Global Sensitivity Analysis for Identifying Important Parameters of Nitrogen Nitrification and Denitrification under Model and Scenario Uncertainties

NASA Astrophysics Data System (ADS)

Ye, M.; Chen, Z.; Shi, L.; Zhu, Y.; Yang, J.

2017-12-01

Nitrogen reactive transport modeling is subject to uncertainty in model parameters, structures, and scenarios. While global sensitivity analysis is a vital tool for identifying the parameters important to nitrogen reactive transport, conventional global sensitivity analysis only considers parametric uncertainty. This may result in inaccurate selection of important parameters, because parameter importance may vary under different models and modeling scenarios. By using a recently developed variance-based global sensitivity analysis method, this paper identifies important parameters with simultaneous consideration of parametric uncertainty, model uncertainty, and scenario uncertainty. In a numerical example of nitrogen reactive transport modeling, a combination of three scenarios of soil temperature and two scenarios of soil moisture leads to a total of six scenarios. Four alternative models are used to evaluate reduction functions used for calculating actual rates of nitrification and denitrification. The model uncertainty is tangled with scenario uncertainty, as the reduction functions depend on soil temperature and moisture content. The results of sensitivity analysis show that parameter importance varies substantially between different models and modeling scenarios, which may lead to inaccurate selection of important parameters if model and scenario uncertainties are not considered. This problem is avoided by using the new method of sensitivity analysis in the context of model averaging and scenario averaging. The new method of sensitivity analysis can be applied to other problems of contaminant transport modeling when model uncertainty and/or scenario uncertainty are present.

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

PubMed Central

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

2012-01-01

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

Search-based model identification of smart-structure damage

NASA Technical Reports Server (NTRS)

Glass, B. J.; Macalou, A.

1991-01-01

This paper describes the use of a combined model and parameter identification approach, based on modal analysis and artificial intelligence (AI) techniques, for identifying damage or flaws in a rotating truss structure incorporating embedded piezoceramic sensors. This smart structure example is representative of a class of structures commonly found in aerospace systems and next generation space structures. Artificial intelligence techniques of classification, heuristic search, and an object-oriented knowledge base are used in an AI-based model identification approach. A finite model space is classified into a search tree, over which a variant of best-first search is used to identify the model whose stored response most closely matches that of the input. Newly-encountered models can be incorporated into the model space. This adaptativeness demonstrates the potential for learning control. Following this output-error model identification, numerical parameter identification is used to further refine the identified model. Given the rotating truss example in this paper, noisy data corresponding to various damage configurations are input to both this approach and a conventional parameter identification method. The combination of the AI-based model identification with parameter identification is shown to lead to smaller parameter corrections than required by the use of parameter identification alone.

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

PubMed Central

Karr, Jonathan R.; Williams, Alex H.; Zucker, Jeremy D.; Raue, Andreas; Steiert, Bernhard; Timmer, Jens; Kreutz, Clemens; Wilkinson, Simon; Allgood, Brandon A.; Bot, Brian M.; Hoff, Bruce R.; Kellen, Michael R.; Covert, Markus W.; Stolovitzky, Gustavo A.; Meyer, Pablo

2015-01-01

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. PMID:26020786

Cellular signaling identifiability analysis: a case study.

PubMed

Roper, Ryan T; Pia Saccomani, Maria; Vicini, Paolo

2010-05-21

Two primary purposes for mathematical modeling in cell biology are (1) simulation for making predictions of experimental outcomes and (2) parameter estimation for drawing inferences from experimental data about unobserved aspects of biological systems. While the former purpose has become common in the biological sciences, the latter is less common, particularly when studying cellular and subcellular phenomena such as signaling-the focus of the current study. Data are difficult to obtain at this level. Therefore, even models of only modest complexity can contain parameters for which the available data are insufficient for estimation. In the present study, we use a set of published cellular signaling models to address issues related to global parameter identifiability. That is, we address the following question: assuming known time courses for some model variables, which parameters is it theoretically impossible to estimate, even with continuous, noise-free data? Following an introduction to this problem and its relevance, we perform a full identifiability analysis on a set of cellular signaling models using DAISY (Differential Algebra for the Identifiability of SYstems). We use our analysis to bring to light important issues related to parameter identifiability in ordinary differential equation (ODE) models. We contend that this is, as of yet, an under-appreciated issue in biological modeling and, more particularly, cell biology. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Comparative Sensitivity Analysis of Muscle Activation Dynamics

PubMed Central

Günther, Michael; Götz, Thomas

2015-01-01

We mathematically compared two models of mammalian striated muscle activation dynamics proposed by Hatze and Zajac. Both models are representative for a broad variety of biomechanical models formulated as ordinary differential equations (ODEs). These models incorporate parameters that directly represent known physiological properties. Other parameters have been introduced to reproduce empirical observations. We used sensitivity analysis to investigate the influence of model parameters on the ODE solutions. In addition, we expanded an existing approach to treating initial conditions as parameters and to calculating second-order sensitivities. Furthermore, we used a global sensitivity analysis approach to include finite ranges of parameter values. Hence, a theoretician striving for model reduction could use the method for identifying particularly low sensitivities to detect superfluous parameters. An experimenter could use it for identifying particularly high sensitivities to improve parameter estimation. Hatze's nonlinear model incorporates some parameters to which activation dynamics is clearly more sensitive than to any parameter in Zajac's linear model. Other than Zajac's model, Hatze's model can, however, reproduce measured shifts in optimal muscle length with varied muscle activity. Accordingly we extracted a specific parameter set for Hatze's model that combines best with a particular muscle force-length relation. PMID:26417379

Optimal input shaping for Fisher identifiability of control-oriented lithium-ion battery models

NASA Astrophysics Data System (ADS)

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-output measurements, and is the approach used in this dissertation. Research in the literature studies optimal current input shaping for high-order electrochemical battery models and focuses on offline laboratory cycling. While this body of research highlights improvements in identifiability through optimal input shaping, each optimal input is a function of nominal parameters, which creates a tautology. The parameter values must be known a priori to determine the optimal input for maximizing estimation speed and accuracy. The system identification literature presents multiple studies containing methods that avoid the challenges of this tautology, but these methods are absent from the battery parameter estimation domain. The gaps in the above literature are addressed in this dissertation through the following five novel and unique contributions. First, this dissertation optimizes the parameter identifiability of a thermal battery model, which Sergio Mendoza experimentally validates through a close collaboration with this dissertation's author. Second, this dissertation extends input-shaping optimization to a linear and nonlinear equivalent-circuit battery model and illustrates the substantial improvements in Fisher identifiability for a periodic optimal signal when compared against automotive benchmark cycles. Third, this dissertation presents an experimental validation study of the simulation work in the previous contribution. The estimation study shows that the automotive benchmark cycles either converge slower than the optimized cycle, or not at all for certain parameters. Fourth, this dissertation examines how automotive battery packs with additional power electronic components that dynamically route current to individual cells/modules can be used for parameter identifiability optimization. While the user and vehicle supervisory controller dictate the current demand for these packs, the optimized internal allocation of current still improves identifiability. Finally, this dissertation presents a robust Bayesian sequential input shaping optimization study to maximize the conditional Fisher information of the battery model parameters without prior knowledge of the nominal parameter set. This iterative algorithm only requires knowledge of the prior parameter distributions to converge to the optimal input trajectory.

Structural identifiability of cyclic graphical models of biological networks with latent variables.

PubMed

Wang, Yulin; Lu, Na; Miao, Hongyu

2016-06-13

Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and is thus of higher resolution in comparison with many existing approaches. Overall, this study provides a basis for systematic examination and refinement of graphical models of biological networks from the identifiability point of view, and it has a significant potential to be extended to more complex network structures or high-dimensional systems.

Parameter screening: the use of a dummy parameter to identify non-influential parameters in a global sensitivity analysis

NASA Astrophysics Data System (ADS)

Khorashadi Zadeh, Farkhondeh; Nossent, Jiri; van Griensven, Ann; Bauwens, Willy

2017-04-01

Parameter estimation is a major concern in hydrological modeling, which may limit the use of complex simulators with a large number of parameters. To support the selection of parameters to include in or exclude from the calibration process, Global Sensitivity Analysis (GSA) is widely applied in modeling practices. Based on the results of GSA, the influential and the non-influential parameters are identified (i.e. parameters screening). Nevertheless, the choice of the screening threshold below which parameters are considered non-influential is a critical issue, which has recently received more attention in GSA literature. In theory, the sensitivity index of a non-influential parameter has a value of zero. However, since numerical approximations, rather than analytical solutions, are utilized in GSA methods to calculate the sensitivity indices, small but non-zero indices may be obtained for the indices of non-influential parameters. In order to assess the threshold that identifies non-influential parameters in GSA methods, we propose to calculate the sensitivity index of a "dummy parameter". This dummy parameter has no influence on the model output, but will have a non-zero sensitivity index, representing the error due to the numerical approximation. Hence, the parameters whose indices are above the sensitivity index of the dummy parameter can be classified as influential, whereas the parameters whose indices are below this index are within the range of the numerical error and should be considered as non-influential. To demonstrated the effectiveness of the proposed "dummy parameter approach", 26 parameters of a Soil and Water Assessment Tool (SWAT) model are selected to be analyzed and screened, using the variance-based Sobol' and moment-independent PAWN methods. The sensitivity index of the dummy parameter is calculated from sampled data, without changing the model equations. Moreover, the calculation does not even require additional model evaluations for the Sobol' method. A formal statistical test validates these parameter screening results. Based on the dummy parameter screening, 11 model parameters are identified as influential. Therefore, it can be denoted that the "dummy parameter approach" can facilitate the parameter screening process and provide guidance for GSA users to define a screening-threshold, with only limited additional resources. Key words: Parameter screening, Global sensitivity analysis, Dummy parameter, Variance-based method, Moment-independent method

Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation

PubMed Central

2017-01-01

The concept of dynamical compensation has been recently introduced to describe the ability of a biological system to keep its output dynamics unchanged in the face of varying parameters. However, the original definition of dynamical compensation amounts to lack of structural identifiability. This is relevant if model parameters need to be estimated, as is often the case in biological modelling. Care should we taken when using an unidentifiable model to extract biological insight: the estimated values of structurally unidentifiable parameters are meaningless, and model predictions about unmeasured state variables can be wrong. Taking this into account, we explore alternative definitions of dynamical compensation that do not necessarily imply structural unidentifiability. Accordingly, we show different ways in which a model can be made identifiable while exhibiting dynamical compensation. Our analyses enable the use of the new concept of dynamical compensation in the context of parameter identification, and reconcile it with the desirable property of structural identifiability. PMID:29186132

Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty

USGS Publications Warehouse

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.

Design of experiments for identification of complex biochemical systems with applications to mitochondrial bioenergetics.

PubMed

Vinnakota, Kalyan C; Beard, Daniel A; Dash, Ranjan K

2009-01-01

Identification of a complex biochemical system model requires appropriate experimental data. Models constructed on the basis of data from the literature often contain parameters that are not identifiable with high sensitivity and therefore require additional experimental data to identify those parameters. Here we report the application of a local sensitivity analysis to design experiments that will improve the identifiability of previously unidentifiable model parameters in a model of mitochondrial oxidative phosphorylation and tricaboxylic acid cycle. Experiments were designed based on measurable biochemical reactants in a dilute suspension of purified cardiac mitochondria with experimentally feasible perturbations to this system. Experimental perturbations and variables yielding the most number of parameters above a 5% sensitivity level are presented and discussed.

Modal Parameter Identification and Numerical Simulation for Self-anchored Suspension Bridges Based on Ambient Vibration

NASA Astrophysics Data System (ADS)

Liu, Bing; Sun, Li Guo

2018-06-01

This paper chooses the Nanjing-Hangzhou high speed overbridge, a self-anchored suspension bridge, as the research target, trying to identify the dynamic characteristic parameters of the bridge by using the peak-picking method to analyze the velocity response data under ambient excitation collected by 7 vibration pickup sensors set on the bridge deck. The ABAQUS is used to set up a three-dimensional finite element model for the full bridge and amends the finite element model of the suspension bridge based on the identified modal parameter, and suspender force picked by the PDV100 laser vibrometer. The study shows that the modal parameter can well be identified by analyzing the bridge vibration velocity collected by 7 survey points. The identified modal parameter and measured suspender force can be used as the basis of the amendment of the finite element model of the suspension bridge. The amended model can truthfully reflect the structural physical features and it can also be the benchmark model for the long-term health monitoring and condition assessment of the bridge.

Identifiability Results for Several Classes of Linear Compartment Models.

PubMed

Meshkat, Nicolette; Sullivant, Seth; Eisenberg, Marisa

2015-08-01

Identifiability concerns finding which unknown parameters of a model can be estimated, uniquely or otherwise, from given input-output data. If some subset of the parameters of a model cannot be determined given input-output data, then we say the model is unidentifiable. In this work, we study linear compartment models, which are a class of biological models commonly used in pharmacokinetics, physiology, and ecology. In past work, we used commutative algebra and graph theory to identify a class of linear compartment models that we call identifiable cycle models, which are unidentifiable but have the simplest possible identifiable functions (so-called monomial cycles). Here we show how to modify identifiable cycle models by adding inputs, adding outputs, or removing leaks, in such a way that we obtain an identifiable model. We also prove a constructive result on how to combine identifiable models, each corresponding to strongly connected graphs, into a larger identifiable model. We apply these theoretical results to several real-world biological models from physiology, cell biology, and ecology.

Optimal Linking Design for Response Model Parameters

ERIC Educational Resources Information Center

Barrett, Michelle D.; van der Linden, Wim J.

2017-01-01

Linking functions adjust for differences between identifiability restrictions used in different instances of the estimation of item response model parameters. These adjustments are necessary when results from those instances are to be compared. As linking functions are derived from estimated item response model parameters, parameter estimation…

Estimation and Identifiability of Model Parameters in Human Nociceptive Processing Using Yes-No Detection Responses to Electrocutaneous Stimulation.

PubMed

Yang, Huan; Meijer, Hil G E; Buitenweg, Jan R; van Gils, Stephan A

2016-01-01

Healthy or pathological states of nociceptive subsystems determine different stimulus-response relations measured from quantitative sensory testing. In turn, stimulus-response 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, i.e., the number of model parameters, 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.

Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models.

PubMed

Cotten, Cameron; Reed, Jennifer L

2013-01-30

Constraint-based modeling uses mass balances, flux capacity, and reaction directionality constraints to predict fluxes through metabolism. Although transcriptional regulation and thermodynamic constraints have been integrated into constraint-based modeling, kinetic rate laws have not been extensively used. In this study, an in vivo kinetic parameter estimation problem was formulated and solved using multi-omic data sets for Escherichia coli. To narrow the confidence intervals for kinetic parameters, a series of kinetic model simplifications were made, resulting in fewer kinetic parameters than the full kinetic model. These new parameter values are able to account for flux and concentration data from 20 different experimental conditions used in our training dataset. Concentration estimates from the simplified kinetic model were within one standard deviation for 92.7% of the 790 experimental measurements in the training set. Gibbs free energy changes of reaction were calculated to identify reactions that were often operating close to or far from equilibrium. In addition, enzymes whose activities were positively or negatively influenced by metabolite concentrations were also identified. The kinetic model was then used to calculate the maximum and minimum possible flux values for individual reactions from independent metabolite and enzyme concentration data that were not used to estimate parameter values. Incorporating these kinetically-derived flux limits into the constraint-based metabolic model improved predictions for uptake and secretion rates and intracellular fluxes in constraint-based models of central metabolism. This study has produced a method for in vivo kinetic parameter estimation and identified strategies and outcomes of kinetic model simplification. We also have illustrated how kinetic constraints can be used to improve constraint-based model predictions for intracellular fluxes and biomass yield and identify potential metabolic limitations through the integrated analysis of multi-omics datasets.

Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models

PubMed Central

2013-01-01

Background Constraint-based modeling uses mass balances, flux capacity, and reaction directionality constraints to predict fluxes through metabolism. Although transcriptional regulation and thermodynamic constraints have been integrated into constraint-based modeling, kinetic rate laws have not been extensively used. Results In this study, an in vivo kinetic parameter estimation problem was formulated and solved using multi-omic data sets for Escherichia coli. To narrow the confidence intervals for kinetic parameters, a series of kinetic model simplifications were made, resulting in fewer kinetic parameters than the full kinetic model. These new parameter values are able to account for flux and concentration data from 20 different experimental conditions used in our training dataset. Concentration estimates from the simplified kinetic model were within one standard deviation for 92.7% of the 790 experimental measurements in the training set. Gibbs free energy changes of reaction were calculated to identify reactions that were often operating close to or far from equilibrium. In addition, enzymes whose activities were positively or negatively influenced by metabolite concentrations were also identified. The kinetic model was then used to calculate the maximum and minimum possible flux values for individual reactions from independent metabolite and enzyme concentration data that were not used to estimate parameter values. Incorporating these kinetically-derived flux limits into the constraint-based metabolic model improved predictions for uptake and secretion rates and intracellular fluxes in constraint-based models of central metabolism. Conclusions This study has produced a method for in vivo kinetic parameter estimation and identified strategies and outcomes of kinetic model simplification. We also have illustrated how kinetic constraints can be used to improve constraint-based model predictions for intracellular fluxes and biomass yield and identify potential metabolic limitations through the integrated analysis of multi-omics datasets. PMID:23360254

Basic research on design analysis methods for rotorcraft vibrations

NASA Technical Reports Server (NTRS)

Hanagud, S.

1991-01-01

The objective of the present work was to develop a method for identifying physically plausible finite element system models of airframe structures from test data. The assumed models were based on linear elastic behavior with general (nonproportional) damping. Physical plausibility of the identified system matrices was insured by restricting the identification process to designated physical parameters only and not simply to the elements of the system matrices themselves. For example, in a large finite element model the identified parameters might be restricted to the moduli for each of the different materials used in the structure. In the case of damping, a restricted set of damping values might be assigned to finite elements based on the material type and on the fabrication processes used. In this case, different damping values might be associated with riveted, bolted and bonded elements. The method itself is developed first, and several approaches are outlined for computing the identified parameter values. The method is applied first to a simple structure for which the 'measured' response is actually synthesized from an assumed model. Both stiffness and damping parameter values are accurately identified. The true test, however, is the application to a full-scale airframe structure. In this case, a NASTRAN model and actual measured modal parameters formed the basis for the identification of a restricted set of physically plausible stiffness and damping parameters.

Insight into model mechanisms through automatic parameter fitting: a new methodological framework for model development

PubMed Central

2014-01-01

Background Striking a balance between the degree of model complexity and parameter identifiability, while still producing biologically feasible simulations using modelling is a major challenge in computational biology. While these two elements of model development are closely coupled, parameter fitting from measured data and analysis of model mechanisms have traditionally been performed separately and sequentially. This process produces potential mismatches between model and data complexities that can compromise the ability of computational frameworks to reveal mechanistic insights or predict new behaviour. In this study we address this issue by presenting a generic framework for combined model parameterisation, comparison of model alternatives and analysis of model mechanisms. Results The presented methodology is based on a combination of multivariate metamodelling (statistical approximation of the input–output relationships of deterministic models) and a systematic zooming into biologically feasible regions of the parameter space by iterative generation of new experimental designs and look-up of simulations in the proximity of the measured data. The parameter fitting pipeline includes an implicit sensitivity analysis and analysis of parameter identifiability, making it suitable for testing hypotheses for model reduction. Using this approach, under-constrained model parameters, as well as the coupling between parameters within the model are identified. The methodology is demonstrated by refitting the parameters of a published model of cardiac cellular mechanics using a combination of measured data and synthetic data from an alternative model of the same system. Using this approach, reduced models with simplified expressions for the tropomyosin/crossbridge kinetics were found by identification of model components that can be omitted without affecting the fit to the parameterising data. Our analysis revealed that model parameters could be constrained to a standard deviation of on average 15% of the mean values over the succeeding parameter sets. Conclusions Our results indicate that the presented approach is effective for comparing model alternatives and reducing models to the minimum complexity replicating measured data. We therefore believe that this approach has significant potential for reparameterising existing frameworks, for identification of redundant model components of large biophysical models and to increase their predictive capacity. PMID:24886522

A comparative modeling study of a dual tracer experiment in a large lysimeter under atmospheric conditions

NASA Astrophysics Data System (ADS)

Stumpp, C.; Nützmann, G.; Maciejewski, S.; Maloszewski, P.

2009-09-01

SummaryIn this paper, five model approaches with different physical and mathematical concepts varying in their model complexity and requirements were applied to identify the transport processes in the unsaturated zone. The applicability of these model approaches were compared and evaluated investigating two tracer breakthrough curves (bromide, deuterium) in a cropped, free-draining lysimeter experiment under natural atmospheric boundary conditions. The data set consisted of time series of water balance, depth resolved water contents, pressure heads and resident concentrations measured during 800 days. The tracer transport parameters were determined using a simple stochastic (stream tube model), three lumped parameter (constant water content model, multi-flow dispersion model, variable flow dispersion model) and a transient model approach. All of them were able to fit the tracer breakthrough curves. The identified transport parameters of each model approach were compared. Despite the differing physical and mathematical concepts the resulting parameters (mean water contents, mean water flux, dispersivities) of the five model approaches were all in the same range. The results indicate that the flow processes are also describable assuming steady state conditions. Homogeneous matrix flow is dominant and a small pore volume with enhanced flow velocities near saturation was identified with variable saturation flow and transport approach. The multi-flow dispersion model also identified preferential flow and additionally suggested a third less mobile flow component. Due to high fitting accuracy and parameter similarity all model approaches indicated reliable results.

Rational selection of experimental readout and intervention sites for reducing uncertainties in computational model predictions.

PubMed

Flassig, Robert J; Migal, Iryna; der Zalm, Esther van; Rihko-Struckmann, Liisa; Sundmacher, Kai

2015-01-16

Understanding the dynamics of biological processes can substantially be supported by computational models in the form of nonlinear ordinary differential equations (ODE). Typically, this model class contains many unknown parameters, which are estimated from inadequate and noisy data. Depending on the ODE structure, predictions based on unmeasured states and associated parameters are highly uncertain, even undetermined. For given data, profile likelihood analysis has been proven to be one of the most practically relevant approaches for analyzing the identifiability of an ODE structure, and thus model predictions. In case of highly uncertain or non-identifiable parameters, rational experimental design based on various approaches has shown to significantly reduce parameter uncertainties with minimal amount of effort. In this work we illustrate how to use profile likelihood samples for quantifying the individual contribution of parameter uncertainty to prediction uncertainty. For the uncertainty quantification we introduce the profile likelihood sensitivity (PLS) index. Additionally, for the case of several uncertain parameters, we introduce the PLS entropy to quantify individual contributions to the overall prediction uncertainty. We show how to use these two criteria as an experimental design objective for selecting new, informative readouts in combination with intervention site identification. The characteristics of the proposed multi-criterion objective are illustrated with an in silico example. We further illustrate how an existing practically non-identifiable model for the chlorophyll fluorescence induction in a photosynthetic organism, D. salina, can be rendered identifiable by additional experiments with new readouts. Having data and profile likelihood samples at hand, the here proposed uncertainty quantification based on prediction samples from the profile likelihood provides a simple way for determining individual contributions of parameter uncertainties to uncertainties in model predictions. The uncertainty quantification of specific model predictions allows identifying regions, where model predictions have to be considered with care. Such uncertain regions can be used for a rational experimental design to render initially highly uncertain model predictions into certainty. Finally, our uncertainty quantification directly accounts for parameter interdependencies and parameter sensitivities of the specific prediction.

Global identifiability of linear compartmental models--a computer algebra algorithm.

PubMed

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.

Selecting Sensitive Parameter Subsets in Dynamical Models With Application to Biomechanical System Identification.

PubMed

Ramadan, Ahmed; Boss, Connor; Choi, Jongeun; Peter Reeves, N; Cholewicki, Jacek; Popovich, John M; Radcliffe, Clark J

2018-07-01

Estimating many parameters of biomechanical systems with limited data may achieve good fit but may also increase 95% confidence intervals in parameter estimates. This results in poor identifiability in the estimation problem. Therefore, we propose a novel method to select sensitive biomechanical model parameters that should be estimated, while fixing the remaining parameters to values obtained from preliminary estimation. Our method relies on identifying the parameters to which the measurement output is most sensitive. The proposed method is based on the Fisher information matrix (FIM). It was compared against the nonlinear least absolute shrinkage and selection operator (LASSO) method to guide modelers on the pros and cons of our FIM method. We present an application identifying a biomechanical parametric model of a head position-tracking task for ten human subjects. Using measured data, our method (1) reduced model complexity by only requiring five out of twelve parameters to be estimated, (2) significantly reduced parameter 95% confidence intervals by up to 89% of the original confidence interval, (3) maintained goodness of fit measured by variance accounted for (VAF) at 82%, (4) reduced computation time, where our FIM method was 164 times faster than the LASSO method, and (5) selected similar sensitive parameters to the LASSO method, where three out of five selected sensitive parameters were shared by FIM and LASSO methods.

Identifiability in N-mixture models: a large-scale screening test with bird data.

PubMed

Kéry, Marc

2018-02-01

Binomial N-mixture models have proven very useful in ecology, conservation, and monitoring: they allow estimation and modeling of abundance separately from detection probability using simple counts. Recently, doubts about parameter identifiability have been voiced. I conducted a large-scale screening test with 137 bird data sets from 2,037 sites. I found virtually no identifiability problems for Poisson and zero-inflated Poisson (ZIP) binomial N-mixture models, but negative-binomial (NB) models had problems in 25% of all data sets. The corresponding multinomial N-mixture models had no problems. Parameter estimates under Poisson and ZIP binomial and multinomial N-mixture models were extremely similar. Identifiability problems became a little more frequent with smaller sample sizes (267 and 50 sites), but were unaffected by whether the models did or did not include covariates. Hence, binomial N-mixture model parameters with Poisson and ZIP mixtures typically appeared identifiable. In contrast, NB mixtures were often unidentifiable, which is worrying since these were often selected by Akaike's information criterion. Identifiability of binomial N-mixture models should always be checked. If problems are found, simpler models, integrated models that combine different observation models or the use of external information via informative priors or penalized likelihoods, may help. © 2017 by the Ecological Society of America.

Identifiability and estimation of multiple transmission pathways in cholera and waterborne disease.

PubMed

Eisenberg, Marisa C; Robertson, Suzanne L; Tien, Joseph H

2013-05-07

Cholera and many waterborne diseases exhibit multiple characteristic timescales or pathways of infection, which can be modeled as direct and indirect transmission. A major public health issue for waterborne diseases involves understanding the modes of transmission in order to improve control and prevention strategies. An important epidemiological question is: given data for an outbreak, can we determine the role and relative importance of direct vs. environmental/waterborne routes of transmission? We examine whether parameters for a differential equation model of waterborne disease transmission dynamics can be identified, both in the ideal setting of noise-free data (structural identifiability) and in the more realistic setting in the presence of noise (practical identifiability). We used a differential algebra approach together with several numerical approaches, with a particular emphasis on identifiability of the transmission rates. To examine these issues in a practical public health context, we apply the model to a recent cholera outbreak in Angola (2006). Our results show that the model parameters-including both water and person-to-person transmission routes-are globally structurally identifiable, although they become unidentifiable when the environmental transmission timescale is fast. Even for water dynamics within the identifiable range, when noisy data are considered, only a combination of the water transmission parameters can practically be estimated. This makes the waterborne transmission parameters difficult to estimate, leading to inaccurate estimates of important epidemiological parameters such as the basic reproduction number (R0). However, measurements of pathogen persistence time in environmental water sources or measurements of pathogen concentration in the water can improve model identifiability and allow for more accurate estimation of waterborne transmission pathway parameters as well as R0. Parameter estimates for the Angola outbreak suggest that both transmission pathways are needed to explain the observed cholera dynamics. These results highlight the importance of incorporating environmental data when examining waterborne disease. Copyright © 2013 Elsevier Ltd. All rights reserved.

Bouc-Wen hysteresis model identification using Modified Firefly Algorithm

NASA Astrophysics Data System (ADS)

Zaman, Mohammad Asif; Sikder, Urmita

2015-12-01

The parameters of Bouc-Wen hysteresis model are identified using a Modified Firefly Algorithm. The proposed algorithm uses dynamic process control parameters to improve its performance. The algorithm is used to find the model parameter values that results in the least amount of error between a set of given data points and points obtained from the Bouc-Wen model. The performance of the algorithm is compared with the performance of conventional Firefly Algorithm, Genetic Algorithm and Differential Evolution algorithm in terms of convergence rate and accuracy. Compared to the other three optimization algorithms, the proposed algorithm is found to have good convergence rate with high degree of accuracy in identifying Bouc-Wen model parameters. Finally, the proposed method is used to find the Bouc-Wen model parameters from experimental data. The obtained model is found to be in good agreement with measured data.

High-order dynamic modeling and parameter identification of structural discontinuities in Timoshenko beams by using reflection coefficients

NASA Astrophysics Data System (ADS)

Fan, Qiang; Huang, Zhenyu; Zhang, Bing; Chen, Dayue

2013-02-01

Properties of discontinuities, such as bolt joints and cracks in the waveguide structures, are difficult to evaluate by either analytical or numerical methods due to the complexity and uncertainty of the discontinuities. In this paper, the discontinuity in a Timoshenko beam is modeled with high-order parameters and then these parameters are identified by using reflection coefficients at the discontinuity. The high-order model is composed of several one-order sub-models in series and each sub-model consists of inertia, stiffness and damping components in parallel. The order of the discontinuity model is determined based on the characteristics of the reflection coefficient curve and the accuracy requirement of the dynamic modeling. The model parameters are identified through the least-square fitting iteration method, of which the undetermined model parameters are updated in iteration to fit the dynamic reflection coefficient curve with the wave-based one. By using the spectral super-element method (SSEM), simulation cases, including one-order discontinuities on infinite- and finite-beams and a two-order discontinuity on an infinite beam, were employed to evaluate both the accuracy of the discontinuity model and the effectiveness of the identification method. For practical considerations, effects of measurement noise on the discontinuity parameter identification are investigated by adding different levels of noise to the simulated data. The simulation results were then validated by the corresponding experiments. Both the simulation and experimental results show that (1) the one-order discontinuities can be identified accurately with the maximum errors of 6.8% and 8.7%, respectively; (2) and the high-order discontinuities can be identified with the maximum errors of 15.8% and 16.2%, respectively; and (3) the high-order model can predict the complex discontinuity much more accurately than the one-order discontinuity model.

Detecting influential observations in nonlinear regression modeling of groundwater flow

USGS Publications Warehouse

Yager, Richard M.

1998-01-01

Nonlinear regression is used to estimate optimal parameter values in models of groundwater flow to ensure that differences between predicted and observed heads and flows do not result from nonoptimal parameter values. Parameter estimates can be affected, however, by observations that disproportionately influence the regression, such as outliers that exert undue leverage on the objective function. Certain statistics developed for linear regression can be used to detect influential observations in nonlinear regression if the models are approximately linear. This paper discusses the application of Cook's D, which measures the effect of omitting a single observation on a set of estimated parameter values, and the statistical parameter DFBETAS, which quantifies the influence of an observation on each parameter. The influence statistics were used to (1) identify the influential observations in the calibration of a three-dimensional, groundwater flow model of a fractured-rock aquifer through nonlinear regression, and (2) quantify the effect of omitting influential observations on the set of estimated parameter values. Comparison of the spatial distribution of Cook's D with plots of model sensitivity shows that influential observations correspond to areas where the model heads are most sensitive to certain parameters, and where predicted groundwater flow rates are largest. Five of the six discharge observations were identified as influential, indicating that reliable measurements of groundwater flow rates are valuable data in model calibration. DFBETAS are computed and examined for an alternative model of the aquifer system to identify a parameterization error in the model design that resulted in overestimation of the effect of anisotropy on horizontal hydraulic conductivity.

Identification of Linear and Nonlinear Sensory Processing Circuits from Spiking Neuron Data.

PubMed

Florescu, Dorian; Coca, Daniel

2018-03-01

Inferring mathematical models of sensory processing systems directly from input-output observations, while making the fewest assumptions about the model equations and the types of measurements available, is still a major issue in computational neuroscience. This letter introduces two new approaches for identifying sensory circuit models consisting of linear and nonlinear filters in series with spiking neuron models, based only on the sampled analog input to the filter and the recorded spike train output of the spiking neuron. For an ideal integrate-and-fire neuron model, the first algorithm can identify the spiking neuron parameters as well as the structure and parameters of an arbitrary nonlinear filter connected to it. The second algorithm can identify the parameters of the more general leaky integrate-and-fire spiking neuron model, as well as the parameters of an arbitrary linear filter connected to it. Numerical studies involving simulated and real experimental recordings are used to demonstrate the applicability and evaluate the performance of the proposed algorithms.

Transfer Function Identification Using Orthogonal Fourier Transform Modeling Functions

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2013-01-01

A method for transfer function identification, including both model structure determination and parameter estimation, was developed and demonstrated. The approach uses orthogonal modeling functions generated from frequency domain data obtained by Fourier transformation of time series data. The method was applied to simulation data to identify continuous-time transfer function models and unsteady aerodynamic models. Model fit error, estimated model parameters, and the associated uncertainties were used to show the effectiveness of the method for identifying accurate transfer function models from noisy data.

Identifying key sources of uncertainty in the modelling of greenhouse gas emissions from wastewater treatment.

PubMed

Sweetapple, Christine; Fu, Guangtao; Butler, David

2013-09-01

This study investigates sources of uncertainty in the modelling of greenhouse gas emissions from wastewater treatment, through the use of local and global sensitivity analysis tools, and contributes to an in-depth understanding of wastewater treatment modelling by revealing critical parameters and parameter interactions. One-factor-at-a-time sensitivity analysis is used to screen model parameters and identify those with significant individual effects on three performance indicators: total greenhouse gas emissions, effluent quality and operational cost. Sobol's method enables identification of parameters with significant higher order effects and of particular parameter pairs to which model outputs are sensitive. Use of a variance-based global sensitivity analysis tool to investigate parameter interactions enables identification of important parameters not revealed in one-factor-at-a-time sensitivity analysis. These interaction effects have not been considered in previous studies and thus provide a better understanding wastewater treatment plant model characterisation. It was found that uncertainty in modelled nitrous oxide emissions is the primary contributor to uncertainty in total greenhouse gas emissions, due largely to the interaction effects of three nitrogen conversion modelling parameters. The higher order effects of these parameters are also shown to be a key source of uncertainty in effluent quality. Copyright © 2013 Elsevier Ltd. All rights reserved.

Uncertainty quantification and propagation in dynamic models using ambient vibration measurements, application to a 10-story building

NASA Astrophysics Data System (ADS)

Behmanesh, Iman; Yousefianmoghadam, Seyedsina; Nozari, Amin; Moaveni, Babak; Stavridis, Andreas

2018-07-01

This paper investigates the application of Hierarchical Bayesian model updating for uncertainty quantification and response prediction of civil structures. In this updating framework, structural parameters of an initial finite element (FE) model (e.g., stiffness or mass) are calibrated by minimizing error functions between the identified modal parameters and the corresponding parameters of the model. These error functions are assumed to have Gaussian probability distributions with unknown parameters to be determined. The estimated parameters of error functions represent the uncertainty of the calibrated model in predicting building's response (modal parameters here). The focus of this paper is to answer whether the quantified model uncertainties using dynamic measurement at building's reference/calibration state can be used to improve the model prediction accuracies at a different structural state, e.g., damaged structure. Also, the effects of prediction error bias on the uncertainty of the predicted values is studied. The test structure considered here is a ten-story concrete building located in Utica, NY. The modal parameters of the building at its reference state are identified from ambient vibration data and used to calibrate parameters of the initial FE model as well as the error functions. Before demolishing the building, six of its exterior walls were removed and ambient vibration measurements were also collected from the structure after the wall removal. These data are not used to calibrate the model; they are only used to assess the predicted results. The model updating framework proposed in this paper is applied to estimate the modal parameters of the building at its reference state as well as two damaged states: moderate damage (removal of four walls) and severe damage (removal of six walls). Good agreement is observed between the model-predicted modal parameters and those identified from vibration tests. Moreover, it is shown that including prediction error bias in the updating process instead of commonly-used zero-mean error function can significantly reduce the prediction uncertainties.

Identification of hydrological model parameter variation using ensemble Kalman filter

NASA Astrophysics Data System (ADS)

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.

The Multiple-Choice Model: Some Solutions for Estimation of Parameters in the Presence of Omitted Responses

ERIC Educational Resources Information Center

Abad, Francisco J.; Olea, Julio; Ponsoda, Vicente

2009-01-01

This article deals with some of the problems that have hindered the application of Samejima's and Thissen and Steinberg's multiple-choice models: (a) parameter estimation difficulties owing to the large number of parameters involved, (b) parameter identifiability problems in the Thissen and Steinberg model, and (c) their treatment of omitted…

Identification of Synchronous Machine Stability - Parameters: AN On-Line Time-Domain Approach.

NASA Astrophysics Data System (ADS)

Le, Loc Xuan

1987-09-01

A time-domain modeling approach is described which enables the stability-study parameters of the synchronous machine to be determined directly from input-output data measured at the terminals of the machine operating under normal conditions. The transient responses due to system perturbations are used to identify the parameters of the equivalent circuit models. The described models are verified by comparing their responses with the machine responses generated from the transient stability models of a small three-generator multi-bus power system and of a single -machine infinite-bus power network. The least-squares method is used for the solution of the model parameters. As a precaution against ill-conditioned problems, the singular value decomposition (SVD) is employed for its inherent numerical stability. In order to identify the equivalent-circuit parameters uniquely, the solution of a linear optimization problem with non-linear constraints is required. Here, the SVD appears to offer a simple solution to this otherwise difficult problem. Furthermore, the SVD yields solutions with small bias and, therefore, physically meaningful parameters even in the presence of noise in the data. The question concerning the need for a more advanced model of the synchronous machine which describes subtransient and even sub-subtransient behavior is dealt with sensibly by the concept of condition number. The concept provides a quantitative measure for determining whether such an advanced model is indeed necessary. Finally, the recursive SVD algorithm is described for real-time parameter identification and tracking of slowly time-variant parameters. The algorithm is applied to identify the dynamic equivalent power system model.

Identifying differentially expressed genes in cancer patients using a non-parameter Ising model.

PubMed

Li, Xumeng; Feltus, Frank A; Sun, Xiaoqian; Wang, James Z; Luo, Feng

2011-10-01

Identification of genes and pathways involved in diseases and physiological conditions is a major task in systems biology. In this study, we developed a novel non-parameter Ising model to integrate protein-protein interaction network and microarray data for identifying differentially expressed (DE) genes. We also proposed a simulated annealing algorithm to find the optimal configuration of the Ising model. The Ising model was applied to two breast cancer microarray data sets. The results showed that more cancer-related DE sub-networks and genes were identified by the Ising model than those by the Markov random field model. Furthermore, cross-validation experiments showed that DE genes identified by Ising model can improve classification performance compared with DE genes identified by Markov random field model. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Analysis of sensitivity of simulated recharge to selected parameters for seven watersheds modeled using the precipitation-runoff modeling system

USGS Publications Warehouse

Ely, D. Matthew

2006-01-01

Recharge is a vital component of the ground-water budget and methods for estimating it range from extremely complex to relatively simple. The most commonly used techniques, however, are limited by the scale of application. One method that can be used to estimate ground-water recharge includes process-based models that compute distributed water budgets on a watershed scale. These models should be evaluated to determine which model parameters are the dominant controls in determining ground-water recharge. Seven existing watershed models from different humid regions of the United States were chosen to analyze the sensitivity of simulated recharge to model parameters. Parameter sensitivities were determined using a nonlinear regression computer program to generate a suite of diagnostic statistics. The statistics identify model parameters that have the greatest effect on simulated ground-water recharge and that compare and contrast the hydrologic system responses to those parameters. Simulated recharge in the Lost River and Big Creek watersheds in Washington State was sensitive to small changes in air temperature. The Hamden watershed model in west-central Minnesota was developed to investigate the relations that wetlands and other landscape features have with runoff processes. Excess soil moisture in the Hamden watershed simulation was preferentially routed to wetlands, instead of to the ground-water system, resulting in little sensitivity of any parameters to recharge. Simulated recharge in the North Fork Pheasant Branch watershed, Wisconsin, demonstrated the greatest sensitivity to parameters related to evapotranspiration. Three watersheds were simulated as part of the Model Parameter Estimation Experiment (MOPEX). Parameter sensitivities for the MOPEX watersheds, Amite River, Louisiana and Mississippi, English River, Iowa, and South Branch Potomac River, West Virginia, were similar and most sensitive to small changes in air temperature and a user-defined flow routing parameter. Although the primary objective of this study was to identify, by geographic region, the importance of the parameter value to the simulation of ground-water recharge, the secondary objectives proved valuable for future modeling efforts. The value of a rigorous sensitivity analysis can (1) make the calibration process more efficient, (2) guide additional data collection, (3) identify model limitations, and (4) explain simulated results.

Structural identifiability analysis of a cardiovascular system model.

PubMed

Pironet, Antoine; Dauby, Pierre C; Chase, J Geoffrey; Docherty, Paul D; Revie, James A; Desaive, Thomas

2016-05-01

The six-chamber cardiovascular system model of Burkhoff and Tyberg has been used in several theoretical and experimental studies. However, this cardiovascular system model (and others derived from it) are not identifiable from any output set. In this work, two such cases of structural non-identifiability are first presented. These cases occur when the model output set only contains a single type of information (pressure or volume). A specific output set is thus chosen, mixing pressure and volume information and containing only a limited number of clinically available measurements. Then, by manipulating the model equations involving these outputs, it is demonstrated that the six-chamber cardiovascular system model is structurally globally identifiable. A further simplification is made, assuming known cardiac valve resistances. Because of the poor practical identifiability of these four parameters, this assumption is usual. Under this hypothesis, the six-chamber cardiovascular system model is structurally identifiable from an even smaller dataset. As a consequence, parameter values computed from limited but well-chosen datasets are theoretically unique. This means that the parameter identification procedure can safely be performed on the model from such a well-chosen dataset. Thus, the model may be considered suitable for use in diagnosis. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

Stochastic mechanical model of vocal folds for producing jitter and for identifying pathologies through real voices.

PubMed

Cataldo, E; Soize, C

2018-06-06

Jitter, in voice production applications, is a random phenomenon characterized by the deviation of the glottal cycle length with respect to a mean value. Its study can help in identifying pathologies related to the vocal folds according to the values obtained through the different ways to measure it. This paper aims to propose a stochastic model, considering three control parameters, to generate jitter based on a deterministic one-mass model for the dynamics of the vocal folds and to identify parameters from the stochastic model taking into account real voice signals experimentally obtained. To solve the corresponding stochastic inverse problem, the cost function used is based on the distance between probability density functions of the random variables associated with the fundamental frequencies obtained by the experimental voices and the simulated ones, and also on the distance between features extracted from the voice signals, simulated and experimental, to calculate jitter. The results obtained show that the model proposed is valid and some samples of voices are synthesized considering the identified parameters for normal and pathological cases. The strategy adopted is also a novelty and mainly because a solution was obtained. In addition to the use of three parameters to construct the model of jitter, it is the discussion of a parameter related to the bandwidth of the power spectral density function of the stochastic process to measure the quality of the signal generated. A study about the influence of all the main parameters is also performed. The identification of the parameters of the model considering pathological cases is maybe of all novelties introduced by the paper the most interesting. Copyright © 2018 Elsevier Ltd. All rights reserved.

Framework for Uncertainty Assessment - Hanford Site-Wide Groundwater Flow and Transport Modeling

NASA Astrophysics Data System (ADS)

Bergeron, M. P.; Cole, C. R.; Murray, C. J.; Thorne, P. D.; Wurstner, S. K.

2002-05-01

Pacific Northwest National Laboratory is in the process of development and implementation of an uncertainty estimation methodology for use in future site assessments that addresses parameter uncertainty as well as uncertainties related to the groundwater conceptual model. The long-term goals of the effort are development and implementation of an uncertainty estimation methodology for use in future assessments and analyses being made with the Hanford site-wide groundwater model. The basic approach in the framework developed for uncertainty assessment consists of: 1) Alternate conceptual model (ACM) identification to identify and document the major features and assumptions of each conceptual model. The process must also include a periodic review of the existing and proposed new conceptual models as data or understanding become available. 2) ACM development of each identified conceptual model through inverse modeling with historical site data. 3) ACM evaluation to identify which of conceptual models are plausible and should be included in any subsequent uncertainty assessments. 4) ACM uncertainty assessments will only be carried out for those ACMs determined to be plausible through comparison with historical observations and model structure identification measures. The parameter uncertainty assessment process generally involves: a) Model Complexity Optimization - to identify the important or relevant parameters for the uncertainty analysis; b) Characterization of Parameter Uncertainty - to develop the pdfs for the important uncertain parameters including identification of any correlations among parameters; c) Propagation of Uncertainty - to propagate parameter uncertainties (e.g., by first order second moment methods if applicable or by a Monte Carlo approach) through the model to determine the uncertainty in the model predictions of interest. 5)Estimation of combined ACM and scenario uncertainty by a double sum with each component of the inner sum (an individual CCDF) representing parameter uncertainty associated with a particular scenario and ACM and the outer sum enumerating the various plausible ACM and scenario combinations in order to represent the combined estimate of uncertainty (a family of CCDFs). A final important part of the framework includes identification, enumeration, and documentation of all the assumptions, which include those made during conceptual model development, required by the mathematical model, required by the numerical model, made during the spatial and temporal descretization process, needed to assign the statistical model and associated parameters that describe the uncertainty in the relevant input parameters, and finally those assumptions required by the propagation method. Pacific Northwest National Laboratory is operated for the U.S. Department of Energy under Contract DE-AC06-76RL01830.

Measurement and modelling of the y-direction apparent mass of sitting human body-cushioned seat system

NASA Astrophysics Data System (ADS)

Stein, George Juraj; Múčka, Peter; Hinz, Barbara; Blüthner, Ralph

2009-04-01

Laboratory tests were conducted using 13 male subjects seated on a cushioned commercial vehicle driver's seat. The hands gripped a mock-up steering wheel and the subjects were in contact with the lumbar region of the backrest. The accelerations and forces in the y-direction were measured during random lateral whole-body vibration with a frequency range between 0.25 and 30 Hz, vibration magnitudes 0.30, 0.98, and 1.92 m s -2 (unweighted root mean square (rms)). Based on these laboratory measurements, a linear multi-degree-of-freedom (mdof) model of the seated human body and cushioned seat in the lateral direction ( y-axis) was developed. Model parameters were identified from averaged measured apparent mass values (modulus and phase) for the three excitation magnitudes mentioned. A preferred model structure was selected from four 3-dof models analysed. The mean subject parameters were identified. In addition, identification of each subject's apparent mass model parameters was performed. The results are compared with previous studies. The developed model structure and the identified parameters can be used for further biodynamical research in seating dynamics.

Identifying parameter regions for multistationarity

PubMed Central

Conradi, Carsten; Mincheva, Maya; Wiuf, Carsten

2017-01-01

Mathematical modelling has become an established tool for studying the dynamics of biological systems. Current applications range from building models that reproduce quantitative data to identifying systems with predefined qualitative features, such as switching behaviour, bistability or oscillations. Mathematically, the latter question amounts to identifying parameter values associated with a given qualitative feature. We introduce a procedure to partition the parameter space of a parameterized system of ordinary differential equations into regions for which the system has a unique or multiple equilibria. The procedure is based on the computation of the Brouwer degree, and it creates a multivariate polynomial with parameter depending coefficients. The signs of the coefficients determine parameter regions with and without multistationarity. A particular strength of the procedure is the avoidance of numerical analysis and parameter sampling. The procedure consists of a number of steps. Each of these steps might be addressed algorithmically using various computer programs and available software, or manually. We demonstrate our procedure on several models of gene transcription and cell signalling, and show that in many cases we obtain a complete partitioning of the parameter space with respect to multistationarity. PMID:28972969

Integrating satellite actual evapotranspiration patterns into distributed model parametrization and evaluation for a mesoscale catchment

NASA Astrophysics Data System (ADS)

Demirel, M. C.; Mai, J.; Stisen, S.; Mendiguren González, G.; Koch, J.; Samaniego, L. E.

2016-12-01

Distributed hydrologic models are traditionally calibrated and evaluated against observations of streamflow. Spatially distributed remote sensing observations offer a great opportunity to enhance spatial model calibration schemes. For that it is important to identify the model parameters that can change spatial patterns before the satellite based hydrologic model calibration. Our study is based on two main pillars: first we use spatial sensitivity analysis to identify the key parameters controlling the spatial distribution of actual evapotranspiration (AET). Second, we investigate the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mesoscale Hydrologic Model (mHM). This distributed model is selected as it allows for a change in the spatial distribution of key soil parameters through the calibration of pedo-transfer function parameters and includes options for using fully distributed daily Leaf Area Index (LAI) directly as input. In addition the simulated AET can be estimated at the spatial resolution suitable for comparison to the spatial patterns observed using MODIS data. We introduce a new dynamic scaling function employing remotely sensed vegetation to downscale coarse reference evapotranspiration. In total, 17 parameters of 47 mHM parameters are identified using both sequential screening and Latin hypercube one-at-a-time sampling methods. The spatial patterns are found to be sensitive to the vegetation parameters whereas streamflow dynamics are sensitive to the PTF parameters. The results of multi-objective model calibration show that calibration of mHM against observed streamflow does not reduce the spatial errors in AET while they improve only the streamflow simulations. We will further examine the results of model calibration using only multi spatial objective functions measuring the association between observed AET and simulated AET maps and another case including spatial and streamflow metrics together.

ON IDENTIFIABILITY OF NONLINEAR ODE MODELS AND APPLICATIONS IN VIRAL DYNAMICS

PubMed Central

MIAO, HONGYU; XIA, XIAOHUA; PERELSON, ALAN S.; WU, HULIN

2011-01-01

Ordinary differential equations (ODE) are a powerful tool for modeling dynamic processes with wide applications in a variety of scientific fields. Over the last 2 decades, ODEs have also emerged as a prevailing tool in various biomedical research fields, especially in infectious disease modeling. In practice, it is important and necessary to determine unknown parameters in ODE models based on experimental data. Identifiability analysis is the first step in determing unknown parameters in ODE models and such analysis techniques for nonlinear ODE models are still under development. In this article, we review identifiability analysis methodologies for nonlinear ODE models developed in the past one to two decades, including structural identifiability analysis, practical identifiability analysis and sensitivity-based identifiability analysis. Some advanced topics and ongoing research are also briefly reviewed. Finally, some examples from modeling viral dynamics of HIV, influenza and hepatitis viruses are given to illustrate how to apply these identifiability analysis methods in practice. PMID:21785515

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

NASA Astrophysics Data System (ADS)

Domanskyi, Sergii; Schilling, Joshua E.; Gorshkov, Vyacheslav; Libert, Sergiy; Privman, Vladimir

2016-09-01

We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model we describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of "stiff" equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

NASA Astrophysics Data System (ADS)

Domanskyi, Sergii; Schilling, Joshua; Gorshkov, Vyacheslav; Libert, Sergiy; Privman, Vladimir

We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model we describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of ``stiff'' equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.

Permutation on hybrid natural inflation

NASA Astrophysics Data System (ADS)

Carone, Christopher D.; Erlich, Joshua; Ramos, Raymundo; Sher, Marc

2014-09-01

We analyze a model of hybrid natural inflation based on the smallest non-Abelian discrete group S3. Leading invariant terms in the scalar potential have an accidental global symmetry that is spontaneously broken, providing a pseudo-Goldstone boson that is identified as the inflaton. The S3 symmetry restricts both the form of the inflaton potential and the couplings of the inflaton field to the waterfall fields responsible for the end of inflation. We identify viable points in the model parameter space. Although the power in tensor modes is small in most of the parameter space of the model, we identify parameter choices that yield potentially observable values of r without super-Planckian initial values of the inflaton field.

Understanding the Day Cent model: Calibration, sensitivity, and identifiability through inverse modeling

USGS Publications Warehouse

Necpálová, Magdalena; Anex, Robert P.; Fienen, Michael N.; Del Grosso, Stephen J.; Castellano, Michael J.; Sawyer, John E.; Iqbal, Javed; Pantoja, Jose L.; Barker, Daniel W.

2015-01-01

The ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soil3NO− compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil 3NO− and 4NH+. Post-processing analyses provided insights into parameter–observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent.

Sensitivity analysis of infectious disease models: methods, advances and their application

PubMed Central

Wu, Jianyong; Dhingra, Radhika; Gambhir, Manoj; Remais, Justin V.

2013-01-01

Sensitivity analysis (SA) can aid in identifying influential model parameters and optimizing model structure, yet infectious disease modelling has yet to adopt advanced SA techniques that are capable of providing considerable insights over traditional methods. We investigate five global SA methods—scatter plots, the Morris and Sobol’ methods, Latin hypercube sampling-partial rank correlation coefficient and the sensitivity heat map method—and detail their relative merits and pitfalls when applied to a microparasite (cholera) and macroparasite (schistosomaisis) transmission model. The methods investigated yielded similar results with respect to identifying influential parameters, but offered specific insights that vary by method. The classical methods differed in their ability to provide information on the quantitative relationship between parameters and model output, particularly over time. The heat map approach provides information about the group sensitivity of all model state variables, and the parameter sensitivity spectrum obtained using this method reveals the sensitivity of all state variables to each parameter over the course of the simulation period, especially valuable for expressing the dynamic sensitivity of a microparasite epidemic model to its parameters. A summary comparison is presented to aid infectious disease modellers in selecting appropriate methods, with the goal of improving model performance and design. PMID:23864497

A Sensitivity Analysis of an Inverted Pendulum Balance Control Model.

PubMed

Pasma, Jantsje H; Boonstra, Tjitske A; van Kordelaar, Joost; Spyropoulou, Vasiliki V; Schouten, Alfred C

2017-01-01

Balance control models are used to describe balance behavior in health and disease. We identified the unique contribution and relative importance of each parameter of a commonly used balance control model, the Independent Channel (IC) model, to identify which parameters are crucial to describe balance behavior. The balance behavior was expressed by transfer functions (TFs), representing the relationship between sensory perturbations and body sway as a function of frequency, in terms of amplitude (i.e., magnitude) and timing (i.e., phase). The model included an inverted pendulum controlled by a neuromuscular system, described by several parameters. Local sensitivity of each parameter was determined for both the magnitude and phase using partial derivatives. Both the intrinsic stiffness and proportional gain shape the magnitude at low frequencies (0.1-1 Hz). The derivative gain shapes the peak and slope of the magnitude between 0.5 and 0.9 Hz. The sensory weight influences the overall magnitude, and does not have any effect on the phase. The effect of the time delay becomes apparent in the phase above 0.6 Hz. The force feedback parameters and intrinsic stiffness have a small effect compared with the other parameters. All parameters shape the TF magnitude and phase and therefore play a role in the balance behavior. The sensory weight, time delay, derivative gain, and the proportional gain have a unique effect on the TFs, while the force feedback parameters and intrinsic stiffness contribute less. More insight in the unique contribution and relative importance of all parameters shows which parameters are crucial and critical to identify underlying differences in balance behavior between different patient groups.

Identifiability of sorption parameters in stirred flow-through reactor experiments and their identification with a Bayesian approach.

PubMed

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 K d 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 K d,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. Copyright © 2016 Elsevier Ltd. All rights reserved.

Efficient Calibration of Distributed Catchment Models Using Perceptual Understanding and Hydrologic Signatures

NASA Astrophysics Data System (ADS)

Hutton, C.; Wagener, T.; Freer, J. E.; Duffy, C.; Han, D.

2015-12-01

Distributed models offer the potential to resolve catchment systems in more detail, and therefore simulate the hydrological impacts of spatial changes in catchment forcing (e.g. landscape change). Such models may contain a large number of model parameters which are computationally expensive to calibrate. Even when calibration is possible, insufficient data can result in model parameter and structural equifinality. In order to help reduce the space of feasible models and supplement traditional outlet discharge calibration data, semi-quantitative information (e.g. knowledge of relative groundwater levels), may also be used to identify behavioural models when applied to constrain spatially distributed predictions of states and fluxes. The challenge is to combine these different sources of information together to identify a behavioural region of state-space, and efficiently search a large, complex parameter space to identify behavioural parameter sets that produce predictions that fall within this behavioural region. Here we present a methodology to incorporate different sources of data to efficiently calibrate distributed catchment models. Metrics of model performance may be derived from multiple sources of data (e.g. perceptual understanding and measured or regionalised hydrologic signatures). For each metric, an interval or inequality is used to define the behaviour of the catchment system, accounting for data uncertainties. These intervals are then combined to produce a hyper-volume in state space. The state space is then recast as a multi-objective optimisation problem, and the Borg MOEA is applied to first find, and then populate the hyper-volume, thereby identifying acceptable model parameter sets. We apply the methodology to calibrate the PIHM model at Plynlimon, UK by incorporating perceptual and hydrologic data into the calibration problem. Furthermore, we explore how to improve calibration efficiency through search initialisation from shorter model runs.

Identifiability, reducibility, and adaptability in allosteric macromolecules.

PubMed

Bohner, Gergő; Venkataraman, Gaurav

2017-05-01

The ability of macromolecules to transduce stimulus information at one site into conformational changes at a distant site, termed "allostery," is vital for cellular signaling. Here, we propose a link between the sensitivity of allosteric macromolecules to their underlying biophysical parameters, the interrelationships between these parameters, and macromolecular adaptability. We demonstrate that the parameters of a canonical model of the mSlo large-conductance Ca 2+ -activated K + (BK) ion channel are non-identifiable with respect to the equilibrium open probability-voltage relationship, a common functional assay. We construct a reduced model with emergent parameters that are identifiable and expressed as combinations of the original mechanistic parameters. These emergent parameters indicate which coordinated changes in mechanistic parameters can leave assay output unchanged. We predict that these coordinated changes are used by allosteric macromolecules to adapt, and we demonstrate how this prediction can be tested experimentally. We show that these predicted parameter compensations are used in the first reported allosteric phenomena: the Bohr effect, by which hemoglobin adapts to varying pH. © 2017 Bohner and Venkataraman.

Identifiability, reducibility, and adaptability in allosteric macromolecules

PubMed Central

Bohner, Gergő

2017-01-01

The ability of macromolecules to transduce stimulus information at one site into conformational changes at a distant site, termed “allostery,” is vital for cellular signaling. Here, we propose a link between the sensitivity of allosteric macromolecules to their underlying biophysical parameters, the interrelationships between these parameters, and macromolecular adaptability. We demonstrate that the parameters of a canonical model of the mSlo large-conductance Ca2+-activated K+ (BK) ion channel are non-identifiable with respect to the equilibrium open probability-voltage relationship, a common functional assay. We construct a reduced model with emergent parameters that are identifiable and expressed as combinations of the original mechanistic parameters. These emergent parameters indicate which coordinated changes in mechanistic parameters can leave assay output unchanged. We predict that these coordinated changes are used by allosteric macromolecules to adapt, and we demonstrate how this prediction can be tested experimentally. We show that these predicted parameter compensations are used in the first reported allosteric phenomena: the Bohr effect, by which hemoglobin adapts to varying pH. PMID:28416647

An Extreme-Value Approach to Anomaly Vulnerability Identification

NASA Technical Reports Server (NTRS)

Everett, Chris; Maggio, Gaspare; Groen, Frank

2010-01-01

The objective of this paper is to present a method for importance analysis in parametric probabilistic modeling where the result of interest is the identification of potential engineering vulnerabilities associated with postulated anomalies in system behavior. In the context of Accident Precursor Analysis (APA), under which this method has been developed, these vulnerabilities, designated as anomaly vulnerabilities, are conditions that produce high risk in the presence of anomalous system behavior. The method defines a parameter-specific Parameter Vulnerability Importance measure (PVI), which identifies anomaly risk-model parameter values that indicate the potential presence of anomaly vulnerabilities, and allows them to be prioritized for further investigation. This entails analyzing each uncertain risk-model parameter over its credible range of values to determine where it produces the maximum risk. A parameter that produces high system risk for a particular range of values suggests that the system is vulnerable to the modeled anomalous conditions, if indeed the true parameter value lies in that range. Thus, PVI analysis provides a means of identifying and prioritizing anomaly-related engineering issues that at the very least warrant improved understanding to reduce uncertainty, such that true vulnerabilities may be identified and proper corrective actions taken.

On 4-degree-of-freedom biodynamic models of seated occupants: Lumped-parameter modeling

NASA Astrophysics Data System (ADS)

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.

Monte Carlo sensitivity analysis of land surface parameters using the Variable Infiltration Capacity model

NASA Astrophysics Data System (ADS)

Demaria, Eleonora M.; Nijssen, Bart; Wagener, Thorsten

2007-06-01

Current land surface models use increasingly complex descriptions of the processes that they represent. Increase in complexity is accompanied by an increase in the number of model parameters, many of which cannot be measured directly at large spatial scales. A Monte Carlo framework was used to evaluate the sensitivity and identifiability of ten parameters controlling surface and subsurface runoff generation in the Variable Infiltration Capacity model (VIC). Using the Monte Carlo Analysis Toolbox (MCAT), parameter sensitivities were studied for four U.S. watersheds along a hydroclimatic gradient, based on a 20-year data set developed for the Model Parameter Estimation Experiment (MOPEX). Results showed that simulated streamflows are sensitive to three parameters when evaluated with different objective functions. Sensitivity of the infiltration parameter (b) and the drainage parameter (exp) were strongly related to the hydroclimatic gradient. The placement of vegetation roots played an important role in the sensitivity of model simulations to the thickness of the second soil layer (thick2). Overparameterization was found in the base flow formulation indicating that a simplified version could be implemented. Parameter sensitivity was more strongly dictated by climatic gradients than by changes in soil properties. Results showed how a complex model can be reduced to a more parsimonious form, leading to a more identifiable model with an increased chance of successful regionalization to ungauged basins. Although parameter sensitivities are strictly valid for VIC, this model is representative of a wider class of macroscale hydrological models. Consequently, the results and methodology will have applicability to other hydrological models.

Individual Colorimetric Observer Model

PubMed Central

Asano, Yuta; Fairchild, Mark D.; Blondé, Laurent

2016-01-01

This study proposes a vision model for individual colorimetric observers. The proposed model can be beneficial in many color-critical applications such as color grading and soft proofing to assess ranges of color matches instead of a single average match. We extended the CIE 2006 physiological observer by adding eight additional physiological parameters to model individual color-normal observers. These eight parameters control lens pigment density, macular pigment density, optical densities of L-, M-, and S-cone photopigments, and λmax shifts of L-, M-, and S-cone photopigments. By identifying the variability of each physiological parameter, the model can simulate color matching functions among color-normal populations using Monte Carlo simulation. The variabilities of the eight parameters were identified through two steps. In the first step, extensive reviews of past studies were performed for each of the eight physiological parameters. In the second step, the obtained variabilities were scaled to fit a color matching dataset. The model was validated using three different datasets: traditional color matching, applied color matching, and Rayleigh matches. PMID:26862905

A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund-Potsdam-Jena (LPJ) model as an example

NASA Astrophysics Data System (ADS)

Sun, Guodong; Mu, Mu

2017-05-01

An important source of uncertainty, which causes further uncertainty in numerical simulations, is that residing in the parameters describing physical processes in numerical models. Therefore, finding a subset among numerous physical parameters in numerical models in the atmospheric and oceanic sciences, which are relatively more sensitive and important parameters, and reducing the errors in the physical parameters in this subset would be a far more efficient way to reduce the uncertainties involved in simulations. In this context, we present a new approach based on the conditional nonlinear optimal perturbation related to parameter (CNOP-P) method. The approach provides a framework to ascertain the subset of those relatively more sensitive and important parameters among the physical parameters. The Lund-Potsdam-Jena (LPJ) dynamical global vegetation model was utilized to test the validity of the new approach in China. The results imply that nonlinear interactions among parameters play a key role in the identification of sensitive parameters in arid and semi-arid regions of China compared to those in northern, northeastern, and southern China. The uncertainties in the numerical simulations were reduced considerably by reducing the errors of the subset of relatively more sensitive and important parameters. The results demonstrate that our approach not only offers a new route to identify relatively more sensitive and important physical parameters but also that it is viable to then apply "target observations" to reduce the uncertainties in model parameters.

Importance analysis for Hudson River PCB transport and fate model parameters using robust sensitivity studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, S.; Toll, J.; Cothern, K.

