Maximum likelihood estimation of finite mixture model for economic data

NASA Astrophysics Data System (ADS)

Phoong, Seuk-Yen; Ismail, Mohd Tahir

2014-06-01

Finite mixture model is a mixture model with finite-dimension. This models are provides a natural representation of heterogeneity in a finite number of latent classes. In addition, finite mixture models also known as latent class models or unsupervised learning models. Recently, maximum likelihood estimation fitted finite mixture models has greatly drawn statistician's attention. The main reason is because maximum likelihood estimation is a powerful statistical method which provides consistent findings as the sample sizes increases to infinity. Thus, the application of maximum likelihood estimation is used to fit finite mixture model in the present paper in order to explore the relationship between nonlinear economic data. In this paper, a two-component normal mixture model is fitted by maximum likelihood estimation in order to investigate the relationship among stock market price and rubber price for sampled countries. Results described that there is a negative effect among rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia.

A Model-Based Approach for Visualizing the Dimensional Structure of Ordered Successive Categories Preference Data

ERIC Educational Resources Information Center

DeSarbo, Wayne S.; Park, Joonwook; Scott, Crystal J.

2008-01-01

A cyclical conditional maximum likelihood estimation procedure is developed for the multidimensional unfolding of two- or three-way dominance data (e.g., preference, choice, consideration) measured on ordered successive category rating scales. The technical description of the proposed model and estimation procedure are discussed, as well as the…

Bayesian inference based on dual generalized order statistics from the exponentiated Weibull model

NASA Astrophysics Data System (ADS)

Al Sobhi, Mashail M.

2015-02-01

Bayesian estimation for the two parameters and the reliability function of the exponentiated Weibull model are obtained based on dual generalized order statistics (DGOS). Also, Bayesian prediction bounds for future DGOS from exponentiated Weibull model are obtained. The symmetric and asymmetric loss functions are considered for Bayesian computations. The Markov chain Monte Carlo (MCMC) methods are used for computing the Bayes estimates and prediction bounds. The results have been specialized to the lower record values. Comparisons are made between Bayesian and maximum likelihood estimators via Monte Carlo simulation.

Kinetic parameter estimation model for anaerobic co-digestion of waste activated sludge and microalgae.

PubMed

Lee, Eunyoung; Cumberbatch, Jewel; Wang, Meng; Zhang, Qiong

2017-03-01

Anaerobic co-digestion has a potential to improve biogas production, but limited kinetic information is available for co-digestion. This study introduced regression-based models to estimate the kinetic parameters for the co-digestion of microalgae and Waste Activated Sludge (WAS). The models were developed using the ratios of co-substrates and the kinetic parameters for the single substrate as indicators. The models were applied to the modified first-order kinetics and Monod model to determine the rate of hydrolysis and methanogenesis for the co-digestion. The results showed that the model using a hyperbola function was better for the estimation of the first-order kinetic coefficients, while the model using inverse tangent function closely estimated the Monod kinetic parameters. The models can be used for estimating kinetic parameters for not only microalgae-WAS co-digestion but also other substrates' co-digestion such as microalgae-swine manure and WAS-aquatic plants. Copyright © 2016 Elsevier Ltd. All rights reserved.

Parameter Estimates in Differential Equation Models for Chemical Kinetics

ERIC Educational Resources Information Center

Winkel, Brian

2011-01-01

We discuss the need for devoting time in differential equations courses to modelling and the completion of the modelling process with efforts to estimate the parameters in the models using data. We estimate the parameters present in several differential equation models of chemical reactions of order n, where n = 0, 1, 2, and apply more general…

Evaluation of Geometrically Nonlinear Reduced Order Models with Nonlinear Normal Modes

DOE PAGES

Kuether, Robert J.; Deaner, Brandon J.; Hollkamp, Joseph J.; ...

2015-09-15

Several reduced-order modeling strategies have been developed to create low-order models of geometrically nonlinear structures from detailed finite element models, allowing one to compute the dynamic response of the structure at a dramatically reduced cost. But, the parameters of these reduced-order models are estimated by applying a series of static loads to the finite element model, and the quality of the reduced-order model can be highly sensitive to the amplitudes of the static load cases used and to the type/number of modes used in the basis. Our paper proposes to combine reduced-order modeling and numerical continuation to estimate the nonlinearmore » normal modes of geometrically nonlinear finite element models. Not only does this make it possible to compute the nonlinear normal modes far more quickly than existing approaches, but the nonlinear normal modes are also shown to be an excellent metric by which the quality of the reduced-order model can be assessed. Hence, the second contribution of this work is to demonstrate how nonlinear normal modes can be used as a metric by which nonlinear reduced-order models can be compared. Moreover, various reduced-order models with hardening nonlinearities are compared for two different structures to demonstrate these concepts: a clamped–clamped beam model, and a more complicated finite element model of an exhaust panel cover.« less

A model for the cost of doing a cost estimate

NASA Technical Reports Server (NTRS)

Remer, D. S.; Buchanan, H. R.

1992-01-01

A model for estimating the cost required to do a cost estimate for Deep Space Network (DSN) projects that range from $0.1 to $100 million is presented. The cost of the cost estimate in thousands of dollars, C(sub E), is found to be approximately given by C(sub E) = K((C(sub p))(sup 0.35)) where C(sub p) is the cost of the project being estimated in millions of dollars and K is a constant depending on the accuracy of the estimate. For an order-of-magnitude estimate, K = 24; for a budget estimate, K = 60; and for a definitive estimate, K = 115. That is, for a specific project, the cost of doing a budget estimate is about 2.5 times as much as that for an order-of-magnitude estimate, and a definitive estimate costs about twice as much as a budget estimate. Use of this model should help provide the level of resources required for doing cost estimates and, as a result, provide insights towards more accurate estimates with less potential for cost overruns.

A Probabilistic Model of Visual Working Memory: Incorporating Higher Order Regularities into Working Memory Capacity Estimates

ERIC Educational Resources Information Center

Brady, Timothy F.; Tenenbaum, Joshua B.

2013-01-01

When remembering a real-world scene, people encode both detailed information about specific objects and higher order information like the overall gist of the scene. However, formal models of change detection, like those used to estimate visual working memory capacity, assume observers encode only a simple memory representation that includes no…

Blind identification of nonlinear models with non-Gaussian inputs

NASA Astrophysics Data System (ADS)

Prakriya, Shankar; Pasupathy, Subbarayan; Hatzinakos, Dimitrios

1995-12-01

Some methods are proposed for the blind identification of finite-order discrete-time nonlinear models with non-Gaussian circular inputs. The nonlinear models consist of two finite memory linear time invariant (LTI) filters separated by a zero-memory nonlinearity (ZMNL) of the polynomial type (the LTI-ZMNL-LTI models). The linear subsystems are allowed to be of non-minimum phase (NMP). The methods base their estimates of the impulse responses on slices of the N plus 1th order polyspectra of the output sequence. It is shown that the identification of LTI-ZMNL systems requires only a 1-D moment or polyspectral slice. The coefficients of the ZMNL are not estimated, and need not be known. The order of the nonlinearity can, in theory, be estimated from the received signal. These methods possess several noise and interference suppression characteristics, and have applications in modeling nonlinearly amplified QAM/QPSK signals in digital satellite and microwave communications.

A function space approach to smoothing with applications to model error estimation for flexible spacecraft control

NASA Technical Reports Server (NTRS)

Rodriguez, G.

1981-01-01

A function space approach to smoothing is used to obtain a set of model error estimates inherent in a reduced-order model. By establishing knowledge of inevitable deficiencies in the truncated model, the error estimates provide a foundation for updating the model and thereby improving system performance. The function space smoothing solution leads to a specification of a method for computation of the model error estimates and development of model error analysis techniques for comparison between actual and estimated errors. The paper summarizes the model error estimation approach as well as an application arising in the area of modeling for spacecraft attitude control.

Nonparametric autocovariance estimation from censored time series by Gaussian imputation.

PubMed

Park, Jung Wook; Genton, Marc G; Ghosh, Sujit K

2009-02-01

One of the most frequently used methods to model the autocovariance function of a second-order stationary time series is to use the parametric framework of autoregressive and moving average models developed by Box and Jenkins. However, such parametric models, though very flexible, may not always be adequate to model autocovariance functions with sharp changes. Furthermore, if the data do not follow the parametric model and are censored at a certain value, the estimation results may not be reliable. We develop a Gaussian imputation method to estimate an autocovariance structure via nonparametric estimation of the autocovariance function in order to address both censoring and incorrect model specification. We demonstrate the effectiveness of the technique in terms of bias and efficiency with simulations under various rates of censoring and underlying models. We describe its application to a time series of silicon concentrations in the Arctic.

Modeling vehicle operating speed on urban roads in Montreal: a panel mixed ordered probit fractional split model.

PubMed

Eluru, Naveen; Chakour, Vincent; Chamberlain, Morgan; Miranda-Moreno, Luis F

2013-10-01

Vehicle operating speed measured on roadways is a critical component for a host of analysis in the transportation field including transportation safety, traffic flow modeling, roadway geometric design, vehicle emissions modeling, and road user route decisions. The current research effort contributes to the literature on examining vehicle speed on urban roads methodologically and substantively. In terms of methodology, we formulate a new econometric model framework for examining speed profiles. The proposed model is an ordered response formulation of a fractional split model. The ordered nature of the speed variable allows us to propose an ordered variant of the fractional split model in the literature. The proposed formulation allows us to model the proportion of vehicles traveling in each speed interval for the entire segment of roadway. We extend the model to allow the influence of exogenous variables to vary across the population. Further, we develop a panel mixed version of the fractional split model to account for the influence of site-specific unobserved effects. The paper contributes substantively by estimating the proposed model using a unique dataset from Montreal consisting of weekly speed data (collected in hourly intervals) for about 50 local roads and 70 arterial roads. We estimate separate models for local roads and arterial roads. The model estimation exercise considers a whole host of variables including geometric design attributes, roadway attributes, traffic characteristics and environmental factors. The model results highlight the role of various street characteristics including number of lanes, presence of parking, presence of sidewalks, vertical grade, and bicycle route on vehicle speed proportions. The results also highlight the presence of site-specific unobserved effects influencing the speed distribution. The parameters from the modeling exercise are validated using a hold-out sample not considered for model estimation. The results indicate that the proposed panel mixed ordered probit fractional split model offers promise for modeling such proportional ordinal variables. Copyright © 2013 Elsevier Ltd. All rights reserved.

Unified framework to evaluate panmixia and migration direction among multiple sampling locations.

PubMed

Beerli, Peter; Palczewski, Michal

2010-05-01

For many biological investigations, groups of individuals are genetically sampled from several geographic locations. These sampling locations often do not reflect the genetic population structure. We describe a framework using marginal likelihoods to compare and order structured population models, such as testing whether the sampling locations belong to the same randomly mating population or comparing unidirectional and multidirectional gene flow models. In the context of inferences employing Markov chain Monte Carlo methods, the accuracy of the marginal likelihoods depends heavily on the approximation method used to calculate the marginal likelihood. Two methods, modified thermodynamic integration and a stabilized harmonic mean estimator, are compared. With finite Markov chain Monte Carlo run lengths, the harmonic mean estimator may not be consistent. Thermodynamic integration, in contrast, delivers considerably better estimates of the marginal likelihood. The choice of prior distributions does not influence the order and choice of the better models when the marginal likelihood is estimated using thermodynamic integration, whereas with the harmonic mean estimator the influence of the prior is pronounced and the order of the models changes. The approximation of marginal likelihood using thermodynamic integration in MIGRATE allows the evaluation of complex population genetic models, not only of whether sampling locations belong to a single panmictic population, but also of competing complex structured population models.

Estimation in SEM: A Concrete Example

ERIC Educational Resources Information Center

Ferron, John M.; Hess, Melinda R.

2007-01-01

A concrete example is used to illustrate maximum likelihood estimation of a structural equation model with two unknown parameters. The fitting function is found for the example, as are the vector of first-order partial derivatives, the matrix of second-order partial derivatives, and the estimates obtained from each iteration of the Newton-Raphson…

Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach

NASA Astrophysics Data System (ADS)

Chen, Chaochao; Vachtsevanos, George; Orchard, Marcos E.

2012-04-01

Machine prognosis can be considered as the generation of long-term predictions that describe the evolution in time of a fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem so that timely maintenance can be performed to avoid catastrophic failures. This paper proposes an integrated RUL prediction method using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which forecasts the time evolution of the fault indicator and estimates the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter as a model describing the fault progression. The high-order particle filter is used to estimate the current state and carry out p-step-ahead predictions via a set of particles. These predictions are used to estimate the RUL pdf. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results demonstrate that it outperforms both the conventional ANFIS predictor and the particle-filter-based predictor where the fault growth model is a first-order model that is trained via the ANFIS.

Malaria transmission rates estimated from serological data.

PubMed Central

Burattini, M. N.; Massad, E.; Coutinho, F. A.

1993-01-01

A mathematical model was used to estimate malaria transmission rates based on serological data. The model is minimally stochastic and assumes an age-dependent force of infection for malaria. The transmission rates estimated were applied to a simple compartmental model in order to mimic the malaria transmission. The model has shown a good retrieving capacity for serological and parasite prevalence data. PMID:8270011

Time to burn: Modeling wildland arson as an autoregressive crime function

Treesearch

Jeffrey P. Prestemon; David T. Butry

2005-01-01

Six Poisson autoregressive models of order p [PAR(p)] of daily wildland arson ignition counts are estimated for five locations in Florida (1994-2001). In addition, a fixed effects time-series Poisson model of annual arson counts is estimated for all Florida counties (1995-2001). PAR(p) model estimates reveal highly significant arson ignition autocorrelation, lasting up...

BAYESIAN PARAMETER ESTIMATION IN A MIXED-ORDER MODEL OF BOD DECAY. (U915590)

EPA Science Inventory

We describe a generalized version of the BOD decay model in which the reaction is allowed to assume an order other than one. This is accomplished by making the exponent on BOD concentration a free parameter to be determined by the data. This "mixed-order" model may be ...

Model reference adaptive control (MRAC)-based parameter identification applied to surface-mounted permanent magnet synchronous motor

NASA Astrophysics Data System (ADS)

Zhong, Chongquan; Lin, Yaoyao

2017-11-01

In this work, a model reference adaptive control-based estimated algorithm is proposed for online multi-parameter identification of surface-mounted permanent magnet synchronous machines. By taking the dq-axis equations of a practical motor as the reference model and the dq-axis estimation equations as the adjustable model, a standard model-reference-adaptive-system-based estimator was established. Additionally, the Popov hyperstability principle was used in the design of the adaptive law to guarantee accurate convergence. In order to reduce the oscillation of identification result, this work introduces a first-order low-pass digital filter to improve precision regarding the parameter estimation. The proposed scheme was then applied to an SPM synchronous motor control system without any additional circuits and implemented using a DSP TMS320LF2812. For analysis, the experimental results reveal the effectiveness of the proposed method.

The national cost of adverse drug events resulting from inappropriate medication-related alert overrides in the United States.

PubMed

Slight, Sarah P; Seger, Diane L; Franz, Calvin; Wong, Adrian; Bates, David W

2018-06-22

To estimate the national cost of ADEs resulting from inappropriate medication-related alert overrides in the U.S. inpatient setting. We used three different regression models (Basic, Model 1, Model 2) with model inputs taken from the medical literature. A random sample of 40 990 adult inpatients at the Brigham and Women's Hospital (BWH) in Boston with a total of 1 639  294 medication orders was taken. We extrapolated BWH medication orders using 2014 National Inpatient Sample (NIS) data. Using three regression models, we estimated that 29.7 million adult inpatient discharges in 2014 resulted in between 1.02 billion and 1.07 billion medication orders, which in turn generated between 75.1 million and 78.8 million medication alerts, respectively. Taking the basic model (78.8 million), we estimated that 5.5 million medication-related alerts might have been inappropriately overridden, resulting in approximately 196 600 ADEs nationally. This was projected to cost between $871 million and $1.8 billion for treating preventable ADEs. We also estimated that clinicians and pharmacists would have jointly spent 175 000 hours responding to 78.8 million alerts with an opportunity cost of $16.9 million. These data suggest that further optimization of hospitals computerized provider order entry systems and their associated clinical decision support is needed and would result in substantial savings. We have erred on the side of caution in developing this range, taking two conservative cost estimates for a preventable ADE that did not include malpractice or litigation costs, or costs of injuries to patients.

Mixture of autoregressive modeling orders and its implication on single trial EEG classification

PubMed Central

Atyabi, Adham; Shic, Frederick; Naples, Adam

2016-01-01

Autoregressive (AR) models are of commonly utilized feature types in Electroencephalogram (EEG) studies due to offering better resolution, smoother spectra and being applicable to short segments of data. Identifying correct AR’s modeling order is an open challenge. Lower model orders poorly represent the signal while higher orders increase noise. Conventional methods for estimating modeling order includes Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Final Prediction Error (FPE). This article assesses the hypothesis that appropriate mixture of multiple AR orders is likely to better represent the true signal compared to any single order. Better spectral representation of underlying EEG patterns can increase utility of AR features in Brain Computer Interface (BCI) systems by increasing timely & correctly responsiveness of such systems to operator’s thoughts. Two mechanisms of Evolutionary-based fusion and Ensemble-based mixture are utilized for identifying such appropriate mixture of modeling orders. The classification performance of the resultant AR-mixtures are assessed against several conventional methods utilized by the community including 1) A well-known set of commonly used orders suggested by the literature, 2) conventional order estimation approaches (e.g., AIC, BIC and FPE), 3) blind mixture of AR features originated from a range of well-known orders. Five datasets from BCI competition III that contain 2, 3 and 4 motor imagery tasks are considered for the assessment. The results indicate superiority of Ensemble-based modeling order mixture and evolutionary-based order fusion methods within all datasets. PMID:28740331

Numerical scheme approximating solution and parameters in a beam equation

NASA Astrophysics Data System (ADS)

Ferdinand, Robert R.

2003-12-01

We present a mathematical model which describes vibration in a metallic beam about its equilibrium position. This model takes the form of a nonlinear second-order (in time) and fourth-order (in space) partial differential equation with boundary and initial conditions. A finite-element Galerkin approximation scheme is used to estimate model solution. Infinite-dimensional model parameters are then estimated numerically using an inverse method procedure which involves the minimization of a least-squares cost functional. Numerical results are presented and future work to be done is discussed.

Assessing doses to terrestrial wildlife at a radioactive waste disposal site: inter-comparison of modelling approaches.

PubMed

Johansen, M P; Barnett, C L; Beresford, N A; Brown, J E; Černe, M; Howard, B J; Kamboj, S; Keum, D-K; Smodiš, B; Twining, J R; Vandenhove, H; Vives i Batlle, J; Wood, M D; Yu, C

2012-06-15

Radiological doses to terrestrial wildlife were examined in this model inter-comparison study that emphasised factors causing variability in dose estimation. The study participants used varying modelling approaches and information sources to estimate dose rates and tissue concentrations for a range of biota types exposed to soil contamination at a shallow radionuclide waste burial site in Australia. Results indicated that the dominant factor causing variation in dose rate estimates (up to three orders of magnitude on mean total dose rates) was the soil-to-organism transfer of radionuclides that included variation in transfer parameter values as well as transfer calculation methods. Additional variation was associated with other modelling factors including: how participants conceptualised and modelled the exposure configurations (two orders of magnitude); which progeny to include with the parent radionuclide (typically less than one order of magnitude); and dose calculation parameters, including radiation weighting factors and dose conversion coefficients (typically less than one order of magnitude). Probabilistic approaches to model parameterisation were used to encompass and describe variable model parameters and outcomes. The study confirms the need for continued evaluation of the underlying mechanisms governing soil-to-organism transfer of radionuclides to improve estimation of dose rates to terrestrial wildlife. The exposure pathways and configurations available in most current codes are limited when considering instances where organisms access subsurface contamination through rooting, burrowing, or using different localised waste areas as part of their habitual routines. Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.

Application of the Zero-Order Reaction Rate Model and Transition State Theory to predict porous Ti6Al4V bending strength.

PubMed

Reig, L; Amigó, V; Busquets, D; Calero, J A; Ortiz, J L

2012-08-01

Porous Ti6Al4V samples were produced by microsphere sintering. The Zero-Order Reaction Rate Model and Transition State Theory were used to model the sintering process and to estimate the bending strength of the porous samples developed. The evolution of the surface area during the sintering process was used to obtain sintering parameters (sintering constant, activation energy, frequency factor, constant of activation and Gibbs energy of activation). These were then correlated with the bending strength in order to obtain a simple model with which to estimate the evolution of the bending strength of the samples when the sintering temperature and time are modified: σY=P+B·[lnT·t-ΔGa/R·T]. Although the sintering parameters were obtained only for the microsphere sizes analysed here, the strength of intermediate sizes could easily be estimated following this model. Copyright © 2012 Elsevier B.V. All rights reserved.

A simplified parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM.

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...

A tridiagonal parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a tridiagonal parsimonious higher-order multivariate Markov chain model (TPHOMMCM). Moreover, estimation method of the parameters in TPHOMMCM is give. Numerical experiments illustrate the effectiveness of TPHOMMCM.

Covariance functions for body weight from birth to maturity in Nellore cows.

PubMed

Boligon, A A; Mercadante, M E Z; Forni, S; Lôbo, R B; Albuquerque, L G

2010-03-01

The objective of this study was to estimate (co)variance functions using random regression models on Legendre polynomials for the analysis of repeated measures of BW from birth to adult age. A total of 82,064 records from 8,145 females were analyzed. Different models were compared. The models included additive direct and maternal effects, and animal and maternal permanent environmental effects as random terms. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of animal age (cubic regression) were considered as random covariables. Eight models with polynomials of third to sixth order were used to describe additive direct and maternal effects, and animal and maternal permanent environmental effects. Residual effects were modeled using 1 (i.e., assuming homogeneity of variances across all ages) or 5 age classes. The model with 5 classes was the best to describe the trajectory of residuals along the growth curve. The model including fourth- and sixth-order polynomials for additive direct and animal permanent environmental effects, respectively, and third-order polynomials for maternal genetic and maternal permanent environmental effects were the best. Estimates of (co)variance obtained with the multi-trait and random regression models were similar. Direct heritability estimates obtained with the random regression models followed a trend similar to that obtained with the multi-trait model. The largest estimates of maternal heritability were those of BW taken close to 240 d of age. In general, estimates of correlation between BW from birth to 8 yr of age decreased with increasing distance between ages.

Accounting for the dissociating properties of organic chemicals in LCIA: an uncertainty analysis applied to micropollutants in the assessment of freshwater ecotoxicity.

PubMed

Morais, Sérgio Alberto; Delerue-Matos, Cristina; Gabarrell, Xavier

2013-03-15

In life cycle impact assessment (LCIA) models, the sorption of the ionic fraction of dissociating organic chemicals is not adequately modeled because conventional non-polar partitioning models are applied. Therefore, high uncertainties are expected when modeling the mobility, as well as the bioavailability for uptake by exposed biota and degradation, of dissociating organic chemicals. Alternative regressions that account for the ionized fraction of a molecule to estimate fate parameters were applied to the USEtox model. The most sensitive model parameters in the estimation of ecotoxicological characterization factors (CFs) of micropollutants were evaluated by Monte Carlo analysis in both the default USEtox model and the alternative approach. Negligible differences of CFs values and 95% confidence limits between the two approaches were estimated for direct emissions to the freshwater compartment; however the default USEtox model overestimates CFs and the 95% confidence limits of basic compounds up to three orders and four orders of magnitude, respectively, relatively to the alternative approach for emissions to the agricultural soil compartment. For three emission scenarios, LCIA results show that the default USEtox model overestimates freshwater ecotoxicity impacts for the emission scenarios to agricultural soil by one order of magnitude, and larger confidence limits were estimated, relatively to the alternative approach. Copyright © 2013 Elsevier B.V. All rights reserved.

A physics-based fractional order model and state of energy estimation for lithium ion batteries. Part I: Model development and observability analysis

NASA Astrophysics Data System (ADS)

Li, Xiaoyu; Fan, Guodong; Pan, Ke; Wei, Guo; Zhu, Chunbo; Rizzoni, Giorgio; Canova, Marcello

2017-11-01

The design of a lumped parameter battery model preserving physical meaning is especially desired by the automotive researchers and engineers due to the strong demand for battery system control, estimation, diagnosis and prognostics. In light of this, a novel simplified fractional order electrochemical model is developed for electric vehicle (EV) applications in this paper. In the model, a general fractional order transfer function is designed for the solid phase lithium ion diffusion approximation. The dynamic characteristics of the electrolyte concentration overpotential are approximated by a first-order resistance-capacitor transfer function in the electrolyte phase. The Ohmic resistances and electrochemical reaction kinetics resistance are simplified to a lumped Ohmic resistance parameter. Overall, the number of model parameters is reduced from 30 to 9, yet the accuracy of the model is still guaranteed. In order to address the dynamics of phase-change phenomenon in the active particle during charging and discharging, variable solid-state diffusivity is taken into consideration in the model. Also, the observability of the model is analyzed on two types of lithium ion batteries subsequently. Results show the fractional order model with variable solid-state diffusivity agrees very well with experimental data at various current input conditions and is suitable for electric vehicle applications.

The determination of third order linear models from a seventh order nonlinear jet engine model

NASA Technical Reports Server (NTRS)

Lalonde, Rick J.; Hartley, Tom T.; De Abreu-Garcia, J. Alex

1989-01-01

Results are presented that demonstrate how good reduced-order models can be obtained directly by recursive parameter identification using input/output (I/O) data of high-order nonlinear systems. Three different methods of obtaining a third-order linear model from a seventh-order nonlinear turbojet engine model are compared. The first method is to obtain a linear model from the original model and then reduce the linear model by standard reduction techniques such as residualization and balancing. The second method is to identify directly a third-order linear model by recursive least-squares parameter estimation using I/O data of the original model. The third method is to obtain a reduced-order model from the original model and then linearize the reduced model. Frequency responses are used as the performance measure to evaluate the reduced models. The reduced-order models along with their Bode plots are presented for comparison purposes.

A Systematic Approach for Model-Based Aircraft Engine Performance Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2010-01-01

A requirement for effective aircraft engine performance estimation is the ability to account for engine degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. This paper presents a linear point design methodology for minimizing the degradation-induced error in model-based aircraft engine performance estimation applications. The technique specifically focuses on the underdetermined estimation problem, where there are more unknown health parameters than available sensor measurements. A condition for Kalman filter-based estimation is that the number of health parameters estimated cannot exceed the number of sensed measurements. In this paper, the estimated health parameter vector will be replaced by a reduced order tuner vector whose dimension is equivalent to the sensed measurement vector. The reduced order tuner vector is systematically selected to minimize the theoretical mean squared estimation error of a maximum a posteriori estimator formulation. This paper derives theoretical estimation errors at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the estimation accuracy achieved through conventional maximum a posteriori and Kalman filter estimation approaches. Maximum a posteriori estimation results demonstrate that reduced order tuning parameter vectors can be found that approximate the accuracy of estimating all health parameters directly. Kalman filter estimation results based on the same reduced order tuning parameter vectors demonstrate that significantly improved estimation accuracy can be achieved over the conventional approach of selecting a subset of health parameters to serve as the tuner vector. However, additional development is necessary to fully extend the methodology to Kalman filter-based estimation applications.

Tracking Skill Acquisition with Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model with Covariates

ERIC Educational Resources Information Center

Wang, Shiyu; Yang, Yan; Culpepper, Steven Andrew; Douglas, Jeffrey A.

2018-01-01

A family of learning models that integrates a cognitive diagnostic model and a higher-order, hidden Markov model in one framework is proposed. This new framework includes covariates to model skill transition in the learning environment. A Bayesian formulation is adopted to estimate parameters from a learning model. The developed methods are…

Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative

PubMed Central

Tiyip, Tashpolat; Ding, Jianli; Zhang, Dong; Liu, Wei; Wang, Fei; Tashpolat, Nigara

2017-01-01

Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration (Rc2), root mean square errors of calibration (RMSEC), determinant coefficients of prediction (Rp2), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance (Rc2 = 0.907, RMSEC = 0.425%, Rp2 = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance (Rc2 = 0.888, RMSEC = 0.446%, Rp2 = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area. PMID:28934274

Comparing Fit and Reliability Estimates of a Psychological Instrument Using Second-Order CFA, Bifactor, and Essentially Tau-Equivalent (Coefficient Alpha) Models via AMOS 22

ERIC Educational Resources Information Center

Black, Ryan A.; Yang, Yanyun; Beitra, Danette; McCaffrey, Stacey

2015-01-01

Estimation of composite reliability within a hierarchical modeling framework has recently become of particular interest given the growing recognition that the underlying assumptions of coefficient alpha are often untenable. Unfortunately, coefficient alpha remains the prominent estimate of reliability when estimating total scores from a scale with…

Inferring Markov chains: Bayesian estimation, model comparison, entropy rate, and out-of-class modeling.

PubMed

Strelioff, Christopher C; Crutchfield, James P; Hübler, Alfred W

2007-07-01

Markov chains are a natural and well understood tool for describing one-dimensional patterns in time or space. We show how to infer kth order Markov chains, for arbitrary k , from finite data by applying Bayesian methods to both parameter estimation and model-order selection. Extending existing results for multinomial models of discrete data, we connect inference to statistical mechanics through information-theoretic (type theory) techniques. We establish a direct relationship between Bayesian evidence and the partition function which allows for straightforward calculation of the expectation and variance of the conditional relative entropy and the source entropy rate. Finally, we introduce a method that uses finite data-size scaling with model-order comparison to infer the structure of out-of-class processes.

Spline Laplacian estimate of EEG potentials over a realistic magnetic resonance-constructed scalp surface model.

PubMed

Babiloni, F; Babiloni, C; Carducci, F; Fattorini, L; Onorati, P; Urbano, A

1996-04-01

This paper presents a realistic Laplacian (RL) estimator based on a tensorial formulation of the surface Laplacian (SL) that uses the 2-D thin plate spline function to obtain a mathematical description of a realistic scalp surface. Because of this tensorial formulation, the RL does not need an orthogonal reference frame placed on the realistic scalp surface. In simulation experiments the RL was estimated with an increasing number of "electrodes" (up to 256) on a mathematical scalp model, the analytic Laplacian being used as a reference. Second and third order spherical spline Laplacian estimates were examined for comparison. Noise of increasing magnitude and spatial frequency was added to the simulated potential distributions. Movement-related potentials and somatosensory evoked potentials sampled with 128 electrodes were used to estimate the RL on a realistically shaped, MR-constructed model of the subject's scalp surface. The RL was also estimated on a mathematical spherical scalp model computed from the real scalp surface. Simulation experiments showed that the performances of the RL estimator were similar to those of the second and third order spherical spline Laplacians. Furthermore, the information content of scalp-recorded potentials was clearly better when the RL estimator computed the SL of the potential on an MR-constructed scalp surface model.

Exposure reconstruction for the TCDD-exposed NIOSH cohort using a concentration- and age-dependent model of elimination.

PubMed

Aylward, Lesa L; Brunet, Robert C; Starr, Thomas B; Carrier, Gaétan; Delzell, Elizabeth; Cheng, Hong; Beall, Colleen

2005-08-01

Recent studies demonstrating a concentration dependence of elimination of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) suggest that previous estimates of exposure for occupationally exposed cohorts may have underestimated actual exposure, resulting in a potential overestimate of the carcinogenic potency of TCDD in humans based on the mortality data for these cohorts. Using a database on U.S. chemical manufacturing workers potentially exposed to TCDD compiled by the National Institute for Occupational Safety and Health (NIOSH), we evaluated the impact of using a concentration- and age-dependent elimination model (CADM) (Aylward et al., 2005) on estimates of serum lipid area under the curve (AUC) for the NIOSH cohort. These data were used previously by Steenland et al. (2001) in combination with a first-order elimination model with an 8.7-year half-life to estimate cumulative serum lipid concentration (equivalent to AUC) for these workers for use in cancer dose-response assessment. Serum lipid TCDD measurements taken in 1988 for a subset of the cohort were combined with the NIOSH job exposure matrix and work histories to estimate dose rates per unit of exposure score. We evaluated the effect of choices in regression model (regression on untransformed vs. ln-transformed data and inclusion of a nonzero regression intercept) as well as the impact of choices of elimination models and parameters on estimated AUCs for the cohort. Central estimates for dose rate parameters derived from the serum-sampled subcohort were applied with the elimination models to time-specific exposure scores for the entire cohort to generate AUC estimates for all cohort members. Use of the CADM resulted in improved model fits to the serum sampling data compared to the first-order models. Dose rates varied by a factor of 50 among different combinations of elimination model, parameter sets, and regression models. Use of a CADM results in increases of up to five-fold in AUC estimates for the more highly exposed members of the cohort compared to estimates obtained using the first-order model with 8.7-year half-life. This degree of variation in the AUC estimates for this cohort would affect substantially the cancer potency estimates derived from the mortality data from this cohort. Such variability and uncertainty in the reconstructed serum lipid AUC estimates for this cohort, depending on elimination model, parameter set, and regression model, have not been described previously and are critical components in evaluating the dose-response data from the occupationally exposed populations.

Modifying high-order aeroelastic math model of a jet transport using maximum likelihood estimation

NASA Technical Reports Server (NTRS)

Anissipour, Amir A.; Benson, Russell A.

1989-01-01

The design of control laws to damp flexible structural modes requires accurate math models. Unlike the design of control laws for rigid body motion (e.g., where robust control is used to compensate for modeling inaccuracies), structural mode damping usually employs narrow band notch filters. In order to obtain the required accuracy in the math model, maximum likelihood estimation technique is employed to improve the accuracy of the math model using flight data. Presented here are all phases of this methodology: (1) pre-flight analysis (i.e., optimal input signal design for flight test, sensor location determination, model reduction technique, etc.), (2) data collection and preprocessing, and (3) post-flight analysis (i.e., estimation technique and model verification). In addition, a discussion is presented of the software tools used and the need for future study in this field.

Dense motion estimation using regularization constraints on local parametric models.

PubMed

Patras, Ioannis; Worring, Marcel; van den Boomgaard, Rein

2004-11-01

This paper presents a method for dense optical flow estimation in which the motion field within patches that result from an initial intensity segmentation is parametrized with models of different order. We propose a novel formulation which introduces regularization constraints between the model parameters of neighboring patches. In this way, we provide the additional constraints for very small patches and for patches whose intensity variation cannot sufficiently constrain the estimation of their motion parameters. In order to preserve motion discontinuities, we use robust functions as a regularization mean. We adopt a three-frame approach and control the balance between the backward and forward constraints by a real-valued direction field on which regularization constraints are applied. An iterative deterministic relaxation method is employed in order to solve the corresponding optimization problem. Experimental results show that the proposed method deals successfully with motions large in magnitude, motion discontinuities, and produces accurate piecewise-smooth motion fields.

Adaptive Disturbance Tracking Theory with State Estimation and State Feedback for Region II Control of Large Wind Turbines

NASA Technical Reports Server (NTRS)

Balas, Mark J.; Thapa Magar, Kaman S.; Frost, Susan A.

2013-01-01

A theory called Adaptive Disturbance Tracking Control (ADTC) is introduced and used to track the Tip Speed Ratio (TSR) of 5 MW Horizontal Axis Wind Turbine (HAWT). Since ADTC theory requires wind speed information, a wind disturbance generator model is combined with lower order plant model to estimate the wind speed as well as partial states of the wind turbine. In this paper, we present a proof of stability and convergence of ADTC theory with lower order estimator and show that the state feedback can be adaptive.

Estimating distributions with increasing failure rate in an imperfect repair model.

PubMed

Kvam, Paul H; Singh, Harshinder; Whitaker, Lyn R

2002-03-01

A failed system is repaired minimally if after failure, it is restored to the working condition of an identical system of the same age. We extend the nonparametric maximum likelihood estimator (MLE) of a system's lifetime distribution function to test units that are known to have an increasing failure rate. Such items comprise a significant portion of working components in industry. The order-restricted MLE is shown to be consistent. Similar results hold for the Brown-Proschan imperfect repair model, which dictates that a failed component is repaired perfectly with some unknown probability, and is otherwise repaired minimally. The estimators derived are motivated and illustrated by failure data in the nuclear industry. Failure times for groups of emergency diesel generators and motor-driven pumps are analyzed using the order-restricted methods. The order-restricted estimators are consistent and show distinct differences from the ordinary MLEs. Simulation results suggest significant improvement in reliability estimation is available in many cases when component failure data exhibit the IFR property.

A-posteriori error estimation for second order mechanical systems

NASA Astrophysics Data System (ADS)

Ruiner, Thomas; Fehr, Jörg; Haasdonk, Bernard; Eberhard, Peter

2012-06-01

One important issue for the simulation of flexible multibody systems is the reduction of the flexible bodies degrees of freedom. As far as safety questions are concerned knowledge about the error introduced by the reduction of the flexible degrees of freedom is helpful and very important. In this work, an a-posteriori error estimator for linear first order systems is extended for error estimation of mechanical second order systems. Due to the special second order structure of mechanical systems, an improvement of the a-posteriori error estimator is achieved. A major advantage of the a-posteriori error estimator is that the estimator is independent of the used reduction technique. Therefore, it can be used for moment-matching based, Gramian matrices based or modal based model reduction techniques. The capability of the proposed technique is demonstrated by the a-posteriori error estimation of a mechanical system, and a sensitivity analysis of the parameters involved in the error estimation process is conducted.

Bayesian deconvolution of [corrected] fMRI data using bilinear dynamical systems.

PubMed

Makni, Salima; Beckmann, Christian; Smith, Steve; Woolrich, Mark

2008-10-01

In Penny et al. [Penny, W., Ghahramani, Z., Friston, K.J. 2005. Bilinear dynamical systems. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457) 983-993], a particular case of the Linear Dynamical Systems (LDSs) was used to model the dynamic behavior of the BOLD response in functional MRI. This state-space model, called bilinear dynamical system (BDS), is used to deconvolve the fMRI time series in order to estimate the neuronal response induced by the different stimuli of the experimental paradigm. The BDS model parameters are estimated using an expectation-maximization (EM) algorithm proposed by Ghahramani and Hinton [Ghahramani, Z., Hinton, G.E. 1996. Parameter Estimation for Linear Dynamical Systems. Technical Report, Department of Computer Science, University of Toronto]. In this paper we introduce modifications to the BDS model in order to explicitly model the spatial variations of the haemodynamic response function (HRF) in the brain using a non-parametric approach. While in Penny et al. [Penny, W., Ghahramani, Z., Friston, K.J. 2005. Bilinear dynamical systems. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457) 983-993] the relationship between neuronal activation and fMRI signals is formulated as a first-order convolution with a kernel expansion using basis functions (typically two or three), in this paper, we argue in favor of a spatially adaptive GLM in which a local non-parametric estimation of the HRF is performed. Furthermore, in order to overcome the overfitting problem typically associated with simple EM estimates, we propose a full Variational Bayes (VB) solution to infer the BDS model parameters. We demonstrate the usefulness of our model which is able to estimate both the neuronal activity and the haemodynamic response function in every voxel of the brain. We first examine the behavior of this approach when applied to simulated data with different temporal and noise features. As an example we will show how this method can be used to improve interpretability of estimates from an independent component analysis (ICA) analysis of fMRI data. We finally demonstrate its use on real fMRI data in one slice of the brain.

Implementation of Kalman filter algorithm on models reduced using singular pertubation approximation method and its application to measurement of water level

NASA Astrophysics Data System (ADS)

Rachmawati, Vimala; Khusnul Arif, Didik; Adzkiya, Dieky

2018-03-01

The systems contained in the universe often have a large order. Thus, the mathematical model has many state variables that affect the computation time. In addition, generally not all variables are known, so estimations are needed to measure the magnitude of the system that cannot be measured directly. In this paper, we discuss the model reduction and estimation of state variables in the river system to measure the water level. The model reduction of a system is an approximation method of a system with a lower order without significant errors but has a dynamic behaviour that is similar to the original system. The Singular Perturbation Approximation method is one of the model reduction methods where all state variables of the equilibrium system are partitioned into fast and slow modes. Then, The Kalman filter algorithm is used to estimate state variables of stochastic dynamic systems where estimations are computed by predicting state variables based on system dynamics and measurement data. Kalman filters are used to estimate state variables in the original system and reduced system. Then, we compare the estimation results of the state and computational time between the original and reduced system.

Recharge and groundwater models: An overview

USGS Publications Warehouse

Sanford, W.

2002-01-01

Recharge is a fundamental component of groundwater systems, and in groundwater-modeling exercises recharge is either measured and specified or estimated during model calibration. The most appropriate way to represent recharge in a groundwater model depends upon both physical factors and study objectives. Where the water table is close to the land surface, as in humid climates or regions with low topographic relief, a constant-head boundary condition is used. Conversely, where the water table is relatively deep, as in drier climates or regions with high relief, a specified-flux boundary condition is used. In most modeling applications, mixed-type conditions are more effective, or a combination of the different types can be used. The relative distribution of recharge can be estimated from water-level data only, but flux observations must be incorporated in order to estimate rates of recharge. Flux measurements are based on either Darcian velocities (e.g., stream base-flow) or seepage velocities (e.g., groundwater age). In order to estimate the effective porosity independently, both types of flux measurements must be available. Recharge is often estimated more efficiently when automated inverse techniques are used. Other important applications are the delineation of areas contributing recharge to wells and the estimation of paleorecharge rates using carbon-14.

Copula-based analysis of rhythm

NASA Astrophysics Data System (ADS)

García, J. E.; González-López, V. A.; Viola, M. L. Lanfredi

2016-06-01

In this paper we establish stochastic profiles of the rhythm for three languages: English, Japanese and Spanish. We model the increase or decrease of the acoustical energy, collected into three bands coming from the acoustic signal. The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain. In this case the size of the database is not large enough for a consistent estimation of the model. We apply a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consist on obtaining a partition of the state space which is constructed from a combination of the partitions corresponding to the three marginal processes, one for each band of energy, and the partition coming from to the multivariate Markov chain. Then, all the partitions are linked using a copula, in order to estimate the transition probabilities.

Building a kinetic Monte Carlo model with a chosen accuracy.

PubMed

Bhute, Vijesh J; Chatterjee, Abhijit

2013-06-28

The kinetic Monte Carlo (KMC) method is a popular modeling approach for reaching large materials length and time scales. The KMC dynamics is erroneous when atomic processes that are relevant to the dynamics are missing from the KMC model. Recently, we had developed for the first time an error measure for KMC in Bhute and Chatterjee [J. Chem. Phys. 138, 084103 (2013)]. The error measure, which is given in terms of the probability that a missing process will be selected in the correct dynamics, requires estimation of the missing rate. In this work, we present an improved procedure for estimating the missing rate. The estimate found using the new procedure is within an order of magnitude of the correct missing rate, unlike our previous approach where the estimate was larger by orders of magnitude. This enables one to find the error in the KMC model more accurately. In addition, we find the time for which the KMC model can be used before a maximum error in the dynamics has been reached.

Aerodynamic loads on buses due to crosswind gusts: extended analysis

NASA Astrophysics Data System (ADS)

Drugge, Lars; Juhlin, Magnus

2010-12-01

The objective of this work is to use inverse simulations on measured vehicle data in order to estimate the aerodynamic loads on a bus when exposed to crosswind situations. Tyre forces, driver input, wind velocity and vehicle response were measured on a typical coach when subjected to natural crosswind gusts. Based on these measurements and a detailed MBS vehicle model, the aerodynamic loads were estimated through inverse simulations. In order to estimate the lift force, roll and pitch moments in addition to the lateral force and yaw moment, the simulation model was extended by also incorporating the estimation of the vertical road disturbances. The proposed method enables the estimation of aerodynamic loads due to crosswind gusts without using a full scale wind tunnel adapted for crosswind excitation.

Reduced order modeling and active flow control of an inlet duct

NASA Astrophysics Data System (ADS)

Ge, Xiaoqing

Many aerodynamic applications require the modeling of compressible flows in or around a body, e.g., the design of aircraft, inlet or exhaust duct, wind turbines, or tall buildings. Traditional methods use wind tunnel experiments and computational fluid dynamics (CFD) to investigate the spatial and temporal distribution of the flows. Although they provide a great deal of insight into the essential characteristics of the flow field, they are not suitable for control analysis and design due to the high physical/computational cost. Many model reduction methods have been studied to reduce the complexity of the flow model. There are two main approaches: linearization based input/output modeling and proper orthogonal decomposition (POD) based model reduction. The former captures mostly the local behavior near a steady state, which is suitable to model laminar flow dynamics. The latter obtains a reduced order model by projecting the governing equation onto an "optimal" subspace and is able to model complex nonlinear flow phenomena. In this research we investigate various model reduction approaches and compare them in flow modeling and control design. We propose an integrated model-based control methodology and apply it to the reduced order modeling and active flow control of compressible flows within a very aggressive (length to exit diameter ratio, L/D, of 1.5) inlet duct and its upstream contraction section. The approach systematically applies reduced order modeling, estimator design, sensor placement and control design to improve the aerodynamic performance. The main contribution of this work is the development of a hybrid model reduction approach that attempts to combine the best features of input/output model identification and POD method. We first identify a linear input/output model by using a subspace algorithm. We next project the difference between CFD response and the identified model response onto a set of POD basis. This trajectory is fit to a nonlinear dynamical model to augment the linear input/output model. Thus, the full system is decomposed into a dominant linear subsystem and a low order nonlinear subsystem. The hybrid model is then used for control design and compared with other modeling methods in CFD simulations. Numerical results indicate that the hybrid model accurately predicts the nonlinear behavior of the flow for a 2D diffuser contraction section model. It also performs best in terms of feedback control design and learning control. Since some outputs of interest (e.g., the AIP pressure recovery) are not observable during normal operations, static and dynamic estimators are designed to recreate the information from available sensor measurements. The latter also provides a state estimation for feedback controller. Based on the reduced order models and estimators, different controllers are designed to improve the aerodynamic performance of the contraction section and inlet duct. The integrated control methodology is evaluated with CFD simulations. Numerical results demonstrate the feasibility and efficacy of the active flow control based on reduced order models. Our reduced order models not only generate a good approximation of the nonlinear flow dynamics over a wide input range, but also help to design controllers that significantly improve the flow response. The tools developed for model reduction, estimator and control design can also be applied to wind tunnel experiment.

Impact of transverse and longitudinal dispersion on first-order degradation rate constant estimation

NASA Astrophysics Data System (ADS)

Stenback, Greg A.; Ong, Say Kee; Rogers, Shane W.; Kjartanson, Bruce H.

2004-09-01

A two-dimensional analytical model is employed for estimating the first-order degradation rate constant of hydrophobic organic compounds (HOCs) in contaminated groundwater under steady-state conditions. The model may utilize all aqueous concentration data collected downgradient of a source area, but does not require that any data be collected along the plume centerline. Using a least squares fit of the model to aqueous concentrations measured in monitoring wells, degradation rate constants were estimated at a former manufactured gas plant (FMGP) site in the Midwest U.S. The estimated degradation rate constants are 0.0014, 0.0034, 0.0031, 0.0019, and 0.0053 day -1 for acenaphthene, naphthalene, benzene, ethylbenzene, and toluene, respectively. These estimated rate constants were as low as one-half those estimated with the one-dimensional (centerline) approach of Buscheck and Alcantar [Buscheck, T.E., Alcantar, C.M., 1995. Regression techniques and analytical solutions to demonstrate intrinsic bioremediation. In: Hinchee, R.E., Wilson, J.T., Downey, D.C. (Eds.), Intrinsic Bioremediation, Battelle Press, Columbus, OH, pp. 109-116] which does not account for transverse dispersivity. Varying the transverse and longitudinal dispersivity values over one order of magnitude for toluene data obtained from the FMGP site resulted in nearly a threefold variation in the estimated degradation rate constant—highlighting the importance of reliable estimates of the dispersion coefficients for obtaining reasonable estimates of the degradation rate constants. These results have significant implications for decision making and site management where overestimation of a degradation rate may result in remediation times and bioconversion factors that exceed expectations. For a complex source area or non-steady-state plume, a superposition of analytical models that incorporate longitudinal and transverse dispersion and time may be used at sites where the centerline method would not be applicable.

Information-geometric measures as robust estimators of connection strengths and external inputs.

PubMed

Tatsuno, Masami; Fellous, Jean-Marc; Amari, Shun-Ichi

2009-08-01

Information geometry has been suggested to provide a powerful tool for analyzing multineuronal spike trains. Among several advantages of this approach, a significant property is the close link between information-geometric measures and neural network architectures. Previous modeling studies established that the first- and second-order information-geometric measures corresponded to the number of external inputs and the connection strengths of the network, respectively. This relationship was, however, limited to a symmetrically connected network, and the number of neurons used in the parameter estimation of the log-linear model needed to be known. Recently, simulation studies of biophysical model neurons have suggested that information geometry can estimate the relative change of connection strengths and external inputs even with asymmetric connections. Inspired by these studies, we analytically investigated the link between the information-geometric measures and the neural network structure with asymmetrically connected networks of N neurons. We focused on the information-geometric measures of orders one and two, which can be derived from the two-neuron log-linear model, because unlike higher-order measures, they can be easily estimated experimentally. Considering the equilibrium state of a network of binary model neurons that obey stochastic dynamics, we analytically showed that the corrected first- and second-order information-geometric measures provided robust and consistent approximation of the external inputs and connection strengths, respectively. These results suggest that information-geometric measures provide useful insights into the neural network architecture and that they will contribute to the study of system-level neuroscience.

Evaluation of FOFEM fuel loading and consumption estimates in pine-oak forests and woodlands of the Ouachita Mountains, Arkansas, USA

Treesearch

Virginia L. McDaniel; Roger W. Perry; Nancy E. Koerth; James M. Guldin

2016-01-01

Accurate fuel load and consumption predictions are important to estimate fire effects and air pollutant emissions. The FOFEM (First Order Fire Effects Model) is a commonly used model developed in the western United States to estimate fire effects such as fuel consumption, soil heating, air pollutant emissions, and tree mortality. However, the accuracy of the model in...

Copula-based prediction of economic movements

NASA Astrophysics Data System (ADS)

García, J. E.; González-López, V. A.; Hirsh, I. D.

2016-06-01

In this paper we model the discretized returns of two paired time series BM&FBOVESPA Dividend Index and BM&FBOVESPA Public Utilities Index using multivariate Markov models. The discretization corresponds to three categories, high losses, high profits and the complementary periods of the series. In technical terms, the maximal memory that can be considered for a Markov model, can be derived from the size of the alphabet and dataset. The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain. In this case the size of the database is not large enough for a consistent estimation of the model. We apply a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consist on obtaining a partition of the state space which is constructed from a combination, of the partitions corresponding to the two marginal processes and the partition corresponding to the multivariate Markov chain. In order to estimate the transition probabilities, all the partitions are linked using a copula. In our application this strategy provides a significant improvement in the movement predictions.

Ionospheric Modelling using GPS to Calibrate the MWA. I: Comparison of First Order Ionospheric Effects between GPS Models and MWA Observations

NASA Astrophysics Data System (ADS)

Arora, B. S.; Morgan, J.; Ord, S. M.; Tingay, S. J.; Hurley-Walker, N.; Bell, M.; Bernardi, G.; Bhat, N. D. R.; Briggs, F.; Callingham, J. R.; Deshpande, A. A.; Dwarakanath, K. S.; Ewall-Wice, A.; Feng, L.; For, B.-Q.; Hancock, P.; Hazelton, B. J.; Hindson, L.; Jacobs, D.; Johnston-Hollitt, M.; Kapińska, A. D.; Kudryavtseva, N.; Lenc, E.; McKinley, B.; Mitchell, D.; Oberoi, D.; Offringa, A. R.; Pindor, B.; Procopio, P.; Riding, J.; Staveley-Smith, L.; Wayth, R. B.; Wu, C.; Zheng, Q.; Bowman, J. D.; Cappallo, R. J.; Corey, B. E.; Emrich, D.; Goeke, R.; Greenhill, L. J.; Kaplan, D. L.; Kasper, J. C.; Kratzenberg, E.; Lonsdale, C. J.; Lynch, M. J.; McWhirter, S. R.; Morales, M. F.; Morgan, E.; Prabu, T.; Rogers, A. E. E.; Roshi, A.; Shankar, N. Udaya; Srivani, K. S.; Subrahmanyan, R.; Waterson, M.; Webster, R. L.; Whitney, A. R.; Williams, A.; Williams, C. L.

2015-08-01

We compare first-order (refractive) ionospheric effects seen by the MWA with the ionosphere as inferred from GPS data. The first-order ionosphere manifests itself as a bulk position shift of the observed sources across an MWA field of view. These effects can be computed from global ionosphere maps provided by GPS analysis centres, namely the CODE. However, for precision radio astronomy applications, data from local GPS networks needs to be incorporated into ionospheric modelling. For GPS observations, the ionospheric parameters are biased by GPS receiver instrument delays, among other effects, also known as receiver DCBs. The receiver DCBs need to be estimated for any non-CODE GPS station used for ionosphere modelling. In this work, single GPS station-based ionospheric modelling is performed at a time resolution of 10 min. Also the receiver DCBs are estimated for selected Geoscience Australia GPS receivers, located at Murchison Radio Observatory, Yarragadee, Mount Magnet and Wiluna. The ionospheric gradients estimated from GPS are compared with that inferred from MWA. The ionospheric gradients at all the GPS stations show a correlation with the gradients observed with the MWA. The ionosphere estimates obtained using GPS measurements show promise in terms of providing calibration information for the MWA.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models.

PubMed

Bignardi, A B; El Faro, L; Cardoso, V L; Machado, P F; Albuquerque, L G

2009-09-01

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

A generic model for a single strain mosquito-transmitted disease with memory on the host and the vector.

PubMed

Sardar, Tridip; Rana, Sourav; Bhattacharya, Sabyasachi; Al-Khaled, Kamel; Chattopadhyay, Joydev

2015-05-01

In the present investigation, three mathematical models on a common single strain mosquito-transmitted diseases are considered. The first one is based on ordinary differential equations, and other two models are based on fractional order differential equations. The proposed models are validated using published monthly dengue incidence data from two provinces of Venezuela during the period 1999-2002. We estimate several parameters of these models like the order of the fractional derivatives (in case of two fractional order systems), the biting rate of mosquito, two probabilities of infection, mosquito recruitment and mortality rates, etc., from the data. The basic reproduction number, R0, for the ODE system is estimated using the data. For two fractional order systems, an upper bound for, R0, is derived and its value is obtained using the published data. The force of infection, and the effective reproduction number, R(t), for the three models are estimated using the data. Sensitivity analysis of the mosquito memory parameter with some important responses is worked out. We use Akaike Information Criterion (AIC) to identify the best model among the three proposed models. It is observed that the model with memory in both the host, and the vector population provides a better agreement with epidemic data. Finally, we provide a control strategy for the vector-borne disease, dengue, using the memory of the host, and the vector. Copyright © 2015 Elsevier Inc. All rights reserved.

Simple, Efficient Estimators of Treatment Effects in Randomized Trials Using Generalized Linear Models to Leverage Baseline Variables

PubMed Central

Rosenblum, Michael; van der Laan, Mark J.

2010-01-01

Models, such as logistic regression and Poisson regression models, are often used to estimate treatment effects in randomized trials. These models leverage information in variables collected before randomization, in order to obtain more precise estimates of treatment effects. However, there is the danger that model misspecification will lead to bias. We show that certain easy to compute, model-based estimators are asymptotically unbiased even when the working model used is arbitrarily misspecified. Furthermore, these estimators are locally efficient. As a special case of our main result, we consider a simple Poisson working model containing only main terms; in this case, we prove the maximum likelihood estimate of the coefficient corresponding to the treatment variable is an asymptotically unbiased estimator of the marginal log rate ratio, even when the working model is arbitrarily misspecified. This is the log-linear analog of ANCOVA for linear models. Our results demonstrate one application of targeted maximum likelihood estimation. PMID:20628636

Estimation of power lithium-ion battery SOC based on fuzzy optimal decision

NASA Astrophysics Data System (ADS)

He, Dongmei; Hou, Enguang; Qiao, Xin; Liu, Guangmin

2018-06-01

In order to improve vehicle performance and safety, need to accurately estimate the power lithium battery state of charge (SOC), analyzing the common SOC estimation methods, according to the characteristics open circuit voltage and Kalman filter algorithm, using T - S fuzzy model, established a lithium battery SOC estimation method based on the fuzzy optimal decision. Simulation results show that the battery model accuracy can be improved.

Efficient estimation of Pareto model: Some modified percentile estimators.

PubMed

Bhatti, Sajjad Haider; Hussain, Shahzad; Ahmad, Tanvir; Aslam, Muhammad; Aftab, Muhammad; Raza, Muhammad Ali

2018-01-01

The article proposes three modified percentile estimators for parameter estimation of the Pareto distribution. These modifications are based on median, geometric mean and expectation of empirical cumulative distribution function of first-order statistic. The proposed modified estimators are compared with traditional percentile estimators through a Monte Carlo simulation for different parameter combinations with varying sample sizes. Performance of different estimators is assessed in terms of total mean square error and total relative deviation. It is determined that modified percentile estimator based on expectation of empirical cumulative distribution function of first-order statistic provides efficient and precise parameter estimates compared to other estimators considered. The simulation results were further confirmed using two real life examples where maximum likelihood and moment estimators were also considered.

Numerical discretization-based estimation methods for ordinary differential equation models via penalized spline smoothing with applications in biomedical research.

PubMed

Wu, Hulin; Xue, Hongqi; Kumar, Arun

2012-06-01

Differential equations are extensively used for modeling dynamics of physical processes in many scientific fields such as engineering, physics, and biomedical sciences. Parameter estimation of differential equation models is a challenging problem because of high computational cost and high-dimensional parameter space. In this article, we propose a novel class of methods for estimating parameters in ordinary differential equation (ODE) models, which is motivated by HIV dynamics modeling. The new methods exploit the form of numerical discretization algorithms for an ODE solver to formulate estimating equations. First, a penalized-spline approach is employed to estimate the state variables and the estimated state variables are then plugged in a discretization formula of an ODE solver to obtain the ODE parameter estimates via a regression approach. We consider three different order of discretization methods, Euler's method, trapezoidal rule, and Runge-Kutta method. A higher-order numerical algorithm reduces numerical error in the approximation of the derivative, which produces a more accurate estimate, but its computational cost is higher. To balance the computational cost and estimation accuracy, we demonstrate, via simulation studies, that the trapezoidal discretization-based estimate is the best and is recommended for practical use. The asymptotic properties for the proposed numerical discretization-based estimators are established. Comparisons between the proposed methods and existing methods show a clear benefit of the proposed methods in regards to the trade-off between computational cost and estimation accuracy. We apply the proposed methods t an HIV study to further illustrate the usefulness of the proposed approaches. © 2012, The International Biometric Society.

Tree STEM and Canopy Biomass Estimates from Terrestrial Laser Scanning Data

NASA Astrophysics Data System (ADS)

Olofsson, K.; Holmgren, J.

2017-10-01

In this study an automatic method for estimating both the tree stem and the tree canopy biomass is presented. The point cloud tree extraction techniques operate on TLS data and models the biomass using the estimated stem and canopy volume as independent variables. The regression model fit error is of the order of less than 5 kg, which gives a relative model error of about 5 % for the stem estimate and 10-15 % for the spruce and pine canopy biomass estimates. The canopy biomass estimate was improved by separating the models by tree species which indicates that the method is allometry dependent and that the regression models need to be recomputed for different areas with different climate and different vegetation.

First-order kinetic gas generation model parameters for wet landfills.

PubMed

Faour, Ayman A; Reinhart, Debra R; You, Huaxin

2007-01-01

Landfill gas collection data from wet landfill cells were analyzed and first-order gas generation model parameters were estimated for the US EPA landfill gas emissions model (LandGEM). Parameters were determined through statistical comparison of predicted and actual gas collection. The US EPA LandGEM model appeared to fit the data well, provided it is preceded by a lag phase, which on average was 1.5 years. The first-order reaction rate constant, k, and the methane generation potential, L(o), were estimated for a set of landfills with short-term waste placement and long-term gas collection data. Mean and 95% confidence parameter estimates for these data sets were found using mixed-effects model regression followed by bootstrap analysis. The mean values for the specific methane volume produced during the lag phase (V(sto)), L(o), and k were 33 m(3)/Megagrams (Mg), 76 m(3)/Mg, and 0.28 year(-1), respectively. Parameters were also estimated for three full scale wet landfills where waste was placed over many years. The k and L(o) estimated for these landfills were 0.21 year(-1), 115 m(3)/Mg, 0.11 year(-1), 95 m(3)/Mg, and 0.12 year(-1) and 87 m(3)/Mg, respectively. A group of data points from wet landfills cells with short-term data were also analyzed. A conservative set of parameter estimates was suggested based on the upper 95% confidence interval parameters as a k of 0.3 year(-1) and a L(o) of 100 m(3)/Mg if design is optimized and the lag is minimized.

Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates.

PubMed

LeDell, Erin; Petersen, Maya; van der Laan, Mark

In binary classification problems, the area under the ROC curve (AUC) is commonly used to evaluate the performance of a prediction model. Often, it is combined with cross-validation in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an estimate for cross-validated AUC, we obtain an estimate of its variance. For massive data sets, the process of generating a single performance estimate can be computationally expensive. Additionally, when using a complex prediction method, the process of cross-validating a predictive model on even a relatively small data set can still require a large amount of computation time. Thus, in many practical settings, the bootstrap is a computationally intractable approach to variance estimation. As an alternative to the bootstrap, we demonstrate a computationally efficient influence curve based approach to obtaining a variance estimate for cross-validated AUC.

Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates

PubMed Central

Petersen, Maya; van der Laan, Mark

2015-01-01

In binary classification problems, the area under the ROC curve (AUC) is commonly used to evaluate the performance of a prediction model. Often, it is combined with cross-validation in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an estimate for cross-validated AUC, we obtain an estimate of its variance. For massive data sets, the process of generating a single performance estimate can be computationally expensive. Additionally, when using a complex prediction method, the process of cross-validating a predictive model on even a relatively small data set can still require a large amount of computation time. Thus, in many practical settings, the bootstrap is a computationally intractable approach to variance estimation. As an alternative to the bootstrap, we demonstrate a computationally efficient influence curve based approach to obtaining a variance estimate for cross-validated AUC. PMID:26279737

Boundary methods for mode estimation

NASA Astrophysics Data System (ADS)

Pierson, William E., Jr.; Ulug, Batuhan; Ahalt, Stanley C.

1999-08-01

This paper investigates the use of Boundary Methods (BMs), a collection of tools used for distribution analysis, as a method for estimating the number of modes associated with a given data set. Model order information of this type is required by several pattern recognition applications. The BM technique provides a novel approach to this parameter estimation problem and is comparable in terms of both accuracy and computations to other popular mode estimation techniques currently found in the literature and automatic target recognition applications. This paper explains the methodology used in the BM approach to mode estimation. Also, this paper quickly reviews other common mode estimation techniques and describes the empirical investigation used to explore the relationship of the BM technique to other mode estimation techniques. Specifically, the accuracy and computational efficiency of the BM technique are compared quantitatively to the a mixture of Gaussian (MOG) approach and a k-means approach to model order estimation. The stopping criteria of the MOG and k-means techniques is the Akaike Information Criteria (AIC).

Estimating Function Approaches for Spatial Point Processes

NASA Astrophysics Data System (ADS)

Deng, Chong

Spatial point pattern data consist of locations of events that are often of interest in biological and ecological studies. Such data are commonly viewed as a realization from a stochastic process called spatial point process. To fit a parametric spatial point process model to such data, likelihood-based methods have been widely studied. However, while maximum likelihood estimation is often too computationally intensive for Cox and cluster processes, pairwise likelihood methods such as composite likelihood, Palm likelihood usually suffer from the loss of information due to the ignorance of correlation among pairs. For many types of correlated data other than spatial point processes, when likelihood-based approaches are not desirable, estimating functions have been widely used for model fitting. In this dissertation, we explore the estimating function approaches for fitting spatial point process models. These approaches, which are based on the asymptotic optimal estimating function theories, can be used to incorporate the correlation among data and yield more efficient estimators. We conducted a series of studies to demonstrate that these estmating function approaches are good alternatives to balance the trade-off between computation complexity and estimating efficiency. First, we propose a new estimating procedure that improves the efficiency of pairwise composite likelihood method in estimating clustering parameters. Our approach combines estimating functions derived from pairwise composite likeli-hood estimation and estimating functions that account for correlations among the pairwise contributions. Our method can be used to fit a variety of parametric spatial point process models and can yield more efficient estimators for the clustering parameters than pairwise composite likelihood estimation. We demonstrate its efficacy through a simulation study and an application to the longleaf pine data. Second, we further explore the quasi-likelihood approach on fitting second-order intensity function of spatial point processes. However, the original second-order quasi-likelihood is barely feasible due to the intense computation and high memory requirement needed to solve a large linear system. Motivated by the existence of geometric regular patterns in the stationary point processes, we find a lower dimension representation of the optimal weight function and propose a reduced second-order quasi-likelihood approach. Through a simulation study, we show that the proposed method not only demonstrates superior performance in fitting the clustering parameter but also merits in the relaxation of the constraint of the tuning parameter, H. Third, we studied the quasi-likelihood type estimating funciton that is optimal in a certain class of first-order estimating functions for estimating the regression parameter in spatial point process models. Then, by using a novel spectral representation, we construct an implementation that is computationally much more efficient and can be applied to more general setup than the original quasi-likelihood method.

