On linear models and parameter identifiability in experimental biological systems.
Lamberton, Timothy O; Condon, Nicholas D; Stow, Jennifer L; Hamilton, Nicholas A
2014-10-07
A key problem in the biological sciences is to be able to reliably estimate model parameters from experimental data. This is the well-known problem of parameter identifiability. Here, methods are developed for biologists and other modelers to design optimal experiments to ensure parameter identifiability at a structural level. The main results of the paper are to provide a general methodology for extracting parameters of linear models from an experimentally measured scalar function - the transfer function - and a framework for the identifiability analysis of complex model structures using linked models. Linked models are composed by letting the output of one model become the input to another model which is then experimentally measured. The linked model framework is shown to be applicable to designing experiments to identify the measured sub-model and recover the input from the unmeasured sub-model, even in cases that the unmeasured sub-model is not identifiable. Applications for a set of common model features are demonstrated, and the results combined in an example application to a real-world experimental system. These applications emphasize the insight into answering "where to measure" and "which experimental scheme" questions provided by both the parameter extraction methodology and the linked model framework. The aim is to demonstrate the tools' usefulness in guiding experimental design to maximize parameter information obtained, based on the model structure.
Baker Syed; Poskar C; Junker Björn
2011-01-01
Abstract In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. Wh...
Parameter Identifiability of Ship Manoeuvring Modeling Using System Identification
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Weilin Luo
2016-01-01
Full Text Available To improve the feasibility of system identification in the prediction of ship manoeuvrability, several measures are presented to deal with the parameter identifiability in the parametric modeling of ship manoeuvring motion based on system identification. Drift of nonlinear hydrodynamic coefficients is explained from the point of view of regression analysis. To diminish the multicollinearity in a complicated manoeuvring model, difference method and additional signal method are employed to reconstruct the samples. Moreover, the structure of manoeuvring model is simplified based on correlation analysis. Manoeuvring simulation is performed to demonstrate the validity of the measures proposed.
Directory of Open Access Journals (Sweden)
Baker Syed
2011-01-01
Full Text Available Abstract In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF, rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison.
Baker, Syed Murtuza; Poskar, C Hart; Junker, Björn H
2011-10-11
In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF), rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison.
IDENTIFYING THE PARAMETERS OF THE MATHEMATICAL EXPENDITURE SYSTEM MODEL
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ANA-PETRINA PĂUN
2013-10-01
Full Text Available This chapter describes an optimum regulation model for the public expenditures system in Romania. The aim of this work is to design an optimal control system of public expenditures in Romania. It contains an offline identification of the total public expenditures system in Romania for a timespan of 15 years. The total public expenditures system is a MISO type one (Multiple Input – Single Output and is identified by the use of the lowest foursquare applied on an OE (Output Error type model.
The identifiability of parameters in a water quality model of the Biebrza River, Poland
Perk, van der M.; Bierkens, M.F.P.
1997-01-01
The identifiability of model parameters of a steady state water quality model of the Biebrza River and the resulting variation in model results was examined by applying the Monte Carlo method which combines calibration, identifiability analysis, uncertainty analysis, and sensitivity analysis. The wa
DEFF Research Database (Denmark)
Ottosen, Thor Bjørn; Ketzel, Matthias; Skov, Henrik
2016-01-01
Pollution Model (OSPM®). To assess the predictive validity of the model, the data is split into an estimation and a prediction data set using two data splitting approaches and data preparation techniques (clustering and outlier detection) are analysed. The sensitivity analysis, being part......Mathematical models are increasingly used in environmental science thus increasing the importance of uncertainty and sensitivity analyses. In the present study, an iterative parameter estimation and identifiability analysis methodology is applied to an atmospheric model – the Operational Street...
Shariati, M M; Korsgaard, I R; Sorensen, D
2009-04-01
Markov chain Monte Carlo (MCMC) enables fitting complex hierarchical models that may adequately reflect the process of data generation. Some of these models may contain more parameters than can be uniquely inferred from the distribution of the data, causing non-identifiability. The reaction norm model with unknown covariates (RNUC) is a model in which unknown environmental effects can be inferred jointly with the remaining parameters. The problem of identifiability of parameters at the level of the likelihood and the associated behaviour of MCMC chains were discussed using the RNUC as an example. It was shown theoretically that when environmental effects (covariates) are considered as random effects, estimable functions of the fixed effects, (co)variance components and genetic effects are identifiable as well as the environmental effects. When the environmental effects are treated as fixed and there are other fixed factors in the model, the contrasts involving environmental effects, the variance of environmental sensitivities (genetic slopes) and the residual variance are the only identifiable parameters. These different identifiability scenarios were generated by changing the formulation of the model and the structure of the data and the models were then implemented via MCMC. The output of MCMC sampling schemes was interpreted in the light of the theoretical findings. The erratic behaviour of the MCMC chains was shown to be associated with identifiability problems in the likelihood, despite propriety of posterior distributions, achieved by arbitrarily chosen uniform (bounded) priors. In some cases, very long chains were needed before the pattern of behaviour of the chain may signal the existence of problems. The paper serves as a warning concerning the implementation of complex models where identifiability problems can be difficult to detect a priori. We conclude that it would be good practice to experiment with a proposed model and to understand its features
Energy Technology Data Exchange (ETDEWEB)
Rafique, Rashid; Kumar, Sandeep; Luo, Yiqi; Kiely, Gerard; Asrar, Ghassem R.
2015-02-01
he accurate calibration of complex biogeochemical models is essential for the robust estimation of soil greenhouse gases (GHG) as well as other environmental conditions and parameters that are used in research and policy decisions. DayCent is a popular biogeochemical model used both nationally and internationally for this purpose. Despite DayCent’s popularity, its complex parameter estimation is often based on experts’ knowledge which is somewhat subjective. In this study we used the inverse modelling parameter estimation software (PEST), to calibrate the DayCent model based on sensitivity and identifi- ability analysis. Using previously published N2 O and crop yield data as a basis of our calibration approach, we found that half of the 140 parameters used in this study were the primary drivers of calibration dif- ferences (i.e. the most sensitive) and the remaining parameters could not be identified given the data set and parameter ranges we used in this study. The post calibration results showed improvement over the pre-calibration parameter set based on, a decrease in residual differences 79% for N2O fluxes and 84% for crop yield, and an increase in coefficient of determination 63% for N2O fluxes and 72% for corn yield. The results of our study suggest that future studies need to better characterize germination tem- perature, number of degree-days and temperature dependency of plant growth; these processes were highly sensitive and could not be adequately constrained by the data used in our study. Furthermore, the sensitivity and identifiability analysis was helpful in providing deeper insight for important processes and associated parameters that can lead to further improvement in calibration of DayCent model.
Humbird, Kelli; Peterson, J. Luc; Brandon, Scott; Field, John; Nora, Ryan; Spears, Brian
2016-10-01
Next-generation supercomputer architecture and in-transit data analysis have been used to create a large collection of 2-D ICF capsule implosion simulations. The database includes metrics for approximately 60,000 implosions, with x-ray images and detailed physics parameters available for over 20,000 simulations. To map and explore this large database, surrogate models for numerous quantities of interest are built using supervised machine learning algorithms. Response surfaces constructed using the predictive capabilities of the surrogates allow for continuous exploration of parameter space without requiring additional simulations. High performing regions of the input space are identified to guide the design of future experiments. In particular, a model for the yield built using a random forest regression algorithm has a cross validation score of 94.3% and is consistently conservative for high yield predictions. The model is used to search for robust volumes of parameter space where high yields are expected, even given variations in other input parameters. Surrogates for additional quantities of interest relevant to ignition are used to further characterize the high yield regions. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, Lawrence Livermore National Security, LLC. LLNL-ABS-697277.
Three-stage Method for Identifying the Dynamic Model Parameters of Stranded Wire Helical Springs
Institute of Scientific and Technical Information of China (English)
ZHAO Yu; WANG Shilong; ZHOU Jie; LI Chuan; SUN Shouli
2015-01-01
The dynamic behavior of the stranded wire helical spring is described by a modified Bouc-Wen model while the model parameters must be identified using an identification method and experimental data. Existing identification methods usually relies either solely nonlinear iterative algorithms or manually trial and error. Therefore, the identification process can be rather time consuming and effort taking. As a result, these methods are not ideal for engineering applications. To come up with a more practical method, a three-stage identification method is proposed. Periodic loading and identification simulations are carried out to verify the effectiveness of the proposed method. Noises are added to the simulated data to test the performance of the proposed method when dealing with noise contaminated data. The simulation results indicate that the proposed method is able to give satisfying results when the noise levels are set to be 0.01, 0.03, 0.05 and 0.07. In addition, the proposed method is also applied to experimental data and compared with an existing method. The experimental data is acquired through a periodic loading test. The experiment results suggest that the proposed method features better accuracy compared with the existing method. An effective approach is proposed for identifying the model parameters of the stranded wire helical spring.
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M. Sudha
2015-12-01
Full Text Available Uncertain atmosphere is a prevalent factor affecting the existing prediction approaches. Rough set and fuzzy set theories as proposed by Pawlak and Zadeh have become an effective tool for handling vagueness and fuzziness in the real world scenarios. This research work describes the impact of Hybrid Intelligent System (HIS for strategic decision support in meteorology. In this research a novel exhaustive search based Rough set reduct Selection using Genetic Algorithm (RSGA is introduced to identify the significant input feature subset. The proposed model could identify the most effective weather parameters efficiently than other existing input techniques. In the model evaluation phase two adaptive techniques were constructed and investigated. The proposed Artificial Neural Network based on Back Propagation learning (ANN-BP and Adaptive Neuro Fuzzy Inference System (ANFIS was compared with existing Fuzzy Unordered Rule Induction Algorithm (FURIA, Structural Learning Algorithm on Vague Environment (SLAVE and Particle Swarm OPtimization (PSO. The proposed rainfall prediction models outperformed when trained with the input generated using RSGA. A meticulous comparison of the performance indicates ANN-BP model as a suitable HIS for effective rainfall prediction. The ANN-BP achieved 97.46% accuracy with a nominal misclassification rate of 0.0254 %.
Meshkat, Nicolette; Anderson, Chris; Distefano, Joseph J
2011-09-01
When examining the structural identifiability properties of dynamic system models, some parameters can take on an infinite number of values and yet yield identical input-output data. These parameters and the model are then said to be unidentifiable. Finding identifiable combinations of parameters with which to reparameterize the model provides a means for quantitatively analyzing the model and computing solutions in terms of the combinations. In this paper, we revisit and explore the properties of an algorithm for finding identifiable parameter combinations using Gröbner Bases and prove useful theoretical properties of these parameter combinations. We prove a set of M algebraically independent identifiable parameter combinations can be found using this algorithm and that there exists a unique rational reparameterization of the input-output equations over these parameter combinations. We also demonstrate application of the procedure to a nonlinear biomodel.
Utilizing Soize's Approach to Identify Parameter and Model Uncertainties
Energy Technology Data Exchange (ETDEWEB)
Bonney, Matthew S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Univ. of Wisconsin, Madison, WI (United States); Brake, Matthew Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2014-10-01
Quantifying uncertainty in model parameters is a challenging task for analysts. Soize has derived a method that is able to characterize both model and parameter uncertainty independently. This method is explained with the assumption that some experimental data is available, and is divided into seven steps. Monte Carlo analyses are performed to select the optimal dispersion variable to match the experimental data. Along with the nominal approach, an alternative distribution can be used along with corrections that can be utilized to expand the scope of this method. This method is one of a very few methods that can quantify uncertainty in the model form independently of the input parameters. Two examples are provided to illustrate the methodology, and example code is provided in the Appendix.
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Popović Slobodan J.
2014-01-01
Full Text Available In this paper, novel method for obtaining information about combustion process in individual cylinders of a multi-cylinder Spark Ignition Engine based on instantaneous crankshaft angular velocity is presented. The method is based on robust box constrained Levenberg-Marquardt minimization of nonlinear Least Squares given for measured and simulated instantaneous crankshaft angular speed which is determined from the solution of the engine dynamics torque balance equation. Combination of in-house developed comprehensive Zero-Dimensional Two-Zone SI engine combustion model and analytical friction loss model in angular domain have been applied to provide sensitivity and error analysis regarding Wiebe combustion model parameters, heat transfer coefficient and compression ratio. The analysis is employed to evaluate the basic starting assumption and possibility to provide reliable combustion analysis based on instantaneous engine crankshaft angular speed. [Projekat Ministarstva nauke Republike Srbije, br. NPEE-290025 and TR-14074
Zhao, He; Sokhansanj, Bahrad A
2007-10-01
Microtubule dynamics play a critical role in cell function and stress response, modulating mitosis, morphology, signaling, and transport. Drugs such as paclitaxel (Taxol) can impact tubulin polymerization and affect microtubule dynamics. While theoretical methods have been previously proposed to simulate microtubule dynamics, we develop a methodology here that can be used to compare model predictions with experimental data. Our model is a hybrid of (1) a simple two-state stochastic formulation of tubulin polymerization kinetics and (2) an equilibrium approximation for the chemical kinetics of Taxol drug binding to microtubule ends. Model parameters are biologically realistic, with values taken directly from experimental measurements. Model validation is conducted against published experimental data comparing optical measurements of microtubule dynamics in cultured cells under normal and Taxol-treated conditions. To compare model predictions with experimental data requires applying a "windowing" strategy on the spatiotemporal resolution of the simulation. From a biological perspective, this is consistent with interpreting the microtubule "pause" phenomenon as at least partially an artifact of spatiotemporal resolution limits on experimental measurement.
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Nicolette Meshkat
Full Text Available Parameter identifiability problems can plague biomodelers when they reach the quantification stage of development, even for relatively simple models. Structural identifiability (SI is the primary question, usually understood as knowing which of P unknown biomodel parameters p1,…, pi,…, pP are-and which are not-quantifiable in principle from particular input-output (I-O biodata. It is not widely appreciated that the same database also can provide quantitative information about the structurally unidentifiable (not quantifiable subset, in the form of explicit algebraic relationships among unidentifiable pi. Importantly, this is a first step toward finding what else is needed to quantify particular unidentifiable parameters of interest from new I-O experiments. We further develop, implement and exemplify novel algorithms that address and solve the SI problem for a practical class of ordinary differential equation (ODE systems biology models, as a user-friendly and universally-accessible web application (app-COMBOS. Users provide the structural ODE and output measurement models in one of two standard forms to a remote server via their web browser. COMBOS provides a list of uniquely and non-uniquely SI model parameters, and-importantly-the combinations of parameters not individually SI. If non-uniquely SI, it also provides the maximum number of different solutions, with important practical implications. The behind-the-scenes symbolic differential algebra algorithms are based on computing Gröbner bases of model attributes established after some algebraic transformations, using the computer-algebra system Maxima. COMBOS was developed for facile instructional and research use as well as modeling. We use it in the classroom to illustrate SI analysis; and have simplified complex models of tumor suppressor p53 and hormone regulation, based on explicit computation of parameter combinations. It's illustrated and validated here for models of moderate
Meshkat, Nicolette; Kuo, Christine Er-zhen; DiStefano, Joseph
2014-01-01
Parameter identifiability problems can plague biomodelers when they reach the quantification stage of development, even for relatively simple models. Structural identifiability (SI) is the primary question, usually understood as knowing which of P unknown biomodel parameters p1,…, pi,…, pP are-and which are not-quantifiable in principle from particular input-output (I-O) biodata. It is not widely appreciated that the same database also can provide quantitative information about the structurally unidentifiable (not quantifiable) subset, in the form of explicit algebraic relationships among unidentifiable pi. Importantly, this is a first step toward finding what else is needed to quantify particular unidentifiable parameters of interest from new I-O experiments. We further develop, implement and exemplify novel algorithms that address and solve the SI problem for a practical class of ordinary differential equation (ODE) systems biology models, as a user-friendly and universally-accessible web application (app)-COMBOS. Users provide the structural ODE and output measurement models in one of two standard forms to a remote server via their web browser. COMBOS provides a list of uniquely and non-uniquely SI model parameters, and-importantly-the combinations of parameters not individually SI. If non-uniquely SI, it also provides the maximum number of different solutions, with important practical implications. The behind-the-scenes symbolic differential algebra algorithms are based on computing Gröbner bases of model attributes established after some algebraic transformations, using the computer-algebra system Maxima. COMBOS was developed for facile instructional and research use as well as modeling. We use it in the classroom to illustrate SI analysis; and have simplified complex models of tumor suppressor p53 and hormone regulation, based on explicit computation of parameter combinations. It's illustrated and validated here for models of moderate complexity, with
Directory of Open Access Journals (Sweden)
Huan Yang
2016-12-01
Full Text Available Healthy or pathological states of nociceptive subsystems determine different stimulus-response relations measured from quantitative sensory testing. In turn, stimulus-responses measurements may be used to assess these states.In a recently developed computational model, six model parameters characterize activation of nerve endings and spinal neurons. However, both model nonlinearity and limited information in yes-no detection responses to electrocutaneous stimuli challenge to estimate model parameters. Here, we address the question whether and how one can overcome these difficulties for reliable parameter estimation. First, we fit the computational model to experimental stimulus-response pairs by maximizing the likelihood. To evaluate the balance between model fit and complexity, we evaluate the Bayesian Information Criterion. We find that the computational model is better than a conventional logistic model regarding the balance. Second, our theoretical analysis suggests to vary the pulse width among applied stimuli as a necessary condition to prevent structural non-identifiability. In addition, the numerically implemented profile likelihood approach reveals structural and practical non-identifiability. Our model-based approach with integration of psychophysical measurements can be useful for a reliable assessment of states of the nociceptive system.
Zhang, Yong; Sun, HongGuang; Lu, Bingqing; Garrard, Rhiannon; Neupauer, Roseanna M.
2017-09-01
Backward models have been applied for four decades by hydrologists to identify the source of pollutants undergoing Fickian diffusion, while analytical tools are not available for source identification of super-diffusive pollutants undergoing decay. This technical note evaluates analytical solutions for the source location and release time of a decaying contaminant undergoing super-diffusion using backward probability density functions (PDFs), where the forward model is the space fractional advection-dispersion equation with decay. Revisit of the well-known MADE-2 tracer test using parameter analysis shows that the peak backward location PDF can predict the tritium source location, while the peak backward travel time PDF underestimates the tracer release time due to the early arrival of tracer particles at the detection well in the maximally skewed, super-diffusive transport. In addition, the first-order decay adds additional skewness toward earlier arrival times in backward travel time PDFs, resulting in a younger release time, although this impact is minimized at the MADE-2 site due to tritium's half-life being relatively longer than the monitoring period. The main conclusion is that, while non-trivial backward techniques are required to identify pollutant source location, the pollutant release time can and should be directly estimated given the speed of the peak resident concentration for super-diffusive pollutants with or without decay.
Bakopoulou, C.; Bulygina, N.; Butler, A. P.; McIntyre, N. R.
2012-04-01
Land surface models (LSMs) are recognised as important components of Global Circulation Models (GCMs). Simulating exchanges of the moisture, carbon and energy between land surface and atmosphere in a consistent manner requires physics-based LSMs of high complexity, fine vertical resolution and a large number of parameters that need to be estimated. The "physics" that is incorporated in such models is generally based on our knowledge of point (or very small) scale hydrological processes. Therefore, while larger GCM grid-scale performance may be the ultimate goal, the ability of the model to simulate the point-scale processes is, intuitively, a pre-requisite for its reliable use at larger scales. Critical evaluation of model performance and parameter uncertainty at point scales is therefore a rational starting point for critical evaluation of LSMs; and identification of optimal parameter sets at the point scale is a significant stage of the model evaluation at larger scales. The Joint UK Land Environment Simulator (JULES) is a complex LSM, which is used to represent surface exchanges in the UK Met Office's forecast and climate change models. This complexity necessitates a large number of model parameters (in total 108) some of which are incapable of being measured directly at large (i.e. kilometer) scales. For this reason, a parameter sensitivity analysis is a vital confidence building process within the framework of every LSM, and as a part of the calibration strategy. The problem of JULES parameter estimation and uncertainty at the point scale with a view to assessing the accuracy and the uncertainty in the default parameter values is addressed. The sensitivity of the JULES output of soil moisture is examined using parameter response surface analysis. The implemented technique is based on the Regional Sensitivity Analysis method (RSA), which evaluates the model response surface over a region of parameter space using Monte Carlo sampling. The modified version of RSA
Regions of constrained maximum likelihood parameter identifiability
Lee, C.-H.; Herget, C. J.
1975-01-01
This paper considers the parameter identification problem of general discrete-time, nonlinear, multiple-input/multiple-output dynamic systems with Gaussian-white distributed measurement errors. Knowledge of the system parameterization is assumed to be known. Regions of constrained maximum likelihood (CML) parameter identifiability are established. A computation procedure employing interval arithmetic is proposed for finding explicit regions of parameter identifiability for the case of linear systems. It is shown that if the vector of true parameters is locally CML identifiable, then with probability one, the vector of true parameters is a unique maximal point of the maximum likelihood function in the region of parameter identifiability and the CML estimation sequence will converge to the true parameters.
Zhang, Tian; Zhang, Daijun; Li, Zhenliang; Cai, Qing
2010-05-01
The calibration of ASMs is a prerequisite for their application to simulation of a wastewater treatment plant. This work should be made based on the evaluation of structural identifiability of model parameters. An EBPR sub-model including denitrification phosphorus removal has been incorporated in ASM2d. Yet no report is presented on the structural identifiability of the parameters in the EBPR sub-model. In this paper, the differential algebra approach was used to address this issue. The results showed that the structural identifiability of parameters in the EBPR sub-model could be improved by increasing the measured variables. The reduction factor eta(NO)(3) was identifiable when combined data of aerobic process and anoxic process were assumed. For K(PP), X(PAO) and q(PHA) of the anaerobic process to be uniquely identifiable, one of them is needed to be determined by other ways. Likewise, if prior information on one of the parameters, K(PHA), X(PAO) and q(PP) of the aerobic process, is known, all the parameters are identifiable. The above results could be of interest to the parameter estimation of the EBPR sub-model. The algorithm proposed in the paper is also suitable for other sub-models of ASMs.
Identifying crucial parameter correlations maintaining bursting activity.
Directory of Open Access Journals (Sweden)
Anca Doloc-Mihu
2014-06-01
Full Text Available Recent experimental and computational studies suggest that linearly correlated sets of parameters (intrinsic and synaptic properties of neurons allow central pattern-generating networks to produce and maintain their rhythmic activity regardless of changing internal and external conditions. To determine the role of correlated conductances in the robust maintenance of functional bursting activity, we used our existing database of half-center oscillator (HCO model instances of the leech heartbeat CPG. From the database, we identified functional activity groups of burster (isolated neuron and half-center oscillator model instances and realistic subgroups of each that showed burst characteristics (principally period and spike frequency similar to the animal. To find linear correlations among the conductance parameters maintaining functional leech bursting activity, we applied Principal Component Analysis (PCA to each of these four groups. PCA identified a set of three maximal conductances (leak current, [Formula: see text]Leak; a persistent K current, [Formula: see text]K2; and of a persistent Na+ current, [Formula: see text]P that correlate linearly for the two groups of burster instances but not for the HCO groups. Visualizations of HCO instances in a reduced space suggested that there might be non-linear relationships between these parameters for these instances. Experimental studies have shown that period is a key attribute influenced by modulatory inputs and temperature variations in heart interneurons. Thus, we explored the sensitivity of period to changes in maximal conductances of [Formula: see text]Leak, [Formula: see text]K2, and [Formula: see text]P, and we found that for our realistic bursters the effect of these parameters on period could not be assessed because when varied individually bursting activity was not maintained.
DEFF Research Database (Denmark)
Shariati, M M; Korsgaard, I R; Sorensen, D
2009-01-01
the formulation of the model and the structure of the data and the models were then implemented via MCMC. The output of MCMC sampling schemes was interpreted in the light of the theoretical findings. The erratic behaviour of the MCMC chains was shown to be associated with identifiability problems...
Identifying tectonic parameters that influence tsunamigenesis
van Zelst, Iris; Brizzi, Silvia; van Dinther, Ylona; Heuret, Arnauld; Funiciello, Francesca
2017-04-01
The role of tectonics in tsunami generation is at present poorly understood. However, the fact that some regions produce more tsunamis than others indicates that tectonics could influence tsunamigenesis. Here, we complement a global earthquake database that contains geometrical, mechanical, and seismicity parameters of subduction zones with tsunami data. We statistically analyse the database to identify the tectonic parameters that affect tsunamigenesis. The Pearson's product-moment correlation coefficients reveal high positive correlations of 0.65 between, amongst others, the maximum water height of tsunamis and the seismic coupling in a subduction zone. However, these correlations are mainly caused by outliers. The Spearman's rank correlation coefficient results in more robust correlations of 0.60 between the number of tsunamis in a subduction zone and subduction velocity (positive correlation) and the sediment thickness at the trench (negative correlation). Interestingly, there is a positive correlation between the latter and tsunami magnitude. In an effort towards multivariate statistics, a binary decision tree analysis is conducted with one variable. However, this shows that the amount of data is too scarce. To complement this limited amount of data and to assess physical causality of the tectonic parameters with regard to tsunamigenesis, we conduct a numerical study of the most promising parameters using a geodynamic seismic cycle model. We show that an increase in sediment thickness on the subducting plate results in a shift in seismic activity from outerrise normal faults to splay faults. We also show that the splay fault is the preferred rupture path for a strongly velocity strengthening friction regime in the shallow part of the subduction zone, which increases the tsunamigenic potential. A larger updip limit of the seismogenic zone results in larger vertical surface displacement.
Parameter identifiability of linear dynamical systems
Glover, K.; Willems, J. C.
1974-01-01
It is assumed that the system matrices of a stationary linear dynamical system were parametrized by a set of unknown parameters. The question considered here is, when can such a set of unknown parameters be identified from the observed data? Conditions for the local identifiability of a parametrization are derived in three situations: (1) when input/output observations are made, (2) when there exists an unknown feedback matrix in the system and (3) when the system is assumed to be driven by white noise and only output observations are made. Also a sufficient condition for global identifiability is derived.
Energy Technology Data Exchange (ETDEWEB)
Ibsen, Lars Bo; Liingaard, M.
2006-12-15
A lumped-parameter model represents the frequency dependent soil-structure interaction of a massless foundation placed on or embedded into an unbounded soil domain. In this technical report the steps of establishing a lumped-parameter model are presented. Following sections are included in this report: Static and dynamic formulation, Simple lumped-parameter models and Advanced lumped-parameter models. (au)
Machado, Vinicius Cunha; Lafuente, Javier; Baeza, Juan Antonio
2014-07-01
The present work developed a model for the description of a full-scale wastewater treatment plant (WWTP) (Manresa, Catalonia, Spain) for further plant upgrades based on the systematic parameter calibration of the activated sludge model 2d (ASM2d) using a methodology based on the Fisher information matrix. The influent was characterized for the application of the ASM2d and the confidence interval of the calibrated parameters was also assessed. No expert knowledge was necessary for model calibration and a huge available plant database was converted into more useful information. The effect of the influent and operating variables on the model fit was also studied using these variables as calibrating parameters and keeping the ASM2d kinetic and stoichiometric parameters, which traditionally are the calibration parameters, at their default values. Such an "inversion" of the traditional way of model fitting allowed evaluating the sensitivity of the main model outputs regarding the influent and the operating variables changes. This new approach is able to evaluate the capacity of the operational variables used by the WWTP feedback control loops to overcome external disturbances in the influent and kinetic/stoichiometric model parameters uncertainties. In addition, the study of the influence of operating variables on the model outputs provides useful information to select input and output variables in decentralized control structures.
Parameter Identifiability in Statistical Machine Learning: A Review.
Ran, Zhi-Yong; Hu, Bao-Gang
2017-05-01
This review examines the relevance of parameter identifiability for statistical models used in machine learning. In addition to defining main concepts, we address several issues of identifiability closely related to machine learning, showing the advantages and disadvantages of state-of-the-art research and demonstrating recent progress. First, we review criteria for determining the parameter structure of models from the literature. This has three related issues: parameter identifiability, parameter redundancy, and reparameterization. Second, we review the deep influence of identifiability on various aspects of machine learning from theoretical and application viewpoints. In addition to illustrating the utility and influence of identifiability, we emphasize the interplay among identifiability theory, machine learning, mathematical statistics, information theory, optimization theory, information geometry, Riemann geometry, symbolic computation, Bayesian inference, algebraic geometry, and others. Finally, we present a new perspective together with the associated challenges.
White, L J; Evans, N D; Lam, T J G M; Schukken, Y H; Medley, G F; Godfrey, K R; Chappell, M J
2002-01-01
A mathematical model for the transmission of two interacting classes of mastitis causing bacterial pathogens in a herd of dairy cows is presented and applied to a specific data set. The data were derived from a field trial of a specific measure used in the control of these pathogens, where half the individuals were subjected to the control and in the others the treatment was discontinued. The resultant mathematical model (eight non-linear simultaneous ordinary differential equations) therefore incorporates heterogeneity in the host as well as the infectious agent and consequently the effects of control are intrinsic in the model structure. A structural identifiability analysis of the model is presented demonstrating that the scope of the novel method used allows application to high order non-linear systems. The results of a simultaneous estimation of six unknown system parameters are presented. Previous work has only estimated a subset of these either simultaneously or individually. Therefore not only are new estimates provided for the parameters relating to the transmission and control of the classes of pathogens under study, but also information about the relationships between them. We exploit the close link between mathematical modelling, structural identifiability analysis, and parameter estimation to obtain biological insights into the system modelled.
A parameter identifiability study of two chalk tracer tests
Directory of Open Access Journals (Sweden)
S. A. Mathias
2006-08-01
Full Text Available As with most fractured rock formations, Chalk is highly heterogeneous. Therefore, meaningful estimates of model parameters must be obtained at a scale comparable with the process of concern. These are frequently obtained by calibrating an appropriate model to observed concentration-time data from radially convergent tracer tests (RCTT. Arguably, an appropriate model should consider radially convergent dispersion (RCD and Fickian matrix diffusion. Such a model requires the estimation of at least four parameters. A question arises as to whether or not this level of model complexity is supported by the information contained within the calibration data. Generally modellers have not answered this question due to the calibration techniques employed. A dual-porosity model with RCD was calibrated to two tracer test datasets from different UK Chalk aquifers. A multivariate sensitivity analysis, which assumed only a priori upper and lower bounds for each model parameter, was undertaken. Rather than looking at measures of uncertainty, the shape of the multivariate objective function surface was used to determine whether a parameter was identifiable. Non-identifiable parameters were then removed and the procedure was repeated until all remaining parameters were identifiable.
It was found that the single fracture model (SFM (which ignores mechanical dispersion obtained the best mass recovery, excellent model performance and best parameter identifiability in both the tests studied. However, there was no objective evidence suggesting that mechanical dispersion was negligible. Moreover, the SFM (with just two parameters was found to be good at approximating the Single Fracture Dispersion Model SFDM (with three parameters when different, and potentially erroneous parameters, were used. Overall, this study emphasises the importance of adequate temporal sampling of breakthrough curve data prior to peak concentrations, to ensure adequate characterisation of
Smith, Jason F.; Chen, Kewei; Pillai, Ajay S.; Horwitz, Barry
2013-01-01
The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define “effective connectivity” using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons. PMID:23717258
Smith, Jason F; Chen, Kewei; Pillai, Ajay S; Horwitz, Barry
2013-01-01
The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define "effective connectivity" using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons.
Directory of Open Access Journals (Sweden)
Jason Fitzgerald Smith
2013-05-01
Full Text Available The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here we explicitly define effective connectivity using a common set of observation and state equations that are appropriate for three connectivity methods: Dynamic Causal Modeling (DCM, Multivariate Autoregressive Modeling (MAR, and Switching Linear Dynamic Systems for fMRI (sLDSf. In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons.
Identifiability in stochastic models
1992-01-01
The problem of identifiability is basic to all statistical methods and data analysis, occurring in such diverse areas as Reliability Theory, Survival Analysis, and Econometrics, where stochastic modeling is widely used. Mathematics dealing with identifiability per se is closely related to the so-called branch of ""characterization problems"" in Probability Theory. This book brings together relevant material on identifiability as it occurs in these diverse fields.
Two statistics for evaluating parameter identifiability and error reduction
Doherty, John; Hunt, Randall J.
2009-01-01
Two statistics are presented that can be used to rank input parameters utilized by a model in terms of their relative identifiability based on a given or possible future calibration dataset. Identifiability is defined here as the capability of model calibration to constrain parameters used by a model. Both statistics require that the sensitivity of each model parameter be calculated for each model output for which there are actual or presumed field measurements. Singular value decomposition (SVD) of the weighted sensitivity matrix is then undertaken to quantify the relation between the parameters and observations that, in turn, allows selection of calibration solution and null spaces spanned by unit orthogonal vectors. The first statistic presented, "parameter identifiability", is quantitatively defined as the direction cosine between a parameter and its projection onto the calibration solution space. This varies between zero and one, with zero indicating complete non-identifiability and one indicating complete identifiability. The second statistic, "relative error reduction", indicates the extent to which the calibration process reduces error in estimation of a parameter from its pre-calibration level where its value must be assigned purely on the basis of prior expert knowledge. This is more sophisticated than identifiability, in that it takes greater account of the noise associated with the calibration dataset. Like identifiability, it has a maximum value of one (which can only be achieved if there is no measurement noise). Conceptually it can fall to zero; and even below zero if a calibration problem is poorly posed. An example, based on a coupled groundwater/surface-water model, is included that demonstrates the utility of the statistics. ?? 2009 Elsevier B.V.
Structural parameter identifiability analysis for dynamic reaction networks
DEFF Research Database (Denmark)
Davidescu, Florin Paul; Jørgensen, Sten Bay
2008-01-01
. The proposed analysis is performed in two phases. The first phase determines the structurally identifiable reaction rates based on reaction network stoichiometry. The second phase assesses the structural parameter identifiability of the specific kinetic rate expressions using a generating series expansion...... method based on Lie derivatives. The proposed systematic two phase methodology is illustrated on a mass action based model for an enzymatically catalyzed reaction pathway network where only a limited set of variables is measured. The methodology clearly pinpoints the structurally identifiable parameters...
DEFF Research Database (Denmark)
Ibsen, Lars Bo; Liingaard, Morten
A lumped-parameter model represents the frequency dependent soil-structure interaction of a massless foundation placed on or embedded into an unbounded soil domain. The lumped-parameter model development have been reported by (Wolf 1991b; Wolf 1991a; Wolf and Paronesso 1991; Wolf and Paronesso 19...
Sparse Linear Identifiable Multivariate Modeling
DEFF Research Database (Denmark)
Henao, Ricardo; Winther, Ole
2011-01-01
In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully...... Bayesian hierarchy for sparse models using slab and spike priors (two-component δ-function and continuous mixtures), non-Gaussian latent factors and a stochastic search over the ordering of the variables. The framework, which we call SLIM (Sparse Linear Identifiable Multivariate modeling), is validated...... and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable...
Response model parameter linking
Barrett, Michelle Derbenwick
2015-01-01
With a few exceptions, the problem of linking item response model parameters from different item calibrations has been conceptualized as an instance of the problem of equating observed scores on different test forms. This thesis argues, however, that the use of item response models does not require
Distributed Parameter Modelling Applications
DEFF Research Database (Denmark)
2011-01-01
Here the issue of distributed parameter models is addressed. Spatial variations as well as time are considered important. Several applications for both steady state and dynamic applications are given. These relate to the processing of oil shale, the granulation of industrial fertilizers and the d......Here the issue of distributed parameter models is addressed. Spatial variations as well as time are considered important. Several applications for both steady state and dynamic applications are given. These relate to the processing of oil shale, the granulation of industrial fertilizers...... sands processing. The fertilizer granulation model considers the dynamics of MAP-DAP (mono and diammonium phosphates) production within an industrial granulator, that involves complex crystallisation, chemical reaction and particle growth, captured through population balances. A final example considers...
Parameter identifiability-based optimal observation remedy for biological networks.
Wang, Yulin; Miao, Hongyu
2017-05-04
To systematically understand the interactions between numerous biological components, a variety of biological networks on different levels and scales have been constructed and made available in public databases or knowledge repositories. Graphical models such as structural equation models have long been used to describe biological networks for various quantitative analysis tasks, especially key biological parameter estimation. However, limited by resources or technical capacities, partial observation is a common problem in experimental observations of biological networks, and it thus becomes an important problem how to select unobserved nodes for additional measurements such that all unknown model parameters become identifiable. To the best knowledge of our authors, a solution to this problem does not exist until this study. The identifiability-based observation problem for biological networks is mathematically formulated for the first time based on linear recursive structural equation models, and then a dynamic programming strategy is developed to obtain the optimal observation strategies. The efficiency of the dynamic programming algorithm is achieved by avoiding both symbolic computation and matrix operations as used in other studies. We also provided necessary theoretical justifications to the proposed method. Finally, we verified the algorithm using synthetic network structures and illustrated the application of the proposed method in practice using a real biological network related to influenza A virus infection. The proposed approach is the first solution to the structural identifiability-based optimal observation remedy problem. It is applicable to an arbitrary directed acyclic biological network (recursive SEMs) without bidirectional edges, and it is a computerizable method. Observation remedy is an important issue in experiment design for biological networks, and we believe that this study provides a solid basis for dealing with more challenging design
Paris, Adrien; André Garambois, Pierre; Calmant, Stéphane; Paiva, Rodrigo; Walter, Collischonn; Santos da Silva, Joecila; Medeiros Moreira, Daniel; Bonnet, Marie-Paule; Seyler, Frédérique; Monnier, Jérôme
2016-04-01
Estimating river discharge for ungauged river reaches from satellite measurements is not straightforward given the nonlinearity of flow behavior with respect to measurable and non measurable hydraulic parameters. As a matter of facts, current satellite datasets do not give access to key parameters such as river bed topography and roughness. A unique set of almost one thousand altimetry-based rating curves was built by fit of ENVISAT and Jason-2 water stages with discharges obtained from the MGB-IPH rainfall-runoff model in the Amazon basin. These rated discharges were successfully validated towards simulated discharges (Ens = 0.70) and in-situ discharges (Ens = 0.71) and are not mission-dependent. The rating curve writes Q = a(Z-Z0)b*sqrt(S), with Z the water surface elevation and S its slope gained from satellite altimetry, a and b power law coefficient and exponent and Z0 the river bed elevation such as Q(Z0) = 0. For several river reaches in the Amazon basin where ADCP measurements are available, the Z0 values are fairly well validated with a relative error lower than 10%. The present contribution aims at relating the identifiability and the physical meaning of a, b and Z0given various hydraulic and geomorphologic conditions. Synthetic river bathymetries sampling a wide range of rivers and inflow discharges are used to perform twin experiments. A shallow water model is run for generating synthetic satellite observations, and then rating curve parameters are determined for each river section thanks to a MCMC algorithm. Thanks to twin experiments, it is shown that rating curve formulation with water surface slope, i.e. closer from Manning equation form, improves parameter identifiability. The compensation between parameters is limited, especially for reaches with little water surface variability. Rating curve parameters are analyzed for riffle and pools for small to large rivers, different river slopes and cross section shapes. It is shown that the river bed
Biased sampling, over-identified parameter problems and beyond
Qin, Jing
2017-01-01
This book is devoted to biased sampling problems (also called choice-based sampling in Econometrics parlance) and over-identified parameter estimation problems. Biased sampling problems appear in many areas of research, including Medicine, Epidemiology and Public Health, the Social Sciences and Economics. The book addresses a range of important topics, including case and control studies, causal inference, missing data problems, meta-analysis, renewal process and length biased sampling problems, capture and recapture problems, case cohort studies, exponential tilting genetic mixture models etc. The goal of this book is to make it easier for Ph. D students and new researchers to get started in this research area. It will be of interest to all those who work in the health, biological, social and physical sciences, as well as those who are interested in survey methodology and other areas of statistical science, among others. .
A New Method for Identifying the Life Parameters via Radar
Directory of Open Access Journals (Sweden)
Lu Guohua
2007-01-01
Full Text Available It has been proved that the vital signs can be detected via radar. To better identify the life parameters such as respiration and heartbeat, a novel method combined with several signal processing techniques is presented. Firstly, to improve the signal-to-noise ratio (SNR of the life signals, the signal accumulation technique by FFT is used. Then, to restrain the interferences produced by moving objects, a dual filtering algorithm (DFA which is able to remove the interferences by tracing the interfering spectral peaks is proposed. Finally, the wavelet transform is applied to separate the heartbeat from the respiration signal. The method cannot only help to automatically detect the existence of human beings effectively, but also identifying the parameters like respiration, heartbeat, and body-moving signals significantly. Experimental results demonstrated that the method is very promising in identifying the life parameters via radar.
Identifying nonlinear biomechanical models by multicriteria analysis
Srdjevic, Zorica; Cveticanin, Livija
2012-02-01
In this study, the methodology developed by Srdjevic and Cveticanin (International Journal of Industrial Ergonomics 34 (2004) 307-318) for the nonbiased (objective) parameter identification of the linear biomechanical model exposed to vertical vibrations is extended to the identification of n-degree of freedom (DOF) nonlinear biomechanical models. The dynamic performance of the n-DOF nonlinear model is described in terms of response functions in the frequency domain, such as the driving-point mechanical impedance and seat-to-head transmissibility function. For randomly generated parameters of the model, nonlinear equations of motion are solved using the Runge-Kutta method. The appropriate data transformation from the time-to-frequency domain is performed by a discrete Fourier transformation. Squared deviations of the response functions from the target values are used as the model performance evaluation criteria, thus shifting the problem into the multicriteria framework. The objective weights of criteria are obtained by applying the Shannon entropy concept. The suggested methodology is programmed in Pascal and tested on a 4-DOF nonlinear lumped parameter biomechanical model. The identification process over the 2000 generated sets of parameters lasts less than 20 s. The model response obtained with the imbedded identified parameters correlates well with the target values, therefore, justifying the use of the underlying concept and the mathematical instruments and numerical tools applied. It should be noted that the identified nonlinear model has an improved accuracy of the biomechanical response compared to the accuracy of a linear model.
Seeley, George W.; Roehrig, Hans; Mockbee, Brent; Hunter, Tim B.; Ovitt, Theron; Claypool, H. R.; Bielland, John C.; Scott, Anne; Yang, Peter; Dallas, William J.
1987-01-01
The digital imaging group at the University of Arizona Health Sciences Center Radiology Department is vigorously pursuing the development of a total digital radiology department (TDRD). One avenue of research being conducted is to define the needed resolutions and capabilities of TDRD systems. Parts of that effort are described in these proceedings and elsewhere. One of these investigations is to assess the general application of computed r adiography (CR) in clinical imaging. Specifically we are comparing images produced by the Toshiba computed radiography system (Model 201) to those produced by conventional imaging techniques. This paper describes one aspect of that work.
Photovoltaic module parameters acquisition model
Cibira, Gabriel; Koščová, Marcela
2014-09-01
This paper presents basic procedures for photovoltaic (PV) module parameters acquisition using MATLAB and Simulink modelling. In first step, MATLAB and Simulink theoretical model are set to calculate I-V and P-V characteristics for PV module based on equivalent electrical circuit. Then, limited I-V data string is obtained from examined PV module using standard measurement equipment at standard irradiation and temperature conditions and stated into MATLAB data matrix as a reference model. Next, the theoretical model is optimized to keep-up with the reference model and to learn its basic parameters relations, over sparse data matrix. Finally, PV module parameters are deliverable for acquisition at different realistic irradiation, temperature conditions as well as series resistance. Besides of output power characteristics and efficiency calculation for PV module or system, proposed model validates computing statistical deviation compared to reference model.
Mode choice model parameters estimation
Strnad, Irena
2010-01-01
The present work focuses on parameter estimation of two mode choice models: multinomial logit and EVA 2 model, where four different modes and five different trip purposes are taken into account. Mode choice model discusses the behavioral aspect of mode choice making and enables its application to a traffic model. Mode choice model includes mode choice affecting trip factors by using each mode and their relative importance to choice made. When trip factor values are known, it...
Identifiability of large phylogenetic mixture models.
Rhodes, John A; Sullivant, Seth
2012-01-01
Phylogenetic mixture models are statistical models of character evolution allowing for heterogeneity. Each of the classes in some unknown partition of the characters may evolve by different processes, or even along different trees. Such models are of increasing interest for data analysis, as they can capture the variety of evolutionary processes that may be occurring across long sequences of DNA or proteins. The fundamental question of whether parameters of such a model are identifiable is difficult to address, due to the complexity of the parameterization. Identifiability is, however, essential to their use for statistical inference.We analyze mixture models on large trees, with many mixture components, showing that both numerical and tree parameters are indeed identifiable in these models when all trees are the same. This provides a theoretical justification for some current empirical studies, and indicates that extensions to even more mixture components should be theoretically well behaved. We also extend our results to certain mixtures on different trees, using the same algebraic techniques.
Delineating Parameter Unidentifiabilities in Complex Models
Raman, Dhruva V; Papachristodoulou, Antonis
2016-01-01
Scientists use mathematical modelling to understand and predict the properties of complex physical systems. In highly parameterised models there often exist relationships between parameters over which model predictions are identical, or nearly so. These are known as structural or practical unidentifiabilities, respectively. They are hard to diagnose and make reliable parameter estimation from data impossible. They furthermore imply the existence of an underlying model simplification. We describe a scalable method for detecting unidentifiabilities, and the functional relations defining them, for generic models. This allows for model simplification, and appreciation of which parameters (or functions thereof) cannot be estimated from data. Our algorithm can identify features such as redundant mechanisms and fast timescale subsystems, as well as the regimes in which such approximations are valid. We base our algorithm on a novel quantification of regional parametric sensitivity: multiscale sloppiness. Traditional...
Ding, Liang; Gao, Haibo; Liu, Zhen; Deng, Zongquan; Liu, Guangjun
2015-12-01
Identifying the mechanical property parameters of planetary soil based on terramechanics models using in-situ data obtained from autonomous planetary exploration rovers is both an important scientific goal and essential for control strategy optimization and high-fidelity simulations of rovers. However, identifying all the terrain parameters is a challenging task because of the nonlinear and coupling nature of the involved functions. Three parameter identification methods are presented in this paper to serve different purposes based on an improved terramechanics model that takes into account the effects of slip, wheel lugs, etc. Parameter sensitivity and coupling of the equations are analyzed, and the parameters are grouped according to their sensitivity to the normal force, resistance moment and drawbar pull. An iterative identification method using the original integral model is developed first. In order to realize real-time identification, the model is then simplified by linearizing the normal and shearing stresses to derive decoupled closed-form analytical equations. Each equation contains one or two groups of soil parameters, making step-by-step identification of all the unknowns feasible. Experiments were performed using six different types of single-wheels as well as a four-wheeled rover moving on planetary soil simulant. All the unknown model parameters were identified using the measured data and compared with the values obtained by conventional experiments. It is verified that the proposed iterative identification method provides improved accuracy, making it suitable for scientific studies of soil properties, whereas the step-by-step identification methods based on simplified models require less calculation time, making them more suitable for real-time applications. The models have less than 10% margin of error comparing with the measured results when predicting the interaction forces and moments using the corresponding identified parameters.
Numerical and Experimental Approach for Identifying Elastic Parameters in Sandwich Plates
Directory of Open Access Journals (Sweden)
Sergio Ferreira Bastos
2002-01-01
Full Text Available This article deals with the identification of elastic parameters (engineering constants in sandwich honeycomb orthotropic rectangular plates. A non-destructive method is introduced to identify the elastic parameters through the experimental measurements of natural frequencies of a plate undergoing free vibrations. Four elastic constant are identified. The estimation of the elastic parameter problem is solved by minimizing the differences between the measured and the calculated natural frequencies. The numerical method to calculate the natural frequencies involves the formulation of Rayleigh-Ritz using a series of characteristic orthogonal polynomials to properly model the free edge boundary conditions. The analysis of the results indicates the efficiency of the method.
Roe, Byron
2013-01-01
The effect of correlations between model parameters and nuisance parameters is discussed, in the context of fitting model parameters to data. Modifications to the usual $\\chi^2$ method are required. Fake data studies, as used at present, will not be optimum. Problems will occur for applications of the Maltoni-Schwetz \\cite{ms} theorem. Neutrino oscillations are used as examples, but the problems discussed here are general ones, which are often not addressed.
DEFF Research Database (Denmark)
Lauridsen, Jonas; Sekunda, André Krabdrup; Santos, Ilmar
2015-01-01
In this paper, a method for identifying uncertain parameters in a rotordynamic system composed of a flexible rotating shaft, rigid discs and two radial active magnetic bearings is presented. Shaft and disc dynamics are mathematically described using a Finite Element (FE) model while magnetic...... bearing forces are represented by linear springs with negative stiffness. Bearing negative stiffness produces an unstable rotordynamic system, demanding implementation of feedback control to stabilize the rotordynamic system. Thus, to identify the system parameters, closed-loop system identification...... techniques are required., The main focus of the paper relies on how to effectively identify uncertain parameters, such as stiffness and damping force coefficients of bearings and seals in rotordynamic systems. Dynamic condensation method, i.e. pseudo-modal reduction, is used to obtain a reduced order model...
Delineating parameter unidentifiabilities in complex models
Raman, Dhruva V.; Anderson, James; Papachristodoulou, Antonis
2017-03-01
Scientists use mathematical modeling as a tool for understanding and predicting the properties of complex physical systems. In highly parametrized models there often exist relationships between parameters over which model predictions are identical, or nearly identical. These are known as structural or practical unidentifiabilities, respectively. They are hard to diagnose and make reliable parameter estimation from data impossible. They furthermore imply the existence of an underlying model simplification. We describe a scalable method for detecting unidentifiabilities, as well as the functional relations defining them, for generic models. This allows for model simplification, and appreciation of which parameters (or functions thereof) cannot be estimated from data. Our algorithm can identify features such as redundant mechanisms and fast time-scale subsystems, as well as the regimes in parameter space over which such approximations are valid. We base our algorithm on a quantification of regional parametric sensitivity that we call `multiscale sloppiness'. Traditionally, the link between parametric sensitivity and the conditioning of the parameter estimation problem is made locally, through the Fisher information matrix. This is valid in the regime of infinitesimal measurement uncertainty. We demonstrate the duality between multiscale sloppiness and the geometry of confidence regions surrounding parameter estimates made where measurement uncertainty is non-negligible. Further theoretical relationships are provided linking multiscale sloppiness to the likelihood-ratio test. From this, we show that a local sensitivity analysis (as typically done) is insufficient for determining the reliability of parameter estimation, even with simple (non)linear systems. Our algorithm can provide a tractable alternative. We finally apply our methods to a large-scale, benchmark systems biology model of necrosis factor (NF)-κ B , uncovering unidentifiabilities.
Systematic parameter inference in stochastic mesoscopic modeling
Lei, Huan; Yang, Xiu; Li, Zhen; Karniadakis, George Em
2017-02-01
We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are "sparse". The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space. Fully access to the response surfaces within the confidence range enables us to infer the optimal force parameters given the desirable values of target properties at the macroscopic scale. Moreover, it enables us to investigate the intrinsic relationship between the model parameters, identify possible degeneracies in the parameter space, and optimize the model by eliminating model redundancies. The proposed method provides an efficient alternative approach for constructing mesoscopic models by inferring model parameters to recover target properties of the physics systems (e.g., from experimental measurements), where those force field parameters and formulation cannot be derived from the microscopic level in a straight forward way.