1995-12-31

The authors have performed robust sensitivity studies of the physico-chemical Hudson River PCB model PCHEPM to identify the parameters and process uncertainties contributing the most to uncertainty in predictions of water column and sediment PCB concentrations, over the time period 1977--1991 in one segment of the lower Hudson River. The term ``robust sensitivity studies`` refers to the use of several sensitivity analysis techniques to obtain a more accurate depiction of the relative importance of different sources of uncertainty. Local sensitivity analysis provided data on the sensitivity of PCB concentration estimates to small perturbations in nominal parameter values. Range sensitivity analysismore » provided information about the magnitude of prediction uncertainty associated with each input uncertainty. Rank correlation analysis indicated which parameters had the most dominant influence on model predictions. Factorial analysis identified important interactions among model parameters. Finally, term analysis looked at the aggregate influence of combinations of parameters representing physico-chemical processes. The authors scored the results of the local and range sensitivity and rank correlation analyses. The authors considered parameters that scored high on two of the three analyses to be important contributors to PCB concentration prediction uncertainty, and treated them probabilistically in simulations. They also treated probabilistically parameters identified in the factorial analysis as interacting with important parameters. The authors used the term analysis to better understand how uncertain parameters were influencing the PCB concentration predictions. The importance analysis allowed us to reduce the number of parameters to be modeled probabilistically from 16 to 5. This reduced the computational complexity of Monte Carlo simulations, and more importantly, provided a more lucid depiction of prediction uncertainty and its causes.« less

From LCAs to simplified models: a generic methodology applied to wind power electricity.

PubMed

Padey, Pierryves; Girard, Robin; le Boulch, Denis; Blanc, Isabelle

2013-02-05

This study presents a generic methodology to produce simplified models able to provide a comprehensive life cycle impact assessment of energy pathways. The methodology relies on the application of global sensitivity analysis to identify key parameters explaining the impact variability of systems over their life cycle. Simplified models are built upon the identification of such key parameters. The methodology is applied to one energy pathway: onshore wind turbines of medium size considering a large sample of possible configurations representative of European conditions. Among several technological, geographical, and methodological parameters, we identified the turbine load factor and the wind turbine lifetime as the most influent parameters. Greenhouse Gas (GHG) performances have been plotted as a function of these key parameters identified. Using these curves, GHG performances of a specific wind turbine can be estimated, thus avoiding the undertaking of an extensive Life Cycle Assessment (LCA). This methodology should be useful for decisions makers, providing them a robust but simple support tool for assessing the environmental performance of energy systems.

DAISY: a new software tool to test global identifiability of biological and physiological systems.

PubMed

Bellu, Giuseppina; Saccomani, Maria Pia; Audoly, Stefania; D'Angiò, Leontina

2007-10-01

A priori global identifiability is a structural property of biological and physiological models. It is considered a prerequisite for well-posed estimation, since it concerns the possibility of recovering uniquely the unknown model parameters from measured input-output data, under ideal conditions (noise-free observations and error-free model structure). Of course, determining if the parameters can be uniquely recovered from observed data is essential before investing resources, time and effort in performing actual biomedical experiments. Many interesting biological models are nonlinear but identifiability analysis for nonlinear system turns out to be a difficult mathematical problem. Different methods have been proposed in the literature to test identifiability of nonlinear models but, to the best of our knowledge, so far no software tools have been proposed for automatically checking identifiability of nonlinear models. In this paper, we describe a software tool implementing a differential algebra algorithm to perform parameter identifiability analysis for (linear and) nonlinear dynamic models described by polynomial or rational equations. Our goal is to provide the biological investigator a completely automatized software, requiring minimum prior knowledge of mathematical modelling and no in-depth understanding of the mathematical tools. The DAISY (Differential Algebra for Identifiability of SYstems) software will potentially be useful in biological modelling studies, especially in physiology and clinical medicine, where research experiments are particularly expensive and/or difficult to perform. Practical examples of use of the software tool DAISY are presented. DAISY is available at the web site http://www.dei.unipd.it/~pia/.

Knowledge modeling in image-guided neurosurgery: application in understanding intraoperative brain shift

NASA Astrophysics Data System (ADS)

Cohen-Adad, Julien; Paul, Perrine; Morandi, Xavier; Jannin, Pierre

2006-03-01

During an image-guided neurosurgery procedure, the neuronavigation system is subject to inaccuracy because of anatomical deformations which induce a gap between the preoperative images and their anatomical reality. Thus, the objective of many research teams is to succeed in quantifying these deformations in order to update preoperative images. Anatomical intraoperative deformations correspond to a complex spatio-temporal phenomenon. Our objective is to identify the parameters implicated in these deformations and to use these parameters as constrains for systems dedicated to updating preoperative images. In order to identify these parameters of deformation we followed the iterative methodology used for cognitive system conception: identification, conceptualization, formalization, implementation and validation. A state of the art about cortical deformations has been established in order to identify relevant parameters probably involved in the deformations. As a first step, 30 parameters have been identified and described following an ontological approach. They were formalized into a Unified Modeling Language (UML) class diagram. We implemented that model into a web-based application in order to fill a database. Two surgical cases have been studied at this moment. After having entered enough surgical cases for data mining purposes, we expect to identify the most relevant and influential parameters and to gain a better ability to understand the deformation phenomenon. This original approach is part of a global system aiming at quantifying and correcting anatomical deformations.

Efficient Screening of Climate Model Sensitivity to a Large Number of Perturbed Input Parameters [plus supporting information

DOE PAGES

Covey, Curt; Lucas, Donald D.; Tannahill, John; ...

2013-07-01

Modern climate models contain numerous input parameters, each with a range of possible values. Since the volume of parameter space increases exponentially with the number of parameters N, it is generally impossible to directly evaluate a model throughout this space even if just 2-3 values are chosen for each parameter. Sensitivity screening algorithms, however, can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination.This can aid both model development and the uncertainty quantification (UQ) process. Here we report results from a parameter sensitivity screening algorithm hitherto untested in climate modeling,more » the Morris one-at-a-time (MOAT) method. This algorithm drastically reduces the computational cost of estimating sensitivities in a high dimensional parameter space because the sample size grows linearly rather than exponentially with N. It nevertheless samples over much of the N-dimensional volume and allows assessment of parameter interactions, unlike traditional elementary one-at-a-time (EOAT) parameter variation. We applied both EOAT and MOAT to the Community Atmosphere Model (CAM), assessing CAM’s behavior as a function of 27 uncertain input parameters related to the boundary layer, clouds, and other subgrid scale processes. For radiation balance at the top of the atmosphere, EOAT and MOAT rank most input parameters similarly, but MOAT identifies a sensitivity that EOAT underplays for two convection parameters that operate nonlinearly in the model. MOAT’s ranking of input parameters is robust to modest algorithmic variations, and it is qualitatively consistent with model development experience. Supporting information is also provided at the end of the full text of the article.« less

An analysis of sensitivity of CLIMEX parameters in mapping species potential distribution and the broad-scale changes observed with minor variations in parameters values: an investigation using open-field Solanum lycopersicum and Neoleucinodes elegantalis as an example

NASA Astrophysics Data System (ADS)

da Silva, Ricardo Siqueira; Kumar, Lalit; Shabani, Farzin; Picanço, Marcelo Coutinho

2018-04-01

A sensitivity analysis can categorize levels of parameter influence on a model's output. Identifying parameters having the most influence facilitates establishing the best values for parameters of models, providing useful implications in species modelling of crops and associated insect pests. The aim of this study was to quantify the response of species models through a CLIMEX sensitivity analysis. Using open-field Solanum lycopersicum and Neoleucinodes elegantalis distribution records, and 17 fitting parameters, including growth and stress parameters, comparisons were made in model performance by altering one parameter value at a time, in comparison to the best-fit parameter values. Parameters that were found to have a greater effect on the model results are termed "sensitive". Through the use of two species, we show that even when the Ecoclimatic Index has a major change through upward or downward parameter value alterations, the effect on the species is dependent on the selection of suitability categories and regions of modelling. Two parameters were shown to have the greatest sensitivity, dependent on the suitability categories of each species in the study. Results enhance user understanding of which climatic factors had a greater impact on both species distributions in our model, in terms of suitability categories and areas, when parameter values were perturbed by higher or lower values, compared to the best-fit parameter values. Thus, the sensitivity analyses have the potential to provide additional information for end users, in terms of improving management, by identifying the climatic variables that are most sensitive.

Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, Huiying; Hou, Zhangshuan; Huang, Maoyi

The Community Land Model (CLM) represents physical, chemical, and biological processes of the terrestrial ecosystems that interact with climate across a range of spatial and temporal scales. As CLM includes numerous sub-models and associated parameters, the high-dimensional parameter space presents a formidable challenge for quantifying uncertainty and improving Earth system predictions needed to assess environmental changes and risks. This study aims to evaluate the potential of transferring hydrologic model parameters in CLM through sensitivity analyses and classification across watersheds from the Model Parameter Estimation Experiment (MOPEX) in the United States. The sensitivity of CLM-simulated water and energy fluxes to hydrologicalmore » parameters across 431 MOPEX basins are first examined using an efficient stochastic sampling-based sensitivity analysis approach. Linear, interaction, and high-order nonlinear impacts are all identified via statistical tests and stepwise backward removal parameter screening. The basins are then classified accordingly to their parameter sensitivity patterns (internal attributes), as well as their hydrologic indices/attributes (external hydrologic factors) separately, using a Principal component analyses (PCA) and expectation-maximization (EM) –based clustering approach. Similarities and differences among the parameter sensitivity-based classification system (S-Class), the hydrologic indices-based classification (H-Class), and the Koppen climate classification systems (K-Class) are discussed. Within each S-class with similar parameter sensitivity characteristics, similar inversion modeling setups can be used for parameter calibration, and the parameters and their contribution or significance to water and energy cycling may also be more transferrable. This classification study provides guidance on identifiable parameters, and on parameterization and inverse model design for CLM but the methodology is applicable to other models. Inverting parameters at representative sites belonging to the same class can significantly reduce parameter calibration efforts.« less

Parameters Identification of Interface Friction Model for Ceramic Matrix Composites Based on Stress-Strain Response

NASA Astrophysics Data System (ADS)

Han, Xiao; Gao, Xiguang; Song, Yingdong

2017-10-01

An approach to identify parameters of interface friction model for Ceramic Matrix composites based on stress-strain response was developed. The stress distribution of fibers in the interface slip region and intact region of the damaged composite was determined by adopting the interface friction model. The relation between maximum strain, secant moduli of hysteresis loop and interface shear stress, interface de-bonding stress was established respectively with the method of symbolic-graphic combination. By comparing the experimental strain, secant moduli of hysteresis loop with computation values, the interface shear stress and interface de-bonding stress corresponding to first cycle were identified. Substituting the identification of parameters into interface friction model, the stress-strain curves were predicted and the predicted results fit experiments well. Besides, the influence of number of data points on identifying the value of interface parameters was discussed. And the approach was compared with the method based on the area of hysteresis loop.

Linear system identification via backward-time observer models

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Phan, Minh Q.

1992-01-01

Presented here is an algorithm to compute the Markov parameters of a backward-time observer for a backward-time model from experimental input and output data. The backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) for the backward-time system identification. The identified backward-time system Markov parameters are used in the Eigensystem Realization Algorithm to identify a backward-time state-space model, which can be easily converted to the usual forward-time representation. If one reverses time in the model to be identified, what were damped true system modes become modes with negative damping, growing as the reversed time increases. On the other hand, the noise modes in the identification still maintain the property that they are stable. The shift from positive damping to negative damping of the true system modes allows one to distinguish these modes from noise modes. Experimental results are given to illustrate when and to what extent this concept works.

A Computational Framework for Identifiability and Ill-Conditioning Analysis of Lithium-Ion Battery Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

López C, Diana C.; Wozny, Günter; Flores-Tlacuahuac, Antonio

2016-03-23

The lack of informative experimental data and the complexity of first-principles battery models make the recovery of kinetic, transport, and thermodynamic parameters complicated. We present a computational framework that combines sensitivity, singular value, and Monte Carlo analysis to explore how different sources of experimental data affect parameter structural ill conditioning and identifiability. Our study is conducted on a modified version of the Doyle-Fuller-Newman model. We demonstrate that the use of voltage discharge curves only enables the identification of a small parameter subset, regardless of the number of experiments considered. Furthermore, we show that the inclusion of a single electrolyte concentrationmore » measurement significantly aids identifiability and mitigates ill-conditioning.« less

The Effect of Nondeterministic Parameters on Shock-Associated Noise Prediction Modeling

NASA Technical Reports Server (NTRS)

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.

User's manual for a parameter identification technique. [with options for model simulation for fixed input forcing functions and identification from wind tunnel and flight measurements

NASA Technical Reports Server (NTRS)

Kanning, G.

1975-01-01

A digital computer program written in FORTRAN is presented that implements the system identification theory for deterministic systems using input-output measurements. The user supplies programs simulating the mathematical model of the physical plant whose parameters are to be identified. The user may choose any one of three options. The first option allows for a complete model simulation for fixed input forcing functions. The second option identifies up to 36 parameters of the model from wind tunnel or flight measurements. The third option performs a sensitivity analysis for up to 36 parameters. The use of each option is illustrated with an example using input-output measurements for a helicopter rotor tested in a wind tunnel.

A general model-based design of experiments approach to achieve practical identifiability of pharmacokinetic and pharmacodynamic models.

PubMed

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.

Identification of time-varying structural dynamic systems - An artificial intelligence approach

NASA Technical Reports Server (NTRS)

Glass, B. J.; Hanagud, S.

1992-01-01

An application of the artificial intelligence-derived methodologies of heuristic search and object-oriented programming to the problem of identifying the form of the model and the associated parameters of a time-varying structural dynamic system is presented in this paper. Possible model variations due to changes in boundary conditions or configurations of a structure are organized into a taxonomy of models, and a variant of best-first search is used to identify the model whose simulated response best matches that of the current physical structure. Simulated model responses are verified experimentally. An output-error approach is used in a discontinuous model space, and an equation-error approach is used in the parameter space. The advantages of the AI methods used, compared with conventional programming techniques for implementing knowledge structuring and inheritance, are discussed. Convergence conditions and example problems have been discussed. In the example problem, both the time-varying model and its new parameters have been identified when changes occur.

Identification of Bouc-Wen hysteretic parameters based on enhanced response sensitivity approach

NASA Astrophysics Data System (ADS)

Wang, Li; Lu, Zhong-Rong

2017-05-01

This paper aims to identify parameters of Bouc-Wen hysteretic model using time-domain measured data. It follows a general inverse identification procedure, that is, identifying model parameters is treated as an optimization problem with the nonlinear least squares objective function. Then, the enhanced response sensitivity approach, which has been shown convergent and proper for such kind of problems, is adopted to solve the optimization problem. Numerical tests are undertaken to verify the proposed identification approach.

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

PubMed Central

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

2017-01-01

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

Machine Learning Techniques for Global Sensitivity Analysis in Climate Models

NASA Astrophysics Data System (ADS)

Safta, C.; Sargsyan, K.; Ricciuto, D. M.

2017-12-01

Climate models studies are not only challenged by the compute intensive nature of these models but also by the high-dimensionality of the input parameter space. In our previous work with the land model components (Sargsyan et al., 2014) we identified subsets of 10 to 20 parameters relevant for each QoI via Bayesian compressive sensing and variance-based decomposition. Nevertheless the algorithms were challenged by the nonlinear input-output dependencies for some of the relevant QoIs. In this work we will explore a combination of techniques to extract relevant parameters for each QoI and subsequently construct surrogate models with quantified uncertainty necessary to future developments, e.g. model calibration and prediction studies. In the first step, we will compare the skill of machine-learning models (e.g. neural networks, support vector machine) to identify the optimal number of classes in selected QoIs and construct robust multi-class classifiers that will partition the parameter space in regions with smooth input-output dependencies. These classifiers will be coupled with techniques aimed at building sparse and/or low-rank surrogate models tailored to each class. Specifically we will explore and compare sparse learning techniques with low-rank tensor decompositions. These models will be used to identify parameters that are important for each QoI. Surrogate accuracy requirements are higher for subsequent model calibration studies and we will ascertain the performance of this workflow for multi-site ALM simulation ensembles.

Systematic parameter estimation and sensitivity analysis using a multidimensional PEMFC model coupled with DAKOTA.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Chao Yang; Luo, Gang; Jiang, Fangming

2010-05-01

Current computational models for proton exchange membrane fuel cells (PEMFCs) include a large number of parameters such as boundary conditions, material properties, and numerous parameters used in sub-models for membrane transport, two-phase flow and electrochemistry. In order to successfully use a computational PEMFC model in design and optimization, it is important to identify critical parameters under a wide variety of operating conditions, such as relative humidity, current load, temperature, etc. Moreover, when experimental data is available in the form of polarization curves or local distribution of current and reactant/product species (e.g., O2, H2O concentrations), critical parameters can be estimated inmore » order to enable the model to better fit the data. Sensitivity analysis and parameter estimation are typically performed using manual adjustment of parameters, which is also common in parameter studies. We present work to demonstrate a systematic approach based on using a widely available toolkit developed at Sandia called DAKOTA that supports many kinds of design studies, such as sensitivity analysis as well as optimization and uncertainty quantification. In the present work, we couple a multidimensional PEMFC model (which is being developed, tested and later validated in a joint effort by a team from Penn State Univ. and Sandia National Laboratories) with DAKOTA through the mapping of model parameters to system responses. Using this interface, we demonstrate the efficiency of performing simple parameter studies as well as identifying critical parameters using sensitivity analysis. Finally, we show examples of optimization and parameter estimation using the automated capability in DAKOTA.« less

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Domanskyi, Sergii; Schilling, Joshua E.; Privman, Vladimir, E-mail: privman@clarkson.edu

We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model wemore » describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of “stiff” equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.« less

Modeling of 2D diffusion processes based on microscopy data: parameter estimation and practical identifiability analysis.

PubMed

Hock, Sabrina; Hasenauer, Jan; Theis, Fabian J

2013-01-01

Diffusion is a key component of many biological processes such as chemotaxis, developmental differentiation and tissue morphogenesis. Since recently, the spatial gradients caused by diffusion can be assessed in-vitro and in-vivo using microscopy based imaging techniques. The resulting time-series of two dimensional, high-resolutions images in combination with mechanistic models enable the quantitative analysis of the underlying mechanisms. However, such a model-based analysis is still challenging due to measurement noise and sparse observations, which result in uncertainties of the model parameters. We introduce a likelihood function for image-based measurements with log-normal distributed noise. Based upon this likelihood function we formulate the maximum likelihood estimation problem, which is solved using PDE-constrained optimization methods. To assess the uncertainty and practical identifiability of the parameters we introduce profile likelihoods for diffusion processes. As proof of concept, we model certain aspects of the guidance of dendritic cells towards lymphatic vessels, an example for haptotaxis. Using a realistic set of artificial measurement data, we estimate the five kinetic parameters of this model and compute profile likelihoods. Our novel approach for the estimation of model parameters from image data as well as the proposed identifiability analysis approach is widely applicable to diffusion processes. The profile likelihood based method provides more rigorous uncertainty bounds in contrast to local approximation methods.

Identifiability of PBPK Models with Applications to ...

EPA Pesticide Factsheets

Any statistical model should be identifiable in order for estimates and tests using it to be meaningful. We consider statistical analysis of physiologically-based pharmacokinetic (PBPK) models in which parameters cannot be estimated precisely from available data, and discuss different types of identifiability that occur in PBPK models and give reasons why they occur. We particularly focus on how the mathematical structure of a PBPK model and lack of appropriate data can lead to statistical models in which it is impossible to estimate at least some parameters precisely. Methods are reviewed which can determine whether a purely linear PBPK model is globally identifiable. We propose a theorem which determines when identifiability at a set of finite and specific values of the mathematical PBPK model (global discrete identifiability) implies identifiability of the statistical model. However, we are unable to establish conditions that imply global discrete identifiability, and conclude that the only safe approach to analysis of PBPK models involves Bayesian analysis with truncated priors. Finally, computational issues regarding posterior simulations of PBPK models are discussed. The methodology is very general and can be applied to numerous PBPK models which can be expressed as linear time-invariant systems. A real data set of a PBPK model for exposure to dimethyl arsinic acid (DMA(V)) is presented to illustrate the proposed methodology. We consider statistical analy

In-vivo viscous properties of the heel pad by stress-relaxation experiment based on a spherical indentation.

PubMed

Suzuki, Ryo; Ito, Kohta; Lee, Taeyong; Ogihara, Naomichi

2017-12-01

Identifying the viscous properties of the plantar soft tissue is crucial not only for understanding the dynamic interaction of the foot with the ground during locomotion, but also for development of improved footwear products and therapeutic footwear interventions. In the present study, the viscous and hyperelastic material properties of the plantar soft tissue were experimentally identified using a spherical indentation test and an analytical contact model of the spherical indentation test. Force-relaxation curves of the heel pads were obtained from the indentation experiment. The curves were fit to the contact model incorporating a five-element Maxwell model to identify the viscous material parameters. The finite element method with the experimentally identified viscoelastic parameters could successfully reproduce the measured force-relaxation curves, indicating the material parameters were correctly estimated using the proposed method. Although there are some methodological limitations, the proposed framework to identify the viscous material properties may facilitate the development of subject-specific finite element modeling of the foot and other biological materials. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Local identifiability and sensitivity analysis of neuromuscular blockade and depth of hypnosis models.

PubMed

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. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Quantifying Parameter Sensitivity, Interaction and Transferability in Hydrologically Enhanced Versions of Noah-LSM over Transition Zones

NASA Technical Reports Server (NTRS)

Rosero, Enrique; Yang, Zong-Liang; Wagener, Thorsten; Gulden, Lindsey E.; Yatheendradas, Soni; Niu, Guo-Yue

2009-01-01

We use sensitivity analysis to identify the parameters that are most responsible for shaping land surface model (LSM) simulations and to understand the complex interactions in three versions of the Noah LSM: the standard version (STD), a version enhanced with a simple groundwater module (GW), and version augmented by a dynamic phenology module (DV). We use warm season, high-frequency, near-surface states and turbulent fluxes collected over nine sites in the US Southern Great Plains. We quantify changes in the pattern of sensitive parameters, the amount and nature of the interaction between parameters, and the covariance structure of the distribution of behavioral parameter sets. Using Sobol s total and first-order sensitivity indexes, we show that very few parameters directly control the variance of the model output. Significant parameter interaction occurs so that not only the optimal parameter values differ between models, but the relationships between parameters change. GW decreases parameter interaction and appears to improve model realism, especially at wetter sites. DV increases parameter interaction and decreases identifiability, implying it is overparameterized and/or underconstrained. A case study at a wet site shows GW has two functional modes: one that mimics STD and a second in which GW improves model function by decoupling direct evaporation and baseflow. Unsupervised classification of the posterior distributions of behavioral parameter sets cannot group similar sites based solely on soil or vegetation type, helping to explain why transferability between sites and models is not straightforward. This evidence suggests a priori assignment of parameters should also consider climatic differences.

A Bayesian Hierarchical Modeling Approach to Predicting Flow in Ungauged Basins

NASA Astrophysics Data System (ADS)

Gronewold, A.; Alameddine, I.; Anderson, R. M.

2009-12-01

Recent innovative approaches to identifying and applying regression-based relationships between land use patterns (such as increasing impervious surface area and decreasing vegetative cover) and rainfall-runoff model parameters represent novel and promising improvements to predicting flow from ungauged basins. In particular, these approaches allow for predicting flows under uncertain and potentially variable future conditions due to rapid land cover changes, variable climate conditions, and other factors. Despite the broad range of literature on estimating rainfall-runoff model parameters, however, the absence of a robust set of modeling tools for identifying and quantifying uncertainties in (and correlation between) rainfall-runoff model parameters represents a significant gap in current hydrological modeling research. Here, we build upon a series of recent publications promoting novel Bayesian and probabilistic modeling strategies for quantifying rainfall-runoff model parameter estimation uncertainty. Our approach applies alternative measures of rainfall-runoff model parameter joint likelihood (including Nash-Sutcliffe efficiency, among others) to simulate samples from the joint parameter posterior probability density function. We then use these correlated samples as response variables in a Bayesian hierarchical model with land use coverage data as predictor variables in order to develop a robust land use-based tool for forecasting flow in ungauged basins while accounting for, and explicitly acknowledging, parameter estimation uncertainty. We apply this modeling strategy to low-relief coastal watersheds of Eastern North Carolina, an area representative of coastal resource waters throughout the world because of its sensitive embayments and because of the abundant (but currently threatened) natural resources it hosts. Consequently, this area is the subject of several ongoing studies and large-scale planning initiatives, including those conducted through the United States Environmental Protection Agency (USEPA) total maximum daily load (TMDL) program, as well as those addressing coastal population dynamics and sea level rise. Our approach has several advantages, including the propagation of parameter uncertainty through a nonparametric probability distribution which avoids common pitfalls of fitting parameters and model error structure to a predetermined parametric distribution function. In addition, by explicitly acknowledging correlation between model parameters (and reflecting those correlations in our predictive model) our model yields relatively efficient prediction intervals (unlike those in the current literature which are often unnecessarily large, and may lead to overly-conservative management actions). Finally, our model helps improve understanding of the rainfall-runoff process by identifying model parameters (and associated catchment attributes) which are most sensitive to current and future land use change patterns. Disclaimer: Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.

A methodology for global-sensitivity analysis of time-dependent outputs in systems biology modelling.

PubMed

Sumner, T; Shephard, E; Bogle, I D L

2012-09-07

One of the main challenges in the development of mathematical and computational models of biological systems is the precise estimation of parameter values. Understanding the effects of uncertainties in parameter values on model behaviour is crucial to the successful use of these models. Global sensitivity analysis (SA) can be used to quantify the variability in model predictions resulting from the uncertainty in multiple parameters and to shed light on the biological mechanisms driving system behaviour. We present a new methodology for global SA in systems biology which is computationally efficient and can be used to identify the key parameters and their interactions which drive the dynamic behaviour of a complex biological model. The approach combines functional principal component analysis with established global SA techniques. The methodology is applied to a model of the insulin signalling pathway, defects of which are a major cause of type 2 diabetes and a number of key features of the system are identified.

DAISY: a new software tool to test global identifiability of biological and physiological systems

PubMed Central

Bellu, Giuseppina; Saccomani, Maria Pia; Audoly, Stefania; D’Angiò, Leontina

2009-01-01

A priori global identifiability is a structural property of biological and physiological models. It is considered a prerequisite for well-posed estimation, since it concerns the possibility of recovering uniquely the unknown model parameters from measured input-output data, under ideal conditions (noise-free observations and error-free model structure). Of course, determining if the parameters can be uniquely recovered from observed data is essential before investing resources, time and effort in performing actual biomedical experiments. Many interesting biological models are nonlinear but identifiability analysis for nonlinear system turns out to be a difficult mathematical problem. Different methods have been proposed in the literature to test identifiability of nonlinear models but, to the best of our knowledge, so far no software tools have been proposed for automatically checking identifiability of nonlinear models. In this paper, we describe a software tool implementing a differential algebra algorithm to perform parameter identifiability analysis for (linear and) nonlinear dynamic models described by polynomial or rational equations. Our goal is to provide the biological investigator a completely automatized software, requiring minimum prior knowledge of mathematical modelling and no in-depth understanding of the mathematical tools. The DAISY (Differential Algebra for Identifiability of SYstems) software will potentially be useful in biological modelling studies, especially in physiology and clinical medicine, where research experiments are particularly expensive and/or difficult to perform. Practical examples of use of the software tool DAISY are presented. DAISY is available at the web site http://www.dei.unipd.it/~pia/. PMID:17707944

Investigating the relationship between a soils classification and the spatial parameters of a conceptual catchment-scale hydrological model

NASA Astrophysics Data System (ADS)

Dunn, S. M.; Lilly, A.

2001-10-01

There are now many examples of hydrological models that utilise the capabilities of Geographic Information Systems to generate spatially distributed predictions of behaviour. However, the spatial variability of hydrological parameters relating to distributions of soils and vegetation can be hard to establish. In this paper, the relationship between a soil hydrological classification Hydrology of Soil Types (HOST) and the spatial parameters of a conceptual catchment-scale model is investigated. A procedure involving inverse modelling using Monte-Carlo simulations on two catchments is developed to identify relative values for soil related parameters of the DIY model. The relative values determine the internal variability of hydrological processes as a function of the soil type. For three out of the four soil parameters studied, the variability between HOST classes was found to be consistent across two catchments when tested independently. Problems in identifying values for the fourth 'fast response distance' parameter have highlighted a potential limitation with the present structure of the model. The present assumption that this parameter can be related simply to soil type rather than topography appears to be inadequate. With the exclusion of this parameter, calibrated parameter sets from one catchment can be converted into equivalent parameter sets for the alternate catchment on the basis of their HOST distributions, to give a reasonable simulation of flow. Following further testing on different catchments, and modifications to the definition of the fast response distance parameter, the technique provides a methodology whereby it is possible to directly derive spatial soil parameters for new catchments.

Identification of internal properties of fibers and micro-swimmers

NASA Astrophysics Data System (ADS)

Plouraboue, Franck; Thiam, Ibrahima; Delmotte, Blaise; Climent, Eric; PSC Collaboration

2016-11-01

In this presentation we discuss the identifiability of constitutive parameters of passive or active micro-swimmers. We first present a general framework for describing fibers or micro-swimmers using a bead-model description. Using a kinematic constraint formulation to describe fibers, flagellum or cilia, we find explicit linear relationship between elastic constitutive parameters and generalised velocities from computing contact forces. This linear formulation then permits to address explicitly identifiability conditions and solve for parameter identification. We show that both active forcing and passive parameters are both identifiable independently but not simultaneously. We also provide unbiased estimators for elastic parameters as well as active ones in the presence of Langevin-like forcing with Gaussian noise using normal linear regression models and maximum likelihood method. These theoretical results are illustrated in various configurations of relaxed or actuated passives fibers, and active filament of known passive properties, showing the efficiency of the proposed approach for direct parameter identification. The convergence of the proposed estimators is successfully tested numerically.

Marginal Utility of Conditional Sensitivity Analyses for Dynamic Models

EPA Science Inventory

Background/Question/MethodsDynamic ecological processes may be influenced by many factors. Simulation models thatmimic these processes often have complex implementations with many parameters. Sensitivityanalyses are subsequently used to identify critical parameters whose uncertai...

On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model

DOE PAGES

Bao, Jie; Hou, Zhangshuan; Huang, Maoyi; ...

2015-12-04

Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalizedmore » linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.« less

On Finding and Using Identifiable Parameter Combinations in Nonlinear Dynamic Systems Biology Models and COMBOS: A Novel Web Implementation

PubMed Central

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 p 1,…, 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 and without initial conditions. Built-in examples include unidentifiable 2 to 4-compartment and HIV dynamics models. PMID:25350289

The structural identifiability and parameter estimation of a multispecies model for the transmission of mastitis in dairy cows with postmilking teat disinfection.

PubMed

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.

Modeling Spacecraft Fuel Slosh at Embry-Riddle Aeronautical University

NASA Technical Reports Server (NTRS)

Schlee, Keith L.

2007-01-01

As a NASA-sponsored GSRP Fellow, I worked with other researchers and analysts at Embry-Riddle Aeronautical University and NASA's ELV Division to investigate the effect of spacecraft fuel slosh. NASA's research into the effects of fuel slosh includes modeling the response in full-sized tanks using equipment such as the Spinning Slosh Test Rig (SSTR), located at Southwest Research Institute (SwRI). NASA and SwRI engineers analyze data taken from SSTR runs and hand-derive equations of motion to identify model parameters and characterize the sloshing motion. With guidance from my faculty advisor, Dr. Sathya Gangadharan, and NASA flight controls analysts James Sudermann and Charles Walker, I set out to automate this parameter identification process by building a simple physical experimental setup to model free surface slosh in a spherical tank with a simple pendulum analog. This setup was then modeled using Simulink and SimMechanics. The Simulink Parameter Estimation Tool was then used to identify the model parameters.

Material and morphology parameter sensitivity analysis in particulate composite materials

NASA Astrophysics Data System (ADS)

Zhang, Xiaoyu; Oskay, Caglar

2017-12-01

This manuscript presents a novel parameter sensitivity analysis framework for damage and failure modeling of particulate composite materials subjected to dynamic loading. The proposed framework employs global sensitivity analysis to study the variance in the failure response as a function of model parameters. In view of the computational complexity of performing thousands of detailed microstructural simulations to characterize sensitivities, Gaussian process (GP) surrogate modeling is incorporated into the framework. In order to capture the discontinuity in response surfaces, the GP models are integrated with a support vector machine classification algorithm that identifies the discontinuities within response surfaces. The proposed framework is employed to quantify variability and sensitivities in the failure response of polymer bonded particulate energetic materials under dynamic loads to material properties and morphological parameters that define the material microstructure. Particular emphasis is placed on the identification of sensitivity to interfaces between the polymer binder and the energetic particles. The proposed framework has been demonstrated to identify the most consequential material and morphological parameters under vibrational and impact loads.

Correlations among the parameters of the spherical model for eclipsing binaries

NASA Technical Reports Server (NTRS)

Sobieski, S.; White, J. E.

1971-01-01

Correlation coefficients were computed to investigate the parameters for describing the spherical model of an eclipsing binary system. Regions in parameter hyperspace were identified where strong correlations exist and, by implication, the solution determinacy is low. The results are presented in tabular form for a large number of system configurations.

Modeling the shape and composition of the human body using dual energy X-ray absorptiometry images

PubMed Central

Shepherd, John A.; Fan, Bo; Schwartz, Ann V.; Cawthon, Peggy; Cummings, Steven R.; Kritchevsky, Stephen; Nevitt, Michael; Santanasto, Adam; Cootes, Timothy F.

2017-01-01

There is growing evidence that body shape and regional body composition are strong indicators of metabolic health. The purpose of this study was to develop statistical models that accurately describe holistic body shape, thickness, and leanness. We hypothesized that there are unique body shape features that are predictive of mortality beyond standard clinical measures. We developed algorithms to process whole-body dual-energy X-ray absorptiometry (DXA) scans into body thickness and leanness images. We performed statistical appearance modeling (SAM) and principal component analysis (PCA) to efficiently encode the variance of body shape, leanness, and thickness across sample of 400 older Americans from the Health ABC study. The sample included 200 cases and 200 controls based on 6-year mortality status, matched on sex, race and BMI. The final model contained 52 points outlining the torso, upper arms, thighs, and bony landmarks. Correlation analyses were performed on the PCA parameters to identify body shape features that vary across groups and with metabolic risk. Stepwise logistic regression was performed to identify sex and race, and predict mortality risk as a function of body shape parameters. These parameters are novel body composition features that uniquely identify body phenotypes of different groups and predict mortality risk. Three parameters from a SAM of body leanness and thickness accurately identified sex (training AUC = 0.99) and six accurately identified race (training AUC = 0.91) in the sample dataset. Three parameters from a SAM of only body thickness predicted mortality (training AUC = 0.66, validation AUC = 0.62). Further study is warranted to identify specific shape/composition features that predict other health outcomes. PMID:28423041

Estimating Colloidal Contact Model Parameters Using Quasi-Static Compression Simulations.

PubMed

Bürger, Vincent; Briesen, Heiko

2016-10-05

For colloidal particles interacting in suspensions, clusters, or gels, contact models should attempt to include all physical phenomena experimentally observed. One critical point when formulating a contact model is to ensure that the interaction parameters can be easily obtained from experiments. Experimental determinations of contact parameters for particles either are based on bulk measurements for simulations on the macroscopic scale or require elaborate setups for obtaining tangential parameters such as using atomic force microscopy. However, on the colloidal scale, a simple method is required to obtain all interaction parameters simultaneously. This work demonstrates that quasi-static compression of a fractal-like particle network provides all the necessary information to obtain particle interaction parameters using a simple spring-based contact model. These springs provide resistances against all degrees of freedom associated with two-particle interactions, and include critical forces or moments where such springs break, indicating a bond-breakage event. A position-based cost function is introduced to show the identifiability of the two-particle contact parameters, and a discrete, nonlinear, and non-gradient-based global optimization method (simplex with simulated annealing, SIMPSA) is used to minimize the cost function calculated from deviations of particle positions. Results show that, in principle, all necessary contact parameters for an arbitrary particle network can be identified, although numerical efficiency as well as experimental noise must be addressed when applying this method. Such an approach lays the groundwork for identifying particle-contact parameters from a position-based particle analysis for a colloidal system using just one experiment. Spring constants also directly influence the time step of the discrete-element method, and a detailed knowledge of all necessary interaction parameters will help to improve the efficiency of colloidal particle simulations.

Atherosclerotic plaque delamination: Experiments and 2D finite element model to simulate plaque peeling in two strains of transgenic mice.

PubMed

Merei, Bilal; Badel, Pierre; Davis, Lindsey; Sutton, Michael A; Avril, Stéphane; Lessner, Susan M

2017-03-01

Finite element analyses using cohesive zone models (CZM) can be used to predict the fracture of atherosclerotic plaques but this requires setting appropriate values of the model parameters. In this study, material parameters of a CZM were identified for the first time on two groups of mice (ApoE -/- and ApoE -/- Col8 -/- ) using the measured force-displacement curves acquired during delamination tests. To this end, a 2D finite-element model of each plaque was solved using an explicit integration scheme. Each constituent of the plaque was modeled with a neo-Hookean strain energy density function and a CZM was used for the interface. The model parameters were calibrated by minimizing the quadratic deviation between the experimental force displacement curves and the model predictions. The elastic parameter of the plaque and the CZM interfacial parameter were successfully identified for a cohort of 11 mice. The results revealed that only the elastic parameter was significantly different between the two groups, ApoE -/- Col8 -/- plaques being less stiff than ApoE -/- plaques. Finally, this study demonstrated that a simple 2D finite element model with cohesive elements can reproduce fairly well the plaque peeling global response. Future work will focus on understanding the main biological determinants of regional and inter-individual variations of the material parameters used in the model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Equivalence between the Lovelock-Cartan action and a constrained gauge theory

NASA Astrophysics Data System (ADS)

Junqueira, O. C.; Pereira, A. D.; Sadovski, G.; Santos, T. R. S.; Sobreiro, R. F.; Tomaz, A. A.

2017-04-01

We show that the four-dimensional Lovelock-Cartan action can be derived from a massless gauge theory for the SO(1, 3) group with an additional BRST trivial part. The model is originally composed of a topological sector and a BRST exact piece and has no explicit dependence on the metric, the vierbein or a mass parameter. The vierbein is introduced together with a mass parameter through some BRST trivial constraints. The effect of the constraints is to identify the vierbein with some of the additional fields, transforming the original action into the Lovelock-Cartan one. In this scenario, the mass parameter is identified with Newton's constant, while the gauge field is identified with the spin connection. The symmetries of the model are also explored. Moreover, the extension of the model to a quantum version is qualitatively discussed.

Knowledge, transparency, and refutability in groundwater models, an example from the Death Valley regional groundwater flow system

USGS Publications Warehouse

Hill, Mary C.; Faunt, Claudia C.; Belcher, Wayne; Sweetkind, Donald; Tiedeman, Claire; Kavetski, Dmitri

2013-01-01

This work demonstrates how available knowledge can be used to build more transparent and refutable computer models of groundwater systems. The Death Valley regional groundwater flow system, which surrounds a proposed site for a high level nuclear waste repository of the United States of America, and the Nevada National Security Site (NNSS), where nuclear weapons were tested, is used to explore model adequacy, identify parameters important to (and informed by) observations, and identify existing old and potential new observations important to predictions. Model development is pursued using a set of fundamental questions addressed with carefully designed metrics. Critical methods include using a hydrogeologic model, managing model nonlinearity by designing models that are robust while maintaining realism, using error-based weighting to combine disparate types of data, and identifying important and unimportant parameters and observations and optimizing parameter values with computationally frugal schemes. The frugal schemes employed in this study require relatively few (10–1000 s), parallelizable model runs. This is beneficial because models able to approximate the complex site geology defensibly tend to have high computational cost. The issue of model defensibility is particularly important given the contentious political issues involved.

Active subspace uncertainty quantification for a polydomain ferroelectric phase-field model

NASA Astrophysics Data System (ADS)

Leon, Lider S.; Smith, Ralph C.; Miles, Paul; Oates, William S.

2018-03-01

Quantum-informed ferroelectric phase field models capable of predicting material behavior, are necessary for facilitating the development and production of many adaptive structures and intelligent systems. Uncertainty is present in these models, given the quantum scale at which calculations take place. A necessary analysis is to determine how the uncertainty in the response can be attributed to the uncertainty in the model inputs or parameters. A second analysis is to identify active subspaces within the original parameter space, which quantify directions in which the model response varies most dominantly, thus reducing sampling effort and computational cost. In this investigation, we identify an active subspace for a poly-domain ferroelectric phase-field model. Using the active variables as our independent variables, we then construct a surrogate model and perform Bayesian inference. Once we quantify the uncertainties in the active variables, we obtain uncertainties for the original parameters via an inverse mapping. The analysis provides insight into how active subspace methodologies can be used to reduce computational power needed to perform Bayesian inference on model parameters informed by experimental or simulated data.

Uncertainty in a monthly water balance model using the generalized likelihood uncertainty estimation methodology

NASA Astrophysics Data System (ADS)

Rivera, Diego; Rivas, Yessica; Godoy, Alex

2015-02-01

Hydrological models are simplified representations of natural processes and subject to errors. Uncertainty bounds are a commonly used way to assess the impact of an input or model architecture uncertainty in model outputs. Different sets of parameters could have equally robust goodness-of-fit indicators, which is known as Equifinality. We assessed the outputs from a lumped conceptual hydrological model to an agricultural watershed in central Chile under strong interannual variability (coefficient of variability of 25%) by using the Equifinality concept and uncertainty bounds. The simulation period ran from January 1999 to December 2006. Equifinality and uncertainty bounds from GLUE methodology (Generalized Likelihood Uncertainty Estimation) were used to identify parameter sets as potential representations of the system. The aim of this paper is to exploit the use of uncertainty bounds to differentiate behavioural parameter sets in a simple hydrological model. Then, we analyze the presence of equifinality in order to improve the identification of relevant hydrological processes. The water balance model for Chillan River exhibits, at a first stage, equifinality. However, it was possible to narrow the range for the parameters and eventually identify a set of parameters representing the behaviour of the watershed (a behavioural model) in agreement with observational and soft data (calculation of areal precipitation over the watershed using an isohyetal map). The mean width of the uncertainty bound around the predicted runoff for the simulation period decreased from 50 to 20 m3s-1 after fixing the parameter controlling the areal precipitation over the watershed. This decrement is equivalent to decreasing the ratio between simulated and observed discharge from 5.2 to 2.5. Despite the criticisms against the GLUE methodology, such as the lack of statistical formality, it is identified as a useful tool assisting the modeller with the identification of critical parameters.

Identifying sensitive ranges in global warming precipitation change dependence on convective parameters

DOE PAGES

Bernstein, Diana N.; Neelin, J. David

2016-04-28

A branch-run perturbed-physics ensemble in the Community Earth System Model estimates impacts of parameters in the deep convection scheme on current hydroclimate and on end-of-century precipitation change projections under global warming. Regional precipitation change patterns prove highly sensitive to these parameters, especially in the tropics with local changes exceeding 3mm/d, comparable to the magnitude of the predicted change and to differences in global warming predictions among the Coupled Model Intercomparison Project phase 5 models. This sensitivity is distributed nonlinearly across the feasible parameter range, notably in the low-entrainment range of the parameter for turbulent entrainment in the deep convection scheme.more » This suggests that a useful target for parameter sensitivity studies is to identify such disproportionately sensitive dangerous ranges. Here, the low-entrainment range is used to illustrate the reduction in global warming regional precipitation sensitivity that could occur if this dangerous range can be excluded based on evidence from current climate.« less

Identifying sensitive ranges in global warming precipitation change dependence on convective parameters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bernstein, Diana N.; Neelin, J. David

A branch-run perturbed-physics ensemble in the Community Earth System Model estimates impacts of parameters in the deep convection scheme on current hydroclimate and on end-of-century precipitation change projections under global warming. Regional precipitation change patterns prove highly sensitive to these parameters, especially in the tropics with local changes exceeding 3mm/d, comparable to the magnitude of the predicted change and to differences in global warming predictions among the Coupled Model Intercomparison Project phase 5 models. This sensitivity is distributed nonlinearly across the feasible parameter range, notably in the low-entrainment range of the parameter for turbulent entrainment in the deep convection scheme.more » This suggests that a useful target for parameter sensitivity studies is to identify such disproportionately sensitive dangerous ranges. Here, the low-entrainment range is used to illustrate the reduction in global warming regional precipitation sensitivity that could occur if this dangerous range can be excluded based on evidence from current climate.« less

Global sensitivity analysis for identifying important parameters of nitrogen nitrification and denitrification under model uncertainty and scenario uncertainty

NASA Astrophysics Data System (ADS)

Chen, Zhuowei; Shi, Liangsheng; Ye, Ming; Zhu, Yan; Yang, Jinzhong

2018-06-01

Nitrogen reactive transport modeling is subject to uncertainty in model parameters, structures, and scenarios. By using a new variance-based global sensitivity analysis method, this paper identifies important parameters for nitrogen reactive transport with simultaneous consideration of these three uncertainties. A combination of three scenarios of soil temperature and two scenarios of soil moisture creates a total of six scenarios. Four alternative models describing the effect of soil temperature and moisture content are used to evaluate the reduction functions used for calculating actual reaction rates. The results show that for nitrogen reactive transport problem, parameter importance varies substantially among different models and scenarios. Denitrification and nitrification process is sensitive to soil moisture content status rather than to the moisture function parameter. Nitrification process becomes more important at low moisture content and low temperature. However, the changing importance of nitrification activity with respect to temperature change highly relies on the selected model. Model-averaging is suggested to assess the nitrification (or denitrification) contribution by reducing the possible model error. Despite the introduction of biochemical heterogeneity or not, fairly consistent parameter importance rank is obtained in this study: optimal denitrification rate (Kden) is the most important parameter; reference temperature (Tr) is more important than temperature coefficient (Q10); empirical constant in moisture response function (m) is the least important one. Vertical distribution of soil moisture but not temperature plays predominant role controlling nitrogen reaction. This study provides insight into the nitrogen reactive transport modeling and demonstrates an effective strategy of selecting the important parameters when future temperature and soil moisture carry uncertainties or when modelers face with multiple ways of establishing nitrogen models.

On the problem of modeling for parameter identification in distributed structures

NASA Technical Reports Server (NTRS)

Norris, Mark A.; Meirovitch, Leonard

1988-01-01

Structures are often characterized by parameters, such as mass and stiffness, that are spatially distributed. Parameter identification of distributed structures is subject to many of the difficulties involved in the modeling problem, and the choice of the model can greatly affect the results of the parameter identification process. Analogously to control spillover in the control of distributed-parameter systems, identification spillover is shown to exist as well and its effect is to degrade the parameter estimates. Moreover, as in modeling by the Rayleigh-Ritz method, it is shown that, for a Rayleigh-Ritz type identification algorithm, an inclusion principle exists in the identification of distributed-parameter systems as well, so that the identified natural frequencies approach the actual natural frequencies monotonically from above.

Sensitivity analysis and calibration of a dynamic physically based slope stability model

NASA Astrophysics Data System (ADS)

Zieher, Thomas; Rutzinger, Martin; Schneider-Muntau, Barbara; Perzl, Frank; Leidinger, David; Formayer, Herbert; Geitner, Clemens

2017-06-01

Physically based modelling of slope stability on a catchment scale is still a challenging task. When applying a physically based model on such a scale (1 : 10 000 to 1 : 50 000), parameters with a high impact on the model result should be calibrated to account for (i) the spatial variability of parameter values, (ii) shortcomings of the selected model, (iii) uncertainties of laboratory tests and field measurements or (iv) parameters that cannot be derived experimentally or measured in the field (e.g. calibration constants). While systematic parameter calibration is a common task in hydrological modelling, this is rarely done using physically based slope stability models. In the present study a dynamic, physically based, coupled hydrological-geomechanical slope stability model is calibrated based on a limited number of laboratory tests and a detailed multitemporal shallow landslide inventory covering two landslide-triggering rainfall events in the Laternser valley, Vorarlberg (Austria). Sensitive parameters are identified based on a local one-at-a-time sensitivity analysis. These parameters (hydraulic conductivity, specific storage, angle of internal friction for effective stress, cohesion for effective stress) are systematically sampled and calibrated for a landslide-triggering rainfall event in August 2005. The identified model ensemble, including 25 behavioural model runs with the highest portion of correctly predicted landslides and non-landslides, is then validated with another landslide-triggering rainfall event in May 1999. The identified model ensemble correctly predicts the location and the supposed triggering timing of 73.0 % of the observed landslides triggered in August 2005 and 91.5 % of the observed landslides triggered in May 1999. Results of the model ensemble driven with raised precipitation input reveal a slight increase in areas potentially affected by slope failure. At the same time, the peak run-off increases more markedly, suggesting that precipitation intensities during the investigated landslide-triggering rainfall events were already close to or above the soil's infiltration capacity.

Numerical Simulation Of Cratering Effects In Adobe

DTIC Science & Technology

2013-07-01

DEVELOPMENT OF MATERIAL PARAMETERS .........................................................7 PROBLEM SETUP...37 PARAMETER ADJUSTMENTS ......................................................................................38 GLOSSARY...dependent yield surface with the Geological Yield Surface (GEO) modeled in CTH using well characterized adobe. By identifying key parameters that

Torsional Vibration in the National Wind Technology Center’s 2.5-Megawatt Dynamometer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sethuraman, Latha; Keller, Jonathan; Wallen, Robb

2016-08-31

This report documents the torsional drivetrain dynamics of the NWTC's 2.5-megawatt dynamometer as identified experimentally and as calculated using lumped parameter models using known inertia and stiffness parameters. The report is presented in two parts beginning with the identification of the primary torsional modes followed by the investigation of approaches to damp the torsional vibrations. The key mechanical parameters for the lumped parameter models and justification for the element grouping used in the derivation of the torsional modes are presented. The sensitivities of the torsional modes to different test article properties are discussed. The oscillations observed from the low-speed andmore » generator torque measurements were used to identify the extent of damping inherently achieved through active and passive compensation techniques. A simplified Simulink model of the dynamometer test article integrating the electro-mechanical power conversion and control features was established to emulate the torque behavior that was observed during testing. The torque response in the high-speed, low-speed, and generator shafts were tested and validated against experimental measurements involving step changes in load with the dynamometer operating under speed-regulation mode. The Simulink model serves as a ready reference to identify the torque sensitivities to various system parameters and to explore opportunities to improve torsional damping under different conditions.« less

On Multi-Objective Based Constitutive Modelling Methodology and Numerical Validation in Small-Hole Drilling of Al6063/SiCp Composites

PubMed Central

Xiang, Junfeng; Xie, Lijing; Gao, Feinong; Zhang, Yu; Yi, Jie; Wang, Tao; Pang, Siqin; Wang, Xibin

2018-01-01

Discrepancies in capturing material behavior of some materials, such as Particulate Reinforced Metal Matrix Composites, by using conventional ad hoc strategy make the applicability of Johnson-Cook constitutive model challenged. Despites applicable efforts, its extended formalism with more fitting parameters would increase the difficulty in identifying constitutive parameters. A weighted multi-objective strategy for identifying any constitutive formalism is developed to predict mechanical behavior in static and dynamic loading conditions equally well. These varying weighting is based on the Gaussian-distributed noise evaluation of experimentally obtained stress-strain data in quasi-static or dynamic mode. This universal method can be used to determine fast and directly whether the constitutive formalism is suitable to describe the material constitutive behavior by measuring goodness-of-fit. A quantitative comparison of different fitting strategies on identifying Al6063/SiCp’s material parameters is made in terms of performance evaluation including noise elimination, correlation, and reliability. Eventually, a three-dimensional (3D) FE model in small-hole drilling of Al6063/SiCp composites, using multi-objective identified constitutive formalism, is developed. Comparison with the experimental observations in thrust force, torque, and chip morphology provides valid evidence on the applicability of the developed multi-objective identification strategy in identifying constitutive parameters. PMID:29324688

Verification Techniques for Parameter Selection and Bayesian Model Calibration Presented for an HIV Model

NASA Astrophysics Data System (ADS)

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 techniques for model calibration. For Bayesian model calibration, we employ adaptive Metropolis algorithms to construct densities for input parameters in the heat model and the HIV model. To quantify the uncertainty in the parameters, we employ two MCMC algorithms: Delayed Rejection Adaptive Metropolis (DRAM) [33] and Differential Evolution Adaptive Metropolis (DREAM) [66, 68]. The densities obtained using these methods are compared to those obtained through the direct numerical evaluation of the Bayes' formula. We also combine uncertainties in input parameters and measurement errors to construct predictive estimates for a model response. A significant emphasis is on the development and illustration of techniques to verify the accuracy of sampling-based Metropolis algorithms. We verify the accuracy of DRAM and DREAM by comparing chains, densities and correlations obtained using DRAM, DREAM and the direct evaluation of Bayes formula. We also perform similar analysis for credible and prediction intervals for responses. Once the parameters are estimated, we employ energy statistics test [63, 64] to compare the densities obtained by different methods for the HIV model. The energy statistics are used to test the equality of distributions. We also consider parameter selection and verification techniques for models having one or more parameters that are noninfluential in the sense that they minimally impact model outputs. We illustrate these techniques for a dynamic HIV model but note that the parameter selection and verification framework is applicable to a wide range of biological and physical models. To accommodate the nonlinear input to output relations, which are typical for such models, we focus on global sensitivity analysis techniques, including those based on partial correlations, Sobol indices based on second-order model representations, and Morris indices, as well as a parameter selection technique based on standard errors. A significant objective is to provide verification strategies to assess the accuracy of those techniques, which we illustrate in the context of the HIV model. Finally, we examine active subspace methods as an alternative to parameter subset selection techniques. The objective of active subspace methods is to determine the subspace of inputs that most strongly affect the model response, and to reduce the dimension of the input space. The major difference between active subspace methods and parameter selection techniques is that parameter selection identifies influential parameters whereas subspace selection identifies a linear combination of parameters that impacts the model responses significantly. We employ active subspace methods discussed in [22] for the HIV model and present a verification that the active subspace successfully reduces the input dimensions.

Hidden symmetries of the extended Kitaev-Heisenberg model: Implications for the honeycomb-lattice iridates A2IrO3

NASA Astrophysics Data System (ADS)

Chaloupka, Jiří; Khaliullin, Giniyat

2015-07-01

We have explored the hidden symmetries of a generic four-parameter nearest-neighbor spin model, allowed in honeycomb-lattice compounds under trigonal compression. Our method utilizes a systematic algorithm to identify all dual transformations of the model that map the Hamiltonian on itself, changing the parameters and providing exact links between different points in its parameter space. We have found the complete set of points of hidden SU(2) symmetry at which a seemingly highly anisotropic model can be mapped back on the Heisenberg model and inherits therefore its properties such as the presence of gapless Goldstone modes. The procedure used to search for the hidden symmetries is quite general and may be extended to other bond-anisotropic spin models and other lattices, such as the triangular, kagome, hyperhoneycomb, or harmonic-honeycomb lattices. We apply our findings to the honeycomb-lattice iridates Na2IrO3 and Li2IrO3 , and illustrate how they help to identify plausible values of the model parameters that are compatible with the available experimental data.

Correlations among the parameters of the spherical model for eclipsing binaries.

NASA Technical Reports Server (NTRS)

Sobieski, S.; White, J.

1973-01-01

Correlation coefficients have been computed to investigate the parameters used to describe the spherical model of an eclipsing binary system. Regions in parameter hyperspace have been identified where strong correlations exist and, by implication, the solution determinacy is low. The results are presented in tabular form for a large number of system configurations.

Advanced multivariate data analysis to determine the root cause of trisulfide bond formation in a novel antibody–peptide fusion

PubMed Central

Goldrick, Stephen; Holmes, William; Bond, Nicholas J.; Lewis, Gareth; Kuiper, Marcel; Turner, Richard

2017-01-01

ABSTRACT Product quality heterogeneities, such as a trisulfide bond (TSB) formation, can be influenced by multiple interacting process parameters. Identifying their root cause is a major challenge in biopharmaceutical production. To address this issue, this paper describes the novel application of advanced multivariate data analysis (MVDA) techniques to identify the process parameters influencing TSB formation in a novel recombinant antibody–peptide fusion expressed in mammalian cell culture. The screening dataset was generated with a high‐throughput (HT) micro‐bioreactor system (AmbrTM 15) using a design of experiments (DoE) approach. The complex dataset was firstly analyzed through the development of a multiple linear regression model focusing solely on the DoE inputs and identified the temperature, pH and initial nutrient feed day as important process parameters influencing this quality attribute. To further scrutinize the dataset, a partial least squares model was subsequently built incorporating both on‐line and off‐line process parameters and enabled accurate predictions of the TSB concentration at harvest. Process parameters identified by the models to promote and suppress TSB formation were implemented on five 7 L bioreactors and the resultant TSB concentrations were comparable to the model predictions. This study demonstrates the ability of MVDA to enable predictions of the key performance drivers influencing TSB formation that are valid also upon scale‐up. Biotechnol. Bioeng. 2017;114: 2222–2234. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc. PMID:28500668

Local overfishing may be avoided by examining parameters of a spatio-temporal model

PubMed Central

Shackell, Nancy; Mills Flemming, Joanna

2017-01-01

Spatial erosion of stock structure through local overfishing can lead to stock collapse because fish often prefer certain locations, and fisheries tend to focus on those locations. Fishery managers are challenged to maintain the integrity of the entire stock and require scientific approaches that provide them with sound advice. Here we propose a Bayesian hierarchical spatio-temporal modelling framework for fish abundance data to estimate key parameters that define spatial stock structure: persistence (similarity of spatial structure over time), connectivity (coherence of temporal pattern over space), and spatial variance (variation across the seascape). The consideration of these spatial parameters in the stock assessment process can help identify the erosion of structure and assist in preventing local overfishing. We use Atlantic cod (Gadus morhua) in eastern Canada as a case study an examine the behaviour of these parameters from the height of the fishery through its collapse. We identify clear signals in parameter behaviour under circumstances of destructive stock erosion as well as for recovery of spatial structure even when combined with a non-recovery in abundance. Further, our model reveals the spatial pattern of areas of high and low density persists over the 41 years of available data and identifies the remnant patches. Models of this sort are crucial to recovery plans if we are to identify and protect remaining sources of recolonization for Atlantic cod. Our method is immediately applicable to other exploited species. PMID:28886179

Local overfishing may be avoided by examining parameters of a spatio-temporal model.

PubMed

Carson, Stuart; Shackell, Nancy; Mills Flemming, Joanna

2017-01-01

Spatial erosion of stock structure through local overfishing can lead to stock collapse because fish often prefer certain locations, and fisheries tend to focus on those locations. Fishery managers are challenged to maintain the integrity of the entire stock and require scientific approaches that provide them with sound advice. Here we propose a Bayesian hierarchical spatio-temporal modelling framework for fish abundance data to estimate key parameters that define spatial stock structure: persistence (similarity of spatial structure over time), connectivity (coherence of temporal pattern over space), and spatial variance (variation across the seascape). The consideration of these spatial parameters in the stock assessment process can help identify the erosion of structure and assist in preventing local overfishing. We use Atlantic cod (Gadus morhua) in eastern Canada as a case study an examine the behaviour of these parameters from the height of the fishery through its collapse. We identify clear signals in parameter behaviour under circumstances of destructive stock erosion as well as for recovery of spatial structure even when combined with a non-recovery in abundance. Further, our model reveals the spatial pattern of areas of high and low density persists over the 41 years of available data and identifies the remnant patches. Models of this sort are crucial to recovery plans if we are to identify and protect remaining sources of recolonization for Atlantic cod. Our method is immediately applicable to other exploited species.

Parameter as a Switch Between Dynamical States of a Network in Population Decoding.

PubMed

Yu, Jiali; Mao, Hua; Yi, Zhang

2017-04-01

Population coding is a method to represent stimuli using the collective activities of a number of neurons. Nevertheless, it is difficult to extract information from these population codes with the noise inherent in neuronal responses. Moreover, it is a challenge to identify the right parameter of the decoding model, which plays a key role for convergence. To address the problem, a population decoding model is proposed for parameter selection. Our method successfully identified the key conditions for a nonzero continuous attractor. Both the theoretical analysis and the application studies demonstrate the correctness and effectiveness of this strategy.

Bayesian inference to identify parameters in viscoelasticity

NASA Astrophysics Data System (ADS)

Rappel, Hussein; Beex, Lars A. A.; Bordas, Stéphane P. A.

2017-08-01

This contribution discusses Bayesian inference (BI) as an approach to identify parameters in viscoelasticity. The aims are: (i) to show that the prior has a substantial influence for viscoelasticity, (ii) to show that this influence decreases for an increasing number of measurements and (iii) to show how different types of experiments influence the identified parameters and their uncertainties. The standard linear solid model is the material description of interest and a relaxation test, a constant strain-rate test and a creep test are the tensile experiments focused on. The experimental data are artificially created, allowing us to make a one-to-one comparison between the input parameters and the identified parameter values. Besides dealing with the aforementioned issues, we believe that this contribution forms a comprehensible start for those interested in applying BI in viscoelasticity.

Parameter dimensionality reduction of a conceptual model for streamflow prediction in Canadian, snowmelt dominated ungauged basins

NASA Astrophysics Data System (ADS)

Arsenault, Richard; Poissant, Dominique; Brissette, François

2015-11-01

This paper evaluated the effects of parametric reduction of a hydrological model on five regionalization methods and 267 catchments in the province of Quebec, Canada. The Sobol' variance-based sensitivity analysis was used to rank the model parameters by their influence on the model results and sequential parameter fixing was performed. The reduction in parameter correlations improved parameter identifiability, however this improvement was found to be minimal and was not transposed in the regionalization mode. It was shown that 11 of the HSAMI models' 23 parameters could be fixed with little or no loss in regionalization skill. The main conclusions were that (1) the conceptual lumped models used in this study did not represent physical processes sufficiently well to warrant parameter reduction for physics-based regionalization methods for the Canadian basins examined and (2) catchment descriptors did not adequately represent the relevant hydrological processes, namely snow accumulation and melt.

A Bayesian approach to identifying structural nonlinearity using free-decay response: Application to damage detection in composites

USGS Publications Warehouse

Nichols, J.M.; Link, W.A.; Murphy, K.D.; Olson, C.C.

2010-01-01

This work discusses a Bayesian approach to approximating the distribution of parameters governing nonlinear structural systems. Specifically, we use a Markov Chain Monte Carlo method for sampling the posterior parameter distributions thus producing both point and interval estimates for parameters. The method is first used to identify both linear and nonlinear parameters in a multiple degree-of-freedom structural systems using free-decay vibrations. The approach is then applied to the problem of identifying the location, size, and depth of delamination in a model composite beam. The influence of additive Gaussian noise on the response data is explored with respect to the quality of the resulting parameter estimates.

Application of identified sensitive physical parameters in reducing the uncertainty of numerical simulation

NASA Astrophysics Data System (ADS)

Sun, Guodong; Mu, Mu

2016-04-01

An important source of uncertainty, which then causes further uncertainty in numerical simulations, is that residing in the parameters describing physical processes in numerical models. There are many physical parameters in numerical models in the atmospheric and oceanic sciences, and it would cost a great deal to reduce uncertainties in all physical parameters. Therefore, finding a subset of these parameters, which are relatively more sensitive and important parameters, and reducing the errors in the physical parameters in this subset would be a far more efficient way to reduce the uncertainties involved in simulations. In this context, we present a new approach based on the conditional nonlinear optimal perturbation related to parameter (CNOP-P) method. The approach provides a framework to ascertain the subset of those relatively more sensitive and important parameters among the physical parameters. The Lund-Potsdam-Jena (LPJ) dynamical global vegetation model was utilized to test the validity of the new approach. The results imply that nonlinear interactions among parameters play a key role in the uncertainty of numerical simulations in arid and semi-arid regions of China compared to those in northern, northeastern and southern China. The uncertainties in the numerical simulations were reduced considerably by reducing the errors of the subset of relatively more sensitive and important parameters. The results demonstrate that our approach not only offers a new route to identify relatively more sensitive and important physical parameters but also that it is viable to then apply "target observations" to reduce the uncertainties in model parameters.

Identifiability of PBPK Models with Applications to Dimethylarsinic Acid Exposure

EPA Science Inventory

Any statistical model should be identifiable in order for estimates and tests using it to be meaningful. We consider statistical analysis of physiologically-based pharmacokinetic (PBPK) models in which parameters cannot be estimated precisely from available data, and discuss diff...