Autonomous frequency domain identification: Theory and experiment

NASA Technical Reports Server (NTRS)

Yam, Yeung; Bayard, D. S.; Hadaegh, F. Y.; Mettler, E.; Milman, M. H.; Scheid, R. E.

1989-01-01

The analysis, design, and on-orbit tuning of robust controllers require more information about the plant than simply a nominal estimate of the plant transfer function. Information is also required concerning the uncertainty in the nominal estimate, or more generally, the identification of a model set within which the true plant is known to lie. The identification methodology that was developed and experimentally demonstrated makes use of a simple but useful characterization of the model uncertainty based on the output error. This is a characterization of the additive uncertainty in the plant model, which has found considerable use in many robust control analysis and synthesis techniques. The identification process is initiated by a stochastic input u which is applied to the plant p giving rise to the output. Spectral estimation (h = P sub uy/P sub uu) is used as an estimate of p and the model order is estimated using the produce moment matrix (PMM) method. A parametric model unit direction vector p is then determined by curve fitting the spectral estimate to a rational transfer function. The additive uncertainty delta sub m = p - unit direction vector p is then estimated by the cross spectral estimate delta = P sub ue/P sub uu where e = y - unit direction vectory y is the output error, and unit direction vector y = unit direction vector pu is the computed output of the parametric model subjected to the actual input u. The experimental results demonstrate the curve fitting algorithm produces the reduced-order plant model which minimizes the additive uncertainty. The nominal transfer function estimate unit direction vector p and the estimate delta of the additive uncertainty delta sub m are subsequently available to be used for optimization of robust controller performance and stability.

Model predictive control based on reduced order models applied to belt conveyor system.

PubMed

Chen, Wei; Li, Xin

2016-11-01

In the paper, a model predictive controller based on reduced order model is proposed to control belt conveyor system, which is an electro-mechanics complex system with long visco-elastic body. Firstly, in order to design low-degree controller, the balanced truncation method is used for belt conveyor model reduction. Secondly, MPC algorithm based on reduced order model for belt conveyor system is presented. Because of the error bound between the full-order model and reduced order model, two Kalman state estimators are applied in the control scheme to achieve better system performance. Finally, the simulation experiments are shown that balanced truncation method can significantly reduce the model order with high-accuracy and model predictive control based on reduced-model performs well in controlling the belt conveyor system. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Nonparametric Model of Smooth Muscle Force Production During Electrical Stimulation.

PubMed

Cole, Marc; Eikenberry, Steffen; Kato, Takahide; Sandler, Roman A; Yamashiro, Stanley M; Marmarelis, Vasilis Z

2017-03-01

A nonparametric model of smooth muscle tension response to electrical stimulation was estimated using the Laguerre expansion technique of nonlinear system kernel estimation. The experimental data consisted of force responses of smooth muscle to energy-matched alternating single pulse and burst current stimuli. The burst stimuli led to at least a 10-fold increase in peak force in smooth muscle from Mytilus edulis, despite the constant energy constraint. A linear model did not fit the data. However, a second-order model fit the data accurately, so the higher-order models were not required to fit the data. Results showed that smooth muscle force response is not linearly related to the stimulation power.

Using Heteroskedastic Ordered Probit Models to Recover Moments of Continuous Test Score Distributions from Coarsened Data

ERIC Educational Resources Information Center

Reardon, Sean F.; Shear, Benjamin R.; Castellano, Katherine E.; Ho, Andrew D.

2017-01-01

Test score distributions of schools or demographic groups are often summarized by frequencies of students scoring in a small number of ordered proficiency categories. We show that heteroskedastic ordered probit (HETOP) models can be used to estimate means and standard deviations of multiple groups' test score distributions from such data. Because…

Estimation of real-time runway surface contamination using flight data recorder parameters

NASA Astrophysics Data System (ADS)

Curry, Donovan

Within this research effort, the development of an analytic process for friction coefficient estimation is presented. Under static equilibrium, the sum of forces and moments acting on the aircraft, in the aircraft body coordinate system, while on the ground at any instant is equal to zero. Under this premise the longitudinal, lateral and normal forces due to landing are calculated along with the individual deceleration components existent when an aircraft comes to a rest during ground roll. In order to validate this hypothesis a six degree of freedom aircraft model had to be created and landing tests had to be simulated on different surfaces. The simulated aircraft model includes a high fidelity aerodynamic model, thrust model, landing gear model, friction model and antiskid model. Three main surfaces were defined in the friction model; dry, wet and snow/ice. Only the parameters recorded by an FDR are used directly from the aircraft model all others are estimated or known a priori. The estimation of unknown parameters is also presented in the research effort. With all needed parameters a comparison and validation with simulated and estimated data, under different runway conditions, is performed. Finally, this report presents results of a sensitivity analysis in order to provide a measure of reliability of the analytic estimation process. Linear and non-linear sensitivity analysis has been performed in order to quantify the level of uncertainty implicit in modeling estimated parameters and how they can affect the calculation of the instantaneous coefficient of friction. Using the approach of force and moment equilibrium about the CG at landing to reconstruct the instantaneous coefficient of friction appears to be a reasonably accurate estimate when compared to the simulated friction coefficient. This is also true when the FDR and estimated parameters are introduced to white noise and when crosswind is introduced to the simulation. After the linear analysis the results show the minimum frequency at which the algorithm still provides moderately accurate data is at 2Hz. In addition, the linear analysis shows that with estimated parameters increased and decreased up to 25% at random, high priority parameters have to be accurate to within at least +/-5% to have an effect of less than 1% change in the average coefficient of friction. Non-linear analysis results show that the algorithm can be considered reasonably accurate for all simulated cases when inaccuracies in the estimated parameters vary randomly and simultaneously up to +/-27%. At worst-case the maximum percentage change in average coefficient of friction is less than 10% for all surfaces.

Physiological responses at five estimates of critical velocity.

PubMed

Bull, Anthony J; Housh, Terry J; Johnson, Glen O; Rana, Sharon R

2008-04-01

The purpose of this study was to compare critical velocity (CV) estimates from five mathematical models, and to examine the oxygen uptake (VO(2)) and heart rate (HR) responses during treadmill runs at the five estimates of CV. Ten subjects (six males and four females) performed one incremental test to determine maximal oxygen consumption (VO(2max)) and four or five randomly ordered constant-velocity trials on a treadmill for the estimation of CV. Five mathematical models were used to estimate CV for each subject including two linear, two nonlinear, and an exponential model. Up to five randomly ordered runs to exhaustion were performed by each subject at treadmill velocities that corresponded to the five CV estimates, and VO(2) and HR responses were monitored throughout each trial. The 3-parameter, nonlinear (Non-3) model produced CV estimates that were significantly (P < 0.05) less than the other four models. During runs at CV estimates, five subjects did not complete 60 min at the their estimate from the Non-3 model, nine did not complete 60 min at their estimate from the Non-2 model, and no subjects completed 60 min at any estimate from the other three models. The mean HR value (179 +/- 18 beats min(-1), HR(peak)) at the end of runs at CV using the Non-3 model was significantly less than the maximal HR (195 +/- 7 beats min(-1), HR(max)) achieved during the incremental trial to exhaustion. However, mean HR(peak) values from runs at all other CV estimates were not significantly different from HR(max). Furthermore, data indicated that mean HR(peak) values increased during runs at CV estimates from the third minute to the end of exercise for all models, and that these increases in VO(2) (range = 367-458 ml min(-1)) were significantly greater than that typically associated with O(2) drift ( approximately 200 ml min(-1)) for all but the exponential model, indicating a VO(2) slow component associated with CV estimates from four of the five models. However, the mean VO(2) values at the end of exercise during the runs at CV estimates for all five mathematical models were significantly less than the mean VO(2max) value. These results suggest that, in most cases, CV estimated from the five models does not represent a fatigueless task. In addition, the mean CV estimates from the five models varied by 18%, and four of the five mean CV estimates were within the heavy exercise domain. Therefore, CV would not represent the demarcation point between heavy and severe exercise domains.

True covariance simulation of the EUVE update filter

NASA Technical Reports Server (NTRS)

Bar-Itzhack, Itzhack Y.; Harman, R. R.

1989-01-01

A covariance analysis of the performance and sensitivity of the attitude determination Extended Kalman Filter (EKF) used by the On Board Computer (OBC) of the Extreme Ultra Violet Explorer (EUVE) spacecraft is presented. The linearized dynamics and measurement equations of the error states are derived which constitute the truth model describing the real behavior of the systems involved. The design model used by the OBC EKF is then obtained by reducing the order of the truth model. The covariance matrix of the EKF which uses the reduced order model is not the correct covariance of the EKF estimation error. A true covariance analysis has to be carried out in order to evaluate the correct accuracy of the OBC generated estimates. The results of such analysis are presented which indicate both the performance and the sensitivity of the OBC EKF.

“A Reduced-form Model to Estimate Near-road Air Quality for Communities: the Community Line Source modeling system (C-LINE)”

EPA Science Inventory

The paper presents the Community Line Source (C-LINE) modeling system that estimates toxic air pollutant (air toxics) concentration gradients within 500 meters of busy roadways for community-sized areas on the order of 100 km2. C-LINE accesses publicly available datasets with nat...

A simplified parsimonious higher order multivariate Markov chain model with new convergence condition

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a simplified parsimonious higher-order multivariate Markov chain model with new convergence condition. (TPHOMMCM-NCC). Moreover, estimation method of the parameters in TPHOMMCM-NCC is give. Numerical experiments illustrate the effectiveness of TPHOMMCM-NCC.

Lateral-Directional Parameter Estimation on the X-48B Aircraft Using an Abstracted, Multi-Objective Effector Model

NASA Technical Reports Server (NTRS)

Ratnayake, Nalin A.; Waggoner, Erin R.; Taylor, Brian R.

2011-01-01

The problem of parameter estimation on hybrid-wing-body aircraft is complicated by the fact that many design candidates for such aircraft involve a large number of aerodynamic control effectors that act in coplanar motion. This adds to the complexity already present in the parameter estimation problem for any aircraft with a closed-loop control system. Decorrelation of flight and simulation data must be performed in order to ascertain individual surface derivatives with any sort of mathematical confidence. Non-standard control surface configurations, such as clamshell surfaces and drag-rudder modes, further complicate the modeling task. In this paper, time-decorrelation techniques are applied to a model structure selected through stepwise regression for simulated and flight-generated lateral-directional parameter estimation data. A virtual effector model that uses mathematical abstractions to describe the multi-axis effects of clamshell surfaces is developed and applied. Comparisons are made between time history reconstructions and observed data in order to assess the accuracy of the regression model. The Cram r-Rao lower bounds of the estimated parameters are used to assess the uncertainty of the regression model relative to alternative models. Stepwise regression was found to be a useful technique for lateral-directional model design for hybrid-wing-body aircraft, as suggested by available flight data. Based on the results of this study, linear regression parameter estimation methods using abstracted effectors are expected to perform well for hybrid-wing-body aircraft properly equipped for the task.

High-Order Model and Dynamic Filtering for Frame Rate Up-Conversion.

PubMed

Bao, Wenbo; Zhang, Xiaoyun; Chen, Li; Ding, Lianghui; Gao, Zhiyong

2018-08-01

This paper proposes a novel frame rate up-conversion method through high-order model and dynamic filtering (HOMDF) for video pixels. Unlike the constant brightness and linear motion assumptions in traditional methods, the intensity and position of the video pixels are both modeled with high-order polynomials in terms of time. Then, the key problem of our method is to estimate the polynomial coefficients that represent the pixel's intensity variation, velocity, and acceleration. We propose to solve it with two energy objectives: one minimizes the auto-regressive prediction error of intensity variation by its past samples, and the other minimizes video frame's reconstruction error along the motion trajectory. To efficiently address the optimization problem for these coefficients, we propose the dynamic filtering solution inspired by video's temporal coherence. The optimal estimation of these coefficients is reformulated into a dynamic fusion of the prior estimate from pixel's temporal predecessor and the maximum likelihood estimate from current new observation. Finally, frame rate up-conversion is implemented using motion-compensated interpolation by pixel-wise intensity variation and motion trajectory. Benefited from the advanced model and dynamic filtering, the interpolated frame has much better visual quality. Extensive experiments on the natural and synthesized videos demonstrate the superiority of HOMDF over the state-of-the-art methods in both subjective and objective comparisons.

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

Phillips, William Scott

This seminar presentation describes amplitude models and yield estimations that look at the data in order to inform legislation. The following points were brought forth in the summary: global models that will predict three-component amplitudes (R-T-Z) were produced; Q models match regional geology; corrected source spectra can be used for discrimination and yield estimation; three-component data increase coverage and reduce scatter in source spectral estimates; three-component efforts must include distance-dependent effects; a community effort on instrument calibration is needed.

Sparse Method for Direction of Arrival Estimation Using Denoised Fourth-Order Cumulants Vector.

PubMed

Fan, Yangyu; Wang, Jianshu; Du, Rui; Lv, Guoyun

2018-06-04

Fourth-order cumulants (FOCs) vector-based direction of arrival (DOA) estimation methods of non-Gaussian sources may suffer from poor performance for limited snapshots or difficulty in setting parameters. In this paper, a novel FOCs vector-based sparse DOA estimation method is proposed. Firstly, by utilizing the concept of a fourth-order difference co-array (FODCA), an advanced FOCs vector denoising or dimension reduction procedure is presented for arbitrary array geometries. Then, a novel single measurement vector (SMV) model is established by the denoised FOCs vector, and efficiently solved by an off-grid sparse Bayesian inference (OGSBI) method. The estimation errors of FOCs are integrated in the SMV model, and are approximately estimated in a simple way. A necessary condition regarding the number of identifiable sources of our method is presented that, in order to uniquely identify all sources, the number of sources K must fulfill K ≤ ( M 4 - 2 M 3 + 7 M 2 - 6 M ) / 8 . The proposed method suits any geometry, does not need prior knowledge of the number of sources, is insensitive to associated parameters, and has maximum identifiability O ( M 4 ) , where M is the number of sensors in the array. Numerical simulations illustrate the superior performance of the proposed method.

An extended Kalman filter approach to non-stationary Bayesian estimation of reduced-order vocal fold model parameters.

PubMed

Hadwin, Paul J; Peterson, Sean D

2017-04-01

The Bayesian framework for parameter inference provides a basis from which subject-specific reduced-order vocal fold models can be generated. Previously, it has been shown that a particle filter technique is capable of producing estimates and associated credibility intervals of time-varying reduced-order vocal fold model parameters. However, the particle filter approach is difficult to implement and has a high computational cost, which can be barriers to clinical adoption. This work presents an alternative estimation strategy based upon Kalman filtering aimed at reducing the computational cost of subject-specific model development. The robustness of this approach to Gaussian and non-Gaussian noise is discussed. The extended Kalman filter (EKF) approach is found to perform very well in comparison with the particle filter technique at dramatically lower computational cost. Based upon the test cases explored, the EKF is comparable in terms of accuracy to the particle filter technique when greater than 6000 particles are employed; if less particles are employed, the EKF actually performs better. For comparable levels of accuracy, the solution time is reduced by 2 orders of magnitude when employing the EKF. By virtue of the approximations used in the EKF, however, the credibility intervals tend to be slightly underpredicted.

Using CV-GLUE procedure in analysis of wetland model predictive uncertainty.

PubMed

Huang, Chun-Wei; Lin, Yu-Pin; Chiang, Li-Chi; Wang, Yung-Chieh

2014-07-01

This study develops a procedure that is related to Generalized Likelihood Uncertainty Estimation (GLUE), called the CV-GLUE procedure, for assessing the predictive uncertainty that is associated with different model structures with varying degrees of complexity. The proposed procedure comprises model calibration, validation, and predictive uncertainty estimation in terms of a characteristic coefficient of variation (characteristic CV). The procedure first performed two-stage Monte-Carlo simulations to ensure predictive accuracy by obtaining behavior parameter sets, and then the estimation of CV-values of the model outcomes, which represent the predictive uncertainties for a model structure of interest with its associated behavior parameter sets. Three commonly used wetland models (the first-order K-C model, the plug flow with dispersion model, and the Wetland Water Quality Model; WWQM) were compared based on data that were collected from a free water surface constructed wetland with paddy cultivation in Taipei, Taiwan. The results show that the first-order K-C model, which is simpler than the other two models, has greater predictive uncertainty. This finding shows that predictive uncertainty does not necessarily increase with the complexity of the model structure because in this case, the more simplistic representation (first-order K-C model) of reality results in a higher uncertainty in the prediction made by the model. The CV-GLUE procedure is suggested to be a useful tool not only for designing constructed wetlands but also for other aspects of environmental management. Copyright © 2014 Elsevier Ltd. All rights reserved.

Distributed Damage Estimation for Prognostics based on Structural Model Decomposition

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

2011-01-01

Model-based prognostics approaches capture system knowledge in the form of physics-based models of components, and how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decomposition approach adapted from the diagnosis community, called possible conflicts, in order to both improve the computational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state estimate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the approach.

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.

Development of collision dynamics models to estimate the results of full-scale rail vehicle impact tests : Tufts University Master's Thesis

DOT National Transportation Integrated Search

2000-11-01

In an effort to study occupant survivability in train collisions, analyses and tests were conducted to understand and improve the crashworthiness of rail vehicles. A collision dynamics model was developed in order to estimate the rigid body motion of...

Using Web-based Interspecies Correlation Estimation (Web-ICE) models as a tool for acute toxicity prediction

EPA Science Inventory

In order to assess risk of contaminants to taxa with limited or no toxicity data available, Interspecies Correlation Estimation (ICE) models have been developed by the U.S. Environmental Protection Agency to extrapolate contaminant sensitivity predictions based on data from commo...

Error associated with a reduced order linear model of a spur gear pair

NASA Technical Reports Server (NTRS)

Kahraman, A.; Singh, R.

1991-01-01

The paper proposes a reduced-order analytical model of a spur gear pair which consists of two identical spur gears, two identical flexible shafts, and four identical rolling element bearings of a given radial stiffness. The error associated with the undamped eigensolution is estimated by a comparison with a benchmark finite element model.

Closed-Loop System Identification Experience for Flight Control Law and Flying Qualities Evaluation of a High Performance Fighter Aircraft

NASA Technical Reports Server (NTRS)

Murphy, Patrick C.

1999-01-01

This paper highlights some of the results and issues associated with estimating models to evaluate control law design methods and design criteria for advanced high performance aircraft. Experimental fighter aircraft such as the NASA High Alpha Research Vehicle (HARV) have the capability to maneuver at very high angles of attack where nonlinear aerodynamics often predominate. HARV is an experimental F/A-18, configured with thrust vectoring and conformal actuated nose strakes. Identifying closed-loop models for this type of aircraft can be made difficult by nonlinearities and high-order characteristics of the system. In this paper only lateral-directional axes are considered since the lateral-directional control law was specifically designed to produce classical airplane responses normally expected with low-order, rigid-body systems. Evaluation of the control design methodology was made using low-order equivalent systems determined from flight and simulation. This allowed comparison of the closed-loop rigid-body dynamics achieved in flight with that designed in simulation. In flight, the On Board Excitation System was used to apply optimal inputs to lateral stick and pedals at five angles of attack: 5, 20, 30, 45, and 60 degrees. Data analysis and closed-loop model identification were done using frequency domain maximum likelihood. The structure of the identified models was a linear state-space model reflecting classical 4th-order airplane dynamics. Input time delays associated with the high-order controller and aircraft system were accounted for in data preprocessing. A comparison of flight estimated models with small perturbation linear design models highlighted nonlinearities in the system and indicated that the estimated closed-loop rigid-body dynamics were sensitive to input amplitudes at 20 and 30 degrees angle of attack.

Blow-up for a three dimensional Keller-Segel model with consumption of chemoattractant

NASA Astrophysics Data System (ADS)

Jiang, Jie; Wu, Hao; Zheng, Songmu

2018-04-01

We investigate blow-up properties for the initial-boundary value problem of a Keller-Segel model with consumption of chemoattractant when the spatial dimension is three. Through a kinetic reformulation of the Keller-Segel system, we first derive some higher-order estimates and obtain certain blow-up criteria for the local classical solutions. These blow-up criteria generalize the results in [4,5] from the whole space R3 to the case of bounded smooth domain Ω ⊂R3. Lower global blow-up estimate on ‖ n ‖ L∞ (Ω) is also obtained based on our higher-order estimates. Moreover, we prove local non-degeneracy for blow-up points.

Using Count Data and Ordered Models in National Forest Recreation Demand Analysis

NASA Astrophysics Data System (ADS)

Simões, Paula; Barata, Eduardo; Cruz, Luis

2013-11-01

This research addresses the need to improve our knowledge on the demand for national forests for recreation and offers an in-depth data analysis supported by the complementary use of count data and ordered models. From a policy-making perspective, while count data models enable the estimation of monetary welfare measures, ordered models allow for the wider use of the database and provide a more flexible analysis of data. The main purpose of this article is to analyse the individual forest recreation demand and to derive a measure of its current use value. To allow a more complete analysis of the forest recreation demand structure the econometric approach supplements the use of count data models with ordered category models using data obtained by means of an on-site survey in the Bussaco National Forest (Portugal). Overall, both models reveal that travel cost and substitute prices are important explanatory variables, visits are a normal good and demographic variables seem to have no influence on demand. In particular, estimated price and income elasticities of demand are quite low. Accordingly, it is possible to argue that travel cost (price) in isolation may be expected to have a low impact on visitation levels.

A Reduced-Order Successive Linear Estimator for Geostatistical Inversion and its Application in Hydraulic Tomography

NASA Astrophysics Data System (ADS)

Zha, Yuanyuan; Yeh, Tian-Chyi J.; Illman, Walter A.; Zeng, Wenzhi; Zhang, Yonggen; Sun, Fangqiang; Shi, Liangsheng

2018-03-01

Hydraulic tomography (HT) is a recently developed technology for characterizing high-resolution, site-specific heterogeneity using hydraulic data (nd) from a series of cross-hole pumping tests. To properly account for the subsurface heterogeneity and to flexibly incorporate additional information, geostatistical inverse models, which permit a large number of spatially correlated unknowns (ny), are frequently used to interpret the collected data. However, the memory storage requirements for the covariance of the unknowns (ny × ny) in these models are prodigious for large-scale 3-D problems. Moreover, the sensitivity evaluation is often computationally intensive using traditional difference method (ny forward runs). Although employment of the adjoint method can reduce the cost to nd forward runs, the adjoint model requires intrusive coding effort. In order to resolve these issues, this paper presents a Reduced-Order Successive Linear Estimator (ROSLE) for analyzing HT data. This new estimator approximates the covariance of the unknowns using Karhunen-Loeve Expansion (KLE) truncated to nkl order, and it calculates the directional sensitivities (in the directions of nkl eigenvectors) to form the covariance and cross-covariance used in the Successive Linear Estimator (SLE). In addition, the covariance of unknowns is updated every iteration by updating the eigenvalues and eigenfunctions. The computational advantages of the proposed algorithm are demonstrated through numerical experiments and a 3-D transient HT analysis of data from a highly heterogeneous field site.

Latent transition models with latent class predictors: attention deficit hyperactivity disorder subtypes and high school marijuana use

PubMed Central

Reboussin, Beth A.; Ialongo, Nicholas S.

2011-01-01

Summary Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder which is most often diagnosed in childhood with symptoms often persisting into adulthood. Elevated rates of substance use disorders have been evidenced among those with ADHD, but recent research focusing on the relationship between subtypes of ADHD and specific drugs is inconsistent. We propose a latent transition model (LTM) to guide our understanding of how drug use progresses, in particular marijuana use, while accounting for the measurement error that is often found in self-reported substance use data. We extend the LTM to include a latent class predictor to represent empirically derived ADHD subtypes that do not rely on meeting specific diagnostic criteria. We begin by fitting two separate latent class analysis (LCA) models by using second-order estimating equations: a longitudinal LCA model to define stages of marijuana use, and a cross-sectional LCA model to define ADHD subtypes. The LTM model parameters describing the probability of transitioning between the LCA-defined stages of marijuana use and the influence of the LCA-defined ADHD subtypes on these transition rates are then estimated by using a set of first-order estimating equations given the LCA parameter estimates. A robust estimate of the LTM parameter variance that accounts for the variation due to the estimation of the two sets of LCA parameters is proposed. Solving three sets of estimating equations enables us to determine the underlying latent class structures independently of the model for the transition rates and simplifying assumptions about the correlation structure at each stage reduces the computational complexity. PMID:21461139

Building unbiased estimators from non-gaussian likelihoods with application to shear estimation

DOE PAGES

Madhavacheril, Mathew S.; McDonald, Patrick; Sehgal, Neelima; ...

2015-01-15

We develop a general framework for generating estimators of a given quantity which are unbiased to a given order in the difference between the true value of the underlying quantity and the fiducial position in theory space around which we expand the likelihood. We apply this formalism to rederive the optimal quadratic estimator and show how the replacement of the second derivative matrix with the Fisher matrix is a generic way of creating an unbiased estimator (assuming choice of the fiducial model is independent of data). Next we apply the approach to estimation of shear lensing, closely following the workmore » of Bernstein and Armstrong (2014). Our first order estimator reduces to their estimator in the limit of zero shear, but it also naturally allows for the case of non-constant shear and the easy calculation of correlation functions or power spectra using standard methods. Both our first-order estimator and Bernstein and Armstrong’s estimator exhibit a bias which is quadratic in true shear. Our third-order estimator is, at least in the realm of the toy problem of Bernstein and Armstrong, unbiased to 0.1% in relative shear errors Δg/g for shears up to |g| = 0.2.« less

Building unbiased estimators from non-Gaussian likelihoods with application to shear estimation

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

Madhavacheril, Mathew S.; Sehgal, Neelima; McDonald, Patrick

2015-01-01

We develop a general framework for generating estimators of a given quantity which are unbiased to a given order in the difference between the true value of the underlying quantity and the fiducial position in theory space around which we expand the likelihood. We apply this formalism to rederive the optimal quadratic estimator and show how the replacement of the second derivative matrix with the Fisher matrix is a generic way of creating an unbiased estimator (assuming choice of the fiducial model is independent of data). Next we apply the approach to estimation of shear lensing, closely following the workmore » of Bernstein and Armstrong (2014). Our first order estimator reduces to their estimator in the limit of zero shear, but it also naturally allows for the case of non-constant shear and the easy calculation of correlation functions or power spectra using standard methods. Both our first-order estimator and Bernstein and Armstrong's estimator exhibit a bias which is quadratic in true shear. Our third-order estimator is, at least in the realm of the toy problem of Bernstein and Armstrong, unbiased to 0.1% in relative shear errors Δg/g for shears up to |g|=0.2.« less

Concurrent hyperthermia estimation schemes based on extended Kalman filtering and reduced-order modelling.

PubMed

Potocki, J K; Tharp, H S

1993-01-01

The success of treating cancerous tissue with heat depends on the temperature elevation, the amount of tissue elevated to that temperature, and the length of time that the tissue temperature is elevated. In clinical situations the temperature of most of the treated tissue volume is unknown, because only a small number of temperature sensors can be inserted into the tissue. A state space model based on a finite difference approximation of the bioheat transfer equation (BHTE) is developed for identification purposes. A full-order extended Kalman filter (EKF) is designed to estimate both the unknown blood perfusion parameters and the temperature at unmeasured locations. Two reduced-order estimators are designed as computationally less intensive alternatives to the full-order EKF. Simulation results show that the success of the estimation scheme depends strongly on the number and location of the temperature sensors. Superior results occur when a temperature sensor exists in each unknown blood perfusion zone, and the number of sensors is at least as large as the number of unknown perfusion zones. Unacceptable results occur when there are more unknown perfusion parameters than temperature sensors, or when the sensors are placed in locations that do not sample the unknown perfusion information.

Genetic parameters for test day somatic cell score in Brazilian Holstein cattle.

PubMed

Costa, C N; Santos, G G; Cobuci, J A; Thompson, G; Carvalheira, J G V

2015-12-29

Selection for lower somatic cell count has been included in the breeding objectives of several countries in order to increase resistance to mastitis. Genetic parameters of somatic cell scores (SCS) were estimated from the first lactation test day records of Brazilian Holstein cows using random-regression models with Legendre polynomials (LP) of the order 3-5. Data consisted of 87,711 TD produced by 10,084 cows, sired by 619 bulls calved from 1993 to 2007. Heritability estimates varied from 0.06 to 0.14 and decreased from the beginning of the lactation up to 60 days in milk (DIM) and increased thereafter to the end of lactation. Genetic correlations between adjacent DIM were very high (>0.83) but decreased to negative values, obtained with LP of order four, between DIM in the extremes of lactation. Despite the favorable trend, genetic changes in SCS were not significant and did not differ among LP. There was little benefit of fitting an LP of an order >3 to model animal genetic and permanent environment effects for SCS. Estimates of variance components found in this study may be used for breeding value estimation for SCS and selection for mastitis resistance in Holstein cattle in Brazil.

Genetic analysis of partial egg production records in Japanese quail using random regression models.

PubMed

Abou Khadiga, G; Mahmoud, B Y F; Farahat, G S; Emam, A M; El-Full, E A

2017-08-01

The main objectives of this study were to detect the most appropriate random regression model (RRM) to fit the data of monthly egg production in 2 lines (selected and control) of Japanese quail and to test the consistency of different criteria of model choice. Data from 1,200 female Japanese quails for the first 5 months of egg production from 4 consecutive generations of an egg line selected for egg production in the first month (EP1) was analyzed. Eight RRMs with different orders of Legendre polynomials were compared to determine the proper model for analysis. All criteria of model choice suggested that the adequate model included the second-order Legendre polynomials for fixed effects, and the third-order for additive genetic effects and permanent environmental effects. Predictive ability of the best model was the highest among all models (ρ = 0.987). According to the best model fitted to the data, estimates of heritability were relatively low to moderate (0.10 to 0.17) showed a descending pattern from the first to the fifth month of production. A similar pattern was observed for permanent environmental effects with greater estimates in the first (0.36) and second (0.23) months of production than heritability estimates. Genetic correlations between separate production periods were higher (0.18 to 0.93) than their phenotypic counterparts (0.15 to 0.87). The superiority of the selected line over the control was observed through significant (P < 0.05) linear contrast estimates. Significant (P < 0.05) estimates of covariate effect (age at sexual maturity) showed a decreased pattern with greater impact on egg production in earlier ages (first and second months) than later ones. A methodology based on random regression animal models can be recommended for genetic evaluation of egg production in Japanese quail. © 2017 Poultry Science Association Inc.

A new model for estimating total body water from bioelectrical resistance

NASA Technical Reports Server (NTRS)

Siconolfi, S. F.; Kear, K. T.

1992-01-01

Estimation of total body water (T) from bioelectrical resistance (R) is commonly done by stepwise regression models with height squared over R, H(exp 2)/R, age, sex, and weight (W). Polynomials of H(exp 2)/R have not been included in these models. We examined the validity of a model with third order polynomials and W. Methods: T was measured with oxygen-18 labled water in 27 subjects. R at 50 kHz was obtained from electrodes placed on the hand and foot while subjects were in the supine position. A stepwise regression equation was developed with 13 subjects (age 31.5 plus or minus 6.2 years, T 38.2 plus or minus 6.6 L, W 65.2 plus or minus 12.0 kg). Correlations, standard error of estimates and mean differences were computed between T and estimated T's from the new (N) model and other models. Evaluations were completed with the remaining 14 subjects (age 32.4 plus or minus 6.3 years, T 40.3 plus or minus 8 L, W 70.2 plus or minus 12.3 kg) and two of its subgroups (high and low) Results: A regression equation was developed from the model. The only significant mean difference was between T and one of the earlier models. Conclusion: Third order polynomials in regression models may increase the accuracy of estimating total body water. Evaluating the model with a larger population is needed.

An Assessment of Global Organic Carbon Flux Along Continental Margins

NASA Technical Reports Server (NTRS)

Thunell, Robert

2004-01-01

This project was designed to use real-time and historical SeaWiFS and AVHRR data, and real-time MODIS data in order to estimate the global vertical carbon flux along continental margins. This required construction of an empirical model relating surface ocean color and physical variables like temperature and wind to vertical settling flux at sites co-located with sediment trap observations (Santa Barbara Basin, Cariaco Basin, Gulf of California, Hawaii, and Bermuda, etc), and application of the model to imagery in order to obtain spatially-weighted estimates.

Critical role for mesoscale eddy diffusion in supplying oxygen to hypoxic ocean waters

NASA Astrophysics Data System (ADS)

Gnanadesikan, Anand; Bianchi, Daniele; Pradal, Marie-Aude

2013-10-01

of the oceanic lateral eddy diffusion coefficient Aredi vary by more than an order of magnitude, ranging from less than a few hundred m2/s to thousands of m2/s. This uncertainty has first-order implications for the intensity of oceanic hypoxia, which is poorly simulated by the current generation of Earth System Models. Using satellite-based estimate of oxygen consumption in hypoxic waters to estimate the required diffusion coefficient for these waters gives a value of order 1000 m2/s. Varying Aredi across a suite of Earth System Models yields a broadly consistent result given a thermocline diapycnal diffusion coefficient of 1 × 10-5 m2/s.

Finite mixture model: A maximum likelihood estimation approach on time series data

NASA Astrophysics Data System (ADS)

Yen, Phoong Seuk; Ismail, Mohd Tahir; Hamzah, Firdaus Mohamad

2014-09-01

Recently, statistician emphasized on the fitting of finite mixture model by using maximum likelihood estimation as it provides asymptotic properties. In addition, it shows consistency properties as the sample sizes increases to infinity. This illustrated that maximum likelihood estimation is an unbiased estimator. Moreover, the estimate parameters obtained from the application of maximum likelihood estimation have smallest variance as compared to others statistical method as the sample sizes increases. Thus, maximum likelihood estimation is adopted in this paper to fit the two-component mixture model in order to explore the relationship between rubber price and exchange rate for Malaysia, Thailand, Philippines and Indonesia. Results described that there is a negative effect among rubber price and exchange rate for all selected countries.

A priori error estimates for an hp-version of the discontinuous Galerkin method for hyperbolic conservation laws

NASA Technical Reports Server (NTRS)

Bey, Kim S.; Oden, J. Tinsley

1993-01-01

A priori error estimates are derived for hp-versions of the finite element method for discontinuous Galerkin approximations of a model class of linear, scalar, first-order hyperbolic conservation laws. These estimates are derived in a mesh dependent norm in which the coefficients depend upon both the local mesh size h(sub K) and a number p(sub k) which can be identified with the spectral order of the local approximations over each element.

Multi-Axis Identifiability Using Single-Surface Parameter Estimation Maneuvers on the X-48B Blended Wing Body

NASA Technical Reports Server (NTRS)

Ratnayake, Nalin A.; Koshimoto, Ed T.; Taylor, Brian R.

2011-01-01

The problem of parameter estimation on hybrid-wing-body type aircraft is complicated by the fact that many design candidates for such aircraft involve a large number of aero- dynamic control effectors that act in coplanar motion. This fact adds to the complexity already present in the parameter estimation problem for any aircraft with a closed-loop control system. Decorrelation of system inputs must be performed in order to ascertain individual surface derivatives with any sort of mathematical confidence. Non-standard control surface configurations, such as clamshell surfaces and drag-rudder modes, further complicate the modeling task. In this paper, asymmetric, single-surface maneuvers are used to excite multiple axes of aircraft motion simultaneously. Time history reconstructions of the moment coefficients computed by the solved regression models are then compared to each other in order to assess relative model accuracy. The reduced flight-test time required for inner surface parameter estimation using multi-axis methods was found to come at the cost of slightly reduced accuracy and statistical confidence for linear regression methods. Since the multi-axis maneuvers captured parameter estimates similar to both longitudinal and lateral-directional maneuvers combined, the number of test points required for the inner, aileron-like surfaces could in theory have been reduced by 50%. While trends were similar, however, individual parameters as estimated by a multi-axis model were typically different by an average absolute difference of roughly 15-20%, with decreased statistical significance, than those estimated by a single-axis model. The multi-axis model exhibited an increase in overall fit error of roughly 1-5% for the linear regression estimates with respect to the single-axis model, when applied to flight data designed for each, respectively.

Evaluation and application of site-specific data to revise the first-order decay model for estimating landfill gas generation and emissions at Danish landfills.

PubMed

Mou, Zishen; Scheutz, Charlotte; Kjeldsen, Peter

2015-06-01

Methane (CH₄) generated from low-organic waste degradation at four Danish landfills was estimated by three first-order decay (FOD) landfill gas (LFG) generation models (LandGEM, IPCC, and Afvalzorg). Actual waste data from Danish landfills were applied to fit model (IPCC and Afvalzorg) required categories. In general, the single-phase model, LandGEM, significantly overestimated CH₄generation, because it applied too high default values for key parameters to handle low-organic waste scenarios. The key parameters were biochemical CH₄potential (BMP) and CH₄generation rate constant (k-value). In comparison to the IPCC model, the Afvalzorg model was more suitable for estimating CH₄generation at Danish landfills, because it defined more proper waste categories rather than traditional municipal solid waste (MSW) fractions. Moreover, the Afvalzorg model could better show the influence of not only the total disposed waste amount, but also various waste categories. By using laboratory-determined BMPs and k-values for shredder, sludge, mixed bulky waste, and street-cleaning waste, the Afvalzorg model was revised. The revised model estimated smaller cumulative CH₄generation results at the four Danish landfills (from the start of disposal until 2020 and until 2100). Through a CH₄mass balance approach, fugitive CH₄emissions from whole sites and a specific cell for shredder waste were aggregated based on the revised Afvalzorg model outcomes. Aggregated results were in good agreement with field measurements, indicating that the revised Afvalzorg model could provide practical and accurate estimation for Danish LFG emissions. This study is valuable for both researchers and engineers aiming to predict, control, and mitigate fugitive CH₄emissions from landfills receiving low-organic waste. Landfill operators use the first-order decay (FOD) models to estimate methane (CH₄) generation. A single-phase model (LandGEM) and a traditional model (IPCC) could result in overestimation when handling a low-organic waste scenario. Site-specific data were important and capable of calibrating key parameter values in FOD models. The comparison study of the revised Afvalzorg model outcomes and field measurements at four Danish landfills provided a guideline for revising the Pollutants Release and Transfer Registers (PRTR) model, as well as indicating noteworthy waste fractions that could emit CH₄at modern landfills.

Lessons from a low-order coupled chemistry meteorology model and applications to a high-dimensional chemical transport model

NASA Astrophysics Data System (ADS)

Haussaire, Jean-Matthieu; Bocquet, Marc

2016-04-01

Atmospheric chemistry models are becoming increasingly complex, with multiphasic chemistry, size-resolved particulate matter, and possibly coupled to numerical weather prediction models. In the meantime, data assimilation methods have also become more sophisticated. Hence, it will become increasingly difficult to disentangle the merits of data assimilation schemes, of models, and of their numerical implementation in a successful high-dimensional data assimilation study. That is why we believe that the increasing variety of problems encountered in the field of atmospheric chemistry data assimilation puts forward the need for simple low-order models, albeit complex enough to capture the relevant dynamics, physics and chemistry that could impact the performance of data assimilation schemes. Following this analysis, we developped a low-order coupled chemistry meteorology model named L95-GRS [1]. The advective wind is simulated by the Lorenz-95 model, while the chemistry is made of 6 reactive species and simulates ozone concentrations. With this model, we carried out data assimilation experiments to estimate the state of the system as well as the forcing parameter of the wind and the emissions of chemical compounds. This model proved to be a powerful playground giving insights on the hardships of online and offline estimation of atmospheric pollution. Building on the results on this low-order model, we test advanced data assimilation methods on a state-of-the-art chemical transport model to check if the conclusions obtained with our low-order model still stand. References [1] Haussaire, J.-M. and Bocquet, M.: A low-order coupled chemistry meteorology model for testing online and offline data assimilation schemes, Geosci. Model Dev. Discuss., 8, 7347-7394, doi:10.5194/gmdd-8-7347-2015, 2015.

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

PubMed

Ding, A Adam; Wu, Hulin

2014-10-01

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

Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression

PubMed Central

Ding, A. Adam; Wu, Hulin

2015-01-01

We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method. PMID:26401093

An Australian stocks and flows model for asbestos.

PubMed

Donovan, Sally; Pickin, Joe

2016-10-01

All available data on asbestos consumption in Australia were collated in order to determine the most common asbestos-containing materials remaining in the built environment. The proportion of asbestos contained within each material and the types of products these materials are most commonly found in was also determined. The lifetime of these asbestos containing products was estimated in order to develop a model that projects stocks and flows of asbestos products in Australia through to the year 2100. The model is based on a Weibull distribution and was built in an excel spreadsheet to make it user-friendly and accessible. The nature of the products under consideration means both their asbestos content and lifetime parameters are highly variable, and so for each of these a high and low estimate is presented along with the estimate used in the model. The user is able to vary the parameters in the model as better data become available. © The Author(s) 2016.

Estimating abundance

USGS Publications Warehouse

Sutherland, Chris; Royle, Andy

2016-01-01

This chapter provides a non-technical overview of ‘closed population capture–recapture’ models, a class of well-established models that are widely applied in ecology, such as removal sampling, covariate models, and distance sampling. These methods are regularly adopted for studies of reptiles, in order to estimate abundance from counts of marked individuals while accounting for imperfect detection. Thus, the chapter describes some classic closed population models for estimating abundance, with considerations for some recent extensions that provide a spatial context for the estimation of abundance, and therefore density. Finally, the chapter suggests some software for use in data analysis, such as the Windows-based program MARK, and provides an example of estimating abundance and density of reptiles using an artificial cover object survey of Slow Worms (Anguis fragilis).

Estimating abundance: Chapter 27

USGS Publications Warehouse

Royle, J. Andrew

2016-01-01

This chapter provides a non-technical overview of ‘closed population capture–recapture’ models, a class of well-established models that are widely applied in ecology, such as removal sampling, covariate models, and distance sampling. These methods are regularly adopted for studies of reptiles, in order to estimate abundance from counts of marked individuals while accounting for imperfect detection. Thus, the chapter describes some classic closed population models for estimating abundance, with considerations for some recent extensions that provide a spatial context for the estimation of abundance, and therefore density. Finally, the chapter suggests some software for use in data analysis, such as the Windows-based program MARK, and provides an example of estimating abundance and density of reptiles using an artificial cover object survey of Slow Worms (Anguis fragilis).

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.

Blind channel estimation and deconvolution in colored noise using higher-order cumulants

NASA Astrophysics Data System (ADS)

Tugnait, Jitendra K.; Gummadavelli, Uma

1994-10-01

Existing approaches to blind channel estimation and deconvolution (equalization) focus exclusively on channel or inverse-channel impulse response estimation. It is well-known that the quality of the deconvolved output depends crucially upon the noise statistics also. Typically it is assumed that the noise is white and the signal-to-noise ratio is known. In this paper we remove these restrictions. Both the channel impulse response and the noise model are estimated from the higher-order (fourth, e.g.) cumulant function and the (second-order) correlation function of the received data via a least-squares cumulant/correlation matching criterion. It is assumed that the noise higher-order cumulant function vanishes (e.g., Gaussian noise, as is the case for digital communications). Consistency of the proposed approach is established under certain mild sufficient conditions. The approach is illustrated via simulation examples involving blind equalization of digital communications signals.

A spatial panel ordered-response model with application to the analysis of urban land-use development intensity patterns

NASA Astrophysics Data System (ADS)

Ferdous, Nazneen; Bhat, Chandra R.

2013-01-01

This paper proposes and estimates a spatial panel ordered-response probit model with temporal autoregressive error terms to analyze changes in urban land development intensity levels over time. Such a model structure maintains a close linkage between the land owner's decision (unobserved to the analyst) and the land development intensity level (observed by the analyst) and accommodates spatial interactions between land owners that lead to spatial spillover effects. In addition, the model structure incorporates spatial heterogeneity as well as spatial heteroscedasticity. The resulting model is estimated using a composite marginal likelihood (CML) approach that does not require any simulation machinery and that can be applied to data sets of any size. A simulation exercise indicates that the CML approach recovers the model parameters very well, even in the presence of high spatial and temporal dependence. In addition, the simulation results demonstrate that ignoring spatial dependency and spatial heterogeneity when both are actually present will lead to bias in parameter estimation. A demonstration exercise applies the proposed model to examine urban land development intensity levels using parcel-level data from Austin, Texas.

Modeling willingness to pay for land conservation easements: treatment of zero and protest bids and application and policy implications

Treesearch

Seong-Hoon Cho; Steven T. Yen; J. Michael Bowker; David H. Newman

2008-01-01

This study compares an ordered probit model and a Tobit model with selection to take into account both true zero and protest zero bids while estimating the willingness to pay (WTP) for conservation easements in Macon County, NC. By comparing the two models, the ordered/Unordered selection issue of the protest responses is analyzed to demonstrate how the treatment of...

Bayesian Image Segmentations by Potts Prior and Loopy Belief Propagation

NASA Astrophysics Data System (ADS)

Tanaka, Kazuyuki; Kataoka, Shun; Yasuda, Muneki; Waizumi, Yuji; Hsu, Chiou-Ting

2014-12-01

This paper presents a Bayesian image segmentation model based on Potts prior and loopy belief propagation. The proposed Bayesian model involves several terms, including the pairwise interactions of Potts models, and the average vectors and covariant matrices of Gauss distributions in color image modeling. These terms are often referred to as hyperparameters in statistical machine learning theory. In order to determine these hyperparameters, we propose a new scheme for hyperparameter estimation based on conditional maximization of entropy in the Potts prior. The algorithm is given based on loopy belief propagation. In addition, we compare our conditional maximum entropy framework with the conventional maximum likelihood framework, and also clarify how the first order phase transitions in loopy belief propagations for Potts models influence our hyperparameter estimation procedures.

Prioritizing Chemicals and Data Requirements for Screening-Level Exposure and Risk Assessment

PubMed Central

Brown, Trevor N.; Wania, Frank; Breivik, Knut; McLachlan, Michael S.

2012-01-01

Background: Scientists and regulatory agencies strive to identify chemicals that may cause harmful effects to humans and the environment; however, prioritization is challenging because of the large number of chemicals requiring evaluation and limited data and resources. Objectives: We aimed to prioritize chemicals for exposure and exposure potential and obtain a quantitative perspective on research needs to better address uncertainty in screening assessments. Methods: We used a multimedia mass balance model to prioritize > 12,000 organic chemicals using four far-field human exposure metrics. The propagation of variance (uncertainty) in key chemical information used as model input for calculating exposure metrics was quantified. Results: Modeled human concentrations and intake rates span approximately 17 and 15 orders of magnitude, respectively. Estimates of exposure potential using human concentrations and a unit emission rate span approximately 13 orders of magnitude, and intake fractions span 7 orders of magnitude. The actual chemical emission rate contributes the greatest variance (uncertainty) in exposure estimates. The human biotransformation half-life is the second greatest source of uncertainty in estimated concentrations. In general, biotransformation and biodegradation half-lives are greater sources of uncertainty in modeled exposure and exposure potential than chemical partition coefficients. Conclusions: Mechanistic exposure modeling is suitable for screening and prioritizing large numbers of chemicals. By including uncertainty analysis and uncertainty in chemical information in the exposure estimates, these methods can help identify and address the important sources of uncertainty in human exposure and risk assessment in a systematic manner. PMID:23008278

Spectrum Modal Analysis for the Detection of Low-Altitude Windshear with Airborne Doppler Radar

NASA Technical Reports Server (NTRS)

Kunkel, Matthew W.

1992-01-01

A major obstacle in the estimation of windspeed patterns associated with low-altitude windshear with an airborne pulsed Doppler radar system is the presence of strong levels of ground clutter which can strongly bias a windspeed estimate. Typical solutions attempt to remove the clutter energy from the return through clutter rejection filtering. Proposed is a method whereby both the weather and clutter modes present in a return spectrum can be identified to yield an unbiased estimate of the weather mode without the need for clutter rejection filtering. An attempt will be made to show that modeling through a second order extended Prony approach is sufficient for the identification of the weather mode. A pattern recognition approach to windspeed estimation from the identified modes is derived and applied to both simulated and actual flight data. Comparisons between windspeed estimates derived from modal analysis and the pulse-pair estimator are included as well as associated hazard factors. Also included is a computationally attractive method for estimating windspeeds directly from the coefficients of a second-order autoregressive model. Extensions and recommendations for further study are included.

Estimation of clear-sky insolation using satellite and ground meteorological data

NASA Technical Reports Server (NTRS)

Staylor, W. F.; Darnell, W. L.; Gupta, S. K.

1983-01-01

Ground based pyranometer measurements were combined with meteorological data from the Tiros N satellite in order to estimate clear-sky insolations at five U.S. sites for five weeks during the spring of 1979. The estimates were used to develop a semi-empirical model of clear-sky insolation for the interpretation of input data from the Tiros Operational Vertical Sounder (TOVS). Using only satellite data, the estimated standard errors in the model were about 2 percent. The introduction of ground based data reduced errors to around 1 percent. It is shown that although the errors in the model were reduced by only 1 percent, TOVS data products are still adequate for estimating clear-sky insolation.

Alignment-free Transcriptomic and Metatranscriptomic Comparison Using Sequencing Signatures with Variable Length Markov Chains.

PubMed

Liao, Weinan; Ren, Jie; Wang, Kun; Wang, Shun; Zeng, Feng; Wang, Ying; Sun, Fengzhu

2016-11-23

The comparison between microbial sequencing data is critical to understand the dynamics of microbial communities. The alignment-based tools analyzing metagenomic datasets require reference sequences and read alignments. The available alignment-free dissimilarity approaches model the background sequences with Fixed Order Markov Chain (FOMC) yielding promising results for the comparison of microbial communities. However, in FOMC, the number of parameters grows exponentially with the increase of the order of Markov Chain (MC). Under a fixed high order of MC, the parameters might not be accurately estimated owing to the limitation of sequencing depth. In our study, we investigate an alternative to FOMC to model background sequences with the data-driven Variable Length Markov Chain (VLMC) in metatranscriptomic data. The VLMC originally designed for long sequences was extended to apply to high-throughput sequencing reads and the strategies to estimate the corresponding parameters were developed. The flexible number of parameters in VLMC avoids estimating the vast number of parameters of high-order MC under limited sequencing depth. Different from the manual selection in FOMC, VLMC determines the MC order adaptively. Several beta diversity measures based on VLMC were applied to compare the bacterial RNA-Seq and metatranscriptomic datasets. Experiments show that VLMC outperforms FOMC to model the background sequences in transcriptomic and metatranscriptomic samples. A software pipeline is available at https://d2vlmc.codeplex.com.

Maneuverability Estimation of High-Speed Craft

DTIC Science & Technology

2015-06-01

derived based on equations by Lewandowski and Denny- Hubble in order to find the fundamental maneuvering characteristics. The model is developed in...characteristic of high- speed craft. A mathematical model is derived based on equations by Lewandowski and Denny- Hubble in order to find the fundamental...33 C. EQUATIONS BY DENNY AND HUBBLE ................................................43 D. NOMOTO

Higher-Order Item Response Models for Hierarchical Latent Traits

ERIC Educational Resources Information Center

Huang, Hung-Yu; Wang, Wen-Chung; Chen, Po-Hsi; Su, Chi-Ming

2013-01-01

Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify…

Intraclass Correlation Coefficients in Hierarchical Designs: Evaluation Using Latent Variable Modeling

ERIC Educational Resources Information Center

Raykov, Tenko

2011-01-01

Interval estimation of intraclass correlation coefficients in hierarchical designs is discussed within a latent variable modeling framework. A method accomplishing this aim is outlined, which is applicable in two-level studies where participants (or generally lower-order units) are clustered within higher-order units. The procedure can also be…

Reduced-order modeling for hyperthermia: an extended balanced-realization-based approach.

PubMed

Mattingly, M; Bailey, E A; Dutton, A W; Roemer, R B; Devasia, S

1998-09-01

Accurate thermal models are needed in hyperthermia cancer treatments for such tasks as actuator and sensor placement design, parameter estimation, and feedback temperature control. The complexity of the human body produces full-order models which are too large for effective execution of these tasks, making use of reduced-order models necessary. However, standard balanced-realization (SBR)-based model reduction techniques require a priori knowledge of the particular placement of actuators and sensors for model reduction. Since placement design is intractable (computationally) on the full-order models, SBR techniques must use ad hoc placements. To alleviate this problem, an extended balanced-realization (EBR)-based model-order reduction approach is presented. The new technique allows model order reduction to be performed over all possible placement designs and does not require ad hoc placement designs. It is shown that models obtained using the EBR method are more robust to intratreatment changes in the placement of the applied power field than those models obtained using the SBR method.

Using Diffraction Tomography to Estimate Marine Animal Size

NASA Astrophysics Data System (ADS)

Jaffe, J. S.; Roberts, P.

In this article we consider the development of acoustic methods which have the potential to size marine animals. The proposed technique uses scattered sound in order to invert for both animal size and shape. The technique uses the Distorted Wave Born Approximation (DWBA) in order to model sound scattered from these organisms. The use of the DWBA also provides a valuable context for formulating data analysis techniques in order to invert for parameters of the animal. Although 3-dimensional observations can be obtained from a complete set of views, due to the difficulty of collecting full 3-dimensional scatter, it is useful to simplify the inversion by approximating the animal by a few parameters. Here, the animals are modeled as 3-dimensional ellipsoids. This reduces the complexity of the problem to a determination of the 3 semi axes for the x, y and z dimensions from just a few radial spokes through the 3-dimensional Fourier Transform. In order to test the idea, simulated scatter data is taken from a 3-dimensional model of a marine animal and the resultant data are inverted in order to estimate animal shape

Los Alamos National Laboratory Economic Analysis Capability Overview

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

Boero, Riccardo; Edwards, Brian Keith; Pasqualini, Donatella

Los Alamos National Laboratory has developed two types of models to compute the economic impact of infrastructure disruptions. FastEcon is a fast running model that estimates first-­order economic impacts of large scale events such as hurricanes and floods and can be used to identify the amount of economic activity that occurs in a specific area. LANL’s Computable General Equilibrium (CGE) model estimates more comprehensive static and dynamic economic impacts of a broader array of events and captures the interactions between sectors and industries when estimating economic impacts.

Cumulus cloud model estimates of trace gas transports

NASA Technical Reports Server (NTRS)

Garstang, Michael; Scala, John; Simpson, Joanne; Tao, Wei-Kuo; Thompson, A.; Pickering, K. E.; Harris, R.

1989-01-01

Draft structures in convective clouds are examined with reference to the results of the NASA Amazon Boundary Layer Experiments (ABLE IIa and IIb) and calculations based on a multidimensional time dependent dynamic and microphysical numerical cloud model. It is shown that some aspects of the draft structures can be calculated from measurements of the cloud environment. Estimated residence times in the lower regions of the cloud based on surface observations (divergence and vertical velocities) are within the same order of magnitude (about 20 min) as model trajectory estimates.

Parameters estimation using the first passage times method in a jump-diffusion model

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

Khaldi, K., E-mail: kkhaldi@umbb.dz; LIMOSE Laboratory, Boumerdes University, 35000; Meddahi, S., E-mail: samia.meddahi@gmail.com

2016-06-02

The main purposes of this paper are two contributions: (1) it presents a new method, which is the first passage time (FPT method) generalized for all passage times (GPT method), in order to estimate the parameters of stochastic Jump-Diffusion process. (2) it compares in a time series model, share price of gold, the empirical results of the estimation and forecasts obtained with the GPT method and those obtained by the moments method and the FPT method applied to the Merton Jump-Diffusion (MJD) model.

True and apparent scaling: The proximity of the Markov-switching multifractal model to long-range dependence

NASA Astrophysics Data System (ADS)

Liu, Ruipeng; Di Matteo, T.; Lux, Thomas

2007-09-01

In this paper, we consider daily financial data of a collection of different stock market indices, exchange rates, and interest rates, and we analyze their multi-scaling properties by estimating a simple specification of the Markov-switching multifractal (MSM) model. In order to see how well the estimated model captures the temporal dependence of the data, we estimate and compare the scaling exponents H(q) (for q=1,2) for both empirical data and simulated data of the MSM model. In most cases the multifractal model appears to generate ‘apparent’ long memory in agreement with the empirical scaling laws.

Assessment of hemoglobin responsiveness to epoetin alfa in patients on hemodialysis using a population pharmacokinetic pharmacodynamic model.

PubMed

Wu, Liviawati; Mould, Diane R; Perez Ruixo, Juan Jose; Doshi, Sameer

2015-10-01

A population pharmacokinetic pharmacodynamic (PK/PD) model describing the effect of epoetin alfa on hemoglobin (Hb) response in hemodialysis patients was developed. Epoetin alfa pharmacokinetics was described using a linear 2-compartment model. PK parameter estimates were similar to previously reported values. A maturation-structured cytokinetic model consisting of 5 compartments linked in a catenary fashion by first-order cell transfer rates following a zero-order input process described the Hb time course. The PD model described 2 subpopulations, one whose Hb response reflected epoetin alfa dosing and a second whose response was unrelated to epoetin alfa dosing. Parameter estimates from the PK/PD model were physiologically reasonable and consistent with published reports. Numerical and visual predictive checks using data from 2 studies were performed. The PK and PD of epoetin alfa were well described by the model. © 2015, The American College of Clinical Pharmacology.

Pattern statistics on Markov chains and sensitivity to parameter estimation

PubMed Central

Nuel, Grégory

2006-01-01

Background: In order to compute pattern statistics in computational biology a Markov model is commonly used to take into account the sequence composition. Usually its parameter must be estimated. The aim of this paper is to determine how sensitive these statistics are to parameter estimation, and what are the consequences of this variability on pattern studies (finding the most over-represented words in a genome, the most significant common words to a set of sequences,...). Results: In the particular case where pattern statistics (overlap counting only) computed through binomial approximations we use the delta-method to give an explicit expression of σ, the standard deviation of a pattern statistic. This result is validated using simulations and a simple pattern study is also considered. Conclusion: We establish that the use of high order Markov model could easily lead to major mistakes due to the high sensitivity of pattern statistics to parameter estimation. PMID:17044916

Pattern statistics on Markov chains and sensitivity to parameter estimation.

PubMed

Nuel, Grégory

2006-10-17

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

Constrained State Estimation for Individual Localization in Wireless Body Sensor Networks

PubMed Central

Feng, Xiaoxue; Snoussi, Hichem; Liang, Yan; Jiao, Lianmeng

2014-01-01

Wireless body sensor networks based on ultra-wideband radio have recently received much research attention due to its wide applications in health-care, security, sports and entertainment. Accurate localization is a fundamental problem to realize the development of effective location-aware applications above. In this paper the problem of constrained state estimation for individual localization in wireless body sensor networks is addressed. Priori knowledge about geometry among the on-body nodes as additional constraint is incorporated into the traditional filtering system. The analytical expression of state estimation with linear constraint to exploit the additional information is derived. Furthermore, for nonlinear constraint, first-order and second-order linearizations via Taylor series expansion are proposed to transform the nonlinear constraint to the linear case. Examples between the first-order and second-order nonlinear constrained filters based on interacting multiple model extended kalman filter (IMM-EKF) show that the second-order solution for higher order nonlinearity as present in this paper outperforms the first-order solution, and constrained IMM-EKF obtains superior estimation than IMM-EKF without constraint. Another brownian motion individual localization example also illustrates the effectiveness of constrained nonlinear iterative least square (NILS), which gets better filtering performance than NILS without constraint. PMID:25390408

Sliding mode observers for automotive alternator

NASA Astrophysics Data System (ADS)

Chen, De-Shiou

Estimator development for synchronous rectification of the automotive alternator is a desirable approach for estimating alternator's back electromotive forces (EMFs) without a direct mechanical sensor of the rotor position. Recent theoretical studies show that estimation of the back EMF may be observed based on system's phase current model by sensing electrical variables (AC phase currents and DC bus voltage) of the synchronous rectifier. Observer design of the back EMF estimation has been developed for constant engine speed. In this work, we are interested in nonlinear observer design of the back EMF estimation for the real case of variable engine speed. Initial back EMF estimate can be obtained from a first-order sliding mode observer (SMO) based on the phase current model. A fourth-order nonlinear asymptotic observer (NAO), complemented by the dynamics of the back EMF with time-varying frequency and amplitude, is then incorporated into the observer design for chattering reduction. Since the cost of required phase current sensors may be prohibitive, the most applicable approach in real implementation by measuring DC current of the synchronous rectifier is carried out in the dissertation. It is shown that the DC link current consists of sequential "windows" with partial information of the phase currents, hence, the cascaded NAO is responsible not only for the purpose of chattering reduction but also for necessarily accomplishing the process of estimation. Stability analyses of the proposed estimators are considered for most linear and time-varying cases. The stability of the NAO without speed information is substantiated by both numerical and experimental results. Prospective estimation algorithms for the case of battery current measurements are investigated. Theoretical study indicates that the convergence of the proposed LAO may be provided by high gain inputs. Since the order of the LAO/NAO for the battery current case is one order higher than that of the link current measurements, it is hard to find moderate values of the input gains for the real-time sampled-data systems. Technical difficulties in implementation of such high order discrete-time nonlinear estimators have been discussed. Directions of further investigations have been provided.

Modeling an alkaline electrolysis cell through reduced-order and loss-estimate approaches

NASA Astrophysics Data System (ADS)

Milewski, Jaroslaw; Guandalini, Giulio; Campanari, Stefano

2014-12-01

The paper presents two approaches to the mathematical modeling of an Alkaline Electrolyzer Cell. The presented models were compared and validated against available experimental results taken from a laboratory test and against literature data. The first modeling approach is based on the analysis of estimated losses due to the different phenomena occurring inside the electrolytic cell, and requires careful calibration of several specific parameters (e.g. those related to the electrochemical behavior of the electrodes) some of which could be hard to define. An alternative approach is based on a reduced-order equivalent circuit, resulting in only two fitting parameters (electrodes specific resistance and parasitic losses) and calculation of the internal electric resistance of the electrolyte. Both models yield satisfactory results with an average error limited below 3% vs. the considered experimental data and show the capability to describe with sufficient accuracy the different operating conditions of the electrolyzer; the reduced-order model could be preferred thanks to its simplicity for implementation within plant simulation tools dealing with complex systems, such as electrolyzers coupled with storage facilities and intermittent renewable energy sources.

Comparison of different models for non-invasive FFR estimation

NASA Astrophysics Data System (ADS)

Mirramezani, Mehran; Shadden, Shawn

2017-11-01

Coronary artery disease is a leading cause of death worldwide. Fractional flow reserve (FFR), derived from invasively measuring the pressure drop across a stenosis, is considered the gold standard to diagnose disease severity and need for treatment. Non-invasive estimation of FFR has gained recent attention for its potential to reduce patient risk and procedural cost versus invasive FFR measurement. Non-invasive FFR can be obtained by using image-based computational fluid dynamics to simulate blood flow and pressure in a patient-specific coronary model. However, 3D simulations require extensive effort for model construction and numerical computation, which limits their routine use. In this study we compare (ordered by increasing computational cost/complexity): reduced-order algebraic models of pressure drop across a stenosis; 1D, 2D (multiring) and 3D CFD models; as well as 3D FSI for the computation of FFR in idealized and patient-specific stenosis geometries. We demonstrate the ability of an appropriate reduced order algebraic model to closely predict FFR when compared to FFR from a full 3D simulation. This work was supported by the NIH, Grant No. R01-HL103419.

Observed galaxy number counts on the lightcone up to second order: I. Main result

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

Bertacca, Daniele; Maartens, Roy; Clarkson, Chris, E-mail: daniele.bertacca@gmail.com, E-mail: roy.maartens@gmail.com, E-mail: chris.clarkson@gmail.com

2014-09-01

We present the galaxy number overdensity up to second order in redshift space on cosmological scales for a concordance model. The result contains all general relativistic effects up to second order that arise from observing on the past light cone, including all redshift effects, lensing distortions from convergence and shear, and contributions from velocities, Sachs-Wolfe, integrated SW and time-delay terms. This result will be important for accurate calculation of the bias on estimates of non-Gaussianity and on precision parameter estimates, introduced by nonlinear projection effects.

