic design parameters, braking distances and braking time.As practice and analysis of energy loading show, they (brake + wheel absorb the aircraftpossessed kinetic energy at the start of braking as much as 60-70%, 70-80%, and 80-90%, respectively, under normal increased, and emergency operating conditions. The paper presents a procedure for the rapid calculation of energy loading of the brake wheel.Currently, the mainline aircrafts use mainly electrohydraulic brake systems in which there are the main, backup, and emergency-parking brake systems. All channels are equipped with automatic anti-skid systems. Their presence in the emergency (the third reserve channel significantly improves the reliability and safety of

PWR system simulation and parameter estimation with neural networks

Energy Technology Data Exchange (ETDEWEB)

Akkurt, Hatice; Colak, Uener E-mail: uc@nuke.hacettepe.edu.tr

2002-11-01

A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within {+-}0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected.

PWR system simulation and parameter estimation with neural networks

International Nuclear Information System (INIS)

Akkurt, Hatice; Colak, Uener

2002-01-01

A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within ±0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected

Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression.

Science.gov (United States)

Ding, A Adam; Wu, Hulin

2014-10-01

We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method.

Nonparametric estimation of location and scale parameters

KAUST Repository

Potgieter, C.J.; Lombard, F.

2012-01-01

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

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 resp...

Parameter estimation and prediction of nonlinear biological systems: some examples

NARCIS (Netherlands)

Doeswijk, T.G.; Keesman, K.J.

2006-01-01

Rearranging and reparameterizing a discrete-time nonlinear model with polynomial quotient structure in input, output and parameters (xk = f(Z, p)) leads to a model linear in its (new) parameters. As a result, the parameter estimation problem becomes a so-called errors-in-variables problem for which

CTER-rapid estimation of CTF parameters with error assessment.

Science.gov (United States)

Penczek, Pawel A; Fang, Jia; Li, Xueming; Cheng, Yifan; Loerke, Justus; Spahn, Christian M T

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 300kV 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 4.03Å without, and 3.85Å with, inclusion of astigmatism parameters. Copyright © 2014 Elsevier B.V. All rights reserved.

Influence of measurement errors and estimated parameters on combustion diagnosis

International Nuclear Information System (INIS)

Payri, F.; Molina, S.; Martin, J.; Armas, O.

2006-01-01

Thermodynamic diagnosis models are valuable tools for the study of Diesel combustion. Inputs required by such models comprise measured mean and instantaneous variables, together with suitable values for adjustable parameters used in different submodels. In the case of measured variables, one may estimate the uncertainty associated with measurement errors; however, the influence of errors in model parameter estimation may not be so easily established on an experimental basis. In this paper, a simulated pressure cycle has been used along with known input parameters, so that any uncertainty in the inputs is avoided. Then, the influence of errors in measured variables and geometric and heat transmission parameters on the results of a diagnosis combustion model for direct injection diesel engines have been studied. This procedure allowed to establish the relative importance of these parameters and to set limits to the maximal errors of the model, accounting for both the maximal expected errors in the input parameters and the sensitivity of the model to those errors

On the Nature of SEM Estimates of ARMA Parameters.

Science.gov (United States)

Hamaker, Ellen L.; Dolan, Conor V.; Molenaar, Peter C. M.

2002-01-01

Reexamined the nature of structural equation modeling (SEM) estimates of autoregressive moving average (ARMA) models, replicated the simulation experiments of P. Molenaar, and examined the behavior of the log-likelihood ratio test. Simulation studies indicate that estimates of ARMA parameters observed with SEM software are identical to those…

Ordinary Mathematical Models in Calculating the Aviation GTE Parameters

Directory of Open Access Journals (Sweden)

E. A. Khoreva

2017-01-01

Full Text Available The paper presents the analytical review results of the ordinary mathematical models of the operating process used to study aviation GTE parameters and characteristics at all stages of its creation and operation. Considers the mathematical models of the zero and the first level, which are mostly used when solving typical problems in calculating parameters and characteristics of engines.Presents a number of practical problems arising in designing aviation GTE for various applications.The application of mathematical models of the zero-level engine can be quite appropriate when the engine is considered as a component in the aircraft system to estimate its calculated individual flight performance or when modeling the flight cycle of the aircrafts of different purpose.The paper demonstrates that introduction of correction functions into the first-level mathematical models in solving typical problems (influence of the Reynolds number, characteristics deterioration of the units during the overhaul period of engine, as well as influence of the flow inhomogeneity at the inlet because of manufacturing tolerance, etc. enables providing a sufficient engineering estimate accuracy to reflect a realistic operating process in the engine and its elements.

Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data

Science.gov (United States)

Gorinevsky, Dimitry; Matthews, Bryan L.; Martin, Rodney

2012-01-01

This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances.

Small sample GEE estimation of regression parameters for longitudinal data.

Science.gov (United States)

Paul, Sudhir; Zhang, Xuemao

2014-09-28

Longitudinal (clustered) response data arise in many bio-statistical applications which, in general, cannot be assumed to be independent. Generalized estimating equation (GEE) is a widely used method to estimate marginal regression parameters for correlated responses. The advantage of the GEE is that the estimates of the regression parameters are asymptotically unbiased even if the correlation structure is misspecified, although their small sample properties are not known. In this paper, two bias adjusted GEE estimators of the regression parameters in longitudinal data are obtained when the number of subjects is small. One is based on a bias correction, and the other is based on a bias reduction. Simulations show that the performances of both the bias-corrected methods are similar in terms of bias, efficiency, coverage probability, average coverage length, impact of misspecification of correlation structure, and impact of cluster size on bias correction. Both these methods show superior properties over the GEE estimates for small samples. Further, analysis of data involving a small number of subjects also shows improvement in bias, MSE, standard error, and length of the confidence interval of the estimates by the two bias adjusted methods over the GEE estimates. For small to moderate sample sizes (N ≤50), either of the bias-corrected methods GEEBc and GEEBr can be used. However, the method GEEBc should be preferred over GEEBr, as the former is computationally easier. For large sample sizes, the GEE method can be used. Copyright © 2014 John Wiley & Sons, Ltd.

Parameter estimation techniques and uncertainty in ground water flow model predictions

International Nuclear Information System (INIS)

Zimmerman, D.A.; Davis, P.A.

1990-01-01

Quantification of uncertainty in predictions of nuclear waste repository performance is a requirement of Nuclear Regulatory Commission regulations governing the licensing of proposed geologic repositories for high-level radioactive waste disposal. One of the major uncertainties in these predictions is in estimating the ground-water travel time of radionuclides migrating from the repository to the accessible environment. The cause of much of this uncertainty has been attributed to a lack of knowledge about the hydrogeologic properties that control the movement of radionuclides through the aquifers. A major reason for this lack of knowledge is the paucity of data that is typically available for characterizing complex ground-water flow systems. Because of this, considerable effort has been put into developing parameter estimation techniques that infer property values in regions where no measurements exist. Currently, no single technique has been shown to be superior or even consistently conservative with respect to predictions of ground-water travel time. This work was undertaken to compare a number of parameter estimation techniques and to evaluate how differences in the parameter estimates and the estimation errors are reflected in the behavior of the flow model predictions. That is, we wished to determine to what degree uncertainties in flow model predictions may be affected simply by the choice of parameter estimation technique used. 3 refs., 2 figs

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

Genetic Parameter Estimates for Metabolizing Two Common Pharmaceuticals in Swine

Science.gov (United States)

Howard, Jeremy T.; Ashwell, Melissa S.; Baynes, Ronald E.; Brooks, James D.; Yeatts, James L.; Maltecca, Christian

2018-01-01

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

Measurement-Based Transmission Line Parameter Estimation with Adaptive Data Selection Scheme

DEFF Research Database (Denmark)

Li, Changgang; Zhang, Yaping; Zhang, Hengxu

2017-01-01

Accurate parameters of transmission lines are critical for power system operation and control decision making. Transmission line parameter estimation based on measured data is an effective way to enhance the validity of the parameters. This paper proposes a multi-point transmission line parameter...

Automatic estimation of elasticity parameters in breast tissue

Science.gov (United States)

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.

Adaptive Parameter Estimation of Person Recognition Model in a Stochastic Human Tracking Process

Science.gov (United States)

Nakanishi, W.; Fuse, T.; Ishikawa, T.

2015-05-01

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model, which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions.

Correction of static pressure on a research aircraft in accelerated flight using differential pressure measurements

Directory of Open Access Journals (Sweden)

A. R. Rodi

2012-11-01

Full Text Available A method is described that estimates the error in the static pressure measurement on an aircraft from differential pressure measurements on the hemispherical surface of a Rosemount model 858AJ air velocity probe mounted on a boom ahead of the aircraft. The theoretical predictions for how the pressure should vary over the surface of the hemisphere, involving an unknown sensitivity parameter, leads to a set of equations that can be solved for the unknowns – angle of attack, angle of sideslip, dynamic pressure and the error in static pressure – if the sensitivity factor can be determined. The sensitivity factor was determined on the University of Wyoming King Air research aircraft by comparisons with the error measured with a carefully designed sonde towed on connecting tubing behind the aircraft – a trailing cone – and the result was shown to have a precision of about ±10 Pa over a wide range of conditions, including various altitudes, power settings, and gear and flap extensions. Under accelerated flight conditions, geometric altitude data from a combined Global Navigation Satellite System (GNSS and inertial measurement unit (IMU system are used to estimate acceleration effects on the error, and the algorithm is shown to predict corrections to a precision of better than ±20 Pa under those conditions. Some limiting factors affecting the precision of static pressure measurement on a research aircraft are discussed.

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.

A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization

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.

Test models for improving filtering with model errors through stochastic parameter estimation

International Nuclear Information System (INIS)

Gershgorin, B.; Harlim, J.; Majda, A.J.

2010-01-01

The filtering skill for turbulent signals from nature is often limited by model errors created by utilizing an imperfect model for filtering. Updating the parameters in the imperfect model through stochastic parameter estimation is one way to increase filtering skill and model performance. Here a suite of stringent test models for filtering with stochastic parameter estimation is developed based on the Stochastic Parameterization Extended Kalman Filter (SPEKF). These new SPEKF-algorithms systematically correct both multiplicative and additive biases and involve exact formulas for propagating the mean and covariance including the parameters in the test model. A comprehensive study is presented of robust parameter regimes for increasing filtering skill through stochastic parameter estimation for turbulent signals as the observation time and observation noise are varied and even when the forcing is incorrectly specified. The results here provide useful guidelines for filtering turbulent signals in more complex systems with significant model errors.

Variational estimates of point-kinetics parameters

International Nuclear Information System (INIS)

Favorite, J.A.; Stacey, W.M. Jr.

1995-01-01

Variational estimates of the effect of flux shifts on the integral reactivity parameter of the point-kinetics equations and on regional power fractions were calculated for a variety of localized perturbations in two light water reactor (LWR) model problems representing a small, tightly coupled core and a large, loosely coupled core. For the small core, the flux shifts resulting from even relatively large localized reactivity changes (∼600 pcm) were small, and the standard point-kinetics approximation estimates of reactivity were in error by only ∼10% or less, while the variational estimates were accurate to within ∼1%. For the larger core, significant (>50%) flux shifts occurred in response to local perturbations, leading to errors of the same magnitude in the standard point-kinetics approximation of the reactivity worth. For positive reactivity, the error in the variational estimate of reactivity was only a few percent in the larger core, and the resulting transient power prediction was 1 to 2 orders of magnitude more accurate than with the standard point-kinetics approximation. For a large, local negative reactivity insertion resulting in a large flux shift, the accuracy of the variational estimate broke down. The variational estimate of the effect of flux shifts on reactivity in point-kinetics calculations of transients in LWR cores was found to generally result in greatly improved accuracy, relative to the standard point-kinetics approximation, the exception being for large negative reactivity insertions with large flux shifts in large, loosely coupled cores

Parameter estimation in tree graph metabolic networks

Directory of Open Access Journals (Sweden)

Laura Astola

2016-09-01

Full Text Available We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis–Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

Parameter estimation in tree graph metabolic networks.

Science.gov (United States)

Astola, Laura; Stigter, Hans; Gomez Roldan, Maria Victoria; van Eeuwijk, Fred; Hall, Robert D; Groenenboom, Marian; Molenaar, Jaap J

2016-01-01

We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis-Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

The relative pose estimation of aircraft based on contour model

Science.gov (United States)

Fu, Tai; Sun, Xiangyi

2017-02-01

This paper proposes a relative pose estimation approach based on object contour model. The first step is to obtain a two-dimensional (2D) projection of three-dimensional (3D)-model-based target, which will be divided into 40 forms by clustering and LDA analysis. Then we proceed by extracting the target contour in each image and computing their Pseudo-Zernike Moments (PZM), thus a model library is constructed in an offline mode. Next, we spot a projection contour that resembles the target silhouette most in the present image from the model library with reference of PZM; then similarity transformation parameters are generated as the shape context is applied to match the silhouette sampling location, from which the identification parameters of target can be further derived. Identification parameters are converted to relative pose parameters, in the premise that these values are the initial result calculated via iterative refinement algorithm, as the relative pose parameter is in the neighborhood of actual ones. At last, Distance Image Iterative Least Squares (DI-ILS) is employed to acquire the ultimate relative pose parameters.