Eberle, Claudia; Ament, Christoph
2012-03-01
Today, diagnostic decisions about pre-diabetes or diabetes are made using static threshold rules for the measured plasma glucose. In order to develop an alternative diagnostic approach, dynamic models as the Minimal Model may be deployed. We present a novel method to analyze the identifiability of model parameters based on the interpretation of the empirical observability Gramian. This allows a unifying view of both, the observability of the system's states (with dynamics) and the identifiability of the system's parameters (without dynamics). We give an iterative algorithm, in order to find an optimized set of states and parameters to be estimated. For this set, estimation results using an Unscented Kalman Filter (UKF) are presented. Two parameters are of special interest for diagnostic purposes: the glucose effectiveness S(G) characterizes the ability of plasma glucose clearance, and the insulin sensitivity S(I) quantifies the impact from the plasma insulin to the interstitial insulin subsystem. Applying the identifiability analysis to the trajectories of the insulin glucose system during an intravenous glucose tolerance test (IVGTT) shows the following result: (1) if only plasma glucose G(t) is measured, plasma insulin I(t) and S(G) can be estimated, but not S(I). (2) If plasma insulin I(t) is captured additionally, identifiability is improved significantly such that up to four model parameters can be estimated including S(I). (3) The situation of the first case can be improved, if a controlled external dosage of insulin is applied. Then, parameters of the insulin subsystem can be identified approximately from measurement of plasma glucose G(t) only.
Parameter Estimation and Experimental Design in Groundwater Modeling
Institute of Scientific and Technical Information of China (English)
SUN Ne-zheng
2004-01-01
This paper reviews the latest developments on parameter estimation and experimental design in the field of groundwater modeling. Special considerations are given when the structure of the identified parameter is complex and unknown. A new methodology for constructing useful groundwater models is described, which is based on the quantitative relationships among the complexity of model structure, the identifiability of parameter, the sufficiency of data, and the reliability of model application.
Identifying and modeling safety hazards
Energy Technology Data Exchange (ETDEWEB)
DANIELS,JESSE; BAHILL,TERRY; WERNER,PAUL W.
2000-03-29
The hazard model described in this paper is designed to accept data over the Internet from distributed databases. A hazard object template is used to ensure that all necessary descriptors are collected for each object. Three methods for combining the data are compared and contrasted. Three methods are used for handling the three types of interactions between the hazard objects.
Sparse Linear Identifiable Multivariate Modeling
DEFF Research Database (Denmark)
Henao, Ricardo; Winther, Ole
2011-01-01
and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable...
Determining extreme parameter correlation in ground water models
DEFF Research Database (Denmark)
Hill, Mary Cole; Østerby, Ole
2003-01-01
In ground water flow system models with hydraulic-head observations but without significant imposed or observed flows, extreme parameter correlation generally exists. As a result, hydraulic conductivity and recharge parameters cannot be uniquely estimated. In complicated problems, such correlation...... correlation coefficients, but it required sensitivities that were one to two significant digits less accurate than those that required using parameter correlation coefficients; and (3) both the SVD and parameter correlation coefficients identified extremely correlated parameters better when the parameters...
Teergele, Jane; Danai, Kourosh
2015-12-01
A method of sensor location selection is introduced for distributed parameter systems. In this method, the sensitivities of spatial outputs to model parameters are computed by a model and transformed via continuous wavelet transforms into the time-scale domain to characterise the shape attributes of output sensitivities and accentuate their differences. Regions are then sought in the time-scale plane wherein the wavelet coefficient of an output sensitivity surpasses all the others' as indication of the output sensitivity's distinctness. This yields a comprehensive account of identifiability each output provides to the model parameters as the basis of output selection. The proposed output selection strategy is demonstrated for a numerical case of pollutant dispersion by advection and diffusion in a two-dimensional area.
Application of Cauchy wavelet transformation to identify time-variant modal parameters of structures
Huang, C. S.; Liu, C. Y.; Su, W. C.
2016-12-01
This work proposes a procedure for accurately identifying instantaneous modal parameters of a linear time-varying system using a time-varying autoregressive with exogenous input (TVARX) model with the continuous Cauchy wavelet transform (CCWT). An appropriate TVARX model is established using the velocity and displacement responses of the system under consideration. The time-varying coefficients of the TVARX are expanded as piecewise polynomial functions. CCWTs with various scale parameters are then applied to the TVARX model to evaluate the instantaneous modal parameters of different modes. The CCWTs of the velocity and displacement responses are analytically obtained from the CCWT of the measured acceleration responses. The effectiveness and accuracy of the proposed procedure are validated by numerical simulations of single and multiple degrees of freedom systems that have periodically varying and sharply varying stiffness and damping coefficients. The effects of noise, the Cauchy wavelet function and the order of the polynomial on the evaluation of the modal parameters are explored in processing the numerically simulated acceleration responses of systems with a single degree of freedom subjected to base excitation. Finally, the proposed procedure is adopted to determine the modal parameters of a five-story symmetric steel frame from its measured acceleration responses in a shaking table test. The measured strains reveal the yielding of columns in the first story. The variations of the identified instantaneous natural frequencies and modal damping ratios with time are consistent with the physical phenomena that are observed from the measured strains and base excitation acceleration.
Order Parameters of the Dilute A Models
Warnaar, S O; Seaton, K A; Nienhuis, B
1993-01-01
The free energy and local height probabilities of the dilute A models with broken $\\Integer_2$ symmetry are calculated analytically using inversion and corner transfer matrix methods. These models possess four critical branches. The first two branches provide new realisations of the unitary minimal series and the other two branches give a direct product of this series with an Ising model. We identify the integrable perturbations which move the dilute A models away from the critical limit. Generalised order parameters are defined and their critical exponents extracted. The associated conformal weights are found to occur on the diagonal of the relevant Kac table. In an appropriate regime the dilute A$_3$ model lies in the universality class of the Ising model in a magnetic field. In this case we obtain the magnetic exponent $\\delta=15$ directly, without the use of scaling relations.
Ebola Virus Infection Modelling and Identifiability Problems
Directory of Open Access Journals (Sweden)
Van-Kinh eNguyen
2015-04-01
Full Text Available The recent outbreaks of Ebola virus (EBOV infections have underlined the impact of the virus as a major threat for human health. Due to the high biosafety classification of EBOV (level 4, basic research is very limited. Therefore, the development of new avenues of thinking to advance quantitative comprehension of the virus and its interaction with the host cells is urgently neededto tackle this lethal disease. Mathematical modelling of the EBOV dynamics can be instrumental to interpret Ebola infection kinetics on quantitative grounds. To the best of our knowledge, a mathematical modelling approach to unravel the interaction between EBOV and the host cells isstill missing. In this paper, a mathematical model based on differential equations is used to represent the basic interactions between EBOV and wild-type Vero cells in vitro. Parameter sets that represent infectivity of pathogens are estimated for EBOV infection and compared with influenza virus infection kinetics. The average infecting time of wild-type Vero cells in EBOV is slower than in influenza infection. Simulation results suggest that the slow infecting time of EBOV could be compensated by its efficient replication. This study reveals several identifiability problems and what kind of experiments are necessary to advance the quantification of EBOV infection. A first mathematical approach of EBOV dynamics and the estimation of standard parametersin viral infections kinetics is the key contribution of this work, paving the way for future modelling work on EBOV infection.
A Method for Identifying the Mechanical Parameters in Resistance Spot Welding Machines
DEFF Research Database (Denmark)
Wu, Pei; Zhang, Wenqi; Bay, Niels
2003-01-01
Mechanical dynamic responses of resistance welding machine have a significant influence on weld quality and electrode service life, it must be considered when the real welding production is carried out or the welding process is stimulated. The mathematical models for characterizing the mechanical...... and differences of machine constructions. In this paper, a method of identifying the machine mechanical parameters based on measured data is presented, which is independent on the construction and the type of machines. The computations are implemented in MATLAB....
Spatio-temporal modeling of nonlinear distributed parameter systems
Li, Han-Xiong
2011-01-01
The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches. In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein s
PARAMETER ESTIMATION OF ENGINEERING TURBULENCE MODEL
Institute of Scientific and Technical Information of China (English)
钱炜祺; 蔡金狮
2001-01-01
A parameter estimation algorithm is introduced and used to determine the parameters in the standard k-ε two equation turbulence model (SKE). It can be found from the estimation results that although the parameter estimation method is an effective method to determine model parameters, it is difficult to obtain a set of parameters for SKE to suit all kinds of separated flow and a modification of the turbulence model structure should be considered. So, a new nonlinear k-ε two-equation model (NNKE) is put forward in this paper and the corresponding parameter estimation technique is applied to determine the model parameters. By implementing the NNKE to solve some engineering turbulent flows, it is shown that NNKE is more accurate and versatile than SKE. Thus, the success of NNKE implies that the parameter estimation technique may have a bright prospect in engineering turbulence model research.
Parameter optimization in S-system models
Directory of Open Access Journals (Sweden)
Vasconcelos Ana
2008-04-01
Full Text Available Abstract Background The inverse problem of identifying the topology of biological networks from their time series responses is a cornerstone challenge in systems biology. We tackle this challenge here through the parameterization of S-system models. It was previously shown that parameter identification can be performed as an optimization based on the decoupling of the differential S-system equations, which results in a set of algebraic equations. Results A novel parameterization solution is proposed for the identification of S-system models from time series when no information about the network topology is known. The method is based on eigenvector optimization of a matrix formed from multiple regression equations of the linearized decoupled S-system. Furthermore, the algorithm is extended to the optimization of network topologies with constraints on metabolites and fluxes. These constraints rejoin the system in cases where it had been fragmented by decoupling. We demonstrate with synthetic time series why the algorithm can be expected to converge in most cases. Conclusion A procedure was developed that facilitates automated reverse engineering tasks for biological networks using S-systems. The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression. The proposed method overcomes convergence issues encountered in alternate regression by identifying nonlinear constraints that restrict the search space to computationally feasible solutions. Because the parameter identification is still performed for each metabolite separately, the modularity and linear time characteristics of the alternating regression method are preserved. Simulation studies illustrate how the proposed algorithm identifies the correct network topology out of a collection of models which all fit the dynamical time series essentially equally well.
On identifiability of certain latent class models.
van Wieringen, W.N.
2005-01-01
Blischke [1962. Moment estimators for the parameters of a mixture of two binomial distributions. Ann. Math. Statist. 33, 444-454] studies a mixture of two binomials, a latent class model. In this article we generalize this model to a mixture of two products of binomials. We show when this generalize
Identifiability and identification of a Synthesis Load Model
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
A Synthesis Load Model (SLM) including both the power load and the distribution network has been proposed in the references. The identifiability of SLM is analyzed at first, it is concluded that the model parameters are identifiable if one of the resistance, reactance and the ratio of them is known. The conclusion is validated through a simulation example. A strategy for parameter identification of SLM is proposed with the combination of the component based approach and the measurement based approach. During parameter identification, only the key parameters playing very important roles in the dynamics of the load and the system are estimated, while the other parameters playing limited role are set as the default values. The proposed strategy is verified by the field measurements.
Directory of Open Access Journals (Sweden)
Johanna M Walz
Full Text Available Vascular endothelial growth factor-A (VEGF-A is intensively investigated in various medical fields. However, comparing VEGF-A measurements is difficult because sample acquisition and pre-analytic procedures differ between studies. We therefore investigated which variables act as confounders of VEGF-A measurements.Following a standardized protocol, blood was taken at three clinical sites from six healthy participants (one male and one female participant at each center twice one week apart. The following pre-analytical parameters were varied in order to analyze their impact on VEGF-A measurements: analyzing center, anticoagulant (EDTA vs. PECT / CTAD, cannula (butterfly vs. neonatal, type of centrifuge (swing-out vs. fixed-angle, time before and after centrifugation, filling level (completely filled vs. half-filled tubes and analyzing method (ELISA vs. multiplex bead array. Additionally, intrapersonal variations over time and sex differences were explored. Statistical analysis was performed using a linear regression model.The following parameters were identified as statistically significant independent confounders of VEGF-A measurements: analyzing center, anticoagulant, centrifuge, analyzing method and sex of the proband. The following parameters were no significant confounders in our data set: intrapersonal variation over one week, cannula, time before and after centrifugation and filling level of collection tubes.VEGF-A measurement results can be affected significantly by the identified pre-analytical parameters. We recommend the use of CTAD anticoagulant, a standardized type of centrifuge and one central laboratory using the same analyzing method for all samples.
Slew, K. Lee; Miller, M.; Matida, E.
2016-09-01
A numerical study was carried out to identify non-dimensional parameters for dual-rotor horizontal axis wind turbines (DRWTs). Based on some important DRWT parameters such as the rotor speeds, rotor diameters and the distance between the rotors, three dimensionless parameters were derived from the Buckingham Pi theorem. Hypothetical DRWT models were created using geometrically-scaled National Renewable Energy Laboratory (NREL) Phase VI rotor geometry and operating conditions in order to confirm the validity of these parameters. The performance of each turbine was simulated using DR_HAWT, an inhouse prediction tool for single and dual-rotor wind turbines created by the current authors. The variation in normalized output power as a function of the dimensionless parameters suggests that an improved performance of DRWTs can be obtained at lower diameter and gap ratios. The NREL Phase VI rotor equipped with a 5 m geometrically-scaled upwind rotor can generate about 88% of the combined power output of two equivalent single-rotors. In addition, the effect of having an auxiliary upwind rotor reduces the angle of attack along the inboard section of the downwind blade.
Global identifiability of linear structural equation models
Drton, Mathias; Sullivant, Seth
2010-01-01
Structural equation models are multivariate statistical models that are defined by specifying noisy functional relationships among random variables. We consider the classical case of linear relationships and additive Gaussian noise terms. We give a necessary and sufficient condition for global identifiability of the model in terms of a mixed graph encoding the linear structural equations and the correlation structure of the error terms. Global identifiability is understood to mean injectivity of the parametrization of the model and is fundamental in particular for applicability of standard statistical methodology.
Model atmospheres - Tool for identifying interstellar features
Frisch, P. C.; Slojkowski, S. E.; Rodriguez-Bell, T.; York, D.
1993-01-01
Model atmosphere parameters are derived for 14 early A stars with rotation velocities, from optical spectra, in excess of 80 km/s. The models are compared with IUE observations of the stars in regions where interstellar lines are expected. In general, with the assumption of solar abundances, excellent fits are obtained in regions longward of 2580 A, and accurate interstellar equivalent widths can be derived using models to establish the continuum. The fits are poorer at shorter wavelengths, particularly at 2026-2062 A, where the stellar model parameters seem inadequate. Features indicating mass flows are evident in stars with known infrared excesses. In gamma TrA, variability in the Mg II lines is seen over the 5-year interval of these data, and also over timescales as short as 26 days. The present technique should be useful in systematic studies of episodic mass flows in A stars and for stellar abundance studies, as well as interstellar features.
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.
PARAMETER ESTIMATION IN BREAD BAKING MODEL
Hadiyanto Hadiyanto; AJB van Boxtel
2012-01-01
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 pro...
Parameter counting in models with global symmetries
Energy Technology Data Exchange (ETDEWEB)
Berger, Joshua [Institute for High Energy Phenomenology, Newman Laboratory of Elementary Particle Physics, Cornell University, Ithaca, NY 14853 (United States)], E-mail: jb454@cornell.edu; Grossman, Yuval [Institute for High Energy Phenomenology, Newman Laboratory of Elementary Particle Physics, Cornell University, Ithaca, NY 14853 (United States)], E-mail: yuvalg@lepp.cornell.edu
2009-05-18
We present rules for determining the number of physical parameters in models with exact flavor symmetries. In such models the total number of parameters (physical and unphysical) needed to described a matrix is less than in a model without the symmetries. Several toy examples are studied in order to demonstrate the rules. The use of global symmetries in studying the minimally supersymmetric standard model (MSSM) is examined.
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...... method is based on a modified version of the EM algorithm. Experimental results for a deformable template used for textile inspection are presented...
Cosmological models with constant deceleration parameter
Energy Technology Data Exchange (ETDEWEB)
Berman, M.S.; de Mello Gomide, F.
1988-02-01
Berman presented elsewhere a law of variation for Hubble's parameter that yields constant deceleration parameter models of the universe. By analyzing Einstein, Pryce-Hoyle and Brans-Dicke cosmologies, we derive here the necessary relations in each model, considering a perfect fluid.
Institute of Scientific and Technical Information of China (English)
Kan Liu; Qiao Zhang; Zi-Qiang Zhu; Jing Zhang; An-Wen Shen; Paul Stewart
2010-01-01
Two model reference adaptive system(MRAS)estimators are developed for identifying the parameters of permanent magnet synchronous motors(PMSM)based on the Lyapunov stability theorem and the Popov stability criterion,respectively.The proposed estimators only need online measurement of currents,voltages,and rotor speed to effectively estimate stator resistance,inductance,and rotor flux-linkage simultaneously.The performance of the estimators is compared and verified through simulations and experiments,which show that the two estimators are simple,have good robustness against parameter variation,and are accurate in parameter tracking.However,the estimator based on the Popov stability criterion,which can overcome parameter variation in a practical system,is superior in terms of response speed and convergence speed since there are both proportional and integral units in the estimator,in contrast to only one integral unit in the estimator based on the Lyapunov stability theorem.In addition,the estimator based on the Popov stability criterion does not need the expertise that is required in designing a Lyapunov function.
Sliding mode identifier for parameter uncertain nonlinear dynamic systems with nonlinear input
Institute of Scientific and Technical Information of China (English)
张克勤; 庄开宇; 苏宏业; 褚健; 高红
2002-01-01
This paper presents a sliding mode (SM) based identifier to deal wit h the parameter identification problem for a class of parameter uncertain nonlin ear dynamic systems with input nonlinearity. A sliding mode controller (SMC) is used to ensure the global reaching condition of the sliding mode for the nonline ar system; an identifier is designed to identify the uncertain parameter of the nonlinear system. A numerical example is studied to show the feasibility of the SM controller and the asymptotical convergence of the identifier.
Sliding mode identifier for parameter uncertain nonlinear dynamic systems with nonlinear input
Institute of Scientific and Technical Information of China (English)
张克勤; 庄开宇; 苏宏业; 褚健; 高红
2002-01-01
This paper presents a sliding mode(SM) based identifier to deal with the parameter idenfification problem for a class of parameter uncertain nonlinear dynamic systems with input nonlinearity. A sliding mode controller (SMC) is used to ensure the global reaching condition of the sliding mode for the nonlinear system;an identifier is designed to identify the uncertain parameter of the nonlinear system. A numerical example is studied to show the feasibility of the SM controller and the asymptotical convergence of the identifier.
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.
Identification of parameters of discrete-continuous models
Energy Technology Data Exchange (ETDEWEB)
Cekus, Dawid, E-mail: cekus@imipkm.pcz.pl; Warys, Pawel, E-mail: warys@imipkm.pcz.pl [Institute of Mechanics and Machine Design Foundations, Czestochowa University of Technology, Dabrowskiego 73, 42-201 Czestochowa (Poland)
2015-03-10
In the paper, the parameters of a discrete-continuous model have been identified on the basis of experimental investigations and formulation of optimization problem. The discrete-continuous model represents a cantilever stepped Timoshenko beam. The mathematical model has been formulated and solved according to the Lagrange multiplier formalism. Optimization has been based on the genetic algorithm. The presented proceeding’s stages make the identification of any parameters of discrete-continuous systems possible.
Trait Characteristics of Diffusion Model Parameters
Directory of Open Access Journals (Sweden)
Anna-Lena Schubert
2016-07-01
Full Text Available Cognitive modeling of response time distributions has seen a huge rise in popularity in individual differences research. In particular, several studies have shown that individual differences in the drift rate parameter of the diffusion model, which reflects the speed of information uptake, are substantially related to individual differences in intelligence. However, if diffusion model parameters are to reflect trait-like properties of cognitive processes, they have to qualify as trait-like variables themselves, i.e., they have to be stable across time and consistent over different situations. To assess their trait characteristics, we conducted a latent state-trait analysis of diffusion model parameters estimated from three response time tasks that 114 participants completed at two laboratory sessions eight months apart. Drift rate, boundary separation, and non-decision time parameters showed a great temporal stability over a period of eight months. However, the coefficients of consistency and reliability were only low to moderate and highest for drift rate parameters. These results show that the consistent variance of diffusion model parameters across tasks can be regarded as temporally stable ability parameters. Moreover, they illustrate the need for using broader batteries of response time tasks in future studies on the relationship between diffusion model parameters and intelligence.
Parameter identification in the logistic STAR model
DEFF Research Database (Denmark)
Ekner, Line Elvstrøm; Nejstgaard, Emil
We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter is that th......We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter...
Parameter Estimation of Partial Differential Equation Models
Xun, Xiaolei
2013-09-01
Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Application of lumped-parameter models
Energy Technology Data Exchange (ETDEWEB)
Ibsen, Lars Bo; Liingaard, M.
2006-12-15
This technical report concerns the lumped-parameter models for a suction caisson with a ratio between skirt length and foundation diameter equal to 1/2, embedded into an viscoelastic soil. The models are presented for three different values of the shear modulus of the subsoil. Subsequently, the assembly of the dynamic stiffness matrix for the foundation is considered, and the solution for obtaining the steady state response, when using lumped-parameter models is given. (au)
Parameter estimation of hydrologic models using data assimilation
Kaheil, Y. H.
2005-12-01
The uncertainties associated with the modeling of hydrologic systems sometimes demand that data should be incorporated in an on-line fashion in order to understand the behavior of the system. This paper represents a Bayesian strategy to estimate parameters for hydrologic models in an iterative mode. The paper presents a modified technique called localized Bayesian recursive estimation (LoBaRE) that efficiently identifies the optimum parameter region, avoiding convergence to a single best parameter set. The LoBaRE methodology is tested for parameter estimation for two different types of models: a support vector machine (SVM) model for predicting soil moisture, and the Sacramento Soil Moisture Accounting (SAC-SMA) model for estimating streamflow. The SAC-SMA model has 13 parameters that must be determined. The SVM model has three parameters. Bayesian inference is used to estimate the best parameter set in an iterative fashion. This is done by narrowing the sampling space by imposing uncertainty bounds on the posterior best parameter set and/or updating the "parent" bounds based on their fitness. The new approach results in fast convergence towards the optimal parameter set using minimum training/calibration data and evaluation of fewer parameter sets. The efficacy of the localized methodology is also compared with the previously used Bayesian recursive estimation (BaRE) algorithm.
A Note on the Identifiability of Generalized Linear Mixed Models
DEFF Research Database (Denmark)
Labouriau, Rodrigo
2014-01-01
I present here a simple proof that, under general regularity conditions, the standard parametrization of generalized linear mixed model is identifiable. The proof is based on the assumptions of generalized linear mixed models on the first and second order moments and some general mild regularity ...... conditions, and, therefore, is extensible to quasi-likelihood based generalized linear models. In particular, binomial and Poisson mixed models with dispersion parameter are identifiable when equipped with the standard parametrization......I present here a simple proof that, under general regularity conditions, the standard parametrization of generalized linear mixed model is identifiable. The proof is based on the assumptions of generalized linear mixed models on the first and second order moments and some general mild regularity...
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
Identifying motifs in folktales using topic models
Karsdorp, F.; Bosch, A.P.J. van den
2013-01-01
With the undertake of various folktale digitalization initiatives, the need for computational aids to explore these collections is increasing. In this paper we compare Labeled LDA (L-LDA) to a simple retrieval model on the task of identifying motifs in folktales. We show that both methods are well a
Identifiability of Causal Graphs using Functional Models
Peters, Jonas; Janzing, Dominik; Schoelkopf, Bernhard
2012-01-01
This work addresses the following question: Under what assumptions on the data generating process can one infer the causal graph from the joint distribution? The approach taken by conditional independence-based causal discovery methods is based on two assumptions: the Markov condition and faithfulness. It has been shown that under these assumptions the causal graph can be identified up to Markov equivalence (some arrows remain undirected) using methods like the PC algorithm. In this work we propose an alternative by defining Identifiable Functional Model Classes (IFMOCs). As our main theorem we prove that if the data generating process belongs to an IFMOC, one can identify the complete causal graph. To the best of our knowledge this is the first identifiability result of this kind that is not limited to linear functional relationships. We discuss how the IFMOC assumption and the Markov and faithfulness assumptions relate to each other and explain why we believe that the IFMOC assumption can be tested more eas...
Glover, K.; Willems, J. C.
1973-01-01
Consider the situation in which the unknown parameters of a stationary linear system may be parametrized by a set of unknown parameters. The question thus arises of when such a set of parameters can be uniquely identified on the basis of observed data. This problem is considered here both in the case of input and output observations and in the case of output observations in the presence of a white noise input. Conditions for local identifiability are derived for both situations and a sufficient condition for global identifiability is given for the former situation, i.e., when simultaneous input and output observations are available.
Statefinder parameters in two dark energy models
Panotopoulos, Grigoris
2007-01-01
The statefinder parameters ($r,s$) in two dark energy models are studied. In the first, we discuss in four-dimensional General Relativity a two fluid model, in which dark energy and dark matter are allowed to interact with each other. In the second model, we consider the DGP brane model generalized by taking a possible energy exchange between the brane and the bulk into account. We determine the values of the statefinder parameters that correspond to the unique attractor of the system at hand. Furthermore, we produce plots in which we show $s,r$ as functions of red-shift, and the ($s-r$) plane for each model.
Parameter Symmetry of the Interacting Boson Model
Shirokov, A M; Smirnov, Yu F; Shirokov, Andrey M.; Smirnov, Yu. F.
1998-01-01
We discuss the symmetry of the parameter space of the interacting boson model (IBM). It is shown that for any set of the IBM Hamiltonian parameters (with the only exception of the U(5) dynamical symmetry limit) one can always find another set that generates the equivalent spectrum. We discuss the origin of the symmetry and its relevance for physical applications.
Parameter estimation and investigation of a bolted joint model
Shiryayev, O. V.; Page, S. M.; Pettit, C. L.; Slater, J. C.
2007-11-01
Mechanical joints are a primary source of variability in the dynamics of built-up structures. Physical phenomena in the joint are quite complex and therefore too impractical to model at the micro-scale. This motivates the development of lumped parameter joint models with discrete interfaces so that they can be easily implemented in finite element codes. Among the most important considerations in choosing a model for dynamically excited systems is its ability to model energy dissipation. This translates into the need for accurate and reliable methods to measure model parameters and estimate their inherent variability from experiments. The adjusted Iwan model was identified as a promising candidate for representing joint dynamics. Recent research focused on this model has exclusively employed impulse excitation in conjunction with neural networks to identify the model parameters. This paper presents an investigation of an alternative parameter estimation approach for the adjusted Iwan model, which employs data from oscillatory forcing. This approach is shown to produce parameter estimates with precision similar to the impulse excitation method for a range of model parameters.
Wind Farm Decentralized Dynamic Modeling With Parameters
DEFF Research Database (Denmark)
Soltani, Mohsen; Shakeri, Sayyed Mojtaba; Grunnet, Jacob Deleuran;
2010-01-01
Development of dynamic wind flow models for wind farms is part of the research in European research FP7 project AEOLUS. The objective of this report is to provide decentralized dynamic wind flow models with parameters. The report presents a structure for decentralized flow models with inputs from...
Setting Parameters for Biological Models With ANIMO
Schivo, Stefano; Scholma, Jetse; Karperien, Hermanus Bernardus Johannes; Post, Janine Nicole; van de Pol, Jan Cornelis; Langerak, Romanus; André, Étienne; Frehse, Goran
2014-01-01
ANIMO (Analysis of Networks with Interactive MOdeling) is a software for modeling biological networks, such as e.g. signaling, metabolic or gene networks. An ANIMO model is essentially the sum of a network topology and a number of interaction parameters. The topology describes the interactions
Parameter Estimation, Model Reduction and Quantum Filtering
Chase, Bradley A
2009-01-01
This dissertation explores the topics of parameter estimation and model reduction in the context of quantum filtering. Chapters 2 and 3 provide a review of classical and quantum probability theory, stochastic calculus and filtering. Chapter 4 studies the problem of quantum parameter estimation and introduces the quantum particle filter as a practical computational method for parameter estimation via continuous measurement. Chapter 5 applies these techniques in magnetometry and studies the estimator's uncertainty scalings in a double-pass atomic magnetometer. Chapter 6 presents an efficient feedback controller for continuous-time quantum error correction. Chapter 7 presents an exact model of symmetric processes of collective qubit systems.
Identifying confounders using additive noise models
Janzing, Dominik; Mooij, Joris; Schoelkopf, Bernhard
2012-01-01
We propose a method for inferring the existence of a latent common cause ('confounder') of two observed random variables. The method assumes that the two effects of the confounder are (possibly nonlinear) functions of the confounder plus independent, additive noise. We discuss under which conditions the model is identifiable (up to an arbitrary reparameterization of the confounder) from the joint distribution of the effects. We state and prove a theoretical result that provides evidence for the conjecture that the model is generically identifiable under suitable technical conditions. In addition, we propose a practical method to estimate the confounder from a finite i.i.d. sample of the effects and illustrate that the method works well on both simulated and real-world data.
Identification of hydrological model parameter variation using ensemble Kalman filter
Deng, Chao; Liu, Pan; Guo, Shenglian; Li, Zejun; Wang, Dingbao
2016-12-01
Hydrological model parameters play an important role in the ability of model prediction. In a stationary context, parameters of hydrological models are treated as constants; however, model parameters may vary with time under climate change and anthropogenic activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model (TWBM) by assimilating the runoff observations. Through a synthetic experiment, the proposed method is evaluated with time-invariant (i.e., constant) parameters and different types of parameter variations, including trend, abrupt change and periodicity. Various levels of observation uncertainty are designed to examine the performance of the EnKF. The results show that the EnKF can successfully capture the temporal variations of the model parameters. The application to the Wudinghe basin shows that the water storage capacity (SC) of the TWBM model has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of SC is explained by land use and land cover changes due to soil and water conservation measures. In contrast, the application to the Tongtianhe basin shows that the estimated SC has no significant variation during the simulation period of 1982-2013, corresponding to the relatively stationary catchment properties. The evapotranspiration parameter (C) has temporal variations while no obvious change patterns exist. The proposed method provides an effective tool for quantifying the temporal variations of the model parameters, thereby improving the accuracy and reliability of model simulations and forecasts.
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.
Uncertainty of Modal Parameters Estimated by ARMA Models
DEFF Research Database (Denmark)
Jensen, Jakob Laigaard; Brincker, Rune; Rytter, Anders
In this paper the uncertainties of identified modal parameters such as eigenfrequencies and damping ratios are assessed. From the measured response of dynamic excited structures the modal parameters may be identified and provide important structural knowledge. However the uncertainty of the param......In this paper the uncertainties of identified modal parameters such as eigenfrequencies and damping ratios are assessed. From the measured response of dynamic excited structures the modal parameters may be identified and provide important structural knowledge. However the uncertainty...... by a simulation study of a lightly damped single degree of freedom system. Identification by ARMA models has been chosen as system identification method. It is concluded that both the sampling interval and number of sampled points may play a significant role with respect to the statistical errors. Furthermore...
Uncertainty of Modal Parameters Estimated by ARMA Models
DEFF Research Database (Denmark)
Jensen, Jacob Laigaard; Brincker, Rune; Rytter, Anders
1990-01-01
In this paper the uncertainties of identified modal parameters such as eidenfrequencies and damping ratios are assed. From the measured response of dynamic excited structures the modal parameters may be identified and provide important structural knowledge. However the uncertainty of the parameters...... by simulation study of a lightly damped single degree of freedom system. Identification by ARMA models has been choosen as system identification method. It is concluded that both the sampling interval and number of sampled points may play a significant role with respect to the statistical errors. Furthermore...
Application of lumped-parameter models
DEFF Research Database (Denmark)
Ibsen, Lars Bo; Liingaard, Morten
This technical report concerns the lumped-parameter models for a suction caisson with a ratio between skirt length and foundation diameter equal to 1/2, embedded into an viscoelastic soil. The models are presented for three different values of the shear modulus of the subsoil (section 1.1). Subse...
Models and parameters for environmental radiological assessments
Energy Technology Data Exchange (ETDEWEB)
Miller, C W [ed.
1984-01-01
This book presents a unified compilation of models and parameters appropriate for assessing the impact of radioactive discharges to the environment. Models examined include those developed for the prediction of atmospheric and hydrologic transport and deposition, for terrestrial and aquatic food-chain bioaccumulation, and for internal and external dosimetry. Chapters have been entered separately into the data base. (ACR)
Towards predictive food process models: A protocol for parameter estimation.
Vilas, Carlos; Arias-Méndez, Ana; Garcia, Miriam R; Alonso, Antonio A; Balsa-Canto, E
2016-05-31
Mathematical models, in particular, physics-based models, are essential tools to food product and process design, optimization and control. The success of mathematical models relies on their predictive capabilities. However, describing physical, chemical and biological changes in food processing requires the values of some, typically unknown, parameters. Therefore, parameter estimation from experimental data is critical to achieving desired model predictive properties. This work takes a new look into the parameter estimation (or identification) problem in food process modeling. First, we examine common pitfalls such as lack of identifiability and multimodality. Second, we present the theoretical background of a parameter identification protocol intended to deal with those challenges. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods.
Estimation of Model Parameters for Steerable Needles
Park, Wooram; Reed, Kyle B.; Okamura, Allison M.; Chirikjian, Gregory S.
2010-01-01
Flexible needles with bevel tips are being developed as useful tools for minimally invasive surgery and percutaneous therapy. When such a needle is inserted into soft tissue, it bends due to the asymmetric geometry of the bevel tip. This insertion with bending is not completely repeatable. We characterize the deviations in needle tip pose (position and orientation) by performing repeated needle insertions into artificial tissue. The base of the needle is pushed at a constant speed without rotating, and the covariance of the distribution of the needle tip pose is computed from experimental data. We develop the closed-form equations to describe how the covariance varies with different model parameters. We estimate the model parameters by matching the closed-form covariance and the experimentally obtained covariance. In this work, we use a needle model modified from a previously developed model with two noise parameters. The modified needle model uses three noise parameters to better capture the stochastic behavior of the needle insertion. The modified needle model provides an improvement of the covariance error from 26.1% to 6.55%. PMID:21643451
Estimation of Model Parameters for Steerable Needles.
Park, Wooram; Reed, Kyle B; Okamura, Allison M; Chirikjian, Gregory S
2010-01-01
Flexible needles with bevel tips are being developed as useful tools for minimally invasive surgery and percutaneous therapy. When such a needle is inserted into soft tissue, it bends due to the asymmetric geometry of the bevel tip. This insertion with bending is not completely repeatable. We characterize the deviations in needle tip pose (position and orientation) by performing repeated needle insertions into artificial tissue. The base of the needle is pushed at a constant speed without rotating, and the covariance of the distribution of the needle tip pose is computed from experimental data. We develop the closed-form equations to describe how the covariance varies with different model parameters. We estimate the model parameters by matching the closed-form covariance and the experimentally obtained covariance. In this work, we use a needle model modified from a previously developed model with two noise parameters. The modified needle model uses three noise parameters to better capture the stochastic behavior of the needle insertion. The modified needle model provides an improvement of the covariance error from 26.1% to 6.55%.
An Optimization Model of Tunnel Support Parameters
Directory of Open Access Journals (Sweden)
Su Lijuan
2015-05-01
Full Text Available An optimization model was developed to obtain the ideal values of the primary support parameters of tunnels, which are wide-ranging in high-speed railway design codes when the surrounding rocks are at the III, IV, and V levels. First, several sets of experiments were designed and simulated using the FLAC3D software under an orthogonal experimental design. Six factors, namely, level of surrounding rock, buried depth of tunnel, lateral pressure coefficient, anchor spacing, anchor length, and shotcrete thickness, were considered. Second, a regression equation was generated by conducting a multiple linear regression analysis following the analysis of the simulation results. Finally, the optimization model of support parameters was obtained by solving the regression equation using the least squares method. In practical projects, the optimized values of support parameters could be obtained by integrating known parameters into the proposed model. In this work, the proposed model was verified on the basis of the Liuyang River Tunnel Project. Results show that the optimization model significantly reduces related costs. The proposed model can also be used as a reliable reference for other high-speed railway tunnels.
Nicoulaud-Gouin, V; Garcia-Sanchez, L; Giacalone, M; Attard, J C; Martin-Garin, A; Bois, F Y
2016-10-01
This paper addresses the methodological conditions -particularly experimental design and statistical inference- ensuring the identifiability of sorption parameters from breakthrough curves measured during stirred flow-through reactor experiments also known as continuous flow stirred-tank reactor (CSTR) experiments. The equilibrium-kinetic (EK) sorption model was selected as nonequilibrium parameterization embedding the Kd approach. Parameter identifiability was studied formally on the equations governing outlet concentrations. It was also studied numerically on 6 simulated CSTR experiments on a soil with known equilibrium-kinetic sorption parameters. EK sorption parameters can not be identified from a single breakthrough curve of a CSTR experiment, because Kd,1 and k(-) were diagnosed collinear. For pairs of CSTR experiments, Bayesian inference allowed to select the correct models of sorption and error among sorption alternatives. Bayesian inference was conducted with SAMCAT software (Sensitivity Analysis and Markov Chain simulations Applied to Transfer models) which launched the simulations through the embedded simulation engine GNU-MCSim, and automated their configuration and post-processing. Experimental designs consisting in varying flow rates between experiments reaching equilibrium at contamination stage were found optimal, because they simultaneously gave accurate sorption parameters and predictions. Bayesian results were comparable to maximum likehood method but they avoided convergence problems, the marginal likelihood allowed to compare all models, and credible interval gave directly the uncertainty of sorption parameters θ. Although these findings are limited to the specific conditions studied here, in particular the considered sorption model, the chosen parameter values and error structure, they help in the conception and analysis of future CSTR experiments with radionuclides whose kinetic behaviour is suspected.
Analysis of Modeling Parameters on Threaded Screws.
Energy Technology Data Exchange (ETDEWEB)
Vigil, Miquela S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Brake, Matthew Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vangoethem, Douglas [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-06-01
Assembled mechanical systems often contain a large number of bolted connections. These bolted connections (joints) are integral aspects of the load path for structural dynamics, and, consequently, are paramount for calculating a structure's stiffness and energy dissipation prop- erties. However, analysts have not found the optimal method to model appropriately these bolted joints. The complexity of the screw geometry cause issues when generating a mesh of the model. This paper will explore different approaches to model a screw-substrate connec- tion. Model parameters such as mesh continuity, node alignment, wedge angles, and thread to body element size ratios are examined. The results of this study will give analysts a better understanding of the influences of these parameters and will aide in finding the optimal method to model bolted connections.
STUDY ON NEW METHOD OF IDENTIFYING GEOMETRIC ERROR PARAMETERS FOR NC MACHINE TOOLS
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The methods of identifying geometric error parameters for NC machine tools are introduced. According to analyzing and comparing the different methods, a new method-displacement method with 9 lines is developed based on the theories of the movement errors of multibody system (MBS). A lot of experiments are also made to obtain 21 terms geometric error parameters by using the error identification software based on the new method.
Identifying Suitable Projection Parameters and Display Configurations for Mobile True-3D Displays
Serrano, Marcos; Hildebrandt, Dale; Subramanian, Sriram; Irani, Pourang
2014-01-01
International audience; We present a two-part exploration on mobile true-3D displays, i.e. displaying volumetric 3D content in mid-air. We first identify and study the parameters of a mobile true-3D projection, in terms of the projection's distance to the phone, angle to the phone, display volume and position within the display. We identify suitable parameters and constraints, which we propose as requirements for developing mobile true-3D systems. We build on the first outcomes to explore met...
The Lund Model at Nonzero Impact Parameter
Janik, R A; Janik, Romuald A.; Peschanski, Robi
2003-01-01
We extend the formulation of the longitudinal 1+1 dimensional Lund model to nonzero impact parameter using the minimal area assumption. Complete formulae for the string breaking probability and the momenta of the produced mesons are derived using the string worldsheet Minkowskian helicoid geometry. For strings stretched into the transverse dimension, we find probability distribution with slope linear in m_T similar to the statistical models but without any thermalization assumptions.
IMPROVEMENT OF FLUID PIPE LUMPED PARAMETER MODEL
Institute of Scientific and Technical Information of China (English)
Kong Xiaowu; Wei Jianhua; Qiu Minxiu; Wu Genmao
2004-01-01
The traditional lumped parameter model of fluid pipe is introduced and its drawbacks are pointed out.Furthermore, two suggestions are put forward to remove these drawbacks.Firstly, the structure of equivalent circuit is modified, and then the evaluation of equivalent fluid resistance is change to take the frequency-dependent friction into account.Both simulation and experiment prove that this model is precise to characterize the dynamic behaviors of fluid in pipe.
Xu, Wenfu; Hu, Zhonghua; Zhang, Yu; Liang, Bin
2017-03-01
After being launched into space to perform some tasks, the inertia parameters of a space robotic system may change due to fuel consumption, hardware reconfiguration, target capturing, and so on. For precision control and simulation, it is required to identify these parameters on orbit. This paper proposes an effective method for identifying the complete inertia parameters (including the mass, inertia tensor and center of mass position) of a space robotic system. The key to the method is to identify two types of simple dynamics systems: equivalent single-body and two-body systems. For the former, all of the joints are locked into a designed configuration and the thrusters are used for orbital maneuvering. The object function for optimization is defined in terms of acceleration and velocity of the equivalent single body. For the latter, only one joint is unlocked and driven to move along a planned (exiting) trajectory in free-floating mode. The object function is defined based on the linear and angular momentum equations. Then, the parameter identification problems are transformed into non-linear optimization problems. The Particle Swarm Optimization (PSO) algorithm is applied to determine the optimal parameters, i.e. the complete dynamic parameters of the two equivalent systems. By sequentially unlocking the 1st to nth joints (or unlocking the nth to 1st joints), the mass properties of body 0 to n (or n to 0) are completely identified. For the proposed method, only simple dynamics equations are needed for identification. The excitation motion (orbit maneuvering and joint motion) is also easily realized. Moreover, the method does not require prior knowledge of the mass properties of any body. It is general and practical for identifying a space robotic system on-orbit.
Consistent Stochastic Modelling of Meteocean Design Parameters
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard; Sterndorff, M. J.
2000-01-01
Consistent stochastic models of metocean design parameters and their directional dependencies are essential for reliability assessment of offshore structures. In this paper a stochastic model for the annual maximum values of the significant wave height, and the associated wind velocity, current...... velocity, and water level is presented. The stochastic model includes statistical uncertainty and dependency between the four stochastic variables. Further, a new stochastic model for annual maximum directional significant wave heights is presented. The model includes dependency between the maximum wave...... height from neighboring directional sectors. Numerical examples are presented where the models are calibrated using the Maximum Likelihood method to data from the central part of the North Sea. The calibration of the directional distributions is made such that the stochastic model for the omnidirectional...
On the Identifiability of Transmission Dynamic Models for Infectious Diseases.
Lintusaari, Jarno; Gutmann, Michael U; Kaski, Samuel; Corander, Jukka
2016-03-01
Understanding the transmission dynamics of infectious diseases is important for both biological research and public health applications. It has been widely demonstrated that statistical modeling provides a firm basis for inferring relevant epidemiological quantities from incidence and molecular data. However, the complexity of transmission dynamic models presents two challenges: (1) the likelihood function of the models is generally not computable, and computationally intensive simulation-based inference methods need to be employed, and (2) the model may not be fully identifiable from the available data. While the first difficulty can be tackled by computational and algorithmic advances, the second obstacle is more fundamental. Identifiability issues may lead to inferences that are driven more by prior assumptions than by the data themselves. We consider a popular and relatively simple yet analytically intractable model for the spread of tuberculosis based on classical IS6110 fingerprinting data. We report on the identifiability of the model, also presenting some methodological advances regarding the inference. Using likelihood approximations, we show that the reproductive value cannot be identified from the data available and that the posterior distributions obtained in previous work have likely been substantially dominated by the assumed prior distribution. Further, we show that the inferences are influenced by the assumed infectious population size, which generally has been kept fixed in previous work. We demonstrate that the infectious population size can be inferred if the remaining epidemiological parameters are already known with sufficient precision.
DEFF Research Database (Denmark)
Sin, Gürkan; Meyer, Anne S.; Gernaey, Krist
2010-01-01
The reliability of cellulose hydrolysis models is studied using the NREL model. An identifiability analysis revealed that only 6 out of 26 parameters are identifiable from the available data (typical hydrolysis experiments). Attempting to identify a higher number of parameters (as done...... are not informative enough (sensitivities of 16 parameters were insignificant). This indicates that the NREL model has severe parameter uncertainty, likely to be the case for other hydrolysis models as well since similar kinetic expressions are used. To overcome this impasse, we have used the Monte Carlo procedure...
Testing Linear Models for Ability Parameters in Item Response Models
Glas, Cees A.W.; Hendrawan, Irene
2005-01-01
Methods for testing hypotheses concerning the regression parameters in linear models for the latent person parameters in item response models are presented. Three tests are outlined: A likelihood ratio test, a Lagrange multiplier test and a Wald test. The tests are derived in a marginal maximum like
MODELING PARAMETERS OF ARC OF ELECTRIC ARC FURNACE
Directory of Open Access Journals (Sweden)
R.N. Khrestin
2015-08-01
Full Text Available Purpose. The aim is to build a mathematical model of the electric arc of arc furnace (EAF. The model should clearly show the relationship between the main parameters of the arc. These parameters determine the properties of the arc and the possibility of optimization of melting mode. Methodology. We have built a fairly simple model of the arc, which satisfies the above requirements. The model is designed for the analysis of electromagnetic processes arc of varying length. We have compared the results obtained when testing the model with the results obtained on actual furnaces. Results. During melting in real chipboard under the influence of changes in temperature changes its properties arc plasma. The proposed model takes into account these changes. Adjusting the length of the arc is the main way to regulate the mode of smelting chipboard. The arc length is controlled by the movement of the drive electrode. The model reflects the dynamic changes in the parameters of the arc when changing her length. We got the dynamic current-voltage characteristics (CVC of the arc for the different stages of melting. We got the arc voltage waveform and identified criteria by which possible identified stage of smelting. Originality. In contrast to the previously known models, this model clearly shows the relationship between the main parameters of the arc EAF: arc voltage Ud, amperage arc id and length arc d. Comparison of the simulation results and experimental data obtained from real particleboard showed the adequacy of the constructed model. It was found that character of change of magnitude Md, helps determine the stage of melting. Practical value. It turned out that the model can be used to simulate smelting in EAF any capacity. Thus, when designing the system of control mechanism for moving the electrode, the model takes into account changes in the parameters of the arc and it can significantly reduce electrode material consumption and energy consumption
A Simple Model for Identifying Critical Structures in Atrial Fibrillation
Christensen, Kim; Peters, Nicholas S
2014-01-01
Atrial fibrillation (AF) is the most common abnormal heart rhythm and the single biggest cause of stroke. Ablation, destroying regions of the atria, is applied largely empirically and can be curative but with a disappointing clinical success rate. We design a simple model of activation wavefront propagation on a structure mimicking the branching network architecture of heart muscle and show how AF emerges spontaneously as age-related parameters change. We identify regions responsible for the initiation and maintenance of AF, the ablation of which terminates AF. The simplicity of the model allows us to calculate analytically the risk of arrhythmia. This analytical result allows us to locate the transition in parameter space and highlights that the transition from regular to fibrillatory behaviour is a finite-size effect present in systems of any size. These clinically testable predictions might inform ablation therapies and arrhythmic risk assessment.
Modelling spin Hamiltonian parameters of molecular nanomagnets.
Gupta, Tulika; Rajaraman, Gopalan
2016-07-12
Molecular nanomagnets encompass a wide range of coordination complexes possessing several potential applications. A formidable challenge in realizing these potential applications lies in controlling the magnetic properties of these clusters. Microscopic spin Hamiltonian (SH) parameters describe the magnetic properties of these clusters, and viable ways to control these SH parameters are highly desirable. Computational tools play a proactive role in this area, where SH parameters such as isotropic exchange interaction (J), anisotropic exchange interaction (Jx, Jy, Jz), double exchange interaction (B), zero-field splitting parameters (D, E) and g-tensors can be computed reliably using X-ray structures. In this feature article, we have attempted to provide a holistic view of the modelling of these SH parameters of molecular magnets. The determination of J includes various class of molecules, from di- and polynuclear Mn complexes to the {3d-Gd}, {Gd-Gd} and {Gd-2p} class of complexes. The estimation of anisotropic exchange coupling includes the exchange between an isotropic metal ion and an orbitally degenerate 3d/4d/5d metal ion. The double-exchange section contains some illustrative examples of mixed valance systems, and the section on the estimation of zfs parameters covers some mononuclear transition metal complexes possessing very large axial zfs parameters. The section on the computation of g-anisotropy exclusively covers studies on mononuclear Dy(III) and Er(III) single-ion magnets. The examples depicted in this article clearly illustrate that computational tools not only aid in interpreting and rationalizing the observed magnetic properties but possess the potential to predict new generation MNMs.
Singh, R.; Archfield, S.A.; Wagener, T.
2014-01-01
Daily streamflow information is critical for solving various hydrologic problems, though observations of continuous streamflow for model calibration are available at only a small fraction of the world’s rivers. One approach to estimate daily streamflow at an ungauged location is to transfer rainfall–runoff model parameters calibrated at a gauged (donor) catchment to an ungauged (receiver) catchment of interest. Central to this approach is the selection of a hydrologically similar donor. No single metric or set of metrics of hydrologic similarity have been demonstrated to consistently select a suitable donor catchment. We design an experiment to diagnose the dominant controls on successful hydrologic model parameter transfer. We calibrate a lumped rainfall–runoff model to 83 stream gauges across the United States. All locations are USGS reference gauges with minimal human influence. Parameter sets from the calibrated models are then transferred to each of the other catchments and the performance of the transferred parameters is assessed. This transfer experiment is carried out both at the scale of the entire US and then for six geographic regions. We use classification and regression tree (CART) analysis to determine the relationship between catchment similarity and performance of transferred parameters. Similarity is defined using physical/climatic catchment characteristics, as well as streamflow response characteristics (signatures such as baseflow index and runoff ratio). Across the entire US, successful parameter transfer is governed by similarity in elevation and climate, and high similarity in streamflow signatures. Controls vary for different geographic regions though. Geology followed by drainage, topography and climate constitute the dominant similarity metrics in forested eastern mountains and plateaus, whereas agricultural land use relates most strongly with successful parameter transfer in the humid plains.
Energy Technology Data Exchange (ETDEWEB)
Samper, J.; Dewonck, S.; Zheng, L.; Yang, Q.; Naves, A.
2009-10-01
DIR (Diffusion of Inert and Reactive tracers) is an experimental program performed by ANDRA at Bure underground research laboratory in Meuse/Haute Marne (France) to characterize diffusion and retention of radionuclides in Callovo-Oxfordian (C-Ox) argillite. In situ diffusion experiments were performed in vertical boreholes to determine diffusion and retention parameters of selected radionuclides. C-Ox clay exhibits a mild diffusion anisotropy due to stratification. Interpretation of in situ diffusion experiments is complicated by several non-ideal effects caused by the presence of a sintered filter, a gap between the filter and borehole wall and an excavation disturbed zone (EdZ). The relevance of such non-ideal effects and their impact on estimated clay parameters have been evaluated with numerical sensitivity analyses and synthetic experiments having similar parameters and geometric characteristics as real DIR experiments. Normalized dimensionless sensitivities of tracer concentrations at the test interval have been computed numerically. Tracer concentrations are found to be sensitive to all key parameters. Sensitivities are tracer dependent and vary with time. These sensitivities are useful to identify which are the parameters that can be estimated with less uncertainty and find the times at which tracer concentrations begin to be sensitive to each parameter. Synthetic experiments generated with prescribed known parameters have been interpreted automatically with INVERSE-CORE{sup 2D} and used to evaluate the relevance of non-ideal effects and ascertain parameter identifiability in the presence of random measurement errors. Identifiability analysis of synthetic experiments reveals that data noise makes difficult the estimation of clay parameters. Parameters of clay and EdZ cannot be estimated simultaneously from noisy data. Models without an EdZ fail to reproduce synthetic data. Proper interpretation of in situ diffusion experiments requires accounting for filter
Joint Dynamics Modeling and Parameter Identification for Space Robot Applications
Directory of Open Access Journals (Sweden)
Adenilson R. da Silva
2007-01-01
Full Text Available Long-term mission identification and model validation for in-flight manipulator control system in almost zero gravity with hostile space environment are extremely important for robotic applications. In this paper, a robot joint mathematical model is developed where several nonlinearities have been taken into account. In order to identify all the required system parameters, an integrated identification strategy is derived. This strategy makes use of a robust version of least-squares procedure (LS for getting the initial conditions and a general nonlinear optimization method (MCS—multilevel coordinate search—algorithm to estimate the nonlinear parameters. The approach is applied to the intelligent robot joint (IRJ experiment that was developed at DLR for utilization opportunity on the International Space Station (ISS. The results using real and simulated measurements have shown that the developed algorithm and strategy have remarkable features in identifying all the parameters with good accuracy.