Probability based remaining capacity estimation using data-driven and neural network model

NASA Astrophysics Data System (ADS)

Wang, Yujie; Yang, Duo; Zhang, Xu; Chen, Zonghai

2016-05-01

Since large numbers of lithium-ion batteries are composed in pack and the batteries are complex electrochemical devices, their monitoring and safety concerns are key issues for the applications of battery technology. An accurate estimation of battery remaining capacity is crucial for optimization of the vehicle control, preventing battery from over-charging and over-discharging and ensuring the safety during its service life. The remaining capacity estimation of a battery includes the estimation of state-of-charge (SOC) and state-of-energy (SOE). In this work, a probability based adaptive estimator is presented to obtain accurate and reliable estimation results for both SOC and SOE. For the SOC estimation, an n ordered RC equivalent circuit model is employed by combining an electrochemical model to obtain more accurate voltage prediction results. For the SOE estimation, a sliding window neural network model is proposed to investigate the relationship between the terminal voltage and the model inputs. To verify the accuracy and robustness of the proposed model and estimation algorithm, experiments under different dynamic operation current profiles are performed on the commercial 1665130-type lithium-ion batteries. The results illustrate that accurate and robust estimation can be obtained by the proposed method.

Keystroke-Level Analysis to Estimate Time to Process Pages in Online Learning Environments

ERIC Educational Resources Information Center

Bälter, Olle; Zimmaro, Dawn

2018-01-01

It is challenging for students to plan their work sessions in online environments, as it is very difficult to make estimates on how much material there is to cover. In order to simplify this estimation, we have extended the Keystroke-level analysis model with individual reading speed of text, figures, and questions. This was used to estimate how…

Estimating sturgeon abundance in the Carolinas using side-scan sonar

USGS Publications Warehouse

Flowers, H. Jared; Hightower, Joseph E.

2015-01-01

Sturgeons (Acipenseridae) are one of the most threatened taxa worldwide, including species in North Carolina and South Carolina. Populations of Atlantic Sturgeon Acipenser oxyrinchus in the Carolinas have been significantly reduced from historical levels by a combination of intense fishing and habitat loss. There is a need for estimates of current abundance, to describe status, and for estimates of historical abundance in order to provide realistic recovery goals. In this study we used N-mixture and distance models with data acquired from side-scan sonar surveys to estimate abundance of sturgeon in six major sturgeon rivers in North Carolina and South Carolina. Estimated abundances of sturgeon greater than 1 m TL in the Carolina distinct population segment (DPS) were 2,031 using the count model and 1,912 via the distance model. The Pee Dee River had the highest overall abundance of any river at 1,944 (count model) or 1,823 (distance model). These estimates do not account for sturgeon less than 1 m TL or occurring in riverine reaches not surveyed or in marine waters. Comparing the two models, the N-mixture model produced similar estimates using less data than the distance model with only a slight reduction of estimated precision.

Man power/cost estimation model: Automated planetary projects

NASA Technical Reports Server (NTRS)

Kitchen, L. D.

1975-01-01

A manpower/cost estimation model is developed which is based on a detailed level of financial analysis of over 30 million raw data points which are then compacted by more than three orders of magnitude to the level at which the model is applicable. The major parameter of expenditure is manpower (specifically direct labor hours) for all spacecraft subsystem and technical support categories. The resultant model is able to provide a mean absolute error of less than fifteen percent for the eight programs comprising the model data base. The model includes cost saving inheritance factors, broken down in four levels, for estimating follow-on type programs where hardware and design inheritance are evident or expected.

Estimating methane emissions from landfills based on rainfall, ambient temperature, and waste composition: The CLEEN model.

PubMed

Karanjekar, Richa V; Bhatt, Arpita; Altouqui, Said; Jangikhatoonabad, Neda; Durai, Vennila; Sattler, Melanie L; Hossain, M D Sahadat; Chen, Victoria

2015-12-01

Accurately estimating landfill methane emissions is important for quantifying a landfill's greenhouse gas emissions and power generation potential. Current models, including LandGEM and IPCC, often greatly simplify treatment of factors like rainfall and ambient temperature, which can substantially impact gas production. The newly developed Capturing Landfill Emissions for Energy Needs (CLEEN) model aims to improve landfill methane generation estimates, but still require inputs that are fairly easy to obtain: waste composition, annual rainfall, and ambient temperature. To develop the model, methane generation was measured from 27 laboratory scale landfill reactors, with varying waste compositions (ranging from 0% to 100%); average rainfall rates of 2, 6, and 12 mm/day; and temperatures of 20, 30, and 37°C, according to a statistical experimental design. Refuse components considered were the major biodegradable wastes, food, paper, yard/wood, and textile, as well as inert inorganic waste. Based on the data collected, a multiple linear regression equation (R(2)=0.75) was developed to predict first-order methane generation rate constant values k as functions of waste composition, annual rainfall, and temperature. Because, laboratory methane generation rates exceed field rates, a second scale-up regression equation for k was developed using actual gas-recovery data from 11 landfills in high-income countries with conventional operation. The Capturing Landfill Emissions for Energy Needs (CLEEN) model was developed by incorporating both regression equations into the first-order decay based model for estimating methane generation rates from landfills. CLEEN model values were compared to actual field data from 6 US landfills, and to estimates from LandGEM and IPCC. For 4 of the 6 cases, CLEEN model estimates were the closest to actual. Copyright © 2015 Elsevier Ltd. All rights reserved.

Finite time state and disturbance estimation for robust performance of motion control systems using sliding modes

NASA Astrophysics Data System (ADS)

Tamhane, Bhagyashri; Kurode, Shailaja

2018-05-01

In this paper, simultaneous state and disturbance estimation of a drive system composed of motor connected to a load is proposed. Such a system is represented by a two mass model realising in a fourth-order plant. Backlash is introduced as the nonlinear disturbance in gears which is proposed to be estimated and in turn compensated. For this motion control system, a two-stage higher order sliding-mode observer is proposed for state and backlash estimation. The novelty lies in the fact that for this fourth-order system, output is considered from the motor end only, i.e. its angular displacement. The unmeasured states consisting of output derivative, load-side angular displacement and its derivative along with backlash are estimated in finite time. This disturbance due to backlash is unmatched in nature. The estimated states and disturbance are used to devise a robust sliding-mode control. This proposed scheme is validated in simulation and experimentation.

Identification of transmissivity fields using a Bayesian strategy and perturbative approach

NASA Astrophysics Data System (ADS)

Zanini, Andrea; Tanda, Maria Giovanna; Woodbury, Allan D.

2017-10-01

The paper deals with the crucial problem of the groundwater parameter estimation that is the basis for efficient modeling and reclamation activities. A hierarchical Bayesian approach is developed: it uses the Akaike's Bayesian Information Criteria in order to estimate the hyperparameters (related to the covariance model chosen) and to quantify the unknown noise variance. The transmissivity identification proceeds in two steps: the first, called empirical Bayesian interpolation, uses Y* (Y = lnT) observations to interpolate Y values on a specified grid; the second, called empirical Bayesian update, improve the previous Y estimate through the addition of hydraulic head observations. The relationship between the head and the lnT has been linearized through a perturbative solution of the flow equation. In order to test the proposed approach, synthetic aquifers from literature have been considered. The aquifers in question contain a variety of boundary conditions (both Dirichelet and Neuman type) and scales of heterogeneities (σY2 = 1.0 and σY2 = 5.3). The estimated transmissivity fields were compared to the true one. The joint use of Y* and head measurements improves the estimation of Y considering both degrees of heterogeneity. Even if the variance of the strong transmissivity field can be considered high for the application of the perturbative approach, the results show the same order of approximation of the non-linear methods proposed in literature. The procedure allows to compute the posterior probability distribution of the target quantities and to quantify the uncertainty in the model prediction. Bayesian updating has advantages related both to the Monte-Carlo (MC) and non-MC approaches. In fact, as the MC methods, Bayesian updating allows computing the direct posterior probability distribution of the target quantities and as non-MC methods it has computational times in the order of seconds.

Assessing first-order emulator inference for physical parameters in nonlinear mechanistic models

USGS Publications Warehouse

Hooten, Mevin B.; Leeds, William B.; Fiechter, Jerome; Wikle, Christopher K.

2011-01-01

We present an approach for estimating physical parameters in nonlinear models that relies on an approximation to the mechanistic model itself for computational efficiency. The proposed methodology is validated and applied in two different modeling scenarios: (a) Simulation and (b) lower trophic level ocean ecosystem model. The approach we develop relies on the ability to predict right singular vectors (resulting from a decomposition of computer model experimental output) based on the computer model input and an experimental set of parameters. Critically, we model the right singular vectors in terms of the model parameters via a nonlinear statistical model. Specifically, we focus our attention on first-order models of these right singular vectors rather than the second-order (covariance) structure.

Firm-Related Training Tracks: A Random Effects Ordered Probit Model

ERIC Educational Resources Information Center

Groot, Wim; van den Brink, Henriette Maassen

2003-01-01

A random effects ordered response model of training is estimated to analyze the existence of training tracks and time varying coefficients in training frequency. Two waves of a Dutch panel survey of workers are used covering the period 1992-1996. The amount of training received by workers increased during the period 1994-1996 compared to…

Reducing uncertainty for estimating forest carbon stocks and dynamics using integrated remote sensing, forest inventory and process-based modeling

NASA Astrophysics Data System (ADS)

Poulter, B.; Ciais, P.; Joetzjer, E.; Maignan, F.; Luyssaert, S.; Barichivich, J.

2015-12-01

Accurately estimating forest biomass and forest carbon dynamics requires new integrated remote sensing, forest inventory, and carbon cycle modeling approaches. Presently, there is an increasing and urgent need to reduce forest biomass uncertainty in order to meet the requirements of carbon mitigation treaties, such as Reducing Emissions from Deforestation and forest Degradation (REDD+). Here we describe a new parameterization and assimilation methodology used to estimate tropical forest biomass using the ORCHIDEE-CAN dynamic global vegetation model. ORCHIDEE-CAN simulates carbon uptake and allocation to individual trees using a mechanistic representation of photosynthesis, respiration and other first-order processes. The model is first parameterized using forest inventory data to constrain background mortality rates, i.e., self-thinning, and productivity. Satellite remote sensing data for forest structure, i.e., canopy height, is used to constrain simulated forest stand conditions using a look-up table approach to match canopy height distributions. The resulting forest biomass estimates are provided for spatial grids that match REDD+ project boundaries and aim to provide carbon estimates for the criteria described in the IPCC Good Practice Guidelines Tier 3 category. With the increasing availability of forest structure variables derived from high-resolution LIDAR, RADAR, and optical imagery, new methodologies and applications with process-based carbon cycle models are becoming more readily available to inform land management.

Genetic parameters of legendre polynomials for first parity lactation curves.

PubMed

Pool, M H; Janss, L L; Meuwissen, T H

2000-11-01

Variance components of the covariance function coefficients in a random regression test-day model were estimated by Legendre polynomials up to a fifth order for first-parity records of Dutch dairy cows using Gibbs sampling. Two Legendre polynomials of equal order were used to model the random part of the lactation curve, one for the genetic component and one for permanent environment. Test-day records from cows registered between 1990 to 1996 and collected by regular milk recording were available. For the data set, 23,700 complete lactations were selected from 475 herds sired by 262 sires. Because the application of a random regression model is limited by computing capacity, we investigated the minimum order needed to fit the variance structure in the data sufficiently. Predictions of genetic and permanent environmental variance structures were compared with bivariate estimates on 30-d intervals. A third-order or higher polynomial modeled the shape of variance curves over DIM with sufficient accuracy for the genetic and permanent environment part. Also, the genetic correlation structure was fitted with sufficient accuracy by a third-order polynomial, but, for the permanent environmental component, a fourth order was needed. Because equal orders are suggested in the literature, a fourth-order Legendre polynomial is recommended in this study. However, a rank of three for the genetic covariance matrix and of four for permanent environment allows a simpler covariance function with a reduced number of parameters based on the eigenvalues and eigenvectors.

A Comparison of Vertical Deformations Derived from Space-based Gravimetry, Ground-based Sensors, and Model-based Hydrologic Loading over the Western United States

NASA Astrophysics Data System (ADS)

Yin, G.; Forman, B. A.; Loomis, B. D.; Luthcke, S. B.

2017-12-01

Vertical deformation of the Earth's crust due to the movement and redistribution of terrestrial freshwater can be studied using satellite measurements, ground-based sensors, hydrologic models, or a combination thereof. This current study explores the relationship between vertical deformation estimates derived from mass concentrations (mascons) from the Gravity Recovery and Climate Experiment (GRACE), vertical deformation from ground-based Global Positioning System (GPS) observations collected from the Plate Boundary Observatory (PBO), and hydrologic loading estimates based on model output from the NASA Catchment Land Surface Model (Catchment). A particular focus is made to snow-dominated basins where mass accumulates during the snow season and subsequently runs off during the ablation season. The mean seasonal cycle and the effects of atmospheric loading, non-tidal ocean loading, and glacier isostatic adjustment (GIA) are removed from the GPS observations in order to derive the vertical displacement caused predominately by hydrological processes. A low-pass filter is applied to GPS observations to remove high frequency noise. Correlation coefficients between GRACE- and GPS-based estimates at all PBO sites are calculated. GRACE-derived and Catchment-derived displacements are subtracted from the GPS height variations, respectively, in order to compute the root mean square (RMS) reduction as a means of studying the consistency between the three different methods. Results show that in most sites, the three methods exhibit good agreement. Exceptions to this generalization include the Central Valley of California where extensive groundwater pumping is witnessed in the GRACE- and GPS-based estimates, but not in the Catchment-based estimates because anthropogenic groundwater pumping activities are not included in the Catchment model. The relatively good agreement between GPS- and GRACE-derived vertical crustal displacements suggests that ground-based GPS has tremendous potential for a Bayesian merger with GRACE-based estimates in order to provide a higher resolution (in space and time) of terrestrial water storage.

Electrochemical state and internal variables estimation using a reduced-order physics-based model of a lithium-ion cell and an extended Kalman filter

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

Stetzel, KD; Aldrich, LL; Trimboli, MS

2015-03-15

This paper addresses the problem of estimating the present value of electrochemical internal variables in a lithium-ion cell in real time, using readily available measurements of cell voltage, current, and temperature. The variables that can be estimated include any desired set of reaction flux and solid and electrolyte potentials and concentrations at any set of one-dimensional spatial locations, in addition to more standard quantities such as state of charge. The method uses an extended Kalman filter along with a one-dimensional physics-based reduced-order model of cell dynamics. Simulations show excellent and robust predictions having dependable error bounds for most internal variables.more » (C) 2014 Elsevier B.V. All rights reserved.« less

Estimating the Global Prevalence of Inadequate Zinc Intake from National Food Balance Sheets: Effects of Methodological Assumptions

PubMed Central

Wessells, K. Ryan; Singh, Gitanjali M.; Brown, Kenneth H.

2012-01-01

Background The prevalence of inadequate zinc intake in a population can be estimated by comparing the zinc content of the food supply with the population’s theoretical requirement for zinc. However, assumptions regarding the nutrient composition of foods, zinc requirements, and zinc absorption may affect prevalence estimates. These analyses were conducted to: (1) evaluate the effect of varying methodological assumptions on country-specific estimates of the prevalence of dietary zinc inadequacy and (2) generate a model considered to provide the best estimates. Methodology and Principal Findings National food balance data were obtained from the Food and Agriculture Organization of the United Nations. Zinc and phytate contents of these foods were estimated from three nutrient composition databases. Zinc absorption was predicted using a mathematical model (Miller equation). Theoretical mean daily per capita physiological and dietary requirements for zinc were calculated using recommendations from the Food and Nutrition Board of the Institute of Medicine and the International Zinc Nutrition Consultative Group. The estimated global prevalence of inadequate zinc intake varied between 12–66%, depending on which methodological assumptions were applied. However, country-specific rank order of the estimated prevalence of inadequate intake was conserved across all models (r = 0.57–0.99, P<0.01). A “best-estimate” model, comprised of zinc and phytate data from a composite nutrient database and IZiNCG physiological requirements for absorbed zinc, estimated the global prevalence of inadequate zinc intake to be 17.3%. Conclusions and Significance Given the multiple sources of uncertainty in this method, caution must be taken in the interpretation of the estimated prevalence figures. However, the results of all models indicate that inadequate zinc intake may be fairly common globally. Inferences regarding the relative likelihood of zinc deficiency as a public health problem in different countries can be drawn based on the country-specific rank order of estimated prevalence of inadequate zinc intake. PMID:23209781

Cost model validation: a technical and cultural approach

NASA Technical Reports Server (NTRS)

Hihn, J.; Rosenberg, L.; Roust, K.; Warfield, K.

2001-01-01

This paper summarizes how JPL's parametric mission cost model (PMCM) has been validated using both formal statistical methods and a variety of peer and management reviews in order to establish organizational acceptance of the cost model estimates.

Applying constraints on model-based methods: Estimation of rate constants in a second order consecutive reaction

NASA Astrophysics Data System (ADS)

Kompany-Zareh, Mohsen; Khoshkam, Maryam

2013-02-01

This paper describes estimation of reaction rate constants and pure ultraviolet/visible (UV-vis) spectra of the component involved in a second order consecutive reaction between Ortho-Amino benzoeic acid (o-ABA) and Diazoniom ions (DIAZO), with one intermediate. In the described system, o-ABA was not absorbing in the visible region of interest and thus, closure rank deficiency problem did not exist. Concentration profiles were determined by solving differential equations of the corresponding kinetic model. In that sense, three types of model-based procedures were applied to estimate the rate constants of the kinetic system, according to Levenberg/Marquardt (NGL/M) algorithm. Original data-based, Score-based and concentration-based objective functions were included in these nonlinear fitting procedures. Results showed that when there is error in initial concentrations, accuracy of estimated rate constants strongly depends on the type of applied objective function in fitting procedure. Moreover, flexibility in application of different constraints and optimization of the initial concentrations estimation during the fitting procedure were investigated. Results showed a considerable decrease in ambiguity of obtained parameters by applying appropriate constraints and adjustable initial concentrations of reagents.

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

NASA Astrophysics Data System (ADS)

Shrivastava, Akash; Mohanty, A. R.

2018-03-01

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

Stochastic process approximation for recursive estimation with guaranteed bound on the error covariance

NASA Technical Reports Server (NTRS)

Menga, G.

1975-01-01

An approach, is proposed for the design of approximate, fixed order, discrete time realizations of stochastic processes from the output covariance over a finite time interval, was proposed. No restrictive assumptions are imposed on the process; it can be nonstationary and lead to a high dimension realization. Classes of fixed order models are defined, having the joint covariance matrix of the combined vector of the outputs in the interval of definition greater or equal than the process covariance; (the difference matrix is nonnegative definite). The design is achieved by minimizing, in one of those classes, a measure of the approximation between the model and the process evaluated by the trace of the difference of the respective covariance matrices. Models belonging to these classes have the notable property that, under the same measurement system and estimator structure, the output estimation error covariance matrix computed on the model is an upper bound of the corresponding covariance on the real process. An application of the approach is illustrated by the modeling of random meteorological wind profiles from the statistical analysis of historical data.

Relaxation limit of a compressible gas-liquid model with well-reservoir interaction

NASA Astrophysics Data System (ADS)

Solem, Susanne; Evje, Steinar

2017-02-01

This paper deals with the relaxation limit of a two-phase compressible gas-liquid model which contains a pressure-dependent well-reservoir interaction term of the form q (P_r - P) where q>0 is the rate of the pressure-dependent influx/efflux of gas, P is the (unknown) wellbore pressure, and P_r is the (known) surrounding reservoir pressure. The model can be used to study gas-kick flow scenarios relevant for various wellbore operations. One extreme case is when the wellbore pressure P is largely dictated by the surrounding reservoir pressure P_r. Formally, this model is obtained by deriving the limiting system as the relaxation parameter q in the full model tends to infinity. The main purpose of this work is to understand to what extent this case can be represented by a well-defined mathematical model for a fixed global time T>0. Well-posedness of the full model has been obtained in Evje (SIAM J Math Anal 45(2):518-546, 2013). However, as the estimates for the full model are dependent on the relaxation parameter q, new estimates must be obtained for the equilibrium model to ensure existence of solutions. By means of appropriate a priori assumptions and some restrictions on the model parameters, necessary estimates (low order and higher order) are obtained. These estimates that depend on the global time T together with smallness assumptions on the initial data are then used to obtain existence of solutions in suitable Sobolev spaces.

Economic Analysis Model Evaluation for Technology Modernization Programs.

DTIC Science & Technology

1983-09-01

program and ’ expost - facto ’ utilized the ASD model to evaluate the accuracy of the ASD model. The following chapter reviews the literature on Tech Mod and...was applied to the CAR 80 Tech Mod project ’ expost - facto ’ in order to develop an ASD model estimated rate o . return. The ASD model was applied in...ASD estimate to achieve a range for the actual IRR. The ASD model was applied expost - facto to the CAR 80 Tech Mod program to derive an IRR. Initial data

Basis material decomposition in spectral CT using a semi-empirical, polychromatic adaption of the Beer-Lambert model.

PubMed

Ehn, S; Sellerer, T; Mechlem, K; Fehringer, A; Epple, M; Herzen, J; Pfeiffer, F; Noël, P B

2017-01-07

Following the development of energy-sensitive photon-counting detectors using high-Z sensor materials, application of spectral x-ray imaging methods to clinical practice comes into reach. However, these detectors require extensive calibration efforts in order to perform spectral imaging tasks like basis material decomposition. In this paper, we report a novel approach to basis material decomposition that utilizes a semi-empirical estimator for the number of photons registered in distinct energy bins in the presence of beam-hardening effects which can be termed as a polychromatic Beer-Lambert model. A maximum-likelihood estimator is applied to the model in order to obtain estimates of the underlying sample composition. Using a Monte-Carlo simulation of a typical clinical CT acquisition, the performance of the proposed estimator was evaluated. The estimator is shown to be unbiased and efficient according to the Cramér-Rao lower bound. In particular, the estimator is capable of operating with a minimum number of calibration measurements. Good results were obtained after calibration using less than 10 samples of known composition in a two-material attenuation basis. This opens up the possibility for fast re-calibration in the clinical routine which is considered an advantage of the proposed method over other implementations reported in the literature.

Basis material decomposition in spectral CT using a semi-empirical, polychromatic adaption of the Beer-Lambert model

NASA Astrophysics Data System (ADS)

Ehn, S.; Sellerer, T.; Mechlem, K.; Fehringer, A.; Epple, M.; Herzen, J.; Pfeiffer, F.; Noël, P. B.

2017-01-01

Following the development of energy-sensitive photon-counting detectors using high-Z sensor materials, application of spectral x-ray imaging methods to clinical practice comes into reach. However, these detectors require extensive calibration efforts in order to perform spectral imaging tasks like basis material decomposition. In this paper, we report a novel approach to basis material decomposition that utilizes a semi-empirical estimator for the number of photons registered in distinct energy bins in the presence of beam-hardening effects which can be termed as a polychromatic Beer-Lambert model. A maximum-likelihood estimator is applied to the model in order to obtain estimates of the underlying sample composition. Using a Monte-Carlo simulation of a typical clinical CT acquisition, the performance of the proposed estimator was evaluated. The estimator is shown to be unbiased and efficient according to the Cramér-Rao lower bound. In particular, the estimator is capable of operating with a minimum number of calibration measurements. Good results were obtained after calibration using less than 10 samples of known composition in a two-material attenuation basis. This opens up the possibility for fast re-calibration in the clinical routine which is considered an advantage of the proposed method over other implementations reported in the literature.

Spacecraft attitude determination using a second-order nonlinear filter

NASA Technical Reports Server (NTRS)

Vathsal, S.

1987-01-01

The stringent attitude determination accuracy and faster slew maneuver requirements demanded by present-day spacecraft control systems motivate the development of recursive nonlinear filters for attitude estimation. This paper presents the second-order filter development for the estimation of attitude quaternion using three-axis gyro and star tracker measurement data. Performance comparisons have been made by computer simulation of system models and filter mechanization. It is shown that the second-order filter consistently performs better than the extended Kalman filter when the performance index of the root sum square estimation error of the quaternion vector is compared. The second-order filter identifies the gyro drift rates faster than the extended Kalman filter. The uniqueness of this algorithm is the online generation of the time-varying process and measurement noise covariance matrices, derived as a function or the process and measurement nonlinearity, respectively.

Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery

NASA Astrophysics Data System (ADS)

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

2016-11-01

Reliable state estimate depends largely on an accurate battery model. However, the parameters of battery model are time varying with operating condition variation and battery aging. The existing co-estimation methods address the model uncertainty by integrating the online model identification with state estimate and have shown improved accuracy. However, the cross interference may arise from the integrated framework to compromise numerical stability and accuracy. Thus this paper proposes the decoupling of model identification and state estimate to eliminate the possibility of cross interference. The model parameters are online adapted with the recursive least squares (RLS) method, based on which a novel joint estimator based on extended Kalman Filter (EKF) is formulated to estimate the state of charge (SOC) and capacity concurrently. The proposed joint estimator effectively compresses the filter order which leads to substantial improvement in the computational efficiency and numerical stability. Lab scale experiment on vanadium redox flow battery shows that the proposed method is highly authentic with good robustness to varying operating conditions and battery aging. The proposed method is further compared with some existing methods and shown to be superior in terms of accuracy, convergence speed, and computational cost.

Further comments on sensitivities, parameter estimation, and sampling design in one-dimensional analysis of solute transport in porous media

USGS Publications Warehouse

Knopman, Debra S.; Voss, Clifford I.

1988-01-01

Sensitivities of solute concentration to parameters associated with first-order chemical decay, boundary conditions, initial conditions, and multilayer transport are examined in one-dimensional analytical models of transient solute transport in porous media. A sensitivity is a change in solute concentration resulting from a change in a model parameter. Sensitivity analysis is important because minimum information required in regression on chemical data for the estimation of model parameters by regression is expressed in terms of sensitivities. Nonlinear regression models of solute transport were tested on sets of noiseless observations from known models that exceeded the minimum sensitivity information requirements. Results demonstrate that the regression models consistently converged to the correct parameters when the initial sets of parameter values substantially deviated from the correct parameters. On the basis of the sensitivity analysis, several statements may be made about design of sampling for parameter estimation for the models examined: (1) estimation of parameters associated with solute transport in the individual layers of a multilayer system is possible even when solute concentrations in the individual layers are mixed in an observation well; (2) when estimating parameters in a decaying upstream boundary condition, observations are best made late in the passage of the front near a time chosen by adding the inverse of an hypothesized value of the source decay parameter to the estimated mean travel time at a given downstream location; (3) estimation of a first-order chemical decay parameter requires observations to be made late in the passage of the front, preferably near a location corresponding to a travel time of √2 times the half-life of the solute; and (4) estimation of a parameter relating to spatial variability in an initial condition requires observations to be made early in time relative to passage of the solute front.

Mixed Effects Modeling Using Stochastic Differential Equations: Illustrated by Pharmacokinetic Data of Nicotinic Acid in Obese Zucker Rats.

PubMed

Leander, Jacob; Almquist, Joachim; Ahlström, Christine; Gabrielsson, Johan; Jirstrand, Mats

2015-05-01

Inclusion of stochastic differential equations in mixed effects models provides means to quantify and distinguish three sources of variability in data. In addition to the two commonly encountered sources, measurement error and interindividual variability, we also consider uncertainty in the dynamical model itself. To this end, we extend the ordinary differential equation setting used in nonlinear mixed effects models to include stochastic differential equations. The approximate population likelihood is derived using the first-order conditional estimation with interaction method and extended Kalman filtering. To illustrate the application of the stochastic differential mixed effects model, two pharmacokinetic models are considered. First, we use a stochastic one-compartmental model with first-order input and nonlinear elimination to generate synthetic data in a simulated study. We show that by using the proposed method, the three sources of variability can be successfully separated. If the stochastic part is neglected, the parameter estimates become biased, and the measurement error variance is significantly overestimated. Second, we consider an extension to a stochastic pharmacokinetic model in a preclinical study of nicotinic acid kinetics in obese Zucker rats. The parameter estimates are compared between a deterministic and a stochastic NiAc disposition model, respectively. Discrepancies between model predictions and observations, previously described as measurement noise only, are now separated into a comparatively lower level of measurement noise and a significant uncertainty in model dynamics. These examples demonstrate that stochastic differential mixed effects models are useful tools for identifying incomplete or inaccurate model dynamics and for reducing potential bias in parameter estimates due to such model deficiencies.

Class Enumeration and Parameter Recovery of Growth Mixture Modeling and Second-Order Growth Mixture Modeling in the Presence of Measurement Noninvariance between Latent Classes

PubMed Central

Kim, Eun Sook; Wang, Yan

2017-01-01

Population heterogeneity in growth trajectories can be detected with growth mixture modeling (GMM). It is common that researchers compute composite scores of repeated measures and use them as multiple indicators of growth factors (baseline performance and growth) assuming measurement invariance between latent classes. Considering that the assumption of measurement invariance does not always hold, we investigate the impact of measurement noninvariance on class enumeration and parameter recovery in GMM through a Monte Carlo simulation study (Study 1). In Study 2, we examine the class enumeration and parameter recovery of the second-order growth mixture modeling (SOGMM) that incorporates measurement models at the first order level. Thus, SOGMM estimates growth trajectory parameters with reliable sources of variance, that is, common factor variance of repeated measures and allows heterogeneity in measurement parameters between latent classes. The class enumeration rates are examined with information criteria such as AIC, BIC, sample-size adjusted BIC, and hierarchical BIC under various simulation conditions. The results of Study 1 showed that the parameter estimates of baseline performance and growth factor means were biased to the degree of measurement noninvariance even when the correct number of latent classes was extracted. In Study 2, the class enumeration accuracy of SOGMM depended on information criteria, class separation, and sample size. The estimates of baseline performance and growth factor mean differences between classes were generally unbiased but the size of measurement noninvariance was underestimated. Overall, SOGMM is advantageous in that it yields unbiased estimates of growth trajectory parameters and more accurate class enumeration compared to GMM by incorporating measurement models. PMID:28928691

Boundary conditions estimation on a road network using compressed sensing.

DOT National Transportation Integrated Search

2016-02-01

This report presents a new boundary condition estimation framework for transportation networks in which : the state is modeled by a first order scalar conservation law. Using an equivalent formulation based on a : Hamilton-Jacobi equation, we pose th...

Inverse modeling for seawater intrusion in coastal aquifers: Insights about parameter sensitivities, variances, correlations and estimation procedures derived from the Henry problem

USGS Publications Warehouse

Sanz, E.; Voss, C.I.

2006-01-01

Inverse modeling studies employing data collected from the classic Henry seawater intrusion problem give insight into several important aspects of inverse modeling of seawater intrusion problems and effective measurement strategies for estimation of parameters for seawater intrusion. Despite the simplicity of the Henry problem, it embodies the behavior of a typical seawater intrusion situation in a single aquifer. Data collected from the numerical problem solution are employed without added noise in order to focus on the aspects of inverse modeling strategies dictated by the physics of variable-density flow and solute transport during seawater intrusion. Covariances of model parameters that can be estimated are strongly dependent on the physics. The insights gained from this type of analysis may be directly applied to field problems in the presence of data errors, using standard inverse modeling approaches to deal with uncertainty in data. Covariance analysis of the Henry problem indicates that in order to generally reduce variance of parameter estimates, the ideal places to measure pressure are as far away from the coast as possible, at any depth, and the ideal places to measure concentration are near the bottom of the aquifer between the center of the transition zone and its inland fringe. These observations are located in and near high-sensitivity regions of system parameters, which may be identified in a sensitivity analysis with respect to several parameters. However, both the form of error distribution in the observations and the observation weights impact the spatial sensitivity distributions, and different choices for error distributions or weights can result in significantly different regions of high sensitivity. Thus, in order to design effective sampling networks, the error form and weights must be carefully considered. For the Henry problem, permeability and freshwater inflow can be estimated with low estimation variance from only pressure or only concentration observations. Permeability, freshwater inflow, solute molecular diffusivity, and porosity can be estimated with roughly equivalent confidence using observations of only the logarithm of concentration. Furthermore, covariance analysis allows a logical reduction of the number of estimated parameters for ill-posed inverse seawater intrusion problems. Ill-posed problems may exhibit poor estimation convergence, have a non-unique solution, have multiple minima, or require excessive computational effort, and the condition often occurs when estimating too many or co-dependent parameters. For the Henry problem, such analysis allows selection of the two parameters that control system physics from among all possible system parameters. ?? 2005 Elsevier Ltd. All rights reserved.

Intertemporal consumption with directly measured welfare functions and subjective expectations

PubMed Central

Kapteyn, Arie; Kleinjans, Kristin J.; van Soest, Arthur

2010-01-01

Euler equation estimation of intertemporal consumption models requires many, often unverifiable assumptions. These include assumptions on expectations and preferences. We aim at reducing some of these requirements by using direct subjective information on respondents’ preferences and expectations. The results suggest that individually measured welfare functions and expectations have predictive power for the variation in consumption across households. Furthermore, estimates of the intertemporal elasticity of substitution based on the estimated welfare functions are plausible and of a similar order of magnitude as other estimates found in the literature. The model favored by the data only requires cross-section data for estimation. PMID:20442798

Documentation of the Retail Price Model

EPA Pesticide Factsheets

The Retail Price Model (RPM) provides a first‐order estimate of average retail electricity prices using information from the EPA Base Case v.5.13 Base Case or other scenarios for each of the 64 Integrated Planing Model (IPM) regions.

Bayesian estimation of realized stochastic volatility model by Hybrid Monte Carlo algorithm

NASA Astrophysics Data System (ADS)

Takaishi, Tetsuya

2014-03-01

The hybrid Monte Carlo algorithm (HMCA) is applied for Bayesian parameter estimation of the realized stochastic volatility (RSV) model. Using the 2nd order minimum norm integrator (2MNI) for the molecular dynamics (MD) simulation in the HMCA, we find that the 2MNI is more efficient than the conventional leapfrog integrator. We also find that the autocorrelation time of the volatility variables sampled by the HMCA is very short. Thus it is concluded that the HMCA with the 2MNI is an efficient algorithm for parameter estimations of the RSV model.

A novel kinetic modeling method for the stabilization phase of the composting process for biodegradation of solid wastes.

PubMed

Ebrahimzadeh, Reza; Ghazanfari Moghaddam, Ahmad; Sarcheshmehpour, Mehdi; Mortezapour, Hamid

2017-12-01

Biomass degradation kinetics of the composting process for kitchen waste, pruned elm tree branches and sheep manure were studied to model changes in volatile solids (VS) over time. Three experimental reactors containing raw mixtures with a carbon to nitrogen (C/N) ratio of 27:1 and a moisture content of 65% were prepared. During the composting process two of the reactors used forced air and the third used natural aeration. The composting stabilization phases in all reactors were completed in 30 days. During this period, composting indexes such as temperature, moisture content and VS changes were recorded. Elementary reactions were used for kinetics modeling of the degradation process. Results showed that the numerical values of rate constant ( k) for zero-order ranged from 0.86 to 1.03 VS×day -1 , for first-order models it ranged from 0.01 to 0.02 day -1 , for second-order the range was from 1.36×10 -5 to 1.78×10 -5 VS -1 ×day -1 and for n-order the rate constant ranged from 0.031 to 0.095 VS (1-n) ×day -1 . The resulting models were validated by comparing statistical parameters. Evaluation of the models showed that, in the aerated reactors, the n-order models (less than 1) successfully estimated the VS changes. In the non-aeration reactor, for the second-order model good agreement was achieved between the simulated and actual quantities of VS. Also, half-life time provided a useful criterion for the estimation of expected time for completion of different phases of composting.

Estimation of Supercapacitor Energy Storage Based on Fractional Differential Equations.

PubMed

Kopka, Ryszard

2017-12-22

In this paper, new results on using only voltage measurements on supercapacitor terminals for estimation of accumulated energy are presented. For this purpose, a study based on application of fractional-order models of supercapacitor charging/discharging circuits is undertaken. Parameter estimates of the models are then used to assess the amount of the energy accumulated in supercapacitor. The obtained results are compared with energy determined experimentally by measuring voltage and current on supercapacitor terminals. All the tests are repeated for various input signal shapes and parameters. Very high consistency between estimated and experimental results fully confirm suitability of the proposed approach and thus applicability of the fractional calculus to modelling of supercapacitor energy storage.

Physician Utilization of a Hospital Information System: A Computer Simulation Model

PubMed Central

Anderson, James G.; Jay, Stephen J.; Clevenger, Stephen J.; Kassing, David R.; Perry, Jane; Anderson, Marilyn M.

1988-01-01

The purpose of this research was to develop a computer simulation model that represents the process through which physicians enter orders into a hospital information system (HIS). Computer simulation experiments were performed to estimate the effects of two methods of order entry on outcome variables. The results of the computer simulation experiments were used to perform a cost-benefit analysis to compare the two different means of entering medical orders into the HIS. The results indicate that the use of personal order sets to enter orders into the HIS will result in a significant reduction in manpower, salaries and fringe benefits, and errors in order entry.

Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle

PubMed Central

Cho, C. I.; Alam, M.; Choi, T. J.; Choy, Y. H.; Choi, J. G.; Lee, S. S.; Cho, K. H.

2016-01-01

The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3–L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of polynomials×3 types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea. PMID:26954184

Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle.

PubMed

Cho, C I; Alam, M; Choi, T J; Choy, Y H; Choi, J G; Lee, S S; Cho, K H

2016-05-01

The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3-L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of polynomials×3 types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea.

A model for dispersion from area sources in convective turbulence. [for air pollution

NASA Technical Reports Server (NTRS)

Crane, G.; Panofsky, H. A.; Zeman, O.

1977-01-01

Four independent estimates of the vertical distribution of the eddy coefficient for dispersion of a passive contaminant from an extensive area source in a convective layer have been presented. The estimates were based on the following methods: (1) a second-order closure prediction, (2) field data of pollutant concentrations over Los Angeles, (3) lab measurements of particle dispersion, and (4) assumption of equality between momentum and mass transfer coefficients in the free convective limit. It is suggested that K-values estimated both from second-order closure theory and from Los Angeles measurements are systematically underestimated.

A framework and a set of tools called Nutting models to estimate retention capacities and loads of nitrogen and phosphorus in rivers at catchment and national level (France)

NASA Astrophysics Data System (ADS)

Legeay, Pierre-Louis; Moatar, Florentina; Dupas, Rémi; Gascuel-Odoux, Chantal

2016-04-01

The Nutting-N and Nutting-P models (Dupas et al., 2013, 2015) have been developed to estimate Nitrogen and Phosphorus nonpoint-source emissions to surface water, using readily available data. These models were inspired from US model SPARROW (Smith al., 1997) and European model GREEN (Grizzetti et al., 2008), i.e. statistical approaches consisting of linking nitrogen and phosphorus surplus to catchment's land and rivers characteristics to find the catchment relative retention capacities. The nutrient load (L) at the outlet of each catchment is expressed as: L=R*(B*DS+PS) [1] where DS is diffuse sources (i.e. surplus in kg.ha-1/yr-1 for N, P storage in soil for P), PS is point sources from domestic and industrial origin (kg.ha-1.yr-1), R and B are the river system and basin reduction factor, respectively and they combine observed variables and calibrated parameters. The model was calibrated on independent catchments for the 2005-2009 and 2008-2012 periods. Variables were selected according to Bayesian Information Criterion (BIC) in order to optimize the predictive performance of the models. From these basic models, different improvements have been realized to build a framework and a set of tools: 1) a routing module has been added in order to improve estimations on 4 or 5 stream order, i.e. upscaling the basic Nutting approach; 2) a territorial module, in order to test the models at local scale (from 500 to 5000 km²); 3) a seasonal estimation has been investigated. The basic approach as well territorial application will be illustrated. These tools allow water manager to identify areas at risk where high nutrients loads are estimated, as well areas where retention is potentially high and can buffer high nutrient sources. References Dupas R., Curie F., Gascuel-Odoux C., Moatar F., Delmas M., Parnaudeau, V., Durand P., 2013. Assessing N emissions in surface water at the national level: Comparison of country-wide vs. regionalized models. Science of the Total Environment 443, 152-162 Dupas R., Delmas M., Dorioz J.M., Garnier J., Moatar F., Gascuel-Odoux C., 2015. Assessing the impact of agricultural pressures on N and P loads andeutrophication risk. Ecological Indicators 48, 396-407. Grizzetti B., Bouraoui F., De Marsily G., 2008. Assessing nitrogen pressures on European surface water. Global Biogeochemical Cycles; 22. Smith R.A., Schwarz G.E., Alexander R.B., 1997. Regional interpretation of water-quality monitoring data. Water Resources Research 1997; 33: 2781-2798.

Geomanetically Induced Currents (GIC) calculation, impact assessment on transmission system and validation using 3-D earth conductivity tensors and GIC measurements.

NASA Astrophysics Data System (ADS)

Sharma, R.; McCalley, J. D.

2016-12-01

Geomagnetic disturbance (GMD) causes the flow of geomagnetically induced currents (GIC) in the power transmission system that may cause large scale power outages and power system equipment damage. In order to plan for defense against GMD, it is necessary to accurately estimate the flow of GICs in the power transmission system. The current calculation as per NERC standards uses the 1-D earth conductivity models that don't reflect the coupling between the geoelectric and geomagnetic field components in the same direction. For accurate estimation of GICs, it is important to have spatially granular 3-D earth conductivity tensors, accurate DC network model of the transmission system and precisely estimated or measured input in the form of geomagnetic or geoelectric field data. Using these models and data the pre event, post event and online planning and assessment can be performed. The pre, post and online planning can be done by calculating GIC, analyzing voltage stability margin, identifying protection system vulnerabilities and estimating heating in transmission equipment. In order to perform the above mentioned tasks, an established GIC calculation and analysis procedure is needed that uses improved geophysical and DC network models obtained by model parameter tuning. The issue is addressed by performing the following tasks; 1) Geomagnetic field data and improved 3-D earth conductivity tensors are used to plot the geoelectric field map of a given area. The obtained geoelectric field map then serves as an input to the PSS/E platform, where through DC circuit analysis the GIC flows are calculated. 2) The computed GIC is evaluated against GIC measurements in order to fine tune the geophysical and DC network model parameters for any mismatch in the calculated and measured GIC. 3) The GIC calculation procedure is then adapted for a one in 100 year storm, in order to assess the impact of the worst case GMD on the power system. 4) Using the transformer models, the voltage stability margin would be analyzed for various real and synthetic geomagnetic or geoelectric field inputs, by calculating the reactive power absorbed by the transformers during an event. All four steps will help the electric utilities and planners to make use of better and accurate estimation techniques for GIC calculation, and impact assessment for future GMD events.

Estimation Of Rheological Law By Inverse Method From Flow And Temperature Measurements With An Extrusion Die

NASA Astrophysics Data System (ADS)

Pujos, Cyril; Regnier, Nicolas; Mousseau, Pierre; Defaye, Guy; Jarny, Yvon

2007-05-01

Simulation quality is determined by the knowledge of the parameters of the model. Yet the rheological models for polymer are often not very accurate, since the viscosity measurements are made under approximations as homogeneous temperature and empirical corrections as Bagley one. Furthermore rheological behaviors are often traduced by mathematical laws as the Cross or the Carreau-Yasuda ones, whose parameters are fitted from viscosity values, obtained with corrected experimental data, and not appropriate for each polymer. To correct these defaults, a table-like rheological model is proposed. This choice makes easier the estimation of model parameters, since each parameter has the same order of magnitude. As the mathematical shape of the model is not imposed, the estimation process is appropriate for each polymer. The proposed method consists in minimizing the quadratic norm of the difference between calculated variables and measured data. In this study an extrusion die is simulated, in order to provide us temperature along the extrusion channel, pressure and flow references. These data allow to characterize thermal transfers and flow phenomena, in which the viscosity is implied. Furthermore the different natures of data allow to estimate viscosity for a large range of shear rates. The estimated rheological model improves the agreement between measurements and simulation: for numerical cases, the error on the flow becomes less than 0.1% for non-Newtonian rheology. This method couples measurements and simulation, constitutes a very accurate mean of rheology determination, and allows to improve the prediction abilities of the model.

Targeted estimation of nuisance parameters to obtain valid statistical inference.

PubMed

van der Laan, Mark J

2014-01-01

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

Development of Flight-Test Performance Estimation Techniques for Small Unmanned Aerial Systems

NASA Astrophysics Data System (ADS)

McCrink, Matthew Henry

This dissertation provides a flight-testing framework for assessing the performance of fixed-wing, small-scale unmanned aerial systems (sUAS) by leveraging sub-system models of components unique to these vehicles. The development of the sub-system models, and their links to broader impacts on sUAS performance, is the key contribution of this work. The sub-system modeling and analysis focuses on the vehicle's propulsion, navigation and guidance, and airframe components. Quantification of the uncertainty in the vehicle's power available and control states is essential for assessing the validity of both the methods and results obtained from flight-tests. Therefore, detailed propulsion and navigation system analyses are presented to validate the flight testing methodology. Propulsion system analysis required the development of an analytic model of the propeller in order to predict the power available over a range of flight conditions. The model is based on the blade element momentum (BEM) method. Additional corrections are added to the basic model in order to capture the Reynolds-dependent scale effects unique to sUAS. The model was experimentally validated using a ground based testing apparatus. The BEM predictions and experimental analysis allow for a parameterized model relating the electrical power, measurable during flight, to the power available required for vehicle performance analysis. Navigation system details are presented with a specific focus on the sensors used for state estimation, and the resulting uncertainty in vehicle state. Uncertainty quantification is provided by detailed calibration techniques validated using quasi-static and hardware-in-the-loop (HIL) ground based testing. The HIL methods introduced use a soft real-time flight simulator to provide inertial quality data for assessing overall system performance. Using this tool, the uncertainty in vehicle state estimation based on a range of sensors, and vehicle operational environments is presented. The propulsion and navigation system models are used to evaluate flight-testing methods for evaluating fixed-wing sUAS performance. A brief airframe analysis is presented to provide a foundation for assessing the efficacy of the flight-test methods. The flight-testing presented in this work is focused on validating the aircraft drag polar, zero-lift drag coefficient, and span efficiency factor. Three methods are detailed and evaluated for estimating these design parameters. Specific focus is placed on the influence of propulsion and navigation system uncertainty on the resulting performance data. Performance estimates are used in conjunction with the propulsion model to estimate the impact sensor and measurement uncertainty on the endurance and range of a fixed-wing sUAS. Endurance and range results for a simplistic power available model are compared to the Reynolds-dependent model presented in this work. Additional parameter sensitivity analysis related to state estimation uncertainties encountered in flight-testing are presented. Results from these analyses indicate that the sub-system models introduced in this work are of first-order importance, on the order of 5-10% change in range and endurance, in assessing the performance of a fixed-wing sUAS.

The 4th order GISS model of the global atmosphere

NASA Technical Reports Server (NTRS)

Kalnay-Rivas, E.; Bayliss, A.; Storch, J.

1977-01-01

The new GISS 4th order model of the global atmosphere is described. It is based on 4th order quadratically conservative differences with the periodic application of a 16th order filter on the sea level pressure and potential temperature equations, a combination which is approximately enstrophy conserving. Several short range forecasts indicate a significant improvement over 2nd order forecasts with the same resolution (approximately 400 km). However the 4th order forecasts are somewhat inferior to 2nd order forecasts with double resolution. This is probably due to the presence of short waves in the range between 1000 km and 2000 km, which are computed more accurately by the 2nd order high resolution model. An operation count of the schemes indicates that with similar code optimization, the 4th order model will require approximately the same amount of computer time as the 2nd order model with the same resolution. It is estimated that the 4th order model with a grid size of 200 km provides enough accuracy to make horizontal truncation errors negligible over a period of a week for all synoptic scales (waves longer than 1000 km).

U.S. ENVIRONMENTAL PROTECTION AGENCY'S LANDFILL GAS EMISSION MODEL (LANDGEM)

EPA Science Inventory

The paper discusses EPA's available software for estimating landfill gas emissions. This software is based on a first-order decomposition rate equation using empirical data from U.S. landfills. The software provides a relatively simple approach to estimating landfill gas emissi...

A GRASS GIS module to obtain an estimation of glacier behavior under climate change: A pilot study on Italian glacier

NASA Astrophysics Data System (ADS)

Strigaro, Daniele; Moretti, Massimiliano; Mattavelli, Matteo; Frigerio, Ivan; Amicis, Mattia De; Maggi, Valter

2016-09-01

The aim of this work is to integrate the Minimal Glacier Model in a Geographic Information System Python module in order to obtain spatial simulations of glacier retreat and to assess the future scenarios with a spatial representation. The Minimal Glacier Models are a simple yet effective way of estimating glacier response to climate fluctuations. This module can be useful for the scientific and glaciological community in order to evaluate glacier behavior, driven by climate forcing. The module, called r.glacio.model, is developed in a GRASS GIS (GRASS Development Team, 2016) environment using Python programming language combined with different libraries as GDAL, OGR, CSV, math, etc. The module is applied and validated on the Rutor glacier, a glacier in the south-western region of the Italian Alps. This glacier is very large in size and features rather regular and lively dynamics. The simulation is calibrated by reconstructing the 3-dimensional dynamics flow line and analyzing the difference between the simulated flow line length variations and the observed glacier fronts coming from ortophotos and DEMs. These simulations are driven by the past mass balance record. Afterwards, the future assessment is estimated by using climatic drivers provided by a set of General Circulation Models participating in the Climate Model Inter-comparison Project 5 effort. The approach devised in r.glacio.model can be applied to most alpine glaciers to obtain a first-order spatial representation of glacier behavior under climate change.

Validation of drift and diffusion coefficients from experimental data

NASA Astrophysics Data System (ADS)

Riera, R.; Anteneodo, C.

2010-04-01

Many fluctuation phenomena, in physics and other fields, can be modeled by Fokker-Planck or stochastic differential equations whose coefficients, associated with drift and diffusion components, may be estimated directly from the observed time series. Its correct characterization is crucial to determine the system quantifiers. However, due to the finite sampling rates of real data, the empirical estimates may significantly differ from their true functional forms. In the literature, low-order corrections, or even no corrections, have been applied to the finite-time estimates. A frequent outcome consists of linear drift and quadratic diffusion coefficients. For this case, exact corrections have been recently found, from Itô-Taylor expansions. Nevertheless, model validation constitutes a necessary step before determining and applying the appropriate corrections. Here, we exploit the consequences of the exact theoretical results obtained for the linear-quadratic model. In particular, we discuss whether the observed finite-time estimates are actually a manifestation of that model. The relevance of this analysis is put into evidence by its application to two contrasting real data examples in which finite-time linear drift and quadratic diffusion coefficients are observed. In one case the linear-quadratic model is readily rejected while in the other, although the model constitutes a very good approximation, low-order corrections are inappropriate. These examples give warning signs about the proper interpretation of finite-time analysis even in more general diffusion processes.

Modeling and estimation of a low degree geopotential model from terrestrial gravity data

NASA Technical Reports Server (NTRS)

Pavlis, Nikolaos K.

1988-01-01

The development of appropriate modeling and adjustment procedures for the estimation of harmonic coefficients of the geopotential, from surface gravity data was studied, in order to provide an optimum way of utilizing the terrestrial gravity information in combination solutions currently developed at NASA/Goddard Space Flight Center, for use in the TOPEX/POSEIDON mission. The mathematical modeling was based on the fundamental boundary condition of the linearized Molodensky boundary value problem. Atmospheric and ellipsoidal corrections were applied to the surface anomalies. Terrestrial gravity solutions were found to be in good agreement with the satellite ones over areas which are well surveyed (gravimetrically), such as North America or Australia. However, systematic differences between the terrestrial only models and GEMT1, over extended regions in Africa, the Soviet Union, and China were found. In Africa, gravity anomaly differences on the order of 20 mgals and undulation differences on the order of 15 meters, over regions extending 2000 km in diameter, occur. Comparisons of the GEMT1 implied undulations with 32 well distributed Doppler derived undulations gave an RMS difference of 2.6 m, while corresponding comparison with undulations implied by the terrestrial solution gave RMS difference on the order of 15 m, which implies that the terrestrial data in that region are substantially in error.

Regression Models of Quarterly Overhead Costs for Six Government Aerospace Contractors.

DTIC Science & Technology

1986-03-01

34 Testing ,, for Serial Correlation After Least Squares %Regression, Econometrica, Vol. 36, No. 1, pp. 133-150, January 1968. Intrili8ator M.D., Econometric ...to be superior. These two estimators are both two-stage estimators that are calculated utilizing Wallis’s test statistic for fourth-order...utilizing Wallis’s test statistic for fourth-order autocorrelation. NTIS C F’,& D tI1C T - .1 I -. . . ..- rJ ,. *p J • - DA 3

On-board monitoring of 2-D spatially-resolved temperatures in cylindrical lithium-ion batteries: Part I. Low-order thermal modelling

NASA Astrophysics Data System (ADS)

Richardson, Robert R.; Zhao, Shi; Howey, David A.

2016-09-01

Estimating the temperature distribution within Li-ion batteries during operation is critical for safety and control purposes. Although existing control-oriented thermal models - such as thermal equivalent circuits (TEC) - are computationally efficient, they only predict average temperatures, and are unable to predict the spatially resolved temperature distribution throughout the cell. We present a low-order 2D thermal model of a cylindrical battery based on a Chebyshev spectral-Galerkin (SG) method, capable of predicting the full temperature distribution with a similar efficiency to a TEC. The model accounts for transient heat generation, anisotropic heat conduction, and non-homogeneous convection boundary conditions. The accuracy of the model is validated through comparison with finite element simulations, which show that the 2-D temperature field (r, z) of a large format (64 mm diameter) cell can be accurately modelled with as few as 4 states. Furthermore, the performance of the model for a range of Biot numbers is investigated via frequency analysis. For larger cells or highly transient thermal dynamics, the model order can be increased for improved accuracy. The incorporation of this model in a state estimation scheme with experimental validation against thermocouple measurements is presented in the companion contribution (http://www.sciencedirect.com/science/article/pii/S0378775316308163)

Heterogeneous autoregressive model with structural break using nearest neighbor truncation volatility estimators for DAX.

PubMed

Chin, Wen Cheong; Lee, Min Cherng; Yap, Grace Lee Ching

2016-01-01

High frequency financial data modelling has become one of the important research areas in the field of financial econometrics. However, the possible structural break in volatile financial time series often trigger inconsistency issue in volatility estimation. In this study, we propose a structural break heavy-tailed heterogeneous autoregressive (HAR) volatility econometric model with the enhancement of jump-robust estimators. The breakpoints in the volatility are captured by dummy variables after the detection by Bai-Perron sequential multi breakpoints procedure. In order to further deal with possible abrupt jump in the volatility, the jump-robust volatility estimators are composed by using the nearest neighbor truncation approach, namely the minimum and median realized volatility. Under the structural break improvements in both the models and volatility estimators, the empirical findings show that the modified HAR model provides the best performing in-sample and out-of-sample forecast evaluations as compared with the standard HAR models. Accurate volatility forecasts have direct influential to the application of risk management and investment portfolio analysis.

Combined Yamamoto approach for simultaneous estimation of adsorption isotherm and kinetic parameters in ion-exchange chromatography.

PubMed

Rüdt, Matthias; Gillet, Florian; Heege, Stefanie; Hitzler, Julian; Kalbfuss, Bernd; Guélat, Bertrand

2015-09-25

Application of model-based design is appealing to support the development of protein chromatography in the biopharmaceutical industry. However, the required efforts for parameter estimation are frequently perceived as time-consuming and expensive. In order to speed-up this work, a new parameter estimation approach for modelling ion-exchange chromatography in linear conditions was developed. It aims at reducing the time and protein demand for the model calibration. The method combines the estimation of kinetic and thermodynamic parameters based on the simultaneous variation of the gradient slope and the residence time in a set of five linear gradient elutions. The parameters are estimated from a Yamamoto plot and a gradient-adjusted Van Deemter plot. The combined approach increases the information extracted per experiment compared to the individual methods. As a proof of concept, the combined approach was successfully applied for a monoclonal antibody on a cation-exchanger and for a Fc-fusion protein on an anion-exchange resin. The individual parameter estimations for the mAb confirmed that the new approach maintained the accuracy of the usual Yamamoto and Van Deemter plots. In the second case, offline size-exclusion chromatography was performed in order to estimate the thermodynamic parameters of an impurity (high molecular weight species) simultaneously with the main product. Finally, the parameters obtained from the combined approach were used in a lumped kinetic model to simulate the chromatography runs. The simulated chromatograms obtained for a wide range of gradient lengths and residence times showed only small deviations compared to the experimental data. Copyright © 2015 Elsevier B.V. All rights reserved.

Spatial capture-recapture models for jointly estimating population density and landscape connectivity

USGS Publications Warehouse

Royle, J. Andrew; Chandler, Richard B.; Gazenski, Kimberly D.; Graves, Tabitha A.

2013-01-01

Population size and landscape connectivity are key determinants of population viability, yet no methods exist for simultaneously estimating density and connectivity parameters. Recently developed spatial capture–recapture (SCR) models provide a framework for estimating density of animal populations but thus far have not been used to study connectivity. Rather, all applications of SCR models have used encounter probability models based on the Euclidean distance between traps and animal activity centers, which implies that home ranges are stationary, symmetric, and unaffected by landscape structure. In this paper we devise encounter probability models based on “ecological distance,” i.e., the least-cost path between traps and activity centers, which is a function of both Euclidean distance and animal movement behavior in resistant landscapes. We integrate least-cost path models into a likelihood-based estimation scheme for spatial capture–recapture models in order to estimate population density and parameters of the least-cost encounter probability model. Therefore, it is possible to make explicit inferences about animal density, distribution, and landscape connectivity as it relates to animal movement from standard capture–recapture data. Furthermore, a simulation study demonstrated that ignoring landscape connectivity can result in negatively biased density estimators under the naive SCR model.

Spatial capture--recapture models for jointly estimating population density and landscape connectivity.

PubMed

Royle, J Andrew; Chandler, Richard B; Gazenski, Kimberly D; Graves, Tabitha A

2013-02-01

Population size and landscape connectivity are key determinants of population viability, yet no methods exist for simultaneously estimating density and connectivity parameters. Recently developed spatial capture--recapture (SCR) models provide a framework for estimating density of animal populations but thus far have not been used to study connectivity. Rather, all applications of SCR models have used encounter probability models based on the Euclidean distance between traps and animal activity centers, which implies that home ranges are stationary, symmetric, and unaffected by landscape structure. In this paper we devise encounter probability models based on "ecological distance," i.e., the least-cost path between traps and activity centers, which is a function of both Euclidean distance and animal movement behavior in resistant landscapes. We integrate least-cost path models into a likelihood-based estimation scheme for spatial capture-recapture models in order to estimate population density and parameters of the least-cost encounter probability model. Therefore, it is possible to make explicit inferences about animal density, distribution, and landscape connectivity as it relates to animal movement from standard capture-recapture data. Furthermore, a simulation study demonstrated that ignoring landscape connectivity can result in negatively biased density estimators under the naive SCR model.

Modeling, estimation and identification methods for static shape determination of flexible structures. [for large space structure design

NASA Technical Reports Server (NTRS)

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

1986-01-01

This paper outlines methods for modeling, identification and estimation for static determination of flexible structures. The shape estimation schemes are based on structural models specified by (possibly interconnected) elliptic partial differential equations. The identification techniques provide approximate knowledge of parameters in elliptic systems. The techniques are based on the method of maximum-likelihood that finds parameter values such that the likelihood functional associated with the system model is maximized. The estimation methods are obtained by means of a function-space approach that seeks to obtain the conditional mean of the state given the data and a white noise characterization of model errors. The solutions are obtained in a batch-processing mode in which all the data is processed simultaneously. After methods for computing the optimal estimates are developed, an analysis of the second-order statistics of the estimates and of the related estimation error is conducted. In addition to outlining the above theoretical results, the paper presents typical flexible structure simulations illustrating performance of the shape determination methods.

Random regression models using different functions to model milk flow in dairy cows.

PubMed

Laureano, M M M; Bignardi, A B; El Faro, L; Cardoso, V L; Tonhati, H; Albuquerque, L G

2014-09-12

We analyzed 75,555 test-day milk flow records from 2175 primiparous Holstein cows that calved between 1997 and 2005. Milk flow was obtained by dividing the mean milk yield (kg) of the 3 daily milking by the total milking time (min) and was expressed as kg/min. Milk flow was grouped into 43 weekly classes. The analyses were performed using a single-trait Random Regression Models that included direct additive genetic, permanent environmental, and residual random effects. In addition, the contemporary group and linear and quadratic effects of cow age at calving were included as fixed effects. Fourth-order orthogonal Legendre polynomial of days in milk was used to model the mean trend in milk flow. The additive genetic and permanent environmental covariance functions were estimated using random regression Legendre polynomials and B-spline functions of days in milk. The model using a third-order Legendre polynomial for additive genetic effects and a sixth-order polynomial for permanent environmental effects, which contained 7 residual classes, proved to be the most adequate to describe variations in milk flow, and was also the most parsimonious. The heritability in milk flow estimated by the most parsimonious model was of moderate to high magnitude.

Poisson regression models outperform the geometrical model in estimating the peak-to-trough ratio of seasonal variation: a simulation study.

PubMed

Christensen, A L; Lundbye-Christensen, S; Dethlefsen, C

2011-12-01

Several statistical methods of assessing seasonal variation are available. Brookhart and Rothman [3] proposed a second-order moment-based estimator based on the geometrical model derived by Edwards [1], and reported that this estimator is superior in estimating the peak-to-trough ratio of seasonal variation compared with Edwards' estimator with respect to bias and mean squared error. Alternatively, seasonal variation may be modelled using a Poisson regression model, which provides flexibility in modelling the pattern of seasonal variation and adjustments for covariates. Based on a Monte Carlo simulation study three estimators, one based on the geometrical model, and two based on log-linear Poisson regression models, were evaluated in regards to bias and standard deviation (SD). We evaluated the estimators on data simulated according to schemes varying in seasonal variation and presence of a secular trend. All methods and analyses in this paper are available in the R package Peak2Trough[13]. Applying a Poisson regression model resulted in lower absolute bias and SD for data simulated according to the corresponding model assumptions. Poisson regression models had lower bias and SD for data simulated to deviate from the corresponding model assumptions than the geometrical model. This simulation study encourages the use of Poisson regression models in estimating the peak-to-trough ratio of seasonal variation as opposed to the geometrical model. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Performance of signal-to-noise ratio estimation for scanning electron microscope using autocorrelation Levinson-Durbin recursion model.

PubMed

Sim, K S; Lim, M S; Yeap, Z X

2016-07-01

A new technique to quantify signal-to-noise ratio (SNR) value of the scanning electron microscope (SEM) images is proposed. This technique is known as autocorrelation Levinson-Durbin recursion (ACLDR) model. To test the performance of this technique, the SEM image is corrupted with noise. The autocorrelation function of the original image and the noisy image are formed. The signal spectrum based on the autocorrelation function of image is formed. ACLDR is then used as an SNR estimator to quantify the signal spectrum of noisy image. The SNR values of the original image and the quantified image are calculated. The ACLDR is then compared with the three existing techniques, which are nearest neighbourhood, first-order linear interpolation and nearest neighbourhood combined with first-order linear interpolation. It is shown that ACLDR model is able to achieve higher accuracy in SNR estimation. © 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.

Maximum Marginal Likelihood Estimation of a Monotonic Polynomial Generalized Partial Credit Model with Applications to Multiple Group Analysis.

PubMed

Falk, Carl F; Cai, Li

2016-06-01

We present a semi-parametric approach to estimating item response functions (IRF) useful when the true IRF does not strictly follow commonly used functions. Our approach replaces the linear predictor of the generalized partial credit model with a monotonic polynomial. The model includes the regular generalized partial credit model at the lowest order polynomial. Our approach extends Liang's (A semi-parametric approach to estimate IRFs, Unpublished doctoral dissertation, 2007) method for dichotomous item responses to the case of polytomous data. Furthermore, item parameter estimation is implemented with maximum marginal likelihood using the Bock-Aitkin EM algorithm, thereby facilitating multiple group analyses useful in operational settings. Our approach is demonstrated on both educational and psychological data. We present simulation results comparing our approach to more standard IRF estimation approaches and other non-parametric and semi-parametric alternatives.