Sliding Mode Fault Tolerant Control with Adaptive Diagnosis for Aircraft Engines

Science.gov (United States)

Xiao, Lingfei; Du, Yanbin; Hu, Jixiang; Jiang, Bin

2018-03-01

In this paper, a novel sliding mode fault tolerant control method is presented for aircraft engine systems with uncertainties and disturbances on the basis of adaptive diagnostic observer. By taking both sensors faults and actuators faults into account, the general model of aircraft engine control systems which is subjected to uncertainties and disturbances, is considered. Then, the corresponding augmented dynamic model is established in order to facilitate the fault diagnosis and fault tolerant controller design. Next, a suitable detection observer is designed to detect the faults effectively. Through creating an adaptive diagnostic observer and based on sliding mode strategy, the sliding mode fault tolerant controller is constructed. Robust stabilization is discussed and the closed-loop system can be stabilized robustly. It is also proven that the adaptive diagnostic observer output errors and the estimations of faults converge to a set exponentially, and the converge rate greater than some value which can be adjusted by choosing designable parameters properly. The simulation on a twin-shaft aircraft engine verifies the applicability of the proposed fault tolerant control 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.

Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks.

Science.gov (United States)

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.

Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation

Science.gov (United States)

Li, Shan; Zhang, Shaoqing; Liu, Zhengyu; Lu, Lv; Zhu, Jiang; Zhang, Xuefeng; Wu, Xinrong; Zhao, Ming; Vecchi, Gabriel A.; Zhang, Rong-Hua; Lin, Xiaopei

2018-04-01

Parametric uncertainty in convection parameterization is one major source of model errors that cause model climate drift. Convection parameter tuning has been widely studied in atmospheric models to help mitigate the problem. However, in a fully coupled general circulation model (CGCM), convection parameters which impact the ocean as well as the climate simulation may have different optimal values. This study explores the possibility of estimating convection parameters with an ensemble coupled data assimilation method in a CGCM. Impacts of the convection parameter estimation on climate analysis and forecast are analyzed. In a twin experiment framework, five convection parameters in the GFDL coupled model CM2.1 are estimated individually and simultaneously under both perfect and imperfect model regimes. Results show that the ensemble data assimilation method can help reduce the bias in convection parameters. With estimated convection parameters, the analyses and forecasts for both the atmosphere and the ocean are generally improved. It is also found that information in low latitudes is relatively more important for estimating convection parameters. This study further suggests that when important parameters in appropriate physical parameterizations are identified, incorporating their estimation into traditional ensemble data assimilation procedure could improve the final analysis and climate prediction.

Model parameters estimation and sensitivity by genetic algorithms

International Nuclear Information System (INIS)

Marseguerra, Marzio; Zio, Enrico; Podofillini, Luca

2003-01-01

In this paper we illustrate the possibility of extracting qualitative information on the importance of the parameters of a model in the course of a Genetic Algorithms (GAs) optimization procedure for the estimation of such parameters. The Genetic Algorithms' search of the optimal solution is performed according to procedures that resemble those of natural selection and genetics: an initial population of alternative solutions evolves within the search space through the four fundamental operations of parent selection, crossover, replacement, and mutation. During the search, the algorithm examines a large amount of solution points which possibly carries relevant information on the underlying model characteristics. A possible utilization of this information amounts to create and update an archive with the set of best solutions found at each generation and then to analyze the evolution of the statistics of the archive along the successive generations. From this analysis one can retrieve information regarding the speed of convergence and stabilization of the different control (decision) variables of the optimization problem. In this work we analyze the evolution strategy followed by a GA in its search for the optimal solution with the aim of extracting information on the importance of the control (decision) variables of the optimization with respect to the sensitivity of the objective function. The study refers to a GA search for optimal estimates of the effective parameters in a lumped nuclear reactor model of literature. The supporting observation is that, as most optimization procedures do, the GA search evolves towards convergence in such a way to stabilize first the most important parameters of the model and later those which influence little the model outputs. In this sense, besides estimating efficiently the parameters values, the optimization approach also allows us to provide a qualitative ranking of their importance in contributing to the model output. The

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)

Pattern statistics on Markov chains and sensitivity to parameter estimation

Directory of Open Access Journals (Sweden)

Nuel Grégory

2006-10-01

Full Text Available Abstract Background: In order to compute pattern statistics in computational biology a Markov model is commonly used to take into account the sequence composition. Usually its parameter must be estimated. The aim of this paper is to determine how sensitive these statistics are to parameter estimation, and what are the consequences of this variability on pattern studies (finding the most over-represented words in a genome, the most significant common words to a set of sequences,.... Results: In the particular case where pattern statistics (overlap counting only computed through binomial approximations we use the delta-method to give an explicit expression of σ, the standard deviation of a pattern statistic. This result is validated using simulations and a simple pattern study is also considered. Conclusion: We establish that the use of high order Markov model could easily lead to major mistakes due to the high sensitivity of pattern statistics to parameter estimation.

Hierarchical parameter estimation of DFIG and drive train system in a wind turbine generator

Institute of Scientific and Technical Information of China (English)

Xueping PAN; Ping JU; Feng WU; Yuqing JIN

2017-01-01

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.

A hybrid optimization approach to the estimation of distributed parameters in two-dimensional confined aquifers

Science.gov (United States)

Heidari, M.; Ranjithan, S.R.

1998-01-01

In using non-linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated-Newton search technique to estimate groundwater parameters for a confined steady-state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near-correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is experimentally demonstrated that only one piece of prior information of the least sensitive parameter is sufficient to arrive at the global or near-global optimum solution. For hydraulic head data with measurement errors, the error in the estimation of parameters increases as the standard deviation of the errors increases. Results from our experiments show that, in general, the accuracy of the estimated parameters depends on the level of noise in the hydraulic head data and the initial values used in the truncated-Newton search technique.In using non-linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated-Newton search technique to estimate groundwater parameters for a confined steady-state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near-correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is

Toxicity of carbon monoxide hydrogen cyanide gas mixtures : exposure concentration, time to incapacitation, carboxyhemoglobin and blood cyanide parameters.

Science.gov (United States)

1994-04-01

During aircraft interior fires, carbon monoxide (CO) and hydrogen cyanide (HCN) are produced in sufficient amounts to cause incapacitation and death. Time-to-incapacitation (ti) is a practical parameter for estimating escape time in fire environments...

A comparison of ground-based and aircraft-based methane emission flux estimates in a western oil and natural gas production basin

Science.gov (United States)

Snare, Dustin A.

Recent increases in oil and gas production from unconventional reservoirs has brought with it an increase of methane emissions. Estimating methane emissions from oil and gas production is complex due to differences in equipment designs, maintenance, and variable product composition. Site access to oil and gas production equipment can be difficult and time consuming, making remote assessment of emissions vital to understanding local point source emissions. This work presents measurements of methane leakage made from a new ground-based mobile laboratory and a research aircraft around oil and gas fields in the Upper Green River Basin (UGRB) of Wyoming in 2014. It was recently shown that the application of the Point Source Gaussian (PSG) method, utilizing atmospheric dispersion tables developed by US EPA (Appendix B), is an effective way to accurately measure methane flux from a ground-based location downwind of a source without the use of a tracer (Brantley et al., 2014). Aircraft measurements of methane enhancement regions downwind of oil and natural gas production and Planetary Boundary Layer observations are utilized to obtain a flux for the entire UGRB. Methane emissions are compared to volumes of natural gas produced to derive a leakage rate from production operations for individual production sites and basin-wide production. Ground-based flux estimates derive a leakage rate of 0.14 - 0.78 % (95 % confidence interval) per site with a mass-weighted average (MWA) of 0.20 % for all sites. Aircraft-based flux estimates derive a MWA leakage rate of 0.54 - 0.91 % for the UGRB.

A Note On the Estimation of the Poisson Parameter

Directory of Open Access Journals (Sweden)

S. S. Chitgopekar

1985-01-01

distribution when there are errors in observing the zeros and ones and obtains both the maximum likelihood and moments estimates of the Poisson mean and the error probabilities. It is interesting to note that either method fails to give unique estimates of these parameters unless the error probabilities are functionally related. However, it is equally interesting to observe that the estimate of the Poisson mean does not depend on the functional relationship between the error probabilities.

Inspection Program Development for an Aircraft Fleet and an Airline on the Basis of the Acceptance Fatigue Test Result

Directory of Open Access Journals (Sweden)

Paramonov Yuri

2015-02-01

Full Text Available An inspection interval planning is considered in order to limit the probability of any fatigue failure (FFP in a fleet of N aircraft (AC and to provide an economical effectiveness of airline (AL under the limitation of fatigue failure rate (FFR. A solution of these two problems is based on the processing of the result of acceptance fatigue test of a new type of aircraft. During this test an estimate of the parameter ϴ, of a fatigue crack growth trajectory has been obtained. If the result of this acceptance test is too bad then this new type of aircraft will not be used in service. A redesign of this project should be done. If the result the acceptance test is pretty good then the reliability of the aircraft fleet and the airline will be provided without inspections. For this strategy there is a maximum of FFP (a maximum of FFR as a function of an unknown parameter ᶿ. This maximum can be limited by the use of the offered here procedure of the choice of the inspection number. The economic effectiveness of the AL operation is considered using the theory of Markov process with rewords.

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

Smart aircraft fastener evaluation (SAFE) system: a condition-based corrosion detection system for aging aircraft

Science.gov (United States)

Schoess, Jeffrey N.; Seifert, Greg; Paul, Clare A.

1996-05-01

The smart aircraft fastener evaluation (SAFE) system is an advanced structural health monitoring effort to detect and characterize corrosion in hidden and inaccessible locations of aircraft structures. Hidden corrosion is the number one logistics problem for the U.S. Air Force, with an estimated maintenance cost of $700M per year in 1990 dollars. The SAFE system incorporates a solid-state electrochemical microsensor and smart sensor electronics in the body of a Hi-Lok aircraft fastener to process and autonomously report corrosion status to aircraft maintenance personnel. The long-term payoff for using SAFE technology will be in predictive maintenance for aging aircraft and rotorcraft systems, fugitive emissions applications such as control valves, chemical pipeline vessels, and industrial boilers. Predictive maintenance capability, service, and repair will replace the current practice of scheduled maintenance to substantially reduce operational costs. A summary of the SAFE concept, laboratory test results, and future field test plans is presented.

Improved Differential Evolution Algorithm for Parameter Estimation to Improve the Production of Biochemical Pathway

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

Acoustic Characterization of a Multi-Rotor Unmanned Aircraft

Science.gov (United States)

Feight, Jordan; Gaeta, Richard; Jacob, Jamey

2017-11-01

In this study, the noise produced by a small multi-rotor rotary wing aircraft, or drone, is measured and characterized. The aircraft is tested in different configurations and environments to investigate specific parameters and how they affect the acoustic signature of the system. The parameters include rotor RPM, the number of rotors, distance and angle of microphone array from the noise source, and the ambient environment. The testing environments include an anechoic chamber for an idealized setting and both indoor and outdoor settings to represent real world conditions. PIV measurements are conducted to link the downwash and vortical flow structures from the rotors with the noise generation. The significant factors that arise from this study are the operational state of the aircraft and the microphone location (or the directivity of the noise source). The directivity in the rotor plane was shown to be omni-directional, regardless of the varying parameters. The tonal noise dominates the low to mid frequencies while the broadband noise dominates the higher frequencies. The fundamental characteristics of the acoustic signature appear to be invariant to the number of rotors. Flight maneuvers of the aircraft also significantly impact the tonal content in the acoustic signature.

Overview and benchmark analysis of fuel cell parameters estimation for energy management purposes

Science.gov (United States)

Kandidayeni, M.; Macias, A.; Amamou, A. A.; Boulon, L.; Kelouwani, S.; Chaoui, H.

2018-03-01

Proton exchange membrane fuel cells (PEMFCs) have become the center of attention for energy conversion in many areas such as automotive industry, where they confront a high dynamic behavior resulting in their characteristics variation. In order to ensure appropriate modeling of PEMFCs, accurate parameters estimation is in demand. However, parameter estimation of PEMFC models is highly challenging due to their multivariate, nonlinear, and complex essence. This paper comprehensively reviews PEMFC models parameters estimation methods with a specific view to online identification algorithms, which are considered as the basis of global energy management strategy design, to estimate the linear and nonlinear parameters of a PEMFC model in real time. In this respect, different PEMFC models with different categories and purposes are discussed first. Subsequently, a thorough investigation of PEMFC parameter estimation methods in the literature is conducted in terms of applicability. Three potential algorithms for online applications, Recursive Least Square (RLS), Kalman filter, and extended Kalman filter (EKF), which has escaped the attention in previous works, have been then utilized to identify the parameters of two well-known semi-empirical models in the literature, Squadrito et al. and Amphlett et al. Ultimately, the achieved results and future challenges are discussed.