Systematic parameter inference in stochastic mesoscopic modeling
Lei, Huan; Li, Zhen; Karniadakis, George
2016-01-01
We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are sparse. The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space....
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.
Parameter estimation and model selection in computational biology.
Directory of Open Access Journals (Sweden)
Gabriele Lillacci
2010-03-01
Full Text Available A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection.
Modelling tourists arrival using time varying parameter
Suciptawati, P.; Sukarsa, K. G.; Kencana, Eka N.
2017-06-01
The importance of tourism and its related sectors to support economic development and poverty reduction in many countries increase researchers’ attentions to study and model tourists’ arrival. This work is aimed to demonstrate time varying parameter (TVP) technique to model the arrival of Korean’s tourists to Bali. The number of Korean tourists whom visiting Bali for period January 2010 to December 2015 were used to model the number of Korean’s tourists to Bali (KOR) as dependent variable. The predictors are the exchange rate of Won to IDR (WON), the inflation rate in Korea (INFKR), and the inflation rate in Indonesia (INFID). Observing tourists visit to Bali tend to fluctuate by their nationality, then the model was built by applying TVP and its parameters were approximated using Kalman Filter algorithm. The results showed all of predictor variables (WON, INFKR, INFID) significantly affect KOR. For in-sample and out-of-sample forecast with ARIMA’s forecasted values for the predictors, TVP model gave mean absolute percentage error (MAPE) as much as 11.24 percent and 12.86 percent, respectively.
Parameter estimation, model reduction and quantum filtering
Chase, Bradley A.
This thesis explores the topics of parameter estimation and model reduction in the context of quantum filtering. The last is a mathematically rigorous formulation of continuous quantum measurement, in which a stream of auxiliary quantum systems is used to infer the state of a target quantum system. Fundamental quantum uncertainties appear as noise which corrupts the probe observations and therefore must be filtered in order to extract information about the target system. This is analogous to the classical filtering problem in which techniques of inference are used to process noisy observations of a system in order to estimate its state. Given the clear similarities between the two filtering problems, I devote the beginning of this thesis to a review of classical and quantum probability theory, stochastic calculus and filtering. This allows for a mathematically rigorous and technically adroit presentation of the quantum filtering problem and solution. Given this foundation, I next consider the related problem of quantum parameter estimation, in which one seeks to infer the strength of a parameter that drives the evolution of a probe quantum system. By embedding this problem in the state estimation problem solved by the quantum filter, I present the optimal Bayesian estimator for a parameter when given continuous measurements of the probe system to which it couples. For cases when the probe takes on a finite number of values, I review a set of sufficient conditions for asymptotic convergence of the estimator. For a continuous-valued parameter, I present a computational method called quantum particle filtering for practical estimation of the parameter. Using these methods, I then study the particular problem of atomic magnetometry and review an experimental method for potentially reducing the uncertainty in the estimate of the magnetic field beyond the standard quantum limit. The technique involves double-passing a probe laser field through the atomic system, giving
Identification of slow molecular order parameters for Markov model construction
Perez-Hernandez, Guillermo; Giorgino, Toni; de Fabritiis, Gianni; Noé, Frank
2013-01-01
A goal in the kinetic characterization of a macromolecular system is the description of its slow relaxation processes, involving (i) identification of the structural changes involved in these processes, and (ii) estimation of the rates or timescales at which these slow processes occur. Most of the approaches to this task, including Markov models, Master-equation models, and kinetic network models, start by discretizing the high-dimensional state space and then characterize relaxation processes in terms of the eigenvectors and eigenvalues of a discrete transition matrix. The practical success of such an approach depends very much on the ability to finely discretize the slow order parameters. How can this task be achieved in a high-dimensional configuration space without relying on subjective guesses of the slow order parameters? In this paper, we use the variational principle of conformation dynamics to derive an optimal way of identifying the "slow subspace" of a large set of prior order parameters - either g...
Environmental Transport Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
M. Wasiolek
2004-09-10
This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the total system performance assessment for the license application (TSPA-LA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA-LA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]) (TWP). This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA). This report is one of the five reports that develop input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the conceptual model and the mathematical model. The input parameter reports, shown to the right of the Biosphere Model Report in Figure 1-1, contain detailed description of the model input parameters. The output of this report is used as direct input in the ''Nominal Performance Biosphere Dose Conversion Factor Analysis'' and in the ''Disruptive Event Biosphere Dose Conversion Factor Analysis'' that calculate the values of biosphere dose conversion factors (BDCFs) for the groundwater and volcanic ash exposure scenarios, respectively. The purpose of this analysis was to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or in volcanic ash). The analysis
Inhalation Exposure Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
K. Rautenstrauch
2004-09-10
This analysis is one of 10 reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN) biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. Inhalation Exposure Input Parameters for the Biosphere Model is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the Technical Work Plan for Biosphere Modeling and Expert Support (BSC 2004 [DIRS 169573]). This analysis report defines and justifies values of mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of ERMYN to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception.
Modeling of Parameters of Subcritical Assembly SAD
Petrochenkov, S; Puzynin, I
2005-01-01
The accepted conceptual design of the experimental Subcritical Assembly in Dubna (SAD) is based on the MOX core with a nominal unit capacity of 25 kW (thermal). This corresponds to the multiplication coefficient $k_{\\rm eff} =0.95$ and accelerator beam power 1 kW. A subcritical assembly driven with the existing 660 MeV proton accelerator at the Joint Institute for Nuclear Research has been modelled in order to make choice of the optimal parameters for the future experiments. The Monte Carlo method was used to simulate neutron spectra, energy deposition and doses calculations. Some of the calculation results are presented in the paper.
Bates, P. D.; Neal, J. C.; Fewtrell, T. J.
2012-12-01
In this we paper we consider two related questions. First, we address the issue of how much physical complexity is necessary in a model in order to simulate floodplain inundation to within validation data error. This is achieved through development of a single code/multiple physics hydraulic model (LISFLOOD-FP) where different degrees of complexity can be switched on or off. Different configurations of this code are applied to four benchmark test cases, and compared to the results of a number of industry standard models. Second we address the issue of how parameter sensitivity and transferability change with increasing complexity using numerical experiments with models of different physical and geometric intricacy. Hydraulic models are a good example system with which to address such generic modelling questions as: (1) they have a strong physical basis; (2) there is only one set of equations to solve; (3) they require only topography and boundary conditions as input data; and (4) they typically require only a single free parameter, namely boundary friction. In terms of complexity required we show that for the problem of sub-critical floodplain inundation a number of codes of different dimensionality and resolution can be found to fit uncertain model validation data equally well, and that in this situation Occam's razor emerges as a useful logic to guide model selection. We find also find that model skill usually improves more rapidly with increases in model spatial resolution than increases in physical complexity, and that standard approaches to testing hydraulic models against laboratory data or analytical solutions may fail to identify this important fact. Lastly, we find that in benchmark testing studies significant differences can exist between codes with identical numerical solution techniques as a result of auxiliary choices regarding the specifics of model implementation that are frequently unreported by code developers. As a consequence, making sound
Moose models with vanishing $S$ parameter
Casalbuoni, R; Dominici, Daniele
2004-01-01
In the linear moose framework, which naturally emerges in deconstruction models, we show that there is a unique solution for the vanishing of the $S$ parameter at the lowest order in the weak interactions. We consider an effective gauge theory based on $K$ SU(2) gauge groups, $K+1$ chiral fields and electroweak groups $SU(2)_L$ and $U(1)_Y$ at the ends of the chain of the moose. $S$ vanishes when a link in the moose chain is cut. As a consequence one has to introduce a dynamical non local field connecting the two ends of the moose. Then the model acquires an additional custodial symmetry which protects this result. We examine also the possibility of a strong suppression of $S$ through an exponential behavior of the link couplings as suggested by Randall Sundrum metric.
Model parameters for simulation of physiological lipids
McGlinchey, Nicholas
2016-01-01
Coarse grain simulation of proteins in their physiological membrane environment can offer insight across timescales, but requires a comprehensive force field. Parameters are explored for multicomponent bilayers composed of unsaturated lipids DOPC and DOPE, mixed‐chain saturation POPC and POPE, and anionic lipids found in bacteria: POPG and cardiolipin. A nonbond representation obtained from multiscale force matching is adapted for these lipids and combined with an improved bonding description of cholesterol. Equilibrating the area per lipid yields robust bilayer simulations and properties for common lipid mixtures with the exception of pure DOPE, which has a known tendency to form nonlamellar phase. The models maintain consistency with an existing lipid–protein interaction model, making the force field of general utility for studying membrane proteins in physiologically representative bilayers. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. PMID:26864972
Environmental Transport Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
M. A. Wasiolek
2003-06-27
This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (TWP) (BSC 2003 [163602]). Some documents in Figure 1-1 may be under development and not available when this report is issued. This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA), but access to the listed documents is not required to understand the contents of this report. This report is one of the reports that develops input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2003 [160699]) describes the conceptual model, the mathematical model, and the input parameters. The purpose of this analysis is to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or volcanic ash). The analysis was performed in accordance with the TWP (BSC 2003 [163602]). This analysis develops values of parameters associated with many features, events, and processes (FEPs) applicable to the reference biosphere (DTN: M00303SEPFEPS2.000 [162452]), which are addressed in the biosphere model (BSC 2003 [160699]). The treatment of these FEPs is described in BSC (2003 [160699
Suminar, Wulan; Saepuloh, Asep; Meilano, Irwan
2016-05-01
Analysis of hazard assessment to active volcanoes is crucial for risk management. The hazard map of volcano provides information to decision makers and communities before, during, and after volcanic crisis. The rapid and accurate hazard assessment, especially to an active volcano is necessary to be developed for better mitigation on the time of volcanic crises in Indonesia. In this paper, we identified the hazard parameters to develop quantitative and dynamic hazard map of an active volcano. The Guntur volcano in Garut Region, West Java, Indonesia was selected as study area due population are resided adjacent to active volcanoes. The development of infrastructures, especially related to tourism at the eastern flank from the Summit, are growing rapidly. The remote sensing and field investigation approaches were used to obtain hazard parameters spatially. We developed a quantitative and dynamic algorithm to map spatially hazard potential of volcano based on index overlay technique. There were identified five volcano hazard parameters based on Landsat 8 and ASTER imageries: volcanic products including pyroclastic fallout, pyroclastic flows, lava and lahar, slope topography, surface brightness temperature, and vegetation density. Following this proposed technique, the hazard parameters were extracted, indexed, and calculated to produce spatial hazard values at and around Guntur Volcano. Based on this method, the hazard potential of low vegetation density is higher than high vegetation density. Furthermore, the slope topography, surface brightness temperature, and fragmental volcanic product such as pyroclastics influenced to the spatial hazard value significantly. Further study to this proposed approach will be aimed for effective and efficient analyses of volcano risk assessment.
Iterative integral parameter identification of a respiratory mechanics model
Directory of Open Access Journals (Sweden)
Schranz Christoph
2012-07-01
Full Text Available Abstract Background Patient-specific respiratory mechanics models can support the evaluation of optimal lung protective ventilator settings during ventilation therapy. Clinical application requires that the individual’s model parameter values must be identified with information available at the bedside. Multiple linear regression or gradient-based parameter identification methods are highly sensitive to noise and initial parameter estimates. Thus, they are difficult to apply at the bedside to support therapeutic decisions. Methods An iterative integral parameter identification method is applied to a second order respiratory mechanics model. The method is compared to the commonly used regression methods and error-mapping approaches using simulated and clinical data. The clinical potential of the method was evaluated on data from 13 Acute Respiratory Distress Syndrome (ARDS patients. Results The iterative integral method converged to error minima 350 times faster than the Simplex Search Method using simulation data sets and 50 times faster using clinical data sets. Established regression methods reported erroneous results due to sensitivity to noise. In contrast, the iterative integral method was effective independent of initial parameter estimations, and converged successfully in each case tested. Conclusion These investigations reveal that the iterative integral method is beneficial with respect to computing time, operator independence and robustness, and thus applicable at the bedside for this clinical application.
Uncertainty Quantification for Optical Model Parameters
Lovell, A E; Sarich, J; Wild, S M
2016-01-01
Although uncertainty quantification has been making its way into nuclear theory, these methods have yet to be explored in the context of reaction theory. For example, it is well known that different parameterizations of the optical potential can result in different cross sections, but these differences have not been systematically studied and quantified. The purpose of this work is to investigate the uncertainties in nuclear reactions that result from fitting a given model to elastic-scattering data, as well as to study how these uncertainties propagate to the inelastic and transfer channels. We use statistical methods to determine a best fit and create corresponding 95\\% confidence bands. A simple model of the process is fit to elastic-scattering data and used to predict either inelastic or transfer cross sections. In this initial work, we assume that our model is correct, and the only uncertainties come from the variation of the fit parameters. We study a number of reactions involving neutron and deuteron p...
Numerical modeling of partial discharges parameters
Directory of Open Access Journals (Sweden)
Kartalović Nenad M.
2016-01-01
Full Text Available In recent testing of the partial discharges or the use for the diagnosis of insulation condition of high voltage generators, transformers, cables and high voltage equipment develops rapidly. It is a result of the development of electronics, as well as, the development of knowledge about the processes of partial discharges. The aim of this paper is to contribute the better understanding of this phenomenon of partial discharges by consideration of the relevant physical processes in isolation materials and isolation systems. Prebreakdown considers specific processes, and development processes at the local level and their impact on specific isolation material. This approach to the phenomenon of partial discharges needed to allow better take into account relevant discharge parameters as well as better numerical model of partial discharges.
Institute of Scientific and Technical Information of China (English)
Yang Haijun; Xu Yongzhong; Huang Zhibin; Chen Shizhong; Yang Zhilin; Wu Gang; Xiao Zhongyao
2011-01-01
With the objective of establishing the necessary conditions for 3-D seismic data from a Permian plutonic oilfield in western China,we compared the technology of several multi-parameter seismic inversion methods in identifying igneous rocks.The most often used inversion methods are Constrained Sparse Spike Inversion (CSSI).Artificial Neural Network Inversion (ANN) and GR Pseudo-impedance Inversion.Through the application of a variety of inversion methods with log curves correction,we obtained relatively high-resolution impedance and velocity sections,effectively identifying the lithology of Permian igneous rocks and inferred lateral variation in the lithology of tgneous rocks.By means of a comprehensive comparative study,we arrived at the following conclusions:the CSSI inversion has good waveform continuity,and the ANN inversion has lower resolution than the CSSI inversion.The inversion results show that multi-parameter seismic inversion methods are an effective solution to the identification of igneous rocks.
Probabilistic Constraint Programming for Parameters Optimisation of Generative Models
Zanin, Massimiliano; Sousa, Pedro A C; Cruz, Jorge
2015-01-01
Complex networks theory has commonly been used for modelling and understanding the interactions taking place between the elements composing complex systems. More recently, the use of generative models has gained momentum, as they allow identifying which forces and mechanisms are responsible for the appearance of given structural properties. In spite of this interest, several problems remain open, one of the most important being the design of robust mechanisms for finding the optimal parameters of a generative model, given a set of real networks. In this contribution, we address this problem by means of Probabilistic Constraint Programming. By using as an example the reconstruction of networks representing brain dynamics, we show how this approach is superior to other solutions, in that it allows a better characterisation of the parameters space, while requiring a significantly lower computational cost.
Considerations for parameter optimization and sensitivity in climate models.
Neelin, J David; Bracco, Annalisa; Luo, Hao; McWilliams, James C; Meyerson, Joyce E
2010-12-14
Climate models exhibit high sensitivity in some respects, such as for differences in predicted precipitation changes under global warming. Despite successful large-scale simulations, regional climatology features prove difficult to constrain toward observations, with challenges including high-dimensionality, computationally expensive simulations, and ambiguity in the choice of objective function. In an atmospheric General Circulation Model forced by observed sea surface temperature or coupled to a mixed-layer ocean, many climatic variables yield rms-error objective functions that vary smoothly through the feasible parameter range. This smoothness occurs despite nonlinearity strong enough to reverse the curvature of the objective function in some parameters, and to imply limitations on multimodel ensemble means as an estimator of global warming precipitation changes. Low-order polynomial fits to the model output spatial fields as a function of parameter (quadratic in model field, fourth-order in objective function) yield surprisingly successful metamodels for many quantities and facilitate a multiobjective optimization approach. Tradeoffs arise as optima for different variables occur at different parameter values, but with agreement in certain directions. Optima often occur at the limit of the feasible parameter range, identifying key parameterization aspects warranting attention--here the interaction of convection with free tropospheric water vapor. Analytic results for spatial fields of leading contributions to the optimization help to visualize tradeoffs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional error under minimization of global objective functions. The approach is sufficiently simple to guide parameter choices and to aid intercomparison of sensitivity properties among climate models.
Yousefi, Saleh; Pourghasemi, Hamid Reza; Hooke, Janet; Navratil, Oldrich; Kidová, Anna
2016-10-01
River meander dynamics and mobility are important indicators of environmental change related to climate changes and anthropogenic activities at local and river basin scales. The aim of the present study is to identify morphological changes of the Karoon River in Iran using high accuracy maps and Landsat satellite images by analyses during the time period 1989-2008. In this study, 20 meandering reaches were analyzed over a 128-km-long river reach located in the middle part of the Karoon River, Iran. Morphometric indicators such as: river width (W), meander neck length (L), axis length (A), radius of curvature (R), water flow length (S), and sinuosity of meander (C) were extracted for the identified meanders. The results of a paired t-test showed that river width (W) and meander neck length (L) have significantly changed during the study period (1989-2008), with an increase of + 3.5 m for W and a decrease of 274 m for L. Spearman correlation analysis has shown that meander parameter changes are highly correlated to each other. The parameters that do not have significant correlation together are C with W and L, W and L, and L with S and A. During the period of the study, the flow length and river sinuosity decreased for the whole river reach, by 4.77 km and 0.11, respectively. Analysis of land use/land cover categories (1989 and 2008) using the support vector machine (SVM) and kernel function method served as one of the tools for interpretation of the meander parameter changes. These changes can be attributed not only to LU/LC (riparian vegetation to agriculture area ratio) but also to dam construction in the upstream part of the river that leads to major hydrological regime and sediment transfer alteration. Sediment extraction may also be an important factor.
Directory of Open Access Journals (Sweden)
R. P. Lepping
2010-08-01
Full Text Available This study is motivated by the unusually low number of magnetic clouds (MCs that are strictly identified within interplanetary coronal mass ejections (ICMEs, as observed at 1 AU; this is usually estimated to be around 30% or lower. But a looser definition of MCs may significantly increase this percentage. Another motivation is the unexpected shape of the occurrence distribution of the observers' "closest approach distances" (measured from a MC's axis, and called CA which drops off somewhat rapidly as |CA| (in % of MC radius approaches 100%, based on earlier studies. We suggest, for various geometrical and physical reasons, that the |CA|-distribution should be somewhere between a uniform one and the one actually observed, and therefore the 30% estimate should be higher. So we ask, When there is a failure to identify a MC within an ICME, is it occasionally due to a large |CA| passage, making MC identification more difficult, i.e., is it due to an event selection effect? In attempting to answer this question we examine WIND data to obtain an accurate distribution of the number of MCs vs. |CA| distance, whether the event is ICME-related or not, where initially a large number of cases (N=98 are considered. This gives a frequence distribution that is far from uniform, confirming earlier studies. This along with the fact that there are many ICME identification-parameters that do not depend on |CA| suggest that, indeed an MC event selection effect may explain at least part of the low ratio of (No. MCs/(No. ICMEs. We also show that there is an acceptable geometrical and physical consistency in the relationships for both average "normalized" magnetic field intensity change and field direction change vs. |CA| within a MC, suggesting that our estimates of |CA|, B_{O} (magnetic field intensity on the axis, and choice of a proper "cloud coordinate" system (all needed in the analysis are acceptably accurate. Therefore, the MC
Directory of Open Access Journals (Sweden)
Xiao-meng SONG
2013-01-01
Full Text Available Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1 a screening method (Morris for qualitative ranking of parameters, and (2 a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol. First, the Morris screening method was used to qualitatively identify the parameters’ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.
A Method for Identifying the Mechanical Parameters in Resistance Spot Welding Machines
DEFF Research Database (Denmark)
Wu, Pei; Zhang, Wenqi; Bay, Niels
2003-01-01
Mechanical dynamic responses of resistance welding machine have a significant influence on weld quality and electrode service life, it must be considered when the real welding production is carried out or the welding process is stimulated. The mathematical models for characterizing the mechanical...... dynamic responses are normally a few coupled differential equations which can be easily created according to the theories of kinematics and dynamics, however the problem is that the parameters contained in the equations are unavailable and hard to be determined directly due to the complexities...
H. Mijderwijk (Herjan); R.J. Stolker (Robert); H.J. Duivenvoorden (Hugo); M. Klimek (Markus); E.W. Steyerberg (Ewout)
2016-01-01
markdownabstract__Background:__ Ambulatory surgery patients are at risk of adverse psychological outcomes such as anxiety, aggression, fatigue, and depression. We developed and validated a clinical prediction model to identify patients who were vulnerable to these psychological outcome parameters.
Parameter Optimisation for the Behaviour of Elastic Models over Time
DEFF Research Database (Denmark)
Mosegaard, Jesper
2004-01-01
Optimisation of parameters for elastic models is essential for comparison or finding equivalent behaviour of elastic models when parameters cannot simply be transferred or converted. This is the case with a large range of commonly used elastic models. In this paper we present a general method...... that will optimise parameters based on the behaviour of the elastic models over time....
Model Identification of Linear Parameter Varying Aircraft Systems
Fujimore, Atsushi; Ljung, Lennart
2007-01-01
This article presents a parameter estimation of continuous-time polytopic models for a linear parameter varying (LPV) system. The prediction error method of linear time invariant (LTI) models is modified for polytopic models. The modified prediction error method is applied to an LPV aircraft system whose varying parameter is the flight velocity and model parameters are the stability and control derivatives (SCDs). In an identification simulation, the polytopic model is more suitable for expre...
Global identifiability of linear compartmental models--a computer algebra algorithm.
Audoly, S; D'Angiò, L; Saccomani, M P; Cobelli, C
1998-01-01
A priori global identifiability deals with the uniqueness of the solution for the unknown parameters of a model and is, thus, a prerequisite for parameter estimation of biological dynamic models. Global identifiability is however difficult to test, since it requires solving a system of algebraic nonlinear equations which increases both in nonlinearity degree and number of terms and unknowns with increasing model order. In this paper, a computer algebra tool, GLOBI (GLOBal Identifiability) is presented, which combines the topological transfer function method with the Buchberger algorithm, to test global identifiability of linear compartmental models. GLOBI allows for the automatic testing of a priori global identifiability of general structure compartmental models from general multi input-multi output experiments. Examples of usage of GLOBI to analyze a priori global identifiability of some complex biological compartmental models are provided.
Institute of Scientific and Technical Information of China (English)
Guangbing Luo; Jing Zeng; Qunsheng Wang
2014-01-01
As a critical component of the railway vehicle, underframe equipment has a great influence on the ride comfort of railway vehicles due to their big mass and active vibration. Therefore, study on the relationship between suspension parameters of underframe equipment and the modal frequency of carbody is extremely crucial for controlling the ride quality of railway vehicles. In this paper, a finite element model of the carbody was developed to investigate the effects of the suspension location, the mass of the suspension equipment, and the suspension frequency on the mode of the carbody. Then, the matching relationship between the suspension parameters and the modal frequency of the carbody was studied through the transfer function. In addition, roller rig tests were performed to verify the numerical simulation model of the carbody. The results show that the suspension parameters of the underframe equipment have a great influence on the mode of the carbody, especially for the frequency of the first bending mode. To improve the frequency of carbody high-frequency bending and reduce energy transfer, equipment with a large mass should be suspended toward the middle of the carbody. The weight of the equipment strongly affects the first bending frequency and energy transfer of the carbody. The frequency of heavy suspended equipment should be sufficiently low to increase the transmissibility of high frequencies and improve the vibration characteristics of the carbody. Although the bending frequency of the carbody can be improved effectively by increasing the suspension stiffness of the suspension equipment, in order to reduce carbody vibration effectively, the suspension frequency of the equipment should be slightly lower than the carbody bending frequency.
Soil-Related Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
A. J. Smith
2004-09-09
This report presents one of the analyses that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the details of the conceptual model as well as the mathematical model and the required input parameters. The biosphere model is one of a series of process models supporting the postclosure Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A schematic representation of the documentation flow for the Biosphere input to TSPA is presented in Figure 1-1. This figure shows the evolutionary relationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (TWP) (BSC 2004 [DIRS 169573]). This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this report. This report, ''Soil-Related Input Parameters for the Biosphere Model'', is one of the five analysis reports that develop input parameters for use in the ERMYN model. This report is the source documentation for the six biosphere parameters identified in Table 1-1. The purpose of this analysis was to develop the biosphere model parameters associated with the accumulation and depletion of radionuclides in the soil. These parameters support the calculation of radionuclide concentrations in soil from on-going irrigation or ash deposition and, as a direct consequence, radionuclide concentration in other environmental media that are affected by radionuclide concentrations in soil. The analysis was performed in accordance with the TWP (BSC 2004 [DIRS 169573]) where the governing procedure
[Calculation of parameters in forest evapotranspiration model].
Wang, Anzhi; Pei, Tiefan
2003-12-01
Forest evapotranspiration is an important component not only in water balance, but also in energy balance. It is a great demand for the development of forest hydrology and forest meteorology to simulate the forest evapotranspiration accurately, which is also a theoretical basis for the management and utilization of water resources and forest ecosystem. Taking the broadleaved Korean pine forest on Changbai Mountain as an example, this paper constructed a mechanism model for estimating forest evapotranspiration, based on the aerodynamic principle and energy balance equation. Using the data measured by the Routine Meteorological Measurement System and Open-Path Eddy Covariance Measurement System mounted on the tower in the broadleaved Korean pine forest, the parameters displacement height d, stability functions for momentum phi m, and stability functions for heat phi h were ascertained. The displacement height of the study site was equal to 17.8 m, near to the mean canopy height, and the functions of phi m and phi h changing with gradient Richarson number R i were constructed.
Rose, Kevin C.; Winslow, Luke A.; Read, Jordan S.; Read, Emily K.; Solomon, Christopher T.; Adrian, Rita; Hanson, Paul C.
2014-01-01
Diel changes in dissolved oxygen are often used to estimate gross primary production (GPP) and ecosystem respiration (ER) in aquatic ecosystems. Despite the widespread use of this approach to understand ecosystem metabolism, we are only beginning to understand the degree and underlying causes of uncertainty for metabolism model parameter estimates. Here, we present a novel approach to improve the precision and accuracy of ecosystem metabolism estimates by identifying physical metrics that indicate when metabolism estimates are highly uncertain. Using datasets from seventeen instrumented GLEON (Global Lake Ecological Observatory Network) lakes, we discovered that many physical characteristics correlated with uncertainty, including PAR (photosynthetically active radiation, 400-700 nm), daily variance in Schmidt stability, and wind speed. Low PAR was a consistent predictor of high variance in GPP model parameters, but also corresponded with low ER model parameter variance. We identified a threshold (30% of clear sky PAR) below which GPP parameter variance increased rapidly and was significantly greater in nearly all lakes compared with variance on days with PAR levels above this threshold. The relationship between daily variance in Schmidt stability and GPP model parameter variance depended on trophic status, whereas daily variance in Schmidt stability was consistently positively related to ER model parameter variance. Wind speeds in the range of ~0.8-3 m s–1 were consistent predictors of high variance for both GPP and ER model parameters, with greater uncertainty in eutrophic lakes. Our findings can be used to reduce ecosystem metabolism model parameter uncertainty and identify potential sources of that uncertainty.
Identifying key parameters to differentiate groundwater flow systems using multifactorial analysis
Menció, Anna; Folch, Albert; Mas-Pla, Josep
2012-11-01
SummaryMultivariate techniques are useful in hydrogeological studies to reduce the complexity of large-scale data sets, and provide more understandable insight into the system hydrology. In this study, principal component analysis (PCA) has been used as an exploratory method to identify the key parameters that define distinct flow systems in the Selva basin (NE Spain). In this statistical analysis, all the information obtained in hydrogeological studies (that is, hydrochemical and isotopic data, but also potentiometric data) is used. Additionally, cluster analysis, based on PCA results, allows the associations between samples to be identified, and thus, corroborates the occurrence of different groundwater fluxes. PCA and cluster analysis reveal that two main groundwater flow systems exist in the Selva basin, each with distinct hydrochemical, isotopic, and potentiometric features. Regional groundwater fluxes are associated with high F- contents, and confined aquifer layers; while local fluxes are linked to nitrate polluted unconfined aquifers with a different recharge rates. In agreement with previous hydrogeological studies, these statistical methods stand as valid screening tools to highlight the fingerprint variables that can be used as indicators to facilitate further, more arduous, analytical approaches and a feasible interpretation of the whole data set.
Enhancing debris flow modeling parameters integrating Bayesian networks
Graf, C.; Stoffel, M.; Grêt-Regamey, A.
2009-04-01
Applied debris-flow modeling requires suitably constraint input parameter sets. Depending on the used model, there is a series of parameters to define before running the model. Normally, the data base describing the event, the initiation conditions, the flow behavior, the deposition process and mainly the potential range of possible debris flow events in a certain torrent is limited. There are only some scarce places in the world, where we fortunately can find valuable data sets describing event history of debris flow channels delivering information on spatial and temporal distribution of former flow paths and deposition zones. Tree-ring records in combination with detailed geomorphic mapping for instance provide such data sets over a long time span. Considering the significant loss potential associated with debris-flow disasters, it is crucial that decisions made in regard to hazard mitigation are based on a consistent assessment of the risks. This in turn necessitates a proper assessment of the uncertainties involved in the modeling of the debris-flow frequencies and intensities, the possible run out extent, as well as the estimations of the damage potential. In this study, we link a Bayesian network to a Geographic Information System in order to assess debris-flow risk. We identify the major sources of uncertainty and show the potential of Bayesian inference techniques to improve the debris-flow model. We model the flow paths and deposition zones of a highly active debris-flow channel in the Swiss Alps using the numerical 2-D model RAMMS. Because uncertainties in run-out areas cause large changes in risk estimations, we use the data of flow path and deposition zone information of reconstructed debris-flow events derived from dendrogeomorphological analysis covering more than 400 years to update the input parameters of the RAMMS model. The probabilistic model, which consistently incorporates this available information, can serve as a basis for spatial risk
Lee, C.-H.; Herget, C. J.
1976-01-01
This short paper considers the parameter-identification problem of general discrete-time, nonlinear, multiple input-multiple output dynamic systems with Gaussian white distributed measurement errors. Knowledge of the system parameterization is assumed to be available. Regions of constrained maximum likelihood (CML) parameter identifiability are established. A computation procedure employing interval arithmetic is proposed for finding explicit regions of parameter identifiability for the case of linear systems.
Transfer function modeling of damping mechanisms in distributed parameter models
Slater, J. C.; Inman, D. J.
1994-01-01
This work formulates a method for the modeling of material damping characteristics in distributed parameter models which may be easily applied to models such as rod, plate, and beam equations. The general linear boundary value vibration equation is modified to incorporate hysteresis effects represented by complex stiffness using the transfer function approach proposed by Golla and Hughes. The governing characteristic equations are decoupled through separation of variables yielding solutions similar to those of undamped classical theory, allowing solution of the steady state as well as transient response. Example problems and solutions are provided demonstrating the similarity of the solutions to those of the classical theories and transient responses of nonviscous systems.
On the modeling of internal parameters in hyperelastic biological materials
Giantesio, Giulia
2016-01-01
This paper concerns the behavior of hyperelastic energies depending on an internal parameter. First, the situation in which the internal parameter is a function of the gradient of the deformation is presented. Second, two models where the parameter describes the activation of skeletal muscle tissue are analyzed. In those models, the activation parameter depends on the strain and it is important to consider the derivative of the parameter with respect to the strain in order to capture the proper behavior of the stress.
Simple Model for Identifying Critical Regions in Atrial Fibrillation
Peters, Nicholas S.
2015-01-01
Atrial fibrillation (AF) is the most common abnormal heart rhythm and the single biggest cause of stroke. Ablation, destroying regions of the atria, is applied largely empirically and can be curative but with a disappointing clinical success rate. We design a simple model of activation wave front propagation on an anisotropic structure mimicking the branching network of heart muscle cells. This integration of phenomenological dynamics and pertinent structure shows how AF emerges spontaneously when the transverse cell-to-cell coupling decreases, as occurs with age, beyond a threshold value. We identify critical regions responsible for the initiation and maintenance of AF, the ablation of which terminates AF. The simplicity of the model allows us to calculate analytically the risk of arrhythmia and express the threshold value of transversal cell-to-cell coupling as a function of the model parameters. This threshold value decreases with increasing refractory period by reducing the number of critical regions which can initiate and sustain microreentrant circuits. These biologically testable predictions might inform ablation therapies and arrhythmic risk assessment. PMID:25635565
Passegger, Vera Maria; Reiners, Ansgar
2016-01-01
M-dwarf stars are the most numerous stars in the Universe; they span a wide range in mass and are in the focus of ongoing and planned exoplanet surveys. To investigate and understand their physical nature, detailed spectral information and accurate stellar models are needed. We use a new synthetic atmosphere model generation and compare model spectra to observations. To test the model accuracy, we compared the models to four benchmark stars with atmospheric parameters for which independent information from interferometric radius measurements is available. We used $\\chi^2$ -based methods to determine parameters from high-resolution spectroscopic observations. Our synthetic spectra are based on the new PHOENIX grid that uses the ACES description for the equation of state. This is a model generation expected to be especially suitable for the low-temperature atmospheres. We identified suitable spectral tracers of atmospheric parameters and determined the uncertainties in $T_{\\rm eff}$, $\\log{g}$, and [Fe/H] resul...
Model comparisons and genetic and environmental parameter ...
African Journals Online (AJOL)
arc
South African Journal of Animal Science 2005, 35 (1) ... Genetic and environmental parameters were estimated for pre- and post-weaning average daily gain ..... and BWT (and medium maternal genetic correlations) indicates that these traits ...
Silva, M M; Lemos, J M; Coito, A; Costa, B A; Wigren, T; Mendonça, T
2014-01-01
This paper addresses the local identifiability and sensitivity properties of two classes of Wiener models for the neuromuscular blockade and depth of hypnosis, when drug dose profiles like the ones commonly administered in the clinical practice are used as model inputs. The local parameter identifiability was assessed based on the singular value decomposition of the normalized sensitivity matrix. For the given input signal excitation, the results show an over-parameterization of the standard pharmacokinetic/pharmacodynamic models. The same identifiability assessment was performed on recently proposed minimally parameterized parsimonious models for both the neuromuscular blockade and the depth of hypnosis. The results show that the majority of the model parameters are identifiable from the available input-output data. This indicates that any identification strategy based on the minimally parameterized parsimonious Wiener models for the neuromuscular blockade and for the depth of hypnosis is likely to be more successful than if standard models are used.
Structural identifiability of a model for the acetic acid fermentation process.
Jiménez-Hornero, Jorge E; Santos-Dueñas, Inés M; Garci A-Garci A, Isidoro
2008-12-01
Modelling has proved an essential tool for addressing research into biotechnological processes, particularly with a view to their optimization and control. Parameter estimation via optimization approaches is among the major steps in the development of biotechnology models. In fact, one of the first tasks in the development process is to determine whether the parameters concerned can be unambiguously determined and provide meaningful physical conclusions as a result. The analysis process is known as 'identifiability' and presents two different aspects: structural or theoretical identifiability and practical identifiability. While structural identifiability is concerned with model structure alone, practical identifiability takes into account both the quantity and quality of experimental data. In this work, we discuss the theoretical identifiability of a new model for the acetic acid fermentation process and review existing methods for this purpose.
NEW DOCTORAL DEGREE Parameter estimation problem in the Weibull model
Marković, Darija
2009-01-01
In this dissertation we consider the problem of the existence of best parameters in the Weibull model, one of the most widely used statistical models in reliability theory and life data theory. Particular attention is given to a 3-parameter Weibull model. We have listed some of the many applications of this model. We have described some of the classical methods for estimating parameters of the Weibull model, two graphical methods (Weibull probability plot and hazard plot), and two analyt...
Parameter optimization model in electrical discharge machining process
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Electrical discharge machining (EDM) process, at present is still an experience process, wherein selected parameters are often far from the optimum, and at the same time selecting optimization parameters is costly and time consuming. In this paper,artificial neural network (ANN) and genetic algorithm (GA) are used together to establish the parameter optimization model. An ANN model which adapts Levenberg-Marquardt algorithm has been set up to represent the relationship between material removal rate (MRR) and input parameters, and GA is used to optimize parameters, so that optimization results are obtained. The model is shown to be effective, and MRR is improved using optimized machining parameters.
Institute of Scientific and Technical Information of China (English)
J.Z.Cui; X.G.Yu
2006-01-01
In this paper,a two-scale method (TSM) is presented for identifying the mechanics parameters such as stiffness and strength of composite materials with small periodic configuration.Firstly,a formulation is briefly given for two-scale analysis (TSA) of the composite materials.And then a two-scale computation formulation of strains and stresses is developed by displacement solution with orthotropic material coefficients for three kinds of such composites structures,I.e.,the tension column with a square cross section,the bending cantilever with a rectangular cross section and the torsion column with a circle cross section.The strength formulas for the three kinds of structures are derived and the TSM procedure is discussed.Finally the numerical results of stiffness and strength are presented and compared with experimental data.It shows that the TSM method in this paper is feasible and valid for predicting both the stiffness and the strength of the composite materials with periodic configuration.
Sensitivity of a Shallow-Water Model to Parameters
Kazantsev, Eugene
2011-01-01
An adjoint based technique is applied to a shallow water model in order to estimate the influence of the model's parameters on the solution. Among parameters the bottom topography, initial conditions, boundary conditions on rigid boundaries, viscosity coefficients Coriolis parameter and the amplitude of the wind stress tension are considered. Their influence is analyzed from three points of view: 1. flexibility of the model with respect to a parameter that is related to the lowest value of the cost function that can be obtained in the data assimilation experiment that controls this parameter; 2. possibility to improve the model by the parameter's control, i.e. whether the solution with the optimal parameter remains close to observations after the end of control; 3. sensitivity of the model solution to the parameter in a classical sense. That implies the analysis of the sensitivity estimates and their comparison with each other and with the local Lyapunov exponents that characterize the sensitivity of the mode...
Estimation of shape model parameters for 3D surfaces
DEFF Research Database (Denmark)
Erbou, Søren Gylling Hemmingsen; Darkner, Sune; Fripp, Jurgen;
2008-01-01
Statistical shape models are widely used as a compact way of representing shape variation. Fitting a shape model to unseen data enables characterizing the data in terms of the model parameters. In this paper a Gauss-Newton optimization scheme is proposed to estimate shape model parameters of 3D s...
Directory of Open Access Journals (Sweden)
Y. Sun
2013-04-01
Full Text Available This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4. Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Two inversion strategies, the deterministic least-square fitting and stochastic Markov-Chain Monte-Carlo (MCMC Bayesian inversion approaches, are evaluated by applying them to CLM4 at selected sites. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the least-square fitting provides little improvements in the model simulations but the sampling-based stochastic inversion approaches are consistent – as more information comes in, the predictive intervals of the calibrated parameters become narrower and the misfits between the calculated and observed responses decrease. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty
Compositional modelling of distributed-parameter systems
Maschke, Bernhard; Schaft, van der Arjan; Lamnabhi-Lagarrigue, F.; Loría, A.; Panteley, E.
2005-01-01
The Hamiltonian formulation of distributed-parameter systems has been a challenging reserach area for quite some time. (A nice introduction, especially with respect to systems stemming from fluid dynamics, can be found in [26], where also a historical account is provided.) The identification of the
Bayesian approach to decompression sickness model parameter estimation.
Howle, L E; Weber, P W; Nichols, J M
2017-03-01
We examine both maximum likelihood and Bayesian approaches for estimating probabilistic decompression sickness model parameters. Maximum likelihood estimation treats parameters as fixed values and determines the best estimate through repeated trials, whereas the Bayesian approach treats parameters as random variables and determines the parameter probability distributions. We would ultimately like to know the probability that a parameter lies in a certain range rather than simply make statements about the repeatability of our estimator. Although both represent powerful methods of inference, for models with complex or multi-peaked likelihoods, maximum likelihood parameter estimates can prove more difficult to interpret than the estimates of the parameter distributions provided by the Bayesian approach. For models of decompression sickness, we show that while these two estimation methods are complementary, the credible intervals generated by the Bayesian approach are more naturally suited to quantifying uncertainty in the model parameters.
Wentworth, Mami Tonoe
Uncertainty quantification plays an important role when making predictive estimates of model responses. In this context, uncertainty quantification is defined as quantifying and reducing uncertainties, and the objective is to quantify uncertainties in parameter, model and measurements, and propagate the uncertainties through the model, so that one can make a predictive estimate with quantified uncertainties. Two of the aspects of uncertainty quantification that must be performed prior to propagating uncertainties are model calibration and parameter selection. There are several efficient techniques for these processes; however, the accuracy of these methods are often not verified. This is the motivation for our work, and in this dissertation, we present and illustrate verification frameworks for model calibration and parameter selection in the context of biological and physical models. First, HIV models, developed and improved by [2, 3, 8], describe the viral infection dynamics of an HIV disease. These are also used to make predictive estimates of viral loads and T-cell counts and to construct an optimal control for drug therapy. Estimating input parameters is an essential step prior to uncertainty quantification. However, not all the parameters are identifiable, implying that they cannot be uniquely determined by the observations. These unidentifiable parameters can be partially removed by performing parameter selection, a process in which parameters that have minimal impacts on the model response are determined. We provide verification techniques for Bayesian model calibration and parameter selection for an HIV model. As an example of a physical model, we employ a heat model with experimental measurements presented in [10]. A steady-state heat model represents a prototypical behavior for heat conduction and diffusion process involved in a thermal-hydraulic model, which is a part of nuclear reactor models. We employ this simple heat model to illustrate verification
DEFF Research Database (Denmark)
Nørgaard, Trine; Møldrup, Per; Olsen, Preben
2013-01-01
characteristics including soil texture, bulk density, dissolved tracer, particle and phosphorus transport parameters identified the northern one-third of the field as a zone with higher leaching risk. This risk assessment based on parameter mapping from measurements on intact samples was in good agreement...
Identifying anaerobic digestion models using simultaneous batch experiments
Energy Technology Data Exchange (ETDEWEB)
Flotats, X.; Palatsi, J.; Fernandez, B.; Colomer, M. A.; Illa, J.
2009-07-01
As in other wastewater treatment processes, anaerobic digestion models have become a valuable tool to increase the understanding of complex biodegradation processes, to teach and to communicate using a common language, to optimize design plants and operating strategies and for trying operators and process engineers. Models require accurate and significant parameter values for being useful. (Author) 2 refs.
Parameters-related uncertainty in modeling sugar cane yield with an agro-Land Surface Model
Valade, A.; Ciais, P.; Vuichard, N.; Viovy, N.; Ruget, F.; Gabrielle, B.
2012-12-01
Agro-Land Surface Models (agro-LSM) have been developed from the coupling of specific crop models and large-scale generic vegetation models. They aim at accounting for the spatial distribution and variability of energy, water and carbon fluxes within soil-vegetation-atmosphere continuum with a particular emphasis on how crop phenology and agricultural management practice influence the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty in these models is related to the many parameters included in the models' equations. In this study, we quantify the parameter-based uncertainty in the simulation of sugar cane biomass production with the agro-LSM ORCHIDEE-STICS on a multi-regional approach with data from sites in Australia, La Reunion and Brazil. First, the main source of uncertainty for the output variables NPP, GPP, and sensible heat flux (SH) is determined through a screening of the main parameters of the model on a multi-site basis leading to the selection of a subset of most sensitive parameters causing most of the uncertainty. In a second step, a sensitivity analysis is carried out on the parameters selected from the screening analysis at a regional scale. For this, a Monte-Carlo sampling method associated with the calculation of Partial Ranked Correlation Coefficients is used. First, we quantify the sensitivity of the output variables to individual input parameters on a regional scale for two regions of intensive sugar cane cultivation in Australia and Brazil. Then, we quantify the overall uncertainty in the simulation's outputs propagated from the uncertainty in the input parameters. Seven parameters are identified by the screening procedure as driving most of the uncertainty in the agro-LSM ORCHIDEE-STICS model output at all sites. These parameters control photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), root
Parameter and Uncertainty Estimation in Groundwater Modelling
DEFF Research Database (Denmark)
Jensen, Jacob Birk
The data basis on which groundwater models are constructed is in general very incomplete, and this leads to uncertainty in model outcome. Groundwater models form the basis for many, often costly decisions and if these are to be made on solid grounds, the uncertainty attached to model results must...... be quantified. This study was motivated by the need to estimate the uncertainty involved in groundwater models.Chapter 2 presents an integrated surface/subsurface unstructured finite difference model that was developed and applied to a synthetic case study.The following two chapters concern calibration...... and uncertainty estimation. Essential issues relating to calibration are discussed. The classical regression methods are described; however, the main focus is on the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The next two chapters describe case studies in which the GLUE methodology...
Energy Technology Data Exchange (ETDEWEB)
Bowong, Samuel, E-mail: sbowong@gmail.co [Laboratory of Applied Mathematics, Department of Mathematics and Computer Science, Faculty of Science, University of Douala, P.O. Box 24157 Douala (Cameroon); Postdam Institute for Climate Impact Research (PIK), Telegraphenberg A 31, 14412 Potsdam (Germany); Kurths, Jurgen [Postdam Institute for Climate Impact Research (PIK), Telegraphenberg A 31, 14412 Potsdam (Germany); Department of Physics, Humboldt Universitat zu Berlin, 12489 Berlin (Germany)
2010-10-04
We propose a method based on synchronization to identify the parameters and to estimate the underlying variables for an epidemic model from real data. We suggest an adaptive synchronization method based on observer approach with an effective guidance parameter to update rule design only from real data. In order, to validate the identifiability and estimation results, numerical simulations of a tuberculosis (TB) model using real data of the region of Center in Cameroon are performed to estimate the parameters and variables. This study shows that some tools of synchronization of nonlinear systems can help to deal with the parameter and state estimation problem in the field of epidemiology. We exploit the close link between mathematical modelling, structural identifiability analysis, synchronization, and parameter estimation to obtain biological insights into the system modelled.
Bowong, Samuel; Kurths, Jurgen
2010-10-01
We propose a method based on synchronization to identify the parameters and to estimate the underlying variables for an epidemic model from real data. We suggest an adaptive synchronization method based on observer approach with an effective guidance parameter to update rule design only from real data. In order, to validate the identifiability and estimation results, numerical simulations of a tuberculosis (TB) model using real data of the region of Center in Cameroon are performed to estimate the parameters and variables. This study shows that some tools of synchronization of nonlinear systems can help to deal with the parameter and state estimation problem in the field of epidemiology. We exploit the close link between mathematical modelling, structural identifiability analysis, synchronization, and parameter estimation to obtain biological insights into the system modelled.
Parameter redundancy in discrete state‐space and integrated models
McCrea, Rachel S.
2016-01-01
Discrete state‐space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state‐space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state‐space models using discrete analogues of methods for continuous state‐space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant. PMID:27362826
Parameter redundancy in discrete state-space and integrated models.
Cole, Diana J; McCrea, Rachel S
2016-09-01
Discrete state-space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state-space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state-space models using discrete analogues of methods for continuous state-space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant. © 2016 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Spatial variability of the parameters of a semi-distributed hydrological model
de Lavenne, Alban; Thirel, Guillaume; Andréassian, Vazken; Perrin, Charles; Ramos, Maria-Helena
2016-05-01
Ideally, semi-distributed hydrologic models should provide better streamflow simulations than lumped models, along with spatially-relevant water resources management solutions. However, the spatial distribution of model parameters raises issues related to the calibration strategy and to the identifiability of the parameters. To analyse these issues, we propose to base the evaluation of a semi-distributed model not only on its performance at streamflow gauging stations, but also on the spatial and temporal pattern of the optimised value of its parameters. We implemented calibration over 21 rolling periods and 64 catchments, and we analysed how well each parameter is identified in time and space. Performance and parameter identifiability are analysed comparatively to the calibration of the lumped version of the same model. We show that the semi-distributed model faces more difficulties to identify stable optimal parameter sets. The main difficulty lies in the identification of the parameters responsible for the closure of the water balance (i.e. for the particular model investigated, the intercatchment groundwater flow parameter).
An automatic and effective parameter optimization method for model tuning
Directory of Open Access Journals (Sweden)
T. Zhang
2015-11-01
simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.
Structural Breaks, Parameter Stability and Energy Demand Modeling in Nigeria
Directory of Open Access Journals (Sweden)
Olusegun A. Omisakin
2012-08-01
Full Text Available This paper extends previous studies in modeling and estimating energy demand functions for both gasoline and kerosene petroleum products for Nigeria from 1977 to 2008. In contrast to earlier studies on Nigeria and other developing countries, this study specifically tests for the possibility of structural breaks/regime shifts and parameter instability in the energy demand functions using more recent and robust techniques. In addition, the study considers an alternative model specification which primarily captures the price-income interaction effects on both gasoline and kerosene demand functions. While the conventional residual-based cointegration tests employed fail to identify any meaningful long run relationship in both functions, the Gregory-Hansen structural break cointegration approach confirms the cointegration relationships despite the breakpoints. Both functions are also found to be stable under the period studied.The elasticity estimates also follow the a priori expectation being inelastic both in the long- and short run for the two functions.
Ternary interaction parameters in calphad solution models
Energy Technology Data Exchange (ETDEWEB)
Eleno, Luiz T.F., E-mail: luizeleno@usp.br [Universidade de Sao Paulo (USP), SP (Brazil). Instituto de Fisica; Schön, Claudio G., E-mail: schoen@usp.br [Universidade de Sao Paulo (USP), SP (Brazil). Computational Materials Science Laboratory. Department of Metallurgical and Materials Engineering
2014-07-01
For random, diluted, multicomponent solutions, the excess chemical potentials can be expanded in power series of the composition, with coefficients that are pressure- and temperature-dependent. For a binary system, this approach is equivalent to using polynomial truncated expansions, such as the Redlich-Kister series for describing integral thermodynamic quantities. For ternary systems, an equivalent expansion of the excess chemical potentials clearly justifies the inclusion of ternary interaction parameters, which arise naturally in the form of correction terms in higher-order power expansions. To demonstrate this, we carry out truncated polynomial expansions of the excess chemical potential up to the sixth power of the composition variables. (author)
On 4-degree-of-freedom biodynamic models of seated occupants: Lumped-parameter modeling
Bai, Xian-Xu; Xu, Shi-Xu; Cheng, Wei; Qian, Li-Jun
2017-08-01
It is useful to develop an effective biodynamic model of seated human occupants to help understand the human vibration exposure to transportation vehicle vibrations and to help design and improve the anti-vibration devices and/or test dummies. This study proposed and demonstrated a methodology for systematically identifying the best configuration or structure of a 4-degree-of-freedom (4DOF) human vibration model and for its parameter identification. First, an equivalent simplification expression for the models was made. Second, all of the possible 23 structural configurations of the models were identified. Third, each of them was calibrated using the frequency response functions recommended in a biodynamic standard. An improved version of non-dominated sorting genetic algorithm (NSGA-II) based on Pareto optimization principle was used to determine the model parameters. Finally, a model evaluation criterion proposed in this study was used to assess the models and to identify the best one, which was based on both the goodness of curve fits and comprehensive goodness of the fits. The identified top configurations were better than those reported in the literature. This methodology may also be extended and used to develop the models with other DOFs.
