Finite-frequency model reduction of continuous-time switched linear systems with average dwell time
NASA Astrophysics Data System (ADS)
Ding, Da-Wei; Du, Xin
2016-11-01
This paper deals with the model reduction problem of continuous-time switched linear systems with finite-frequency input signals. The objective of the paper is to propose a finite-frequency model reduction method for such systems. A finite-frequency ? performance index is first defined in frequency domain, and then a finite-frequency performance analysis condition is derived by Parseval's theorem. Combined with the average dwell time approach, sufficient conditions for the existence of exponentially stable reduced-order models are derived. An algorithm is proposed to construct the desired reduced-order models. The effectiveness of the proposed method is illustrated by a numerical example.
Bayesian recursive mixed linear model for gene expression analyses with continuous covariates.
Casellas, J; Ibáñez-Escriche, N
2012-01-01
The analysis of microarray gene expression data has experienced a remarkable growth in scientific research over the last few years and is helping to decipher the genetic background of several productive traits. Nevertheless, most analytical approaches have relied on the comparison of 2 (or a few) well-defined groups of biological conditions where the continuous covariates have no sense (e.g., healthy vs. cancerous cells). Continuous effects could be of special interest when analyzing gene expression in animal production-oriented studies (e.g., birth weight), although very few studies address this peculiarity in the animal science framework. Within this context, we have developed a recursive linear mixed model where not only are linear covariates accounted for during gene expression analyses but also hierarchized and the effects of their genetic, environmental, and residual components on differential gene expression inferred independently. This parameterization allows a step forward in the inference of differential gene expression linked to a given quantitative trait such as birth weight. The statistical performance of this recursive model was exemplified under simulation by accounting for different sample sizes (n), heritabilities for the quantitative trait (h(2)), and magnitudes of differential gene expression (λ). It is important to highlight that statistical power increased with n, h(2), and λ, and the recursive model exceeded the standard linear mixed model with linear (nonrecursive) covariates in the majority of scenarios. This new parameterization would provide new insights about gene expression in the animal science framework, opening a new research scenario where within-covariate sources of differential gene expression could be individualized and estimated. The source code of the program accommodating these analytical developments and additional information about practical aspects on running the program are freely available by request to the corresponding
ERIC Educational Resources Information Center
Ferrando, Pere J.
2004-01-01
This study used kernel-smoothing procedures to estimate the item characteristic functions (ICFs) of a set of continuous personality items. The nonparametric ICFs were compared with the ICFs estimated (a) by the linear model and (b) by Samejima's continuous-response model. The study was based on a conditioned approach and used an error-in-variables…
Minimal state space realisation of continuous-time linear time-variant input-output models
NASA Astrophysics Data System (ADS)
Goos, J.; Pintelon, R.
2016-04-01
In the linear time-invariant (LTI) framework, the transformation from an input-output equation into state space representation is well understood. Several canonical forms exist that realise the same dynamic behaviour. If the coefficients become time-varying however, the LTI transformation no longer holds. We prove by induction that there exists a closed-form expression for the observability canonical state space model, using binomial coefficients.
ERIC Educational Resources Information Center
Ferrando, Pere J.
2008-01-01
This paper develops results and procedures for obtaining linear composites of factor scores that maximize: (a) test information, and (b) validity with respect to external variables in the multiple factor analysis (FA) model. I treat FA as a multidimensional item response theory model, and use Ackerman's multidimensional information approach based…
Raabe, Joshua K.; Gardner, Beth; Hightower, Joseph E.
2013-01-01
We developed a spatial capture–recapture model to evaluate survival and activity centres (i.e., mean locations) of tagged individuals detected along a linear array. Our spatially explicit version of the Cormack–Jolly–Seber model, analyzed using a Bayesian framework, correlates movement between periods and can incorporate environmental or other covariates. We demonstrate the model using 2010 data for anadromous American shad (Alosa sapidissima) tagged with passive integrated transponders (PIT) at a weir near the mouth of a North Carolina river and passively monitored with an upstream array of PIT antennas. The river channel constrained migrations, resulting in linear, one-dimensional encounter histories that included both weir captures and antenna detections. Individual activity centres in a given time period were a function of the individual’s previous estimated location and the river conditions (i.e., gage height). Model results indicate high within-river spawning mortality (mean weekly survival = 0.80) and more extensive movements during elevated river conditions. This model is applicable for any linear array (e.g., rivers, shorelines, and corridors), opening new opportunities to study demographic parameters, movement or migration, and habitat use.
Tan, Ziwen; Qin, Guoyou; Zhou, Haibo
2016-10-01
Outcome-dependent sampling (ODS) designs have been well recognized as a cost-effective way to enhance study efficiency in both statistical literature and biomedical and epidemiologic studies. A partially linear additive model (PLAM) is widely applied in real problems because it allows for a flexible specification of the dependence of the response on some covariates in a linear fashion and other covariates in a nonlinear non-parametric fashion. Motivated by an epidemiological study investigating the effect of prenatal polychlorinated biphenyls exposure on children's intelligence quotient (IQ) at age 7 years, we propose a PLAM in this article to investigate a more flexible non-parametric inference on the relationships among the response and covariates under the ODS scheme. We propose the estimation method and establish the asymptotic properties of the proposed estimator. Simulation studies are conducted to show the improved efficiency of the proposed ODS estimator for PLAM compared with that from a traditional simple random sampling design with the same sample size. The data of the above-mentioned study is analyzed to illustrate the proposed method.
Tan, Ziwen; Qin, Guoyou; Zhou, Haibo
2016-10-01
Outcome-dependent sampling (ODS) designs have been well recognized as a cost-effective way to enhance study efficiency in both statistical literature and biomedical and epidemiologic studies. A partially linear additive model (PLAM) is widely applied in real problems because it allows for a flexible specification of the dependence of the response on some covariates in a linear fashion and other covariates in a nonlinear non-parametric fashion. Motivated by an epidemiological study investigating the effect of prenatal polychlorinated biphenyls exposure on children's intelligence quotient (IQ) at age 7 years, we propose a PLAM in this article to investigate a more flexible non-parametric inference on the relationships among the response and covariates under the ODS scheme. We propose the estimation method and establish the asymptotic properties of the proposed estimator. Simulation studies are conducted to show the improved efficiency of the proposed ODS estimator for PLAM compared with that from a traditional simple random sampling design with the same sample size. The data of the above-mentioned study is analyzed to illustrate the proposed method. PMID:27006375
Linear models: permutation methods
Cade, B.S.; Everitt, B.S.; Howell, D.C.