A new M w estimation parameter for use in earthquake early warning systems

Science.gov (United States)

Wang, Zijun; Zhao, Boming

2018-01-01

We propose a method that employs the squared displacement integral ( ID2) to estimate earthquake magnitudes in real time for use in earthquake early warning (EEW) systems. Moreover, using τ c and P d for comparison, we establish formulas for estimating the moment magnitudes of these three parameters based on the selected aftershocks (4.0 ≤ M s ≤ 6.5) of the 2008 Wenchuan earthquake. In this comparison, the proposed ID2 method displays the highest accuracy. Furthermore, we investigate the applicability of the initial parameters to large earthquakes by estimating the magnitude of the Wenchuan M s 8.0 mainshock using a 3-s time window. Although these three parameters all display problems with saturation, the proposed ID2 parameter is relatively accurate. The evolutionary estimation of ID2 as a function of the time window shows that the estimation equation established with ID2 Ref determined from the first 8-s of P wave data can be directly applicable to predicate the magnitudes of 8.0. Therefore, the proposed ID2 parameter provides a robust estimator of earthquake moment magnitudes and can be used for EEW purposes.

Synchronous Generator Model Parameter Estimation Based on Noisy Dynamic Waveforms

Science.gov (United States)

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.

Determination of Paris' law constants and crack length evolution via Extended and Unscented Kalman filter: An application to aircraft fuselage panels

Science.gov (United States)

Wang, Yiwei; Binaud, Nicolas; Gogu, Christian; Bes, Christian; Fu, Jian

2016-12-01

Prediction of fatigue crack length in aircraft fuselage panels is one of the key issues for aircraft structural safety since it helps prevent catastrophic failures. Accurate estimation of crack length propagation is also meaningful for helping develop aircraft maintenance strategies. Paris' law is often used to capture the dynamics of fatigue crack propagation in metallic material. However, uncertainties are often present in the crack growth model, measured crack size and pressure differential in each flight and need to be accounted for accurate prediction. The aim of this paper is to estimate the two unknown Paris' law constants m and C as well as the crack length evolution by taking into account these uncertainties. Due to the nonlinear nature of the Paris' law, we propose here an on-line estimation algorithm based on two widespread nonlinear filtering techniques, Extended Kalman filter (EKF) and Unscented Kalman filter (UKF). The numerical experiments indicate that both EKF and UKF estimated the crack length well and accurately identified the unknown parameters. Although UKF is theoretical superior to EKF, in this Paris' law application EKF is comparable in accuracy to UKF and requires less computational expense.

Estimation of power feedback parameters of pulse reactor IBR-2M on transients

International Nuclear Information System (INIS)

Pepyolyshev, Yu.N.; Popov, A.K.

2013-01-01

Parameters of the IBR-2M reactor power feedback (PFB) on a model of the reactor dynamics by mathematical treatment of two registered transients are estimated. Frequency characteristics and the pulse transient characteristics corresponding to these PFB parameters are calculated. PFB parameters received thus can be considered as their express tentative estimation as real measurements in this case occupy no more than 30 minutes. Total PFB is negative at 1 and 2 MW. At the received estimations of PFB parameters in a self-regulation mode it is possible to consider the stability margins of the IBR-2M reactor satisfactory

Estimation of single plane unbalance parameters of a rotor-bearing system using Kalman filtering based force estimation technique

Science.gov (United States)

Shrivastava, Akash; Mohanty, A. R.

2018-03-01

This paper proposes a model-based method to estimate single plane unbalance parameters (amplitude and phase angle) in a rotor using Kalman filter and recursive least square based input force estimation technique. Kalman filter based input force estimation technique requires state-space model and response measurements. A modified system equivalent reduction expansion process (SEREP) technique is employed to obtain a reduced-order model of the rotor system so that limited response measurements can be used. The method is demonstrated using numerical simulations on a rotor-disk-bearing system. Results are presented for different measurement sets including displacement, velocity, and rotational response. Effects of measurement noise level, filter parameters (process noise covariance and forgetting factor), and modeling error are also presented and it is observed that the unbalance parameter estimation is robust with respect to measurement noise.

Choice of the parameters of the cusum algorithms for parameter estimation in the markov modulated poisson process

OpenAIRE

Burkatovskaya, Yuliya Borisovna; Kabanova, T.; Khaustov, Pavel Aleksandrovich

2016-01-01

CUSUM algorithm for controlling chain state switching in the Markov modulated Poissonprocess was investigated via simulation. Recommendations concerning the parameter choice were givensubject to characteristics of the process. Procedure of the process parameter estimation was described.

Comprehensive analysis of transport aircraft flight performance

Science.gov (United States)

Filippone, Antonio

2008-04-01

This paper reviews the state-of-the art in comprehensive performance codes for fixed-wing aircraft. The importance of system analysis in flight performance is discussed. The paper highlights the role of aerodynamics, propulsion, flight mechanics, aeroacoustics, flight operation, numerical optimisation, stochastic methods and numerical analysis. The latter discipline is used to investigate the sensitivities of the sub-systems to uncertainties in critical state parameters or functional parameters. The paper discusses critically the data used for performance analysis, and the areas where progress is required. Comprehensive analysis codes can be used for mission fuel planning, envelope exploration, competition analysis, a wide variety of environmental studies, marketing analysis, aircraft certification and conceptual aircraft design. A comprehensive program that uses the multi-disciplinary approach for transport aircraft is presented. The model includes a geometry deck, a separate engine input deck with the main parameters, a database of engine performance from an independent simulation, and an operational deck. The comprehensive code has modules for deriving the geometry from bitmap files, an aerodynamics model for all flight conditions, a flight mechanics model for flight envelopes and mission analysis, an aircraft noise model and engine emissions. The model is validated at different levels. Validation of the aerodynamic model is done against the scale models DLR-F4 and F6. A general model analysis and flight envelope exploration are shown for the Boeing B-777-300 with GE-90 turbofan engines with intermediate passenger capacity (394 passengers in 2 classes). Validation of the flight model is done by sensitivity analysis on the wetted area (or profile drag), on the specific air range, the brake-release gross weight and the aircraft noise. A variety of results is shown, including specific air range charts, take-off weight-altitude charts, payload-range performance

An approach of parameter estimation for non-synchronous systems

International Nuclear Information System (INIS)

Xu Daolin; Lu Fangfang

2005-01-01

Synchronization-based parameter estimation is simple and effective but only available to synchronous systems. To come over this limitation, we propose a technique that the parameters of an unknown physical process (possibly a non-synchronous system) can be identified from a time series via a minimization procedure based on a synchronization control. The feasibility of this approach is illustrated in several chaotic systems

Using Genetic Algorithm to Estimate Hydraulic Parameters of Unconfined Aquifers

Directory of Open Access Journals (Sweden)

Asghar Asghari Moghaddam

2009-03-01

Full Text Available Nowadays, optimization techniques such as Genetic Algorithms (GA have attracted wide attention among scientists for solving complicated engineering problems. In this article, pumping test data are used to assess the efficiency of GA in estimating unconfined aquifer parameters and a sensitivity analysis is carried out to propose an optimal arrangement of GA. For this purpose, hydraulic parameters of three sets of pumping test data are calculated by GA and they are compared with the results of graphical methods. The results indicate that the GA technique is an efficient, reliable, and powerful method for estimating the hydraulic parameters of unconfined aquifer and, further, that in cases of deficiency in pumping test data, it has a better performance than graphical methods.

Probabilistic parameter estimation of activated sludge processes using Markov Chain Monte Carlo.

Science.gov (United States)

Sharifi, Soroosh; Murthy, Sudhir; Takács, Imre; Massoudieh, Arash

2014-03-01

One of the most important challenges in making activated sludge models (ASMs) applicable to design problems is identifying the values of its many stoichiometric and kinetic parameters. When wastewater characteristics data from full-scale biological treatment systems are used for parameter estimation, several sources of uncertainty, including uncertainty in measured data, external forcing (e.g. influent characteristics), and model structural errors influence the value of the estimated parameters. This paper presents a Bayesian hierarchical modeling framework for the probabilistic estimation of activated sludge process parameters. The method provides the joint probability density functions (JPDFs) of stoichiometric and kinetic parameters by updating prior information regarding the parameters obtained from expert knowledge and literature. The method also provides the posterior correlations between the parameters, as well as a measure of sensitivity of the different constituents with respect to the parameters. This information can be used to design experiments to provide higher information content regarding certain parameters. The method is illustrated using the ASM1 model to describe synthetically generated data from a hypothetical biological treatment system. The results indicate that data from full-scale systems can narrow down the ranges of some parameters substantially whereas the amount of information they provide regarding other parameters is small, due to either large correlations between some of the parameters or a lack of sensitivity with respect to the parameters. Copyright © 2013 Elsevier Ltd. All rights reserved.

AEROSTATIC AND AERODYNAMIC MODULES OF A HYBRID BUOYANT AIRCRAFT: AN ANALYTICAL APPROACH

Directory of Open Access Journals (Sweden)

Anwar Ul Haque

2015-05-01

Full Text Available An analytical approach is essential for the estimation of the requirements of aerodynamic and aerostatic lift for a hybrid buoyant aircraft. Such aircrafts have two different modules to balance the weight of aircraft; aerostatic module and aerodynamic module. Both these modules are to be treated separately for estimation of the mass budget of propulsion systems and required power. In the present work, existing relationships of aircraft and airship are reviewed for its further application for these modules. Limitations of such relationships are also disussed and it is precieved that it will provide a strating point for better understanding of design anatomy of such aircraft.

Cycle Counting Methods of the Aircraft Engine

Science.gov (United States)

Fedorchenko, Dmitrii G.; Novikov, Dmitrii K.

2016-01-01

The concept of condition-based gas turbine-powered aircraft operation is realized all over the world, which implementation requires knowledge of the end-of-life information related to components of aircraft engines in service. This research proposes an algorithm for estimating the equivalent cyclical running hours. This article provides analysis…

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...

Errors and parameter estimation in precipitation-runoff modeling: 1. Theory

Science.gov (United States)

Troutman, Brent M.

1985-01-01

Errors in complex conceptual precipitation-runoff models may be analyzed by placing them into a statistical framework. This amounts to treating the errors as random variables and defining the probabilistic structure of the errors. By using such a framework, a large array of techniques, many of which have been presented in the statistical literature, becomes available to the modeler for quantifying and analyzing the various sources of error. A number of these techniques are reviewed in this paper, with special attention to the peculiarities of hydrologic models. Known methodologies for parameter estimation (calibration) are particularly applicable for obtaining physically meaningful estimates and for explaining how bias in runoff prediction caused by model error and input error may contribute to bias in parameter estimation.

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.

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...

Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography

Science.gov (United States)

Lähivaara, Timo; Kärkkäinen, Leo; Huttunen, Janne M. J.; Hesthaven, Jan S.

2018-02-01

We study the feasibility of data based machine learning applied to ultrasound tomography to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled poroviscoelastic-viscoelastic-acoustic media. As the forward model, we consider a high-order discontinuous Galerkin method while deep convolutional neural networks are used to solve the parameter estimation problem. In the numerical experiment, we estimate the material porosity and tortuosity while the remaining parameters which are of less interest are successfully marginalized in the neural networks-based inversion. Computational examples confirms the feasibility and accuracy of this approach.

Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes

International Nuclear Information System (INIS)

Bachoc, Francois

2014-01-01

Covariance parameter estimation of Gaussian processes is analyzed in an asymptotic framework. The spatial sampling is a randomly perturbed regular grid and its deviation from the perfect regular grid is controlled by a single scalar regularity parameter. Consistency and asymptotic normality are proved for the Maximum Likelihood and Cross Validation estimators of the covariance parameters. The asymptotic covariance matrices of the covariance parameter estimators are deterministic functions of the regularity parameter. By means of an exhaustive study of the asymptotic covariance matrices, it is shown that the estimation is improved when the regular grid is strongly perturbed. Hence, an asymptotic confirmation is given to the commonly admitted fact that using groups of observation points with small spacing is beneficial to covariance function estimation. Finally, the prediction error, using a consistent estimator of the covariance parameters, is analyzed in detail. (authors)

Impact of relativistic effects on cosmological parameter estimation

Science.gov (United States)

Lorenz, Christiane S.; Alonso, David; Ferreira, Pedro G.