Parameter sensitivity in satellite-gravity-constrained geothermal modelling
Pastorutti, Alberto; Braitenberg, Carla
2017-04-01
The use of satellite gravity data in thermal structure estimates require identifying the factors that affect the gravity field and are related to the thermal characteristics of the lithosphere. We propose a set of forward-modelled synthetics, investigating the model response in terms of heat flow, temperature, and gravity effect at satellite altitude. The sensitivity analysis concerns the parameters involved, as heat production, thermal conductivity, density and their temperature dependence. We discuss the effect of the horizontal smoothing due to heat conduction, the superposition of the bulk thermal effect of near-surface processes (e.g. advection in ground-water and permeable faults, paleoclimatic effects, blanketing by sediments), and the out-of equilibrium conditions due to tectonic transients. All of them have the potential to distort the gravity-derived estimates.We find that the temperature-conductivity relationship has a small effect with respect to other parameter uncertainties on the modelled temperature depth variation, surface heat flow, thermal lithosphere thickness. We conclude that the global gravity is useful for geothermal studies.
Optimization of Experimental Model Parameter Identification for Energy Storage Systems
Directory of Open Access Journals (Sweden)
Rosario Morello
2013-09-01
Full Text Available The smart grid approach is envisioned to take advantage of all available modern technologies in transforming the current power system to provide benefits to all stakeholders in the fields of efficient energy utilisation and of wide integration of renewable sources. Energy storage systems could help to solve some issues that stem from renewable energy usage in terms of stabilizing the intermittent energy production, power quality and power peak mitigation. With the integration of energy storage systems into the smart grids, their accurate modeling becomes a necessity, in order to gain robust real-time control on the network, in terms of stability and energy supply forecasting. In this framework, this paper proposes a procedure to identify the values of the battery model parameters in order to best fit experimental data and integrate it, along with models of energy sources and electrical loads, in a complete framework which represents a real time smart grid management system. The proposed method is based on a hybrid optimisation technique, which makes combined use of a stochastic and a deterministic algorithm, with low computational burden and can therefore be repeated over time in order to account for parameter variations due to the battery’s age and usage.
Parameter estimation and error analysis in environmental modeling and computation
Kalmaz, E. E.
1986-01-01
A method for the estimation of parameters and error analysis in the development of nonlinear modeling for environmental impact assessment studies is presented. The modular computer program can interactively fit different nonlinear models to the same set of data, dynamically changing the error structure associated with observed values. Parameter estimation techniques and sequential estimation algorithms employed in parameter identification and model selection are first discussed. Then, least-square parameter estimation procedures are formulated, utilizing differential or integrated equations, and are used to define a model for association of error with experimentally observed data.
Local identifiability for two and three-compartment pharmacokinetic models with time-lags.
Merino, J A; De Biasi, J; Plusquellec, Y; Houin, G
1998-06-01
In this paper, we show that time-lags between compartments in a 2 and 3 compartment pharmacokinetic model may be taken into account but that separate identification for model parameters and for time-lags would not be suitable. Furthermore, it may happen that a time-lag model is locally identifiable while the corresponding model without delay is not. For two-compartment delayed models, with only one observation, it is not necessary to have two different inputs contrary to the case without time-lag. Both the Laplace transformation and a Jacobian matrix are used in an identifiability study. For all two-compartment models we have investigated which kind of parameters or lags are identifiable from amount (Q) or concentration (C) measures.
Statistical Evaluation of the Identified Structural Parameters of an idling Offshore Wind Turbine
Kramers, Hendrik C.; van der Valk, Paul L. C.; van Wingerden, Jan-Willem
2016-09-01
With the increased need for renewable energy, new offshore wind farms are being developed at an unprecedented scale. However, as the costs of offshore wind energy are still too high, design optimization and new innovations are required for lowering its cost. The design of modern day offshore wind turbines relies on numerical models for estimating ultimate and fatigue loads of the turbines. The dynamic behavior and the resulting structural loading of the turbines is determined for a large part by its structural properties, such as the natural frequencies and damping ratios. Hence, it is important to obtain accurate estimates of these modal properties. For this purpose stochastic subspace identification (SSI), in combination with clustering and statistical evaluation methods, is used to obtain the variance of the identified modal properties of an installed 3.6MW offshore wind turbine in idling conditions. It is found that one is able to obtain confidence intervals for the means of eigenfrequencies and damping ratios of the fore-aft and side-side modes of the wind turbine.
Optimal input shaping for Fisher identifiability of control-oriented lithium-ion battery models
Rothenberger, Michael J.
This dissertation examines the fundamental challenge of optimally shaping input trajectories to maximize parameter identifiability of control-oriented lithium-ion battery models. Identifiability is a property from information theory that determines the solvability of parameter estimation for mathematical models using input-output measurements. This dissertation creates a framework that exploits the Fisher information metric to quantify the level of battery parameter identifiability, optimizes this metric through input shaping, and facilitates faster and more accurate estimation. The popularity of lithium-ion batteries is growing significantly in the energy storage domain, especially for stationary and transportation applications. While these cells have excellent power and energy densities, they are plagued with safety and lifespan concerns. These concerns are often resolved in the industry through conservative current and voltage operating limits, which reduce the overall performance and still lack robustness in detecting catastrophic failure modes. New advances in automotive battery management systems mitigate these challenges through the incorporation of model-based control to increase performance, safety, and lifespan. To achieve these goals, model-based control requires accurate parameterization of the battery model. While many groups in the literature study a variety of methods to perform battery parameter estimation, a fundamental issue of poor parameter identifiability remains apparent for lithium-ion battery models. This fundamental challenge of battery identifiability is studied extensively in the literature, and some groups are even approaching the problem of improving the ability to estimate the model parameters. The first approach is to add additional sensors to the battery to gain more information that is used for estimation. The other main approach is to shape the input trajectories to increase the amount of information that can be gained from input
GIS-Based Hydrogeological-Parameter Modeling
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A regression model is proposed to relate the variation of water well depth with topographic properties (area and slope), the variation of hydraulic conductivity and vertical decay factor. The implementation of this model in GIS environment (ARC/TNFO) based on known water data and DEM is used to estimate the variation of hydraulic conductivity and decay factor of different lithoiogy units in watershed context.
Challenges of Identifying Communities with Shared Semantics in Enterprise Modeling
Hoppenbrouwers, Stijn; Linden, D. van der
2012-01-01
In this paper we discuss the use and challenges of identifying communities with shared semantics in Enterprise Modeling. People tend to understand modeling meta-concepts (i.e., a modeling language’s constructs or types) in a certain way and can be grouped by this understanding. Having an insight int
Estimation of Kinetic Parameters in an Automotive SCR Catalyst Model
DEFF Research Database (Denmark)
Åberg, Andreas; Widd, Anders; Abildskov, Jens;
2016-01-01
A challenge during the development of models for simulation of the automotive Selective Catalytic Reduction catalyst is the parameter estimation of the kinetic parameters, which can be time consuming and problematic. The parameter estimation is often carried out on small-scale reactor tests, or p...
Mirror symmetry for two parameter models, 2
Candelas, Philip; Katz, S; Morrison, Douglas Robert Ogston; Philip Candelas; Anamaria Font; Sheldon Katz; David R Morrison
1994-01-01
We describe in detail the space of the two K\\"ahler parameters of the Calabi--Yau manifold \\P_4^{(1,1,1,6,9)}[18] by exploiting mirror symmetry. The large complex structure limit of the mirror, which corresponds to the classical large radius limit, is found by studying the monodromy of the periods about the discriminant locus, the boundary of the moduli space corresponding to singular Calabi--Yau manifolds. A symplectic basis of periods is found and the action of the Sp(6,\\Z) generators of the modular group is determined. From the mirror map we compute the instanton expansion of the Yukawa couplings and the generalized N=2 index, arriving at the numbers of instantons of genus zero and genus one of each degree. We also investigate an SL(2,\\Z) symmetry that acts on a boundary of the moduli space.
Accuracy of Parameter Estimation in Gibbs Sampling under the Two-Parameter Logistic Model.
Kim, Seock-Ho; Cohen, Allan S.
The accuracy of Gibbs sampling, a Markov chain Monte Carlo procedure, was considered for estimation of item and ability parameters under the two-parameter logistic model. Memory test data were analyzed to illustrate the Gibbs sampling procedure. Simulated data sets were analyzed using Gibbs sampling and the marginal Bayesian method. The marginal…
The Effect of Nondeterministic Parameters on Shock-Associated Noise Prediction Modeling
Dahl, Milo D.; Khavaran, Abbas
2010-01-01
Engineering applications for aircraft noise prediction contain models for physical phenomenon that enable solutions to be computed quickly. These models contain parameters that have an uncertainty not accounted for in the solution. To include uncertainty in the solution, nondeterministic computational methods are applied. Using prediction models for supersonic jet broadband shock-associated noise, fixed model parameters are replaced by probability distributions to illustrate one of these methods. The results show the impact of using nondeterministic parameters both on estimating the model output uncertainty and on the model spectral level prediction. In addition, a global sensitivity analysis is used to determine the influence of the model parameters on the output, and to identify the parameters with the least influence on model output.
CHAMP: Changepoint Detection Using Approximate Model Parameters
2014-06-01
positions as a Markov chain in which the transition probabilities are defined by the time since the last changepoint: p(τi+1 = t|τi = s) = g(t− s), (1...experimentally verified using artifi- cially generated data and are compared to those of Fearnhead and Liu [5]. 2 Related work Hidden Markov Models (HMMs) are...length α, and maximum number of particles M . Output: Viterbi path of changepoint times and models // Initialize data structures 1: max path, prev queue
A New Method for Identifying the Model Error of Adjustment System
Institute of Scientific and Technical Information of China (English)
TAO Benzao; ZHANG Chaoyu
2005-01-01
Some theory problems affecting parameter estimation are discussed in this paper. Influence and transformation between errors of stochastic and functional models is pointed out as well. For choosing the best adjustment model, a formula, which is different from the literatures existing methods, for estimating and identifying the model error, is proposed. On the basis of the proposed formula, an effective approach of selecting the best model of adjustment system is given.
WINKLER'S SINGLE-PARAMETER SUBGRADE MODEL FROM ...
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[3, 9]. However, mainly due to the simplicity of Winkler's model in practical applications and .... this case, the coefficient B takes the dimension of a ... In plane-strain problems, the assumption of ... loaded circular region; s is the radial coordinate.
Kennedy, M.; McKenzie, D.
2013-12-01
It is imperative for resource managers to understand how a changing climate might modify future watershed and hydrological processes, and such an understanding is incomplete if disturbances such as fire are not integrated with hydrological projections. Can a robust fire spread model be developed that approximates patterns of fire spread in response to varying topography wind patterns, and fuel loads and moistures, without requiring intensive calibration to each new study area or time frame? We assessed the performance of a stochastic model of fire spread (WMFire), integrated with the Regional Hydro-Ecological Simulation System (RHESSys), for projecting the effects of climatic change on mountain watersheds. We first use Monte Carlo inference to determine that the fire spread model is able to replicate the spatial pattern of fire spread for a contemporary wildfire in Washington State (the Tripod fire), measured by the lacunarity and fractal dimension of the fire. We then integrate a version of WMFire able to replicate the contemporary wildfire with RHESSys and simulate a New Mexico watershed over the calibration period of RHESSys (1941-1997). In comparing the fire spread model to a single contemporary wildfire we found issues in parameter identifiability for several of the nine parameters, due to model input uncertainty and insensitivity of the mathematical function to certain ranges of the parameter values. Model input uncertainty is caused by the inherent difficulty in reconstructing fuel loads and fuel moistures for a fire event after the fire has occurred, as well as by issues in translating variables relevant to hydrological processes produced by the hydrological model to those known to affect fire spread and fire severity. The first stage in the model evaluation aided the improvement of the model in both of these regards. In transporting the model to a new landscape in order to evaluate fire regimes in addition to patterns of fire spread, we find reasonable
Identifiability of Model Properties in Over-Parameterized Model Classes
DEFF Research Database (Denmark)
Jaeger, Manfred
2013-01-01
in the data. In this paper we make some initial steps to extend and adapt basic concepts of computational learnability and statistical identifiability to provide a foundation for investigating learnability in such broader contexts. We exemplify the use of the framework in three different applications...
A New Statistical Parameter for Identifying the Main Transition Velocities in Bubble Columns.
Nedeltchev, Stoyan; Rabha, Swapna; Hampel, Uwe; Schubert, Markus
2015-11-01
The identification of the main flow regime boundaries in bubble columns is essential since the degrees of mixing and mass and heat transfer vary with the flow regime. In this work, a new statistical parameter was extracted from the time series of the cross-sectional averaged gas holdup. The measurements were performed in bubble columns by means of conductivity wire-mesh sensors at very high sampling frequency. The columns were operated with an air/deionized water system under ambient conditions. As a flow regime indicator, a new dimensionless statistical parameter called "relative maximum number of visits in a region" was introduced. This new parameter is a function of the difference between the maximum numbers of visits in a region, calculated from two different division schemes of the signal range.
Improved Methodology for Parameter Inference in Nonlinear, Hydrologic Regression Models
Bates, Bryson C.
1992-01-01
A new method is developed for the construction of reliable marginal confidence intervals and joint confidence regions for the parameters of nonlinear, hydrologic regression models. A parameter power transformation is combined with measures of the asymptotic bias and asymptotic skewness of maximum likelihood estimators to determine the transformation constants which cause the bias or skewness to vanish. These optimized constants are used to construct confidence intervals and regions for the transformed model parameters using linear regression theory. The resulting confidence intervals and regions can be easily mapped into the original parameter space to give close approximations to likelihood method confidence intervals and regions for the model parameters. Unlike many other approaches to parameter transformation, the procedure does not use a grid search to find the optimal transformation constants. An example involving the fitting of the Michaelis-Menten model to velocity-discharge data from an Australian gauging station is used to illustrate the usefulness of the methodology.
Identifiability of causal effect for a simple causal model
Institute of Scientific and Technical Information of China (English)
郑忠国; 张艳艳; 童行伟
2002-01-01
Counterfactual model is put forward to discuss the causal inference in the directed acyclic graph and its corresponding identifiability is thus studied with the ancillary information based on conditional independence. It is shown that the assumption of ignorability can be expanded to the assumption of replaceability,under which the causal efiects are identifiable.
A simulation of water pollution model parameter estimation
Kibler, J. F.
1976-01-01
A parameter estimation procedure for a water pollution transport model is elaborated. A two-dimensional instantaneous-release shear-diffusion model serves as representative of a simple transport process. Pollution concentration levels are arrived at via modeling of a remote-sensing system. The remote-sensed data are simulated by adding Gaussian noise to the concentration level values generated via the transport model. Model parameters are estimated from the simulated data using a least-squares batch processor. Resolution, sensor array size, and number and location of sensor readings can be found from the accuracies of the parameter estimates.
Behmanesh, Iman; Moaveni, Babak
2016-07-01
This paper presents a Hierarchical Bayesian model updating framework to account for the effects of ambient temperature and excitation amplitude. The proposed approach is applied for model calibration, response prediction and damage identification of a footbridge under changing environmental/ambient conditions. The concrete Young's modulus of the footbridge deck is the considered updating structural parameter with its mean and variance modeled as functions of temperature and excitation amplitude. The identified modal parameters over 27 months of continuous monitoring of the footbridge are used to calibrate the updating parameters. One of the objectives of this study is to show that by increasing the levels of information in the updating process, the posterior variation of the updating structural parameter (concrete Young's modulus) is reduced. To this end, the calibration is performed at three information levels using (1) the identified modal parameters, (2) modal parameters and ambient temperatures, and (3) modal parameters, ambient temperatures, and excitation amplitudes. The calibrated model is then validated by comparing the model-predicted natural frequencies and those identified from measured data after deliberate change to the structural mass. It is shown that accounting for modeling error uncertainties is crucial for reliable response prediction, and accounting only the estimated variability of the updating structural parameter is not sufficient for accurate response predictions. Finally, the calibrated model is used for damage identification of the footbridge.
On retrial queueing model with fuzzy parameters
Ke, Jau-Chuan; Huang, Hsin-I.; Lin, Chuen-Horng
2007-01-01
This work constructs the membership functions of the system characteristics of a retrial queueing model with fuzzy customer arrival, retrial and service rates. The α-cut approach is used to transform a fuzzy retrial-queue into a family of conventional crisp retrial queues in this context. By means of the membership functions of the system characteristics, a set of parametric non-linear programs is developed to describe the family of crisp retrial queues. A numerical example is solved successfully to illustrate the validity of the proposed approach. Because the system characteristics are expressed and governed by the membership functions, more information is provided for use by management. By extending this model to the fuzzy environment, fuzzy retrial-queue is represented more accurately and analytic results are more useful for system designers and practitioners.
Solar parameters for modeling interplanetary background
Bzowski, M; Tokumaru, M; Fujiki, K; Quemerais, E; Lallement, R; Ferron, S; Bochsler, P; McComas, D J
2011-01-01
The goal of the Fully Online Datacenter of Ultraviolet Emissions (FONDUE) Working Team of the International Space Science Institute in Bern, Switzerland, was to establish a common calibration of various UV and EUV heliospheric observations, both spectroscopic and photometric. Realization of this goal required an up-to-date model of spatial distribution of neutral interstellar hydrogen in the heliosphere, and to that end, a credible model of the radiation pressure and ionization processes was needed. This chapter describes the solar factors shaping the distribution of neutral interstellar H in the heliosphere. Presented are the solar Lyman-alpha flux and the solar Lyman-alpha resonant radiation pressure force acting on neutral H atoms in the heliosphere, solar EUV radiation and the photoionization of heliospheric hydrogen, and their evolution in time and the still hypothetical variation with heliolatitude. Further, solar wind and its evolution with solar activity is presented in the context of the charge excha...
Linear Sigma Models With Strongly Coupled Phases -- One Parameter Models
Hori, Kentaro
2013-01-01
We systematically construct a class of two-dimensional $(2,2)$ supersymmetric gauged linear sigma models with phases in which a continuous subgroup of the gauge group is totally unbroken. We study some of their properties by employing a recently developed technique. The focus of the present work is on models with one K\\"ahler parameter. The models include those corresponding to Calabi-Yau threefolds, extending three examples found earlier by a few more, as well as Calabi-Yau manifolds of other dimensions and non-Calabi-Yau manifolds. The construction leads to predictions of equivalences of D-brane categories, systematically extending earlier examples. There is another type of surprise. Two distinct superconformal field theories corresponding to Calabi-Yau threefolds with different Hodge numbers, $h^{2,1}=23$ versus $h^{2,1}=59$, have exactly the same quantum K\\"ahler moduli space. The strong-weak duality plays a crucial r\\^ole in confirming this, and also is useful in the actual computation of the metric on t...
Parameter identification in tidal models with uncertain boundaries
Bagchi, Arunabha; ten Brummelhuis, P.G.J.; ten Brummelhuis, Paul
1994-01-01
In this paper we consider a simultaneous state and parameter estimation procedure for tidal models with random inputs, which is formulated as a minimization problem. It is assumed that some model parameters are unknown and that the random noise inputs only act upon the open boundaries. The
Exploring the interdependencies between parameters in a material model.
Energy Technology Data Exchange (ETDEWEB)
Silling, Stewart Andrew; Fermen-Coker, Muge
2014-01-01
A method is investigated to reduce the number of numerical parameters in a material model for a solid. The basis of the method is to detect interdependencies between parameters within a class of materials of interest. The method is demonstrated for a set of material property data for iron and steel using the Johnson-Cook plasticity model.
An Alternative Three-Parameter Logistic Item Response Model.
Pashley, Peter J.
Birnbaum's three-parameter logistic function has become a common basis for item response theory modeling, especially within situations where significant guessing behavior is evident. This model is formed through a linear transformation of the two-parameter logistic function in order to facilitate a lower asymptote. This paper discusses an…
Parameter identification in tidal models with uncertain boundaries
Bagchi, Arunabha; Brummelhuis, ten Paul
1994-01-01
In this paper we consider a simultaneous state and parameter estimation procedure for tidal models with random inputs, which is formulated as a minimization problem. It is assumed that some model parameters are unknown and that the random noise inputs only act upon the open boundaries. The hyperboli
A compact cyclic plasticity model with parameter evolution
DEFF Research Database (Denmark)
Krenk, Steen; Tidemann, L.
2017-01-01
, and it is demonstrated that this simple formulation enables very accurate representation of experimental results. An extension of the theory to account for model parameter evolution effects, e.g. in the form of changing yield level, is included in the form of extended evolution equations for the model parameters...
Regionalization of SWAT Model Parameters for Use in Ungauged Watersheds
Directory of Open Access Journals (Sweden)
Indrajeet Chaubey
2010-11-01
Full Text Available There has been a steady shift towards modeling and model-based approaches as primary methods of assessing watershed response to hydrologic inputs and land management, and of quantifying watershed-wide best management practice (BMP effectiveness. Watershed models often require some degree of calibration and validation to achieve adequate watershed and therefore BMP representation. This is, however, only possible for gauged watersheds. There are many watersheds for which there are very little or no monitoring data available, thus the question as to whether it would be possible to extend and/or generalize model parameters obtained through calibration of gauged watersheds to ungauged watersheds within the same region. This study explored the possibility of developing regionalized model parameter sets for use in ungauged watersheds. The study evaluated two regionalization methods: global averaging, and regression-based parameters, on the SWAT model using data from priority watersheds in Arkansas. Resulting parameters were tested and model performance determined on three gauged watersheds. Nash-Sutcliffe efficiencies (NS for stream flow obtained using regression-based parameters (0.53–0.83 compared well with corresponding values obtained through model calibration (0.45–0.90. Model performance obtained using global averaged parameter values was also generally acceptable (0.4 ≤ NS ≤ 0.75. Results from this study indicate that regionalized parameter sets for the SWAT model can be obtained and used for making satisfactory hydrologic response predictions in ungauged watersheds.
NWP model forecast skill optimization via closure parameter variations
Järvinen, H.; Ollinaho, P.; Laine, M.; Solonen, A.; Haario, H.
2012-04-01
We present results of a novel approach to tune predictive skill of numerical weather prediction (NWP) models. These models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. The current practice is to specify manually the numerical parameter values, based on expert knowledge. We developed recently a concept and method (QJRMS 2011) for on-line estimation of the NWP model parameters via closure parameter variations. The method called EPPES ("Ensemble prediction and parameter estimation system") utilizes ensemble prediction infra-structure for parameter estimation in a very cost-effective way: practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating an ensemble of predictions so that each member uses different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In this presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an ensemble prediction system emulator, based on the ECHAM5 atmospheric GCM show that the model tuning capability of EPPES scales up to realistic models and ensemble prediction systems. Finally, preliminary results of EPPES in the context of ECMWF forecasting system are presented.
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
Structural identifiability of systems biology models: a critical comparison of methods.
Directory of Open Access Journals (Sweden)
Oana-Teodora Chis
Full Text Available Analysing the properties of a biological system through in silico experimentation requires a satisfactory mathematical representation of the system including accurate values of the model parameters. Fortunately, modern experimental techniques allow obtaining time-series data of appropriate quality which may then be used to estimate unknown parameters. However, in many cases, a subset of those parameters may not be uniquely estimated, independently of the experimental data available or the numerical techniques used for estimation. This lack of identifiability is related to the structure of the model, i.e. the system dynamics plus the observation function. Despite the interest in knowing a priori whether there is any chance of uniquely estimating all model unknown parameters, the structural identifiability analysis for general non-linear dynamic models is still an open question. There is no method amenable to every model, thus at some point we have to face the selection of one of the possibilities. This work presents a critical comparison of the currently available techniques. To this end, we perform the structural identifiability analysis of a collection of biological models. The results reveal that the generating series approach, in combination with identifiability tableaus, offers the most advantageous compromise among range of applicability, computational complexity and information provided.
Empirically modelled Pc3 activity based on solar wind parameters
Directory of Open Access Journals (Sweden)
T. Raita
2010-09-01
Full Text Available It is known that under certain solar wind (SW/interplanetary magnetic field (IMF conditions (e.g. high SW speed, low cone angle the occurrence of ground-level Pc3–4 pulsations is more likely. In this paper we demonstrate that in the event of anomalously low SW particle density, Pc3 activity is extremely low regardless of otherwise favourable SW speed and cone angle. We re-investigate the SW control of Pc3 pulsation activity through a statistical analysis and two empirical models with emphasis on the influence of SW density on Pc3 activity. We utilise SW and IMF measurements from the OMNI project and ground-based magnetometer measurements from the MM100 array to relate SW and IMF measurements to the occurrence of Pc3 activity. Multiple linear regression and artificial neural network models are used in iterative processes in order to identify sets of SW-based input parameters, which optimally reproduce a set of Pc3 activity data. The inclusion of SW density in the parameter set significantly improves the models. Not only the density itself, but other density related parameters, such as the dynamic pressure of the SW, or the standoff distance of the magnetopause work equally well in the model. The disappearance of Pc3s during low-density events can have at least four reasons according to the existing upstream wave theory: 1. Pausing the ion-cyclotron resonance that generates the upstream ultra low frequency waves in the absence of protons, 2. Weakening of the bow shock that implies less efficient reflection, 3. The SW becomes sub-Alfvénic and hence it is not able to sweep back the waves propagating upstream with the Alfvén-speed, and 4. The increase of the standoff distance of the magnetopause (and of the bow shock. Although the models cannot account for the lack of Pc3s during intervals when the SW density is extremely low, the resulting sets of optimal model inputs support the generation of mid latitude Pc3 activity predominantly through
4-DOF biodynamic lumped-parameter models for a seated occupant
Cheng, Wei; Xu, Shi-Xu; Qian, Li-Jun; Bai, Xian-Xu
2016-04-01
In order to study how vibrations from ground vehicles/aircraft will impact on the seated occupants, it is of significance to develop an effective biodynamic model for the seated occupants. In this paper, a wide variety of 4-degree-of-freedom (4- DOF) lumped-parameter models for a seated occupant is investigated. A linear 4-DOF model with 18 parameters is deduced and employed as an example. The parameters of the 4-DOF model are identified based on the Pareto optimization principle. The goodness of fit (ɛ) is established and employed to evaluate the effectiveness of the models. Then, all possible linear 4-DOF models are analyzed and discussed with the same parameters identification and effectiveness evaluation. The most-effective two models are obtained and compared with two other existing models. The research results show that: (i) The total types of linear 4-DOF models is limited and all the parameters of models are identifiable; (ii) The number of parameters of the 4-DOF models affects little on the goodness of fit (ɛ); and (iii) The presented models are more effective than the two existing models.
Some tests for parameter constancy in cointegrated VAR-models
DEFF Research Database (Denmark)
Hansen, Henrik; Johansen, Søren
1999-01-01
Some methods for the evaluation of parameter constancy in vector autoregressive (VAR) models are discussed. Two different ways of re-estimating the VAR model are proposed; one in which all parameters are estimated recursively based upon the likelihood function for the first observations, and anot...... be applied to test the constancy of the long-run parameters in the cointegrated VAR-model. All results are illustrated using a model for the term structure of interest rates on US Treasury securities. ...
Galvanin, Federico; Ballan, Carlo C; Barolo, Massimiliano; Bezzo, Fabrizio
2013-08-01
The use of pharmacokinetic (PK) and pharmacodynamic (PD) models is a common and widespread practice in the preliminary stages of drug development. However, PK-PD models may be affected by structural identifiability issues intrinsically related to their mathematical formulation. A preliminary structural identifiability analysis is usually carried out to check if the set of model parameters can be uniquely determined from experimental observations under the ideal assumptions of noise-free data and no model uncertainty. However, even for structurally identifiable models, real-life experimental conditions and model uncertainty may strongly affect the practical possibility to estimate the model parameters in a statistically sound way. A systematic procedure coupling the numerical assessment of structural identifiability with advanced model-based design of experiments formulations is presented in this paper. The objective is to propose a general approach to design experiments in an optimal way, detecting a proper set of experimental settings that ensure the practical identifiability of PK-PD models. Two simulated case studies based on in vitro bacterial growth and killing models are presented to demonstrate the applicability and generality of the methodology to tackle model identifiability issues effectively, through the design of feasible and highly informative experiments.
Directory of Open Access Journals (Sweden)
Issa Ahmed Abed
2016-12-01
Full Text Available This paper present a method to enhance the firefly algorithm by coupling with a local search. The constructed technique is applied to identify the solar parameters model where the method has been proved its ability to obtain the photovoltaic parameters model. Standard firefly algorithm (FA, electromagnetism-like (EM algorithm, and electromagnetism-like without local (EMW search algorithm all are compared with the suggested method to test its capability to solve this model.
Weigand, M.; Kemna, A.
2016-06-01
Spectral induced polarization (SIP) data are commonly analysed using phenomenological models. Among these models the Cole-Cole (CC) model is the most popular choice to describe the strength and frequency dependence of distinct polarization peaks in the data. More flexibility regarding the shape of the spectrum is provided by decomposition schemes. Here the spectral response is decomposed into individual responses of a chosen elementary relaxation model, mathematically acting as kernel in the involved integral, based on a broad range of relaxation times. A frequently used kernel function is the Debye model, but also the CC model with some other a priorly specified frequency dispersion (e.g. Warburg model) has been proposed as kernel in the decomposition. The different decomposition approaches in use, also including conductivity and resistivity formulations, pose the question to which degree the integral spectral parameters typically derived from the obtained relaxation time distribution are biased by the approach itself. Based on synthetic SIP data sampled from an ideal CC response, we here investigate how the two most important integral output parameters deviate from the corresponding CC input parameters. We find that the total chargeability may be underestimated by up to 80 per cent and the mean relaxation time may be off by up to three orders of magnitude relative to the original values, depending on the frequency dispersion of the analysed spectrum and the proximity of its peak to the frequency range limits considered in the decomposition. We conclude that a quantitative comparison of SIP parameters across different studies, or the adoption of parameter relationships from other studies, for example when transferring laboratory results to the field, is only possible on the basis of a consistent spectral analysis procedure. This is particularly important when comparing effective CC parameters with spectral parameters derived from decomposition results.
Azam, M.; Rahman, Z.; Talib, F.; Singh, K.J.
2012-01-01
PURPOSE: The purpose of this article is to identify and critically analyze healthcare establishment (HCE) quality parameters described in the literature. It aims to propose an integrated quality model that includes technical quality and associated supportive quality parameters to achieve optimum
Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model
Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami
2017-06-01
A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.
Universally sloppy parameter sensitivities in systems biology models.
Directory of Open Access Journals (Sweden)
Ryan N Gutenkunst
2007-10-01
Full Text Available Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.
Tube-Load Model Parameter Estimation for Monitoring Arterial Hemodynamics
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Guanqun eZhang
2011-11-01
Full Text Available A useful model of the arterial system is the uniform, lossless tube with parametric load. This tube-load model is able to account for wave propagation and reflection (unlike lumped-parameter models such as the Windkessel while being defined by only a few parameters (unlike comprehensive distributed-parameter models. As a result, the parameters may be readily estimated by accurate fitting of the model to available arterial pressure and flow waveforms so as to permit improved monitoring of arterial hemodynamics. In this paper, we review tube-load model parameter estimation techniques that have appeared in the literature for monitoring wave reflection, large artery compliance, pulse transit time, and central aortic pressure. We begin by motivating the use of the tube-load model for parameter estimation. We then describe the tube-load model, its assumptions and validity, and approaches for estimating its parameters. We next summarize the various techniques and their experimental results while highlighting their advantages over conventional techniques. We conclude the review by suggesting future research directions and describing potential applications.
Tube-Load Model Parameter Estimation for Monitoring Arterial Hemodynamics
Zhang, Guanqun; Hahn, Jin-Oh; Mukkamala, Ramakrishna
2011-01-01
A useful model of the arterial system is the uniform, lossless tube with parametric load. This tube-load model is able to account for wave propagation and reflection (unlike lumped-parameter models such as the Windkessel) while being defined by only a few parameters (unlike comprehensive distributed-parameter models). As a result, the parameters may be readily estimated by accurate fitting of the model to available arterial pressure and flow waveforms so as to permit improved monitoring of arterial hemodynamics. In this paper, we review tube-load model parameter estimation techniques that have appeared in the literature for monitoring wave reflection, large artery compliance, pulse transit time, and central aortic pressure. We begin by motivating the use of the tube-load model for parameter estimation. We then describe the tube-load model, its assumptions and validity, and approaches for estimating its parameters. We next summarize the various techniques and their experimental results while highlighting their advantages over conventional techniques. We conclude the review by suggesting future research directions and describing potential applications. PMID:22053157
Identifying interacting pairs of sites in infinite range Ising models
Galves, Antonio; Takahashi, Daniel Yasumasa
2010-01-01
We consider Ising models (pairwise interaction Gibbs probability measures) in $\\Z^d$ with an infinite range potential. We address the problem of identifying pairs of interacting sites from a finite sample of independent realisations of the Ising model. The sample contains only the values assigned by the Ising model to a finite set of sites in $\\Z^d$. Our main result is an upperbound for the probability with our estimator to misidentify the pairs of interacting sites in this finite set.
Half-trek criterion for generic identifiability of linear structural equation models
Foygel, Rina; Drton, Mathias
2011-01-01
A linear structural equation model relates random variables of interest and corresponding Gaussian noise terms via a linear equation system. Each such model can be represented by a mixed graph in which directed edges encode the linear equations, and bidirected edges indicate possible correlations among noise terms. We study parameter identifiability in these models, that is, we ask for conditions that ensure that the edge coefficients and correlations appearing in a linear structural equation model can be uniquely recovered from the covariance matrix of the associated normal distribution. We treat the case of generic identifiability, where unique recovery is possible for almost every choice of parameters. We give a new graphical criterion that is sufficient for generic identifiability. It improves criteria from prior work and does not require the directed part of the graph to be acyclic. We also develop a related necessary condition and examine the "gap" between sufficient and necessary conditions through sim...
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.
Parameter estimation of hidden periodic model in random fields
Institute of Scientific and Technical Information of China (English)
何书元
1999-01-01
Two-dimensional hidden periodic model is an important model in random fields. The model is used in the field of two-dimensional signal processing, prediction and spectral analysis. A method of estimating the parameters for the model is designed. The strong consistency of the estimators is proved.
Flight dynamics modeling of a small ducted fan aerial vehicle based on parameter identification
Institute of Scientific and Technical Information of China (English)
Wang Zhengjie; Liu Zhijun; Fan Ningjun; Guo Meifang
2013-01-01
This paper presents a simple and useful modeling method to acquire a dynamics model of an aerial vehicle containing unknown parameters using mechanism modeling, and then to design different identification experiments to identify the parameters based on the sources and features of its unknown parameters. Based on the mathematical model of the aerial vehicle acquired by modeling and identification, a design for the structural parameters of the attitude control system is carried out, and the results of the attitude control flaps are verified by simulation experiments and flight tests of the aerial vehicle. Results of the mathematical simulation and flight tests show that the mathematical model acquired using parameter identification is comparatively accurate and of clear mechanics, and can be used as the reference and basis for the structural design.
Estimating parameters for generalized mass action models with connectivity information
Directory of Open Access Journals (Sweden)
Voit Eberhard O
2009-05-01
Full Text Available Abstract Background Determining the parameters of a mathematical model from quantitative measurements is the main bottleneck of modelling biological systems. Parameter values can be estimated from steady-state data or from dynamic data. The nature of suitable data for these two types of estimation is rather different. For instance, estimations of parameter values in pathway models, such as kinetic orders, rate constants, flux control coefficients or elasticities, from steady-state data are generally based on experiments that measure how a biochemical system responds to small perturbations around the steady state. In contrast, parameter estimation from dynamic data requires time series measurements for all dependent variables. Almost no literature has so far discussed the combined use of both steady-state and transient data for estimating parameter values of biochemical systems. Results In this study we introduce a constrained optimization method for estimating parameter values of biochemical pathway models using steady-state information and transient measurements. The constraints are derived from the flux connectivity relationships of the system at the steady state. Two case studies demonstrate the estimation results with and without flux connectivity constraints. The unconstrained optimal estimates from dynamic data may fit the experiments well, but they do not necessarily maintain the connectivity relationships. As a consequence, individual fluxes may be misrepresented, which may cause problems in later extrapolations. By contrast, the constrained estimation accounting for flux connectivity information reduces this misrepresentation and thereby yields improved model parameters. Conclusion The method combines transient metabolic profiles and steady-state information and leads to the formulation of an inverse parameter estimation task as a constrained optimization problem. Parameter estimation and model selection are simultaneously carried out
Similarity transformation approach to identifiability analysis of nonlinear compartmental models.
Vajda, S; Godfrey, K R; Rabitz, H
1989-04-01
Through use of the local state isomorphism theorem instead of the algebraic equivalence theorem of linear systems theory, the similarity transformation approach is extended to nonlinear models, resulting in finitely verifiable sufficient and necessary conditions for global and local identifiability. The approach requires testing of certain controllability and observability conditions, but in many practical examples these conditions prove very easy to verify. In principle the method also involves nonlinear state variable transformations, but in all of the examples presented in the paper the transformations turn out to be linear. The method is applied to an unidentifiable nonlinear model and a locally identifiable nonlinear model, and these are the first nonlinear models other than bilinear models where the reason for lack of global identifiability is nontrivial. The method is also applied to two models with Michaelis-Menten elimination kinetics, both of considerable importance in pharmacokinetics, and for both of which the complicated nature of the algebraic equations arising from the Taylor series approach has hitherto defeated attempts to establish identifiability results for specific input functions.
Estimation of the input parameters in the Feller neuronal model
Ditlevsen, Susanne; Lansky, Petr
2006-06-01
The stochastic Feller neuronal model is studied, and estimators of the model input parameters, depending on the firing regime of the process, are derived. Closed expressions for the first two moments of functionals of the first-passage time (FTP) through a constant boundary in the suprathreshold regime are derived, which are used to calculate moment estimators. In the subthreshold regime, the exponentiality of the FTP is utilized to characterize the input parameters. The methods are illustrated on simulated data. Finally, approximations of the first-passage-time moments are suggested, and biological interpretations and comparisons of the parameters in the Feller and the Ornstein-Uhlenbeck models are discussed.
An automatic and effective parameter optimization method for model tuning
Directory of Open Access Journals (Sweden)
T. Zhang
2015-05-01
Full Text Available Physical parameterizations in General Circulation Models (GCMs, having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determines parameter sensitivity and the other chooses the optimum initial value of sensitive parameters, are introduced before the downhill simplex method to reduce the computational cost and improve the tuning performance. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9%. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameters tuning during the model development stage.
Optimal parameters for the FFA-Beddoes dynamic stall model
Energy Technology Data Exchange (ETDEWEB)
Bjoerck, A.; Mert, M. [FFA, The Aeronautical Research Institute of Sweden, Bromma (Sweden); Madsen, H.A. [Risoe National Lab., Roskilde (Denmark)
1999-03-01
Unsteady aerodynamic effects, like dynamic stall, must be considered in calculation of dynamic forces for wind turbines. Models incorporated in aero-elastic programs are of semi-empirical nature. Resulting aerodynamic forces therefore depend on values used for the semi-empiricial parameters. In this paper a study of finding appropriate parameters to use with the Beddoes-Leishman model is discussed. Minimisation of the `tracking error` between results from 2D wind tunnel tests and simulation with the model is used to find optimum values for the parameters. The resulting optimum parameters show a large variation from case to case. Using these different sets of optimum parameters in the calculation of blade vibrations, give rise to quite different predictions of aerodynamic damping which is discussed. (au)
On the Identifiability of the Post-Nonlinear Causal Model
Zhang, Kun
2012-01-01
By taking into account the nonlinear effect of the cause, the inner noise effect, and the measurement distortion effect in the observed variables, the post-nonlinear (PNL) causal model has demonstrated its excellent performance in distinguishing the cause from effect. However, its identifiability has not been properly addressed, and how to apply it in the case of more than two variables is also a problem. In this paper, we conduct a systematic investigation on its identifiability in the two-variable case. We show that this model is identifiable in most cases; by enumerating all possible situations in which the model is not identifiable, we provide sufficient conditions for its identifiability. Simulations are given to support the theoretical results. Moreover, in the case of more than two variables, we show that the whole causal structure can be found by applying the PNL causal model to each structure in the Markov equivalent class and testing if the disturbance is independent of the direct causes for each va...
Do Lumped-Parameter Models Provide the Correct Geometrical Damping?
DEFF Research Database (Denmark)
Andersen, Lars
This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines and other models applied to fast evaluation of struct......This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines and other models applied to fast evaluation...... response during excitation and the geometrical damping related to free vibrations of a hexagonal footing. The optimal order of a lumped-parameter model is determined for each degree of freedom, i.e. horizontal and vertical translation as well as torsion and rocking. In particular, the necessity of coupling...... between horizontal sliding and rocking is discussed....
A New Approach for Parameter Optimization in Land Surface Model
Institute of Scientific and Technical Information of China (English)
LI Hongqi; GUO Weidong; SUN Guodong; ZHANG Yaocun; FU Congbin
2011-01-01
In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation (CNOP) in a land surface model (LSM) using long-term enhanced field observations at Tongyn station in Jilin Province,China,combined with a sophisticated LSM (common land model,CoLM).Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project (CEOP) that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments:First,a single-parameter optimization was performed,while the second and third experiments performed triple- and six-parameter optinizations,respectively.Notable improvements in simulating sensible heat flux (SH),latent heat flux (LH),soil temperature (TS),and moisture (MS) at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.
Identifiability analysis of the CSTR river water quality model.
Chen, J; Deng, Y
2006-01-01
Conceptual river water quality models are widely known to lack identifiability. The causes for that can be due to model structure errors, observational errors and less frequent samplings. Although significant efforts have been directed towards better identification of river water quality models, it is not clear whether a given model is structurally identifiable. Information is also limited regarding the contribution of different unidentifiability sources. Taking the widely applied CSTR river water quality model as an example, this paper presents a theoretical proof that the CSTR model is indeed structurally identifiable. Its uncertainty is thus dominantly from observational errors and less frequent samplings. Given the current monitoring accuracy and sampling frequency, the unidentifiability from sampling frequency is found to be more significant than that from observational errors. It is also noted that there is a crucial sampling frequency between 0.1 and 1 day, over which the simulated river system could be represented by different illusions and the model application could be far less reliable.
Parameter Estimation for a Computable General Equilibrium Model
DEFF Research Database (Denmark)
Arndt, Channing; Robinson, Sherman; Tarp, Finn
. Second, it permits incorporation of prior information on parameter values. Third, it can be applied in the absence of copious data. Finally, it supplies measures of the capacity of the model to reproduce the historical record and the statistical significance of parameter estimates. The method is applied...
Estimating winter wheat phenological parameters: Implications for crop modeling
Crop parameters, such as the timing of developmental events, are critical for accurate simulation results in crop simulation models, yet uncertainty often exists in determining the parameters. Factors contributing to the uncertainty include: a) sources of variation within a plant (i.e., within diffe...
Retrospective forecast of ETAS model with daily parameters estimate
Falcone, Giuseppe; Murru, Maura; Console, Rodolfo; Marzocchi, Warner; Zhuang, Jiancang
2016-04-01
We present a retrospective ETAS (Epidemic Type of Aftershock Sequence) model based on the daily updating of free parameters during the background, the learning and the test phase of a seismic sequence. The idea was born after the 2011 Tohoku-Oki earthquake. The CSEP (Collaboratory for the Study of Earthquake Predictability) Center in Japan provided an appropriate testing benchmark for the five 1-day submitted models. Of all the models, only one was able to successfully predict the number of events that really happened. This result was verified using both the real time and the revised catalogs. The main cause of the failure was in the underestimation of the forecasted events, due to model parameters maintained fixed during the test. Moreover, the absence in the learning catalog of an event similar to the magnitude of the mainshock (M9.0), which drastically changed the seismicity in the area, made the learning parameters not suitable to describe the real seismicity. As an example of this methodological development we show the evolution of the model parameters during the last two strong seismic sequences in Italy: the 2009 L'Aquila and the 2012 Reggio Emilia episodes. The achievement of the model with daily updated parameters is compared with that of same model where the parameters remain fixed during the test time.
Al-Hinnawi, Abdel-Razzak M; Al-Naami, Bassam O; Al-Latayfeh, Motasem M
2017-08-01
To investigate monitoring slope-based features of the optic nerve head (ONH) cup as open-angle glaucoma (OAG) occurs. A dataset of 46 retrospective OCT cases was acquired from the SPECTRALIS Heidelberg Engineering OCT device. A set of five parameters, which are based on the ONH cup-incline, are measured on the OAG and normal subjects in the dataset. Then, three new ONH cup-shape indices were deduced. The ONH cup-incline parameters and ONH cup-shape indices are analyzed to estimate their clinical value. The statistical difference between measurements on normal and glaucoma eyes was remarkably significant for all of the analyzed parameters and indices (p value < 0.001). The geometric shape of the ONH cup can be transferred to numerical parameters and indices. The proposed ONH cup-incline parameters and ONH cup-shape indices have shown suggestive clinical value to identify the development of OAG. As OAG appears, the top ONH cup-incline parameters decrease while the bottom ONH cup-incline parameters increase. The ONH cup-shape indices suggest capability to discriminate OAG from normal eyes.
Data Mining and Pattern Recognition Models for Identifying Inherited Diseases
DEFF Research Database (Denmark)
Iddamalgoda, Lahiru; Das, Partha S; Aponso, Achala;
2016-01-01
Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately...... prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification- and scoring-based prioritization methods...
Parameter Estimates in Differential Equation Models for Population Growth
Winkel, Brian J.
2011-01-01
We estimate the parameters present in several differential equation models of population growth, specifically logistic growth models and two-species competition models. We discuss student-evolved strategies and offer "Mathematica" code for a gradient search approach. We use historical (1930s) data from microbial studies of the Russian biologist,…
Dynamic Modeling and Parameter Identification of Power Systems
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
@@ The generator, the excitation system, the steam turbine and speed governor, and the load are the so called four key models of power systems. Mathematical modeling and parameter identification for the four key models are of great importance as the basis for designing, operating, and analyzing power systems.
Dynamic Load Model using PSO-Based Parameter Estimation
Taoka, Hisao; Matsuki, Junya; Tomoda, Michiya; Hayashi, Yasuhiro; Yamagishi, Yoshio; Kanao, Norikazu
This paper presents a new method for estimating unknown parameters of dynamic load model as a parallel composite of a constant impedance load and an induction motor behind a series constant reactance. An adequate dynamic load model is essential for evaluating power system stability, and this model can represent the behavior of actual load by using appropriate parameters. However, the problem of this model is that a lot of parameters are necessary and it is not easy to estimate a lot of unknown parameters. We propose an estimating method based on Particle Swarm Optimization (PSO) which is a non-linear optimization method by using the data of voltage, active power and reactive power measured at voltage sag.
Parameter Estimation for the Thurstone Case III Model.
Mackay, David B.; Chaiy, Seoil
1982-01-01
The ability of three estimation criteria to recover parameters of the Thurstone Case V and Case III models from comparative judgment data was investigated via Monte Carlo techniques. Significant differences in recovery are shown to exist. (Author/JKS)
Bazin, L.; Lemieux-Dudon, B.; Landais, A.; Guillevic, M.; Kindler, P.; Parrenin, F.; Martinerie, P.
2014-08-01
A~recent coherent chronology has been built for 4 Antarctic ice cores and the NorthGRIP (NGRIP) Greenland ice core (Antarctic Ice Core Chronology 2012, AICC2012) using a bayesian approach for ice core dating (Datice). When building the AICC2012 chronology, and in order to prevent any confusion with official ice cores chronology, it has been imposed that the AICC2012 chronology for NGRIP should respect exactly the GICC05 chronology based on layer counting. However, such a strong tuning did not satisfy the hypothesis of independence of background parameters and observations for the NGRIP core as required by Datice. We present here the implementation in Datice of a new type of markers that is better suited to constraints deduced from layer counting: the markers of age-difference. Using this type of markers for NGRIP in a 5 cores dating exercise with Datice, we have performed several sensitivity tests and show that the new ice core chronologies obtained with these new markers do not differ by more than 400 years from AICC2012 for Antarctic ice cores and by more than 130 years from GICC05 for NGRIP over the last 60 000 years. With this new parameterization, the accumulation rate and lock-in depth associated with NGRIP are more coherent with independent estimates than those obtained in AICC2012. While these new chronologies should not be used yet as new ice core chronologies, the improved methodology presented here should be considered in the next coherent ice core dating exercise.
Institute of Scientific and Technical Information of China (English)
Youlong XIA; Zong-Liang YANG; Paul L. STOFFA; Mrinal K. SEN
2005-01-01
Most previous land-surface model calibration studies have defined global ranges for their parameters to search for optimal parameter sets. Little work has been conducted to study the impacts of realistic versus global ranges as well as model complexities on the calibration and uncertainty estimates. The primary purpose of this paper is to investigate these impacts by employing Bayesian Stochastic Inversion (BSI)to the Chameleon Surface Model (CHASM). The CHASM was designed to explore the general aspects of land-surface energy balance representation within a common modeling framework that can be run from a simple energy balance formulation to a complex mosaic type structure. The BSI is an uncertainty estimation technique based on Bayes theorem, importance sampling, and very fast simulated annealing.The model forcing data and surface flux data were collected at seven sites representing a wide range of climate and vegetation conditions. For each site, four experiments were performed with simple and complex CHASM formulations as well as realistic and global parameter ranges. Twenty eight experiments were conducted and 50 000 parameter sets were used for each run. The results show that the use of global and realistic ranges gives similar simulations for both modes for most sites, but the global ranges tend to produce some unreasonable optimal parameter values. Comparison of simple and complex modes shows that the simple mode has more parameters with unreasonable optimal values. Use of parameter ranges and model complexities have significant impacts on frequency distribution of parameters, marginal posterior probability density functions, and estimates of uncertainty of simulated sensible and latent heat fluxes.Comparison between model complexity and parameter ranges shows that the former has more significant impacts on parameter and uncertainty estimations.
Multi-objective global sensitivity analysis of the WRF model parameters
Quan, Jiping; Di, Zhenhua; Duan, Qingyun; Gong, Wei; Wang, Chen
2015-04-01
Tuning model parameters to match model simulations with observations can be an effective way to enhance the performance of numerical weather prediction (NWP) models such as Weather Research and Forecasting (WRF) model. However, this is a very complicated process as a typical NWP model involves many model parameters and many output variables. One must take a multi-objective approach to ensure all of the major simulated model outputs are satisfactory. This talk presents the results of an investigation of multi-objective parameter sensitivity analysis of the WRF model to different model outputs, including conventional surface meteorological variables such as precipitation, surface temperature, humidity and wind speed, as well as atmospheric variables such as total precipitable water, cloud cover, boundary layer height and outgoing long radiation at the top of the atmosphere. The goal of this study is to identify the most important parameters that affect the predictive skill of short-range meteorological forecasts by the WRF model. The study was performed over the Greater Beijing Region of China. A total of 23 adjustable parameters from seven different physical parameterization schemes were considered. Using a multi-objective global sensitivity analysis method, we examined the WRF model parameter sensitivities to the 5-day simulations of the aforementioned model outputs. The results show that parameter sensitivities vary with different model outputs. But three to four of the parameters are shown to be sensitive to all model outputs considered. The sensitivity results from this research can be the basis for future model parameter optimization of the WRF model.
Robust global identifiability theory using potentials--Application to compartmental models.