2005-01-01
Permutation tests (see Permutation Based Inference) for the linear model have applications in behavioral studies when traditional parametric assumptions about the error term in a linear model are not tenable. Improved validity of Type I error rates can be achieved with properly constructed permutation tests. Perhaps more importantly, increased statistical power, improved robustness to effects of outliers, and detection of alternative distributional differences can be achieved by coupling permutation inference with alternative linear model estimators. For example, it is well-known that estimates of the mean in linear model are extremely sensitive to even a single outlying value of the dependent variable compared to estimates of the median [7, 19]. Traditionally, linear modeling focused on estimating changes in the center of distributions (means or medians). However, quantile regression allows distributional changes to be estimated in all or any selected part of a distribution or responses, providing a more complete statistical picture that has relevance to many biological questions [6]...
Memory in linear recurrent neural networks in continuous time.
Hermans, Michiel; Schrauwen, Benjamin
2010-04-01
Reservoir Computing is a novel technique which employs recurrent neural networks while circumventing difficult training algorithms. A very recent trend in Reservoir Computing is the use of real physical dynamical systems as implementation platforms, rather than the customary digital emulations. Physical systems operate in continuous time, creating a fundamental difference with the classic discrete time definitions of Reservoir Computing. The specific goal of this paper is to study the memory properties of such systems, where we will limit ourselves to linear dynamics. We develop an analytical model which allows the calculation of the memory function for continuous time linear dynamical systems, which can be considered as networks of linear leaky integrator neurons. We then use this model to research memory properties for different types of reservoir. We start with random connection matrices with a shifted eigenvalue spectrum, which perform very poorly. Next, we transform two specific reservoir types, which are known to give good performance in discrete time, to the continuous time domain. Reservoirs based on uniform spreading of connection matrix eigenvalues on the unit disk in discrete time give much better memory properties than reservoirs with random connection matrices, where reservoirs based on orthogonal connection matrices in discrete time are very robust against noise and their memory properties can be tuned. The overall results found in this work yield important insights into how to design networks for continuous time.
Continuous-mode operation of a noiseless linear amplifier
NASA Astrophysics Data System (ADS)
Li, Yi; Carvalho, André R. R.; James, Matthew R.
2016-05-01
We develop a dynamical model to describe the operation of the nondeterministic noiseless linear amplifier (NLA) in the regime of continuous-mode inputs. We analyze the dynamics conditioned on the detection of photons and show that the amplification gain depends on detection times and on the temporal profile of the input state and the auxiliary single-photon state required by the NLA. We also show that the output amplified state inherits the pulse shape of the ancilla photon.
Generalized Linear Models in Family Studies
ERIC Educational Resources Information Center
Wu, Zheng
2005-01-01
Generalized linear models (GLMs), as defined by J. A. Nelder and R. W. M. Wedderburn (1972), unify a class of regression models for categorical, discrete, and continuous response variables. As an extension of classical linear models, GLMs provide a common body of theory and methodology for some seemingly unrelated models and procedures, such as…
Optimal piecewise locally linear modeling
NASA Astrophysics Data System (ADS)
Harris, Chris J.; Hong, Xia; Feng, M.
1999-03-01
Associative memory networks such as Radial Basis Functions, Neurofuzzy and Fuzzy Logic used for modelling nonlinear processes suffer from the curse of dimensionality (COD), in that as the input dimension increases the parameterization, computation cost, training data requirements, etc. increase exponentially. Here a new algorithm is introduced for the construction of a Delaunay input space partitioned optimal piecewise locally linear models to overcome the COD as well as generate locally linear models directly amenable to linear control and estimation algorithms. The training of the model is configured as a new mixture of experts network with a new fast decision rule derived using convex set theory. A very fast simulated reannealing (VFSR) algorithm is utilized to search a global optimal solution of the Delaunay input space partition. A benchmark non-linear time series is used to demonstrate the new approach.
Some estimation formulae for continuous time-invariant linear systems
NASA Technical Reports Server (NTRS)
Bierman, G. J.; Sidhu, G. S.
1975-01-01
In this brief paper we examine a Riccati equation decomposition due to Reid and Lainiotis and apply the result to the continuous time-invariant linear filtering problem. Exploitation of the time-invariant structure leads to integration-free covariance recursions which are of use in covariance analyses and in filter implementations. A super-linearly convergent iterative solution to the algebraic Riccati equation (ARE) is developed. The resulting algorithm, arranged in a square-root form, is thought to be numerically stable and competitive with other ARE solution methods. Certain covariance relations that are relevant to the fixed-point and fixed-lag smoothing problems are also discussed.
Parameterized Linear Longitudinal Airship Model
NASA Technical Reports Server (NTRS)
Kulczycki, Eric; Elfes, Alberto; Bayard, David; Quadrelli, Marco; Johnson, Joseph
2010-01-01
A parameterized linear mathematical model of the longitudinal dynamics of an airship is undergoing development. This model is intended to be used in designing control systems for future airships that would operate in the atmospheres of Earth and remote planets. Heretofore, the development of linearized models of the longitudinal dynamics of airships has been costly in that it has been necessary to perform extensive flight testing and to use system-identification techniques to construct models that fit the flight-test data. The present model is a generic one that can be relatively easily specialized to approximate the dynamics of specific airships at specific operating points, without need for further system identification, and with significantly less flight testing. The approach taken in the present development is to merge the linearized dynamical equations of an airship with techniques for estimation of aircraft stability derivatives, and to thereby make it possible to construct a linearized dynamical model of the longitudinal dynamics of a specific airship from geometric and aerodynamic data pertaining to that airship. (It is also planned to develop a model of the lateral dynamics by use of the same methods.) All of the aerodynamic data needed to construct the model of a specific airship can be obtained from wind-tunnel testing and computational fluid dynamics
Linearized Bekenstein varying α models
NASA Astrophysics Data System (ADS)
Avelino, P. P.; Martins, C. J.; Oliveira, J. C.
2004-10-01
We study the simplest class of Bekenstein-type, varying α models, in which the two available free functions (potential and gauge kinetic function) are Taylor-expanded up to linear order. Any realistic model of this type reduces to a model in this class for a certain time interval around the present day. Nevertheless, we show that no such model is consistent with all existing observational results. We discuss possible implications of these findings, and, in particular, clarify the ambiguous statement (often found in the literature) that “the Webb results are inconsistent with Oklo.”
LINEAR - DERIVATION AND DEFINITION OF A LINEAR AIRCRAFT MODEL
NASA Technical Reports Server (NTRS)
Duke, E. L.