2018-01-01

Future surveys will access large volumes of space and hence very long wavelength fluctuations of the matter density and gravitational field. It has been argued that the set of secondary effects that affect the galaxy distribution, relativistic in nature, will bring new, complementary cosmological constraints. We study this claim in detail by focusing on a subset of wide-area future surveys: Stage-4 cosmic microwave background experiments and photometric redshift surveys. In particular, we look at the magnification lensing contribution to galaxy clustering and general-relativistic corrections to all observables. We quantify the amount of information encoded in these effects in terms of the tightening of the final cosmological constraints as well as the potential bias in inferred parameters associated with neglecting them. We do so for a wide range of cosmological parameters, covering neutrino masses, standard dark-energy parametrizations and scalar-tensor gravity theories. Our results show that, while the effect of lensing magnification to number counts does not contain a significant amount of information when galaxy clustering is combined with cosmic shear measurements, this contribution does play a significant role in biasing estimates on a host of parameter families if unaccounted for. Since the amplitude of the magnification term is controlled by the slope of the source number counts with apparent magnitude, s (z ), we also estimate the accuracy to which this quantity must be known to avoid systematic parameter biases, finding that future surveys will need to determine s (z ) to the ˜5 %- 10 % level. On the contrary, large-scale general-relativistic corrections are irrelevant both in terms of information content and parameter bias for most cosmological parameters but significant for the level of primordial non-Gaussianity.

Nonlinear Adaptive Descriptor Observer for the Joint States and Parameters Estimation

KAUST Repository

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.

Nonlinear Adaptive Descriptor Observer for the Joint States and Parameters Estimation

KAUST Repository

Unknown author

2016-01-01

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.

Bayesian Parameter Estimation via Filtering and Functional Approximations

KAUST Repository

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.

Bayesian Parameter Estimation via Filtering and Functional Approximations

KAUST Repository

Matthies, Hermann G.; Litvinenko, Alexander; Rosic, Bojana V.; Zander, Elmar

2016-01-01

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.

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

Response-based estimation of sea state parameters - Influence of filtering

DEFF Research Database (Denmark)

Nielsen, Ulrik Dam

2007-01-01

Reliable estimation of the on-site sea state parameters is essential to decision support systems for safe navigation of ships. The wave spectrum can be estimated from procedures based on measured ship responses. The paper deals with two procedures—Bayesian Modelling and Parametric Modelling...

Nonlinear Parameter Estimation in Microbiological Degradation Systems and Statistic Test for Common Estimation

DEFF Research Database (Denmark)

Sommer, Helle Mølgaard; Holst, Helle; Spliid, Henrik

1995-01-01

Three identical microbiological experiments were carried out and analysed in order to examine the variability of the parameter estimates. The microbiological system consisted of a substrate (toluene) and a biomass (pure culture) mixed together in an aquifer medium. The degradation of the substrate...

Preliminary Estimation of Kappa Parameter in Croatia

Science.gov (United States)

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.

A framework for scalable parameter estimation of gene circuit models using structural information

KAUST Repository

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.

A framework for scalable parameter estimation of gene circuit models using structural information

KAUST Repository

Kuwahara, Hiroyuki; Fan, Ming; Wang, Suojin; Gao, Xin

2013-01-01

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.

Online state of charge and model parameter co-estimation based on a novel multi-timescale estimator for vanadium redox flow battery

International Nuclear Information System (INIS)

Wei, Zhongbao; Lim, Tuti Mariana; Skyllas-Kazacos, Maria; Wai, Nyunt; Tseng, King Jet

2016-01-01

Highlights: • Battery model parameters and SOC co-estimation is investigated. • The model parameters and OCV are decoupled and estimated independently. • Multiple timescales are adopted to improve precision and stability. • SOC is online estimated without using the open-circuit cell. • The method is robust to aging levels, flow rates, and battery chemistries. - Abstract: A key function of battery management system (BMS) is to provide accurate information of the state of charge (SOC) in real time, and this depends directly on the precise model parameterization. In this paper, a novel multi-timescale estimator is proposed to estimate the model parameters and SOC for vanadium redox flow battery (VRB) in real time. The model parameters and OCV are decoupled and estimated independently, effectively avoiding the possibility of cross interference between them. The analysis of model sensitivity, stability, and precision suggests the necessity of adopting different timescales for each estimator independently. Experiments are conducted to assess the performance of the proposed method. Results reveal that the model parameters are online adapted accurately thus the periodical calibration on them can be avoided. The online estimated terminal voltage and SOC are both benchmarked with the reference values. The proposed multi-timescale estimator has the merits of fast convergence, high precision, and good robustness against the initialization uncertainty, aging states, flow rates, and also battery chemistries.

Estimation of Adjoint-Weighted Kinetics Parameters in Monte Carlo Wieland Calculations

International Nuclear Information System (INIS)

Choi, Sung Hoon; Shim, Hyung Jin

2013-01-01

The effective delayed neutron fraction, β eff , and the prompt neutron generation time, Λ, in the point kinetics equation are weighted by the adjoint flux to improve the accuracy of the reactivity estimate. Recently the Monte Carlo (MC) kinetics parameter estimation methods by using the self-consistent adjoint flux calculated in the MC forward simulations have been developed and successfully applied for the research reactor analyses. However these adjoint estimation methods based on the cycle-by-cycle genealogical table require a huge memory size to store the pedigree hierarchy. In this paper, we present a new adjoint estimation in which the pedigree of a single history is utilized by applying the MC Wielandt method. The effectiveness of the new method is demonstrated in the kinetics parameter estimations for infinite homogeneous two-group problems and the Godiva critical facility

FLIGHT EXPERT RISK ASSESSMENT OF AIRCRAFT GROUP AT THEIR PROXIMITY USING A PROGRAM-MANAGER

Directory of Open Access Journals (Sweden)

D. A. Mikhaylin

2017-01-01

Full Text Available The paper presents an approach to solving the problem of aircraft flight safety. External threats in the form of aircraft-offenders are considered. The algorithm of collision danger coefficients with aircraft-offenders is presented, оn the basis of which the side-program manager of flight safety monitoring is formed.Two danger coefficients in the horizontal and vertical planes are introduced. Based on various flight situations four possible decisions are offered: absence of any aircraft activity, flight level change, deviation in the horizontal plane and both in vertical and horizontal planes. For each case the formulas of double evaluation are received. They take into account different parameters of aircraft relative motion. Based on these estimates it is possible to build a final expert evaluation for the considered flight situations. It is implemented in the onboard program-manager. The structure of the program is presented. At the program-manager output the expected minimized risk evaluation and the selected alternative of the avoidance of aircraft from the meeting point are formed. The paper presents a detailed description of the procedures to test the performance of the program-manager algorithms. The initial conditions for different flight situations are provided. The simulation results of the algorithm are given. The danger coefficients comparison when performing maneuvers to prevent dangerous approach and in their absence is illustrated. It is shown that the maneuver implementation recommended by program-manager algorithms decreases the resulting danger coefficient. Particular attention was paid to aircraft landing, especially if the landing area had several conflicting aircraft.

ADAPTIVE PARAMETER ESTIMATION OF PERSON RECOGNITION MODEL IN A STOCHASTIC HUMAN TRACKING PROCESS

OpenAIRE

W. Nakanishi; T. Fuse; T. Ishikawa

2015-01-01

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation ...

A quasi-sequential parameter estimation for nonlinear dynamic systems based on multiple data profiles

Energy Technology Data Exchange (ETDEWEB)

Zhao, Chao [FuZhou University, FuZhou (China); Vu, Quoc Dong; Li, Pu [Ilmenau University of Technology, Ilmenau (Germany)

2013-02-15

A three-stage computation framework for solving parameter estimation problems for dynamic systems with multiple data profiles is developed. The dynamic parameter estimation problem is transformed into a nonlinear programming (NLP) problem by using collocation on finite elements. The model parameters to be estimated are treated in the upper stage by solving an NLP problem. The middle stage consists of multiple NLP problems nested in the upper stage, representing the data reconciliation step for each data profile. We use the quasi-sequential dynamic optimization approach to solve these problems. In the lower stage, the state variables and their gradients are evaluated through ntegrating the model equations. Since the second-order derivatives are not required in the computation framework this proposed method will be efficient for solving nonlinear dynamic parameter estimation problems. The computational results obtained on a parameter estimation problem for two CSTR models demonstrate the effectiveness of the proposed approach.

A quasi-sequential parameter estimation for nonlinear dynamic systems based on multiple data profiles

International Nuclear Information System (INIS)

Zhao, Chao; Vu, Quoc Dong; Li, Pu

2013-01-01

A three-stage computation framework for solving parameter estimation problems for dynamic systems with multiple data profiles is developed. The dynamic parameter estimation problem is transformed into a nonlinear programming (NLP) problem by using collocation on finite elements. The model parameters to be estimated are treated in the upper stage by solving an NLP problem. The middle stage consists of multiple NLP problems nested in the upper stage, representing the data reconciliation step for each data profile. We use the quasi-sequential dynamic optimization approach to solve these problems. In the lower stage, the state variables and their gradients are evaluated through ntegrating the model equations. Since the second-order derivatives are not required in the computation framework this proposed method will be efficient for solving nonlinear dynamic parameter estimation problems. The computational results obtained on a parameter estimation problem for two CSTR models demonstrate the effectiveness of the proposed approach

Statistical methods of parameter estimation for deterministically chaotic time series

Science.gov (United States)

Pisarenko, V. F.; Sornette, D.

2004-03-01

We discuss the possibility of applying some standard statistical methods (the least-square method, the maximum likelihood method, and the method of statistical moments for estimation of parameters) to deterministically chaotic low-dimensional dynamic system (the logistic map) containing an observational noise. A “segmentation fitting” maximum likelihood (ML) method is suggested to estimate the structural parameter of the logistic map along with the initial value x1 considered as an additional unknown parameter. The segmentation fitting method, called “piece-wise” ML, is similar in spirit but simpler and has smaller bias than the “multiple shooting” previously proposed. Comparisons with different previously proposed techniques on simulated numerical examples give favorable results (at least, for the investigated combinations of sample size N and noise level). Besides, unlike some suggested techniques, our method does not require the a priori knowledge of the noise variance. We also clarify the nature of the inherent difficulties in the statistical analysis of deterministically chaotic time series and the status of previously proposed Bayesian approaches. We note the trade off between the need of using a large number of data points in the ML analysis to decrease the bias (to guarantee consistency of the estimation) and the unstable nature of dynamical trajectories with exponentially fast loss of memory of the initial condition. The method of statistical moments for the estimation of the parameter of the logistic map is discussed. This method seems to be the unique method whose consistency for deterministically chaotic time series is proved so far theoretically (not only numerically).

Data Based Parameter Estimation Method for Circular-scanning SAR Imaging

Directory of Open Access Journals (Sweden)

Chen Gong-bo

2013-06-01

Full Text Available The circular-scanning Synthetic Aperture Radar (SAR is a novel working mode and its image quality is closely related to the accuracy of the imaging parameters, especially considering the inaccuracy of the real speed of the motion. According to the characteristics of the circular-scanning mode, a new data based method for estimating the velocities of the radar platform and the scanning-angle of the radar antenna is proposed in this paper. By referring to the basic conception of the Doppler navigation technique, the mathematic model and formulations for the parameter estimation are firstly improved. The optimal parameter approximation based on the least square criterion is then realized in solving those equations derived from the data processing. The simulation results verified the validity of the proposed scheme.

On Using Exponential Parameter Estimators with an Adaptive Controller

Science.gov (United States)

Patre, Parag; Joshi, Suresh M.

2011-01-01

Typical adaptive controllers are restricted to using a specific update law to generate parameter estimates. This paper investigates the possibility of using any exponential parameter estimator with an adaptive controller such that the system tracks a desired trajectory. The goal is to provide flexibility in choosing any update law suitable for a given application. The development relies on a previously developed concept of controller/update law modularity in the adaptive control literature, and the use of a converse Lyapunov-like theorem. Stability analysis is presented to derive gain conditions under which this is possible, and inferences are made about the tracking error performance. The development is based on a class of Euler-Lagrange systems that are used to model various engineering systems including space robots and manipulators.

The estimation of parameter compaction values for pavement subgrade stabilized with lime

Science.gov (United States)

Lubis, A. S.; Muis, Z. A.; Simbolon, C. A.

2018-02-01

The type of soil material, field control, maintenance and availability of funds are several factors that must be considered in compaction of the pavement subgrade. In determining the compaction parameters in laboratory desperately requires considerable materials, time and funds, and reliable laboratory operators. If the result of soil classification values can be used to estimate the compaction parameters of a subgrade material, so it would save time, energy, materials and cost on the execution of this work. This is also a clarification (cross check) of the work that has been done by technicians in the laboratory. The study aims to estimate the compaction parameter values ie. maximum dry unit weight (γdmax) and optimum water content (Wopt) of the soil subgrade that stabilized with lime. The tests that conducted in the laboratory of soil mechanics were to determine the index properties (Fines and Liquid Limit/LL) and Standard Compaction Test. Soil samples that have Plasticity Index (PI) > 10% were made with additional 3% lime for 30 samples. By using the Goswami equation, the compaction parameter values can be estimated by equation γd max # = -0,1686 Log G + 1,8434 and Wopt # = 2,9178 log G + 17,086. From the validation calculation, there was a significant positive correlation between the compaction parameter values laboratory and the compaction parameter values estimated, with a 95% confidence interval as a strong relationship.