Wongvanich, N; Hann, C E; Sirisena, H R
2015-04-01
This paper presents a global practical identifiability theory for analyzing and identifying linear and nonlinear compartmental models. The compartmental system is prolonged onto the potential jet space to formulate a set of input-output equations that are integrals in terms of the measured data, which allows for robust identification of parameters without requiring any simulation of the model differential equations. Two classes of linear and non-linear compartmental models are considered. The theory is first applied to analyze the linear nitrous oxide (N2O) uptake model. The fitting accuracy of the identified models from differential jet space and potential jet space identifiability theories is compared with a realistic noise level of 3% which is derived from sensor noise data in the literature. The potential jet space approach gave a match that was well within the coefficient of variation. The differential jet space formulation was unstable and not suitable for parameter identification. The proposed theory is then applied to a nonlinear immunological model for mastitis in cows. In addition, the model formulation is extended to include an iterative method which allows initial conditions to be accurately identified. With up to 10% noise, the potential jet space theory predicts the normalized population concentration infected with pathogens, to within 9% of the true curve. Copyright © 2015 Elsevier Inc. All rights reserved.
Model parameter uncertainty analysis for an annual field-scale P loss model
Bolster, Carl H.; Vadas, Peter A.; Boykin, Debbie
2016-08-01
Phosphorous (P) fate and transport models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. Because all models are simplifications of complex systems, there will exist an inherent amount of uncertainty associated with their predictions. It is therefore important that efforts be directed at identifying, quantifying, and communicating the different sources of model uncertainties. In this study, we conducted an uncertainty analysis with the Annual P Loss Estimator (APLE) model. Our analysis included calculating parameter uncertainties and confidence and prediction intervals for five internal regression equations in APLE. We also estimated uncertainties of the model input variables based on values reported in the literature. We then predicted P loss for a suite of fields under different management and climatic conditions while accounting for uncertainties in the model parameters and inputs and compared the relative contributions of these two sources of uncertainty to the overall uncertainty associated with predictions of P loss. Both the overall magnitude of the prediction uncertainties and the relative contributions of the two sources of uncertainty varied depending on management practices and field characteristics. This was due to differences in the number of model input variables and the uncertainties in the regression equations associated with each P loss pathway. Inspection of the uncertainties in the five regression equations brought attention to a previously unrecognized limitation with the equation used to partition surface-applied fertilizer P between leaching and runoff losses. As a result, an alternate equation was identified that provided similar predictions with much less uncertainty. Our results demonstrate how a thorough uncertainty and model residual analysis can be used to identify limitations with a model. Such insight can then be used to guide future data collection and model
Directory of Open Access Journals (Sweden)
Rutao Luo
Full Text Available 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.
Comparing spatial and temporal transferability of hydrological model parameters
Patil, Sopan D.; Stieglitz, Marc
2015-06-01
Operational use of hydrological models requires the transfer of calibrated parameters either in time (for streamflow forecasting) or space (for prediction at ungauged catchments) or both. Although the effects of spatial and temporal parameter transfer on catchment streamflow predictions have been well studied individually, a direct comparison of these approaches is much less documented. Here, we compare three different schemes of parameter transfer, viz., temporal, spatial, and spatiotemporal, using a spatially lumped hydrological model called EXP-HYDRO at 294 catchments across the continental United States. Results show that the temporal parameter transfer scheme performs best, with lowest decline in prediction performance (median decline of 4.2%) as measured using the Kling-Gupta efficiency metric. More interestingly, negligible difference in prediction performance is observed between the spatial and spatiotemporal parameter transfer schemes (median decline of 12.4% and 13.9% respectively). We further demonstrate that the superiority of temporal parameter transfer scheme is preserved even when: (1) spatial distance between donor and receiver catchments is reduced, or (2) temporal lag between calibration and validation periods is increased. Nonetheless, increase in the temporal lag between calibration and validation periods reduces the overall performance gap between the three parameter transfer schemes. Results suggest that spatiotemporal transfer of hydrological model parameters has the potential to be a viable option for climate change related hydrological studies, as envisioned in the "trading space for time" framework. However, further research is still needed to explore the relationship between spatial and temporal aspects of catchment hydrological variability.
Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty
Energy Technology Data Exchange (ETDEWEB)
Meyer, Philip D.; Ye, Ming; Neuman, Shlomo P.; Cantrell, Kirk J.
2004-03-01
The objective of the research described in this report is the development and application of a methodology for comprehensively assessing the hydrogeologic uncertainties involved in dose assessment, including uncertainties associated with conceptual models, parameters, and scenarios. This report describes and applies a statistical method to quantitatively estimate the combined uncertainty in model predictions arising from conceptual model and parameter uncertainties. The method relies on model averaging to combine the predictions of a set of alternative models. Implementation is driven by the available data. When there is minimal site-specific data the method can be carried out with prior parameter estimates based on generic data and subjective prior model probabilities. For sites with observations of system behavior (and optionally data characterizing model parameters), the method uses model calibration to update the prior parameter estimates and model probabilities based on the correspondence between model predictions and site observations. The set of model alternatives can contain both simplified and complex models, with the requirement that all models be based on the same set of data. The method was applied to the geostatistical modeling of air permeability at a fractured rock site. Seven alternative variogram models of log air permeability were considered to represent data from single-hole pneumatic injection tests in six boreholes at the site. Unbiased maximum likelihood estimates of variogram and drift parameters were obtained for each model. Standard information criteria provided an ambiguous ranking of the models, which would not justify selecting one of them and discarding all others as is commonly done in practice. Instead, some of the models were eliminated based on their negligibly small updated probabilities and the rest were used to project the measured log permeabilities by kriging onto a rock volume containing the six boreholes. These four
Feng, Xia-Ting; Zhao, Hongbo; Li, Shaojun
2004-09-01
Displacement back analysis is a common method to identify mechanical geo-material parameters using the monitored displacement. How to obtain a global optimum solution in large space search of highly non-linear multimodal is a key point of optimum back analysis. The paper presents a new back analysis that is an integration of evolutionary support vector machines (SVMs), numerical analysis and genetic algorithm. The non-linear relationship between the mechanical geo-material parameters to be identified and the corresponding displacement values of key points is learned and represented by evolutionary SVMs in global optimum. Numerical analysis is used to create training and testing samples for recognition of SVMs. Then, performing a global optimum search on the obtained SVMs using genetic algorithm can identify the mechanical geo-material parameters. The proposed algorithm is tested by back analysis of an elastic plate and an elastic-plastic plate and used to recognize mechanical parameters of subclay, strongly weathered tuff and weakly weathered tuff of Bachimen slope, Funing expressway, Fujian, China. The results indicate that applicability of the proposed algorithm with enough accuracy. Copyright
Parameter Estimation for Groundwater Models under Uncertain Irrigation Data.
Demissie, Yonas; Valocchi, Albert; Cai, Ximing; Brozovic, Nicholas; Senay, Gabriel; Gebremichael, Mekonnen
2015-01-01
The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.
Parameter estimation for groundwater models under uncertain irrigation data
Demissie, Yonas; Valocchi, Albert J.; Cai, Ximing; Brozovic, Nicholas; Senay, Gabriel; Gebremichael, Mekonnen
2015-01-01
The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.
Parameter estimation in stochastic rainfall-runoff models
DEFF Research Database (Denmark)
Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur
2006-01-01
the parameters, including the noise terms. The parameter estimation method is a maximum likelihood method (ML) where the likelihood function is evaluated using a Kalman filter technique. The ML method estimates the parameters in a prediction error settings, i.e. the sum of squared prediction error is minimized....... For a comparison the parameters are also estimated by an output error method, where the sum of squared simulation error is minimized. The former methodology is optimal for short-term prediction whereas the latter is optimal for simulations. Hence, depending on the purpose it is possible to select whether...... the parameter values are optimal for simulation or prediction. The data originates from Iceland and the model is designed for Icelandic conditions, including a snow routine for mountainous areas. The model demands only two input data series, precipitation and temperature and one output data series...
Modeling and parameter estimation for hydraulic system of excavator's arm
Institute of Scientific and Technical Information of China (English)
HE Qing-hua; HAO Peng; ZHANG Da-qing
2008-01-01
A retrofitted electro-bydraulic proportional system for hydraulic excavator was introduced firstly. According to the principle and characteristic of load independent flow distribution(LUDV)system, taking boom hydraulic system as an example and ignoring the leakage of hydraulic cylinder and the mass of oil in it,a force equilibrium equation and a continuous equation of hydraulic cylinder were set up.Based On the flow equation of electro-hydraulic proportional valve, the pressure passing through the valve and the difference of pressure were tested and analyzed.The results show that the difference of pressure does not change with load, and it approximates to 2.0 MPa. And then, assume the flow across the valve is directly proportional to spool displacement andis not influenced by load, a simplified model of electro-hydraulic system was put forward. At the same time, by analyzing the structure and load-bearing of boom instrument, and combining moment equivalent equation of manipulator with rotating law, the estimation methods and equations for such parameters as equivalent mass and bearing force of hydraulic cylinder were set up. Finally, the step response of flow of boom cylinder was tested when the electro-hydraulic proportional valve was controlled by the stepcurrent. Based on the experiment curve, the flow gain coefficient of valve is identified as 2.825×10-4m3/(s·A)and the model is verified.
Application of Artificial Bee Colony in Model Parameter Identification of Solar Cells
Directory of Open Access Journals (Sweden)
Rongjie Wang
2015-07-01
Full Text Available The identification of values of solar cell parameters is of great interest for evaluating solar cell performances. The algorithm of an artificial bee colony was used to extract model parameters of solar cells from current-voltage characteristics. Firstly, the best-so-for mechanism was introduced to the original artificial bee colony. Then, a method was proposed to identify parameters for a single diode model and double diode model using this improved artificial bee colony. Experimental results clearly demonstrate the effectiveness of the proposed method and its superior performance compared to other competing methods.
Transformations among CE–CVM model parameters for multicomponent systems
Indian Academy of Sciences (India)
B Nageswara Sarma; Shrikant Lele
2005-06-01
In the development of thermodynamic databases for multicomponent systems using the cluster expansion–cluster variation methods, we need to have a consistent procedure for expressing the model parameters (CECs) of a higher order system in terms of those of the lower order subsystems and to an independent set of parameters which exclusively represent interactions of the higher order systems. Such a procedure is presented in detail in this communication. Furthermore, the details of transformations required to express the model parameters in one basis from those defined in another basis for the same system are also presented.
SPOTting Model Parameters Using a Ready-Made Python Package.
Houska, Tobias; Kraft, Philipp; Chamorro-Chavez, Alejandro; Breuer, Lutz
2015-01-01
The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.
SPOTting Model Parameters Using a Ready-Made Python Package.
Directory of Open Access Journals (Sweden)
Tobias Houska
Full Text Available The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool, an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI. We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.
Numerical modeling of piezoelectric transducers using physical parameters.
Cappon, Hans; Keesman, Karel J
2012-05-01
Design of ultrasonic equipment is frequently facilitated with numerical models. These numerical models, however, need a calibration step, because usually not all characteristics of the materials used are known. Characterization of material properties combined with numerical simulations and experimental data can be used to acquire valid estimates of the material parameters. In our design application, a finite element (FE) model of an ultrasonic particle separator, driven by an ultrasonic transducer in thickness mode, is required. A limited set of material parameters for the piezoelectric transducer were obtained from the manufacturer, thus preserving prior physical knowledge to a large extent. The remaining unknown parameters were estimated from impedance analysis with a simple experimental setup combined with a numerical optimization routine using 2-D and 3-D FE models. Thus, a full set of physically interpretable material parameters was obtained for our specific purpose. The approach provides adequate accuracy of the estimates of the material parameters, near 1%. These parameter estimates will subsequently be applied in future design simulations, without the need to go through an entire series of characterization experiments. Finally, a sensitivity study showed that small variations of 1% in the main parameters caused changes near 1% in the eigenfrequency, but changes up to 7% in the admittance peak, thus influencing the efficiency of the system. Temperature will already cause these small variations in response; thus, a frequency control unit is required when actually manufacturing an efficient ultrasonic separation system.
An Effective Parameter Screening Strategy for High Dimensional Watershed Models
Khare, Y. P.; Martinez, C. J.; Munoz-Carpena, R.
2014-12-01
Watershed simulation models can assess the impacts of natural and anthropogenic disturbances on natural systems. These models have become important tools for tackling a range of water resources problems through their implementation in the formulation and evaluation of Best Management Practices, Total Maximum Daily Loads, and Basin Management Action Plans. For accurate applications of watershed models they need to be thoroughly evaluated through global uncertainty and sensitivity analyses (UA/SA). However, due to the high dimensionality of these models such evaluation becomes extremely time- and resource-consuming. Parameter screening, the qualitative separation of important parameters, has been suggested as an essential step before applying rigorous evaluation techniques such as the Sobol' and Fourier Amplitude Sensitivity Test (FAST) methods in the UA/SA framework. The method of elementary effects (EE) (Morris, 1991) is one of the most widely used screening methodologies. Some of the common parameter sampling strategies for EE, e.g. Optimized Trajectories [OT] (Campolongo et al., 2007) and Modified Optimized Trajectories [MOT] (Ruano et al., 2012), suffer from inconsistencies in the generated parameter distributions, infeasible sample generation time, etc. In this work, we have formulated a new parameter sampling strategy - Sampling for Uniformity (SU) - for parameter screening which is based on the principles of the uniformity of the generated parameter distributions and the spread of the parameter sample. A rigorous multi-criteria evaluation (time, distribution, spread and screening efficiency) of OT, MOT, and SU indicated that SU is superior to other sampling strategies. Comparison of the EE-based parameter importance rankings with those of Sobol' helped to quantify the qualitativeness of the EE parameter screening approach, reinforcing the fact that one should use EE only to reduce the resource burden required by FAST/Sobol' analyses but not to replace it.
Energy Technology Data Exchange (ETDEWEB)
Hamimid, M., E-mail: Hamimid_mourad@hotmail.com [Laboratoire de modelisation des systemes energetiques LMSE, Universite de Biskra, BP 145, 07000 Biskra (Algeria); Mimoune, S.M., E-mail: s.m.mimoune@mselab.org [Laboratoire de modelisation des systemes energetiques LMSE, Universite de Biskra, BP 145, 07000 Biskra (Algeria); Feliachi, M., E-mail: mouloud.feliachi@univ-nantes.fr [IREENA-IUT, CRTT, 37 Boulevard de l' Universite, BP 406, 44602 Saint Nazaire Cedex (France)
2012-07-01
In this present work, the minor hysteresis loops model based on parameters scaling of the modified Jiles-Atherton model is evaluated by using judicious expressions. These expressions give the minor hysteresis loops parameters as a function of the major hysteresis loop ones. They have exponential form and are obtained by parameters identification using the stochastic optimization method 'simulated annealing'. The main parameters influencing the data fitting are three parameters, the pinning parameter k, the mean filed parameter {alpha} and the parameter which characterizes the shape of anhysteretic magnetization curve a. To validate this model, calculated minor hysteresis loops are compared with measured ones and good agreements are obtained.
MODELING OF FUEL SPRAY CHARACTERISTICS AND DIESEL COMBUSTION CHAMBER PARAMETERS
Directory of Open Access Journals (Sweden)
G. M. Kukharonak
2011-01-01
Full Text Available The computer model for coordination of fuel spray characteristics with diesel combustion chamber parameters has been created in the paper. The model allows to observe fuel sprays develоpment in diesel cylinder at any moment of injection, to calculate characteristics of fuel sprays with due account of a shape and dimensions of a combustion chamber, timely to change fuel injection characteristics and supercharging parameters, shape and dimensions of a combustion chamber. Moreover the computer model permits to determine parameters of holes in an injector nozzle that provides the required fuel sprays characteristics at the stage of designing a diesel engine. Combustion chamber parameters for 4ЧН11/12.5 diesel engine have been determined in the paper.
Mathematically Modeling Parameters Influencing Surface Roughness in CNC Milling
Directory of Open Access Journals (Sweden)
Engin Nas
2012-01-01
Full Text Available In this study, steel AISI 1050 is subjected to process of face milling in CNC milling machine and such parameters as cutting speed, feed rate, cutting tip, depth of cut influencing the surface roughness are investigated experimentally. Four different experiments are conducted by creating different combinations for parameters. In conducted experiments, cutting tools, which are coated by PVD method used in forcing steel and spheroidal graphite cast iron are used. Surface roughness values, which are obtained by using specified parameters with cutting tools, are measured and correlation between measured surface roughness values and parameters is modeled mathematically by using curve fitting algorithm. Mathematical models are evaluated according to coefficients of determination (R2 and the most ideal one is suggested for theoretical works. Mathematical models, which are proposed for each experiment, are estipulated.
Regionalization parameters of conceptual rainfall-runoff model
Osuch, M.
2003-04-01
Main goal of this study was to develop techniques for the a priori estimation parameters of hydrological model. Conceptual hydrological model CLIRUN was applied to around 50 catchment in Poland. The size of catchments range from 1 000 to 100 000 km2. The model was calibrated for a number of gauged catchments with different catchment characteristics. The parameters of model were related to different climatic and physical catchment characteristics (topography, land use, vegetation and soil type). The relationships were tested by comparing observed and simulated runoff series from the gauged catchment that were not used in the calibration. The model performance using regional parameters was promising for most of the calibration and validation catchments.
Ratnayake, Nalin A.; Koshimoto, Ed T.; Taylor, Brian R.
2011-01-01
The problem of parameter estimation on hybrid-wing-body type aircraft is complicated by the fact that many design candidates for such aircraft involve a large number of aero- dynamic control effectors that act in coplanar motion. This fact adds to the complexity already present in the parameter estimation problem for any aircraft with a closed-loop control system. Decorrelation of system inputs must be performed in order to ascertain individual surface derivatives with any sort of mathematical confidence. Non-standard control surface configurations, such as clamshell surfaces and drag-rudder modes, further complicate the modeling task. In this paper, asymmetric, single-surface maneuvers are used to excite multiple axes of aircraft motion simultaneously. Time history reconstructions of the moment coefficients computed by the solved regression models are then compared to each other in order to assess relative model accuracy. The reduced flight-test time required for inner surface parameter estimation using multi-axis methods was found to come at the cost of slightly reduced accuracy and statistical confidence for linear regression methods. Since the multi-axis maneuvers captured parameter estimates similar to both longitudinal and lateral-directional maneuvers combined, the number of test points required for the inner, aileron-like surfaces could in theory have been reduced by 50%. While trends were similar, however, individual parameters as estimated by a multi-axis model were typically different by an average absolute difference of roughly 15-20%, with decreased statistical significance, than those estimated by a single-axis model. The multi-axis model exhibited an increase in overall fit error of roughly 1-5% for the linear regression estimates with respect to the single-axis model, when applied to flight data designed for each, respectively.
Parameter Identification on Lumped Parameters of the Hydraulic Engine Mount Model
Directory of Open Access Journals (Sweden)
Li Qian
2016-01-01
Full Text Available Hydraulic Engine Mounts (HEM are important vibration isolation components with compound structure in the vehicle powertrain mounting system. They have the characteristic that large damping and high dynamic stiffness in the high frequency region, and small damping and low dynamic stiffness in the low frequency region, which can meet the requirements of the vehicle powertrain mounting system better. The method to identify the lumped parameters of the HEM is not only the necessary work for the analysis and calculation in dynamic performance and can also provide the theory for the performance optimization and structure optimization of product in the future. The parameter identification method based on coupled fluid-structure interaction (FSI and finite element analysis (FEA was established in this study to identify the equivalent piston area of the rubber spring, the volume stiffness of the upper chamber, as well as the inertia coefficient and damping coefficient of the liquid through the inertia track. The simulated dynamic characteristic curves of the HEM with the parameters identified are in accordance with the measured dynamic characteristic curves well.
Weibull Parameters Estimation Based on Physics of Failure Model
DEFF Research Database (Denmark)
Kostandyan, Erik; Sørensen, John Dalsgaard
2012-01-01
Reliability estimation procedures are discussed for the example of fatigue development in solder joints using a physics of failure model. The accumulated damage is estimated based on a physics of failure model, the Rainflow counting algorithm and the Miner’s rule. A threshold model is used...... distribution. Methods from structural reliability analysis are used to model the uncertainties and to assess the reliability for fatigue failure. Maximum Likelihood and Least Square estimation techniques are used to estimate fatigue life distribution parameters....
Energy Technology Data Exchange (ETDEWEB)
Dai, Heng [Pacific Northwest National Laboratory, Richland Washington USA; Ye, Ming [Department of Scientific Computing, Florida State University, Tallahassee Florida USA; Walker, Anthony P. [Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge Tennessee USA; Chen, Xingyuan [Pacific Northwest National Laboratory, Richland Washington USA
2017-04-01
Hydrological models are always composed of multiple components that represent processes key to intended model applications. When a process can be simulated by multiple conceptual-mathematical models (process models), model uncertainty in representing the process arises. While global sensitivity analysis methods have been widely used for identifying important processes in hydrologic modeling, the existing methods consider only parametric uncertainty but ignore the model uncertainty for process representation. To address this problem, this study develops a new method to probe multimodel process sensitivity by integrating the model averaging methods into the framework of variance-based global sensitivity analysis, given that the model averaging methods quantify both parametric and model uncertainty. A new process sensitivity index is derived as a metric of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and model parameters. For demonstration, the new index is used to evaluate the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that converting precipitation to recharge, and the geology process is also simulated by two models of different parameterizations of hydraulic conductivity; each process model has its own random parameters. The new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.
Essa, Mohamed; Sayed, Tarek
2015-11-01
Several studies have investigated the relationship between field-measured conflicts and the conflicts obtained from micro-simulation models using the Surrogate Safety Assessment Model (SSAM). Results from recent studies have shown that while reasonable correlation between simulated and real traffic conflicts can be obtained especially after proper calibration, more work is still needed to confirm that simulated conflicts provide safety measures beyond what can be expected from exposure. As well, the results have emphasized that using micro-simulation model to evaluate safety without proper model calibration should be avoided. The calibration process adjusts relevant simulation parameters to maximize the correlation between field-measured and simulated conflicts. The main objective of this study is to investigate the transferability of calibrated parameters of the traffic simulation model (VISSIM) for safety analysis between different sites. The main purpose is to examine whether the calibrated parameters, when applied to other sites, give reasonable results in terms of the correlation between the field-measured and the simulated conflicts. Eighty-three hours of video data from two signalized intersections in Surrey, BC were used in this study. Automated video-based computer vision techniques were used to extract vehicle trajectories and identify field-measured rear-end conflicts. Calibrated VISSIM parameters obtained from the first intersection which maximized the correlation between simulated and field-observed conflicts were used to estimate traffic conflicts at the second intersection and to compare the results to parameters optimized specifically for the second intersection. The results show that the VISSIM parameters are generally transferable between the two locations as the transferred parameters provided better correlation between simulated and field-measured conflicts than using the default VISSIM parameters. Of the six VISSIM parameters identified as
Sman, van der R.G.M.; Willigenburg, van G.; Vollebregt, H.M.; Eisner, V.; Mepschen, A.
2015-01-01
A first principles microfiltration model based on shear-induced diffusion is compared to experiments performed on the clarification of beer. After performing an identifiability and sensitivity analysis, the model parameters are estimated using global minimization of the sum of least squares. The
Scheibehenne, Benjamin; Pachur, Thorsten
2015-04-01
To be useful, cognitive models with fitted parameters should show generalizability across time and allow accurate predictions of future observations. It has been proposed that hierarchical procedures yield better estimates of model parameters than do nonhierarchical, independent approaches, because the formers' estimates for individuals within a group can mutually inform each other. Here, we examine Bayesian hierarchical approaches to evaluating model generalizability in the context of two prominent models of risky choice-cumulative prospect theory (Tversky & Kahneman, 1992) and the transfer-of-attention-exchange model (Birnbaum & Chavez, 1997). Using empirical data of risky choices collected for each individual at two time points, we compared the use of hierarchical versus independent, nonhierarchical Bayesian estimation techniques to assess two aspects of model generalizability: parameter stability (across time) and predictive accuracy. The relative performance of hierarchical versus independent estimation varied across the different measures of generalizability. The hierarchical approach improved parameter stability (in terms of a lower absolute discrepancy of parameter values across time) and predictive accuracy (in terms of deviance; i.e., likelihood). With respect to test-retest correlations and posterior predictive accuracy, however, the hierarchical approach did not outperform the independent approach. Further analyses suggested that this was due to strong correlations between some parameters within both models. Such intercorrelations make it difficult to identify and interpret single parameters and can induce high degrees of shrinkage in hierarchical models. Similar findings may also occur in the context of other cognitive models of choice.
A Regularized SNPOM for Stable Parameter Estimation of RBF-AR(X) Model.
Zeng, Xiaoyong; Peng, Hui; Zhou, Feng
2017-01-20
Recently, the radial basis function (RBF) network-style coefficients AutoRegressive (with exogenous inputs) [RBF-AR(X)] model identified by the structured nonlinear parameter optimization method (SNPOM) has attracted considerable interest because of its significant performance in nonlinear system modeling. However, this promising technique may occasionally confront the problem that the parameters are divergent in the optimization process, which may be a potential issue ignored by most researchers. In this paper, a regularized SNPOM, together with the regularization parameter detection technique, is presented to estimate the parameters of RBF-AR(X) models. This approach first separates the parameters of an RBF-AR(X) model into a linear parameters set and a nonlinear parameters set, and then combines a gradient-based nonlinear optimization algorithm for estimating the nonlinear parameters and the regularized least squares method for estimating the linear parameters. Several examples demonstrate that the proposed approach is effective to cope with the potential unstable problem in the parameters search process, and may also yield better or similar multistep forecasting accuracy and better robustness than the previous method.
Structural identifiability analyses of candidate models for in vitro Pitavastatin hepatic uptake.
Grandjean, Thomas R B; Chappell, Michael J; Yates, James W T; Evans, Neil D
2014-05-01
In this paper a review of the application of four different techniques (a version of the similarity transformation approach for autonomous uncontrolled systems, a non-differential input/output observable normal form approach, the characteristic set differential algebra and a recent algebraic input/output relationship approach) to determine the structural identifiability of certain in vitro nonlinear pharmacokinetic models is provided. The Organic Anion Transporting Polypeptide (OATP) substrate, Pitavastatin, is used as a probe on freshly isolated animal and human hepatocytes. Candidate pharmacokinetic non-linear compartmental models have been derived to characterise the uptake process of Pitavastatin. As a prerequisite to parameter estimation, structural identifiability analyses are performed to establish that all unknown parameters can be identified from the experimental observations available.
Construction of constant-Q viscoelastic model with three parameters
Institute of Scientific and Technical Information of China (English)
SUN Cheng-yu; YIN Xing-yao
2007-01-01
The popularly used viscoelastic models have some shortcomings in describing relationship between quality factor (Q) and frequency, which is not consistent with the observation data. Based on the theory of viscoelasticity, a new approach to construct constant-Q viscoelastic model in given frequency band with three parameters is developed. The designed model describes the frequency-independence feature of quality factor very well, and the effect of viscoelasticity on seismic wave field can be studied relatively accurate in theory with this model. Furthermore, the number of required parameters in this model has been reduced fewer than that of other constant-Q models, this can simplify the solution of the viscoelastic problems to some extent. At last, the accuracy and application range have been analyzed through numerical tests. The effect of viscoelasticity on wave propagation has been briefly illustrated through the change of frequency spectra and waveform in several different viscoelastic models.
Global-scale regionalization of hydrologic model parameters
Beck, Hylke E.; van Dijk, Albert I. J. M.; de Roo, Ad; Miralles, Diego G.; McVicar, Tim R.; Schellekens, Jaap; Bruijnzeel, L. Adrian
2016-05-01
Current state-of-the-art models typically applied at continental to global scales (hereafter called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments (10-10,000 km2) and the simple conceptual HBV model to set up and test the scheme. Each catchment was calibrated against observed daily Q, after which 674 catchments with high calibration and validation scores, and thus presumably good-quality observed Q and forcing data, were selected to serve as donor catchments. The calibrated parameter sets for the donors were subsequently transferred to 0.5° grid cells with similar climatic and physiographic characteristics, resulting in parameter maps for HBV with global coverage. For each grid cell, we used the 10 most similar donor catchments, rather than the single most similar donor, and averaged the resulting simulated Q, which enhanced model performance. The 1113 catchments not used as donors were used to independently evaluate the scheme. The regionalized parameters outperformed spatially uniform (i.e., averaged calibrated) parameters for 79% of the evaluation catchments. Substantial improvements were evident for all major Köppen-Geiger climate types and even for evaluation catchments > 5000 km distant from the donors. The median improvement was about half of the performance increase achieved through calibration. HBV with regionalized parameters outperformed nine state-of-the-art macroscale models, suggesting these might also benefit from the new regionalization scheme. The produced HBV parameter maps including ancillary data are available via www.gloh2o.org.
Bayesian parameter estimation for nonlinear modelling of biological pathways
Directory of Open Access Journals (Sweden)
Ghasemi Omid
2011-12-01
Full Text Available Abstract Background The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data. Results We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC method. We applied this approach to the biological pathways involved in the left ventricle (LV response to myocardial infarction (MI and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly
Mirror symmetry for two-parameter models, 1
Candelas, Philip; Font, A; Katz, S; Morrison, Douglas Robert Ogston; Candelas, Philip; Ossa, Xenia de la; Font, Anamaria; Katz, Sheldon; Morrison, David R.
1994-01-01
We study, by means of mirror symmetry, the quantum geometry of the K\\"ahler-class parameters of a number of Calabi-Yau manifolds that have $b_{11}=2$. Our main interest lies in the structure of the moduli space and in the loci corresponding to singular models. This structure is considerably richer when there are two parameters than in the various one-parameter models that have been studied hitherto. We describe the intrinsic structure of the point in the (compactification of the) moduli space that corresponds to the large complex structure or classical limit. The instanton expansions are of interest owing to the fact that some of the instantons belong to families with continuous parameters. We compute the Yukawa couplings and their expansions in terms of instantons of genus zero. By making use of recent results of Bershadsky et al. we compute also the instanton numbers for instantons of genus one. For particular values of the parameters the models become birational to certain models with one parameter. The co...
Do Lumped-Parameter Models Provide the Correct Geometrical Damping?
DEFF Research Database (Denmark)
Andersen, Lars
2007-01-01
This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil with focus on the horizontal sliding and rocking. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines...
Muscle parameters for musculoskeletal modelling of the human neck
Borst, J.; Forbes, P.A.; Happee, R.; Veeger, H.E.J.
2011-01-01
Background: To study normal or pathological neuromuscular control, a musculoskeletal model of the neck has great potential but a complete and consistent anatomical dataset which comprises the muscle geometry parameters to construct such a model is not yet available. Methods: A dissection experiment
Do Lumped-Parameter Models Provide the Correct Geometrical Damping?
DEFF Research Database (Denmark)
Andersen, Lars
2007-01-01
This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil with focus on the horizontal sliding and rocking. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines...
Multiplicity Control in Structural Equation Modeling: Incorporating Parameter Dependencies
Smith, Carrie E.; Cribbie, Robert A.
2013-01-01
When structural equation modeling (SEM) analyses are conducted, significance tests for all important model relationships (parameters including factor loadings, covariances, etc.) are typically conducted at a specified nominal Type I error rate ([alpha]). Despite the fact that many significance tests are often conducted in SEM, rarely is…
Muscle parameters for musculoskeletal modelling of the human neck
Borst, J.; Forbes, P.A.; Happee, R.; Veeger, H.E.J.
2011-01-01
Background: To study normal or pathological neuromuscular control, a musculoskeletal model of the neck has great potential but a complete and consistent anatomical dataset which comprises the muscle geometry parameters to construct such a model is not yet available. Methods: A dissection experiment
Geometry parameters for musculoskeletal modelling of the shoulder system
Van der Helm, F C; Veeger, DirkJan (H. E. J.); Pronk, G M; Van der Woude, L H; Rozendal, R H
1992-01-01
A dynamical finite-element model of the shoulder mechanism consisting of thorax, clavicula, scapula and humerus is outlined. The parameters needed for the model are obtained in a cadaver experiment consisting of both shoulders of seven cadavers. In this paper, in particular, the derivation of geomet
Precise correction to parameter ρ in the littlest Higgs model
Institute of Scientific and Technical Information of China (English)
Farshid Tabbak; F.Farnoudi
2008-01-01
In this paper tree-level violation of weak isospin parameter,ρ in the flame of the littlest Higgs model is studied.The potentially large deviation from the standard model prediction for the ρ in terms of the littlest Higgs model parameters is calculated.The maximum value for ρ for f ＝ 1 TeV,c ＝ 0.05,c'＝ 0.05and v'= 1.5 GeV is ρ = 1.2973 which means a large enhancement than the SM.
Comparative Analysis of Visco-elastic Models with Variable Parameters
Directory of Open Access Journals (Sweden)
Silviu Nastac
2010-01-01
Full Text Available The paper presents a theoretical comparative study for computational behaviour analysis of vibration isolation elements based on viscous and elastic models with variable parameters. The changing of elastic and viscous parameters can be produced by natural timed evolution demo-tion or by heating developed into the elements during their working cycle. It was supposed both linear and non-linear numerical viscous and elastic models, and their combinations. The results show the impor-tance of numerical model tuning with the real behaviour, as such the characteristics linearity, and the essential parameters for damping and rigidity. Multiple comparisons between linear and non-linear simulation cases dignify the basis of numerical model optimization regarding mathematical complexity vs. results reliability.
Improvement of Continuous Hydrologic Models and HMS SMA Parameters Reduction
Rezaeian Zadeh, Mehdi; Zia Hosseinipour, E.; Abghari, Hirad; Nikian, Ashkan; Shaeri Karimi, Sara; Moradzadeh Azar, Foad
2010-05-01
Hydrological models can help us to predict stream flows and associated runoff volumes of rainfall events within a watershed. There are many different reasons why we need to model the rainfall-runoff processes of for a watershed. However, the main reason is the limitation of hydrological measurement techniques and the costs of data collection at a fine scale. Generally, we are not able to measure all that we would like to know about a given hydrological systems. This is very particularly the case for ungauged catchments. Since the ultimate aim of prediction using models is to improve decision-making about a hydrological problem, therefore, having a robust and efficient modeling tool becomes an important factor. Among several hydrologic modeling approaches, continuous simulation has the best predictions because it can model dry and wet conditions during a long-term period. Continuous hydrologic models, unlike event based models, account for a watershed's soil moisture balance over a long-term period and are suitable for simulating daily, monthly, and seasonal streamflows. In this paper, we describe a soil moisture accounting (SMA) algorithm added to the hydrologic modeling system (HEC-HMS) computer program. As is well known in the hydrologic modeling community one of the ways for improving a model utility is the reduction of input parameters. The enhanced model developed in this study is applied to Khosrow Shirin Watershed, located in the north-west part of Fars Province in Iran, a data limited watershed. The HMS SMA algorithm divides the potential path of rainfall onto a watershed into five zones. The results showed that the output of HMS SMA is insensitive with the variation of many parameters such as soil storage and soil percolation rate. The study's objective is to remove insensitive parameters from the model input using Multi-objective sensitivity analysis. Keywords: Continuous Hydrologic Modeling, HMS SMA, Multi-objective sensitivity analysis, SMA Parameters
Rácz, A; Bajusz, D; Héberger, K
2015-01-01
Recent implementations of QSAR modelling software provide the user with numerous models and a wealth of information. In this work, we provide some guidance on how one should interpret the results of QSAR modelling, compare and assess the resulting models, and select the best and most consistent ones. Two QSAR datasets are applied as case studies for the comparison of model performance parameters and model selection methods. We demonstrate the capabilities of sum of ranking differences (SRD) in model selection and ranking, and identify the best performance indicators and models. While the exchange of the original training and (external) test sets does not affect the ranking of performance parameters, it provides improved models in certain cases (despite the lower number of molecules in the training set). Performance parameters for external validation are substantially separated from the other merits in SRD analyses, highlighting their value in data fusion.
Condition Parameter Modeling for Anomaly Detection in Wind Turbines
Directory of Open Access Journals (Sweden)
Yonglong Yan
2014-05-01
Full Text Available Data collected from the supervisory control and data acquisition (SCADA system, used widely in wind farms to obtain operational and condition information about wind turbines (WTs, is of important significance for anomaly detection in wind turbines. The paper presents a novel model for wind turbine anomaly detection mainly based on SCADA data and a back-propagation neural network (BPNN for automatic selection of the condition parameters. The SCADA data sets are determined through analysis of the cumulative probability distribution of wind speed and the relationship between output power and wind speed. The automatic BPNN-based parameter selection is for reduction of redundant parameters for anomaly detection in wind turbines. Through investigation of cases of WT faults, the validity of the automatic parameter selection-based model for WT anomaly detection is verified.
Parameter Estimation of Photovoltaic Models via Cuckoo Search
Directory of Open Access Journals (Sweden)
Jieming Ma
2013-01-01
Full Text Available Since conventional methods are incapable of estimating the parameters of Photovoltaic (PV models with high accuracy, bioinspired algorithms have attracted significant attention in the last decade. Cuckoo Search (CS is invented based on the inspiration of brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior. In this paper, a CS-based parameter estimation method is proposed to extract the parameters of single-diode models for commercial PV generators. Simulation results and experimental data show that the CS algorithm is capable of obtaining all the parameters with extremely high accuracy, depicted by a low Root-Mean-Squared-Error (RMSE value. The proposed method outperforms other algorithms applied in this study.
Identifying gene regulatory network rewiring using latent differential graphical models.
Tian, Dechao; Gu, Quanquan; Ma, Jian
2016-09-30
Gene regulatory networks (GRNs) are highly dynamic among different tissue types. Identifying tissue-specific gene regulation is critically important to understand gene function in a particular cellular context. Graphical models have been used to estimate GRN from gene expression data to distinguish direct interactions from indirect associations. However, most existing methods estimate GRN for a specific cell/tissue type or in a tissue-naive way, or do not specifically focus on network rewiring between different tissues. Here, we describe a new method called Latent Differential Graphical Model (LDGM). The motivation of our method is to estimate the differential network between two tissue types directly without inferring the network for individual tissues, which has the advantage of utilizing much smaller sample size to achieve reliable differential network estimation. Our simulation results demonstrated that LDGM consistently outperforms other Gaussian graphical model based methods. We further evaluated LDGM by applying to the brain and blood gene expression data from the GTEx consortium. We also applied LDGM to identify network rewiring between cancer subtypes using the TCGA breast cancer samples. Our results suggest that LDGM is an effective method to infer differential network using high-throughput gene expression data to identify GRN dynamics among different cellular conditions.
Parameter Estimation for Single Diode Models of Photovoltaic Modules
Energy Technology Data Exchange (ETDEWEB)
Hansen, Clifford [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Photovoltaic and Distributed Systems Integration Dept.
2015-03-01
Many popular models for photovoltaic system performance employ a single diode model to compute the I - V curve for a module or string of modules at given irradiance and temperature conditions. A single diode model requires a number of parameters to be estimated from measured I - V curves. Many available parameter estimation methods use only short circuit, o pen circuit and maximum power points for a single I - V curve at standard test conditions together with temperature coefficients determined separately for individual cells. In contrast, module testing frequently records I - V curves over a wide range of irradi ance and temperature conditions which, when available , should also be used to parameterize the performance model. We present a parameter estimation method that makes use of a fu ll range of available I - V curves. We verify the accuracy of the method by recov ering known parameter values from simulated I - V curves . We validate the method by estimating model parameters for a module using outdoor test data and predicting the outdoor performance of the module.
Automatic Determination of the Conic Coronal Mass Ejection Model Parameters
Pulkkinen, A.; Oates, T.; Taktakishvili, A.
2009-01-01
Characterization of the three-dimensional structure of solar transients using incomplete plane of sky data is a difficult problem whose solutions have potential for societal benefit in terms of space weather applications. In this paper transients are characterized in three dimensions by means of conic coronal mass ejection (CME) approximation. A novel method for the automatic determination of cone model parameters from observed halo CMEs is introduced. The method uses both standard image processing techniques to extract the CME mass from white-light coronagraph images and a novel inversion routine providing the final cone parameters. A bootstrap technique is used to provide model parameter distributions. When combined with heliospheric modeling, the cone model parameter distributions will provide direct means for ensemble predictions of transient propagation in the heliosphere. An initial validation of the automatic method is carried by comparison to manually determined cone model parameters. It is shown using 14 halo CME events that there is reasonable agreement, especially between the heliocentric locations of the cones derived with the two methods. It is argued that both the heliocentric locations and the opening half-angles of the automatically determined cones may be more realistic than those obtained from the manual analysis
Estimation of the parameters of ETAS models by Simulated Annealing
Lombardi, Anna Maria
2015-01-01
This paper proposes a new algorithm to estimate the maximum likelihood parameters of an Epidemic Type Aftershock Sequences (ETAS) model. It is based on Simulated Annealing, a versatile method that solves problems of global optimization and ensures convergence to a global optimum. The procedure is tested on both simulated and real catalogs. The main conclusion is that the method performs poorly as the size of the catalog decreases because the effect of the correlation of the ETAS parameters is...
CADLIVE optimizer: web-based parameter estimation for dynamic models
Directory of Open Access Journals (Sweden)
Inoue Kentaro
2012-08-01
Full Text Available Abstract Computer simulation has been an important technique to capture the dynamics of biochemical networks. In most networks, however, few kinetic parameters have been measured in vivo because of experimental complexity. We develop a kinetic parameter estimation system, named the CADLIVE Optimizer, which comprises genetic algorithms-based solvers with a graphical user interface. This optimizer is integrated into the CADLIVE Dynamic Simulator to attain efficient simulation for dynamic models.
Reference physiological parameters for pharmacodynamic modeling of liver cancer
Energy Technology Data Exchange (ETDEWEB)
Travis, C.C.; Arms, A.D.
1988-01-01
This document presents a compilation of measured values for physiological parameters used in pharamacodynamic modeling of liver cancer. The physiological parameters include body weight, liver weight, the liver weight/body weight ratio, and number of hepatocytes. Reference values for use in risk assessment are given for each of the physiological parameters based on analyses of valid measurements taken from the literature and other reliable sources. The proposed reference values for rodents include sex-specific measurements for the B6C3F{sub 1}, mice and Fishcer 344/N, Sprague-Dawley, and Wistar rats. Reference values are also provided for humans. 102 refs., 65 tabs.
X-Parameter Based Modelling of Polar Modulated Power Amplifiers
DEFF Research Database (Denmark)
Wang, Yelin; Nielsen, Troels Studsgaard; Sira, Daniel
2013-01-01
X-parameters are developed as an extension of S-parameters capable of modelling non-linear devices driven by large signals. They are suitable for devices having only radio frequency (RF) and DC ports. In a polar power amplifier (PA), phase and envelope of the input modulated signal are applied...... at separate ports and the envelope port is neither an RF nor a DC port. As a result, X-parameters may fail to characterise the effect of the envelope port excitation and consequently the polar PA. This study introduces a solution to the problem for a commercial polar PA. In this solution, the RF-phase path...
A Bayesian framework for parameter estimation in dynamical models.
Directory of Open Access Journals (Sweden)
Flávio Codeço Coelho
Full Text Available Mathematical models in biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system. Proper handling of such uncertainties is key to the successful usage of models to predict experimental or field observations. This problem has been addressed over the years by many tools for model calibration and parameter estimation. In this article we present a general framework for uncertainty analysis and parameter estimation that is designed to handle uncertainties associated with the modeling of dynamic biological systems while remaining agnostic as to the type of model used. We apply the framework to fit an SIR-like influenza transmission model to 7 years of incidence data in three European countries: Belgium, the Netherlands and Portugal.
Modelling of Water Turbidity Parameters in a Water Treatment Plant
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A. S. KOVO
2005-01-01
Full Text Available The high cost of chemical analysis of water has necessitated various researches into finding alternative method of determining portable water quality. This paper is aimed at modelling the turbidity value as a water quality parameter. Mathematical models for turbidity removal were developed based on the relationships between water turbidity and other water criteria. Results showed that the turbidity of water is the cumulative effect of the individual parameters/factors affecting the system. A model equation for the evaluation and prediction of a clarifier’s performance was developed:Model: T = T0(-1.36729 + 0.037101∙10λpH + 0.048928t + 0.00741387∙alkThe developed model will aid the predictive assessment of water treatment plant performance. The limitations of the models are as a result of insufficient variable considered during the conceptualization.
Simultaneous estimation of parameters in the bivariate Emax model.
Magnusdottir, Bergrun T; Nyquist, Hans
2015-12-10
In this paper, we explore inference in multi-response, nonlinear models. By multi-response, we mean models with m > 1 response variables and accordingly m relations. Each parameter/explanatory variable may appear in one or more of the relations. We study a system estimation approach for simultaneous computation and inference of the model and (co)variance parameters. For illustration, we fit a bivariate Emax model to diabetes dose-response data. Further, the bivariate Emax model is used in a simulation study that compares the system estimation approach to equation-by-equation estimation. We conclude that overall, the system estimation approach performs better for the bivariate Emax model when there are dependencies among relations. The stronger the dependencies, the more we gain in precision by using system estimation rather than equation-by-equation estimation.
Schiavazzi, Daniele E; Baretta, Alessia; Pennati, Giancarlo; Hsia, Tain-Yen; Marsden, Alison L
2017-03-01
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. Copyright © 2016 John Wiley & Sons, Ltd.
Shape parameter estimate for a glottal model without time position
Degottex, Gilles; Roebel, Axel; Rodet, Xavier
2009-01-01
cote interne IRCAM: Degottex09a; None / None; National audience; From a recorded speech signal, we propose to estimate a shape parameter of a glottal model without estimating his time position. Indeed, the literature usually propose to estimate the time position first (ex. by detecting Glottal Closure Instants). The vocal-tract filter estimate is expressed as a minimum-phase envelope estimation after removing the glottal model and a standard lips radiation model. Since this filter is mainly b...
Light-Front Spin-1 Model: Parameters Dependence
Mello, Clayton S; de Melo, J P B C; Frederico, T
2015-01-01
We study the structure of the $\\rho$-meson within a light-front model with constituent quark degrees of freedom. We calculate electroweak static observables: magnetic and quadrupole moments, decay constant and charge radius. The prescription used to compute the electroweak quantities is free of zero modes, which makes the calculation implicitly covariant. We compare the results of our model with other ones found in the literature. Our model parameters give a decay constant close to the experimental one.
Cosmological Models with Variable Deceleration Parameter in Lyra's Manifold
Pradhan, A; Singh, C B
2006-01-01
FRW models of the universe have been studied in the cosmological theory based on Lyra's manifold. A new class of exact solutions has been obtained by considering a time dependent displacement field for variable deceleration parameter from which three models of the universe are derived (i) exponential (ii) polynomial and (iii) sinusoidal form respectively. The behaviour of these models of the universe are also discussed. Finally some possibilities of further problems and their investigations have been pointed out.
Directory of Open Access Journals (Sweden)
Filip Górski
2013-09-01
Full Text Available The paper presents the results of experimental study – part of research of additive technology using thermoplastics as a build material, namely Fused Deposition Modelling (FDM. Aim of the study was to identify the relation between basic parameter of the FDM process – model orientation during manufacturing – and a dimensional accuracy and repeatability of obtained products. A set of samples was prepared – they were manufactured with variable process parameters and they were measured using 3D scanner. Significant differences in accuracy of products of the same geometry, but manufactured with different set of process parameters were observed.
DEFF Research Database (Denmark)
Ruano, MV; Ribes, J; de Pauw, DJW
2007-01-01
In this work we address the issue of parameter subset selection within the scope of activated sludge model calibration. To this end, we evaluate two approaches: (i) systems analysis and (ii) experience-based approach. The evaluation has been carried out using a dynamic model (ASM2d) calibrated...... based approaches which excluded them from their analysis. Systems analysis reveals that parameter significance ranking and size of the identifiable parameter subset depend on the information content of data available for calibration. However, it suffers from heavy computational demand. In contrast...
Chen, W. J.; Tan, X. J.; Cai, M.
2017-07-01
Parameter identification method of equivalent circuit models for Li-ion batteries using the advanced tree seeds algorithm is proposed. On one hand, since the electrochemical models are not suitable for the design of battery management system, the equivalent circuit models are commonly adopted for on-board applications. On the other hand, by building up the objective function for optimization, the tree seeds algorithm can be used to identify the parameters of equivalent circuit models. Experimental verifications under different profiles demonstrate the suggested method can achieve a better result with lower complexity, more accuracy and robustness, which make it a reasonable alternative for other identification algorithms.
A Numerical Procedure for Model Identifiability Analysis Applied to Enzyme Kinetics
DEFF Research Database (Denmark)
Daele, Timothy, Van; Van Hoey, Stijn; Gernaey, Krist;
2015-01-01
exercise, thereby bypassing the challenging task of model structure determination and identification. Parameter identification problems can thus lead to ill-calibrated models with low predictive power and large model uncertainty. Every calibration exercise should therefore be precededby a proper model...... and Pronzato (1997) and which can be easily set up for any type of model. In this paper the proposed approach is applied to the forward reaction rate of the enzyme kinetics proposed by Shin and Kim(1998). Structural identifiability analysis showed that no local structural model problems were occurring......The proper calibration of models describing enzyme kinetics can be quite challenging. In the literature, different procedures are available to calibrate these enzymatic models in an efficient way. However, in most cases the model structure is already decided on prior to the actual calibration...
Bayesian parameter inference and model selection by population annealing in systems biology.
Murakami, Yohei
2014-01-01
Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named "posterior parameter ensemble". We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor.
Solar Model Parameters and Direct Measurements of Solar Neutrino Fluxes
Bandyopadhyay, A; Goswami, S; Petcov, S T; Bandyopadhyay, Abhijit; Choubey, Sandhya; Goswami, Srubabati
2006-01-01
We explore a novel possibility of determining the solar model parameters, which serve as input in the calculations of the solar neutrino fluxes, by exploiting the data from direct measurements of the fluxes. More specifically, we use the rather precise value of the $^8B$ neutrino flux, $\\phi_B$ obtained from the global analysis of the solar neutrino and KamLAND data, to derive constraints on each of the solar model parameters on which $\\phi_B$ depends. We also use more precise values of $^7Be$ and $pp$ fluxes as can be obtained from future prospective data and discuss whether such measurements can help in reducing the uncertainties of one or more input parameters of the Standard Solar Model.
IP-Sat: Impact-Parameter dependent Saturation model; revised
Rezaeian, Amir H; Van de Klundert, Merijn; Venugopalan, Raju
2013-01-01
In this talk, we present a global analysis of available small-x data on inclusive DIS and exclusive diffractive processes, including the latest data from the combined HERA analysis on reduced cross sections within the Impact-Parameter dependent Saturation (IP-Sat) Model. The impact-parameter dependence of dipole amplitude is crucial in order to have a unified description of both inclusive and exclusive diffractive processes. With the parameters of model fixed via a fit to the high-precision reduced cross-section, we compare model predictions to data for the structure functions, the longitudinal structure function, the charm structure function, exclusive vector mesons production and Deeply Virtual Compton Scattering (DVCS). Excellent agreement is obtained for the processes considered at small x in a wide range of Q^2.
QCD-inspired determination of NJL model parameters
Springer, Paul; Rechenberger, Stefan; Rennecke, Fabian
2016-01-01
The QCD phase diagram at finite temperature and density has attracted considerable interest over many decades now, not least because of its relevance for a better understanding of heavy-ion collision experiments. Models provide some insight into the QCD phase structure but usually rely on various parameters. Based on renormalization group arguments, we discuss how the parameters of QCD low-energy models can be determined from the fundamental theory of the strong interaction. We particularly focus on a determination of the temperature dependence of these parameters in this work and comment on the effect of a finite quark chemical potential. We present first results and argue that our findings can be used to improve the predictive power of future model calculations.
Compartmental analysis of dynamic nuclear medicine data: models and identifiability
Delbary, Fabrice; Garbarino, Sara; Vivaldi, Valentina
2016-12-01
Compartmental models based on tracer mass balance are extensively used in clinical and pre-clinical nuclear medicine in order to obtain quantitative information on tracer metabolism in the biological tissue. This paper is the first of a series of two that deal with the problem of tracer coefficient estimation via compartmental modelling in an inverse problem framework. Specifically, here we discuss the identifiability problem for a general n-dimension compartmental system and provide uniqueness results in the case of two-compartment and three-compartment compartmental models. The second paper will utilize this framework in order to show how nonlinear regularization schemes can be applied to obtain numerical estimates of the tracer coefficients in the case of nuclear medicine data corresponding to brain, liver and kidney physiology.