Global asymptotic stability of density dependent integral population projection models.

PubMed

Rebarber, Richard; Tenhumberg, Brigitte; Townley, Stuart

2012-02-01

Many stage-structured density dependent populations with a continuum of stages can be naturally modeled using nonlinear integral projection models. In this paper, we study a trichotomy of global stability result for a class of density dependent systems which include a Platte thistle model. Specifically, we identify those systems parameters for which zero is globally asymptotically stable, parameters for which there is a positive asymptotically stable equilibrium, and parameters for which there is no asymptotically stable equilibrium. Copyright © 2011 Elsevier Inc. All rights reserved.

Bayesian inference for unidirectional misclassification of a binary response trait.

PubMed

Xia, Michelle; Gustafson, Paul

2018-03-15

When assessing association between a binary trait and some covariates, the binary response may be subject to unidirectional misclassification. Unidirectional misclassification can occur when revealing a particular level of the trait is associated with a type of cost, such as a social desirability or financial cost. The feasibility of addressing misclassification is commonly obscured by model identification issues. The current paper attempts to study the efficacy of inference when the binary response variable is subject to unidirectional misclassification. From a theoretical perspective, we demonstrate that the key model parameters possess identifiability, except for the case with a single binary covariate. From a practical standpoint, the logistic model with quantitative covariates can be weakly identified, in the sense that the Fisher information matrix may be near singular. This can make learning some parameters difficult under certain parameter settings, even with quite large samples. In other cases, the stronger identification enables the model to provide more effective adjustment for unidirectional misclassification. An extension to the Poisson approximation of the binomial model reveals the identifiability of the Poisson and zero-inflated Poisson models. For fully identified models, the proposed method adjusts for misclassification based on learning from data. For binary models where there is difficulty in identification, the method is useful for sensitivity analyses on the potential impact from unidirectional misclassification. Copyright © 2017 John Wiley & Sons, Ltd.

Development and validation of a habitat suitability model for the non-indigenous seagrass Zostera japonica in North America

EPA Science Inventory

We developed a spatially-explicit, flexible 3-parameter habitat suitability model that can be used to identify and predict areas at higher risk for non-native dwarf eelgrass (Zostera japonica) invasion. The model uses simple environmental parameters (depth, nearshore slope, and s...

Parameter Uncertainty Analysis Using Monte Carlo Simulations for a Regional-Scale Groundwater Model

NASA Astrophysics Data System (ADS)

Zhang, Y.; Pohlmann, K.

2016-12-01

Regional-scale grid-based groundwater models for flow and transport often contain multiple types of parameters that can intensify the challenge of parameter uncertainty analysis. We propose a Monte Carlo approach to systematically quantify the influence of various types of model parameters on groundwater flux and contaminant travel times. The Monte Carlo simulations were conducted based on the steady-state conversion of the original transient model, which was then combined with the PEST sensitivity analysis tool SENSAN and particle tracking software MODPATH. Results identified hydrogeologic units whose hydraulic conductivity can significantly affect groundwater flux, and thirteen out of 173 model parameters that can cause large variation in travel times for contaminant particles originating from given source zones.

Experimental parameter identification of a multi-scale musculoskeletal model controlled by electrical stimulation: application to patients with spinal cord injury.

PubMed

Benoussaad, Mourad; Poignet, Philippe; Hayashibe, Mitsuhiro; Azevedo-Coste, Christine; Fattal, Charles; Guiraud, David

2013-06-01

We investigated the parameter identification of a multi-scale physiological model of skeletal muscle, based on Huxley's formulation. We focused particularly on the knee joint controlled by quadriceps muscles under electrical stimulation (ES) in subjects with a complete spinal cord injury. A noninvasive and in vivo identification protocol was thus applied through surface stimulation in nine subjects and through neural stimulation in one ES-implanted subject. The identification protocol included initial identification steps, which are adaptations of existing identification techniques to estimate most of the parameters of our model. Then we applied an original and safer identification protocol in dynamic conditions, which required resolution of a nonlinear programming (NLP) problem to identify the serial element stiffness of quadriceps. Each identification step and cross validation of the estimated model in dynamic condition were evaluated through a quadratic error criterion. The results highlighted good accuracy, the efficiency of the identification protocol and the ability of the estimated model to predict the subject-specific behavior of the musculoskeletal system. From the comparison of parameter values between subjects, we discussed and explored the inter-subject variability of parameters in order to select parameters that have to be identified in each patient.

Structural identifiability analyses of candidate models for in vitro Pitavastatin hepatic uptake.

PubMed

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. Copyright © 2013. Published by Elsevier Ireland Ltd.

Characterization of human passive muscles for impact loads using genetic algorithm and inverse finite element methods.

PubMed

Chawla, A; Mukherjee, S; Karthikeyan, B

2009-02-01

The objective of this study is to identify the dynamic material properties of human passive muscle tissues for the strain rates relevant to automobile crashes. A novel methodology involving genetic algorithm (GA) and finite element method is implemented to estimate the material parameters by inverse mapping the impact test data. Isolated unconfined impact tests for average strain rates ranging from 136 s(-1) to 262 s(-1) are performed on muscle tissues. Passive muscle tissues are modelled as isotropic, linear and viscoelastic material using three-element Zener model available in PAMCRASH(TM) explicit finite element software. In the GA based identification process, fitness values are calculated by comparing the estimated finite element forces with the measured experimental forces. Linear viscoelastic material parameters (bulk modulus, short term shear modulus and long term shear modulus) are thus identified at strain rates 136 s(-1), 183 s(-1) and 262 s(-1) for modelling muscles. Extracted optimal parameters from this study are comparable with reported parameters in literature. Bulk modulus and short term shear modulus are found to be more influential in predicting the stress-strain response than long term shear modulus for the considered strain rates. Variations within the set of parameters identified at different strain rates indicate the need for new or improved material model, which is capable of capturing the strain rate dependency of passive muscle response with single set of material parameters for wide range of strain rates.

Lumped parametric model of the human ear for sound transmission.

PubMed

Feng, Bin; Gan, Rong Z

2004-09-01

A lumped parametric model of the human auditoria peripherals consisting of six masses suspended with six springs and ten dashpots was proposed. This model will provide the quantitative basis for the construction of a physical model of the human middle ear. The lumped model parameters were first identified using published anatomical data, and then determined through a parameter optimization process. The transfer function of the middle ear obtained from human temporal bone experiments with laser Doppler interferometers was used for creating the target function during the optimization process. It was found that, among 14 spring and dashpot parameters, there were five parameters which had pronounced effects on the dynamic behaviors of the model. The detailed discussion on the sensitivity of those parameters was provided with appropriate applications for sound transmission in the ear. We expect that the methods for characterizing the lumped model of the human ear and the model parameters will be useful for theoretical modeling of the ear function and construction of the ear physical model.

Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions.

PubMed

Vernon, Ian; Liu, Junli; Goldstein, Michael; Rowe, James; Topping, Jen; Lindsey, Keith

2018-01-02

Many mathematical models have now been employed across every area of systems biology. These models increasingly involve large numbers of unknown parameters, have complex structure which can result in substantial evaluation time relative to the needs of the analysis, and need to be compared to observed data of various forms. The correct analysis of such models usually requires a global parameter search, over a high dimensional parameter space, that incorporates and respects the most important sources of uncertainty. This can be an extremely difficult task, but it is essential for any meaningful inference or prediction to be made about any biological system. It hence represents a fundamental challenge for the whole of systems biology. Bayesian statistical methodology for the uncertainty analysis of complex models is introduced, which is designed to address the high dimensional global parameter search problem. Bayesian emulators that mimic the systems biology model but which are extremely fast to evaluate are embeded within an iterative history match: an efficient method to search high dimensional spaces within a more formal statistical setting, while incorporating major sources of uncertainty. The approach is demonstrated via application to a model of hormonal crosstalk in Arabidopsis root development, which has 32 rate parameters, for which we identify the sets of rate parameter values that lead to acceptable matches between model output and observed trend data. The multiple insights into the model's structure that this analysis provides are discussed. The methodology is applied to a second related model, and the biological consequences of the resulting comparison, including the evaluation of gene functions, are described. Bayesian uncertainty analysis for complex models using both emulators and history matching is shown to be a powerful technique that can greatly aid the study of a large class of systems biology models. It both provides insight into model behaviour and identifies the sets of rate parameters of interest.

Effects of correlated parameters and uncertainty in electronic-structure-based chemical kinetic modelling

NASA Astrophysics Data System (ADS)

Sutton, Jonathan E.; Guo, Wei; Katsoulakis, Markos A.; Vlachos, Dionisios G.

2016-04-01

Kinetic models based on first principles are becoming common place in heterogeneous catalysis because of their ability to interpret experimental data, identify the rate-controlling step, guide experiments and predict novel materials. To overcome the tremendous computational cost of estimating parameters of complex networks on metal catalysts, approximate quantum mechanical calculations are employed that render models potentially inaccurate. Here, by introducing correlative global sensitivity analysis and uncertainty quantification, we show that neglecting correlations in the energies of species and reactions can lead to an incorrect identification of influential parameters and key reaction intermediates and reactions. We rationalize why models often underpredict reaction rates and show that, despite the uncertainty being large, the method can, in conjunction with experimental data, identify influential missing reaction pathways and provide insights into the catalyst active site and the kinetic reliability of a model. The method is demonstrated in ethanol steam reforming for hydrogen production for fuel cells.

Near Identifiability of Dynamical Systems

NASA Technical Reports Server (NTRS)

Hadaegh, F. Y.; Bekey, G. A.

1987-01-01

Concepts regarding approximate mathematical models treated rigorously. Paper presents new results in analysis of structural identifiability, equivalence, and near equivalence between mathematical models and physical processes they represent. Helps establish rigorous mathematical basis for concepts related to structural identifiability and equivalence revealing fundamental requirements, tacit assumptions, and sources of error. "Structural identifiability," as used by workers in this field, loosely translates as meaning ability to specify unique mathematical model and set of model parameters that accurately predict behavior of corresponding physical system.

Nonlinear Parameter Identification of a Resonant Electrostatic MEMS Actuator

PubMed Central

Al-Ghamdi, Majed S.; Alneamy, Ayman M.; Park, Sangtak; Li, Beichen; Khater, Mahmoud E.; Abdel-Rahman, Eihab M.; Heppler, Glenn R.; Yavuz, Mustafa

2017-01-01

We experimentally investigate the primary superharmonic of order two and subharmonic of order one-half resonances of an electrostatic MEMS actuator under direct excitation. We identify the parameters of a one degree of freedom (1-DOF) generalized Duffing oscillator model representing it. The experiments were conducted in soft vacuum to reduce squeeze-film damping, and the actuator response was measured optically using a laser vibrometer. The predictions of the identified model were found to be in close agreement with the experimental results. We also identified the noise spectral density of process (actuation voltage) and measurement noise. PMID:28505097

Nonlinear Parameter Identification of a Resonant Electrostatic MEMS Actuator.

PubMed

Al-Ghamdi, Majed S; Alneamy, Ayman M; Park, Sangtak; Li, Beichen; Khater, Mahmoud E; Abdel-Rahman, Eihab M; Heppler, Glenn R; Yavuz, Mustafa

2017-05-13

We experimentally investigate the primary superharmonic of order two and subharmonic of order one-half resonances of an electrostatic MEMS actuator under direct excitation. We identify the parameters of a one degree of freedom (1-DOF) generalized Duffing oscillator model representing it. The experiments were conducted in soft vacuum to reduce squeeze-film damping, and the actuator response was measured optically using a laser vibrometer. The predictions of the identified model were found to be in close agreement with the experimental results. We also identified the noise spectral density of process (actuation voltage) and measurement noise.

Advanced multivariate data analysis to determine the root cause of trisulfide bond formation in a novel antibody-peptide fusion.

PubMed

Goldrick, Stephen; Holmes, William; Bond, Nicholas J; Lewis, Gareth; Kuiper, Marcel; Turner, Richard; Farid, Suzanne S

2017-10-01

Product quality heterogeneities, such as a trisulfide bond (TSB) formation, can be influenced by multiple interacting process parameters. Identifying their root cause is a major challenge in biopharmaceutical production. To address this issue, this paper describes the novel application of advanced multivariate data analysis (MVDA) techniques to identify the process parameters influencing TSB formation in a novel recombinant antibody-peptide fusion expressed in mammalian cell culture. The screening dataset was generated with a high-throughput (HT) micro-bioreactor system (Ambr TM 15) using a design of experiments (DoE) approach. The complex dataset was firstly analyzed through the development of a multiple linear regression model focusing solely on the DoE inputs and identified the temperature, pH and initial nutrient feed day as important process parameters influencing this quality attribute. To further scrutinize the dataset, a partial least squares model was subsequently built incorporating both on-line and off-line process parameters and enabled accurate predictions of the TSB concentration at harvest. Process parameters identified by the models to promote and suppress TSB formation were implemented on five 7 L bioreactors and the resultant TSB concentrations were comparable to the model predictions. This study demonstrates the ability of MVDA to enable predictions of the key performance drivers influencing TSB formation that are valid also upon scale-up. Biotechnol. Bioeng. 2017;114: 2222-2234. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.

An almost-parameter-free harmony search algorithm for groundwater pollution source identification.

PubMed

Jiang, Simin; Zhang, Yali; Wang, Pei; Zheng, Maohui

2013-01-01

The spatiotemporal characterization of unknown sources of groundwater pollution is frequently encountered in environmental problems. This study adopts a simulation-optimization approach that combines a contaminant transport simulation model with a heuristic harmony search algorithm to identify unknown pollution sources. In the proposed methodology, an almost-parameter-free harmony search algorithm is developed. The performance of this methodology is evaluated on an illustrative groundwater pollution source identification problem, and the identified results indicate that the proposed almost-parameter-free harmony search algorithm-based optimization model can give satisfactory estimations, even when the irregular geometry, erroneous monitoring data, and prior information shortage of potential locations are considered.

A Real-time Breakdown Prediction Method for Urban Expressway On-ramp Bottlenecks

NASA Astrophysics Data System (ADS)

Ye, Yingjun; Qin, Guoyang; Sun, Jian; Liu, Qiyuan

2018-01-01

Breakdown occurrence on expressway is considered to relate with various factors. Therefore, to investigate the association between breakdowns and these factors, a Bayesian network (BN) model is adopted in this paper. Based on the breakdown events identified at 10 urban expressways on-ramp in Shanghai, China, 23 parameters before breakdowns are extracted, including dynamic environment conditions aggregated with 5-minutes and static geometry features. Different time periods data are used to predict breakdown. Results indicate that the models using 5-10 min data prior to breakdown performs the best prediction, with the prediction accuracies higher than 73%. Moreover, one unified model for all bottlenecks is also built and shows reasonably good prediction performance with the classification accuracy of breakdowns about 75%, at best. Additionally, to simplify the model parameter input, the random forests (RF) model is adopted to identify the key variables. Modeling with the selected 7 parameters, the refined BN model can predict breakdown with adequate accuracy.

Simulation modeling for stratified breast cancer screening - a systematic review of cost and quality of life assumptions.

PubMed

Arnold, Matthias

2017-12-02

The economic evaluation of stratified breast cancer screening gains momentum, but produces also very diverse results. Systematic reviews so far focused on modeling techniques and epidemiologic assumptions. However, cost and utility parameters received only little attention. This systematic review assesses simulation models for stratified breast cancer screening based on their cost and utility parameters in each phase of breast cancer screening and care. A literature review was conducted to compare economic evaluations with simulation models of personalized breast cancer screening. Study quality was assessed using reporting guidelines. Cost and utility inputs were extracted, standardized and structured using a care delivery framework. Studies were then clustered according to their study aim and parameters were compared within the clusters. Eighteen studies were identified within three study clusters. Reporting quality was very diverse in all three clusters. Only two studies in cluster 1, four studies in cluster 2 and one study in cluster 3 scored high in the quality appraisal. In addition to the quality appraisal, this review assessed if the simulation models were consistent in integrating all relevant phases of care, if utility parameters were consistent and methodological sound and if cost were compatible and consistent in the actual parameters used for screening, diagnostic work up and treatment. Of 18 studies, only three studies did not show signs of potential bias. This systematic review shows that a closer look into the cost and utility parameter can help to identify potential bias. Future simulation models should focus on integrating all relevant phases of care, using methodologically sound utility parameters and avoiding inconsistent cost parameters.

GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models.

PubMed

Ligon, Thomas S; Fröhlich, Fabian; Chis, Oana T; Banga, Julio R; Balsa-Canto, Eva; Hasenauer, Jan

2018-04-15

Mathematical modeling using ordinary differential equations is used in systems biology to improve the understanding of dynamic biological processes. The parameters of ordinary differential equation models are usually estimated from experimental data. To analyze a priori the uniqueness of the solution of the estimation problem, structural identifiability analysis methods have been developed. We introduce GenSSI 2.0, an advancement of the software toolbox GenSSI (Generating Series for testing Structural Identifiability). GenSSI 2.0 is the first toolbox for structural identifiability analysis to implement Systems Biology Markup Language import, state/parameter transformations and multi-experiment structural identifiability analysis. In addition, GenSSI 2.0 supports a range of MATLAB versions and is computationally more efficient than its previous version, enabling the analysis of more complex models. GenSSI 2.0 is an open-source MATLAB toolbox and available at https://github.com/genssi-developer/GenSSI. thomas.ligon@physik.uni-muenchen.de or jan.hasenauer@helmholtz-muenchen.de. Supplementary data are available at Bioinformatics online.

Robust global identifiability theory using potentials--Application to compartmental models.

PubMed

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.

Suspension parameter estimation in the frequency domain using a matrix inversion approach

NASA Astrophysics Data System (ADS)

Thite, A. N.; Banvidi, S.; Ibicek, T.; Bennett, L.

2011-12-01

The dynamic lumped parameter models used to optimise the ride and handling of a vehicle require base values of the suspension parameters. These parameters are generally experimentally identified. The accuracy of identified parameters can depend on the measurement noise and the validity of the model used. The existing publications on suspension parameter identification are generally based on the time domain and use a limited degree of freedom. Further, the data used are either from a simulated 'experiment' or from a laboratory test on an idealised quarter or a half-car model. In this paper, a method is developed in the frequency domain which effectively accounts for the measurement noise. Additional dynamic constraining equations are incorporated and the proposed formulation results in a matrix inversion approach. The nonlinearities in damping are estimated, however, using a time-domain approach. Full-scale 4-post rig test data of a vehicle are used. The variations in the results are discussed using the modal resonant behaviour. Further, a method is implemented to show how the results can be improved when the matrix inverted is ill-conditioned. The case study shows a good agreement between the estimates based on the proposed frequency-domain approach and measurable physical parameters.

Estimating cellular parameters through optimization procedures: elementary principles and applications.

PubMed

Kimura, Akatsuki; Celani, Antonio; Nagao, Hiromichi; Stasevich, Timothy; Nakamura, Kazuyuki

2015-01-01

Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE) in a prediction or to maximize likelihood. A (local) maximum of likelihood or (local) minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge of parameter optimization, cell and developmental biologists can develop realistic models that reproduce their observations and thus, obtain mechanistic insights into phenomena of interest.

Unsteady hovering wake parameters identified from dynamic model tests, part 1

NASA Technical Reports Server (NTRS)

Hohenemser, K. H.; Crews, S. T.

1977-01-01

The development of a 4-bladed model rotor is reported that can be excited with a simple eccentric mechanism in progressing and regressing modes with either harmonic or transient inputs. Parameter identification methods were applied to the problem of extracting parameters for linear perturbation models, including rotor dynamic inflow effects, from the measured blade flapping responses to transient pitch stirring excitations. These perturbation models were then used to predict blade flapping response to other pitch stirring transient inputs, and rotor wake and blade flapping responses to harmonic inputs. The viability and utility of using parameter identification methods for extracting the perturbation models from transients are demonstrated through these combined analytical and experimental studies.

Wayside Bearing Fault Diagnosis Based on a Data-Driven Doppler Effect Eliminator and Transient Model Analysis

PubMed Central

Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

2014-01-01

A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects. PMID:24803197

Three novel approaches to structural identifiability analysis in mixed-effects models.

PubMed

Janzén, David L I; Jirstrand, Mats; Chappell, Michael J; Evans, Neil D

2016-05-06

Structural identifiability is a concept that considers whether the structure of a model together with a set of input-output relations uniquely determines the model parameters. In the mathematical modelling of biological systems, structural identifiability is an important concept since biological interpretations are typically made from the parameter estimates. For a system defined by ordinary differential equations, several methods have been developed to analyse whether the model is structurally identifiable or otherwise. Another well-used modelling framework, which is particularly useful when the experimental data are sparsely sampled and the population variance is of interest, is mixed-effects modelling. However, established identifiability analysis techniques for ordinary differential equations are not directly applicable to such models. In this paper, we present and apply three different methods that can be used to study structural identifiability in mixed-effects models. The first method, called the repeated measurement approach, is based on applying a set of previously established statistical theorems. The second method, called the augmented system approach, is based on augmenting the mixed-effects model to an extended state-space form. The third method, called the Laplace transform mixed-effects extension, is based on considering the moment invariants of the systems transfer function as functions of random variables. To illustrate, compare and contrast the application of the three methods, they are applied to a set of mixed-effects models. Three structural identifiability analysis methods applicable to mixed-effects models have been presented in this paper. As method development of structural identifiability techniques for mixed-effects models has been given very little attention, despite mixed-effects models being widely used, the methods presented in this paper provides a way of handling structural identifiability in mixed-effects models previously not possible. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Observation model and parameter partials for the JPL geodetic (GPS) modeling software 'GPSOMC'

NASA Technical Reports Server (NTRS)

Sovers, O. J.

1990-01-01

The physical models employed in GPSOMC, the modeling module of the GIPSY software system developed at JPL for analysis of geodetic Global Positioning Satellite (GPS) measurements are described. Details of the various contributions to range and phase observables are given, as well as the partial derivatives of the observed quantities with respect to model parameters. A glossary of parameters is provided to enable persons doing data analysis to identify quantities with their counterparts in the computer programs. The present version is the second revision of the original document which it supersedes. The modeling is expanded to provide the option of using Cartesian station coordinates; parameters for the time rates of change of universal time and polar motion are also introduced.

Constraining ecosystem model with adaptive Metropolis algorithm using boreal forest site eddy covariance measurements

NASA Astrophysics Data System (ADS)

Mäkelä, Jarmo; Susiluoto, Jouni; Markkanen, Tiina; Aurela, Mika; Järvinen, Heikki; Mammarella, Ivan; Hagemann, Stefan; Aalto, Tuula

2016-12-01

We examined parameter optimisation in the JSBACH (Kaminski et al., 2013; Knorr and Kattge, 2005; Reick et al., 2013) ecosystem model, applied to two boreal forest sites (Hyytiälä and Sodankylä) in Finland. We identified and tested key parameters in soil hydrology and forest water and carbon-exchange-related formulations, and optimised them using the adaptive Metropolis (AM) algorithm for Hyytiälä with a 5-year calibration period (2000-2004) followed by a 4-year validation period (2005-2008). Sodankylä acted as an independent validation site, where optimisations were not made. The tuning provided estimates for full distribution of possible parameters, along with information about correlation, sensitivity and identifiability. Some parameters were correlated with each other due to a phenomenological connection between carbon uptake and water stress or other connections due to the set-up of the model formulations. The latter holds especially for vegetation phenology parameters. The least identifiable parameters include phenology parameters, parameters connecting relative humidity and soil dryness, and the field capacity of the skin reservoir. These soil parameters were masked by the large contribution from vegetation transpiration. In addition to leaf area index and the maximum carboxylation rate, the most effective parameters adjusting the gross primary production (GPP) and evapotranspiration (ET) fluxes in seasonal tuning were related to soil wilting point, drainage and moisture stress imposed on vegetation. For daily and half-hourly tunings the most important parameters were the ratio of leaf internal CO2 concentration to external CO2 and the parameter connecting relative humidity and soil dryness. Effectively the seasonal tuning transferred water from soil moisture into ET, and daily and half-hourly tunings reversed this process. The seasonal tuning improved the month-to-month development of GPP and ET, and produced the most stable estimates of water use efficiency. When compared to the seasonal tuning, the daily tuning is worse on the seasonal scale. However, daily parametrisation reproduced the observations for average diurnal cycle best, except for the GPP for Sodankylä validation period, where half-hourly tuned parameters were better. In general, the daily tuning provided the largest reduction in model-data mismatch. The models response to drought was unaffected by our parametrisations and further studies are needed into enhancing the dry response in JSBACH.

Estimation and identification study for flexible vehicles

NASA Technical Reports Server (NTRS)

Jazwinski, A. H.; Englar, T. S., Jr.

1973-01-01

Techniques are studied for the estimation of rigid body and bending states and the identification of model parameters associated with the single-axis attitude dynamics of a flexible vehicle. This problem is highly nonlinear but completely observable provided sufficient attitude and attitude rate data is available and provided all system bending modes are excited in the observation interval. A sequential estimator tracks the system states in the presence of model parameter errors. A batch estimator identifies all model parameters with high accuracy.

Parameter identification of civil engineering structures

NASA Technical Reports Server (NTRS)

Juang, J. N.; Sun, C. T.

1980-01-01

This paper concerns the development of an identification method required in determining structural parameter variations for systems subjected to an extended exposure to the environment. The concept of structural identifiability of a large scale structural system in the absence of damping is presented. Three criteria are established indicating that a large number of system parameters (the coefficient parameters of the differential equations) can be identified by a few actuators and sensors. An eight-bay-fifteen-story frame structure is used as example. A simple model is employed for analyzing the dynamic response of the frame structure.

Additional Research Needs to Support the GENII Biosphere Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Napier, Bruce A.; Snyder, Sandra F.; Arimescu, Carmen

In the course of evaluating the current parameter needs for the GENII Version 2 code (Snyder et al. 2013), areas of possible improvement for both the data and the underlying models have been identified. As the data review was implemented, PNNL staff identified areas where the models can be improved both to accommodate the locally significant pathways identified and also to incorporate newer models. The areas are general data needs for the existing models and improved formulations for the pathway models.

Volume effects of late term normal tissue toxicity in prostate cancer radiotherapy

NASA Astrophysics Data System (ADS)

Bonta, Dacian Viorel

Modeling of volume effects for treatment toxicity is paramount for optimization of radiation therapy. This thesis proposes a new model for calculating volume effects in gastro-intestinal and genito-urinary normal tissue complication probability (NTCP) following radiation therapy for prostate carcinoma. The radiobiological and the pathological basis for this model and its relationship to other models are detailed. A review of the radiobiological experiments and published clinical data identified salient features and specific properties a biologically adequate model has to conform to. The new model was fit to a set of actual clinical data. In order to verify the goodness of fit, two established NTCP models and a non-NTCP measure for complication risk were fitted to the same clinical data. The method of fit for the model parameters was maximum likelihood estimation. Within the framework of the maximum likelihood approach I estimated the parameter uncertainties for each complication prediction model. The quality-of-fit was determined using the Aikaike Information Criterion. Based on the model that provided the best fit, I identified the volume effects for both types of toxicities. Computer-based bootstrap resampling of the original dataset was used to estimate the bias and variance for the fitted parameter values. Computer simulation was also used to estimate the population size that generates a specific uncertainty level (3%) in the value of predicted complication probability. The same method was used to estimate the size of the patient population needed for accurate choice of the model underlying the NTCP. The results indicate that, depending on the number of parameters of a specific NTCP model, 100 (for two parameter models) and 500 patients (for three parameter models) are needed for accurate parameter fit. Correlation of complication occurrence in patients was also investigated. The results suggest that complication outcomes are correlated in a patient, although the correlation coefficient is rather small.

A modified Leslie-Gower predator-prey interaction model and parameter identifiability

NASA Astrophysics Data System (ADS)

Tripathi, Jai Prakash; Meghwani, Suraj S.; Thakur, Manoj; Abbas, Syed

2018-01-01

In this work, bifurcation and a systematic approach for estimation of identifiable parameters of a modified Leslie-Gower predator-prey system with Crowley-Martin functional response and prey refuge is discussed. Global asymptotic stability is discussed by applying fluctuation lemma. The system undergoes into Hopf bifurcation with respect to parameters intrinsic growth rate of predators (s) and prey reserve (m). The stability of Hopf bifurcation is also discussed by calculating Lyapunov number. The sensitivity analysis of the considered model system with respect to all variables is performed which also supports our theoretical study. To estimate the unknown parameter from the data, an optimization procedure (pseudo-random search algorithm) is adopted. System responses and phase plots for estimated parameters are also compared with true noise free data. It is found that the system dynamics with true set of parametric values is similar to the estimated parametric values. Numerical simulations are presented to substantiate the analytical findings.

Statistical properties of a filtered Poisson process with additive random noise: distributions, correlations and moment estimation

NASA Astrophysics Data System (ADS)

Theodorsen, A.; E Garcia, O.; Rypdal, M.

2017-05-01

Filtered Poisson processes are often used as reference models for intermittent fluctuations in physical systems. Such a process is here extended by adding a noise term, either as a purely additive term to the process or as a dynamical term in a stochastic differential equation. The lowest order moments, probability density function, auto-correlation function and power spectral density are derived and used to identify and compare the effects of the two different noise terms. Monte-Carlo studies of synthetic time series are used to investigate the accuracy of model parameter estimation and to identify methods for distinguishing the noise types. It is shown that the probability density function and the three lowest order moments provide accurate estimations of the model parameters, but are unable to separate the noise types. The auto-correlation function and the power spectral density also provide methods for estimating the model parameters, as well as being capable of identifying the noise type. The number of times the signal crosses a prescribed threshold level in the positive direction also promises to be able to differentiate the noise type.

Subjective Prior Distributions for Modeling Longitudinal Continuous Outcomes with Non-Ignorable Dropout

PubMed Central

Paddock, Susan M.; Ebener, Patricia

2010-01-01

Substance abuse treatment research is complicated by the pervasive problem of non-ignorable missing data – i.e., the occurrence of the missing data is related to the unobserved outcomes. Missing data frequently arise due to early client departure from treatment. Pattern-mixture models (PMMs) are often employed in such situations to jointly model the outcome and the missing data mechanism. PMMs require non-testable assumptions to identify model parameters. Several approaches to parameter identification have therefore been explored for longitudinal modeling of continuous outcomes, and informative priors have been developed in other contexts. In this paper, we describe an expert interview conducted with five substance abuse treatment clinical experts who have familiarity with the Therapeutic Community modality of substance abuse treatment and with treatment process scores collected using the Dimensions of Change Instrument. The goal of the interviews was to obtain expert opinion about the rate of change in continuous client-level treatment process scores for clients who leave before completing two assessments and whose rate of change (slope) in treatment process scores is unidentified by the data. We find that the experts’ opinions differed dramatically from widely-utilized assumptions used to identify parameters in the PMM. Further, subjective prior assessment allows one to properly address the uncertainty inherent in the subjective decisions required to identify parameters in the PMM and to measure their effect on conclusions drawn from the analysis. PMID:19012279

Significance of settling model structures and parameter subsets in modelling WWTPs under wet-weather flow and filamentous bulking conditions.

PubMed

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. Copyright © 2014 Elsevier Ltd. All rights reserved.

Population models for passerine birds: structure, parameterization, and analysis

USGS Publications Warehouse

Noon, B.R.; Sauer, J.R.; McCullough, D.R.; Barrett, R.H.

1992-01-01

Population models have great potential as management tools, as they use infonnation about the life history of a species to summarize estimates of fecundity and survival into a description of population change. Models provide a framework for projecting future populations, determining the effects of management decisions on future population dynamics, evaluating extinction probabilities, and addressing a variety of questions of ecological and evolutionary interest. Even when insufficient information exists to allow complete identification of the model, the modelling procedure is useful because it forces the investigator to consider the life history of the species when determining what parameters should be estimated from field studies and provides a context for evaluating the relative importance of demographic parameters. Models have been little used in the study of the population dynamics of passerine birds because of: (1) widespread misunderstandings of the model structures and parameterizations, (2) a lack of knowledge of life histories of many species, (3) difficulties in obtaining statistically reliable estimates of demographic parameters for most passerine species, and (4) confusion about functional relationships among demographic parameters. As a result, studies of passerine demography are often designed inappropriately and fail to provide essential data. We review appropriate models for passerine bird populations and illustrate their possible uses in evaluating the effects of management or other environmental influences on population dynamics. We identify environmental influences on population dynamics. We identify parameters that must be estimated from field data, briefly review existing statistical methods for obtaining valid estimates, and evaluate the present status of knowledge of these parameters.

Parameter Estimation and Sensitivity Analysis of an Urban Surface Energy Balance Parameterization at a Tropical Suburban Site

NASA Astrophysics Data System (ADS)

Harshan, S.; Roth, M.; Velasco, E.

2014-12-01

Forecasting of the urban weather and climate is of great importance as our cities become more populated and considering the combined effects of global warming and local land use changes which make urban inhabitants more vulnerable to e.g. heat waves and flash floods. In meso/global scale models, urban parameterization schemes are used to represent the urban effects. However, these schemes require a large set of input parameters related to urban morphological and thermal properties. Obtaining all these parameters through direct measurements are usually not feasible. A number of studies have reported on parameter estimation and sensitivity analysis to adjust and determine the most influential parameters for land surface schemes in non-urban areas. Similar work for urban areas is scarce, in particular studies on urban parameterization schemes in tropical cities have so far not been reported. In order to address above issues, the town energy balance (TEB) urban parameterization scheme (part of the SURFEX land surface modeling system) was subjected to a sensitivity and optimization/parameter estimation experiment at a suburban site in, tropical Singapore. The sensitivity analysis was carried out as a screening test to identify the most sensitive or influential parameters. Thereafter, an optimization/parameter estimation experiment was performed to calibrate the input parameter. The sensitivity experiment was based on the "improved Sobol's global variance decomposition method" . The analysis showed that parameters related to road, roof and soil moisture have significant influence on the performance of the model. The optimization/parameter estimation experiment was performed using the AMALGM (a multi-algorithm genetically adaptive multi-objective method) evolutionary algorithm. The experiment showed a remarkable improvement compared to the simulations using the default parameter set. The calibrated parameters from this optimization experiment can be used for further model validation studies to identify inherent deficiencies in model physics.

Estimation of Handling Qualities Parameters of the Tu-144 Supersonic Transport Aircraft from Flight Test Data

NASA Technical Reports Server (NTRS)

Curry, Timothy J.; Batterson, James G. (Technical Monitor)

2000-01-01

Low order equivalent system (LOES) models for the Tu-144 supersonic transport aircraft were identified from flight test data. The mathematical models were given in terms of transfer functions with a time delay by the military standard MIL-STD-1797A, "Flying Qualities of Piloted Aircraft," and the handling qualities were predicted from the estimated transfer function coefficients. The coefficients and the time delay in the transfer functions were estimated using a nonlinear equation error formulation in the frequency domain. Flight test data from pitch, roll, and yaw frequency sweeps at various flight conditions were used for parameter estimation. Flight test results are presented in terms of the estimated parameter values, their standard errors, and output fits in the time domain. Data from doublet maneuvers at the same flight conditions were used to assess the predictive capabilities of the identified models. The identified transfer function models fit the measured data well and demonstrated good prediction capabilities. The Tu-144 was predicted to be between level 2 and 3 for all longitudinal maneuvers and level I for all lateral maneuvers. High estimates of the equivalent time delay in the transfer function model caused the poor longitudinal rating.

Are quantitative sensitivity analysis methods always reliable?

NASA Astrophysics Data System (ADS)

Huang, X.

2016-12-01

Physical parameterizations developed to represent subgrid-scale physical processes include various uncertain parameters, leading to large uncertainties in today's Earth System Models (ESMs). Sensitivity Analysis (SA) is an efficient approach to quantitatively determine how the uncertainty of the evaluation metric can be apportioned to each parameter. Also, SA can identify the most influential parameters, as a result to reduce the high dimensional parametric space. In previous studies, some SA-based approaches, such as Sobol' and Fourier amplitude sensitivity testing (FAST), divide the parameters into sensitive and insensitive groups respectively. The first one is reserved but the other is eliminated for certain scientific study. However, these approaches ignore the disappearance of the interactive effects between the reserved parameters and the eliminated ones, which are also part of the total sensitive indices. Therefore, the wrong sensitive parameters might be identified by these traditional SA approaches and tools. In this study, we propose a dynamic global sensitivity analysis method (DGSAM), which iteratively removes the least important parameter until there are only two parameters left. We use the CLM-CASA, a global terrestrial model, as an example to verify our findings with different sample sizes ranging from 7000 to 280000. The result shows DGSAM has abilities to identify more influential parameters, which is confirmed by parameter calibration experiments using four popular optimization methods. For example, optimization using Top3 parameters filtered by DGSAM could achieve substantial improvement against Sobol' by 10%. Furthermore, the current computational cost for calibration has been reduced to 1/6 of the original one. In future, it is necessary to explore alternative SA methods emphasizing parameter interactions.

Mixture Rasch model for guessing group identification

NASA Astrophysics Data System (ADS)

Siow, Hoo Leong; Mahdi, Rasidah; Siew, Eng Ling

2013-04-01

Several alternative dichotomous Item Response Theory (IRT) models have been introduced to account for guessing effect in multiple-choice assessment. The guessing effect in these models has been considered to be itemrelated. In the most classic case, pseudo-guessing in the three-parameter logistic IRT model is modeled to be the same for all the subjects but may vary across items. This is not realistic because subjects can guess worse or better than the pseudo-guessing. Derivation from the three-parameter logistic IRT model improves the situation by incorporating ability in guessing. However, it does not model non-monotone function. This paper proposes to study guessing from a subject-related aspect which is guessing test-taking behavior. Mixture Rasch model is employed to detect latent groups. A hybrid of mixture Rasch and 3-parameter logistic IRT model is proposed to model the behavior based guessing from the subjects' ways of responding the items. The subjects are assumed to simply choose a response at random. An information criterion is proposed to identify the behavior based guessing group. Results show that the proposed model selection criterion provides a promising method to identify the guessing group modeled by the hybrid model.

Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters.

PubMed

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.

The structure of binding curves and practical identifiability of equilibrium ligand-binding parameters

PubMed Central

Middendorf, Thomas R.

2017-01-01

A critical but often overlooked question in the study of ligands binding to proteins is whether the parameters obtained from analyzing binding data are practically identifiable (PI), i.e., whether the estimates obtained from fitting models to noisy data are accurate and unique. Here we report a general approach to assess and understand binding parameter identifiability, which provides a toolkit to assist experimentalists in the design of binding studies and in the analysis of binding data. The partial fraction (PF) expansion technique is used to decompose binding curves for proteins with n ligand-binding sites exactly and uniquely into n components, each of which has the form of a one-site binding curve. The association constants of the PF component curves, being the roots of an n-th order polynomial, may be real or complex. We demonstrate a fundamental connection between binding parameter identifiability and the nature of these one-site association constants: all binding parameters are identifiable if the constants are all real and distinct; otherwise, at least some of the parameters are not identifiable. The theory is used to construct identifiability maps from which the practical identifiability of binding parameters for any two-, three-, or four-site binding curve can be assessed. Instructions for extending the method to generate identifiability maps for proteins with more than four binding sites are also given. Further analysis of the identifiability maps leads to the simple rule that the maximum number of structurally identifiable binding parameters (shown in the previous paper to be equal to n) will also be PI only if the binding curve line shape contains n resolved components. PMID:27993951

A lateral dynamics of a wheelchair: identification and analysis of tire parameters.

PubMed

Silva, L C A; Corrêa, F C; Eckert, J J; Santiciolli, F M; Dedini, F G

2017-02-01

In vehicle dynamics studies, the tire behaviour plays an important role in planar motion of the vehicle. Therefore, a correct representation of tire is a necessity. This paper describes a mathematical model for wheelchair tire based on the Magic Formula model. This model is widely used to represent forces and moments between the tire and the ground; however some experimental parameters must be determined. The purpose of this work is to identify the tire parameters for the wheelchair tire model, implementing them in a dynamic model of the wheelchair. For this, we developed an experimental test rig to measure the tires parameters for the lateral dynamics of a wheelchair. This dynamic model was made using a multi-body software and the wheelchair behaviour was analysed and discussed according to the tire parameters. The result of this work is one step further towards the understanding of wheelchair dynamics.

Quantitative body DW-MRI biomarkers uncertainty estimation using unscented wild-bootstrap.

PubMed

Freiman, M; Voss, S D; Mulkern, R V; Perez-Rossello, J M; Warfield, S K

2011-01-01

We present a new method for the uncertainty estimation of diffusion parameters for quantitative body DW-MRI assessment. Diffusion parameters uncertainty estimation from DW-MRI is necessary for clinical applications that use these parameters to assess pathology. However, uncertainty estimation using traditional techniques requires repeated acquisitions, which is undesirable in routine clinical use. Model-based bootstrap techniques, for example, assume an underlying linear model for residuals rescaling and cannot be utilized directly for body diffusion parameters uncertainty estimation due to the non-linearity of the body diffusion model. To offset this limitation, our method uses the Unscented transform to compute the residuals rescaling parameters from the non-linear body diffusion model, and then applies the wild-bootstrap method to infer the body diffusion parameters uncertainty. Validation through phantom and human subject experiments shows that our method identify the regions with higher uncertainty in body DWI-MRI model parameters correctly with realtive error of -36% in the uncertainty values.

Identification of internal properties of fibres and micro-swimmers

NASA Astrophysics Data System (ADS)

Plouraboué, Franck; Thiam, E. Ibrahima; Delmotte, Blaise; Climent, Eric

2017-01-01

In this paper, we address the identifiability of constitutive parameters of passive or active micro-swimmers. We first present a general framework for describing fibres or micro-swimmers using a bead-model description. Using a kinematic constraint formulation to describe fibres, flagellum or cilia, we find explicit linear relationship between elastic constitutive parameters and generalized velocities from computing contact forces. This linear formulation then permits one to address explicitly identifiability conditions and solve for parameter identification. We show that both active forcing and passive parameters are both identifiable independently but not simultaneously. We also provide unbiased estimators for generalized elastic parameters in the presence of Langevin-like forcing with Gaussian noise using a Bayesian approach. These theoretical results are illustrated in various configurations showing the efficiency of the proposed approach for direct parameter identification. The convergence of the proposed estimators is successfully tested numerically.

Matching experimental and three dimensional numerical models for structural vibration problems with uncertainties

NASA Astrophysics Data System (ADS)

Langer, P.; Sepahvand, K.; Guist, C.; Bär, J.; Peplow, A.; Marburg, S.

2018-03-01

The simulation model which examines the dynamic behavior of real structures needs to address the impact of uncertainty in both geometry and material parameters. This article investigates three-dimensional finite element models for structural dynamics problems with respect to both model and parameter uncertainties. The parameter uncertainties are determined via laboratory measurements on several beam-like samples. The parameters are then considered as random variables to the finite element model for exploring the uncertainty effects on the quality of the model outputs, i.e. natural frequencies. The accuracy of the output predictions from the model is compared with the experimental results. To this end, the non-contact experimental modal analysis is conducted to identify the natural frequency of the samples. The results show a good agreement compared with experimental data. Furthermore, it is demonstrated that geometrical uncertainties have more influence on the natural frequencies compared to material parameters and material uncertainties are about two times higher than geometrical uncertainties. This gives valuable insights for improving the finite element model due to various parameter ranges required in a modeling process involving uncertainty.

Study of the method of water-injected meat identifying based on low-field nuclear magnetic resonance

NASA Astrophysics Data System (ADS)

Xu, Jianmei; Lin, Qing; Yang, Fang; Zheng, Zheng; Ai, Zhujun

2018-01-01

The aim of this study to apply low-field nuclear magnetic resonance technique was to study regular variation of the transverse relaxation spectral parameters of water-injected meat with the proportion of water injection. Based on this, the method of one-way ANOVA and discriminant analysis was used to analyse the differences between these parameters in the capacity of distinguishing water-injected proportion, and established a model for identifying water-injected meat. The results show that, except for T 21b, T 22e and T 23b, the other parameters of the T 2 relaxation spectrum changed regularly with the change of water-injected proportion. The ability of different parameters to distinguish water-injected proportion was different. Based on S, P 22 and T 23m as the prediction variable, the Fisher model and the Bayes model were established by discriminant analysis method, qualitative and quantitative classification of water-injected meat can be realized. The rate of correct discrimination of distinguished validation and cross validation were 88%, the model was stable.

Detection of Rossby Waves in Multi-Parameters in Multi-Mission Satellite Observations and HYCOM Simulations in the Indian Ocean

NASA Technical Reports Server (NTRS)

Subrahmanyam, Bulusu; Heffner, David M.; Cromwell, David; Shriver, Jay F.

2009-01-01

Rossby waves are difficult to detect with in situ methods. However, as we show in this paper, they can be clearly identified in multi-parameters in multi-mission satellite observations of sea surface height (SSH), sea surface temperature (SST) and ocean color observations of chlorophyll-a (chl-a), as well as 1/12-deg global HYbrid Coordinate Ocean Model (HYCOM) simulations of SSH, SST and sea surface salinity (SSS) in the Indian Ocean. While the surface structure of Rossby waves can be elucidated from comparisons of the signal in different sea surface parameters, models are needed to gain direct information about how these waves affect the ocean at depth. The first three baroclinic modes of the Rossby waves are inferred from the Fast Fourier Transform (FFT), and two-dimensional Radon Transform (2D RT). At many latitudes the first and second baroclinic mode Rossby wave phase speeds from satellite observations and model parameters are identified.

Sensitivity-based virtual fields for the non-linear virtual fields method

NASA Astrophysics Data System (ADS)

Marek, Aleksander; Davis, Frances M.; Pierron, Fabrice

2017-09-01

The virtual fields method is an approach to inversely identify material parameters using full-field deformation data. In this manuscript, a new set of automatically-defined virtual fields for non-linear constitutive models has been proposed. These new sensitivity-based virtual fields reduce the influence of noise on the parameter identification. The sensitivity-based virtual fields were applied to a numerical example involving small strain plasticity; however, the general formulation derived for these virtual fields is applicable to any non-linear constitutive model. To quantify the improvement offered by these new virtual fields, they were compared with stiffness-based and manually defined virtual fields. The proposed sensitivity-based virtual fields were consistently able to identify plastic model parameters and outperform the stiffness-based and manually defined virtual fields when the data was corrupted by noise.

Robust design of configurations and parameters of adaptable products

NASA Astrophysics Data System (ADS)

Zhang, Jian; Chen, Yongliang; Xue, Deyi; Gu, Peihua

2014-03-01

An adaptable product can satisfy different customer requirements by changing its configuration and parameter values during the operation stage. Design of adaptable products aims at reducing the environment impact through replacement of multiple different products with single adaptable ones. Due to the complex architecture, multiple functional requirements, and changes of product configurations and parameter values in operation, impact of uncertainties to the functional performance measures needs to be considered in design of adaptable products. In this paper, a robust design approach is introduced to identify the optimal design configuration and parameters of an adaptable product whose functional performance measures are the least sensitive to uncertainties. An adaptable product in this paper is modeled by both configurations and parameters. At the configuration level, methods to model different product configuration candidates in design and different product configuration states in operation to satisfy design requirements are introduced. At the parameter level, four types of product/operating parameters and relations among these parameters are discussed. A two-level optimization approach is developed to identify the optimal design configuration and its parameter values of the adaptable product. A case study is implemented to illustrate the effectiveness of the newly developed robust adaptable design method.

Systems biology as a conceptual framework for research in family medicine; use in predicting response to influenza vaccination.

PubMed

Majnarić-Trtica, Ljiljana; Vitale, Branko

2011-10-01

To introduce systems biology as a conceptual framework for research in family medicine, based on empirical data from a case study on the prediction of influenza vaccination outcomes. This concept is primarily oriented towards planning preventive interventions and includes systematic data recording, a multi-step research protocol and predictive modelling. Factors known to affect responses to influenza vaccination include older age, past exposure to influenza viruses, and chronic diseases; however, constructing useful prediction models remains a challenge, because of the need to identify health parameters that are appropriate for general use in modelling patients' responses. The sample consisted of 93 patients aged 50-89 years (median 69), with multiple medical conditions, who were vaccinated against influenza. Literature searches identified potentially predictive health-related parameters, including age, gender, diagnoses of the main chronic ageing diseases, anthropometric measures, and haematological and biochemical tests. By applying data mining algorithms, patterns were identified in the data set. Candidate health parameters, selected in this way, were then combined with information on past influenza virus exposure to build the prediction model using logistic regression. A highly significant prediction model was obtained, indicating that by using a systems biology approach it is possible to answer unresolved complex medical uncertainties. Adopting this systems biology approach can be expected to be useful in identifying the most appropriate target groups for other preventive programmes.

Reliability of a new biokinetic model of zirconium in internal dosimetry: part II, parameter sensitivity analysis.

PubMed

Li, Wei Bo; Greiter, Matthias; Oeh, Uwe; Hoeschen, Christoph

2011-12-01

The reliability of biokinetic models is essential for the assessment of internal doses and a radiation risk analysis for the public and occupational workers exposed to radionuclides. In the present study, a method for assessing the reliability of biokinetic models by means of uncertainty and sensitivity analysis was developed. In the first part of the paper, the parameter uncertainty was analyzed for two biokinetic models of zirconium (Zr); one was reported by the International Commission on Radiological Protection (ICRP), and one was developed at the Helmholtz Zentrum München-German Research Center for Environmental Health (HMGU). In the second part of the paper, the parameter uncertainties and distributions of the Zr biokinetic models evaluated in Part I are used as the model inputs for identifying the most influential parameters in the models. Furthermore, the most influential model parameter on the integral of the radioactivity of Zr over 50 y in source organs after ingestion was identified. The results of the systemic HMGU Zr model showed that over the first 10 d, the parameters of transfer rates between blood and other soft tissues have the largest influence on the content of Zr in the blood and the daily urinary excretion; however, after day 1,000, the transfer rate from bone to blood becomes dominant. For the retention in bone, the transfer rate from blood to bone surfaces has the most influence out to the endpoint of the simulation; the transfer rate from blood to the upper larger intestine contributes a lot in the later days; i.e., after day 300. The alimentary tract absorption factor (fA) influences mostly the integral of radioactivity of Zr in most source organs after ingestion.

Estimating procedure times for surgeries by determining location parameters for the lognormal model.

PubMed

Spangler, William E; Strum, David P; Vargas, Luis G; May, Jerrold H

2004-05-01

We present an empirical study of methods for estimating the location parameter of the lognormal distribution. Our results identify the best order statistic to use, and indicate that using the best order statistic instead of the median may lead to less frequent incorrect rejection of the lognormal model, more accurate critical value estimates, and higher goodness-of-fit. Using simulation data, we constructed and compared two models for identifying the best order statistic, one based on conventional nonlinear regression and the other using a data mining/machine learning technique. Better surgical procedure time estimates may lead to improved surgical operations.

A simplified fractional order impedance model and parameter identification method for lithium-ion batteries

PubMed Central

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

Review: To be or not to be an identifiable model. Is this a relevant question in animal science modelling?

PubMed

Muñoz-Tamayo, R; Puillet, L; Daniel, J B; Sauvant, D; Martin, O; Taghipoor, M; Blavy, P

2018-04-01

What is a good (useful) mathematical model in animal science? For models constructed for prediction purposes, the question of model adequacy (usefulness) has been traditionally tackled by statistical analysis applied to observed experimental data relative to model-predicted variables. However, little attention has been paid to analytic tools that exploit the mathematical properties of the model equations. For example, in the context of model calibration, before attempting a numerical estimation of the model parameters, we might want to know if we have any chance of success in estimating a unique best value of the model parameters from available measurements. This question of uniqueness is referred to as structural identifiability; a mathematical property that is defined on the sole basis of the model structure within a hypothetical ideal experiment determined by a setting of model inputs (stimuli) and observable variables (measurements). Structural identifiability analysis applied to dynamic models described by ordinary differential equations (ODEs) is a common practice in control engineering and system identification. This analysis demands mathematical technicalities that are beyond the academic background of animal science, which might explain the lack of pervasiveness of identifiability analysis in animal science modelling. To fill this gap, in this paper we address the analysis of structural identifiability from a practitioner perspective by capitalizing on the use of dedicated software tools. Our objectives are (i) to provide a comprehensive explanation of the structural identifiability notion for the community of animal science modelling, (ii) to assess the relevance of identifiability analysis in animal science modelling and (iii) to motivate the community to use identifiability analysis in the modelling practice (when the identifiability question is relevant). We focus our study on ODE models. By using illustrative examples that include published mathematical models describing lactation in cattle, we show how structural identifiability analysis can contribute to advancing mathematical modelling in animal science towards the production of useful models and, moreover, highly informative experiments via optimal experiment design. Rather than attempting to impose a systematic identifiability analysis to the modelling community during model developments, we wish to open a window towards the discovery of a powerful tool for model construction and experiment design.

A Simulation Model for Studying Effects of Pollution and Freshwater Inflow on Secondary Productivity in an Ecosystem. Ph.D. Thesis - North Carolina State Univ.

NASA Technical Reports Server (NTRS)

Johnson, R. W.

1974-01-01

A mathematical model of an ecosystem is developed. Secondary productivity is evaluated in terms of man related and controllable factors. Information from an existing physical parameters model is used as well as pertinent biological measurements. Predictive information of value to estuarine management is presented. Biological, chemical, and physical parameters measured in order to develop models of ecosystems are identified.

Probabilistic evaluation of damage potential in earthquake-induced liquefaction in a 3-D soil deposit

NASA Astrophysics Data System (ADS)

Halder, A.; Miller, F. J.

1982-03-01

A probabilistic model to evaluate the risk of liquefaction at a site and to limit or eliminate damage during earthquake induced liquefaction is proposed. The model is extended to consider three dimensional nonhomogeneous soil properties. The parameters relevant to the liquefaction phenomenon are identified, including: (1) soil parameters; (2) parameters required to consider laboratory test and sampling effects; and (3) loading parameters. The fundamentals of risk based design concepts pertient to liquefaction are reviewed. A detailed statistical evaluation of the soil parameters in the proposed liquefaction model is provided and the uncertainty associated with the estimation of in situ relative density is evaluated for both direct and indirect methods. It is found that the liquefaction potential the uncertainties in the load parameters could be higher than those in the resistance parameters.

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

NASA Technical Reports Server (NTRS)

Duval, R. W.; Bahrami, M.

1985-01-01

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

Multiple-Shrinkage Multinomial Probit Models with Applications to Simulating Geographies in Public Use Data.

PubMed

Burgette, Lane F; Reiter, Jerome P

2013-06-01

Multinomial outcomes with many levels can be challenging to model. Information typically accrues slowly with increasing sample size, yet the parameter space expands rapidly with additional covariates. Shrinking all regression parameters towards zero, as often done in models of continuous or binary response variables, is unsatisfactory, since setting parameters equal to zero in multinomial models does not necessarily imply "no effect." We propose an approach to modeling multinomial outcomes with many levels based on a Bayesian multinomial probit (MNP) model and a multiple shrinkage prior distribution for the regression parameters. The prior distribution encourages the MNP regression parameters to shrink toward a number of learned locations, thereby substantially reducing the dimension of the parameter space. Using simulated data, we compare the predictive performance of this model against two other recently-proposed methods for big multinomial models. The results suggest that the fully Bayesian, multiple shrinkage approach can outperform these other methods. We apply the multiple shrinkage MNP to simulating replacement values for areal identifiers, e.g., census tract indicators, in order to protect data confidentiality in public use datasets.

Exploratory Study for Continuous-time Parameter Estimation of Ankle Dynamics

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.; Boyle, Richard D.

2014-01-01

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

Simultaneous Estimation of Microphysical Parameters and Atmospheric State Variables With Radar Data and Ensemble Square-root Kalman Filter

NASA Astrophysics Data System (ADS)

Tong, M.; Xue, M.

2006-12-01

An important source of model error for convective-scale data assimilation and prediction is microphysical parameterization. This study investigates the possibility of estimating up to five fundamental microphysical parameters, which are closely involved in the definition of drop size distribution of microphysical species in a commonly used single-moment ice microphysics scheme, using radar observations and the ensemble Kalman filter method. The five parameters include the intercept parameters for rain, snow and hail/graupel, and the bulk densities of hail/graupel and snow. Parameter sensitivity and identifiability are first examined. The ensemble square-root Kalman filter (EnSRF) is employed for simultaneous state and parameter estimation. OSS experiments are performed for a model-simulated supercell storm, in which the five microphysical parameters are estimated individually or in different combinations starting from different initial guesses. When error exists in only one of the microphysical parameters, the parameter can be successfully estimated without exception. The estimation of multiple parameters is found to be less robust, with end results of estimation being sensitive to the realization of the initial parameter perturbation. This is believed to be because of the reduced parameter identifiability and the existence of non-unique solutions. The results of state estimation are, however, always improved when simultaneous parameter estimation is performed, even when the estimated parameters values are not accurate.

Influence of Population Variation of Physiological Parameters in Computational Models of Space Physiology

NASA Technical Reports Server (NTRS)

Myers, J. G.; Feola, A.; Werner, C.; Nelson, E. S.; Raykin, J.; Samuels, B.; Ethier, C. R.

2016-01-01

The earliest manifestations of Visual Impairment and Intracranial Pressure (VIIP) syndrome become evident after months of spaceflight and include a variety of ophthalmic changes, including posterior globe flattening and distension of the optic nerve sheath. Prevailing evidence links the occurrence of VIIP to the cephalic fluid shift induced by microgravity and the subsequent pressure changes around the optic nerve and eye. Deducing the etiology of VIIP is challenging due to the wide range of physiological parameters that may be influenced by spaceflight and are required to address a realistic spectrum of physiological responses. Here, we report on the application of an efficient approach to interrogating physiological parameter space through computational modeling. Specifically, we assess the influence of uncertainty in input parameters for two models of VIIP syndrome: a lumped-parameter model (LPM) of the cardiovascular and central nervous systems, and a finite-element model (FEM) of the posterior eye, optic nerve head (ONH) and optic nerve sheath. Methods: To investigate the parameter space in each model, we employed Latin hypercube sampling partial rank correlation coefficient (LHSPRCC) strategies. LHS techniques outperform Monte Carlo approaches by enforcing efficient sampling across the entire range of all parameters. The PRCC method estimates the sensitivity of model outputs to these parameters while adjusting for the linear effects of all other inputs. The LPM analysis addressed uncertainties in 42 physiological parameters, such as initial compartmental volume and nominal compartment percentage of total cardiac output in the supine state, while the FEM evaluated the effects on biomechanical strain from uncertainties in 23 material and pressure parameters for the ocular anatomy. Results and Conclusion: The LPM analysis identified several key factors including high sensitivity to the initial fluid distribution. The FEM study found that intraocular pressure and intracranial pressure had dominant impact on the peak strains in the ONH and retro-laminar optic nerve, respectively; optic nerve and lamina cribrosa stiffness were also important. This investigation illustrates the ability of LHSPRCC to identify the most influential physiological parameters, which must therefore be well-characterized to produce the most accurate numerical results.

Competitive Modes for the Detection of Chaotic Parameter Regimes in the General Chaotic Bilinear System of Lorenz Type

NASA Astrophysics Data System (ADS)

Mallory, Kristina; van Gorder, Robert A.

We study chaotic behavior of solutions to the bilinear system of Lorenz type developed by Celikovsky and Vanecek [1994] through an application of competitive modes. This bilinear system of Lorenz type is one possible canonical form holding the Lorenz equation as a special case. Using a competitive modes analysis, which is a completely analytical method allowing one to identify parameter regimes for which chaos may occur, we are able to demonstrate a number of parameter regimes which admit a variety of distinct chaotic behaviors. Indeed, we are able to draw some interesting conclusions which relate the behavior of the mode frequencies arising from writing the state variables for the Celikovsky-Vanecek model as coupled oscillators, and the types of emergent chaotic behaviors observed. The competitive modes analysis is particularly useful if all but one of the model parameters are fixed, and the remaining free parameter is used to modify the chaos observed, in a manner analogous to a bifurcation parameter. Through a thorough application of the method, we are able to identify several parameter regimes which give new dynamics (such as specific forms of chaos) which were not observed or studied previously in the Celikovsky-Vanecek model. Therefore, the results demonstrate the advantage of the competitive modes approach for detecting new parameter regimes leading to chaos in third-order dynamical systems.

A new multimedia contaminant fate model for China: how important are environmental parameters in influencing chemical persistence and long-range transport potential?

PubMed

Zhu, Ying; Price, Oliver R; Tao, Shu; Jones, Kevin C; Sweetman, Andy J

2014-08-01

We present a new multimedia chemical fate model (SESAMe) which was developed to assess chemical fate and behaviour across China. We apply the model to quantify the influence of environmental parameters on chemical overall persistence (POV) and long-range transport potential (LRTP) in China, which has extreme diversity in environmental conditions. Sobol sensitivity analysis was used to identify the relative importance of input parameters. Physicochemical properties were identified as more influential than environmental parameters on model output. Interactive effects of environmental parameters on POV and LRTP occur mainly in combination with chemical properties. Hypothetical chemicals and emission data were used to model POV and LRTP for neutral and acidic chemicals with different KOW/DOW, vapour pressure and pKa under different precipitation, wind speed, temperature and soil organic carbon contents (fOC). Generally for POV, precipitation was more influential than the other environmental parameters, whilst temperature and wind speed did not contribute significantly to POV variation; for LRTP, wind speed was more influential than the other environmental parameters, whilst the effects of other environmental parameters relied on specific chemical properties. fOC had a slight effect on POV and LRTP, and higher fOC always increased POV and decreased LRTP. Example case studies were performed on real test chemicals using SESAMe to explore the spatial variability of model output and how environmental properties affect POV and LRTP. Dibenzofuran released to multiple media had higher POV in northwest of Xinjiang, part of Gansu, northeast of Inner Mongolia, Heilongjiang and Jilin. Benzo[a]pyrene released to the air had higher LRTP in south Xinjiang and west Inner Mongolia, whilst acenaphthene had higher LRTP in Tibet and west Inner Mongolia. TCS released into water had higher LRTP in Yellow River and Yangtze River catchments. The initial case studies demonstrated that SESAMe performed well on comparing POV and LRTP of chemicals in different regions across China in order to potentially identify the most sensitive regions. This model should not only be used to estimate POV and LRTP for screening and risk assessments of chemicals, but could potentially be used to help design chemical monitoring programmes across China in the future. Copyright © 2014 Elsevier Ltd. All rights reserved.

Validation of Slosh Model Parameters and Anti-Slosh Baffle Designs of Propellant Tanks by Using Lateral Slosh Testing

NASA Technical Reports Server (NTRS)

Perez, Jose G.; Parks, Russel, A.; Lazor, Daniel R.

2012-01-01

The slosh dynamics of propellant tanks can be represented by an equivalent mass-pendulum-dashpot mechanical model. The parameters of this equivalent model, identified as slosh mechanical model parameters, are slosh frequency, slosh mass, and pendulum hinge point location. They can be obtained by both analysis and testing for discrete fill levels. Anti-slosh baffles are usually needed in propellant tanks to control the movement of the fluid inside the tank. Lateral slosh testing, involving both random excitation testing and free-decay testing, are performed to validate the slosh mechanical model parameters and the damping added to the fluid by the anti-slosh baffles. Traditional modal analysis procedures were used to extract the parameters from the experimental data. Test setup of sub-scale tanks will be described. A comparison between experimental results and analysis will be presented.

Validation of Slosh Model Parameters and Anti-Slosh Baffle Designs of Propellant Tanks by Using Lateral Slosh Testing

NASA Technical Reports Server (NTRS)

Perez, Jose G.; Parks, Russel A.; Lazor, Daniel R.

2012-01-01

The slosh dynamics of propellant tanks can be represented by an equivalent pendulum-mass mechanical model. The parameters of this equivalent model, identified as slosh model parameters, are slosh mass, slosh mass center of gravity, slosh frequency, and smooth-wall damping. They can be obtained by both analysis and testing for discrete fill heights. Anti-slosh baffles are usually needed in propellant tanks to control the movement of the fluid inside the tank. Lateral slosh testing, involving both random testing and free-decay testing, are performed to validate the slosh model parameters and the damping added to the fluid by the anti-slosh baffles. Traditional modal analysis procedures are used to extract the parameters from the experimental data. Test setup of sub-scale test articles of cylindrical and spherical shapes will be described. A comparison between experimental results and analysis will be presented.

A New Formulation of the Filter-Error Method for Aerodynamic Parameter Estimation in Turbulence

NASA Technical Reports Server (NTRS)

Grauer, Jared A.; Morelli, Eugene A.