Estimation of riverbank soil erodibility parameters using genetic ...

Indian Academy of Sciences (India)

Tapas Karmaker

2017-11-07

Nov 7, 2017 ... process. Therefore, this is a study to verify the applicability of inverse parameter ... successful modelling of the riverbank erosion, precise estimation of ..... For this simulation, about 40 iterations are found to attain the convergence. ..... rithm for function optimization: a Matlab implementation. NCSU-IE TR ...

Determination of power system component parameters using nonlinear dead beat estimation method

Science.gov (United States)

Kolluru, Lakshmi

Power systems are considered the most complex man-made wonders in existence today. In order to effectively supply the ever increasing demands of the consumers, power systems are required to remain stable at all times. Stability and monitoring of these complex systems are achieved by strategically placed computerized control centers. State and parameter estimation is an integral part of these facilities, as they deal with identifying the unknown states and/or parameters of the systems. Advancements in measurement technologies and the introduction of phasor measurement units (PMU) provide detailed and dynamic information of all measurements. Accurate availability of dynamic measurements provides engineers the opportunity to expand and explore various possibilities in power system dynamic analysis/control. This thesis discusses the development of a parameter determination algorithm for nonlinear power systems, using dynamic data obtained from local measurements. The proposed algorithm was developed by observing the dead beat estimator used in state space estimation of linear systems. The dead beat estimator is considered to be very effective as it is capable of obtaining the required results in a fixed number of steps. The number of steps required is related to the order of the system and the number of parameters to be estimated. The proposed algorithm uses the idea of dead beat estimator and nonlinear finite difference methods to create an algorithm which is user friendly and can determine the parameters fairly accurately and effectively. The proposed algorithm is based on a deterministic approach, which uses dynamic data and mathematical models of power system components to determine the unknown parameters. The effectiveness of the algorithm is tested by implementing it to identify the unknown parameters of a synchronous machine. MATLAB environment is used to create three test cases for dynamic analysis of the system with assumed known parameters. Faults are

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

Application of genetic algorithms for parameter estimation in liquid chromatography

International Nuclear Information System (INIS)

Hernandez Torres, Reynier; Irizar Mesa, Mirtha; Tavares Camara, Leoncio Diogenes

2012-01-01

In chromatography, complex inverse problems related to the parameters estimation and process optimization are presented. Metaheuristics methods are known as general purpose approximated algorithms which seek and hopefully find good solutions at a reasonable computational cost. These methods are iterative process to perform a robust search of a solution space. Genetic algorithms are optimization techniques based on the principles of genetics and natural selection. They have demonstrated very good performance as global optimizers in many types of applications, including inverse problems. In this work, the effectiveness of genetic algorithms is investigated to estimate parameters in liquid chromatography

HIV Model Parameter Estimates from Interruption Trial Data including Drug Efficacy and Reservoir Dynamics

Science.gov (United States)

Luo, Rutao; Piovoso, Michael J.; Martinez-Picado, Javier; Zurakowski, Ryan

2012-01-01

Mathematical models based on ordinary differential equations (ODE) have had significant impact on understanding HIV disease dynamics and optimizing patient treatment. A model that characterizes the essential disease dynamics can be used for prediction only if the model parameters are identifiable from clinical data. Most previous parameter identification studies for HIV have used sparsely sampled data from the decay phase following the introduction of therapy. In this paper, model parameters are identified from frequently sampled viral-load data taken from ten patients enrolled in the previously published AutoVac HAART interruption study, providing between 69 and 114 viral load measurements from 3–5 phases of viral decay and rebound for each patient. This dataset is considerably larger than those used in previously published parameter estimation studies. Furthermore, the measurements come from two separate experimental conditions, which allows for the direct estimation of drug efficacy and reservoir contribution rates, two parameters that cannot be identified from decay-phase data alone. A Markov-Chain Monte-Carlo method is used to estimate the model parameter values, with initial estimates obtained using nonlinear least-squares methods. The posterior distributions of the parameter estimates are reported and compared for all patients. PMID:22815727

PARAMETER ESTIMATION OF THE HYBRID CENSORED LOMAX DISTRIBUTION

Directory of Open Access Journals (Sweden)

Samir Kamel Ashour

2010-12-01

Full Text Available Survival analysis is used in various fields for analyzing data involving the duration between two events. It is also known as event history analysis, lifetime data analysis, reliability analysis or time to event analysis. One of the difficulties which arise in this area is the presence of censored data. The lifetime of an individual is censored when it cannot be exactly measured but partial information is available. Different circumstances can produce different types of censoring. The two most common censoring schemes used in life testing experiments are Type-I and Type-II censoring schemes. Hybrid censoring scheme is mixture of Type-I and Type-II censoring scheme. In this paper we consider the estimation of parameters of Lomax distribution based on hybrid censored data. The parameters are estimated by the maximum likelihood and Bayesian methods. The Fisher information matrix has been obtained and it can be used for constructing asymptotic confidence intervals.

Parameter estimation and hypothesis testing in linear models

CERN Document Server

Koch, Karl-Rudolf

1999-01-01

The necessity to publish the second edition of this book arose when its third German edition had just been published. This second English edition is there­ fore a translation of the third German edition of Parameter Estimation and Hypothesis Testing in Linear Models, published in 1997. It differs from the first English edition by the addition of a new chapter on robust estimation of parameters and the deletion of the section on discriminant analysis, which has been more completely dealt with by the author in the book Bayesian In­ ference with Geodetic Applications, Springer-Verlag, Berlin Heidelberg New York, 1990. Smaller additions and deletions have been incorporated, to im­ prove the text, to point out new developments or to eliminate errors which became apparent. A few examples have been also added. I thank Springer-Verlag for publishing this second edition and for the assistance in checking the translation, although the responsibility of errors remains with the author. I also want to express my thanks...

Correcting the bias of empirical frequency parameter estimators in codon models.

Directory of Open Access Journals (Sweden)

Sergei Kosakovsky Pond

2010-07-01

Full Text Available Markov models of codon substitution are powerful inferential tools for studying biological processes such as natural selection and preferences in amino acid substitution. The equilibrium character distributions of these models are almost always estimated using nucleotide frequencies observed in a sequence alignment, primarily as a matter of historical convention. In this note, we demonstrate that a popular class of such estimators are biased, and that this bias has an adverse effect on goodness of fit and estimates of substitution rates. We propose a "corrected" empirical estimator that begins with observed nucleotide counts, but accounts for the nucleotide composition of stop codons. We show via simulation that the corrected estimates outperform the de facto standard estimates not just by providing better estimates of the frequencies themselves, but also by leading to improved estimation of other parameters in the evolutionary models. On a curated collection of sequence alignments, our estimators show a significant improvement in goodness of fit compared to the approach. Maximum likelihood estimation of the frequency parameters appears to be warranted in many cases, albeit at a greater computational cost. Our results demonstrate that there is little justification, either statistical or computational, for continued use of the -style estimators.

Bridging the gaps between non-invasive genetic sampling and population parameter estimation

Science.gov (United States)

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 capture­recapture (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,...

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

An improved method to estimate reflectance parameters for high dynamic range imaging

Science.gov (United States)

Li, Shiying; Deguchi, Koichiro; Li, Renfa; Manabe, Yoshitsugu; Chihara, Kunihiro

2008-01-01

Two methods are described to accurately estimate diffuse and specular reflectance parameters for colors, gloss intensity and surface roughness, over the dynamic range of the camera used to capture input images. Neither method needs to segment color areas on an image, or to reconstruct a high dynamic range (HDR) image. The second method improves on the first, bypassing the requirement for specific separation of diffuse and specular reflection components. For the latter method, diffuse and specular reflectance parameters are estimated separately, using the least squares method. Reflection values are initially assumed to be diffuse-only reflection components, and are subjected to the least squares method to estimate diffuse reflectance parameters. Specular reflection components, obtained by subtracting the computed diffuse reflection components from reflection values, are then subjected to a logarithmically transformed equation of the Torrance-Sparrow reflection model, and specular reflectance parameters for gloss intensity and surface roughness are finally estimated using the least squares method. Experiments were carried out using both methods, with simulation data at different saturation levels, generated according to the Lambert and Torrance-Sparrow reflection models, and the second method, with spectral images captured by an imaging spectrograph and a moving light source. Our results show that the second method can estimate the diffuse and specular reflectance parameters for colors, gloss intensity and surface roughness more accurately and faster than the first one, so that colors and gloss can be reproduced more efficiently for HDR imaging.

A termination criterion for parameter estimation in stochastic models in systems biology.

Science.gov (United States)

Zimmer, Christoph; Sahle, Sven

2015-11-01

Parameter estimation procedures are a central aspect of modeling approaches in systems biology. They are often computationally expensive, especially when the models take stochasticity into account. Typically parameter estimation involves the iterative optimization of an objective function that describes how well the model fits some measured data with a certain set of parameter values. In order to limit the computational expenses it is therefore important to apply an adequate stopping criterion for the optimization process, so that the optimization continues at least until a reasonable fit is obtained, but not much longer. In the case of stochastic modeling, at least some parameter estimation schemes involve an objective function that is itself a random variable. This means that plain convergence tests are not a priori suitable as stopping criteria. This article suggests a termination criterion suited to optimization problems in parameter estimation arising from stochastic models in systems biology. The termination criterion is developed for optimization algorithms that involve populations of parameter sets, such as particle swarm or evolutionary algorithms. It is based on comparing the variance of the objective function over the whole population of parameter sets with the variance of repeated evaluations of the objective function at the best parameter set. The performance is demonstrated for several different algorithms. To test the termination criterion we choose polynomial test functions as well as systems biology models such as an Immigration-Death model and a bistable genetic toggle switch. The genetic toggle switch is an especially challenging test case as it shows a stochastic switching between two steady states which is qualitatively different from the model behavior in a deterministic model. Copyright © 2015. Published by Elsevier Ireland Ltd.

A framework for scalable parameter estimation of gene circuit models using structural information.

Science.gov (United States)

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.

Modified polarimetric bidirectional reflectance distribution function with diffuse scattering: surface parameter estimation

Science.gov (United States)

Zhan, Hanyu; Voelz, David G.

2016-12-01

The polarimetric bidirectional reflectance distribution function (pBRDF) describes the relationships between incident and scattered Stokes parameters, but the familiar surface-only microfacet pBRDF cannot capture diffuse scattering contributions and depolarization phenomena. We propose a modified pBRDF model with a diffuse scattering component developed from the Kubelka-Munk and Le Hors et al. theories, and apply it in the development of a method to jointly estimate refractive index, slope variance, and diffuse scattering parameters from a series of Stokes parameter measurements of a surface. An application of the model and estimation approach to experimental data published by Priest and Meier shows improved correspondence with measurements of normalized Mueller matrix elements. By converting the Stokes/Mueller calculus formulation of the model to a degree of polarization (DOP) description, the estimation results of the parameters from measured DOP values are found to be consistent with a previous DOP model and results.

Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

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.

Estimating Propensity Parameters Using Google PageRank and Genetic Algorithms.

Science.gov (United States)

Murrugarra, David; Miller, Jacob; Mueller, Alex N

2016-01-01

Stochastic Boolean networks, or more generally, stochastic discrete networks, are an important class of computational models for molecular interaction networks. The stochasticity stems from the updating schedule. Standard updating schedules include the synchronous update, where all the nodes are updated at the same time, and the asynchronous update where a random node is updated at each time step. The former produces a deterministic dynamics while the latter a stochastic dynamics. A more general stochastic setting considers propensity parameters for updating each node. Stochastic Discrete Dynamical Systems (SDDS) are a modeling framework that considers two propensity parameters for updating each node and uses one when the update has a positive impact on the variable, that is, when the update causes the variable to increase its value, and uses the other when the update has a negative impact, that is, when the update causes it to decrease its value. This framework offers additional features for simulations but also adds a complexity in parameter estimation of the propensities. This paper presents a method for estimating the propensity parameters for SDDS. The method is based on adding noise to the system using the Google PageRank approach to make the system ergodic and thus guaranteeing the existence of a stationary distribution. Then with the use of a genetic algorithm, the propensity parameters are estimated. Approximation techniques that make the search algorithms efficient are also presented and Matlab/Octave code to test the algorithms are available at http://www.ms.uky.edu/~dmu228/GeneticAlg/Code.html.