Unscented predictive variable structure filter for satellite attitude estimation with model errors when using low precision sensors

NASA Astrophysics Data System (ADS)

Cao, Lu; Li, Hengnian

2016-10-01

For the satellite attitude estimation problem, the serious model errors always exist and hider the estimation performance of the Attitude Determination and Control System (ACDS), especially for a small satellite with low precision sensors. To deal with this problem, a new algorithm for the attitude estimation, referred to as the unscented predictive variable structure filter (UPVSF) is presented. This strategy is proposed based on the variable structure control concept and unscented transform (UT) sampling method. It can be implemented in real time with an ability to estimate the model errors on-line, in order to improve the state estimation precision. In addition, the model errors in this filter are not restricted only to the Gaussian noises; therefore, it has the advantages to deal with the various kinds of model errors or noises. It is anticipated that the UT sampling strategy can further enhance the robustness and accuracy of the novel UPVSF. Numerical simulations show that the proposed UPVSF is more effective and robustness in dealing with the model errors and low precision sensors compared with the traditional unscented Kalman filter (UKF).

Autoregressive linear least square single scanning electron microscope image signal-to-noise ratio estimation.

PubMed

Sim, Kok Swee; NorHisham, Syafiq

2016-11-01

A technique based on linear Least Squares Regression (LSR) model is applied to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. In order to test the accuracy of this technique on SNR estimation, a number of SEM images are initially corrupted with white noise. The autocorrelation function (ACF) of the original and the corrupted SEM images are formed to serve as the reference point to estimate the SNR value of the corrupted image. The LSR technique is then compared with the previous three existing techniques known as nearest neighbourhood, first-order interpolation, and the combination of both nearest neighborhood and first-order interpolation. The actual and the estimated SNR values of all these techniques are then calculated for comparison purpose. It is shown that the LSR technique is able to attain the highest accuracy compared to the other three existing techniques as the absolute difference between the actual and the estimated SNR value is relatively small. SCANNING 38:771-782, 2016. © 2016 Wiley Periodicals, Inc. © Wiley Periodicals, Inc.

Hybrid estimation of complex systems.

PubMed

Hofbaur, Michael W; Williams, Brian C

2004-10-01

Modern automated systems evolve both continuously and discretely, and hence require estimation techniques that go well beyond the capability of a typical Kalman Filter. Multiple model (MM) estimation schemes track these system evolutions by applying a bank of filters, one for each discrete system mode. Modern systems, however, are often composed of many interconnected components that exhibit rich behaviors, due to complex, system-wide interactions. Modeling these systems leads to complex stochastic hybrid models that capture the large number of operational and failure modes. This large number of modes makes a typical MM estimation approach infeasible for online estimation. This paper analyzes the shortcomings of MM estimation, and then introduces an alternative hybrid estimation scheme that can efficiently estimate complex systems with large number of modes. It utilizes search techniques from the toolkit of model-based reasoning in order to focus the estimation on the set of most likely modes, without missing symptoms that might be hidden amongst the system noise. In addition, we present a novel approach to hybrid estimation in the presence of unknown behavioral modes. This leads to an overall hybrid estimation scheme for complex systems that robustly copes with unforeseen situations in a degraded, but fail-safe manner.

Cloud-based shaft torque estimation for electric vehicle equipped with integrated motor-transmission system

NASA Astrophysics Data System (ADS)

Zhu, Xiaoyuan; Zhang, Hui; Yang, Bo; Zhang, Guichen

2018-01-01

In order to improve oscillation damping control performance as well as gear shift quality of electric vehicle equipped with integrated motor-transmission system, a cloud-based shaft torque estimation scheme is proposed in this paper by using measurable motor and wheel speed signals transmitted by wireless network. It can help reduce computational burden of onboard controllers and also relief network bandwidth requirement of individual vehicle. Considering possible delays during signal wireless transmission, delay-dependent full-order observer design is proposed to estimate the shaft torque in cloud server. With these random delays modeled by using homogenous Markov chain, robust H∞ performance is adopted to minimize the effect of wireless network-induced delays, signal measurement noise as well as system modeling uncertainties on shaft torque estimation error. Observer parameters are derived by solving linear matrix inequalities, and simulation results using acceleration test and tip-in, tip-out test demonstrate the effectiveness of proposed shaft torque observer design.

Basic Investigations of Dynamic Travel Time Estimation Model for Traffic Signals Control Using Information from Optical Beacons

NASA Astrophysics Data System (ADS)

Okutani, Iwao; Mitsui, Tatsuro; Nakada, Yusuke

In this paper put forward are neuron-type models, i.e., neural network model, wavelet neuron model and three layered wavelet neuron model(WV3), for estimating traveling time between signalized intersections in order to facilitate adaptive setting of traffic signal parameters such as green time and offset. Model validation tests using simulated data reveal that compared to other models, WV3 model works very fast in learning process and can produce more accurate estimates of travel time. Also, it is exhibited that up-link information obtainable from optical beacons, i.e., travel time observed during the former cycle time in this case, makes a crucial input variable to the models in that there isn't any substantial difference between the change of estimated and simulated travel time with the change of green time or offset when up-link information is employed as input while there appears big discrepancy between them when not employed.

Dynamic characteristics of oxygen consumption.

PubMed

Ye, Lin; Argha, Ahmadreza; Yu, Hairong; Celler, Branko G; Nguyen, Hung T; Su, Steven

2018-04-23

Previous studies have indicated that oxygen uptake ([Formula: see text]) is one of the most accurate indices for assessing the cardiorespiratory response to exercise. In most existing studies, the response of [Formula: see text] is often roughly modelled as a first-order system due to the inadequate stimulation and low signal to noise ratio. To overcome this difficulty, this paper proposes a novel nonparametric kernel-based method for the dynamic modelling of [Formula: see text] response to provide a more robust estimation. Twenty healthy non-athlete participants conducted treadmill exercises with monotonous stimulation (e.g., single step function as input). During the exercise, [Formula: see text] was measured and recorded by a popular portable gas analyser ([Formula: see text], COSMED). Based on the recorded data, a kernel-based estimation method was proposed to perform the nonparametric modelling of [Formula: see text]. For the proposed method, a properly selected kernel can represent the prior modelling information to reduce the dependence of comprehensive stimulations. Furthermore, due to the special elastic net formed by [Formula: see text] norm and kernelised [Formula: see text] norm, the estimations are smooth and concise. Additionally, the finite impulse response based nonparametric model which estimated by the proposed method can optimally select the order and fit better in terms of goodness-of-fit comparing to classical methods. Several kernels were introduced for the kernel-based [Formula: see text] modelling method. The results clearly indicated that the stable spline (SS) kernel has the best performance for [Formula: see text] modelling. Particularly, based on the experimental data from 20 participants, the estimated response from the proposed method with SS kernel was significantly better than the results from the benchmark method [i.e., prediction error method (PEM)] ([Formula: see text] vs [Formula: see text]). The proposed nonparametric modelling method is an effective method for the estimation of the impulse response of VO 2 -Speed system. Furthermore, the identified average nonparametric model method can dynamically predict [Formula: see text] response with acceptable accuracy during treadmill exercise.

Prediction of in vivo neutral detergent fiber digestibility and digestion rate of potentially digestible neutral detergent fiber: comparison of models.

PubMed

Huhtanen, P; Seppälä, A; Ahvenjärvi, S; Rinne, M

2008-10-01

Eleven 1-pool, seven 2-pool, and three 3-pool models were compared in fitting gas production data and predicting in vivo NDF digestibility and effective first-order digestion rate of potentially digestible NDF (pdNDF). Isolated NDF from 15 grass silages harvested at different stages of maturity was incubated in triplicate in rumen fluid-buffer solution for 72 h to estimate the digestion kinetics from cumulative gas production profiles. In vivo digestibility was estimated by the total fecal collection method in sheep fed at a maintenance level of feeding. The concentration of pdNDF was estimated by a 12-d in situ incubation. The parameter values from gas production profiles and pdNDF were used in a 2-compartment rumen model to predict pdNDF digestibility using 50 h of rumen residence time distributed in a ratio of 0.4:0.6 between the non-escapable and escapable pools. The effective first-order digestion rate was computed both from observed in vivo and model-predicted pdNDF digestibility assuming the passage kinetic model described above. There were marked differences between the models in fitting the gas production data. The fit improved with increasing number of pools, suggesting that silage pdNDF is not a homogenous substrate. Generally, the models predicted in vivo NDF digestibility and digestion rate accurately. However, a good fit of gas production data was not necessarily translated into improved predictions of the in vivo data. The models overestimating the asymptotic gas volumes tended to underestimate the in vivo digestibility. Investigating the time-related residuals during the later phases of fermentation is important when the data are used to estimate the first-order digestion rate of pdNDF. Relatively simple models such as the France model or even a single exponential model with discrete lag period satisfied the minimum criteria for a good model. Further, the comparison of feedstuffs on the basis of parameter values is more unequivocal than in the case of multiple-pool models.

A numerical identifiability test for state-space models--application to optimal experimental design.

PubMed

Hidalgo, M E; Ayesa, E

2001-01-01

This paper describes a mathematical tool for identifiability analysis, easily applicable to high order non-linear systems modelled in state-space and implementable in simulators with a time-discrete approach. This procedure also permits a rigorous analysis of the expected estimation errors (average and maximum) in calibration experiments. The methodology is based on the recursive numerical evaluation of the information matrix during the simulation of a calibration experiment and in the setting-up of a group of information parameters based on geometric interpretations of this matrix. As an example of the utility of the proposed test, the paper presents its application to an optimal experimental design of ASM Model No. 1 calibration, in order to estimate the maximum specific growth rate microH and the concentration of heterotrophic biomass XBH.

An assessment of the feasibility of the use of satellite-only rainfall estimates for the hydrological monitoring in central Italy

NASA Astrophysics Data System (ADS)

Campo, Lorenzo; Caparrini, Francesca

2013-04-01

The need for accurate distributed hydrological modelling has constantly increased in last years for several purposes: agricultural applications, water resources management, hydrological balance at watershed scale, floods forecast. The main input for the hydrological numerical models is rainfall data that present, at the same time, a large availability of measures (in gauged regions, with respect to other micro-meteorological variables) and the most complex spatial patterns. While also in presence of densely gauged watersheds the spatial interpolation of the rainfall is a non-trivial problem, due to the spatial intermittence of the variable (especially at finer temporal scales), ungauged regions need an alternative source of rainfall data in order to perform the hydrological modelling. Such source can be constituted by the satellite-estimated rainfall fields, with reference to both geostationary and polar-orbit platforms. In this work the rainfall product obtained by the Aqua-AIRS sensor were used in order to assess the feasibility of the use of satellite-based rainfall as input for distributed hydrological modelling. The MOBIDIC (MOdello di BIlancio Distribuito e Continuo) model, developed at the Department of civil and Environmental Engineering of the University of Florence and operationally used by Tuscany Region and Umbria Region for flood prediction and management, was used for the experiments. In particular three experiments were carried on: a) hydrological simulation with the use of rain-gauges data, b) simulation with the use of satellite-only rainfall estimates, c) simulation with the combined use of the two sources of data in order to obtain an optimal estimate of the actual rainfall fields. The domain of the study was the central Italy. Several critical events occurred in the area were analyzed. A discussion of the results is provided.

Iterative Procedures for Exact Maximum Likelihood Estimation in the First-Order Gaussian Moving Average Model

DTIC Science & Technology

1990-11-01

1 = Q- 1 - 1 QlaaQ- 1.1 + a’Q-1a This is a simple case of a general formula called Woodbury’s formula by some authors; see, for example, Phadke and...1 2. The First-Order Moving Average Model ..... .................. 3. Some Approaches to the Iterative...the approximate likelihood function in some time series models. Useful suggestions have been the Cholesky decomposition of the covariance matrix and

Genetic evaluation of weekly body weight in Japanese quail using random regression models.

PubMed

Karami, K; Zerehdaran, S; Tahmoorespur, M; Barzanooni, B; Lotfi, E

2017-02-01

1. A total of 11 826 records from 2489 quails, hatched between 2012 and 2013, were used to estimate genetic parameters for BW (body weight) of Japanese quail using random regression models. Weekly BW was measured from hatch until 49 d of age. WOMBAT software (University of New England, Australia) was used for estimating genetic and phenotypic parameters. 2. Nineteen models were evaluated to identify the best orders of Legendre polynomials. A model with Legendre polynomial of order 3 for additive genetic effect, order 3 for permanent environmental effects and order 1 for maternal permanent environmental effects was chosen as the best model. 3. According to the best model, phenotypic and genetic variances were higher at the end of the rearing period. Although direct heritability for BW reduced from 0.18 at hatch to 0.12 at 7 d of age, it gradually increased to 0.42 at 49 d of age. It indicates that BW at older ages is more controlled by genetic components in Japanese quail. 4. Phenotypic and genetic correlations between adjacent periods except hatching weight were more closely correlated than remote periods. The present results suggested that BW at earlier ages, especially at hatch, are different traits compared to BW at older ages. Therefore, BW at earlier ages could not be used as a selection criterion for improving BW at slaughter age.

High-order computer-assisted estimates of topological entropy

NASA Astrophysics Data System (ADS)

Grote, Johannes

The concept of Taylor Models is introduced, which offers highly accurate C0-estimates for the enclosures of functional dependencies, combining high-order Taylor polynomial approximation of functions and rigorous estimates of the truncation error, performed using verified interval arithmetic. The focus of this work is on the application of Taylor Models in algorithms for strongly nonlinear dynamical systems. A method to obtain sharp rigorous enclosures of Poincare maps for certain types of flows and surfaces is developed and numerical examples are presented. Differential algebraic techniques allow the efficient and accurate computation of polynomial approximations for invariant curves of certain planar maps around hyperbolic fixed points. Subsequently we introduce a procedure to extend these polynomial curves to verified Taylor Model enclosures of local invariant manifolds with C0-errors of size 10-10--10 -14, and proceed to generate the global invariant manifold tangle up to comparable accuracy through iteration in Taylor Model arithmetic. Knowledge of the global manifold structure up to finite iterations of the local manifold pieces enables us to find all homoclinic and heteroclinic intersections in the generated manifold tangle. Combined with the mapping properties of the homoclinic points and their ordering we are able to construct a subshift of finite type as a topological factor of the original planar system to obtain rigorous lower bounds for its topological entropy. This construction is fully automatic and yields homoclinic tangles with several hundred homoclinic points. As an example rigorous lower bounds for the topological entropy of the Henon map are computed, which to the best knowledge of the authors yield the largest such estimates published so far.

Comparison of risk estimates using life-table methods.

PubMed

Sullivan, R E; Weng, P S

1987-08-01

Risk estimates promulgated by various radiation protection authorities in recent years have become increasingly more complex. Early "integral" estimates in the form of health effects per 0.01 person-Gy (per person-rad) or per 10(4) person-Gy (per 10(6) person-rad) have tended to be replaced by "differential" estimates which are age- and sex-dependent and specify both minimum induction (latency) and duration of risk expression (plateau) periods. These latter types of risk estimate must be used in conjunction with a life table in order to reduce them to integral form. In this paper, the life table has been used to effect a comparison of the organ and tissue risk estimates derived in several recent reports. In addition, a brief review of life-table methodology is presented and some features of the models used in deriving differential coefficients are discussed. While the great number of permutations possible with dose-response models, detailed risk estimates and proposed projection models precludes any unique result, the reduced integral coefficients are required to conform to the linear, absolute-risk model recommended for use with the integral risk estimates reviewed.

ARM Best Estimate Data (ARMBE) Products for Climate Science for a Sustainable Energy Future (CSSEF)

DOE Data Explorer

Riihimaki, Laura; Gaustad, Krista; McFarlane, Sally

2014-06-12

This data set was created for the Climate Science for a Sustainable Energy Future (CSSEF) model testbed project and is an extension of the hourly average ARMBE dataset to other extended facility sites and to include uncertainty estimates. Uncertainty estimates were needed in order to use uncertainty quantification (UQ) techniques with the data.

Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering.

PubMed

Wu, Dongjin; Xia, Linyuan; Geng, Jijun

2018-06-19

Pedestrian dead reckoning (PDR) using smart phone-embedded micro-electro-mechanical system (MEMS) sensors plays a key role in ubiquitous localization indoors and outdoors. However, as a relative localization method, it suffers from the problem of error accumulation which prevents it from long term independent running. Heading estimation error is one of the main location error sources, and therefore, in order to improve the location tracking performance of the PDR method in complex environments, an approach based on robust adaptive Kalman filtering (RAKF) for estimating accurate headings is proposed. In our approach, outputs from gyroscope, accelerometer, and magnetometer sensors are fused using the solution of Kalman filtering (KF) that the heading measurements derived from accelerations and magnetic field data are used to correct the states integrated from angular rates. In order to identify and control measurement outliers, a maximum likelihood-type estimator (M-estimator)-based model is used. Moreover, an adaptive factor is applied to resist the negative effects of state model disturbances. Extensive experiments under static and dynamic conditions were conducted in indoor environments. The experimental results demonstrate the proposed approach provides more accurate heading estimates and supports more robust and dynamic adaptive location tracking, compared with methods based on conventional KF.

Robust learning for optimal treatment decision with NP-dimensionality

PubMed Central

Shi, Chengchun; Song, Rui; Lu, Wenbin

2016-01-01

In order to identify important variables that are involved in making optimal treatment decision, Lu, Zhang and Zeng (2013) proposed a penalized least squared regression framework for a fixed number of predictors, which is robust against the misspecification of the conditional mean model. Two problems arise: (i) in a world of explosively big data, effective methods are needed to handle ultra-high dimensional data set, for example, with the dimension of predictors is of the non-polynomial (NP) order of the sample size; (ii) both the propensity score and conditional mean models need to be estimated from data under NP dimensionality. In this paper, we propose a robust procedure for estimating the optimal treatment regime under NP dimensionality. In both steps, penalized regressions are employed with the non-concave penalty function, where the conditional mean model of the response given predictors may be misspecified. The asymptotic properties, such as weak oracle properties, selection consistency and oracle distributions, of the proposed estimators are investigated. In addition, we study the limiting distribution of the estimated value function for the obtained optimal treatment regime. The empirical performance of the proposed estimation method is evaluated by simulations and an application to a depression dataset from the STAR*D study. PMID:28781717

Cause and cure of sloppiness in ordinary differential equation models.

PubMed

Tönsing, Christian; Timmer, Jens; Kreutz, Clemens

2014-08-01

Data-based mathematical modeling of biochemical reaction networks, e.g., by nonlinear ordinary differential equation (ODE) models, has been successfully applied. In this context, parameter estimation and uncertainty analysis is a major task in order to assess the quality of the description of the system by the model. Recently, a broadened eigenvalue spectrum of the Hessian matrix of the objective function covering orders of magnitudes was observed and has been termed as sloppiness. In this work, we investigate the origin of sloppiness from structures in the sensitivity matrix arising from the properties of the model topology and the experimental design. Furthermore, we present strategies using optimal experimental design methods in order to circumvent the sloppiness issue and present nonsloppy designs for a benchmark model.

Cause and cure of sloppiness in ordinary differential equation models

NASA Astrophysics Data System (ADS)

Tönsing, Christian; Timmer, Jens; Kreutz, Clemens

2014-08-01

Data-based mathematical modeling of biochemical reaction networks, e.g., by nonlinear ordinary differential equation (ODE) models, has been successfully applied. In this context, parameter estimation and uncertainty analysis is a major task in order to assess the quality of the description of the system by the model. Recently, a broadened eigenvalue spectrum of the Hessian matrix of the objective function covering orders of magnitudes was observed and has been termed as sloppiness. In this work, we investigate the origin of sloppiness from structures in the sensitivity matrix arising from the properties of the model topology and the experimental design. Furthermore, we present strategies using optimal experimental design methods in order to circumvent the sloppiness issue and present nonsloppy designs for a benchmark model.

Crash protectiveness to occupant injury and vehicle damage: An investigation on major car brands.

PubMed

Huang, Helai; Li, Chunyang; Zeng, Qiang

2016-01-01

This study sets out to investigate vehicles' crash protectiveness on occupant injury and vehicle damage, which can be deemed as an extension of the traditional crash worthiness. A Bayesian bivariate hierarchical ordered logistic (BVHOL) model is developed to estimate the occupant protectiveness (OP) and vehicle protectiveness (VP) of 23 major car brands in Florida, with considering vehicles' crash aggressivity and controlling external factors. The proposed model not only takes over the strength of the existing hierarchical ordered logistic (HOL) model, i.e. specifying the order characteristics of crash outcomes and cross-crash heterogeneities, but also accounts for the correlation between the two crash responses, driver injury and vehicle damage. A total of 7335 two-vehicle-crash records with 14,670 cars involved in Florida are used for the investigation. From the estimation results, it's found that most of the luxury cars such as Cadillac, Volvo and Lexus possess excellent OP and VP while some brands such as KIA and Saturn perform very badly in both aspects. The ranks of the estimated safety performance indices are even compared to the counterparts in Huang et al. study [Huang, H., Hu, S., Abdel-Aty, M., 2014. Indexing crash worthiness and crash aggressivity by major car brands. Safety Science 62, 339-347]. The results show that the rank of occupant protectiveness index (OPI) is relatively coherent with that of crash worthiness index, but the ranks of crash aggressivity index in both studies is more different from each other. Meanwhile, a great discrepancy between the OPI rank and that of vehicle protectiveness index is found. What's more, the results of control variables and hyper-parameters estimation as well as comparison to HOL models with separate or identical threshold errors, demonstrate the validity and advancement of the proposed model and the robustness of the estimated OP and VP. Copyright © 2015 Elsevier Ltd. All rights reserved.

Reduced Order Modeling in General Relativity

NASA Astrophysics Data System (ADS)

Tiglio, Manuel

2014-03-01

Reduced Order Modeling is an emerging yet fast developing filed in gravitational wave physics. The main goals are to enable fast modeling and parameter estimation of any detected signal, along with rapid matched filtering detecting. I will focus on the first two. Some accomplishments include being able to replace, with essentially no lost of physical accuracy, the original models with surrogate ones (which are not effective ones, that is, they do not simplify the physics but go on a very different track, exploiting the particulars of the waveform family under consideration and state of the art dimensional reduction techniques) which are very fast to evaluate. For example, for EOB models they are at least around 3 orders of magnitude faster than solving the original equations, with physically equivalent results. For numerical simulations the speedup is at least 11 orders of magnitude. For parameter estimation our current numbers are about bringing ~100 days for a single SPA inspiral binary neutron star Bayesian parameter estimation analysis to under a day. More recently, it has been shown that the full precessing problem for, say, 200 cycles, can be represented, through some new ideas, by a remarkably compact set of carefully chosen reduced basis waveforms (~10-100, depending on the accuracy requirements). I will highlight what I personally believe are the challenges to face next in this subarea of GW physics and where efforts should be directed. This talk will summarize work in collaboration with: Harbir Antil (GMU), Jonathan Blackman (Caltech), Priscila Canizares (IoA, Cambridge, UK), Sarah Caudill (UWM), Jonathan Gair (IoA. Cambridge. UK), Scott Field (UMD), Chad R. Galley (Caltech), Frank Herrmann (Germany), Han Hestahven (EPFL, Switzerland), Jason Kaye (Brown, Stanford & Courant). Evan Ochsner (UWM), Ricardo Nochetto (UMD), Vivien Raymond (LIGO, Caltech), Rory Smith (LIGO, Caltech) Bela Ssilagyi (Caltech) and MT (UMD & Caltech).

IDC Reengineering Phase 2 & 3 Rough Order of Magnitude (ROM) Cost Estimate Summary (Leveraged NDC Case).

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

Harris, James M.; Prescott, Ryan; Dawson, Jericah M.

2014-11-01

Sandia National Laboratories has prepared a ROM cost estimate for budgetary planning for the IDC Reengineering Phase 2 & 3 effort, based on leveraging a fully funded, Sandia executed NDC Modernization project. This report provides the ROM cost estimate and describes the methodology, assumptions, and cost model details used to create the ROM cost estimate. ROM Cost Estimate Disclaimer Contained herein is a Rough Order of Magnitude (ROM) cost estimate that has been provided to enable initial planning for this proposed project. This ROM cost estimate is submitted to facilitate informal discussions in relation to this project and is NOTmore » intended to commit Sandia National Laboratories (Sandia) or its resources. Furthermore, as a Federally Funded Research and Development Center (FFRDC), Sandia must be compliant with the Anti-Deficiency Act and operate on a full-cost recovery basis. Therefore, while Sandia, in conjunction with the Sponsor, will use best judgment to execute work and to address the highest risks and most important issues in order to effectively manage within cost constraints, this ROM estimate and any subsequent approved cost estimates are on a 'full-cost recovery' basis. Thus, work can neither commence nor continue unless adequate funding has been accepted and certified by DOE.« less

Forecasting the mortality rates of Malaysian population using Heligman-Pollard model

NASA Astrophysics Data System (ADS)

Ibrahim, Rose Irnawaty; Mohd, Razak; Ngataman, Nuraini; Abrisam, Wan Nur Azifah Wan Mohd

2017-08-01

Actuaries, demographers and other professionals have always been aware of the critical importance of mortality forecasting due to declining trend of mortality and continuous increases in life expectancy. Heligman-Pollard model was introduced in 1980 and has been widely used by researchers in modelling and forecasting future mortality. This paper aims to estimate an eight-parameter model based on Heligman and Pollard's law of mortality. Since the model involves nonlinear equations that are explicitly difficult to solve, the Matrix Laboratory Version 7.0 (MATLAB 7.0) software will be used in order to estimate the parameters. Statistical Package for the Social Sciences (SPSS) will be applied to forecast all the parameters according to Autoregressive Integrated Moving Average (ARIMA). The empirical data sets of Malaysian population for period of 1981 to 2015 for both genders will be considered, which the period of 1981 to 2010 will be used as "training set" and the period of 2011 to 2015 as "testing set". In order to investigate the accuracy of the estimation, the forecast results will be compared against actual data of mortality rates. The result shows that Heligman-Pollard model fit well for male population at all ages while the model seems to underestimate the mortality rates for female population at the older ages.

Exact docking flight controller for autonomous aerial refueling with back-stepping based high order sliding mode

NASA Astrophysics Data System (ADS)

Su, Zikang; Wang, Honglun; Li, Na; Yu, Yue; Wu, Jianfa

2018-02-01

Autonomous aerial refueling (AAR) exact docking control has always been an intractable problem due to the strong nonlinearity, the tight coupling of the 6 DOF aircraft model and the complex disturbances of the multiple environment flows. In this paper, the strongly coupled nonlinear 6 DOF model of the receiver aircraft which considers the multiple flow disturbances is established in the affine nonlinear form to facilitate the nonlinear controller design. The items reflecting the influence of the unknown flow disturbances in the receiver dynamics are taken as the components of the "lumped disturbances" together with the items which have no linear correlation with the virtual control variables. These unmeasurable lumped disturbances are estimated and compensated by a specially designed high order sliding mode observer (HOSMO) with excellent estimation property. With the compensation of the estimated lumped disturbances, a back-stepping high order sliding mode based exact docking flight controller is proposed for AAR in the presence of multiple flow disturbances. Extensive simulation results demonstrate the feasibility and superiority of the proposed docking controller.

Fitting a Structured Juvenile-Adult Model for Green Tree Frogs to Population Estimates from Capture-Mark-Recapture Field Data

USGS Publications Warehouse

Ackleh, A.S.; Carter, J.; Deng, K.; Huang, Q.; Pal, N.; Yang, X.

2012-01-01

We derive point and interval estimates for an urban population of green tree frogs (Hyla cinerea) from capture-mark-recapture field data obtained during the years 2006-2009. We present an infinite-dimensional least-squares approach which compares a mathematical population model to the statistical population estimates obtained from the field data. The model is composed of nonlinear first-order hyperbolic equations describing the dynamics of the amphibian population where individuals are divided into juveniles (tadpoles) and adults (frogs). To solve the least-squares problem, an explicit finite difference approximation is developed. Convergence results for the computed parameters are presented. Parameter estimates for the vital rates of juveniles and adults are obtained, and standard deviations for these estimates are computed. Numerical results for the model sensitivity with respect to these parameters are given. Finally, the above-mentioned parameter estimates are used to illustrate the long-time behavior of the population under investigation. ?? 2011 Society for Mathematical Biology.

[Estimation of individual breast cancer risk: relevance and limits of risk estimation models].

PubMed

De Pauw, A; Stoppa-Lyonnet, D; Andrieu, N; Asselain, B

2009-10-01

Several risk estimation models for breast or ovarian cancers have been developed these last decades. All these models take into account the family history, with different levels of sophistication. Gail model was developed in 1989 taking into account the family history (0, 1 or > or = 2 affected relatives) and several environmental factors. In 1990, Claus model was the first to integrate explicit assumptions about genetic effects, assuming a single gene dominantly inherited occurring with a low frequency in the population. BRCAPRO model, posterior to the identification of BRCA1 and BRCA2, assumes a restricted transmission with only these two dominantly inherited genes. BOADICEA model adds the effect of a polygenic component to the effect of BRCA1 and BRCA2 to explain the residual clustering of breast cancer. At last, IBIS model assumes a third dominantly inherited gene to explain this residual clustering. Moreover, this model incorporates environmental factors. We applied the Claus, BRCAPRO, BOADICEA and IBIS models to four clinical situations, corresponding to more or less heavy family histories, in order to study the consistency of the risk estimates. The three more recent models (BRCAPRO, BOADICEA and IBIS) gave the closer estimations. These estimates could be useful in clinical practice in front of complex analysis of breast and/or ovarian cancers family history.

Evaluation of Model Fit in Cognitive Diagnosis Models

ERIC Educational Resources Information Center

Hu, Jinxiang; Miller, M. David; Huggins-Manley, Anne Corinne; Chen, Yi-Hsin

2016-01-01

Cognitive diagnosis models (CDMs) estimate student ability profiles using latent attributes. Model fit to the data needs to be ascertained in order to determine whether inferences from CDMs are valid. This study investigated the usefulness of some popular model fit statistics to detect CDM fit including relative fit indices (AIC, BIC, and CAIC),…

Assessing Interval Estimation Methods for Hill Model ...

EPA Pesticide Factsheets

The Hill model of concentration-response is ubiquitous in toxicology, perhaps because its parameters directly relate to biologically significant metrics of toxicity such as efficacy and potency. Point estimates of these parameters obtained through least squares regression or maximum likelihood are commonly used in high-throughput risk assessment, but such estimates typically fail to include reliable information concerning confidence in (or precision of) the estimates. To address this issue, we examined methods for assessing uncertainty in Hill model parameter estimates derived from concentration-response data. In particular, using a sample of ToxCast concentration-response data sets, we applied four methods for obtaining interval estimates that are based on asymptotic theory, bootstrapping (two varieties), and Bayesian parameter estimation, and then compared the results. These interval estimation methods generally did not agree, so we devised a simulation study to assess their relative performance. We generated simulated data by constructing four statistical error models capable of producing concentration-response data sets comparable to those observed in ToxCast. We then applied the four interval estimation methods to the simulated data and compared the actual coverage of the interval estimates to the nominal coverage (e.g., 95%) in order to quantify performance of each of the methods in a variety of cases (i.e., different values of the true Hill model paramet

Estimation of outer-middle ear transmission using DPOAEs and fractional-order modeling of human middle ear

NASA Astrophysics Data System (ADS)

Naghibolhosseini, Maryam

Our ability to hear depends primarily on sound waves traveling through the outer and middle ear toward the inner ear. Hence, the characteristics of the outer and middle ear affect sound transmission to/from the inner ear. The role of the middle and outer ear in sound transmission is particularly important for otoacoustic emissions (OAEs), which are sound signals generated in a healthy cochlea, and recorded by a sensitive microphone placed in the ear canal. OAEs are used to evaluate the health and function of the cochlea; however, they are also affected by outer and middle ear characteristics. To better assess cochlear health using OAEs, it is critical to quantify the impact of the outer and middle ear on sound transmission. The reported research introduces a noninvasive approach to estimate outer-middle ear transmission using distortion product otoacoustic emissions (DPOAEs). In addition, the role of the outer and middle ear on sound transmission was investigated by developing a physical/mathematical model, which employed fractional-order lumped elements to include the viscoelastic characteristics of biological tissues. Impedance estimations from wideband refectance measurements were used for parameter fitting of the model. The model was validated comparing its estimates of the outer-middle ear sound transmission with those given by DPOAEs. The outer-middle ear transmission by the model was defined as the sum of forward and reverse outer-middle ear transmissions. To estimate the reverse transmission by the model, the probe-microphone impedance was calculated through estimating the Thevenin-equivalent circuit of the probe-microphone. The Thevenin-equivalent circuit was calculated using measurements in a number of test cavities. Such modeling enhances our understanding of the roles of different parts of the outer and middle ear and how they work together to determine their function. In addition, the model would be potentially helpful in diagnosing pathologies of cochlear or middle ear origin.

Neural network uncertainty assessment using Bayesian statistics: a remote sensing application

NASA Technical Reports Server (NTRS)

Aires, F.; Prigent, C.; Rossow, W. B.

2004-01-01

Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component analysis is proposed to suppress the multicollinearities in order to make these Jacobians robust and physically meaningful.

Near-infrared reflectance spectra of mixtures of kaolin-group minerals: Use in clay mineral studies

USGS Publications Warehouse

Crowley, James K.; Vergo, Norma

1988-01-01

Near-infrared (NIR) reflectance spectra for mixtures of ordered kaolinite and ordered dickite have been found to simulate the spectral response of disordered kaolinite. The amount of octahedral vacancy disorder in nine disordered kaolinite samples was estimated by comparing the sample spectra to the spectra of reference mixtures. The resulting estimates are consistent with previously published estimates of vacancy disorder for similar kaolin minerals that were modeled from calculated X-ray diffraction patterns. The ordered kaolinite and dickite samples used in the reference mixtures were carefully selected to avoid undesirable particle size effects that could bias the spectral results.NIR spectra were also recorded for laboratory mixtures of ordered kaolinite and halloysite to assess whether the spectra could be potentially useful for determining mineral proportions in natural physical mixtures of these two clays. Although the kaolinite-halloysite proportions could only be roughly estimated from the mixture spectra, the halloysite component was evident even when halloysite was present in only minor amounts. A similar approach using NIR spectra for laboratory mixtures may have applications in other studies of natural clay mixtures.

Dynamic RSA: Examining parasympathetic regulatory dynamics via vector-autoregressive modeling of time-varying RSA and heart period.

PubMed

Fisher, Aaron J; Reeves, Jonathan W; Chi, Cyrus

2016-07-01

Expanding on recently published methods, the current study presents an approach to estimating the dynamic, regulatory effect of the parasympathetic nervous system on heart period on a moment-to-moment basis. We estimated second-to-second variation in respiratory sinus arrhythmia (RSA) in order to estimate the contemporaneous and time-lagged relationships among RSA, interbeat interval (IBI), and respiration rate via vector autoregression. Moreover, we modeled these relationships at lags of 1 s to 10 s, in order to evaluate the optimal latency for estimating dynamic RSA effects. The IBI (t) on RSA (t-n) regression parameter was extracted from individual models as an operationalization of the regulatory effect of RSA on IBI-referred to as dynamic RSA (dRSA). Dynamic RSA positively correlated with standard averages of heart rate and negatively correlated with standard averages of RSA. We propose that dRSA reflects the active downregulation of heart period by the parasympathetic nervous system and thus represents a novel metric that provides incremental validity in the measurement of autonomic cardiac control-specifically, a method by which parasympathetic regulatory effects can be measured in process. © 2016 Society for Psychophysiological Research.

Measurement of the PPN parameter γ by testing the geometry of near-Earth space

NASA Astrophysics Data System (ADS)

Luo, Jie; Tian, Yuan; Wang, Dian-Hong; Qin, Cheng-Gang; Shao, Cheng-Gang

2016-06-01

The Beyond Einstein Advanced Coherent Optical Network (BEACON) mission was designed to achieve an accuracy of 10^{-9} in measuring the Eddington parameter γ , which is perhaps the most fundamental Parameterized Post-Newtonian parameter. However, this ideal accuracy was just estimated as a ratio of the measurement accuracy of the inter-spacecraft distances to the magnitude of the departure from Euclidean geometry. Based on the BEACON concept, we construct a measurement model to estimate the parameter γ with the least squares method. Influences of the measurement noise and the out-of-plane error on the estimation accuracy are evaluated based on the white noise model. Though the BEACON mission does not require expensive drag-free systems and avoids physical dynamical models of spacecraft, the relatively low accuracy of initial inter-spacecraft distances poses a great challenge, which reduces the estimation accuracy in about two orders of magnitude. Thus the noise requirements may need to be more stringent in the design in order to achieve the target accuracy, which is demonstrated in the work. Considering that, we have given the limits on the power spectral density of both noise sources for the accuracy of 10^{-9}.

Estimation of rates-across-sites distributions in phylogenetic substitution models.

PubMed

Susko, Edward; Field, Chris; Blouin, Christian; Roger, Andrew J

2003-10-01

Previous work has shown that it is often essential to account for the variation in rates at different sites in phylogenetic models in order to avoid phylogenetic artifacts such as long branch attraction. In most current models, the gamma distribution is used for the rates-across-sites distributions and is implemented as an equal-probability discrete gamma. In this article, we introduce discrete distribution estimates with large numbers of equally spaced rate categories allowing us to investigate the appropriateness of the gamma model. With large numbers of rate categories, these discrete estimates are flexible enough to approximate the shape of almost any distribution. Likelihood ratio statistical tests and a nonparametric bootstrap confidence-bound estimation procedure based on the discrete estimates are presented that can be used to test the fit of a parametric family. We applied the methodology to several different protein data sets, and found that although the gamma model often provides a good parametric model for this type of data, rate estimates from an equal-probability discrete gamma model with a small number of categories will tend to underestimate the largest rates. In cases when the gamma model assumption is in doubt, rate estimates coming from the discrete rate distribution estimate with a large number of rate categories provide a robust alternative to gamma estimates. An alternative implementation of the gamma distribution is proposed that, for equal numbers of rate categories, is computationally more efficient during optimization than the standard gamma implementation and can provide more accurate estimates of site rates.

Coupled orbit-attitude dynamics and relative state estimation of spacecraft near small Solar System bodies

NASA Astrophysics Data System (ADS)

Misra, Gaurav; Izadi, Maziar; Sanyal, Amit; Scheeres, Daniel

2016-04-01

The effects of dynamical coupling between the rotational (attitude) and translational (orbital) motion of spacecraft near small Solar System bodies is investigated. This coupling arises due to the weak gravity of these bodies, as well as solar radiation pressure. The traditional approach assumes a point-mass spacecraft model to describe the translational motion of the spacecraft, while the attitude motion is considered to be completely decoupled from the translational motion. The model used here to describe the rigid-body spacecraft dynamics includes the non-uniform rotating gravity field of the small body up to second degree and order along with the attitude dependent terms, solar tide, and solar radiation pressure. This model shows that the second degree and order gravity terms due to the small body affect the dynamics of the spacecraft to the same extent as the orbit-attitude coupling due to the primary gravity (zeroth order) term. Variational integrators are used to simulate the dynamics of both the rigid spacecraft and the point mass. The small bodies considered here are modeled after Near-Earth Objects (NEO) 101955 Bennu, and 25143 Itokawa, and are assumed to be triaxial ellipsoids with uniform density. Differences in the numerically obtained trajectories of a rigid spacecraft and a point mass are then compared, to illustrate the impact of the orbit-attitude coupling on spacecraft dynamics in proximity of small bodies. Possible implications on the performance of model-based spacecraft control and on the station-keeping budget, if the orbit-attitude coupling is not accounted for in the model of the dynamics, are also discussed. An almost globally asymptotically stable motion estimation scheme based solely on visual/optical feedback that estimates the relative motion of the asteroid with respect to the spacecraft is also obtained. This estimation scheme does not require a model of the dynamics of the asteroid, which makes it perfectly suited for asteroids whose properties are not well known.

Multivariate Non-Symmetric Stochastic Models for Spatial Dependence Models

NASA Astrophysics Data System (ADS)

Haslauer, C. P.; Bárdossy, A.

2017-12-01

A copula based multivariate framework allows more flexibility to describe different kind of dependences than what is possible using models relying on the confining assumption of symmetric Gaussian models: different quantiles can be modelled with a different degree of dependence; it will be demonstrated how this can be expected given process understanding. maximum likelihood based multivariate quantitative parameter estimation yields stable and reliable results; not only improved results in cross-validation based measures of uncertainty are obtained but also a more realistic spatial structure of uncertainty compared to second order models of dependence; as much information as is available is included in the parameter estimation: incorporation of censored measurements (e.g., below detection limit, or ones that are above the sensitive range of the measurement device) yield to more realistic spatial models; the proportion of true zeros can be jointly estimated with and distinguished from censored measurements which allow estimates about the age of a contaminant in the system; secondary information (categorical and on the rational scale) has been used to improve the estimation of the primary variable; These copula based multivariate statistical techniques are demonstrated based on hydraulic conductivity observations at the Borden (Canada) site, the MADE site (USA), and a large regional groundwater quality data-set in south-west Germany. Fields of spatially distributed K were simulated with identical marginal simulation, identical second order spatial moments, yet substantially differing solute transport characteristics when numerical tracer tests were performed. A statistical methodology is shown that allows the delineation of a boundary layer separating homogenous parts of a spatial data-set. The effects of this boundary layer (macro structure) and the spatial dependence of K (micro structure) on solute transport behaviour is shown.

Genetic parameters for test-day yield of milk, fat and protein in buffaloes estimated by random regression models.

PubMed

Aspilcueta-Borquis, Rúsbel R; Araujo Neto, Francisco R; Baldi, Fernando; Santos, Daniel J A; Albuquerque, Lucia G; Tonhati, Humberto

2012-08-01

The test-day yields of milk, fat and protein were analysed from 1433 first lactations of buffaloes of the Murrah breed, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, born between 1985 and 2007. For the test-day yields, 10 monthly classes of lactation days were considered. The contemporary groups were defined as the herd-year-month of the test day. Random additive genetic, permanent environmental and residual effects were included in the model. The fixed effects considered were the contemporary group, number of milkings (1 or 2 milkings), linear and quadratic effects of the covariable cow age at calving and the mean lactation curve of the population (modelled by third-order Legendre orthogonal polynomials). The random additive genetic and permanent environmental effects were estimated by means of regression on third- to sixth-order Legendre orthogonal polynomials. The residual variances were modelled with a homogenous structure and various heterogeneous classes. According to the likelihood-ratio test, the best model for milk and fat production was that with four residual variance classes, while a third-order Legendre polynomial was best for the additive genetic effect for milk and fat yield, a fourth-order polynomial was best for the permanent environmental effect for milk production and a fifth-order polynomial was best for fat production. For protein yield, the best model was that with three residual variance classes and third- and fourth-order Legendre polynomials were best for the additive genetic and permanent environmental effects, respectively. The heritability estimates for the characteristics analysed were moderate, varying from 0·16±0·05 to 0·29±0·05 for milk yield, 0·20±0·05 to 0·30±0·08 for fat yield and 0·18±0·06 to 0·27±0·08 for protein yield. The estimates of the genetic correlations between the tests varied from 0·18±0·120 to 0·99±0·002; from 0·44±0·080 to 0·99±0·004; and from 0·41±0·080 to 0·99±0·004, for milk, fat and protein production, respectively, indicating that whatever the selection criterion used, indirect genetic gains can be expected throughout the lactation curve.

Bayesian dynamic modeling of time series of dengue disease case counts.

PubMed

Martínez-Bello, Daniel Adyro; López-Quílez, Antonio; Torres-Prieto, Alexander

2017-07-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.

A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors

NASA Astrophysics Data System (ADS)

Zou, Changfu; Zhang, Lei; Hu, Xiaosong; Wang, Zhenpo; Wik, Torsten; Pecht, Michael

2018-06-01

Electrochemical energy storage systems play an important role in diverse applications, such as electrified transportation and integration of renewable energy with the electrical grid. To facilitate model-based management for extracting full system potentials, proper mathematical models are imperative. Due to extra degrees of freedom brought by differentiation derivatives, fractional-order models may be able to better describe the dynamic behaviors of electrochemical systems. This paper provides a critical overview of fractional-order techniques for managing lithium-ion batteries, lead-acid batteries, and supercapacitors. Starting with the basic concepts and technical tools from fractional-order calculus, the modeling principles for these energy systems are presented by identifying disperse dynamic processes and using electrochemical impedance spectroscopy. Available battery/supercapacitor models are comprehensively reviewed, and the advantages of fractional types are discussed. Two case studies demonstrate the accuracy and computational efficiency of fractional-order models. These models offer 15-30% higher accuracy than their integer-order analogues, but have reasonable complexity. Consequently, fractional-order models can be good candidates for the development of advanced battery/supercapacitor management systems. Finally, the main technical challenges facing electrochemical energy storage system modeling, state estimation, and control in the fractional-order domain, as well as future research directions, are highlighted.

Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference

PubMed Central

Campbell, Kieran R.

2016-01-01

Single cell gene expression profiling can be used to quantify transcriptional dynamics in temporal processes, such as cell differentiation, using computational methods to label each cell with a ‘pseudotime’ where true time series experimentation is too difficult to perform. However, owing to the high variability in gene expression between individual cells, there is an inherent uncertainty in the precise temporal ordering of the cells. Pre-existing methods for pseudotime estimation have predominantly given point estimates precluding a rigorous analysis of the implications of uncertainty. We use probabilistic modelling techniques to quantify pseudotime uncertainty and propagate this into downstream differential expression analysis. We demonstrate that reliance on a point estimate of pseudotime can lead to inflated false discovery rates and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference. PMID:27870852

A Coupled Approach for Structural Damage Detection with Incomplete Measurements

NASA Technical Reports Server (NTRS)

James, George; Cao, Timothy; Kaouk, Mo; Zimmerman, David

2013-01-01

This historical work couples model order reduction, damage detection, dynamic residual/mode shape expansion, and damage extent estimation to overcome the incomplete measurements problem by using an appropriate undamaged structural model. A contribution of this work is the development of a process to estimate the full dynamic residuals using the columns of a spring connectivity matrix obtained by disassembling the structural stiffness matrix. Another contribution is the extension of an eigenvector filtering procedure to produce full-order mode shapes that more closely match the measured active partition of the mode shapes using a set of modified Ritz vectors. The full dynamic residuals and full mode shapes are used as inputs to the minimum rank perturbation theory to provide an estimate of damage location and extent. The issues associated with this process are also discussed as drivers of near-term development activities to understand and improve this approach.

Model-based spectral estimation of Doppler signals using parallel genetic algorithms.

PubMed

Solano González, J; Rodríguez Vázquez, K; García Nocetti, D F

2000-05-01

Conventional spectral analysis methods use a fast Fourier transform (FFT) on consecutive or overlapping windowed data segments. For Doppler ultrasound signals, this approach suffers from an inadequate frequency resolution due to the time segment duration and the non-stationarity characteristics of the signals. Parametric or model-based estimators can give significant improvements in the time-frequency resolution at the expense of a higher computational complexity. This work describes an approach which implements in real-time a parametric spectral estimator method using genetic algorithms (GAs) in order to find the optimum set of parameters for the adaptive filter that minimises the error function. The aim is to reduce the computational complexity of the conventional algorithm by using the simplicity associated to GAs and exploiting its parallel characteristics. This will allow the implementation of higher order filters, increasing the spectrum resolution, and opening a greater scope for using more complex methods.

Identification of Low Order Equivalent System Models From Flight Test Data

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2000-01-01

Identification of low order equivalent system dynamic models from flight test data was studied. Inputs were pilot control deflections, and outputs were aircraft responses, so the models characterized the total aircraft response including bare airframe and flight control system. Theoretical investigations were conducted and related to results found in the literature. Low order equivalent system modeling techniques using output error and equation error parameter estimation in the frequency domain were developed and validated on simulation data. It was found that some common difficulties encountered in identifying closed loop low order equivalent system models from flight test data could be overcome using the developed techniques. Implications for data requirements and experiment design were discussed. The developed methods were demonstrated using realistic simulation cases, then applied to closed loop flight test data from the NASA F-18 High Alpha Research Vehicle.

Estimation of Solar Radiation on Building Roofs in Mountainous Areas

NASA Astrophysics Data System (ADS)

Agugiaro, G.; Remondino, F.; Stevanato, G.; De Filippi, R.; Furlanello, C.

2011-04-01

The aim of this study is estimating solar radiation on building roofs in complex mountain landscape areas. A multi-scale solar radiation estimation methodology is proposed that combines 3D data ranging from regional scale to the architectural one. Both the terrain and the nearby building shadowing effects are considered. The approach is modular and several alternative roof models, obtained by surveying and modelling techniques at varying level of detail, can be embedded in a DTM, e.g. that of an Alpine valley surrounded by mountains. The solar radiation maps obtained from raster models at different resolutions are compared and evaluated in order to obtain information regarding the benefits and disadvantages tied to each roof modelling approach. The solar radiation estimation is performed within the open-source GRASS GIS environment using r.sun and its ancillary modules.

Estimation of the Cesium-137 Source Term from the Fukushima Daiichi Power Plant Using Air Concentration and Deposition Data

NASA Astrophysics Data System (ADS)

Winiarek, Victor; Bocquet, Marc; Duhanyan, Nora; Roustan, Yelva; Saunier, Olivier; Mathieu, Anne

2013-04-01

A major difficulty when inverting the source term of an atmospheric tracer dispersion problem is the estimation of the prior errors: those of the atmospheric transport model, those ascribed to the representativeness of the measurements, the instrumental errors, and those attached to the prior knowledge on the variables one seeks to retrieve. In the case of an accidental release of pollutant, and specially in a situation of sparse observability, the reconstructed source is sensitive to these assumptions. This sensitivity makes the quality of the retrieval dependent on the methods used to model and estimate the prior errors of the inverse modeling scheme. In Winiarek et al. (2012), we proposed to use an estimation method for the errors' amplitude based on the maximum likelihood principle. Under semi-Gaussian assumptions, it takes into account, without approximation, the positivity assumption on the source. We applied the method to the estimation of the Fukushima Daiichi cesium-137 and iodine-131 source terms using activity concentrations in the air. The results were compared to an L-curve estimation technique, and to Desroziers's scheme. Additionally to the estimations of released activities, we provided related uncertainties (12 PBq with a std. of 15 - 20 % for cesium-137 and 190 - 380 PBq with a std. of 5 - 10 % for iodine-131). We also enlightened that, because of the low number of available observations (few hundreds) and even if orders of magnitude were consistent, the reconstructed activities significantly depended on the method used to estimate the prior errors. In order to use more data, we propose to extend the methods to the use of several data types, such as activity concentrations in the air and fallout measurements. The idea is to simultaneously estimate the prior errors related to each dataset, in order to fully exploit the information content of each one. Using the activity concentration measurements, but also daily fallout data from prefectures and cumulated deposition data over a region lying approximately 150 km around the nuclear power plant, we can use a few thousands of data in our inverse modeling algorithm to reconstruct the Cesium-137 source term. To improve the parameterization of removal processes, rainfall fields have also been corrected using outputs from the mesoscale meteorological model WRF and ground station rainfall data. As expected, the different methods yield closer results as the number of data increases. Reference : Winiarek, V., M. Bocquet, O. Saunier, A. Mathieu (2012), Estimation of errors in the inverse modeling of accidental release of atmospheric pollutant : Application to the reconstruction of the cesium-137 and iodine-131 source terms from the Fukushima Daiichi power plant, J. Geophys. Res., 117, D05122, doi:10.1029/2011JD016932.

Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes.

PubMed

Borquis, Rusbel Raul Aspilcueta; Neto, Francisco Ribeiro de Araujo; Baldi, Fernando; Hurtado-Lugo, Naudin; de Camargo, Gregório M F; Muñoz-Berrocal, Milthon; Tonhati, Humberto

2013-09-01

In this study, genetic parameters for test-day milk, fat, and protein yield were estimated for the first lactation. The data analyzed consisted of 1,433 first lactations of Murrah buffaloes, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, with calvings from 1985 to 2007. Ten-month classes of lactation days were considered for the test-day yields. The (co)variance components for the 3 traits were estimated using the regression analyses by Bayesian inference applying an animal model by Gibbs sampling. The contemporary groups were defined as herd-year-month of the test day. In the model, the random effects were additive genetic, permanent environment, and residual. The fixed effects were contemporary group and number of milkings (1 or 2), the linear and quadratic effects of the covariable age of the buffalo at calving, as well as the mean lactation curve of the population, which was modeled by orthogonal Legendre polynomials of fourth order. The random effects for the traits studied were modeled by Legendre polynomials of third and fourth order for additive genetic and permanent environment, respectively, the residual variances were modeled considering 4 residual classes. The heritability estimates for the traits were moderate (from 0.21-0.38), with higher estimates in the intermediate lactation phase. The genetic correlation estimates within and among the traits varied from 0.05 to 0.99. The results indicate that the selection for any trait test day will result in an indirect genetic gain for milk, fat, and protein yield in all periods of the lactation curve. The accuracy associated with estimated breeding values obtained using multi-trait random regression was slightly higher (around 8%) compared with single-trait random regression. This difference may be because to the greater amount of information available per animal. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A reduced-order adaptive neuro-fuzzy inference system model as a software sensor for rapid estimation of five-day biochemical oxygen demand

NASA Astrophysics Data System (ADS)

Noori, Roohollah; Safavi, Salman; Nateghi Shahrokni, Seyyed Afshin

2013-07-01

The five-day biochemical oxygen demand (BOD5) is one of the key parameters in water quality management. In this study, a novel approach, i.e., reduced-order adaptive neuro-fuzzy inference system (ROANFIS) model was developed for rapid estimation of BOD5. In addition, an uncertainty analysis of adaptive neuro-fuzzy inference system (ANFIS) and ROANFIS models was carried out based on Monte-Carlo simulation. Accuracy analysis of ANFIS and ROANFIS models based on both developed discrepancy ratio and threshold statistics revealed that the selected ROANFIS model was superior. Pearson correlation coefficient (R) and root mean square error for the best fitted ROANFIS model were 0.96 and 7.12, respectively. Furthermore, uncertainty analysis of the developed models indicated that the selected ROANFIS had less uncertainty than the ANFIS model and accurately forecasted BOD5 in the Sefidrood River Basin. Besides, the uncertainty analysis also showed that bracketed predictions by 95% confidence bound and d-factor in the testing steps for the selected ROANFIS model were 94% and 0.83, respectively.

Pole-zero form fractional model identification in frequency domain

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

Mansouri, R.; Djamah, T.; Djennoune, S.

2009-03-05

This paper deals with system identification in the frequency domain using non integer order models given in the pole-zero form. The usual identification techniques cannot be used in this case because of the non integer orders of differentiation which makes the problem strongly nonlinear. A general identification method based on Levenberg-Marquardt algorithm is developed and allows to estimate the (2n+2m+1) parameters of the model. Its application to identify the ''skin effect'' of a squirrel cage induction machine modeling is then presented.

An effective and optimal quality control approach for green energy manufacturing using design of experiments framework and evolutionary algorithm

NASA Astrophysics Data System (ADS)

Saavedra, Juan Alejandro

Quality Control (QC) and Quality Assurance (QA) strategies vary significantly across industries in the manufacturing sector depending on the product being built. Such strategies range from simple statistical analysis and process controls, decision-making process of reworking, repairing, or scraping defective product. This study proposes an optimal QC methodology in order to include rework stations during the manufacturing process by identifying the amount and location of these workstations. The factors that are considered to optimize these stations are cost, cycle time, reworkability and rework benefit. The goal is to minimize the cost and cycle time of the process, but increase the reworkability and rework benefit. The specific objectives of this study are: (1) to propose a cost estimation model that includes energy consumption, and (2) to propose an optimal QC methodology to identify quantity and location of rework workstations. The cost estimation model includes energy consumption as part of the product direct cost. The cost estimation model developed allows the user to calculate product direct cost as the quality sigma level of the process changes. This provides a benefit because a complete cost estimation calculation does not need to be performed every time the processes yield changes. This cost estimation model is then used for the QC strategy optimization process. In order to propose a methodology that provides an optimal QC strategy, the possible factors that affect QC were evaluated. A screening Design of Experiments (DOE) was performed on seven initial factors and identified 3 significant factors. It reflected that one response variable was not required for the optimization process. A full factorial DOE was estimated in order to verify the significant factors obtained previously. The QC strategy optimization is performed through a Genetic Algorithm (GA) which allows the evaluation of several solutions in order to obtain feasible optimal solutions. The GA evaluates possible solutions based on cost, cycle time, reworkability and rework benefit. Finally it provides several possible solutions because this is a multi-objective optimization problem. The solutions are presented as chromosomes that clearly state the amount and location of the rework stations. The user analyzes these solutions in order to select one by deciding which of the four factors considered is most important depending on the product being manufactured or the company's objective. The major contribution of this study is to provide the user with a methodology used to identify an effective and optimal QC strategy that incorporates the number and location of rework substations in order to minimize direct product cost, and cycle time, and maximize reworkability, and rework benefit.

The retention time of inorganic mercury in the brain — A systematic review of the evidence

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

Rooney, James P.K., E-mail: jrooney@rcsi.ie

2014-02-01

Reports from human case studies indicate a half-life for inorganic mercury in the brain in the order of years—contradicting older radioisotope studies that estimated half-lives in the order of weeks to months in duration. This study systematically reviews available evidence on the retention time of inorganic mercury in humans and primates to better understand this conflicting evidence. A broad search strategy was used to capture 16,539 abstracts on the Pubmed database. Abstracts were screened to include only study types containing relevant information. 131 studies of interest were identified. Only 1 primate study made a numeric estimate for the half-life ofmore » inorganic mercury (227–540 days). Eighteen human mercury poisoning cases were followed up long term including autopsy. Brain inorganic mercury concentrations at death were consistent with a half-life of several years or longer. 5 radionucleotide studies were found, one of which estimated head half-life (21 days). This estimate has sometimes been misinterpreted to be equivalent to brain half-life—which ignores several confounding factors including limited radioactive half-life and radioactive decay from surrounding tissues including circulating blood. No autopsy cohort study estimated a half-life for inorganic mercury, although some noted bioaccumulation of brain mercury with age. Modelling studies provided some extreme estimates (69 days vs 22 years). Estimates from modelling studies appear sensitive to model assumptions, however predications based on a long half-life (27.4 years) are consistent with autopsy findings. In summary, shorter estimates of half-life are not supported by evidence from animal studies, human case studies, or modelling studies based on appropriate assumptions. Evidence from such studies point to a half-life of inorganic mercury in human brains of several years to several decades. This finding carries important implications for pharmcokinetic modelling of mercury and potentially for the regulatory toxicology of mercury.« less

Estimating the Depth of the Navy Recruiting Market

DTIC Science & Technology

2016-09-01

recommend that NRC make use of the Poisson regression model in order to determine high-yield ZIP codes for market depth. 14. SUBJECT...recommend that NRC make use of the Poisson regression model in order to determine high-yield ZIP codes for market depth. vi THIS PAGE INTENTIONALLY LEFT...DEPTH OF THE NAVY RECRUITING MARKET by Emilie M. Monaghan September 2016 Thesis Advisor: Lyn R. Whitaker Second Reader: Jonathan K. Alt

Magnetic Properties of Strongly Correlated Hubbard Model and Quantum Spin-One Ferromagnets with Arbitrary Crystal-Field Potential: Linked Cluster Series Expansion Approach

NASA Astrophysics Data System (ADS)

Pan, Kok-Kwei

We have generalized the linked cluster expansion method to solve more many-body quantum systems, such as quantum spin systems with crystal-field potentials and the Hubbard model. The technique sums up all connected diagrams to a certain order of the perturbative Hamiltonian. The modified multiple-site Wick reduction theorem and the simple tau dependence of the standard basis operators have been used to facilitate the evaluation of the integration procedures in the perturbation expansion. Computational methods are developed to calculate all terms in the series expansion. As a first example, the perturbation series expansion of thermodynamic quantities of the single-band Hubbard model has been obtained using a linked cluster series expansion technique. We have made corrections to all previous results of several papers (up to fourth order). The behaviors of the three dimensional simple cubic and body-centered cubic systems have been discussed from the qualitative analysis of the perturbation series up to fourth order. We have also calculated the sixth-order perturbation series of this model. As a second example, we present the magnetic properties of spin-one Heisenberg model with arbitrary crystal-field potential using a linked cluster series expansion. The calculation of the thermodynamic properties using this method covers the whole range of temperature, in both magnetically ordered and disordered phases. The series for the susceptibility and magnetization have been obtained up to fourth order for this model. The method sums up all perturbation terms to certain order and estimates the result using a well -developed and highly successful extrapolation method (the standard ratio method). The dependence of critical temperature on the crystal-field potential and the magnetization as a function of temperature and crystal-field potential are shown. The critical behaviors at zero temperature are also shown. The range of the crystal-field potential for Ni(2+) compounds is roughly estimated based on this model using known experimental results.

Bounded influence function based inference in joint modelling of ordinal partial linear model and accelerated failure time model.

PubMed

Chakraborty, Arindom

2016-12-01

A common objective in longitudinal studies is to characterize the relationship between a longitudinal response process and a time-to-event data. Ordinal nature of the response and possible missing information on covariates add complications to the joint model. In such circumstances, some influential observations often present in the data may upset the analysis. In this paper, a joint model based on ordinal partial mixed model and an accelerated failure time model is used, to account for the repeated ordered response and time-to-event data, respectively. Here, we propose an influence function-based robust estimation method. Monte Carlo expectation maximization method-based algorithm is used for parameter estimation. A detailed simulation study has been done to evaluate the performance of the proposed method. As an application, a data on muscular dystrophy among children is used. Robust estimates are then compared with classical maximum likelihood estimates. © The Author(s) 2014.

Comparison of two recent models for estimating actual evapotranspiration using only regularly recorded data

NASA Astrophysics Data System (ADS)

Ali, M. F.; Mawdsley, J. A.

1987-09-01

An advection-aridity model for estimating actual evapotranspiration ET is tested with over 700 days of lysimeter evapotranspiration and meteorological data from barley, turf and rye-grass from three sites in the U.K. The performance of the model is also compared with the API model . It is observed from the test that the advection-aridity model overestimates nonpotential ET and tends to underestimate potential ET, but when tested with potential and nonpotential data together, the tendencies appear to cancel each other. On a daily basis the performance level of this model is found to be of the same order as the API model: correlation coefficients were obtained between the model estimates and lysimeter data of 0.62 and 0.68 respectively. For periods greater than one day, generally the performance of the models are improved. Proposed by Mawdsley and Ali (1979)

Municipal solid waste characterization and its assessment for potential methane generation: a case study.

PubMed

Mor, Suman; Ravindra, Khaiwal; De Visscher, Alex; Dahiya, R P; Chandra, A

2006-12-01

There has been a significant increase in municipal solid waste (MSW) generation in India during the last few decades and its management has become a major issue because the poor waste management practices affect the health and amenity of the cities. In the present study, various physico-chemical parameters of the MSW were analyzed to characterize the waste dumped at Gazipur landfill site in Delhi, India, which shows that it contains a high fraction of degradable organic components. The decomposition of organic components produces methane, a significant contributor to global warming. Based on the waste composition, waste age and the total amount dumped, a first-order decay model (FOD) was applied to estimate the methane generation potential of the Gazipur landfill site, which yields an estimate of 15.3 Gg/year. This value accounts to about 1-3% of existing Indian landfill methane emission estimates. Based on the investigation of Gazipur landfill, we estimate Indian landfill methane emissions at 1.25 Tg/year or 1.68 Tg/year of methane generation potential. These values are within the range of existing estimates. A comparison of FOD with a recently proposed triangular model was also performed and it shows that both models can be used for the estimation of methane generation. However, the decrease of the emission after closure is more gradual in the case of the first-order model, leading to larger gas production predictions after more than 10 years of closure. The regional and global implications of national landfill methane emission are also discussed.

Assimilating Merged Remote Sensing and Ground based Snowpack Information for Runoff Simulation and Forecasting using Hydrological Models

NASA Astrophysics Data System (ADS)

Infante Corona, J. A.; Lakhankar, T.; Khanbilvardi, R.; Pradhanang, S. M.

2013-12-01

Stream flow estimation and flood prediction influenced by snow melting processes have been studied for the past couple of decades because of their destruction potential, money losses and demises. It has been observed that snow, that was very stationary during its seasons, now is variable in shorter time-scales (daily and hourly) and rapid snowmelt can contribute or been the cause of floods. Therefore, good estimates of snowpack properties on ground are necessary in order to have an accurate prediction of these destructive events. The snow thermal model (SNTHERM) is a 1-dimensional model that analyzes the snowpack properties given the climatological conditions of a particular area. Gridded data from both, in-situ meteorological observations and remote sensing data will be produced using interpolation methods; thus, snow water equivalent (SWE) and snowmelt estimations can be obtained. The soil and water assessment tool (SWAT) is a hydrological model capable of predicting runoff quantity and quality of a watershed given its main physical and hydrological properties. The results from SNTHERM will be used as an input for SWAT in order to have simulated runoff under snowmelt conditions. This project attempts to improve the river discharge estimation considering both, excess rainfall runoff and the snow melting process. Obtaining a better estimation of the snowpack properties and evolution is expected. A coupled use of SNTHERM and SWAT based on meteorological in situ and remote sensed data will improve the temporal and spatial resolution of the snowpack characterization and river discharge estimations, and thus flood prediction.