1994-01-01
The Derivation and Definition of a Linear Model program, LINEAR, provides the user with a powerful and flexible tool for the linearization of aircraft aerodynamic models. LINEAR was developed to provide a standard, documented, and verified tool to derive linear models for aircraft stability analysis and control law design. Linear system models define the aircraft system in the neighborhood of an analysis point and are determined by the linearization of the nonlinear equations defining vehicle dynamics and sensors. LINEAR numerically determines a linear system model using nonlinear equations of motion and a user supplied linear or nonlinear aerodynamic model. The nonlinear equations of motion used are six-degree-of-freedom equations with stationary atmosphere and flat, nonrotating earth assumptions. LINEAR is capable of extracting both linearized engine effects, such as net thrust, torque, and gyroscopic effects and including these effects in the linear system model. The point at which this linear model is defined is determined either by completely specifying the state and control variables, or by specifying an analysis point on a trajectory and directing the program to determine the control variables and the remaining state variables. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to provide easy selection of state, control, and observation variables to be used in a particular model. Thus, the order of the system model is completely under user control. Further, the program provides the flexibility of allowing alternate formulations of both the state and observation equations. Data describing the aircraft and the test case is input to the program through a terminal or formatted data files. All data can be modified interactively from case to case. The aerodynamic model can be defined in two ways: a set of nondimensional stability and control derivatives for the flight point of
Multicollinearity in hierarchical linear models.
Yu, Han; Jiang, Shanhe; Land, Kenneth C
2015-09-01
This study investigates an ill-posed problem (multicollinearity) in Hierarchical Linear Models from both the data and the model perspectives. We propose an intuitive, effective approach to diagnosing the presence of multicollinearity and its remedies in this class of models. A simulation study demonstrates the impacts of multicollinearity on coefficient estimates, associated standard errors, and variance components at various levels of multicollinearity for finite sample sizes typical in social science studies. We further investigate the role multicollinearity plays at each level for estimation of coefficient parameters in terms of shrinkage. Based on these analyses, we recommend a top-down method for assessing multicollinearity in HLMs that first examines the contextual predictors (Level-2 in a two-level model) and then the individual predictors (Level-1) and uses the results for data collection, research problem redefinition, model re-specification, variable selection and estimation of a final model.
Continuum eigenmodes in some linear stellar models
NASA Astrophysics Data System (ADS)
Winfield, Christopher J.
2016-10-01
We apply parallel approaches in the study of continuous spectra to adiabatic stellar models. We seek continuum eigenmodes for the LAWE formulated as both finite difference and linear differential equations. In particular, we apply methods of Jacobi matrices and methods of subordinancy theory in these respective formulations. We find certain pressure-density conditions which admit positive-measured sets of continuous oscillation spectra under plausible conditions on density and pressure. We arrive at results of unbounded oscillations and computational or, perhaps, dynamic instability.
Foster, Guy M.; Graham, Jennifer L.
2016-04-06
The Kansas River is a primary source of drinking water for about 800,000 people in northeastern Kansas. Source-water supplies are treated by a combination of chemical and physical processes to remove contaminants before distribution. Advanced notification of changing water-quality conditions and cyanobacteria and associated toxin and taste-and-odor compounds provides drinking-water treatment facilities time to develop and implement adequate treatment strategies. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas State Water Plan Fund), and the City of Lawrence, the City of Topeka, the City of Olathe, and Johnson County Water One, began a study in July 2012 to develop statistical models at two Kansas River sites located upstream from drinking-water intakes. Continuous water-quality monitors have been operated and discrete-water quality samples have been collected on the Kansas River at Wamego (USGS site number 06887500) and De Soto (USGS site number 06892350) since July 2012. Continuous and discrete water-quality data collected during July 2012 through June 2015 were used to develop statistical models for constituents of interest at the Wamego and De Soto sites. Logistic models to continuously estimate the probability of occurrence above selected thresholds were developed for cyanobacteria, microcystin, and geosmin. Linear regression models to continuously estimate constituent concentrations were developed for major ions, dissolved solids, alkalinity, nutrients (nitrogen and phosphorus species), suspended sediment, indicator bacteria (Escherichia coli, fecal coliform, and enterococci), and actinomycetes bacteria. These models will be used to provide real-time estimates of the probability that cyanobacteria and associated compounds exceed thresholds and of the concentrations of other water-quality constituents in the Kansas River. The models documented in this report are useful for characterizing changes
Linear equality constraints in the general linear mixed model.
Edwards, L J; Stewart, P W; Muller, K E; Helms, R W
2001-12-01
Scientists may wish to analyze correlated outcome data with constraints among the responses. For example, piecewise linear regression in a longitudinal data analysis can require use of a general linear mixed model combined with linear parameter constraints. Although well developed for standard univariate models, there are no general results that allow a data analyst to specify a mixed model equation in conjunction with a set of constraints on the parameters. We resolve the difficulty by precisely describing conditions that allow specifying linear parameter constraints that insure the validity of estimates and tests in a general linear mixed model. The recommended approach requires only straightforward and noniterative calculations to implement. We illustrate the convenience and advantages of the methods with a comparison of cognitive developmental patterns in a study of individuals from infancy to early adulthood for children from low-income families.
Inverse Modeling Via Linearized Functional Minimization
NASA Astrophysics Data System (ADS)
Barajas-Solano, D. A.; Wohlberg, B.; Vesselinov, V. V.; Tartakovsky, D. M.
2014-12-01
We present a novel parameter estimation methodology for transient models of geophysical systems with uncertain, spatially distributed, heterogeneous and piece-wise continuous parameters.The methodology employs a bayesian approach to propose an inverse modeling problem for the spatial configuration of the model parameters.The likelihood of the configuration is formulated using sparse measurements of both model parameters and transient states.We propose using total variation regularization (TV) as the prior reflecting the heterogeneous, piece-wise continuity assumption on the parameter distribution.The maximum a posteriori (MAP) estimator of the parameter configuration is then computed by minimizing the negative bayesian log-posterior using a linearized functional minimization approach. The computation of the MAP estimator is a large-dimensional nonlinear minimization problem with two sources of nonlinearity: (1) the TV operator, and (2) the nonlinear relation between states and parameters provided by the model's governing equations.We propose a a hybrid linearized functional minimization (LFM) algorithm in two stages to efficiently treat both sources of nonlinearity.The relation between states and parameters is linearized, resulting in a linear minimization sub-problem equipped with the TV operator; this sub-problem is then minimized using the Alternating Direction Method of Multipliers (ADMM). The methodology is illustrated with a transient saturated groundwater flow application in a synthetic domain, stimulated by external point-wise loadings representing aquifer pumping, together with an array of discrete measurements of hydraulic conductivity and transient measurements of hydraulic head.We show that our inversion strategy is able to recover the overall large-scale features of the parameter configuration, and that the reconstruction is improved by the addition of transient information of the state variable.