2015-01-01

A new formulation of the filter-error method for estimating aerodynamic parameters in nonlinear aircraft dynamic models during turbulence was developed and demonstrated. The approach uses an estimate of the measurement noise covariance to identify the model parameters, their uncertainties, and the process noise covariance, in a relaxation method analogous to the output-error method. Prior information on the model parameters and uncertainties can be supplied, and a post-estimation correction to the uncertainty was included to account for colored residuals not considered in the theory. No tuning parameters, needing adjustment by the analyst, are used in the estimation. The method was demonstrated in simulation using the NASA Generic Transport Model, then applied to the subscale T-2 jet-engine transport aircraft flight. Modeling results in different levels of turbulence were compared with results from time-domain output error and frequency- domain equation error methods to demonstrate the effectiveness of the approach.

Performance of stochastic approaches for forecasting river water quality.

PubMed

Ahmad, S; Khan, I H; Parida, B P

2001-12-01

This study analysed water quality data collected from the river Ganges in India from 1981 to 1990 for forecasting using stochastic models. Initially the box and whisker plots and Kendall's tau test were used to identify the trends during the study period. For detecting the possible intervention in the data the time series plots and cusum charts were used. The three approaches of stochastic modelling which account for the effect of seasonality in different ways. i.e. multiplicative autoregressive integrated moving average (ARIMA) model. deseasonalised model and Thomas-Fiering model were used to model the observed pattern in water quality. The multiplicative ARIMA model having both nonseasonal and seasonal components were, in general, identified as appropriate models. In the deseasonalised modelling approach, the lower order ARIMA models were found appropriate for the stochastic component. The set of Thomas-Fiering models were formed for each month for all water quality parameters. These models were then used to forecast the future values. The error estimates of forecasts from the three approaches were compared to identify the most suitable approach for the reliable forecast. The deseasonalised modelling approach was recommended for forecasting of water quality parameters of a river.

Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models

PubMed Central

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

Bayesian Statistical Inference in Ion-Channel Models with Exact Missed Event Correction.

PubMed

Epstein, Michael; Calderhead, Ben; Girolami, Mark A; Sivilotti, Lucia G

2016-07-26

The stochastic behavior of single ion channels is most often described as an aggregated continuous-time Markov process with discrete states. For ligand-gated channels each state can represent a different conformation of the channel protein or a different number of bound ligands. Single-channel recordings show only whether the channel is open or shut: states of equal conductance are aggregated, so transitions between them have to be inferred indirectly. The requirement to filter noise from the raw signal further complicates the modeling process, as it limits the time resolution of the data. The consequence of the reduced bandwidth is that openings or shuttings that are shorter than the resolution cannot be observed; these are known as missed events. Postulated models fitted using filtered data must therefore explicitly account for missed events to avoid bias in the estimation of rate parameters and therefore assess parameter identifiability accurately. In this article, we present the first, to our knowledge, Bayesian modeling of ion-channels with exact missed events correction. Bayesian analysis represents uncertain knowledge of the true value of model parameters by considering these parameters as random variables. This allows us to gain a full appreciation of parameter identifiability and uncertainty when estimating values for model parameters. However, Bayesian inference is particularly challenging in this context as the correction for missed events increases the computational complexity of the model likelihood. Nonetheless, we successfully implemented a two-step Markov chain Monte Carlo method that we called "BICME", which performs Bayesian inference in models of realistic complexity. The method is demonstrated on synthetic and real single-channel data from muscle nicotinic acetylcholine channels. We show that parameter uncertainty can be characterized more accurately than with maximum-likelihood methods. Our code for performing inference in these ion channel models is publicly available. Copyright © 2016 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Designing novel cellulase systems through agent-based modeling and global sensitivity analysis.

PubMed

Apte, Advait A; Senger, Ryan S; Fong, Stephen S

2014-01-01

Experimental techniques allow engineering of biological systems to modify functionality; however, there still remains a need to develop tools to prioritize targets for modification. In this study, agent-based modeling (ABM) was used to build stochastic models of complexed and non-complexed cellulose hydrolysis, including enzymatic mechanisms for endoglucanase, exoglucanase, and β-glucosidase activity. Modeling results were consistent with experimental observations of higher efficiency in complexed systems than non-complexed systems and established relationships between specific cellulolytic mechanisms and overall efficiency. Global sensitivity analysis (GSA) of model results identified key parameters for improving overall cellulose hydrolysis efficiency including: (1) the cellulase half-life, (2) the exoglucanase activity, and (3) the cellulase composition. Overall, the following parameters were found to significantly influence cellulose consumption in a consolidated bioprocess (CBP): (1) the glucose uptake rate of the culture, (2) the bacterial cell concentration, and (3) the nature of the cellulase enzyme system (complexed or non-complexed). Broadly, these results demonstrate the utility of combining modeling and sensitivity analysis to identify key parameters and/or targets for experimental improvement.

Designing novel cellulase systems through agent-based modeling and global sensitivity analysis

PubMed Central

Apte, Advait A; Senger, Ryan S; Fong, Stephen S

2014-01-01

Experimental techniques allow engineering of biological systems to modify functionality; however, there still remains a need to develop tools to prioritize targets for modification. In this study, agent-based modeling (ABM) was used to build stochastic models of complexed and non-complexed cellulose hydrolysis, including enzymatic mechanisms for endoglucanase, exoglucanase, and β-glucosidase activity. Modeling results were consistent with experimental observations of higher efficiency in complexed systems than non-complexed systems and established relationships between specific cellulolytic mechanisms and overall efficiency. Global sensitivity analysis (GSA) of model results identified key parameters for improving overall cellulose hydrolysis efficiency including: (1) the cellulase half-life, (2) the exoglucanase activity, and (3) the cellulase composition. Overall, the following parameters were found to significantly influence cellulose consumption in a consolidated bioprocess (CBP): (1) the glucose uptake rate of the culture, (2) the bacterial cell concentration, and (3) the nature of the cellulase enzyme system (complexed or non-complexed). Broadly, these results demonstrate the utility of combining modeling and sensitivity analysis to identify key parameters and/or targets for experimental improvement. PMID:24830736

Taking the mystery out of mathematical model applications to karst aquifers—A primer

USGS Publications Warehouse

Kuniansky, Eve L.

2014-01-01

Advances in mathematical model applications toward the understanding of the complex flow, characterization, and water-supply management issues for karst aquifers have occurred in recent years. Different types of mathematical models can be applied successfully if appropriate information is available and the problems are adequately identified. The mathematical approaches discussed in this paper are divided into three major categories: 1) distributed parameter models, 2) lumped parameter models, and 3) fitting models. The modeling approaches are described conceptually with examples (but without equations) to help non-mathematicians understand the applications.

Imposing constraints on parameter values of a conceptual hydrological model using baseflow response

NASA Astrophysics Data System (ADS)

Dunn, S. M.

Calibration of conceptual hydrological models is frequently limited by a lack of data about the area that is being studied. The result is that a broad range of parameter values can be identified that will give an equally good calibration to the available observations, usually of stream flow. The use of total stream flow can bias analyses towards interpretation of rapid runoff, whereas water quality issues are more frequently associated with low flow condition. This paper demonstrates how model distinctions between surface an sub-surface runoff can be used to define a likelihood measure based on the sub-surface (or baseflow) response. This helps to provide more information about the model behaviour, constrain the acceptable parameter sets and reduce uncertainty in streamflow prediction. A conceptual model, DIY, is applied to two contrasting catchments in Scotland, the Ythan and the Carron Valley. Parameter ranges and envelopes of prediction are identified using criteria based on total flow efficiency, baseflow efficiency and combined efficiencies. The individual parameter ranges derived using the combined efficiency measures still cover relatively wide bands, but are better constrained for the Carron than the Ythan. This reflects the fact that hydrological behaviour in the Carron is dominated by a much flashier surface response than in the Ythan. Hence, the total flow efficiency is more strongly controlled by surface runoff in the Carron and there is a greater contrast with the baseflow efficiency. Comparisons of the predictions using different efficiency measures for the Ythan also suggest that there is a danger of confusing parameter uncertainties with data and model error, if inadequate likelihood measures are defined.

Utilization of Short-Simulations for Tuning High-Resolution Climate Model

NASA Astrophysics Data System (ADS)

Lin, W.; Xie, S.; Ma, P. L.; Rasch, P. J.; Qian, Y.; Wan, H.; Ma, H. Y.; Klein, S. A.

2016-12-01

Many physical parameterizations in atmospheric models are sensitive to resolution. Tuning the models that involve a multitude of parameters at high resolution is computationally expensive, particularly when relying primarily on multi-year simulations. This work describes a complementary set of strategies for tuning high-resolution atmospheric models, using ensembles of short simulations to reduce the computational cost and elapsed time. Specifically, we utilize the hindcast approach developed through the DOE Cloud Associated Parameterization Testbed (CAPT) project for high-resolution model tuning, which is guided by a combination of short (< 10 days ) and longer ( 1 year) Perturbed Parameters Ensemble (PPE) simulations at low resolution to identify model feature sensitivity to parameter changes. The CAPT tests have been found to be effective in numerous previous studies in identifying model biases due to parameterized fast physics, and we demonstrate that it is also useful for tuning. After the most egregious errors are addressed through an initial "rough" tuning phase, longer simulations are performed to "hone in" on model features that evolve over longer timescales. We explore these strategies to tune the DOE ACME (Accelerated Climate Modeling for Energy) model. For the ACME model at 0.25° resolution, it is confirmed that, given the same parameters, major biases in global mean statistics and many spatial features are consistent between Atmospheric Model Intercomparison Project (AMIP)-type simulations and CAPT-type hindcasts, with just a small number of short-term simulations for the latter over the corresponding season. The use of CAPT hindcasts to find parameter choice for the reduction of large model biases dramatically improves the turnaround time for the tuning at high resolution. Improvement seen in CAPT hindcasts generally translates to improved AMIP-type simulations. An iterative CAPT-AMIP tuning approach is therefore adopted during each major tuning cycle, with the former to survey the likely responses and narrow the parameter space, and the latter to verify the results in climate context along with assessment in greater detail once an educated set of parameter choice is selected. Limitations on using short-term simulations for tuning climate model are also discussed.

Discovery and characterization of 3000+ main-sequence binaries from APOGEE spectra

NASA Astrophysics Data System (ADS)

El-Badry, Kareem; Ting, Yuan-Sen; Rix, Hans-Walter; Quataert, Eliot; Weisz, Daniel R.; Cargile, Phillip; Conroy, Charlie; Hogg, David W.; Bergemann, Maria; Liu, Chao

2018-05-01

We develop a data-driven spectral model for identifying and characterizing spatially unresolved multiple-star systems and apply it to APOGEE DR13 spectra of main-sequence stars. Binaries and triples are identified as targets whose spectra can be significantly better fit by a superposition of two or three model spectra, drawn from the same isochrone, than any single-star model. From an initial sample of ˜20 000 main-sequence targets, we identify ˜2500 binaries in which both the primary and secondary stars contribute detectably to the spectrum, simultaneously fitting for the velocities and stellar parameters of both components. We additionally identify and fit ˜200 triple systems, as well as ˜700 velocity-variable systems in which the secondary does not contribute detectably to the spectrum. Our model simplifies the process of simultaneously fitting single- or multi-epoch spectra with composite models and does not depend on a velocity offset between the two components of a binary, making it sensitive to traditionally undetectable systems with periods of hundreds or thousands of years. In agreement with conventional expectations, almost all the spectrally identified binaries with measured parallaxes fall above the main sequence in the colour-magnitude diagram. We find excellent agreement between spectrally and dynamically inferred mass ratios for the ˜600 binaries in which a dynamical mass ratio can be measured from multi-epoch radial velocities. We obtain full orbital solutions for 64 systems, including 14 close binaries within hierarchical triples. We make available catalogues of stellar parameters, abundances, mass ratios, and orbital parameters.

The feasibility of utilizing remotely sensed data to assess and monitor oceanic gamefish

NASA Technical Reports Server (NTRS)

Savastano, K. J.; Leming, T. D.

1975-01-01

An investigation was conducted to establish the feasibility of utilizing remotely sensed data acquired from aircraft and satellite platforms to provide information concerning the distribution and abundance of oceanic gamefish. The data from the test area was jointly acquired by NASA, the Navy, the Air Force and NOAA/NMFS elements and private and professional fishermen in the northeastern Gulf of Mexico. The data collected has made it possible to identify fisheries significant environmental parameters for white marlin. Prediction models, based on catch data and surface truth information, were developed and demonstrated a potential for significantly reducing search by identifying areas that have a high probability of productivity. Three of the parameters utilized by the models, chlorophyll-a, sea surface temperature, and turbidity were inferred from aircraft sensor data and were tested. Effective use of Skylab data was inhibited by cloud cover and delayed delivery. Initial efforts toward establishing the feasibility of utilizing remotely sensed data to assess and monitor the distribution of oceanic gamefish has successfully identified fisheries significant oceanographic parameters and demonstrated the capability of remotely measuring most of the parameters.

Modeling Real-Time Human-Automation Collaborative Scheduling of Unmanned Vehicles

DTIC Science & Technology

2013-06-01

that they can only take into account those quantifiable variables, parameters, objectives, and constraints identified in the design stages that were... account those quantifiable variables, parameters, objectives, and constraints identified in the design stages that were deemed to be critical. Previous...increased training and operating costs (Haddal & Gertler, 2010) and challenges in meeting the ever-increasing demand for more UV operations (U.S. Air

Development of uncertainty-based work injury model using Bayesian structural equation modelling.

PubMed

Chatterjee, Snehamoy

2014-01-01

This paper proposed a Bayesian method-based structural equation model (SEM) of miners' work injury for an underground coal mine in India. The environmental and behavioural variables for work injury were identified and causal relationships were developed. For Bayesian modelling, prior distributions of SEM parameters are necessary to develop the model. In this paper, two approaches were adopted to obtain prior distribution for factor loading parameters and structural parameters of SEM. In the first approach, the prior distributions were considered as a fixed distribution function with specific parameter values, whereas, in the second approach, prior distributions of the parameters were generated from experts' opinions. The posterior distributions of these parameters were obtained by applying Bayesian rule. The Markov Chain Monte Carlo sampling in the form Gibbs sampling was applied for sampling from the posterior distribution. The results revealed that all coefficients of structural and measurement model parameters are statistically significant in experts' opinion-based priors, whereas, two coefficients are not statistically significant when fixed prior-based distributions are applied. The error statistics reveals that Bayesian structural model provides reasonably good fit of work injury with high coefficient of determination (0.91) and less mean squared error as compared to traditional SEM.

Identification and calibration of the structural model of historical masonry building damaged during the 2016 Italian earthquakes: The case study of Palazzo del Podestà in Montelupone

NASA Astrophysics Data System (ADS)

Catinari, Federico; Pierdicca, Alessio; Clementi, Francesco; Lenci, Stefano

2017-11-01

The results of an ambient-vibration based investigation conducted on the "Palazzo del Podesta" in Montelupone (Italy) is presented. The case study was damaged during the 20I6 Italian earthquakes that stroke the central part of the Italy. The assessment procedure includes full-scale ambient vibration testing, modal identification from ambient vibration responses, finite element modeling and dynamic-based identification of the uncertain structural parameters of the model. A very good match between theoretical and experimental modal parameters was reached and the model updating has been performed identifying some structural parameters.

Observation model and parameter partials for the JPL geodetic GPS modeling software GPSOMC

NASA Technical Reports Server (NTRS)

Sovers, O. J.; Border, J. S.

1988-01-01

The physical models employed in GPSOMC and the modeling module of the GIPSY software system developed at JPL for analysis of geodetic Global Positioning Satellite (GPS) measurements are described. Details of the various contributions to range and phase observables are given, as well as the partial derivatives of the observed quantities with respect to model parameters. A glossary of parameters is provided to enable persons doing data analysis to identify quantities in the current report with their counterparts in the computer programs. There are no basic model revisions, with the exceptions of an improved ocean loading model and some new options for handling clock parametrization. Such misprints as were discovered were corrected. Further revisions include modeling improvements and assurances that the model description is in accord with the current software.

An adaptive model for vanadium redox flow battery and its application for online peak power estimation

NASA Astrophysics Data System (ADS)

Wei, Zhongbao; Meng, Shujuan; Tseng, King Jet; Lim, Tuti Mariana; Soong, Boon Hee; Skyllas-Kazacos, Maria

2017-03-01

An accurate battery model is the prerequisite for reliable state estimate of vanadium redox battery (VRB). As the battery model parameters are time varying with operating condition variation and battery aging, the common methods where model parameters are empirical or prescribed offline lacks accuracy and robustness. To address this issue, this paper proposes to use an online adaptive battery model to reproduce the VRB dynamics accurately. The model parameters are online identified with both the recursive least squares (RLS) and the extended Kalman filter (EKF). Performance comparison shows that the RLS is superior with respect to the modeling accuracy, convergence property, and computational complexity. Based on the online identified battery model, an adaptive peak power estimator which incorporates the constraints of voltage limit, SOC limit and design limit of current is proposed to fully exploit the potential of the VRB. Experiments are conducted on a lab-scale VRB system and the proposed peak power estimator is verified with a specifically designed "two-step verification" method. It is shown that different constraints dominate the allowable peak power at different stages of cycling. The influence of prediction time horizon selection on the peak power is also analyzed.

Ensemble urban flood simulation in comparison with laboratory-scale experiments: Impact of interaction models for manhole, sewer pipe, and surface flow

NASA Astrophysics Data System (ADS)

Noh, Seong Jin; Lee, Seungsoo; An, Hyunuk; Kawaike, Kenji; Nakagawa, Hajime

2016-11-01

An urban flood is an integrated phenomenon that is affected by various uncertainty sources such as input forcing, model parameters, complex geometry, and exchanges of flow among different domains in surfaces and subsurfaces. Despite considerable advances in urban flood modeling techniques, limited knowledge is currently available with regard to the impact of dynamic interaction among different flow domains on urban floods. In this paper, an ensemble method for urban flood modeling is presented to consider the parameter uncertainty of interaction models among a manhole, a sewer pipe, and surface flow. Laboratory-scale experiments on urban flood and inundation are performed under various flow conditions to investigate the parameter uncertainty of interaction models. The results show that ensemble simulation using interaction models based on weir and orifice formulas reproduces experimental data with high accuracy and detects the identifiability of model parameters. Among interaction-related parameters, the parameters of the sewer-manhole interaction show lower uncertainty than those of the sewer-surface interaction. Experimental data obtained under unsteady-state conditions are more informative than those obtained under steady-state conditions to assess the parameter uncertainty of interaction models. Although the optimal parameters vary according to the flow conditions, the difference is marginal. Simulation results also confirm the capability of the interaction models and the potential of the ensemble-based approaches to facilitate urban flood simulation.

FEAST: sensitive local alignment with multiple rates of evolution.

PubMed

Hudek, Alexander K; Brown, Daniel G

2011-01-01

We present a pairwise local aligner, FEAST, which uses two new techniques: a sensitive extension algorithm for identifying homologous subsequences, and a descriptive probabilistic alignment model. We also present a new procedure for training alignment parameters and apply it to the human and mouse genomes, producing a better parameter set for these sequences. Our extension algorithm identifies homologous subsequences by considering all evolutionary histories. It has higher maximum sensitivity than Viterbi extensions, and better balances specificity. We model alignments with several submodels, each with unique statistical properties, describing strongly similar and weakly similar regions of homologous DNA. Training parameters using two submodels produces superior alignments, even when we align with only the parameters from the weaker submodel. Our extension algorithm combined with our new parameter set achieves sensitivity 0.59 on synthetic tests. In contrast, LASTZ with default settings achieves sensitivity 0.35 with the same false positive rate. Using the weak submodel as parameters for LASTZ increases its sensitivity to 0.59 with high error. FEAST is available at http://monod.uwaterloo.ca/feast/.

Static shape control for flexible structures

NASA Technical Reports Server (NTRS)

Rodriguez, G.; Scheid, R. E., Jr.

1986-01-01

An integrated methodology is described for defining static shape control laws for large flexible structures. The techniques include modeling, identifying and estimating the control laws of distributed systems characterized in terms of infinite dimensional state and parameter spaces. The models are expressed as interconnected elliptic partial differential equations governing a range of static loads, with the capability of analyzing electromagnetic fields around antenna systems. A second-order analysis is carried out for statistical errors, and model parameters are determined by maximizing an appropriate defined likelihood functional which adjusts the model to observational data. The parameter estimates are derived from the conditional mean of the observational data, resulting in a least squares superposition of shape functions obtained from the structural model.

Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model

NASA Astrophysics Data System (ADS)

Yang, B.; Qian, Y.; Lin, G.; Leung, R.; Zhang, Y.

2011-12-01

The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. While the latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic important-sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e., the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Uncertainty Quantification and Parameter Tuning: A Case Study of Convective Parameterization Scheme in the WRF Regional Climate Model

NASA Astrophysics Data System (ADS)

Qian, Y.; Yang, B.; Lin, G.; Leung, R.; Zhang, Y.

2012-04-01

The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. The latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic important-sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e., the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model

NASA Astrophysics Data System (ADS)

Yang, B.; Qian, Y.; Lin, G.; Leung, R.; Zhang, Y.

2012-03-01

The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. While the latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic importance sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e. the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

A Sensitivity Analysis of fMRI Balloon Model.

PubMed

Zayane, Chadia; Laleg-Kirati, Taous Meriem

2015-01-01

Functional magnetic resonance imaging (fMRI) allows the mapping of the brain activation through measurements of the Blood Oxygenation Level Dependent (BOLD) contrast. The characterization of the pathway from the input stimulus to the output BOLD signal requires the selection of an adequate hemodynamic model and the satisfaction of some specific conditions while conducting the experiment and calibrating the model. This paper, focuses on the identifiability of the Balloon hemodynamic model. By identifiability, we mean the ability to estimate accurately the model parameters given the input and the output measurement. Previous studies of the Balloon model have somehow added knowledge either by choosing prior distributions for the parameters, freezing some of them, or looking for the solution as a projection on a natural basis of some vector space. In these studies, the identification was generally assessed using event-related paradigms. This paper justifies the reasons behind the need of adding knowledge, choosing certain paradigms, and completing the few existing identifiability studies through a global sensitivity analysis of the Balloon model in the case of blocked design experiment.

Modeling the Severity of Drinking Consequences in First-Year College Women: An Item Response Theory Analysis of the Rutgers Alcohol Problem Index*

PubMed Central

Cohn, Amy M.; Hagman, Brett T.; Graff, Fiona S.; Noel, Nora E.

2011-01-01

Objective: The present study examined the latent continuum of alcohol-related negative consequences among first-year college women using methods from item response theory and classical test theory. Method: Participants (N = 315) were college women in their freshman year who reported consuming any alcohol in the past 90 days and who completed assessments of alcohol consumption and alcohol-related negative consequences using the Rutgers Alcohol Problem Index. Results: Item response theory analyses showed poor model fit for five items identified in the Rutgers Alcohol Problem Index. Two-parameter item response theory logistic models were applied to the remaining 18 items to examine estimates of item difficulty (i.e., severity) and discrimination parameters. The item difficulty parameters ranged from 0.591 to 2.031, and the discrimination parameters ranged from 0.321 to 2.371. Classical test theory analyses indicated that the omission of the five misfit items did not significantly alter the psychometric properties of the construct. Conclusions: Findings suggest that those consequences that had greater severity and discrimination parameters may be used as screening items to identify female problem drinkers at risk for an alcohol use disorder. PMID:22051212

Generalized Processing Tree Models: Jointly Modeling Discrete and Continuous Variables.

PubMed

Heck, Daniel W; Erdfelder, Edgar; Kieslich, Pascal J

2018-05-24

Multinomial processing tree models assume that discrete cognitive states determine observed response frequencies. Generalized processing tree (GPT) models extend this conceptual framework to continuous variables such as response times, process-tracing measures, or neurophysiological variables. GPT models assume finite-mixture distributions, with weights determined by a processing tree structure, and continuous components modeled by parameterized distributions such as Gaussians with separate or shared parameters across states. We discuss identifiability, parameter estimation, model testing, a modeling syntax, and the improved precision of GPT estimates. Finally, a GPT version of the feature comparison model of semantic categorization is applied to computer-mouse trajectories.

Model parameter estimation approach based on incremental analysis for lithium-ion batteries without using open circuit voltage

NASA Astrophysics Data System (ADS)

Wu, Hongjie; Yuan, Shifei; Zhang, Xi; Yin, Chengliang; Ma, Xuerui

2015-08-01

To improve the suitability of lithium-ion battery model under varying scenarios, such as fluctuating temperature and SoC variation, dynamic model with parameters updated realtime should be developed. In this paper, an incremental analysis-based auto regressive exogenous (I-ARX) modeling method is proposed to eliminate the modeling error caused by the OCV effect and improve the accuracy of parameter estimation. Then, its numerical stability, modeling error, and parametric sensitivity are analyzed at different sampling rates (0.02, 0.1, 0.5 and 1 s). To identify the model parameters recursively, a bias-correction recursive least squares (CRLS) algorithm is applied. Finally, the pseudo random binary sequence (PRBS) and urban dynamic driving sequences (UDDSs) profiles are performed to verify the realtime performance and robustness of the newly proposed model and algorithm. Different sampling rates (1 Hz and 10 Hz) and multiple temperature points (5, 25, and 45 °C) are covered in our experiments. The experimental and simulation results indicate that the proposed I-ARX model can present high accuracy and suitability for parameter identification without using open circuit voltage.

Joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis.

PubMed

Sudell, Maria; Kolamunnage-Dona, Ruwanthi; Tudur-Smith, Catrin

2016-12-05

Joint models for longitudinal and time-to-event data are commonly used to simultaneously analyse correlated data in single study cases. Synthesis of evidence from multiple studies using meta-analysis is a natural next step but its feasibility depends heavily on the standard of reporting of joint models in the medical literature. During this review we aim to assess the current standard of reporting of joint models applied in the literature, and to determine whether current reporting standards would allow or hinder future aggregate data meta-analyses of model results. We undertook a literature review of non-methodological studies that involved joint modelling of longitudinal and time-to-event medical data. Study characteristics were extracted and an assessment of whether separate meta-analyses for longitudinal, time-to-event and association parameters were possible was made. The 65 studies identified used a wide range of joint modelling methods in a selection of software. Identified studies concerned a variety of disease areas. The majority of studies reported adequate information to conduct a meta-analysis (67.7% for longitudinal parameter aggregate data meta-analysis, 69.2% for time-to-event parameter aggregate data meta-analysis, 76.9% for association parameter aggregate data meta-analysis). In some cases model structure was difficult to ascertain from the published reports. Whilst extraction of sufficient information to permit meta-analyses was possible in a majority of cases, the standard of reporting of joint models should be maintained and improved. Recommendations for future practice include clear statement of model structure, of values of estimated parameters, of software used and of statistical methods applied.

Models of Pilot Behavior and Their Use to Evaluate the State of Pilot Training

NASA Astrophysics Data System (ADS)

Jirgl, Miroslav; Jalovecky, Rudolf; Bradac, Zdenek

2016-07-01

This article discusses the possibilities of obtaining new information related to human behavior, namely the changes or progressive development of pilots' abilities during training. The main assumption is that a pilot's ability can be evaluated based on a corresponding behavioral model whose parameters are estimated using mathematical identification procedures. The mean values of the identified parameters are obtained via statistical methods. These parameters are then monitored and their changes evaluated. In this context, the paper introduces and examines relevant mathematical models of human (pilot) behavior, the pilot-aircraft interaction, and an example of the mathematical analysis.

Bayesian calibration of the Community Land Model using surrogates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ray, Jaideep; Hou, Zhangshuan; Huang, Maoyi

2014-02-01

We present results from the Bayesian calibration of hydrological parameters of the Community Land Model (CLM), which is often used in climate simulations and Earth system models. A statistical inverse problem is formulated for three hydrological parameters, conditional on observations of latent heat surface fluxes over 48 months. Our calibration method uses polynomial and Gaussian process surrogates of the CLM, and solves the parameter estimation problem using a Markov chain Monte Carlo sampler. Posterior probability densities for the parameters are developed for two sites with different soil and vegetation covers. Our method also allows us to examine the structural errormore » in CLM under two error models. We find that surrogate models can be created for CLM in most cases. The posterior distributions are more predictive than the default parameter values in CLM. Climatologically averaging the observations does not modify the parameters' distributions significantly. The structural error model reveals a correlation time-scale which can be used to identify the physical process that could be contributing to it. While the calibrated CLM has a higher predictive skill, the calibration is under-dispersive.« less

Design Space Toolbox V2: Automated Software Enabling a Novel Phenotype-Centric Modeling Strategy for Natural and Synthetic Biological Systems

PubMed Central

Lomnitz, Jason G.; Savageau, Michael A.

2016-01-01

Mathematical models of biochemical systems provide a means to elucidate the link between the genotype, environment, and phenotype. A subclass of mathematical models, known as mechanistic models, quantitatively describe the complex non-linear mechanisms that capture the intricate interactions between biochemical components. However, the study of mechanistic models is challenging because most are analytically intractable and involve large numbers of system parameters. Conventional methods to analyze them rely on local analyses about a nominal parameter set and they do not reveal the vast majority of potential phenotypes possible for a given system design. We have recently developed a new modeling approach that does not require estimated values for the parameters initially and inverts the typical steps of the conventional modeling strategy. Instead, this approach relies on architectural features of the model to identify the phenotypic repertoire and then predict values for the parameters that yield specific instances of the system that realize desired phenotypic characteristics. Here, we present a collection of software tools, the Design Space Toolbox V2 based on the System Design Space method, that automates (1) enumeration of the repertoire of model phenotypes, (2) prediction of values for the parameters for any model phenotype, and (3) analysis of model phenotypes through analytical and numerical methods. The result is an enabling technology that facilitates this radically new, phenotype-centric, modeling approach. We illustrate the power of these new tools by applying them to a synthetic gene circuit that can exhibit multi-stability. We then predict values for the system parameters such that the design exhibits 2, 3, and 4 stable steady states. In one example, inspection of the basins of attraction reveals that the circuit can count between three stable states by transient stimulation through one of two input channels: a positive channel that increases the count, and a negative channel that decreases the count. This example shows the power of these new automated methods to rapidly identify behaviors of interest and efficiently predict parameter values for their realization. These tools may be applied to understand complex natural circuitry and to aid in the rational design of synthetic circuits. PMID:27462346

A discrete element modelling approach for block impacts on trees

NASA Astrophysics Data System (ADS)

Toe, David; Bourrier, Franck; Olmedo, Ignatio; Berger, Frederic

2015-04-01

These past few year rockfall models explicitly accounting for block shape, especially those using the Discrete Element Method (DEM), have shown a good ability to predict rockfall trajectories. Integrating forest effects into those models still remain challenging. This study aims at using a DEM approach to model impacts of blocks on trees and identify the key parameters controlling the block kinematics after the impact on a tree. A DEM impact model of a block on a tree was developed and validated using laboratory experiments. Then, key parameters were assessed using a global sensitivity analyse. Modelling the impact of a block on a tree using DEM allows taking into account large displacements, material non-linearities and contacts between the block and the tree. Tree stems are represented by flexible cylinders model as plastic beams sustaining normal, shearing, bending, and twisting loading. Root soil interactions are modelled using a rotation stiffness acting on the bending moment at the bottom of the tree and a limit bending moment to account for tree overturning. The crown is taken into account using an additional mass distribute uniformly on the upper part of the tree. The block is represented by a sphere. The contact model between the block and the stem consists of an elastic frictional model. The DEM model was validated using laboratory impact tests carried out on 41 fresh beech (Fagus Sylvatica) stems. Each stem was 1,3 m long with a diameter between 3 to 7 cm. Wood stems were clamped on a rigid structure and impacted by a 149 kg charpy pendulum. Finally an intensive simulation campaign of blocks impacting trees was done to identify the input parameters controlling the block kinematics after the impact on a tree. 20 input parameters were considered in the DEM simulation model : 12 parameters were related to the tree and 8 parameters to the block. The results highlight that the impact velocity, the stem diameter, and the block volume are the three input parameters that control the block kinematics after impact.

Characterizing a porous road pavement using surface impedance measurement: a guided numerical inversion procedure.

PubMed

Benoit, Gaëlle; Heinkélé, Christophe; Gourdon, Emmanuel

2013-12-01

This paper deals with a numerical procedure to identify the acoustical parameters of road pavement from surface impedance measurements. This procedure comprises three steps. First, a suitable equivalent fluid model for the acoustical properties porous media is chosen, the variation ranges for the model parameters are set, and a sensitivity analysis for this model is performed. Second, this model is used in the parameter inversion process, which is performed with simulated annealing in a selected frequency range. Third, the sensitivity analysis and inversion process are repeated to estimate each parameter in turn. This approach is tested on data obtained for porous bituminous concrete and using the Zwikker and Kosten equivalent fluid model. This work provides a good foundation for the development of non-destructive in situ methods for the acoustical characterization of road pavements.

Inverse problems in the design, modeling and testing of engineering systems

NASA Technical Reports Server (NTRS)

Alifanov, Oleg M.

1991-01-01

Formulations, classification, areas of application, and approaches to solving different inverse problems are considered for the design of structures, modeling, and experimental data processing. Problems in the practical implementation of theoretical-experimental methods based on solving inverse problems are analyzed in order to identify mathematical models of physical processes, aid in input data preparation for design parameter optimization, help in design parameter optimization itself, and to model experiments, large-scale tests, and real tests of engineering systems.

Dynamic causal modelling of effective connectivity from fMRI: Are results reproducible and sensitive to Parkinson's disease and its treatment?

PubMed Central

Rowe, J.B.; Hughes, L.E.; Barker, R.A.; Owen, A.M.

2010-01-01

Dynamic causal modelling (DCM) of functional magnetic resonance imaging (fMRI) data offers new insights into the pathophysiology of neurological disease and mechanisms of effective therapies. Current applications can be used both to identify the most likely functional brain network underlying observed data and estimate the networks' connectivity parameters. We examined the reproducibility of DCM in healthy subjects (young 18–48 years, n = 27; old 50–80 years, n = 15) in the context of action selection. We then examined the effects of Parkinson's disease (50–78 years, Hoehn and Yahr stage 1–2.5, n = 16) and dopaminergic therapy. Forty-eight models were compared, for each of 90 sessions from 58 subjects. Model-evidences clustered according to sets of structurally similar models, with high correlations over two sessions in healthy older subjects. The same model was identified as most likely in healthy controls on both sessions and in medicated patients. In this most likely network model, the selection of action was associated with enhanced coupling between prefrontal cortex and the pre-supplementary motor area. However, the parameters for intrinsic connectivity and contextual modulation in this model were poorly correlated across sessions. A different model was identified in patients with Parkinson's disease after medication withdrawal. In “off” patients, action selection was associated with enhanced connectivity from prefrontal to lateral premotor cortex. This accords with independent evidence of a dopamine-dependent functional disconnection of the SMA in Parkinson's disease. Together, these results suggest that DCM model selection is robust and sensitive enough to study clinical populations and their pharmacological treatment. For critical inferences, model selection may be sufficient. However, caution is required when comparing groups or drug effects in terms of the connectivity parameter estimates, if there are significant posterior covariances among parameters. PMID:20056151

Comment on “Two statistics for evaluating parameter identifiability and error reduction” by John Doherty and Randall J. Hunt

USGS Publications Warehouse

Hill, Mary C.

2010-01-01

Doherty and Hunt (2009) present important ideas for first-order-second moment sensitivity analysis, but five issues are discussed in this comment. First, considering the composite-scaled sensitivity (CSS) jointly with parameter correlation coefficients (PCC) in a CSS/PCC analysis addresses the difficulties with CSS mentioned in the introduction. Second, their new parameter identifiability statistic actually is likely to do a poor job of parameter identifiability in common situations. The statistic instead performs the very useful role of showing how model parameters are included in the estimated singular value decomposition (SVD) parameters. Its close relation to CSS is shown. Third, the idea from p. 125 that a suitable truncation point for SVD parameters can be identified using the prediction variance is challenged using results from Moore and Doherty (2005). Fourth, the relative error reduction statistic of Doherty and Hunt is shown to belong to an emerging set of statistics here named perturbed calculated variance statistics. Finally, the perturbed calculated variance statistics OPR and PPR mentioned on p. 121 are shown to explicitly include the parameter null-space component of uncertainty. Indeed, OPR and PPR results that account for null-space uncertainty have appeared in the literature since 2000.

Computationally inexpensive identification of noninformative model parameters by sequential screening

NASA Astrophysics Data System (ADS)

Cuntz, Matthias; Mai, Juliane; Zink, Matthias; Thober, Stephan; Kumar, Rohini; Schäfer, David; Schrön, Martin; Craven, John; Rakovec, Oldrich; Spieler, Diana; Prykhodko, Vladyslav; Dalmasso, Giovanni; Musuuza, Jude; Langenberg, Ben; Attinger, Sabine; Samaniego, Luis

2015-08-01

Environmental models tend to require increasing computational time and resources as physical process descriptions are improved or new descriptions are incorporated. Many-query applications such as sensitivity analysis or model calibration usually require a large number of model evaluations leading to high computational demand. This often limits the feasibility of rigorous analyses. Here we present a fully automated sequential screening method that selects only informative parameters for a given model output. The method requires a number of model evaluations that is approximately 10 times the number of model parameters. It was tested using the mesoscale hydrologic model mHM in three hydrologically unique European river catchments. It identified around 20 informative parameters out of 52, with different informative parameters in each catchment. The screening method was evaluated with subsequent analyses using all 52 as well as only the informative parameters. Subsequent Sobol's global sensitivity analysis led to almost identical results yet required 40% fewer model evaluations after screening. mHM was calibrated with all and with only informative parameters in the three catchments. Model performances for daily discharge were equally high in both cases with Nash-Sutcliffe efficiencies above 0.82. Calibration using only the informative parameters needed just one third of the number of model evaluations. The universality of the sequential screening method was demonstrated using several general test functions from the literature. We therefore recommend the use of the computationally inexpensive sequential screening method prior to rigorous analyses on complex environmental models.

Computationally inexpensive identification of noninformative model parameters by sequential screening

NASA Astrophysics Data System (ADS)

Mai, Juliane; Cuntz, Matthias; Zink, Matthias; Thober, Stephan; Kumar, Rohini; Schäfer, David; Schrön, Martin; Craven, John; Rakovec, Oldrich; Spieler, Diana; Prykhodko, Vladyslav; Dalmasso, Giovanni; Musuuza, Jude; Langenberg, Ben; Attinger, Sabine; Samaniego, Luis

2016-04-01

Environmental models tend to require increasing computational time and resources as physical process descriptions are improved or new descriptions are incorporated. Many-query applications such as sensitivity analysis or model calibration usually require a large number of model evaluations leading to high computational demand. This often limits the feasibility of rigorous analyses. Here we present a fully automated sequential screening method that selects only informative parameters for a given model output. The method requires a number of model evaluations that is approximately 10 times the number of model parameters. It was tested using the mesoscale hydrologic model mHM in three hydrologically unique European river catchments. It identified around 20 informative parameters out of 52, with different informative parameters in each catchment. The screening method was evaluated with subsequent analyses using all 52 as well as only the informative parameters. Subsequent Sobol's global sensitivity analysis led to almost identical results yet required 40% fewer model evaluations after screening. mHM was calibrated with all and with only informative parameters in the three catchments. Model performances for daily discharge were equally high in both cases with Nash-Sutcliffe efficiencies above 0.82. Calibration using only the informative parameters needed just one third of the number of model evaluations. The universality of the sequential screening method was demonstrated using several general test functions from the literature. We therefore recommend the use of the computationally inexpensive sequential screening method prior to rigorous analyses on complex environmental models.

The Early Eocene equable climate problem: can perturbations of climate model parameters identify possible solutions?

PubMed

Sagoo, Navjit; Valdes, Paul; Flecker, Rachel; Gregoire, Lauren J

2013-10-28

Geological data for the Early Eocene (56-47.8 Ma) indicate extensive global warming, with very warm temperatures at both poles. However, despite numerous attempts to simulate this warmth, there are remarkable data-model differences in the prediction of these polar surface temperatures, resulting in the so-called 'equable climate problem'. In this paper, for the first time an ensemble with a perturbed climate-sensitive model parameters approach has been applied to modelling the Early Eocene climate. We performed more than 100 simulations with perturbed physics parameters, and identified two simulations that have an optimal fit with the proxy data. We have simulated the warmth of the Early Eocene at 560 ppmv CO2, which is a much lower CO2 level than many other models. We investigate the changes in atmospheric circulation, cloud properties and ocean circulation that are common to these simulations and how they differ from the remaining simulations in order to understand what mechanisms contribute to the polar warming. The parameter set from one of the optimal Early Eocene simulations also produces a favourable fit for the last glacial maximum boundary climate and outperforms the control parameter set for the present day. Although this does not 'prove' that this model is correct, it is very encouraging that there is a parameter set that creates a climate model able to simulate well very different palaeoclimates and the present-day climate. Interestingly, to achieve the great warmth of the Early Eocene this version of the model does not have a strong future climate change Charney climate sensitivity. It produces a Charney climate sensitivity of 2.7(°)C, whereas the mean value of the 18 models in the IPCC Fourth Assessment Report (AR4) is 3.26(°)C±0.69(°)C. Thus, this value is within the range and below the mean of the models included in the AR4.

Global sensitivity analysis of the BSM2 dynamic influent disturbance scenario generator.

PubMed

Flores-Alsina, Xavier; Gernaey, Krist V; Jeppsson, Ulf

2012-01-01

This paper presents the results of a global sensitivity analysis (GSA) of a phenomenological model that generates dynamic wastewater treatment plant (WWTP) influent disturbance scenarios. This influent model is part of the Benchmark Simulation Model (BSM) family and creates realistic dry/wet weather files describing diurnal, weekend and seasonal variations through the combination of different generic model blocks, i.e. households, industry, rainfall and infiltration. The GSA is carried out by combining Monte Carlo simulations and standardized regression coefficients (SRC). Cluster analysis is then applied, classifying the influence of the model parameters into strong, medium and weak. The results show that the method is able to decompose the variance of the model predictions (R(2)> 0.9) satisfactorily, thus identifying the model parameters with strongest impact on several flow rate descriptors calculated at different time resolutions. Catchment size (PE) and the production of wastewater per person equivalent (QperPE) are two parameters that strongly influence the yearly average dry weather flow rate and its variability. Wet weather conditions are mainly affected by three parameters: (1) the probability of occurrence of a rain event (Llrain); (2) the catchment size, incorporated in the model as a parameter representing the conversion from mm rain · day(-1) to m(3) · day(-1) (Qpermm); and, (3) the quantity of rain falling on permeable areas (aH). The case study also shows that in both dry and wet weather conditions the SRC ranking changes when the time scale of the analysis is modified, thus demonstrating the potential to identify the effect of the model parameters on the fast/medium/slow dynamics of the flow rate. The paper ends with a discussion on the interpretation of GSA results and of the advantages of using synthetic dynamic flow rate data for WWTP influent scenario generation. This section also includes general suggestions on how to use the proposed methodology to any influent generator to adapt the created time series to a modeller's demands.

Seven protective miRNA signatures for prognosis of cervical cancer.

PubMed

Liu, Bei; Ding, Jin-Feng; Luo, Jian; Lu, Li; Yang, Fen; Tan, Xiao-Dong

2016-08-30

Cervical cancer is the second cause of cancer death in females in their 20s and 30s, but there were limited studies about its prognosis. This study aims to identify miRNA related to prognosis and study their functions. TCGA data of patients with cervical cancer were used to build univariate Cox's model with single clinical parameter or miRNA expression level. Multivariate Cox's model was built using both clinical information and miRNA expression levels. At last, STRING was used to enrich gene ontology or pathway for validated targets of significant miRNAs, and visualize the interactions among them. Using univariate Cox's model with clinical parameters, we found that two clinical parameters, tobacco use and clinical stage, and seven miRNAs were highly correlated with the survival status. Only using the expression level of miRNA signatures, the model could separate patients into high-risk and low-risk groups successfully. An optimal feature-selected model was proposed based on two clinical parameters and seven miRNAs. Functional analysis of these seven miRNAs showed they were associated to various pathways related to cancer, including MAPK, VEGF and P53 pathways. These results helped the research of identifying targets for targeted therapy which could potentially allow tailoring of treatment for cervical cancer patients.

Parameter identification of hyperelastic material properties of the heel pad based on an analytical contact mechanics model of a spherical indentation.

PubMed

Suzuki, Ryo; Ito, Kohta; Lee, Taeyong; Ogihara, Naomichi

2017-01-01

Accurate identification of the material properties of the plantar soft tissue is important for computer-aided analysis of foot pathologies and design of therapeutic footwear interventions based on subject-specific models of the foot. However, parameter identification of the hyperelastic material properties of plantar soft tissues usually requires an inverse finite element analysis due to the lack of a practical contact model of the indentation test. In the present study, we derive an analytical contact model of a spherical indentation test in order to directly estimate the material properties of the plantar soft tissue. Force-displacement curves of the heel pads are obtained through an indentation experiment. The experimental data are fit to the analytical stress-strain solution of the spherical indentation in order to obtain the parameters. A spherical indentation approach successfully predicted the non-linear material properties of the heel pad without iterative finite element calculation. The force-displacement curve obtained in the present study was found to be situated lower than those identified in previous studies. The proposed framework for identifying the hyperelastic material parameters may facilitate the development of subject-specific FE modeling of the foot for possible clinical and ergonomic applications. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

From global to local: exploring the relationship between parameters and behaviors in models of electrical excitability.

PubMed

Fletcher, Patrick; Bertram, Richard; Tabak, Joel

2016-06-01

Models of electrical activity in excitable cells involve nonlinear interactions between many ionic currents. Changing parameters in these models can produce a variety of activity patterns with sometimes unexpected effects. Further more, introducing new currents will have different effects depending on the initial parameter set. In this study we combined global sampling of parameter space and local analysis of representative parameter sets in a pituitary cell model to understand the effects of adding K (+) conductances, which mediate some effects of hormone action on these cells. Global sampling ensured that the effects of introducing K (+) conductances were captured across a wide variety of contexts of model parameters. For each type of K (+) conductance we determined the types of behavioral transition that it evoked. Some transitions were counterintuitive, and may have been missed without the use of global sampling. In general, the wide range of transitions that occurred when the same current was applied to the model cell at different locations in parameter space highlight the challenge of making accurate model predictions in light of cell-to-cell heterogeneity. Finally, we used bifurcation analysis and fast/slow analysis to investigate why specific transitions occur in representative individual models. This approach relies on the use of a graphics processing unit (GPU) to quickly map parameter space to model behavior and identify parameter sets for further analysis. Acceleration with modern low-cost GPUs is particularly well suited to exploring the moderate-sized (5-20) parameter spaces of excitable cell and signaling models.

Misspecification in Latent Change Score Models: Consequences for Parameter Estimation, Model Evaluation, and Predicting Change.

PubMed

Clark, D Angus; Nuttall, Amy K; Bowles, Ryan P

2018-01-01

Latent change score models (LCS) are conceptually powerful tools for analyzing longitudinal data (McArdle & Hamagami, 2001). However, applications of these models typically include constraints on key parameters over time. Although practically useful, strict invariance over time in these parameters is unlikely in real data. This study investigates the robustness of LCS when invariance over time is incorrectly imposed on key change-related parameters. Monte Carlo simulation methods were used to explore the impact of misspecification on parameter estimation, predicted trajectories of change, and model fit in the dual change score model, the foundational LCS. When constraints were incorrectly applied, several parameters, most notably the slope (i.e., constant change) factor mean and autoproportion coefficient, were severely and consistently biased, as were regression paths to the slope factor when external predictors of change were included. Standard fit indices indicated that the misspecified models fit well, partly because mean level trajectories over time were accurately captured. Loosening constraint improved the accuracy of parameter estimates, but estimates were more unstable, and models frequently failed to converge. Results suggest that potentially common sources of misspecification in LCS can produce distorted impressions of developmental processes, and that identifying and rectifying the situation is a challenge.

Parameter Estimation of Computationally Expensive Watershed Models Through Efficient Multi-objective Optimization and Interactive Decision Analytics

NASA Astrophysics Data System (ADS)

Akhtar, Taimoor; Shoemaker, Christine

2016-04-01

Watershed model calibration is inherently a multi-criteria problem. Conflicting trade-offs exist between different quantifiable calibration criterions indicating the non-existence of a single optimal parameterization. Hence, many experts prefer a manual approach to calibration where the inherent multi-objective nature of the calibration problem is addressed through an interactive, subjective, time-intensive and complex decision making process. Multi-objective optimization can be used to efficiently identify multiple plausible calibration alternatives and assist calibration experts during the parameter estimation process. However, there are key challenges to the use of multi objective optimization in the parameter estimation process which include: 1) multi-objective optimization usually requires many model simulations, which is difficult for complex simulation models that are computationally expensive; and 2) selection of one from numerous calibration alternatives provided by multi-objective optimization is non-trivial. This study proposes a "Hybrid Automatic Manual Strategy" (HAMS) for watershed model calibration to specifically address the above-mentioned challenges. HAMS employs a 3-stage framework for parameter estimation. Stage 1 incorporates the use of an efficient surrogate multi-objective algorithm, GOMORS, for identification of numerous calibration alternatives within a limited simulation evaluation budget. The novelty of HAMS is embedded in Stages 2 and 3 where an interactive visual and metric based analytics framework is available as a decision support tool to choose a single calibration from the numerous alternatives identified in Stage 1. Stage 2 of HAMS provides a goodness-of-fit measure / metric based interactive framework for identification of a small subset (typically less than 10) of meaningful and diverse set of calibration alternatives from the numerous alternatives obtained in Stage 1. Stage 3 incorporates the use of an interactive visual analytics framework for decision support in selection of one parameter combination from the alternatives identified in Stage 2. HAMS is applied for calibration of flow parameters of a SWAT model, (Soil and Water Assessment Tool) designed to simulate flow in the Cannonsville watershed in upstate New York. Results from the application of HAMS to Cannonsville indicate that efficient multi-objective optimization and interactive visual and metric based analytics can bridge the gap between the effective use of both automatic and manual strategies for parameter estimation of computationally expensive watershed models.

Appropriate evidence sources for populating decision analytic models within health technology assessment (HTA): a systematic review of HTA manuals and health economic guidelines.

PubMed

Zechmeister-Koss, Ingrid; Schnell-Inderst, Petra; Zauner, Günther

2014-04-01

An increasing number of evidence sources are relevant for populating decision analytic models. What is needed is detailed methodological advice on which type of data is to be used for what type of model parameter. We aim to identify standards in health technology assessment manuals and economic (modeling) guidelines on appropriate evidence sources and on the role different types of data play within a model. Documents were identified via a call among members of the International Network of Agencies for Health Technology Assessment and by hand search. We included documents from Europe, the United States, Canada, Australia, and New Zealand as well as transnational guidelines written in English or German. We systematically summarized in a narrative manner information on appropriate evidence sources for model parameters, their advantages and limitations, data identification methods, and data quality issues. A large variety of evidence sources for populating models are mentioned in the 28 documents included. They comprise research- and non-research-based sources. Valid and less appropriate sources are identified for informing different types of model parameters, such as clinical effect size, natural history of disease, resource use, unit costs, and health state utility values. Guidelines do not provide structured and detailed advice on this issue. The article does not include information from guidelines in languages other than English or German, and the information is not tailored to specific modeling techniques. The usability of guidelines and manuals for modeling could be improved by addressing the issue of evidence sources in a more structured and comprehensive format.

Joint Model and Parameter Dimension Reduction for Bayesian Inversion Applied to an Ice Sheet Flow Problem

NASA Astrophysics Data System (ADS)

Ghattas, O.; Petra, N.; Cui, T.; Marzouk, Y.; Benjamin, P.; Willcox, K.

2016-12-01

Model-based projections of the dynamics of the polar ice sheets play a central role in anticipating future sea level rise. However, a number of mathematical and computational challenges place significant barriers on improving predictability of these models. One such challenge is caused by the unknown model parameters (e.g., in the basal boundary conditions) that must be inferred from heterogeneous observational data, leading to an ill-posed inverse problem and the need to quantify uncertainties in its solution. In this talk we discuss the problem of estimating the uncertainty in the solution of (large-scale) ice sheet inverse problems within the framework of Bayesian inference. Computing the general solution of the inverse problem--i.e., the posterior probability density--is intractable with current methods on today's computers, due to the expense of solving the forward model (3D full Stokes flow with nonlinear rheology) and the high dimensionality of the uncertain parameters (which are discretizations of the basal sliding coefficient field). To overcome these twin computational challenges, it is essential to exploit problem structure (e.g., sensitivity of the data to parameters, the smoothing property of the forward model, and correlations in the prior). To this end, we present a data-informed approach that identifies low-dimensional structure in both parameter space and the forward model state space. This approach exploits the fact that the observations inform only a low-dimensional parameter space and allows us to construct a parameter-reduced posterior. Sampling this parameter-reduced posterior still requires multiple evaluations of the forward problem, therefore we also aim to identify a low dimensional state space to reduce the computational cost. To this end, we apply a proper orthogonal decomposition (POD) approach to approximate the state using a low-dimensional manifold constructed using ``snapshots'' from the parameter reduced posterior, and the discrete empirical interpolation method (DEIM) to approximate the nonlinearity in the forward problem. We show that using only a limited number of forward solves, the resulting subspaces lead to an efficient method to explore the high-dimensional posterior.

Estimation of adsorption isotherm and mass transfer parameters in protein chromatography using artificial neural networks.

PubMed

Wang, Gang; Briskot, Till; Hahn, Tobias; Baumann, Pascal; Hubbuch, Jürgen

2017-03-03

Mechanistic modeling has been repeatedly successfully applied in process development and control of protein chromatography. For each combination of adsorbate and adsorbent, the mechanistic models have to be calibrated. Some of the model parameters, such as system characteristics, can be determined reliably by applying well-established experimental methods, whereas others cannot be measured directly. In common practice of protein chromatography modeling, these parameters are identified by applying time-consuming methods such as frontal analysis combined with gradient experiments, curve-fitting, or combined Yamamoto approach. For new components in the chromatographic system, these traditional calibration approaches require to be conducted repeatedly. In the presented work, a novel method for the calibration of mechanistic models based on artificial neural network (ANN) modeling was applied. An in silico screening of possible model parameter combinations was performed to generate learning material for the ANN model. Once the ANN model was trained to recognize chromatograms and to respond with the corresponding model parameter set, it was used to calibrate the mechanistic model from measured chromatograms. The ANN model's capability of parameter estimation was tested by predicting gradient elution chromatograms. The time-consuming model parameter estimation process itself could be reduced down to milliseconds. The functionality of the method was successfully demonstrated in a study with the calibration of the transport-dispersive model (TDM) and the stoichiometric displacement model (SDM) for a protein mixture. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Important parameters for smoke plume rise simulation with Daysmoke

Treesearch

L. Liu; G.L. Achtemeier; S.L. Goodrick; W. Jackson

2010-01-01

Daysmoke is a local smoke transport model and has been used to provide smoke plume rise information. It includes a large number of parameters describing the dynamic and stochastic processes of particle upward movement, fallout, fluctuation, and burn emissions. This study identifies the important parameters for Daysmoke simulations of plume rise and seeks to understand...

Computational Algorithms or Identification of Distributed Parameter Systems

DTIC Science & Technology

1993-04-24

delay-differential equations, Volterra integral equations, and partial differential equations with memory terms . In particular we investigated a...tested for estimating parameters in a Volterra integral equation arising from a viscoelastic model of a flexible structure with Boltzmann damping. In...particular, one of the parameters identified was the order of the derivative in Volterra integro-differential equations containing fractional

An adaptive control scheme for a flexible manipulator

NASA Technical Reports Server (NTRS)

Yang, T. C.; Yang, J. C. S.; Kudva, P.

1987-01-01

The problem of controlling a single link flexible manipulator is considered. A self-tuning adaptive control scheme is proposed which consists of a least squares on-line parameter identification of an equivalent linear model followed by a tuning of the gains of a pole placement controller using the parameter estimates. Since the initial parameter values for this model are assumed unknown, the use of arbitrarily chosen initial parameter estimates in the adaptive controller would result in undesirable transient effects. Hence, the initial stage control is carried out with a PID controller. Once the identified parameters have converged, control is transferred to the adaptive controller. Naturally, the relevant issues in this scheme are tests for parameter convergence and minimization of overshoots during control switch-over. To demonstrate the effectiveness of the proposed scheme, simulation results are presented with an analytical nonlinear dynamic model of a single link flexible manipulator.

Towards a consensus-based biokinetic model for green microalgae - The ASM-A.

PubMed

Wágner, Dorottya S; Valverde-Pérez, Borja; Sæbø, Mariann; Bregua de la Sotilla, Marta; Van Wagenen, Jonathan; Smets, Barth F; Plósz, Benedek Gy

2016-10-15

Cultivation of microalgae in open ponds and closed photobioreactors (PBRs) using wastewater resources offers an opportunity for biochemical nutrient recovery. Effective reactor system design and process control of PBRs requires process models. Several models with different complexities have been developed to predict microalgal growth. However, none of these models can effectively describe all the relevant processes when microalgal growth is coupled with nutrient removal and recovery from wastewaters. Here, we present a mathematical model developed to simulate green microalgal growth (ASM-A) using the systematic approach of the activated sludge modelling (ASM) framework. The process model - identified based on a literature review and using new experimental data - accounts for factors influencing photoautotrophic and heterotrophic microalgal growth, nutrient uptake and storage (i.e. Droop model) and decay of microalgae. Model parameters were estimated using laboratory-scale batch and sequenced batch experiments using the novel Latin Hypercube Sampling based Simplex (LHSS) method. The model was evaluated using independent data obtained in a 24-L PBR operated in sequenced batch mode. Identifiability of the model was assessed. The model can effectively describe microalgal biomass growth, ammonia and phosphate concentrations as well as the phosphorus storage using a set of average parameter values estimated with the experimental data. A statistical analysis of simulation and measured data suggests that culture history and substrate availability can introduce significant variability on parameter values for predicting the reaction rates for bulk nitrate and the intracellularly stored nitrogen state-variables, thereby requiring scenario specific model calibration. ASM-A was identified using standard cultivation medium and it can provide a platform for extensions accounting for factors influencing algal growth and nutrient storage using wastewater resources. Copyright © 2016 Elsevier Ltd. All rights reserved.

Advanced Method to Estimate Fuel Slosh Simulation Parameters

NASA Technical Reports Server (NTRS)

Schlee, Keith; Gangadharan, Sathya; Ristow, James; Sudermann, James; Walker, Charles; Hubert, Carl

2005-01-01

The nutation (wobble) of a spinning spacecraft in the presence of energy dissipation is a well-known problem in dynamics and is of particular concern for space missions. The nutation of a spacecraft spinning about its minor axis typically grows exponentially and the rate of growth is characterized by the Nutation Time Constant (NTC). For launch vehicles using spin-stabilized upper stages, fuel slosh in the spacecraft propellant tanks is usually the primary source of energy dissipation. For analytical prediction of the NTC this fuel slosh is commonly modeled using simple mechanical analogies such as pendulums or rigid rotors coupled to the spacecraft. Identifying model parameter values which adequately represent the sloshing dynamics is the most important step in obtaining an accurate NTC estimate. Analytic determination of the slosh model parameters has met with mixed success and is made even more difficult by the introduction of propellant management devices and elastomeric diaphragms. By subjecting full-sized fuel tanks with actual flight fuel loads to motion similar to that experienced in flight and measuring the forces experienced by the tanks these parameters can be determined experimentally. Currently, the identification of the model parameters is a laborious trial-and-error process in which the equations of motion for the mechanical analog are hand-derived, evaluated, and their results are compared with the experimental results. The proposed research is an effort to automate the process of identifying the parameters of the slosh model using a MATLAB/SimMechanics-based computer simulation of the experimental setup. Different parameter estimation and optimization approaches are evaluated and compared in order to arrive at a reliable and effective parameter identification process. To evaluate each parameter identification approach, a simple one-degree-of-freedom pendulum experiment is constructed and motion is induced using an electric motor. By applying the estimation approach to a simple, accurately modeled system, its effectiveness and accuracy can be evaluated. The same experimental setup can then be used with fluid-filled tanks to further evaluate the effectiveness of the process. Ultimately, the proven process can be applied to the full-sized spinning experimental setup to quickly and accurately determine the slosh model parameters for a particular spacecraft mission. Automating the parameter identification process will save time, allow more changes to be made to proposed designs, and lower the cost in the initial design stages.

Testing alternative ground water models using cross-validation and other methods

USGS Publications Warehouse

Foglia, L.; Mehl, S.W.; Hill, M.C.; Perona, P.; Burlando, P.

2007-01-01

Many methods can be used to test alternative ground water models. Of concern in this work are methods able to (1) rank alternative models (also called model discrimination) and (2) identify observations important to parameter estimates and predictions (equivalent to the purpose served by some types of sensitivity analysis). Some of the measures investigated are computationally efficient; others are computationally demanding. The latter are generally needed to account for model nonlinearity. The efficient model discrimination methods investigated include the information criteria: the corrected Akaike information criterion, Bayesian information criterion, and generalized cross-validation. The efficient sensitivity analysis measures used are dimensionless scaled sensitivity (DSS), composite scaled sensitivity, and parameter correlation coefficient (PCC); the other statistics are DFBETAS, Cook's D, and observation-prediction statistic. Acronyms are explained in the introduction. Cross-validation (CV) is a computationally intensive nonlinear method that is used for both model discrimination and sensitivity analysis. The methods are tested using up to five alternative parsimoniously constructed models of the ground water system of the Maggia Valley in southern Switzerland. The alternative models differ in their representation of hydraulic conductivity. A new method for graphically representing CV and sensitivity analysis results for complex models is presented and used to evaluate the utility of the efficient statistics. The results indicate that for model selection, the information criteria produce similar results at much smaller computational cost than CV. For identifying important observations, the only obviously inferior linear measure is DSS; the poor performance was expected because DSS does not include the effects of parameter correlation and PCC reveals large parameter correlations. ?? 2007 National Ground Water Association.

In silico simulations of experimental protocols for cardiac modeling.

PubMed

Carro, Jesus; Rodriguez, Jose Felix; Pueyo, Esther

2014-01-01

A mathematical model of the AP involves the sum of different transmembrane ionic currents and the balance of intracellular ionic concentrations. To each ionic current corresponds an equation involving several effects. There are a number of model parameters that must be identified using specific experimental protocols in which the effects are considered as independent. However, when the model complexity grows, the interaction between effects becomes increasingly important. Therefore, model parameters identified considering the different effects as independent might be misleading. In this work, a novel methodology consisting in performing in silico simulations of the experimental protocol and then comparing experimental and simulated outcomes is proposed for parameter model identification and validation. The potential of the methodology is demonstrated by validating voltage-dependent L-type calcium current (ICaL) inactivation in recently proposed human ventricular AP models with different formulations. Our results show large differences between ICaL inactivation as calculated from the model equation and ICaL inactivation from the in silico simulations due to the interaction between effects and/or to the experimental protocol. Our results suggest that, when proposing any new model formulation, consistency between such formulation and the corresponding experimental data that is aimed at being reproduced needs to be first verified considering all involved factors.

Linear system identification via backward-time observer models

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Phan, Minh

1993-01-01

This paper presents an algorithm to identify a state-space model of a linear system using a backward-time approach. The procedure consists of three basic steps. First, the Markov parameters of a backward-time observer are computed from experimental input-output data. Second, the backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) from which a backward-time state-space model is realized using the Eigensystem Realization Algorithm. Third, the obtained backward-time state space model is converted to the usual forward-time representation. Stochastic properties of this approach will be discussed. Experimental results are given to illustrate when and to what extent this concept works.

The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model

NASA Astrophysics Data System (ADS)

Ricciuto, Daniel; Sargsyan, Khachik; Thornton, Peter

2018-02-01

We conduct a global sensitivity analysis (GSA) of the Energy Exascale Earth System Model (E3SM), land model (ELM) to calculate the sensitivity of five key carbon cycle outputs to 68 model parameters. This GSA is conducted by first constructing a Polynomial Chaos (PC) surrogate via new Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth leading to a sparse, high-dimensional PC surrogate with 3,000 model evaluations. The PC surrogate allows efficient extraction of GSA information leading to further dimensionality reduction. The GSA is performed at 96 FLUXNET sites covering multiple plant functional types (PFTs) and climate conditions. About 20 of the model parameters are identified as sensitive with the rest being relatively insensitive across all outputs and PFTs. These sensitivities are dependent on PFT, and are relatively consistent among sites within the same PFT. The five model outputs have a majority of their highly sensitive parameters in common. A common subset of sensitive parameters is also shared among PFTs, but some parameters are specific to certain types (e.g., deciduous phenology). The relative importance of these parameters shifts significantly among PFTs and with climatic variables such as mean annual temperature.