Fast maximum likelihood estimation using continuous-time neural point process models.

PubMed

Lepage, Kyle Q; MacDonald, Christopher J

2015-06-01

A recent report estimates that the number of simultaneously recorded neurons is growing exponentially. A commonly employed statistical paradigm using discrete-time point process models of neural activity involves the computation of a maximum-likelihood estimate. The time to computate this estimate, per neuron, is proportional to the number of bins in a finely spaced discretization of time. By using continuous-time models of neural activity and the optimally efficient Gaussian quadrature, memory requirements and computation times are dramatically decreased in the commonly encountered situation where the number of parameters p is much less than the number of time-bins n. In this regime, with q equal to the quadrature order, memory requirements are decreased from O(np) to O(qp), and the number of floating-point operations are decreased from O(np(2)) to O(qp(2)). Accuracy of the proposed estimates is assessed based upon physiological consideration, error bounds, and mathematical results describing the relation between numerical integration error and numerical error affecting both parameter estimates and the observed Fisher information. A check is provided which is used to adapt the order of numerical integration. The procedure is verified in simulation and for hippocampal recordings. It is found that in 95 % of hippocampal recordings a q of 60 yields numerical error negligible with respect to parameter estimate standard error. Statistical inference using the proposed methodology is a fast and convenient alternative to statistical inference performed using a discrete-time point process model of neural activity. It enables the employment of the statistical methodology available with discrete-time inference, but is faster, uses less memory, and avoids any error due to discretization.

Modeling the global positioning system signal propagation through the ionosphere

NASA Technical Reports Server (NTRS)

Bassiri, S.; Hajj, G. A.

1992-01-01

Based on realistic modeling of the electron density of the ionosphere and using a dipole moment approximation for the Earth's magnetic field, one is able to estimate the effect of the ionosphere on the Global Positioning System (GPS) signal for a ground user. The lowest order effect, which is on the order of 0.1-100 m of group delay, is subtracted out by forming a linear combination of the dual frequencies of the GPS signal. One is left with second- and third-order effects that are estimated typically to be approximately 0-2 cm and approximately 0-2 mm at zenith, respectively, depending on the geographical location, the time of day, the time of year, the solar cycle, and the relative geometry of the magnetic field and the line of sight. Given the total electron content along a line of sight, the authors derive an approximation to the second-order term which is accurate to approximately 90 percent within the magnetic dipole moment model; this approximation can be used to reduce the second-order term to the millimeter level, thus potentially improving precise positioning in space and on the ground. The induced group delay, or phase advance, due to second- and third-order effects is examined for two ground receivers located at equatorial and mid-latitude regions tracking several GPS satellites.

Analysis and testing of Koornstra-type induced exposure models

DOT National Transportation Integrated Search

1985-10-01

Induced exposure models postulate a structure for accident data which permits the : estimation of two factors: exposure and proneness. Since information on exposure : is needed in order to assess the accident risk of different driver, vehicle, and : ...

Dynamic Modeling of the Human Coagulation Cascade Using Reduced Order Effective Kinetic Models (Open Access)

DTIC Science & Technology

2015-03-16

shaded region around each total sensitivity value was the maximum uncertainty in that value estimated by the Sobol method. 2.4. Global Sensitivity...Performance We conducted a global sensitivity analysis, using the variance-based method of Sobol , to estimate which parameters controlled the...Hunter, J.D. Matplotlib: A 2D Graphics Environment. Comput. Sci. Eng. 2007, 9, 90–95. 69. Sobol , I. Global sensitivity indices for nonlinear

Rotorcraft system identification techniques for handling qualities and stability and control evaluation

NASA Technical Reports Server (NTRS)

Hall, W. E., Jr.; Gupta, N. K.; Hansen, R. S.

1978-01-01

An integrated approach to rotorcraft system identification is described. This approach consists of sequential application of (1) data filtering to estimate states of the system and sensor errors, (2) model structure estimation to isolate significant model effects, and (3) parameter identification to quantify the coefficient of the model. An input design algorithm is described which can be used to design control inputs which maximize parameter estimation accuracy. Details of each aspect of the rotorcraft identification approach are given. Examples of both simulated and actual flight data processing are given to illustrate each phase of processing. The procedure is shown to provide means of calibrating sensor errors in flight data, quantifying high order state variable models from the flight data, and consequently computing related stability and control design models.

Parameters estimation for reactive transport: A way to test the validity of a reactive model

NASA Astrophysics Data System (ADS)

Aggarwal, Mohit; Cheikh Anta Ndiaye, Mame; Carrayrou, Jérôme

The chemical parameters used in reactive transport models are not known accurately due to the complexity and the heterogeneous conditions of a real domain. We will present an efficient algorithm in order to estimate the chemical parameters using Monte-Carlo method. Monte-Carlo methods are very robust for the optimisation of the highly non-linear mathematical model describing reactive transport. Reactive transport of tributyltin (TBT) through natural quartz sand at seven different pHs is taken as the test case. Our algorithm will be used to estimate the chemical parameters of the sorption of TBT onto the natural quartz sand. By testing and comparing three models of surface complexation, we show that the proposed adsorption model cannot explain the experimental data.

Theoretical study of gas hydrate decomposition kinetics--model development.

PubMed

Windmeier, Christoph; Oellrich, Lothar R

2013-10-10

In order to provide an estimate of the order of magnitude of intrinsic gas hydrate dissolution and dissociation kinetics, the "Consecutive Desorption and Melting Model" (CDM) is developed by applying only theoretical considerations. The process of gas hydrate decomposition is assumed to comprise two consecutive and repetitive quasi chemical reaction steps. These are desorption of the guest molecule followed by local solid body melting. The individual kinetic steps are modeled according to the "Statistical Rate Theory of Interfacial Transport" and the Wilson-Frenkel approach. All missing required model parameters are directly linked to geometric considerations and a thermodynamic gas hydrate equilibrium model.

A Bayesian Approach to Model Selection in Hierarchical Mixtures-of-Experts Architectures.

PubMed

Tanner, Martin A.; Peng, Fengchun; Jacobs, Robert A.

1997-03-01

There does not exist a statistical model that shows good performance on all tasks. Consequently, the model selection problem is unavoidable; investigators must decide which model is best at summarizing the data for each task of interest. This article presents an approach to the model selection problem in hierarchical mixtures-of-experts architectures. These architectures combine aspects of generalized linear models with those of finite mixture models in order to perform tasks via a recursive "divide-and-conquer" strategy. Markov chain Monte Carlo methodology is used to estimate the distribution of the architectures' parameters. One part of our approach to model selection attempts to estimate the worth of each component of an architecture so that relatively unused components can be pruned from the architecture's structure. A second part of this approach uses a Bayesian hypothesis testing procedure in order to differentiate inputs that carry useful information from nuisance inputs. Simulation results suggest that the approach presented here adheres to the dictum of Occam's razor; simple architectures that are adequate for summarizing the data are favored over more complex structures. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

A continuous optimization approach for inferring parameters in mathematical models of regulatory networks.

PubMed

Deng, Zhimin; Tian, Tianhai

2014-07-29

The advances of systems biology have raised a large number of sophisticated mathematical models for describing the dynamic property of complex biological systems. One of the major steps in developing mathematical models is to estimate unknown parameters of the model based on experimentally measured quantities. However, experimental conditions limit the amount of data that is available for mathematical modelling. The number of unknown parameters in mathematical models may be larger than the number of observation data. The imbalance between the number of experimental data and number of unknown parameters makes reverse-engineering problems particularly challenging. To address the issue of inadequate experimental data, we propose a continuous optimization approach for making reliable inference of model parameters. This approach first uses a spline interpolation to generate continuous functions of system dynamics as well as the first and second order derivatives of continuous functions. The expanded dataset is the basis to infer unknown model parameters using various continuous optimization criteria, including the error of simulation only, error of both simulation and the first derivative, or error of simulation as well as the first and second derivatives. We use three case studies to demonstrate the accuracy and reliability of the proposed new approach. Compared with the corresponding discrete criteria using experimental data at the measurement time points only, numerical results of the ERK kinase activation module show that the continuous absolute-error criteria using both function and high order derivatives generate estimates with better accuracy. This result is also supported by the second and third case studies for the G1/S transition network and the MAP kinase pathway, respectively. This suggests that the continuous absolute-error criteria lead to more accurate estimates than the corresponding discrete criteria. We also study the robustness property of these three models to examine the reliability of estimates. Simulation results show that the models with estimated parameters using continuous fitness functions have better robustness properties than those using the corresponding discrete fitness functions. The inference studies and robustness analysis suggest that the proposed continuous optimization criteria are effective and robust for estimating unknown parameters in mathematical models.

Modelling the Spread of an Oil-Slick with Irregular Information

ERIC Educational Resources Information Center

Winkel, Brian

2010-01-01

We describe a modelling activity for students in a course in which modelling with differential equations is appropriate. We have used this model in our coursework for years and have found that it enlightens students as to the model building process and parameter estimation for a linear, first-order, ordinary differential equation. The activity…

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

Sensitivity of quantitative groundwater recharge estimates to volumetric and distribution uncertainty in rainfall forcing products

NASA Astrophysics Data System (ADS)

Werner, Micha; Westerhoff, Rogier; Moore, Catherine

2017-04-01

Quantitative estimates of recharge due to precipitation excess are an important input to determining sustainable abstraction of groundwater resources, as well providing one of the boundary conditions required for numerical groundwater modelling. Simple water balance models are widely applied for calculating recharge. In these models, precipitation is partitioned between different processes and stores; including surface runoff and infiltration, storage in the unsaturated zone, evaporation, capillary processes, and recharge to groundwater. Clearly the estimation of recharge amounts will depend on the estimation of precipitation volumes, which may vary, depending on the source of precipitation data used. However, the partitioning between the different processes is in many cases governed by (variable) intensity thresholds. This means that the estimates of recharge will not only be sensitive to input parameters such as soil type, texture, land use, potential evaporation; but mainly to the precipitation volume and intensity distribution. In this paper we explore the sensitivity of recharge estimates due to difference in precipitation volumes and intensity distribution in the rainfall forcing over the Canterbury region in New Zealand. We compare recharge rates and volumes using a simple water balance model that is forced using rainfall and evaporation data from; the NIWA Virtual Climate Station Network (VCSN) data (which is considered as the reference dataset); the ERA-Interim/WATCH dataset at 0.25 degrees and 0.5 degrees resolution; the TRMM-3B42 dataset; the CHIRPS dataset; and the recently releases MSWEP dataset. Recharge rates are calculated at a daily time step over the 14 year period from the 2000 to 2013 for the full Canterbury region, as well as at eight selected points distributed over the region. Lysimeter data with observed estimates of recharge are available at four of these points, as well as recharge estimates from the NGRM model, an independent model constructed using the same base data and forced with the VCSN precipitation dataset. Results of the comparison of the rainfall products show that there are significant differences in precipitation volume between the forcing products; in the order of 20% at most points. Even more significant differences can be seen, however, in the distribution of precipitation. For the VCSN data wet days (defined as >0.1mm precipitation) occur on some 20-30% of days (depending on location). This is reasonably reflected in the TRMM and CHIRPS data, while for the re-analysis based products some 60%to 80% of days are wet, albeit at lower intensities. These differences are amplified in the recharge estimates. At most points, volumetric differences are in the order of 40-60%, though difference may range into several orders of magnitude. The frequency distributions of recharge also differ significantly, with recharge over 0.1 mm occurring on 4-6% of days for the VCNS, CHIRPS, and TRMM datasets, but up to the order of 12% of days for the re-analysis data. Comparison against the lysimeter data show estimates to be reasonable, in particular for the reference datasets. Surprisingly some estimates of the lower resolution re-analysis datasets are reasonable, though this does seem to be due to lower recharge being compensated by recharge occurring more frequently. These results underline the importance of correct representation of rainfall volumes, as well as of distribution, particularly when evaluating possible changes to for example changes in precipitation intensity and volume. This holds for precipitation data derived from satellite based and re-analysis products, but also for interpolated data from gauges, where the distribution of intensities is strongly influenced by the interpolation process.

Optimal firing rate estimation

NASA Technical Reports Server (NTRS)

Paulin, M. G.; Hoffman, L. F.

2001-01-01

We define a measure for evaluating the quality of a predictive model of the behavior of a spiking neuron. This measure, information gain per spike (Is), indicates how much more information is provided by the model than if the prediction were made by specifying the neuron's average firing rate over the same time period. We apply a maximum Is criterion to optimize the performance of Gaussian smoothing filters for estimating neural firing rates. With data from bullfrog vestibular semicircular canal neurons and data from simulated integrate-and-fire neurons, the optimal bandwidth for firing rate estimation is typically similar to the average firing rate. Precise timing and average rate models are limiting cases that perform poorly. We estimate that bullfrog semicircular canal sensory neurons transmit in the order of 1 bit of stimulus-related information per spike.

Improved estimation of hydraulic conductivity by combining stochastically simulated hydrofacies with geophysical data.

PubMed

Zhu, Lin; Gong, Huili; Chen, Yun; Li, Xiaojuan; Chang, Xiang; Cui, Yijiao

2016-03-01

Hydraulic conductivity is a major parameter affecting the output accuracy of groundwater flow and transport models. The most commonly used semi-empirical formula for estimating conductivity is Kozeny-Carman equation. However, this method alone does not work well with heterogeneous strata. Two important parameters, grain size and porosity, often show spatial variations at different scales. This study proposes a method for estimating conductivity distributions by combining a stochastic hydrofacies model with geophysical methods. The Markov chain model with transition probability matrix was adopted to re-construct structures of hydrofacies for deriving spatial deposit information. The geophysical and hydro-chemical data were used to estimate the porosity distribution through the Archie's law. Results show that the stochastic simulated hydrofacies model reflects the sedimentary features with an average model accuracy of 78% in comparison with borehole log data in the Chaobai alluvial fan. The estimated conductivity is reasonable and of the same order of magnitude of the outcomes of the pumping tests. The conductivity distribution is consistent with the sedimentary distributions. This study provides more reliable spatial distributions of the hydraulic parameters for further numerical modeling.

Satellite constraints on surface concentrations of particulate matter

NASA Astrophysics Data System (ADS)

Ford Hotmann, Bonne

Because of the increasing evidence of the widespread adverse effects on human health from exposure to poor air quality and the recommendations of the World Health Organization to significantly reduce PM2.5 in order to reduce these risks, better estimates of surface air quality globally are required. However, surface measurements useful for monitoring particulate exposure are scarce, especially in developing countries which often experience the worst air pollution. Therefore, other methods are necessary to augment estimates in regions with limited surface observations. The prospect of using satellite observations to infer surface air quality is attractive; however, it requires knowledge of the complicated relationship between satellite-observed aerosol optical depth (AOD) and surface concentrations. This dissertation explores how satellite observations can be used in conjunction with a chemical transport model (GEOS-Chem) to better understand this relationship. First, we investigate the seasonality in aerosols over the Southeastern United States using observations from several satellite instruments (MODIS, MISR, CALIOP) and surface network sites (IMPROVE, SEARCH, AERONET). We find that the strong summertime enhancement in satellite-observed aerosol optical depth (factor 2-3 enhancement over wintertime AOD) is not present in surface mass concentrations (25-55% summertime enhancement). Goldstein et al. [2009] previously attributed this seasonality in AOD to biogenic organic aerosol; however, surface observations show that organic aerosol only accounts for ~35% of PM2.5 mass and exhibits similar seasonality to total surface PM2.5. The GEOS-Chem model generally reproduces these surface aerosol measurements, but under represents the AOD seasonality observed by satellites. We show that seasonal differences in water uptake cannot sufficiently explain the magnitude of AOD increase. As CALIOP profiles indicate the presence of additional aerosol in the lower troposphere (below 700 hPa), which cannot be explained by vertical mixing; we conclude that the discrepancy is due to a missing source of aerosols above the surface layer in summer. Next, we examine the usefulness of deriving premature mortality estimates from "satellite-based" PM2.5 concentrations. In particular, we examine how uncertainties in the model AOD-to-surface-PM2.5 relationship, satellite retrieved AOD, and particulars of the concentration-response function can impact these mortality estimates. We find that the satellite-based estimates suggest premature mortality due to chronic PM2.5 exposure is 2-16% higher in the U.S. and 4-13% lower in China compared to model-based estimates. However, this difference is overshadowed by the uncertainty in the methodology, which we quantify to be on order of 20% for the model-to- surface-PM2.5 relationship, 10% for the satellite AOD and 30-60% or greater with regards to the application of concentration response functions. Because there is a desire for acute exposure estimates, especially with regards to extreme events, we also examine how premature mortality due to acute exposure can be estimated from global models and satellite-observations. We find similar differences between model and satellite-based mortality estimates as with chronic exposure. However the range of uncertainty is much larger on these shorter timescales. This work suggests that although satellites can be useful for constraining model estimates of PM2.5, national mortality estimates from the two methods are not significantly different. In order to improve the efficacy of satellite-based PM2.5 mortality estimates, future work will need to focus on improving the model representation of the regional AOD-to-surface-PM2.5 relationship, reducing biases in satellite-retrieved AOD and advancing our understanding of personal and population-level responses to PM2.5 exposure.

Effect of 3-D heterogeneous-earth on rheology inference of postseismic model following the 2012 Indian Ocean earthquake

NASA Astrophysics Data System (ADS)

Pratama, C.; Ito, T.; Sasajima, R.; Tabei, T.; Kimata, F.; Gunawan, E.; Ohta, Y.; Yamashina, T.; Ismail, N.; Muksin, U.; Maulida, P.; Meilano, I.; Nurdin, I.; Sugiyanto, D.; Efendi, J.

2017-12-01

Postseismic deformation following the 2012 Indian Ocean earthquake has been modeled by several studies (Han et al. 2015, Hu et al. 2016, Masuti et al. 2016). Although each study used different method and dataset, the previous studies constructed a significant difference of earth structure. Han et al. (2015) ignored subducting slab beneath Sumatra while Masuti et al. (2016) neglect sphericity of the earth. Hu et al. (2016) incorporated elastic slab and spherical earth but used uniform rigidity in each layer of the model. As a result, Han et al. (2015) model estimated one order higher Maxwell viscosity than the Hu et al. (2016) and half order lower Kelvin viscosity than the Masuti et al. (2016) model predicted. In the present study, we conduct a quantitative analysis of each heterogeneous geometry and parameter effect on rheology inference. We develop heterogeneous three-dimensional spherical-earth finite element models. We investigate the effect of subducting slab, spherical earth, and three-dimensional earth rigidity on estimated lithosphere-asthenosphere rheology beneath the Indian Ocean. A wide range of viscosity structure from time constant rheology to time dependent rheology was chosen as previous studies have been modeled. In order to evaluate actual displacement, we compared the model to the Global Navigation Satellite System (GNSS) observation. We incorporate the GNSS data from previous studies and introduce new GNSS site as a part of the Indonesian Continuously Operating Reference Stations (InaCORS) located in Sumatra that has not been used in the last analysis. As a preliminary result, we obtained the effect of the spherical earth and elastic slab when we assumed burgers rheology. The model that incorporates the sphericity of the earth needs a one third order lower viscosity than the model that neglects earth curvature. The model that includes elastic slab needs half order lower viscosity than the model that excluding the elastic slab.

Closed-Loop System Identification Experience for Flight Control Law and Flying Qualities Evaluation of a High Performance Fighter Aircraft

NASA Technical Reports Server (NTRS)

Murphy, Patrick C.

1996-01-01

This paper highlights some of the results and issues associated with estimating models to evaluate control law design methods and design criteria for advanced high performance aircraft. Experimental fighter aircraft such as the NASA-High Alpha Research Vehicle (HARV) have the capability to maneuver at very high angles of attack where nonlinear aerodynamics often predominate. HARV is an experimental F/A-18, configured with thrust vectoring and conformal actuated nose strakes. Identifying closed-loop models for this type of aircraft can be made difficult by nonlinearities and high order characteristics of the system. In this paper, only lateral-directional axes are considered since the lateral-directional control law was specifically designed to produce classical airplane responses normally expected with low-order, rigid-body systems. Evaluation of the control design methodology was made using low-order equivalent systems determined from flight and simulation. This allowed comparison of the closed-loop rigid-body dynamics achieved in flight with that designed in simulation. In flight, the On Board Excitation System was used to apply optimal inputs to lateral stick and pedals at five angles at attack : 5, 20, 30, 45, and 60 degrees. Data analysis and closed-loop model identification were done using frequency domain maximum likelihood. The structure of identified models was a linear state-space model reflecting classical 4th-order airplane dynamics. Input time delays associated with the high-order controller and aircraft system were accounted for in data preprocessing. A comparison of flight estimated models with small perturbation linear design models highlighted nonlinearities in the system and indicated that the closed-loop rigid-body dynamics were sensitive to input amplitudes at 20 and 30 degrees angle of attack.

Prediction of Material Properties of Nanostructured Polymer Composites Using Atomistic Simulations

NASA Technical Reports Server (NTRS)

Hinkley, J.A.; Clancy, T.C.; Frankland, S.J.V.

2009-01-01

Atomistic models of epoxy polymers were built in order to assess the effect of structure at the nanometer scale on the resulting bulk properties such as elastic modulus and thermal conductivity. Atomistic models of both bulk polymer and carbon nanotube polymer composites were built. For the bulk models, the effect of moisture content and temperature on the resulting elastic constants was calculated. A relatively consistent decrease in modulus was seen with increasing temperature. The dependence of modulus on moisture content was less consistent. This behavior was seen for two different epoxy systems, one containing a difunctional epoxy molecule and the other a tetrafunctional epoxy molecule. Both epoxy structures were crosslinked with diamine curing agents. Multifunctional properties were calculated with the nanocomposite models. Molecular dynamics simulation was used to estimate the interfacial thermal (Kapitza) resistance between the carbon nanotube and the surrounding epoxy matrix. These estimated values were used in a multiscale model in order to predict the thermal conductivity of a nanocomposite as a function of the nanometer scaled molecular structure.

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.

Buying and Selling Prices of Investments: Configural Weight Model of Interactions Predicts Violations of Joint Independence.

PubMed

Birnbaum; Zimmermann

1998-05-01

Judges evaluated buying and selling prices of hypothetical investments, based on the previous price of each investment and estimates of the investment's future value given by advisors of varied expertise. Effect of a source's estimate varied in proportion to the source's expertise, and it varied inversely with the number and expertise of other sources. There was also a configural effect in which the effect of a source's estimate was affected by the rank order of that source's estimate, in relation to other estimates of the same investment. These interactions were fit with a configural weight averaging model in which buyers and sellers place different weights on estimates of different ranks. This model implies that one can design a new experiment in which there will be different violations of joint independence in different viewpoints. Experiment 2 confirmed patterns of violations of joint independence predicted from the model fit in Experiment 1. Experiment 2 also showed that preference reversals between viewpoints can be predicted by the model of Experiment 1. Configural weighting provides a better account of buying and selling prices than either of two models of loss aversion or the theory of anchoring and insufficient adjustment. Copyright 1998 Academic Press.

Planning Inmarsat's second generation of spacecraft

NASA Astrophysics Data System (ADS)

Williams, W. P.

1982-09-01

The next generation of studies of the Inmarsat service are outlined, such as traffic forecasting studies, communications capacity estimates, space segment design, cost estimates, and financial analysis. Traffic forecasting will require future demand estimates, and a computer model has been developed which estimates demand over the Atlantic, Pacific, and Indian ocean regions. Communications estimates are based on traffic estimates, as a model converts traffic demand into a required capacity figure for a given area. The Erlang formula is used, requiring additional data such as peak hour ratios and distribution estimates. Basic space segment technical requirements are outlined (communications payload, transponder arrangements, etc), and further design studies involve such areas as space segment configuration, launcher and spacecraft studies, transmission planning, and earth segment configurations. Cost estimates of proposed design parameters will be performed, but options must be reduced to make construction feasible. Finally, a financial analysis will be carried out in order to calculate financial returns.

Electroencephalography in ellipsoidal geometry with fourth-order harmonics.

PubMed

Alcocer-Sosa, M; Gutierrez, D

2016-08-01

We present a solution to the electroencephalographs (EEG) forward problem of computing the scalp electric potentials for the case when the head's geometry is modeled using a four-shell ellipsoidal geometry and the brain sources with an equivalent current dipole (ECD). The proposed solution includes terms up to the fourth-order ellipsoidal harmonics and we compare this new approximation against those that only considered up to second- and third-order harmonics. Our comparisons use as reference a solution in which a tessellated volume approximates the head and the forward problem is solved through the boundary element method (BEM). We also assess the solution to the inverse problem of estimating the magnitude of an ECD through different harmonic approximations. Our results show that the fourth-order solution provides a better estimate of the ECD in comparison to lesser order ones.

Influence of conservative corrections on parameter estimation for extreme-mass-ratio inspirals

NASA Astrophysics Data System (ADS)

Huerta, E. A.; Gair, Jonathan R.

2009-04-01

We present an improved numerical kludge waveform model for circular, equatorial extreme-mass-ratio inspirals (EMRIs). The model is based on true Kerr geodesics, augmented by radiative self-force corrections derived from perturbative calculations, and in this paper for the first time we include conservative self-force corrections that we derive by comparison to post-Newtonian results. We present results of a Monte Carlo simulation of parameter estimation errors computed using the Fisher matrix and also assess the theoretical errors that would arise from omitting the conservative correction terms we include here. We present results for three different types of system, namely, the inspirals of black holes, neutron stars, or white dwarfs into a supermassive black hole (SMBH). The analysis shows that for a typical source (a 10M⊙ compact object captured by a 106M⊙ SMBH at a signal to noise ratio of 30) we expect to determine the two masses to within a fractional error of ˜10-4, measure the spin parameter q to ˜10-4.5, and determine the location of the source on the sky and the spin orientation to within 10-3 steradians. We show that, for this kludge model, omitting the conservative corrections leads to a small error over much of the parameter space, i.e., the ratio R of the theoretical model error to the Fisher matrix error is R<1 for all ten parameters in the model. For the few systems with larger errors typically R<3 and hence the conservative corrections can be marginally ignored. In addition, we use our model and first-order self-force results for Schwarzschild black holes to estimate the error that arises from omitting the second-order radiative piece of the self-force. This indicates that it may not be necessary to go beyond first order to recover accurate parameter estimates.

Hybrid Air Quality Modeling Approach for use in the Hear-road Exposures to Urban air pollutant Study(NEXUS)

EPA Science Inventory

The paper presents a hybrid air quality modeling approach and its application in NEXUS in order to provide spatial and temporally varying exposure estimates and identification of the mobile source contribution to the total pollutant exposure. Model-based exposure metrics, associa...

A Note on the Computation of the Second-Order Derivatives of the Elementary Symmetric Functions in the Rasch Model.

ERIC Educational Resources Information Center

Formann, Anton K.

1986-01-01

It is shown that for equal parameters explicit formulas exist, facilitating the application of the Newton-Raphson procedure to estimate the parameters in the Rasch model and related models according to the conditional maximum likelihood principle. (Author/LMO)

Probabilistic models and uncertainty quantification for the ionization reaction rate of atomic Nitrogen

NASA Astrophysics Data System (ADS)

Miki, K.; Panesi, M.; Prudencio, E. E.; Prudhomme, S.

2012-05-01

The objective in this paper is to analyze some stochastic models for estimating the ionization reaction rate constant of atomic Nitrogen (N + e- → N+ + 2e-). Parameters of the models are identified by means of Bayesian inference using spatially resolved absolute radiance data obtained from the Electric Arc Shock Tube (EAST) wind-tunnel. The proposed methodology accounts for uncertainties in the model parameters as well as physical model inadequacies, providing estimates of the rate constant that reflect both types of uncertainties. We present four different probabilistic models by varying the error structure (either additive or multiplicative) and by choosing different descriptions of the statistical correlation among data points. In order to assess the validity of our methodology, we first present some calibration results obtained with manufactured data and then proceed by using experimental data collected at EAST experimental facility. In order to simulate the radiative signature emitted in the shock-heated air plasma, we use a one-dimensional flow solver with Park's two-temperature model that simulates non-equilibrium effects. We also discuss the implications of the choice of the stochastic model on the estimation of the reaction rate and its uncertainties. Our analysis shows that the stochastic models based on correlated multiplicative errors are the most plausible models among the four models proposed in this study. The rate of the atomic Nitrogen ionization is found to be (6.2 ± 3.3) × 1011 cm3 mol-1 s-1 at 10,000 K.

Estimation of groundwater recharge parameters by time series analysis

USGS Publications Warehouse

Naff, Richard L.; Gutjahr, Allan L.

1983-01-01

A model is proposed that relates water level fluctuations in a Dupuit aquifer to effective precipitaton at the top of the unsaturated zone. Effective precipitation, defined herein as that portion of precipitation which becomes recharge, is related to precipitation measured in a nearby gage by a two-parameter function. A second-order stationary assumption is used to connect the spectra of effective precipitation and water level fluctuations. Measured precipitation is assumed to be Gaussian, in order to develop a transfer function that relates the spectra of measured and effective precipitation. A nonlinear least squares technique is proposed for estimating parameters of the effective-precipitation function. Although sensitivity analyses indicate difficulties that may be encountered in the estimation procedure, the methods developed did yield convergent estimates for two case studies.

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

Software for Estimating Costs of Testing Rocket Engines

NASA Technical Reports Server (NTRS)

Hines, Merlon M.

2004-01-01

A high-level parametric mathematical model for estimating the costs of testing rocket engines and components at Stennis Space Center has been implemented as a Microsoft Excel program that generates multiple spreadsheets. The model and the program are both denoted, simply, the Cost Estimating Model (CEM). The inputs to the CEM are the parameters that describe particular tests, including test types (component or engine test), numbers and duration of tests, thrust levels, and other parameters. The CEM estimates anticipated total project costs for a specific test. Estimates are broken down into testing categories based on a work-breakdown structure and a cost-element structure. A notable historical assumption incorporated into the CEM is that total labor times depend mainly on thrust levels. As a result of a recent modification of the CEM to increase the accuracy of predicted labor times, the dependence of labor time on thrust level is now embodied in third- and fourth-order polynomials.

Software for Estimating Costs of Testing Rocket Engines

NASA Technical Reports Server (NTRS)

Hines, Merion M.

2002-01-01

A high-level parametric mathematical model for estimating the costs of testing rocket engines and components at Stennis Space Center has been implemented as a Microsoft Excel program that generates multiple spreadsheets. The model and the program are both denoted, simply, the Cost Estimating Model (CEM). The inputs to the CEM are the parameters that describe particular tests, including test types (component or engine test), numbers and duration of tests, thrust levels, and other parameters. The CEM estimates anticipated total project costs for a specific test. Estimates are broken down into testing categories based on a work-breakdown structure and a cost-element structure. A notable historical assumption incorporated into the CEM is that total labor times depend mainly on thrust levels. As a result of a recent modification of the CEM to increase the accuracy of predicted labor times, the dependence of labor time on thrust level is now embodied in third- and fourth-order polynomials.

Software for Estimating Costs of Testing Rocket Engines

NASA Technical Reports Server (NTRS)

Hines, Merlon M.

2003-01-01

A high-level parametric mathematical model for estimating the costs of testing rocket engines and components at Stennis Space Center has been implemented as a Microsoft Excel program that generates multiple spreadsheets. The model and the program are both denoted, simply, the Cost Estimating Model (CEM). The inputs to the CEM are the parameters that describe particular tests, including test types (component or engine test), numbers and duration of tests, thrust levels, and other parameters. The CEM estimates anticipated total project costs for a specific test. Estimates are broken down into testing categories based on a work-breakdown structure and a cost-element structure. A notable historical assumption incorporated into the CEM is that total labor times depend mainly on thrust levels. As a result of a recent modification of the CEM to increase the accuracy of predicted labor times, the dependence of labor time on thrust level is now embodied in third- and fourth-order polynomials.

Uncertainty in flood damage estimates and its potential effect on investment decisions

NASA Astrophysics Data System (ADS)

Wagenaar, Dennis; de Bruijn, Karin; Bouwer, Laurens; de Moel, Hans

2015-04-01

This paper addresses the large differences that are found between damage estimates of different flood damage models. It explains how implicit assumptions in flood damage models can lead to large uncertainties in flood damage estimates. This explanation is used to quantify this uncertainty with a Monte Carlo Analysis. This Monte Carlo analysis uses a damage function library with 272 functions from 7 different flood damage models. This results in uncertainties in the order of magnitude of a factor 2 to 5. This uncertainty is typically larger for small water depths and for smaller flood events. The implications of the uncertainty in damage estimates for flood risk management are illustrated by a case study in which the economic optimal investment strategy for a dike segment in the Netherlands is determined. The case study shows that the uncertainty in flood damage estimates can lead to significant over- or under-investments.

Uncertainty in flood damage estimates and its potential effect on investment decisions

NASA Astrophysics Data System (ADS)

Wagenaar, D. J.; de Bruijn, K. M.; Bouwer, L. M.; De Moel, H.

2015-01-01

This paper addresses the large differences that are found between damage estimates of different flood damage models. It explains how implicit assumptions in flood damage models can lead to large uncertainties in flood damage estimates. This explanation is used to quantify this uncertainty with a Monte Carlo Analysis. As input the Monte Carlo analysis uses a damage function library with 272 functions from 7 different flood damage models. This results in uncertainties in the order of magnitude of a factor 2 to 5. The resulting uncertainty is typically larger for small water depths and for smaller flood events. The implications of the uncertainty in damage estimates for flood risk management are illustrated by a case study in which the economic optimal investment strategy for a dike segment in the Netherlands is determined. The case study shows that the uncertainty in flood damage estimates can lead to significant over- or under-investments.

Wind and wave extremes over the world oceans from very large ensembles

NASA Astrophysics Data System (ADS)

Breivik, Øyvind; Aarnes, Ole Johan; Abdalla, Saleh; Bidlot, Jean-Raymond; Janssen, Peter A. E. M.

2014-07-01

Global return values of marine wind speed and significant wave height are estimated from very large aggregates of archived ensemble forecasts at +240 h lead time. Long lead time ensures that the forecasts represent independent draws from the model climate. Compared with ERA-Interim, a reanalysis, the ensemble yields higher return estimates for both wind speed and significant wave height. Confidence intervals are much tighter due to the large size of the data set. The period (9 years) is short enough to be considered stationary even with climate change. Furthermore, the ensemble is large enough for nonparametric 100 year return estimates to be made from order statistics. These direct return estimates compare well with extreme value estimates outside areas with tropical cyclones. Like any method employing modeled fields, it is sensitive to tail biases in the numerical model, but we find that the biases are moderate outside areas with tropical cyclones.

Sampling schemes and parameter estimation for nonlinear Bernoulli-Gaussian sparse models

NASA Astrophysics Data System (ADS)

Boudineau, Mégane; Carfantan, Hervé; Bourguignon, Sébastien; Bazot, Michael

2016-06-01

We address the sparse approximation problem in the case where the data are approximated by the linear combination of a small number of elementary signals, each of these signals depending non-linearly on additional parameters. Sparsity is explicitly expressed through a Bernoulli-Gaussian hierarchical model in a Bayesian framework. Posterior mean estimates are computed using Markov Chain Monte-Carlo algorithms. We generalize the partially marginalized Gibbs sampler proposed in the linear case in [1], and build an hybrid Hastings-within-Gibbs algorithm in order to account for the nonlinear parameters. All model parameters are then estimated in an unsupervised procedure. The resulting method is evaluated on a sparse spectral analysis problem. It is shown to converge more efficiently than the classical joint estimation procedure, with only a slight increase of the computational cost per iteration, consequently reducing the global cost of the estimation procedure.

Eikonal solutions to optical model coupled-channel equations

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.; Khandelwal, Govind S.; Maung, Khin M.; Townsend, Lawrence W.; Wilson, John W.

1988-01-01

Methods of solution are presented for the Eikonal form of the nucleus-nucleus coupled-channel scattering amplitudes. Analytic solutions are obtained for the second-order optical potential for elastic scattering. A numerical comparison is made between the first and second order optical model solutions for elastic and inelastic scattering of H-1 and He-4 on C-12. The effects of bound-state excitations on total and reaction cross sections are also estimated.

CrowdWater - Can people observe what models need?

NASA Astrophysics Data System (ADS)

van Meerveld, I. H. J.; Seibert, J.; Vis, M.; Etter, S.; Strobl, B.

2017-12-01

CrowdWater (www.crowdwater.ch) is a citizen science project that explores the usefulness of crowd-sourced data for hydrological model calibration and prediction. Hydrological models are usually calibrated based on observed streamflow data but it is likely easier for people to estimate relative stream water levels, such as the water level above or below a rock, than streamflow. Relative stream water levels may, therefore, be a more suitable variable for citizen science projects than streamflow. In order to test this assumption, we held surveys near seven different sized rivers in Switzerland and asked more than 450 volunteers to estimate the water level class based on a picture with a virtual staff gauge. The results show that people can generally estimate the relative water level well, although there were also a few outliers. We also asked the volunteers to estimate streamflow based on the stick method. The median estimated streamflow was close to the observed streamflow but the spread in the streamflow estimates was large and there were very large outliers, suggesting that crowd-based streamflow data is highly uncertain. In order to determine the potential value of water level class data for model calibration, we converted streamflow time series for 100 catchments in the US to stream level class time series and used these to calibrate the HBV model. The model was then validated using the streamflow data. The results of this modeling exercise show that stream level class data are useful for constraining a simple runoff model. Time series of only two stream level classes, e.g. above or below a rock in the stream, were already informative, especially when the class boundary was chosen towards the highest stream levels. There was hardly any improvement in model performance when more than five water level classes were used. This suggests that if crowd-sourced stream level observations are available for otherwise ungauged catchments, these data can be used to constrain a simple runoff model and to generate simulated streamflow time series from the level observations.

Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation.

PubMed

Zhang, Xiangjun; Wu, Xiaolin

2008-06-01

The challenge of image interpolation is to preserve spatial details. We propose a soft-decision interpolation technique that estimates missing pixels in groups rather than one at a time. The new technique learns and adapts to varying scene structures using a 2-D piecewise autoregressive model. The model parameters are estimated in a moving window in the input low-resolution image. The pixel structure dictated by the learnt model is enforced by the soft-decision estimation process onto a block of pixels, including both observed and estimated. The result is equivalent to that of a high-order adaptive nonseparable 2-D interpolation filter. This new image interpolation approach preserves spatial coherence of interpolated images better than the existing methods, and it produces the best results so far over a wide range of scenes in both PSNR measure and subjective visual quality. Edges and textures are well preserved, and common interpolation artifacts (blurring, ringing, jaggies, zippering, etc.) are greatly reduced.

SIR-B ocean-wave enhancement with fast Fourier transform techniques

NASA Technical Reports Server (NTRS)

Tilley, David G.

1987-01-01

Shuttle Imaging Radar (SIR-B) imagery is Fourier filtered to remove the estimated system-transfer function, reduce speckle noise, and produce ocean scenes with a gray scale that is proportional to wave height. The SIR-B system response to speckled scenes of uniform surfaces yields an estimate of the stationary wavenumber response of the imaging radar, modeled by the 15 even terms of an eighth-order two-dimensional polynomial. Speckle can also be used to estimate the dynamic wavenumber response of the system due to surface motion during the aperture synthesis period, modeled with a single adaptive parameter describing an exponential correlation along track. A Fourier filter can then be devised to correct for the wavenumber response of the remote sensor and scene correlation, with subsequent subtraction of an estimate of the speckle noise component. A linearized velocity bunching model, combined with a surface tilt and hydrodynamic model, is incorporated in the Fourier filter to derive estimates of wave height from the radar intensities corresponding to individual picture elements.

Modelling average maximum daily temperature using r largest order statistics: An application to South African data

PubMed Central

2018-01-01

Natural hazards (events that may cause actual disasters) are established in the literature as major causes of various massive and destructive problems worldwide. The occurrences of earthquakes, floods and heat waves affect millions of people through several impacts. These include cases of hospitalisation, loss of lives and economic challenges. The focus of this study was on the risk reduction of the disasters that occur because of extremely high temperatures and heat waves. Modelling average maximum daily temperature (AMDT) guards against the disaster risk and may also help countries towards preparing for extreme heat. This study discusses the use of the r largest order statistics approach of extreme value theory towards modelling AMDT over the period of 11 years, that is, 2000–2010. A generalised extreme value distribution for r largest order statistics is fitted to the annual maxima. This is performed in an effort to study the behaviour of the r largest order statistics. The method of maximum likelihood is used in estimating the target parameters and the frequency of occurrences of the hottest days is assessed. The study presents a case study of South Africa in which the data for the non-winter season (September–April of each year) are used. The meteorological data used are the AMDT that are collected by the South African Weather Service and provided by Eskom. The estimation of the shape parameter reveals evidence of a Weibull class as an appropriate distribution for modelling AMDT in South Africa. The extreme quantiles for specified return periods are estimated using the quantile function and the best model is chosen through the use of the deviance statistic with the support of the graphical diagnostic tools. The Entropy Difference Test (EDT) is used as a specification test for diagnosing the fit of the models to the data.

Estimation of genetic parameters related to eggshell strength using random regression models.

PubMed

Guo, J; Ma, M; Qu, L; Shen, M; Dou, T; Wang, K

2015-01-01

This study examined the changes in eggshell strength and the genetic parameters related to this trait throughout a hen's laying life using random regression. The data were collected from a crossbred population between 2011 and 2014, where the eggshell strength was determined repeatedly for 2260 hens. Using random regression models (RRMs), several Legendre polynomials were employed to estimate the fixed, direct genetic and permanent environment effects. The residual effects were treated as independently distributed with heterogeneous variance for each test week. The direct genetic variance was included with second-order Legendre polynomials and the permanent environment with third-order Legendre polynomials. The heritability of eggshell strength ranged from 0.26 to 0.43, the repeatability ranged between 0.47 and 0.69, and the estimated genetic correlations between test weeks was high at > 0.67. The first eigenvalue of the genetic covariance matrix accounted for about 97% of the sum of all the eigenvalues. The flexibility and statistical power of RRM suggest that this model could be an effective method to improve eggshell quality and to reduce losses due to cracked eggs in a breeding plan.

A mathematical approach to molecular organization and proteolytic disintegration of bacterial inclusion bodies.

PubMed

Cubarsi, R; Carrió, M M; Villaverde, A

2005-09-01

The in vivo proteolytic digestion of bacterial inclusion bodies (IBs) and the kinetic analysis of the resulting protein fragments is an interesting approach to investigate the molecular organization of these unconventional protein aggregates. In this work, we describe a set of mathematical instruments useful for such analysis and interpretation of observed data. These methods combine numerical estimation of digestion rate and approximation of its high-order derivatives, modelling of fragmentation events from a mixture of Poisson processes associated with differentiated protein species, differential equations techniques in order to estimate the mixture parameters, an iterative predictor-corrector algorithm for describing the flow diagram along the cascade process, as well as least squares procedures with minimum variance estimates. The models are formulated and compared with data, and successively refined to better match experimental observations. By applying such procedures as well as newer improved algorithms of formerly developed equations, it has been possible to model, for two kinds of bacterially produced aggregation prone recombinant proteins, their cascade digestion process that has revealed intriguing features of the IB-forming polypeptides.

Integral-equation based methods for parameter estimation in output pulses of radiation detectors: Application in nuclear medicine and spectroscopy

NASA Astrophysics Data System (ADS)

Mohammadian-Behbahani, Mohammad-Reza; Saramad, Shahyar

2018-04-01

Model based analysis methods are relatively new approaches for processing the output data of radiation detectors in nuclear medicine imaging and spectroscopy. A class of such methods requires fast algorithms for fitting pulse models to experimental data. In order to apply integral-equation based methods for processing the preamplifier output pulses, this article proposes a fast and simple method for estimating the parameters of the well-known bi-exponential pulse model by solving an integral equation. The proposed method needs samples from only three points of the recorded pulse as well as its first and second order integrals. After optimizing the sampling points, the estimation results were calculated and compared with two traditional integration-based methods. Different noise levels (signal-to-noise ratios from 10 to 3000) were simulated for testing the functionality of the proposed method, then it was applied to a set of experimental pulses. Finally, the effect of quantization noise was assessed by studying different sampling rates. Promising results by the proposed method endorse it for future real-time applications.

Mars Propellant Liquefaction and Storage Performance Modeling using Thermal Desktop with an Integrated Cryocooler Model

NASA Technical Reports Server (NTRS)

Desai, Pooja; Hauser, Dan; Sutherlin, Steven

2017-01-01

NASAs current Mars architectures are assuming the production and storage of 23 tons of liquid oxygen on the surface of Mars over a duration of 500+ days. In order to do this in a mass efficient manner, an energy efficient refrigeration system will be required. Based on previous analysis NASA has decided to do all liquefaction in the propulsion vehicle storage tanks. In order to allow for transient Martian environmental effects, a propellant liquefaction and storage system for a Mars Ascent Vehicle (MAV) was modeled using Thermal Desktop. The model consisted of a propellant tank containing a broad area cooling loop heat exchanger integrated with a reverse turbo Brayton cryocooler. Cryocooler sizing and performance modeling was conducted using MAV diurnal heat loads and radiator rejection temperatures predicted from a previous thermal model of the MAV. A system was also sized and modeled using an alternative heat rejection system that relies on a forced convection heat exchanger. Cryocooler mass, input power, and heat rejection for both systems were estimated and compared against sizing based on non-transient sizing estimates.

Nonlinear aeroacoustic characterization of Helmholtz resonators with a local-linear neuro-fuzzy network model

NASA Astrophysics Data System (ADS)

Förner, K.; Polifke, W.

2017-10-01

The nonlinear acoustic behavior of Helmholtz resonators is characterized by a data-based reduced-order model, which is obtained by a combination of high-resolution CFD simulation and system identification. It is shown that even in the nonlinear regime, a linear model is capable of describing the reflection behavior at a particular amplitude with quantitative accuracy. This observation motivates to choose a local-linear model structure for this study, which consists of a network of parallel linear submodels. A so-called fuzzy-neuron layer distributes the input signal over the linear submodels, depending on the root mean square of the particle velocity at the resonator surface. The resulting model structure is referred to as an local-linear neuro-fuzzy network. System identification techniques are used to estimate the free parameters of this model from training data. The training data are generated by CFD simulations of the resonator, with persistent acoustic excitation over a wide range of frequencies and sound pressure levels. The estimated nonlinear, reduced-order models show good agreement with CFD and experimental data over a wide range of amplitudes for several test cases.

Aeroelastic Modeling of X-56A Stiff-Wing Configuration Flight Test Data

NASA Technical Reports Server (NTRS)

Grauer, Jared A.; Boucher, Matthew J.

2017-01-01

Aeroelastic stability and control derivatives for the X-56A Multi-Utility Technology Testbed (MUTT), in the stiff-wing configuration, were estimated from flight test data using the output-error method. Practical aspects of the analysis are discussed. The orthogonal phase-optimized multisine inputs provided excellent data information for aeroelastic modeling. Consistent parameter estimates were determined using output error in both the frequency and time domains. The frequency domain analysis converged faster and was less sensitive to starting values for the model parameters, which was useful for determining the aeroelastic model structure and obtaining starting values for the time domain analysis. Including a modal description of the structure from a finite element model reduced the complexity of the estimation problem and improved the modeling results. Effects of reducing the model order on the short period stability and control derivatives were investigated.

Bayesian Modeling for Identification and Estimation of the Learning Effects of Pointing Tasks

NASA Astrophysics Data System (ADS)

Kyo, Koki

Recently, in the field of human-computer interaction, a model containing the systematic factor and human factor has been proposed to evaluate the performance of the input devices of a computer. This is called the SH-model. In this paper, in order to extend the range of application of the SH-model, we propose some new models based on the Box-Cox transformation and apply a Bayesian modeling method for identification and estimation of the learning effects of pointing tasks. We consider the parameters describing the learning effect as random variables and introduce smoothness priors for them. Illustrative results show that the newly-proposed models work well.

Performance of a reduced-order FSI model for flow-induced vocal fold vibration

NASA Astrophysics Data System (ADS)

Chang, Siyuan; Luo, Haoxiang; Luo's lab Team

2016-11-01

Vocal fold vibration during speech production involves a three-dimensional unsteady glottal jet flow and three-dimensional nonlinear tissue mechanics. A full 3D fluid-structure interaction (FSI) model is computationally expensive even though it provides most accurate information about the system. On the other hand, an efficient reduced-order FSI model is useful for fast simulation and analysis of the vocal fold dynamics, which is often needed in procedures such as optimization and parameter estimation. In this work, we study the performance of a reduced-order model as compared with the corresponding full 3D model in terms of its accuracy in predicting the vibration frequency and deformation mode. In the reduced-order model, we use a 1D flow model coupled with a 3D tissue model. Two different hyperelastic tissue behaviors are assumed. In addition, the vocal fold thickness and subglottal pressure are varied for systematic comparison. The result shows that the reduced-order model provides consistent predictions as the full 3D model across different tissue material assumptions and subglottal pressures. However, the vocal fold thickness has most effect on the model accuracy, especially when the vocal fold is thin. Supported by the NSF.

Simultaneous Estimation of Overall and Domain Abilities: A Higher-Order IRT Model Approach

ERIC Educational Resources Information Center

de la Torre, Jimmy; Song, Hao

2009-01-01

Assessments consisting of different domains (e.g., content areas, objectives) are typically multidimensional in nature but are commonly assumed to be unidimensional for estimation purposes. The different domains of these assessments are further treated as multi-unidimensional tests for the purpose of obtaining diagnostic information. However, when…

Computational methods for estimation of parameters in hyperbolic systems

NASA Technical Reports Server (NTRS)

Banks, H. T.; Ito, K.; Murphy, K. A.

1983-01-01

Approximation techniques for estimating spatially varying coefficients and unknown boundary parameters in second order hyperbolic systems are discussed. Methods for state approximation (cubic splines, tau-Legendre) and approximation of function space parameters (interpolatory splines) are outlined and numerical findings for use of the resulting schemes in model "one dimensional seismic inversion' problems are summarized.

Maximum likelihood estimation for periodic autoregressive moving average models

USGS Publications Warehouse

Vecchia, A.V.

1985-01-01

A useful class of models for seasonal time series that cannot be filtered or standardized to achieve second-order stationarity is that of periodic autoregressive moving average (PARMA) models, which are extensions of ARMA models that allow periodic (seasonal) parameters. An approximation to the exact likelihood for Gaussian PARMA processes is developed, and a straightforward algorithm for its maximization is presented. The algorithm is tested on several periodic ARMA(1, 1) models through simulation studies and is compared to moment estimation via the seasonal Yule-Walker equations. Applicability of the technique is demonstrated through an analysis of a seasonal stream-flow series from the Rio Caroni River in Venezuela.

Parameter Estimation of the Thermal Network Model of a Machine Tool Spindle by Self-made Bluetooth Temperature Sensor Module

PubMed Central

Lo, Yuan-Chieh; Hu, Yuh-Chung; Chang, Pei-Zen

2018-01-01

Thermal characteristic analysis is essential for machine tool spindles because sudden failures may occur due to unexpected thermal issue. This article presents a lumped-parameter Thermal Network Model (TNM) and its parameter estimation scheme, including hardware and software, in order to characterize both the steady-state and transient thermal behavior of machine tool spindles. For the hardware, the authors develop a Bluetooth Temperature Sensor Module (BTSM) which accompanying with three types of temperature-sensing probes (magnetic, screw, and probe). Its specification, through experimental test, achieves to the precision ±(0.1 + 0.0029|t|) °C, resolution 0.00489 °C, power consumption 7 mW, and size Ø40 mm × 27 mm. For the software, the heat transfer characteristics of the machine tool spindle correlative to rotating speed are derived based on the theory of heat transfer and empirical formula. The predictive TNM of spindles was developed by grey-box estimation and experimental results. Even under such complicated operating conditions as various speeds and different initial conditions, the experiments validate that the present modeling methodology provides a robust and reliable tool for the temperature prediction with normalized mean square error of 99.5% agreement, and the present approach is transferable to the other spindles with a similar structure. For realizing the edge computing in smart manufacturing, a reduced-order TNM is constructed by Model Order Reduction (MOR) technique and implemented into the real-time embedded system. PMID:29473877

Parameter Estimation of the Thermal Network Model of a Machine Tool Spindle by Self-made Bluetooth Temperature Sensor Module.

PubMed

Lo, Yuan-Chieh; Hu, Yuh-Chung; Chang, Pei-Zen

2018-02-23

Thermal characteristic analysis is essential for machine tool spindles because sudden failures may occur due to unexpected thermal issue. This article presents a lumped-parameter Thermal Network Model (TNM) and its parameter estimation scheme, including hardware and software, in order to characterize both the steady-state and transient thermal behavior of machine tool spindles. For the hardware, the authors develop a Bluetooth Temperature Sensor Module (BTSM) which accompanying with three types of temperature-sensing probes (magnetic, screw, and probe). Its specification, through experimental test, achieves to the precision ±(0.1 + 0.0029|t|) °C, resolution 0.00489 °C, power consumption 7 mW, and size Ø40 mm × 27 mm. For the software, the heat transfer characteristics of the machine tool spindle correlative to rotating speed are derived based on the theory of heat transfer and empirical formula. The predictive TNM of spindles was developed by grey-box estimation and experimental results. Even under such complicated operating conditions as various speeds and different initial conditions, the experiments validate that the present modeling methodology provides a robust and reliable tool for the temperature prediction with normalized mean square error of 99.5% agreement, and the present approach is transferable to the other spindles with a similar structure. For realizing the edge computing in smart manufacturing, a reduced-order TNM is constructed by Model Order Reduction (MOR) technique and implemented into the real-time embedded system.

Extracting volatility signal using maximum a posteriori estimation

NASA Astrophysics Data System (ADS)

Neto, David

2016-11-01

This paper outlines a methodology to estimate a denoised volatility signal for foreign exchange rates using a hidden Markov model (HMM). For this purpose a maximum a posteriori (MAP) estimation is performed. A double exponential prior is used for the state variable (the log-volatility) in order to allow sharp jumps in realizations and then log-returns marginal distributions with heavy tails. We consider two routes to choose the regularization and we compare our MAP estimate to realized volatility measure for three exchange rates.

A Quantitative Description of Suicide Inhibition of Dichloroacetic Acid in Rats and Mice

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

Keys, Deborah A.; Schultz, Irv R.; Mahle, Deirdre A.

Dichloroacetic acid (DCA), a minor metabolite of trichloroethylene (TCE) and water disinfection byproduct, remains an important risk assessment issue because of its carcinogenic potency. DCA has been shown to inhibit its own metabolism by irreversibly inactivating glutathione transferase zeta (GSTzeta). To better predict internal dosimetry of DCA, a physiologically based pharmacokinetic (PBPK) model of DCA was developed. Suicide inhibition was described dynamically by varying the rate of maximal GSTzeta mediated metabolism of DCA (Vmax) over time. Resynthesis (zero-order) and degradation (first-order) of metabolic activity were described. Published iv pharmacokinetic studies in native rats were used to estimate an initial Vmaxmore » value, with Km set to an in vitro determined value. Degradation and resynthesis rates were set to estimated values from a published immunoreactive GSTzeta protein time course. The first-order inhibition rate, kd, was estimated to this same time course. A secondary, linear non-GSTzeta-mediated metabolic pathway is proposed to fit DCA time courses following treatment with DCA in drinking water. The PBPK model predictions were validated by comparing predicted DCA concentrations to measured concentrations in published studies of rats pretreated with DCA following iv exposure to 0.05 to 20 mg/kg DCA. The same model structure was parameterized to simulate DCA time courses following iv exposure in native and pretreated mice. Blood and liver concentrations during and postexposure to DCA in drinking water were predicted. Comparisons of PBPK model predicted to measured values were favorable, lending support for the further development of this model for application to DCA or TCE human health risk assessment.« less

Disturbance rejection control for vibration suppression of piezoelectric laminated thin-walled structures

NASA Astrophysics Data System (ADS)

Zhang, S. Q.; Li, H. N.; Schmidt, R.; Müller, P. C.

2014-02-01

Thin-walled piezoelectric integrated smart structures are easily excited to vibrate by unknown disturbances. In order to design and simulate a control strategy, firstly, an electro-mechanically coupled dynamic finite element (FE) model of smart structures is developed based on first-order shear deformation (FOSD) hypothesis. Linear piezoelectric constitutive equations and the assumption of constant electric field through the thickness are considered. Based on the dynamic FE model, a disturbance rejection (DR) control with proportional-integral (PI) observer using step functions as the fictitious model of disturbances is developed for vibration suppression of smart structures. In order to achieve a better dynamic behavior of the fictitious model of disturbances, the PI observer is extended to generalized proportional-integral (GPI) observer, in which sine or polynomial functions can be used to represent disturbances resulting in better dynamics. Therefore the disturbances can be estimated either by PI or GPI observer, and then the estimated signals are fed back to the controller. The DR control is validated by various kinds of unknown disturbances, and compared with linear-quadratic regulator (LQR) control. The results illustrate that the vibrations are better suppressed by the proposed DR control.

State estimation of stochastic non-linear hybrid dynamic system using an interacting multiple model algorithm.

PubMed

Elenchezhiyan, M; Prakash, J

2015-09-01

In this work, state estimation schemes for non-linear hybrid dynamic systems subjected to stochastic state disturbances and random errors in measurements using interacting multiple-model (IMM) algorithms are formulated. In order to compute both discrete modes and continuous state estimates of a hybrid dynamic system either an IMM extended Kalman filter (IMM-EKF) or an IMM based derivative-free Kalman filters is proposed in this study. The efficacy of the proposed IMM based state estimation schemes is demonstrated by conducting Monte-Carlo simulation studies on the two-tank hybrid system and switched non-isothermal continuous stirred tank reactor system. Extensive simulation studies reveal that the proposed IMM based state estimation schemes are able to generate fairly accurate continuous state estimates and discrete modes. In the presence and absence of sensor bias, the simulation studies reveal that the proposed IMM unscented Kalman filter (IMM-UKF) based simultaneous state and parameter estimation scheme outperforms multiple-model UKF (MM-UKF) based simultaneous state and parameter estimation scheme. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Modeling Fetal Weight for Gestational Age: A Comparison of a Flexible Multi-level Spline-based Model with Other Approaches

PubMed Central

Villandré, Luc; Hutcheon, Jennifer A; Perez Trejo, Maria Esther; Abenhaim, Haim; Jacobsen, Geir; Platt, Robert W

2011-01-01

We present a model for longitudinal measures of fetal weight as a function of gestational age. We use a linear mixed model, with a Box-Cox transformation of fetal weight values, and restricted cubic splines, in order to flexibly but parsimoniously model median fetal weight. We systematically compare our model to other proposed approaches. All proposed methods are shown to yield similar median estimates, as evidenced by overlapping pointwise confidence bands, except after 40 completed weeks, where our method seems to produce estimates more consistent with observed data. Sex-based stratification affects the estimates of the random effects variance-covariance structure, without significantly changing sex-specific fitted median values. We illustrate the benefits of including sex-gestational age interaction terms in the model over stratification. The comparison leads to the conclusion that the selection of a model for fetal weight for gestational age can be based on the specific goals and configuration of a given study without affecting the precision or value of median estimates for most gestational ages of interest. PMID:21931571

Unscented Kalman filter with parameter identifiability analysis for the estimation of multiple parameters in kinetic models

PubMed Central

2011-01-01

In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF), rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison. PMID:21989173

Sequential fitting-and-separating reflectance components for analytical bidirectional reflectance distribution function estimation.

PubMed

Lee, Yu; Yu, Chanki; Lee, Sang Wook

2018-01-10

We present a sequential fitting-and-separating algorithm for surface reflectance components that separates individual dominant reflectance components and simultaneously estimates the corresponding bidirectional reflectance distribution function (BRDF) parameters from the separated reflectance values. We tackle the estimation of a Lafortune BRDF model, which combines a nonLambertian diffuse reflection and multiple specular reflectance components with a different specular lobe. Our proposed method infers the appropriate number of BRDF lobes and their parameters by separating and estimating each of the reflectance components using an interval analysis-based branch-and-bound method in conjunction with iterative K-ordered scale estimation. The focus of this paper is the estimation of the Lafortune BRDF model. Nevertheless, our proposed method can be applied to other analytical BRDF models such as the Cook-Torrance and Ward models. Experiments were carried out to validate the proposed method using isotropic materials from the Mitsubishi Electric Research Laboratories-Massachusetts Institute of Technology (MERL-MIT) BRDF database, and the results show that our method is superior to a conventional minimization algorithm.

A Particle Smoother with Sequential Importance Resampling for soil hydraulic parameter estimation: A lysimeter experiment

NASA Astrophysics Data System (ADS)

Montzka, Carsten; Hendricks Franssen, Harrie-Jan; Moradkhani, Hamid; Pütz, Thomas; Han, Xujun; Vereecken, Harry

2013-04-01

An adequate description of soil hydraulic properties is essential for a good performance of hydrological forecasts. So far, several studies showed that data assimilation could reduce the parameter uncertainty by considering soil moisture observations. However, these observations and also the model forcings were recorded with a specific measurement error. It seems a logical step to base state updating and parameter estimation on observations made at multiple time steps, in order to reduce the influence of outliers at single time steps given measurement errors and unknown model forcings. Such outliers could result in erroneous state estimation as well as inadequate parameters. This has been one of the reasons to use a smoothing technique as implemented for Bayesian data assimilation methods such as the Ensemble Kalman Filter (i.e. Ensemble Kalman Smoother). Recently, an ensemble-based smoother has been developed for state update with a SIR particle filter. However, this method has not been used for dual state-parameter estimation. In this contribution we present a Particle Smoother with sequentially smoothing of particle weights for state and parameter resampling within a time window as opposed to the single time step data assimilation used in filtering techniques. This can be seen as an intermediate variant between a parameter estimation technique using global optimization with estimation of single parameter sets valid for the whole period, and sequential Monte Carlo techniques with estimation of parameter sets evolving from one time step to another. The aims are i) to improve the forecast of evaporation and groundwater recharge by estimating hydraulic parameters, and ii) to reduce the impact of single erroneous model inputs/observations by a smoothing method. In order to validate the performance of the proposed method in a real world application, the experiment is conducted in a lysimeter environment.

An adaptive discontinuous Galerkin solver for aerodynamic flows

NASA Astrophysics Data System (ADS)

Burgess, Nicholas K.

This work considers the accuracy, efficiency, and robustness of an unstructured high-order accurate discontinuous Galerkin (DG) solver for computational fluid dynamics (CFD). Recently, there has been a drive to reduce the discretization error of CFD simulations using high-order methods on unstructured grids. However, high-order methods are often criticized for lacking robustness and having high computational cost. The goal of this work is to investigate methods that enhance the robustness of high-order discontinuous Galerkin (DG) methods on unstructured meshes, while maintaining low computational cost and high accuracy of the numerical solutions. This work investigates robustness enhancement of high-order methods by examining effective non-linear solvers, shock capturing methods, turbulence model discretizations and adaptive refinement techniques. The goal is to develop an all encompassing solver that can simulate a large range of physical phenomena, where all aspects of the solver work together to achieve a robust, efficient and accurate solution strategy. The components and framework for a robust high-order accurate solver that is capable of solving viscous, Reynolds Averaged Navier-Stokes (RANS) and shocked flows is presented. In particular, this work discusses robust discretizations of the turbulence model equation used to close the RANS equations, as well as stable shock capturing strategies that are applicable across a wide range of discretization orders and applicable to very strong shock waves. Furthermore, refinement techniques are considered as both efficiency and robustness enhancement strategies. Additionally, efficient non-linear solvers based on multigrid and Krylov subspace methods are presented. The accuracy, efficiency, and robustness of the solver is demonstrated using a variety of challenging aerodynamic test problems, which include turbulent high-lift and viscous hypersonic flows. Adaptive mesh refinement was found to play a critical role in obtaining a robust and efficient high-order accurate flow solver. A goal-oriented error estimation technique has been developed to estimate the discretization error of simulation outputs. For high-order discretizations, it is shown that functional output error super-convergence can be obtained, provided the discretization satisfies a property known as dual consistency. The dual consistency of the DG methods developed in this work is shown via mathematical analysis and numerical experimentation. Goal-oriented error estimation is also used to drive an hp-adaptive mesh refinement strategy, where a combination of mesh or h-refinement, and order or p-enrichment, is employed based on the smoothness of the solution. The results demonstrate that the combination of goal-oriented error estimation and hp-adaptation yield superior accuracy, as well as enhanced robustness and efficiency for a variety of aerodynamic flows including flows with strong shock waves. This work demonstrates that DG discretizations can be the basis of an accurate, efficient, and robust CFD solver. Furthermore, enhancing the robustness of DG methods does not adversely impact the accuracy or efficiency of the solver for challenging and complex flow problems. In particular, when considering the computation of shocked flows, this work demonstrates that the available shock capturing techniques are sufficiently accurate and robust, particularly when used in conjunction with adaptive mesh refinement . This work also demonstrates that robust solutions of the Reynolds Averaged Navier-Stokes (RANS) and turbulence model equations can be obtained for complex and challenging aerodynamic flows. In this context, the most robust strategy was determined to be a low-order turbulence model discretization coupled to a high-order discretization of the RANS equations. Although RANS solutions using high-order accurate discretizations of the turbulence model were obtained, the behavior of current-day RANS turbulence models discretized to high-order was found to be problematic, leading to solver robustness issues. This suggests that future work is warranted in the area of turbulence model formulation for use with high-order discretizations. Alternately, the use of Large-Eddy Simulation (LES) subgrid scale models with high-order DG methods offers the potential to leverage the high accuracy of these methods for very high fidelity turbulent simulations. This thesis has developed the algorithmic improvements that will lay the foundation for the development of a three-dimensional high-order flow solution strategy that can be used as the basis for future LES simulations.