Structure and the failure of the linear theory of continuous ordering
Gross, N.; Klein, W.; Ludwig, K. Center for Polymer Physics, Boston University, Boston, Massachusetts 02215 )
1994-11-14
We present the results of numerical investigations of Ising models undergoing continuous ordering which indicate, for systems with large but finite interaction range [ital R], that the time interval during which the linear theory of Cahn, Hilliard, and Cook fits the simulation data depends strongly on the length scale of observation. We associate the initial linear theory deviation at small length scales with the appearance of isolated structures or domains which form after a quench into the unstable region of thermodynamic space. These domains cannot be described by a linear theory; this implies, in contrast to earlier results, that the linear theory breakdown'' cannot be obtained by investigation of the linear itself for inconsistencies.
Computing Linear Mathematical Models Of Aircraft
NASA Technical Reports Server (NTRS)
Duke, Eugene L.; Antoniewicz, Robert F.; Krambeer, Keith D.
1991-01-01
Derivation and Definition of Linear Aircraft Model (LINEAR) computer program provides user with powerful, and flexible, standard, documented, and verified software tool for linearization of mathematical models of aerodynamics of aircraft. Intended for use in software tool to drive linear analysis of stability and design of control laws for aircraft. Capable of both extracting such linearized engine effects as net thrust, torque, and gyroscopic effects, and including these effects in linear model of system. Designed to provide easy selection of state, control, and observation variables used in particular model. Also provides flexibility of allowing alternate formulations of both state and observation equations. Written in FORTRAN.
Wealth redistribution in conservative linear kinetic models
NASA Astrophysics Data System (ADS)
Toscani, G.
2009-10-01
We introduce and discuss kinetic models for wealth distribution which include both taxation and uniform redistribution. The evolution of the continuous density of wealth obeys a linear Boltzmann equation where the background density represents the action of an external subject on the taxation mechanism. The case in which the mean wealth is conserved is analyzed in full details, by recovering the analytical form of the steady states. These states are probability distributions of convergent random series of a special structure, called perpetuities. Among others, Gibbs distribution appears as steady state in case of total taxation and uniform redistribution.
Fractional non-linear modelling of ultracapacitors
NASA Astrophysics Data System (ADS)
Bertrand, Nicolas; Sabatier, Jocelyn; Briat, Olivier; Vinassa, Jean-Michel
2010-05-01
In this paper, it is demonstrated that an ultracapacitor exhibits a non-linear behaviour in relation to the operating voltage. A set of fractional order linear systems resulting from a frequency analysis of the ultracapacitor at various operating points is first obtained. Then, a non-linear model is deduced from the linear systems set, so that its Taylor linearization around the considered operating points (for the frequency analysis), produces the linear system set. The resulting non-linear model is validated on a Hybrid Electric Vehicle (HEV) application.
On high-continuity transfinite element formulations for linear-nonlinear transient thermal problems
NASA Technical Reports Server (NTRS)
Tamma, Kumar K.; Railkar, Sudhir B.
1987-01-01
This paper describes recent developments in the applicability of a hybrid transfinite element methodology with emphasis on high-continuity formulations for linear/nonlinear transient thermal problems. The proposed concepts furnish accurate temperature distributions and temperature gradients making use of a relatively smaller number of degrees of freedom; and the methodology is applicable to linear/nonlinear thermal problems. Characteristic features of the formulations are described in technical detail as the proposed hybrid approach combines the major advantages and modeling features of high-continuity thermal finite elements in conjunction with transform methods and classical Galerkin schemes. Several numerical test problems are evaluated and the results obtained validate the proposed concepts for linear/nonlinear thermal problems.
Nonlinear Modeling by Assembling Piecewise Linear Models
NASA Technical Reports Server (NTRS)
Yao, Weigang; Liou, Meng-Sing
2013-01-01
To preserve nonlinearity of a full order system over a parameters range of interest, we propose a simple modeling approach by assembling a set of piecewise local solutions, including the first-order Taylor series terms expanded about some sampling states. The work by Rewienski and White inspired our use of piecewise linear local solutions. The assembly of these local approximations is accomplished by assigning nonlinear weights, through radial basis functions in this study. The efficacy of the proposed procedure is validated for a two-dimensional airfoil moving at different Mach numbers and pitching motions, under which the flow exhibits prominent nonlinear behaviors. All results confirm that our nonlinear model is accurate and stable for predicting not only aerodynamic forces but also detailed flowfields. Moreover, the model is robustness-accurate for inputs considerably different from the base trajectory in form and magnitude. This modeling preserves nonlinearity of the problems considered in a rather simple and accurate manner.
Linear Logistic Test Modeling with R
ERIC Educational Resources Information Center
Baghaei, Purya; Kubinger, Klaus D.
2015-01-01
The present paper gives a general introduction to the linear logistic test model (Fischer, 1973), an extension of the Rasch model with linear constraints on item parameters, along with eRm (an R package to estimate different types of Rasch models; Mair, Hatzinger, & Mair, 2014) functions to estimate the model and interpret its parameters. The…
Continuous Quantitative Measurements on a Linear Air Track
ERIC Educational Resources Information Center
Vogel, Eric
1973-01-01
Describes the construction and operational procedures of a spark-timing apparatus which is designed to record the back and forth motion of one or two carts on linear air tracks. Applications to measurements of velocity, acceleration, simple harmonic motion, and collision problems are illustrated. (CC)
Composite Linear Models | Division of Cancer Prevention
By Stuart G. Baker The composite linear models software is a matrix approach to compute maximum likelihood estimates and asymptotic standard errors for models for incomplete multinomial data. It implements the method described in Baker SG. Composite linear models for incomplete multinomial data. Statistics in Medicine 1994;13:609-622. The software includes a library of thirty examples from the literature. |
Generalization of continuous-variable quantum cloning with linear optics
NASA Astrophysics Data System (ADS)
Zhai, Zehui; Guo, Juan; Gao, Jiangrui
2006-05-01
We propose an asymmetric quantum cloning scheme. Based on the proposal and experiment by Andersen [Phys. Rev. Lett. 94, 240503 (2005)], we generalize it to two asymmetric cases: quantum cloning with asymmetry between output clones and between quadrature variables. These optical implementations also employ linear elements and homodyne detection only. Finally, we also compare the utility of symmetric and asymmetric cloning in an analysis of a squeezed-state quantum key distribution protocol and find that the asymmetric one is more advantageous.