Cell death, perfusion and electrical parameters are critical in models of hepatic radiofrequency ablation

PubMed Central

Hall, Sheldon K.; Ooi, Ean H.; Payne, Stephen J.

2015-01-01

Abstract Purpose: A sensitivity analysis has been performed on a mathematical model of radiofrequency ablation (RFA) in the liver. The purpose of this is to identify the most important parameters in the model, defined as those that produce the largest changes in the prediction. This is important in understanding the role of uncertainty and when comparing the model predictions to experimental data. Materials and methods: The Morris method was chosen to perform the sensitivity analysis because it is ideal for models with many parameters or that take a significant length of time to obtain solutions. A comprehensive literature review was performed to obtain ranges over which the model parameters are expected to vary, crucial input information. Results: The most important parameters in predicting the ablation zone size in our model of RFA are those representing the blood perfusion, electrical conductivity and the cell death model. The size of the 50 °C isotherm is sensitive to the electrical properties of tissue while the heat source is active, and to the thermal parameters during cooling. Conclusions: The parameter ranges chosen for the sensitivity analysis are believed to represent all that is currently known about their values in combination. The Morris method is able to compute global parameter sensitivities taking into account the interaction of all parameters, something that has not been done before. Research is needed to better understand the uncertainties in the cell death, electrical conductivity and perfusion models, but the other parameters are only of second order, providing a significant simplification. PMID:26000972

Modelling decremental ramps using 2- and 3-parameter "critical power" models.

PubMed

Morton, R Hugh; Billat, Veronique

2013-01-01

The "Critical Power" (CP) model of human bioenergetics provides a valuable way to identify both limits of tolerance to exercise and mechanisms that underpin that tolerance. It applies principally to cycling-based exercise, but with suitable adjustments for analogous units it can be applied to other exercise modalities; in particular to incremental ramp exercise. It has not yet been applied to decremental ramps which put heavy early demand on the anaerobic energy supply system. This paper details cycling-based bioenergetics of decremental ramps using 2- and 3-parameter CP models. It derives equations that, for an individual of known CP model parameters, define those combinations of starting intensity and decremental gradient which will or will not lead to exhaustion before ramping to zero; and equations that predict time to exhaustion on those decremental ramps that will. These are further detailed with suitably chosen numerical and graphical illustrations. These equations can be used for parameter estimation from collected data, or to make predictions when parameters are known.

Modeling of weak blast wave propagation in the lung.

PubMed

D'yachenko, A I; Manyuhina, O V

2006-01-01

Blast injuries of the lung are the most life-threatening after an explosion. The choice of physical parameters responsible for trauma is important to understand its mechanism. We developed a one-dimensional linear model of an elastic wave propagation in foam-like pulmonary parenchyma to identify the possible cause of edema due to the impact load. The model demonstrates different injury localizations for free and rigid boundary conditions. The following parameters were considered: strain, velocity, pressure in the medium and stresses in structural elements, energy dissipation, parameter of viscous criterion. Maximum underpressure is the most suitable wave parameter to be the criterion for edema formation in a rabbit lung. We supposed that observed scattering of experimental data on edema severity is induced by the physiological variety of rabbit lungs. The criterion and the model explain this scattering. The model outlines the demands for experimental data to make an unambiguous choice of physical parameters responsible for lung trauma due to impact load.

Prediction of biomechanical parameters of the proximal femur using statistical appearance models and support vector regression.

PubMed

Fritscher, Karl; Schuler, Benedikt; Link, Thomas; Eckstein, Felix; Suhm, Norbert; Hänni, Markus; Hengg, Clemens; Schubert, Rainer

2008-01-01

Fractures of the proximal femur are one of the principal causes of mortality among elderly persons. Traditional methods for the determination of femoral fracture risk use methods for measuring bone mineral density. However, BMD alone is not sufficient to predict bone failure load for an individual patient and additional parameters have to be determined for this purpose. In this work an approach that uses statistical models of appearance to identify relevant regions and parameters for the prediction of biomechanical properties of the proximal femur will be presented. By using Support Vector Regression the proposed model based approach is capable of predicting two different biomechanical parameters accurately and fully automatically in two different testing scenarios.

The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model

NASA Astrophysics Data System (ADS)

Cuntz, Matthias; Mai, Juliane; Samaniego, Luis; Clark, Martyn; Wulfmeyer, Volker; Branch, Oliver; Attinger, Sabine; Thober, Stephan

2016-09-01

Land surface models incorporate a large number of process descriptions, containing a multitude of parameters. These parameters are typically read from tabulated input files. Some of these parameters might be fixed numbers in the computer code though, which hinder model agility during calibration. Here we identified 139 hard-coded parameters in the model code of the Noah land surface model with multiple process options (Noah-MP). We performed a Sobol' global sensitivity analysis of Noah-MP for a specific set of process options, which includes 42 out of the 71 standard parameters and 75 out of the 139 hard-coded parameters. The sensitivities of the hydrologic output fluxes latent heat and total runoff as well as their component fluxes were evaluated at 12 catchments within the United States with very different hydrometeorological regimes. Noah-MP's hydrologic output fluxes are sensitive to two thirds of its applicable standard parameters (i.e., Sobol' indexes above 1%). The most sensitive parameter is, however, a hard-coded value in the formulation of soil surface resistance for direct evaporation, which proved to be oversensitive in other land surface models as well. Surface runoff is sensitive to almost all hard-coded parameters of the snow processes and the meteorological inputs. These parameter sensitivities diminish in total runoff. Assessing these parameters in model calibration would require detailed snow observations or the calculation of hydrologic signatures of the runoff data. Latent heat and total runoff exhibit very similar sensitivities because of their tight coupling via the water balance. A calibration of Noah-MP against either of these fluxes should therefore give comparable results. Moreover, these fluxes are sensitive to both plant and soil parameters. Calibrating, for example, only soil parameters hence limit the ability to derive realistic model parameters. It is thus recommended to include the most sensitive hard-coded model parameters that were exposed in this study when calibrating Noah-MP.

Individual heterogeneity and identifiability in capture-recapture models

USGS Publications Warehouse

Link, W.A.

2004-01-01

Individual heterogeneity in detection probabilities is a far more serious problem for capture-recapture modeling than has previously been recognized. In this note, I illustrate that population size is not an identifiable parameter under the general closed population mark-recapture model Mh. The problem of identifiability is obvious if the population includes individuals with pi = 0, but persists even when it is assumed that individual detection probabilities are bounded away from zero. Identifiability may be attained within parametric families of distributions for pi, but not among parametric families of distributions. Consequently, in the presence of individual heterogeneity in detection probability, capture-recapture analysis is strongly model dependent.

Application of Bayesian model averaging to measurements of the primordial power spectrum

NASA Astrophysics Data System (ADS)

Parkinson, David; Liddle, Andrew R.

2010-11-01

Cosmological parameter uncertainties are often stated assuming a particular model, neglecting the model uncertainty, even when Bayesian model selection is unable to identify a conclusive best model. Bayesian model averaging is a method for assessing parameter uncertainties in situations where there is also uncertainty in the underlying model. We apply model averaging to the estimation of the parameters associated with the primordial power spectra of curvature and tensor perturbations. We use CosmoNest and MultiNest to compute the model evidences and posteriors, using cosmic microwave data from WMAP, ACBAR, BOOMERanG, and CBI, plus large-scale structure data from the SDSS DR7. We find that the model-averaged 95% credible interval for the spectral index using all of the data is 0.940

Simulation of dynamics of beam structures with bolted joints using adjusted Iwan beam elements

NASA Astrophysics Data System (ADS)

Song, Y.; Hartwigsen, C. J.; McFarland, D. M.; Vakakis, A. F.; Bergman, L. A.

2004-05-01

Mechanical joints often affect structural response, causing localized non-linear stiffness and damping changes. As many structures are assemblies, incorporating the effects of joints is necessary to produce predictive finite element models. In this paper, we present an adjusted Iwan beam element (AIBE) for dynamic response analysis of beam structures containing joints. The adjusted Iwan model consists of a combination of springs and frictional sliders that exhibits non-linear behavior due to the stick-slip characteristic of the latter. The beam element developed is two-dimensional and consists of two adjusted Iwan models and maintains the usual complement of degrees of freedom: transverse displacement and rotation at each of the two nodes. The resulting element includes six parameters, which must be determined. To circumvent the difficulty arising from the non-linear nature of the inverse problem, a multi-layer feed-forward neural network (MLFF) is employed to extract joint parameters from measured structural acceleration responses. A parameter identification procedure is implemented on a beam structure with a bolted joint. In this procedure, acceleration responses at one location on the beam structure due to one known impulsive forcing function are simulated for sets of combinations of varying joint parameters. A MLFF is developed and trained using the patterns of envelope data corresponding to these acceleration histories. The joint parameters are identified through the trained MLFF applied to the measured acceleration response. Then, using the identified joint parameters, acceleration responses of the jointed beam due to a different impulsive forcing function are predicted. The validity of the identified joint parameters is assessed by comparing simulated acceleration responses with experimental measurements. The capability of the AIBE to capture the effects of bolted joints on the dynamic responses of beam structures, and the efficacy of the MLFF parameter identification procedure, are demonstrated.

Use of Linear Prediction Uncertainty Analysis to Guide Conditioning of Models Simulating Surface-Water/Groundwater Interactions

NASA Astrophysics Data System (ADS)

Hughes, J. D.; White, J.; Doherty, J.

2011-12-01

Linear prediction uncertainty analysis in a Bayesian framework was applied to guide the conditioning of an integrated surface water/groundwater model that will be used to predict the effects of groundwater withdrawals on surface-water and groundwater flows. Linear prediction uncertainty analysis is an effective approach for identifying (1) raw and processed data most effective for model conditioning prior to inversion, (2) specific observations and periods of time critically sensitive to specific predictions, and (3) additional observation data that would reduce model uncertainty relative to specific predictions. We present results for a two-dimensional groundwater model of a 2,186 km2 area of the Biscayne aquifer in south Florida implicitly coupled to a surface-water routing model of the actively managed canal system. The model domain includes 5 municipal well fields withdrawing more than 1 Mm3/day and 17 operable surface-water control structures that control freshwater releases from the Everglades and freshwater discharges to Biscayne Bay. More than 10 years of daily observation data from 35 groundwater wells and 24 surface water gages are available to condition model parameters. A dense parameterization was used to fully characterize the contribution of the inversion null space to predictive uncertainty and included bias-correction parameters. This approach allows better resolution of the boundary between the inversion null space and solution space. Bias-correction parameters (e.g., rainfall, potential evapotranspiration, and structure flow multipliers) absorb information that is present in structural noise that may otherwise contaminate the estimation of more physically-based model parameters. This allows greater precision in predictions that are entirely solution-space dependent, and reduces the propensity for bias in predictions that are not. Results show that application of this analysis is an effective means of identifying those surface-water and groundwater data, both raw and processed, that minimize predictive uncertainty, while simultaneously identifying the maximum solution-space dimensionality of the inverse problem supported by the data.

New Uses for Sensitivity Analysis: How Different Movement Tasks Effect Limb Model Parameter Sensitivity

NASA Technical Reports Server (NTRS)

Winters, J. M.; Stark, L.

1984-01-01

Original results for a newly developed eight-order nonlinear limb antagonistic muscle model of elbow flexion and extension are presented. A wider variety of sensitivity analysis techniques are used and a systematic protocol is established that shows how the different methods can be used efficiently to complement one another for maximum insight into model sensitivity. It is explicitly shown how the sensitivity of output behaviors to model parameters is a function of the controller input sequence, i.e., of the movement task. When the task is changed (for instance, from an input sequence that results in the usual fast movement task to a slower movement that may also involve external loading, etc.) the set of parameters with high sensitivity will in general also change. Such task-specific use of sensitivity analysis techniques identifies the set of parameters most important for a given task, and even suggests task-specific model reduction possibilities.

Model parameter uncertainty analysis for an annual field-scale P loss model

NASA Astrophysics Data System (ADS)

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 development and evaluation efforts.

Properties of added variable plots in Cox's regression model.

PubMed

Lindkvist, M

2000-03-01

The added variable plot is useful for examining the effect of a covariate in regression models. The plot provides information regarding the inclusion of a covariate, and is useful in identifying influential observations on the parameter estimates. Hall et al. (1996) proposed a plot for Cox's proportional hazards model derived by regarding the Cox model as a generalized linear model. This paper proves and discusses properties of this plot. These properties make the plot a valuable tool in model evaluation. Quantities considered include parameter estimates, residuals, leverage, case influence measures and correspondence to previously proposed residuals and diagnostics.

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

PubMed

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

2014-03-01

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

Subject-specific cardiovascular system model-based identification and diagnosis of septic shock with a minimally invasive data set: animal experiments and proof of concept.

PubMed

Chase, J Geoffrey; Lambermont, Bernard; Starfinger, Christina; Hann, Christopher E; Shaw, Geoffrey M; Ghuysen, Alexandre; Kolh, Philippe; Dauby, Pierre C; Desaive, Thomas

2011-01-01

A cardiovascular system (CVS) model and parameter identification method have previously been validated for identifying different cardiac and circulatory dysfunctions in simulation and using porcine models of pulmonary embolism, hypovolemia with PEEP titrations and induced endotoxic shock. However, these studies required both left and right heart catheters to collect the data required for subject-specific monitoring and diagnosis-a maximally invasive data set in a critical care setting although it does occur in practice. Hence, use of this model-based diagnostic would require significant additional invasive sensors for some subjects, which is unacceptable in some, if not all, cases. The main goal of this study is to prove the concept of using only measurements from one side of the heart (right) in a 'minimal' data set to identify an effective patient-specific model that can capture key clinical trends in endotoxic shock. This research extends existing methods to a reduced and minimal data set requiring only a single catheter and reducing the risk of infection and other complications-a very common, typical situation in critical care patients, particularly after cardiac surgery. The extended methods and assumptions that found it are developed and presented in a case study for the patient-specific parameter identification of pig-specific parameters in an animal model of induced endotoxic shock. This case study is used to define the impact of this minimal data set on the quality and accuracy of the model application for monitoring, detecting and diagnosing septic shock. Six anesthetized healthy pigs weighing 20-30 kg received a 0.5 mg kg(-1) endotoxin infusion over a period of 30 min from T0 to T30. For this research, only right heart measurements were obtained. Errors for the identified model are within 8% when the model is identified from data, re-simulated and then compared to the experimentally measured data, including measurements not used in the identification process for validation. Importantly, all identified parameter trends match physiologically and clinically and experimentally expected changes, indicating that no diagnostic power is lost. This work represents a further with human subjects validation for this model-based approach to cardiovascular diagnosis and therapy guidance in monitoring endotoxic disease states. The results and methods obtained can be readily extended from this case study to the other animal model results presented previously. Overall, these results provide further support for prospective, proof of concept clinical testing with humans.

A new hyper-elastic model for predicting multi-axial behaviour of rubber-like materials: formulation and computational aspects

NASA Astrophysics Data System (ADS)

Yaya, Kamel; Bechir, Hocine

2018-05-01

We propose a new hyper-elastic model that is based on the standard invariants of Green-Cauchy. Experimental data reported by Treloar (Trans. Faraday Soc. 40:59, 1944) are used to identify the model parameters. To this end, the data of uni-axial tension and equi-bi-axial tension are used simultaneously. The new model has four material parameters, their identification leads to linear optimisation problem and it is able to predict multi-axial behaviour of rubber-like materials. We show that the response quality of the new model is equivalent to that of the well-known Ogden six parameters model. Thereafter, the new model is implemented in FE code. Then, we investigate the inflation of a rubber balloon with the new model and Ogden models. We compare both the analytic and numerical solutions derived from these models.

Influence of parameter values on the oscillation sensitivities of two p53-Mdm2 models.

PubMed

Cuba, Christian E; Valle, Alexander R; Ayala-Charca, Giancarlo; Villota, Elizabeth R; Coronado, Alberto M

2015-09-01

Biomolecular networks that present oscillatory behavior are ubiquitous in nature. While some design principles for robust oscillations have been identified, it is not well understood how these oscillations are affected when the kinetic parameters are constantly changing or are not precisely known, as often occurs in cellular environments. Many models of diverse complexity level, for systems such as circadian rhythms, cell cycle or the p53 network, have been proposed. Here we assess the influence of hundreds of different parameter sets on the sensitivities of two configurations of a well-known oscillatory system, the p53 core network. We show that, for both models and all parameter sets, the parameter related to the p53 positive feedback, i.e. self-promotion, is the only one that presents sizeable sensitivities on extrema, periods and delay. Moreover, varying the parameter set values to change the dynamical characteristics of the response is more restricted in the simple model, whereas the complex model shows greater tunability. These results highlight the importance of the presence of specific network patterns, in addition to the role of parameter values, when we want to characterize oscillatory biochemical systems.

Identifiability of altimetry-based rating curve parameters in function of river morphological parameters

NASA Astrophysics Data System (ADS)

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 elevation Z0is systematically well identified with relative errors on the order of a few %. Eventually, these altimetry-based rating curves provide morphological parameters of river reaches that can be used as inputs into hydraulic models and a priori information that could be useful for SWOT inversion algorithms.

Efficient design based on perturbed parameter ensembles to identify plausible and diverse variants of a model for climate change projections

NASA Astrophysics Data System (ADS)

Karmalkar, A.; Sexton, D.; Murphy, J.

2017-12-01

We present exploratory work towards developing an efficient strategy to select variants of a state-of-the-art but expensive climate model suitable for climate projection studies. The strategy combines information from a set of idealized perturbed parameter ensemble (PPE) and CMIP5 multi-model ensemble (MME) experiments, and uses two criteria as basis to select model variants for a PPE suitable for future projections: a) acceptable model performance at two different timescales, and b) maintaining diversity in model response to climate change. We demonstrate that there is a strong relationship between model errors at weather and climate timescales for a variety of key variables. This relationship is used to filter out parts of parameter space that do not give credible simulations of historical climate, while minimizing the impact on ranges in forcings and feedbacks that drive model responses to climate change. We use statistical emulation to explore the parameter space thoroughly, and demonstrate that about 90% can be filtered out without affecting diversity in global-scale climate change responses. This leads to identification of plausible parts of parameter space from which model variants can be selected for projection studies.

A comprehensive approach to identify dominant controls of the behavior of a land surface-hydrology model across various hydroclimatic conditions

NASA Astrophysics Data System (ADS)

Haghnegahdar, Amin; Elshamy, Mohamed; Yassin, Fuad; Razavi, Saman; Wheater, Howard; Pietroniro, Al

2017-04-01

Complex physically-based environmental models are being increasingly used as the primary tool for watershed planning and management due to advances in computation power and data acquisition. Model sensitivity analysis plays a crucial role in understanding the behavior of these complex models and improving their performance. Due to the non-linearity and interactions within these complex models, Global sensitivity analysis (GSA) techniques should be adopted to provide a comprehensive understanding of model behavior and identify its dominant controls. In this study we adopt a multi-basin multi-criteria GSA approach to systematically assess the behavior of the Modélisation Environmentale-Surface et Hydrologie (MESH) across various hydroclimatic conditions in Canada including areas in the Great Lakes Basin, Mackenzie River Basin, and South Saskatchewan River Basin. MESH is a semi-distributed physically-based coupled land surface-hydrology modelling system developed by Environment and Climate Change Canada (ECCC) for various water resources management purposes in Canada. We use a novel method, called Variogram Analysis of Response Surfaces (VARS), to perform sensitivity analysis. VARS is a variogram-based GSA technique that can efficiently provide a spectrum of sensitivity information across a range of scales within the parameter space. We use multiple metrics to identify dominant controls of model response (e.g. streamflow) to model parameters under various conditions such as high flows, low flows, and flow volume. We also investigate the influence of initial conditions on model behavior as part of this study. Our preliminary results suggest that this type of GSA can significantly help with estimating model parameters, decreasing calibration computational burden, and reducing prediction uncertainty.

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

PubMed

Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R

2012-08-01

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.

A global sensitivity analysis approach for morphogenesis models.

PubMed

Boas, Sonja E M; Navarro Jimenez, Maria I; Merks, Roeland M H; Blom, Joke G

2015-11-21

Morphogenesis is a developmental process in which cells organize into shapes and patterns. Complex, non-linear and multi-factorial models with images as output are commonly used to study morphogenesis. It is difficult to understand the relation between the uncertainty in the input and the output of such 'black-box' models, giving rise to the need for sensitivity analysis tools. In this paper, we introduce a workflow for a global sensitivity analysis approach to study the impact of single parameters and the interactions between them on the output of morphogenesis models. To demonstrate the workflow, we used a published, well-studied model of vascular morphogenesis. The parameters of this cellular Potts model (CPM) represent cell properties and behaviors that drive the mechanisms of angiogenic sprouting. The global sensitivity analysis correctly identified the dominant parameters in the model, consistent with previous studies. Additionally, the analysis provided information on the relative impact of single parameters and of interactions between them. This is very relevant because interactions of parameters impede the experimental verification of the predicted effect of single parameters. The parameter interactions, although of low impact, provided also new insights in the mechanisms of in silico sprouting. Finally, the analysis indicated that the model could be reduced by one parameter. We propose global sensitivity analysis as an alternative approach to study the mechanisms of morphogenesis. Comparison of the ranking of the impact of the model parameters to knowledge derived from experimental data and from manipulation experiments can help to falsify models and to find the operand mechanisms in morphogenesis. The workflow is applicable to all 'black-box' models, including high-throughput in vitro models in which output measures are affected by a set of experimental perturbations.

Parameter sensitivity analysis and optimization for a satellite-based evapotranspiration model across multiple sites using Moderate Resolution Imaging Spectroradiometer and flux data

NASA Astrophysics Data System (ADS)

Zhang, Kun; Ma, Jinzhu; Zhu, Gaofeng; Ma, Ting; Han, Tuo; Feng, Li Li

2017-01-01

Global and regional estimates of daily evapotranspiration are essential to our understanding of the hydrologic cycle and climate change. In this study, we selected the radiation-based Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) model and assessed it at a daily time scale by using 44 flux towers. These towers distributed in a wide range of ecological systems: croplands, deciduous broadleaf forest, evergreen broadleaf forest, evergreen needleleaf forest, grasslands, mixed forests, savannas, and shrublands. A regional land surface evapotranspiration model with a relatively simple structure, the PT-JPL model largely uses ecophysiologically-based formulation and parameters to relate potential evapotranspiration to actual evapotranspiration. The results using the original model indicate that the model always overestimates evapotranspiration in arid regions. This likely results from the misrepresentation of water limitation and energy partition in the model. By analyzing physiological processes and determining the sensitive parameters, we identified a series of parameter sets that can increase model performance. The model with optimized parameters showed better performance (R2 = 0.2-0.87; Nash-Sutcliffe efficiency (NSE) = 0.1-0.87) at each site than the original model (R2 = 0.19-0.87; NSE = -12.14-0.85). The results of the optimization indicated that the parameter β (water control of soil evaporation) was much lower in arid regions than in relatively humid regions. Furthermore, the optimized value of parameter m1 (plant control of canopy transpiration) was mostly between 1 to 1.3, slightly lower than the original value. Also, the optimized parameter Topt correlated well to the actual environmental temperature at each site. We suggest that using optimized parameters with the PT-JPL model could provide an efficient way to improve the model performance.

Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology

PubMed Central

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. PMID:25089832

Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model

NASA Astrophysics Data System (ADS)

Sun, Y.; Hou, Z.; Huang, M.; Tian, F.; Leung, L. Ruby

2013-12-01

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. Both deterministic least-square fitting and stochastic Markov-chain Monte Carlo (MCMC)-Bayesian inversion approaches are evaluated by applying them to CLM4 at selected sites with different climate and soil conditions. 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 sampling-based stochastic inversion approaches provides significant improvements in the model simulations compared to using default CLM4 parameter values, and that as more information comes in, the predictive intervals (ranges of posterior distributions) of the calibrated parameters become narrower. 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 bounds.

AAA gunnermodel based on observer theory. [predicting a gunner's tracking response

NASA Technical Reports Server (NTRS)

Kou, R. S.; Glass, B. C.; Day, C. N.; Vikmanis, M. M.

1978-01-01

The Luenberger observer theory is used to develop a predictive model of a gunner's tracking response in antiaircraft artillery systems. This model is composed of an observer, a feedback controller and a remnant element. An important feature of the model is that the structure is simple, hence a computer simulation requires only a short execution time. A parameter identification program based on the least squares curve fitting method and the Gauss Newton gradient algorithm is developed to determine the parameter values of the gunner model. Thus, a systematic procedure exists for identifying model parameters for a given antiaircraft tracking task. Model predictions of tracking errors are compared with human tracking data obtained from manned simulation experiments. Model predictions are in excellent agreement with the empirical data for several flyby and maneuvering target trajectories.

Variation of yield loci in finite element analysis by considering texture evolution for AA5042 aluminum sheets

NASA Astrophysics Data System (ADS)

Yoon, Jonghun; Kim, Kyungjin; Yoon, Jeong Whan

2013-12-01

Yield function has various material parameters that describe how materials respond plastically in given conditions. However, a significant number of mechanical tests are required to identify the many material parameters for yield function. In this study, an effective method using crystal plasticity through a virtual experiment is introduced to develop the anisotropic yield function for AA5042. The crystal plasticity approach was used to predict the anisotropic response of the material in order to consider a number of stress or strain modes that would not otherwise be evident through mechanical testing. A rate-independent crystal plasticity model based on a smooth single crystal yield surface, which removes the innate ambiguity problem within the rate-independent model and Taylor model for polycrystalline deformation behavior were employed to predict the material's response in the balanced biaxial stress, pure shear, and plane strain states to identify the parameters for the anisotropic yield function of AA5042.

Thermodynamic, electronic and magnetic properties of intermetallic compounds through statistical models

NASA Astrophysics Data System (ADS)

Cadeville, M. C.; Pierron-Bohnes, V.; Bouzidi, L.; Sanchez, J. M.

1993-01-01

Local and average electronic and magnetic properties of transition metal alloys are strongly correlated to the distribution of atoms on the lattice sites. The ability of some systems to form long range ordered structures at low temperature allows to discuss their properties in term of well identified occupation operators as those related to long range order (LRO) parameters. We show that using theoretical determinations of these LRO parameters through statistical models like the cluster variation method (CVM) developed to simulate the experimental phase diagrams, we are able to reproduce a lot of physical properties. In this paper we focus on two points: (i) a comparison between CVM results and an experimental determination of the LRO parameter by NMR at 59Co in a CoPt3 compound, and (ii) the modelling of the resistivity of ferromagnetic and paramagnetic intermetallic compounds belonging to Co-Pt, Ni-Pt and Fe-Al systems. All experiments were performed on samples in identified thermodynamic states, implying that kinetic effects are thoroughly taken into account.

Calibration of micromechanical parameters for DEM simulations by using the particle filter

NASA Astrophysics Data System (ADS)

Cheng, Hongyang; Shuku, Takayuki; Thoeni, Klaus; Yamamoto, Haruyuki

2017-06-01

The calibration of DEM models is typically accomplished by trail and error. However, the procedure lacks of objectivity and has several uncertainties. To deal with these issues, the particle filter is employed as a novel approach to calibrate DEM models of granular soils. The posterior probability distribution of the microparameters that give numerical results in good agreement with the experimental response of a Toyoura sand specimen is approximated by independent model trajectories, referred as `particles', based on Monte Carlo sampling. The soil specimen is modeled by polydisperse packings with different numbers of spherical grains. Prepared in `stress-free' states, the packings are subjected to triaxial quasistatic loading. Given the experimental data, the posterior probability distribution is incrementally updated, until convergence is reached. The resulting `particles' with higher weights are identified as the calibration results. The evolutions of the weighted averages and posterior probability distribution of the micro-parameters are plotted to show the advantage of using a particle filter, i.e., multiple solutions are identified for each parameter with known probabilities of reproducing the experimental response.

Identification of quasi-steady compressor characteristics from transient data

NASA Technical Reports Server (NTRS)

Nunes, K. B.; Rock, S. M.

1984-01-01

The principal goal was to demonstrate that nonlinear compressor map parameters, which govern an in-stall response, can be identified from test data using parameter identification techniques. The tasks included developing and then applying an identification procedure to data generated by NASA LeRC on a hybrid computer. Two levels of model detail were employed. First was a lumped compressor rig model; second was a simplified turbofan model. The main outputs are the tools and procedures generated to accomplish the identification.

An Overview of Kinematic and Calibration Models Using Internal/External Sensors or Constraints to Improve the Behavior of Spatial Parallel Mechanisms

PubMed Central

Majarena, Ana C.; Santolaria, Jorge; Samper, David; Aguilar, Juan J.

2010-01-01

This paper presents an overview of the literature on kinematic and calibration models of parallel mechanisms, the influence of sensors in the mechanism accuracy and parallel mechanisms used as sensors. The most relevant classifications to obtain and solve kinematic models and to identify geometric and non-geometric parameters in the calibration of parallel robots are discussed, examining the advantages and disadvantages of each method, presenting new trends and identifying unsolved problems. This overview tries to answer and show the solutions developed by the most up-to-date research to some of the most frequent questions that appear in the modelling of a parallel mechanism, such as how to measure, the number of sensors and necessary configurations, the type and influence of errors or the number of necessary parameters. PMID:22163469

Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang; Huang, Zhihong; Yan, Wende

2014-01-01

Ecosystem models are useful tools for understanding ecological processes and for sustainable management of resources. In biogeochemical field, numerical models have been widely used for investigating carbon dynamics under global changes from site to regional and global scales. However, it is still challenging to optimize parameters and estimate parameterization uncertainty for complex process-based models such as the Erosion Deposition Carbon Model (EDCM), a modified version of CENTURY, that consider carbon, water, and nutrient cycles of ecosystems. This study was designed to conduct the parameter identifiability, optimization, sensitivity, and uncertainty analysis of EDCM using our developed EDCM-Auto, which incorporated a comprehensive R package—Flexible Modeling Framework (FME) and the Shuffled Complex Evolution (SCE) algorithm. Using a forest flux tower site as a case study, we implemented a comprehensive modeling analysis involving nine parameters and four target variables (carbon and water fluxes) with their corresponding measurements based on the eddy covariance technique. The local sensitivity analysis shows that the plant production-related parameters (e.g., PPDF1 and PRDX) are most sensitive to the model cost function. Both SCE and FME are comparable and performed well in deriving the optimal parameter set with satisfactory simulations of target variables. Global sensitivity and uncertainty analysis indicate that the parameter uncertainty and the resulting output uncertainty can be quantified, and that the magnitude of parameter-uncertainty effects depends on variables and seasons. This study also demonstrates that using the cutting-edge R functions such as FME can be feasible and attractive for conducting comprehensive parameter analysis for ecosystem modeling.

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection

PubMed Central

Alam, Maksudul; Deng, Xinwei; Philipson, Casandra; Bassaganya-Riera, Josep; Bisset, Keith; Carbo, Adria; Eubank, Stephen; Hontecillas, Raquel; Hoops, Stefan; Mei, Yongguo; Abedi, Vida; Marathe, Madhav

2015-01-01

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa. PMID:26327290

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection.

PubMed

Alam, Maksudul; Deng, Xinwei; Philipson, Casandra; Bassaganya-Riera, Josep; Bisset, Keith; Carbo, Adria; Eubank, Stephen; Hontecillas, Raquel; Hoops, Stefan; Mei, Yongguo; Abedi, Vida; Marathe, Madhav

2015-01-01

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close "neighborhood" of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.

Model Performance Evaluation and Scenario Analysis ...

EPA Pesticide Factsheets

This tool consists of two parts: model performance evaluation and scenario analysis (MPESA). The model performance evaluation consists of two components: model performance evaluation metrics and model diagnostics. These metrics provides modelers with statistical goodness-of-fit measures that capture magnitude only, sequence only, and combined magnitude and sequence errors. The performance measures include error analysis, coefficient of determination, Nash-Sutcliffe efficiency, and a new weighted rank method. These performance metrics only provide useful information about the overall model performance. Note that MPESA is based on the separation of observed and simulated time series into magnitude and sequence components. The separation of time series into magnitude and sequence components and the reconstruction back to time series provides diagnostic insights to modelers. For example, traditional approaches lack the capability to identify if the source of uncertainty in the simulated data is due to the quality of the input data or the way the analyst adjusted the model parameters. This report presents a suite of model diagnostics that identify if mismatches between observed and simulated data result from magnitude or sequence related errors. MPESA offers graphical and statistical options that allow HSPF users to compare observed and simulated time series and identify the parameter values to adjust or the input data to modify. The scenario analysis part of the too

A Path Model for Evaluating Dosing Parameters for Children With Cerebral Palsy

PubMed Central

Christy, Jennifer B.; Heathcock, Jill C.; Kolobe, Thubi H.A.

2014-01-01

Dosing of pediatric rehabilitation services for children with cerebral palsy (CP) has been identified as a national priority. Establishing dosing parameters for pediatric physical therapy interventions is critical for informing clinical decision making, health policy, and guidelines for reimbursement. The purpose of this perspective article is to describe a path model for evaluating dosing parameters of interventions for children with CP. The model is intended for dose-related and effectiveness studies of pediatric physical therapy interventions. The premise of the model is: Intervention type (focus on body structures, activity, or the environment) acts on a child first through the family, then through the dose (frequency, intensity, time), to yield structural and behavioral changes. As a result, these changes are linked to improvements in functional independence. Community factors affect dose as well as functional independence (performance and capacity), influencing the relationships between type of intervention and intervention responses. The constructs of family characteristics; child characteristics (eg, age, level of severity, comorbidities, readiness to change, preferences); plastic changes in bone, muscle, and brain; motor skill acquisition; and community access warrant consideration from researchers who are designing intervention studies. Multiple knowledge gaps are identified, and a framework is provided for conceptualizing dosing parameters for children with CP. PMID:24231231

The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems.

PubMed

White, Andrew; Tolman, Malachi; Thames, Howard D; Withers, Hubert Rodney; Mason, Kathy A; Transtrum, Mark K

2016-12-01

We explore the relationship among experimental design, parameter estimation, and systematic error in sloppy models. We show that the approximate nature of mathematical models poses challenges for experimental design in sloppy models. In many models of complex biological processes it is unknown what are the relevant physical mechanisms that must be included to explain system behaviors. As a consequence, models are often overly complex, with many practically unidentifiable parameters. Furthermore, which mechanisms are relevant/irrelevant vary among experiments. By selecting complementary experiments, experimental design may inadvertently make details that were ommitted from the model become relevant. When this occurs, the model will have a large systematic error and fail to give a good fit to the data. We use a simple hyper-model of model error to quantify a model's discrepancy and apply it to two models of complex biological processes (EGFR signaling and DNA repair) with optimally selected experiments. We find that although parameters may be accurately estimated, the discrepancy in the model renders it less predictive than it was in the sloppy regime where systematic error is small. We introduce the concept of a sloppy system-a sequence of models of increasing complexity that become sloppy in the limit of microscopic accuracy. We explore the limits of accurate parameter estimation in sloppy systems and argue that identifying underlying mechanisms controlling system behavior is better approached by considering a hierarchy of models of varying detail rather than focusing on parameter estimation in a single model.

Method for Constructing Composite Response Surfaces by Combining Neural Networks with Polynominal Interpolation or Estimation Techniques

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor); Madavan, Nateri K. (Inventor)

2007-01-01

A method and system for data modeling that incorporates the advantages of both traditional response surface methodology (RSM) and neural networks is disclosed. The invention partitions the parameters into a first set of s simple parameters, where observable data are expressible as low order polynomials, and c complex parameters that reflect more complicated variation of the observed data. Variation of the data with the simple parameters is modeled using polynomials; and variation of the data with the complex parameters at each vertex is analyzed using a neural network. Variations with the simple parameters and with the complex parameters are expressed using a first sequence of shape functions and a second sequence of neural network functions. The first and second sequences are multiplicatively combined to form a composite response surface, dependent upon the parameter values, that can be used to identify an accurate mode

An effective automatic procedure for testing parameter identifiability of HIV/AIDS models.

PubMed

Saccomani, Maria Pia

2011-08-01

Realistic HIV models tend to be rather complex and many recent models proposed in the literature could not yet be analyzed by traditional identifiability testing techniques. In this paper, we check a priori global identifiability of some of these nonlinear HIV models taken from the recent literature, by using a differential algebra algorithm based on previous work of the author. The algorithm is implemented in a software tool, called DAISY (Differential Algebra for Identifiability of SYstems), which has been recently released (DAISY is freely available on the web site http://www.dei.unipd.it/~pia/ ). The software can be used to automatically check global identifiability of (linear and) nonlinear models described by polynomial or rational differential equations, thus providing a general and reliable tool to test global identifiability of several HIV models proposed in the literature. It can be used by researchers with a minimum of mathematical background.

A new methodology based on sensitivity analysis to simplify the recalibration of functional-structural plant models in new conditions.

PubMed

Mathieu, Amélie; Vidal, Tiphaine; Jullien, Alexandra; Wu, QiongLi; Chambon, Camille; Bayol, Benoit; Cournède, Paul-Henry

2018-06-19

Functional-structural plant models (FSPMs) describe explicitly the interactions between plants and their environment at organ to plant scale. However, the high level of description of the structure or model mechanisms makes this type of model very complex and hard to calibrate. A two-step methodology to facilitate the calibration process is proposed here. First, a global sensitivity analysis method was applied to the calibration loss function. It provided first-order and total-order sensitivity indexes that allow parameters to be ranked by importance in order to select the most influential ones. Second, the Akaike information criterion (AIC) was used to quantify the model's quality of fit after calibration with different combinations of selected parameters. The model with the lowest AIC gives the best combination of parameters to select. This methodology was validated by calibrating the model on an independent data set (same cultivar, another year) with the parameters selected in the second step. All the parameters were set to their nominal value; only the most influential ones were re-estimated. Sensitivity analysis applied to the calibration loss function is a relevant method to underline the most significant parameters in the estimation process. For the studied winter oilseed rape model, 11 out of 26 estimated parameters were selected. Then, the model could be recalibrated for a different data set by re-estimating only three parameters selected with the model selection method. Fitting only a small number of parameters dramatically increases the efficiency of recalibration, increases the robustness of the model and helps identify the principal sources of variation in varying environmental conditions. This innovative method still needs to be more widely validated but already gives interesting avenues to improve the calibration of FSPMs.

Systematic parameter inference in stochastic mesoscopic modeling

NASA Astrophysics Data System (ADS)

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.

Modeling and experimental validation of a Hybridized Energy Storage System for automotive applications

NASA Astrophysics Data System (ADS)

Fiorenti, Simone; Guanetti, Jacopo; Guezennec, Yann; Onori, Simona

2013-11-01

This paper presents the development and experimental validation of a dynamic model of a Hybridized Energy Storage System (HESS) consisting of a parallel connection of a lead acid (PbA) battery and double layer capacitors (DLCs), for automotive applications. The dynamic modeling of both the PbA battery and the DLC has been tackled via the equivalent electric circuit based approach. Experimental tests are designed for identification purposes. Parameters of the PbA battery model are identified as a function of state of charge and current direction, whereas parameters of the DLC model are identified for different temperatures. A physical HESS has been assembled at the Center for Automotive Research The Ohio State University and used as a test-bench to validate the model against a typical current profile generated for Start&Stop applications. The HESS model is then integrated into a vehicle simulator to assess the effects of the battery hybridization on the vehicle fuel economy and mitigation of the battery stress.

Inverse Modeling of Hydrologic Parameters Using Surface Flux and Runoff Observations in the Community Land Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Yu; Hou, Zhangshuan; Huang, Maoyi

2013-12-10

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 thatmore » 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 the 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 bounds.« less

The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems

PubMed Central

Tolman, Malachi; Thames, Howard D.; Mason, Kathy A.

2016-01-01

We explore the relationship among experimental design, parameter estimation, and systematic error in sloppy models. We show that the approximate nature of mathematical models poses challenges for experimental design in sloppy models. In many models of complex biological processes it is unknown what are the relevant physical mechanisms that must be included to explain system behaviors. As a consequence, models are often overly complex, with many practically unidentifiable parameters. Furthermore, which mechanisms are relevant/irrelevant vary among experiments. By selecting complementary experiments, experimental design may inadvertently make details that were ommitted from the model become relevant. When this occurs, the model will have a large systematic error and fail to give a good fit to the data. We use a simple hyper-model of model error to quantify a model’s discrepancy and apply it to two models of complex biological processes (EGFR signaling and DNA repair) with optimally selected experiments. We find that although parameters may be accurately estimated, the discrepancy in the model renders it less predictive than it was in the sloppy regime where systematic error is small. We introduce the concept of a sloppy system–a sequence of models of increasing complexity that become sloppy in the limit of microscopic accuracy. We explore the limits of accurate parameter estimation in sloppy systems and argue that identifying underlying mechanisms controlling system behavior is better approached by considering a hierarchy of models of varying detail rather than focusing on parameter estimation in a single model. PMID:27923060

Origin of the sensitivity in modeling the glide behaviour of dislocations

DOE PAGES

Pei, Zongrui; Stocks, George Malcolm

2018-03-26

The sensitivity in predicting glide behaviour of dislocations has been a long-standing problem in the framework of the Peierls-Nabarro model. The predictions of both the model itself and the analytic formulas based on it are too sensitive to the input parameters. In order to reveal the origin of this important problem in materials science, a new empirical-parameter-free formulation is proposed in the same framework. Unlike previous formulations, it includes only a limited small set of parameters all of which can be determined by convergence tests. Under special conditions the new formulation is reduced to its classic counterpart. In the lightmore » of this formulation, new relationships between Peierls stresses and the input parameters are identified, where the sensitivity is greatly reduced or even removed.« less

Incorporating rainfall uncertainty in a SWAT model: the river Zenne basin (Belgium) case study

NASA Astrophysics Data System (ADS)

Tolessa Leta, Olkeba; Nossent, Jiri; van Griensven, Ann; Bauwens, Willy

2013-04-01

The European Union Water Framework Directive (EU-WFD) called its member countries to achieve a good ecological status for all inland and coastal water bodies by 2015. According to recent studies, the river Zenne (Belgium) is far from this objective. Therefore, an interuniversity and multidisciplinary project "Towards a Good Ecological Status in the river Zenne (GESZ)" was launched to evaluate the effects of wastewater management plans on the river. In this project, different models have been developed and integrated using the Open Modelling Interface (OpenMI). The hydrologic, semi-distributed Soil and Water Assessment Tool (SWAT) is hereby used as one of the model components in the integrated modelling chain in order to model the upland catchment processes. The assessment of the uncertainty of SWAT is an essential aspect of the decision making process, in order to design robust management strategies that take the predicted uncertainties into account. Model uncertainty stems from the uncertainties on the model parameters, the input data (e.g, rainfall), the calibration data (e.g., stream flows) and on the model structure itself. The objective of this paper is to assess the first three sources of uncertainty in a SWAT model of the river Zenne basin. For the assessment of rainfall measurement uncertainty, first, we identified independent rainfall periods, based on the daily precipitation and stream flow observations and using the Water Engineering Time Series PROcessing tool (WETSPRO). Secondly, we assigned a rainfall multiplier parameter for each of the independent rainfall periods, which serves as a multiplicative input error corruption. Finally, we treated these multipliers as latent parameters in the model optimization and uncertainty analysis (UA). For parameter uncertainty assessment, due to the high number of parameters of the SWAT model, first, we screened out its most sensitive parameters using the Latin Hypercube One-factor-At-a-Time (LH-OAT) technique. Subsequently, we only considered the most sensitive parameters for parameter optimization and UA. To explicitly account for the stream flow uncertainty, we assumed that the stream flow measurement error increases linearly with the stream flow value. To assess the uncertainty and infer posterior distributions of the parameters, we used a Markov Chain Monte Carlo (MCMC) sampler - differential evolution adaptive metropolis (DREAM) that uses sampling from an archive of past states to generate candidate points in each individual chain. It is shown that the marginal posterior distributions of the rainfall multipliers vary widely between individual events, as a consequence of rainfall measurement errors and the spatial variability of the rain. Only few of the rainfall events are well defined. The marginal posterior distributions of the SWAT model parameter values are well defined and identified by DREAM, within their prior ranges. The posterior distributions of output uncertainty parameter values also show that the stream flow data is highly uncertain. The approach of using rainfall multipliers to treat rainfall uncertainty for a complex model has an impact on the model parameter marginal posterior distributions and on the model results Corresponding author: Tel.: +32 (0)2629 3027; fax: +32(0)2629 3022. E-mail: otolessa@vub.ac.be

The Value of Information in Decision-Analytic Modeling for Malaria Vector Control in East Africa.

PubMed

Kim, Dohyeong; Brown, Zachary; Anderson, Richard; Mutero, Clifford; Miranda, Marie Lynn; Wiener, Jonathan; Kramer, Randall

2017-02-01

Decision analysis tools and mathematical modeling are increasingly emphasized in malaria control programs worldwide to improve resource allocation and address ongoing challenges with sustainability. However, such tools require substantial scientific evidence, which is costly to acquire. The value of information (VOI) has been proposed as a metric for gauging the value of reduced model uncertainty. We apply this concept to an evidenced-based Malaria Decision Analysis Support Tool (MDAST) designed for application in East Africa. In developing MDAST, substantial gaps in the scientific evidence base were identified regarding insecticide resistance in malaria vector control and the effectiveness of alternative mosquito control approaches, including larviciding. We identify four entomological parameters in the model (two for insecticide resistance and two for larviciding) that involve high levels of uncertainty and to which outputs in MDAST are sensitive. We estimate and compare a VOI for combinations of these parameters in evaluating three policy alternatives relative to a status quo policy. We find having perfect information on the uncertain parameters could improve program net benefits by up to 5-21%, with the highest VOI associated with jointly eliminating uncertainty about reproductive speed of malaria-transmitting mosquitoes and initial efficacy of larviciding at reducing the emergence of new adult mosquitoes. Future research on parameter uncertainty in decision analysis of malaria control policy should investigate the VOI with respect to other aspects of malaria transmission (such as antimalarial resistance), the costs of reducing uncertainty in these parameters, and the extent to which imperfect information about these parameters can improve payoffs. © 2016 Society for Risk Analysis.

Global sensitivity analysis of a local water balance model predicting evaporation, water yield and drought

NASA Astrophysics Data System (ADS)

Speich, Matthias; Zappa, Massimiliano; Lischke, Heike

2017-04-01

Evaporation and transpiration affect both catchment water yield and the growing conditions for vegetation. They are driven by climate, but also depend on vegetation, soil and land surface properties. In hydrological and land surface models, these properties may be included as constant parameters, or as state variables. Often, little is known about the effect of these variables on model outputs. In the present study, the effect of surface properties on evaporation was assessed in a global sensitivity analysis. To this effect, we developed a simple local water balance model combining state-of-the-art process formulations for evaporation, transpiration and soil water balance. The model is vertically one-dimensional, and the relative simplicity of its process formulations makes it suitable for integration in a spatially distributed model at regional scale. The main model outputs are annual total evaporation (TE, i.e. the sum of transpiration, soil evaporation and interception), and a drought index (DI), which is based on the ratio of actual and potential transpiration. This index represents the growing conditions for forest trees. The sensitivity analysis was conducted in two steps. First, a screening analysis was applied to identify unimportant parameters out of an initial set of 19 parameters. In a second step, a statistical meta-model was applied to a sample of 800 model runs, in which the values of the important parameters were varied. Parameter effect and interactions were analyzed with effects plots. The model was driven with forcing data from ten meteorological stations in Switzerland, representing a wide range of precipitation regimes across a strong temperature gradient. Of the 19 original parameters, eight were identified as important in the screening analysis. Both steps highlighted the importance of Plant Available Water Capacity (AWC) and Leaf Area Index (LAI). However, their effect varies greatly across stations. For example, while a transition from a sparse to a closed forest canopy has almost no effect on annual TE at warm and dry sites, it increases TE by up to 100 mm/year at cold-humid and warm-humid sites. Further parameters of importance describe infiltration, as well as canopy resistance and its response to environmental variables. This study offers insights for future development of hydrological and ecohydrological models. First, it shows that although local water balance is primarily controlled by climate, the vegetation and soil parameters may have a large impact on the outputs. Second, it indicates that modeling studies should prioritize a realistic parameterization of LAI and AWC, while other parameters may be set to fixed values. Third, it illustrates to which extent parameter effect and interactions depend on local climate.

Scalar field cosmology in f(R,T) gravity via Noether symmetry

NASA Astrophysics Data System (ADS)

Sharif, M.; Nawazish, Iqra

2018-04-01

This paper investigates the existence of Noether symmetries of isotropic universe model in f(R,T) gravity admitting minimal coupling of matter and scalar fields. The scalar field incorporates two dark energy models such as quintessence and phantom models. We determine symmetry generators and corresponding conserved quantities for two particular f(R,T) models. We also evaluate exact solutions and investigate their physical behavior via different cosmological parameters. For the first model, the graphical behavior of these parameters indicate consistency with recent observations representing accelerated expansion of the universe. For the second model, these parameters identify a transition form accelerated to decelerated expansion of the universe. The potential function is found to be constant for the first model while it becomes V(φ )≈ φ 2 for the second model. We conclude that the Noether symmetry generators and corresponding conserved quantities appear in all cases.

ARMA models for earthquake ground motions. Seismic safety margins research program

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, M. K.; Kwiatkowski, J. W.; Nau, R. F.

1981-02-01

Four major California earthquake records were analyzed by use of a class of discrete linear time-domain processes commonly referred to as ARMA (Autoregressive/Moving-Average) models. It was possible to analyze these different earthquakes, identify the order of the appropriate ARMA model(s), estimate parameters, and test the residuals generated by these models. It was also possible to show the connections, similarities, and differences between the traditional continuous models (with parameter estimates based on spectral analyses) and the discrete models with parameters estimated by various maximum-likelihood techniques applied to digitized acceleration data in the time domain. The methodology proposed is suitable for simulatingmore » earthquake ground motions in the time domain, and appears to be easily adapted to serve as inputs for nonlinear discrete time models of structural motions. 60 references, 19 figures, 9 tables.« less

Dynamic characterization of high damping viscoelastic materials from vibration test data

NASA Astrophysics Data System (ADS)

Martinez-Agirre, Manex; Elejabarrieta, María Jesús

2011-08-01

The numerical analysis and design of structural systems involving viscoelastic damping materials require knowledge of material properties and proper mathematical models. A new inverse method for the dynamic characterization of high damping and strong frequency-dependent viscoelastic materials from vibration test data measured by forced vibration tests with resonance is presented. Classical material parameter extraction methods are reviewed; their accuracy for characterizing high damping materials is discussed; and the bases of the new analysis method are detailed. The proposed inverse method minimizes the residue between the experimental and theoretical dynamic response at certain discrete frequencies selected by the user in order to identify the parameters of the material constitutive model. Thus, the material properties are identified in the whole bandwidth under study and not just at resonances. Moreover, the use of control frequencies makes the method insensitive to experimental noise and the efficiency is notably enhanced. Therefore, the number of tests required is drastically reduced and the overall process is carried out faster and more accurately. The effectiveness of the proposed method is demonstrated with the characterization of a CLD (constrained layer damping) cantilever beam. First, the elastic properties of the constraining layers are identified from the dynamic response of a metallic cantilever beam. Then, the viscoelastic properties of the core, represented by a four-parameter fractional derivative model, are identified from the dynamic response of a CLD cantilever beam.

Hyperbolic Discounting: Value and Time Processes of Substance Abusers and Non-Clinical Individuals in Intertemporal Choice

PubMed Central

2014-01-01

The single parameter hyperbolic model has been frequently used to describe value discounting as a function of time and to differentiate substance abusers and non-clinical participants with the model's parameter k. However, k says little about the mechanisms underlying the observed differences. The present study evaluates several alternative models with the purpose of identifying whether group differences stem from differences in subjective valuation, and/or time perceptions. Using three two-parameter models, plus secondary data analyses of 14 studies with 471 indifference point curves, results demonstrated that adding a valuation, or a time perception function led to better model fits. However, the gain in fit due to the flexibility granted by a second parameter did not always lead to a better understanding of the data patterns and corresponding psychological processes. The k parameter consistently indexed group and context (magnitude) differences; it is thus a mixed measure of person and task level effects. This was similar for a parameter meant to index payoff devaluation. A time perception parameter, on the other hand, fluctuated with contexts in a non-predicted fashion and the interpretation of its values was inconsistent with prior findings that supported enlarged perceived delays for substance abusers compared to controls. Overall, the results provide mixed support for hyperbolic models of intertemporal choice in terms of the psychological meaning afforded by their parameters. PMID:25390941

Comparison of Two Global Sensitivity Analysis Methods for Hydrologic Modeling over the Columbia River Basin

NASA Astrophysics Data System (ADS)

Hameed, M.; Demirel, M. C.; Moradkhani, H.

2015-12-01

Global Sensitivity Analysis (GSA) approach helps identify the effectiveness of model parameters or inputs and thus provides essential information about the model performance. In this study, the effects of the Sacramento Soil Moisture Accounting (SAC-SMA) model parameters, forcing data, and initial conditions are analysed by using two GSA methods: Sobol' and Fourier Amplitude Sensitivity Test (FAST). The simulations are carried out over five sub-basins within the Columbia River Basin (CRB) for three different periods: one-year, four-year, and seven-year. Four factors are considered and evaluated by using the two sensitivity analysis methods: the simulation length, parameter range, model initial conditions, and the reliability of the global sensitivity analysis methods. The reliability of the sensitivity analysis results is compared based on 1) the agreement between the two sensitivity analysis methods (Sobol' and FAST) in terms of highlighting the same parameters or input as the most influential parameters or input and 2) how the methods are cohered in ranking these sensitive parameters under the same conditions (sub-basins and simulation length). The results show the coherence between the Sobol' and FAST sensitivity analysis methods. Additionally, it is found that FAST method is sufficient to evaluate the main effects of the model parameters and inputs. Another conclusion of this study is that the smaller parameter or initial condition ranges, the more consistency and coherence between the sensitivity analysis methods results.

FACTORS INFLUENCING TOTAL DIETARY EXPOSURES OF YOUNG CHILDREN

EPA Science Inventory

A deterministic model was developed to identify the critical input parameters needed to assess dietary intakes of young children. The model was used as a framework for understanding the important factors in data collection and data analysis. Factors incorporated into the model i...

Dynamical modeling and multi-experiment fitting with PottersWheel

PubMed Central

Maiwald, Thomas; Timmer, Jens

2008-01-01

Motivation: Modelers in Systems Biology need a flexible framework that allows them to easily create new dynamic models, investigate their properties and fit several experimental datasets simultaneously. Multi-experiment-fitting is a powerful approach to estimate parameter values, to check the validity of a given model, and to discriminate competing model hypotheses. It requires high-performance integration of ordinary differential equations and robust optimization. Results: We here present the comprehensive modeling framework Potters-Wheel (PW) including novel functionalities to satisfy these requirements with strong emphasis on the inverse problem, i.e. data-based modeling of partially observed and noisy systems like signal transduction pathways and metabolic networks. PW is designed as a MATLAB toolbox and includes numerous user interfaces. Deterministic and stochastic optimization routines are combined by fitting in logarithmic parameter space allowing for robust parameter calibration. Model investigation includes statistical tests for model-data-compliance, model discrimination, identifiability analysis and calculation of Hessian- and Monte-Carlo-based parameter confidence limits. A rich application programming interface is available for customization within own MATLAB code. Within an extensive performance analysis, we identified and significantly improved an integrator–optimizer pair which decreases the fitting duration for a realistic benchmark model by a factor over 3000 compared to MATLAB with optimization toolbox. Availability: PottersWheel is freely available for academic usage at http://www.PottersWheel.de/. The website contains a detailed documentation and introductory videos. The program has been intensively used since 2005 on Windows, Linux and Macintosh computers and does not require special MATLAB toolboxes. Contact: maiwald@fdm.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:18614583

Parameter identification of thermophilic anaerobic degradation of valerate.

PubMed

Flotats, Xavier; Ahring, Birgitte K; Angelidaki, Irini

2003-01-01

The considered mathematical model of the decomposition of valerate presents three unknown kinetic parameters, two unknown stoichiometric coefficients, and three unknown initial concentrations for biomass. Applying a structural identifiability study, we concluded that it is necessary to perform simultaneous batch experiments with different initial conditions for estimating these parameters. Four simultaneous batch experiments were conducted at 55 degrees C, characterized by four different initial acetate concentrations. Product inhibition of valerate degradation by acetate was considered. Practical identification was done optimizing the sum of the multiple determination coefficients for all measured state variables and for all experiments simultaneously. The estimated values of kinetic parameters and stoichiometric coefficients were characterized by the parameter correlation matrix, the confidence interval, and the student's t-test at 5% significance level with positive results except for the saturation constant, for which more experiments for improving its identifiability should be conducted. In this article, we discuss kinetic parameter estimation methods.

Computational Difficulties in the Identification and Optimization of Control Systems.

DTIC Science & Technology

1980-01-01

necessary and Identify by block number) - -. 3. iABSTRACT (Continue on revers, side It necessary and Identify by block number) As more realistic models ...Island 02912 ABSTRACT As more realistic models for resource management are developed, the need for efficient computational techniques for parameter...optimization (optimal control) in "state" models which This research was supported in part by ttfe National Science Foundation under grant NSF-MCS 79-05774

Exploring Several Methods of Groundwater Model Selection

NASA Astrophysics Data System (ADS)

Samani, Saeideh; Ye, Ming; Asghari Moghaddam, Asghar

2017-04-01

Selecting reliable models for simulating groundwater flow and solute transport is essential to groundwater resources management and protection. This work is to explore several model selection methods for avoiding over-complex and/or over-parameterized groundwater models. We consider six groundwater flow models with different numbers (6, 10, 10, 13, 13 and 15) of model parameters. These models represent alternative geological interpretations, recharge estimates, and boundary conditions at a study site in Iran. The models were developed with Model Muse, and calibrated against observations of hydraulic head using UCODE. Model selection was conducted by using the following four approaches: (1) Rank the models using their root mean square error (RMSE) obtained after UCODE-based model calibration, (2) Calculate model probability using GLUE method, (3) Evaluate model probability using model selection criteria (AIC, AICc, BIC, and KIC), and (4) Evaluate model weights using the Fuzzy Multi-Criteria-Decision-Making (MCDM) approach. MCDM is based on the fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance, which is to identify the ideal solution by a gradual expansion from the local to the global scale of model parameters. The KIC and MCDM methods are superior to other methods, as they consider not only the fit between observed and simulated data and the number of parameter, but also uncertainty in model parameters. Considering these factors can prevent from occurring over-complexity and over-parameterization, when selecting the appropriate groundwater flow models. These methods selected, as the best model, one with average complexity (10 parameters) and the best parameter estimation (model 3).

Study on feed forward neural network convex optimization for LiFePO4 battery parameters

NASA Astrophysics Data System (ADS)

Liu, Xuepeng; Zhao, Dongmei

2017-08-01

Based on the modern facility agriculture automatic walking equipment LiFePO4 Battery, the parameter identification of LiFePO4 Battery is analyzed. An improved method for the process model of li battery is proposed, and the on-line estimation algorithm is presented. The parameters of the battery are identified using feed forward network neural convex optimization algorithm.

Theoretic aspects of the identification of the parameters in the optimal control model

NASA Technical Reports Server (NTRS)

Vanwijk, R. A.; Kok, J. J.

1977-01-01

The identification of the parameters of the optimal control model from input-output data of the human operator is considered. Accepting the basic structure of the model as a cascade of a full-order observer and a feedback law, and suppressing the inherent optimality of the human controller, the parameters to be identified are the feedback matrix, the observer gain matrix, and the intensity matrices of the observation noise and the motor noise. The identification of the parameters is a statistical problem, because the system and output are corrupted by noise, and therefore the solution must be based on the statistics (probability density function) of the input and output data of the human operator. However, based on the statistics of the input-output data of the human operator, no distinction can be made between the observation and the motor noise, which shows that the model suffers from overparameterization.

The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ricciuto, Daniel; Sargsyan, Khachik; Thornton, Peter

We conduct a global sensitivity analysis (GSA) of the Energy Exascale Earth System Model (E3SM), land model (ELM) to calculate the sensitivity of five key carbon cycle outputs to 68 model parameters. This GSA is conducted by first constructing a Polynomial Chaos (PC) surrogate via new Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth leading to a sparse, high-dimensional PC surrogate with 3,000 model evaluations. The PC surrogate allows efficient extraction of GSA information leading to further dimensionality reduction. The GSA is performed at 96 FLUXNET sites covering multiple plant functional types (PFTs) and climate conditions. Aboutmore » 20 of the model parameters are identified as sensitive with the rest being relatively insensitive across all outputs and PFTs. These sensitivities are dependent on PFT, and are relatively consistent among sites within the same PFT. The five model outputs have a majority of their highly sensitive parameters in common. A common subset of sensitive parameters is also shared among PFTs, but some parameters are specific to certain types (e.g., deciduous phenology). In conclusion, the relative importance of these parameters shifts significantly among PFTs and with climatic variables such as mean annual temperature.« less

The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model

DOE PAGES

Ricciuto, Daniel; Sargsyan, Khachik; Thornton, Peter

2018-02-27

We conduct a global sensitivity analysis (GSA) of the Energy Exascale Earth System Model (E3SM), land model (ELM) to calculate the sensitivity of five key carbon cycle outputs to 68 model parameters. This GSA is conducted by first constructing a Polynomial Chaos (PC) surrogate via new Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth leading to a sparse, high-dimensional PC surrogate with 3,000 model evaluations. The PC surrogate allows efficient extraction of GSA information leading to further dimensionality reduction. The GSA is performed at 96 FLUXNET sites covering multiple plant functional types (PFTs) and climate conditions. Aboutmore » 20 of the model parameters are identified as sensitive with the rest being relatively insensitive across all outputs and PFTs. These sensitivities are dependent on PFT, and are relatively consistent among sites within the same PFT. The five model outputs have a majority of their highly sensitive parameters in common. A common subset of sensitive parameters is also shared among PFTs, but some parameters are specific to certain types (e.g., deciduous phenology). In conclusion, the relative importance of these parameters shifts significantly among PFTs and with climatic variables such as mean annual temperature.« less

A simple approach for the modeling of an ODS steel mechanical behavior in pilgering conditions

NASA Astrophysics Data System (ADS)

Vanegas-Márquez, E.; Mocellin, K.; Toualbi, L.; de Carlan, Y.; Logé, R. E.

2012-01-01

The optimization of the forming of ODS tubes is linked to the choice of an appropriated constitutive model for modeling the metal forming process. In the framework of a unified plastic constitutive theory, the strain-controlled cyclic characteristics of a ferritic ODS steel were analyzed and modeled with two different tests. The first test is a classical tension-compression test, and leads to cyclic softening at low to intermediate strain amplitudes. The second test consists in alternated uniaxial compressions along two perpendicular axes, and is selected based on the similarities with the loading path induced by the Fe-14Cr-1W-Ti ODS cladding tube pilgering process. This second test exhibits cyclic hardening at all tested strain amplitudes. Since variable strain amplitudes prevail in pilgering conditions, the parameters of the considered constitutive law were identified based on a loading sequence including strain amplitude changes. A proposed semi automated inverse analysis methodology is shown to efficiently provide optimal sets of parameters for the considered loading sequences. When compared to classical approaches, the model involves a reduced number of parameters, while keeping a good ability to capture stress changes induced by strain amplitude changes. Furthermore, the methodology only requires one test, which is an advantage when the amount of available material is limited. As two distinct sets of parameters were identified for the two considered tests, it is recommended to consider the loading path when modeling cold forming of the ODS steel.

On the treatment of evapotranspiration, soil moisture accounting, and aquifer recharge in monthly water balance models

USGS Publications Warehouse

Alley, William M.

1984-01-01

Several two- to six-parameter regional water balance models are examined by using 50-year records of monthly streamflow at 10 sites in New Jersey. These models include variants of the Thornthwaite-Mather model, the Palmer model, and the more recent Thomas abcd model. Prediction errors are relatively similar among the models. However, simulated values of state variables such as soil moisture storage differ substantially among the models, and fitted parameter values for different models sometimes indicated an entirely different type of basin response to precipitation. Some problems in parameter identification are noted, including difficulties in identifying an appropriate time lag factor for the Thornthwaite-Mather-type model for basins with little groundwater storage, very high correlations between upper and lower storages in the Palmer-type model, and large sensitivity of parameter a of the abcd model to bias in estimates of precipitation and potential evapotranspiration. Modifications to the threshold concept of the Thornthwaite-Mather model were statistically valid for the six stations in northern New Jersey. The abcd model resulted in a simulated seasonal cycle of groundwater levels similar to fluctuations observed in nearby wells but with greater persistence. These results suggest that extreme caution should be used in attaching physical significance to model parameters and in using the state variables of the models in indices of drought and basin productivity.

Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway.

PubMed

Shi, Qi; Abdel-Aty, Mohamed; Yu, Rongjie

2016-03-01

In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature. Copyright © 2015 Elsevier Ltd. All rights reserved.

Dynamic parameter identification of robot arms with servo-controlled electrical motors

NASA Astrophysics Data System (ADS)

Jiang, Zhao-Hui; Senda, Hiroshi

2005-12-01

This paper addresses the issue of dynamic parameter identification of the robot manipulator with servo-controlled electrical motors. An assumption is made that all kinematical parameters, such as link lengths, are known, and only dynamic parameters containing mass, moment of inertia, and their functions need to be identified. First, we derive dynamics of the robot arm with a linear form of the unknown dynamic parameters by taking dynamic characteristics of the motor and servo unit into consideration. Then, we implement the parameter identification approach to identify the unknown parameters with respect to individual link separately. A pseudo-inverse matrix is used for formulation of the parameter identification. The optimal solution is guaranteed in a sense of least-squares of the mean errors. A Direct Drive (DD) SCARA type industrial robot arm AdeptOne is used as an application example of the parameter identification. Simulations and experiments for both open loop and close loop controls are carried out. Comparison of the results confirms the correctness and usefulness of the parameter identification and the derived dynamic model.

Dynamic Modelling under Uncertainty: The Case of Trypanosoma brucei Energy Metabolism

PubMed Central

Achcar, Fiona; Kerkhoven, Eduard J.; Bakker, Barbara M.; Barrett, Michael P.; Breitling, Rainer

2012-01-01

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies. PMID:22379410

Using fixed-parameter and random-parameter ordered regression models to identify significant factors that affect the severity of drivers' injuries in vehicle-train collisions.

PubMed

Dabbour, Essam; Easa, Said; Haider, Murtaza

2017-10-01

This study attempts to identify significant factors that affect the severity of drivers' injuries when colliding with trains at railroad-grade crossings by analyzing the individual-specific heterogeneity related to those factors over a period of 15 years. Both fixed-parameter and random-parameter ordered regression models were used to analyze records of all vehicle-train collisions that occurred in the United States from January 1, 2001 to December 31, 2015. For fixed-parameter ordered models, both probit and negative log-log link functions were used. The latter function accounts for the fact that lower injury severity levels are more probable than higher ones. Separate models were developed for heavy and light-duty vehicles. Higher train and vehicle speeds, female, and young drivers (below the age of 21 years) were found to be consistently associated with higher severity of drivers' injuries for both heavy and light-duty vehicles. Furthermore, favorable weather, light-duty trucks (including pickup trucks, panel trucks, mini-vans, vans, and sports-utility vehicles), and senior drivers (above the age of 65 years) were found be consistently associated with higher severity of drivers' injuries for light-duty vehicles only. All other factors (e.g. air temperature, the type of warning devices, darkness conditions, and highway pavement type) were found to be temporally unstable, which may explain the conflicting findings of previous studies related to those factors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Toward a better understanding of helicopter stability derivatives

NASA Technical Reports Server (NTRS)

Hansen, R. S.

1982-01-01

An amended six degree of freedom helicopter stability and control derivative model was developed in which body acceleration and control rate derivatives were included in the Taylor series expansion. These additional derivatives were derived from consideration of the effects of the higher order rotor flapping dynamics, which are known to be inadequately represented in the conventional six degree of freedom, quasistatic stability derivative model. The amended model was a substantial improvement over the conventional model, effectively doubling the unsable bandwidth and providing a more accurate representation of the short period and cross axis characteristics. Further investigations assessed the applicability of the two stability derivative model structures for flight test parameter identification. Parameters were identified using simulation data generated from a higher order base line model having sixth order rotor tip path plane dynamics. Three lower order models were identified: one using the conventional stability derivative model structure, a second using the amended six degree of freedom model structure, and a third model having eight degrees of freedom that included a simplified rotor tip path plane tilt representation.

Calibrated Hydrothermal Parameters, Barrow, Alaska, 2013

DOE Data Explorer

Atchley, Adam; Painter, Scott; Harp, Dylan; Coon, Ethan; Wilson, Cathy; Liljedahl, Anna; Romanovsky, Vladimir

2015-01-29

A model-observation-experiment process (ModEx) is used to generate three 1D models of characteristic micro-topographical land-formations, which are capable of simulating present active thaw layer (ALT) from current climate conditions. Each column was used in a coupled calibration to identify moss, peat and mineral soil hydrothermal properties to be used in up-scaled simulations. Observational soil temperature data from a tundra site located near Barrow, AK (Area C) is used to calibrate thermal properties of moss, peat, and sandy loam soil to be used in the multiphysics Advanced Terrestrial Simulator (ATS) models. Simulation results are a list of calibrated hydrothermal parameters for moss, peat, and mineral soil hydrothermal parameters.

Online vegetation parameter estimation using passive microwave remote sensing observations

USDA-ARS?s Scientific Manuscript database

In adaptive system identification the Kalman filter can be used to identify the coefficient of the observation operator of a linear system. Here the ensemble Kalman filter is tested for adaptive online estimation of the vegetation opacity parameter of a radiative transfer model. A state augmentatio...

KABAM Version 1.0 User's Guide and Technical Documentation - Appendix E - Selection of Bird Species of Concern and Corresponding Biological Parameters

EPA Pesticide Factsheets

Bird species of concern were identified to define default parameters (body weight and diet composition) to represent birds in KABAM. KABAM is a simulation model used to predict pesticide concentrations in aquatic regions for use in exposure assessments.

Modal Parameter Identification of a Flexible Arm System

NASA Technical Reports Server (NTRS)

Barrington, Jason; Lew, Jiann-Shiun; Korbieh, Edward; Wade, Montanez; Tantaris, Richard

1998-01-01

In this paper an experiment is designed for the modal parameter identification of a flexible arm system. This experiment uses a function generator to provide input signal and an oscilloscope to save input and output response data. For each vibrational mode, many sets of sine-wave inputs with frequencies close to the natural frequency of the arm system are used to excite the vibration of this mode. Then a least-squares technique is used to analyze the experimental input/output data to obtain the identified parameters for this mode. The identified results are compared with the analytical model obtained by applying finite element analysis.

Using Indirect Turbulence Measurements for Real-Time Parameter Estimation in Turbulent Air

NASA Technical Reports Server (NTRS)

Martos, Borja; Morelli, Eugene A.

2012-01-01

The use of indirect turbulence measurements for real-time estimation of parameters in a linear longitudinal dynamics model in atmospheric turbulence was studied. It is shown that measuring the atmospheric turbulence makes it possible to treat the turbulence as a measured explanatory variable in the parameter estimation problem. Commercial off-the-shelf sensors were researched and evaluated, then compared to air data booms. Sources of colored noise in the explanatory variables resulting from typical turbulence measurement techniques were identified and studied. A major source of colored noise in the explanatory variables was identified as frequency dependent upwash and time delay. The resulting upwash and time delay corrections were analyzed and compared to previous time shift dynamic modeling research. Simulation data as well as flight test data in atmospheric turbulence were used to verify the time delay behavior. Recommendations are given for follow on flight research and instrumentation.

Variations in embodied energy and carbon emission intensities of construction materials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wan Omar, Wan-Mohd-Sabki; School of Environmental Engineering, Universiti Malaysia Perlis, 02600 Arau, Perlis; Doh, Jeung-Hwan, E-mail: j.doh@griffith.edu.au

2014-11-15

Identification of parameter variation allows us to conduct more detailed life cycle assessment (LCA) of energy and carbon emission material over their lifecycle. Previous research studies have demonstrated that hybrid LCA (HLCA) can generally overcome the problems of incompleteness and accuracy of embodied energy (EE) and carbon (EC) emission assessment. Unfortunately, the current interpretation and quantification procedure has not been extensively and empirically studied in a qualitative manner, especially in hybridising between the process LCA and I-O LCA. To determine this weakness, this study empirically demonstrates the changes in EE and EC intensities caused by variations to key parameters inmore » material production. Using Australia and Malaysia as a case study, the results are compared with previous hybrid models to identify key parameters and issues. The parameters considered in this study are technological changes, energy tariffs, primary energy factors, disaggregation constant, emission factors, and material price fluctuation. It was found that changes in technological efficiency, energy tariffs and material prices caused significant variations in the model. Finally, the comparison of hybrid models revealed that non-energy intensive materials greatly influence the variations due to high indirect energy and carbon emission in upstream boundary of material production, and as such, any decision related to these materials should be considered carefully. - Highlights: • We investigate the EE and EC intensity variation in Australia and Malaysia. • The influences of parameter variations on hybrid LCA model were evaluated. • Key significant contribution to the EE and EC intensity variation were identified. • High indirect EE and EC content caused significant variation in hybrid LCA models. • Non-energy intensive material caused variation between hybrid LCA models.« less

An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N V

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.

An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters

PubMed Central

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N. V.

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test. PMID:23469172

Climate Modeling and Causal Identification for Sea Ice Predictability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hunke, Elizabeth Clare; Urrego Blanco, Jorge Rolando; Urban, Nathan Mark

This project aims to better understand causes of ongoing changes in the Arctic climate system, particularly as decreasing sea ice trends have been observed in recent decades and are expected to continue in the future. As part of the Sea Ice Prediction Network, a multi-agency effort to improve sea ice prediction products on seasonal-to-interannual time scales, our team is studying sensitivity of sea ice to a collection of physical process and feedback mechanism in the coupled climate system. During 2017 we completed a set of climate model simulations using the fully coupled ACME-HiLAT model. The simulations consisted of experiments inmore » which cloud, sea ice, and air-ocean turbulent exchange parameters previously identified as important for driving output uncertainty in climate models were perturbed to account for parameter uncertainty in simulated climate variables. We conducted a sensitivity study to these parameters, which built upon a previous study we made for standalone simulations (Urrego-Blanco et al., 2016, 2017). Using the results from the ensemble of coupled simulations, we are examining robust relationships between climate variables that emerge across the experiments. We are also using causal discovery techniques to identify interaction pathways among climate variables which can help identify physical mechanisms and provide guidance in predictability studies. This work further builds on and leverages the large ensemble of standalone sea ice simulations produced in our previous w14_seaice project.« less

Tracking slow modulations in synaptic gain using dynamic causal modelling: validation in epilepsy.

PubMed

Papadopoulou, Margarita; Leite, Marco; van Mierlo, Pieter; Vonck, Kristl; Lemieux, Louis; Friston, Karl; Marinazzo, Daniele

2015-02-15

In this work we propose a proof of principle that dynamic causal modelling can identify plausible mechanisms at the synaptic level underlying brain state changes over a timescale of seconds. As a benchmark example for validation we used intracranial electroencephalographic signals in a human subject. These data were used to infer the (effective connectivity) architecture of synaptic connections among neural populations assumed to generate seizure activity. Dynamic causal modelling allowed us to quantify empirical changes in spectral activity in terms of a trajectory in parameter space - identifying key synaptic parameters or connections that cause observed signals. Using recordings from three seizures in one patient, we considered a network of two sources (within and just outside the putative ictal zone). Bayesian model selection was used to identify the intrinsic (within-source) and extrinsic (between-source) connectivity. Having established the underlying architecture, we were able to track the evolution of key connectivity parameters (e.g., inhibitory connections to superficial pyramidal cells) and test specific hypotheses about the synaptic mechanisms involved in ictogenesis. Our key finding was that intrinsic synaptic changes were sufficient to explain seizure onset, where these changes showed dissociable time courses over several seconds. Crucially, these changes spoke to an increase in the sensitivity of principal cells to intrinsic inhibitory afferents and a transient loss of excitatory-inhibitory balance. Copyright © 2014. Published by Elsevier Inc.

JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language.

PubMed

Bassen, David M; Vilkhovoy, Michael; Minot, Mason; Butcher, Jonathan T; Varner, Jeffrey D

2017-01-25

Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open source, available under an MIT license, and can be installed using the Julia package manager from the JuPOETs GitHub repository.

Ultra wideband (0.5-16 kHz) MR elastography for robust shear viscoelasticity model identification.

PubMed

Liu, Yifei; Yasar, Temel K; Royston, Thomas J

2014-12-21

Changes in the viscoelastic parameters of soft biological tissues often correlate with progression of disease, trauma or injury, and response to treatment. Identifying the most appropriate viscoelastic model, then estimating and monitoring the corresponding parameters of that model can improve insight into the underlying tissue structural changes. MR Elastography (MRE) provides a quantitative method of measuring tissue viscoelasticity. In a previous study by the authors (Yasar et al 2013 Magn. Reson. Med. 70 479-89), a silicone-based phantom material was examined over the frequency range of 200 Hz-7.75 kHz using MRE, an unprecedented bandwidth at that time. Six viscoelastic models including four integer order models and two fractional order models, were fit to the wideband viscoelastic data (measured storage and loss moduli as a function of frequency). The 'fractional Voigt' model (spring and springpot in parallel) exhibited the best fit and was even able to fit the entire frequency band well when it was identified based only on a small portion of the band. This paper is an extension of that study with a wider frequency range from 500 Hz to 16 kHz. Furthermore, more fractional order viscoelastic models are added to the comparison pool. It is found that added complexity of the viscoelastic model provides only marginal improvement over the 'fractional Voigt' model. And, again, the fractional order models show significant improvement over integer order viscoelastic models that have as many or more fitting parameters.

Dynamic Stability Analysis of Blunt Body Entry Vehicles Using Time-Lagged Aftbody Pitching Moments

NASA Technical Reports Server (NTRS)

Kazemba, Cole D.; Braun, Robert D.; Schoenenberger, Mark; Clark, Ian G.

2013-01-01

This analysis defines an analytic model for the pitching motion of blunt bodies during atmospheric entry. The proposed model is independent of the pitch damping sum coefficient present in the standard formulation of the equations of motion describing pitch oscillations of a decelerating blunt body, instead using the principle of a time-lagged aftbody moment as the forcing function for oscillation divergence. Four parameters, all with intuitive physical relevance, are introduced to fully define the aftbody moment and the associated time delay. It is shown that the dynamic oscillation responses typical to blunt bodies can be produced using hysteresis of the aftbody moment in place of the pitch damping coefficient. The approach used in this investigation is shown to be useful in understanding the governing physical mechanisms for blunt body dynamic stability and in guiding vehicle and mission design requirements. A validation case study using simulated ballistic range test data is conducted. From this, parameter identification is carried out through the use of a least squares optimizing routine. Results show good agreement with the limited existing literature for the parameters identified, suggesting that the model proposed could be validated by an experimental ballistic range test series. The trajectories produced by the identified parameters were found to match closely those from the MER ballistic range tests for a wide array of initial conditions and can be identified with a reasonable number of ballistic range shots and computational effort.

ANCA-Associated Glomerulonephritis: Risk Factors for Renal Relapse.

PubMed

Göçeroğlu, Arda; Berden, Annelies E; Fiocco, Marta; Floßmann, Oliver; Westman, Kerstin W; Ferrario, Franco; Gaskin, Gill; Pusey, Charles D; Hagen, E Christiaan; Noël, Laure-Hélène; Rasmussen, Niels; Waldherr, Rüdiger; Walsh, Michael; Bruijn, Jan A; Jayne, David R W; Bajema, Ingeborg M

2016-01-01

Relapse in ANCA-associated vasculitis (AAV) has been studied previously, but there are few studies on renal relapse in particular. Identifying patients at high risk of renal relapse may aid in optimizing clinical management. We investigated which clinical and histological parameters are risk factors for renal relapse in ANCA-associated glomerulonephritis (AAGN). Patients (n = 174) were newly diagnosed and had mild-moderate or severe renal involvement. Data were derived from two trials of the European Vasculitis Society: MEPEX and CYCAZAREM. The Cox regression model was used to identify parameters increasing the instantaneous risk (= rate) of renal relapse (useful for instant clinical decisions). For identifying predictors of renal relapse during follow-up, we used Fine & Gray's regression model. Competing events were end-stage renal failure and death. The cumulative incidence of renal relapse at 5 years was 9.5% (95% CI: 4.8-14.3%). In the Cox model, sclerotic class AAGN increased the instantaneous risk of renal relapse. In Fine & Gray's model, the absence of interstitial infiltrates at diagnosis was predictive for renal relapse. In this study we used two different models to identify possible relationships between clinical and histopathological parameters at time of diagnosis of AAV with the risk of experiencing renal relapse. Sclerotic class AAGN increased the instantaneous risk of renal relapse. This association is most likely due to the high proportion of sclerosed glomeruli reducing the compensatory capacity. The absence of interstitial infiltrates increased the risk of renal relapse which is a warning sign that patients with a relatively benign onset of disease may also be prone to renal relapse. Renal relapses occurring in patients with sclerotic class AAGN and renal relapses occurring in patients without interstitial infiltrates were mutually exclusive, which may indicate that they are essentially different.

ANCA-Associated Glomerulonephritis: Risk Factors for Renal Relapse

PubMed Central

Göçeroğlu, Arda; Berden, Annelies E.; Fiocco, Marta; Floßmann, Oliver; Westman, Kerstin W.; Ferrario, Franco; Gaskin, Gill; Pusey, Charles D.; Hagen, E. Christiaan; Noël, Laure-Hélène; Rasmussen, Niels; Waldherr, Rüdiger; Walsh, Michael; Bruijn, Jan A.; Jayne, David R. W.; Bajema, Ingeborg M.

2016-01-01

Relapse in ANCA-associated vasculitis (AAV) has been studied previously, but there are few studies on renal relapse in particular. Identifying patients at high risk of renal relapse may aid in optimizing clinical management. We investigated which clinical and histological parameters are risk factors for renal relapse in ANCA-associated glomerulonephritis (AAGN). Patients (n = 174) were newly diagnosed and had mild–moderate or severe renal involvement. Data were derived from two trials of the European Vasculitis Society: MEPEX and CYCAZAREM. The Cox regression model was used to identify parameters increasing the instantaneous risk (= rate) of renal relapse (useful for instant clinical decisions). For identifying predictors of renal relapse during follow-up, we used Fine & Gray’s regression model. Competing events were end-stage renal failure and death. The cumulative incidence of renal relapse at 5 years was 9.5% (95% CI: 4.8–14.3%). In the Cox model, sclerotic class AAGN increased the instantaneous risk of renal relapse. In Fine & Gray’s model, the absence of interstitial infiltrates at diagnosis was predictive for renal relapse. In this study we used two different models to identify possible relationships between clinical and histopathological parameters at time of diagnosis of AAV with the risk of experiencing renal relapse. Sclerotic class AAGN increased the instantaneous risk of renal relapse. This association is most likely due to the high proportion of sclerosed glomeruli reducing the compensatory capacity. The absence of interstitial infiltrates increased the risk of renal relapse which is a warning sign that patients with a relatively benign onset of disease may also be prone to renal relapse. Renal relapses occurring in patients with sclerotic class AAGN and renal relapses occurring in patients without interstitial infiltrates were mutually exclusive, which may indicate that they are essentially different. PMID:27973575

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network

PubMed Central

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins. PMID:27418910

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network.

PubMed

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins.

Calibration of a flexible measurement system based on industrial articulated robot and structured light sensor

NASA Astrophysics Data System (ADS)

Mu, Nan; Wang, Kun; Xie, Zexiao; Ren, Ping

2017-05-01

To realize online rapid measurement for complex workpieces, a flexible measurement system based on an articulated industrial robot with a structured light sensor mounted on the end-effector is developed. A method for calibrating the system parameters is proposed in which the hand-eye transformation parameters and the robot kinematic parameters are synthesized in the calibration process. An initial hand-eye calibration is first performed using a standard sphere as the calibration target. By applying the modified complete and parametrically continuous method, we establish a synthesized kinematic model that combines the initial hand-eye transformation and distal link parameters as a whole with the sensor coordinate system as the tool frame. According to the synthesized kinematic model, an error model is constructed based on spheres' center-to-center distance errors. Consequently, the error model parameters can be identified in a calibration experiment using a three-standard-sphere target. Furthermore, the redundancy of error model parameters is eliminated to ensure the accuracy and robustness of the parameter identification. Calibration and measurement experiments are carried out based on an ER3A-C60 robot. The experimental results show that the proposed calibration method enjoys high measurement accuracy, and this efficient and flexible system is suitable for online measurement in industrial scenes.

Reduction of low frequency vibration of truck driver and seating system through system parameter identification, sensitivity analysis and active control

NASA Astrophysics Data System (ADS)

Wang, Xu; Bi, Fengrong; Du, Haiping

2018-05-01

This paper aims to develop an 5-degree-of-freedom driver and seating system model for optimal vibration control. A new method for identification of the driver seating system parameters from experimental vibration measurement has been developed. The parameter sensitivity analysis has been conducted considering the random excitation frequency and system parameter uncertainty. The most and least sensitive system parameters for the transmissibility ratio have been identified. The optimised PID controllers have been developed to reduce the driver's body vibration.

Predicting what helminth parasites a fish species should have using Parasite Co-occurrence Modeler (PaCo)

USGS Publications Warehouse

Strona, Giovanni; Lafferty, Kevin D.

2013-01-01

Fish pathologists are often interested in which parasites would likely be present in a particular host. Parasite Co-occurrence Modeler (PaCo) is a tool for identifying a list of parasites known from fish species that are similar ecologically, phylogenetically, and geographically to the host of interest. PaCo uses data from FishBase (maximum length, growth rate, life span, age at maturity, trophic level, phylogeny, and biogeography) to estimate compatibility between a target host and parasite species–genera from the major helminth groups (Acanthocephala, Cestoda, Monogenea, Nematoda, and Trematoda). Users can include any combination of host attributes in a model. These unique features make PaCo an innovative tool for addressing both theoretical and applied questions in parasitology. In addition to predicting the occurrence of parasites, PaCo can be used to investigate how host characteristics shape parasite communities. To test the performance of the PaCo algorithm, we created 12,400 parasite lists by applying any possible combination of model parameters (248) to 50 fish hosts. We then measured the relative importance of each parameter by assessing their frequency in the best models for each host. Host phylogeny and host geography were identified as the most important factors, with both present in 88% of the best models. Habitat (64%) was identified in more than half of the best models. Among ecological parameters, trophic level (41%) was the most relevant while life span (34%), growth rate (32%), maximum length (28%), and age at maturity (20%) were less commonly linked to best models. PaCo is free to use at www.purl.oclc.org/fishpest.

Predicting what helminth parasites a fish species should have using Parasite Co-occurrence Modeler (PaCo).

PubMed

Strona, Giovanni; Lafferty, Kevin D

2013-02-01

Fish pathologists are often interested in which parasites would likely be present in a particular host. Parasite Co-occurrence Modeler (PaCo) is a tool for identifying a list of parasites known from fish species that are similar ecologically, phylogenetically, and geographically to the host of interest. PaCo uses data from FishBase (maximum length, growth rate, life span, age at maturity, trophic level, phylogeny, and biogeography) to estimate compatibility between a target host and parasite species-genera from the major helminth groups (Acanthocephala, Cestoda, Monogenea, Nematoda, and Trematoda). Users can include any combination of host attributes in a model. These unique features make PaCo an innovative tool for addressing both theoretical and applied questions in parasitology. In addition to predicting the occurrence of parasites, PaCo can be used to investigate how host characteristics shape parasite communities. To test the performance of the PaCo algorithm, we created 12,400 parasite lists by applying any possible combination of model parameters (248) to 50 fish hosts. We then measured the relative importance of each parameter by assessing their frequency in the best models for each host. Host phylogeny and host geography were identified as the most important factors, with both present in 88% of the best models. Habitat (64%) was identified in more than half of the best models. Among ecological parameters, trophic level (41%) was the most relevant while life span (34%), growth rate (32%), maximum length (28%), and age at maturity (20%) were less commonly linked to best models. PaCo is free to use at www.purl.oclc.org/fishpest.

Utility of a novel error-stepping method to improve gradient-based parameter identification by increasing the smoothness of the local objective surface: a case-study of pulmonary mechanics.

PubMed

Docherty, Paul D; Schranz, Christoph; Chase, J Geoffrey; Chiew, Yeong Shiong; Möller, Knut

2014-05-01

Accurate model parameter identification relies on accurate forward model simulations to guide convergence. However, some forward simulation methodologies lack the precision required to properly define the local objective surface and can cause failed parameter identification. The role of objective surface smoothness in identification of a pulmonary mechanics model was assessed using forward simulation from a novel error-stepping method and a proprietary Runge-Kutta method. The objective surfaces were compared via the identified parameter discrepancy generated in a Monte Carlo simulation and the local smoothness of the objective surfaces they generate. The error-stepping method generated significantly smoother error surfaces in each of the cases tested (p<0.0001) and more accurate model parameter estimates than the Runge-Kutta method in three of the four cases tested (p<0.0001) despite a 75% reduction in computational cost. Of note, parameter discrepancy in most cases was limited to a particular oblique plane, indicating a non-intuitive multi-parameter trade-off was occurring. The error-stepping method consistently improved or equalled the outcomes of the Runge-Kutta time-integration method for forward simulations of the pulmonary mechanics model. This study indicates that accurate parameter identification relies on accurate definition of the local objective function, and that parameter trade-off can occur on oblique planes resulting prematurely halted parameter convergence. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

PubMed Central

Trame, MN; Lesko, LJ

2015-01-01

A systems pharmacology model typically integrates pharmacokinetic, biochemical network, and systems biology concepts into a unifying approach. It typically consists of a large number of parameters and reaction species that are interlinked based upon the underlying (patho)physiology and the mechanism of drug action. The more complex these models are, the greater the challenge of reliably identifying and estimating respective model parameters. Global sensitivity analysis provides an innovative tool that can meet this challenge. CPT Pharmacometrics Syst. Pharmacol. (2015) 4, 69–79; doi:10.1002/psp4.6; published online 25 February 2015 PMID:27548289

Uncertainty Analysis of Runoff Simulations and Parameter Identifiability in the Community Land Model – Evidence from MOPEX Basins

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Maoyi; Hou, Zhangshuan; Leung, Lai-Yung R.

2013-12-01

With the emergence of earth system models as important tools for understanding and predicting climate change and implications to mitigation and adaptation, it has become increasingly important to assess the fidelity of the land component within earth system models to capture realistic hydrological processes and their response to the changing climate and quantify the associated uncertainties. This study investigates the sensitivity of runoff simulations to major hydrologic parameters in version 4 of the Community Land Model (CLM4) by integrating CLM4 with a stochastic exploratory sensitivity analysis framework at 20 selected watersheds from the Model Parameter Estimation Experiment (MOPEX) spanning amore » wide range of climate and site conditions. We found that for runoff simulations, the most significant parameters are those related to the subsurface runoff parameterizations. Soil texture related parameters and surface runoff parameters are of secondary significance. Moreover, climate and soil conditions play important roles in the parameter sensitivity. In general, site conditions within water-limited hydrologic regimes and with finer soil texture result in stronger sensitivity of output variables, such as runoff and its surface and subsurface components, to the input parameters in CLM4. This study demonstrated the feasibility of parameter inversion for CLM4 using streamflow observations to improve runoff simulations. By ranking the significance of the input parameters, we showed that the parameter set dimensionality could be reduced for CLM4 parameter calibration under different hydrologic and climatic regimes so that the inverse problem is less ill posed.« less

Automatic Calibration of a Semi-Distributed Hydrologic Model Using Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Bekele, E. G.; Nicklow, J. W.

2005-12-01

Hydrologic simulation models need to be calibrated and validated before using them for operational predictions. Spatially-distributed hydrologic models generally have a large number of parameters to capture the various physical characteristics of a hydrologic system. Manual calibration of such models is a very tedious and daunting task, and its success depends on the subjective assessment of a particular modeler, which includes knowledge of the basic approaches and interactions in the model. In order to alleviate these shortcomings, an automatic calibration model, which employs an evolutionary optimization technique known as Particle Swarm Optimizer (PSO) for parameter estimation, is developed. PSO is a heuristic search algorithm that is inspired by social behavior of bird flocking or fish schooling. The newly-developed calibration model is integrated to the U.S. Department of Agriculture's Soil and Water Assessment Tool (SWAT). SWAT is a physically-based, semi-distributed hydrologic model that was developed to predict the long term impacts of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use, and management conditions. SWAT was calibrated for streamflow and sediment concentration. The calibration process involves parameter specification, whereby sensitive model parameters are identified, and parameter estimation. In order to reduce the number of parameters to be calibrated, parameterization was performed. The methodology is applied to a demonstration watershed known as Big Creek, which is located in southern Illinois. Application results show the effectiveness of the approach and model predictions are significantly improved.

A model of the normal and null states of pulsars

NASA Astrophysics Data System (ADS)

Jones, P. B.

1981-12-01

A solvable three-dimensional polar cap model of pair creation and charged particle acceleration has been derived. There are no free parameters of significance apart from the polar surface magnetic flux density. The parameter determining the acceleration potential difference has been obtained by calculation of elementary nuclear and electromagnetic processes. Solutions of the model exist for both normal and null states of a pulsar, and the instability in the normal state leading to the normal to null transition has been identified. The predicted necessary condition for the transition is entirely consistent with observation.

A model of the normal and null states of pulsars

NASA Astrophysics Data System (ADS)

Jones, P. B.

A solvable three dimensional polar cap model of pair creation and charged particle acceleration is derived. There are no free parameters of significance apart from the polar surface magnetic flux density. The parameter CO determining the acceleration potential difference was obtained by calculation of elementary nuclear and electromagnetic processes. Solutions of the model exist for both normal and null states of a pulsar, and the instability in the normal state leading to the normal to null transition is identified. The predicted necessary condition for the transition is entirely consistent with observation.

Validation of a mathematical model of the bovine estrous cycle for cows with different estrous cycle characteristics.

PubMed

Boer, H M T; Butler, S T; Stötzel, C; Te Pas, M F W; Veerkamp, R F; Woelders, H

2017-11-01

A recently developed mechanistic mathematical model of the bovine estrous cycle was parameterized to fit empirical data sets collected during one estrous cycle of 31 individual cows, with the main objective to further validate the model. The a priori criteria for validation were (1) the resulting model can simulate the measured data correctly (i.e. goodness of fit), and (2) this is achieved without needing extreme, probably non-physiological parameter values. We used a least squares optimization procedure to identify parameter configurations for the mathematical model to fit the empirical in vivo measurements of follicle and corpus luteum sizes, and the plasma concentrations of progesterone, estradiol, FSH and LH for each cow. The model was capable of accommodating normal variation in estrous cycle characteristics of individual cows. With the parameter sets estimated for the individual cows, the model behavior changed for 21 cows, with improved fit of the simulated output curves for 18 of these 21 cows. Moreover, the number of follicular waves was predicted correctly for 18 of the 25 two-wave and three-wave cows, without extreme parameter value changes. Estimation of specific parameters confirmed results of previous model simulations indicating that parameters involved in luteolytic signaling are very important for regulation of general estrous cycle characteristics, and are likely responsible for differences in estrous cycle characteristics between cows.

Delineating parameter unidentifiabilities in complex models

NASA Astrophysics Data System (ADS)

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lei, Huan; Yang, Xiu; Li, Zhen

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 priormore » 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.« less

Multivariate modelling of prostate cancer combining magnetic resonance derived T2, diffusion, dynamic contrast-enhanced and spectroscopic parameters.

PubMed

Riches, S F; Payne, G S; Morgan, V A; Dearnaley, D; Morgan, S; Partridge, M; Livni, N; Ogden, C; deSouza, N M

2015-05-01

The objectives are determine the optimal combination of MR parameters for discriminating tumour within the prostate using linear discriminant analysis (LDA) and to compare model accuracy with that of an experienced radiologist. Multiparameter MRIs in 24 patients before prostatectomy were acquired. Tumour outlines from whole-mount histology, T2-defined peripheral zone (PZ), and central gland (CG) were superimposed onto slice-matched parametric maps. T2, Apparent Diffusion Coefficient, initial area under the gadolinium curve, vascular parameters (K(trans),Kep,Ve), and (choline+polyamines+creatine)/citrate were compared between tumour and non-tumour tissues. Receiver operating characteristic (ROC) curves determined sensitivity and specificity at spectroscopic voxel resolution and per lesion, and LDA determined the optimal multiparametric model for identifying tumours. Accuracy was compared with an expert observer. Tumours were significantly different from PZ and CG for all parameters (all p < 0.001). Area under the ROC curve for discriminating tumour from non-tumour was significantly greater (p < 0.001) for the multiparametric model than for individual parameters; at 90 % specificity, sensitivity was 41 % (MRSI voxel resolution) and 59 % per lesion. At this specificity, an expert observer achieved 28 % and 49 % sensitivity, respectively. The model was more accurate when parameters from all techniques were included and performed better than an expert observer evaluating these data. • The combined model increases diagnostic accuracy in prostate cancer compared with individual parameters • The optimal combined model includes parameters from diffusion, spectroscopy, perfusion, and anatominal MRI • The computed model improves tumour detection compared to an expert viewing parametric maps.

Model-based analysis of fatigued human knee extensors : Effects of isometrically induced fatigue on Hill-type model parameters and ballistic contractions.

PubMed

Penasso, Harald; Thaller, Sigrid

2018-05-05

This study investigated the effect of isometrically induced fatigue on Hill-type muscle model parameters and related task-dependent effects. Parameter identification methods were used to extract fatigue-related parameter trends from isometric and ballistic dynamic maximum voluntary knee extensions. Nine subjects, who completed ten fatiguing sets, each consisting of nine 3 s isometric maximum voluntary contractions with 3 s rest plus two ballistic contractions with different loads, were analyzed. Only at the isometric task, the identified optimized model parameter values of muscle activation rate and maximum force generating capacity of the contractile element decreased from [Formula: see text] to [Formula: see text] Hz and from [Formula: see text] to [Formula: see text] N, respectively. For all tasks, the maximum efficiency of the contractile element, mathematically related to the curvature of the force-velocity relation, increased from [Formula: see text] to [Formula: see text]. The model parameter maximum contraction velocity decreased from [Formula: see text] to [Formula: see text] m/s and the stiffness of the serial elastic element from [Formula: see text] to [Formula: see text] N/mm. Thus, models of fatigue should consider fatigue dependencies in active as well as in passive elements, and muscle activation dynamics should account for the task dependency of fatigue.

Mathematical modeling of a Ti:sapphire solid-state laser

NASA Technical Reports Server (NTRS)

Swetits, John J.

1987-01-01

The project initiated a study of a mathematical model of a tunable Ti:sapphire solid-state laser. A general mathematical model was developed for the purpose of identifying design parameters which will optimize the system, and serve as a useful predictor of the system's behavior.

Literature review of outcome parameters used in studies of Geriatric Fracture Centers.

PubMed

Liem, I S L; Kammerlander, C; Suhm, N; Kates, S L; Blauth, M

2014-02-01

A variety of multidisciplinary treatment models have been described to improve outcome after osteoporotic hip fractures. There is a tendency toward better outcomes after implementation of the most sophisticated model with a shared leadership for orthopedic surgeons and geriatricians; the Geriatric Fracture Center. The purpose of this review is to evaluate the use of outcome parameters in published literature on the Geriatric Fracture Center evaluation studies. A literature search was performed using Medline and the Cochrane Library to identify Geriatric Fracture Center evaluation studies. The outcome parameters used in the included studies were evaluated. A total of 16 outcome parameters were used in 11 studies to evaluate patient outcome in 8 different Geriatric Fracture Centers. Two of these outcome parameters are patient-reported outcome measures and 14 outcome parameters were objective measures. In-hospital mortality, length of stay, time to surgery, place of residence and complication rate are the most frequently used outcome parameters. The patient-reported outcomes included activities of daily living and mobility scores. There is a need for generally agreed upon outcome measures to facilitate comparison of different care models.

Parameter Estimation and Model Selection in Computational Biology

PubMed Central

Lillacci, Gabriele; Khammash, Mustafa

2010-01-01

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. PMID:20221262

Utilization of Expert Knowledge in a Multi-Objective Hydrologic Model Automatic Calibration Process

NASA Astrophysics Data System (ADS)

Quebbeman, J.; Park, G. H.; Carney, S.; Day, G. N.; Micheletty, P. D.

2016-12-01

Spatially distributed continuous simulation hydrologic models have a large number of parameters for potential adjustment during the calibration process. Traditional manual calibration approaches of such a modeling system is extremely laborious, which has historically motivated the use of automatic calibration procedures. With a large selection of model parameters, achieving high degrees of objective space fitness - measured with typical metrics such as Nash-Sutcliffe, Kling-Gupta, RMSE, etc. - can easily be achieved using a range of evolutionary algorithms. A concern with this approach is the high degree of compensatory calibration, with many similarly performing solutions, and yet grossly varying parameter set solutions. To help alleviate this concern, and mimic manual calibration processes, expert knowledge is proposed for inclusion within the multi-objective functions, which evaluates the parameter decision space. As a result, Pareto solutions are identified with high degrees of fitness, but also create parameter sets that maintain and utilize available expert knowledge resulting in more realistic and consistent solutions. This process was tested using the joint SNOW-17 and Sacramento Soil Moisture Accounting method (SAC-SMA) within the Animas River basin in Colorado. Three different elevation zones, each with a range of parameters, resulted in over 35 model parameters simultaneously calibrated. As a result, high degrees of fitness were achieved, in addition to the development of more realistic and consistent parameter sets such as those typically achieved during manual calibration procedures.

Parameter sensitivity analysis of a 1-D cold region lake model for land-surface schemes

NASA Astrophysics Data System (ADS)

Guerrero, José-Luis; Pernica, Patricia; Wheater, Howard; Mackay, Murray; Spence, Chris

2017-12-01

Lakes might be sentinels of climate change, but the uncertainty in their main feedback to the atmosphere - heat-exchange fluxes - is often not considered within climate models. Additionally, these fluxes are seldom measured, hindering critical evaluation of model output. Analysis of the Canadian Small Lake Model (CSLM), a one-dimensional integral lake model, was performed to assess its ability to reproduce diurnal and seasonal variations in heat fluxes and the sensitivity of simulated fluxes to changes in model parameters, i.e., turbulent transport parameters and the light extinction coefficient (Kd). A C++ open-source software package, Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE), was used to perform sensitivity analysis (SA) and identify the parameters that dominate model behavior. The generalized likelihood uncertainty estimation (GLUE) was applied to quantify the fluxes' uncertainty, comparing daily-averaged eddy-covariance observations to the output of CSLM. Seven qualitative and two quantitative SA methods were tested, and the posterior likelihoods of the modeled parameters, obtained from the GLUE analysis, were used to determine the dominant parameters and the uncertainty in the modeled fluxes. Despite the ubiquity of the equifinality issue - different parameter-value combinations yielding equivalent results - the answer to the question was unequivocal: Kd, a measure of how much light penetrates the lake, dominates sensible and latent heat fluxes, and the uncertainty in their estimates is strongly related to the accuracy with which Kd is determined. This is important since accurate and continuous measurements of Kd could reduce modeling uncertainty.

A Bayesian Hierarchical Modeling Approach to Predicting Flow in Ungauged Basins

EPA Science Inventory

Recent innovative approaches to identifying and applying regression-based relationships between land use patterns (such as increasing impervious surface area and decreasing vegetative cover) and rainfall-runoff model parameters represent novel and promising improvements to predic...

Global sensitivity analysis of DRAINMOD-FOREST, an integrated forest ecosystem model

Treesearch

Shiying Tian; Mohamed A. Youssef; Devendra M. Amatya; Eric D. Vance

2014-01-01

Global sensitivity analysis is a useful tool to understand process-based ecosystem models by identifying key parameters and processes controlling model predictions. This study reported a comprehensive global sensitivity analysis for DRAINMOD-FOREST, an integrated model for simulating water, carbon (C), and nitrogen (N) cycles and plant growth in lowland forests. The...

Users manual for an expert system (HSPEXP) for calibration of the hydrological simulation program; Fortran

USGS Publications Warehouse

Lumb, A.M.; McCammon, R.B.; Kittle, J.L.

1994-01-01

Expert system software was developed to assist less experienced modelers with calibration of a watershed model and to facilitate the interaction between the modeler and the modeling process not provided by mathematical optimization. A prototype was developed with artificial intelligence software tools, a knowledge engineer, and two domain experts. The manual procedures used by the domain experts were identified and the prototype was then coded by the knowledge engineer. The expert system consists of a set of hierarchical rules designed to guide the calibration of the model through a systematic evaluation of model parameters. When the prototype was completed and tested, it was rewritten for portability and operational use and was named HSPEXP. The watershed model Hydrological Simulation Program--Fortran (HSPF) is used in the expert system. This report is the users manual for HSPEXP and contains a discussion of the concepts and detailed steps and examples for using the software. The system has been tested on watersheds in the States of Washington and Maryland, and the system correctly identified the model parameters to be adjusted and the adjustments led to improved calibration.

Dynamic model of production enterprises based on accounting registers and its identification

NASA Astrophysics Data System (ADS)

Sirazetdinov, R. T.; Samodurov, A. V.; Yenikeev, I. A.; Markov, D. S.

2016-06-01

The report focuses on the mathematical modeling of economic entities based on accounting registers. Developed the dynamic model of financial and economic activity of the enterprise as a system of differential equations. Created algorithms for identification of parameters of the dynamic model. Constructed and identified the model of Russian machine-building enterprises.

Assessment of reduced-order unscented Kalman filter for parameter identification in 1-dimensional blood flow models using experimental data.

PubMed

Caiazzo, A; Caforio, Federica; Montecinos, Gino; Muller, Lucas O; Blanco, Pablo J; Toro, Eluterio F

2016-10-25

This work presents a detailed investigation of a parameter estimation approach on the basis of the reduced-order unscented Kalman filter (ROUKF) in the context of 1-dimensional blood flow models. In particular, the main aims of this study are (1) to investigate the effects of using real measurements versus synthetic data for the estimation procedure (i.e., numerical results of the same in silico model, perturbed with noise) and (2) to identify potential difficulties and limitations of the approach in clinically realistic applications to assess the applicability of the filter to such setups. For these purposes, the present numerical study is based on a recently published in vitro model of the arterial network, for which experimental flow and pressure measurements are available at few selected locations. To mimic clinically relevant situations, we focus on the estimation of terminal resistances and arterial wall parameters related to vessel mechanics (Young's modulus and wall thickness) using few experimental observations (at most a single pressure or flow measurement per vessel). In all cases, we first perform a theoretical identifiability analysis on the basis of the generalized sensitivity function, comparing then the results owith the ROUKF, using either synthetic or experimental data, to results obtained using reference parameters and to available measurements. Copyright © 2016 John Wiley & Sons, Ltd.

A parameter optimization tool for evaluating the physical consistency of the plot-scale water budget of the integrated eco-hydrological model GEOtop in complex terrain

NASA Astrophysics Data System (ADS)

Bertoldi, Giacomo; Cordano, Emanuele; Brenner, Johannes; Senoner, Samuel; Della Chiesa, Stefano; Niedrist, Georg

2017-04-01

In mountain regions, the plot- and catchment-scale water and energy budgets are controlled by a complex interplay of different abiotic (i.e. topography, geology, climate) and biotic (i.e. vegetation, land management) controlling factors. When integrated, physically-based eco-hydrological models are used in mountain areas, there are a large number of parameters, topographic and boundary conditions that need to be chosen. However, data on soil and land-cover properties are relatively scarce and do not reflect the strong variability at the local scale. For this reason, tools for uncertainty quantification and optimal parameters identification are essential not only to improve model performances, but also to identify most relevant parameters to be measured in the field and to evaluate the impact of different assumptions for topographic and boundary conditions (surface, lateral and subsurface water and energy fluxes), which are usually unknown. In this contribution, we present the results of a sensitivity analysis exercise for a set of 20 experimental stations located in the Italian Alps, representative of different conditions in terms of topography (elevation, slope, aspect), land use (pastures, meadows, and apple orchards), soil type and groundwater influence. Besides micrometeorological parameters, each station provides soil water content at different depths, and in three stations (one for each land cover) eddy covariance fluxes. The aims of this work are: (I) To present an approach for improving calibration of plot-scale soil moisture and evapotranspiration (ET). (II) To identify the most sensitive parameters and relevant factors controlling temporal and spatial differences among sites. (III) Identify possible model structural deficiencies or uncertainties in boundary conditions. Simulations have been performed with the GEOtop 2.0 model, which is a physically-based, fully distributed integrated eco-hydrological model that has been specifically designed for mountain regions, since it considers the effect of topography on radiation and water fluxes and integrates a snow module. A new automatic sensitivity and optimization tool based on the Particle Swarm Optimization theory has been developed, available as R package on https://github.com/EURAC-Ecohydro/geotopOptim2. The model, once calibrated for soil and vegetation parameters, predicts the plot-scale temporal SMC dynamics of SMC and ET with a RMSE of about 0.05 m3/m3 and 40 W/m2, respectively. However, the model tends to underestimate ET during summer months over apple orchards. Results show how most sensitive parameters are both soil and canopy structural properties. However, ranking is affected by the choice of the target function and local topographic conditions. In particular, local slope/aspect influences results in stations located over hillslopes, but with marked seasonal differences. Results for locations in the valley floor are strongly controlled by the choice of the bottom water flux boundary condition. The poorer model performances in simulating ET over apple orchards could be explained by a model structural deficiency in representing the stomatal control on vapor pressure deficit for this particular type of vegetation. The results of this sensitivity could be extended to other physically distributed models, and also provide valuable insights for optimizing new experimental designs.

A Doppler Transient Model Based on the Laplace Wavelet and Spectrum Correlation Assessment for Locomotive Bearing Fault Diagnosis

PubMed Central

Shen, Changqing; Liu, Fang; Wang, Dong; Zhang, Ao; Kong, Fanrang; Tse, Peter W.

2013-01-01

The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully. PMID:24253191

A Doppler transient model based on the laplace wavelet and spectrum correlation assessment for locomotive bearing fault diagnosis.

PubMed

Shen, Changqing; Liu, Fang; Wang, Dong; Zhang, Ao; Kong, Fanrang; Tse, Peter W

2013-11-18

The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully.

Granular-flow rheology: Role of shear-rate number in transition regime

USGS Publications Warehouse

Chen, C.-L.; Ling, C.-H.

1996-01-01

This paper examines the rationale behind the semiempirical formulation of a generalized viscoplastic fluid (GVF) model in the light of the Reiner-Rivlin constitutive theory and the viscoplastic theory, thereby identifying the parameters that control the rheology of granular flow. The shear-rate number (N) proves to be among the most significant parameters identified from the GVF model. As N ??? 0 and N ??? ???, the GVF model can reduce asymptotically to the theoretical stress versus shear-rate relations in the macroviscous and graininertia regimes, respectively, where the grain concentration (C) also plays a major role in the rheology of granular flow. Using available data obtained from the rotating-cylinder experiments of neutrally buoyant solid spheres dispersing in an interstitial fluid, the shear stress for granular flow in transition between the two regimes proves dependent on N and C in addition to some material constants, such as the coefficient of restitution. The insufficiency of data on rotating-cylinder experiments cannot presently allow the GVF model to predict how a granular flow may behave in the entire range of N; however, the analyzed data provide an insight on the interrelation among the relevant dimensionless parameters.

Identification of coal seam strata from geophysical logs of borehole using Adaptive Neuro-Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Yegireddi, Satyanarayana; Uday Bhaskar, G.

2009-01-01

Different parameters obtained through well-logging geophysical sensors such as SP, resistivity, gamma-gamma, neutron, natural gamma and acoustic, help in identification of strata and estimation of the physical, electrical and acoustical properties of the subsurface lithology. Strong and conspicuous changes in some of the log parameters associated with any particular stratigraphy formation, are function of its composition, physical properties and help in classification. However some substrata show moderate values in respective log parameters and make difficult to identify or assess the type of strata, if we go by the standard variability ranges of any log parameters and visual inspection. The complexity increases further with more number of sensors involved. An attempt is made to identify the type of stratigraphy from borehole geophysical log data using a combined approach of neural networks and fuzzy logic, known as Adaptive Neuro-Fuzzy Inference System. A model is built based on a few data sets (geophysical logs) of known stratigraphy of in coal areas of Kothagudem, Godavari basin and further the network model is used as test model to infer the lithology of a borehole from their geophysical logs, not used in simulation. The results are very encouraging and the model is able to decipher even thin cola seams and other strata from borehole geophysical logs. The model can be further modified to assess the physical properties of the strata, if the corresponding ground truth is made available for simulation.

Issues in the inverse modeling of a soil infiltration process

NASA Astrophysics Data System (ADS)

Kuraz, Michal; Jacka, Lukas; Leps, Matej

2017-04-01

This contribution addresses issues in evaluation of the soil hydraulic parameters (SHP) from the Richards equation based inverse model. The inverse model was representing single ring infiltration experiment on mountainous podzolic soil profile, and was searching for the SHP parameters of the top soil layer. Since the thickness of the top soil layer is often much lower than the depth required to embed the single ring or Guelph permeameter device, the SHPs for the top soil layer are very difficult to measure directly. The SHPs for the top soil layer were therefore identified here by inverse modeling of the single ring infiltration process, where, especially, the initial unsteady part of the experiment is expected to provide very useful data for evaluating the retention curve parameters (excluding the residual water content) and the saturated hydraulic conductivity. The main issue, which is addressed in this contribution, is the uniqueness of the Richards equation inverse model. We tried to answer the question whether is it possible to characterize the unsteady infiltration experiment with a unique set of SHPs values, and whether are all SHP parameters vulnerable with the non-uniqueness. Which is an important issue, since we could further conclude whether the popular gradient methods are appropriate here. Further the issues in assigning the initial and boundary condition setup, the influence of spatial and temporal discretization on the values of the identified SHPs, and the convergence issues with the Richards equation nonlinear operator during automatic calibration procedure are also covered here.

On the applicability of surrogate-based Markov chain Monte Carlo-Bayesian inversion to the Community Land Model: Case studies at flux tower sites: SURROGATE-BASED MCMC FOR CLM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Maoyi; Ray, Jaideep; Hou, Zhangshuan

2016-07-04

The Community Land Model (CLM) has been widely used in climate and Earth system modeling. Accurate estimation of model parameters is needed for reliable model simulations and predictions under current and future conditions, respectively. In our previous work, a subset of hydrological parameters has been identified to have significant impact on surface energy fluxes at selected flux tower sites based on parameter screening and sensitivity analysis, which indicate that the parameters could potentially be estimated from surface flux observations at the towers. To date, such estimates do not exist. In this paper, we assess the feasibility of applying a Bayesianmore » model calibration technique to estimate CLM parameters at selected flux tower sites under various site conditions. The parameters are estimated as a joint probability density function (PDF) that provides estimates of uncertainty of the parameters being inverted, conditional on climatologically-average latent heat fluxes derived from observations. We find that the simulated mean latent heat fluxes from CLM using the calibrated parameters are generally improved at all sites when compared to those obtained with CLM simulations using default parameter sets. Further, our calibration method also results in credibility bounds around the simulated mean fluxes which bracket the measured data. The modes (or maximum a posteriori values) and 95% credibility intervals of the site-specific posterior PDFs are tabulated as suggested parameter values for each site. Analysis of relationships between the posterior PDFs and site conditions suggests that the parameter values are likely correlated with the plant functional type, which needs to be confirmed in future studies by extending the approach to more sites.« less

On the applicability of surrogate-based MCMC-Bayesian inversion to the Community Land Model: Case studies at Flux tower sites

DOE PAGES

Huang, Maoyi; Ray, Jaideep; Hou, Zhangshuan; ...

2016-06-01

The Community Land Model (CLM) has been widely used in climate and Earth system modeling. Accurate estimation of model parameters is needed for reliable model simulations and predictions under current and future conditions, respectively. In our previous work, a subset of hydrological parameters has been identified to have significant impact on surface energy fluxes at selected flux tower sites based on parameter screening and sensitivity analysis, which indicate that the parameters could potentially be estimated from surface flux observations at the towers. To date, such estimates do not exist. In this paper, we assess the feasibility of applying a Bayesianmore » model calibration technique to estimate CLM parameters at selected flux tower sites under various site conditions. The parameters are estimated as a joint probability density function (PDF) that provides estimates of uncertainty of the parameters being inverted, conditional on climatologically average latent heat fluxes derived from observations. We find that the simulated mean latent heat fluxes from CLM using the calibrated parameters are generally improved at all sites when compared to those obtained with CLM simulations using default parameter sets. Further, our calibration method also results in credibility bounds around the simulated mean fluxes which bracket the measured data. The modes (or maximum a posteriori values) and 95% credibility intervals of the site-specific posterior PDFs are tabulated as suggested parameter values for each site. As a result, analysis of relationships between the posterior PDFs and site conditions suggests that the parameter values are likely correlated with the plant functional type, which needs to be confirmed in future studies by extending the approach to more sites.« less

On the applicability of surrogate-based MCMC-Bayesian inversion to the Community Land Model: Case studies at Flux tower sites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Maoyi; Ray, Jaideep; Hou, Zhangshuan

The Community Land Model (CLM) has been widely used in climate and Earth system modeling. Accurate estimation of model parameters is needed for reliable model simulations and predictions under current and future conditions, respectively. In our previous work, a subset of hydrological parameters has been identified to have significant impact on surface energy fluxes at selected flux tower sites based on parameter screening and sensitivity analysis, which indicate that the parameters could potentially be estimated from surface flux observations at the towers. To date, such estimates do not exist. In this paper, we assess the feasibility of applying a Bayesianmore » model calibration technique to estimate CLM parameters at selected flux tower sites under various site conditions. The parameters are estimated as a joint probability density function (PDF) that provides estimates of uncertainty of the parameters being inverted, conditional on climatologically average latent heat fluxes derived from observations. We find that the simulated mean latent heat fluxes from CLM using the calibrated parameters are generally improved at all sites when compared to those obtained with CLM simulations using default parameter sets. Further, our calibration method also results in credibility bounds around the simulated mean fluxes which bracket the measured data. The modes (or maximum a posteriori values) and 95% credibility intervals of the site-specific posterior PDFs are tabulated as suggested parameter values for each site. As a result, analysis of relationships between the posterior PDFs and site conditions suggests that the parameter values are likely correlated with the plant functional type, which needs to be confirmed in future studies by extending the approach to more sites.« less

On the applicability of surrogate-based Markov chain Monte Carlo-Bayesian inversion to the Community Land Model: Case studies at flux tower sites

NASA Astrophysics Data System (ADS)

Huang, Maoyi; Ray, Jaideep; Hou, Zhangshuan; Ren, Huiying; Liu, Ying; Swiler, Laura

2016-07-01

The Community Land Model (CLM) has been widely used in climate and Earth system modeling. Accurate estimation of model parameters is needed for reliable model simulations and predictions under current and future conditions, respectively. In our previous work, a subset of hydrological parameters has been identified to have significant impact on surface energy fluxes at selected flux tower sites based on parameter screening and sensitivity analysis, which indicate that the parameters could potentially be estimated from surface flux observations at the towers. To date, such estimates do not exist. In this paper, we assess the feasibility of applying a Bayesian model calibration technique to estimate CLM parameters at selected flux tower sites under various site conditions. The parameters are estimated as a joint probability density function (PDF) that provides estimates of uncertainty of the parameters being inverted, conditional on climatologically average latent heat fluxes derived from observations. We find that the simulated mean latent heat fluxes from CLM using the calibrated parameters are generally improved at all sites when compared to those obtained with CLM simulations using default parameter sets. Further, our calibration method also results in credibility bounds around the simulated mean fluxes which bracket the measured data. The modes (or maximum a posteriori values) and 95% credibility intervals of the site-specific posterior PDFs are tabulated as suggested parameter values for each site. Analysis of relationships between the posterior PDFs and site conditions suggests that the parameter values are likely correlated with the plant functional type, which needs to be confirmed in future studies by extending the approach to more sites.

Parametric uncertainties in global model simulations of black carbon column mass concentration

NASA Astrophysics Data System (ADS)

Pearce, Hana; Lee, Lindsay; Reddington, Carly; Carslaw, Ken; Mann, Graham

2016-04-01

Previous studies have deduced that the annual mean direct radiative forcing from black carbon (BC) aerosol may regionally be up to 5 W m-2 larger than expected due to underestimation of global atmospheric BC absorption in models. We have identified the magnitude and important sources of parametric uncertainty in simulations of BC column mass concentration from a global aerosol microphysics model (GLOMAP-Mode). A variance-based uncertainty analysis of 28 parameters has been performed, based on statistical emulators trained on model output from GLOMAP-Mode. This is the largest number of uncertain model parameters to be considered in a BC uncertainty analysis to date and covers primary aerosol emissions, microphysical processes and structural parameters related to the aerosol size distribution. We will present several recommendations for further research to improve the fidelity of simulated BC. In brief, we find that the standard deviation around the simulated mean annual BC column mass concentration varies globally between 2.5 x 10-9 g cm-2 in remote marine regions and 1.25 x 10-6 g cm-2 near emission sources due to parameter uncertainty Between 60 and 90% of the variance over source regions is due to uncertainty associated with primary BC emission fluxes, including biomass burning, fossil fuel and biofuel emissions. While the contributions to BC column uncertainty from microphysical processes, for example those related to dry and wet deposition, are increased over remote regions, we find that emissions still make an important contribution in these areas. It is likely, however, that the importance of structural model error, i.e. differences between models, is greater than parametric uncertainty. We have extended our analysis to emulate vertical BC profiles at several locations in the mid-Pacific Ocean and identify the parameters contributing to uncertainty in the vertical distribution of black carbon at these locations. We will present preliminary comparisons of emulated BC vertical profiles from the AeroCom multi-model ensemble and Hiaper Pole-to-Pole (HIPPO) observations.

The use of multiobjective calibration and regional sensitivity analysis in simulating hyporheic exchange

USGS Publications Warehouse

Naranjo, Ramon C.; Niswonger, Richard G.; Stone, Mark; Davis, Clinton; McKay, Alan

2012-01-01

We describe an approach for calibrating a two-dimensional (2-D) flow model of hyporheic exchange using observations of temperature and pressure to estimate hydraulic and thermal properties. A longitudinal 2-D heat and flow model was constructed for a riffle-pool sequence to simulate flow paths and flux rates for variable discharge conditions. A uniform random sampling approach was used to examine the solution space and identify optimal values at local and regional scales. We used a regional sensitivity analysis to examine the effects of parameter correlation and nonuniqueness commonly encountered in multidimensional modeling. The results from this study demonstrate the ability to estimate hydraulic and thermal parameters using measurements of temperature and pressure to simulate exchange and flow paths. Examination of the local parameter space provides the potential for refinement of zones that are used to represent sediment heterogeneity within the model. The results indicate vertical hydraulic conductivity was not identifiable solely using pressure observations; however, a distinct minimum was identified using temperature observations. The measured temperature and pressure and estimated vertical hydraulic conductivity values indicate the presence of a discontinuous low-permeability deposit that limits the vertical penetration of seepage beneath the riffle, whereas there is a much greater exchange where the low-permeability deposit is absent. Using both temperature and pressure to constrain the parameter estimation process provides the lowest overall root-mean-square error as compared to using solely temperature or pressure observations. This study demonstrates the benefits of combining continuous temperature and pressure for simulating hyporheic exchange and flow in a riffle-pool sequence. Copyright 2012 by the American Geophysical Union.

Fuzzy inference system for identification of geological stratigraphy off Prydz Bay, East Antarctica

NASA Astrophysics Data System (ADS)

Singh, Upendra K.

2011-12-01

The analysis of well logging data plays key role in the exploration and development of hydrocarbon reservoirs. Various well log parameters such as porosity, gamma ray, density, transit time and resistivity, help in classification of strata and estimation of the physical, electrical and acoustical properties of the subsurface lithology. Strong and conspicuous changes in some of the log parameters associated with any particular geological stratigraphy formation are function of its composition, physical properties that help in classification. However some substrata show moderate values in respective log parameters and make difficult to identify the kind of strata, if we go by the standard variability ranges of any log parameters and visual inspection. The complexity increases further with more number of sensors involved. An attempt is made to identify the kinds of stratigraphy from well logs over Prydz bay basin, East Antarctica using fuzzy inference system. A model is built based on few data sets of known stratigraphy and further the network model is used as test model to infer the lithology of a borehole from their geophysical logs, not used in simulation. Initially the fuzzy based algorithm is trained, validated and tested on well log data and finally identifies the formation lithology of a hydrocarbon reservoir system of study area. The effectiveness of this technique is demonstrated by the analysis of the results for actual lithologs and coring data of ODP Leg 188. The fuzzy results show that the training performance equals to 82.95% while the prediction ability is 87.69%. The fuzzy results are very encouraging and the model is able to decipher even thin layer seams and other strata from geophysical logs. The result provides the significant sand formation of depth range 316.0- 341.0 m, where core recovery is incomplete.

Predicting Performance on a Firefighter's Ability Test from Fitness Parameters

ERIC Educational Resources Information Center

Michaelides, Marcos A.; Parpa, Koulla M.; Thompson, Jerald; Brown, Barry

2008-01-01

The purpose of this project was to identify the relationships between various fitness parameters such as upper body muscular endurance, upper and lower body strength, flexibility, body composition and performance on an ability test (AT) that included simulated firefighting tasks. A second intent was to create a regression model that would predict…

Preliminary results of fisheries investigation associated with Skylab-3. [remotely sensed distribution and abundance of gamefish in Gulf of Mexico

NASA Technical Reports Server (NTRS)

Savastano, K. J. (Principal Investigator); Pastula, E. J., Jr.; Woods, G.; Faller, K.

1974-01-01

The author has identified the following significant results. This investigation is to establish the feasibility of utilizing remotely sensed data acquired from aircraft and satellite platforms to provide information concerning the distribution and abundance of oceanic gamefish. Data from the test area in the northeastern Gulf of Mexico has made possible the identification of fisheries significant environmental parameters for white marlin. Predictive models based on catch data and surface truth information have been developed and have demonstrated potential for reducing search significantly by identifying areas which have a high probability of being productive. Three of the parameters utilized by the model, chlorophyll-a, sea surface temperature, and turbidity have been inferred from aircraft sensor data. Cloud cover and delayed receipt have inhibited the use of Skylab data. The first step toward establishing the feasibility of utilizing remotely sensed data to assess amd monitor the distribution of ocean gamefish has been taken with the successful identification of fisheries significant oceanographic parameters and the demonstration of the capability of measuring most of these parameters remotely.

Application of Quality by Design to the characterization of the cell culture process of an Fc-Fusion protein.

PubMed

Rouiller, Yolande; Solacroup, Thomas; Deparis, Véronique; Barbafieri, Marco; Gleixner, Ralf; Broly, Hervé; Eon-Duval, Alex

2012-06-01

The production bioreactor step of an Fc-Fusion protein manufacturing cell culture process was characterized following Quality by Design principles. Using scientific knowledge derived from the literature and process knowledge gathered during development studies and manufacturing to support clinical trials, potential critical and key process parameters with a possible impact on product quality and process performance, respectively, were determined during a risk assessment exercise. The identified process parameters were evaluated using a design of experiment approach. The regression models generated from the data allowed characterizing the impact of the identified process parameters on quality attributes. The main parameters having an impact on product titer were pH and dissolved oxygen, while those having the highest impact on process- and product-related impurities and variants were pH and culture duration. The models derived from characterization studies were used to define the cell culture process design space. The design space limits were set in such a way as to ensure that the drug substance material would consistently have the desired quality. Copyright © 2012 Elsevier B.V. All rights reserved.

Study of. lambda. parameters and crossover phenomena in SU(N) x SU(N) sigma models in two dimensions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shigemitsu, J; Kogut, J B

1981-01-01

The spin system analogues of recent studies of the string tension and ..lambda.. parameters of SU(N) gauge theories in 4 dimensions are carried out for the SU(N) x SU(N) and O(N) models in 2 dimensions. The relations between the ..lambda.. parameters of both the Euclidean and Hamiltonian formulation of the lattice models and the ..lambda.. parameter of the continuum models are obtained. The one loop finite renormalization of the speed of light in the lattice Hamiltonian formulations of the O(N) and SU(N) x SU(N) models is calculated. Strong coupling calculations of the mass gaps of these spin models are donemore » for all N and the constants of proportionality between the gap and the ..lambda.. parameter of the continuum models are obtained. These results are contrasted with similar calculations for the SU(N) gauge models in 3+1 dimensions. Identifying suitable coupling constants for discussing the N ..-->.. infinity limits, the numerical results suggest that the crossover from weak to strong coupling in the lattice O(N) models becomes less abrupt as N increases while the crossover for the SU(N) x SU(N) models becomes more abrupt. The crossover in SU(N) gauge theories also becomes more abrupt with increasing N, however, at an even greater rate than in the SU(N) x SU(N) spin models.« less

Aerobic stabilization of biological sludge characterized by an extremely low decay rate: modeling, identifiability analysis and parameter estimation.