Cosmic shear measurement with maximum likelihood and maximum a posteriori inference

NASA Astrophysics Data System (ADS)

Hall, Alex; Taylor, Andy

2017-06-01

We investigate the problem of noise bias in maximum likelihood and maximum a posteriori estimators for cosmic shear. We derive the leading and next-to-leading order biases and compute them in the context of galaxy ellipticity measurements, extending previous work on maximum likelihood inference for weak lensing. We show that a large part of the bias on these point estimators can be removed using information already contained in the likelihood when a galaxy model is specified, without the need for external calibration. We test these bias-corrected estimators on simulated galaxy images similar to those expected from planned space-based weak lensing surveys, with promising results. We find that the introduction of an intrinsic shape prior can help with mitigation of noise bias, such that the maximum a posteriori estimate can be made less biased than the maximum likelihood estimate. Second-order terms offer a check on the convergence of the estimators, but are largely subdominant. We show how biases propagate to shear estimates, demonstrating in our simple set-up that shear biases can be reduced by orders of magnitude and potentially to within the requirements of planned space-based surveys at mild signal-to-noise ratio. We find that second-order terms can exhibit significant cancellations at low signal-to-noise ratio when Gaussian noise is assumed, which has implications for inferring the performance of shear-measurement algorithms from simplified simulations. We discuss the viability of our point estimators as tools for lensing inference, arguing that they allow for the robust measurement of ellipticity and shear.

Measuring and partitioning the high-order linkage disequilibrium by multiple order Markov chains.

PubMed

Kim, Yunjung; Feng, Sheng; Zeng, Zhao-Bang

2008-05-01

A map of the background levels of disequilibrium between nearby markers can be useful for association mapping studies. In order to assess the background levels of linkage disequilibrium (LD), multilocus LD measures are more advantageous than pairwise LD measures because the combined analysis of pairwise LD measures is not adequate to detect simultaneous allele associations among multiple markers. Various multilocus LD measures based on haplotypes have been proposed. However, most of these measures provide a single index of association among multiple markers and does not reveal the complex patterns and different levels of LD structure. In this paper, we employ non-homogeneous, multiple order Markov Chain models as a statistical framework to measure and partition the LD among multiple markers into components due to different orders of marker associations. Using a sliding window of multiple markers on phased haplotype data, we compute corresponding likelihoods for different Markov Chain (MC) orders in each window. The log-likelihood difference between the lowest MC order model (MC0) and the highest MC order model in each window is used as a measure of the total LD or the overall deviation from the gametic equilibrium for the window. Then, we partition the total LD into lower order disequilibria and estimate the effects from two-, three-, and higher order disequilibria. The relationship between different orders of LD and the log-likelihood difference involving two different orders of MC models are explored. By applying our method to the phased haplotype data in the ENCODE regions of the HapMap project, we are able to identify high/low multilocus LD regions. Our results reveal that the most LD in the HapMap data is attributed to the LD between adjacent pairs of markers across the whole region. LD between adjacent pairs of markers appears to be more significant in high multilocus LD regions than in low multilocus LD regions. We also find that as the multilocus total LD increases, the effects of high-order LD tends to get weaker due to the lack of observed multilocus haplotypes. The overall estimates of first, second, third, and fourth order LD across the ENCODE regions are 64, 23, 9, and 3%.

Random regression models using Legendre orthogonal polynomials to evaluate the milk production of Alpine goats.

PubMed

Silva, F G; Torres, R A; Brito, L F; Euclydes, R F; Melo, A L P; Souza, N O; Ribeiro, J I; Rodrigues, M T

2013-12-11

The objective of this study was to identify the best random regression model using Legendre orthogonal polynomials to evaluate Alpine goats genetically and to estimate the parameters for test day milk yield. On the test day, we analyzed 20,710 records of milk yield of 667 goats from the Goat Sector of the Universidade Federal de Viçosa. The evaluated models had combinations of distinct fitting orders for polynomials (2-5), random genetic (1-7), and permanent environmental (1-7) fixed curves and a number of classes for residual variance (2, 4, 5, and 6). WOMBAT software was used for all genetic analyses. A random regression model using the best Legendre orthogonal polynomial for genetic evaluation of milk yield on the test day of Alpine goats considered a fixed curve of order 4, curve of genetic additive effects of order 2, curve of permanent environmental effects of order 7, and a minimum of 5 classes of residual variance because it was the most economical model among those that were equivalent to the complete model by the likelihood ratio test. Phenotypic variance and heritability were higher at the end of the lactation period, indicating that the length of lactation has more genetic components in relation to the production peak and persistence. It is very important that the evaluation utilizes the best combination of fixed, genetic additive and permanent environmental regressions, and number of classes of heterogeneous residual variance for genetic evaluation using random regression models, thereby enhancing the precision and accuracy of the estimates of parameters and prediction of genetic values.

Regional oxygen reduction and denitrification rates in groundwater from multi-model residence time distributions, San Joaquin Valley, USA

USGS Publications Warehouse

Green, Christopher T.; Jurgens, Bryant; Zhang, Yong; Starn, Jeffrey; Singleton, Michael J.; Esser, Bradley K.

2016-01-01

Rates of oxygen and nitrate reduction are key factors in determining the chemical evolution of groundwater. Little is known about how these rates vary and covary in regional groundwater settings, as few studies have focused on regional datasets with multiple tracers and methods of analysis that account for effects of mixed residence times on apparent reaction rates. This study provides insight into the characteristics of residence times and rates of O2 reduction and denitrification (NO3− reduction) by comparing reaction rates using multi-model analytical residence time distributions (RTDs) applied to a data set of atmospheric tracers of groundwater age and geochemical data from 141 well samples in the Central Eastern San Joaquin Valley, CA. The RTD approach accounts for mixtures of residence times in a single sample to provide estimates of in-situ rates. Tracers included SF6, CFCs, 3H, He from 3H (tritiogenic He),14C, and terrigenic He. Parameter estimation and multi-model averaging were used to establish RTDs with lower error variances than those produced by individual RTD models. The set of multi-model RTDs was used in combination with NO3− and dissolved gas data to estimate zero order and first order rates of O2 reduction and denitrification. Results indicated that O2 reduction and denitrification rates followed approximately log-normal distributions. Rates of O2 and NO3− reduction were correlated and, on an electron milliequivalent basis, denitrification rates tended to exceed O2 reduction rates. Estimated historical NO3− trends were similar to historical measurements. Results show that the multi-model approach can improve estimation of age distributions, and that relatively easily measured O2 rates can provide information about trends in denitrification rates, which are more difficult to estimate.

Regional oxygen reduction and denitrification rates in groundwater from multi-model residence time distributions, San Joaquin Valley, USA

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

Green, Christopher T.; Jurgens, Bryant C.; Zhang, Yong

Rates of oxygen and nitrate reduction are key factors in determining the chemical evolution of groundwater. Little is known about how these rates vary and covary in regional groundwater settings, as few studies have focused on regional datasets with multiple tracers and methods of analysis that account for effects of mixed residence times on apparent reaction rates. This study provides insight into the characteristics of residence times and rates of O 2 reduction and denitrification (NO 3 – reduction) by comparing reaction rates using multi-model analytical residence time distributions (RTDs) applied to a data set of atmospheric tracers of groundwatermore » age and geochemical data from 141 well samples in the Central Eastern San Joaquin Valley, CA. The RTD approach accounts for mixtures of residence times in a single sample to provide estimates of in-situ rates. Tracers included SF 6, CFCs, 3H, He from 3H (tritiogenic He), 14C, and terrigenic He. Parameter estimation and multi-model averaging were used to establish RTDs with lower error variances than those produced by individual RTD models. The set of multi-model RTDs was used in combination with NO 3 – and dissolved gas data to estimate zero order and first order rates of O 2 reduction and denitrification. Results indicated that O 2 reduction and denitrification rates followed approximately log-normal distributions. Rates of O 2 and NO 3 – reduction were correlated and, on an electron milliequivalent basis, denitrification rates tended to exceed O 2 reduction rates. Estimated historical NO 3 – trends were similar to historical measurements. Here, results show that the multi-model approach can improve estimation of age distributions, and that relatively easily measured O 2 rates can provide information about trends in denitrification rates, which are more difficult to estimate.« less

Regional oxygen reduction and denitrification rates in groundwater from multi-model residence time distributions, San Joaquin Valley, USA

DOE PAGES

Green, Christopher T.; Jurgens, Bryant C.; Zhang, Yong; ...

2016-05-14

Rates of oxygen and nitrate reduction are key factors in determining the chemical evolution of groundwater. Little is known about how these rates vary and covary in regional groundwater settings, as few studies have focused on regional datasets with multiple tracers and methods of analysis that account for effects of mixed residence times on apparent reaction rates. This study provides insight into the characteristics of residence times and rates of O 2 reduction and denitrification (NO 3 – reduction) by comparing reaction rates using multi-model analytical residence time distributions (RTDs) applied to a data set of atmospheric tracers of groundwatermore » age and geochemical data from 141 well samples in the Central Eastern San Joaquin Valley, CA. The RTD approach accounts for mixtures of residence times in a single sample to provide estimates of in-situ rates. Tracers included SF 6, CFCs, 3H, He from 3H (tritiogenic He), 14C, and terrigenic He. Parameter estimation and multi-model averaging were used to establish RTDs with lower error variances than those produced by individual RTD models. The set of multi-model RTDs was used in combination with NO 3 – and dissolved gas data to estimate zero order and first order rates of O 2 reduction and denitrification. Results indicated that O 2 reduction and denitrification rates followed approximately log-normal distributions. Rates of O 2 and NO 3 – reduction were correlated and, on an electron milliequivalent basis, denitrification rates tended to exceed O 2 reduction rates. Estimated historical NO 3 – trends were similar to historical measurements. Here, results show that the multi-model approach can improve estimation of age distributions, and that relatively easily measured O 2 rates can provide information about trends in denitrification rates, which are more difficult to estimate.« less

Estimating the Spatial Distribution of Groundwater Age Using Synoptic Surveys of Environmental Tracers in Streams

NASA Astrophysics Data System (ADS)

Gardner, W. P.

2017-12-01

A model which simulates tracer concentration in surface water as a function the age distribution of groundwater discharge is used to characterize groundwater flow systems at a variety of spatial scales. We develop the theory behind the model and demonstrate its application in several groundwater systems of local to regional scale. A 1-D stream transport model, which includes: advection, dispersion, gas exchange, first-order decay and groundwater inflow is coupled a lumped parameter model that calculates the concentration of environmental tracers in discharging groundwater as a function of the groundwater residence time distribution. The lumped parameters, which describe the residence time distribution, are allowed to vary spatially, and multiple environmental tracers can be simulated. This model allows us to calculate the longitudinal profile of tracer concentration in streams as a function of the spatially variable groundwater age distribution. By fitting model results to observations of stream chemistry and discharge, we can then estimate the spatial distribution of groundwater age. The volume of groundwater discharge to streams can be estimated using a subset of environmental tracers, applied tracers, synoptic stream gauging or other methods, and the age of groundwater then estimated using the previously calculated groundwater discharge and observed environmental tracer concentrations. Synoptic surveys of SF6, CFC's, 3H and 222Rn, along with measured stream discharge are used to estimate the groundwater inflow distribution and mean age for regional scale surveys of the Berland River in west-central Alberta. We find that groundwater entering the Berland has observable age, and that the age estimated using our stream survey is of similar order to limited samples from groundwater wells in the region. Our results show that the stream can be used as an easily accessible location to constrain the regional scale spatial distribution of groundwater age.

Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation

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

Mbamalu, G.A.N.; El-Hawary, M.E.

The authors propose suboptimal least squares or IRWLS procedures for estimating the parameters of a seasonal multiplicative AR model encountered during power system load forecasting. The proposed method involves using an interactive computer environment to estimate the parameters of a seasonal multiplicative AR process. The method comprises five major computational steps. The first determines the order of the seasonal multiplicative AR process, and the second uses the least squares or the IRWLS to estimate the optimal nonseasonal AR model parameters. In the third step one obtains the intermediate series by back forecast, which is followed by using the least squaresmore » or the IRWLS to estimate the optimal season AR parameters. The final step uses the estimated parameters to forecast future load. The method is applied to predict the Nova Scotia Power Corporation's 168 lead time hourly load. The results obtained are documented and compared with results based on the Box and Jenkins method.« less

Vehicle States Observer Using Adaptive Tire-Road Friction Estimator

NASA Astrophysics Data System (ADS)

Kwak, Byunghak; Park, Youngjin

Vehicle stability control system is a new idea which can enhance the vehicle stability and handling in the emergency situation. This system requires the information of the yaw rate, sideslip angle and road friction in order to control the traction and braking forces at the individual wheels. This paper proposes an observer for the vehicle stability control system. This observer consisted of the state observer for vehicle motion estimation and the road condition estimator for the identification of the coefficient of the road friction. The state observer uses 2 degrees-of-freedom bicycle model and estimates the system variables based on the Kalman filter. The road condition estimator uses the same vehicle model and identifies the coefficient of the tire-road friction based on the recursive least square method. Both estimators make use of each other information. We show the effectiveness and feasibility of the proposed scheme under various road conditions through computer simulations of a fifteen degree-of-freedom non-linear vehicle model.

Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research.

PubMed

Hanigan, Ivan C; Williamson, Grant J; Knibbs, Luke D; Horsley, Joshua; Rolfe, Margaret I; Cope, Martin; Barnett, Adrian G; Cowie, Christine T; Heyworth, Jane S; Serre, Marc L; Jalaludin, Bin; Morgan, Geoffrey G

2017-11-07

Exposure to traffic related nitrogen dioxide (NO 2 ) air pollution is associated with adverse health outcomes. Average pollutant concentrations for fixed monitoring sites are often used to estimate exposures for health studies, however these can be imprecise due to difficulty and cost of spatial modeling at the resolution of neighborhoods (e.g., a scale of tens of meters) rather than at a coarse scale (around several kilometers). The objective of this study was to derive improved estimates of neighborhood NO 2 concentrations by blending measurements with modeled predictions in Sydney, Australia (a low pollution environment). We implemented the Bayesian maximum entropy approach to blend data with uncertainty defined using informative priors. We compiled NO 2 data from fixed-site monitors, chemical transport models, and satellite-based land use regression models to estimate neighborhood annual average NO 2 . The spatial model produced a posterior probability density function of estimated annual average concentrations that spanned an order of magnitude from 3 to 35 ppb. Validation using independent data showed improvement, with root mean squared error improvement of 6% compared with the land use regression model and 16% over the chemical transport model. These estimates will be used in studies of health effects and should minimize misclassification bias.

Nonlinear observation of internal states of fuel cell cathode utilizing a high-order sliding-mode algorithm

NASA Astrophysics Data System (ADS)

Xu, Liangfei; Hu, Junming; Cheng, Siliang; Fang, Chuan; Li, Jianqiu; Ouyang, Minggao; Lehnert, Werner

2017-07-01

A scheme for designing a second-order sliding-mode (SOSM) observer that estimates critical internal states on the cathode side of a polymer electrolyte membrane (PEM) fuel cell system is presented. A nonlinear, isothermal dynamic model for the cathode side and a membrane electrolyte assembly are first described. A nonlinear observer topology based on an SOSM algorithm is then introduced, and equations for the SOSM observer deduced. Online calculation of the inverse matrix produces numerical errors, so a modified matrix is introduced to eliminate the negative effects of these on the observer. The simulation results indicate that the SOSM observer performs well for the gas partial pressures and air stoichiometry. The estimation results follow the simulated values in the model with relative errors within ± 2% at stable status. Large errors occur during the fast dynamic processes (<1 s). Moreover, the nonlinear observer shows good robustness against variations in the initial values of the internal states, but less robustness against variations in system parameters. The partial pressures are more sensitive than the air stoichiometry to system parameters. Finally, the order of effects of parameter uncertainties on the estimation results is outlined and analyzed.

Fourth order difference methods for hyperbolic IBVP's

NASA Technical Reports Server (NTRS)

Gustafsson, Bertil; Olsson, Pelle

1994-01-01

Fourth order difference approximations of initial-boundary value problems for hyperbolic partial differential equations are considered. We use the method of lines approach with both explicit and compact implicit difference operators in space. The explicit operator satisfies an energy estimate leading to strict stability. For the implicit operator we develop boundary conditions and give a complete proof of strong stability using the Laplace transform technique. We also present numerical experiments for the linear advection equation and Burgers' equation with discontinuities in the solution or in its derivative. The first equation is used for modeling contact discontinuities in fluid dynamics, the second one for modeling shocks and rarefaction waves. The time discretization is done with a third order Runge-Kutta TVD method. For solutions with discontinuities in the solution itself we add a filter based on second order viscosity. In case of the non-linear Burger's equation we use a flux splitting technique that results in an energy estimate for certain different approximations, in which case also an entropy condition is fulfilled. In particular we shall demonstrate that the unsplit conservative form produces a non-physical shock instead of the physically correct rarefaction wave. In the numerical experiments we compare our fourth order methods with a standard second order one and with a third order TVD-method. The results show that the fourth order methods are the only ones that give good results for all the considered test problems.

The Finite-Size Scaling Relation for the Order-Parameter Probability Distribution of the Six-Dimensional Ising Model

NASA Astrophysics Data System (ADS)

Merdan, Ziya; Karakuş, Özlem

2016-11-01

The six dimensional Ising model with nearest-neighbor pair interactions has been simulated and verified numerically on the Creutz Cellular Automaton by using five bit demons near the infinite-lattice critical temperature with the linear dimensions L=4,6,8,10. The order parameter probability distribution for six dimensional Ising model has been calculated at the critical temperature. The constants of the analytical function have been estimated by fitting to probability function obtained numerically at the finite size critical point.

Estimation of Supersonic Stage Separation Aerodynamics of Winged-Body Launch Vehicles Using Response Surface Methods

NASA Technical Reports Server (NTRS)

Erickson, Gary E.

2010-01-01

Response surface methodology was used to estimate the longitudinal stage separation aerodynamic characteristics of a generic, bimese, winged multi-stage launch vehicle configuration at supersonic speeds in the NASA LaRC Unitary Plan Wind Tunnel. The Mach 3 staging was dominated by shock wave interactions between the orbiter and booster vehicles throughout the relative spatial locations of interest. The inference space was partitioned into several contiguous regions within which the separation aerodynamics were presumed to be well-behaved and estimable using central composite designs capable of fitting full second-order response functions. The underlying aerodynamic response surfaces of the booster vehicle in belly-to-belly proximity to the orbiter vehicle were estimated using piecewise-continuous lower-order polynomial functions. The quality of fit and prediction capabilities of the empirical models were assessed in detail, and the issue of subspace boundary discontinuities was addressed. Augmenting the central composite designs to full third-order using computer-generated D-optimality criteria was evaluated. The usefulness of central composite designs, the subspace sizing, and the practicality of fitting lower-order response functions over a partitioned inference space dominated by highly nonlinear and possibly discontinuous shock-induced aerodynamics are discussed.

Modeling of geogenic radon in Switzerland based on ordered logistic regression.

PubMed

Kropat, Georg; Bochud, François; Murith, Christophe; Palacios Gruson, Martha; Baechler, Sébastien

2017-01-01

The estimation of the radon hazard of a future construction site should ideally be based on the geogenic radon potential (GRP), since this estimate is free of anthropogenic influences and building characteristics. The goal of this study was to evaluate terrestrial gamma dose rate (TGD), geology, fault lines and topsoil permeability as predictors for the creation of a GRP map based on logistic regression. Soil gas radon measurements (SRC) are more suited for the estimation of GRP than indoor radon measurements (IRC) since the former do not depend on ventilation and heating habits or building characteristics. However, SRC have only been measured at a few locations in Switzerland. In former studies a good correlation between spatial aggregates of IRC and SRC has been observed. That's why we used IRC measurements aggregated on a 10 km × 10 km grid to calibrate an ordered logistic regression model for geogenic radon potential (GRP). As predictors we took into account terrestrial gamma doserate, regrouped geological units, fault line density and the permeability of the soil. The classification success rate of the model results to 56% in case of the inclusion of all 4 predictor variables. Our results suggest that terrestrial gamma doserate and regrouped geological units are more suited to model GRP than fault line density and soil permeability. Ordered logistic regression is a promising tool for the modeling of GRP maps due to its simplicity and fast computation time. Future studies should account for additional variables to improve the modeling of high radon hazard in the Jura Mountains of Switzerland. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Examining driver injury severity outcomes in rural non-interstate roadway crashes using a hierarchical ordered logit model.

PubMed

Chen, Cong; Zhang, Guohui; Huang, Helai; Wang, Jiangfeng; Tarefder, Rafiqul A

2016-11-01

Rural non-interstate crashes induce a significant amount of severe injuries and fatalities. Examination of such injury patterns and the associated contributing factors is of practical importance. Taking into account the ordinal nature of injury severity levels and the hierarchical feature of crash data, this study employs a hierarchical ordered logit model to examine the significant factors in predicting driver injury severities in rural non-interstate crashes based on two-year New Mexico crash records. Bayesian inference is utilized in model estimation procedure and 95% Bayesian Credible Interval (BCI) is applied to testing variable significance. An ordinary ordered logit model omitting the between-crash variance effect is evaluated as well for model performance comparison. Results indicate that the model employed in this study outperforms ordinary ordered logit model in model fit and parameter estimation. Variables regarding crash features, environment conditions, and driver and vehicle characteristics are found to have significant influence on the predictions of driver injury severities in rural non-interstate crashes. Factors such as road segments far from intersection, wet road surface condition, collision with animals, heavy vehicle drivers, male drivers and driver seatbelt used tend to induce less severe driver injury outcomes than the factors such as multiple-vehicle crashes, severe vehicle damage in a crash, motorcyclists, females, senior drivers, driver with alcohol or drug impairment, and other major collision types. Research limitations regarding crash data and model assumptions are also discussed. Overall, this research provides reasonable results and insight in developing effective road safety measures for crash injury severity reduction and prevention. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Bayesian Hierarchical Modeling Scheme for Estimating Erosion Rates Under Current Climate Conditions

NASA Astrophysics Data System (ADS)

Lowman, L.; Barros, A. P.

2014-12-01

Computational modeling of surface erosion processes is inherently difficult because of the four-dimensional nature of the problem and the multiple temporal and spatial scales that govern individual mechanisms. Landscapes are modified via surface and fluvial erosion and exhumation, each of which takes place over a range of time scales. Traditional field measurements of erosion/exhumation rates are scale dependent, often valid for a single point-wise location or averaging over large aerial extents and periods with intense and mild erosion. We present a method of remotely estimating erosion rates using a Bayesian hierarchical model based upon the stream power erosion law (SPEL). A Bayesian approach allows for estimating erosion rates using the deterministic relationship given by the SPEL and data on channel slopes and precipitation at the basin and sub-basin scale. The spatial scale associated with this framework is the elevation class, where each class is characterized by distinct morphologic behavior observed through different modes in the distribution of basin outlet elevations. Interestingly, the distributions of first-order outlets are similar in shape and extent to the distribution of precipitation events (i.e. individual storms) over a 14-year period between 1998-2011. We demonstrate an application of the Bayesian hierarchical modeling framework for five basins and one intermontane basin located in the central Andes between 5S and 20S. Using remotely sensed data of current annual precipitation rates from the Tropical Rainfall Measuring Mission (TRMM) and topography from a high resolution (3 arc-seconds) digital elevation map (DEM), our erosion rate estimates are consistent with decadal-scale estimates based on landslide mapping and sediment flux observations and 1-2 orders of magnitude larger than most millennial and million year timescale estimates from thermochronology and cosmogenic nuclides.

Fourier modeling of the BOLD response to a breath-hold task: Optimization and reproducibility.

PubMed

Pinto, Joana; Jorge, João; Sousa, Inês; Vilela, Pedro; Figueiredo, Patrícia

2016-07-15

Cerebrovascular reactivity (CVR) reflects the capacity of blood vessels to adjust their caliber in order to maintain a steady supply of brain perfusion, and it may provide a sensitive disease biomarker. Measurement of the blood oxygen level dependent (BOLD) response to a hypercapnia-inducing breath-hold (BH) task has been frequently used to map CVR noninvasively using functional magnetic resonance imaging (fMRI). However, the best modeling approach for the accurate quantification of CVR maps remains an open issue. Here, we compare and optimize Fourier models of the BOLD response to a BH task with a preparatory inspiration, and assess the test-retest reproducibility of the associated CVR measurements, in a group of 10 healthy volunteers studied over two fMRI sessions. Linear combinations of sine-cosine pairs at the BH task frequency and its successive harmonics were added sequentially in a nested models approach, and were compared in terms of the adjusted coefficient of determination and corresponding variance explained (VE) of the BOLD signal, as well as the number of voxels exhibiting significant BOLD responses, the estimated CVR values, and their test-retest reproducibility. The brain average VE increased significantly with the Fourier model order, up to the 3rd order. However, the number of responsive voxels increased significantly only up to the 2nd order, and started to decrease from the 3rd order onwards. Moreover, no significant relative underestimation of CVR values was observed beyond the 2nd order. Hence, the 2nd order model was concluded to be the optimal choice for the studied paradigm. This model also yielded the best test-retest reproducibility results, with intra-subject coefficients of variation of 12 and 16% and an intra-class correlation coefficient of 0.74. In conclusion, our results indicate that a Fourier series set consisting of a sine-cosine pair at the BH task frequency and its two harmonics is a suitable model for BOLD-fMRI CVR measurements based on a BH task with preparatory inspiration, yielding robust estimates of this important physiological parameter. Copyright © 2016 Elsevier Inc. All rights reserved.

Dual Quaternions as Constraints in 4D-DPM Models for Pose Estimation.

PubMed

Martinez-Berti, Enrique; Sánchez-Salmerón, Antonio-José; Ricolfe-Viala, Carlos

2017-08-19

The goal of this research work is to improve the accuracy of human pose estimation using the Deformation Part Model (DPM) without increasing computational complexity. First, the proposed method seeks to improve pose estimation accuracy by adding the depth channel to DPM, which was formerly defined based only on red-green-blue (RGB) channels, in order to obtain a four-dimensional DPM (4D-DPM). In addition, computational complexity can be controlled by reducing the number of joints by taking it into account in a reduced 4D-DPM. Finally, complete solutions are obtained by solving the omitted joints by using inverse kinematics models. In this context, the main goal of this paper is to analyze the effect on pose estimation timing cost when using dual quaternions to solve the inverse kinematics.

Determination of Time Dependent Virus Inactivation Rates

NASA Astrophysics Data System (ADS)

Chrysikopoulos, C. V.; Vogler, E. T.

2003-12-01

A methodology is developed for estimating temporally variable virus inactivation rate coefficients from experimental virus inactivation data. The methodology consists of a technique for slope estimation of normalized virus inactivation data in conjunction with a resampling parameter estimation procedure. The slope estimation technique is based on a relatively flexible geostatistical method known as universal kriging. Drift coefficients are obtained by nonlinear fitting of bootstrap samples and the corresponding confidence intervals are obtained by bootstrap percentiles. The proposed methodology yields more accurate time dependent virus inactivation rate coefficients than those estimated by fitting virus inactivation data to a first-order inactivation model. The methodology is successfully applied to a set of poliovirus batch inactivation data. Furthermore, the importance of accurate inactivation rate coefficient determination on virus transport in water saturated porous media is demonstrated with model simulations.

Online machining error estimation method of numerical control gear grinding machine tool based on data analysis of internal sensors

NASA Astrophysics Data System (ADS)

Zhao, Fei; Zhang, Chi; Yang, Guilin; Chen, Chinyin

2016-12-01

This paper presents an online estimation method of cutting error by analyzing of internal sensor readings. The internal sensors of numerical control (NC) machine tool are selected to avoid installation problem. The estimation mathematic model of cutting error was proposed to compute the relative position of cutting point and tool center point (TCP) from internal sensor readings based on cutting theory of gear. In order to verify the effectiveness of the proposed model, it was simulated and experimented in gear generating grinding process. The cutting error of gear was estimated and the factors which induce cutting error were analyzed. The simulation and experiments verify that the proposed approach is an efficient way to estimate the cutting error of work-piece during machining process.

An architecture for efficient gravitational wave parameter estimation with multimodal linear surrogate models

NASA Astrophysics Data System (ADS)

O'Shaughnessy, Richard; Blackman, Jonathan; Field, Scott E.

2017-07-01

The recent direct observation of gravitational waves has further emphasized the desire for fast, low-cost, and accurate methods to infer the parameters of gravitational wave sources. Due to expense in waveform generation and data handling, the cost of evaluating the likelihood function limits the computational performance of these calculations. Building on recently developed surrogate models and a novel parameter estimation pipeline, we show how to quickly generate the likelihood function as an analytic, closed-form expression. Using a straightforward variant of a production-scale parameter estimation code, we demonstrate our method using surrogate models of effective-one-body and numerical relativity waveforms. Our study is the first time these models have been used for parameter estimation and one of the first ever parameter estimation calculations with multi-modal numerical relativity waveforms, which include all \\ell ≤slant 4 modes. Our grid-free method enables rapid parameter estimation for any waveform with a suitable reduced-order model. The methods described in this paper may also find use in other data analysis studies, such as vetting coincident events or the computation of the coalescing-compact-binary detection statistic.

Estimation of stochastic volatility with long memory for index prices of FTSE Bursa Malaysia KLCI

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

Chen, Kho Chia; Kane, Ibrahim Lawal; Rahman, Haliza Abd

In recent years, modeling in long memory properties or fractionally integrated processes in stochastic volatility has been applied in the financial time series. A time series with structural breaks can generate a strong persistence in the autocorrelation function, which is an observed behaviour of a long memory process. This paper considers the structural break of data in order to determine true long memory time series data. Unlike usual short memory models for log volatility, the fractional Ornstein-Uhlenbeck process is neither a Markovian process nor can it be easily transformed into a Markovian process. This makes the likelihood evaluation and parametermore » estimation for the long memory stochastic volatility (LMSV) model challenging tasks. The drift and volatility parameters of the fractional Ornstein-Unlenbeck model are estimated separately using the least square estimator (lse) and quadratic generalized variations (qgv) method respectively. Finally, the empirical distribution of unobserved volatility is estimated using the particle filtering with sequential important sampling-resampling (SIR) method. The mean square error (MSE) between the estimated and empirical volatility indicates that the performance of the model towards the index prices of FTSE Bursa Malaysia KLCI is fairly well.« less

Estimation of stochastic volatility with long memory for index prices of FTSE Bursa Malaysia KLCI

NASA Astrophysics Data System (ADS)

Chen, Kho Chia; Bahar, Arifah; Kane, Ibrahim Lawal; Ting, Chee-Ming; Rahman, Haliza Abd

2015-02-01

In recent years, modeling in long memory properties or fractionally integrated processes in stochastic volatility has been applied in the financial time series. A time series with structural breaks can generate a strong persistence in the autocorrelation function, which is an observed behaviour of a long memory process. This paper considers the structural break of data in order to determine true long memory time series data. Unlike usual short memory models for log volatility, the fractional Ornstein-Uhlenbeck process is neither a Markovian process nor can it be easily transformed into a Markovian process. This makes the likelihood evaluation and parameter estimation for the long memory stochastic volatility (LMSV) model challenging tasks. The drift and volatility parameters of the fractional Ornstein-Unlenbeck model are estimated separately using the least square estimator (lse) and quadratic generalized variations (qgv) method respectively. Finally, the empirical distribution of unobserved volatility is estimated using the particle filtering with sequential important sampling-resampling (SIR) method. The mean square error (MSE) between the estimated and empirical volatility indicates that the performance of the model towards the index prices of FTSE Bursa Malaysia KLCI is fairly well.

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.

Evaluation of the Agro-EcoSystem-Watershed (AgES-W)model for estimating nutrient dynamics on a midwest agricultural watershed

USDA-ARS?s Scientific Manuscript database

In order to satisfy the requirements of Conservation Effects Assessment Project (CEAP) Watershed Assessment Study (WAS) Objective 5 (“develop and verify regional watershed models that quantify environmental outcomes of conservation practices in major agricultural regions”), a new watershed model dev...

Modelling Sublimation of Carbon Dioxide

ERIC Educational Resources Information Center

Winkel, Brian

2012-01-01

In this article, the author reports results in their efforts to model sublimation of carbon dioxide and the associated kinetics order and parameter estimation issues in their model. They have offered the reader two sets of data and several approaches to determine the rate of sublimation of a piece of solid dry ice. They presented several models…

Mars Propellant Liquefaction Modeling in Thermal Desktop

NASA Technical Reports Server (NTRS)

Desai, Pooja; Hauser, Dan; Sutherlin, Steven

2017-01-01

NASAs current Mars architectures are assuming the production and storage of 23 tons of liquid oxygen on the surface of Mars over a duration of 500+ days. In order to do this in a mass efficient manner, an energy efficient refrigeration system will be required. Based on previous analysis NASA has decided to do all liquefaction in the propulsion vehicle storage tanks. In order to allow for transient Martian environmental effects, a propellant liquefaction and storage system for a Mars Ascent Vehicle (MAV) was modeled using Thermal Desktop. The model consisted of a propellant tank containing a broad area cooling loop heat exchanger integrated with a reverse turbo Brayton cryocooler. Cryocooler sizing and performance modeling was conducted using MAV diurnal heat loads and radiator rejection temperatures predicted from a previous thermal model of the MAV. A system was also sized and modeled using an alternative heat rejection system that relies on a forced convection heat exchanger. Cryocooler mass, input power, and heat rejection for both systems were estimated and compared against sizing based on non-transient sizing estimates.

A reduced-order nonlinear sliding mode observer for vehicle slip angle and tyre forces

NASA Astrophysics Data System (ADS)

Chen, Yuhang; Ji, Yunfeng; Guo, Konghui

2014-12-01

In this paper, a reduced-order sliding mode observer (RO-SMO) is developed for vehicle state estimation. Several improvements are achieved in this paper. First, the reference model accuracy is improved by considering vehicle load transfers and using a precise nonlinear tyre model 'UniTire'. Second, without the reference model accuracy degraded, the computing burden of the state observer is decreased by a reduced-order approach. Third, nonlinear system damping is integrated into the SMO to speed convergence and reduce chattering. The proposed RO-SMO is evaluated through simulation and experiments based on an in-wheel motor electric vehicle. The results show that the proposed observer accurately predicts the vehicle states.

Application of the method of temporal moments to interpret solute transport with sorption and degradation

NASA Astrophysics Data System (ADS)

Pang, Liping; Goltz, Mark; Close, Murray

2003-01-01

In this note, we applied the temporal moment solutions of [Das and Kluitenberg, 1996. Soil Sci. Am. J. 60, 1724] for one-dimensional advective-dispersive solute transport with linear equilibrium sorption and first-order degradation for time pulse sources to analyse soil column experimental data. Unlike most other moment solutions, these solutions consider the interplay of degradation and sorption. This permits estimation of a first-order degradation rate constant using the zeroth moment of column breakthrough data, as well as estimation of the retardation factor or sorption distribution coefficient of a degrading solute using the first moment. The method of temporal moment (MOM) formulae was applied to analyse breakthrough data from a laboratory column study of atrazine, hexazinone and rhodamine WT transport in volcanic pumice sand, as well as experimental data from the literature. Transport and degradation parameters obtained using the MOM were compared to parameters obtained by fitting breakthrough data from an advective-dispersive transport model with equilibrium sorption and first-order degradation, using the nonlinear least-square curve-fitting program CXTFIT. The results derived from using the literature data were also compared with estimates reported in the literature using different equilibrium models. The good agreement suggests that the MOM could provide an additional useful means of parameter estimation for transport involving equilibrium sorption and first-order degradation. We found that the MOM fitted breakthrough curves with tailing better than curve fitting. However, the MOM analysis requires complete breakthrough curves and relatively frequent data collection to ensure the accuracy of the moments obtained from the breakthrough data.

Nonlinear circuits for naturalistic visual motion estimation

PubMed Central

Fitzgerald, James E; Clark, Damon A

2015-01-01

Many animals use visual signals to estimate motion. Canonical models suppose that animals estimate motion by cross-correlating pairs of spatiotemporally separated visual signals, but recent experiments indicate that humans and flies perceive motion from higher-order correlations that signify motion in natural environments. Here we show how biologically plausible processing motifs in neural circuits could be tuned to extract this information. We emphasize how known aspects of Drosophila's visual circuitry could embody this tuning and predict fly behavior. We find that segregating motion signals into ON/OFF channels can enhance estimation accuracy by accounting for natural light/dark asymmetries. Furthermore, a diversity of inputs to motion detecting neurons can provide access to more complex higher-order correlations. Collectively, these results illustrate how non-canonical computations improve motion estimation with naturalistic inputs. This argues that the complexity of the fly's motion computations, implemented in its elaborate circuits, represents a valuable feature of its visual motion estimator. DOI: http://dx.doi.org/10.7554/eLife.09123.001 PMID:26499494

Automatic Estimation of the Radiological Inventory for the Dismantling of Nuclear Facilities

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

Garcia-Bermejo, R.; Felipe, A.; Gutierrez, S.

The estimation of the radiological inventory of Nuclear Facilities to be dismantled is a process that included information related with the physical inventory of all the plant and radiological survey. Estimation of the radiological inventory for all the components and civil structure of the plant could be obtained with mathematical models with statistical approach. A computer application has been developed in order to obtain the radiological inventory in an automatic way. Results: A computer application that is able to estimate the radiological inventory from the radiological measurements or the characterization program has been developed. In this computer applications has beenmore » included the statistical functions needed for the estimation of the central tendency and variability, e.g. mean, median, variance, confidence intervals, variance coefficients, etc. This computer application is a necessary tool in order to be able to estimate the radiological inventory of a nuclear facility and it is a powerful tool for decision taken in future sampling surveys.« less

Strategic sophistication of individuals and teams. Experimental evidence

PubMed Central

Sutter, Matthias; Czermak, Simon; Feri, Francesco

2013-01-01

Many important decisions require strategic sophistication. We examine experimentally whether teams act more strategically than individuals. We let individuals and teams make choices in simple games, and also elicit first- and second-order beliefs. We find that teams play the Nash equilibrium strategy significantly more often, and their choices are more often a best response to stated first order beliefs. Distributional preferences make equilibrium play less likely. Using a mixture model, the estimated probability to play strategically is 62% for teams, but only 40% for individuals. A model of noisy introspection reveals that teams differ from individuals in higher order beliefs. PMID:24926100

Decomposition of conditional probability for high-order symbolic Markov chains.

PubMed

Melnik, S S; Usatenko, O V

2017-07-01

The main goal of this paper is to develop an estimate for the conditional probability function of random stationary ergodic symbolic sequences with elements belonging to a finite alphabet. We elaborate on a decomposition procedure for the conditional probability function of sequences considered to be high-order Markov chains. We represent the conditional probability function as the sum of multilinear memory function monomials of different orders (from zero up to the chain order). This allows us to introduce a family of Markov chain models and to construct artificial sequences via a method of successive iterations, taking into account at each step increasingly high correlations among random elements. At weak correlations, the memory functions are uniquely expressed in terms of the high-order symbolic correlation functions. The proposed method fills the gap between two approaches, namely the likelihood estimation and the additive Markov chains. The obtained results may have applications for sequential approximation of artificial neural network training.

Decomposition of conditional probability for high-order symbolic Markov chains

NASA Astrophysics Data System (ADS)

Melnik, S. S.; Usatenko, O. V.

2017-07-01

The main goal of this paper is to develop an estimate for the conditional probability function of random stationary ergodic symbolic sequences with elements belonging to a finite alphabet. We elaborate on a decomposition procedure for the conditional probability function of sequences considered to be high-order Markov chains. We represent the conditional probability function as the sum of multilinear memory function monomials of different orders (from zero up to the chain order). This allows us to introduce a family of Markov chain models and to construct artificial sequences via a method of successive iterations, taking into account at each step increasingly high correlations among random elements. At weak correlations, the memory functions are uniquely expressed in terms of the high-order symbolic correlation functions. The proposed method fills the gap between two approaches, namely the likelihood estimation and the additive Markov chains. The obtained results may have applications for sequential approximation of artificial neural network training.

Model-based estimates of long-term persistence of induced HPV antibodies: a flexible subject-specific approach.

PubMed

Aregay, Mehreteab; Shkedy, Ziv; Molenberghs, Geert; David, Marie-Pierre; Tibaldi, Fabián

2013-01-01

In infectious diseases, it is important to predict the long-term persistence of vaccine-induced antibodies and to estimate the time points where the individual titers are below the threshold value for protection. This article focuses on HPV-16/18, and uses a so-called fractional-polynomial model to this effect, derived in a data-driven fashion. Initially, model selection was done from among the second- and first-order fractional polynomials on the one hand and from the linear mixed model on the other. According to a functional selection procedure, the first-order fractional polynomial was selected. Apart from the fractional polynomial model, we also fitted a power-law model, which is a special case of the fractional polynomial model. Both models were compared using Akaike's information criterion. Over the observation period, the fractional polynomials fitted the data better than the power-law model; this, of course, does not imply that it fits best over the long run, and hence, caution ought to be used when prediction is of interest. Therefore, we point out that the persistence of the anti-HPV responses induced by these vaccines can only be ascertained empirically by long-term follow-up analysis.

MRAS state estimator for speed sensorless ISFOC induction motor drives with Luenberger load torque estimation.

PubMed

Zorgani, Youssef Agrebi; Koubaa, Yassine; Boussak, Mohamed

2016-03-01

This paper presents a novel method for estimating the load torque of a sensorless indirect stator flux oriented controlled (ISFOC) induction motor drive based on the model reference adaptive system (MRAS) scheme. As a matter of fact, this method is meant to inter-connect a speed estimator with the load torque observer. For this purpose, a MRAS has been applied to estimate the rotor speed with tuned load torque in order to obtain a high performance ISFOC induction motor drive. The reference and adjustable models, developed in the stationary stator reference frame, are used in the MRAS scheme in an attempt to estimate the speed of the measured terminal voltages and currents. The load torque is estimated by means of a Luenberger observer defined throughout the mechanical equation. Every observer state matrix depends on the mechanical characteristics of the machine taking into account the vicious friction coefficient and inertia moment. Accordingly, some simulation results are presented to validate the proposed method and to highlight the influence of the variation of the inertia moment and the friction coefficient on the speed and the estimated load torque. The experimental results, concerning to the sensorless speed with a load torque estimation, are elaborated in order to validate the effectiveness of the proposed method. The complete sensorless ISFOC with load torque estimation is successfully implemented in real time using a digital signal processor board DSpace DS1104 for a laboratory 3 kW induction motor. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Estimation Methods for Non-Homogeneous Regression - Minimum CRPS vs Maximum Likelihood

NASA Astrophysics Data System (ADS)

Gebetsberger, Manuel; Messner, Jakob W.; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Non-homogeneous regression models are widely used to statistically post-process numerical weather prediction models. Such regression models correct for errors in mean and variance and are capable to forecast a full probability distribution. In order to estimate the corresponding regression coefficients, CRPS minimization is performed in many meteorological post-processing studies since the last decade. In contrast to maximum likelihood estimation, CRPS minimization is claimed to yield more calibrated forecasts. Theoretically, both scoring rules used as an optimization score should be able to locate a similar and unknown optimum. Discrepancies might result from a wrong distributional assumption of the observed quantity. To address this theoretical concept, this study compares maximum likelihood and minimum CRPS estimation for different distributional assumptions. First, a synthetic case study shows that, for an appropriate distributional assumption, both estimation methods yield to similar regression coefficients. The log-likelihood estimator is slightly more efficient. A real world case study for surface temperature forecasts at different sites in Europe confirms these results but shows that surface temperature does not always follow the classical assumption of a Gaussian distribution. KEYWORDS: ensemble post-processing, maximum likelihood estimation, CRPS minimization, probabilistic temperature forecasting, distributional regression models

A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve

NASA Astrophysics Data System (ADS)

Yang, Duo; Zhang, Xu; Pan, Rui; Wang, Yujie; Chen, Zonghai

2018-04-01

The state-of-health (SOH) estimation is always a crucial issue for lithium-ion batteries. In order to provide an accurate and reliable SOH estimation, a novel Gaussian process regression (GPR) model based on charging curve is proposed in this paper. Different from other researches where SOH is commonly estimated by cycle life, in this work four specific parameters extracted from charging curves are used as inputs of the GPR model instead of cycle numbers. These parameters can reflect the battery aging phenomenon from different angles. The grey relational analysis method is applied to analyze the relational grade between selected features and SOH. On the other hand, some adjustments are made in the proposed GPR model. Covariance function design and the similarity measurement of input variables are modified so as to improve the SOH estimate accuracy and adapt to the case of multidimensional input. Several aging data from NASA data repository are used for demonstrating the estimation effect by the proposed method. Results show that the proposed method has high SOH estimation accuracy. Besides, a battery with dynamic discharging profile is used to verify the robustness and reliability of this method.

PROPOSED MODELS FOR ESTIMATING RELEVANT DOSE RESULTING FROM EXPOSURES BY THE GASTROINTESTINAL ROUTE

EPA Science Inventory

Simple first-order intestinal absorption commonly used in physiologically-based pharmacokinetic(PBPK) models can be made to fit many clinical administrations but may not provide relevant information to extrapolate to real-world exposure scenarios for risk assessment. Small hydr...

Estimating linear-nonlinear models using Rényi divergences

PubMed Central

Kouh, Minjoon; Sharpee, Tatyana O.

2009-01-01

This paper compares a family of methods for characterizing neural feature selectivity using natural stimuli in the framework of the linear-nonlinear model. In this model, the spike probability depends in a nonlinear way on a small number of stimulus dimensions. The relevant stimulus dimensions can be found by optimizing a Rényi divergence that quantifies a change in the stimulus distribution associated with the arrival of single spikes. Generally, good reconstructions can be obtained based on optimization of Rényi divergence of any order, even in the limit of small numbers of spikes. However, the smallest error is obtained when the Rényi divergence of order 1 is optimized. This type of optimization is equivalent to information maximization, and is shown to saturate the Cramér-Rao bound describing the smallest error allowed for any unbiased method. We also discuss conditions under which information maximization provides a convenient way to perform maximum likelihood estimation of linear-nonlinear models from neural data. PMID:19568981

Estimating linear-nonlinear models using Renyi divergences.

PubMed

Kouh, Minjoon; Sharpee, Tatyana O

2009-01-01

This article compares a family of methods for characterizing neural feature selectivity using natural stimuli in the framework of the linear-nonlinear model. In this model, the spike probability depends in a nonlinear way on a small number of stimulus dimensions. The relevant stimulus dimensions can be found by optimizing a Rényi divergence that quantifies a change in the stimulus distribution associated with the arrival of single spikes. Generally, good reconstructions can be obtained based on optimization of Rényi divergence of any order, even in the limit of small numbers of spikes. However, the smallest error is obtained when the Rényi divergence of order 1 is optimized. This type of optimization is equivalent to information maximization, and is shown to saturate the Cramer-Rao bound describing the smallest error allowed for any unbiased method. We also discuss conditions under which information maximization provides a convenient way to perform maximum likelihood estimation of linear-nonlinear models from neural data.

The model for estimation production cost of embroidery handicraft

NASA Astrophysics Data System (ADS)

Nofierni; Sriwana, IK; Septriani, Y.

2017-12-01

Embroidery industry is one of type of micro industry that produce embroidery handicraft. These industries are emerging in some rural areas of Indonesia. Embroidery clothing are produce such as scarves and clothes that show cultural value of certain region. The owner of an enterprise must calculate the cost of production before making a decision on how many products are received from the customer. A calculation approach to production cost analysis is needed to consider the feasibility of each order coming. This study is proposed to design the expert system (ES) in order to improve production management in the embroidery industry. The model will design used Fuzzy inference system as a model to estimate production cost. Research conducted based on survey and knowledge acquisitions from stakeholder of supply chain embroidery handicraft industry at Bukittinggi, West Sumatera, Indonesia. This paper will use fuzzy input where the quality, the complexity of the design and the working hours required and the result of the model are useful to manage production cost on embroidery production.

Point cloud modeling using the homogeneous transformation for non-cooperative pose estimation

NASA Astrophysics Data System (ADS)

Lim, Tae W.

2015-06-01

A modeling process to simulate point cloud range data that a lidar (light detection and ranging) sensor produces is presented in this paper in order to support the development of non-cooperative pose (relative attitude and position) estimation approaches which will help improve proximity operation capabilities between two adjacent vehicles. The algorithms in the modeling process were based on the homogeneous transformation, which has been employed extensively in robotics and computer graphics, as well as in recently developed pose estimation algorithms. Using a flash lidar in a laboratory testing environment, point cloud data of a test article was simulated and compared against the measured point cloud data. The simulated and measured data sets match closely, validating the modeling process. The modeling capability enables close examination of the characteristics of point cloud images of an object as it undergoes various translational and rotational motions. Relevant characteristics that will be crucial in non-cooperative pose estimation were identified such as shift, shadowing, perspective projection, jagged edges, and differential point cloud density. These characteristics will have to be considered in developing effective non-cooperative pose estimation algorithms. The modeling capability will allow extensive non-cooperative pose estimation performance simulations prior to field testing, saving development cost and providing performance metrics of the pose estimation concepts and algorithms under evaluation. The modeling process also provides "truth" pose of the test objects with respect to the sensor frame so that the pose estimation error can be quantified.

Temporal rainfall estimation using input data reduction and model inversion

NASA Astrophysics Data System (ADS)

Wright, A. J.; Vrugt, J. A.; Walker, J. P.; Pauwels, V. R. N.

2016-12-01

Floods are devastating natural hazards. To provide accurate, precise and timely flood forecasts there is a need to understand the uncertainties associated with temporal rainfall and model parameters. The estimation of temporal rainfall and model parameter distributions from streamflow observations in complex dynamic catchments adds skill to current areal rainfall estimation methods, allows for the uncertainty of rainfall input to be considered when estimating model parameters and provides the ability to estimate rainfall from poorly gauged catchments. Current methods to estimate temporal rainfall distributions from streamflow are unable to adequately explain and invert complex non-linear hydrologic systems. This study uses the Discrete Wavelet Transform (DWT) to reduce rainfall dimensionality for the catchment of Warwick, Queensland, Australia. The reduction of rainfall to DWT coefficients allows the input rainfall time series to be simultaneously estimated along with model parameters. The estimation process is conducted using multi-chain Markov chain Monte Carlo simulation with the DREAMZS algorithm. The use of a likelihood function that considers both rainfall and streamflow error allows for model parameter and temporal rainfall distributions to be estimated. Estimation of the wavelet approximation coefficients of lower order decomposition structures was able to estimate the most realistic temporal rainfall distributions. These rainfall estimates were all able to simulate streamflow that was superior to the results of a traditional calibration approach. It is shown that the choice of wavelet has a considerable impact on the robustness of the inversion. The results demonstrate that streamflow data contains sufficient information to estimate temporal rainfall and model parameter distributions. The extent and variance of rainfall time series that are able to simulate streamflow that is superior to that simulated by a traditional calibration approach is a demonstration of equifinality. The use of a likelihood function that considers both rainfall and streamflow error combined with the use of the DWT as a model data reduction technique allows the joint inference of hydrologic model parameters along with rainfall.

Global Precipitation Measurement: Methods, Datasets and Applications

NASA Technical Reports Server (NTRS)

Tapiador, Francisco; Turk, Francis J.; Petersen, Walt; Hou, Arthur Y.; Garcia-Ortega, Eduardo; Machado, Luiz, A. T.; Angelis, Carlos F.; Salio, Paola; Kidd, Chris; Huffman, George J.;

Estimation of inflation parameters for Perturbed Power Law model using recent CMB measurements

NASA Astrophysics Data System (ADS)

Mukherjee, Suvodip; Das, Santanu; Joy, Minu; Souradeep, Tarun

2015-01-01

Cosmic Microwave Background (CMB) is an important probe for understanding the inflationary era of the Universe. We consider the Perturbed Power Law (PPL) model of inflation which is a soft deviation from Power Law (PL) inflationary model. This model captures the effect of higher order derivative of Hubble parameter during inflation, which in turn leads to a non-zero effective mass meff for the inflaton field. The higher order derivatives of Hubble parameter at leading order sources constant difference in the spectral index for scalar and tensor perturbation going beyond PL model of inflation. PPL model have two observable independent parameters, namely spectral index for tensor perturbation νt and change in spectral index for scalar perturbation νst to explain the observed features in the scalar and tensor power spectrum of perturbation. From the recent measurements of CMB power spectra by WMAP, Planck and BICEP-2 for temperature and polarization, we estimate the feasibility of PPL model with standard ΛCDM model. Although BICEP-2 claimed a detection of r=0.2, estimates of dust contamination provided by Planck have left open the possibility that only upper bound on r will be expected in a joint analysis. As a result we consider different upper bounds on the value of r and show that PPL model can explain a lower value of tensor to scalar ratio (r<0.1 or r<0.01) for a scalar spectral index of ns=0.96 by having a non-zero value of effective mass of the inflaton field m2eff/H2. The analysis with WP + Planck likelihood shows a non-zero detection of m2eff/H2 with 5.7 σ and 8.1 σ respectively for r<0.1 and r<0.01. Whereas, with BICEP-2 likelihood m2eff/H2 = -0.0237 ± 0.0135 which is consistent with zero.

Nonequilibrium critical dynamics of the two-dimensional Ashkin-Teller model at the Baxter line

NASA Astrophysics Data System (ADS)

Fernandes, H. A.; da Silva, R.; Caparica, A. A.; de Felício, J. R. Drugowich

2017-04-01

We investigate the short-time universal behavior of the two-dimensional Ashkin-Teller model at the Baxter line by performing time-dependent Monte Carlo simulations. First, as preparatory results, we obtain the critical parameters by searching the optimal power-law decay of the magnetization. Thus, the dynamic critical exponents θm and θp, related to the magnetic and electric order parameters, as well as the persistence exponent θg, are estimated using heat-bath Monte Carlo simulations. In addition, we estimate the dynamic exponent z and the static critical exponents β and ν for both order parameters. We propose a refined method to estimate the static exponents that considers two different averages: one that combines an internal average using several seeds with another, which is taken over temporal variations in the power laws. Moreover, we also performed the bootstrapping method for a complementary analysis. Our results show that the ratio β /ν exhibits universal behavior along the critical line corroborating the conjecture for both magnetization and polarization.

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

PubMed Central

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-01-01

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation.

PubMed

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-12-19

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms.

Numerical modeling of space-time wave extremes using WAVEWATCH III

NASA Astrophysics Data System (ADS)

Barbariol, Francesco; Alves, Jose-Henrique G. M.; Benetazzo, Alvise; Bergamasco, Filippo; Bertotti, Luciana; Carniel, Sandro; Cavaleri, Luigi; Y. Chao, Yung; Chawla, Arun; Ricchi, Antonio; Sclavo, Mauro; Tolman, Hendrik

2017-04-01

A novel implementation of parameters estimating the space-time wave extremes within the spectral wave model WAVEWATCH III (WW3) is presented. The new output parameters, available in WW3 version 5.16, rely on the theoretical model of Fedele (J Phys Oceanogr 42(9):1601-1615, 2012) extended by Benetazzo et al. (J Phys Oceanogr 45(9):2261-2275, 2015) to estimate the maximum second-order nonlinear crest height over a given space-time region. In order to assess the wave height associated to the maximum crest height and the maximum wave height (generally different in a broad-band stormy sea state), the linear quasi-determinism theory of Boccotti (2000) is considered. The new WW3 implementation is tested by simulating sea states and space-time extremes over the Mediterranean Sea (forced by the wind fields produced by the COSMO-ME atmospheric model). Model simulations are compared to space-time wave maxima observed on March 10th, 2014, in the northern Adriatic Sea (Italy), by a stereo camera system installed on-board the "Acqua Alta" oceanographic tower. Results show that modeled space-time extremes are in general agreement with observations. Differences are mostly ascribed to the accuracy of the wind forcing and, to a lesser extent, to the approximations introduced in the space-time extremes parameterizations. Model estimates are expected to be even more accurate over areas larger than the mean wavelength (for instance, the model grid size).

Correcting for static shift of magnetotelluric data with airborne electromagnetic measurements: a case study from Rathlin Basin, Northern Ireland

NASA Astrophysics Data System (ADS)

Delhaye, Robert; Rath, Volker; Jones, Alan G.; Muller, Mark R.; Reay, Derek

2017-05-01

Galvanic distortions of magnetotelluric (MT) data, such as the static-shift effect, are a known problem that can lead to incorrect estimation of resistivities and erroneous modelling of geometries with resulting misinterpretation of subsurface electrical resistivity structure. A wide variety of approaches have been proposed to account for these galvanic distortions, some depending on the target area, with varying degrees of success. The natural laboratory for our study is a hydraulically permeable volume of conductive sediment at depth, the internal resistivity structure of which can be used to estimate reservoir viability for geothermal purposes; however, static-shift correction is required in order to ensure robust and precise modelling accuracy.We present here a possible method to employ frequency-domain electromagnetic data in order to correct static-shift effects, illustrated by a case study from Northern Ireland. In our survey area, airborne frequency domain electromagnetic (FDEM) data are regionally available with high spatial density. The spatial distributions of the derived static-shift corrections are analysed and applied to the uncorrected MT data prior to inversion. Two comparative inversion models are derived, one with and one without static-shift corrections, with instructive results. As expected from the one-dimensional analogy of static-shift correction, at shallow model depths, where the structure is controlled by a single local MT site, the correction of static-shift effects leads to vertical scaling of resistivity-thickness products in the model, with the corrected model showing improved correlation to existing borehole wireline resistivity data. In turn, as these vertical scalings are effectively independent of adjacent sites, lateral resistivity distributions are also affected, with up to half a decade of resistivity variation between the models estimated at depths down to 2000 m. Simple estimation of differences in bulk porosity, derived using Archie's Law, between the two models reinforces our conclusion that the suborder of magnitude resistivity contrasts induced by the correction of static shifts correspond to similar contrasts in estimated porosities, and hence, for purposes of reservoir investigation or similar cases requiring accurate absolute resistivity estimates, galvanic distortion correction, especially static-shift correction, is essential.

Parameter estimation for terrain modeling from gradient data. [navigation system for Martian rover

NASA Technical Reports Server (NTRS)

Dangelo, K. R.

1974-01-01

A method is developed for modeling terrain surfaces for use on an unmanned Martian roving vehicle. The modeling procedure employs a two-step process which uses gradient as well as height data in order to improve the accuracy of the model's gradient. Least square approximation is used in order to stochastically determine the parameters which describe the modeled surface. A complete error analysis of the modeling procedure is included which determines the effect of instrumental measurement errors on the model's accuracy. Computer simulation is used as a means of testing the entire modeling process which includes the acquisition of data points, the two-step modeling process and the error analysis. Finally, to illustrate the procedure, a numerical example is included.

Data on industrial new orders for the euro area.

PubMed

de Bondt, Gabe J; Dieden, Heinz C; Muzikarova, Sona; Pavlova, Iskra

2016-12-01

This data article provides time series on euro area industrial new orders and is related to the research article entitled "Modelling industrial new orders" (G.J. de Bondt, H.C. Dieden, S. Muzikarova, I. Vincze, 2014b) [3]. The data are in index format with a fixed base year (currently 2010) for total new orders as well as a number of breakdowns. The euro area data are based on the official national data for countries that still collect data and on European Central Bank (ECB) model estimates for countries that discontinued the data collection. The weighting scheme to calculate euro area aggregates makes use of country weights derived from industrial turnover statistics as published by Eurostat.

A Monte-Carlo Bayesian framework for urban rainfall error modelling

NASA Astrophysics Data System (ADS)

Ochoa Rodriguez, Susana; Wang, Li-Pen; Willems, Patrick; Onof, Christian

2016-04-01

Rainfall estimates of the highest possible accuracy and resolution are required for urban hydrological applications, given the small size and fast response which characterise urban catchments. While significant progress has been made in recent years towards meeting rainfall input requirements for urban hydrology -including increasing use of high spatial resolution radar rainfall estimates in combination with point rain gauge records- rainfall estimates will never be perfect and the true rainfall field is, by definition, unknown [1]. Quantifying the residual errors in rainfall estimates is crucial in order to understand their reliability, as well as the impact that their uncertainty may have in subsequent runoff estimates. The quantification of errors in rainfall estimates has been an active topic of research for decades. However, existing rainfall error models have several shortcomings, including the fact that they are limited to describing errors associated to a single data source (i.e. errors associated to rain gauge measurements or radar QPEs alone) and to a single representative error source (e.g. radar-rain gauge differences, spatial temporal resolution). Moreover, rainfall error models have been mostly developed for and tested at large scales. Studies at urban scales are mostly limited to analyses of propagation of errors in rain gauge records-only through urban drainage models and to tests of model sensitivity to uncertainty arising from unmeasured rainfall variability. Only few radar rainfall error models -originally developed for large scales- have been tested at urban scales [2] and have been shown to fail to well capture small-scale storm dynamics, including storm peaks, which are of utmost important for urban runoff simulations. In this work a Monte-Carlo Bayesian framework for rainfall error modelling at urban scales is introduced, which explicitly accounts for relevant errors (arising from insufficient accuracy and/or resolution) in multiple data sources (in this case radar and rain gauge estimates typically available at present), while at the same time enabling dynamic combination of these data sources (thus not only quantifying uncertainty, but also reducing it). This model generates an ensemble of merged rainfall estimates, which can then be used as input to urban drainage models in order to examine how uncertainties in rainfall estimates propagate to urban runoff estimates. The proposed model is tested using as case study a detailed rainfall and flow dataset, and a carefully verified urban drainage model of a small (~9 km2) pilot catchment in North-East London. The model has shown to well characterise residual errors in rainfall data at urban scales (which remain after the merging), leading to improved runoff estimates. In fact, the majority of measured flow peaks are bounded within the uncertainty area produced by the runoff ensembles generated with the ensemble rainfall inputs. REFERENCES: [1] Ciach, G. J. & Krajewski, W. F. (1999). On the estimation of radar rainfall error variance. Advances in Water Resources, 22 (6), 585-595. [2] Rico-Ramirez, M. A., Liguori, S. & Schellart, A. N. A. (2015). Quantifying radar-rainfall uncertainties in urban drainage flow modelling. Journal of Hydrology, 528, 17-28.

Theoretical study of gas hydrate decomposition kinetics: model predictions.

PubMed

Windmeier, Christoph; Oellrich, Lothar R

2013-11-27

In order to provide an estimate of intrinsic gas hydrate dissolution and dissociation kinetics, the Consecutive Desorption and Melting Model (CDM) was developed in a previous publication (Windmeier, C.; Oellrich, L. R. J. Phys. Chem. A 2013, 117, 10151-10161). In this work, an extensive summary of required model data is given. Obtained model predictions are discussed with respect to their temperature dependence as well as their significance for technically relevant areas of gas hydrate decomposition. As a result, an expression for determination of the intrinsic gas hydrate decomposition kinetics for various hydrate formers is given together with an estimate for the maximum possible rates of gas hydrate decomposition.

Improved estimation of hydraulic conductivity by combining stochastically simulated hydrofacies with geophysical data

PubMed Central

Zhu, Lin; Gong, Huili; Chen, Yun; Li, Xiaojuan; Chang, Xiang; Cui, Yijiao

2016-01-01

Hydraulic conductivity is a major parameter affecting the output accuracy of groundwater flow and transport models. The most commonly used semi-empirical formula for estimating conductivity is Kozeny-Carman equation. However, this method alone does not work well with heterogeneous strata. Two important parameters, grain size and porosity, often show spatial variations at different scales. This study proposes a method for estimating conductivity distributions by combining a stochastic hydrofacies model with geophysical methods. The Markov chain model with transition probability matrix was adopted to re-construct structures of hydrofacies for deriving spatial deposit information. The geophysical and hydro-chemical data were used to estimate the porosity distribution through the Archie’s law. Results show that the stochastic simulated hydrofacies model reflects the sedimentary features with an average model accuracy of 78% in comparison with borehole log data in the Chaobai alluvial fan. The estimated conductivity is reasonable and of the same order of magnitude of the outcomes of the pumping tests. The conductivity distribution is consistent with the sedimentary distributions. This study provides more reliable spatial distributions of the hydraulic parameters for further numerical modeling. PMID:26927886

Estimating the biophysical properties of neurons with intracellular calcium dynamics.