Generalization of continuous-variable quantum cloning with linear optics
Zhai Zehui; Guo Juan; Gao Jiangrui
2006-05-15
We propose an asymmetric quantum cloning scheme. Based on the proposal and experiment by Andersen et al. [Phys. Rev. Lett. 94, 240503 (2005)], we generalize it to two asymmetric cases: quantum cloning with asymmetry between output clones and between quadrature variables. These optical implementations also employ linear elements and homodyne detection only. Finally, we also compare the utility of symmetric and asymmetric cloning in an analysis of a squeezed-state quantum key distribution protocol and find that the asymmetric one is more advantageous.
"Analytic continuation" of = 2 minimal model
NASA Astrophysics Data System (ADS)
Sugawara, Yuji
2014-04-01
In this paper we discuss what theory should be identified as the "analytic continuation" with N rArr -N of the {mathcal N}=2 minimal model with the central charge hat {c} = 1 - frac {2}{N}. We clarify how the elliptic genus of the expected model is written in terms of holomorphic linear combinations of the "modular completions" introduced in [T. Eguchi and Y. Sugawara, JHEP 1103, 107 (2011)] in the SL(2)_{N+2}/U(1) supercoset theory. We further discuss how this model could be interpreted as a kind of model of the SL(2)_{N+2}/U(1) supercoset in the (widetilde {{R}},widetilde {R}) sector, in which only the discrete spectrum appears in the torus partition function and the potential IR divergence due to the non-compactness of the target space is removed. We also briefly discuss possible definitions of the sectors with other spin structures.
Spaghetti Bridges: Modeling Linear Relationships
ERIC Educational Resources Information Center
Kroon, Cindy D.
2016-01-01
Mathematics and science are natural partners. One of many examples of this partnership occurs when scientific observations are made, thus providing data that can be used for mathematical modeling. Developing mathematical relationships elucidates such scientific principles. This activity describes a data-collection activity in which students employ…
A Symbolic Logic for Representing Linear Models.
ERIC Educational Resources Information Center
Hall, Charles E.
A set of symbols is presented along with logical operators which represent the possible manipulations of the linear model. The use of these symbols and operators is to simplify the representation of analysis of variance models, correlation models and factor analysis models. (Author)
Extended Generalized Linear Latent and Mixed Model
ERIC Educational Resources Information Center
Segawa, Eisuke; Emery, Sherry; Curry, Susan J.
2008-01-01
The generalized linear latent and mixed modeling (GLLAMM framework) includes many models such as hierarchical and structural equation models. However, GLLAMM cannot currently accommodate some models because it does not allow some parameters to be random. GLLAMM is extended to overcome the limitation by adding a submodel that specifies a…
Aircraft engine mathematical model - linear system approach
NASA Astrophysics Data System (ADS)
Rotaru, Constantin; Roateşi, Simona; Cîrciu, Ionicǎ
2016-06-01
This paper examines a simplified mathematical model of the aircraft engine, based on the theory of linear and nonlinear systems. The dynamics of the engine was represented by a linear, time variant model, near a nominal operating point within a finite time interval. The linearized equations were expressed in a matrix form, suitable for the incorporation in the MAPLE program solver. The behavior of the engine was included in terms of variation of the rotational speed following a deflection of the throttle. The engine inlet parameters can cover a wide range of altitude and Mach numbers.
Dissipative Continuous Spontaneous Localization (CSL) model
Smirne, Andrea; Bassi, Angelo
2015-01-01
Collapse models explain the absence of quantum superpositions at the macroscopic scale, while giving practically the same predictions as quantum mechanics for microscopic systems. The Continuous Spontaneous Localization (CSL) model is the most refined and studied among collapse models. A well-known problem of this model, and of similar ones, is the steady and unlimited increase of the energy induced by the collapse noise. Here we present the dissipative version of the CSL model, which guarantees a finite energy during the entire system’s evolution, thus making a crucial step toward a realistic energy-conserving collapse model. This is achieved by introducing a non-linear stochastic modification of the Schrödinger equation, which represents the action of a dissipative finite-temperature collapse noise. The possibility to introduce dissipation within collapse models in a consistent way will have relevant impact on the experimental investigations of the CSL model, and therefore also on the testability of the quantum superposition principle. PMID:26243034
A Vernacular for Linear Latent Growth Models
ERIC Educational Resources Information Center
Hancock, Gregory R.; Choi, Jaehwa
2006-01-01
In its most basic form, latent growth modeling (latent curve analysis) allows an assessment of individuals' change in a measured variable X over time. For simple linear models, as with other growth models, parameter estimates associated with the a construct (amount of X at a chosen temporal reference point) and b construct (growth in X per unit…
Theoretical and Empirical Comparisons between Two Models for Continuous Item Responses.
ERIC Educational Resources Information Center
Ferrando, Pere J.
2002-01-01
Analyzed the relations between two continuous response models intended for typical response items: the linear congeneric model and Samejima's continuous response model (CRM). Illustrated the relations described using an empirical example and assessed the relations through a simulation study. (SLD)
Congeneric Models and Levine's Linear Equating Procedures.
ERIC Educational Resources Information Center
Brennan, Robert L.
In 1955, R. Levine introduced two linear equating procedures for the common-item non-equivalent populations design. His procedures make the same assumptions about true scores; they differ in terms of the nature of the equating function used. In this paper, two parameterizations of a classical congeneric model are introduced to model the variables…
ERIC Educational Resources Information Center
Ker, H. W.
2014-01-01
Multilevel data are very common in educational research. Hierarchical linear models/linear mixed-effects models (HLMs/LMEs) are often utilized to analyze multilevel data nowadays. This paper discusses the problems of utilizing ordinary regressions for modeling multilevel educational data, compare the data analytic results from three regression…
Orthogonal nilpotent superfields from linear models
NASA Astrophysics Data System (ADS)
Kallosh, Renata; Karlsson, Anna; Mosk, Benjamin; Murli, Divyanshu
2016-05-01
We derive supersymmetry/supergravity models with constrained orthogonal nilpotent superfields from the linear models in the formal limit where the masses of the sgoldstino, inflatino and sinflaton tend to infinity. The case where the sinflaton mass remains finite leads to a model with a `relaxed' constraint, where the sinflaton remains an independent field. Our procedure is equivalent to a requirement that some of the components of the curvature of the moduli space tend to infinity.