PubMed

Martínez-García, C G; Olguín, M T; Fall, C

2014-08-01

Aerobic digestion batch tests were run on a sludge model that contained only two fractions, the heterotrophic biomass (XH) and its endogenous residue (XP). The objective was to describe the stabilization of the sludge and estimate the endogenous decay parameters. Modeling was performed with Aquasim, based on long-term data of volatile suspended solids and chemical oxygen demand (VSS, COD). Sensitivity analyses were carried out to determine the conditions for unique identifiability of the parameters. Importantly, it was found that the COD/VSS ratio of the endogenous residues (1.06) was significantly lower than for the active biomass fraction (1.48). The decay rate constant of the studied sludge (low bH, 0.025 d(-1)) was one-tenth that usually observed (0.2d(-1)), which has two main practical significances. Digestion time required is much more long; also the oxygen uptake rate might be <1.5 mg O₂/gTSSh (biosolids standards), without there being significant decline in the biomass. Copyright © 2014 Elsevier Ltd. All rights reserved.

Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond.

PubMed

Perdikaris, Paris; Karniadakis, George Em

2016-05-01

We present a computational framework for model inversion based on multi-fidelity information fusion and Bayesian optimization. The proposed methodology targets the accurate construction of response surfaces in parameter space, and the efficient pursuit to identify global optima while keeping the number of expensive function evaluations at a minimum. We train families of correlated surrogates on available data using Gaussian processes and auto-regressive stochastic schemes, and exploit the resulting predictive posterior distributions within a Bayesian optimization setting. This enables a smart adaptive sampling procedure that uses the predictive posterior variance to balance the exploration versus exploitation trade-off, and is a key enabler for practical computations under limited budgets. The effectiveness of the proposed framework is tested on three parameter estimation problems. The first two involve the calibration of outflow boundary conditions of blood flow simulations in arterial bifurcations using multi-fidelity realizations of one- and three-dimensional models, whereas the last one aims to identify the forcing term that generated a particular solution to an elliptic partial differential equation. © 2016 The Author(s).

Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond

PubMed Central

Perdikaris, Paris; Karniadakis, George Em

2016-01-01

We present a computational framework for model inversion based on multi-fidelity information fusion and Bayesian optimization. The proposed methodology targets the accurate construction of response surfaces in parameter space, and the efficient pursuit to identify global optima while keeping the number of expensive function evaluations at a minimum. We train families of correlated surrogates on available data using Gaussian processes and auto-regressive stochastic schemes, and exploit the resulting predictive posterior distributions within a Bayesian optimization setting. This enables a smart adaptive sampling procedure that uses the predictive posterior variance to balance the exploration versus exploitation trade-off, and is a key enabler for practical computations under limited budgets. The effectiveness of the proposed framework is tested on three parameter estimation problems. The first two involve the calibration of outflow boundary conditions of blood flow simulations in arterial bifurcations using multi-fidelity realizations of one- and three-dimensional models, whereas the last one aims to identify the forcing term that generated a particular solution to an elliptic partial differential equation. PMID:27194481

Modal parameter identification of a CMUT membrane using response data only

NASA Astrophysics Data System (ADS)

Lardiès, Joseph; Bourbon, Gilles; Moal, Patrice Le; Kacem, Najib; Walter, Vincent; Le, Thien-Phu

2018-03-01

Capacitive micromachined ultrasonic transducers (CMUTs) are microelectromechanical systems used for the generation of ultrasounds. The fundamental element of the transducer is a clamped thin metallized membrane that vibrates under voltage variations. To control such oscillations and to optimize its dynamic response it is necessary to know the modal parameters of the membrane such as resonance frequency, damping and stiffness coefficients. The purpose of this work is to identify these parameters using only the time data obtained from the membrane center displacement. Dynamic measurements are conducted in time domain and we use two methods to identify the modal parameters: a subspace method based on an innovation model of the state-space representation and the continuous wavelet transform method based on the use of the ridge of the wavelet transform of the displacement. Experimental results are presented showing the effectiveness of these two procedures in modal parameter identification.

Automated palpation for breast tissue discrimination based on viscoelastic biomechanical properties.

PubMed

Tsukune, Mariko; Kobayashi, Yo; Miyashita, Tomoyuki; Fujie, G Masakatsu

2015-05-01

Accurate, noninvasive methods are sought for breast tumor detection and diagnosis. In particular, a need for noninvasive techniques that measure both the nonlinear elastic and viscoelastic properties of breast tissue has been identified. For diagnostic purposes, it is important to select a nonlinear viscoelastic model with a small number of parameters that highly correlate with histological structure. However, the combination of conventional viscoelastic models with nonlinear elastic models requires a large number of parameters. A nonlinear viscoelastic model of breast tissue based on a simple equation with few parameters was developed and tested. The nonlinear viscoelastic properties of soft tissues in porcine breast were measured experimentally using fresh ex vivo samples. Robotic palpation was used for measurements employed in a finite element model. These measurements were used to calculate nonlinear viscoelastic parameters for fat, fibroglandular breast parenchyma and muscle. The ability of these parameters to distinguish the tissue types was evaluated in a two-step statistical analysis that included Holm's pairwise [Formula: see text] test. The discrimination error rate of a set of parameters was evaluated by the Mahalanobis distance. Ex vivo testing in porcine breast revealed significant differences in the nonlinear viscoelastic parameters among combinations of three tissue types. The discrimination error rate was low among all tested combinations of three tissue types. Although tissue discrimination was not achieved using only a single nonlinear viscoelastic parameter, a set of four nonlinear viscoelastic parameters were able to reliably and accurately discriminate fat, breast fibroglandular tissue and muscle.

Assimilating AmeriFlux Site Data into the Community Land Model with Carbon-Nitrogen Coupling via the Ensemble Kalman Filter

NASA Astrophysics Data System (ADS)

Pettijohn, J. C.; Law, B. E.; Williams, M. D.; Stoeckli, R.; Thornton, P. E.; Hudiburg, T. M.; Thomas, C. K.; Martin, J.; Hill, T. C.

2009-12-01

The assimilation of terrestrial carbon, water and nutrient cycle measurements into land surface models of these processes is fundamental to improving our ability to predict how these ecosystems may respond to climate change. A combination of measurements and models, each with their own systematic biases, must be considered when constraining the nonlinear behavior of these coupled dynamics. As such, we use the sequential Ensemble Kalman Filter (EnKF) to assimilate eddy covariance (EC) and other site-level AmeriFlux measurements into the NCAR Community Land Model with Carbon-Nitrogen coupling (CLM-CN v3.5), run in single-column mode at a 30-minute time step, to improve estimates of relatively unconstrained model state variables and parameters. Specifically, we focus on a semi-arid ponderosa pine site (US-ME2) in the Pacific Northwest to identify the mechanisms by which this ecosystem responds to severe late summer drought. Our EnKF analysis includes water, carbon, energy and nitrogen state variables (e.g., 10 volumetric soil moisture levels (0-3.43 m), ponderosa pine and shrub evapotranspiration and net ecosystem exchange of carbon dioxide stocks and flux components, snow depth, etc.) and associated parameters (e.g., PFT-level rooting distribution parameters, maximum subsurface runoff coefficient, soil hydraulic conductivity decay factor, snow aging parameters, maximum canopy conductance, C:N ratios, etc.). The effectiveness of the EnKF in constraining state variables and associated parameters is sensitive to their relative frequencies, in that C-N state variables and parameters with long time constants require similarly long time series in the analysis. We apply the EnKF kernel perturbation routine to disrupt preliminary convergence of covariances, which has been found in recent studies to be a problem more characteristic of low frequency vegetation state variables and parameters than high frequency ones more heavily coupled with highly varying climate (e.g., shallow soil moisture, snow depth). Preliminary results demonstrate that the assimilation of EC and other available AmeriFlux site physical, chemical and biological data significantly helps quantify and reduce CLM-CN model uncertainties and helps to constrain ‘hidden’ states and parameters that are essential in the coupled water, carbon, energy and nutrient dynamics of these sites. Such site-level calibration of CLM-CN is an initial step in identifying model deficiencies and in forecasts of future ecosystem responses to climate change.

Multi-frequency parameter mapping of electrical impedance scanning using two kinds of circuit model.

PubMed

Liu, Ruigang; Dong, Xiuzhen; Fu, Feng; You, Fusheng; Shi, Xuetao; Ji, Zhenyu; Wang, Kan

2007-07-01

Electrical impedance scanning (EIS) is a kind of potential bio-impedance measurement technology, especially aiding the diagnosis of breast cancer in women. By changing the frequency of the driving signal in turn while keeping the other conditions stable, multi-frequency measurement results on the object can be obtained. According to the least square method and circuit theory, the parameters in two models are deduced when measured with data at multiple driving frequencies. The arcs, in the real and imaginary parts of a trans-admittance coordinate, made by the evaluated parameters fit well the realistic data measured by our EIS device on female subjects. The Cole-Cole model in the form of admittance is closer to the measured data than the three-element model. Based on the evaluation of the multi-frequency parameters, we presented parameter mapping of EIS using two kinds of circuit model: one is the three-element model in the form of admittance and the other is the Cole-Cole model in the form of admittance. Comparing with classical admittance mapping at a single frequency, the multi-frequency parameter mapping will provide a novel vision to study EIS. The multi-frequency approach can provide the mappings of four parameters, which is helpful to identify different diseases with a similar characteristic in classical EIS mapping. From plots of the real and imaginary parts of the admittance, it is easy to make sure whether there exists abnormal tissue.

Using geometry to improve model fitting and experiment design for glacial isostasy

NASA Astrophysics Data System (ADS)

Kachuck, S. B.; Cathles, L. M.

2017-12-01

As scientists we routinely deal with models, which are geometric objects at their core - the manifestation of a set of parameters as predictions for comparison with observations. When the number of observations exceeds the number of parameters, the model is a hypersurface (the model manifold) in the space of all possible predictions. The object of parameter fitting is to find the parameters corresponding to the point on the model manifold as close to the vector of observations as possible. But the geometry of the model manifold can make this difficult. By curving, ending abruptly (where, for instance, parameters go to zero or infinity), and by stretching and compressing the parameters together in unexpected directions, it can be difficult to design algorithms that efficiently adjust the parameters. Even at the optimal point on the model manifold, parameters might not be individually resolved well enough to be applied to new contexts. In our context of glacial isostatic adjustment, models of sparse surface observations have a broad spread of sensitivity to mixtures of the earth's viscous structure and the surface distribution of ice over the last glacial cycle. This impedes precise statements about crucial geophysical processes, such as the planet's thermal history or the climates that controlled the ice age. We employ geometric methods developed in the field of systems biology to improve the efficiency of fitting (geodesic accelerated Levenberg-Marquardt) and to identify the maximally informative sources of additional data to make better predictions of sea levels and ice configurations (optimal experiment design). We demonstrate this in particular in reconstructions of the Barents Sea Ice Sheet, where we show that only certain kinds of data from the central Barents have the power to distinguish between proposed models.

On the effect of model parameters on forecast objects

NASA Astrophysics Data System (ADS)

Marzban, Caren; Jones, Corinne; Li, Ning; Sandgathe, Scott

2018-04-01

Many physics-based numerical models produce a gridded, spatial field of forecasts, e.g., a temperature map. The field for some quantities generally consists of spatially coherent and disconnected objects. Such objects arise in many problems, including precipitation forecasts in atmospheric models, eddy currents in ocean models, and models of forest fires. Certain features of these objects (e.g., location, size, intensity, and shape) are generally of interest. Here, a methodology is developed for assessing the impact of model parameters on the features of forecast objects. The main ingredients of the methodology include the use of (1) Latin hypercube sampling for varying the values of the model parameters, (2) statistical clustering algorithms for identifying objects, (3) multivariate multiple regression for assessing the impact of multiple model parameters on the distribution (across the forecast domain) of object features, and (4) methods for reducing the number of hypothesis tests and controlling the resulting errors. The final output of the methodology is a series of box plots and confidence intervals that visually display the sensitivities. The methodology is demonstrated on precipitation forecasts from a mesoscale numerical weather prediction model.

An evaluation of the hemiplegic subject based on the Bobath approach. Part I: The model.

PubMed

Guarna, F; Corriveau, H; Chamberland, J; Arsenault, A B; Dutil, E; Drouin, G

1988-01-01

An evaluation, based on the Bobath approach to treatment has been developed. A model, substantiating this evaluation is presented. In this model, the three stages of motor recovery presented by Bobath have been extended to six, to better follow the progression of the patient. Six parameters have also been identified. These are the elements to be quantified so that the progress of the patient through the stages of motor recovery can be followed. Four of these parameters are borrowed from the Bobath approach, that is: postural reaction, muscle tone, reflex activity and active movement. Two have been added: sensorium and pain. An accompanying paper presents the evaluation protocol along with the operational definition of each of these parameters.

FACTORS INFLUENCING TOTAL DIETARY EXPOSURE OF YOUNG CHILDREN

EPA Science Inventory

A deterministic model was developed to identify critical input parameters to assess dietary intake of young children. The model was used as a framework for understanding important factors in data collection and analysis. Factors incorporated included transfer efficiencies of pest...

Subspace algorithms for identifying separable-in-denominator 2D systems with deterministic-stochastic inputs

NASA Astrophysics Data System (ADS)

Ramos, José A.; Mercère, Guillaume

2016-12-01

In this paper, we present an algorithm for identifying two-dimensional (2D) causal, recursive and separable-in-denominator (CRSD) state-space models in the Roesser form with deterministic-stochastic inputs. The algorithm implements the N4SID, PO-MOESP and CCA methods, which are well known in the literature on 1D system identification, but here we do so for the 2D CRSD Roesser model. The algorithm solves the 2D system identification problem by maintaining the constraint structure imposed by the problem (i.e. Toeplitz and Hankel) and computes the horizontal and vertical system orders, system parameter matrices and covariance matrices of a 2D CRSD Roesser model. From a computational point of view, the algorithm has been presented in a unified framework, where the user can select which of the three methods to use. Furthermore, the identification task is divided into three main parts: (1) computing the deterministic horizontal model parameters, (2) computing the deterministic vertical model parameters and (3) computing the stochastic components. Specific attention has been paid to the computation of a stabilised Kalman gain matrix and a positive real solution when required. The efficiency and robustness of the unified algorithm have been demonstrated via a thorough simulation example.

Assessing the sensitivity of a land-surface scheme to the parameter values using a single column model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pitman, A.J.

The sensitivity of a land-surface scheme (the Biosphere Atmosphere Transfer Scheme, BATS) to its parameter values was investigated using a single column model. Identifying which parameters were important in controlling the turbulent energy fluxes, temperature, soil moisture, and runoff was dependent upon many factors. In the simulation of a nonmoisture-stressed tropical forest, results were dependent on a combination of reservoir terms (soil depth, root distribution), flux efficiency terms (roughness length, stomatal resistance), and available energy (albedo). If moisture became limited, the reservoir terms increased in importance because the total fluxes predicted depended on moisture availability and not on the ratemore » of transfer between the surface and the atmosphere. The sensitivity shown by BATS depended on which vegetation type was being simulated, which variable was used to determine sensitivity, the magnitude and sign of the parameter change, the climate regime (precipitation amount and frequency), and soil moisture levels and proximity to wilting. The interactions between these factors made it difficult to identify the most important parameters in BATS. Therefore, this paper does not argue that a particular set of parameters is important in BATS, rather it shows that no general ranking of parameters is possible. It is also emphasized that using `stand-alone` forcing to examine the sensitivity of a land-surface scheme to perturbations, in either parameters or the atmosphere, is unreliable due to the lack of surface-atmospheric feedbacks.« less

Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress

PubMed Central

Gu, Junfei; Yin, Xinyou; Zhang, Chengwei; Wang, Huaqi; Struik, Paul C.

2014-01-01

Background and Aims Genetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress. Methods Using the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameters were estimated from additive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions. Key Results To account for genotypic yield differences, it was necessary to parameterize the model for differences in an additional trait ‘total crop nitrogen uptake’ (Nmax) among the ILs. Genetic variation in Nmax had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10–36 % more yield than those based on markers for yield per se. Conclusions This study outlines a genotype-to-phenotype approach that exploits the potential value of marker-based crop modelling in developing new plant types with high yields. The approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for marker design for improved rice yields and for ideotyping under contrasting conditions. PMID:24984712

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pei, Zongrui; Stocks, George Malcolm

The sensitivity in predicting glide behaviour of dislocations has been a long-standing problem in the framework of the Peierls-Nabarro model. The predictions of both the model itself and the analytic formulas based on it are too sensitive to the input parameters. In order to reveal the origin of this important problem in materials science, a new empirical-parameter-free formulation is proposed in the same framework. Unlike previous formulations, it includes only a limited small set of parameters all of which can be determined by convergence tests. Under special conditions the new formulation is reduced to its classic counterpart. In the lightmore » of this formulation, new relationships between Peierls stresses and the input parameters are identified, where the sensitivity is greatly reduced or even removed.« less

Construction of robust dynamic genome-scale metabolic model structures of Saccharomyces cerevisiae through iterative re-parameterization.

PubMed

Sánchez, Benjamín J; Pérez-Correa, José R; Agosin, Eduardo

2014-09-01

Dynamic flux balance analysis (dFBA) has been widely employed in metabolic engineering to predict the effect of genetic modifications and environmental conditions in the cell׳s metabolism during dynamic cultures. However, the importance of the model parameters used in these methodologies has not been properly addressed. Here, we present a novel and simple procedure to identify dFBA parameters that are relevant for model calibration. The procedure uses metaheuristic optimization and pre/post-regression diagnostics, fixing iteratively the model parameters that do not have a significant role. We evaluated this protocol in a Saccharomyces cerevisiae dFBA framework calibrated for aerobic fed-batch and anaerobic batch cultivations. The model structures achieved have only significant, sensitive and uncorrelated parameters and are able to calibrate different experimental data. We show that consumption, suboptimal growth and production rates are more useful for calibrating dynamic S. cerevisiae metabolic models than Boolean gene expression rules, biomass requirements and ATP maintenance. Copyright © 2014 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Modeling and experimental investigation of thermal-mechanical-electric coupling dynamics in a standing wave ultrasonic motor

NASA Astrophysics Data System (ADS)

Li, Xiang; Yao, Zhiyuan; He, Yigang; Dai, Shichao

2017-09-01

Ultrasonic motor operation relies on high-frequency vibration of a piezoelectric vibrator and interface friction between the stator and rotor/slider, which can cause temperature rise of the motor under continuous operation, and can affect motor parameters and performance in turn. In this paper, an integral model is developed to study the thermal-mechanical-electric coupling dynamics in a typical standing wave ultrasonic motor. Stick-slip motion at the contact interface and the temperature dependence of material parameters of the stator are taken into account in this model. The elastic, piezoelectric and dielectric material coefficients of the piezoelectric ceramic, as a function of temperature, are determined experimentally using a resonance method. The critical parameters in the model are identified via measured results. The resulting model can be used to evaluate the variation in output characteristics of the motor caused by the thermal-mechanical-electric coupling effects. Furthermore, the dynamic temperature rise of the motor can be accurately predicted under different input parameters using the developed model, which will contribute to improving the reliable life of a motor for long-term running.

Likelihood analysis of supersymmetric SU(5) GUTs

DOE PAGES

Bagnaschi, Emanuele; Costa, J. C.; Sakurai, K.; ...

2017-02-16

Here, we perform a likelihood analysis of the constraints from accelerator experiments and astrophysical observations on supersymmetric (SUSY) models with SU(5) boundary conditions on soft SUSY-breaking parameters at the GUT scale. The parameter space of the models studied has 7 parameters: a universal gaugino massmore » $$m_{1/2}$$, distinct masses for the scalar partners of matter fermions in five- and ten-dimensional representations of SU(5), $$m_5$$ and $$m_{10}$$, and for the $$\\mathbf{5}$$ and $$\\mathbf{\\bar 5}$$ Higgs representations $$m_{H_u}$$ and $$m_{H_d}$$, a universal trilinear soft SUSY-breaking parameter $$A_0$$, and the ratio of Higgs vevs $$\\tan \\beta$$. In addition to previous constraints from direct sparticle searches, low-energy and flavour observables, we incorporate constraints based on preliminary results from 13 TeV LHC searches for jets + MET events and long-lived particles, as well as the latest PandaX-II and LUX searches for direct Dark Matter detection. In addition to previously-identified mechanisms for bringing the supersymmetric relic density into the range allowed by cosmology, we identify a novel $${\\tilde u_R}/{\\tilde c_R} - \\tilde{\\chi}^0_1$$ coannihilation mechanism that appears in the supersymmetric SU(5) GUT model and discuss the role of $${\\tilde \

Estimation of transversely isotropic material properties from magnetic resonance elastography using the optimised virtual fields method.

PubMed

Miller, Renee; Kolipaka, Arunark; Nash, Martyn P; Young, Alistair A

2018-03-12

Magnetic resonance elastography (MRE) has been used to estimate isotropic myocardial stiffness. However, anisotropic stiffness estimates may give insight into structural changes that occur in the myocardium as a result of pathologies such as diastolic heart failure. The virtual fields method (VFM) has been proposed for estimating material stiffness from image data. This study applied the optimised VFM to identify transversely isotropic material properties from both simulated harmonic displacements in a left ventricular (LV) model with a fibre field measured from histology as well as isotropic phantom MRE data. Two material model formulations were implemented, estimating either 3 or 5 material properties. The 3-parameter formulation writes the transversely isotropic constitutive relation in a way that dissociates the bulk modulus from other parameters. Accurate identification of transversely isotropic material properties in the LV model was shown to be dependent on the loading condition applied, amount of Gaussian noise in the signal, and frequency of excitation. Parameter sensitivity values showed that shear moduli are less sensitive to noise than the other parameters. This preliminary investigation showed the feasibility and limitations of using the VFM to identify transversely isotropic material properties from MRE images of a phantom as well as simulated harmonic displacements in an LV geometry. Copyright © 2018 John Wiley & Sons, Ltd.

Impact of the hard-coded parameters on the hydrologic fluxes of the land surface model Noah-MP

NASA Astrophysics Data System (ADS)

Cuntz, Matthias; Mai, Juliane; Samaniego, Luis; Clark, Martyn; Wulfmeyer, Volker; Attinger, Sabine; Thober, Stephan

2016-04-01

Land surface models incorporate a large number of processes, described by physical, chemical and empirical equations. The process descriptions contain a number of parameters that can be soil or plant type dependent and are typically read from tabulated input files. Land surface models may have, however, process descriptions that contain fixed, hard-coded numbers in the computer code, which are not identified as model parameters. Here we searched for hard-coded parameters in the computer code of the land surface model Noah with multiple process options (Noah-MP) to assess the importance of the fixed values on restricting the model's agility during parameter estimation. We found 139 hard-coded values in all Noah-MP process options, which are mostly spatially constant values. This is in addition to the 71 standard parameters of Noah-MP, which mostly get distributed spatially by given vegetation and soil input maps. We performed a Sobol' global sensitivity analysis of Noah-MP to variations of the standard and hard-coded parameters for a specific set of process options. 42 standard parameters and 75 hard-coded parameters were active with the chosen process options. The sensitivities of the hydrologic output fluxes latent heat and total runoff as well as their component fluxes were evaluated. These sensitivities were evaluated at twelve catchments of the Eastern United States with very different hydro-meteorological regimes. Noah-MP's hydrologic output fluxes are sensitive to two thirds of its standard parameters. The most sensitive parameter is, however, a hard-coded value in the formulation of soil surface resistance for evaporation, which proved to be oversensitive in other land surface models as well. Surface runoff is sensitive to almost all hard-coded parameters of the snow processes and the meteorological inputs. These parameter sensitivities diminish in total runoff. Assessing these parameters in model calibration would require detailed snow observations or the calculation of hydrologic signatures of the runoff data. Latent heat and total runoff exhibit very similar sensitivities towards standard and hard-coded parameters in Noah-MP because of their tight coupling via the water balance. It should therefore be comparable to calibrate Noah-MP either against latent heat observations or against river runoff data. Latent heat and total runoff are sensitive to both, plant and soil parameters. Calibrating only a parameter sub-set of only soil parameters, for example, thus limits the ability to derive realistic model parameters. It is thus recommended to include the most sensitive hard-coded model parameters that were exposed in this study when calibrating Noah-MP.

Application of Non-Deterministic Methods to Assess Modeling Uncertainties for Reinforced Carbon-Carbon Debris Impacts

NASA Technical Reports Server (NTRS)

Lyle, Karen H.; Fasanella, Edwin L.; Melis, Matthew; Carney, Kelly; Gabrys, Jonathan

2004-01-01

The Space Shuttle Columbia Accident Investigation Board (CAIB) made several recommendations for improving the NASA Space Shuttle Program. An extensive experimental and analytical program has been developed to address two recommendations related to structural impact analysis. The objective of the present work is to demonstrate the application of probabilistic analysis to assess the effect of uncertainties on debris impacts on Space Shuttle Reinforced Carbon-Carbon (RCC) panels. The probabilistic analysis is used to identify the material modeling parameters controlling the uncertainty. A comparison of the finite element results with limited experimental data provided confidence that the simulations were adequately representing the global response of the material. Five input parameters were identified as significantly controlling the response.

Foot Type Biomechanics Part 2: are structure and anthropometrics related to function?

PubMed

Mootanah, Rajshree; Song, Jinsup; Lenhoff, Mark W; Hafer, Jocelyn F; Backus, Sherry I; Gagnon, David; Deland, Jonathan T; Hillstrom, Howard J

2013-03-01

Many foot pathologies are associated with specific foot types. If foot structure and function are related, measurement of either could assist with differential diagnosis of pedal pathologies. Biomechanical measures of foot structure and function are related in asymptomatic healthy individuals. Sixty-one healthy subjects' left feet were stratified into cavus (n=12), rectus (n=27) and planus (n=22) foot types. Foot structure was assessed by malleolar valgus index, arch height index, and arch height flexibility. Anthropometrics (height and weight), age, and walking speed were measured. Foot function was assessed by center of pressure excursion index, peak plantar pressure, maximum force, and gait pattern parameters. Foot structure and anthropometric variables were entered into stepwise linear regression models to identify predictors of function. Measures of foot structure and anthropometrics explained 10-37% of the model variance (adjusted R(2)) for gait pattern parameters. When walking speed was included, the adjusted R(2) increased to 45-77% but foot structure was no longer a factor. Foot structure and anthropometrics predicted 7-47% of the model variance for plantar pressure and 16-64% for maximum force parameters. All multivariate models were significant (p<0.05), supporting acceptance of the hypothesis. Foot structure and function are related in asymptomatic healthy individuals. The structural parameters employed are basic measurements that do not require ionizing radiation and could be used in a clinical setting. Further research is needed to identify additional predictive parameters (plantar soft tissue characteristics, skeletal alignment, and neuromuscular control) and to include individuals with pathology. Copyright © 2012. Published by Elsevier B.V.

Foot Type Biomechanics Part 2: Are structure and anthropometrics related to function?

PubMed Central

Mootanah, Rajshree; Song, Jinsup; Lenhoff, Mark W.; Hafer, Jocelyn F.; Backus, Sherry I.; Gagnon, David; Deland, Jonathan T.; Hillstrom, Howard J.

2013-01-01

Background Many foot pathologies are associated with specific foot types. If foot structure and function are related, measurement of either could assist with differential diagnosis of pedal pathologies. Hypothesis Biomechanical measures of foot structure and function are related in asymptomatic healthy individuals. Methods Sixty-one healthy subjects' left feet were stratified into cavus (n = 12), rectus (n = 27) and planus (n = 22) foot types. Foot structure was assessed by malleolar valgus index, arch height index, and arch height flexibility. Anthropometrics (height and weight), age, and walking speed were measured. Foot function was assessed by center of pressure excursion index, peak plantar pressure, maximum force, and gait pattern parameters. Foot structure and anthropometric variables were entered into stepwise linear regression models to identify predictors of function. Results Measures of foot structure and anthropometrics explained 10–37% of the model variance (adjusted R2) for gait pattern parameters. When walking speed was included, the adjusted R2 increased to 45–77% but foot structure was no longer a factor. Foot structure and anthropometrics predicted 7–47% of the model variance for plantar pressure and 16–64% for maximum force parameters. All multivariate models were significant (p < 0.05), supporting acceptance of the hypothesis. Discussion and conclusion Foot structure and function are related in asymptomatic healthy individuals. The structural parameters employed are basic measurements that do not require ionizing radiation and could be used in a clinical setting. Further research is needed to identify additional predictive parameters (plantar soft tissue characteristics, skeletal alignment, and neuromuscular control) and to include individuals with pathology. PMID:23107624

Sensitivity Analysis and Parameter Estimation for a Reactive Transport Model of Uranium Bioremediation

NASA Astrophysics Data System (ADS)

Meyer, P. D.; Yabusaki, S.; Curtis, G. P.; Ye, M.; Fang, Y.

2011-12-01

A three-dimensional, variably-saturated flow and multicomponent biogeochemical reactive transport model of uranium bioremediation was used to generate synthetic data . The 3-D model was based on a field experiment at the U.S. Dept. of Energy Rifle Integrated Field Research Challenge site that used acetate biostimulation of indigenous metal reducing bacteria to catalyze the conversion of aqueous uranium in the +6 oxidation state to immobile solid-associated uranium in the +4 oxidation state. A key assumption in past modeling studies at this site was that a comprehensive reaction network could be developed largely through one-dimensional modeling. Sensitivity analyses and parameter estimation were completed for a 1-D reactive transport model abstracted from the 3-D model to test this assumption, to identify parameters with the greatest potential to contribute to model predictive uncertainty, and to evaluate model structure and data limitations. Results showed that sensitivities of key biogeochemical concentrations varied in space and time, that model nonlinearities and/or parameter interactions have a significant impact on calculated sensitivities, and that the complexity of the model's representation of processes affecting Fe(II) in the system may make it difficult to correctly attribute observed Fe(II) behavior to modeled processes. Non-uniformity of the 3-D simulated groundwater flux and averaging of the 3-D synthetic data for use as calibration targets in the 1-D modeling resulted in systematic errors in the 1-D model parameter estimates and outputs. This occurred despite using the same reaction network for 1-D modeling as used in the data-generating 3-D model. Predictive uncertainty of the 1-D model appeared to be significantly underestimated by linear parameter uncertainty estimates.

Modeling the bidirectional reflectance distribution function of mixed finite plant canopies and soil

NASA Technical Reports Server (NTRS)

Schluessel, G.; Dickinson, R. E.; Privette, J. L.; Emery, W. J.; Kokaly, R.

1994-01-01

An analytical model of the bidirectional reflectance for optically semi-infinite plant canopies has been extended to describe the reflectance of finite depth canopies contributions from the underlying soil. The model depends on 10 independent parameters describing vegetation and soil optical and structural properties. The model is inverted with a nonlinear minimization routine using directional reflectance data for lawn (leaf area index (LAI) is equal to 9.9), soybeans (LAI, 2.9) and simulated reflectance data (LAI, 1.0) from a numerical bidirectional reflectance distribution function (BRDF) model (Myneni et al., 1988). While the ten-parameter model results in relatively low rms differences for the BRDF, most of the retrieved parameters exhibit poor stability. The most stable parameter was the single-scattering albedo of the vegetation. Canopy albedo could be derived with an accuracy of less than 5% relative error in the visible and less than 1% in the near-infrared. Sensitivity were performed to determine which of the 10 parameters were most important and to assess the effects of Gaussian noise on the parameter retrievals. Out of the 10 parameters, three were identified which described most of the BRDF variability. At low LAI values the most influential parameters were the single-scattering albedos (both soil and vegetation) and LAI, while at higher LAI values (greater than 2.5) these shifted to the two scattering phase function parameters for vegetation and the single-scattering albedo of the vegetation. The three-parameter model, formed by fixing the seven least significant parameters, gave higher rms values but was less sensitive to noise in the BRDF than the full ten-parameter model. A full hemispherical reflectance data set for lawn was then interpolated to yield BRDF values corresponding to advanced very high resolution radiometer (AVHRR) scan geometries collected over a period of nine days. The resulting parameters and BRDFs are similar to those for the full sampling geometry, suggesting that the limited geometry of AVHRR measurements might be used to reliably retrieve BRDF and canopy albedo with this model.

Performance of Transit Model Fitting in Processing Four Years of Kepler Science Data

NASA Astrophysics Data System (ADS)

Li, Jie; Burke, Christopher J.; Jenkins, Jon Michael; Quintana, Elisa V.; Rowe, Jason; Seader, Shawn; Tenenbaum, Peter; Twicken, Joseph D.

2014-06-01

We present transit model fitting performance of the Kepler Science Operations Center (SOC) Pipeline in processing four years of science data, which were collected by the Kepler spacecraft from May 13, 2009 to May 12, 2013. Threshold Crossing Events (TCEs), which represent transiting planet detections, are generated by the Transiting Planet Search (TPS) component of the pipeline and subsequently processed in the Data Validation (DV) component. The transit model is used in DV to fit TCEs and derive parameters that are used in various diagnostic tests to validate planetary candidates. The standard transit model includes five fit parameters: transit epoch time (i.e. central time of first transit), orbital period, impact parameter, ratio of planet radius to star radius and ratio of semi-major axis to star radius. In the latest Kepler SOC pipeline codebase, the light curve of the target for which a TCE is generated is initially fitted by a trapezoidal model with four parameters: transit epoch time, depth, duration and ingress time. The trapezoidal model fit, implemented with repeated Levenberg-Marquardt minimization, provides a quick and high fidelity assessment of the transit signal. The fit parameters of the trapezoidal model with the minimum chi-square metric are converted to set initial values of the fit parameters of the standard transit model. Additional parameters, such as the equilibrium temperature and effective stellar flux of the planet candidate, are derived from the fit parameters of the standard transit model to characterize pipeline candidates for the search of Earth-size planets in the Habitable Zone. The uncertainties of all derived parameters are updated in the latest codebase to take into account for the propagated errors of the fit parameters as well as the uncertainties in stellar parameters. The results of the transit model fitting of the TCEs identified by the Kepler SOC Pipeline, including fitted and derived parameters, fit goodness metrics and diagnostic figures, are included in the DV report and one-page report summary, which are accessible by the science community at NASA Exoplanet Archive. Funding for the Kepler Mission has been provided by the NASA Science Mission Directorate.

Development and parameter identification of a visco-hyperelastic model for the periodontal ligament.

PubMed

Huang, Huixiang; Tang, Wencheng; Tan, Qiyan; Yan, Bin

2017-04-01

The present study developed and implemented a new visco-hyperelastic model that is capable of predicting the time-dependent biomechanical behavior of the periodontal ligament. The constitutive model has been implemented into the finite element package ABAQUS by means of a user-defined material subroutine (UMAT). The stress response is decomposed into two constitutive parts in parallel which are a hyperelastic and a time-dependent viscoelastic stress response. In order to identify the model parameters, the indentation equation based on V-W hyperelastic model and the indentation creep model are developed. Then the parameters are determined by fitting them to the corresponding nanoindentation experimental data of the PDL. The nanoindentation experiment was simulated by finite element analysis to validate the visco-hyperelastic model. The simulated results are in good agreement with the experimental data, which demonstrates that the visco-hyperelastic model developed is able to accurately predict the time-dependent mechanical behavior of the PDL. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modeling association among demographic parameters in analysis of open population capture-recapture data.

PubMed

Link, William A; Barker, Richard J

2005-03-01

We present a hierarchical extension of the Cormack-Jolly-Seber (CJS) model for open population capture-recapture data. In addition to recaptures of marked animals, we model first captures of animals and losses on capture. The parameter set includes capture probabilities, survival rates, and birth rates. The survival rates and birth rates are treated as a random sample from a bivariate distribution, thus the model explicitly incorporates correlation in these demographic rates. A key feature of the model is that the likelihood function, which includes a CJS model factor, is expressed entirely in terms of identifiable parameters; losses on capture can be factored out of the model. Since the computational complexity of classical likelihood methods is prohibitive, we use Markov chain Monte Carlo in a Bayesian analysis. We describe an efficient candidate-generation scheme for Metropolis-Hastings sampling of CJS models and extensions. The procedure is illustrated using mark-recapture data for the moth Gonodontis bidentata.

Modeling association among demographic parameters in analysis of open population capture-recapture data

USGS Publications Warehouse

Link, William A.; Barker, Richard J.

2005-01-01

We present a hierarchical extension of the Cormack–Jolly–Seber (CJS) model for open population capture–recapture data. In addition to recaptures of marked animals, we model first captures of animals and losses on capture. The parameter set includes capture probabilities, survival rates, and birth rates. The survival rates and birth rates are treated as a random sample from a bivariate distribution, thus the model explicitly incorporates correlation in these demographic rates. A key feature of the model is that the likelihood function, which includes a CJS model factor, is expressed entirely in terms of identifiable parameters; losses on capture can be factored out of the model. Since the computational complexity of classical likelihood methods is prohibitive, we use Markov chain Monte Carlo in a Bayesian analysis. We describe an efficient candidate-generation scheme for Metropolis–Hastings sampling of CJS models and extensions. The procedure is illustrated using mark-recapture data for the moth Gonodontis bidentata.

CAUSAL INFERENCE WITH A GRAPHICAL HIERARCHY OF INTERVENTIONS

PubMed Central

Shpitser, Ilya; Tchetgen, Eric Tchetgen

2017-01-01

Identifying causal parameters from observational data is fraught with subtleties due to the issues of selection bias and confounding. In addition, more complex questions of interest, such as effects of treatment on the treated and mediated effects may not always be identified even in data where treatment assignment is known and under investigator control, or may be identified under one causal model but not another. Increasingly complex effects of interest, coupled with a diversity of causal models in use resulted in a fragmented view of identification. This fragmentation makes it unnecessarily difficult to determine if a given parameter is identified (and in what model), and what assumptions must hold for this to be the case. This, in turn, complicates the development of estimation theory and sensitivity analysis procedures. In this paper, we give a unifying view of a large class of causal effects of interest, including novel effects not previously considered, in terms of a hierarchy of interventions, and show that identification theory for this large class reduces to an identification theory of random variables under interventions from this hierarchy. Moreover, we show that one type of intervention in the hierarchy is naturally associated with queries identified under the Finest Fully Randomized Causally Interpretable Structure Tree Graph (FFRCISTG) model of Robins (via the extended g-formula), and another is naturally associated with queries identified under the Non-Parametric Structural Equation Model with Independent Errors (NPSEM-IE) of Pearl, via a more general functional we call the edge g-formula. Our results motivate the study of estimation theory for the edge g-formula, since we show it arises both in mediation analysis, and in settings where treatment assignment has unobserved causes, such as models associated with Pearl’s front-door criterion. PMID:28919652

CAUSAL INFERENCE WITH A GRAPHICAL HIERARCHY OF INTERVENTIONS.

PubMed

Shpitser, Ilya; Tchetgen, Eric Tchetgen

2016-12-01

Identifying causal parameters from observational data is fraught with subtleties due to the issues of selection bias and confounding. In addition, more complex questions of interest, such as effects of treatment on the treated and mediated effects may not always be identified even in data where treatment assignment is known and under investigator control, or may be identified under one causal model but not another. Increasingly complex effects of interest, coupled with a diversity of causal models in use resulted in a fragmented view of identification. This fragmentation makes it unnecessarily difficult to determine if a given parameter is identified (and in what model), and what assumptions must hold for this to be the case. This, in turn, complicates the development of estimation theory and sensitivity analysis procedures. In this paper, we give a unifying view of a large class of causal effects of interest, including novel effects not previously considered, in terms of a hierarchy of interventions, and show that identification theory for this large class reduces to an identification theory of random variables under interventions from this hierarchy. Moreover, we show that one type of intervention in the hierarchy is naturally associated with queries identified under the Finest Fully Randomized Causally Interpretable Structure Tree Graph (FFRCISTG) model of Robins (via the extended g-formula), and another is naturally associated with queries identified under the Non-Parametric Structural Equation Model with Independent Errors (NPSEM-IE) of Pearl, via a more general functional we call the edge g-formula. Our results motivate the study of estimation theory for the edge g-formula, since we show it arises both in mediation analysis, and in settings where treatment assignment has unobserved causes, such as models associated with Pearl's front-door criterion.

First evidence of non-locality in real band-gap metamaterials: determining parameters in the relaxed micromorphic model

PubMed Central

Barbagallo, Gabriele; d’Agostino, Marco Valerio; Placidi, Luca; Neff, Patrizio

2016-01-01

In this paper, we propose the first estimate of some elastic parameters of the relaxed micromorphic model on the basis of real experiments of transmission of longitudinal plane waves across an interface separating a classical Cauchy material (steel plate) and a phononic crystal (steel plate with fluid-filled holes). A procedure is set up in order to identify the parameters of the relaxed micromorphic model by superimposing the experimentally based profile of the reflection coefficient (plotted as function of the wave-frequency) with the analogous profile obtained via numerical simulations. We determine five out of six constitutive parameters which are featured by the relaxed micromorphic model in the isotropic case, plus the determination of the micro-inertia parameter. The sixth elastic parameter, namely the Cosserat couple modulus μc, still remains undetermined, since experiments on transverse incident waves are not yet available. A fundamental result of this paper is the estimate of the non-locality intrinsically associated with the underlying microstructure of the metamaterial. We show that the characteristic length Lc measuring the non-locality of the phononic crystal is of the order of 13 of the diameter of its fluid-filled holes. PMID:27436984

First evidence of non-locality in real band-gap metamaterials: determining parameters in the relaxed micromorphic model.

PubMed

Madeo, Angela; Barbagallo, Gabriele; d'Agostino, Marco Valerio; Placidi, Luca; Neff, Patrizio

2016-06-01

In this paper, we propose the first estimate of some elastic parameters of the relaxed micromorphic model on the basis of real experiments of transmission of longitudinal plane waves across an interface separating a classical Cauchy material (steel plate) and a phononic crystal (steel plate with fluid-filled holes). A procedure is set up in order to identify the parameters of the relaxed micromorphic model by superimposing the experimentally based profile of the reflection coefficient (plotted as function of the wave-frequency) with the analogous profile obtained via numerical simulations. We determine five out of six constitutive parameters which are featured by the relaxed micromorphic model in the isotropic case, plus the determination of the micro-inertia parameter. The sixth elastic parameter, namely the Cosserat couple modulus μ c , still remains undetermined, since experiments on transverse incident waves are not yet available. A fundamental result of this paper is the estimate of the non-locality intrinsically associated with the underlying microstructure of the metamaterial. We show that the characteristic length L c measuring the non-locality of the phononic crystal is of the order of [Formula: see text] of the diameter of its fluid-filled holes.

Stepwise sensitivity analysis from qualitative to quantitative: Application to the terrestrial hydrological modeling of a Conjunctive Surface-Subsurface Process (CSSP) land surface model

NASA Astrophysics Data System (ADS)

Gan, Yanjun; Liang, Xin-Zhong; Duan, Qingyun; Choi, Hyun Il; Dai, Yongjiu; Wu, Huan

2015-06-01

An uncertainty quantification framework was employed to examine the sensitivities of 24 model parameters from a newly developed Conjunctive Surface-Subsurface Process (CSSP) land surface model (LSM). The sensitivity analysis (SA) was performed over 18 representative watersheds in the contiguous United States to examine the influence of model parameters in the simulation of terrestrial hydrological processes. Two normalized metrics, relative bias (RB) and Nash-Sutcliffe efficiency (NSE), were adopted to assess the fit between simulated and observed streamflow discharge (SD) and evapotranspiration (ET) for a 14 year period. SA was conducted using a multiobjective two-stage approach, in which the first stage was a qualitative SA using the Latin Hypercube-based One-At-a-Time (LH-OAT) screening, and the second stage was a quantitative SA using the Multivariate Adaptive Regression Splines (MARS)-based Sobol' sensitivity indices. This approach combines the merits of qualitative and quantitative global SA methods, and is effective and efficient for understanding and simplifying large, complex system models. Ten of the 24 parameters were identified as important across different watersheds. The contribution of each parameter to the total response variance was then quantified by Sobol' sensitivity indices. Generally, parameter interactions contribute the most to the response variance of the CSSP, and only 5 out of 24 parameters dominate model behavior. Four photosynthetic and respiratory parameters are shown to be influential to ET, whereas reference depth for saturated hydraulic conductivity is the most influential parameter for SD in most watersheds. Parameter sensitivity patterns mainly depend on hydroclimatic regime, as well as vegetation type and soil texture. This article was corrected on 26 JUN 2015. See the end of the full text for details.

Leaf Photosynthetic Parameters Related to Biomass Accumulation in a Global Rice Diversity Survey1[OPEN

PubMed Central

Zheng, Guangyong; Hamdani, Saber; Essemine, Jemaa; Song, Qingfeng; Wang, Hongru

2017-01-01

Mining natural variations is a major approach to identify new options to improve crop light use efficiency. So far, successes in identifying photosynthetic parameters positively related to crop biomass accumulation through this approach are scarce, possibly due to the earlier emphasis on properties related to leaf instead of canopy photosynthetic efficiency. This study aims to uncover rice (Oryza sativa) natural variations to identify leaf physiological parameters that are highly correlated with biomass accumulation, a surrogate of canopy photosynthesis. To do this, we systematically investigated 14 photosynthetic parameters and four morphological traits in a rice population, which consists of 204 U.S. Department of Agriculture-curated minicore accessions collected globally and 11 elite Chinese rice cultivars in both Beijing and Shanghai. To identify key components responsible for the variance of biomass accumulation, we applied a stepwise feature-selection approach based on linear regression models. Although there are large variations in photosynthetic parameters measured in different environments, we observed that photosynthetic rate under low light (Alow) was highly related to biomass accumulation and also exhibited high genomic inheritability in both environments, suggesting its great potential to be used as a target for future rice breeding programs. Large variations in Alow among modern rice cultivars further suggest the great potential of using this parameter in contemporary rice breeding for the improvement of biomass and, hence, yield potential. PMID:28739819

An integrated approach for the knowledge discovery in computer simulation models with a multi-dimensional parameter space

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khawli, Toufik Al; Eppelt, Urs; Hermanns, Torsten

2016-06-08

In production industries, parameter identification, sensitivity analysis and multi-dimensional visualization are vital steps in the planning process for achieving optimal designs and gaining valuable information. Sensitivity analysis and visualization can help in identifying the most-influential parameters and quantify their contribution to the model output, reduce the model complexity, and enhance the understanding of the model behavior. Typically, this requires a large number of simulations, which can be both very expensive and time consuming when the simulation models are numerically complex and the number of parameter inputs increases. There are three main constituent parts in this work. The first part ismore » to substitute the numerical, physical model by an accurate surrogate model, the so-called metamodel. The second part includes a multi-dimensional visualization approach for the visual exploration of metamodels. In the third part, the metamodel is used to provide the two global sensitivity measures: i) the Elementary Effect for screening the parameters, and ii) the variance decomposition method for calculating the Sobol indices that quantify both the main and interaction effects. The application of the proposed approach is illustrated with an industrial application with the goal of optimizing a drilling process using a Gaussian laser beam.« less

An integrated approach for the knowledge discovery in computer simulation models with a multi-dimensional parameter space

NASA Astrophysics Data System (ADS)

Khawli, Toufik Al; Gebhardt, Sascha; Eppelt, Urs; Hermanns, Torsten; Kuhlen, Torsten; Schulz, Wolfgang

2016-06-01

In production industries, parameter identification, sensitivity analysis and multi-dimensional visualization are vital steps in the planning process for achieving optimal designs and gaining valuable information. Sensitivity analysis and visualization can help in identifying the most-influential parameters and quantify their contribution to the model output, reduce the model complexity, and enhance the understanding of the model behavior. Typically, this requires a large number of simulations, which can be both very expensive and time consuming when the simulation models are numerically complex and the number of parameter inputs increases. There are three main constituent parts in this work. The first part is to substitute the numerical, physical model by an accurate surrogate model, the so-called metamodel. The second part includes a multi-dimensional visualization approach for the visual exploration of metamodels. In the third part, the metamodel is used to provide the two global sensitivity measures: i) the Elementary Effect for screening the parameters, and ii) the variance decomposition method for calculating the Sobol indices that quantify both the main and interaction effects. The application of the proposed approach is illustrated with an industrial application with the goal of optimizing a drilling process using a Gaussian laser beam.

Modern control concepts in hydrology. [parameter identification in adaptive stochastic control approach

NASA Technical Reports Server (NTRS)

Duong, N.; Winn, C. B.; Johnson, G. R.

1975-01-01

Two approaches to an identification problem in hydrology are presented, based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time-invariant or time-dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and confirm the results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.

Parameters Identification for Photovoltaic Module Based on an Improved Artificial Fish Swarm Algorithm

PubMed Central

Wang, Hong-Hua

2014-01-01

A precise mathematical model plays a pivotal role in the simulation, evaluation, and optimization of photovoltaic (PV) power systems. Different from the traditional linear model, the model of PV module has the features of nonlinearity and multiparameters. Since conventional methods are incapable of identifying the parameters of PV module, an excellent optimization algorithm is required. Artificial fish swarm algorithm (AFSA), originally inspired by the simulation of collective behavior of real fish swarms, is proposed to fast and accurately extract the parameters of PV module. In addition to the regular operation, a mutation operator (MO) is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated by various parameters of PV module under different environmental conditions, and the testing results are compared with other studied methods in terms of final solutions and computational time. The simulation results show that the proposed method is capable of obtaining higher parameters identification precision. PMID:25243233

An Eigensystem Realization Algorithm (ERA) for modal parameter identification and model reduction

NASA Technical Reports Server (NTRS)

Juang, J. N.; Pappa, R. S.

1985-01-01

A method, called the Eigensystem Realization Algorithm (ERA), is developed for modal parameter identification and model reduction of dynamic systems from test data. A new approach is introduced in conjunction with the singular value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm. The basic formulation is then transformed into modal space for modal parameter identification. Two accuracy indicators are developed to quantitatively identify the system modes and noise modes. For illustration of the algorithm, examples are shown using simulation data and experimental data for a rectangular grid structure.

Testing Saliency Parameters for Automatic Target Recognition

NASA Technical Reports Server (NTRS)

Pandya, Sagar

2012-01-01

A bottom-up visual attention model (the saliency model) is tested to enhance the performance of Automated Target Recognition (ATR). JPL has developed an ATR system that identifies regions of interest (ROI) using a trained OT-MACH filter, and then classifies potential targets as true- or false-positives using machine-learning techniques. In this project, saliency is used as a pre-processing step to reduce the space for performing OT-MACH filtering. Saliency parameters, such as output level and orientation weight, are tuned to detect known target features. Preliminary results are promising and future work entails a rigrous and parameter-based search to gain maximum insight about this method.

A Systematic Approach to Determining the Identifiability of Multistage Carcinogenesis Models.

PubMed

Brouwer, Andrew F; Meza, Rafael; Eisenberg, Marisa C

2017-07-01

Multistage clonal expansion (MSCE) models of carcinogenesis are continuous-time Markov process models often used to relate cancer incidence to biological mechanism. Identifiability analysis determines what model parameter combinations can, theoretically, be estimated from given data. We use a systematic approach, based on differential algebra methods traditionally used for deterministic ordinary differential equation (ODE) models, to determine identifiable combinations for a generalized subclass of MSCE models with any number of preinitation stages and one clonal expansion. Additionally, we determine the identifiable combinations of the generalized MSCE model with up to four clonal expansion stages, and conjecture the results for any number of clonal expansion stages. The results improve upon previous work in a number of ways and provide a framework to find the identifiable combinations for further variations on the MSCE models. Finally, our approach, which takes advantage of the Kolmogorov backward equations for the probability generating functions of the Markov process, demonstrates that identifiability methods used in engineering and mathematics for systems of ODEs can be applied to continuous-time Markov processes. © 2016 Society for Risk Analysis.

Assessment of the viscoelastic mechanical properties of polycarbonate urethane for medical devices.

PubMed

Beckmann, Agnes; Heider, Yousef; Stoffel, Marcus; Markert, Bernd

2018-06-01

The underlying research work introduces a study of the mechanical properties of polycarbonate urethane (PCU), used in the construction of various medical devices. This comprises the discussion of a suitable material model, the application of elemental experiments to identify the related parameters and the numerical simulation of the applied experiments in order to calibrate and validate the mathematical model. In particular, the model of choice for the simulation of PCU response is the non-linear viscoelastic Bergström-Boyce material model, applied in the finite-element (FE) package Abaqus®. For the parameter identification, uniaxial tension and unconfined compression tests under in-laboratory physiological conditions were carried out. The geometry of the samples together with the applied loadings were simulated in Abaqus®, to insure the suitability of the modelling approach. The obtained parameters show a very good agreement between the numerical and the experimental results. Copyright © 2018 Elsevier Ltd. All rights reserved.

High-order sliding-mode control for blood glucose regulation in the presence of uncertain dynamics.

PubMed

Hernández, Ana Gabriela Gallardo; Fridman, Leonid; Leder, Ron; Andrade, Sergio Islas; Monsalve, Cristina Revilla; Shtessel, Yuri; Levant, Arie

2011-01-01

The success of blood glucose automatic regulation depends on the robustness of the control algorithm used. It is a difficult task to perform due to the complexity of the glucose-insulin regulation system. The variety of model existing reflects the great amount of phenomena involved in the process, and the inter-patient variability of the parameters represent another challenge. In this research a High-Order Sliding-Mode Control is proposed. It is applied to two well known models, Bergman Minimal Model, and Sorensen Model, to test its robustness with respect to uncertain dynamics, and patients' parameter variability. The controller designed based on the simulations is tested with the specific Bergman Minimal Model of a diabetic patient whose parameters were identified from an in vivo assay. To minimize the insulin infusion rate, and avoid the hypoglycemia risk, the glucose target is a dynamical profile.

Using Active Learning for Speeding up Calibration in Simulation Models.

PubMed

Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2016-07-01

Most cancer simulation models include unobservable parameters that determine disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality, and their values are typically estimated via a lengthy calibration procedure, which involves evaluating a large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We developed an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs and therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using the previously developed University of Wisconsin breast cancer simulation model (UWBCS). In a recent study, calibration of the UWBCS required the evaluation of 378 000 input parameter combinations to build a race-specific model, and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378 000 combinations. Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. © The Author(s) 2015.

Using Active Learning for Speeding up Calibration in Simulation Models

PubMed Central

Cevik, Mucahit; Ali Ergun, Mehmet; Stout, Natasha K.; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2015-01-01

Background Most cancer simulation models include unobservable parameters that determine the disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality and their values are typically estimated via lengthy calibration procedure, which involves evaluating large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Methods Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We develop an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs, therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using previously developed University of Wisconsin Breast Cancer Simulation Model (UWBCS). Results In a recent study, calibration of the UWBCS required the evaluation of 378,000 input parameter combinations to build a race-specific model and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378,000 combinations. Conclusion Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. PMID:26471190

Parameter estimation using meta-heuristics in systems biology: a comprehensive review.

PubMed

Sun, Jianyong; Garibaldi, Jonathan M; Hodgman, Charlie

2012-01-01

This paper gives a comprehensive review of the application of meta-heuristics to optimization problems in systems biology, mainly focussing on the parameter estimation problem (also called the inverse problem or model calibration). It is intended for either the system biologist who wishes to learn more about the various optimization techniques available and/or the meta-heuristic optimizer who is interested in applying such techniques to problems in systems biology. First, the parameter estimation problems emerging from different areas of systems biology are described from the point of view of machine learning. Brief descriptions of various meta-heuristics developed for these problems follow, along with outlines of their advantages and disadvantages. Several important issues in applying meta-heuristics to the systems biology modelling problem are addressed, including the reliability and identifiability of model parameters, optimal design of experiments, and so on. Finally, we highlight some possible future research directions in this field.

Aspects of metallic low-temperature transport in Mott-insulator/band-insulator superlattices: Optical conductivity and thermoelectricity

NASA Astrophysics Data System (ADS)

Rüegg, Andreas; Pilgram, Sebastian; Sigrist, Manfred

2008-06-01

We investigate the low-temperature electrical and thermal transport properties in atomically precise metallic heterostructures involving strongly correlated electron systems. The model of the Mott-insulator/band-insulator superlattice was discussed in the framework of the slave-boson mean-field approximation and transport quantities were derived by use of the Boltzmann transport equation in the relaxation-time approximation. The results for the optical conductivity are in good agreement with recently published experimental data on (LaTiO3)N/(SrTiO3)M superlattices and allow us to estimate the values of key parameters of the model. Furthermore, predictions for the thermoelectric response were made and the dependence of the Seebeck coefficient on model parameters was studied in detail. The width of the Mott-insulating material was identified as the most relevant parameter, in particular, this parameter provides a way to optimize the thermoelectric power factor at low temperatures.

Shapes, rotation, and pole solutions of the selected Hilda and Trojan asteroids

NASA Astrophysics Data System (ADS)

Gritsevich, Maria; Sonnett, Sarah; Torppa, Johanna; Mainzer, Amy; Muinonen, Karri; Penttilä, Antti; Grav, Thomas; Masiero, Joseph; Bauer, James; Kramer, Emily

2017-04-01

Binary asteroid systems contain key information about the dynamical and chemical environments in which they formed. For example, determining the formation environments of Trojan and Hilda asteroids (in 1:1 and 3:2 mean-motion resonance with Jupiter, respectively) will provide critical constraints on how small bodies and the planets that drive their migration must have moved throughout Solar System history, see e.g. [1-3]. Therefore, identifying and characterizing binary asteroids within the Trojan and Hilda populations could offer a powerful means of discerning between Solar System evolution models. Dozens of possibly close or contact binary Trojans and Hildas were identified within the data obtained by NEOWISE [4]. Densely sampled light curves of these candidate binaries have been obtained in order to resolve rotational light curve features that are indicative of binarity (e.g., [5-7]). We present analysis of the shapes, rotation, and pole solutions of some of the follow-up targets observed with optical ground-based telescopes. For modelling the asteroid photometric properties, we use parameters describing the shape, surface light scattering properties and spin state of the asteroid. Scattering properties of the asteroid surface are modeled using a two parameter H-G12 magnitude system. Determination of the initial best-fit parameters is carried out by first using a triaxial ellipsoid shape model, and scanning over the period values and spin axis orientations, while fitting the other parameters, after which all parameters were fitted, taking the initial values for spin properties from the spin scanning. In addition to the best-fit parameters, we also provide the distribution of the possible solution, which should cover the inaccuracies of the solution, caused by the observing errors and model. The distribution of solutions is generated by Markov-Chain Monte Carlo sampling the spin and shape model parameters, using both an ellipsoid shape model and a convex model, Gaussian curvature of which is defined as a spherical harmonics series [8]. References: [1] Marzari F. and Scholl H. (1998), A&A, 339, 278. [2] Morbidelli A. et al. (2005), Nature, 435, 462. [3] Nesvorny D. et al. (2013), ApJ, 768, 45. [4] Sonnett S. et al. (2015), ApJ, 799, 191. [5] Behrend R. et al. (2006), A&A, 446, 1177. [6] Lacerda P. and Jewitt D. C. (2007), AJ, 133, 1393. [7] Oey J. (2016), MPB, 43, 45. [8] Muinonen et al., ACM 2017.

Two-compartment modeling of tissue microcirculation revisited.

PubMed

Brix, Gunnar; Salehi Ravesh, Mona; Griebel, Jürgen

2017-05-01

Conventional two-compartment modeling of tissue microcirculation is used for tracer kinetic analysis of dynamic contrast-enhanced (DCE) computed tomography or magnetic resonance imaging studies although it is well-known that the underlying assumption of an instantaneous mixing of the administered contrast agent (CA) in capillaries is far from being realistic. It was thus the aim of the present study to provide theoretical and computational evidence in favor of a conceptually alternative modeling approach that makes it possible to characterize the bias inherent to compartment modeling and, moreover, to approximately correct for it. Starting from a two-region distributed-parameter model that accounts for spatial gradients in CA concentrations within blood-tissue exchange units, a modified lumped two-compartment exchange model was derived. It has the same analytical structure as the conventional two-compartment model, but indicates that the apparent blood flow identifiable from measured DCE data is substantially overestimated, whereas the three other model parameters (i.e., the permeability-surface area product as well as the volume fractions of the plasma and interstitial distribution space) are unbiased. Furthermore, a simple formula was derived to approximately compute a bias-corrected flow from the estimates of the apparent flow and permeability-surface area product obtained by model fitting. To evaluate the accuracy of the proposed modeling and bias correction method, representative noise-free DCE curves were analyzed. They were simulated for 36 microcirculation and four input scenarios by an axially distributed reference model. As analytically proven, the considered two-compartment exchange model is structurally identifiable from tissue residue data. The apparent flow values estimated for the 144 simulated tissue/input scenarios were considerably biased. After bias-correction, the deviations between estimated and actual parameter values were (11.2 ± 6.4) % (vs. (105 ± 21) % without correction) for the flow, (3.6 ± 6.1) % for the permeability-surface area product, (5.8 ± 4.9) % for the vascular volume and (2.5 ± 4.1) % for the interstitial volume; with individual deviations of more than 20% being the exception and just marginal. Increasing the duration of CA administration only had a statistically significant but opposite effect on the accuracy of the estimated flow (declined) and intravascular volume (improved). Physiologically well-defined tissue parameters are structurally identifiable and accurately estimable from DCE data by the conceptually modified two-compartment model in combination with the bias correction. The accuracy of the bias-corrected flow is nearly comparable to that of the three other (theoretically unbiased) model parameters. As compared to conventional two-compartment modeling, this feature constitutes a major advantage for tracer kinetic analysis of both preclinical and clinical DCE imaging studies. © 2017 American Association of Physicists in Medicine.

Application of Differential Evolutionary Optimization Methodology for Parameter Structure Identification in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Chiu, Y.; Nishikawa, T.

2013-12-01

With the increasing complexity of parameter-structure identification (PSI) in groundwater modeling, there is a need for robust, fast, and accurate optimizers in the groundwater-hydrology field. For this work, PSI is defined as identifying parameter dimension, structure, and value. In this study, Voronoi tessellation and differential evolution (DE) are used to solve the optimal PSI problem. Voronoi tessellation is used for automatic parameterization, whereby stepwise regression and the error covariance matrix are used to determine the optimal parameter dimension. DE is a novel global optimizer that can be used to solve nonlinear, nondifferentiable, and multimodal optimization problems. It can be viewed as an improved version of genetic algorithms and employs a simple cycle of mutation, crossover, and selection operations. DE is used to estimate the optimal parameter structure and its associated values. A synthetic numerical experiment of continuous hydraulic conductivity distribution was conducted to demonstrate the proposed methodology. The results indicate that DE can identify the global optimum effectively and efficiently. A sensitivity analysis of the control parameters (i.e., the population size, mutation scaling factor, crossover rate, and mutation schemes) was performed to examine their influence on the objective function. The proposed DE was then applied to solve a complex parameter-estimation problem for a small desert groundwater basin in Southern California. Hydraulic conductivity, specific yield, specific storage, fault conductance, and recharge components were estimated simultaneously. Comparison of DE and a traditional gradient-based approach (PEST) shows DE to be more robust and efficient. The results of this work not only provide an alternative for PSI in groundwater models, but also extend DE applications towards solving complex, regional-scale water management optimization problems.

Regionalization of post-processed ensemble runoff forecasts

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

2016-05-01

For many years, meteorological models have been run with perturbated initial conditions or parameters to produce ensemble forecasts that are used as a proxy of the uncertainty of the forecasts. However, the ensembles are usually both biased (the mean is systematically too high or too low, compared with the observed weather), and has dispersion errors (the ensemble variance indicates a too low or too high confidence in the forecast, compared with the observed weather). The ensembles are therefore commonly post-processed to correct for these shortcomings. Here we look at one of these techniques, referred to as Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). Originally, the post-processing parameters were identified as a fixed set of parameters for a region. The application of our work is the European Flood Awareness System (http://www.efas.eu), where a distributed model is run with meteorological ensembles as input. We are therefore dealing with a considerably larger data set than previous analyses. We also want to regionalize the parameters themselves for other locations than the calibration gauges. The post-processing parameters are therefore estimated for each calibration station, but with a spatial penalty for deviations from neighbouring stations, depending on the expected semivariance between the calibration catchment and these stations. The estimated post-processed parameters can then be used for regionalization of the postprocessing parameters also for uncalibrated locations using top-kriging in the rtop-package (Skøien et al., 2006, 2014). We will show results from cross-validation of the methodology and although our interest is mainly in identifying exceedance probabilities for certain return levels, we will also show how the rtop package can be used for creating a set of post-processed ensembles through simulations.