PubMed

Ye, Jingxin; Rozdeba, Paul J; Morone, Uriel I; Daou, Arij; Abarbanel, Henry D I

2014-06-01

We investigate the dynamics of a conductance-based neuron model coupled to a model of intracellular calcium uptake and release by the endoplasmic reticulum. The intracellular calcium dynamics occur on a time scale that is orders of magnitude slower than voltage spiking behavior. Coupling these mechanisms sets the stage for the appearance of chaotic dynamics, which we observe within certain ranges of model parameter values. We then explore the question of whether one can, using observed voltage data alone, estimate the states and parameters of the voltage plus calcium (V+Ca) dynamics model. We find the answer is negative. Indeed, we show that voltage plus another observed quantity must be known to allow the estimation to be accurate. We show that observing both the voltage time course V(t) and the intracellular Ca time course will permit accurate estimation, and from the estimated model state, accurate prediction after observations are completed. This sets the stage for how one will be able to use a more detailed model of V+Ca dynamics in neuron activity in the analysis of experimental data on individual neurons as well as functional networks in which the nodes (neurons) have these biophysical properties.

Estimating the biophysical properties of neurons with intracellular calcium dynamics

NASA Astrophysics Data System (ADS)

Ye, Jingxin; Rozdeba, Paul J.; Morone, Uriel I.; Daou, Arij; Abarbanel, Henry D. I.

2014-06-01

We investigate the dynamics of a conductance-based neuron model coupled to a model of intracellular calcium uptake and release by the endoplasmic reticulum. The intracellular calcium dynamics occur on a time scale that is orders of magnitude slower than voltage spiking behavior. Coupling these mechanisms sets the stage for the appearance of chaotic dynamics, which we observe within certain ranges of model parameter values. We then explore the question of whether one can, using observed voltage data alone, estimate the states and parameters of the voltage plus calcium (V+Ca) dynamics model. We find the answer is negative. Indeed, we show that voltage plus another observed quantity must be known to allow the estimation to be accurate. We show that observing both the voltage time course V (t) and the intracellular Ca time course will permit accurate estimation, and from the estimated model state, accurate prediction after observations are completed. This sets the stage for how one will be able to use a more detailed model of V+Ca dynamics in neuron activity in the analysis of experimental data on individual neurons as well as functional networks in which the nodes (neurons) have these biophysical properties.

Model-data integration for developing the Cropland Carbon Monitoring System (CCMS)

NASA Astrophysics Data System (ADS)

Jones, C. D.; Bandaru, V.; Pnvr, K.; Jin, H.; Reddy, A.; Sahajpal, R.; Sedano, F.; Skakun, S.; Wagle, P.; Gowda, P. H.; Hurtt, G. C.; Izaurralde, R. C.

2017-12-01

The Cropland Carbon Monitoring System (CCMS) has been initiated to improve regional estimates of carbon fluxes from croplands in the conterminous United States through integration of terrestrial ecosystem modeling, use of remote-sensing products and publically available datasets, and development of improved landscape and management databases. In order to develop these improved carbon flux estimates, experimental datasets are essential for evaluating the skill of estimates, characterizing the uncertainty of these estimates, characterizing parameter sensitivities, and calibrating specific modeling components. Experiments were sought that included flux tower measurement of CO2 fluxes under production of major agronomic crops. Currently data has been collected from 17 experiments comprising 117 site-years from 12 unique locations. Calibration of terrestrial ecosystem model parameters using available crop productivity and net ecosystem exchange (NEE) measurements resulted in improvements in RMSE of NEE predictions of between 3.78% to 7.67%, while improvements in RMSE for yield ranged from -1.85% to 14.79%. Model sensitivities were dominated by parameters related to leaf area index (LAI) and spring growth, demonstrating considerable capacity for model improvement through development and integration of remote-sensing products. Subsequent analyses will assess the impact of such integrated approaches on skill of cropland carbon flux estimates.

Estimation of Longitudinal Force and Sideslip Angle for Intelligent Four-Wheel Independent Drive Electric Vehicles by Observer Iteration and Information Fusion.

PubMed

Chen, Te; Chen, Long; Xu, Xing; Cai, Yingfeng; Jiang, Haobin; Sun, Xiaoqiang

2018-04-20

Exact estimation of longitudinal force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for longitudinal force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for longitudinal force estimation, the longitudinal force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for longitudinal force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated longitudinal force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified.

Estimation of Longitudinal Force and Sideslip Angle for Intelligent Four-Wheel Independent Drive Electric Vehicles by Observer Iteration and Information Fusion

PubMed Central

Chen, Long; Xu, Xing; Cai, Yingfeng; Jiang, Haobin; Sun, Xiaoqiang

2018-01-01

Exact estimation of longitudinal force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for longitudinal force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for longitudinal force estimation, the longitudinal force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for longitudinal force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated longitudinal force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified. PMID:29677124

Integrated modeling approach using SELECT and SWAT models to simulate source loading and in-stream conditions of fecal indicator bacteria.

NASA Astrophysics Data System (ADS)

Ranatunga, T.

2016-12-01

Modeling of fate and transport of fecal bacteria in a watershed is generally a processed based approach that considers releases from manure, point sources, and septic systems. Overland transport with water and sediments, infiltration into soils, transport in the vadose zone and groundwater, die-off and growth processes, and in-stream transport are considered as the other major processes in bacteria simulation. This presentation will discuss a simulation of fecal indicator bacteria (E.coli) source loading and in-stream conditions of a non-tidal watershed (Cedar Bayou Watershed) in South Central Texas using two models; Spatially Explicit Load Enrichment Calculation Tool (SELECT) and Soil and Water Assessment Tool (SWAT). Furthermore, it will discuss a probable approach of bacteria source load reduction in order to meet the water quality standards in the streams. The selected watershed is listed as having levels of fecal indicator bacteria that posed a risk for contact recreation and wading by the Texas Commission of Environmental Quality (TCEQ). The SELECT modeling approach was used in estimating the bacteria source loading from land categories. Major bacteria sources considered were, failing septic systems, discharges from wastewater treatment facilities, excreta from livestock (Cattle, Horses, Sheep and Goat), excreta from Wildlife (Feral Hogs, and Deer), Pet waste (mainly from Dogs), and runoff from urban surfaces. The estimated source loads were input to the SWAT model in order to simulate the transport through the land and in-stream conditions. The calibrated SWAT model was then used to estimate the indicator bacteria in-stream concentrations for future years based on H-GAC's regional land use, population and household projections (up to 2040). Based on the in-stream reductions required to meet the water quality standards, the corresponding required source load reductions were estimated.

Quaternion-Based Texture Analysis of Multiband Satellite Images: Application to the Estimation of Aboveground Biomass in the East Region of Cameroon.

PubMed

Djiongo Kenfack, Cedrigue Boris; Monga, Olivier; Mpong, Serge Moto; Ndoundam, René

2018-03-01

Within the last decade, several approaches using quaternion numbers to handle and model multiband images in a holistic manner were introduced. The quaternion Fourier transform can be efficiently used to model texture in multidimensional data such as color images. For practical application, multispectral satellite data appear as a primary source for measuring past trends and monitoring changes in forest carbon stocks. In this work, we propose a texture-color descriptor based on the quaternion Fourier transform to extract relevant information from multiband satellite images. We propose a new multiband image texture model extraction, called FOTO++, in order to address biomass estimation issues. The first stage consists in removing noise from the multispectral data while preserving the edges of canopies. Afterward, color texture descriptors are extracted thanks to a discrete form of the quaternion Fourier transform, and finally the support vector regression method is used to deduce biomass estimation from texture indices. Our texture features are modeled using a vector composed with the radial spectrum coming from the amplitude of the quaternion Fourier transform. We conduct several experiments in order to study the sensitivity of our model to acquisition parameters. We also assess its performance both on synthetic images and on real multispectral images of Cameroonian forest. The results show that our model is more robust to acquisition parameters than the classical Fourier Texture Ordination model (FOTO). Our scheme is also more accurate for aboveground biomass estimation. We stress that a similar methodology could be implemented using quaternion wavelets. These results highlight the potential of the quaternion-based approach to study multispectral satellite images.

Reduced order modeling, statistical analysis and system identification for a bladed rotor with geometric mistuning

NASA Astrophysics Data System (ADS)

Vishwakarma, Vinod

Modified Modal Domain Analysis (MMDA) is a novel method for the development of a reduced-order model (ROM) of a bladed rotor. This method utilizes proper orthogonal decomposition (POD) of Coordinate Measurement Machine (CMM) data of blades' geometries and sector analyses using ANSYS. For the first time ROM of a geometrically mistuned industrial scale rotor (Transonic rotor) with large size of Finite Element (FE) model is generated using MMDA. Two methods for estimating mass and stiffness mistuning matrices are used a) exact computation from sector FE analysis, b) estimates based on POD mistuning parameters. Modal characteristics such as mistuned natural frequencies, mode shapes and forced harmonic response are obtained from ROM for various cases, and results are compared with full rotor ANSYS analysis and other ROM methods such as Subset of Nominal Modes (SNM) and Fundamental Model of Mistuning (FMM). Accuracy of MMDA ROM is demonstrated with variations in number of POD features and geometric mistuning parameters. It is shown for the aforementioned case b) that the high accuracy of ROM studied in previous work with Academic rotor does not directly translate to the Transonic rotor. Reasons for such mismatch in results are investigated and attributed to higher mistuning in Transonic rotor. Alternate solutions such as estimation of sensitivities via least squares, and interpolation of mass and stiffness matrices on manifolds are developed, and their results are discussed. Statistics such as mean and standard deviations of forced harmonic response peak amplitude are obtained from random permutations, and are shown to have similar results as those of Monte Carlo simulations. These statistics are obtained and compared for 3 degree of freedom (DOF) lumped parameter model (LPM) of rotor, Academic rotor and Transonic rotor. A state -- estimator based on MMDA ROM and Kalman filter is also developed for offline or online estimation of harmonic forcing function from measurements of forced response. Forcing function is estimated for synchronous excitation of 3DOF rotor model, Academic rotor and Transonic rotor from measurement of response at few nodes. For asynchronous excitation forcing function is estimated only for 3DOF rotor model and Academic rotor from measurement of response. The impact of number of measurement locations and accuracy of ROM on the estimation of forcing function is discussed. iv.

Vector Observation-Aided/Attitude-Rate Estimation Using Global Positioning System Signals

NASA Technical Reports Server (NTRS)

Oshman, Yaakov; Markley, F. Landis

1997-01-01

A sequential filtering algorithm is presented for attitude and attitude-rate estimation from Global Positioning System (GPS) differential carrier phase measurements. A third-order, minimal-parameter method for solving the attitude matrix kinematic equation is used to parameterize the filter's state, which renders the resulting estimator computationally efficient. Borrowing from tracking theory concepts, the angular acceleration is modeled as an exponentially autocorrelated stochastic process, thus avoiding the use of the uncertain spacecraft dynamic model. The new formulation facilitates the use of aiding vector observations in a unified filtering algorithm, which can enhance the method's robustness and accuracy. Numerical examples are used to demonstrate the performance of the method.

The pension incentive to retire: empirical evidence for West Germany.

PubMed

Siddiqui, S

1997-01-01

"In this paper, the impact of the West German pension system on the retirement decisions of elderly citizens is analyzed within the framework of a discrete-time hazard rate model deduced from a micro-economic decision rule. The model is estimated using a panel dataset of elderly West German citizens. In order to improve the precision of the estimates obtained, the data from the sample are combined with aggregate-level information on the labour force participation behaviour of the elderly. Policy simulations based on the estimates reveal that the probability of early retirement can be reduced significantly by appropriate changes in the pension system." excerpt

Integrating geographic information systems and remote sensing with spatial econometric and mixed logit models for environmental valuation

NASA Astrophysics Data System (ADS)

Wells, Aaron Raymond

This research focuses on the Emory and Obed Watersheds in the Cumberland Plateau in Central Tennessee and the Lower Hatchie River Watershed in West Tennessee. A framework based on market and nonmarket valuation techniques was used to empirically estimate economic values for environmental amenities and negative externalities in these areas. The specific techniques employed include a variation of hedonic pricing and discrete choice conjoint analysis (i.e., choice modeling), in addition to geographic information systems (GIS) and remote sensing. Microeconomic models of agent behavior, including random utility theory and profit maximization, provide the principal theoretical foundation linking valuation techniques and econometric models. The generalized method of moments estimator for a first-order spatial autoregressive function and mixed logit models are the principal econometric methods applied within the framework. The dissertation is subdivided into three separate chapters written in a manuscript format. The first chapter provides the necessary theoretical and mathematical conditions that must be satisfied in order for a forest amenity enhancement program to be implemented. These conditions include utility, value, and profit maximization. The second chapter evaluates the effect of forest land cover and information about future land use change on respondent preferences and willingness to pay for alternative hypothetical forest amenity enhancement options. Land use change information and the amount of forest land cover significantly influenced respondent preferences, choices, and stated willingness to pay. Hicksian welfare estimates for proposed enhancement options ranged from 57.42 to 25.53, depending on the policy specification, information level, and econometric model. The third chapter presents economic values for negative externalities associated with channelization that affect the productivity and overall market value of forested wetlands. Results of robust, generalized moments estimation of a double logarithmic first-order spatial autoregressive error model (inverse distance weights with spatial dependence up to 1500m) indicate that the implicit cost of damages to forested wetlands caused by channelization equaled -$5,438 ha-1. Collectively, the results of this dissertation provide economic measures of the damages to and benefits of environmental assets, help private landowners and policy makers identify the amenity attributes preferred by the public, and improve the management of natural resources.

A Semi-Parametric Bayesian Mixture Modeling Approach for the Analysis of Judge Mediated Data

ERIC Educational Resources Information Center

Muckle, Timothy Joseph

2010-01-01

Existing methods for the analysis of ordinal-level data arising from judge ratings, such as the Multi-Facet Rasch model (MFRM, or the so-called Facets model) have been widely used in assessment in order to render fair examinee ability estimates in situations where the judges vary in their behavior or severity. However, this model makes certain…

Comparison of regression models for estimation of isometric wrist joint torques using surface electromyography

PubMed Central

2011-01-01

Background Several regression models have been proposed for estimation of isometric joint torque using surface electromyography (SEMG) signals. Common issues related to torque estimation models are degradation of model accuracy with passage of time, electrode displacement, and alteration of limb posture. This work compares the performance of the most commonly used regression models under these circumstances, in order to assist researchers with identifying the most appropriate model for a specific biomedical application. Methods Eleven healthy volunteers participated in this study. A custom-built rig, equipped with a torque sensor, was used to measure isometric torque as each volunteer flexed and extended his wrist. SEMG signals from eight forearm muscles, in addition to wrist joint torque data were gathered during the experiment. Additional data were gathered one hour and twenty-four hours following the completion of the first data gathering session, for the purpose of evaluating the effects of passage of time and electrode displacement on accuracy of models. Acquired SEMG signals were filtered, rectified, normalized and then fed to models for training. Results It was shown that mean adjusted coefficient of determination (Ra2) values decrease between 20%-35% for different models after one hour while altering arm posture decreased mean Ra2 values between 64% to 74% for different models. Conclusions Model estimation accuracy drops significantly with passage of time, electrode displacement, and alteration of limb posture. Therefore model retraining is crucial for preserving estimation accuracy. Data resampling can significantly reduce model training time without losing estimation accuracy. Among the models compared, ordinary least squares linear regression model (OLS) was shown to have high isometric torque estimation accuracy combined with very short training times. PMID:21943179

A computational framework for simultaneous estimation of muscle and joint contact forces and body motion using optimization and surrogate modeling.

PubMed

Eskinazi, Ilan; Fregly, Benjamin J

2018-04-01

Concurrent estimation of muscle activations, joint contact forces, and joint kinematics by means of gradient-based optimization of musculoskeletal models is hindered by computationally expensive and non-smooth joint contact and muscle wrapping algorithms. We present a framework that simultaneously speeds up computation and removes sources of non-smoothness from muscle force optimizations using a combination of parallelization and surrogate modeling, with special emphasis on a novel method for modeling joint contact as a surrogate model of a static analysis. The approach allows one to efficiently introduce elastic joint contact models within static and dynamic optimizations of human motion. We demonstrate the approach by performing two optimizations, one static and one dynamic, using a pelvis-leg musculoskeletal model undergoing a gait cycle. We observed convergence on the order of seconds for a static optimization time frame and on the order of minutes for an entire dynamic optimization. The presented framework may facilitate model-based efforts to predict how planned surgical or rehabilitation interventions will affect post-treatment joint and muscle function. Copyright © 2018 IPEM. Published by Elsevier Ltd. All rights reserved.

Extremely low order time-fractional differential equation and application in combustion process

NASA Astrophysics Data System (ADS)

Xu, Qinwu; Xu, Yufeng

2018-11-01

Fractional blow-up model, especially which is of very low order of fractional derivative, plays a significant role in combustion process. The order of time-fractional derivative in diffusion model essentially distinguishes the super-diffusion and sub-diffusion processes when it is relatively high or low accordingly. In this paper, the blow-up phenomenon and condition of its appearance are theoretically proved. The blow-up moment is estimated by using differential inequalities. To numerically study the behavior around blow-up point, a mixed numerical method based on adaptive finite difference on temporal direction and highly effective discontinuous Galerkin method on spatial direction is proposed. The time of blow-up is calculated accurately. In simulation, we analyze the dynamics of fractional blow-up model under different orders of fractional derivative. It is found that the lower the order, the earlier the blow-up comes, by fixing the other parameters in the model. Our results confirm the physical truth that a combustor for explosion cannot be too small.

Performance of a reduced-order FSI model for flow-induced vocal fold vibration

NASA Astrophysics Data System (ADS)

Luo, Haoxiang; Chang, Siyuan; Chen, Ye; Rousseau, Bernard; PhonoSim Team

2017-11-01

Vocal fold vibration during speech production involves a three-dimensional unsteady glottal jet flow and three-dimensional nonlinear tissue mechanics. A full 3D fluid-structure interaction (FSI) model is computationally expensive even though it provides most accurate information about the system. On the other hand, an efficient reduced-order FSI model is useful for fast simulation and analysis of the vocal fold dynamics, which can be applied in procedures such as optimization and parameter estimation. In this work, we study performance of a reduced-order model as compared with the corresponding full 3D model in terms of its accuracy in predicting the vibration frequency and deformation mode. In the reduced-order model, we use a 1D flow model coupled with a 3D tissue model that is the same as in the full 3D model. Two different hyperelastic tissue behaviors are assumed. In addition, the vocal fold thickness and subglottal pressure are varied for systematic comparison. The result shows that the reduced-order model provides consistent predictions as the full 3D model across different tissue material assumptions and subglottal pressures. However, the vocal fold thickness has most effect on the model accuracy, especially when the vocal fold is thin.

Comparison of robustness to outliers between robust poisson models and log-binomial models when estimating relative risks for common binary outcomes: a simulation study.

PubMed

Chen, Wansu; Shi, Jiaxiao; Qian, Lei; Azen, Stanley P

2014-06-26

To estimate relative risks or risk ratios for common binary outcomes, the most popular model-based methods are the robust (also known as modified) Poisson and the log-binomial regression. Of the two methods, it is believed that the log-binomial regression yields more efficient estimators because it is maximum likelihood based, while the robust Poisson model may be less affected by outliers. Evidence to support the robustness of robust Poisson models in comparison with log-binomial models is very limited. In this study a simulation was conducted to evaluate the performance of the two methods in several scenarios where outliers existed. The findings indicate that for data coming from a population where the relationship between the outcome and the covariate was in a simple form (e.g. log-linear), the two models yielded comparable biases and mean square errors. However, if the true relationship contained a higher order term, the robust Poisson models consistently outperformed the log-binomial models even when the level of contamination is low. The robust Poisson models are more robust (or less sensitive) to outliers compared to the log-binomial models when estimating relative risks or risk ratios for common binary outcomes. Users should be aware of the limitations when choosing appropriate models to estimate relative risks or risk ratios.

Spectral Properties, Generation Order Parameters, and Luminosities for Spin-powered X-Ray Pulsars

NASA Astrophysics Data System (ADS)

Wang, Wei; Zhao, Yongheng

2004-02-01

We show the spectral properties of 15 spin-powered X-ray pulsars, and the correlation between the average power-law photon index and spin-down rate. Generation order parameters (GOPs) based on polar cap models are introduced to characterize the X-ray pulsars. We calculate three definitions of generation order parameters arising from the different effects of magnetic and electric fields on photon absorption during cascade processes, and study the relations between the GOPs and spectral properties of X-ray pulsars. There exists a possible correlation between the photon index and GOP in our pulsar sample. Furthermore, we present a method stemming from the concept of GOPs to estimate the nonthermal X-ray luminosity for spin-powered pulsars. Then X-ray luminosity is calculated in the context of our polar cap accelerator model, which is consistent with most observed X-ray pulsar data. The ratio between the X-ray luminosity estimated by our method and the pulsar's spin-down power is consistent with the LX~10-3Lsd feature.

GARCH modelling of covariance in dynamical estimation of inverse solutions

NASA Astrophysics Data System (ADS)

Galka, Andreas; Yamashita, Okito; Ozaki, Tohru

2004-12-01

The problem of estimating unobserved states of spatially extended dynamical systems poses an inverse problem, which can be solved approximately by a recently developed variant of Kalman filtering; in order to provide the model of the dynamics with more flexibility with respect to space and time, we suggest to combine the concept of GARCH modelling of covariance, well known in econometrics, with Kalman filtering. We formulate this algorithm for spatiotemporal systems governed by stochastic diffusion equations and demonstrate its feasibility by presenting a numerical simulation designed to imitate the situation of the generation of electroencephalographic recordings by the human cortex.

Some analytical models to estimate maternal age at birth using age-specific fertility rates.

PubMed

Pandey, A; Suchindran, C M

1995-01-01

"A class of analytical models to study the distribution of maternal age at different births from the data on age-specific fertility rates has been presented. Deriving the distributions and means of maternal age at birth of any specific order, final parity and at next-to-last birth, we have extended the approach to estimate parity progression ratios and the ultimate parity distribution of women in the population.... We illustrate computations of various components of the model expressions with the current fertility experiences of the United States for 1970." excerpt

Parameter estimation of a pulp digester model with derivative-free optimization strategies

NASA Astrophysics Data System (ADS)

Seiça, João C.; Romanenko, Andrey; Fernandes, Florbela P.; Santos, Lino O.; Fernandes, Natércia C. P.

2017-07-01

The work concerns the parameter estimation in the context of the mechanistic modelling of a pulp digester. The problem is cast as a box bounded nonlinear global optimization problem in order to minimize the mismatch between the model outputs with the experimental data observed at a real pulp and paper plant. MCSFilter and Simulated Annealing global optimization methods were used to solve the optimization problem. While the former took longer to converge to the global minimum, the latter terminated faster at a significantly higher value of the objective function and, thus, failed to find the global solution.

EMG-Based Estimation of Limb Movement Using Deep Learning With Recurrent Convolutional Neural Networks.

PubMed

Xia, Peng; Hu, Jie; Peng, Yinghong

2017-10-25

A novel model based on deep learning is proposed to estimate kinematic information for myoelectric control from multi-channel electromyogram (EMG) signals. The neural information of limb movement is embedded in EMG signals that are influenced by all kinds of factors. In order to overcome the negative effects of variability in signals, the proposed model employs the deep architecture combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The EMG signals are transformed to time-frequency frames as the input to the model. The limb movement is estimated by the model that is trained with the gradient descent and backpropagation procedure. We tested the model for simultaneous and proportional estimation of limb movement in eight healthy subjects and compared it with support vector regression (SVR) and CNNs on the same data set. The experimental studies show that the proposed model has higher estimation accuracy and better robustness with respect to time. The combination of CNNs and RNNs can improve the model performance compared with using CNNs alone. The model of deep architecture is promising in EMG decoding and optimization of network structures can increase the accuracy and robustness. © 2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

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.

A conceptual snow model with an analytic resolution of the heat and phase change equations

NASA Astrophysics Data System (ADS)

Riboust, Philippe; Le Moine, Nicolas; Thirel, Guillaume; Ribstein, Pierre

2017-04-01

Compared to degree-day snow models, physically-based snow models resolve more processes in an attempt to achieve a better representation of reality. Often these physically-based models resolve the heat transport equations in snow using a vertical discretization of the snowpack. The snowpack is decomposed into several layers in which the mechanical and thermal states of the snow are calculated. A higher number of layers in the snowpack allow for better accuracy but it also tends to increase the computational costs. In order to develop a snow model that estimates the temperature profile of snow with a lower computational cost, we used an analytical decomposition of the vertical profile using eigenfunctions (i.e. trigonometric functions adapted to the specific boundary conditions). The mass transfer of snow melt has also been estimated using an analytical conceptualization of runoff fingering and matrix flow. As external meteorological forcing, the model uses solar and atmospheric radiation, air temperature, atmospheric humidity and precipitations. It has been tested and calibrated at point scale at two different stations in the Alps: Col de Porte (France, 1325 m) and Weissfluhjoch (Switzerland, 2540 m). A sensitivity analysis of model parameters and model inputs will be presented together with a comparison with measured snow surface temperature, SWE, snow depth, temperature profile and snow melt data. The snow model is created in order to be ultimately coupled with hydrological models for rainfall-runoff modeling in mountainous areas. We hope to create a model faster than physically-based models but capable to estimate more physical processes than degree-day snow models. This should help to build a more reliable snow model capable of being easily calibrated by remote sensing and in situ observation or to assimilate these data for forecasting purposes.

Reconstructing the hidden states in time course data of stochastic models.

PubMed

Zimmer, Christoph

2015-11-01

Parameter estimation is central for analyzing models in Systems Biology. The relevance of stochastic modeling in the field is increasing. Therefore, the need for tailored parameter estimation techniques is increasing as well. Challenges for parameter estimation are partial observability, measurement noise, and the computational complexity arising from the dimension of the parameter space. This article extends the multiple shooting for stochastic systems' method, developed for inference in intrinsic stochastic systems. The treatment of extrinsic noise and the estimation of the unobserved states is improved, by taking into account the correlation between unobserved and observed species. This article demonstrates the power of the method on different scenarios of a Lotka-Volterra model, including cases in which the prey population dies out or explodes, and a Calcium oscillation system. Besides showing how the new extension improves the accuracy of the parameter estimates, this article analyzes the accuracy of the state estimates. In contrast to previous approaches, the new approach is well able to estimate states and parameters for all the scenarios. As it does not need stochastic simulations, it is of the same order of speed as conventional least squares parameter estimation methods with respect to computational time. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Yield estimation of corn based on multitemporal LANDSAT-TM data as input for an agrometeorological model

NASA Astrophysics Data System (ADS)

Bach, Heike

1998-07-01

In order to test remote sensing data with advanced yield formation models for accuracy and timeliness of yield estimation of corn, a project was conducted for the State Ministry for Rural Environment, Food, and Forestry of Baden-Württemberg (Germany). This project was carried out during the course of the `Special Yield Estimation', a regular procedure conducted for the European Union, to more accurately estimate agricultural yield. The methodology employed uses field-based plant parameter estimation from atmospherically corrected multitemporal/multispectral LANDSAT-TM data. An agrometeorological plant-production-model is used for yield prediction. Based solely on four LANDSAT-derived estimates (between May and August) and daily meteorological data, the grain yield of corn fields was determined for 1995. The modelled yields were compared with results gathered independently within the Special Yield Estimation for 23 test fields in the upper Rhine valley. The agreement between LANDSAT-based estimates (six weeks before harvest) and Special Yield Estimation (at harvest) shows a relative error of 2.3%. The comparison of the results for single fields shows that six weeks before harvest, the grain yield of corn was estimated with a mean relative accuracy of 13% using satellite information. The presented methodology can be transferred to other crops and geographical regions. For future applications hyperspectral sensors show great potential to further enhance the results for yield prediction with remote sensing.

Inference of Markovian properties of molecular sequences from NGS data and applications to comparative genomics.

PubMed

Ren, Jie; Song, Kai; Deng, Minghua; Reinert, Gesine; Cannon, Charles H; Sun, Fengzhu

2016-04-01

Next-generation sequencing (NGS) technologies generate large amounts of short read data for many different organisms. The fact that NGS reads are generally short makes it challenging to assemble the reads and reconstruct the original genome sequence. For clustering genomes using such NGS data, word-count based alignment-free sequence comparison is a promising approach, but for this approach, the underlying expected word counts are essential.A plausible model for this underlying distribution of word counts is given through modeling the DNA sequence as a Markov chain (MC). For single long sequences, efficient statistics are available to estimate the order of MCs and the transition probability matrix for the sequences. As NGS data do not provide a single long sequence, inference methods on Markovian properties of sequences based on single long sequences cannot be directly used for NGS short read data. Here we derive a normal approximation for such word counts. We also show that the traditional Chi-square statistic has an approximate gamma distribution ,: using the Lander-Waterman model for physical mapping. We propose several methods to estimate the order of the MC based on NGS reads and evaluate those using simulations. We illustrate the applications of our results by clustering genomic sequences of several vertebrate and tree species based on NGS reads using alignment-free sequence dissimilarity measures. We find that the estimated order of the MC has a considerable effect on the clustering results ,: and that the clustering results that use a N: MC of the estimated order give a plausible clustering of the species. Our implementation of the statistics developed here is available as R package 'NGS.MC' at http://www-rcf.usc.edu/∼fsun/Programs/NGS-MC/NGS-MC.html fsun@usc.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Time Domain Estimation of Arterial Parameters using the Windkessel Model and the Monte Carlo Method

NASA Astrophysics Data System (ADS)

Gostuski, Vladimir; Pastore, Ignacio; Rodriguez Palacios, Gaspar; Vaca Diez, Gustavo; Moscoso-Vasquez, H. Marcela; Risk, Marcelo

2016-04-01

Numerous parameter estimation techniques exist for characterizing the arterial system using electrical circuit analogs. However, they are often limited by their requirements and usually high computational burdain. Therefore, a new method for estimating arterial parameters based on Monte Carlo simulation is proposed. A three element Windkessel model was used to represent the arterial system. The approach was to reduce the error between the calculated and physiological aortic pressure by randomly generating arterial parameter values, while keeping constant the arterial resistance. This last value was obtained for each subject using the arterial flow, and was a necessary consideration in order to obtain a unique set of values for the arterial compliance and peripheral resistance. The estimation technique was applied to in vivo data containing steady beats in mongrel dogs, and it reliably estimated Windkessel arterial parameters. Further, this method appears to be computationally efficient for on-line time-domain estimation of these parameters.

Haptic exploration of fingertip-sized geometric features using a multimodal tactile sensor

NASA Astrophysics Data System (ADS)

Ponce Wong, Ruben D.; Hellman, Randall B.; Santos, Veronica J.

2014-06-01

Haptic perception remains a grand challenge for artificial hands. Dexterous manipulators could be enhanced by "haptic intelligence" that enables identification of objects and their features via touch alone. Haptic perception of local shape would be useful when vision is obstructed or when proprioceptive feedback is inadequate, as observed in this study. In this work, a robot hand outfitted with a deformable, bladder-type, multimodal tactile sensor was used to replay four human-inspired haptic "exploratory procedures" on fingertip-sized geometric features. The geometric features varied by type (bump, pit), curvature (planar, conical, spherical), and footprint dimension (1.25 - 20 mm). Tactile signals generated by active fingertip motions were used to extract key parameters for use as inputs to supervised learning models. A support vector classifier estimated order of curvature while support vector regression models estimated footprint dimension once curvature had been estimated. A distal-proximal stroke (along the long axis of the finger) enabled estimation of order of curvature with an accuracy of 97%. Best-performing, curvature-specific, support vector regression models yielded R2 values of at least 0.95. While a radial-ulnar stroke (along the short axis of the finger) was most helpful for estimating feature type and size for planar features, a rolling motion was most helpful for conical and spherical features. The ability to haptically perceive local shape could be used to advance robot autonomy and provide haptic feedback to human teleoperators of devices ranging from bomb defusal robots to neuroprostheses.

Large-Signal Lyapunov-Based Stability Analysis of DC/AC Inverters and Inverter-Based Microgrids

NASA Astrophysics Data System (ADS)

Kabalan, Mahmoud

Microgrid stability studies have been largely based on small-signal linearization techniques. However, the validity and magnitude of the linearization domain is limited to small perturbations. Thus, there is a need to examine microgrids with large-signal nonlinear techniques to fully understand and examine their stability. Large-signal stability analysis can be accomplished by Lyapunov-based mathematical methods. These Lyapunov methods estimate the domain of asymptotic stability of the studied system. A survey of Lyapunov-based large-signal stability studies showed that few large-signal studies have been completed on either individual systems (dc/ac inverters, dc/dc rectifiers, etc.) or microgrids. The research presented in this thesis addresses the large-signal stability of droop-controlled dc/ac inverters and inverter-based microgrids. Dc/ac power electronic inverters allow microgrids to be technically feasible. Thus, as a prelude to examining the stability of microgrids, the research presented in Chapter 3 analyzes the stability of inverters. First, the 13 th order large-signal nonlinear model of a droop-controlled dc/ac inverter connected to an infinite bus is presented. The singular perturbation method is used to decompose the nonlinear model into 11th, 9th, 7th, 5th, 3rd and 1st order models. Each model ignores certain control or structural components of the full order model. The aim of the study is to understand the accuracy and validity of the reduced order models in replicating the performance of the full order nonlinear model. The performance of each model is studied in three different areas: time domain simulations, Lyapunov's indirect method and domain of attraction estimation. The work aims to present the best model to use in each of the three domains of study. Results show that certain reduced order models are capable of accurately reproducing the performance of the full order model while others can be used to gain insights into those three areas of study. This will enable future studies to save computational effort and produce the most accurate results according to the needs of the study being performed. Moreover, the effect of grid (line) impedance on the accuracy of droop control is explored using the 5th order model. Simulation results show that traditional droop control is valid up to R/X line impedance value of 2. Furthermore, the 3rd order nonlinear model improves the currently available inverter-infinite bus models by accounting for grid impedance, active power-frequency droop and reactive power-voltage droop. Results show the 3rd order model's ability to account for voltage and reactive power changes during a transient event. Finally, the large-signal Lyapunov-based stability analysis is completed for a 3 bus microgrid system (made up of 2 inverters and 1 linear load). The thesis provides a systematic state space large-signal nonlinear mathematical modeling method of inverter-based microgrids. The inverters include the dc-side dynamics associated with dc sources. The mathematical model is then used to estimate the domain of asymptotic stability of the 3 bus microgrid. The three bus microgrid system was used as a case study to highlight the design and optimization capability of a large-signal-based approach. The study explores the effect of system component sizing, load transient and generation variations on the asymptotic stability of the microgrid. Essentially, this advancement gives microgrid designers and engineers the ability to manipulate the domain of asymptotic stability depending on performance requirements. Especially important, this research was able to couple the domain of asymptotic stability of the ac microgrid with that of the dc side voltage source. Time domain simulations were used to demonstrate the mathematical nonlinear analysis results.

Atmospheric Turbulence Estimates from a Pulsed Lidar

NASA Technical Reports Server (NTRS)

Pruis, Matthew J.; Delisi, Donald P.; Ahmad, Nash'at N.; Proctor, Fred H.

2013-01-01

Estimates of the eddy dissipation rate (EDR) were obtained from measurements made by a coherent pulsed lidar and compared with estimates from mesoscale model simulations and measurements from an in situ sonic anemometer at the Denver International Airport and with EDR estimates from the last observation time of the trailing vortex pair. The estimates of EDR from the lidar were obtained using two different methodologies. The two methodologies show consistent estimates of the vertical profiles. Comparison of EDR derived from the Weather Research and Forecast (WRF) mesoscale model with the in situ lidar estimates show good agreement during the daytime convective boundary layer, but the WRF simulations tend to overestimate EDR during the nighttime. The EDR estimates from a sonic anemometer located at 7.3 meters above ground level are approximately one order of magnitude greater than both the WRF and lidar estimates - which are from greater heights - during the daytime convective boundary layer and substantially greater during the nighttime stable boundary layer. The consistency of the EDR estimates from different methods suggests a reasonable ability to predict the temporal evolution of a spatially averaged vertical profile of EDR in an airport terminal area using a mesoscale model during the daytime convective boundary layer. In the stable nighttime boundary layer, there may be added value to EDR estimates provided by in situ lidar measurements.

Bayesian dynamic modeling of time series of dengue disease case counts

PubMed Central

López-Quílez, Antonio; Torres-Prieto, Alexander

2017-01-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health. PMID:28671941

The E-Step of the MGROUP EM Algorithm. Program Statistics Research Technical Report No. 93-37.

ERIC Educational Resources Information Center

Thomas, Neal

Mislevy (1984, 1985) introduced an EM algorithm for estimating the parameters of a latent distribution model that is used extensively by the National Assessment of Educational Progress. Second order asymptotic corrections are derived and applied along with more common first order asymptotic corrections to approximate the expectations required by…

Forest canopy height estimation using double-frequency repeat pass interferometry

NASA Astrophysics Data System (ADS)

Karamvasis, Kleanthis; Karathanassi, Vassilia

2015-06-01

In recent years, many efforts have been made in order to assess forest stand parameters from remote sensing data, as a mean to estimate the above-ground carbon stock of forests in the context of the Kyoto protocol. Synthetic aperture radar interferometry (InSAR) techniques have gained traction in last decade as a viable technology for vegetation parameter estimation. Many works have shown that forest canopy height, which is a critical parameter for quantifying the terrestrial carbon cycle, can be estimated with InSAR. However, research is still needed to understand further the interaction of SAR signals with forest canopy and to develop an operational method for forestry applications. This work discusses the use of repeat pass interferometry with ALOS PALSAR (L band) HH polarized and COSMO Skymed (X band) HH polarized acquisitions over the Taxiarchis forest (Chalkidiki, Greece), in order to produce accurate digital elevation models (DEMs) and estimate canopy height with interferometric processing. The effect of wavelength-dependent penetration depth into the canopy is known to be strong, and could potentially lead to forest canopy height mapping using dual-wavelength SAR interferometry at X- and L-band. The method is based on scattering phase center separation at different wavelengths. It involves the generation of a terrain elevation model underneath the forest canopy from repeat-pass L-band InSAR data as well as the generation of a canopy surface elevation model from repeat pass X-band InSAR data. The terrain model is then used to remove the terrain component from the repeat pass interferometric X-band elevation model, so as to enable the forest canopy height estimation. The canopy height results were compared to a field survey with 6.9 m root mean square error (RMSE). The effects of vegetation characteristics, SAR incidence angle and view geometry, and terrain slope on the accuracy of the results have also been studied in this work.

Effective wind speed estimation: Comparison between Kalman Filter and Takagi-Sugeno observer techniques.

PubMed

Gauterin, Eckhard; Kammerer, Philipp; Kühn, Martin; Schulte, Horst

2016-05-01

Advanced model-based control of wind turbines requires knowledge of the states and the wind speed. This paper benchmarks a nonlinear Takagi-Sugeno observer for wind speed estimation with enhanced Kalman Filter techniques: The performance and robustness towards model-structure uncertainties of the Takagi-Sugeno observer, a Linear, Extended and Unscented Kalman Filter are assessed. Hence the Takagi-Sugeno observer and enhanced Kalman Filter techniques are compared based on reduced-order models of a reference wind turbine with different modelling details. The objective is the systematic comparison with different design assumptions and requirements and the numerical evaluation of the reconstruction quality of the wind speed. Exemplified by a feedforward loop employing the reconstructed wind speed, the benefit of wind speed estimation within wind turbine control is illustrated. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A gentle introduction to quantile regression for ecologists

USGS Publications Warehouse

Cade, B.S.; Noon, B.R.

2003-01-01

Quantile regression is a way to estimate the conditional quantiles of a response variable distribution in the linear model that provides a more complete view of possible causal relationships between variables in ecological processes. Typically, all the factors that affect ecological processes are not measured and included in the statistical models used to investigate relationships between variables associated with those processes. As a consequence, there may be a weak or no predictive relationship between the mean of the response variable (y) distribution and the measured predictive factors (X). Yet there may be stronger, useful predictive relationships with other parts of the response variable distribution. This primer relates quantile regression estimates to prediction intervals in parametric error distribution regression models (eg least squares), and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of the estimates for homogeneous and heterogeneous regression models.

Efficient fuzzy Bayesian inference algorithms for incorporating expert knowledge in parameter estimation

NASA Astrophysics Data System (ADS)

Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad

2016-05-01

Bayesian inference has traditionally been conceived as the proper framework for the formal incorporation of expert knowledge in parameter estimation of groundwater models. However, conventional Bayesian inference is incapable of taking into account the imprecision essentially embedded in expert provided information. In order to solve this problem, a number of extensions to conventional Bayesian inference have been introduced in recent years. One of these extensions is 'fuzzy Bayesian inference' which is the result of integrating fuzzy techniques into Bayesian statistics. Fuzzy Bayesian inference has a number of desirable features which makes it an attractive approach for incorporating expert knowledge in the parameter estimation process of groundwater models: (1) it is well adapted to the nature of expert provided information, (2) it allows to distinguishably model both uncertainty and imprecision, and (3) it presents a framework for fusing expert provided information regarding the various inputs of the Bayesian inference algorithm. However an important obstacle in employing fuzzy Bayesian inference in groundwater numerical modeling applications is the computational burden, as the required number of numerical model simulations often becomes extremely exhaustive and often computationally infeasible. In this paper, a novel approach of accelerating the fuzzy Bayesian inference algorithm is proposed which is based on using approximate posterior distributions derived from surrogate modeling, as a screening tool in the computations. The proposed approach is first applied to a synthetic test case of seawater intrusion (SWI) in a coastal aquifer. It is shown that for this synthetic test case, the proposed approach decreases the number of required numerical simulations by an order of magnitude. Then the proposed approach is applied to a real-world test case involving three-dimensional numerical modeling of SWI in Kish Island, located in the Persian Gulf. An expert elicitation methodology is developed and applied to the real-world test case in order to provide a road map for the use of fuzzy Bayesian inference in groundwater modeling applications.

Bayesian Estimation of Circumplex Models Subject to Prior Theory Constraints and Scale-Usage Bias

ERIC Educational Resources Information Center

Lenk, Peter; Wedel, Michel; Bockenholt, Ulf

2006-01-01

This paper presents a hierarchical Bayes circumplex model for ordinal ratings data. The circumplex model was proposed to represent the circular ordering of items in psychological testing by imposing inequalities on the correlations of the items. We provide a specification of the circumplex, propose identifying constraints and conjugate priors for…

Testing Mediation in Structural Equation Modeling: The Effectiveness of the Test of Joint Significance

ERIC Educational Resources Information Center

Leth-Steensen, Craig; Gallitto, Elena

2016-01-01

A large number of approaches have been proposed for estimating and testing the significance of indirect effects in mediation models. In this study, four sets of Monte Carlo simulations involving full latent variable structural equation models were run in order to contrast the effectiveness of the currently popular bias-corrected bootstrapping…

A statistical methodology for estimating transport parameters: Theory and applications to one-dimensional advectivec-dispersive systems

USGS Publications Warehouse

Wagner, Brian J.; Gorelick, Steven M.

1986-01-01

A simulation nonlinear multiple-regression methodology for estimating parameters that characterize the transport of contaminants is developed and demonstrated. Finite difference contaminant transport simulation is combined with a nonlinear weighted least squares multiple-regression procedure. The technique provides optimal parameter estimates and gives statistics for assessing the reliability of these estimates under certain general assumptions about the distributions of the random measurement errors. Monte Carlo analysis is used to estimate parameter reliability for a hypothetical homogeneous soil column for which concentration data contain large random measurement errors. The value of data collected spatially versus data collected temporally was investigated for estimation of velocity, dispersion coefficient, effective porosity, first-order decay rate, and zero-order production. The use of spatial data gave estimates that were 2–3 times more reliable than estimates based on temporal data for all parameters except velocity. Comparison of estimated linear and nonlinear confidence intervals based upon Monte Carlo analysis showed that the linear approximation is poor for dispersion coefficient and zero-order production coefficient when data are collected over time. In addition, examples demonstrate transport parameter estimation for two real one-dimensional systems. First, the longitudinal dispersivity and effective porosity of an unsaturated soil are estimated using laboratory column data. We compare the reliability of estimates based upon data from individual laboratory experiments versus estimates based upon pooled data from several experiments. Second, the simulation nonlinear regression procedure is extended to include an additional governing equation that describes delayed storage during contaminant transport. The model is applied to analyze the trends, variability, and interrelationship of parameters in a mourtain stream in northern California.

Median nitrate concentrations in groundwater in the New Jersey Highlands Region estimated using regression models and land-surface characteristics

USGS Publications Warehouse

Baker, Ronald J.; Chepiga, Mary M.; Cauller, Stephen J.

2015-01-01

The Kaplan-Meier method of estimating summary statistics from left-censored data was applied in order to include nondetects (left-censored data) in median nitrate-concentration calculations. Median concentrations also were determined using three alternative methods of handling nondetects. Treatment of the 23 percent of samples that were nondetects had little effect on estimated median nitrate concentrations because method detection limits were mostly less than median values.

Population pharmacokinetics of golimumab in patients with ankylosing spondylitis: impact of body weight and immunogenicity.

PubMed

Xu, Z H; Lee, H; Vu, T; Hu, C; Yan, H; Baker, D; Hsu, B; Pendley, C; Wagner, C; Davis, H M; Zhou, H

2010-09-01

To develop a population pharmacokinetic (PK) model of subcutaneously administered golimumab, a human anti-tumor necrosis factor monoclonal antibody, in patients with ankylosing spondylitis (AS), estimate typical fixed and random population PK parameters, and identify significant covariates on golimumab pharmacokinetics. Serum concentration data through Week 24 of a randomized, double-blind, placebo-controlled Phase III trial of golimumab (50 or 100 mg every 4 weeks) were analyzed using a nonlinear mixed-effects modeling approach. The effects of potential covariates on golimumab were evaluated. A one-compartment PK model with first-order absorption and elimination was chosen to describe the observed golimumab concentration-time data in patients with AS. Population estimates obtained from the final model for a typical 70-kg patient were: apparent systemic clearance (CL/F), 1.41 l/day (95% confidence interval (CI): 1.31 - 1.51) and apparent volume of distribution (V/F), 22.6 L (95% CI: 20.7 - 24.4). The first-order absorption rate constant (Ka) was estimated to be 1.01 day-1 (95% CI: 0.760 - 1.46). The between-subject variabilities for CL/F, V/F, and Ka were 35.2%, 38.6%, and 78.6%, respectively. Body weight was the most significant covariate, affecting both CL/F and V/F. Antibody-to-golimumab status, baseline C-reactive protein level, and sex were also identified as significant covariates on CL/F. A one-compartment model with first-order absorption and elimination adequately described the PK of golimumab following subcutaneous administrations in patients with AS. Body weight and anti-golimumab antibody status were found to significantly influence golimumab clearance. When a patient does not respond to the prescribed golimumab therapy, the possibility of the development of antibodies to golimumab has to be considered.

The Use of Satellite Remote Sensing in Epidemiological Studies

PubMed Central

Sorek-Hamer, Meytar; Just, Allan C.; Kloog, Itai

2016-01-01

Purpose of review Particulate matter (PM) air pollution is a ubiquitous exposure linked with multiple adverse health outcomes for children and across the life course. The recent development of satellite based remote sensing models for air pollution enables the quantification of these risks and addresses many limitations of previous air pollution research strategies. We review the recent literature on the applications of satellite remote sensing in air quality research, with a focus on their use in epidemiological studies. Recent findings Aerosol optical depth (AOD) is a focus of this review and a significant number of studies show that ground-level PM can be estimated from columnar AOD. Satellite measurements have been found to be an important source of data for PM model-based exposure estimates, and recently have been used in health studies to increase the spatial breadth and temporal resolution of these estimates. Summary It is suggested that satellite-based models improve our understanding of the spatial characteristics of air quality. Although the adoption of satellite-based measures of air quality in health studies is in its infancy, it is rapidly growing. Nevertheless, further investigation is still needed in order to have a better understanding of the AOD contribution to these prediction models in order to use them with higher accuracy in epidemiological studies. PMID:26859287

One-Dimensional Transport with Inflow and Storage (OTIS): A Solute Transport Model for Streams and Rivers

USGS Publications Warehouse

Runkel, Robert L.

1998-01-01

OTIS is a mathematical simulation model used to characterize the fate and transport of water-borne solutes in streams and rivers. The governing equation underlying the model is the advection-dispersion equation with additional terms to account for transient storage, lateral inflow, first-order decay, and sorption. This equation and the associated equations describing transient storage and sorption are solved using a Crank-Nicolson finite-difference solution. OTIS may be used in conjunction with data from field-scale tracer experiments to quantify the hydrologic parameters affecting solute transport. This application typically involves a trial-and-error approach wherein parameter estimates are adjusted to obtain an acceptable match between simulated and observed tracer concentrations. Additional applications include analyses of nonconservative solutes that are subject to sorption processes or first-order decay. OTIS-P, a modified version of OTIS, couples the solution of the governing equation with a nonlinear regression package. OTIS-P determines an optimal set of parameter estimates that minimize the squared differences between the simulated and observed concentrations, thereby automating the parameter estimation process. This report details the development and application of OTIS and OTIS-P. Sections of the report describe model theory, input/output specifications, sample applications, and installation instructions.

Satellite remote sensing in epidemiological studies.

PubMed

Sorek-Hamer, Meytar; Just, Allan C; Kloog, Itai

2016-04-01

Particulate matter air pollution is a ubiquitous exposure linked with multiple adverse health outcomes for children and across the life course. The recent development of satellite-based remote-sensing models for air pollution enables the quantification of these risks and addresses many limitations of previous air pollution research strategies. We review the recent literature on the applications of satellite remote sensing in air quality research, with a focus on their use in epidemiological studies. Aerosol optical depth (AOD) is a focus of this review and a significant number of studies show that ground-level particulate matter can be estimated from columnar AOD. Satellite measurements have been found to be an important source of data for particulate matter model-based exposure estimates, and recently have been used in health studies to increase the spatial breadth and temporal resolution of these estimates. It is suggested that satellite-based models improve our understanding of the spatial characteristics of air quality. Although the adoption of satellite-based measures of air quality in health studies is in its infancy, it is rapidly growing. Nevertheless, further investigation is still needed in order to have a better understanding of the AOD contribution to these prediction models in order to use them with higher accuracy in epidemiological studies.

A Robust Approach to Risk Assessment Based on Species Sensitivity Distributions.

PubMed

Monti, Gianna S; Filzmoser, Peter; Deutsch, Roland C

2018-05-03

The guidelines for setting environmental quality standards are increasingly based on probabilistic risk assessment due to a growing general awareness of the need for probabilistic procedures. One of the commonly used tools in probabilistic risk assessment is the species sensitivity distribution (SSD), which represents the proportion of species affected belonging to a biological assemblage as a function of exposure to a specific toxicant. Our focus is on the inverse use of the SSD curve with the aim of estimating the concentration, HCp, of a toxic compound that is hazardous to p% of the biological community under study. Toward this end, we propose the use of robust statistical methods in order to take into account the presence of outliers or apparent skew in the data, which may occur without any ecological basis. A robust approach exploits the full neighborhood of a parametric model, enabling the analyst to account for the typical real-world deviations from ideal models. We examine two classic HCp estimation approaches and consider robust versions of these estimators. In addition, we also use data transformations in conjunction with robust estimation methods in case of heteroscedasticity. Different scenarios using real data sets as well as simulated data are presented in order to illustrate and compare the proposed approaches. These scenarios illustrate that the use of robust estimation methods enhances HCp estimation. © 2018 Society for Risk Analysis.

Real-time estimation of ionospheric delay using GPS measurements

NASA Astrophysics Data System (ADS)

Lin, Lao-Sheng

1997-12-01

When radio waves such as the GPS signals propagate through the ionosphere, they experience an extra time delay. The ionospheric delay can be eliminated (to the first order) through a linear combination of L1 and L2 observations from dual-frequency GPS receivers. Taking advantage of this dispersive principle, one or more dual- frequency GPS receivers can be used to determine a model of the ionospheric delay across a region of interest and, if implemented in real-time, can support single-frequency GPS positioning and navigation applications. The research objectives of this thesis were: (1) to develop algorithms to obtain accurate absolute Total Electron Content (TEC) estimates from dual-frequency GPS observables, and (2) to develop an algorithm to improve the accuracy of real-time ionosphere modelling. In order to fulfil these objectives, four algorithms have been proposed in this thesis. A 'multi-day multipath template technique' is proposed to mitigate the pseudo-range multipath effects at static GPS reference stations. This technique is based on the assumption that the multipath disturbance at a static station will be constant if the physical environment remains unchanged from day to day. The multipath template, either single-day or multi-day, can be generated from the previous days' GPS data. A 'real-time failure detection and repair algorithm' is proposed to detect and repair the GPS carrier phase 'failures', such as the occurrence of cycle slips. The proposed algorithm uses two procedures: (1) application of a statistical test on the state difference estimated from robust and conventional Kalman filters in order to detect and identify the carrier phase failure, and (2) application of a Kalman filter algorithm to repair the 'identified carrier phase failure'. A 'L1/L2 differential delay estimation algorithm' is proposed to estimate GPS satellite transmitter and receiver L1/L2 differential delays. This algorithm, based on the single-site modelling technique, is able to estimate the sum of the satellite and receiver L1/L2 differential delay for each tracked GPS satellite. A 'UNSW grid-based algorithm' is proposed to improve the accuracy of real-time ionosphere modelling. The proposed algorithm is similar to the conventional grid-based algorithm. However, two modifications were made to the algorithm: (1) an 'exponential function' is adopted as the weighting function, and (2) the 'grid-based ionosphere model' estimated from the previous day is used to predict the ionospheric delay ratios between the grid point and reference points. (Abstract shortened by UMI.)

Estimating order statistics of network degrees

NASA Astrophysics Data System (ADS)

Chu, J.; Nadarajah, S.

2018-01-01

We model the order statistics of network degrees of big data sets by a range of generalised beta distributions. A three parameter beta distribution due to Libby and Novick (1982) is shown to give the best overall fit for at least four big data sets. The fit of this distribution is significantly better than the fit suggested by Olhede and Wolfe (2012) across the whole range of order statistics for all four data sets.

Reduced uncertainty of regional scale CLM predictions of net carbon fluxes and leaf area indices with estimated plant-specific parameters

NASA Astrophysics Data System (ADS)

Post, Hanna; Hendricks Franssen, Harrie-Jan; Han, Xujun; Baatz, Roland; Montzka, Carsten; Schmidt, Marius; Vereecken, Harry

2016-04-01

Reliable estimates of carbon fluxes and states at regional scales are required to reduce uncertainties in regional carbon balance estimates and to support decision making in environmental politics. In this work the Community Land Model version 4.5 (CLM4.5-BGC) was applied at a high spatial resolution (1 km2) for the Rur catchment in western Germany. In order to improve the model-data consistency of net ecosystem exchange (NEE) and leaf area index (LAI) for this study area, five plant functional type (PFT)-specific CLM4.5-BGC parameters were estimated with time series of half-hourly NEE data for one year in 2011/2012, using the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm, a Markov Chain Monte Carlo (MCMC) approach. The parameters were estimated separately for four different plant functional types (needleleaf evergreen temperate tree, broadleaf deciduous temperate tree, C3-grass and C3-crop) at four different sites. The four sites are located inside or close to the Rur catchment. We evaluated modeled NEE for one year in 2012/2013 with NEE measured at seven eddy covariance sites in the catchment, including the four parameter estimation sites. Modeled LAI was evaluated by means of LAI derived from remotely sensed RapidEye images of about 18 days in 2011/2012. Performance indices were based on a comparison between measurements and (i) a reference run with CLM default parameters, and (ii) a 60 instance CLM ensemble with parameters sampled from the DREAM posterior probability density functions (pdfs). The difference between the observed and simulated NEE sum reduced 23% if estimated parameters instead of default parameters were used as input. The mean absolute difference between modeled and measured LAI was reduced by 59% on average. Simulated LAI was not only improved in terms of the absolute value but in some cases also in terms of the timing (beginning of vegetation onset), which was directly related to a substantial improvement of the NEE estimates in spring. In order to obtain a more comprehensive estimate of the model uncertainty, a second CLM ensemble was set up, where initial conditions and atmospheric forcings were perturbed in addition to the parameter estimates. This resulted in very high standard deviations (STD) of the modeled annual NEE sums for C3-grass and C3-crop PFTs, ranging between 24.1 and 225.9 gC m-2 y-1, compared to STD = 0.1 - 3.4 gC m-2 y-1 (effect of parameter uncertainty only, without additional perturbation of initial states and atmospheric forcings). The higher spread of modeled NEE for the C3-crop and C3-grass indicated that the model uncertainty was notably higher for those PFTs compared to the forest-PFTs. Our findings highlight the potential of parameter and uncertainty estimation to support the understanding and further development of land surface models such as CLM.

Diversity of charge orderings in correlated systems

NASA Astrophysics Data System (ADS)

Kapcia, Konrad Jerzy; Barański, Jan; Ptok, Andrzej

2017-10-01

The phenomenon associated with inhomogeneous distribution of electron density is known as a charge ordering. In this work, we study the zero-bandwidth limit of the extended Hubbard model, which can be considered as a simple effective model of charge ordered insulators. It consists of the on-site interaction U and the intersite density-density interactions W1 and W2 between nearest neighbors and next-nearest neighbors, respectively. We derived the exact ground state diagrams for different lattice dimensionalities and discuss effects of small finite temperatures in the limit of high dimensions. In particular, we estimated the critical interactions for which new ordered phases emerge (laminar or stripe and four-sublattice-type). Our analysis show that the ground state of the model is highly degenerated. One of the most intriguing finding is that the nonzero temperature removes these degenerations.

Explicit least squares system parameter identification for exact differential input/output models

NASA Technical Reports Server (NTRS)

Pearson, A. E.

1993-01-01

The equation error for a class of systems modeled by input/output differential operator equations has the potential to be integrated exactly, given the input/output data on a finite time interval, thereby opening up the possibility of using an explicit least squares estimation technique for system parameter identification. The paper delineates the class of models for which this is possible and shows how the explicit least squares cost function can be obtained in a way that obviates dealing with unknown initial and boundary conditions. The approach is illustrated by two examples: a second order chemical kinetics model and a third order system of Lorenz equations.

Quantifying annual internal effective 137Cesium dose utilizing direct body-burden measurement and ecological dose modeling.

PubMed

Jelin, Benjamin A; Sun, Wenjie; Kravets, Alexandra; Naboka, Maryna; Stepanova, Eugenia I; Vdovenko, Vitaliy Y; Karmaus, Wilfried J; Lichosherstov, Alex; Svendsen, Erik R

2016-11-01

The Chernobyl Nuclear Power Plant (CNPP) accident represents one of the most significant civilian releases of 137 Cesium ( 137 Cs, radiocesium) in human history. In the Chernobyl-affected region, radiocesium is considered to be the greatest on-going environmental hazard to human health by radiobiologists and public health scientists. The goal of this study was to characterize dosimetric patterns and predictive factors for whole-body count (WBC)-derived radiocesium internal dose estimations in a CNPP-affected children's cohort, and cross-validate these estimations with a soil-based ecological dose estimation model. WBC data were used to estimate the internal effective dose using the International Commission on Radiological Protection (ICRP) 67 dose conversion coefficient for 137 Cs and MONDAL Version 3.01 software. Geometric mean dose estimates from each model were compared utilizing paired t-tests and intra-class correlation coefficients. Additionally, we developed predictive models for WBC-derived dose estimation in order to determine the appropriateness of EMARC to estimate dose for this population. The two WBC-derived dose predictive models identified 137 Cs soil concentration (P<0.0001) as the strongest predictor of annual internal effective dose from radiocesium validating the use of the soil-based EMARC model. The geometric mean internal effective dose estimate of the EMARC model (0.183 mSv/y) was the highest followed by the ICRP 67 dose estimates (0.165 mSv/y) and the MONDAL model estimates (0.149 mSv/y). All three models yielded significantly different geometric mean dose (P<0.05) estimates for this cohort when stratified by sex, age at time of exam and season of exam, except for the mean MONDAL and EMARC estimates for 15- and 16-year olds and mean ICRP and MONDAL estimates for children examined in Winter. Further prospective and retrospective radio-epidemiological studies utilizing refined WBC measurements and ecological model dose estimations, in conjunction with findings from animal toxicological studies, should help elucidate possible deterministic radiogenic health effects associated with chronic low-dose internal exposure to 137 Cs.

Adaptive model reduction for continuous systems via recursive rational interpolation

NASA Technical Reports Server (NTRS)

Lilly, John H.

1994-01-01

A method for adaptive identification of reduced-order models for continuous stable SISO and MIMO plants is presented. The method recursively finds a model whose transfer function (matrix) matches that of the plant on a set of frequencies chosen by the designer. The algorithm utilizes the Moving Discrete Fourier Transform (MDFT) to continuously monitor the frequency-domain profile of the system input and output signals. The MDFT is an efficient method of monitoring discrete points in the frequency domain of an evolving function of time. The model parameters are estimated from MDFT data using standard recursive parameter estimation techniques. The algorithm has been shown in simulations to be quite robust to additive noise in the inputs and outputs. A significant advantage of the method is that it enables a type of on-line model validation. This is accomplished by simultaneously identifying a number of models and comparing each with the plant in the frequency domain. Simulations of the method applied to an 8th-order SISO plant and a 10-state 2-input 2-output plant are presented. An example of on-line model validation applied to the SISO plant is also presented.

Determination of suitable drying curve model for bread moisture loss during baking

NASA Astrophysics Data System (ADS)

Soleimani Pour-Damanab, A. R.; Jafary, A.; Rafiee, S.

2013-03-01

This study presents mathematical modelling of bread moisture loss or drying during baking in a conventional bread baking process. In order to estimate and select the appropriate moisture loss curve equation, 11 different models, semi-theoretical and empirical, were applied to the experimental data and compared according to their correlation coefficients, chi-squared test and root mean square error which were predicted by nonlinear regression analysis. Consequently, of all the drying models, a Page model was selected as the best one, according to the correlation coefficients, chi-squared test, and root mean square error values and its simplicity. Mean absolute estimation error of the proposed model by linear regression analysis for natural and forced convection modes was 2.43, 4.74%, respectively.

Customizable Optical Force Sensor for Fast Prototyping and Cost-Effective Applications.

PubMed

Díez, Jorge A; Catalán, José M; Blanco, Andrea; García-Perez, José V; Badesa, Francisco J; Gacía-Aracil, Nicolás

2018-02-07

This paper presents the development of an optical force sensor architecture directed to prototyping and cost-effective applications, where the actual force requirements are still not well defined or the most suitable commercial technologies would highly increase the cost of the device. The working principle of this sensor consists of determining the displacement of a lens by measuring the distortion of a refracted light beam. This lens is attached to an elastic interface whose elastic constant is known, allowing the estimation of the force that disturbs the optical system. In order to satisfy the requirements of the design process in an inexpensive way, this sensor can be built by fast prototyping technologies and using non-optical grade elements. To deal with the imperfections of this kind of manufacturing procedures and materials, four fitting models are proposed to calibrate the implemented sensor. In order to validate the system, two different sensor implementations with measurement ranges of ±45 N and ±10 N are tested with the proposed models, comparing the resulting force estimation with respect to an industrial-grade load cell. Results show that all models can estimate the loads with an error of about 6% of the measurement range.

Customizable Optical Force Sensor for Fast Prototyping and Cost-Effective Applications

PubMed Central

Díez, Jorge A.; Catalán, José M.; Blanco, Andrea; García-Perez, José V.; Badesa, Francisco J.

2018-01-01

This paper presents the development of an optical force sensor architecture directed to prototyping and cost-effective applications, where the actual force requirements are still not well defined or the most suitable commercial technologies would highly increase the cost of the device. The working principle of this sensor consists of determining the displacement of a lens by measuring the distortion of a refracted light beam. This lens is attached to an elastic interface whose elastic constant is known, allowing the estimation of the force that disturbs the optical system. In order to satisfy the requirements of the design process in an inexpensive way, this sensor can be built by fast prototyping technologies and using non-optical grade elements. To deal with the imperfections of this kind of manufacturing procedures and materials, four fitting models are proposed to calibrate the implemented sensor. In order to validate the system, two different sensor implementations with measurement ranges of ±45 N and ±10 N are tested with the proposed models, comparing the resulting force estimation with respect to an industrial-grade load cell. Results show that all models can estimate the loads with an error of about 6% of the measurement range. PMID:29414861

Key Results of Interaction Models with Centering

ERIC Educational Resources Information Center

Afshartous, David; Preston, Richard A.