Estimation of apparent kinetic parameters of polymer pyrolysis with complex thermal degradation behavior

International Nuclear Information System (INIS)

Srimachai, Taranee; Anantawaraskul, Siripon

2010-01-01

Full text: Thermal degradation behavior during polymer pyrolysis can typically be described using three apparent kinetic parameters (i.e., pre-exponential factor, activation energy, and reaction order). Several efficient techniques have been developed to estimate these apparent kinetic parameters for simple thermal degradation behavior (i.e., single apparent pyrolysis reaction). Unfortunately, these techniques cannot be directly extended to the case of polymer pyrolysis with complex thermal degradation behavior (i.e., multiple concurrent reactions forming single or multiple DTG peaks). In this work, we proposed a deconvolution method to determine the number of apparent reactions and estimate three apparent kinetic parameters and contribution of each reaction for polymer pyrolysis with complex thermal degradation behavior. The proposed technique was validated with the model and experimental pyrolysis data of several polymer blends with known compositions. The results showed that (1) the number of reaction and (2) three apparent kinetic parameters and contribution of each reaction can be estimated reasonably. The simulated DTG curves with estimated parameters also agree well with experimental DTG curves. (author)

Efficiency Optimization Control of IPM Synchronous Motor Drives with Online Parameter Estimation

Directory of Open Access Journals (Sweden)

Sadegh Vaez-Zadeh

2011-04-01

Full Text Available This paper describes an efficiency optimization control method for high performance interior permanent magnet synchronous motor drives with online estimation of motor parameters. The control system is based on an input-output feedback linearization method which provides high performance control and simultaneously ensures the minimization of the motor losses. The controllable electrical loss can be minimized by the optimal control of the armature current vector. It is shown that parameter variations except at near the nominal conditions have undesirable effect on the controller performance. Therefore, a parameter estimation method based on the second method of Lyapunov is presented which guarantees the stability and convergence of the estimation. The extensive simulation results show the feasibility of the proposed controller and observer and their desirable performances.

Estimating unknown parameters in haemophilia using expert judgement elicitation.

Science.gov (United States)

Fischer, K; Lewandowski, D; Janssen, M P

2013-09-01

The increasing attention to healthcare costs and treatment efficiency has led to an increasing demand for quantitative data concerning patient and treatment characteristics in haemophilia. However, most of these data are difficult to obtain. The aim of this study was to use expert judgement elicitation (EJE) to estimate currently unavailable key parameters for treatment models in severe haemophilia A. Using a formal expert elicitation procedure, 19 international experts provided information on (i) natural bleeding frequency according to age and onset of bleeding, (ii) treatment of bleeds, (iii) time needed to control bleeding after starting secondary prophylaxis, (iv) dose requirements for secondary prophylaxis according to onset of bleeding, and (v) life-expectancy. For each parameter experts provided their quantitative estimates (median, P10, P90), which were combined using a graphical method. In addition, information was obtained concerning key decision parameters of haemophilia treatment. There was most agreement between experts regarding bleeding frequencies for patients treated on demand with an average onset of joint bleeding (1.7 years): median 12 joint bleeds per year (95% confidence interval 0.9-36) for patients ≤ 18, and 11 (0.8-61) for adult patients. Less agreement was observed concerning estimated effective dose for secondary prophylaxis in adults: median 2000 IU every other day The majority (63%) of experts expected that a single minor joint bleed could cause irreversible damage, and would accept up to three minor joint bleeds or one trauma related joint bleed annually on prophylaxis. Expert judgement elicitation allowed structured capturing of quantitative expert estimates. It generated novel data to be used in computer modelling, clinical care, and trial design. © 2013 John Wiley & Sons Ltd.

An iterative stochastic ensemble method for parameter estimation of subsurface flow models

International Nuclear Information System (INIS)

Elsheikh, Ahmed H.; Wheeler, Mary F.; Hoteit, Ibrahim

2013-01-01

Parameter estimation for subsurface flow models is an essential step for maximizing the value of numerical simulations for future prediction and the development of effective control strategies. We propose the iterative stochastic ensemble method (ISEM) as a general method for parameter estimation based on stochastic estimation of gradients using an ensemble of directional derivatives. ISEM eliminates the need for adjoint coding and deals with the numerical simulator as a blackbox. The proposed method employs directional derivatives within a Gauss–Newton iteration. The update equation in ISEM resembles the update step in ensemble Kalman filter, however the inverse of the output covariance matrix in ISEM is regularized using standard truncated singular value decomposition or Tikhonov regularization. We also investigate the performance of a set of shrinkage based covariance estimators within ISEM. The proposed method is successfully applied on several nonlinear parameter estimation problems for subsurface flow models. The efficiency of the proposed algorithm is demonstrated by the small size of utilized ensembles and in terms of error convergence rates

An iterative stochastic ensemble method for parameter estimation of subsurface flow models

KAUST Repository

Elsheikh, Ahmed H.

2013-06-01

Parameter estimation for subsurface flow models is an essential step for maximizing the value of numerical simulations for future prediction and the development of effective control strategies. We propose the iterative stochastic ensemble method (ISEM) as a general method for parameter estimation based on stochastic estimation of gradients using an ensemble of directional derivatives. ISEM eliminates the need for adjoint coding and deals with the numerical simulator as a blackbox. The proposed method employs directional derivatives within a Gauss-Newton iteration. The update equation in ISEM resembles the update step in ensemble Kalman filter, however the inverse of the output covariance matrix in ISEM is regularized using standard truncated singular value decomposition or Tikhonov regularization. We also investigate the performance of a set of shrinkage based covariance estimators within ISEM. The proposed method is successfully applied on several nonlinear parameter estimation problems for subsurface flow models. The efficiency of the proposed algorithm is demonstrated by the small size of utilized ensembles and in terms of error convergence rates. © 2013 Elsevier Inc.

Parameter and State Estimation of Large-Scale Complex Systems Using Python Tools

Directory of Open Access Journals (Sweden)

M. Anushka S. Perera

2015-07-01

Full Text Available This paper discusses the topics related to automating parameter, disturbance and state estimation analysis of large-scale complex nonlinear dynamic systems using free programming tools. For large-scale complex systems, before implementing any state estimator, the system should be analyzed for structural observability and the structural observability analysis can be automated using Modelica and Python. As a result of structural observability analysis, the system may be decomposed into subsystems where some of them may be observable --- with respect to parameter, disturbances, and states --- while some may not. The state estimation process is carried out for those observable subsystems and the optimum number of additional measurements are prescribed for unobservable subsystems to make them observable. In this paper, an industrial case study is considered: the copper production process at Glencore Nikkelverk, Kristiansand, Norway. The copper production process is a large-scale complex system. It is shown how to implement various state estimators, in Python, to estimate parameters and disturbances, in addition to states, based on available measurements.

Probabilistic risk assessment of aircraft impact on a spent nuclear fuel dry storage

Energy Technology Data Exchange (ETDEWEB)

Almomani, Belal, E-mail: balmomani@kaist.ac.kr [Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701 (Korea, Republic of); Lee, Sanghoon, E-mail: shlee1222@kmu.ac.kr [Department of Mechanical and Automotive Engineering, Keimyung University, Dalgubeol-daero 1095, Dalseo-gu, Daegu (Korea, Republic of); Jang, Dongchan, E-mail: dongchan.jang@kaist.ac.kr [Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701 (Korea, Republic of); Kang, Hyun Gook, E-mail: kangh6@rpi.edu [Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 (United States)

2017-01-15

Highlights: • A new risk assessment frame is proposed for aircraft impact into an interim dry storage. • It uses event tree analysis, response-structural analysis, consequence analysis, and Monte Carlo simulation. • A case study of the proposed procedure is presented to illustrate the methodology’s application. - Abstract: This paper proposes a systematic risk evaluation framework for one of the most significant impact events on an interim dry storage facility, an aircraft crash, by using a probabilistic approach. A realistic case study that includes a specific cask model and selected impact conditions is performed to demonstrate the practical applicability of the proposed framework. An event tree analysis of an occurred aircraft crash that defines a set of impact conditions and storage cask response is constructed. The Monte-Carlo simulation is employed for the probabilistic approach in consideration of sources of uncertainty associated with the impact loads onto the internal storage casks. The parameters for representing uncertainties that are managed probabilistically include the aircraft impact velocity, the compressive strength of the reinforced concrete wall, the missile shape factor, and the facility wall thickness. Failure probabilities of the impacted wall and a single storage cask under direct mechanical impact load caused by the aircraft crash are estimated. A finite element analysis is applied to simulate the postulated direct engine impact load onto the cask body, and a source term analysis for associated releases of radioactive materials as well as an off-site consequence analysis are performed. Finally, conditional risk contribution calculations are represented by an event tree model. Case study results indicate that no severe risk is presented, as the radiological consequences do not exceed regulatory exposure limits to the public. This risk model can be used with any other representative detailed parameters and reference design concepts for

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. 

Analytic Investigation Into Effect of Population Heterogeneity on Parameter Ratio Estimates

International Nuclear Information System (INIS)

Schinkel, Colleen; Carlone, Marco; Warkentin, Brad; Fallone, B. Gino

2007-01-01

Purpose: A homogeneous tumor control probability (TCP) model has previously been used to estimate the α/β ratio for prostate cancer from clinical dose-response data. For the ratio to be meaningful, it must be assumed that parameter ratios are not sensitive to the type of tumor control model used. We investigated the validity of this assumption by deriving analytic relationships between the α/β estimates from a homogeneous TCP model, ignoring interpatient heterogeneity, and those of the corresponding heterogeneous (population-averaged) model that incorporated heterogeneity. Methods and Materials: The homogeneous and heterogeneous TCP models can both be written in terms of the geometric parameters D 50 and γ 50 . We show that the functional forms of these models are similar. This similarity was used to develop an expression relating the homogeneous and heterogeneous estimates for the α/β ratio. The expression was verified numerically by generating pseudo-data from a TCP curve with known parameters and then using the homogeneous and heterogeneous TCP models to estimate the α/β ratio for the pseudo-data. Results: When the dominant form of interpatient heterogeneity is that of radiosensitivity, the homogeneous and heterogeneous α/β estimates differ. This indicates that the presence of this heterogeneity affects the value of the α/β ratio derived from analysis of TCP curves. Conclusions: The α/β ratio estimated from clinical dose-response data is model dependent-a heterogeneous TCP model that accounts for heterogeneity in radiosensitivity will produce a greater α/β estimate than that resulting from a homogeneous TCP model

Applicability of genetic algorithms to parameter estimation of economic models

Directory of Open Access Journals (Sweden)

Marcel Ševela

2004-01-01

Full Text Available The paper concentrates on capability of genetic algorithms for parameter estimation of non-linear economic models. In the paper we test the ability of genetic algorithms to estimate of parameters of demand function for durable goods and simultaneously search for parameters of genetic algorithm that lead to maximum effectiveness of the computation algorithm. The genetic algorithms connect deterministic iterative computation methods with stochastic methods. In the genteic aůgorithm approach each possible solution is represented by one individual, those life and lifes of all generations of individuals run under a few parameter of genetic algorithm. Our simulations resulted in optimal mutation rate of 15% of all bits in chromosomes, optimal elitism rate 20%. We can not set the optimal extend of generation, because it proves positive correlation with effectiveness of genetic algorithm in all range under research, but its impact is degreasing. The used genetic algorithm was sensitive to mutation rate at most, than to extend of generation. The sensitivity to elitism rate is not so strong.

Parameter Estimation as a Problem in Statistical Thermodynamics.

Science.gov (United States)

Earle, Keith A; Schneider, David J

2011-03-14

In this work, we explore the connections between parameter fitting and statistical thermodynamics using the maxent principle of Jaynes as a starting point. In particular, we show how signal averaging may be described by a suitable one particle partition function, modified for the case of a variable number of particles. These modifications lead to an entropy that is extensive in the number of measurements in the average. Systematic error may be interpreted as a departure from ideal gas behavior. In addition, we show how to combine measurements from different experiments in an unbiased way in order to maximize the entropy of simultaneous parameter fitting. We suggest that fit parameters may be interpreted as generalized coordinates and the forces conjugate to them may be derived from the system partition function. From this perspective, the parameter fitting problem may be interpreted as a process where the system (spectrum) does work against internal stresses (non-optimum model parameters) to achieve a state of minimum free energy/maximum entropy. Finally, we show how the distribution function allows us to define a geometry on parameter space, building on previous work[1, 2]. This geometry has implications for error estimation and we outline a program for incorporating these geometrical insights into an automated parameter fitting algorithm.

Non-Cooperative Target Imaging and Parameter Estimation with Narrowband Radar Echoes

Directory of Open Access Journals (Sweden)

Chun-mao Yeh

2016-01-01

Full Text Available This study focuses on the rotating target imaging and parameter estimation with narrowband radar echoes, which is essential for radar target recognition. First, a two-dimensional (2D imaging model with narrowband echoes is established in this paper, and two images of the target are formed on the velocity-acceleration plane at two neighboring coherent processing intervals (CPIs. Then, the rotating velocity (RV is proposed to be estimated by utilizing the relationship between the positions of the scattering centers among two images. Finally, the target image is rescaled to the range-cross-range plane with the estimated rotational parameter. The validity of the proposed approach is confirmed using numerical simulations.