Managing Clustered Data Using Hierarchical Linear Modeling
ERIC Educational Resources Information Center
Warne, Russell T.; Li, Yan; McKyer, E. Lisako J.; Condie, Rachel; Diep, Cassandra S.; Murano, Peter S.
2012-01-01
Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence…
Bayesian Methods for High Dimensional Linear Models
Mallick, Himel; Yi, Nengjun
2013-01-01
In this article, we present a selective overview of some recent developments in Bayesian model and variable selection methods for high dimensional linear models. While most of the reviews in literature are based on conventional methods, we focus on recently developed methods, which have proven to be successful in dealing with high dimensional variable selection. First, we give a brief overview of the traditional model selection methods (viz. Mallow’s Cp, AIC, BIC, DIC), followed by a discussion on some recently developed methods (viz. EBIC, regularization), which have occupied the minds of many statisticians. Then, we review high dimensional Bayesian methods with a particular emphasis on Bayesian regularization methods, which have been used extensively in recent years. We conclude by briefly addressing the asymptotic behaviors of Bayesian variable selection methods for high dimensional linear models under different regularity conditions. PMID:24511433
Bayesian Methods for High Dimensional Linear Models.
Mallick, Himel; Yi, Nengjun
2013-06-01
In this article, we present a selective overview of some recent developments in Bayesian model and variable selection methods for high dimensional linear models. While most of the reviews in literature are based on conventional methods, we focus on recently developed methods, which have proven to be successful in dealing with high dimensional variable selection. First, we give a brief overview of the traditional model selection methods (viz. Mallow's Cp, AIC, BIC, DIC), followed by a discussion on some recently developed methods (viz. EBIC, regularization), which have occupied the minds of many statisticians. Then, we review high dimensional Bayesian methods with a particular emphasis on Bayesian regularization methods, which have been used extensively in recent years. We conclude by briefly addressing the asymptotic behaviors of Bayesian variable selection methods for high dimensional linear models under different regularity conditions.
Oliver-Rodríguez, B; Zafra-Gómez, A; Reis, M S; Duarte, B P M; Verge, C; de Ferrer, J A; Pérez-Pascual, M; Vílchez, J L
2015-07-01
The behaviour of Linear Alkylbenzene Sulfonate (LAS) in agricultural soil is investigated in the laboratory using continuous-flow soil column studies in order to simultaneously analyze the three main underlying phenomena (adsorption/desorption, degradation and transport). The continuous-flow soil column experiments generated the breakthrough curves for each LAS homologue, C10, C11, C12 and C13, and by adding them up, for total LAS, from which the relevant retention, degradation and transport parameters could be estimated, after proposing adequate models. Several transport equations were considered, including the degradation of the sorbate in solution and its retention by soil, under equilibrium and non-equilibrium conditions between the sorbent and the sorbate. In general, the results obtained for the estimates of those parameters that were common to the various models studied (such as the isotherm slope, first order degradation rate coefficient and the hydrodynamic dispersion coefficient) were rather consistent, meaning that mass transfer limitations are not playing a major role in the experiments. These three parameters increase with the length of the LAS homologue chain. The study will provide the underlying conceptual framework and fundamental parameters to understand, simulate and predict the environmental behaviour of LAS compounds in agricultural soils. PMID:25765258
Oliver-Rodríguez, B; Zafra-Gómez, A; Reis, M S; Duarte, B P M; Verge, C; de Ferrer, J A; Pérez-Pascual, M; Vílchez, J L
2015-07-01
The behaviour of Linear Alkylbenzene Sulfonate (LAS) in agricultural soil is investigated in the laboratory using continuous-flow soil column studies in order to simultaneously analyze the three main underlying phenomena (adsorption/desorption, degradation and transport). The continuous-flow soil column experiments generated the breakthrough curves for each LAS homologue, C10, C11, C12 and C13, and by adding them up, for total LAS, from which the relevant retention, degradation and transport parameters could be estimated, after proposing adequate models. Several transport equations were considered, including the degradation of the sorbate in solution and its retention by soil, under equilibrium and non-equilibrium conditions between the sorbent and the sorbate. In general, the results obtained for the estimates of those parameters that were common to the various models studied (such as the isotherm slope, first order degradation rate coefficient and the hydrodynamic dispersion coefficient) were rather consistent, meaning that mass transfer limitations are not playing a major role in the experiments. These three parameters increase with the length of the LAS homologue chain. The study will provide the underlying conceptual framework and fundamental parameters to understand, simulate and predict the environmental behaviour of LAS compounds in agricultural soils.
Linear Deterministic Accumulator Models of Simple Choice
Heathcote, Andrew; Love, Jonathon
2012-01-01
We examine theories of simple choice as a race among evidence accumulation processes. We focus on the class of deterministic race models, which assume that the effects of fluctuations in the parameters of the accumulation processes between-choice trials (between-choice noise) dominate the effects of fluctuations occurring while making a choice (within-choice noise) in behavioral data (i.e., response times and choices). The latter deterministic approximation, when combined with the assumption that accumulation is linear, leads to a class of models that can be readily applied to simple-choice behavior because they are computationally tractable. We develop a new and mathematically simple exemplar within the class of linear deterministic models, the Lognormal race (LNR). We then examine how the LNR, and another widely applied linear deterministic model, Brown and Heathcote’s (2008) LBA, account for a range of benchmark simple-choice effects in lexical-decision task data reported by Wagenmakers et al. (2008). Our results indicate that the LNR provides an accurate description of this data. Although the LBA model provides a slightly better account, both models support similar psychological conclusions. PMID:22936920
Linear Mixed Models: Gum and Beyond
NASA Astrophysics Data System (ADS)
Arendacká, Barbora; Täubner, Angelika; Eichstädt, Sascha; Bruns, Thomas; Elster, Clemens
2014-04-01
In Annex H.5, the Guide to the Evaluation of Uncertainty in Measurement (GUM) [1] recognizes the necessity to analyze certain types of experiments by applying random effects ANOVA models. These belong to the more general family of linear mixed models that we focus on in the current paper. Extending the short introduction provided by the GUM, our aim is to show that the more general, linear mixed models cover a wider range of situations occurring in practice and can be beneficial when employed in data analysis of long-term repeated experiments. Namely, we point out their potential as an aid in establishing an uncertainty budget and as means for gaining more insight into the measurement process. We also comment on computational issues and to make the explanations less abstract, we illustrate all the concepts with the help of a measurement campaign conducted in order to challenge the uncertainty budget in calibration of accelerometers.