2011-01-01

We consider the effect on estimation of simultaneous variable centering and interaction effects in linear regression. We technically define, review, and amplify many of the statistical issues for interaction models with centering in order to create a useful and compact reference for teachers, students, and applied researchers. In addition, we…

The isentropic quantum drift-diffusion model in two or three space dimensions

NASA Astrophysics Data System (ADS)

Chen, Xiuqing

2009-05-01

We investigate the isentropic quantum drift-diffusion model, a fourth order parabolic system, in space dimensions d = 2, 3. First, we establish the global weak solutions with large initial value and periodic boundary conditions. Then we show the semiclassical limit by delicate interpolation estimates and compactness argument.

PROJECTING THE BIOLOGICAL CONDITION OF STREAMS UNDER ALTERNATIVE SCENARIOS OF HUMAN LAND USE

EPA Science Inventory

We present empirical models for estimating the status of fish and aquatic invertebrate communities in all second to fourth-order streams (1:100,000 scale; total stream length = 6476 km) throughout the Willamette River Basin, Oregon. The models project fish and invertebrate status...

Development of a biologically based dose response (BBDR) model for arsenic induced cancer

EPA Science Inventory

We are developing a biologically based dose response (BBDR) model for arsenic carcinogenicity in order to reduce uncertainty in estimates of low dose risk by maximizing the use of relevant data on the mode of action. Expert consultation and literature review are being conducted t...

An Avoidance Model for Short-Range Order Induced by Soft Repulsions in Systems of Rigid Rods

NASA Astrophysics Data System (ADS)

Han, Jining; Herzfeld, Judith

1996-03-01

The effects of soft repulsions on hard particle systems are calculated using an avoidance model which improves upon the simple mean field approximation. Avoidance reduces, but does not eliminate, the energy due to soft repulsions. On the other hand, it also reduces the configurational entropy. Under suitable conditions, this simple trade-off yields a free energy that is lower than the mean field value. In these cases, the variationally determined avoidance gives an estimate for the short-range positional order induced by soft repulsions. The results indicate little short-range order for isotropically oriented rods. However, for parallel rods, short-range order increases to significant levels as the particle axial ratio increases. The implications for long- range positional ordering are also discussed. In particular, avoidance may explain the smectic ordering of tobacco mosaic virus at volume fractions lower than those necessary for smectic ordering of hard particles.

Impurity Effects in Highly Frustrated Diamond-Lattice Antiferromagnets

NASA Astrophysics Data System (ADS)

Savary, Lucile

2012-02-01

We consider the effects of local impurities in highly frustrated diamond lattice antiferromagnets, which exhibit large but non-extensive ground state degeneracies. Such models are appropriate to many A-site magnetic spinels. We argue very generally that sufficiently dilute impurities induce an ordered magnetic ground state, and provide a mechanism of degeneracy breaking. The states which are selected can be determined by a ``swiss cheese model'' analysis, which we demonstrate numerically for a particular impurity model in this case. Moreover, we present criteria for estimating the stability of the resulting ordered phase to a competing frozen (spin glass) one. The results may explain the contrasting finding of frozen and ordered ground states in CoAl2O4 and MnSc2S4, respectively.

Cosmology from galaxy clusters as observed by Planck

NASA Astrophysics Data System (ADS)

Pierpaoli, Elena

We propose to use current all-sky data on galaxy clusters in the radio/infrared bands in order to constrain cosmology. This will be achieved performing parameter estimation with number counts and power spectra for galaxy clusters detected by Planck through their Sunyaev—Zeldovich signature. The ultimate goal of this proposal is to use clusters as tracers of matter density in order to provide information about fundamental properties of our Universe, such as the law of gravity on large scale, early Universe phenomena, structure formation and the nature of dark matter and dark energy. We will leverage on the availability of a larger and deeper cluster catalog from the latest Planck data release in order to include, for the first time, the cluster power spectrum in the cosmological parameter determination analysis. Furthermore, we will extend clusters' analysis to cosmological models not yet investigated by the Planck collaboration. These aims require a diverse set of activities, ranging from the characterization of the clusters' selection function, the choice of the cosmological cluster sample to be used for parameter estimation, the construction of mock samples in the various cosmological models with correct correlation properties in order to produce reliable selection functions and noise covariance matrices, and finally the construction of the appropriate likelihood for number counts and power spectra. We plan to make the final code available to the community and compatible with the most widely used cosmological parameter estimation code. This research makes use of data from the NASA satellites Planck and, less directly, Chandra, in order to constrain cosmology; and therefore perfectly fits the NASA objectives and the specifications of this solicitation.

Simultaneous measurement of lipid and aqueous layers of tear film using optical coherence tomography and statistical decision theory

NASA Astrophysics Data System (ADS)

Huang, Jinxin; Clarkson, Eric; Kupinski, Matthew; Rolland, Jannick P.

2014-03-01

The prevalence of Dry Eye Disease (DED) in the USA is approximately 40 million in aging adults with about $3.8 billion economic burden. However, a comprehensive understanding of tear film dynamics, which is the prerequisite to advance the management of DED, is yet to be realized. To extend our understanding of tear film dynamics, we investigate the simultaneous estimation of the lipid and aqueous layers thicknesses with the combination of optical coherence tomography (OCT) and statistical decision theory. In specific, we develop a mathematical model for Fourier-domain OCT where we take into account the different statistical processes associated with the imaging chain. We formulate the first-order and second-order statistical quantities of the output of the OCT system, which can generate some simulated OCT spectra. A tear film model, which includes a lipid and aqueous layer on top of a rough corneal surface, is the object being imaged. Then we further implement a Maximum-likelihood (ML) estimator to interpret the simulated OCT data to estimate the thicknesses of both layers of the tear film. Results show that an axial resolution of 1 μm allows estimates down to nanometers scale. We use the root mean square error of the estimates as a metric to evaluate the system parameters, such as the tradeoff between the imaging speed and the precision of estimation. This framework further provides the theoretical basics to optimize the imaging setup for a specific thickness estimation task.

Multicentric genesis of material structure: Development of the percolation model and some applications

NASA Astrophysics Data System (ADS)

Herega, Alexander; Sukhanov, Volodymyr; Vyrovoy, Valery

2016-11-01

The multiplicative measure and estimation method of ordering of the nearest neighborhood at the multiscale "site" percolation problem are considered. In the report also is shown the possibility of quantifying a relative degree of order of two nearest neighborhoods, which is based on the algorithm proposed by one of the authors. Moreover, the model of the oscillatory component of interaction of inner boundaries of different scales is proposed. In the context of our report, the concept of lacunarity and effective dimension (introduced by B. Mandelbrot) is discussed as effective tools of mathematical modeling.

A multi-species reactive transport model to estimate biogeochemical rates based on single-well push-pull test data

NASA Astrophysics Data System (ADS)

Phanikumar, Mantha S.; McGuire, Jennifer T.

2010-08-01

Push-pull tests are a popular technique to investigate various aquifer properties and microbial reaction kinetics in situ. Most previous studies have interpreted push-pull test data using approximate analytical solutions to estimate (generally first-order) reaction rate coefficients. Though useful, these analytical solutions may not be able to describe important complexities in rate data. This paper reports the development of a multi-species, radial coordinate numerical model (PPTEST) that includes the effects of sorption, reaction lag time and arbitrary reaction order kinetics to estimate rates in the presence of mixing interfaces such as those created between injected "push" water and native aquifer water. The model has the ability to describe an arbitrary number of species and user-defined reaction rate expressions including Monod/Michelis-Menten kinetics. The FORTRAN code uses a finite-difference numerical model based on the advection-dispersion-reaction equation and was developed to describe the radial flow and transport during a push-pull test. The accuracy of the numerical solutions was assessed by comparing numerical results with analytical solutions and field data available in the literature. The model described the observed breakthrough data for tracers (chloride and iodide-131) and reactive components (sulfate and strontium-85) well and was found to be useful for testing hypotheses related to the complex set of processes operating near mixing interfaces.

A simulation of probabilistic wildfire risk components for the continental United States

Treesearch

Mark A. Finney; Charles W. McHugh; Isaac C. Grenfell; Karin L. Riley; Karen C. Short

2011-01-01

This simulation research was conducted in order to develop a large-fire risk assessment system for the contiguous land area of the United States. The modeling system was applied to each of 134 Fire Planning Units (FPUs) to estimate burn probabilities and fire size distributions. To obtain stable estimates of these quantities, fire ignition and growth was simulated for...

Bayesian analysis of physiologically based toxicokinetic and toxicodynamic models.

PubMed

Hack, C Eric

2006-04-17

Physiologically based toxicokinetic (PBTK) and toxicodynamic (TD) models of bromate in animals and humans would improve our ability to accurately estimate the toxic doses in humans based on available animal studies. These mathematical models are often highly parameterized and must be calibrated in order for the model predictions of internal dose to adequately fit the experimentally measured doses. Highly parameterized models are difficult to calibrate and it is difficult to obtain accurate estimates of uncertainty or variability in model parameters with commonly used frequentist calibration methods, such as maximum likelihood estimation (MLE) or least squared error approaches. The Bayesian approach called Markov chain Monte Carlo (MCMC) analysis can be used to successfully calibrate these complex models. Prior knowledge about the biological system and associated model parameters is easily incorporated in this approach in the form of prior parameter distributions, and the distributions are refined or updated using experimental data to generate posterior distributions of parameter estimates. The goal of this paper is to give the non-mathematician a brief description of the Bayesian approach and Markov chain Monte Carlo analysis, how this technique is used in risk assessment, and the issues associated with this approach.

Development and Application of a Cohesive Sediment Transport Model in Coastal Louisiana

NASA Astrophysics Data System (ADS)

Sorourian, S.; Nistor, I.

2017-12-01

The Louisiana coast has suffered from rapid land loss due to the combined effects of increasing the rate of eustatic sea level rise, insufficient riverine sediment input and subsidence. The sediment in this region is dominated by cohesive sediments (up to 80% of clay). This study presents a new model for calculating suspended sediment concentration (SSC) of cohesive sediments. Several new concepts are incorporated into the proposed model, which is capable of estimating the spatial and temporal variation in the concentration of cohesive sediment. First, the model incorporates the effect of electrochemical forces between cohesive sediment particles. Second, the wave friction factor is expressed in terms of the median particle size diameter in order to enhance the accuracy of the estimation of bed shear stress. Third, the erosion rate of cohesive sediments is also expressed in time-dependent form. Simulated SSC profiles are compared with field data collected from Vermilion Bay, Louisiana. The results of the proposed model agree well with the experimental data, as soon as steady state condition is achieved. The results of the new numerical models provide a better estimation of the suspended sediment concentration profile compared to the initial model developed by Mehta and Li, 2003. Among the proposed developments, the formulation of a time-dependent erosion rate shows the most accurate results. Coupling of present model with the Finite-Volume, primitive equation Community Ocean Model (FVCOM) would shed light on the fate of fine-grained sediments in order to increase overall retention and restoration of the Louisiana coastal plain.

Towards a more consistent picture of isopycnal mixing in climate models

NASA Astrophysics Data System (ADS)

Gnanadesikan, A.; Pradal, M. A. S.; Koszalka, I.; Abernathey, R. P.

2014-12-01

The stirring of tracers by mesoscale eddies along isopycnal surfaces is often represented in coarse-resolution models by the Redi diffusion parameter ARedi. Theoretical treatments of ARedi often assume it should scale as the eddy energy or the growth rate of mesoscale eddies,. producing a picture where it is high in boundary currents and low )of order a few hundred m2/s) in the gyre interiors. However, observational estimates suggest that ARedi should be very large (of order thousands of m2/s) in the gyre interior. We present results of recent simulations comparing a range of spatially constant values ARedi (with values of 400, 800, 1200 and 2400 m2/s) to a spatially resolved estimate based on altimetry and a zonally averaged version of the same estimate. In general, increasing the ARedi coefficient destratifies and warms the high latitudes. Relative to our control simulation, the spatially dependent coefficient is lower in the Southern Ocean, but high in the North Pacific, and so the temperature changes mirror this. We also examine the response of ocean hypoxia to these changes. In general, the zonally averaged version of the altimetry-based estimate of ARedi does not capture the full 2d representation.

ESTIMATING THE RADIUS OF THE CONVECTIVE CORE OF MAIN-SEQUENCE STARS FROM OBSERVED OSCILLATION FREQUENCIES

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

Yang, Wuming, E-mail: yangwuming@bnu.edu.cn, E-mail: yangwuming@ynao.ac.cn

The determination of the size of the convective core of main-sequence stars is usually dependent on the construction of models of stars. Here we introduce a method to estimate the radius of the convective core of main-sequence stars with masses between about 1.1 and 1.5 M {sub ⊙} from observed frequencies of low-degree p -modes. A formula is proposed to achieve the estimation. The values of the radius of the convective core of four known stars are successfully estimated by the formula. The radius of the convective core of KIC 9812850 estimated by the formula is 0.140 ± 0.028 Rmore » {sub ⊙}. In order to confirm this prediction, a grid of evolutionary models was computed. The value of the convective-core radius of the best-fit model of KIC 9812850 is 0.149 R {sub ⊙}, which is in good agreement with that estimated by the formula from observed frequencies. The formula aids in understanding the interior structure of stars directly from observed frequencies. The understanding is not dependent on the construction of models.« less

Spatio-Temporal EEG Models for Brain Interfaces

PubMed Central

Gonzalez-Navarro, P.; Moghadamfalahi, M.; Akcakaya, M.; Erdogmus, D.

2016-01-01

Multichannel electroencephalography (EEG) is widely used in non-invasive brain computer interfaces (BCIs) for user intent inference. EEG can be assumed to be a Gaussian process with unknown mean and autocovariance, and the estimation of parameters is required for BCI inference. However, the relatively high dimensionality of the EEG feature vectors with respect to the number of labeled observations lead to rank deficient covariance matrix estimates. In this manuscript, to overcome ill-conditioned covariance estimation, we propose a structure for the covariance matrices of the multichannel EEG signals. Specifically, we assume that these covariances can be modeled as a Kronecker product of temporal and spatial covariances. Our results over the experimental data collected from the users of a letter-by-letter typing BCI show that with less number of parameter estimations, the system can achieve higher classification accuracies compared to a method that uses full unstructured covariance estimation. Moreover, in order to illustrate that the proposed Kronecker product structure could enable shortening the BCI calibration data collection sessions, using Cramer-Rao bound analysis on simulated data, we demonstrate that a model with structured covariance matrices will achieve the same estimation error as a model with no covariance structure using fewer labeled EEG observations. PMID:27713590

Chloramine demand estimation using surrogate chemical and microbiological parameters.

PubMed

Moradi, Sina; Liu, Sanly; Chow, Christopher W K; van Leeuwen, John; Cook, David; Drikas, Mary; Amal, Rose

2017-07-01

A model is developed to enable estimation of chloramine demand in full scale drinking water supplies based on chemical and microbiological factors that affect chloramine decay rate via nonlinear regression analysis method. The model is based on organic character (specific ultraviolet absorbance (SUVA)) of the water samples and a laboratory measure of the microbiological (F m ) decay of chloramine. The applicability of the model for estimation of chloramine residual (and hence chloramine demand) was tested on several waters from different water treatment plants in Australia through statistical test analysis between the experimental and predicted data. Results showed that the model was able to simulate and estimate chloramine demand at various times in real drinking water systems. To elucidate the loss of chloramine over the wide variation of water quality used in this study, the model incorporates both the fast and slow chloramine decay pathways. The significance of estimated fast and slow decay rate constants as the kinetic parameters of the model for three water sources in Australia was discussed. It was found that with the same water source, the kinetic parameters remain the same. This modelling approach has the potential to be used by water treatment operators as a decision support tool in order to manage chloramine disinfection. Copyright © 2017. Published by Elsevier B.V.

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.

Venus - Global gravity and topography

NASA Technical Reports Server (NTRS)

Mcnamee, J. B.; Borderies, N. J.; Sjogren, W. L.

1993-01-01

A new gravity field determination that has been produced combines both the Pioneer Venus Orbiter (PVO) and the Magellan Doppler radio data. Comparisonsbetween this estimate, a spherical harmonic model of degree and order 21, and previous models show that significant improvements have been made. Results are displayed as gravity contours overlaying a topographic map. We also calculate a new spherical harmonic model of topography based on Magellan altimetry, with PVO altimetry included where gaps exist in the Magellan data. This model is also of degree and order 21, so in conjunction with the gravity model, Bouguer and isostatic anomaly maps can be produced. These results are very consistent with previous results, but reveal more spatial resolution in the higher latitudes.

Probing Higgs self-coupling of a classically scale invariant model in e+e- → Zhh: Evaluation at physical point

NASA Astrophysics Data System (ADS)

Fujitani, Y.; Sumino, Y.

2018-04-01

A classically scale invariant extension of the standard model predicts large anomalous Higgs self-interactions. We compute missing contributions in previous studies for probing the Higgs triple coupling of a minimal model using the process e+e- → Zhh. Employing a proper order counting, we compute the total and differential cross sections at the leading order, which incorporate the one-loop corrections between zero external momenta and their physical values. Discovery/exclusion potential of a future e+e- collider for this model is estimated. We also find a unique feature in the momentum dependence of the Higgs triple vertex for this class of models.

Comparison of 3 estimation methods of mycophenolic acid AUC based on a limited sampling strategy in renal transplant patients.

PubMed

Hulin, Anne; Blanchet, Benoît; Audard, Vincent; Barau, Caroline; Furlan, Valérie; Durrbach, Antoine; Taïeb, Fabrice; Lang, Philippe; Grimbert, Philippe; Tod, Michel

2009-04-01

A significant relationship between mycophenolic acid (MPA) area under the plasma concentration-time curve (AUC) and the risk for rejection has been reported. Based on 3 concentration measurements, 3 approaches have been proposed for the estimation of MPA AUC, involving either a multilinear regression approach model (MLRA) or a Bayesian estimation using either gamma absorption or zero-order absorption population models. The aim of the study was to compare the 3 approaches for the estimation of MPA AUC in 150 renal transplant patients treated with mycophenolate mofetil and tacrolimus. The population parameters were determined in 77 patients (learning study). The AUC estimation methods were compared in the learning population and in 73 patients from another center (validation study). In the latter study, the reference AUCs were estimated by the trapezoidal rule on 8 measurements. MPA concentrations were measured by liquid chromatography. The gamma absorption model gave the best fit. In the learning study, the AUCs estimated by both Bayesian methods were very similar, whereas the multilinear approach was highly correlated but yielded estimates about 20% lower than Bayesian methods. This resulted in dosing recommendations differing by 250 mg/12 h or more in 27% of cases. In the validation study, AUC estimates based on the Bayesian method with gamma absorption model and multilinear regression approach model were, respectively, 12% higher and 7% lower than the reference values. To conclude, the bicompartmental model with gamma absorption rate gave the best fit. The 3 AUC estimation methods are highly correlated but not concordant. For a given patient, the same estimation method should always be used.

Multivariate Probabilistic Analysis of an Hydrological Model

NASA Astrophysics Data System (ADS)

Franceschini, Samuela; Marani, Marco

2010-05-01

Model predictions derived based on rainfall measurements and hydrological model results are often limited by the systematic error of measuring instruments, by the intrinsic variability of the natural processes and by the uncertainty of the mathematical representation. We propose a means to identify such sources of uncertainty and to quantify their effects based on point-estimate approaches, as a valid alternative to cumbersome Montecarlo methods. We present uncertainty analyses on the hydrologic response to selected meteorological events, in the mountain streamflow-generating portion of the Brenta basin at Bassano del Grappa, Italy. The Brenta river catchment has a relatively uniform morphology and quite a heterogeneous rainfall-pattern. In the present work, we evaluate two sources of uncertainty: data uncertainty (the uncertainty due to data handling and analysis) and model uncertainty (the uncertainty related to the formulation of the model). We thus evaluate the effects of the measurement error of tipping-bucket rain gauges, the uncertainty in estimating spatially-distributed rainfall through block kriging, and the uncertainty associated with estimated model parameters. To this end, we coupled a deterministic model based on the geomorphological theory of the hydrologic response to probabilistic methods. In particular we compare the results of Monte Carlo Simulations (MCS) to the results obtained, in the same conditions, using Li's Point Estimate Method (LiM). The LiM is a probabilistic technique that approximates the continuous probability distribution function of the considered stochastic variables by means of discrete points and associated weights. This allows to satisfactorily reproduce results with only few evaluations of the model function. The comparison between the LiM and MCS results highlights the pros and cons of using an approximating method. LiM is less computationally demanding than MCS, but has limited applicability especially when the model response is highly nonlinear. Higher-order approximations can provide more accurate estimations, but reduce the numerical advantage of the LiM. The results of the uncertainty analysis identify the main sources of uncertainty in the computation of river discharge. In this particular case the spatial variability of rainfall and the model parameters uncertainty are shown to have the greatest impact on discharge evaluation. This, in turn, highlights the need to support any estimated hydrological response with probability information and risk analysis results in order to provide a robust, systematic framework for decision making.

Trimming a hazard logic tree with a new model-order-reduction technique

USGS Publications Warehouse

Porter, Keith; Field, Edward; Milner, Kevin R

2017-01-01

The size of the logic tree within the Uniform California Earthquake Rupture Forecast Version 3, Time-Dependent (UCERF3-TD) model can challenge risk analyses of large portfolios. An insurer or catastrophe risk modeler concerned with losses to a California portfolio might have to evaluate a portfolio 57,600 times to estimate risk in light of the hazard possibility space. Which branches of the logic tree matter most, and which can one ignore? We employed two model-order-reduction techniques to simplify the model. We sought a subset of parameters that must vary, and the specific fixed values for the remaining parameters, to produce approximately the same loss distribution as the original model. The techniques are (1) a tornado-diagram approach we employed previously for UCERF2, and (2) an apparently novel probabilistic sensitivity approach that seems better suited to functions of nominal random variables. The new approach produces a reduced-order model with only 60 of the original 57,600 leaves. One can use the results to reduce computational effort in loss analyses by orders of magnitude.

Improvements to Fidelity, Generation and Implementation of Physics-Based Lithium-Ion Reduced-Order Models

NASA Astrophysics Data System (ADS)

Rodriguez Marco, Albert

Battery management systems (BMS) require computationally simple but highly accurate models of the battery cells they are monitoring and controlling. Historically, empirical equivalent-circuit models have been used, but increasingly researchers are focusing their attention on physics-based models due to their greater predictive capabilities. These models are of high intrinsic computational complexity and so must undergo some kind of order-reduction process to make their use by a BMS feasible: we favor methods based on a transfer-function approach of battery cell dynamics. In prior works, transfer functions have been found from full-order PDE models via two simplifying assumptions: (1) a linearization assumption--which is a fundamental necessity in order to make transfer functions--and (2) an assumption made out of expedience that decouples the electrolyte-potential and electrolyte-concentration PDEs in order to render an approach to solve for the transfer functions from the PDEs. This dissertation improves the fidelity of physics-based models by eliminating the need for the second assumption and, by linearizing nonlinear dynamics around different constant currents. Electrochemical transfer functions are infinite-order and cannot be expressed as a ratio of polynomials in the Laplace variable s. Thus, for practical use, these systems need to be approximated using reduced-order models that capture the most significant dynamics. This dissertation improves the generation of physics-based reduced-order models by introducing different realization algorithms, which produce a low-order model from the infinite-order electrochemical transfer functions. Physics-based reduced-order models are linear and describe cell dynamics if operated near the setpoint at which they have been generated. Hence, multiple physics-based reduced-order models need to be generated at different setpoints (i.e., state-of-charge, temperature and C-rate) in order to extend the cell operating range. This dissertation improves the implementation of physics-based reduced-order models by introducing different blending approaches that combine the pre-computed models generated (offline) at different setpoints in order to produce good electrochemical estimates (online) along the cell state-of-charge, temperature and C-rate range.

Modeling trends from North American Breeding Bird Survey data: a spatially explicit approach

USGS Publications Warehouse

Bled, Florent; Sauer, John R.; Pardieck, Keith L.; Doherty, Paul; Royle, J. Andy

2013-01-01

Population trends, defined as interval-specific proportional changes in population size, are often used to help identify species of conservation interest. Efficient modeling of such trends depends on the consideration of the correlation of population changes with key spatial and environmental covariates. This can provide insights into causal mechanisms and allow spatially explicit summaries at scales that are of interest to management agencies. We expand the hierarchical modeling framework used in the North American Breeding Bird Survey (BBS) by developing a spatially explicit model of temporal trend using a conditional autoregressive (CAR) model. By adopting a formal spatial model for abundance, we produce spatially explicit abundance and trend estimates. Analyses based on large-scale geographic strata such as Bird Conservation Regions (BCR) can suffer from basic imbalances in spatial sampling. Our approach addresses this issue by providing an explicit weighting based on the fundamental sample allocation unit of the BBS. We applied the spatial model to three species from the BBS. Species have been chosen based upon their well-known population change patterns, which allows us to evaluate the quality of our model and the biological meaning of our estimates. We also compare our results with the ones obtained for BCRs using a nonspatial hierarchical model (Sauer and Link 2011). Globally, estimates for mean trends are consistent between the two approaches but spatial estimates provide much more precise trend estimates in regions on the edges of species ranges that were poorly estimated in non-spatial analyses. Incorporating a spatial component in the analysis not only allows us to obtain relevant and biologically meaningful estimates for population trends, but also enables us to provide a flexible framework in order to obtain trend estimates for any area.

Methods to assess performance of models estimating risk of death in intensive care patients: a review.

PubMed

Cook, D A

2006-04-01

Models that estimate the probability of death of intensive care unit patients can be used to stratify patients according to the severity of their condition and to control for casemix and severity of illness. These models have been used for risk adjustment in quality monitoring, administration, management and research and as an aid to clinical decision making. Models such as the Mortality Prediction Model family, SAPS II, APACHE II, APACHE III and the organ system failure models provide estimates of the probability of in-hospital death of ICU patients. This review examines methods to assess the performance of these models. The key attributes of a model are discrimination (the accuracy of the ranking in order of probability of death) and calibration (the extent to which the model's prediction of probability of death reflects the true risk of death). These attributes should be assessed in existing models that predict the probability of patient mortality, and in any subsequent model that is developed for the purposes of estimating these probabilities. The literature contains a range of approaches for assessment which are reviewed and a survey of the methodologies used in studies of intensive care mortality models is presented. The systematic approach used by Standards for Reporting Diagnostic Accuracy provides a framework to incorporate these theoretical considerations of model assessment and recommendations are made for evaluation and presentation of the performance of models that estimate the probability of death of intensive care patients.

Reduced rank models for travel time estimation of low order mode pulses.

PubMed

Chandrayadula, Tarun K; Wage, Kathleen E; Worcester, Peter F; Dzieciuch, Matthew A; Mercer, James A; Andrew, Rex K; Howe, Bruce M

2013-10-01

Mode travel time estimation in the presence of internal waves (IWs) is a challenging problem. IWs perturb the sound speed, which results in travel time wander and mode scattering. A standard approach to travel time estimation is to pulse compress the broadband signal, pick the peak of the compressed time series, and average the peak time over multiple receptions to reduce variance. The peak-picking approach implicitly assumes there is a single strong arrival and does not perform well when there are multiple arrivals due to scattering. This article presents a statistical model for the scattered mode arrivals and uses the model to design improved travel time estimators. The model is based on an Empirical Orthogonal Function (EOF) analysis of the mode time series. Range-dependent simulations and data from the Long-range Ocean Acoustic Propagation Experiment (LOAPEX) indicate that the modes are represented by a small number of EOFs. The reduced-rank EOF model is used to construct a travel time estimator based on the Matched Subspace Detector (MSD). Analysis of simulation and experimental data show that the MSDs are more robust to IW scattering than peak picking. The simulation analysis also highlights how IWs affect the mode excitation by the source.

Bayesian structural inference for hidden processes.

PubMed

Strelioff, Christopher C; Crutchfield, James P

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ε-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ε-machines, irrespective of estimated transition probabilities. Properties of ε-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Bayesian structural inference for hidden processes

NASA Astrophysics Data System (ADS)

Strelioff, Christopher C.; Crutchfield, James P.

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ɛ-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ɛ-machines, irrespective of estimated transition probabilities. Properties of ɛ-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Development of a multi-body nonlinear model for a seat-occupant system

NASA Astrophysics Data System (ADS)

Azizi, Yousof

A car seat is an important component of today's cars, which directly affects ride comfort experienced by occupants. Currently, the process of ride comfort evaluation is subjective. Alternatively, the ride comfort can be evaluated by a series of objective metrics in the dynamic response of the occupant. From previous studies it is well known that the dynamic behavior of a seat-occupant system is greatly affected by soft nonlinear viscoelastic materials used in the seat cushion. Therefore, in this research, especial attention was given to efficiently modeling the behavior of seat cushion. In the first part of this research, a phenomenological nonlinear viscoelastic foam model was proposed and its ability to capture uniaxial behavior of foam was investigated. The model is based on the assumption that the total stress can be decomposed into the sum of a nonlinear elastic component, modeled by a higher order polynomial of strain, and a nonlinear hereditary type viscoelastic component. System identification procedures were developed to estimate the model parameters using uniaxial cyclic compression data from experiments conducted at different rates on two types of low density polyurethane foams and three types of high density CONFOR foams. The performance of the proposed model was compared to that of other traditional continuum models. For each foam type, it was observed that lower order models are sufficient to describe the uniaxial behavior of the foam compressed at different rates. Although, the estimated model parameters were functions of the input strain rate. Alternatively, higher order comprehensive models, with strain independent parameters, were estimated as well. The estimated comprehensive model predicts foam responses under different compression rates. Also, a methodology was proposed to predict the stress-response of a layered foam system using the estimated models of each foam in the layers. Next, the estimated foam model was incorporated into a single-degree of freedom foam-mass model which is also the simplest model of seat-occupant systems. The steady-state response of the system when it is subjected to harmonic base excitation was studied using the incremental harmonic balance method. The incremental harmonic balance method was used to reduce the time required to generate the steady-state response of the system. The incremental harmonic balance method was used to reduce the time required to generate the steady-state response of the system. Experiments are conducted on a single-degree of freedom foam-mass system subjected to harmonic base excitation. Initially, the simulated response predictions were found to deviate from the experimental results. The foam-mass model was then modified to incorporate rate dependency of foam parameters resulting in response predictions that were in good agreement with experimental results. In the second part of this research, the dynamic response of a seat-occupant system was examined through a more realistic planar multi-body seat-occupant model. A constraint Lagrangian formulation was used to derive the governing equations for the seat-occupant model. First, the governing equations were solved numerically to obtain the occupant transient response, the occupant's H-Point location and the interfacial pressure distribution. Variations in the H-Point location and the seat-occupant pressure distribution with changes in the seat-occupant parameters, including the seat geometry and the occupant's characteristics, were studied. The estimated pressure was also investigated experimentally and was found to match with the results obtained using the seat-occupant model. Next, the incremental harmonic balance method was modified and used to obtain the occupant's steady-state response when the seat-occupant system was subjected to harmonic base excitation at different frequencies. The system frequency response and mode shapes at different frequencies were also obtained and compared to the previously measured experimental frequency responses. Finally, variations in the estimated frequency response with changes in the seat-occupant parameters, including the seat geometry and the occupant characteristics, were studied.

A Probabilistic Model for Estimating the Depth and Threshold Temperature of C-fiber Nociceptors

PubMed Central

Dezhdar, Tara; Moshourab, Rabih A.; Fründ, Ingo; Lewin, Gary R.; Schmuker, Michael

2015-01-01

The subjective experience of thermal pain follows the detection and encoding of noxious stimuli by primary afferent neurons called nociceptors. However, nociceptor morphology has been hard to access and the mechanisms of signal transduction remain unresolved. In order to understand how heat transducers in nociceptors are activated in vivo, it is important to estimate the temperatures that directly activate the skin-embedded nociceptor membrane. Hence, the nociceptor’s temperature threshold must be estimated, which in turn will depend on the depth at which transduction happens in the skin. Since the temperature at the receptor cannot be accessed experimentally, such an estimation can currently only be achieved through modeling. However, the current state-of-the-art model to estimate temperature at the receptor suffers from the fact that it cannot account for the natural stochastic variability of neuronal responses. We improve this model using a probabilistic approach which accounts for uncertainties and potential noise in system. Using a data set of 24 C-fibers recorded in vitro, we show that, even without detailed knowledge of the bio-thermal properties of the system, the probabilistic model that we propose here is capable of providing estimates of threshold and depth in cases where the classical method fails. PMID:26638830

ESTIMATING TREATMENT EFFECTS ON HEALTHCARE COSTS UNDER EXOGENEITY: IS THERE A ‘MAGIC BULLET’?

PubMed Central

Polsky, Daniel; Manning, Willard G.

2011-01-01

Methods for estimating average treatment effects, under the assumption of no unmeasured confounders, include regression models; propensity score adjustments using stratification, weighting, or matching; and doubly robust estimators (a combination of both). Researchers continue to debate about the best estimator for outcomes such as health care cost data, as they are usually characterized by an asymmetric distribution and heterogeneous treatment effects,. Challenges in finding the right specifications for regression models are well documented in the literature. Propensity score estimators are proposed as alternatives to overcoming these challenges. Using simulations, we find that in moderate size samples (n= 5000), balancing on propensity scores that are estimated from saturated specifications can balance the covariate means across treatment arms but fails to balance higher-order moments and covariances amongst covariates. Therefore, unlike regression model, even if a formal model for outcomes is not required, propensity score estimators can be inefficient at best and biased at worst for health care cost data. Our simulation study, designed to take a ‘proof by contradiction’ approach, proves that no one estimator can be considered the best under all data generating processes for outcomes such as costs. The inverse-propensity weighted estimator is most likely to be unbiased under alternate data generating processes but is prone to bias under misspecification of the propensity score model and is inefficient compared to an unbiased regression estimator. Our results show that there are no ‘magic bullets’ when it comes to estimating treatment effects in health care costs. Care should be taken before naively applying any one estimator to estimate average treatment effects in these data. We illustrate the performance of alternative methods in a cost dataset on breast cancer treatment. PMID:22199462

Lunar gravitational field estimation and the effects of mismodeling upon lunar satellite orbit prediction. M.S. Thesis

NASA Technical Reports Server (NTRS)

Davis, John H.

1993-01-01

Lunar spherical harmonic gravity coefficients are estimated from simulated observations of a near-circular low altitude polar orbiter disturbed by lunar mascons. Lunar gravity sensing missions using earth-based nearside observations with and without satellite-based far-side observations are simulated and least squares maximum likelihood estimates are developed for spherical harmonic expansion fit models. Simulations and parameter estimations are performed by a modified version of the Smithsonian Astrophysical Observatory's Planetary Ephemeris Program. Two different lunar spacecraft mission phases are simulated to evaluate the estimated fit models. Results for predicting state covariances one orbit ahead are presented along with the state errors resulting from the mismodeled gravity field. The position errors from planning a lunar landing maneuver with a mismodeled gravity field are also presented. These simulations clearly demonstrate the need to include observations of satellite motion over the far side in estimating the lunar gravity field. The simulations also illustrate that the eighth degree and order expansions used in the simulated fits were unable to adequately model lunar mascons.

Centrality dependence of chemical freeze-out parameters from net-proton and net-charge fluctuations using a hadron resonance gas model

NASA Astrophysics Data System (ADS)

Adak, Rama Prasad; Das, Supriya; Ghosh, Sanjay K.; Ray, Rajarshi; Samanta, Subhasis

2017-07-01

We estimate chemical freeze-out parameters in Hadron Resonance Gas (HRG) and Excluded Volume HRG (EVHRG) models by fitting the experimental information of net-proton and net-charge fluctuations measured in Au + Au collisions by the STAR Collaboration at the BNL Relativistic Heavy Ion Collider (RHIC). We observe that chemical freeze-out parameters obtained from lower and higher order fluctuations are almost the same for √{sNN}>27 GeV, but tend to deviate from each other at lower √{sNN}. Moreover, these separations increase with decrease of √{sNN}, and for a fixed √{sNN} increase towards central collisions. Furthermore, we observe an approximate scaling behavior of (μB/T ) /(μB/T)central with (Npart) /(Npart)central for the parameters estimated from lower order fluctuations for 11.5 ≤√{sNN}≤200 GeV. Scaling is violated for the parameters estimated from higher order fluctuations for √{sNN}=11.5 and 19.6 GeV. It is observed that the chemical freeze-out parameter, which can describe σ2/M of net protons very well in all energies and centralities, cannot describe the s σ equally well, and vice versa.

Performance comparison of first-order conditional estimation with interaction and Bayesian estimation methods for estimating the population parameters and its distribution from data sets with a low number of subjects.

PubMed

Pradhan, Sudeep; Song, Byungjeong; Lee, Jaeyeon; Chae, Jung-Woo; Kim, Kyung Im; Back, Hyun-Moon; Han, Nayoung; Kwon, Kwang-Il; Yun, Hwi-Yeol

2017-12-01

Exploratory preclinical, as well as clinical trials, may involve a small number of patients, making it difficult to calculate and analyze the pharmacokinetic (PK) parameters, especially if the PK parameters show very high inter-individual variability (IIV). In this study, the performance of a classical first-order conditional estimation with interaction (FOCE-I) and expectation maximization (EM)-based Markov chain Monte Carlo Bayesian (BAYES) estimation methods were compared for estimating the population parameters and its distribution from data sets having a low number of subjects. In this study, 100 data sets were simulated with eight sampling points for each subject and with six different levels of IIV (5%, 10%, 20%, 30%, 50%, and 80%) in their PK parameter distribution. A stochastic simulation and estimation (SSE) study was performed to simultaneously simulate data sets and estimate the parameters using four different methods: FOCE-I only, BAYES(C) (FOCE-I and BAYES composite method), BAYES(F) (BAYES with all true initial parameters and fixed ω 2 ), and BAYES only. Relative root mean squared error (rRMSE) and relative estimation error (REE) were used to analyze the differences between true and estimated values. A case study was performed with a clinical data of theophylline available in NONMEM distribution media. NONMEM software assisted by Pirana, PsN, and Xpose was used to estimate population PK parameters, and R program was used to analyze and plot the results. The rRMSE and REE values of all parameter (fixed effect and random effect) estimates showed that all four methods performed equally at the lower IIV levels, while the FOCE-I method performed better than other EM-based methods at higher IIV levels (greater than 30%). In general, estimates of random-effect parameters showed significant bias and imprecision, irrespective of the estimation method used and the level of IIV. Similar performance of the estimation methods was observed with theophylline dataset. The classical FOCE-I method appeared to estimate the PK parameters more reliably than the BAYES method when using a simple model and data containing only a few subjects. EM-based estimation methods can be considered for adapting to the specific needs of a modeling project at later steps of modeling.

Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows.

PubMed

Bignardi, A B; El Faro, L; Torres Júnior, R A A; Cardoso, V L; Machado, P F; Albuquerque, L G

2011-10-31

We analyzed 152,145 test-day records from 7317 first lactations of Holstein cows recorded from 1995 to 2003. Our objective was to model variations in test-day milk yield during the first lactation of Holstein cows by random regression model (RRM), using various functions in order to obtain adequate and parsimonious models for the estimation of genetic parameters. Test-day milk yields were grouped into weekly classes of days in milk, ranging from 1 to 44 weeks. The contemporary groups were defined as herd-test-day. The analyses were performed using a single-trait RRM, including the direct additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. The mean trend of milk yield was modeled with a fourth-order orthogonal Legendre polynomial. The additive genetic and permanent environmental covariance functions were estimated by random regression on two parametric functions, Ali and Schaeffer and Wilmink, and on B-spline functions of days in milk. The covariance components and the genetic parameters were estimated by the restricted maximum likelihood method. Results from RRM parametric and B-spline functions were compared to RRM on Legendre polynomials and with a multi-trait analysis, using the same data set. Heritability estimates presented similar trends during mid-lactation (13 to 31 weeks) and between week 37 and the end of lactation, for all RRM. Heritabilities obtained by multi-trait analysis were of a lower magnitude than those estimated by RRM. The RRMs with a higher number of parameters were more useful to describe the genetic variation of test-day milk yield throughout the lactation. RRM using B-spline and Legendre polynomials as base functions appears to be the most adequate to describe the covariance structure of the data.

The Exponent of High-frequency Source Spectral Falloff and Contribution to Source Parameter Estimates

NASA Astrophysics Data System (ADS)

Kiuchi, R.; Mori, J. J.

2015-12-01

As a way to understand the characteristics of the earthquake source, studies of source parameters (such as radiated energy and stress drop) and their scaling are important. In order to estimate source parameters reliably, often we must use appropriate source spectrum models and the omega-square model is most frequently used. In this model, the spectrum is flat in lower frequencies and the falloff is proportional to the angular frequency squared. However, Some studies (e.g. Allmann and Shearer, 2009; Yagi et al., 2012) reported that the exponent of the high frequency falloff is other than -2. Therefore, in this study we estimate the source parameters using a spectral model for which the falloff exponent is not fixed. We analyze the mainshock and larger aftershocks of the 2008 Iwate-Miyagi Nairiku earthquake. Firstly, we calculate the P wave and SH wave spectra using empirical Green functions (EGF) to remove the path effect (such as attenuation) and site effect. For the EGF event, we select a smaller earthquake that is highly-correlated with the target event. In order to obtain the stable results, we calculate the spectral ratios using a multitaper spectrum analysis (Prieto et al., 2009). Then we take a geometric mean from multiple stations. Finally, using the obtained spectra ratios, we perform a grid search to determine the high frequency falloffs, as well as corner frequency of both of events. Our results indicate the high frequency falloff exponent is often less than 2.0. We do not observe any regional, focal mechanism, or depth dependencies for the falloff exponent. In addition, our estimated corner frequencies and falloff exponents are consistent between the P wave and SH wave analysis. In our presentation, we show differences in estimated source parameters using a fixed omega-square model and a model allowing variable high-frequency falloff.

Impact of planetary boundary layer turbulence on model climate and tracer transport

NASA Astrophysics Data System (ADS)

McGrath-Spangler, E. L.; Molod, A.; Ott, L. E.; Pawson, S.

2014-12-01

Planetary boundary layer (PBL) processes are important for weather, climate, and tracer transport and concentration. One measure of the strength of these processes is the PBL depth. However, no single PBL depth definition exists and several studies have found that the estimated depth can vary substantially based on the definition used. In the Goddard Earth Observing System (GEOS-5) atmospheric general circulation model, the PBL depth is particularly important because it is used to calculate the turbulent length scale that is used in the estimation of turbulent mixing. This study analyzes the impact of using three different PBL depth definitions in this calculation. Two definitions are based on the scalar eddy diffusion coefficient and the third is based on the bulk Richardson number. Over land, the bulk Richardson number definition estimates shallower nocturnal PBLs than the other estimates while over water this definition generally produces deeper PBLs. The near surface wind velocity, temperature, and specific humidity responses to the change in turbulence are spatially and temporally heterogeneous, resulting in changes to tracer transport and concentrations. Near surface wind speed increases in the bulk Richardson number experiment cause Saharan dust increases on the order of 1 × 10-4 kg m-2 downwind over the Atlantic Ocean. Carbon monoxide (CO) surface concentrations are modified over Africa during boreal summer, producing differences on the order of 20 ppb, due to the model's treatment of emissions from biomass burning. While differences in carbon dioxide (CO2) are small in the time mean, instantaneous differences are on the order of 10 ppm and these are especially prevalent at high latitude during boreal winter. Understanding the sensitivity of trace gas and aerosol concentration estimates to PBL depth is important for studies seeking to calculate surface fluxes based on near-surface concentrations and to studies projecting future concentrations.

Impact of planetary boundary layer turbulence on model climate and tracer transport

NASA Astrophysics Data System (ADS)

McGrath-Spangler, E. L.; Molod, A.; Ott, L. E.; Pawson, S.

2015-07-01

Planetary boundary layer (PBL) processes are important for weather, climate, and tracer transport and concentration. One measure of the strength of these processes is the PBL depth. However, no single PBL depth definition exists and several studies have found that the estimated depth can vary substantially based on the definition used. In the Goddard Earth Observing System (GEOS-5) atmospheric general circulation model, the PBL depth is particularly important because it is used to calculate the turbulent length scale that is used in the estimation of turbulent mixing. This study analyzes the impact of using three different PBL depth definitions in this calculation. Two definitions are based on the scalar eddy diffusion coefficient and the third is based on the bulk Richardson number. Over land, the bulk Richardson number definition estimates shallower nocturnal PBLs than the other estimates while over water this definition generally produces deeper PBLs. The near-surface wind velocity, temperature, and specific humidity responses to the change in turbulence are spatially and temporally heterogeneous, resulting in changes to tracer transport and concentrations. Near-surface wind speed increases in the bulk Richardson number experiment cause Saharan dust increases on the order of 1 × 10-4 kg m-2 downwind over the Atlantic Ocean. Carbon monoxide (CO) surface concentrations are modified over Africa during boreal summer, producing differences on the order of 20 ppb, due to the model's treatment of emissions from biomass burning. While differences in carbon dioxide (CO2) are small in the time mean, instantaneous differences are on the order of 10 ppm and these are especially prevalent at high latitude during boreal winter. Understanding the sensitivity of trace gas and aerosol concentration estimates to PBL depth is important for studies seeking to calculate surface fluxes based on near-surface concentrations and for studies projecting future concentrations.

EQUILGAS: Program to estimate temperatures and in situ two-phase conditions in geothermal reservoirs using three combined FT-HSH gas equilibria models

NASA Astrophysics Data System (ADS)

Barragán, Rosa María; Núñez, José; Arellano, Víctor Manuel; Nieva, David

2016-03-01

Exploration and exploitation of geothermal resources require the estimation of important physical characteristics of reservoirs including temperatures, pressures and in situ two-phase conditions, in order to evaluate possible uses and/or investigate changes due to exploitation. As at relatively high temperatures (>150 °C) reservoir fluids usually attain chemical equilibrium in contact with hot rocks, different models based on the chemistry of fluids have been developed that allow deep conditions to be estimated. Currently either in water-dominated or steam-dominated reservoirs the chemistry of steam has been useful for working out reservoir conditions. In this context, three methods based on the Fischer-Tropsch (FT) and combined H2S-H2 (HSH) mineral-gas reactions have been developed for estimating temperatures and the quality of the in situ two-phase mixture prevailing in the reservoir. For these methods the mineral buffers considered to be controlling H2S-H2 composition of fluids are as follows. The pyrite-magnetite buffer (FT-HSH1); the pyrite-hematite buffer (FT-HSH2) and the pyrite-pyrrhotite buffer (FT-HSH3). Currently from such models the estimations of both, temperature and steam fraction in the two-phase fluid are obtained graphically by using a blank diagram with a background theoretical solution as reference. Thus large errors are involved since the isotherms are highly nonlinear functions while reservoir steam fractions are taken from a logarithmic scale. In order to facilitate the use of the three FT-HSH methods and minimize visual interpolation errors, the EQUILGAS program that numerically solves the equations of the FT-HSH methods was developed. In this work the FT-HSH methods and the EQUILGAS program are described. Illustrative examples for Mexican fields are also given in order to help the users in deciding which method could be more suitable for every specific data set.

Proper orthogonal decomposition-based spectral higher-order stochastic estimation

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

Baars, Woutijn J., E-mail: wbaars@unimelb.edu.au; Tinney, Charles E.

A unique routine, capable of identifying both linear and higher-order coherence in multiple-input/output systems, is presented. The technique combines two well-established methods: Proper Orthogonal Decomposition (POD) and Higher-Order Spectra Analysis. The latter of these is based on known methods for characterizing nonlinear systems by way of Volterra series. In that, both linear and higher-order kernels are formed to quantify the spectral (nonlinear) transfer of energy between the system's input and output. This reduces essentially to spectral Linear Stochastic Estimation when only first-order terms are considered, and is therefore presented in the context of stochastic estimation as spectral Higher-Order Stochastic Estimationmore » (HOSE). The trade-off to seeking higher-order transfer kernels is that the increased complexity restricts the analysis to single-input/output systems. Low-dimensional (POD-based) analysis techniques are inserted to alleviate this void as POD coefficients represent the dynamics of the spatial structures (modes) of a multi-degree-of-freedom system. The mathematical framework behind this POD-based HOSE method is first described. The method is then tested in the context of jet aeroacoustics by modeling acoustically efficient large-scale instabilities as combinations of wave packets. The growth, saturation, and decay of these spatially convecting wave packets are shown to couple both linearly and nonlinearly in the near-field to produce waveforms that propagate acoustically to the far-field for different frequency combinations.« less

Prediction of the flooding of a mining reservoir in NW Spain.

PubMed

Álvarez, R; Ordóñez, A; De Miguel, E; Loredo, C

2016-12-15

Abandoned and flooded mines constitute underground reservoirs which must be managed. When pumping is stopped in a closed mine, the process of flooding should be anticipated in order to avoid environmentally undesirable or unexpected mine water discharges at the surface, particularly in populated areas. The Candín-Fondón mining reservoir in Asturias (NW Spain) has an estimated void volume of 8 million m 3 and some urban areas are susceptible to be flooded if the water is freely released from the lowest mine adit/pithead. A conceptual model of this reservoir was undertaken and the flooding process was numerically modelled in order to estimate the time that the flooding would take. Additionally, the maximum safe height for the filling of the reservoir is discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Estimating reaction rate coefficients within a travel-time modeling framework.

PubMed

Gong, R; Lu, C; Wu, W-M; Cheng, H; Gu, B; Watson, D; Jardine, P M; Brooks, S C; Criddle, C S; Kitanidis, P K; Luo, J

2011-01-01

A generalized, efficient, and practical approach based on the travel-time modeling framework is developed to estimate in situ reaction rate coefficients for groundwater remediation in heterogeneous aquifers. The required information for this approach can be obtained by conducting tracer tests with injection of a mixture of conservative and reactive tracers and measurements of both breakthrough curves (BTCs). The conservative BTC is used to infer the travel-time distribution from the injection point to the observation point. For advection-dominant reactive transport with well-mixed reactive species and a constant travel-time distribution, the reactive BTC is obtained by integrating the solutions to advective-reactive transport over the entire travel-time distribution, and then is used in optimization to determine the in situ reaction rate coefficients. By directly working on the conservative and reactive BTCs, this approach avoids costly aquifer characterization and improves the estimation for transport in heterogeneous aquifers which may not be sufficiently described by traditional mechanistic transport models with constant transport parameters. Simplified schemes are proposed for reactive transport with zero-, first-, nth-order, and Michaelis-Menten reactions. The proposed approach is validated by a reactive transport case in a two-dimensional synthetic heterogeneous aquifer and a field-scale bioremediation experiment conducted at Oak Ridge, Tennessee. The field application indicates that ethanol degradation for U(VI)-bioremediation is better approximated by zero-order reaction kinetics than first-order reaction kinetics. Copyright © 2010 The Author(s). Journal compilation © 2010 National Ground Water Association.

Estimating Reaction Rate Coefficients Within a Travel-Time Modeling Framework

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

Gong, R; Lu, C; Luo, Jian

A generalized, efficient, and practical approach based on the travel-time modeling framework is developed to estimate in situ reaction rate coefficients for groundwater remediation in heterogeneous aquifers. The required information for this approach can be obtained by conducting tracer tests with injection of a mixture of conservative and reactive tracers and measurements of both breakthrough curves (BTCs). The conservative BTC is used to infer the travel-time distribution from the injection point to the observation point. For advection-dominant reactive transport with well-mixed reactive species and a constant travel-time distribution, the reactive BTC is obtained by integrating the solutions to advective-reactive transportmore » over the entire travel-time distribution, and then is used in optimization to determine the in situ reaction rate coefficients. By directly working on the conservative and reactive BTCs, this approach avoids costly aquifer characterization and improves the estimation for transport in heterogeneous aquifers which may not be sufficiently described by traditional mechanistic transport models with constant transport parameters. Simplified schemes are proposed for reactive transport with zero-, first-, nth-order, and Michaelis-Menten reactions. The proposed approach is validated by a reactive transport case in a two-dimensional synthetic heterogeneous aquifer and a field-scale bioremediation experiment conducted at Oak Ridge, Tennessee. The field application indicates that ethanol degradation for U(VI)-bioremediation is better approximated by zero-order reaction kinetics than first-order reaction kinetics.« less

Application of empirical and dynamical closure methods to simple climate models

NASA Astrophysics Data System (ADS)

Padilla, Lauren Elizabeth

This dissertation applies empirically- and physically-based methods for closure of uncertain parameters and processes to three model systems that lie on the simple end of climate model complexity. Each model isolates one of three sources of closure uncertainty: uncertain observational data, large dimension, and wide ranging length scales. They serve as efficient test systems toward extension of the methods to more realistic climate models. The empirical approach uses the Unscented Kalman Filter (UKF) to estimate the transient climate sensitivity (TCS) parameter in a globally-averaged energy balance model. Uncertainty in climate forcing and historical temperature make TCS difficult to determine. A range of probabilistic estimates of TCS computed for various assumptions about past forcing and natural variability corroborate ranges reported in the IPCC AR4 found by different means. Also computed are estimates of how quickly uncertainty in TCS may be expected to diminish in the future as additional observations become available. For higher system dimensions the UKF approach may become prohibitively expensive. A modified UKF algorithm is developed in which the error covariance is represented by a reduced-rank approximation, substantially reducing the number of model evaluations required to provide probability densities for unknown parameters. The method estimates the state and parameters of an abstract atmospheric model, known as Lorenz 96, with accuracy close to that of a full-order UKF for 30-60% rank reduction. The physical approach to closure uses the Multiscale Modeling Framework (MMF) to demonstrate closure of small-scale, nonlinear processes that would not be resolved directly in climate models. A one-dimensional, abstract test model with a broad spatial spectrum is developed. The test model couples the Kuramoto-Sivashinsky equation to a transport equation that includes cloud formation and precipitation-like processes. In the test model, three main sources of MMF error are evaluated independently. Loss of nonlinear multi-scale interactions and periodic boundary conditions in closure models were dominant sources of error. Using a reduced order modeling approach to maximize energy content allowed reduction of the closure model dimension up to 75% without loss in accuracy. MMF and a comparable alternative model peformed equally well compared to direct numerical simulation.

An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and t-distributed deviations

NASA Astrophysics Data System (ADS)

Kargoll, Boris; Omidalizarandi, Mohammad; Loth, Ina; Paffenholz, Jens-André; Alkhatib, Hamza

2018-03-01

In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student's) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.

Flash flood hazard assessment through modelling in small semi-arid watersheds. The example of the Beni Mellal watershed in Morocco

NASA Astrophysics Data System (ADS)

Werren, G.; Balin, D.; Reynard, E.; Lane, S. N.

2012-04-01

Flood modelling is essential for flood hazard assessment. Modelling becomes a challenge in small, ungauged watersheds prone to flash floods, like the ones draining the town of Beni Mellal (Morocco). Four temporary streams meet in the urban area of Beni Mellal, producing every year sheet floods, harmful to infrastructure and to people. Here, statistical analysis may not give realistic results, but the study of these repeated real flash flood events may provide a better understanding of watershed specific hydrology. This study integrates a larger cooperation project between Switzerland and Morroco, aimed at knowledge transfer in disaster risk reduction, especially through hazard mapping and land-use planning, related to implementation of hazard maps. Hydrologic and hydraulic modelling was carried out to obtain hazard maps. An important point was to find open source data and methods that could still produce a realistic model for the area concerned, in order to provide easy-to-use, cost-effective tools for risk management in developing countries like Morocco, where routine data collection is largely lacking. The data used for modelling is the Web available TRMM 3-Hour 0.25 degree rainfall data provided by the Tropical Rainfall Measurement Mission Project (TRMM). Hydrologic modelling for discharge estimation was undertaken using methods available in the HEC-HMS software provided by the US Army Corps of Engineers® (USACE). Several transfer models were used, so as to choose the best-suited method available. As no model calibration was possible for no measured flow data was available, a one-at-the-time sensitivity analysis was performed on the parameters chosen, in order to detect their influence on the results. But the most important verification method remained field observation, through post-flood field campaigns aimed at mapping water surfaces and depths in the flooded areas, as well as river section monitoring, where rough discharge estimates could be obtained using empirical equations. Another information source was local knowledge, as people could give a rough estimation of concentration time by describing flood evolution. Finally, hydraulic modelling of the flooded areas in the urban perimeter was performed using the USACE HEC-RAS® software capabilities. A specific challenge at this stage was field morphology, as the flooded areas form large alluvial fans, with very different flood behaviour compared to flood plains. Model "calibration" at this stage was undertaken using the mapped water surfaces and depths. Great care was taken for field geometry design, where field observations, measured cross sections and field images were used to improve the existing DTM data. The model included protection dikes already built by local authorities in their flood-fight effort. Because of flash-flood specific behaviour, only maximal flooded surfaces and flow velocities were simulated through steady flow analysis in HEC-RAS. The discharge estimates obtained for the chosen event were comparable to 10-year return periods as estimated by the watershed authorities. Times of concentration correspond to this previous estimation and to local people descriptions. The modelled water surfaces reflect field reality. Flash-flood modelling demands extensive knowledge of the studied field in order to compensate data scarcity. However, more precise data, like radar rainfall estimates available in Morocco, would definitely improve outputs. In this perspective, better data access at the local level and good use of the available methods could benefit the disaster risk reduction effort as a whole.

Time-Varying Delay Estimation Applied to the Surface Electromyography Signals Using the Parametric Approach

NASA Astrophysics Data System (ADS)

Luu, Gia Thien; Boualem, Abdelbassit; Duy, Tran Trung; Ravier, Philippe; Butteli, Olivier

Muscle Fiber Conduction Velocity (MFCV) can be calculated from the time delay between the surface electromyographic (sEMG) signals recorded by electrodes aligned with the fiber direction. In order to take into account the non-stationarity during the dynamic contraction (the most daily life situation) of the data, the developed methods have to consider that the MFCV changes over time, which induces time-varying delays and the data is non-stationary (change of Power Spectral Density (PSD)). In this paper, the problem of TVD estimation is considered using a parametric method. First, the polynomial model of TVD has been proposed. Then, the TVD model parameters are estimated by using a maximum likelihood estimation (MLE) strategy solved by a deterministic optimization technique (Newton) and stochastic optimization technique, called simulated annealing (SA). The performance of the two techniques is also compared. We also derive two appropriate Cramer-Rao Lower Bounds (CRLB) for the estimated TVD model parameters and for the TVD waveforms. Monte-Carlo simulation results show that the estimation of both the model parameters and the TVD function is unbiased and that the variance obtained is close to the derived CRBs. A comparison with non-parametric approaches of the TVD estimation is also presented and shows the superiority of the method proposed.

Estimating Animal Abundance in Ground Beef Batches Assayed with Molecular Markers

PubMed Central

Hu, Xin-Sheng; Simila, Janika; Platz, Sindey Schueler; Moore, Stephen S.; Plastow, Graham; Meghen, Ciaran N.

2012-01-01

Estimating animal abundance in industrial scale batches of ground meat is important for mapping meat products through the manufacturing process and for effectively tracing the finished product during a food safety recall. The processing of ground beef involves a potentially large number of animals from diverse sources in a single product batch, which produces a high heterogeneity in capture probability. In order to estimate animal abundance through DNA profiling of ground beef constituents, two parameter-based statistical models were developed for incidence data. Simulations were applied to evaluate the maximum likelihood estimate (MLE) of a joint likelihood function from multiple surveys, showing superiority in the presence of high capture heterogeneity with small sample sizes, or comparable estimation in the presence of low capture heterogeneity with a large sample size when compared to other existing models. Our model employs the full information on the pattern of the capture-recapture frequencies from multiple samples. We applied the proposed models to estimate animal abundance in six manufacturing beef batches, genotyped using 30 single nucleotide polymorphism (SNP) markers, from a large scale beef grinding facility. Results show that between 411∼1367 animals were present in six manufacturing beef batches. These estimates are informative as a reference for improving recall processes and tracing finished meat products back to source. PMID:22479559

Quadratic semiparametric Von Mises calculus

PubMed Central

Robins, James; Li, Lingling; Tchetgen, Eric

2009-01-01

We discuss a new method of estimation of parameters in semiparametric and nonparametric models. The method is based on U-statistics constructed from quadratic influence functions. The latter extend ordinary linear influence functions of the parameter of interest as defined in semiparametric theory, and represent second order derivatives of this parameter. For parameters for which the matching cannot be perfect the method leads to a bias-variance trade-off, and results in estimators that converge at a slower than n–1/2-rate. In a number of examples the resulting rate can be shown to be optimal. We are particularly interested in estimating parameters in models with a nuisance parameter of high dimension or low regularity, where the parameter of interest cannot be estimated at n–1/2-rate. PMID:23087487

Uncertainty in flood damage estimates and its potential effect on investment decisions

NASA Astrophysics Data System (ADS)

Wagenaar, D. J.; de Bruijn, K. M.; Bouwer, L. M.; de Moel, H.

2016-01-01

This paper addresses the large differences that are found between damage estimates of different flood damage models. It explains how implicit assumptions in flood damage functions and maximum damages can have large effects on flood damage estimates. This explanation is then used to quantify the uncertainty in the damage estimates with a Monte Carlo analysis. The Monte Carlo analysis uses a damage function library with 272 functions from seven different flood damage models. The paper shows that the resulting uncertainties in estimated damages are in the order of magnitude of a factor of 2 to 5. The uncertainty is typically larger for flood events with small water depths and for smaller flood events. The implications of the uncertainty in damage estimates for flood risk management are illustrated by a case study in which the economic optimal investment strategy for a dike segment in the Netherlands is determined. The case study shows that the uncertainty in flood damage estimates can lead to significant over- or under-investments.

Optimization of the lithium/thionyl chloride battery

NASA Technical Reports Server (NTRS)

White, Ralph E.

1989-01-01

A 1-D math model for the lithium/thionyl chloride primary cell is used in conjunction with a parameter estimation technique in order to estimate the electro-kinetic parameters of this electrochemical system. The electro-kinetic parameters include the anodic transfer coefficient and exchange current density of the lithium oxidation, alpha sub a,1 and i sub o,i,ref, the cathodic transfer coefficient and the effective exchange current density of the thionyl chloride reduction, alpha sub c,2 and a sup o i sub o,2,ref, and a morphology parameter, Xi. The parameter estimation is performed on simulated data first in order to gain confidence in the method. Data, reported in the literature, for a high rate discharge of an experimental lithium/thionyl chloride cell is used for an analysis.

The Effects of Population Size Histories on Estimates of Selection Coefficients from Time-Series Genetic Data

PubMed Central

Jewett, Ethan M.; Steinrücken, Matthias; Song, Yun S.

2016-01-01

Many approaches have been developed for inferring selection coefficients from time series data while accounting for genetic drift. These approaches have been motivated by the intuition that properly accounting for the population size history can significantly improve estimates of selective strengths. However, the improvement in inference accuracy that can be attained by modeling drift has not been characterized. Here, by comparing maximum likelihood estimates of selection coefficients that account for the true population size history with estimates that ignore drift by assuming allele frequencies evolve deterministically in a population of infinite size, we address the following questions: how much can modeling the population size history improve estimates of selection coefficients? How much can mis-inferred population sizes hurt inferences of selection coefficients? We conduct our analysis under the discrete Wright–Fisher model by deriving the exact probability of an allele frequency trajectory in a population of time-varying size and we replicate our results under the diffusion model. For both models, we find that ignoring drift leads to estimates of selection coefficients that are nearly as accurate as estimates that account for the true population history, even when population sizes are small and drift is high. This result is of interest because inference methods that ignore drift are widely used in evolutionary studies and can be many orders of magnitude faster than methods that account for population sizes. PMID:27550904

Cost estimating methods for advanced space systems

NASA Technical Reports Server (NTRS)

Cyr, Kelley

1988-01-01

Parametric cost estimating methods for space systems in the conceptual design phase are developed. The approach is to identify variables that drive cost such as weight, quantity, development culture, design inheritance, and time. The relationship between weight and cost is examined in detail. A theoretical model of cost is developed and tested statistically against a historical data base of major research and development programs. It is concluded that the technique presented is sound, but that it must be refined in order to produce acceptable cost estimates.

Inferring Instantaneous, Multivariate and Nonlinear Sensitivities for the Analysis of Feedback Processes in a Dynamical System: Lorenz Model Case Study

NASA Technical Reports Server (NTRS)

Aires, Filipe; Rossow, William B.; Hansen, James E. (Technical Monitor)

2001-01-01

A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.