SPOTting model parameters using a ready-made Python package
Houska, Tobias; Kraft, Philipp; Breuer, Lutz
2015-04-01
The selection and parameterization of reliable process descriptions in ecological modelling is driven by several uncertainties. The procedure is highly dependent on various criteria, like the used algorithm, the likelihood function selected and the definition of the prior parameter distributions. A wide variety of tools have been developed in the past decades to optimize parameters. Some of the tools are closed source. Due to this, the choice for a specific parameter estimation method is sometimes more dependent on its availability than the performance. A toolbox with a large set of methods can support users in deciding about the most suitable method. Further, it enables to test and compare different methods. We developed the SPOT (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of modules, to analyze and optimize parameters of (environmental) models. SPOT comes along with a selected set of algorithms for parameter optimization and uncertainty analyses (Monte Carlo, MC; Latin Hypercube Sampling, LHS; Maximum Likelihood, MLE; Markov Chain Monte Carlo, MCMC; Scuffled Complex Evolution, SCE-UA; Differential Evolution Markov Chain, DE-MCZ), together with several likelihood functions (Bias, (log-) Nash-Sutcliff model efficiency, Correlation Coefficient, Coefficient of Determination, Covariance, (Decomposed-, Relative-, Root-) Mean Squared Error, Mean Absolute Error, Agreement Index) and prior distributions (Binomial, Chi-Square, Dirichlet, Exponential, Laplace, (log-, multivariate-) Normal, Pareto, Poisson, Cauchy, Uniform, Weibull) to sample from. The model-independent structure makes it suitable to analyze a wide range of applications. We apply all algorithms of the SPOT package in three different case studies. Firstly, we investigate the response of the Rosenbrock function, where the MLE algorithm shows its strengths. Secondly, we study the Griewank function, which has a challenging response surface for
Synchronous Generator Model Parameter Estimation Based on Noisy Dynamic Waveforms
Berhausen, Sebastian; Paszek, Stefan
2016-01-01
In recent years, there have occurred system failures in many power systems all over the world. They have resulted in a lack of power supply to a large number of recipients. To minimize the risk of occurrence of power failures, it is necessary to perform multivariate investigations, including simulations, of power system operating conditions. To conduct reliable simulations, the current base of parameters of the models of generating units, containing the models of synchronous generators, is necessary. In the paper, there is presented a method for parameter estimation of a synchronous generator nonlinear model based on the analysis of selected transient waveforms caused by introducing a disturbance (in the form of a pseudorandom signal) in the generator voltage regulation channel. The parameter estimation was performed by minimizing the objective function defined as a mean square error for deviations between the measurement waveforms and the waveforms calculated based on the generator mathematical model. A hybrid algorithm was used for the minimization of the objective function. In the paper, there is described a filter system used for filtering the noisy measurement waveforms. The calculation results of the model of a 44 kW synchronous generator installed on a laboratory stand of the Institute of Electrical Engineering and Computer Science of the Silesian University of Technology are also given. The presented estimation method can be successfully applied to parameter estimation of different models of high-power synchronous generators operating in a power system.
Modelling of intermittent microwave convective drying: parameter sensitivity
Directory of Open Access Journals (Sweden)
Zhang Zhijun
2017-06-01
Full Text Available The reliability of the predictions of a mathematical model is a prerequisite to its utilization. A multiphase porous media model of intermittent microwave convective drying is developed based on the literature. The model considers the liquid water, gas and solid matrix inside of food. The model is simulated by COMSOL software. Its sensitivity parameter is analysed by changing the parameter values by ±20%, with the exception of several parameters. The sensitivity analysis of the process of the microwave power level shows that each parameter: ambient temperature, effective gas diffusivity, and evaporation rate constant, has significant effects on the process. However, the surface mass, heat transfer coefficient, relative and intrinsic permeability of the gas, and capillary diffusivity of water do not have a considerable effect. The evaporation rate constant has minimal parameter sensitivity with a ±20% value change, until it is changed 10-fold. In all results, the temperature and vapour pressure curves show the same trends as the moisture content curve. However, the water saturation at the medium surface and in the centre show different results. Vapour transfer is the major mass transfer phenomenon that affects the drying process.
Optimizing Muscle Parameters in Musculoskeletal Modeling Using Monte Carlo Simulations
Hanson, Andrea; Reed, Erik; Cavanagh, Peter
2011-01-01
Astronauts assigned to long-duration missions experience bone and muscle atrophy in the lower limbs. The use of musculoskeletal simulation software has become a useful tool for modeling joint and muscle forces during human activity in reduced gravity as access to direct experimentation is limited. Knowledge of muscle and joint loads can better inform the design of exercise protocols and exercise countermeasure equipment. In this study, the LifeModeler(TM) (San Clemente, CA) biomechanics simulation software was used to model a squat exercise. The initial model using default parameters yielded physiologically reasonable hip-joint forces. However, no activation was predicted in some large muscles such as rectus femoris, which have been shown to be active in 1-g performance of the activity. Parametric testing was conducted using Monte Carlo methods and combinatorial reduction to find a muscle parameter set that more closely matched physiologically observed activation patterns during the squat exercise. Peak hip joint force using the default parameters was 2.96 times body weight (BW) and increased to 3.21 BW in an optimized, feature-selected test case. The rectus femoris was predicted to peak at 60.1% activation following muscle recruitment optimization, compared to 19.2% activation with default parameters. These results indicate the critical role that muscle parameters play in joint force estimation and the need for exploration of the solution space to achieve physiologically realistic muscle activation.
Modelling of intermittent microwave convective drying: parameter sensitivity
Zhang, Zhijun; Qin, Wenchao; Shi, Bin; Gao, Jingxin; Zhang, Shiwei
2017-06-01
The reliability of the predictions of a mathematical model is a prerequisite to its utilization. A multiphase porous media model of intermittent microwave convective drying is developed based on the literature. The model considers the liquid water, gas and solid matrix inside of food. The model is simulated by COMSOL software. Its sensitivity parameter is analysed by changing the parameter values by ±20%, with the exception of several parameters. The sensitivity analysis of the process of the microwave power level shows that each parameter: ambient temperature, effective gas diffusivity, and evaporation rate constant, has significant effects on the process. However, the surface mass, heat transfer coefficient, relative and intrinsic permeability of the gas, and capillary diffusivity of water do not have a considerable effect. The evaporation rate constant has minimal parameter sensitivity with a ±20% value change, until it is changed 10-fold. In all results, the temperature and vapour pressure curves show the same trends as the moisture content curve. However, the water saturation at the medium surface and in the centre show different results. Vapour transfer is the major mass transfer phenomenon that affects the drying process.
Extraction of Spatial Parameters from Classified LIDAR Data and Aerial Photograph for Sound Modeling
Biswas, S.; Lohani, B.
2012-07-01
Prediction of outdoor sound levels in 3D space is important for noise management, soundscaping etc. Sound levels at outdoor can be predicted using sound propagation models which need terrain parameters. The existing practices of incorporating terrain parameters into models are often limited due to inadequate data or inability to determine accurate sound transmission paths through a terrain. This leads to poor accuracy in modelling. LIDAR data and Aerial Photograph (or Satellite Images) provide opportunity to incorporate high resolution data into sound models. To realize this, identification of building and other objects and their use for extraction of terrain parameters are fundamental. However, development of a suitable technique, to incorporate terrain parameters from classified LIDAR data and Aerial Photograph, for sound modelling is a challenge. Determination of terrain parameters along various transmission paths of sound from sound source to a receiver becomes very complex in an urban environment due to the presence of varied and complex urban features. This paper presents a technique to identify the principal paths through which sound transmits from source to receiver. Further, the identified principal paths are incorporated inside the sound model for sound prediction. Techniques based on plane cutting and line tracing are developed for determining principal paths and terrain parameters, which use various information, e.g., building corner and edges, triangulated ground, tree points and locations of source and receiver. The techniques developed are validated through a field experiment. Finally efficacy of the proposed technique is demonstrated by developing a noise map for a test site.
Comparing spatial and temporal transferability of hydrological model parameters
Patil, Sopan; Stieglitz, Marc
2015-04-01
Operational use of hydrological models requires the transfer of calibrated parameters either in time (for streamflow forecasting) or space (for prediction at ungauged catchments) or both. Although the effects of spatial and temporal parameter transfer on catchment streamflow predictions have been well studied individually, a direct comparison of these approaches is much less documented. In our view, such comparison is especially pertinent in the context of increasing appeal and popularity of the "trading space for time" approaches that are proposed for assessing the hydrological implications of anthropogenic climate change. Here, we compare three different schemes of parameter transfer, viz., temporal, spatial, and spatiotemporal, using a spatially lumped hydrological model called EXP-HYDRO at 294 catchments across the continental United States. Results show that the temporal parameter transfer scheme performs best, with lowest decline in prediction performance (median decline of 4.2%) as measured using the Kling-Gupta efficiency metric. More interestingly, negligible difference in prediction performance is observed between the spatial and spatiotemporal parameter transfer schemes (median decline of 12.4% and 13.9% respectively). We further demonstrate that the superiority of temporal parameter transfer scheme is preserved even when: (1) spatial distance between donor and receiver catchments is reduced, or (2) temporal lag between calibration and validation periods is increased. Nonetheless, increase in the temporal lag between calibration and validation periods reduces the overall performance gap between the three parameter transfer schemes. Results suggest that spatiotemporal transfer of hydrological model parameters has the potential to be a viable option for climate change related hydrological studies, as envisioned in the "trading space for time" framework. However, further research is still needed to explore the relationship between spatial and temporal
Ye, Yan; Song, Xiaomeng; Zhang, Jianyun; Kong, Fanzhe; Ma, Guangwen
2014-06-01
Practical experience has demonstrated that single objective functions, no matter how carefully chosen, prove to be inadequate in providing proper measurements for all of the characteristics of the observed data. One strategy to circumvent this problem is to define multiple fitting criteria that measure different aspects of system behavior, and to use multi-criteria optimization to identify non-dominated optimal solutions. Unfortunately, these analyses require running original simulation models thousands of times. As such, they demand prohibitively large computational budgets. As a result, surrogate models have been used in combination with a variety of multi-objective optimization algorithms to approximate the true Pareto-front within limited evaluations for the original model. In this study, multi-objective optimization based on surrogate modeling (multivariate adaptive regression splines, MARS) for a conceptual rainfall-runoff model (Xin'anjiang model, XAJ) was proposed. Taking the Yanduhe basin of Three Gorges in the upper stream of the Yangtze River in China as a case study, three evaluation criteria were selected to quantify the goodness-of-fit of observations against calculated values from the simulation model. The three criteria chosen were the Nash-Sutcliffe efficiency coefficient, the relative error of peak flow, and runoff volume (REPF and RERV). The efficacy of this method is demonstrated on the calibration of the XAJ model. Compared to the single objective optimization results, it was indicated that the multi-objective optimization method can infer the most probable parameter set. The results also demonstrate that the use of surrogate-modeling enables optimization that is much more efficient; and the total computational cost is reduced by about 92.5%, compared to optimization without using surrogate modeling. The results obtained with the proposed method support the feasibility of applying parameter optimization to computationally intensive simulation
Energy Technology Data Exchange (ETDEWEB)
Andersen, Erlend K.F. [Department of Medical Physics, Oslo University Hospital, Oslo (Norway); Hole, Knut Hakon; Lund, Kjersti V. [Department of Radiology, Oslo University Hospital, Oslo (Norway); Sundfor, Kolbein [Department of Gynaecological Oncology, Oslo University Hospital, Oslo (Norway); Kristensen, Gunnar B. [Department of Gynaecological Oncology, Oslo University Hospital, Oslo (Norway); Institute for Medical Informatics, Oslo University Hospital, Oslo (Norway); Lyng, Heidi [Department of Radiation Biology, Oslo University Hospital, Oslo (Norway); Malinen, Eirik, E-mail: eirik.malinen@fys.uio.no [Department of Medical Physics, Oslo University Hospital, Oslo (Norway); Department of Physics, University of Oslo, Oslo (Norway)
2012-03-01
Purpose: To systematically screen the tumor contrast enhancement of locally advanced cervical cancers to assess the prognostic value of two descriptive parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods and Materials: This study included a prospectively collected cohort of 81 patients who underwent DCE-MRI with gadopentetate dimeglumine before chemoradiotherapy. The following descriptive DCE-MRI parameters were extracted voxel by voxel and presented as histograms for each time point in the dynamic series: normalized relative signal increase (nRSI) and normalized area under the curve (nAUC). The first to 100th percentiles of the histograms were included in a log-rank survival test, resulting in p value and relative risk maps of all percentile-time intervals for each DCE-MRI parameter. The maps were used to evaluate the robustness of the individual percentile-time pairs and to construct prognostic parameters. Clinical endpoints were locoregional control and progression-free survival. The study was approved by the institutional ethics committee. Results: The p value maps of nRSI and nAUC showed a large continuous region of percentile-time pairs that were significantly associated with locoregional control (p < 0.05). These parameters had prognostic impact independent of tumor stage, volume, and lymph node status on multivariate analysis. Only a small percentile-time interval of nRSI was associated with progression-free survival. Conclusions: The percentile-time screening identified DCE-MRI parameters that predict long-term locoregional control after chemoradiotherapy of cervical cancer.
Estimation of the parameters of ETAS models by Simulated Annealing
Lombardi, Anna Maria
2015-02-01
This paper proposes a new algorithm to estimate the maximum likelihood parameters of an Epidemic Type Aftershock Sequences (ETAS) model. It is based on Simulated Annealing, a versatile method that solves problems of global optimization and ensures convergence to a global optimum. The procedure is tested on both simulated and real catalogs. The main conclusion is that the method performs poorly as the size of the catalog decreases because the effect of the correlation of the ETAS parameters is more significant. These results give new insights into the ETAS model and the efficiency of the maximum-likelihood method within this context.
J-A Hysteresis Model Parameters Estimation using GA
Directory of Open Access Journals (Sweden)
Bogomir Zidaric
2005-01-01
Full Text Available This paper presents the Jiles and Atherton (J-A hysteresis model parameter estimation for soft magnetic composite (SMC material. The calculation of Jiles and Atherton hysteresis model parameters is based on experimental data and genetic algorithms (GA. Genetic algorithms operate in a given area of possible solutions. Finding the best solution of a problem in wide area of possible solutions is uncertain. A new approach in use of genetic algorithms is proposed to overcome this uncertainty. The basis of this approach is in genetic algorithm built in another genetic algorithm.
A new estimate of the parameters in linear mixed models
Institute of Scientific and Technical Information of China (English)
王松桂; 尹素菊
2002-01-01
In linear mixed models, there are two kinds of unknown parameters: one is the fixed effect, theother is the variance component. In this paper, new estimates of these parameters, called the spectral decom-position estimates, are proposed, Some important statistical properties of the new estimates are established,in particular the linearity of the estimates of the fixed effects with many statistical optimalities. A new methodis applied to two important models which are used in economics, finance, and mechanical fields. All estimatesobtained have good statistical and practical meaning.
Models wagging the dog: are circuits constructed with disparate parameters?
Nowotny, Thomas; Szücs, Attila; Levi, Rafael; Selverston, Allen I
2007-08-01
In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken.
Do land parameters matter in large-scale hydrological modelling?
Gudmundsson, Lukas; Seneviratne, Sonia I.
2013-04-01
Many of the most pending issues in large-scale hydrology are concerned with predicting hydrological variability at ungauged locations. However, current-generation hydrological and land surface models that are used for their estimation suffer from large uncertainties. These models rely on mathematical approximations of the physical system as well as on mapped values of land parameters (e.g. topography, soil types, land cover) to predict hydrological variables (e.g. evapotranspiration, soil moisture, stream flow) as a function of atmospheric forcing (e.g. precipitation, temperature, humidity). Despite considerable progress in recent years, it remains unclear whether better estimates of land parameters can improve predictions - or - if a refinement of model physics is necessary. To approach this question we suggest scrutinizing our perception of hydrological systems by confronting it with the radical assumption that hydrological variability at any location in space depends on past and present atmospheric forcing only, and not on location-specific land parameters. This so called "Constant Land Parameter Hypothesis (CLPH)" assumes that variables like runoff can be predicted without taking location specific factors such as topography or soil types into account. We demonstrate, using a modern statistical tool, that monthly runoff in Europe can be skilfully estimated using atmospheric forcing alone, without accounting for locally varying land parameters. The resulting runoff estimates are used to benchmark state-of-the-art process models. These are found to have inferior performance, despite their explicit process representation, which accounts for locally varying land parameters. This suggests that progress in the theory of hydrological systems is likely to yield larger improvements in model performance than more precise land parameter estimates. The results also question the current modelling paradigm that is dominated by the attempt to account for locally varying land
Edouard, C.; Petit, M.; Forgez, C.; Bernard, J.; Revel, R.
2016-09-01
In this work, a simplified electrochemical and thermal model that can predict both physicochemical and aging behavior of Li-ion batteries is studied. A sensitivity analysis of all its physical parameters is performed in order to find out their influence on the model output based on simulations under various conditions. The results gave hints on whether a parameter needs particular attention when measured or identified and on the conditions (e.g. temperature, discharge rate) under which it is the most sensitive. A specific simulation profile is designed for parameters involved in aging equations in order to determine their sensitivity. Finally, a step-wise method is followed to limit the influence of parameter values when identifying some of them, according to their relative sensitivity from the study. This sensitivity analysis and the subsequent step-wise identification method show very good results, such as a better fitting of the simulated cell voltage with experimental data.
Nonlinear model predictive control using parameter varying BP-ARX combination model
Yang, J.-F.; Xiao, L.-F.; Qian, J.-X.; Li, H.
2012-03-01
A novel back-propagation AutoRegressive with eXternal input (BP-ARX) combination model is constructed for model predictive control (MPC) of MIMO nonlinear systems, whose steady-state relation between inputs and outputs can be obtained. The BP neural network represents the steady-state relation, and the ARX model represents the linear dynamic relation between inputs and outputs of the nonlinear systems. The BP-ARX model is a global model and is identified offline, while the parameters of the ARX model are rescaled online according to BP neural network and operating data. Sequential quadratic programming is employed to solve the quadratic objective function online, and a shift coefficient is defined to constrain the effect time of the recursive least-squares algorithm. Thus, a parameter varying nonlinear MPC (PVNMPC) algorithm that responds quickly to large changes in system set-points and shows good dynamic performance when system outputs approach set-points is proposed. Simulation results in a multivariable stirred tank and a multivariable pH neutralisation process illustrate the applicability of the proposed method and comparisons of the control effect between PVNMPC and multivariable recursive generalised predictive controller are also performed.
Sleeping Beauty mouse models identify candidate genes involved in gliomagenesis.
Vyazunova, Irina; Maklakova, Vilena I; Berman, Samuel; De, Ishani; Steffen, Megan D; Hong, Won; Lincoln, Hayley; Morrissy, A Sorana; Taylor, Michael D; Akagi, Keiko; Brennan, Cameron W; Rodriguez, Fausto J; Collier, Lara S
2014-01-01
Genomic studies of human high-grade gliomas have discovered known and candidate tumor drivers. Studies in both cell culture and mouse models have complemented these approaches and have identified additional genes and processes important for gliomagenesis. Previously, we found that mobilization of Sleeping Beauty transposons in mice ubiquitously throughout the body from the Rosa26 locus led to gliomagenesis with low penetrance. Here we report the characterization of mice in which transposons are mobilized in the Glial Fibrillary Acidic Protein (GFAP) compartment. Glioma formation in these mice did not occur on an otherwise wild-type genetic background, but rare gliomas were observed when mobilization occurred in a p19Arf heterozygous background. Through cloning insertions from additional gliomas generated by transposon mobilization in the Rosa26 compartment, several candidate glioma genes were identified. Comparisons to genetic, epigenetic and mRNA expression data from human gliomas implicates several of these genes as tumor suppressor genes and oncogenes in human glioblastoma.
Institute of Scientific and Technical Information of China (English)
Lukas Graber; Diomar Infante; Michael Steurer; William W. Brey
2011-01-01
Careful analysis of transients in shipboard power systems is important to achieve long life times of the com ponents in future all-electric ships. In order to accomplish results with high accuracy, it is recommended to validate cable models as they have significant influence on the amplitude and frequency spectrum of voltage transients. The authors propose comparison of model and measurement using scattering parameters. They can be easily obtained from measurement and simulation and deliver broadband information about the accuracy of the model. The measurement can be performed using a vector network analyzer. The process to extract scattering parameters from simulation models is explained in detail. Three different simulation models of a 5 kV XLPE power cable have been validated. The chosen approach delivers an efficient tool to quickly estimate the quality of a model.
Inhalation Exposure Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
M. Wasiolek
2006-06-05
This analysis is one of the technical reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), referred to in this report as the biosphere model. ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. ''Inhalation Exposure Input Parameters for the Biosphere Model'' is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the biosphere model is presented in Figure 1-1 (based on BSC 2006 [DIRS 176938]). This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and how this analysis report contributes to biosphere modeling. This analysis report defines and justifies values of atmospheric mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of the biosphere model to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception. This
Ramakrishnan, Rajasekhar; Ramakrishnan, Janak D
2010-11-01
Tracer studies are analyzed almost universally by multicompartmental models where the state variables are tracer amounts or activities in the different pools. The model parameters are rate constants, defined naturally by expressing fluxes as fractions of the source pools. We consider an alternative state space with tracer enrichments or specific activities as the state variables, with the rate constants redefined by expressing fluxes as fractions of the destination pools. Although the redefinition may seem unphysiological, the commonly computed fractional synthetic rate actually expresses synthetic flux as a fraction of the product mass (destination pool). We show that, for a variety of structures, provided the structure is linear and stationary, the model in the enrichment state space has fewer parameters than that in the activities state space, and is hence better both to study identifiability and to estimate parameters. The superiority of enrichment modeling is shown for structures where activity model unidentifiability is caused by multiple exit pathways; on the other hand, with a single exit pathway but with multiple untraced entry pathways, activity modeling is shown to be superior. With the present-day emphasis on mass isotopes, the tracer in human studies is often of a precursor, labeling most or all entry pathways. It is shown that for these tracer studies, models in the activities state space are always unidentifiable when there are multiple exit pathways, even if the enrichment in every pool is observed; on the other hand, the corresponding models in the enrichment state space have fewer parameters and are more often identifiable. Our results suggest that studies with labeled precursors are modeled best with enrichments.
Identifying and modeling the structural discontinuities of human interactions
Grauwin, Sebastian; Szell, Michael; Sobolevsky, Stanislav; Hövel, Philipp; Simini, Filippo; Vanhoof, Maarten; Smoreda, Zbigniew; Barabási, Albert-László; Ratti, Carlo
2017-04-01
The idea of a hierarchical spatial organization of society lies at the core of seminal theories in human geography that have strongly influenced our understanding of social organization. Along the same line, the recent availability of large-scale human mobility and communication data has offered novel quantitative insights hinting at a strong geographical confinement of human interactions within neighboring regions, extending to local levels within countries. However, models of human interaction largely ignore this effect. Here, we analyze several country-wide networks of telephone calls - both, mobile and landline - and in either case uncover a systematic decrease of communication induced by borders which we identify as the missing variable in state-of-the-art models. Using this empirical evidence, we propose an alternative modeling framework that naturally stylizes the damping effect of borders. We show that this new notion substantially improves the predictive power of widely used interaction models. This increases our ability to understand, model and predict social activities and to plan the development of infrastructures across multiple scales.
Directory of Open Access Journals (Sweden)
S. Vignesh
2017-04-01
Full Text Available Flow based Erosion – corrosion problems are very common in fluid handling equipments such as propellers, impellers, pumps in warships, submarine. Though there are many coating materials available to combat erosion–corrosion damage in the above components, iron based amorphous coatings are considered to be more effective to combat erosion–corrosion problems. High velocity oxy-fuel (HVOF spray process is considered to be a better process to coat the iron based amorphous powders. In this investigation, iron based amorphous metallic coating was developed on 316 stainless steel substrate using HVOF spray technique. Empirical relationships were developed to predict the porosity and micro hardness of iron based amorphous coating incorporating HVOF spray parameters such as oxygen flow rate, fuel flow rate, powder feed rate, carrier gas flow rate, and spray distance. Response surface methodology (RSM was used to identify the optimal HVOF spray parameters to attain coating with minimum porosity and maximum hardness.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Firstly, using the damage model for rock based on Lemaitre hypothesis about strain equivalence, a new technique for measuring strength of rock micro-cells by adopting the Mohr-Coulomb criterion was developed, and a statistical damage evolution equation was established based on the property that strength of micro-cells is consistent with normal distribution function, through discussing the characteristics of random distributions for strength of micro-cells, then a statistical damage constitutive model that can simulate the full process of rock strain softening under specific confining pressure was set up. Secondly, a new method to determine the model parameters which can be applied to the situations under different confining pressures was proposed, by deeply studying the relations between the model parameters and characteristic parameters of the full stress-strain curve under different confining pressures. Therefore, a unified statistical damage constitutive model for rock softening which can reflect the effect of different confining pressures was set up. This model makes the physical property of model parameters explicit, contains only conventional mechanical parameters, and leads its application more convenient. Finally, the rationality of this model and its parameters-determining method were identified via comparative analyses between theoretical and experimental curves.
Yang, Qingxia; Xu, Jun; Cao, Binggang; Li, Xiuqing
2017-01-01
Identification of internal parameters of lithium-ion batteries is a useful tool to evaluate battery performance, and requires an effective model and algorithm. Based on the least square genetic algorithm, a simplified fractional order impedance model for lithium-ion batteries and the corresponding parameter identification method were developed. The simplified model was derived from the analysis of the electrochemical impedance spectroscopy data and the transient response of lithium-ion batteries with different states of charge. In order to identify the parameters of the model, an equivalent tracking system was established, and the method of least square genetic algorithm was applied using the time-domain test data. Experiments and computer simulations were carried out to verify the effectiveness and accuracy of the proposed model and parameter identification method. Compared with a second-order resistance-capacitance (2-RC) model and recursive least squares method, small tracing voltage fluctuations were observed. The maximum battery voltage tracing error for the proposed model and parameter identification method is within 0.5%; this demonstrates the good performance of the model and the efficiency of the least square genetic algorithm to estimate the internal parameters of lithium-ion batteries. PMID:28212405
Sensitivity analysis of CLIMEX parameters in modelling potential distribution of Lantana camara L.
Directory of Open Access Journals (Sweden)
Subhashni Taylor
Full Text Available A process-based niche model of L. camara L. (lantana, a highly invasive shrub species, was developed to estimate its potential distribution using CLIMEX. Model development was carried out using its native and invasive distribution and validation was carried out with the extensive Australian distribution. A good fit was observed, with 86.7% of herbarium specimens collected in Australia occurring within the suitable and highly suitable categories. A sensitivity analysis was conducted to identify the model parameters that had the most influence on lantana distribution. The changes in suitability were assessed by mapping the regions where the distribution changed with each parameter alteration. This allowed an assessment of where, within Australia, the modification of each parameter was having the most impact, particularly in terms of the suitable and highly suitable locations. The sensitivity of various parameters was also evaluated by calculating the changes in area within the suitable and highly suitable categories. The limiting low temperature (DV0, limiting high temperature (DV3 and limiting low soil moisture (SM0 showed highest sensitivity to change. The other model parameters were relatively insensitive to change. Highly sensitive parameters require extensive research and data collection to be fitted accurately in species distribution models. The results from this study can inform more cost effective development of species distribution models for lantana. Such models form an integral part of the management of invasive species and the results can be used to streamline data collection requirements for potential distribution modelling.
Yang, Qingxia; Xu, Jun; Cao, Binggang; Li, Xiuqing
2017-01-01
Identification of internal parameters of lithium-ion batteries is a useful tool to evaluate battery performance, and requires an effective model and algorithm. Based on the least square genetic algorithm, a simplified fractional order impedance model for lithium-ion batteries and the corresponding parameter identification method were developed. The simplified model was derived from the analysis of the electrochemical impedance spectroscopy data and the transient response of lithium-ion batteries with different states of charge. In order to identify the parameters of the model, an equivalent tracking system was established, and the method of least square genetic algorithm was applied using the time-domain test data. Experiments and computer simulations were carried out to verify the effectiveness and accuracy of the proposed model and parameter identification method. Compared with a second-order resistance-capacitance (2-RC) model and recursive least squares method, small tracing voltage fluctuations were observed. The maximum battery voltage tracing error for the proposed model and parameter identification method is within 0.5%; this demonstrates the good performance of the model and the efficiency of the least square genetic algorithm to estimate the internal parameters of lithium-ion batteries.
SBMLSimulator: A Java Tool for Model Simulation and Parameter Estimation in Systems Biology
Directory of Open Access Journals (Sweden)
Alexander Dörr
2014-12-01
Full Text Available The identification of suitable model parameters for biochemical reactions has been recognized as a quite difficult endeavor. Parameter values from literature or experiments can often not directly be combined in complex reaction systems. Nature-inspired optimization techniques can find appropriate sets of parameters that calibrate a model to experimentally obtained time series data. We present SBMLsimulator, a tool that combines the Systems Biology Simulation Core Library for dynamic simulation of biochemical models with the heuristic optimization framework EvA2. SBMLsimulator provides an intuitive graphical user interface with various options as well as a fully-featured command-line interface for large-scale and script-based model simulation and calibration. In a parameter estimation study based on a published model and artificial data we demonstrate the capability of SBMLsimulator to identify parameters. SBMLsimulator is useful for both, the interactive simulation and exploration of the parameter space and for the large-scale model calibration and estimation of uncertain parameter values.
Estimation of growth parameters using a nonlinear mixed Gompertz model.
Wang, Z; Zuidhof, M J
2004-06-01
In order to maximize the utility of simulation models for decision making, accurate estimation of growth parameters and associated variances is crucial. A mixed Gompertz growth model was used to account for between-bird variation and heterogeneous variance. The mixed model had several advantages over the fixed effects model. The mixed model partitioned BW variation into between- and within-bird variation, and the covariance structure assumed with the random effect accounted for part of the BW correlation across ages in the same individual. The amount of residual variance decreased by over 55% with the mixed model. The mixed model reduced estimation biases that resulted from selective sampling. For analysis of longitudinal growth data, the mixed effects growth model is recommended.
Modelling Biophysical Parameters of Maize Using Landsat 8 Time Series
Dahms, Thorsten; Seissiger, Sylvia; Conrad, Christopher; Borg, Erik
2016-06-01
Open and free access to multi-frequent high-resolution data (e.g. Sentinel - 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area index (LAI) and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD): R² = 0.80; RMSE=4.9. Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing datasets to model
Mathematical Modelling and Parameter Optimization of Pulsating Heat Pipes
Yang, Xin-She; Luan, Tao; Koziel, Slawomir
2014-01-01
Proper heat transfer management is important to key electronic components in microelectronic applications. Pulsating heat pipes (PHP) can be an efficient solution to such heat transfer problems. However, mathematical modelling of a PHP system is still very challenging, due to the complexity and multiphysics nature of the system. In this work, we present a simplified, two-phase heat transfer model, and our analysis shows that it can make good predictions about startup characteristics. Furthermore, by considering parameter estimation as a nonlinear constrained optimization problem, we have used the firefly algorithm to find parameter estimates efficiently. We have also demonstrated that it is possible to obtain good estimates of key parameters using very limited experimental data.
The influences of model parameters on the characteristics of memristors
Institute of Scientific and Technical Information of China (English)
Zhou Jing; Huang Da
2012-01-01
As the fourth passive circuit component,a memristor is a nonlinear resistor that can "remember" the amount of charge passing through it.The characteristic of "remembering" the charge and non-volatility makes memristors great potential candidates in many fields.Nowadays,only a few groups have the ability to fabricate memristors,and most researchers study them by theoretic analysis and simulation.In this paper,we first analyse the theoretical base and characteristics of memristors,then use a simulation program with integrated circuit emphasis as our tool to simulate the theoretical model of memristors and change the parameters in the model to see the influence of each parameter on the characteristics.Our work supplies researchers engaged in memristor-based circuits with advice on how to choose the proper parameters.
Saccomani, Maria Pia; Audoly, Stefania; Bellu, Giuseppina; D'Angiò, Leontina
2010-04-01
DAISY (Differential Algebra for Identifiability of SYstems) is a recently developed computer algebra software tool which can be used to automatically check global identifiability of (linear and) nonlinear dynamic models described by differential equations involving polynomial or rational functions. Global identifiability is a fundamental prerequisite for model identification which is important not only for biological or medical systems but also for many physical and engineering systems derived from first principles. Lack of identifiability implies that the parameter estimation techniques may not fail but any obtained numerical estimates will be meaningless. The software does not require understanding of the underlying mathematical principles and can be used by researchers in applied fields with a minimum of mathematical background. We illustrate the DAISY software by checking the a priori global identifiability of two benchmark nonlinear models taken from the literature. The analysis of these two examples includes comparison with other methods and demonstrates how identifiability analysis is simplified by this tool. Thus we illustrate the identifiability analysis of other two examples, by including discussion of some specific aspects related to the role of observability and knowledge of initial conditions in testing identifiability and to the computational complexity of the software. The main focus of this paper is not on the description of the mathematical background of the algorithm, which has been presented elsewhere, but on illustrating its use and on some of its more interesting features. DAISY is available on the web site http://www.dei.unipd.it/ approximately pia/.
Prediction of interest rate using CKLS model with stochastic parameters
Energy Technology Data Exchange (ETDEWEB)
Ying, Khor Chia [Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Selangor (Malaysia); Hin, Pooi Ah [Sunway University Business School, No. 5, Jalan Universiti, Bandar Sunway, 47500 Subang Jaya, Selangor (Malaysia)
2014-06-19
The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ{sup (j)} of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j′-th time point where j≤j′≤j+n. To model the variation of φ{sup (j)}, we assume that φ{sup (j)} depends on φ{sup (j−m)}, φ{sup (j−m+1)},…, φ{sup (j−1)} and the interest rate r{sub j+n} at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r{sub j+n+1} of the interest rate at the next time point when the value r{sub j+n} of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r{sub j+n+d} at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters.
Revised Parameters for the AMOEBA Polarizable Atomic Multipole Water Model.
Laury, Marie L; Wang, Lee-Ping; Pande, Vijay S; Head-Gordon, Teresa; Ponder, Jay W
2015-07-23
A set of improved parameters for the AMOEBA polarizable atomic multipole water model is developed. An automated procedure, ForceBalance, is used to adjust model parameters to enforce agreement with ab initio-derived results for water clusters and experimental data for a variety of liquid phase properties across a broad temperature range. The values reported here for the new AMOEBA14 water model represent a substantial improvement over the previous AMOEBA03 model. The AMOEBA14 model accurately predicts the temperature of maximum density and qualitatively matches the experimental density curve across temperatures from 249 to 373 K. Excellent agreement is observed for the AMOEBA14 model in comparison to experimental properties as a function of temperature, including the second virial coefficient, enthalpy of vaporization, isothermal compressibility, thermal expansion coefficient, and dielectric constant. The viscosity, self-diffusion constant, and surface tension are also well reproduced. In comparison to high-level ab initio results for clusters of 2-20 water molecules, the AMOEBA14 model yields results similar to AMOEBA03 and the direct polarization iAMOEBA models. With advances in computing power, calibration data, and optimization techniques, we recommend the use of the AMOEBA14 water model for future studies employing a polarizable water model.
Parameter identification theory of a complex model based on global optimization method
Institute of Scientific and Technical Information of China (English)
2008-01-01
With the development of computer technology and numerical simulation technol- ogy, computer aided engineering (CAE) technology has been widely applied to many fields. One of the main obstacles, which hinder the further application of CAE technology, is how to successfully identify the parameters of the selected model. An elementary framework for parameter identification of a complex model is pro-vided in this paper. The framework includes the construction of objective function, the design of the optimization method and the evaluation of the identified results, etc. The parameter identification process is described in this framework, taking the parameter identification of the superplastic constitutive model considering grain growth for Ti-6Al-4V at 927℃ as an example. The objective function is the weighted quadratic sums of the difference between the experimental and computational data for the stress-strain relationship and the grain growth relationship; the designed optimization method is a hybrid global optimization method, which is based on the feature of the objective function and incorporates the strengths of genetic algo-rithm (GA), the Levenberg-Marquardt algorithm and the augmented Gauss-Newton algorithm. The reliability evaluation of parameter identification result is made through the comparison between the calculated and experimental results and be-tween the theoretical values of the parameters and the identified ones.
Comparison of Parameter Estimation Methods for Transformer Weibull Lifetime Modelling
Institute of Scientific and Technical Information of China (English)
ZHOU Dan; LI Chengrong; WANG Zhongdong
2013-01-01
Two-parameter Weibull distribution is the most widely adopted lifetime model for power transformers.An appropriate parameter estimation method is essential to guarantee the accuracy of a derived Weibull lifetime model.Six popular parameter estimation methods (i.e.the maximum likelihood estimation method,two median rank regression methods including the one regressing X on Y and the other one regressing Y on X,the Kaplan-Meier method,the method based on cumulative hazard plot,and the Li's method) are reviewed and compared in order to find the optimal one that suits transformer's Weibull lifetime modelling.The comparison took several different scenarios into consideration:10 000 sets of lifetime data,each of which had a sampling size of 40 ～ 1 000 and a censoring rate of 90％,were obtained by Monte-Carlo simulations for each scienario.Scale and shape parameters of Weibull distribution estimated by the six methods,as well as their mean value,median value and 90％ confidence band are obtained.The cross comparison of these results reveals that,among the six methods,the maximum likelihood method is the best one,since it could provide the most accurate Weibull parameters,i.e.parameters having the smallest bias in both mean and median values,as well as the shortest length of the 90％ confidence band.The maximum likelihood method is therefore recommended to be used over the other methods in transformer Weibull lifetime modelling.
Calculation of Thermodynamic Parameters for Freundlich and Temkin Isotherm Models
Institute of Scientific and Technical Information of China (English)
ZHANGZENGQIANG; ZHANGYIPING; 等
1999-01-01
Derivation of the Freundlich and Temkin isotherm models from the kinetic adsorption/desorption equations was carried out to calculate their thermodynamic equilibrium constants.The calculation formulase of three thermodynamic parameters,the standard molar Gibbs free energy change,the standard molar enthalpy change and the standard molar entropy change,of isothermal adsorption processes for Freundlich and Temkin isotherm models were deduced according to the relationship between the thermodynamic equilibrium constants and the temperature.
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...
Parabolic problems with parameters arising in evolution model for phytromediation
Sahmurova, Aida; Shakhmurov, Veli
2012-12-01
The past few decades, efforts have been made to clean sites polluted by heavy metals as chromium. One of the new innovative methods of eradicating metals from soil is phytoremediation. This uses plants to pull metals from the soil through the roots. This work develops a system of differential equations with parameters to model the plant metal interaction of phytoremediation (see [1]).
Lumped-parameter Model of a Bucket Foundation
DEFF Research Database (Denmark)
Andersen, Lars; Ibsen, Lars Bo; Liingaard, Morten
2009-01-01
As an alternative to gravity footings or pile foundations, offshore wind turbines at shallow water can be placed on a bucket foundation. The present analysis concerns the development of consistent lumped-parameter models for this type of foundation. The aim is to formulate a computationally effic...
Improved parameter estimation for hydrological models using weighted object functions
Stein, A.; Zaadnoordijk, W.J.
1999-01-01
This paper discusses the sensitivity of calibration of hydrological model parameters to different objective functions. Several functions are defined with weights depending upon the hydrological background. These are compared with an objective function based upon kriging. Calibration is applied to pi
Parameter Estimation for a Computable General Equilibrium Model
DEFF Research Database (Denmark)
Arndt, Channing; Robinson, Sherman; Tarp, Finn
We introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of nonlinear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints...
PARAMETER ESTIMATION IN LINEAR REGRESSION MODELS FOR LONGITUDINAL CONTAMINATED DATA
Institute of Scientific and Technical Information of China (English)
QianWeimin; LiYumei
2005-01-01
The parameter estimation and the coefficient of contamination for the regression models with repeated measures are studied when its response variables are contaminated by another random variable sequence. Under the suitable conditions it is proved that the estimators which are established in the paper are strongly consistent estimators.
Modeling and simulation of HTS cables for scattering parameter analysis
Bang, Su Sik; Lee, Geon Seok; Kwon, Gu-Young; Lee, Yeong Ho; Chang, Seung Jin; Lee, Chun-Kwon; Sohn, Songho; Park, Kijun; Shin, Yong-June
2016-11-01
Most of modeling and simulation of high temperature superconducting (HTS) cables are inadequate for high frequency analysis since focus of the simulation's frequency is fundamental frequency of the power grid, which does not reflect transient characteristic. However, high frequency analysis is essential process to research the HTS cables transient for protection and diagnosis of the HTS cables. Thus, this paper proposes a new approach for modeling and simulation of HTS cables to derive the scattering parameter (S-parameter), an effective high frequency analysis, for transient wave propagation characteristics in high frequency range. The parameters sweeping method is used to validate the simulation results to the measured data given by a network analyzer (NA). This paper also presents the effects of the cable-to-NA connector in order to minimize the error between the simulated and the measured data under ambient and superconductive conditions. Based on the proposed modeling and simulation technique, S-parameters of long-distance HTS cables can be accurately derived in wide range of frequency. The results of proposed modeling and simulation can yield the characteristics of the HTS cables and will contribute to analyze the HTS cables.
Directory of Open Access Journals (Sweden)
H. C. Winsemius
2006-01-01
Full Text Available Variations of water stocks in the upper Zambezi river basin have been determined by 2 different hydrological modelling approaches. The purpose was to provide preliminary terrestrial storage estimates in the upper Zambezi, which will be compared with estimates derived from the Gravity Recovery And Climate Experiment (GRACE in a future study. The first modelling approach is GIS-based, distributed and conceptual (STREAM. The second approach uses Lumped Elementary Watersheds identified and modelled conceptually (LEW. The STREAM model structure has been assessed using GLUE (Generalized Likelihood Uncertainty Estimation a posteriori to determine parameter identifiability. The LEW approach could, in addition, be tested for model structure, because computational efforts of LEW are low. Both models are threshold models, where the non-linear behaviour of the Zambezi river basin is explained by a combination of thresholds and linear reservoirs. The models were forced by time series of gauged and interpolated rainfall. Where available, runoff station data was used to calibrate the models. Ungauged watersheds were generally given the same parameter sets as their neighbouring calibrated watersheds. It appeared that the LEW model structure could be improved by applying GLUE iteratively. Eventually, it led to better identifiability of parameters and consequently a better model structure than the STREAM model. Hence, the final model structure obtained better represents the true hydrology. After calibration, both models show a comparable efficiency in representing discharge. However the LEW model shows a far greater storage amplitude than the STREAM model. This emphasizes the storage uncertainty related to hydrological modelling in data-scarce environments such as the Zambezi river basin. It underlines the need and potential for independent observations of terrestrial storage to enhance our understanding and modelling capacity of the hydrological processes. GRACE
FRIGA, A New Approach To Identify Isotopes and Hypernuclei In N-Body Transport Models
Fèvre, A Le; Aichelin, J; Hartnack, Ch; Kireyev, V; Bratkovskaya, E
2015-01-01
We present a new algorithm to identify fragments in computer simulations of relativistic heavy ion collisions. It is based on the simulated annealing technique and can be applied to n-body transport models like the Quantum Molecular Dynamics. This new approach is able to predict isotope yields as well as hyper-nucleus production. In order to illustrate its predicting power, we confront this new method to experimental data, and show the sensitivity on the parameters which govern the cluster formation.
Identifiability of Gaussian Structural Equation Models with Same Error Variances
Peters, Jonas
2012-01-01
We consider structural equation models (SEMs) in which variables can be written as a function of their parents and noise terms (the latter are assumed to be jointly independent). Corresponding to each SEM, there is a directed acyclic graph (DAG) G_0 describing the relationships between the variables. In Gaussian SEMs with linear functions, the graph can be identified from the joint distribution only up to Markov equivalence classes (assuming faithfulness). It has been shown, however, that this constitutes an exceptional case. In the case of linear functions and non-Gaussian noise, the DAG becomes identifiable. Apart from few exceptions the same is true for non-linear functions and arbitrarily distributed additive noise. In this work, we prove identifiability for a third modification: if we require all noise variables to have the same variances, again, the DAG can be recovered from the joint Gaussian distribution. Our result can be applied to the problem of causal inference. If the data follow a Gaussian SEM w...
Evaluation of some infiltration models and hydraulic parameters
Energy Technology Data Exchange (ETDEWEB)
Haghighi, F.; Gorji, M.; Shorafa, M.; Sarmadian, F.; Mohammadi, M. H.
2010-07-01
The evaluation of infiltration characteristics and some parameters of infiltration models such as sorptivity and final steady infiltration rate in soils are important in agriculture. The aim of this study was to evaluate some of the most common models used to estimate final soil infiltration rate. The equality of final infiltration rate with saturated hydraulic conductivity (Ks) was also tested. Moreover, values of the estimated sorptivity from the Philips model were compared to estimates by selected pedotransfer functions (PTFs). The infiltration experiments used the doublering method on soils with two different land uses in the Taleghan watershed of Tehran province, Iran, from September to October, 2007. The infiltration models of Kostiakov-Lewis, Philip two-term and Horton were fitted to observed infiltration data. Some parameters of the models and the coefficient of determination goodness of fit were estimated using MATLAB software. The results showed that, based on comparing measured and model-estimated infiltration rate using root mean squared error (RMSE), Hortons model gave the best prediction of final infiltration rate in the experimental area. Laboratory measured Ks values gave significant differences and higher values than estimated final infiltration rates from the selected models. The estimated final infiltration rate was not equal to laboratory measured Ks values in the study area. Moreover, the estimated sorptivity factor by Philips model was significantly different to those estimated by selected PTFs. It is suggested that the applicability of PTFs is limited to specific, similar conditions. (Author) 37 refs.
Agricultural and Environmental Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
K. Rasmuson; K. Rautenstrauch
2004-09-14
This analysis is one of 10 technical reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) (i.e., the biosphere model). It documents development of agricultural and environmental input parameters for the biosphere model, and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for the repository at Yucca Mountain. The ERMYN provides the TSPA with the capability to perform dose assessments. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships between the major activities and their products (the analysis and model reports) that were planned in ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the ERMYN and its input parameters.
Estimating model parameters in nonautonomous chaotic systems using synchronization
Yang, Xiaoli; Xu, Wei; Sun, Zhongkui
2007-05-01
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.
Estimating model parameters in nonautonomous chaotic systems using synchronization
Energy Technology Data Exchange (ETDEWEB)
Yang, Xiaoli [Department of Applied Mathematics, Northwestern Polytechnical University, Xi' an 710072 (China)]. E-mail: yangxl205@mail.nwpu.edu.cn; Xu, Wei [Department of Applied Mathematics, Northwestern Polytechnical University, Xi' an 710072 (China); Sun, Zhongkui [Department of Applied Mathematics, Northwestern Polytechnical University, Xi' an 710072 (China)
2007-05-07
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.
Multiscale Parameter Regionalization for consistent global water resources modelling
Wanders, Niko; Wood, Eric; Pan, Ming; Samaniego, Luis; Thober, Stephan; Kumar, Rohini; Sutanudjaja, Edwin; van Beek, Rens; Bierkens, Marc F. P.
2017-04-01
Due to an increasing demand for high- and hyper-resolution water resources information, it has become increasingly important to ensure consistency in model simulations across scales. This consistency can be ensured by scale independent parameterization of the land surface processes, even after calibration of the water resource model. Here, we use the Multiscale Parameter Regionalization technique (MPR, Samaniego et al. 2010, WRR) to allow for a novel, spatially consistent, scale independent parameterization of the global water resource model PCR-GLOBWB. The implementation of MPR in PCR-GLOBWB allows for calibration at coarse resolutions and subsequent parameter transfer to the hyper-resolution. In this study, the model was calibrated at 50 km resolution over Europe and validation carried out at resolutions of 50 km, 10 km and 1 km. MPR allows for a direct transfer of the calibrated transfer function parameters across scales and we find that we can maintain consistent land-atmosphere fluxes across scales. Here we focus on the 2003 European drought and show that the new parameterization allows for high-resolution calibrated simulations of water resources during the drought. For example, we find a reduction from 29% to 9.4% in the percentile difference in the annual evaporative flux across scales when compared against default simulations. Soil moisture errors are reduced from 25% to 6.9%, clearly indicating the benefits of the MPR implementation. This new parameterization allows us to show more spatial detail in water resources simulations that are consistent across scales and also allow validation of discharge for smaller catchments, even with calibrations at a coarse 50 km resolution. The implementation of MPR allows for novel high-resolution calibrated simulations of a global water resources model, providing calibrated high-resolution model simulations with transferred parameter sets from coarse resolutions. The applied methodology can be transferred to other
Model and parameter uncertainty in IDF relationships under climate change
Chandra, Rupa; Saha, Ujjwal; Mujumdar, P. P.
2015-05-01
Quantifying distributional behavior of extreme events is crucial in hydrologic designs. Intensity Duration Frequency (IDF) relationships are used extensively in engineering especially in urban hydrology, to obtain return level of extreme rainfall event for a specified return period and duration. Major sources of uncertainty in the IDF relationships are due to insufficient quantity and quality of data leading to parameter uncertainty due to the distribution fitted to the data and uncertainty as a result of using multiple GCMs. It is important to study these uncertainties and propagate them to future for accurate assessment of return levels for future. The objective of this study is to quantify the uncertainties arising from parameters of the distribution fitted to data and the multiple GCM models using Bayesian approach. Posterior distribution of parameters is obtained from Bayes rule and the parameters are transformed to obtain return levels for a specified return period. Markov Chain Monte Carlo (MCMC) method using Metropolis Hastings algorithm is used to obtain the posterior distribution of parameters. Twenty six CMIP5 GCMs along with four RCP scenarios are considered for studying the effects of climate change and to obtain projected IDF relationships for the case study of Bangalore city in India. GCM uncertainty due to the use of multiple GCMs is treated using Reliability Ensemble Averaging (REA) technique along with the parameter uncertainty. Scale invariance theory is employed for obtaining short duration return levels from daily data. It is observed that the uncertainty in short duration rainfall return levels is high when compared to the longer durations. Further it is observed that parameter uncertainty is large compared to the model uncertainty.
Parameters in dynamic models of complex traits are containers of missing heritability.
Directory of Open Access Journals (Sweden)
Yunpeng Wang
Full Text Available Polymorphisms identified in genome-wide association studies of human traits rarely explain more than a small proportion of the heritable variation, and improving this situation within the current paradigm appears daunting. Given a well-validated dynamic model of a complex physiological trait, a substantial part of the underlying genetic variation must manifest as variation in model parameters. These parameters are themselves phenotypic traits. By linking whole-cell phenotypic variation to genetic variation in a computational model of a single heart cell, incorporating genotype-to-parameter maps, we show that genome-wide association studies on parameters reveal much more genetic variation than when using higher-level cellular phenotypes. The results suggest that letting such studies be guided by computational physiology may facilitate a causal understanding of the genotype-to-phenotype map of complex traits, with strong implications for the development of phenomics technology.
Reduced parameter model on trajectory tracking data with applications
Institute of Scientific and Technical Information of China (English)
王正明; 朱炬波
1999-01-01
The data fusion in tracking the same trajectory by multi-measurernent unit (MMU) is considered. Firstly, the reduced parameter model (RPM) of trajectory parameter (TP), system error and random error are presented,and then the RPM on trajectory tracking data (TTD) is obtained, a weighted method on measuring elements (ME) is studied and criteria on selection of ME based on residual and accuracy estimation are put forward. According to RPM,the problem about selection of ME and self-calibration of TTD is thoroughly investigated. The method improves data accuracy in trajectory tracking obviously and gives accuracy evaluation of trajectory tracking system simultaneously.