Parameter Estimation of a Closed Loop Coupled Tank Time Varying System using Recursive Methods

International Nuclear Information System (INIS)

Basir, Siti Nora; Yussof, Hanafiah; Shamsuddin, Syamimi; Selamat, Hazlina; Zahari, Nur Ismarrubie

2013-01-01

This project investigates the direct identification of closed loop plant using discrete-time approach. The uses of Recursive Least Squares (RLS), Recursive Instrumental Variable (RIV) and Recursive Instrumental Variable with Centre-Of-Triangle (RIV + COT) in the parameter estimation of closed loop time varying system have been considered. The algorithms were applied in a coupled tank system that employs covariance resetting technique where the time of parameter changes occur is unknown. The performances of all the parameter estimation methods, RLS, RIV and RIV + COT were compared. The estimation of the system whose output was corrupted with white and coloured noises were investigated. Covariance resetting technique successfully executed when the parameters change. RIV + COT gives better estimates than RLS and RIV in terms of convergence and maximum overshoot

When celibacy matters: incorporating non-breeders improves demographic parameter estimates.

Science.gov (United States)

Pardo, Deborah; Weimerskirch, Henri; Barbraud, Christophe

2013-01-01

In long-lived species only a fraction of a population breeds at a given time. Non-breeders can represent more than half of adult individuals, calling in doubt the relevance of estimating demographic parameters from the sole breeders. Here we demonstrate the importance of considering observable non-breeders to estimate reliable demographic traits: survival, return, breeding, hatching and fledging probabilities. We study the long-lived quasi-biennial breeding wandering albatross (Diomedea exulans). In this species, the breeding cycle lasts almost a year and birds that succeed a given year tend to skip the next breeding occasion while birds that fail tend to breed again the following year. Most non-breeders remain unobservable at sea, but still a substantial number of observable non-breeders (ONB) was identified on breeding sites. Using multi-state capture-mark-recapture analyses, we used several measures to compare the performance of demographic estimates between models incorporating or ignoring ONB: bias (difference in mean), precision (difference is standard deviation) and accuracy (both differences in mean and standard deviation). Our results highlight that ignoring ONB leads to bias and loss of accuracy on breeding probability and survival estimates. These effects are even stronger when studied in an age-dependent framework. Biases on breeding probabilities and survival increased with age leading to overestimation of survival at old age and thus actuarial senescence and underestimation of reproductive senescence. We believe our study sheds new light on the difficulties of estimating demographic parameters in species/taxa where a significant part of the population does not breed every year. Taking into account ONB appeared important to improve demographic parameter estimates, models of population dynamics and evolutionary conclusions regarding senescence within and across taxa.

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 ...

Airport acoustics: Aircraft noise distribution and modelling of some ...

African Journals Online (AJOL)

Airport acoustics: Aircraft noise distribution and modelling of some aircraft parameters. MU Onuu, EO Obisung. Abstract. No Abstract. Nigerian Journal of Physics Vol. 17 (Supplement) 2005: pp. 177-186. Full Text: EMAIL FREE FULL TEXT EMAIL FREE FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT.

SBML-PET-MPI: a parallel parameter estimation tool for Systems Biology Markup Language based models.

Science.gov (United States)

Zi, Zhike

2011-04-01

Parameter estimation is crucial for the modeling and dynamic analysis of biological systems. However, implementing parameter estimation is time consuming and computationally demanding. Here, we introduced a parallel parameter estimation tool for Systems Biology Markup Language (SBML)-based models (SBML-PET-MPI). SBML-PET-MPI allows the user to perform parameter estimation and parameter uncertainty analysis by collectively fitting multiple experimental datasets. The tool is developed and parallelized using the message passing interface (MPI) protocol, which provides good scalability with the number of processors. SBML-PET-MPI is freely available for non-commercial use at http://www.bioss.uni-freiburg.de/cms/sbml-pet-mpi.html or http://sites.google.com/site/sbmlpetmpi/.

Sinusoidal Parameter Estimation Using Quadratic Interpolation around Power-Scaled Magnitude Spectrum Peaks

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Kurt James Werner

2016-10-01

Full Text Available The magnitude of the Discrete Fourier Transform (DFT of a discrete-time signal has a limited frequency definition. Quadratic interpolation over the three DFT samples surrounding magnitude peaks improves the estimation of parameters (frequency and amplitude of resolved sinusoids beyond that limit. Interpolating on a rescaled magnitude spectrum using a logarithmic scale has been shown to improve those estimates. In this article, we show how to heuristically tune a power scaling parameter to outperform linear and logarithmic scaling at an equivalent computational cost. Although this power scaling factor is computed heuristically rather than analytically, it is shown to depend in a structured way on window parameters. Invariance properties of this family of estimators are studied and the existence of a bias due to noise is shown. Comparing to two state-of-the-art estimators, we show that an optimized power scaling has a lower systematic bias and lower mean-squared-error in noisy conditions for ten out of twelve common windowing functions.

REML estimates of genetic parameters of sexual dimorphism for ...

Indian Academy of Sciences (India)

Administrator

Full and half sibs were distinguished, in contrast to usual isofemale studies in which animals ... studies. Thus, the aim of this study was to estimate genetic parameters of sexual dimorphism in isofemale lines using ..... Muscovy ducks. Genet.

A parameter estimation for DC servo motor by using optimization process

International Nuclear Information System (INIS)

Arjoni Amir

2010-01-01

Modeling and simulation parameters of DC servo motor using Matlab Simulink software have been done. The objective to define the DC servo motor parameter estimation is to get DC servo motor parameter values (B, La, Ra, Km, J) which are significant value that can be used for actuation process of control systems. In the analysis of control systems DC the servo motor expressed by transfer function equation to make faster to be analyzed as a component of the actuator. To obtain the data model parameters and initial conditions of the DC servo motors is then carried out the processor modeling and simulation in which the DC servo motor combined with other components. To obtain preliminary data of the DC servo motor parameters as estimated venue, it is obtained from the data factory of the DC servo motor. The initial data parameters of the DC servo motor are applied for the optimization process by using nonlinear least square algorithm and minimize the cost function value so that the DC servo motors parameter values are obtained significantly. The result of the optimization process of the DC servo motor parameter values are B = 0.039881, J= 1.2608e-007, Km = 0.069648, La = 2.3242e-006 and Ra = 1.8837. (author)

Estimating parameters for probabilistic linkage of privacy-preserved datasets.

Science.gov (United States)

Brown, Adrian P; Randall, Sean M; Ferrante, Anna M; Semmens, James B; Boyd, James H

2017-07-10

Probabilistic record linkage is a process used to bring together person-based records from within the same dataset (de-duplication) or from disparate datasets using pairwise comparisons and matching probabilities. The linkage strategy and associated match probabilities are often estimated through investigations into data quality and manual inspection. However, as privacy-preserved datasets comprise encrypted data, such methods are not possible. In this paper, we present a method for estimating the probabilities and threshold values for probabilistic privacy-preserved record linkage using Bloom filters. Our method was tested through a simulation study using synthetic data, followed by an application using real-world administrative data. Synthetic datasets were generated with error rates from zero to 20% error. Our method was used to estimate parameters (probabilities and thresholds) for de-duplication linkages. Linkage quality was determined by F-measure. Each dataset was privacy-preserved using separate Bloom filters for each field. Match probabilities were estimated using the expectation-maximisation (EM) algorithm on the privacy-preserved data. Threshold cut-off values were determined by an extension to the EM algorithm allowing linkage quality to be estimated for each possible threshold. De-duplication linkages of each privacy-preserved dataset were performed using both estimated and calculated probabilities. Linkage quality using the F-measure at the estimated threshold values was also compared to the highest F-measure. Three large administrative datasets were used to demonstrate the applicability of the probability and threshold estimation technique on real-world data. Linkage of the synthetic datasets using the estimated probabilities produced an F-measure that was comparable to the F-measure using calculated probabilities, even with up to 20% error. Linkage of the administrative datasets using estimated probabilities produced an F-measure that was higher

Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations

Science.gov (United States)

We analyzed 10 established and 4 new satellite reflectance algorithms for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense water truth collected within one hour of image acquisition to develop si...

Comparison of methodologies estimating emissions of aircraft pollutants, environmental impact assessment around airports

International Nuclear Information System (INIS)

Kurniawan, Jermanto S.; Khardi, S.

2011-01-01

Air transportation growth has increased continuously over the years. The rise in air transport activity has been accompanied by an increase in the amount of energy used to provide air transportation services. It is also assumed to increase environmental impacts, in particular pollutant emissions. Traditionally, the environmental impacts of atmospheric emissions from aircraft have been addressed in two separate ways; aircraft pollutant emissions occurring during the landing and take-off (LTO) phase (local pollutant emissions) which is the focus of this study, and the non-LTO phase (global/regional pollutant emissions). Aircraft pollutant emissions are an important source of pollution and directly or indirectly harmfully affect human health, ecosystems and cultural heritage. There are many methods to asses pollutant emissions used by various countries. However, using different and separate methodology will cause a variation in results, some lack of information and the use of certain methods will require justification and reliability that must be demonstrated and proven. In relation to this issue, this paper presents identification, comparison and reviews of some of the methodologies of aircraft pollutant assessment from the past, present and future expectations of some studies and projects focusing on emissions factors, fuel consumption, and uncertainty. This paper also provides reliable information on the impacts of aircraft pollutant emissions in short term and long term predictions.

Forest parameter estimation using polarimetric SAR interferometry techniques at low frequencies

International Nuclear Information System (INIS)

Lee, Seung-Kuk

2013-01-01

Polarimetric Synthetic Aperture Radar Interferometry (Pol-InSAR) is an active radar remote sensing technique based on the coherent combination of both polarimetric and interferometric observables. The Pol-InSAR technique provided a step forward in quantitative forest parameter estimation. In the last decade, airborne SAR experiments evaluated the potential of Pol-InSAR techniques to estimate forest parameters (e.g., the forest height and biomass) with high accuracy over various local forest test sites. This dissertation addresses the actual status, potentials and limitations of Pol-InSAR inversion techniques for 3-D forest parameter estimations on a global scale using lower frequencies such as L- and P-band. The multi-baseline Pol-InSAR inversion technique is applied to optimize the performance with respect to the actual level of the vertical wave number and to mitigate the impact of temporal decorrelation on the Pol-InSAR forest parameter inversion. Temporal decorrelation is a critical issue for successful Pol-InSAR inversion in the case of repeat-pass Pol-InSAR data, as provided by conventional satellites or airborne SAR systems. Despite the limiting impact of temporal decorrelation in Pol-InSAR inversion, it remains a poorly understood factor in forest height inversion. Therefore, the main goal of this dissertation is to provide a quantitative estimation of the temporal decorrelation effects by using multi-baseline Pol-InSAR data. A new approach to quantify the different temporal decorrelation components is proposed and discussed. Temporal decorrelation coefficients are estimated for temporal baselines ranging from 10 minutes to 54 days and are converted to height inversion errors. In addition, the potential of Pol-InSAR forest parameter estimation techniques is addressed and projected onto future spaceborne system configurations and mission scenarios (Tandem-L and BIOMASS satellite missions at L- and P-band). The impact of the system parameters (e.g., bandwidth

A new method to estimate parameters of linear compartmental models using artificial neural networks

International Nuclear Information System (INIS)

Gambhir, Sanjiv S.; Keppenne, Christian L.; Phelps, Michael E.; Banerjee, Pranab K.

1998-01-01

At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models. (author)

Mammalian Cell Culture Process for Monoclonal Antibody Production: Nonlinear Modelling and Parameter Estimation

Directory of Open Access Journals (Sweden)

Dan Selişteanu

2015-01-01

Full Text Available Monoclonal antibodies (mAbs are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies.

Parameter estimation techniques for LTP system identification

Science.gov (United States)

Nofrarias Serra, Miquel

LISA Pathfinder (LPF) is the precursor mission of LISA (Laser Interferometer Space Antenna) and the first step towards gravitational waves detection in space. The main instrument onboard the mission is the LTP (LISA Technology Package) whose scientific goal is to test LISA's drag-free control loop by reaching a differential acceleration noise level between two masses in √ geodesic motion of 3 × 10-14 ms-2 / Hz in the milliHertz band. The mission is not only challenging in terms of technology readiness but also in terms of data analysis. As with any gravitational wave detector, attaining the instrument performance goals will require an extensive noise hunting campaign to measure all contributions with high accuracy. But, opposite to on-ground experiments, LTP characterisation will be only possible by setting parameters via telecommands and getting a selected amount of information through the available telemetry downlink. These two conditions, high accuracy and high reliability, are the main restrictions that the LTP data analysis must overcome. A dedicated object oriented Matlab Toolbox (LTPDA) has been set up by the LTP analysis team for this purpose. Among the different toolbox methods, an essential part for the mission are the parameter estimation tools that will be used for system identification during operations: Linear Least Squares, Non-linear Least Squares and Monte Carlo Markov Chain methods have been implemented as LTPDA methods. The data analysis team has been testing those methods with a series of mock data exercises with the following objectives: to cross-check parameter estimation methods and compare the achievable accuracy for each of them, and to develop the best strategies to describe the physics underlying a complex controlled experiment as the LTP. In this contribution we describe how these methods were tested with simulated LTP-like data to recover the parameters of the model and we report on the latest results of these mock data exercises.