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R(2)) indicates the importance of independent variables in the outcome.
On the Limit Cycles for a Class of Continuous Piecewise Linear Differential Systems with Three Zones
NASA Astrophysics Data System (ADS)
Lima, Maurício Firmino Silva; Pessoa, Claudio; Pereira, Weber F.
Lima and Llibre [2012] have studied a class of planar continuous piecewise linear vector fields with three zones. Using the Poincaré map, they proved that this class admits always a unique limit cycle, which is hyperbolic. The class studied in [Lima & Llibre, 2012] belongs to a larger set of planar continuous piecewise linear vector fields with three zones that can be separated into four other classes. Here, we consider some of these classes and we prove that some of them always admit a unique limit cycle, which is hyperbolic. However we find a class that does not have limit cycles.
User's manual for LINEAR, a FORTRAN program to derive linear aircraft models
NASA Technical Reports Server (NTRS)
Duke, Eugene L.; Patterson, Brian P.; Antoniewicz, Robert F.
1987-01-01
This report documents a FORTRAN program that provides a powerful and flexible tool for the linearization of aircraft models. The program LINEAR numerically determines a linear system model using nonlinear equations of motion and a user-supplied nonlinear aerodynamic model. The system model determined by LINEAR consists of matrices for both state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model.
Synaptic dynamics: linear model and adaptation algorithm.
Yousefi, Ali; Dibazar, Alireza A; Berger, Theodore W
2014-08-01
In this research, temporal processing in brain neural circuitries is addressed by a dynamic model of synaptic connections in which the synapse model accounts for both pre- and post-synaptic processes determining its temporal dynamics and strength. Neurons, which are excited by the post-synaptic potentials of hundred of the synapses, build the computational engine capable of processing dynamic neural stimuli. Temporal dynamics in neural models with dynamic synapses will be analyzed, and learning algorithms for synaptic adaptation of neural networks with hundreds of synaptic connections are proposed. The paper starts by introducing a linear approximate model for the temporal dynamics of synaptic transmission. The proposed linear model substantially simplifies the analysis and training of spiking neural networks. Furthermore, it is capable of replicating the synaptic response of the non-linear facilitation-depression model with an accuracy better than 92.5%. In the second part of the paper, a supervised spike-in-spike-out learning rule for synaptic adaptation in dynamic synapse neural networks (DSNN) is proposed. The proposed learning rule is a biologically plausible process, and it is capable of simultaneously adjusting both pre- and post-synaptic components of individual synapses. The last section of the paper starts with presenting the rigorous analysis of the learning algorithm in a system identification task with hundreds of synaptic connections which confirms the learning algorithm's accuracy, repeatability and scalability. The DSNN is utilized to predict the spiking activity of cortical neurons and pattern recognition tasks. The DSNN model is demonstrated to be a generative model capable of producing different cortical neuron spiking patterns and CA1 Pyramidal neurons recordings. A single-layer DSNN classifier on a benchmark pattern recognition task outperforms a 2-Layer Neural Network and GMM classifiers while having fewer numbers of free parameters and
Linear transport models for adsorbing solutes
NASA Astrophysics Data System (ADS)
Roth, K.; Jury, W. A.
1993-04-01
A unified linear theory for the transport of adsorbing solutes through soils is presented and applied to analyze movement of napropamide through undisturbed soil columns. The transport characteristics of the soil are expressed in terms of the travel time distribution of the mobile phase which is then used to incorporate local interaction processes. This approach permits the analysis of all linear transport processes, not only the small subset for which a differential description is known. From a practical point of view, it allows the direct use of measured concentrations or fluxes of conservative solutes to characterize the mobile phase without first subjecting them to any model. For complicated flow regimes, this may vastly improve the identification of models and estimation of their parameters for the local adsorption processes.
Comparing the Discrete and Continuous Logistic Models
ERIC Educational Resources Information Center
Gordon, Sheldon P.
2008-01-01
The solutions of the discrete logistic growth model based on a difference equation and the continuous logistic growth model based on a differential equation are compared and contrasted. The investigation is conducted using a dynamic interactive spreadsheet. (Contains 5 figures.)
B-737 Linear Autoland Simulink Model
NASA Technical Reports Server (NTRS)
Belcastro, Celeste (Technical Monitor); Hogge, Edward F.
2004-01-01
The Linear Autoland Simulink model was created to be a modular test environment for testing of control system components in commercial aircraft. The input variables, physical laws, and referenced frames used are summarized. The state space theory underlying the model is surveyed and the location of the control actuators described. The equations used to realize the Dryden gust model to simulate winds and gusts are derived. A description of the pseudo-random number generation method used in the wind gust model is included. The longitudinal autopilot, lateral autopilot, automatic throttle autopilot, engine model and automatic trim devices are considered as subsystems. The experience in converting the Airlabs FORTRAN aircraft control system simulation to a graphical simulation tool (Matlab/Simulink) is described.
Drawbacks of using linear mixture modeling on hyperspectral images
NASA Astrophysics Data System (ADS)
Rodricks, Neena; Kirkland, Laurel E.
2004-10-01
Hyperspectral spectroscopy can be used remotely to measure emitted radiation from minerals and rocks at a series of narrow and continuous wavelength bands resulting in a continuous spectrum for each pixel, thereby providing ample spectral information to identify and distinguish spectrally unique materials. Linear mixture modeling ("spectral unmixing"), a commonly used method, is based on the theory that the radiance in the thermal infrared region (8-12 μm) from a multi-mineral surface can be modeled as a linear combination of the endmembers. A linear mixture model can thus potentially model the minerals present on planetary surfaces. It works by scaling the endmember spectra so that the sum of the scaled endmember spectra matches the measured spectrum with the smallest "error" (difference). But one of the drawbacks of this established method is that mathematically, a fit with an inverted spectrum is valid, which effectively returns a negative abundance of a material. Current models usually address the problem by elimination of endmembers that have negative scale factors. Eliminating the negative abundance problem is not a major issue when the endmembers are known. However, identifying unknown target composition (like on Mars) can be a problem. The goal of this study is to improve the understanding and find a subsequent solution of the negative abundance problem for Mars analog field data obtained from airborne and ground spectrometers. We are using a well-defined library of spectra to test the accuracy of hyperspectral analysis for the identification of minerals on planetary surfaces.
Reduced Order ODE Model for Linear Contrails
NASA Astrophysics Data System (ADS)
Inamdar, A. R.; Lele, S. K.; Jacobson, M. Z.