Parameter Estimation of the Extended Vasiček Model
Rujivan, Sanae
2010-01-01
In this paper, an estimate of the drift and diffusion parameters of the extended Vasiček model is presented. The estimate is based on the method of maximum likelihood. We derive a closed-form expansion for the transition (probability) density of the extended Vasiček process and use the expansion to construct an approximate log-likelihood function of a discretely sampled data of the process. Approximate maximum likelihood estimators (AMLEs) of the parameters are obtained by maximizing the appr...
Prediction of mortality rates using a model with stochastic parameters
Tan, Chon Sern; Pooi, Ah Hin
2016-10-01
Prediction of future mortality rates is crucial to insurance companies because they face longevity risks while providing retirement benefits to a population whose life expectancy is increasing. In the past literature, a time series model based on multivariate power-normal distribution has been applied on mortality data from the United States for the years 1933 till 2000 to forecast the future mortality rates for the years 2001 till 2010. In this paper, a more dynamic approach based on the multivariate time series will be proposed where the model uses stochastic parameters that vary with time. The resulting prediction intervals obtained using the model with stochastic parameters perform better because apart from having good ability in covering the observed future mortality rates, they also tend to have distinctly shorter interval lengths.
Mark-recapture models with parameters constant in time.
Jolly, G M
1982-06-01
The Jolly-Seber method, which allows for both death and immigration, is easy to apply but often requires a larger number of parameters to be estimated tha would otherwise be necessary. If (i) survival rate, phi, or (ii) probability of capture, p, or (iii) both phi and p can be assumed constant over the experimental period, models with a reduced number of parameters are desirable. In the present paper, maximum likelihood (ML) solutions for these three situations are derived from the general ML equations of Jolly [1979, in Sampling Biological Populations, R. M. Cormack, G. P. Patil and D. S. Robson (eds), 277-282]. A test is proposed for heterogeneity arising from a breakdown of assumptions in the general Jolly-Seber model. Tests for constancy of phi and p are provided. An example is given, in which these models are fitted to data from a local butterfly population.
DEFF Research Database (Denmark)
Darula, Radoslav; Stein, George Juraj; Kallesøe, Carsten Skovmose
2012-01-01
Electromechanical systems for vibration control exhibit complex non-linear behaviour. Therefore advanced mathematical tools and appropriate simplifications are required for their modelling. To properly understand the dynamics of such a non-linear system, it is necessary to identify the parameters...
Identifying Spatially Variable Sensitivity of Model Predictions and Calibrations
McKenna, S. A.; Hart, D. B.
2005-12-01
Stochastic inverse modeling provides an ensemble of stochastic property fields, each calibrated to measured steady-state and transient head data. These calibrated fields are used as input for predictions of other processes (e.g., contaminant transport, advective travel time). Use of the entire ensemble of fields transfers spatial uncertainty in hydraulic properties to uncertainty in the predicted performance measures. A sampling-based sensitivity coefficient is proposed to determine the sensitivity of the performance measures to the uncertain values of hydraulic properties at every cell in the model domain. The basis of this sensitivity coefficient is the Spearman rank correlation coefficient. Sampling-based sensitivity coefficients are demonstrated using a recent set of transmissivity (T) fields created through a stochastic inverse calibration process for the Culebra dolomite in the vicinity of the WIPP site in southeastern New Mexico. The stochastic inverse models were created using a unique approach to condition a geologically-based conceptual model of T to measured T values via a multiGaussian residual field. This field is calibrated to both steady-state and transient head data collected over an 11 year period. Maps of these sensitivity coefficients provide a means of identifying the locations in the study area to which both the value of the model calibration objective function and the predicted travel times to a regulatory boundary are most sensitive to the T and head values. These locations can be targeted for deployment of additional long-term monitoring resources. Comparison of areas where the calibration objective function and the travel time have high sensitivity shows that these are not necessarily coincident with regions of high uncertainty. The sampling-based sensitivity coefficients are compared to analytically derived sensitivity coefficients at the 99 pilot point locations. Results of the sensitivity mapping exercise are being used in combination
Bailer-Jones, C. A. L.
2010-03-01
I introduce an algorithm for estimating parameters from multidimensional data based on forward modelling. It performs an iterative local search to effectively achieve a non-linear interpolation of a template grid. In contrast to many machine-learning approaches, it avoids fitting an inverse model and the problems associated with this. The algorithm makes explicit use of the sensitivities of the data to the parameters, with the goal of better treating parameters which only have a weak impact on the data. The forward modelling approach provides uncertainty (full covariance) estimates in the predicted parameters as well as a goodness-of-fit for observations, thus providing a simple means of identifying outliers. I demonstrate the algorithm, ILIUM, with the estimation of stellar astrophysical parameters (APs) from simulations of the low-resolution spectrophotometry to be obtained by Gaia. The AP accuracy is competitive with that obtained by a support vector machine. For zero extinction stars covering a wide range of metallicity, surface gravity and temperature, ILIUM can estimate Teff to an accuracy of 0.3 per cent at G = 15 and to 4 per cent for (lower signal-to-noise ratio) spectra at G = 20, the Gaia limiting magnitude (mean absolute errors are quoted). [Fe/H] and logg can be estimated to accuracies of 0.1-0.4dex for stars with G <= 18.5, depending on the magnitude and what priors we can place on the APs. If extinction varies a priori over a wide range (0-10mag) - which will be the case with Gaia because it is an all-sky survey - then logg and [Fe/H] can still be estimated to 0.3 and 0.5dex, respectively, at G = 15, but much poorer at G = 18.5. Teff and AV can be estimated quite accurately (3-4 per cent and 0.1-0.2mag, respectively, at G = 15), but there is a strong and ubiquitous degeneracy in these parameters which limits our ability to estimate either accurately at faint magnitudes. Using the forward model, we can map these degeneracies (in advance) and thus
Singularity of Some Software Reliability Models and Parameter Estimation Method
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
According to the principle, “The failure data is the basis of software reliability analysis”, we built a software reliability expert system (SRES) by adopting the artificial intelligence technology. By reasoning out the conclusion from the fitting results of failure data of a software project, the SRES can recommend users “the most suitable model” as a software reliability measurement model. We believe that the SRES can overcome the inconsistency in applications of software reliability models well. We report investigation results of singularity and parameter estimation methods of experimental models in SRES.
Energy Technology Data Exchange (ETDEWEB)
Morrison, J.L.
1992-12-01
The objective of this research is to develop a simple, yet accurate, lumped parameter mathematical model for an explosively driven magnetohydrodynamic generator that can predict the pulse power variables of voltage and current from startup through regenerative operation. The inputs to the model will be the plasma properties entering the generator as predicted by the explosive shock model of Reference [1]. The strategy used was to simplify electromagnetic and thermodynamic three dimensional effects into a zero dimensional model. The model will provide a convenient tool for researchers to optimize designs to be used in pulse power applications. The model is validated using experimental data of Reference [1]. An overview of the operation of the explosively driven generator is first presented. Then a simplified electrical circuit model that describes basic performance of the device is developed. Then a lumped parameter model that incorporates the coupled electromagnetic and thermodynamic effects that govern generator performance is described and developed. The model is based on fundamental physical principles and parameters that were either obtained directly from design data or estimated from experimental data. The model was used to obtain parameter sensitivities and predict beyond the limits observed in the experiments to the levels desired by the potential Department of Defense sponsors. The model identifies process limitations that provide direction for future research.
Directory of Open Access Journals (Sweden)
Farzin Shabani
Full Text Available Using CLIMEX and the Taguchi Method, a process-based niche model was developed to estimate potential distributions of Phoenix dactylifera L. (date palm, an economically important crop in many counties. Development of the model was based on both its native and invasive distribution and validation was carried out in terms of its extensive distribution in Iran. To identify model parameters having greatest influence on distribution of date palm, a sensitivity analysis was carried out. Changes in suitability were established by mapping of regions where the estimated distribution changed with parameter alterations. This facilitated the assessment of certain areas in Iran where parameter modifications impacted the most, particularly in relation to suitable and highly suitable locations. Parameter sensitivities were also evaluated by the calculation of area changes within the suitable and highly suitable categories. The low temperature limit (DV2, high temperature limit (DV3, upper optimal temperature (SM2 and high soil moisture limit (SM3 had the greatest impact on sensitivity, while other parameters showed relatively less sensitivity or were insensitive to change. For an accurate fit in species distribution models, highly sensitive parameters require more extensive research and data collection methods. Results of this study demonstrate a more cost effective method for developing date palm distribution models, an integral element in species management, and may prove useful for streamlining requirements for data collection in potential distribution modeling for other species as well.
Directory of Open Access Journals (Sweden)
Kottner R.
2013-12-01
Full Text Available Adhesively bonded joints can be numerically simulated using the cohesive crack model. The critical strain energy release rate and the critical opening displacement are the parameters which must be known when cohesive elements in MSC.Marc software are used. In this work, the parameters of two industrial adhesives Hunstman Araldite 2021 and Gurit Spabond 345 for bonding of epoxy composites are identified. Double Cantilever Beam (DCB and End Notched Flexure (ENF test data were used for the identification. The critical opening displacements were identified using an optimization algorithm where the tests and their numerical simulations were compared.
Robust linear parameter varying induction motor control with polytopic models
Directory of Open Access Journals (Sweden)
Dalila Khamari
2013-01-01
Full Text Available This paper deals with a robust controller for an induction motor which is represented as a linear parameter varying systems. To do so linear matrix inequality (LMI based approach and robust Lyapunov feedback controller are associated. This new approach is related to the fact that the synthesis of a linear parameter varying (LPV feedback controller for the inner loop take into account rotor resistance and mechanical speed as varying parameter. An LPV flux observer is also synthesized to estimate rotor flux providing reference to cited above regulator. The induction motor is described as a polytopic model because of speed and rotor resistance affine dependence their values can be estimated on line during systems operations. The simulation results are presented to confirm the effectiveness of the proposed approach where robustness stability and high performances have been achieved over the entire operating range of the induction motor.
Minimum information modelling of structural systems with uncertain parameters
Hyland, D. C.
1983-01-01
Work is reviewed wherein the design of active structural control is formulated as the mean-square optimal control of a linear mechanical system with stochastic parameters. In practice, a complete probabilistic description of model parameters can never be provided by empirical determinations, and a suitable design approach must accept very limited a priori data on parameter statistics. In consequence, the mean-square optimization problem is formulated using a complete probability assignment which is made to be consistent with available data but maximally unconstrained otherwise through use of a maximum entropy principle. The ramifications of this approach for both robustness and large dimensionality are illustrated by consideration of the full-state feedback regulation problem.
Parameter estimation in a spatial unit root autoregressive model
Baran, Sándor
2011-01-01
Spatial autoregressive model $X_{k,\\ell}=\\alpha X_{k-1,\\ell}+\\beta X_{k,\\ell-1}+\\gamma X_{k-1,\\ell-1}+\\epsilon_{k,\\ell}$ is investigated in the unit root case, that is when the parameters are on the boundary of the domain of stability that forms a tetrahedron with vertices $(1,1,-1), \\ (1,-1,1),\\ (-1,1,1)$ and $(-1,-1,-1)$. It is shown that the limiting distribution of the least squares estimator of the parameters is normal and the rate of convergence is $n$ when the parameters are in the faces or on the edges of the tetrahedron, while on the vertices the rate is $n^{3/2}$.
Rheumatoid arthritis: identifying and characterising polymorphisms using rat models
2016-01-01
ABSTRACT Rheumatoid arthritis is a chronic inflammatory joint disorder characterised by erosive inflammation of the articular cartilage and by destruction of the synovial joints. It is regulated by both genetic and environmental factors, and, currently, there is no preventative treatment or cure for this disease. Genome-wide association studies have identified ∼100 new loci associated with rheumatoid arthritis, in addition to the already known locus within the major histocompatibility complex II region. However, together, these loci account for only a modest fraction of the genetic variance associated with this disease and very little is known about the pathogenic roles of most of the risk loci identified. Here, we discuss how rat models of rheumatoid arthritis are being used to detect quantitative trait loci that regulate different arthritic traits by genetic linkage analysis and to positionally clone the underlying causative genes using congenic strains. By isolating specific loci on a fixed genetic background, congenic strains overcome the challenges of genetic heterogeneity and environmental interactions associated with human studies. Most importantly, congenic strains allow functional experimental studies be performed to investigate the pathological consequences of natural genetic polymorphisms, as illustrated by the discovery of several major disease genes that contribute to arthritis in rats. We discuss how these advances have provided new biological insights into arthritis in humans. PMID:27736747
Sleeping Beauty mouse models identify candidate genes involved in gliomagenesis.
Directory of Open Access Journals (Sweden)
Irina Vyazunova
Full Text Available Genomic studies of human high-grade gliomas have discovered known and candidate tumor drivers. Studies in both cell culture and mouse models have complemented these approaches and have identified additional genes and processes important for gliomagenesis. Previously, we found that mobilization of Sleeping Beauty transposons in mice ubiquitously throughout the body from the Rosa26 locus led to gliomagenesis with low penetrance. Here we report the characterization of mice in which transposons are mobilized in the Glial Fibrillary Acidic Protein (GFAP compartment. Glioma formation in these mice did not occur on an otherwise wild-type genetic background, but rare gliomas were observed when mobilization occurred in a p19Arf heterozygous background. Through cloning insertions from additional gliomas generated by transposon mobilization in the Rosa26 compartment, several candidate glioma genes were identified. Comparisons to genetic, epigenetic and mRNA expression data from human gliomas implicates several of these genes as tumor suppressor genes and oncogenes in human glioblastoma.
Sleeping Beauty Mouse Models Identify Candidate Genes Involved in Gliomagenesis
Vyazunova, Irina; Maklakova, Vilena I.; Berman, Samuel; De, Ishani; Steffen, Megan D.; Hong, Won; Lincoln, Hayley; Morrissy, A. Sorana; Taylor, Michael D.; Akagi, Keiko; Brennan, Cameron W.; Rodriguez, Fausto J.; Collier, Lara S.
2014-01-01
Genomic studies of human high-grade gliomas have discovered known and candidate tumor drivers. Studies in both cell culture and mouse models have complemented these approaches and have identified additional genes and processes important for gliomagenesis. Previously, we found that mobilization of Sleeping Beauty transposons in mice ubiquitously throughout the body from the Rosa26 locus led to gliomagenesis with low penetrance. Here we report the characterization of mice in which transposons are mobilized in the Glial Fibrillary Acidic Protein (GFAP) compartment. Glioma formation in these mice did not occur on an otherwise wild-type genetic background, but rare gliomas were observed when mobilization occurred in a p19Arf heterozygous background. Through cloning insertions from additional gliomas generated by transposon mobilization in the Rosa26 compartment, several candidate glioma genes were identified. Comparisons to genetic, epigenetic and mRNA expression data from human gliomas implicates several of these genes as tumor suppressor genes and oncogenes in human glioblastoma. PMID:25423036
Recursive modular modelling methodology for lumped-parameter dynamic systems.
Orsino, Renato Maia Matarazzo
2017-08-01
This paper proposes a novel approach to the modelling of lumped-parameter dynamic systems, based on representing them by hierarchies of mathematical models of increasing complexity instead of a single (complex) model. Exploring the multilevel modularity that these systems typically exhibit, a general recursive modelling methodology is proposed, in order to conciliate the use of the already existing modelling techniques. The general algorithm is based on a fundamental theorem that states the conditions for computing projection operators recursively. Three procedures for these computations are discussed: orthonormalization, use of orthogonal complements and use of generalized inverses. The novel methodology is also applied for the development of a recursive algorithm based on the Udwadia-Kalaba equation, which proves to be identical to the one of a Kalman filter for estimating the state of a static process, given a sequence of noiseless measurements representing the constraints that must be satisfied by the system.
Zhou, Liming; Yang, Yuxing; Yuan, Shiying
2006-02-01
A new algorithm, the coordinates transform iterative optimizing method based on the least square curve fitting model, is presented. This arithmetic is used for extracting the bio-impedance model parameters. It is superior to other methods, for example, its speed of the convergence is quicker, and its calculating precision is higher. The objective to extract the model parameters, such as Ri, Re, Cm and alpha, has been realized rapidly and accurately. With the aim at lowering the power consumption, decreasing the price and improving the price-to-performance ratio, a practical bio-impedance measure system with double CPUs has been built. It can be drawn from the preliminary results that the intracellular resistance Ri increased largely with an increase in working load during sitting, which reflects the ischemic change of lower limbs.
Hussain, Faraz; Jha, Sumit K; Jha, Susmit; Langmead, Christopher J
2014-01-01
Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.
Propagation channel characterization, parameter estimation, and modeling for wireless communications
Yin, Xuefeng
2016-01-01
Thoroughly covering channel characteristics and parameters, this book provides the knowledge needed to design various wireless systems, such as cellular communication systems, RFID and ad hoc wireless communication systems. It gives a detailed introduction to aspects of channels before presenting the novel estimation and modelling techniques which can be used to achieve accurate models. To systematically guide readers through the topic, the book is organised in three distinct parts. The first part covers the fundamentals of the characterization of propagation channels, including the conventional single-input single-output (SISO) propagation channel characterization as well as its extension to multiple-input multiple-output (MIMO) cases. Part two focuses on channel measurements and channel data post-processing. Wideband channel measurements are introduced, including the equipment, technology and advantages and disadvantages of different data acquisition schemes. The channel parameter estimation methods are ...
Auxiliary Parameter MCMC for Exponential Random Graph Models
Byshkin, Maksym; Stivala, Alex; Mira, Antonietta; Krause, Rolf; Robins, Garry; Lomi, Alessandro
2016-11-01
Exponential random graph models (ERGMs) are a well-established family of statistical models for analyzing social networks. Computational complexity has so far limited the appeal of ERGMs for the analysis of large social networks. Efficient computational methods are highly desirable in order to extend the empirical scope of ERGMs. In this paper we report results of a research project on the development of snowball sampling methods for ERGMs. We propose an auxiliary parameter Markov chain Monte Carlo (MCMC) algorithm for sampling from the relevant probability distributions. The method is designed to decrease the number of allowed network states without worsening the mixing of the Markov chains, and suggests a new approach for the developments of MCMC samplers for ERGMs. We demonstrate the method on both simulated and actual (empirical) network data and show that it reduces CPU time for parameter estimation by an order of magnitude compared to current MCMC methods.
Directory of Open Access Journals (Sweden)
Man Zhu
2017-03-01
Full Text Available Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS, are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM, is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.
Determining avalanche modelling input parameters using terrestrial laser scanning technology
2013-01-01
International audience; In dynamic avalanche modelling, data about the volumes and areas of the snow released, mobilized and deposited are key input parameters, as well as the fracture height. The fracture height can sometimes be measured in the field, but it is often difficult to access the starting zone due to difficult or dangerous terrain and avalanche hazards. More complex is determining the areas and volumes of snow involved in an avalanche. Such calculations require high-resolution spa...
Numerical model for thermal parameters in optical materials
Sato, Yoichi; Taira, Takunori
2016-04-01
Thermal parameters of optical materials, such as thermal conductivity, thermal expansion, temperature coefficient of refractive index play a decisive role for the thermal design inside laser cavities. Therefore, numerical value of them with temperature dependence is quite important in order to develop the high intense laser oscillator in which optical materials generate excessive heat across mode volumes both of lasing output and optical pumping. We already proposed a novel model of thermal conductivity in various optical materials. Thermal conductivity is a product of isovolumic specific heat and thermal diffusivity, and independent modeling of these two figures should be required from the viewpoint of a clarification of physical meaning. Our numerical model for thermal conductivity requires one material parameter for specific heat and two parameters for thermal diffusivity in the calculation of each optical material. In this work we report thermal conductivities of various optical materials as Y3Al5O12 (YAG), YVO4 (YVO), GdVO4 (GVO), stoichiometric and congruent LiTaO3, synthetic quartz, YAG ceramics and Y2O3 ceramics. The dependence on Nd3+-doping in laser gain media in YAG, YVO and GVO is also studied. This dependence can be described by only additional three parameters. Temperature dependence of thermal expansion and temperature coefficient of refractive index for YAG, YVO, and GVO: these are also included in this work for convenience. We think our numerical model is quite useful for not only thermal analysis in laser cavities or optical waveguides but also the evaluation of physical properties in various transparent materials.
Land Building Models: Uncertainty in and Sensitivity to Input Parameters
2013-08-01
Louisiana Coastal Area Ecosystem Restoration Projects Study , Vol. 3, Final integrated ERDC/CHL CHETN-VI-44 August 2013 24 feasibility study and... Nourishment Module, Chapter 8. In Coastal Louisiana Ecosystem Assessment and Restoration (CLEAR) Model of Louisiana Coastal Area (LCA) Comprehensive...to Input Parameters by Ty V. Wamsley PURPOSE: The purpose of this Coastal and Hydraulics Engineering Technical Note (CHETN) is to document a
The oblique S parameter in higgsless electroweak models
Rosell, Ignasi
2012-01-01
We present a one-loop calculation of the oblique S parameter within Higgsless models of electroweak symmetry breaking. We have used a general effective Lagrangian with at most two derivatives, implementing the chiral symmetry breaking SU(2)_L x SU(2)_R -> SU(2)_{L+R} with Goldstones, gauge bosons and one multiplet of vector and axial-vector resonances. The estimation is based on the short-distance constraints and the dispersive approach proposed by Peskin and Takeuchi.
A statistical model of proton with no parameter
Zhang, Y; Zhang, Yongjun; Yang, Li-Ming
2001-01-01
In this text, the protons are taken as an ensemble of Fock states. Using detailed balancing principle and equal probability principle, the unpolarized parton distribution of proton is gained through Monte Carlo without any parameter. A new origin of the light flavor sea-quark asymmetry is given here beside known models as Pauli blocking, meson-cloud, chiral-field, chiral-soliton and instantons.
Model of the Stochastic Vacuum and QCD Parameters
Ferreira, E; Ferreira, Erasmo; Pereira, Flávio
1997-01-01
Accounting for the two independent correlation functions of the QCD vacuum, we improve the simple and consistent description given by the model of the stochastic vacuum to the high-energy pp and pbar-p data, with a new determination of parameters of non-perturbative QCD. The increase of the hadronic radii with the energy accounts for the energy dependence of the observables.
Directory of Open Access Journals (Sweden)
Guang-zhou Chen
2015-01-01
Full Text Available Parameter identification plays a crucial role for simulating and using model. This paper firstly carried out the sensitivity analysis of the 2-chlorophenol oxidation model in supercritical water using the Monte Carlo method. Then, to address the nonlinearity of the model, two improved differential search (DS algorithms were proposed to carry out the parameter identification of the model. One strategy is to adopt the Latin hypercube sampling method to replace the uniform distribution of initial population; the other is to combine DS with simplex method. The results of sensitivity analysis reveal the sensitivity and the degree of difficulty identified for every model parameter. Furthermore, the posteriori probability distribution of parameters and the collaborative relationship between any two parameters can be obtained. To verify the effectiveness of the improved algorithms, the optimization performance of improved DS in kinetic parameter estimation is studied and compared with that of the basic DS algorithm, differential evolution, artificial bee colony optimization, and quantum-behaved particle swarm optimization. And the experimental results demonstrate that the DS with the Latin hypercube sampling method does not present better performance, while the hybrid methods have the advantages of strong global search ability and local search ability and are more effective than the other algorithms.
An Improved Lumped Parameter Model for a Piezoelectric Energy Harvester in Transverse Vibration
Directory of Open Access Journals (Sweden)
Guang-qing Wang
2014-01-01
Full Text Available An improved lumped parameter model (ILPM is proposed which predicts the output characteristics of a piezoelectric vibration energy harvester (PVEH. A correction factor is derived for improving the precisions of lumped parameter models for transverse vibration, by considering the dynamic mode shape and the strain distribution of the PVEH. For a tip mass, variations of the correction factor with PVEH length are presented with curve fitting from numerical solutions. The improved governing motion equations and exact analytical solution of the PVEH excited by persistent base motions are developed. Steady-state electrical and mechanical response expressions are derived for arbitrary frequency excitations. Effects of the structural parameters on the electromechanical outputs of the PVEH and important characteristics of the PVEH, such as short-circuit and open-circuit behaviors, are analyzed numerically in detail. Accuracy of the output performances of the ILPM is identified from the available lumped parameter models and the coupled distributed parameter model. Good agreement is found between the analytical results of the ILPM and the coupled distributed parameter model. The results demonstrate the feasibility of the ILPM as a simple and effective means for enhancing the predictions of the PVEH.
Is flow velocity a significant parameter in flood damage modelling?
Directory of Open Access Journals (Sweden)
H. Kreibich
2009-10-01
Full Text Available Flow velocity is generally presumed to influence flood damage. However, this influence is hardly quantified and virtually no damage models take it into account. Therefore, the influences of flow velocity, water depth and combinations of these two impact parameters on various types of flood damage were investigated in five communities affected by the Elbe catchment flood in Germany in 2002. 2-D hydraulic models with high to medium spatial resolutions were used to calculate the impact parameters at the sites in which damage occurred. A significant influence of flow velocity on structural damage, particularly on roads, could be shown in contrast to a minor influence on monetary losses and business interruption. Forecasts of structural damage to road infrastructure should be based on flow velocity alone. The energy head is suggested as a suitable flood impact parameter for reliable forecasting of structural damage to residential buildings above a critical impact level of 2 m of energy head or water depth. However, general consideration of flow velocity in flood damage modelling, particularly for estimating monetary loss, cannot be recommended.
A robust approach for the determination of Gurson model parameters
Directory of Open Access Journals (Sweden)
R. Sepe
2016-07-01
Full Text Available Among the most promising models introduced in recent years, with which it is possible to obtain very useful results for a better understanding of the physical phenomena involved in the macroscopic mechanism of crack propagation, the one proposed by Gurson and Tvergaard links the propagation of a crack to the nucleation, growth and coalescence of micro-voids, which is likely to connect the micromechanical characteristics of the component under examination to crack initiation and propagation up to a macroscopic scale. It must be pointed out that, even if the statistical character of some of the many physical parameters involved in the said model has been put in evidence, no serious attempt has been made insofar to link the corresponding statistic to the experimental and macroscopic results, as for example crack initiation time, material toughness, residual strength of the cracked component (R-Curve, and so on. In this work, such an analysis was carried out in a twofold way: the former concerned the study of the influence exerted by each of the physical parameters on the material toughness, and the latter concerned the use of the Stochastic Design Improvement (SDI technique to perform a “robust” numerical calibration of the model evaluating the nominal values of the physical and correction parameters, which fit a particular experimental result even in the presence of their “natural” variability.
Nonlinear Inverse Problem for an Ion-Exchange Filter Model: Numerical Recovery of Parameters
Directory of Open Access Journals (Sweden)
Balgaisha Mukanova
2015-01-01
Full Text Available This paper considers the problem of identifying unknown parameters for a mathematical model of an ion-exchange filter via measurement at the outlet of the filter. The proposed mathematical model consists of a material balance equation, an equation describing the kinetics of ion-exchange for the nonequilibrium case, and an equation for the ion-exchange isotherm. The material balance equation includes a nonlinear term that depends on the kinetics of ion-exchange and several parameters. First, a numerical solution of the direct problem, the calculation of the impurities concentration at the outlet of the filter, is provided. Then, the inverse problem, finding the parameters of the ion-exchange process in nonequilibrium conditions, is formulated. A method for determining the approximate values of these parameters from the impurities concentration measured at the outlet of the filter is proposed.
Pulungan, Ditho
2017-03-31
In this paper, we propose a micromechanical approach to predict damage mechanisms and their interactions in glass fibers/polypropylene thermoplastic composites. First, a representative volume element (RVE) of such materials was rigorously determined using a geometrical two-point probability function and the eigenvalue stabilization of homogenized elastic tensor obtained by Hill-Mandel kinematic homogenization. Next, the 3D finite element models of the RVE were developed accordingly. The fibers were modeled with an isotropic linear elastic material. The matrix was modeled with an isotropic linear elastic, rate-independent hyperbolic Drucker-Prager plasticity coupled with a ductile damage model that is able to show pressure dependency of the yield and damage behavior often found in a thermoplastic material. In addition, cohesive elements were inserted into the fiber-matrix interfaces to simulate debonding. The RVE faces are imposed with periodical boundary conditions to minimize the edge effect. The RVE was then subjected to transverse tensile loading in accordance with experimental tensile tests on [90]8 laminates. The model prediction was found to be in very good agreement with the experimental results in terms of the global stress-strain curves, including the linear and nonlinear portion of the response and also the failure point, making it a useful virtual testing tool for composite material design. Furthermore, the effect of tailoring the main parameters of thermoplastic composites is investigated to provide guidelines for future improvements of these materials.
Reduced-order modeling for cardiac electrophysiology. Application to parameter identification
Boulakia, Muriel; Gerbeau, Jean-Frédéric
2011-01-01
A reduced-order model based on Proper Orthogonal Decomposition (POD) is proposed for the bidomain equations of cardiac electrophysiology. Its accuracy is assessed through electrocardiograms in various configurations, including myocardium infarctions and long-time simulations. We show in particular that a restitution curve can efficiently be approximated by this approach. The reduced-order model is then used in an inverse problem solved by an evolutionary algorithm. Some attempts are presented to identify ionic parameters and infarction locations from synthetic ECGs.
Kim, Kyung Yong; Lee, Won-Chan
2017-01-01
This article provides a detailed description of three factors (specification of the ability distribution, numerical integration, and frame of reference for the item parameter estimates) that might affect the item parameter estimation of the three-parameter logistic model, and compares five item calibration methods, which are combinations of the…
Information Theoretic Tools for Parameter Fitting in Coarse Grained Models
Kalligiannaki, Evangelia
2015-01-07
We study the application of information theoretic tools for model reduction in the case of systems driven by stochastic dynamics out of equilibrium. The model/dimension reduction is considered by proposing parametrized coarse grained dynamics and finding the optimal parameter set for which the relative entropy rate with respect to the atomistic dynamics is minimized. The minimization problem leads to a generalization of the force matching methods to non equilibrium systems. A multiplicative noise example reveals the importance of the diffusion coefficient in the optimization problem.
Nonlocal order parameters for the 1D Hubbard model.
Montorsi, Arianna; Roncaglia, Marco
2012-12-07
We characterize the Mott-insulator and Luther-Emery phases of the 1D Hubbard model through correlators that measure the parity of spin and charge strings along the chain. These nonlocal quantities order in the corresponding gapped phases and vanish at the critical point U(c)=0, thus configuring as hidden order parameters. The Mott insulator consists of bound doublon-holon pairs, which in the Luther-Emery phase turn into electron pairs with opposite spins, both unbinding at U(c). The behavior of the parity correlators is captured by an effective free spinless fermion model.
Surrogate based approaches to parameter inference in ocean models
Knio, Omar
2016-01-06
This talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. Non-intrusive spectral projections and regularized regressions are used for this purpose. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to infer dependence of wind drag, bottom drag, and internal mixing coefficients.
Kuczera, George; Kavetski, Dmitri; Franks, Stewart; Thyer, Mark
2006-11-01
SummaryCalibration and prediction in conceptual rainfall-runoff (CRR) modelling is affected by the uncertainty in the observed forcing/response data and the structural error in the model. This study works towards the goal of developing a robust framework for dealing with these sources of error and focuses on model error. The characterisation of model error in CRR modelling has been thwarted by the convenient but indefensible treatment of CRR models as deterministic descriptions of catchment dynamics. This paper argues that the fluxes in CRR models should be treated as stochastic quantities because their estimation involves spatial and temporal averaging. Acceptance that CRR models are intrinsically stochastic paves the way for a more rational characterisation of model error. The hypothesis advanced in this paper is that CRR model error can be characterised by storm-dependent random variation of one or more CRR model parameters. A simple sensitivity analysis is used to identify the parameters most likely to behave stochastically, with variation in these parameters yielding the largest changes in model predictions as measured by the Nash-Sutcliffe criterion. A Bayesian hierarchical model is then formulated to explicitly differentiate between forcing, response and model error. It provides a very general framework for calibration and prediction, as well as for testing hypotheses regarding model structure and data uncertainty. A case study calibrating a six-parameter CRR model to daily data from the Abercrombie catchment (Australia) demonstrates the considerable potential of this approach. Allowing storm-dependent variation in just two model parameters (with one of the parameters characterising model error and the other reflecting input uncertainty) yields a substantially improved model fit raising the Nash-Sutcliffe statistic from 0.74 to 0.94. Of particular significance is the use of posterior diagnostics to test the key assumptions about the data and model errors
Athira, P.; Sudheer, K.
2013-12-01
Parameter estimation is one of the major tasks in the application of any physics based distributed model. Generally the calibration does not consider the heterogeneity of the parameters across the basin, and as a result the model simulation conforms to the location for which it has been calibrated for. However, the major advantage of distributed hydrological models is to have reasonably good simulations on various locations in the watershed, including ungauged locations. While multi-site calibration can address this issue to some extent, the availability of more gauge sites in a watershed is always not guaranteed. When single site calibration is performed, generally a uniform variation of the parameters is considered across the basin which does not ensure the true heterogeneity of the parameters in the basin. The primary objective of this study is to compare the effect of uniform variation of the parameter with a procedure that identifies actual heterogeneity of the parameters across the basin, while performing calibration of distributed hydrological models. In order to demonstrate the objective, a case study of two watersheds in the USA using the model, Soil and Water Assessment Tool (SWAT) is presented and discussed. Initially, the SWAT model is calibrated for both the watersheds in the traditional way considering uniform variation of the sensitive parameters during the calibration. Further, the hydrological response units (HRU) delineated in the SWAT are classified into various clusters based the land use, soil type and slope. A random perturbation of the parameters is performed in these clusters during calibration. The rationale behind this approach was to identify plausible parameter values that simulate the hydrological process in these clusters appropriately. The proposed procedure is applied to both the basins. The results indicate that the simulations obtained for upstream ungauged locations (other than that used for calibration) are much better when a
Comparison of parameter estimation algorithms in hydrological modelling
DEFF Research Database (Denmark)
Blasone, Roberta-Serena; Madsen, Henrik; Rosbjerg, Dan
2006-01-01
Local search methods have been applied successfully in calibration of simple groundwater models, but might fail in locating the optimum for models of increased complexity, due to the more complex shape of the response surface. Global search algorithms have been demonstrated to perform well...... for these types of models, although at a more expensive computational cost. The main purpose of this study is to investigate the performance of a global and a local parameter optimization algorithm, respectively, the Shuffled Complex Evolution (SCE) algorithm and the gradient-based Gauss......-Marquardt-Levenberg algorithm (implemented in the PEST software), when applied to a steady-state and a transient groundwater model. The results show that PEST can have severe problems in locating the global optimum and in being trapped in local regions of attractions. The global SCE procedure is, in general, more effective...
Moolenaar, H.E.; Selten, F.M.
2004-01-01
Climate models contain numerous parameters for which the numeric values are uncertain. In the context of climate simulation and prediction, a relevant question is what range of climate outcomes is possible given the range of parameter uncertainties. Which parameter perturbation changes the climate i
Energy Technology Data Exchange (ETDEWEB)
Maynard, K.; Royer, J.F. [Meteo-France CNRM, 42 Avenue G. Coriolis, 31057, Toulouse Cedex 1 (France)
2004-06-01
During the last two decades, several land surface schemes for use in climate, regional and/or mesoscale, hydrological and ecological models have been designed. Incorrect parametrization of land-surface processes and prescription of the surface parameters in atmospheric modeling, can result in artificial changes of the horizontal gradient of the sensible heat flux. Thus, an error in horizontal temperature gradient within the lower atmosphere may be introduced. The reliability of the model depends on the quality of boundary layer scheme implemented and its sensitivity to the bare soil and vegetation parameters. In this study, a series of sensitivity experiments has been conducted over broad time scales, using a version of the ARPEGE Climate Model coupled to the ISBA land surface scheme in order to investigate model sensitivity to separate changes in land surface parameters over Africa. Effects of perturbing vegetation cover, distribution of soil depth, albedo of vegetation, roughness length, leaf area index and minimum stomatal resistance were explored by using a simple statistical analysis. Identifying which parameters are important in controlling turbulent energy fluxes, temperature and soil moisture is dependent on which variables are used to determine sensibility, which type of vegetation and climate regime is being simulated and the magnitude and sign of the parameter change. This study does not argue that a particular parameter is important in ISBA, rather it shows that no general ranking of parameters is possible. So, it is essential to specify all land surface parameters with greater precision when attempting to determine the climate response to modification of the land surface. The implication of ISBA being sensitive to parameters that cannot be validated suggests that there will always be considerable doubt over the predictive quality of land-surface schemes. (orig.)
Integral-based identification of patient specific parameters for a minimal cardiac model.
Hann, C E; Chase, J G; Shaw, G M
2006-02-01
A minimal cardiac model has been developed which accurately captures the essential dynamics of the cardiovascular system (CVS). However, identifying patient specific parameters with the limited measurements often available, hinders the clinical application of the model for diagnosis and therapy selection. This paper presents an integral-based parameter identification method for fast, accurate identification of patient specific parameters using limited measured data. The integral method turns a previously non-linear and non-convex optimization problem into a linear and convex identification problem. The model includes ventricular interaction and physiological valve dynamics. A healthy human state and four disease states, valvular stenosis, pulmonary embolism, cardiogenic shock and septic shock are used to test the method. Parameters for the healthy and disease states are accurately identified using only discretized flows into and out of the two cardiac chambers, the minimum and maximum volumes of the left and right ventricles, and the pressure waveforms through the aorta and pulmonary artery. These input values can be readily obtained non-invasively using echo-cardiography and ultra-sound, or invasively via catheters that are often used in Intensive Care. The method enables rapid identification of model parameters to match a particular patient condition in clinical real time (3-5 min) to within a mean value of 4-10% in the presence of 5-15% uniformly distributed measurement noise. The specific changes made to simulate each disease state are correctly identified in each case to within 10% without false identification of any other patient specific parameters. Clinically, the resulting patient specific model can then be used to assist medical staff in understanding, diagnosis and treatment selection.
Parameter and uncertainty estimation for mechanistic, spatially explicit epidemiological models
Finger, Flavio; Schaefli, Bettina; Bertuzzo, Enrico; Mari, Lorenzo; Rinaldo, Andrea
2014-05-01
Epidemiological models can be a crucially important tool for decision-making during disease outbreaks. The range of possible applications spans from real-time forecasting and allocation of health-care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. Our spatially explicit, mechanistic models for cholera epidemics have been successfully applied to several epidemics including, the one that struck Haiti in late 2010 and is still ongoing. Calibration and parameter estimation of such models represents a major challenge because of properties unusual in traditional geoscientific domains such as hydrology. Firstly, the epidemiological data available might be subject to high uncertainties due to error-prone diagnosis as well as manual (and possibly incomplete) data collection. Secondly, long-term time-series of epidemiological data are often unavailable. Finally, the spatially explicit character of the models requires the comparison of several time-series of model outputs with their real-world counterparts, which calls for an appropriate weighting scheme. It follows that the usual assumption of a homoscedastic Gaussian error distribution, used in combination with classical calibration techniques based on Markov chain Monte Carlo algorithms, is likely to be violated, whereas the construction of an appropriate formal likelihood function seems close to impossible. Alternative calibration methods, which allow for accurate estimation of total model uncertainty, particularly regarding the envisaged use of the models for decision-making, are thus needed. Here we present the most recent developments regarding methods for parameter and uncertainty estimation to be used with our mechanistic, spatially explicit models for cholera epidemics, based on informal measures of goodness of fit.
Modeling secondary accidents identified by traffic shock waves.
Junhua, Wang; Boya, Liu; Lanfang, Zhang; Ragland, David R
2016-02-01
The high potential for occurrence and the negative consequences of secondary accidents make them an issue of great concern affecting freeway safety. Using accident records from a three-year period together with California interstate freeway loop data, a dynamic method for more accurate classification based on the traffic shock wave detecting method was used to identify secondary accidents. Spatio-temporal gaps between the primary and secondary accident were proven be fit via a mixture of Weibull and normal distribution. A logistic regression model was developed to investigate major factors contributing to secondary accident occurrence. Traffic shock wave speed and volume at the occurrence of a primary accident were explicitly considered in the model, as a secondary accident is defined as an accident that occurs within the spatio-temporal impact scope of the primary accident. Results show that the shock waves originating in the wake of a primary accident have a more significant impact on the likelihood of a secondary accident occurrence than the effects of traffic volume. Primary accidents with long durations can significantly increase the possibility of secondary accidents. Unsafe speed and weather are other factors contributing to secondary crash occurrence. It is strongly suggested that when police or rescue personnel arrive at the scene of an accident, they should not suddenly block, decrease, or unblock the traffic flow, but instead endeavor to control traffic in a smooth and controlled manner. Also it is important to reduce accident processing time to reduce the risk of secondary accident. Copyright © 2015 Elsevier Ltd. All rights reserved.
Identification of ecosystem parameters by SDE-modelling
DEFF Research Database (Denmark)
describing interactions between phytoplankton and water-column nitrogen with light as forcing, using data form a Danish estuary covering a 16 years period (1988-2003), and modelling primary production as a random walk, it is demonstrated how non-linear relationships between states can be identified...
Order-parameter model for unstable multilane traffic flow
Lubashevsky; Mahnke
2000-11-01
We discuss a phenomenological approach to the description of unstable vehicle motion on multilane highways that explains in a simple way the observed sequence of the "free flow synchronized mode jam" phase transitions as well as the hysteresis in these transitions. We introduce a variable called an order parameter that accounts for possible correlations in the vehicle motion at different lanes. So, it is principally due to the "many-body" effects in the car interaction in contrast to such variables as the mean car density and velocity being actually the zeroth and first moments of the "one-particle" distribution function. Therefore, we regard the order parameter as an additional independent state variable of traffic flow. We assume that these correlations are due to a small group of "fast" drivers and by taking into account the general properties of the driver behavior we formulate a governing equation for the order parameter. In this context we analyze the instability of homogeneous traffic flow that manifested itself in the above-mentioned phase transitions and gave rise to the hysteresis in both of them. Besides, the jam is characterized by the vehicle flows at different lanes which are independent of one another. We specify a certain simplified model in order to study the general features of the car cluster self-formation under the "free flow synchronized motion" phase transition. In particular, we show that the main local parameters of the developed cluster are determined by the state characteristics of vehicle motion only.
Accelerated gravitational wave parameter estimation with reduced order modeling.
Canizares, Priscilla; Field, Scott E; Gair, Jonathan; Raymond, Vivien; Smith, Rory; Tiglio, Manuel
2015-02-20
Inferring the astrophysical parameters of coalescing compact binaries is a key science goal of the upcoming advanced LIGO-Virgo gravitational-wave detector network and, more generally, gravitational-wave astronomy. However, current approaches to parameter estimation for these detectors require computationally expensive algorithms. Therefore, there is a pressing need for new, fast, and accurate Bayesian inference techniques. In this Letter, we demonstrate that a reduced order modeling approach enables rapid parameter estimation to be performed. By implementing a reduced order quadrature scheme within the LIGO Algorithm Library, we show that Bayesian inference on the 9-dimensional parameter space of nonspinning binary neutron star inspirals can be sped up by a factor of ∼30 for the early advanced detectors' configurations (with sensitivities down to around 40 Hz) and ∼70 for sensitivities down to around 20 Hz. This speedup will increase to about 150 as the detectors improve their low-frequency limit to 10 Hz, reducing to hours analyses which could otherwise take months to complete. Although these results focus on interferometric gravitational wave detectors, the techniques are broadly applicable to any experiment where fast Bayesian analysis is desirable.
Parameter Identification Model for Accelerometer%加速度计参数辨识建模
Institute of Scientific and Technical Information of China (English)
刘畅; 黄玉清
2014-01-01
In this paper , according to the need of the accelerometer parameters model identification , the accelerometer zero bias through gathering , analyzes the characteristics of the data , combining three meth-ods of system identification , the least squares , the recursive least squares and maximum likelihood esti-mate, relevant mathematics models were established zero deflection data , the simulation based on the model, to observe parameters changing trend of curve , the model parameters and to identify the parame-ters in the model after comparison , proves the reliability of the model , for accelerometer parameters change with time of level provides a reference method .%根据加速度计参数模型辨识的需要，通过采集加速度计零偏，分析了数据的特点，结合最小二乘、递推最小二乘、最大似然估计3种系统辨识方法，建立起零偏数据相关的数学模型，再通过对模型进行仿真，观察参数变化曲线趋势，把模型参数和辨识之后的模型参数作对比，证明了模型的可靠性。
Estimasi Parameter Item dan Latent Class dengan Model Dina untuk Diagnosis Kesulitan Belajar
Directory of Open Access Journals (Sweden)
- Kusaeri
2013-07-01
Full Text Available Abstract: Estimation of Item Parameter and Latent Class with DINA Model to Diagnose Learning Difficulties. This study aims to estimate item parameter of diagnostic test developed with DINA model and identify attribute profiles of each test participant. The instrument of this study was diagnostic test using multiple choice format with 4 options. The data were analyzed using Mplus software, R program and ITEMAN. The results show that out of 8 items measuring social arithmetic and comparison, there was on ly one item that had low guessing and slip parameter. The study also found that basic operation and concept in arithmetic and verbal questions were problematic f or most students. Abstrak: Estimasi Parameter Item dan Latent Class dengan Model DINA untuk Diagnosis Kesulitan Belajar. Penelitian ini bertujuan untuk mengestimasi parameter item dari tes diagnostik yang dikembangkan dengan model DINA dan mengidentifikasi profil atribut setiap peserta tes. Instrumen penelitian ini berupa tes diagnostik berbentuk pilihan ganda dengan 4 pilihan jawaban. Data dianalisis dengan menggunakan software Mplus, program R dan ITEMAN. Hasil penelitian menunjukkan bahwa dari 8 item yang mengukur materi aritmetika sosial dan perbandingan, hanya ada satu item dengan parameter guessing dan slip rendah. Temuan lain operasi dan konsep dasar dalam aritmetika serta soal bentuk verbal masih menjadi m asalah bagi sebagian besar siswa.
Optimal vibration control of curved beams using distributed parameter models
Liu, Fushou; Jin, Dongping; Wen, Hao
2016-12-01
The design of linear quadratic optimal controller using spectral factorization method is studied for vibration suppression of curved beam structures modeled as distributed parameter models. The equations of motion for active control of the in-plane vibration of a curved beam are developed firstly considering its shear deformation and rotary inertia, and then the state space model of the curved beam is established directly using the partial differential equations of motion. The functional gains for the distributed parameter model of curved beam are calculated by extending the spectral factorization method. Moreover, the response of the closed-loop control system is derived explicitly in frequency domain. Finally, the suppression of the vibration at the free end of a cantilevered curved beam by point control moment is studied through numerical case studies, in which the benefit of the presented method is shown by comparison with a constant gain velocity feedback control law, and the performance of the presented method on avoidance of control spillover is demonstrated.
Parameter and Process Significance in Mechanistic Modeling of Cellulose Hydrolysis
Rotter, B.; Barry, A.; Gerhard, J.; Small, J.; Tahar, B.
2005-12-01
The rate of cellulose hydrolysis, and of associated microbial processes, is important in determining the stability of landfills and their potential impact on the environment, as well as associated time scales. To permit further exploration in this field, a process-based model of cellulose hydrolysis was developed. The model, which is relevant to both landfill and anaerobic digesters, includes a novel approach to biomass transfer between a cellulose-bound biofilm and biomass in the surrounding liquid. Model results highlight the significance of the bacterial colonization of cellulose particles by attachment through contact in solution. Simulations revealed that enhanced colonization, and therefore cellulose degradation, was associated with reduced cellulose particle size, higher biomass populations in solution, and increased cellulose-binding ability of the biomass. A sensitivity analysis of the system parameters revealed different sensitivities to model parameters for a typical landfill scenario versus that for an anaerobic digester. The results indicate that relative surface area of cellulose and proximity of hydrolyzing bacteria are key factors determining the cellulose degradation rate.
Identification of parameters in nonlinear geotechnical models using extenden Kalman filter
Directory of Open Access Journals (Sweden)
Nestorović Tamara
2014-01-01
Full Text Available Direct measurement of relevant system parameters often represents a problem due to different limitations. In geomechanics, measurement of geotechnical material constants which constitute a material model is usually a very diffcult task even with modern test equipment. Back-analysis has proved to be a more effcient and more economic method for identifying material constants because it needs measurement data such as settlements, pore pressures, etc., which are directly measurable, as inputs. Among many model parameter identification methods, the Kalman filter method has been applied very effectively in recent years. In this paper, the extended Kalman filter – local iteration procedure incorporated with finite element analysis (FEA software has been implemented. In order to prove the effciency of the method, parameter identification has been performed for a nonlinear geotechnical model.
Institute of Scientific and Technical Information of China (English)
DU XiuLi; WANG FengQuan
2009-01-01
A new time-domain modal identification method of linear time-lnvariant system driven by the non-stationary Gaussian random excitation is introduced based on the continuous time AR model.The method can identify physical parameters of the system from response data.In order to identify the parameters of the system, the structural dynamic equation is first transformed into the continuous time AR model, and subsequently written into the forms of observation equation and state equation which is just a stochastic differential equation.Secondly, under the assumption that the uniformly modulated function is approximately equal to a constant matrix in a very short time period, the uniformly modulated func-tion is identified piecewise.Then, we present the exact maximum likelihood estimators of parameters by virtue of the Girsanov theorem.Finally, the modal parameters are identified by eigenanalysis.Nu-merical results show that the method we introduce here not only has high precision and robustness, but also has very high computing efficiency.Therefore, it is suitable for real-time modal identification.
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
A new time-domain modal identification method of linear time-invariant system driven by the non-stationary Gaussian random excitation is introduced based on the continuous time AR model. The method can identify physical parameters of the system from response data. In order to identify the parameters of the system, the structural dynamic equation is first transformed into the continuous time AR model, and subsequently written into the forms of observation equation and state equation which is just a stochastic differential equation. Secondly, under the assumption that the uniformly modulated function is approximately equal to a constant matrix in a very short time period, the uniformly modulated function is identified piecewise. Then, we present the exact maximum likelihood estimators of parameters by virtue of the Girsanov theorem. Finally, the modal parameters are identified by eigenanalysis. Numerical results show that the method we introduce here not only has high precision and robustness, but also has very high computing efficiency. Therefore, it is suitable for real-time modal identification.
Parameter estimation for models of ligninolytic and cellulolytic enzyme kinetics
Energy Technology Data Exchange (ETDEWEB)
Wang, Gangsheng [ORNL; Post, Wilfred M [ORNL; Mayes, Melanie [ORNL; Frerichs, Joshua T [ORNL; Jagadamma, Sindhu [ORNL
2012-01-01
While soil enzymes have been explicitly included in the soil organic carbon (SOC) decomposition models, there is a serious lack of suitable data for model parameterization. This study provides well-documented enzymatic parameters for application in enzyme-driven SOC decomposition models from a compilation and analysis of published measurements. In particular, we developed appropriate kinetic parameters for five typical ligninolytic and cellulolytic enzymes ( -glucosidase, cellobiohydrolase, endo-glucanase, peroxidase, and phenol oxidase). The kinetic parameters included the maximum specific enzyme activity (Vmax) and half-saturation constant (Km) in the Michaelis-Menten equation. The activation energy (Ea) and the pH optimum and sensitivity (pHopt and pHsen) were also analyzed. pHsen was estimated by fitting an exponential-quadratic function. The Vmax values, often presented in different units under various measurement conditions, were converted into the same units at a reference temperature (20 C) and pHopt. Major conclusions are: (i) Both Vmax and Km were log-normal distributed, with no significant difference in Vmax exhibited between enzymes originating from bacteria or fungi. (ii) No significant difference in Vmax was found between cellulases and ligninases; however, there was significant difference in Km between them. (iii) Ligninases had higher Ea values and lower pHopt than cellulases; average ratio of pHsen to pHopt ranged 0.3 0.4 for the five enzymes, which means that an increase or decrease of 1.1 1.7 pH units from pHopt would reduce Vmax by 50%. (iv) Our analysis indicated that the Vmax values from lab measurements with purified enzymes were 1 2 orders of magnitude higher than those for use in SOC decomposition models under field conditions.