The application of sensitivity analysis to models of large scale physiological systems

NASA Technical Reports Server (NTRS)

Leonard, J. I.

1974-01-01

A survey of the literature of sensitivity analysis as it applies to biological systems is reported as well as a brief development of sensitivity theory. A simple population model and a more complex thermoregulatory model illustrate the investigatory techniques and interpretation of parameter sensitivity analysis. The role of sensitivity analysis in validating and verifying models, and in identifying relative parameter influence in estimating errors in model behavior due to uncertainty in input data is presented. This analysis is valuable to the simulationist and the experimentalist in allocating resources for data collection. A method for reducing highly complex, nonlinear models to simple linear algebraic models that could be useful for making rapid, first order calculations of system behavior is presented.

Ranges of applicability for the continuum beam model in the mechanics of carbon nanotubes and nanorods

NASA Technical Reports Server (NTRS)

Harik, V. M.

2001-01-01

Limitations in the validity of the continuum beam model for carbon nanotubes (NTs) and nanorods are examined. Applicability of all assumptions used in the model is restricted by the two criteria for geometric parameters that characterize the structure of NTs. The key non-dimensional parameters that control the NT buckling behavior are derived via dimensional analysis of the nanomechanical problem. A mechanical law of geometric similitude for NT buckling is extended from continuum mechanics for different molecular structures. A model applicability map, where two classes of beam-like NTs are identified, is constructed for distinct ranges of non-dimensional parameters. Expressions for the critical buckling loads and strains are tailored for two classes of NTs and compared with the data provided by the molecular dynamics simulations. copyright 2001 Elsevier Science Ltd. All rights reserved.

Influence of the model's degree of freedom on human body dynamics identification.

PubMed

Maita, Daichi; Venture, Gentiane

2013-01-01

In fields of sports and rehabilitation, opportunities of using motion analysis of the human body have dramatically increased. To analyze the motion dynamics, a number of subject specific parameters and measurements are required. For example the contact forces measurement and the inertial parameters of each segment of the human body are necessary to compute the joint torques. In this study, in order to perform accurate dynamic analysis we propose to identify the inertial parameters of the human body and to evaluate the influence of the model's number of degrees of freedom (DoF) on the results. We use a method to estimate the inertial parameters without torque sensor, using generalized coordinates of the base link, joint angles and external forces information. We consider a 34DoF model, a 58DoF model, as well as the case when the human is manipulating a tool (here a tennis racket). We compare the obtained in results in terms of contact force estimation.

A method to investigate the diffusion properties of nuclear calcium.

PubMed

Queisser, Gillian; Wittum, Gabriel

2011-10-01

Modeling biophysical processes in general requires knowledge about underlying biological parameters. The quality of simulation results is strongly influenced by the accuracy of these parameters, hence the identification of parameter values that the model includes is a major part of simulating biophysical processes. In many cases, secondary data can be gathered by experimental setups, which are exploitable by mathematical inverse modeling techniques. Here we describe a method for parameter identification of diffusion properties of calcium in the nuclei of rat hippocampal neurons. The method is based on a Gauss-Newton method for solving a least-squares minimization problem and was formulated in such a way that it is ideally implementable in the simulation platform uG. Making use of independently published space- and time-dependent calcium imaging data, generated from laser-assisted calcium uncaging experiments, here we could identify the diffusion properties of nuclear calcium and were able to validate a previously published model that describes nuclear calcium dynamics as a diffusion process.

Complex bifurcation patterns in a discrete predator-prey model with periodic environmental modulation

NASA Astrophysics Data System (ADS)

Harikrishnan, K. P.

2018-02-01

We consider the simplest model in the family of discrete predator-prey system and introduce for the first time an environmental factor in the evolution of the system by periodically modulating the natural death rate of the predator. We show that with the introduction of environmental modulation, the bifurcation structure becomes much more complex with bubble structure and inverse period doubling bifurcation. The model also displays the peculiar phenomenon of coexistence of multiple limit cycles in the domain of attraction for a given parameter value that combine and finally gets transformed into a single strange attractor as the control parameter is increased. To identify the chaotic regime in the parameter plane of the model, we apply the recently proposed scheme based on the correlation dimension analysis. We show that the environmental modulation is more favourable for the stable coexistence of the predator and the prey as the regions of fixed point and limit cycle in the parameter plane increase at the expense of chaotic domain.

Uncertainty in temperature-based determination of time of death

NASA Astrophysics Data System (ADS)

Weiser, Martin; Erdmann, Bodo; Schenkl, Sebastian; Muggenthaler, Holger; Hubig, Michael; Mall, Gita; Zachow, Stefan

2018-03-01

Temperature-based estimation of time of death (ToD) can be performed either with the help of simple phenomenological models of corpse cooling or with detailed mechanistic (thermodynamic) heat transfer models. The latter are much more complex, but allow a higher accuracy of ToD estimation as in principle all relevant cooling mechanisms can be taken into account. The potentially higher accuracy depends on the accuracy of tissue and environmental parameters as well as on the geometric resolution. We investigate the impact of parameter variations and geometry representation on the estimated ToD. For this, numerical simulation of analytic heat transport models is performed on a highly detailed 3D corpse model, that has been segmented and geometrically reconstructed from a computed tomography (CT) data set, differentiating various organs and tissue types. From that and prior information available on thermal parameters and their variability, we identify the most crucial parameters to measure or estimate, and obtain an a priori uncertainty quantification for the ToD.

Analysis of acute radiation-induced esophagitis in non-small-cell lung cancer patients using the Lyman NTCP model.

PubMed

Zhu, Jian; Zhang, Zi-Cheng; Li, Bao-Sheng; Liu, Min; Yin, Yong; Yu, Jin-Ming; Luo, Li-Min; Shu, Hua-Zhong; De Crevoisier, Renaud

2010-12-01

To analyze acute esophagitis (AE) in a Chinese population receiving 3D conformal radiotherapy (3DCRT) for non-small cell lung cancer (NSCLC), combined or not with chemotherapy (CT), using the Lyman-Kutcher-Burman (LKB) normal tissue complication probability (NTCP) model. 157 Chinese patients (pts) presented with NSCLC received 3DCRT: alone (34 pts) or combined with sequential CT (59 pts) (group 1) or with concomitant CT (64 pts) (group 2). Parameters (TD(50), n, and m) of the LKB NTCP model predicting for>grade 2 AE (RTOG grading) were identified using maximum likelihood analysis. Univariate and multivariate analyses using a binary regression logistic model were performed to identify patient, tumor and dosimetric predictors of AE. Grade 2 or 3 AE occurred in 24% and 52% of pts in group 1 and 2, respectively (p<0.001). For the 93 group 1 pts, the fitted LKB model parameters were: m=0.15, n=0.29 and TD(50)=46 Gy. For the 64 group 2 pts, the parameters were: m=0.42, n=0.09 and TD(50)=36 Gy. In multivariate analysis, the only significant predictors of AE were: NTCP (p<0.001) and V(50), as continuous variable (RR=1.03, p=0.03) or being more than a threshold value of 11% (RR=3.6, p=0.009). A LKB NTCP model has been established to predict AE in a Chinese population, receiving thoracic RT, alone or combined with CT. The parameters of the models appear slightly different than the previous one described in Western countries, with a lower volume effect for Chinese patients. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Assessing the quality of life history information in publicly available databases.

PubMed

Thorson, James T; Cope, Jason M; Patrick, Wesley S

2014-01-01

Single-species life history parameters are central to ecological research and management, including the fields of macro-ecology, fisheries science, and ecosystem modeling. However, there has been little independent evaluation of the precision and accuracy of the life history values in global and publicly available databases. We therefore develop a novel method based on a Bayesian errors-in-variables model that compares database entries with estimates from local experts, and we illustrate this process by assessing the accuracy and precision of entries in FishBase, one of the largest and oldest life history databases. This model distinguishes biases among seven life history parameters, two types of information available in FishBase (i.e., published values and those estimated from other parameters), and two taxa (i.e., bony and cartilaginous fishes) relative to values from regional experts in the United States, while accounting for additional variance caused by sex- and region-specific life history traits. For published values in FishBase, the model identifies a small positive bias in natural mortality and negative bias in maximum age, perhaps caused by unacknowledged mortality caused by fishing. For life history values calculated by FishBase, the model identified large and inconsistent biases. The model also demonstrates greatest precision for body size parameters, decreased precision for values derived from geographically distant populations, and greatest between-sex differences in age at maturity. We recommend that our bias and precision estimates be used in future errors-in-variables models as a prior on measurement errors. This approach is broadly applicable to global databases of life history traits and, if used, will encourage further development and improvements in these databases.

Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods

NASA Astrophysics Data System (ADS)

Lu, Dan; Ricciuto, Daniel; Walker, Anthony; Safta, Cosmin; Munger, William

2017-09-01

Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results in a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. The result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.

Nonlinear soil parameter effects on dynamic embedment of offshore pipeline on soft clay

NASA Astrophysics Data System (ADS)

Yu, Su Young; Choi, Han Suk; Lee, Seung Keon; Park, Kyu-Sik; Kim, Do Kyun

2015-06-01

In this paper, the effects of nonlinear soft clay on dynamic embedment of offshore pipeline were investigated. Seabed embedment by pipe-soil interactions has impacts on the structural boundary conditions for various subsea structures such as pipeline, riser, pile, and many other systems. A number of studies have been performed to estimate real soil behavior, but their estimation of seabed embedment has not been fully identified and there are still many uncertainties. In this regards, comparison of embedment between field survey and existing empirical models has been performed to identify uncertainties and investigate the effect of nonlinear soil parameter on dynamic embedment. From the comparison, it is found that the dynamic embedment with installation effects based on nonlinear soil model have an influence on seabed embedment. Therefore, the pipe embedment under dynamic condition by nonlinear parameters of soil models was investigated by Dynamic Embedment Factor (DEF) concept, which is defined as the ratio of the dynamic and static embedment of pipeline, in order to overcome the gap between field embedment and currently used empirical and numerical formula. Although DEF through various researches is suggested, its range is too wide and it does not consider dynamic laying effect. It is difficult to find critical parameters that are affecting to the embedment result. Therefore, the study on dynamic embedment factor by soft clay parameters of nonlinear soil model was conducted and the sensitivity analyses about parameters of nonlinear soil model were performed as well. The tendency on dynamic embedment factor was found by conducting numerical analyses using OrcaFlex software. It is found that DEF was influenced by shear strength gradient than other factors. The obtained results will be useful to understand the pipe embedment on soft clay seabed for applying offshore pipeline designs such as on-bottom stability and free span analyses.

Artificial neural network model for ozone concentration estimation and Monte Carlo analysis

NASA Astrophysics Data System (ADS)

Gao, Meng; Yin, Liting; Ning, Jicai

2018-07-01

Air pollution in urban atmosphere directly affects public-health; therefore, it is very essential to predict air pollutant concentrations. Air quality is a complex function of emissions, meteorology and topography, and artificial neural networks (ANNs) provide a sound framework for relating these variables. In this study, we investigated the feasibility of using ANN model with meteorological parameters as input variables to predict ozone concentration in the urban area of Jinan, a metropolis in Northern China. We firstly found that the architecture of network of neurons had little effect on the predicting capability of ANN model. A parsimonious ANN model with 6 routinely monitored meteorological parameters and one temporal covariate (the category of day, i.e. working day, legal holiday and regular weekend) as input variables was identified, where the 7 input variables were selected following the forward selection procedure. Compared with the benchmarking ANN model with 9 meteorological and photochemical parameters as input variables, the predicting capability of the parsimonious ANN model was acceptable. Its predicting capability was also verified in term of warming success ratio during the pollution episodes. Finally, uncertainty and sensitivity analysis were also performed based on Monte Carlo simulations (MCS). It was concluded that the ANN could properly predict the ambient ozone level. Maximum temperature, atmospheric pressure, sunshine duration and maximum wind speed were identified as the predominate input variables significantly influencing the prediction of ambient ozone concentrations.

A new adaptive estimation method of spacecraft thermal mathematical model with an ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Akita, T.; Takaki, R.; Shima, E.

2012-04-01

An adaptive estimation method of spacecraft thermal mathematical model is presented. The method is based on the ensemble Kalman filter, which can effectively handle the nonlinearities contained in the thermal model. The state space equations of the thermal mathematical model is derived, where both temperature and uncertain thermal characteristic parameters are considered as the state variables. In the method, the thermal characteristic parameters are automatically estimated as the outputs of the filtered state variables, whereas, in the usual thermal model correlation, they are manually identified by experienced engineers using trial-and-error approach. A numerical experiment of a simple small satellite is provided to verify the effectiveness of the presented method.

Extended Kalman Filter framework for forecasting shoreline evolution

USGS Publications Warehouse

Long, Joseph; Plant, Nathaniel G.

2012-01-01

A shoreline change model incorporating both long- and short-term evolution is integrated into a data assimilation framework that uses sparse observations to generate an updated forecast of shoreline position and to estimate unobserved geophysical variables and model parameters. Application of the assimilation algorithm provides quantitative statistical estimates of combined model-data forecast uncertainty which is crucial for developing hazard vulnerability assessments, evaluation of prediction skill, and identifying future data collection needs. Significant attention is given to the estimation of four non-observable parameter values and separating two scales of shoreline evolution using only one observable morphological quantity (i.e. shoreline position).

Solar collector parameter identification from unsteady data by a discrete-gradient optimization algorithm

NASA Technical Reports Server (NTRS)

Hotchkiss, G. B.; Burmeister, L. C.; Bishop, K. A.

1980-01-01

A discrete-gradient optimization algorithm is used to identify the parameters in a one-node and a two-node capacitance model of a flat-plate collector. Collector parameters are first obtained by a linear-least-squares fit to steady state data. These parameters, together with the collector heat capacitances, are then determined from unsteady data by use of the discrete-gradient optimization algorithm with less than 10 percent deviation from the steady state determination. All data were obtained in the indoor solar simulator at the NASA Lewis Research Center.

An open simulation approach to identify chances and limitations for vulnerable road user (VRU) active safety.

PubMed

Seiniger, Patrick; Bartels, Oliver; Pastor, Claus; Wisch, Marcus

2013-01-01

It is commonly agreed that active safety will have a significant impact on reducing accident figures for pedestrians and probably also bicyclists. However, chances and limitations for active safety systems have only been derived based on accident data and the current state of the art, based on proprietary simulation models. The objective of this article is to investigate these chances and limitations by developing an open simulation model. This article introduces a simulation model, incorporating accident kinematics, driving dynamics, driver reaction times, pedestrian dynamics, performance parameters of different autonomous emergency braking (AEB) generations, as well as legal and logical limitations. The level of detail for available pedestrian accident data is limited. Relevant variables, especially timing of the pedestrian appearance and the pedestrian's moving speed, are estimated using assumptions. The model in this article uses the fact that a pedestrian and a vehicle in an accident must have been in the same spot at the same time and defines the impact position as a relevant accident parameter, which is usually available from accident data. The calculations done within the model identify the possible timing available for braking by an AEB system as well as the possible speed reduction for different accident scenarios as well as for different system configurations. The simulation model identifies the lateral impact position of the pedestrian as a significant parameter for system performance, and the system layout is designed to brake when the accident becomes unavoidable by the vehicle driver. Scenarios with a pedestrian running from behind an obstruction are the most demanding scenarios and will very likely never be avoidable for all vehicle speeds due to physical limits. Scenarios with an unobstructed person walking will very likely be treatable for a wide speed range for next generation AEB systems.

SUMMA and Model Mimicry: Understanding Differences Among Land Models

NASA Astrophysics Data System (ADS)

Nijssen, B.; Nearing, G. S.; Ou, G.; Clark, M. P.

2016-12-01

Model inter-comparison and model ensemble experiments suffer from an inability to explain the mechanisms behind differences in model outcomes. We can clearly demonstrate that the models are different, but we cannot necessarily identify the reasons why, because most models exhibit myriad differences in process representations, model parameterizations, model parameters and numerical solution methods. This inability to identify the reasons for differences in model performance hampers our understanding and limits model improvement, because we cannot easily identify the most promising paths forward. We have developed the Structure for Unifying Multiple Modeling Alternatives (SUMMA) to allow for controlled experimentation with model construction, numerical techniques, and parameter values and therefore isolate differences in model outcomes to specific choices during the model development process. In developing SUMMA, we recognized that hydrologic models can be thought of as individual instantiations of a master modeling template that is based on a common set of conservation equations for energy and water. Given this perspective, SUMMA provides a unified approach to hydrologic modeling that integrates different modeling methods into a consistent structure with the ability to instantiate alternative hydrologic models at runtime. Here we employ SUMMA to revisit a previous multi-model experiment and demonstrate its use for understanding differences in model performance. Specifically, we implement SUMMA to mimic the spread of behaviors exhibited by the land models that participated in the Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) and draw conclusions about the relative performance of specific model parameterizations for water and energy fluxes through the soil-vegetation continuum. SUMMA's ability to mimic the spread of model ensembles and the behavior of individual models can be an important tool in focusing model development and improvement efforts.

Optimization-Based Inverse Identification of the Parameters of a Concrete Cap Material Model

NASA Astrophysics Data System (ADS)

Král, Petr; Hokeš, Filip; Hušek, Martin; Kala, Jiří; Hradil, Petr

2017-10-01

Issues concerning the advanced numerical analysis of concrete building structures in sophisticated computing systems currently require the involvement of nonlinear mechanics tools. The efforts to design safer, more durable and mainly more economically efficient concrete structures are supported via the use of advanced nonlinear concrete material models and the geometrically nonlinear approach. The application of nonlinear mechanics tools undoubtedly presents another step towards the approximation of the real behaviour of concrete building structures within the framework of computer numerical simulations. However, the success rate of this application depends on having a perfect understanding of the behaviour of the concrete material models used and having a perfect understanding of the used material model parameters meaning. The effective application of nonlinear concrete material models within computer simulations often becomes very problematic because these material models very often contain parameters (material constants) whose values are difficult to obtain. However, getting of the correct values of material parameters is very important to ensure proper function of a concrete material model used. Today, one possibility, which permits successful solution of the mentioned problem, is the use of optimization algorithms for the purpose of the optimization-based inverse material parameter identification. Parameter identification goes hand in hand with experimental investigation while it trying to find parameter values of the used material model so that the resulting data obtained from the computer simulation will best approximate the experimental data. This paper is focused on the optimization-based inverse identification of the parameters of a concrete cap material model which is known under the name the Continuous Surface Cap Model. Within this paper, material parameters of the model are identified on the basis of interaction between nonlinear computer simulations, gradient based and nature inspired optimization algorithms and experimental data, the latter of which take the form of a load-extension curve obtained from the evaluation of uniaxial tensile test results. The aim of this research was to obtain material model parameters corresponding to the quasi-static tensile loading which may be further used for the research involving dynamic and high-speed tensile loading. Based on the obtained results it can be concluded that the set goal has been reached.

Identification of atmospheric boundary layer thickness using doppler radar datas and WRF - ARW model in Merauke

NASA Astrophysics Data System (ADS)

Putri, R. J. A.; Setyawan, T.

2017-01-01

In the synoptic scale, one of the important meteorological parameter is the atmospheric boundary layer. Aside from being a supporter of the parameters in weather and climate models, knowing the thickness of the layer of the atmosphere can help identify aerosols and the strength of the vertical mixing of pollutants in it. The vertical wind profile data from C-band Doppler radar Mopah-Merauke which is operated by BMKG through Mopah-Merauke Meteorological Station can be used to identify the peak of Atmospheric Boundaryu Layer (ABL). ABL peak marked by increasing wind shear over the layer blending. Samples in January 2015 as a representative in the wet and in July 2015 as the representation of a dry month, shows that ABL heights using WRF models show that in July (sunny weather) ABL height values higher than in January (cloudy)

A compendium of chameleon constraints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burrage, Clare; Sakstein, Jeremy, E-mail: clare.burrage@nottingham.ac.uk, E-mail: jeremy.sakstein@port.ac.uk

2016-11-01

The chameleon model is a scalar field theory with a screening mechanism that explains how a cosmologically relevant light scalar can avoid the constraints of intra-solar-system searches for fifth-forces. The chameleon is a popular dark energy candidate and also arises in f ( R ) theories of gravity. Whilst the chameleon is designed to avoid historical searches for fifth-forces it is not unobservable and much effort has gone into identifying the best observables and experiments to detect it. These results are not always presented for the same models or in the same language, a particular problem when comparing astrophysical andmore » laboratory searches making it difficult to understand what regions of parameter space remain. Here we present combined constraints on the chameleon model from astrophysical and laboratory searches for the first time and identify the remaining windows of parameter space. We discuss the implications for cosmological chameleon searches and future small-scale probes.« less

Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses

ERIC Educational Resources Information Center

Huang, Guan-Hua; Wang, Su-Mei; Hsu, Chung-Chu

2011-01-01

Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the…

ESTIMATION OF CONSTANT AND TIME-VARYING DYNAMIC PARAMETERS OF HIV INFECTION IN A NONLINEAR DIFFERENTIAL EQUATION MODEL.

PubMed

Liang, Hua; Miao, Hongyu; Wu, Hulin

2010-03-01

Modeling viral dynamics in HIV/AIDS studies has resulted in deep understanding of pathogenesis of HIV infection from which novel antiviral treatment guidance and strategies have been derived. Viral dynamics models based on nonlinear differential equations have been proposed and well developed over the past few decades. However, it is quite challenging to use experimental or clinical data to estimate the unknown parameters (both constant and time-varying parameters) in complex nonlinear differential equation models. Therefore, investigators usually fix some parameter values, from the literature or by experience, to obtain only parameter estimates of interest from clinical or experimental data. However, when such prior information is not available, it is desirable to determine all the parameter estimates from data. In this paper, we intend to combine the newly developed approaches, a multi-stage smoothing-based (MSSB) method and the spline-enhanced nonlinear least squares (SNLS) approach, to estimate all HIV viral dynamic parameters in a nonlinear differential equation model. In particular, to the best of our knowledge, this is the first attempt to propose a comparatively thorough procedure, accounting for both efficiency and accuracy, to rigorously estimate all key kinetic parameters in a nonlinear differential equation model of HIV dynamics from clinical data. These parameters include the proliferation rate and death rate of uninfected HIV-targeted cells, the average number of virions produced by an infected cell, and the infection rate which is related to the antiviral treatment effect and is time-varying. To validate the estimation methods, we verified the identifiability of the HIV viral dynamic model and performed simulation studies. We applied the proposed techniques to estimate the key HIV viral dynamic parameters for two individual AIDS patients treated with antiretroviral therapies. We demonstrate that HIV viral dynamics can be well characterized and quantified for individual patients. As a result, personalized treatment decision based on viral dynamic models is possible.

Electro-thermal battery model identification for automotive applications

NASA Astrophysics Data System (ADS)

Hu, Y.; Yurkovich, S.; Guezennec, Y.; Yurkovich, B. J.

This paper describes a model identification procedure for identifying an electro-thermal model of lithium ion batteries used in automotive applications. The dynamic model structure adopted is based on an equivalent circuit model whose parameters are scheduled on the state-of-charge, temperature, and current direction. Linear spline functions are used as the functional form for the parametric dependence. The model identified in this way is valid inside a large range of temperatures and state-of-charge, so that the resulting model can be used for automotive applications such as on-board estimation of the state-of-charge and state-of-health. The model coefficients are identified using a multiple step genetic algorithm based optimization procedure designed for large scale optimization problems. The validity of the procedure is demonstrated experimentally for an A123 lithium ion iron-phosphate battery.

Case-Deletion Diagnostics for Maximum Likelihood Multipoint Quantitative Trait Locus Linkage Analysis

PubMed Central

Mendoza, Maria C.B.; Burns, Trudy L.; Jones, Michael P.

2009-01-01

Objectives Case-deletion diagnostic methods are tools that allow identification of influential observations that may affect parameter estimates and model fitting conclusions. The goal of this paper was to develop two case-deletion diagnostics, the exact case deletion (ECD) and the empirical influence function (EIF), for detecting outliers that can affect results of sib-pair maximum likelihood quantitative trait locus (QTL) linkage analysis. Methods Subroutines to compute the ECD and EIF were incorporated into the maximum likelihood QTL variance estimation components of the linkage analysis program MAPMAKER/SIBS. Performance of the diagnostics was compared in simulation studies that evaluated the proportion of outliers correctly identified (sensitivity), and the proportion of non-outliers correctly identified (specificity). Results Simulations involving nuclear family data sets with one outlier showed EIF sensitivities approximated ECD sensitivities well for outlier-affected parameters. Sensitivities were high, indicating the outlier was identified a high proportion of the time. Simulations also showed the enormous computational time advantage of the EIF. Diagnostics applied to body mass index in nuclear families detected observations influential on the lod score and model parameter estimates. Conclusions The EIF is a practical diagnostic tool that has the advantages of high sensitivity and quick computation. PMID:19172086

Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

NASA Astrophysics Data System (ADS)

Bassiouni, Maoya; Higgins, Chad W.; Still, Christopher J.; Good, Stephen P.

2018-06-01

Vegetation controls on soil moisture dynamics are challenging to measure and translate into scale- and site-specific ecohydrological parameters for simple soil water balance models. We hypothesize that empirical probability density functions (pdfs) of relative soil moisture or soil saturation encode sufficient information to determine these ecohydrological parameters. Further, these parameters can be estimated through inverse modeling of the analytical equation for soil saturation pdfs, derived from the commonly used stochastic soil water balance framework. We developed a generalizable Bayesian inference framework to estimate ecohydrological parameters consistent with empirical soil saturation pdfs derived from observations at point, footprint, and satellite scales. We applied the inference method to four sites with different land cover and climate assuming (i) an annual rainfall pattern and (ii) a wet season rainfall pattern with a dry season of negligible rainfall. The Nash-Sutcliffe efficiencies of the analytical model's fit to soil observations ranged from 0.89 to 0.99. The coefficient of variation of posterior parameter distributions ranged from < 1 to 15 %. The parameter identifiability was not significantly improved in the more complex seasonal model; however, small differences in parameter values indicate that the annual model may have absorbed dry season dynamics. Parameter estimates were most constrained for scales and locations at which soil water dynamics are more sensitive to the fitted ecohydrological parameters of interest. In these cases, model inversion converged more slowly but ultimately provided better goodness of fit and lower uncertainty. Results were robust using as few as 100 daily observations randomly sampled from the full records, demonstrating the advantage of analyzing soil saturation pdfs instead of time series to estimate ecohydrological parameters from sparse records. Our work combines modeling and empirical approaches in ecohydrology and provides a simple framework to obtain scale- and site-specific analytical descriptions of soil moisture dynamics consistent with soil moisture observations.

The Role of Model Complexity in Determining Patterns of Chlorophyll Variability in the Coastal Northwest North Atlantic

NASA Astrophysics Data System (ADS)

Kuhn, A. M.; Fennel, K.; Bianucci, L.

2016-02-01

A key feature of the North Atlantic Ocean's biological dynamics is the annual phytoplankton spring bloom. In the region comprising the continental shelf and adjacent deep ocean of the northwest North Atlantic, we identified two patterns of bloom development: 1) locations with cold temperatures and deep winter mixed layers, where the spring bloom peaks around April and the annual chlorophyll cycle has a large amplitude, and 2) locations with warmer temperatures and shallow winter mixed layers, where the spring bloom peaks earlier in the year, sometimes indiscernible from the fall bloom. These patterns result from a combination of limiting environmental factors and interactions among planktonic groups with different optimal requirements. Simple models that represent the ecosystem with a single phytoplankton (P) and a single zooplankton (Z) group are challenged to reproduce these ecological interactions. Here we investigate the effect that added complexity has on determining spatio-temporal chlorophyll. We compare two ecosystem models, one that contains one P and one Z group, and one with two P and three Z groups. We consider three types of changes in complexity: 1) added dependencies among variables (e.g., temperature dependent rates), 2) modified structural pathways, and 3) added pathways. Subsets of the most sensitive parameters are optimized in each model to replicate observations in the region. For computational efficiency, the parameter optimization is performed using 1D surrogates of a 3D model. We evaluate how model complexity affects model skill, and whether the optimized parameter sets found for each model modify the interpretation of ecosystem functioning. Spatial differences in the parameter sets that best represent different areas hint at the existence of different ecological communities or at physical-biological interactions that are not represented in the simplest model. Our methodology emphasizes the combined use of observations, 1D models to help identifying patterns, and 3D models able to simulate the environment modre realistically, as a means to acquire predictive understanding of the ocean's ecology.

Modeling of biodynamic responses distributed at the fingers and the palm of the human hand-arm system.

PubMed

Dong, Ren G; Dong, Jennie H; Wu, John Z; Rakheja, Subhash

2007-01-01

The objective of this study is to develop analytical models for simulating driving-point biodynamic responses distributed at the fingers and palm of the hand under vibration along the forearm direction (z(h)-axis). Two different clamp-like model structures are formulated to analyze the distributed responses at the fingers-handle and palm-handle interfaces, as opposed to the single driving point invariably considered in the reported models. The parameters of the proposed four- and five degrees-of-freedom models are identified through minimization of an rms error function of the model and measured responses under different hand actions, namely, fingers pull, push only, grip only, and combined push and grip. The results show that the responses predicted from both models agree reasonably well with the measured data in terms of distributed as well total impedance magnitude and phase. The variations in the identified model parameters under different hand actions are further discussed in view of the biological system behavior. The proposed models are considered to serve as useful tools for design and assessment of vibration isolation methods, and for developing a hand-arm simulator for vibration analysis of power tools.

Efficient Global Aerodynamic Modeling from Flight Data

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2012-01-01

A method for identifying global aerodynamic models from flight data in an efficient manner is explained and demonstrated. A novel experiment design technique was used to obtain dynamic flight data over a range of flight conditions with a single flight maneuver. Multivariate polynomials and polynomial splines were used with orthogonalization techniques and statistical modeling metrics to synthesize global nonlinear aerodynamic models directly and completely from flight data alone. Simulation data and flight data from a subscale twin-engine jet transport aircraft were used to demonstrate the techniques. Results showed that global multivariate nonlinear aerodynamic dependencies could be accurately identified using flight data from a single maneuver. Flight-derived global aerodynamic model structures, model parameter estimates, and associated uncertainties were provided for all six nondimensional force and moment coefficients for the test aircraft. These models were combined with a propulsion model identified from engine ground test data to produce a high-fidelity nonlinear flight simulation very efficiently. Prediction testing using a multi-axis maneuver showed that the identified global model accurately predicted aircraft responses.

A bifurcation study to guide the design of a landing gear with a combined uplock/downlock mechanism.

PubMed

Knowles, James A C; Lowenberg, Mark H; Neild, Simon A; Krauskopf, Bernd

2014-12-08

This paper discusses the insights that a bifurcation analysis can provide when designing mechanisms. A model, in the form of a set of coupled steady-state equations, can be derived to describe the mechanism. Solutions to this model can be traced through the mechanism's state versus parameter space via numerical continuation, under the simultaneous variation of one or more parameters. With this approach, crucial features in the response surface, such as bifurcation points, can be identified. By numerically continuing these points in the appropriate parameter space, the resulting bifurcation diagram can be used to guide parameter selection and optimization. In this paper, we demonstrate the potential of this technique by considering an aircraft nose landing gear, with a novel locking strategy that uses a combined uplock/downlock mechanism. The landing gear is locked when in the retracted or deployed states. Transitions between these locked states and the unlocked state (where the landing gear is a mechanism) are shown to depend upon the positions of two fold point bifurcations. By performing a two-parameter continuation, the critical points are traced to identify operational boundaries. Following the variation of the fold points through parameter space, a minimum spring stiffness is identified that enables the landing gear to be locked in the retracted state. The bifurcation analysis also shows that the unlocking of a retracted landing gear should use an unlock force measure, rather than a position indicator, to de-couple the effects of the retraction and locking actuators. Overall, the study demonstrates that bifurcation analysis can enhance the understanding of the influence of design choices over a wide operating range where nonlinearity is significant.

Identifying cognitive remediation change through computational modelling--effects on reinforcement learning in schizophrenia.

PubMed

Cella, Matteo; Bishara, Anthony J; Medin, Evelina; Swan, Sarah; Reeder, Clare; Wykes, Til

2014-11-01

Converging research suggests that individuals with schizophrenia show a marked impairment in reinforcement learning, particularly in tasks requiring flexibility and adaptation. The problem has been associated with dopamine reward systems. This study explores, for the first time, the characteristics of this impairment and how it is affected by a behavioral intervention-cognitive remediation. Using computational modelling, 3 reinforcement learning parameters based on the Wisconsin Card Sorting Test (WCST) trial-by-trial performance were estimated: R (reward sensitivity), P (punishment sensitivity), and D (choice consistency). In Study 1 the parameters were compared between a group of individuals with schizophrenia (n = 100) and a healthy control group (n = 50). In Study 2 the effect of cognitive remediation therapy (CRT) on these parameters was assessed in 2 groups of individuals with schizophrenia, one receiving CRT (n = 37) and the other receiving treatment as usual (TAU, n = 34). In Study 1 individuals with schizophrenia showed impairment in the R and P parameters compared with healthy controls. Study 2 demonstrated that sensitivity to negative feedback (P) and reward (R) improved in the CRT group after therapy compared with the TAU group. R and P parameter change correlated with WCST outputs. Improvements in R and P after CRT were associated with working memory gains and reduction of negative symptoms, respectively. Schizophrenia reinforcement learning difficulties negatively influence performance in shift learning tasks. CRT can improve sensitivity to reward and punishment. Identifying parameters that show change may be useful in experimental medicine studies to identify cognitive domains susceptible to improvement. © The Author 2013. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

A bifurcation study to guide the design of a landing gear with a combined uplock/downlock mechanism

PubMed Central

Knowles, James A. C.; Lowenberg, Mark H.; Neild, Simon A.; Krauskopf, Bernd

2014-01-01

This paper discusses the insights that a bifurcation analysis can provide when designing mechanisms. A model, in the form of a set of coupled steady-state equations, can be derived to describe the mechanism. Solutions to this model can be traced through the mechanism's state versus parameter space via numerical continuation, under the simultaneous variation of one or more parameters. With this approach, crucial features in the response surface, such as bifurcation points, can be identified. By numerically continuing these points in the appropriate parameter space, the resulting bifurcation diagram can be used to guide parameter selection and optimization. In this paper, we demonstrate the potential of this technique by considering an aircraft nose landing gear, with a novel locking strategy that uses a combined uplock/downlock mechanism. The landing gear is locked when in the retracted or deployed states. Transitions between these locked states and the unlocked state (where the landing gear is a mechanism) are shown to depend upon the positions of two fold point bifurcations. By performing a two-parameter continuation, the critical points are traced to identify operational boundaries. Following the variation of the fold points through parameter space, a minimum spring stiffness is identified that enables the landing gear to be locked in the retracted state. The bifurcation analysis also shows that the unlocking of a retracted landing gear should use an unlock force measure, rather than a position indicator, to de-couple the effects of the retraction and locking actuators. Overall, the study demonstrates that bifurcation analysis can enhance the understanding of the influence of design choices over a wide operating range where nonlinearity is significant. PMID:25484601

FITPOP, a heuristic simulation model of population dynamics and genetics with special reference to fisheries

USGS Publications Warehouse

McKenna, James E.

2000-01-01

Although, perceiving genetic differences and their effects on fish population dynamics is difficult, simulation models offer a means to explore and illustrate these effects. I partitioned the intrinsic rate of increase parameter of a simple logistic-competition model into three components, allowing specification of effects of relative differences in fitness and mortality, as well as finite rate of increase. This model was placed into an interactive, stochastic environment to allow easy manipulation of model parameters (FITPOP). Simulation results illustrated the effects of subtle differences in genetic and population parameters on total population size, overall fitness, and sensitivity of the system to variability. Several consequences of mixing genetically distinct populations were illustrated. For example, behaviors such as depression of population size after initial introgression and extirpation of native stocks due to continuous stocking of genetically inferior fish were reproduced. It also was shown that carrying capacity relative to the amount of stocking had an important influence on population dynamics. Uncertainty associated with parameter estimates reduced confidence in model projections. The FITPOP model provided a simple tool to explore population dynamics, which may assist in formulating management strategies and identifying research needs.

W3MAMCAT: a world wide web based tool for mammillary and catenary compartmental modeling and expert system distinguishability.

PubMed

Russell, Solomon; Distefano, Joseph J

2006-07-01

W(3)MAMCAT is a new web-based and interactive system for building and quantifying the parameters or parameter ranges of n-compartment mammillary and catenary model structures, with input and output in the first compartment, from unstructured multiexponential (sum-of-n-exponentials) models. It handles unidentifiable as well as identifiable models and, as such, provides finite parameter interval solutions for unidentifiable models, whereas direct parameter search programs typically do not. It also tutorially develops the theory of model distinguishability for same order mammillary versus catenary models, as did its desktop application predecessor MAMCAT+. This includes expert system analysis for distinguishing mammillary from catenary structures, given input and output in similarly numbered compartments. W(3)MAMCAT provides for universal deployment via the internet and enhanced application error checking. It uses supported Microsoft technologies to form an extensible application framework for maintaining a stable and easily updatable application. Most important, anybody, anywhere, is welcome to access it using Internet Explorer 6.0 over the internet for their teaching or research needs. It is available on the Biocybernetics Laboratory website at UCLA: www.biocyb.cs.ucla.edu.

Uncertainty analyses of the calibrated parameter values of a water quality model

NASA Astrophysics Data System (ADS)

Rode, M.; Suhr, U.; Lindenschmidt, K.-E.

2003-04-01

For river basin management water quality models are increasingly used for the analysis and evaluation of different management measures. However substantial uncertainties exist in parameter values depending on the available calibration data. In this paper an uncertainty analysis for a water quality model is presented, which considers the impact of available model calibration data and the variance of input variables. The investigation was conducted based on four extensive flowtime related longitudinal surveys in the River Elbe in the years 1996 to 1999 with varying discharges and seasonal conditions. For the model calculations the deterministic model QSIM of the BfG (Germany) was used. QSIM is a one dimensional water quality model and uses standard algorithms for hydrodynamics and phytoplankton dynamics in running waters, e.g. Michaelis Menten/Monod kinetics, which are used in a wide range of models. The multi-objective calibration of the model was carried out with the nonlinear parameter estimator PEST. The results show that for individual flow time related measuring surveys very good agreements between model calculation and measured values can be obtained. If these parameters are applied to deviating boundary conditions, substantial errors in model calculation can occur. These uncertainties can be decreased with an increased calibration database. More reliable model parameters can be identified, which supply reasonable results for broader boundary conditions. The extension of the application of the parameter set on a wider range of water quality conditions leads to a slight reduction of the model precision for the specific water quality situation. Moreover the investigations show that highly variable water quality variables like the algal biomass always allow a smaller forecast accuracy than variables with lower coefficients of variation like e.g. nitrate.

Quantifying the sensitivity of feedstock properties and process conditions on hydrochar yield, carbon content, and energy content.

PubMed

Li, Liang; Wang, Yiying; Xu, Jiting; Flora, Joseph R V; Hoque, Shamia; Berge, Nicole D

2018-08-01

Hydrothermal carbonization (HTC) is a wet, low temperature thermal conversion process that continues to gain attention for the generation of hydrochar. The importance of specific process conditions and feedstock properties on hydrochar characteristics is not well understood. To evaluate this, linear and non-linear models were developed to describe hydrochar characteristics based on data collected from HTC-related literature. A Sobol analysis was subsequently conducted to identify parameters that most influence hydrochar characteristics. Results from this analysis indicate that for each investigated hydrochar property, the model fit and predictive capability associated with the random forest models is superior to both the linear and regression tree models. Based on results from the Sobol analysis, the feedstock properties and process conditions most influential on hydrochar yield, carbon content, and energy content were identified. In addition, a variational process parameter sensitivity analysis was conducted to determine how feedstock property importance changes with process conditions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Predictive Model for the Design of Zwitterionic Polymer Brushes: A Statistical Design of Experiments Approach.

PubMed

Kumar, Ramya; Lahann, Joerg

2016-07-06

The performance of polymer interfaces in biology is governed by a wide spectrum of interfacial properties. With the ultimate goal of identifying design parameters for stem cell culture coatings, we developed a statistical model that describes the dependence of brush properties on surface-initiated polymerization (SIP) parameters. Employing a design of experiments (DOE) approach, we identified operating boundaries within which four gel architecture regimes can be realized, including a new regime of associated brushes in thin films. Our statistical model can accurately predict the brush thickness and the degree of intermolecular association of poly[{2-(methacryloyloxy) ethyl} dimethyl-(3-sulfopropyl) ammonium hydroxide] (PMEDSAH), a previously reported synthetic substrate for feeder-free and xeno-free culture of human embryonic stem cells. DOE-based multifunctional predictions offer a powerful quantitative framework for designing polymer interfaces. For example, model predictions can be used to decrease the critical thickness at which the wettability transition occurs by simply increasing the catalyst quantity from 1 to 3 mol %.

Parameter identification of piezoelectric hysteresis model based on improved artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Wang, Geng; Zhou, Kexin; Zhang, Yeming

2018-04-01

The widely used Bouc-Wen hysteresis model can be utilized to accurately simulate the voltage-displacement curves of piezoelectric actuators. In order to identify the unknown parameters of the Bouc-Wen model, an improved artificial bee colony (IABC) algorithm is proposed in this paper. A guiding strategy for searching the current optimal position of the food source is proposed in the method, which can help balance the local search ability and global exploitation capability. And the formula for the scout bees to search for the food source is modified to increase the convergence speed. Some experiments were conducted to verify the effectiveness of the IABC algorithm. The results show that the identified hysteresis model agreed well with the actual actuator response. Moreover, the identification results were compared with the standard particle swarm optimization (PSO) method, and it can be seen that the search performance in convergence rate of the IABC algorithm is better than that of the standard PSO method.

Noise source and reactor stability estimation in a boiling water reactor using a multivariate autoregressive model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kanemoto, S.; Andoh, Y.; Sandoz, S.A.

1984-10-01

A method for evaluating reactor stability in boiling water reactors has been developed. The method is based on multivariate autoregressive (M-AR) modeling of steady-state neutron and process noise signals. In this method, two kinds of power spectral densities (PSDs) for the measured neutron signal and the corresponding noise source signal are separately identified by the M-AR modeling. The closed- and open-loop stability parameters are evaluated from these PSDs. The method is applied to actual plant noise data that were measured together with artificial perturbation test data. Stability parameters identified from noise data are compared to those from perturbation test data,more » and it is shown that both results are in good agreement. In addition to these stability estimations, driving noise sources for the neutron signal are evaluated by the M-AR modeling. Contributions from void, core flow, and pressure noise sources are quantitatively evaluated, and the void noise source is shown to be the most dominant.« less

Fine-Tuning ADAS Algorithm Parameters for Optimizing Traffic ...

EPA Pesticide Factsheets

With the development of the Connected Vehicle technology that facilitates wirelessly communication among vehicles and road-side infrastructure, the Advanced Driver Assistance Systems (ADAS) can be adopted as an effective tool for accelerating traffic safety and mobility optimization at various highway facilities. To this end, the traffic management centers identify the optimal ADAS algorithm parameter set that enables the maximum improvement of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. After adopting the optimal parameter set, the ADAS-equipped drivers become active agents in the traffic stream that work collectively and consistently to prevent traffic conflicts, lower the intensity of traffic disturbances, and suppress the development of traffic oscillations into heavy traffic jams. Successful implementation of this objective requires the analysis capability of capturing the impact of the ADAS on driving behaviors, and measuring traffic safety and mobility performance under the influence of the ADAS. To address this challenge, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through an optimization programming framework to enable th

Herneneutics of Sansei Management: Some Conceptual Parameters.

ERIC Educational Resources Information Center

Tanaka, Ronald

1988-01-01

Explores the managerial behavior of sansei, third-generation Japanese-Americans, which directly conforms to neither Western nor native Japanese models, but which contains identifiable elements of each. (BJV)

Thermal modeling of a cryogenic turbopump for space shuttle applications.

NASA Technical Reports Server (NTRS)

Knowles, P. J.

1971-01-01

Thermal modeling of a cryogenic pump and a hot-gas turbine in a turbopump assembly proposed for the Space Shuttle is described in this paper. A model, developed by identifying the heat-transfer regimes and incorporating their dependencies into a turbopump system model, included heat transfer for two-phase cryogen, hot-gas (200 R) impingement on turbine blades, gas impingement on rotating disks and parallel plate fluid flow. The ?thermal analyzer' program employed to develop this model was the TRW Systems Improved Numerical Differencing Analyzer (SINDA). This program uses finite differencing with lumped parameter representation for each node. Also discussed are model development, simulations of turbopump startup/shutdown operations, and the effects of varying turbopump parameters on the thermal performance.

RANS computations for identification of 1-D cavitation model parameters: application to full load cavitation vortex rope

NASA Astrophysics Data System (ADS)

Alligné, S.; Decaix, J.; Müller, A.; Nicolet, C.; Avellan, F.; Münch, C.

2017-04-01

Due to the massive penetration of alternative renewable energies, hydropower is a key energy conversion technology for stabilizing the electrical power network by using hydraulic machines at off design operating conditions. At full load, the axisymmetric cavitation vortex rope developing in Francis turbines acts as an internal source of energy, leading to an instability commonly referred to as self-excited surge. 1-D models are developed to predict this phenomenon and to define the range of safe operating points for a hydropower plant. These models require a calibration of several parameters. The present work aims at identifying these parameters by using CFD results as objective functions for an optimization process. A 2-D Venturi and 3-D Francis turbine are considered.

Development of advanced techniques for rotorcraft state estimation and parameter identification

NASA Technical Reports Server (NTRS)

Hall, W. E., Jr.; Bohn, J. G.; Vincent, J. H.

1980-01-01

An integrated methodology for rotorcraft system identification consists of rotorcraft mathematical modeling, three distinct data processing steps, and a technique for designing inputs to improve the identifiability of the data. These elements are as follows: (1) a Kalman filter smoother algorithm which estimates states and sensor errors from error corrupted data. Gust time histories and statistics may also be estimated; (2) a model structure estimation algorithm for isolating a model which adequately explains the data; (3) a maximum likelihood algorithm for estimating the parameters and estimates for the variance of these estimates; and (4) an input design algorithm, based on a maximum likelihood approach, which provides inputs to improve the accuracy of parameter estimates. Each step is discussed with examples to both flight and simulated data cases.

Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding

NASA Astrophysics Data System (ADS)

Kelleher, Christa; McGlynn, Brian; Wagener, Thorsten

2017-07-01

Distributed catchment models are widely used tools for predicting hydrologic behavior. While distributed models require many parameters to describe a system, they are expected to simulate behavior that is more consistent with observed processes. However, obtaining a single set of acceptable parameters can be problematic, as parameter equifinality often results in several behavioral sets that fit observations (typically streamflow). In this study, we investigate the extent to which equifinality impacts a typical distributed modeling application. We outline a hierarchical approach to reduce the number of behavioral sets based on regional, observation-driven, and expert-knowledge-based constraints. For our application, we explore how each of these constraint classes reduced the number of behavioral parameter sets and altered distributions of spatiotemporal simulations, simulating a well-studied headwater catchment, Stringer Creek, Montana, using the distributed hydrology-soil-vegetation model (DHSVM). As a demonstrative exercise, we investigated model performance across 10 000 parameter sets. Constraints on regional signatures, the hydrograph, and two internal measurements of snow water equivalent time series reduced the number of behavioral parameter sets but still left a small number with similar goodness of fit. This subset was ultimately further reduced by incorporating pattern expectations of groundwater table depth across the catchment. Our results suggest that utilizing a hierarchical approach based on regional datasets, observations, and expert knowledge to identify behavioral parameter sets can reduce equifinality and bolster more careful application and simulation of spatiotemporal processes via distributed modeling at the catchment scale.

Analysis of Family Structures Reveals Robustness or Sensitivity of Bursting Activity to Parameter Variations in a Half-Center Oscillator (HCO) Model.

PubMed

Doloc-Mihu, Anca; Calabrese, Ronald L

2016-01-01

The underlying mechanisms that support robustness in neuronal networks are as yet unknown. However, recent studies provide evidence that neuronal networks are robust to natural variations, modulation, and environmental perturbations of parameters, such as maximal conductances of intrinsic membrane and synaptic currents. Here we sought a method for assessing robustness, which might easily be applied to large brute-force databases of model instances. Starting with groups of instances with appropriate activity (e.g., tonic spiking), our method classifies instances into much smaller subgroups, called families, in which all members vary only by the one parameter that defines the family. By analyzing the structures of families, we developed measures of robustness for activity type. Then, we applied these measures to our previously developed model database, HCO-db, of a two-neuron half-center oscillator (HCO), a neuronal microcircuit from the leech heartbeat central pattern generator where the appropriate activity type is alternating bursting. In HCO-db, the maximal conductances of five intrinsic and two synaptic currents were varied over eight values (leak reversal potential also varied, five values). We focused on how variations of particular conductance parameters maintain normal alternating bursting activity while still allowing for functional modulation of period and spike frequency. We explored the trade-off between robustness of activity type and desirable change in activity characteristics when intrinsic conductances are altered and identified the hyperpolarization-activated (h) current as an ideal target for modulation. We also identified ensembles of model instances that closely approximate physiological activity and can be used in future modeling studies.

Combining the ‘bottom up’ and ‘top down’ approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data

PubMed Central

Tsamandouras, Nikolaos; Rostami-Hodjegan, Amin; Aarons, Leon

2015-01-01

Pharmacokinetic models range from being entirely exploratory and empirical, to semi-mechanistic and ultimately complex physiologically based pharmacokinetic (PBPK) models. This choice is conditional on the modelling purpose as well as the amount and quality of the available data. The main advantage of PBPK models is that they can be used to extrapolate outside the studied population and experimental conditions. The trade-off for this advantage is a complex system of differential equations with a considerable number of model parameters. When these parameters cannot be informed from in vitro or in silico experiments they are usually optimized with respect to observed clinical data. Parameter estimation in complex models is a challenging task associated with many methodological issues which are discussed here with specific recommendations. Concepts such as structural and practical identifiability are described with regards to PBPK modelling and the value of experimental design and sensitivity analyses is sketched out. Parameter estimation approaches are discussed, while we also highlight the importance of not neglecting the covariance structure between model parameters and the uncertainty and population variability that is associated with them. Finally the possibility of using model order reduction techniques and minimal semi-mechanistic models that retain the physiological-mechanistic nature only in the parts of the model which are relevant to the desired modelling purpose is emphasized. Careful attention to all the above issues allows us to integrate successfully information from in vitro or in silico experiments together with information deriving from observed clinical data and develop mechanistically sound models with clinical relevance. PMID:24033787

The use and misuse of V(c,max) in Earth System Models.

PubMed

Rogers, Alistair

2014-02-01

Earth System Models (ESMs) aim to project global change. Central to this aim is the need to accurately model global carbon fluxes. Photosynthetic carbon dioxide assimilation by the terrestrial biosphere is the largest of these fluxes, and in many ESMs is represented by the Farquhar, von Caemmerer and Berry (FvCB) model of photosynthesis. The maximum rate of carboxylation by the enzyme Rubisco, commonly termed V c,max, is a key parameter in the FvCB model. This study investigated the derivation of the values of V c,max used to represent different plant functional types (PFTs) in ESMs. Four methods for estimating V c,max were identified; (1) an empirical or (2) mechanistic relationship was used to relate V c,max to leaf N content, (3) V c,max was estimated using an approach based on the optimization of photosynthesis and respiration or (4) calibration of a user-defined V c,max to obtain a target model output. Despite representing the same PFTs, the land model components of ESMs were parameterized with a wide range of values for V c,max (-46 to +77% of the PFT mean). In many cases, parameterization was based on limited data sets and poorly defined coefficients that were used to adjust model parameters and set PFT-specific values for V c,max. Examination of the models that linked leaf N mechanistically to V c,max identified potential changes to fixed parameters that collectively would decrease V c,max by 31% in C3 plants and 11% in C4 plants. Plant trait data bases are now available that offer an excellent opportunity for models to update PFT-specific parameters used to estimate V c,max. However, data for parameterizing some PFTs, particularly those in the Tropics and the Arctic are either highly variable or largely absent.

Modeling the biomechanical and injury response of human liver parenchyma under tensile loading.

PubMed

Untaroiu, Costin D; Lu, Yuan-Chiao; Siripurapu, Sundeep K; Kemper, Andrew R

2015-01-01

The rapid advancement in computational power has made human finite element (FE) models one of the most efficient tools for assessing the risk of abdominal injuries in a crash event. In this study, specimen-specific FE models were employed to quantify material and failure properties of human liver parenchyma using a FE optimization approach. Uniaxial tensile tests were performed on 34 parenchyma coupon specimens prepared from two fresh human livers. Each specimen was tested to failure at one of four loading rates (0.01s(-1), 0.1s(-1), 1s(-1), and 10s(-1)) to investigate the effects of rate dependency on the biomechanical and failure response of liver parenchyma. Each test was simulated by prescribing the end displacements of specimen-specific FE models based on the corresponding test data. The parameters of a first-order Ogden material model were identified for each specimen by a FE optimization approach while simulating the pre-tear loading region. The mean material model parameters were then determined for each loading rate from the characteristic averages of the stress-strain curves, and a stochastic optimization approach was utilized to determine the standard deviations of the material model parameters. A hyperelastic material model using a tabulated formulation for rate effects showed good predictions in terms of tensile material properties of human liver parenchyma. Furthermore, the tissue tearing was numerically simulated using a cohesive zone modeling (CZM) approach. A layer of cohesive elements was added at the failure location, and the CZM parameters were identified by fitting the post-tear force-time history recorded in each test. The results show that the proposed approach is able to capture both the biomechanical and failure response, and accurately model the overall force-deflection response of liver parenchyma over a large range of tensile loadings rates. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automated evaluation of liver fibrosis in thioacetamide, carbon tetrachloride, and bile duct ligation rodent models using second-harmonic generation/two-photon excited fluorescence microscopy.

PubMed

Liu, Feng; Chen, Long; Rao, Hui-Ying; Teng, Xiao; Ren, Ya-Yun; Lu, Yan-Qiang; Zhang, Wei; Wu, Nan; Liu, Fang-Fang; Wei, Lai

2017-01-01

Animal models provide a useful platform for developing and testing new drugs to treat liver fibrosis. Accordingly, we developed a novel automated system to evaluate liver fibrosis in rodent models. This system uses second-harmonic generation (SHG)/two-photon excited fluorescence (TPEF) microscopy to assess a total of four mouse and rat models, using chemical treatment with either thioacetamide (TAA) or carbon tetrachloride (CCl 4 ), and a surgical method, bile duct ligation (BDL). The results obtained by the new technique were compared with that using Ishak fibrosis scores and two currently used quantitative methods for determining liver fibrosis: the collagen proportionate area (CPA) and measurement of hydroxyproline (HYP) content. We show that 11 shared morphological parameters faithfully recapitulate Ishak fibrosis scores in the models, with high area under the receiver operating characteristic (ROC) curve (AUC) performance. The AUC values of 11 shared parameters were greater than that of the CPA (TAA: 0.758-0.922 vs 0.752-0.908; BDL: 0.874-0.989 vs 0.678-0.966) in the TAA mice and BDL rat models and similar to that of the CPA in the TAA rat and CCl 4 mouse models. Similarly, based on the trends in these parameters at different time points, 9, 10, 7, and 2 model-specific parameters were selected for the TAA rats, TAA mice, CCl 4 mice, and BDL rats, respectively. These parameters identified differences among the time points in the four models, with high AUC accuracy, and the corresponding AUC values of these parameters were greater compared with those of the CPA in the TAA rat and mouse models (rats: 0.769-0.894 vs 0.64-0.799; mice: 0.87-0.93 vs 0.739-0.836) and similar to those of the CPA in the CCl 4 mouse and BDL rat models. Similarly, the AUC values of 11 shared parameters and model-specific parameters were greater than those of HYP in the TAA rats, TAA mice, and CCl 4 mouse models and were similar to those of HYP in the BDL rat models. The automated evaluation system, combined with 11 shared parameters and model-specific parameters, could specifically, accurately, and quantitatively stage liver fibrosis in animal models.

Constraining Distributed Catchment Models by Incorporating Perceptual Understanding of Spatial Hydrologic Behaviour

NASA Astrophysics Data System (ADS)

Hutton, Christopher; Wagener, Thorsten; Freer, Jim; Han, Dawei

2016-04-01

Distributed models offer the potential to resolve catchment systems in more detail, and therefore simulate the hydrological impacts of spatial changes in catchment forcing (e.g. landscape change). Such models tend to contain a large number of poorly defined and spatially varying model parameters which are therefore computationally expensive to calibrate. Insufficient data can result in model parameter and structural equifinality, particularly when calibration is reliant on catchment outlet discharge behaviour alone. Evaluating spatial patterns of internal hydrological behaviour has the potential to reveal simulations that, whilst consistent with measured outlet discharge, are qualitatively dissimilar to our perceptual understanding of how the system should behave. We argue that such understanding, which may be derived from stakeholder knowledge across different catchments for certain process dynamics, is a valuable source of information to help reject non-behavioural models, and therefore identify feasible model structures and parameters. The challenge, however, is to convert different sources of often qualitative and/or semi-qualitative information into robust quantitative constraints of model states and fluxes, and combine these sources of information together to reject models within an efficient calibration framework. Here we present the development of a framework to incorporate different sources of data to efficiently calibrate distributed catchment models. For each source of information, an interval or inequality is used to define the behaviour of the catchment system. These intervals are then combined to produce a hyper-volume in state space, which is used to identify behavioural models. We apply the methodology to calibrate the Penn State Integrated Hydrological Model (PIHM) at the Wye catchment, Plynlimon, UK. Outlet discharge behaviour is successfully simulated when perceptual understanding of relative groundwater levels between lowland peat, upland peat and valley slopes within the catchment are used to identify behavioural models. The process of converting qualitative information into quantitative constraints forces us to evaluate the assumptions behind our perceptual understanding in order to derive robust constraints, and therefore fairly reject models and avoid type II errors. Likewise, consideration needs to be given to the commensurability problem when mapping perceptual understanding to constrain model states.

Dynamic tissue phantoms and their use in assessment of a noninvasive optical plethysmography imaging device

NASA Astrophysics Data System (ADS)

Thatcher, Jeffrey E.; Plant, Kevin D.; King, Darlene R.; Block, Kenneth L.; Fan, Wensheng; DiMaio, J. Michael

2014-05-01

Non-contact photoplethysmography (PPG) has been studied as a method to provide low-cost and non-invasive medical imaging for a variety of near-surface pathologies and two dimensional blood oxygenation measurements. Dynamic tissue phantoms were developed to evaluate this technology in a laboratory setting. The purpose of these phantoms was to generate a tissue model with tunable parameters including: blood vessel volume change; pulse wave frequency; and optical scattering and absorption parameters. A non-contact PPG imaging system was evaluated on this model and compared against laser Doppler imaging (LDI) and a traditional pulse oximeter. Results indicate non-contact PPG accurately identifies pulse frequency and appears to identify signals from optically dense phantoms with significantly higher detection thresholds than LDI.

Towards parameter-free classification of sound effects in movies

NASA Astrophysics Data System (ADS)

Chu, Selina; Narayanan, Shrikanth; Kuo, C.-C. J.

2005-08-01

The problem of identifying intense events via multimedia data mining in films is investigated in this work. Movies are mainly characterized by dialog, music, and sound effects. We begin our investigation with detecting interesting events through sound effects. Sound effects are neither speech nor music, but are closely associated with interesting events such as car chases and gun shots. In this work, we utilize low-level audio features including MFCC and energy to identify sound effects. It was shown in previous work that the Hidden Markov model (HMM) works well for speech/audio signals. However, this technique requires a careful choice in designing the model and choosing correct parameters. In this work, we introduce a framework that will avoid such necessity and works well with semi- and non-parametric learning algorithms.

Superheavy dark matter through Higgs portal operators

NASA Astrophysics Data System (ADS)

Kolb, Edward W.; Long, Andrew J.

2017-11-01

The WIMPzilla hypothesis is that the dark matter is a super-weakly-interacting and superheavy particle. Conventionally, the WIMPzilla abundance is set by gravitational particle production during or at the end of inflation. In this study we allow the WIMPzilla to interact directly with Standard Model fields through the Higgs portal, and we calculate the thermal production (freeze-in) of WIMPzilla dark matter from the annihilation of Higgs boson pairs in the plasma. The two particle-physics model parameters are the WIMPzilla mass and the Higgs-WIMPzilla coupling. The two cosmological parameters are the reheating temperature and the expansion rate of the universe at the end of inflation. We delineate the regions of parameter space where either gravitational or thermal production is dominant, and within those regions we identify the parameters that predict the observed dark matter relic abundance. Allowing for thermal production opens up the parameter space, even for Planck-suppressed Higgs-WIMPzilla interactions.

User Selection Criteria of Airspace Designs in Flexible Airspace Management

NASA Technical Reports Server (NTRS)

Lee, Hwasoo E.; Lee, Paul U.; Jung, Jaewoo; Lai, Chok Fung

2011-01-01

A method for identifying global aerodynamic models from flight data in an efficient manner is explained and demonstrated. A novel experiment design technique was used to obtain dynamic flight data over a range of flight conditions with a single flight maneuver. Multivariate polynomials and polynomial splines were used with orthogonalization techniques and statistical modeling metrics to synthesize global nonlinear aerodynamic models directly and completely from flight data alone. Simulation data and flight data from a subscale twin-engine jet transport aircraft were used to demonstrate the techniques. Results showed that global multivariate nonlinear aerodynamic dependencies could be accurately identified using flight data from a single maneuver. Flight-derived global aerodynamic model structures, model parameter estimates, and associated uncertainties were provided for all six nondimensional force and moment coefficients for the test aircraft. These models were combined with a propulsion model identified from engine ground test data to produce a high-fidelity nonlinear flight simulation very efficiently. Prediction testing using a multi-axis maneuver showed that the identified global model accurately predicted aircraft responses.

Model of succession in degraded areas based on carabid beetles (Coleoptera, Carabidae).

PubMed

Schwerk, Axel; Szyszko, Jan

2011-01-01

Degraded areas constitute challenging tasks with respect to sustainable management of natural resources. Maintaining or even establishing certain successional stages seems to be particularly important. This paper presents a model of the succession in five different types of degraded areas in Poland based on changes in the carabid fauna. Mean Individual Biomass of Carabidae (MIB) was used as a numerical measure for the stage of succession. The run of succession differed clearly among the different types of degraded areas. Initial conditions (origin of soil and origin of vegetation) and landscape related aspects seem to be important with respect to these differences. As characteristic phases, a 'delay phase', an 'increase phase' and a 'stagnation phase' were identified. In general, the runs of succession could be described by four different parameters: (1) 'Initial degradation level', (2) 'delay', (3) 'increase rate' and (4) 'recovery level'. Applying the analytic solution of the logistic equation, characteristic values for the parameters were identified for each of the five area types. The model is of practical use, because it provides a possibility to compare the values of the parameters elaborated in different areas, to give hints for intervention and to provide prognoses about future succession in the areas. Furthermore, it is possible to transfer the model to other indicators of succession.

A delay differential equation model of follicle waves in women.

PubMed

Panza, Nicole M; Wright, Andrew A; Selgrade, James F

2016-01-01

This article presents a mathematical model for hormonal regulation of the menstrual cycle which predicts the occurrence of follicle waves in normally cycling women. Several follicles of ovulatory size that develop sequentially during one menstrual cycle are referred to as follicle waves. The model consists of 13 nonlinear, delay differential equations with 51 parameters. Model simulations exhibit a unique stable periodic cycle and this menstrual cycle accurately approximates blood levels of ovarian and pituitary hormones found in the biological literature. Numerical experiments illustrate that the number of follicle waves corresponds to the number of rises in pituitary follicle stimulating hormone. Modifications of the model equations result in simulations which predict the possibility of two ovulations at different times during the same menstrual cycle and, hence, the occurrence of dizygotic twins via a phenomenon referred to as superfecundation. Sensitive parameters are identified and bifurcations in model behaviour with respect to parameter changes are discussed. Studying follicle waves may be helpful for improving female fertility and for understanding some aspects of female reproductive ageing.