Application of Novel Lateral Tire Force Sensors to Vehicle Parameter Estimation of Electric Vehicles.

Science.gov (United States)

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.

Parameter estimation in a simple stochastic differential equation for phytoplankton modelling

DEFF Research Database (Denmark)

Møller, Jan Kloppenborg; Madsen, Henrik; Carstensen, Jacob

2011-01-01

The use of stochastic differential equations (SDEs) for simulation of aquatic ecosystems has attracted increasing attention in recent years. The SDE setting also provides the opportunity for statistical estimation of ecosystem parameters. We present an estimation procedure, based on Kalman...

Estimating Propensity Parameters using Google PageRank and Genetic Algorithms

Directory of Open Access Journals (Sweden)

David Murrugarra

2016-11-01

Full Text Available Stochastic Boolean networks, or more generally, stochastic discrete networks, are an important class of computational models for molecular interaction networks. The stochasticity stems from the updating schedule. Standard updating schedules include the synchronous update, where all the nodes are updated at the same time, and the asynchronous update where a random node is updated at each time step. The former produces a deterministic dynamics while the latter a stochastic dynamics. A more general stochastic setting considers propensity parameters for updating each node. Stochastic Discrete Dynamical Systems (SDDS are a modeling framework that considers two propensity parameters for updating each node and uses one when the update has a positive impact on the variable, that is, when the update causes the variable to increase its value, and uses the other when the update has a negative impact, that is, when the update causes it to decrease its value. This framework offers additional features for simulations but also adds a complexity in parameter estimation of the propensities. This paper presents a method for estimating the propensity parameters for SDDS. The method is based on adding noise to the system using the Google PageRank approach to make the system ergodic and thus guaranteeing the existence of a stationary distribution. Then with the use of a genetic algorithm, the propensity parameters are estimated. Approximation techniques that make the search algorithms efficient are also presented and Matlab/Octave code to test the algorithms are available at~href{http://www.ms.uky.edu/~dmu228/GeneticAlg/Code.html}{http://www.ms.uky.edu/$sim$dmu228GeneticAlgCode.html}.

Estimation of solid earth tidal parameters and FCN with VLBI

International Nuclear Information System (INIS)

Krásná, H.

2012-01-01

Measurements of a space-geodetic technique VLBI (Very Long Baseline Interferometry) are influenced by a variety of processes which have to be modelled and put as a priori information into the analysis of the space-geodetic data. The increasing accuracy of the VLBI measurements allows access to these parameters and provides possibilities to validate them directly from the measured data. The gravitational attraction of the Moon and the Sun causes deformation of the Earth's surface which can reach several decimetres in radial direction during a day. The displacement is a function of the so-called Love and Shida numbers. Due to the present accuracy of the VLBI measurements the parameters have to be specified as complex numbers, where the imaginary parts describe the anelasticity of the Earth's mantle. Moreover, it is necessary to distinguish between the single tides within the various frequency bands. In this thesis, complex Love and Shida numbers of twelve diurnal and five long-period tides included in the solid Earth tidal displacement modelling are estimated directly from the 27 years of VLBI measurements (1984.0 - 2011.0). In this work, the period of the Free Core Nutation (FCN) is estimated which shows up in the frequency dependent solid Earth tidal displacement as well as in a nutation model describing the motion of the Earth's axis in space. The FCN period in both models is treated as a single parameter and it is estimated in a rigorous global adjustment of the VLBI data. The obtained value of -431.18 ± 0.10 sidereal days differs slightly from the conventional value -431.39 sidereal days given in IERS Conventions 2010. An empirical FCN model based on variable amplitude and phase is determined, whose parameters are estimated in yearly steps directly within VLBI global solutions. (author) [de

ESTIMATION OF DISTANCES TO STARS WITH STELLAR PARAMETERS FROM LAMOST

Energy Technology Data Exchange (ETDEWEB)

Carlin, Jeffrey L.; Newberg, Heidi Jo [Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute, Troy, NY 12180 (United States); Liu, Chao; Deng, Licai; Li, Guangwei; Luo, A-Li; Wu, Yue; Yang, Ming; Zhang, Haotong [Key Lab of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012 (China); Beers, Timothy C. [Department of Physics and JINA: Joint Institute for Nuclear Astrophysics, University of Notre Dame, 225 Nieuwland Science Hall, Notre Dame, IN 46556 (United States); Chen, Li; Hou, Jinliang; Smith, Martin C. [Shanghai Astronomical Observatory, 80 Nandan Road, Shanghai 200030 (China); Guhathakurta, Puragra [UCO/Lick Observatory, Department of Astronomy and Astrophysics, University of California, Santa Cruz, CA 95064 (United States); Hou, Yonghui [Nanjing Institute of Astronomical Optics and Technology, National Astronomical Observatories, Chinese Academy of Sciences, Nanjing 210042 (China); Lépine, Sébastien [Department of Physics and Astronomy, Georgia State University, 25 Park Place, Suite 605, Atlanta, GA 30303 (United States); Yanny, Brian [Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510 (United States); Zheng, Zheng, E-mail: jeffreylcarlin@gmail.com [Department of Physics and Astronomy, University of Utah, UT 84112 (United States)

2015-07-15

We present a method to estimate distances to stars with spectroscopically derived stellar parameters. The technique is a Bayesian approach with likelihood estimated via comparison of measured parameters to a grid of stellar isochrones, and returns a posterior probability density function for each star’s absolute magnitude. This technique is tailored specifically to data from the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) survey. Because LAMOST obtains roughly 3000 stellar spectra simultaneously within each ∼5° diameter “plate” that is observed, we can use the stellar parameters of the observed stars to account for the stellar luminosity function and target selection effects. This removes biasing assumptions about the underlying populations, both due to predictions of the luminosity function from stellar evolution modeling, and from Galactic models of stellar populations along each line of sight. Using calibration data of stars with known distances and stellar parameters, we show that our method recovers distances for most stars within ∼20%, but with some systematic overestimation of distances to halo giants. We apply our code to the LAMOST database, and show that the current precision of LAMOST stellar parameters permits measurements of distances with ∼40% error bars. This precision should improve as the LAMOST data pipelines continue to be refined.

A model-based initial guess for estimating parameters in systems of ordinary differential equations.

Science.gov (United States)

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.

Patient-specific parameter estimation in single-ventricle lumped circulation models under uncertainty

Science.gov (United States)

Schiavazzi, Daniele E.; Baretta, Alessia; Pennati, Giancarlo; Hsia, Tain-Yen; Marsden, Alison L.

2017-01-01

Summary Computational models of cardiovascular physiology can inform clinical decision-making, providing a physically consistent framework to assess vascular pressures and flow distributions, and aiding in treatment planning. In particular, lumped parameter network (LPN) models that make an analogy to electrical circuits offer a fast and surprisingly realistic method to reproduce the circulatory physiology. The complexity of LPN models can vary significantly to account, for example, for cardiac and valve function, respiration, autoregulation, and time-dependent hemodynamics. More complex models provide insight into detailed physiological mechanisms, but their utility is maximized if one can quickly identify patient specific parameters. The clinical utility of LPN models with many parameters will be greatly enhanced by automated parameter identification, particularly if parameter tuning can match non-invasively obtained clinical data. We present a framework for automated tuning of 0D lumped model parameters to match clinical data. We demonstrate the utility of this framework through application to single ventricle pediatric patients with Norwood physiology. Through a combination of local identifiability, Bayesian estimation and maximum a posteriori simplex optimization, we show the ability to automatically determine physiologically consistent point estimates of the parameters and to quantify uncertainty induced by errors and assumptions in the collected clinical data. We show that multi-level estimation, that is, updating the parameter prior information through sub-model analysis, can lead to a significant reduction in the parameter marginal posterior variance. We first consider virtual patient conditions, with clinical targets generated through model solutions, and second application to a cohort of four single-ventricle patients with Norwood physiology. PMID:27155892

Parameter estimation of compact binaries using the inspiral and ringdown waveforms

International Nuclear Information System (INIS)

Luna, Manuel; Sintes, Alicia M

2006-01-01

We analyse the problem of parameter estimation for compact binary systems that could be detected by ground-based gravitational wave detectors. So far, this problem has only been dealt with for the inspiral and the ringdown phases separately. In this paper, we combine the information from both signals, and we study the improvement in parameter estimation, at a fixed signal-to-noise ratio, by including the ringdown signal without making any assumption on the merger phase. The study is performed for both initial and advanced LIGO and VIRGO detectors

Improving aircraft accident forecasting for an integrated plutonium storage facility

International Nuclear Information System (INIS)

Rock, J.C.; Kiffe, J.; McNerney, M.T.; Turen, T.A.

1998-06-01

Aircraft accidents pose a quantifiable threat to facilities used to store and process surplus weapon-grade plutonium. The Department of Energy (DOE) recently published its first aircraft accident analysis guidelines: Accident Analysis for Aircraft Crash into Hazardous Facilities. This document establishes a hierarchy of procedures for estimating the small annual frequency for aircraft accidents that impact Pantex facilities and the even smaller frequency of hazardous material released to the environment. The standard establishes a screening threshold of 10 -6 impacts per year; if the initial estimate of impact frequency for a facility is below this level, no further analysis is required. The Pantex Site-Wide Environmental Impact Statement (SWEIS) calculates the aircraft impact frequency to be above this screening level. The DOE Standard encourages more detailed analyses in such cases. This report presents three refinements, namely, removing retired small military aircraft from the accident rate database, correcting the conversion factor from military accident rates (accidents per 100,000 hours) to the rates used in the DOE model (accidents per flight phase), and adjusting the conditional probability of impact for general aviation to more accurately reflect pilot training and local conditions. This report documents a halving of the predicted frequency of an aircraft impact at Pantex and points toward further reductions

Low Complexity Parameter Estimation For Off-the-Grid Targets

KAUST Repository

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.

Effects of censoring on parameter estimates and power in genetic modeling

NARCIS (Netherlands)

Derks, Eske M.; Dolan, Conor V.; Boomsma, Dorret I.

2004-01-01

Genetic and environmental influences on variance in phenotypic traits may be estimated with normal theory Maximum Likelihood (ML). However, when the assumption of multivariate normality is not met, this method may result in biased parameter estimates and incorrect likelihood ratio tests. We

Effects of censoring on parameter estimates and power in genetic modeling.

NARCIS (Netherlands)

Derks, E.M.; Dolan, C.V.; Boomsma, D.I.

2004-01-01

Genetic and environmental influences on variance in phenotypic traits may be estimated with normal theory Maximum Likelihood (ML). However, when the assumption of multivariate normality is not met, this method may result in biased parameter estimates and incorrect likelihood ratio tests. We

Modal Parameters Evaluation in a Full-Scale Aircraft Demonstrator under Different Environmental Conditions Using HS 3D-DIC

Directory of Open Access Journals (Sweden)

Ángel Jesús Molina-Viedma

2018-02-01

Full Text Available In real aircraft structures the comfort and the occupational performance of crewmembers and passengers are affected by the presence of noise. In this sense, special attention is focused on mechanical and material design for isolation and vibration control. Experimental characterization and, in particular, experimental modal analysis, provides information for adequate cabin noise control. Traditional sensors employed in the aircraft industry for this purpose are invasive and provide a low spatial resolution. This paper presents a methodology for experimental modal characterization of a front fuselage full-scale demonstrator using high-speed 3D digital image correlation, which is non-invasive, ensuring that the structural response is unperturbed by the instrumentation mass. Specifically, full-field measurements on the passenger window area were conducted when the structure was excited using an electrodynamic shaker. The spectral analysis of the measured time-domain displacements made it possible to identify natural frequencies and full-field operational deflection shapes. Changes in the modal parameters due to cabin pressurization and the behavior of different local structural modifications were assessed using this methodology. The proposed full-field methodology allowed the characterization of relevant dynamic response patterns, complementing the capabilities provided by accelerometers.

Improved Shape Parameter Estimation in Pareto Distributed Clutter with Neural Networks

Directory of Open Access Journals (Sweden)

José Raúl Machado-Fernández

2016-12-01

Full Text Available The main problem faced by naval radars is the elimination of the clutter input which is a distortion signal appearing mixed with target reflections. Recently, the Pareto distribution has been related to sea clutter measurements suggesting that it may provide a better fit than other traditional distributions. The authors propose a new method for estimating the Pareto shape parameter based on artificial neural networks. The solution achieves a precise estimation of the parameter, having a low computational cost, and outperforming the classic method which uses Maximum Likelihood Estimates (MLE. The presented scheme contributes to the development of the NATE detector for Pareto clutter, which uses the knowledge of clutter statistics for improving the stability of the detection, among other applications.