Modeling of state parameter and hardening function for granular materials
Institute of Scientific and Technical Information of China (English)
彭芳乐; 李建中
2004-01-01
A modified plastic strain energy as hardening state parameter for dense sand was proposed, based on the results from a series of drained plane strain tests on saturated dense Japanese Toyoura sand with precise stress and strain measurements along many stress paths. In addition, a unique hardening function between the plastic strain energy and the instantaneous stress path was also presented, which was independent of stress history. The proposed state parameter and hardening function was directly verified by the simple numerical integration method. It is shown that the proposed hardening function is independent of stress history and stress path and is appropriate to be used as the hardening rule in constitutive modeling for dense sand, and it is also capable of simulating the effects on the deformation characteristics of stress history and stress path for dense sand.
Parameter Estimation of the Extended Vasiček Model
Directory of Open Access Journals (Sweden)
Sanae RUJIVAN
2010-01-01
Full Text Available In this paper, an estimate of the drift and diffusion parameters of the extended Vasiček model is presented. The estimate is based on the method of maximum likelihood. We derive a closed-form expansion for the transition (probability density of the extended Vasiček process and use the expansion to construct an approximate log-likelihood function of a discretely sampled data of the process. Approximate maximum likelihood estimators (AMLEs of the parameters are obtained by maximizing the approximate log-likelihood function. The convergence of the AMLEs to the true maximum likelihood estimators is obtained by increasing the number of terms in the expansions with a small time step size.
Energy Technology Data Exchange (ETDEWEB)
Ducoste, J.; Brauer, R.
1999-07-01
Analysis of a computational fluid dynamics (CFD) model for a water treatment plant clearwell was done. Model parameters were analyzed to determine their influence on the effluent-residence time distribution (RTD) function. The study revealed that several model parameters could have significant impact on the shape of the RTD function and consequently raise the level of uncertainty on accurate predictions of clearwell hydraulics. The study also revealed that although the modeler could select a distribution of values for some of the model parameters, most of these values can be ruled out by requiring the difference between the calculated and theoretical hydraulic retention time to within 5% of the theoretical value.
Strong parameter renormalization from optimum lattice model orbitals
Brosco, Valentina; Ying, Zu-Jian; Lorenzana, José
2017-01-01
Which is the best single-particle basis to express a Hubbard-like lattice model? A rigorous variational answer to this question leads to equations the solution of which depends in a self-consistent manner on the lattice ground state. Contrary to naive expectations, for arbitrary small interactions, the optimized orbitals differ from the noninteracting ones, leading also to substantial changes in the model parameters as shown analytically and in an explicit numerical solution for a simple double-well one-dimensional case. At strong coupling, we obtain the direct exchange interaction with a very large renormalization with important consequences for the explanation of ferromagnetism with model Hamiltonians. Moreover, in the case of two atoms and two fermions we show that the optimization equations are closely related to reduced density-matrix functional theory, thus establishing an unsuspected correspondence between continuum and lattice approaches.
Multi-parameter models of innovation diffusion on complex networks
McCullen, Nicholas J; Bale, Catherine S E; Foxon, Tim J; Gale, William F
2012-01-01
A model, applicable to a range of innovation diffusion applications with a strong peer to peer component, is developed and studied, along with methods for its investigation and analysis. A particular application is to individual households deciding whether to install an energy efficiency measure in their home. The model represents these individuals as nodes on a network, each with a variable representing their current state of adoption of the innovation. The motivation to adopt is composed of three terms, representing personal preference, an average of each individual's network neighbours' states and a system average, which is a measure of the current social trend. The adoption state of a node changes if a weighted linear combination of these factors exceeds some threshold. Numerical simulations have been carried out, computing the average uptake after a sufficient number of time-steps over many realisations at a range of model parameter values, on various network topologies, including random (Erdos-Renyi), s...
Reconstructing parameters of spreading models from partial observations
Lokhov, Andrey Y
2016-01-01
Spreading processes are often modelled as a stochastic dynamics occurring on top of a given network with edge weights corresponding to the transmission probabilities. Knowledge of veracious transmission probabilities is essential for prediction, optimization, and control of diffusion dynamics. Unfortunately, in most cases the transmission rates are unknown and need to be reconstructed from the spreading data. Moreover, in realistic settings it is impossible to monitor the state of each node at every time, and thus the data is highly incomplete. We introduce an efficient dynamic message-passing algorithm, which is able to reconstruct parameters of the spreading model given only partial information on the activation times of nodes in the network. The method is generalizable to a large class of dynamic models, as well to the case of temporal graphs.
Dynamic systems models new methods of parameter and state estimation
2016-01-01
This monograph is an exposition of a novel method for solving inverse problems, a method of parameter estimation for time series data collected from simulations of real experiments. These time series might be generated by measuring the dynamics of aircraft in flight, by the function of a hidden Markov model used in bioinformatics or speech recognition or when analyzing the dynamics of asset pricing provided by the nonlinear models of financial mathematics. Dynamic Systems Models demonstrates the use of algorithms based on polynomial approximation which have weaker requirements than already-popular iterative methods. Specifically, they do not require a first approximation of a root vector and they allow non-differentiable elements in the vector functions being approximated. The text covers all the points necessary for the understanding and use of polynomial approximation from the mathematical fundamentals, through algorithm development to the application of the method in, for instance, aeroplane flight dynamic...
Connecting Global to Local Parameters in Barred Galaxy Models
Indian Academy of Sciences (India)
N. D. Caranicolas
2002-09-01
We present connections between global and local parameters in a realistic dynamical model, describing motion in a barred galaxy. Expanding the global model in the vicinity of a stable Lagrange point, we find the potential of a two-dimensional perturbed harmonic oscillator, which describes local motion near the centre of the global model. The frequencies of oscillations and the coefficients of the perturbing terms are not arbitrary but are connected to the mass, the angular rotation velocity, the scale length and the strength of the galactic bar. The local energy is also connected to the global energy. A comparison of the properties of orbits in the global and local potential is also made.
Multi-Objective Calibration of Hydrological Model Parameters Using MOSCEM-UA
Wang, Yuhui; Lei, Xiaohui; Jiang, Yunzhong; Wang, Hao
2010-05-01
In the past two decades, many evolutionary algorithms have been adopted in the auto-calibration of hydrological model such as NSGA-II, SCEM, etc., some of which has shown ideal performance. In this article, a detailed hydrological model auto-calibration algorithm Multi-objective Shuffled Complex Evolution Metropolis (MOSCEM-UA) has been introduced to carry out auto-calibration of hydrological model in order to clarify the equilibrium and the uncertainty of model parameters. The development and the implement flow chart of the advanced multi-objective algorithm (MOSCEM-UA) were interpreted in detail. Hymod, a conceptual hydrological model depending on Moore's concept, was then introduced as a lumped Rain-Runoff simulation approach with several principal parameters involved. The five important model parameters subjected to calibration includes maximum storage capacity, spatial variability of the soil moisture capacity, flow distributing factor between slow and quick reservoirs as well as slow tank and quick tank distribution factor. In this study, a test case on the up-stream area of KuanCheng hydrometric station in Haihe basin was studied to verify the performance of calibration. Two objectives including objective for high flow process and objective for low flow process are chosen in the process of calibration. The results emphasized that the interrelationship between objective functions could be described in correlation Pareto Front by using MOSCEM-UA. The Pareto Front can be draw after the iteration. Further more, post range of parameters corresponding to Pareto sets could also be drawn to identify the prediction range of the model. Then a set of balanced parameter was chosen to validate the model and the result showed an ideal prediction. Meanwhile, the correlation among parameters and their effects on the model performance could also be achieved.
Analysis report for WIPP colloid model constraints and performance assessment parameters
Energy Technology Data Exchange (ETDEWEB)
Mariner, Paul E.; Sassani, David Carl
2014-03-01
An analysis of the Waste Isolation Pilot Plant (WIPP) colloid model constraints and parameter values was performed. The focus of this work was primarily on intrinsic colloids, mineral fragment colloids, and humic substance colloids, with a lesser focus on microbial colloids. Comments by the US Environmental Protection Agency (EPA) concerning intrinsic Th(IV) colloids and Mg-Cl-OH mineral fragment colloids were addressed in detail, assumptions and data used to constrain colloid model calculations were evaluated, and inconsistencies between data and model parameter values were identified. This work resulted in a list of specific conclusions regarding model integrity, model conservatism, and opportunities for improvement related to each of the four colloid types included in the WIPP performance assessment.
Mattern, Jann Paul; Edwards, Christopher A.
2017-01-01
Parameter estimation is an important part of numerical modeling and often required when a coupled physical-biogeochemical ocean model is first deployed. However, 3-dimensional ocean model simulations are computationally expensive and models typically contain upwards of 10 parameters suitable for estimation. Hence, manual parameter tuning can be lengthy and cumbersome. Here, we present four easy to implement and flexible parameter estimation techniques and apply them to two 3-dimensional biogeochemical models of different complexities. Based on a Monte Carlo experiment, we first develop a cost function measuring the model-observation misfit based on multiple data types. The parameter estimation techniques are then applied and yield a substantial cost reduction over ∼ 100 simulations. Based on the outcome of multiple replicate experiments, they perform on average better than random, uninformed parameter search but performance declines when more than 40 parameters are estimated together. Our results emphasize the complex cost function structure for biogeochemical parameters and highlight dependencies between different parameters as well as different cost function formulations.
Parameter Identification for a New Circuit Model Aimed to Predict Body Water Volume
Directory of Open Access Journals (Sweden)
GHEORGHE, A.-G.
2012-11-01
Full Text Available Intracellular and extracellular water volumes in the human body have been computed using a sequence of models starting with a linear first order RC circuit (Cole model and finishing with the De Lorenzo model. This last model employs a fractional order impedance whose parameters are identified using the frequency characteristics of the impedance module and phase, the latter being not unique. While the Cole model has a two octaves frequency validity range, the De Lorenzo model can be used for three decades. A new linear RC model, valid for a three decades frequency range, is proposed. This circuit can be viewed as an extension of the Cole model for a larger frequency interval, unlike similar models proposed by the same authors.
Multiobjective Automatic Parameter Calibration of a Hydrological Model
Directory of Open Access Journals (Sweden)
Donghwi Jung
2017-03-01
Full Text Available This study proposes variable balancing approaches for the exploration (diversification and exploitation (intensification of the non-dominated sorting genetic algorithm-II (NSGA-II with simulated binary crossover (SBX and polynomial mutation (PM in the multiobjective automatic parameter calibration of a lumped hydrological model, the HYMOD model. Two objectives—minimizing the percent bias and minimizing three peak flow differences—are considered in the calibration of the six parameters of the model. The proposed balancing approaches, which migrate the focus between exploration and exploitation over generations by varying the crossover and mutation distribution indices of SBX and PM, respectively, are compared with traditional static balancing approaches (the two dices value is fixed during optimization in a benchmark hydrological calibration problem for the Leaf River (1950 km2 near Collins, Mississippi. Three performance metrics—solution quality, spacing, and convergence—are used to quantify and compare the quality of the Pareto solutions obtained by the two different balancing approaches. The variable balancing approaches that migrate the focus of exploration and exploitation differently for SBX and PM outperformed other methods.
Constraints on the parameters of the Left Right Mirror Model
Cerón, V E; Díaz-Cruz, J L; Maya, M; Ceron, Victoria E.; Cotti, Umberto; Maya, Mario
1998-01-01
We study some phenomenological constraints on the parameters of a left right model with mirror fermions (LRMM) that solves the strong CP problem. In particular, we evaluate the contribution of mirror neutrinos to the invisible Z decay width (\\Gamma_Z^{inv}), and we find that the present experimental value on \\Gamma_Z^{inv}, can be used to place an upper bound on the Z-Z' mixing angle that is consistent with limits obtained previously from other low-energy observables. In this model the charged fermions that correspond to the standard model (SM) mix with its mirror counterparts. This mixing, simultaneously with the Z-Z' one, leads to modifications of the \\Gamma(Z --> f \\bar{f}) decay width. By comparing with LEP data, we obtain bounds on the standard-mirror lepton mixing angles. We also find that the bottom quark mixing parameters can be chosen to fit the experimental values of R_b, and the resulting values for the Z-Z' mixing angle do not agree with previous bounds. However, this disagreement disappears if on...
Application of a free parameter model to plastic scintillation samples
Energy Technology Data Exchange (ETDEWEB)
Tarancon Sanz, Alex, E-mail: alex.tarancon@ub.edu [Departament de Quimica Analitica, Universitat de Barcelona, Diagonal 647, E-08028 Barcelona (Spain); Kossert, Karsten, E-mail: Karsten.Kossert@ptb.de [Physikalisch-Technische Bundesanstalt (PTB), Bundesallee 100, 38116 Braunschweig (Germany)
2011-08-21
In liquid scintillation (LS) counting, the CIEMAT/NIST efficiency tracing method and the triple-to-double coincidence ratio (TDCR) method have proved their worth for reliable activity measurements of a number of radionuclides. In this paper, an extended approach to apply a free-parameter model to samples containing a mixture of solid plastic scintillation microspheres and radioactive aqueous solutions is presented. Several beta-emitting radionuclides were measured in a TDCR system at PTB. For the application of the free parameter model, the energy loss in the aqueous phase must be taken into account, since this portion of the particle energy does not contribute to the creation of scintillation light. The energy deposit in the aqueous phase is determined by means of Monte Carlo calculations applying the PENELOPE software package. To this end, great efforts were made to model the geometry of the samples. Finally, a new geometry parameter was defined, which was determined by means of a tracer radionuclide with known activity. This makes the analysis of experimental TDCR data of other radionuclides possible. The deviations between the determined activity concentrations and reference values were found to be lower than 3%. The outcome of this research work is also important for a better understanding of liquid scintillation counting. In particular the influence of (inverse) micelles, i.e. the aqueous spaces embedded in the organic scintillation cocktail, can be investigated. The new approach makes clear that it is important to take the energy loss in the aqueous phase into account. In particular for radionuclides emitting low-energy electrons (e.g. M-Auger electrons from {sup 125}I), this effect can be very important.
Model parameters for representative wetland plant functional groups
Williams, Amber S.; Kiniry, James R.; Mushet, David M.; Smith, Loren M.; McMurry, Scott T.; Attebury, Kelly; Lang, Megan; McCarty, Gregory W.; Shaffer, Jill A.; Effland, William R.; Johnson, Mari-Vaughn V.
2017-01-01
Wetlands provide a wide variety of ecosystem services including water quality remediation, biodiversity refugia, groundwater recharge, and floodwater storage. Realistic estimation of ecosystem service benefits associated with wetlands requires reasonable simulation of the hydrology of each site and realistic simulation of the upland and wetland plant growth cycles. Objectives of this study were to quantify leaf area index (LAI), light extinction coefficient (k), and plant nitrogen (N), phosphorus (P), and potassium (K) concentrations in natural stands of representative plant species for some major plant functional groups in the United States. Functional groups in this study were based on these parameters and plant growth types to enable process-based modeling. We collected data at four locations representing some of the main wetland regions of the United States. At each site, we collected on-the-ground measurements of fraction of light intercepted, LAI, and dry matter within the 2013–2015 growing seasons. Maximum LAI and k variables showed noticeable variations among sites and years, while overall averages and functional group averages give useful estimates for multisite simulation modeling. Variation within each species gives an indication of what can be expected in such natural ecosystems. For P and K, the concentrations from highest to lowest were spikerush (Eleocharis macrostachya), reed canary grass (Phalaris arundinacea), smartweed (Polygonum spp.), cattail (Typha spp.), and hardstem bulrush (Schoenoplectus acutus). Spikerush had the highest N concentration, followed by smartweed, bulrush, reed canary grass, and then cattail. These parameters will be useful for the actual wetland species measured and for the wetland plant functional groups they represent. These parameters and the associated process-based models offer promise as valuable tools for evaluating environmental benefits of wetlands and for evaluating impacts of various agronomic practices in
Parameter optimization in differential geometry based solvation models.
Wang, Bao; Wei, G W
2015-10-01
Differential geometry (DG) based solvation models are a new class of variational implicit solvent approaches that are able to avoid unphysical solvent-solute boundary definitions and associated geometric singularities, and dynamically couple polar and non-polar interactions in a self-consistent framework. Our earlier study indicates that DG based non-polar solvation model outperforms other methods in non-polar solvation energy predictions. However, the DG based full solvation model has not shown its superiority in solvation analysis, due to its difficulty in parametrization, which must ensure the stability of the solution of strongly coupled nonlinear Laplace-Beltrami and Poisson-Boltzmann equations. In this work, we introduce new parameter learning algorithms based on perturbation and convex optimization theories to stabilize the numerical solution and thus achieve an optimal parametrization of the DG based solvation models. An interesting feature of the present DG based solvation model is that it provides accurate solvation free energy predictions for both polar and non-polar molecules in a unified formulation. Extensive numerical experiment demonstrates that the present DG based solvation model delivers some of the most accurate predictions of the solvation free energies for a large number of molecules.
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...
Allowed Parameter Regions for a Tree-Level Inflation Model
Institute of Scientific and Technical Information of China (English)
MENG Xin-He
2001-01-01
The early universe inflation is well known as a promising theory to explain the origin of large-scale structure of universe and to solve the early universe pressing problems. For a reasonable inflation model, the potential during inflation must be very flat, at least, in the direction of the inflaton. To construct the inflaton potential all the known related astrophysics observations should be included. For a general tree-level hybrid inflation potential, which is notdiscussed fully so far, the parameters in it are shown how to be constrained via the astrophysics data observed and to be obtained to the expected accuracy, and to be consistent with cosmology requirements.``
Modelling of bio-optical parameters of open ocean waters
Directory of Open Access Journals (Sweden)
Vadim N. Pelevin
2001-12-01
Full Text Available An original method for estimating the concentration of chlorophyll pigments, absorption of yellow substance and absorption of suspended matter without pigments and yellow substance in detritus using spectral diffuse attenuation coefficient for downwelling irradiance and irradiance reflectance data has been applied to sea waters of different types in the open ocean (case 1. Using the effective numerical single parameter classification with the water type optical index m as a parameter over the whole range of the open ocean waters, the calculations have been carried out and the light absorption spectra of sea waters tabulated. These spectra are used to optimize the absorption models and thus to estimate the concentrations of the main admixtures in sea water. The value of m can be determined from direct measurements of the downward irradiance attenuation coefficient at 500 nm or calculated from remote sensing data using the regressions given in the article. The sea water composition can then be readily estimated from the tables given for any open ocean area if that one parameter m characterizing the basin is known.
Development of class model based on blood biochemical parameters as a diagnostic tool of PSE meat.
Qu, Daofeng; Zhou, Xu; Yang, Feng; Tian, Shiyi; Zhang, Xiaojun; Ma, Lin; Han, Jianzhong
2017-06-01
A fast, sensitive and effective method based on the blood biochemical parameters for the detection of PSE meat was developed in this study. A total of 200 pigs were slaughtered in the same slaughterhouse. Meat quality was evaluated by measuring pH, electrical conductivity and color at 45min, 2h and 24h after slaughtering in M. longissimus thoracis et lumborum (LD). Blood biochemical parameters were determined in blood samples collected during carcass bleeding. Principal component analysis (PCA) biplot showed that high levels of exsanguination Creatine Kinase, Lactate Dehydrogenase, Aspertate aminotransferase, blood glucose and lactate were associated with the PSE meat, and the five biochemical parameters were found to be good indicators of PSE meat Discriminant function analysis (DFA) was able to clearly identify PSE meat using the five biochemical parameters as input data, and the class model is an effective diagnostic tool in pigs which can be used to detect the PSE meat and reduce economic loss for the company.
Parameter-free methods distinguish Wnt pathway models and guide design of experiments
MacLean, Adam L.
2015-02-17
The canonical Wnt signaling pathway, mediated by β-catenin, is crucially involved in development, adult stem cell tissue maintenance, and a host of diseases including cancer. We analyze existing mathematical models of Wnt and compare them to a new Wnt signaling model that targets spatial localization; our aim is to distinguish between the models and distill biological insight from them. Using Bayesian methods we infer parameters for each model from mammalian Wnt signaling data and find that all models can fit this time course. We appeal to algebraic methods (concepts from chemical reaction network theory and matroid theory) to analyze the models without recourse to specific parameter values. These approaches provide insight into aspects of Wnt regulation: the new model, via control of shuttling and degradation parameters, permits multiple stable steady states corresponding to stem-like vs. committed cell states in the differentiation hierarchy. Our analysis also identifies groups of variables that should be measured to fully characterize and discriminate between competing models, and thus serves as a guide for performing minimal experiments for model comparison.
Breakdown parameter for kinetic modeling of multiscale gas flows.
Meng, Jianping; Dongari, Nishanth; Reese, Jason M; Zhang, Yonghao
2014-06-01
Multiscale methods built purely on the kinetic theory of gases provide information about the molecular velocity distribution function. It is therefore both important and feasible to establish new breakdown parameters for assessing the appropriateness of a fluid description at the continuum level by utilizing kinetic information rather than macroscopic flow quantities alone. We propose a new kinetic criterion to indirectly assess the errors introduced by a continuum-level description of the gas flow. The analysis, which includes numerical demonstrations, focuses on the validity of the Navier-Stokes-Fourier equations and corresponding kinetic models and reveals that the new criterion can consistently indicate the validity of continuum-level modeling in both low-speed and high-speed flows at different Knudsen numbers.
Batstone, D J; Torrijos, M; Ruiz, C; Schmidt, J E
2004-01-01
The model structure in anaerobic digestion has been clarified following publication of the IWA Anaerobic Digestion Model No. 1 (ADM1). However, parameter values are not well known, and uncertainty and variability in the parameter values given is almost unknown. Additionally, platforms for identification of parameters, namely continuous-flow laboratory digesters, and batch tests suffer from disadvantages such as long run times, and difficulty in defining initial conditions, respectively. Anaerobic sequencing batch reactors (ASBRs) are sequenced into fill-react-settle-decant phases, and offer promising possibilities for estimation of parameters, as they are by nature, dynamic in behaviour, and allow repeatable behaviour to establish initial conditions, and evaluate parameters. In this study, we estimated parameters describing winery wastewater (most COD as ethanol) degradation using data from sequencing operation, and validated these parameters using unsequenced pulses of ethanol and acetate. The model used was the ADM1, with an extension for ethanol degradation. Parameter confidence spaces were found by non-linear, correlated analysis of the two main Monod parameters; maximum uptake rate (k(m)), and half saturation concentration (K(S)). These parameters could be estimated together using only the measured acetate concentration (20 points per cycle). From interpolating the single cycle acetate data to multiple cycles, we estimate that a practical "optimal" identifiability could be achieved after two cycles for the acetate parameters, and three cycles for the ethanol parameters. The parameters found performed well in the short term, and represented the pulses of acetate and ethanol (within 4 days of the winery-fed cycles) very well. The main discrepancy was poor prediction of pH dynamics, which could be due to an unidentified buffer with an overall influence the same as a weak base (possibly CaCO3). Based on this work, ASBR systems are effective for parameter
Probing the dynamics of identified neurons with a data-driven modeling approach.
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Thomas Nowotny
Full Text Available In controlling animal behavior the nervous system has to perform within the operational limits set by the requirements of each specific behavior. The implications for the corresponding range of suitable network, single neuron, and ion channel properties have remained elusive. In this article we approach the question of how well-constrained properties of neuronal systems may be on the neuronal level. We used large data sets of the activity of isolated invertebrate identified cells and built an accurate conductance-based model for this cell type using customized automated parameter estimation techniques. By direct inspection of the data we found that the variability of the neurons is larger when they are isolated from the circuit than when in the intact system. Furthermore, the responses of the neurons to perturbations appear to be more consistent than their autonomous behavior under stationary conditions. In the developed model, the constraints on different parameters that enforce appropriate model dynamics vary widely from some very tightly controlled parameters to others that are almost arbitrary. The model also allows predictions for the effect of blocking selected ionic currents and to prove that the origin of irregular dynamics in the neuron model is proper chaoticity and that this chaoticity is typical in an appropriate sense. Our results indicate that data driven models are useful tools for the in-depth analysis of neuronal dynamics. The better consistency of responses to perturbations, in the real neurons as well as in the model, suggests a paradigm shift away from measuring autonomous dynamics alone towards protocols of controlled perturbations. Our predictions for the impact of channel blockers on the neuronal dynamics and the proof of chaoticity underscore the wide scope of our approach.
A novel criterion for determination of material model parameters
Andrade-Campos, A.; de-Carvalho, R.; Valente, R. A. F.
2011-05-01
Parameter identification problems have emerged due to the increasing demanding of precision in the numerical results obtained by Finite Element Method (FEM) software. High result precision can only be obtained with confident input data and robust numerical techniques. The determination of parameters should always be performed confronting numerical and experimental results leading to the minimum difference between them. However, the success of this task is dependent of the specification of the cost/objective function, defined as the difference between the experimental and the numerical results. Recently, various objective functions have been formulated to assess the errors between the experimental and computed data (Lin et al., 2002; Cao and Lin, 2008; among others). The objective functions should be able to efficiently lead the optimisation process. An ideal objective function should have the following properties: (i) all the experimental data points on the curve and all experimental curves should have equal opportunity to be optimised; and (ii) different units and/or the number of curves in each sub-objective should not affect the overall performance of the fitting. These two criteria should be achieved without manually choosing the weighting factors. However, for some non-analytical specific problems, this is very difficult in practice. Null values of experimental or numerical values also turns the task difficult. In this work, a novel objective function for constitutive model parameter identification is presented. It is a generalization of the work of Cao and Lin and it is suitable for all kinds of constitutive models and mechanical tests, including cyclic tests and Baushinger tests with null values.
Identifying errors in dust models from data assimilation.
Pope, R J; Marsham, J H; Knippertz, P; Brooks, M E; Roberts, A J
2016-09-16
Airborne mineral dust is an important component of the Earth system and is increasingly predicted prognostically in weather and climate models. The recent development of data assimilation for remotely sensed aerosol optical depths (AODs) into models offers a new opportunity to better understand the characteristics and sources of model error. Here we examine assimilation increments from Moderate Resolution Imaging Spectroradiometer AODs over northern Africa in the Met Office global forecast model. The model underpredicts (overpredicts) dust in light (strong) winds, consistent with (submesoscale) mesoscale processes lifting dust in reality but being missed by the model. Dust is overpredicted in the Sahara and underpredicted in the Sahel. Using observations of lighting and rain, we show that haboobs (cold pool outflows from moist convection) are an important dust source in reality but are badly handled by the model's convection scheme. The approach shows promise to serve as a useful framework for future model development.
Standard model parameters and the search for new physics
Energy Technology Data Exchange (ETDEWEB)
Marciano, W.J.
1988-04-01
In these lectures, my aim is to present an up-to-date status report on the standard model and some key tests of electroweak unification. Within that context, I also discuss how and where hints of new physics may emerge. To accomplish those goals, I have organized my presentation as follows: I discuss the standard model parameters with particular emphasis on the gauge coupling constants and vector boson masses. Examples of new physics appendages are also briefly commented on. In addition, because these lectures are intended for students and thus somewhat pedagogical, I have included an appendix on dimensional regularization and a simple computational example that employs that technique. Next, I focus on weak charged current phenomenology. Precision tests of the standard model are described and up-to-date values for the Cabibbo-Kobayashi-Maskawa (CKM) mixing matrix parameters are presented. Constraints implied by those tests for a 4th generation, supersymmetry, extra Z/prime/ bosons, and compositeness are also discussed. I discuss weak neutral current phenomenology and the extraction of sin/sup 2/ /theta//sub W/ from experiment. The results presented there are based on a recently completed global analysis of all existing data. I have chosen to concentrate that discussion on radiative corrections, the effect of a heavy top quark mass, and implications for grand unified theories (GUTS). The potential for further experimental progress is also commented on. I depart from the narrowest version of the standard model and discuss effects of neutrino masses and mixings. I have chosen to concentrate on oscillations, the Mikheyev-Smirnov- Wolfenstein (MSW) effect, and electromagnetic properties of neutrinos. On the latter topic, I will describe some recent work on resonant spin-flavor precession. Finally, I conclude with a prospectus on hopes for the future. 76 refs.
Optimization routine for identification of model parameters in soil plasticity
Mattsson, Hans; Klisinski, Marek; Axelsson, Kennet
2001-04-01
The paper presents an optimization routine especially developed for the identification of model parameters in soil plasticity on the basis of different soil tests. Main focus is put on the mathematical aspects and the experience from application of this optimization routine. Mathematically, for the optimization, an objective function and a search strategy are needed. Some alternative expressions for the objective function are formulated. They capture the overall soil behaviour and can be used in a simultaneous optimization against several laboratory tests. Two different search strategies, Rosenbrock's method and the Simplex method, both belonging to the category of direct search methods, are utilized in the routine. Direct search methods have generally proved to be reliable and their relative simplicity make them quite easy to program into workable codes. The Rosenbrock and simplex methods are modified to make the search strategies as efficient and user-friendly as possible for the type of optimization problem addressed here. Since these search strategies are of a heuristic nature, which makes it difficult (or even impossible) to analyse their performance in a theoretical way, representative optimization examples against both simulated experimental results as well as performed triaxial tests are presented to show the efficiency of the optimization routine. From these examples, it has been concluded that the optimization routine is able to locate a minimum with a good accuracy, fast enough to be a very useful tool for identification of model parameters in soil plasticity.
Ramin, Elham; Sin, Gürkan; Mikkelsen, Peter Steen; Plósz, Benedek Gy
2014-10-15
Current research focuses on predicting and mitigating the impacts of high hydraulic loadings on centralized wastewater treatment plants (WWTPs) under wet-weather conditions. The maximum permissible inflow to WWTPs depends not only on the settleability of activated sludge in secondary settling tanks (SSTs) but also on the hydraulic behaviour of SSTs. The present study investigates the impacts of ideal and non-ideal flow (dry and wet weather) and settling (good settling and bulking) boundary conditions on the sensitivity of WWTP model outputs to uncertainties intrinsic to the one-dimensional (1-D) SST model structures and parameters. We identify the critical sources of uncertainty in WWTP models through global sensitivity analysis (GSA) using the Benchmark simulation model No. 1 in combination with first- and second-order 1-D SST models. The results obtained illustrate that the contribution of settling parameters to the total variance of the key WWTP process outputs significantly depends on the influent flow and settling conditions. The magnitude of the impact is found to vary, depending on which type of 1-D SST model is used. Therefore, we identify and recommend potential parameter subsets for WWTP model calibration, and propose optimal choice of 1-D SST models under different flow and settling boundary conditions. Additionally, the hydraulic parameters in the second-order SST model are found significant under dynamic wet-weather flow conditions. These results highlight the importance of developing a more mechanistic based flow-dependent hydraulic sub-model in second-order 1-D SST models in the future.
Outlier-Tolerance RML Identification of Parameters in CAR Model
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Hong Teng-teng
2016-10-01
Full Text Available The measured data inevitably contain abnormal data under the normal operating conditions. Most of the existing algorithms, such as least squares identification and maximum likelihood estimation, are easily affected by abnormal data and appear large indentation deviation. It is a difficult task needed to be addressed that how to improve the sensitivity of the existing algorithm or build a new parameter identifying algorithm with outlier-tolerance ability to abnormal data in system identification technology application. In this paper, the sensitivity of the RML to the sampled abnormal data was analyzed and a new improvement algorithm of CAR process is established to improve outlier-tolerance ability of the RML identification when there are outliers in the sampling series. The improved algorithm not only effectively inhibits the negative impact of the abnormal data but also effectively improve the quality of the parameter identification results. Some simulation given in this paper shows that the improved RML algorithm has strong outlier-tolerance. This paper’s research results play an important role in engineering control, signal processing, industrial automation and aerospace or other fields.
Liu, M.; He, B.; Lü, A.; Zhou, L.; Wu, J.
2014-06-01
Parameters sensitivity analysis is a crucial step in effective model calibration. It quantitatively apportions the variation of model output to different sources of variation, and identifies how "sensitive" a model is to changes in the values of model parameters. Through calibration of parameters that are sensitive to model outputs, parameter estimation becomes more efficient. Due to uncertainties associated with yield estimates in a regional assessment, field-based models that perform well at field scale are not accurate enough to model at regional scale. Conducting parameters sensitivity analysis at the regional scale and analyzing the differences of parameter sensitivity between stations would make model calibration and validation in different sub-regions more efficient. Further, it would benefit the model applied to the regional scale. Through simulating 2000 × 22 samples for 10 stations in the Huanghuaihai Plain, this study discovered that TB (Optimal temperature), HI (Normal harvest index), WA (Potential radiation use efficiency), BN2 (Normal fraction of N in crop biomass at mid-season) and RWPC1 (Fraction of root weight at emergency) are more sensitive than other parameters. Parameters that determine nutrition supplement and LAI development have higher global sensitivity indices than first-order indices. For spatial application, soil diversity is crucial because soil is responsible for crop parameters sensitivity index differences between sites.
Reimer, Joscha; Piwonski, Jaroslaw; Slawig, Thomas
2016-04-01
The statistical significance of any model-data comparison strongly depends on the quality of the used data and the criterion used to measure the model-to-data misfit. The statistical properties (such as mean values, variances and covariances) of the data should be taken into account by choosing a criterion as, e.g., ordinary, weighted or generalized least squares. Moreover, the criterion can be restricted onto regions or model quantities which are of special interest. This choice influences the quality of the model output (also for not measured quantities) and the results of a parameter estimation or optimization process. We have estimated the parameters of a three-dimensional and time-dependent marine biogeochemical model describing the phosphorus cycle in the ocean. For this purpose, we have developed a statistical model for measurements of phosphate and dissolved organic phosphorus. This statistical model includes variances and correlations varying with time and location of the measurements. We compared the obtained estimations of model output and parameters for different criteria. Another question is if (and which) further measurements would increase the model's quality at all. Using experimental design criteria, the information content of measurements can be quantified. This may refer to the uncertainty in unknown model parameters as well as the uncertainty regarding which model is closer to reality. By (another) optimization, optimal measurement properties such as locations, time instants and quantities to be measured can be identified. We have optimized such properties for additional measurement for the parameter estimation of the marine biogeochemical model. For this purpose, we have quantified the uncertainty in the optimal model parameters and the model output itself regarding the uncertainty in the measurement data using the (Fisher) information matrix. Furthermore, we have calculated the uncertainty reduction by additional measurements depending on time
Parameters of AMMI Model for Yield Stability Analysis in Durum Wheat
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Naser Sabaghnia
2013-06-01
Full Text Available The improvement of new genotypes with acceptable yield stability in different environments is an important issue in breeding programs. In order to study genotype × environment (GE interaction and to determine the most stable durum wheat genotypes, field experiments were conducted with 20 genotypes for three years (2007-2009. Results showed highly significant GE interaction indicating the possibility of selection for the most stable genotypes. The AMMI (additive main effect and multiplicative interaction analysis indicated that the first five axes were significant based on F-test of Gollob while the other tests (FGH1 and FGH2 identified first three axes as significant AMMI model components. Furthermore, according to FRatio test and cross validation results, only first two axes were significant. According to these distinct numbers of significant axes, sixteen AMMI stability parameters plus ASV(AMMI stability value were computed. Our results showed that EV- and D-based parameters, displayed G7 and G8, SIPC-based parameters indicated G3 and G4 and AMGE-based parameters identified G15 as the most stable genotypes. Genotypes G15 and G7 were the highest mean yielding genotypes and so they could be regarded as the most favorable durum wheat genotypes. The results of this investigation proved that the most of AMMI stability parameters are suitable indices for discriminating stable genotypes and AMGE-based parameters can detect highly seed yield genotypes with good stability.
Parameters of AMMI Model for Yield Stability Analysis in Durum Wheat
Directory of Open Access Journals (Sweden)
Naser Sabaghnia
2013-07-01
Full Text Available The improvement of new genotypes with acceptable yield stability in different environments is an important issue in breeding programs. In order to study genotype × environment (GE interaction and to determine the most stable durum wheat genotypes, field experiments were conducted with 20 genotypes for three years (2007-2009. Results showed highly significant GE interaction indicating the possibility of selection for the most stable genotypes. The AMMI (additive main effect and multiplicative interaction analysis indicated that the first five axes were significant based on F-test of Gollob while the other tests (FGH1 and FGH2 identified first three axes as significant AMMI model components. Furthermore, according to FRatio test and cross validation results, only first two axes were significant. According to these distinct numbers of significant axes, sixteen AMMI stability parameters plus ASV (AMMI stability value were computed. Our results showed that EV- and D-based parameters, displayed G7 and G8, SIPC-based parameters indicated G3 and G4 and AMGE-based parameters identified G15 as the most stable genotypes. Genotypes G15 and G7 were the highest mean yielding genotypes and so they could be regarded as the most favorable durum wheat genotypes. The results of this investigation proved that the most of AMMI stability parameters are suitable indices for discriminating stable genotypes and AMGE-based parameters can detect highly seed yield genotypes with good stability.
Estimating input parameters from intracellular recordings in the Feller neuronal model
Bibbona, Enrico; Lansky, Petr; Sirovich, Roberta
2010-03-01
We study the estimation of the input parameters in a Feller neuronal model from a trajectory of the membrane potential sampled at discrete times. These input parameters are identified with the drift and the infinitesimal variance of the underlying stochastic diffusion process with multiplicative noise. The state space of the process is restricted from below by an inaccessible boundary. Further, the model is characterized by the presence of an absorbing threshold, the first hitting of which determines the length of each trajectory and which constrains the state space from above. We compare, both in the presence and in the absence of the absorbing threshold, the efficiency of different known estimators. In addition, we propose an estimator for the drift term, which is proved to be more efficient than the others, at least in the explored range of the parameters. The presence of the threshold makes the estimates of the drift term biased, and two methods to correct it are proposed.
Modeling, Parameters Identification, and Control of High Pressure Fuel Cell Back-Pressure Valve
Directory of Open Access Journals (Sweden)
Fengxiang Chen
2014-01-01
Full Text Available The reactant pressure is crucial to the efficiency and lifespan of a high pressure PEMFC engine. This paper analyses a regulated back-pressure valve (BPV for the cathode outlet flow in a high pressure PEMFC engine, which can achieve precisely pressure control. The modeling, parameters identification, and nonlinear controller design of a BPV system are considered. The identified parameters are used in designing active disturbance rejection controller (ADRC. Simulations and extensive experiments are conducted with the xPC Target and show that the proposed controller can not only achieve good dynamic and static performance but also have strong robustness against parameters’ disturbance and external disturbance.
Data Assimilation and Sensitivity of the Black Sea Model to Parameters
Kazantsev, Eugene
2011-01-01
An adjoint based technique is applied to a Shallow Water Model in order to estimate influence of the model's parameters on the solution. Among parameters the bottom topography, initial conditions, boundary conditions on rigid boundaries, viscosity coefficients and the amplitude of the wind stress tension are considered. Their influence is analyzed from different points of view. Two configurations have been analyzed: an academic case of the model in a square box and a more realistic case simulating Black Sea currents. It is shown in both experiments that the boundary conditions near a rigid boundary influence the most the solution. This fact points out the necessity to identify optimal boundary approximation during a model development.
Variational methods to estimate terrestrial ecosystem model parameters
Delahaies, Sylvain; Roulstone, Ian
2016-04-01
Carbon is at the basis of the chemistry of life. Its ubiquity in the Earth system is the result of complex recycling processes. Present in the atmosphere in the form of carbon dioxide it is adsorbed by marine and terrestrial ecosystems and stored within living biomass and decaying organic matter. Then soil chemistry and a non negligible amount of time transform the dead matter into fossil fuels. Throughout this cycle, carbon dioxide is released in the atmosphere through respiration and combustion of fossils fuels. Model-data fusion techniques allow us to combine our understanding of these complex processes with an ever-growing amount of observational data to help improving models and predictions. The data assimilation linked ecosystem carbon (DALEC) model is a simple box model simulating the carbon budget allocation for terrestrial ecosystems. Over the last decade several studies have demonstrated the relative merit of various inverse modelling strategies (MCMC, ENKF, 4DVAR) to estimate model parameters and initial carbon stocks for DALEC and to quantify the uncertainty in the predictions. Despite its simplicity, DALEC represents the basic processes at the heart of more sophisticated models of the carbon cycle. Using adjoint based methods we study inverse problems for DALEC with various data streams (8 days MODIS LAI, monthly MODIS LAI, NEE). The framework of constraint optimization allows us to incorporate ecological common sense into the variational framework. We use resolution matrices to study the nature of the inverse problems and to obtain data importance and information content for the different type of data. We study how varying the time step affect the solutions, and we show how "spin up" naturally improves the conditioning of the inverse problems.
Simple Empirical Model for Identifying Rheological Properties of Soft Biological Tissues
Kobayashi, Yo; Miyashita, Tomoyuki; Fujie, Masakatsu G
2015-01-01
Understanding the rheological properties of soft biological tissue is a key issue for mechanical systems used in the healthcare field. We propose a simple empirical model using Fractional Dynamics and Exponential Nonlinearity (FDEN) to identify the rheological properties of soft biological tissue. The model is derived from detailed material measurements using samples isolated from porcine liver. We conducted dynamic viscoelastic and creep tests on liver samples using a rheometer. The experimental results indicated that biological tissue has specific properties: i) power law increases in storage elastic modulus and loss elastic modulus with the same slope; ii) power law gain decrease and constant phase delay in the frequency domain over two decades; iii) log-log scale linearity between time and strain relationships under constant force; and iv) linear and log scale linearity between strain and stress relationships. Our simple FDEN model uses only three dependent parameters and represents the specific propertie...
A parameter model for dredge plume sediment source terms
Decrop, Boudewijn; De Mulder, Tom; Toorman, Erik; Sas, Marc
2017-01-01
, which is not available in all situations. For example, to allow correct representation of overflow plume dispersion in a real-time forecasting model, a fast assessment of the near-field behaviour is needed. For this reason, a semi-analytical parameter model has been developed that reproduces the near-field sediment dispersion obtained with the CFD model in a relatively accurate way. In this paper, this so-called grey-box model is presented.
Recommended Parameter Values for INEEL Subsurface Disposal Area Source Release Modeling
Energy Technology Data Exchange (ETDEWEB)
Riley, Robert G.; Lopresti, Charles A.
2004-06-23
The purpose of this report is to summarize 1) associated information and values for key release model parameters (i.e., best estimate, minimum and maximum) obtained where possible from published experimental data, 2) a structure for selection of sensitivity tests cases that can be used to identify test cases, and 3) recommended test cases for selected contaminants of potential concern to assess remedy effectiveness against a no-treatment base case.
Pressure pulsation in roller pumps: a validated lumped parameter model.
Moscato, Francesco; Colacino, Francesco M; Arabia, Maurizio; Danieli, Guido A
2008-11-01
During open-heart surgery roller pumps are often used to keep the circulation of blood through the patient body. They present numerous key features, but they suffer from several limitations: (a) they normally deliver uncontrolled pulsatile inlet and outlet pressure; (b) blood damage appears to be more than that encountered with centrifugal pumps. A lumped parameter mathematical model of a roller pump (Sarns 7000, Terumo CVS, Ann Arbor, MI, USA) was developed to dynamically simulate pressures at the pump inlet and outlet in order to clarify the uncontrolled pulsation mechanism. Inlet and outlet pressures obtained by the mathematical model have been compared with those measured in various operating conditions: different rollers' rotating speed, different tube occlusion rates, and different clamping degree at the pump inlet and outlet. Model results agree with measured pressure waveforms, whose oscillations are generated by the tube compression/release mechanism during the rollers' engaging and disengaging phases. Average Euclidean Error (AEE) was 20mmHg and 33mmHg for inlet and outlet pressure estimates, respectively. The normalized AEE never exceeded 0.16. The developed model can be exploited for designing roller pumps with improved performances aimed at reducing the undesired pressure pulsation.
Basavaraj, C. K.; Vishwas, M.
2016-09-01
This paper discusses the process parameters for fused deposition modelling (FDM). Layer thickness, Orientation angle and shell thickness are the process variables considered for studies. Ultimate tensile strength, dimensional accuracy and manufacturing time are the response parameters. For number of experimental runs the taguchi's L9 orthogonal array is used. Taguchis S/N ratio was used to identify a set of process parameters which give good results for respective response characteristics. Effectiveness of each parameter is investigated by using analysis of variance. The material used for the studies of process parameter is Nylon.
Uniqueness, scale, and resolution issues in groundwater model parameter identification
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Tian-chyi J. Yeh
2015-07-01
Full Text Available This paper first visits uniqueness, scale, and resolution issues in groundwater flow forward modeling problems. It then makes the point that non-unique solutions to groundwater flow inverse problems arise from a lack of information necessary to make the problems well defined. Subsequently, it presents the necessary conditions for a well-defined inverse problem. They are full specifications of (1 flux boundaries and sources/sinks, and (2 heads everywhere in the domain at at least three times (one of which is t = 0, with head change everywhere at those times must being nonzero for transient flow. Numerical experiments are presented to corroborate the fact that, once the necessary conditions are met, the inverse problem has a unique solution. We also demonstrate that measurement noise, instability, and sensitivity are issues related to solution techniques rather than the inverse problems themselves. In addition, we show that a mathematically well-defined inverse problem, based on an equivalent homogeneous or a layered conceptual model, may yield physically incorrect and scenario-dependent parameter values. These issues are attributed to inconsistency between the scale of the head observed and that implied by these models. Such issues can be reduced only if a sufficiently large number of observation wells are used in the equivalent homogeneous domain or each layer. With a large number of wells, we then show that increase in parameterization can lead to a higher-resolution depiction of heterogeneity if an appropriate inverse methodology is used. Furthermore, we illustrate that, using the same number of wells, a highly parameterized model in conjunction with hydraulic tomography can yield better characterization of the aquifer and minimize the scale and scenario-dependent problems. Lastly, benefits of the highly parameterized model and hydraulic tomography are tested according to their ability to improve predictions of aquifer responses induced by
Finding model parameters: Genetic algorithms and the numerical modelling of quartz luminescence
Energy Technology Data Exchange (ETDEWEB)
Adamiec, Grzegorz [Department of Radioisotopes, Institute of Physics, Silesian University of Technology, ul. Krzywoustego 2, 44-100 Gliwice (Poland)]. E-mail: grzegorz.adamiec@polsl.pl; Bluszcz, Andrzej [Department of Radioisotopes, Institute of Physics, Silesian University of Technology, ul. Krzywoustego 2, 44-100 Gliwice (Poland); Bailey, Richard [Department of Geography, Royal Holloway, University of London, Egham, Surrey, TW20 0EX (United Kingdom); Garcia-Talavera, Marta [LIBRA, Centro I-D, Campus Miguel Delibes, 47011 Valladolid (Spain)
2006-08-15
The paper presents an application of genetic algorithms (GAs) to the problem of finding appropriate parameter values for the numerical simulation of quartz thermoluminescence (TL). We show that with the use of GAs it is possible to achieve a very good match between simulated and experimentally measured characteristics of quartz, for example the thermal activation characteristics of fired quartz. The rate equations of charge transport in the numerical model of luminescence in quartz contain a large number of parameters (trap depths, frequency factors, populations, charge capture probabilities, optical detrapping probabilities, and recombination probabilities). Given that comprehensive models consist of over 10 traps, finding model parameters proves a very difficult task. Manual parameter changes are very time consuming and allow only a limited degree of accuracy. GAs provide a semi-automatic way of finding appropriate parameters.
Relevant parameters in models of cell division control
Grilli, Jacopo; Osella, Matteo; Kennard, Andrew S.; Lagomarsino, Marco Cosentino
2017-03-01
A recent burst of dynamic single-cell data makes it possible to characterize the stochastic dynamics of cell division control in bacteria. Different models were used to propose specific mechanisms, but the links between them are poorly explored. The lack of comparative studies makes it difficult to appreciate how well any particular mechanism is supported by the data. Here, we describe a simple and generic framework in which two common formalisms can be used interchangeably: (i) a continuous-time division process described by a hazard function and (ii) a discrete-time equation describing cell size across generations (where the unit of time is a cell cycle). In our framework, this second process is a discrete-time Langevin equation with simple physical analogues. By perturbative expansion around the mean initial size (or interdivision time), we show how this framework describes a wide range of division control mechanisms, including combinations of time and size control, as well as the constant added size mechanism recently found to capture several aspects of the cell division behavior of different bacteria. As we show by analytical estimates and numerical simulations, the available data are described precisely by the first-order approximation of this expansion, i.e., by a "linear response" regime for the correction of size fluctuations. Hence, a single dimensionless parameter defines the strength and action of the division control against cell-to-cell variability (quantified by a single "noise" parameter). However, the same strength of linear response may emerge from several mechanisms, which are distinguished only by higher-order terms in the perturbative expansion. Our analytical estimate of the sample size needed to distinguish between second-order effects shows that this value is close to but larger than the values of the current datasets. These results provide a unified framework for future studies and clarify the relevant parameters at play in the control of
Modelling intelligence-led policing to identify its potential
Hengst-Bruggeling, M. den; Graaf, H.A.L.M. de; Scheepstal, P.G.M. van
2014-01-01
lntelligence-led policing is a concept of policing that has been applied throughout the world. Despite some encouraging reports, the effect of intelligence-led policing is largely unknown. This paper presents a method with which it is possible to identify intelligence-led policing's potential to
Modelling intelligence-led policing to identify its potential
Hengst-Bruggeling, M. den; Graaf, H.A.L.M. de; Scheepstal, P.G.M. van
2014-01-01
lntelligence-led policing is a concept of policing that has been applied throughout the world. Despite some encouraging reports, the effect of intelligence-led policing is largely unknown. This paper presents a method with which it is possible to identify intelligence-led policing's potential to inc
Sadiqi, Said; Verlaan, Jorrit Jan; Lehr, A. M.; Dvorak, Marcel F.; Kandziora, Frank; Rajasekaran, S.; Schnake, Klaus J.; Vaccaro, Alexander R.; Oner, F. C.
2016-01-01
STUDY DESIGN.: International web-based survey OBJECTIVE.: To identify clinical and radiological parameters that spine surgeons consider most relevant when evaluating clinical and functional outcomes of subaxial cervical spine trauma patients. SUMMARY OF BACKGROUND DATA.: While an outcome instrument
Sadiqi, Said; Verlaan, Jorrit Jan; Lehr, A. M.; Dvorak, Marcel F.; Kandziora, Frank; Rajasekaran, S.; Schnake, Klaus J.; Vaccaro, Alexander R.; Oner, F. C.
2016-01-01
STUDY DESIGN.: International web-based survey OBJECTIVE.: To identify clinical and radiological parameters that spine surgeons consider most relevant when evaluating clinical and functional outcomes of subaxial cervical spine trauma patients. SUMMARY OF BACKGROUND DATA.: While an outcome instrument
Modeling soil detachment capacity by rill flow using hydraulic parameters
Wang, Dongdong; Wang, Zhanli; Shen, Nan; Chen, Hao
2016-04-01
The relationship between soil detachment capacity (Dc) by rill flow and hydraulic parameters (e.g., flow velocity, shear stress, unit stream power, stream power, and unit energy) at low flow rates is investigated to establish an accurate experimental model. Experiments are conducted using a 4 × 0.1 m rill hydraulic flume with a constant artificial roughness on the flume bed. The flow rates range from 0.22 × 10-3 m2 s-1 to 0.67 × 10-3 m2 s-1, and the slope gradients vary from 15.8% to 38.4%. Regression analysis indicates that the Dc by rill flow can be predicted using the linear equations of flow velocity, stream power, unit stream power, and unit energy. Dc by rill flow that is fitted to shear stress can be predicted with a power function equation. Predictions based on flow velocity, unit energy, and stream power are powerful, but those based on shear stress, especially on unit stream power, are relatively poor. The prediction based on flow velocity provides the best estimates of Dc by rill flow because of the simplicity and availability of its measurements. Owing to error in measuring flow velocity at low flow rates, the predictive abilities of Dc by rill flow using all hydraulic parameters are relatively lower in this study compared with the results of previous research. The measuring accuracy of experiments for flow velocity should be improved in future research.