2015-12-01
It is widely acknowledged that the large uncertainties in predictions of climate impact of linear contrails stem from inadequate parametrization of contrails in GCMs. But, the parameter space on which contrail dynamics and optical properties depend is very large and spanning it using high fidelity LES is prohibitively expensive. This study leverages the large dataset of LES done so far to understand the most important physical process that governs the evolution of contrails in different stages of its life and proposes a simple, low-cost and robust ODE model to capture the evolution of quantities of interest such as ice mass, vortex downwash and contrail cross-sectional dimensions. A direct consequence of modeling the contrail using parameters impacting the most important physical process is the reduction of the original parameter space to only those groupings of parameters that impact linear contrails independently. We are able to capture the most prominent features of the contrail at every stage of the life of the contrail - the induction of the jet exhaust by the trailing vortex pair, the vortex downwash and eventual destruction and the subsequent spreading of the contrail by ambient turbulence. A simplified version of GATOR-GCMOM - a GCM - is initialized using inputs from the new ODE model to test if the inclusion of the impact of the aforementioned parameter groups has significant persistent effects. Results from the GATOR-GCMOM box model calculations show which parameter groupings show persistent effects.
ATOPS B-737 inner-loop control system linear model construction and verification
NASA Technical Reports Server (NTRS)
Broussard, J. R.
1983-01-01
Nonlinear models and block diagrams of an inner-loop control system for the ATOPS B-737 Research Aircraft are presented. Continuous time linear model representations of the nonlinear inner-loop control systems are derived. Closed-loop aircraft simulations comparing nonlinear and linear dynamic responses to step inputs are used to verify the inner-loop control system models.
Modeling pan evaporation for Kuwait by multiple linear regression.
Almedeij, Jaber
2012-01-01
Evaporation is an important parameter for many projects related to hydrology and water resources systems. This paper constitutes the first study conducted in Kuwait to obtain empirical relations for the estimation of daily and monthly pan evaporation as functions of available meteorological data of temperature, relative humidity, and wind speed. The data used here for the modeling are daily measurements of substantial continuity coverage, within a period of 17 years between January 1993 and December 2009, which can be considered representative of the desert climate of the urban zone of the country. Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. The evaporation models suggested with the best variable combinations were shown to produce results that are in a reasonable agreement with observation values.
User's manual for interactive LINEAR: A FORTRAN program to derive linear aircraft models
NASA Technical Reports Server (NTRS)
Antoniewicz, Robert F.; Duke, Eugene L.; Patterson, Brian P.
1988-01-01
An interactive FORTRAN program that provides the user with a powerful and flexible tool for the linearization of aircraft aerodynamic models is documented in this report. The program LINEAR numerically determines a linear system model using nonlinear equations of motion and a user-supplied linear or nonlinear aerodynamic model. The nonlinear equations of motion used are six-degree-of-freedom equations with stationary atmosphere and flat, nonrotating earth assumptions. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model.
Estimating population trends with a linear model
Bart, J.; Collins, B.; Morrison, R.I.G.
2003-01-01
We describe a simple and robust method for estimating trends in population size. The method may be used with Breeding Bird Survey data, aerial surveys, point counts, or any other program of repeated surveys at permanent locations. Surveys need not be made at each location during each survey period. The method differs from most existing methods in being design based, rather than model based. The only assumptions are that the nominal sampling plan is followed and that sample size is large enough for use of the t-distribution. Simulations based on two bird data sets from natural populations showed that the point estimate produced by the linear model was essentially unbiased even when counts varied substantially and 25% of the complete data set was missing. The estimating-equation approach, often used to analyze Breeding Bird Survey data, performed similarly on one data set but had substantial bias on the second data set, in which counts were highly variable. The advantages of the linear model are its simplicity, flexibility, and that it is self-weighting. A user-friendly computer program to carry out the calculations is available from the senior author.
Linear modelling of attentional resource allocation
NASA Technical Reports Server (NTRS)
Pierce, B.
1978-01-01
Eight subjects time-shared performance of two compensatory tracking tasks under conditions when both were of constant difficulty, and when the control order of one task (designated primary) was varied over time within a trial. On line performance feedback was presented on half of the trials. The data are interpreted in terms of a linear model of the operator's attention allocation system, and suggest that this allocation is strongly suboptimal. Furthermore, the limitations in reallocating attentional resources between tasks, in response to difficulty fluctuations were not reduced by augmented performance feedback. Some characteristics of the allocation system are described, and reasons for its limitations suggested.
The Piecewise Linear Reactive Flow Rate Model
Vitello, P; Souers, P C
2005-07-22
Conclusions are: (1) Early calibrations of the Piece Wise Linear reactive flow model have shown that it allows for very accurate agreement with data for a broad range of detonation wave strengths. (2) The ability to vary the rate at specific pressures has shown that corner turning involves competition between the strong wave that travels roughly in a straight line and growth at low pressure of a new wave that turns corners sharply. (3) The inclusion of a low pressure de-sensitization rate is essential to preserving the dead zone at large times as is observed.
The Piece Wise Linear Reactive Flow Model
Vitello, P; Souers, P C
2005-08-18
For non-ideal explosives a wide range of behavior is observed in experiments dealing with differing sizes and geometries. A predictive detonation model must be able to reproduce many phenomena including such effects as: variations in the detonation velocity with the radial diameter of rate sticks; slowing of the detonation velocity around gentle corners; production of dead zones for abrupt corner turning; failure of small diameter rate sticks; and failure for rate sticks with sufficiently wide cracks. Most models have been developed to explain one effect at a time. Often, changes are made in the input parameters used to fit each succeeding case with the implication that this is sufficient for the model to be valid over differing regimes. We feel that it is important to develop a model that is able to fit experiments with one set of parameters. To address this we are creating a new generation of models that are able to produce better fitting to individual data sets than prior models and to simultaneous fit distinctly different regimes of experiments. Presented here are details of our new Piece Wise Linear reactive flow model applied to LX-17.
Model Selection with the Linear Mixed Model for Longitudinal Data
ERIC Educational Resources Information Center
Ryoo, Ji Hoon
2011-01-01
Model building or model selection with linear mixed models (LMMs) is complicated by the presence of both fixed effects and random effects. The fixed effects structure and random effects structure are codependent, so selection of one influences the other. Most presentations of LMM in psychology and education are based on a multilevel or…
A study on the fabrication of main scale of linear encoder using continuous roller imprint method
NASA Astrophysics Data System (ADS)
Fan, Shanjin; Shi, Yongsheng; Yin, Lei; Feng, Long; Liu, Hongzhong
2013-10-01
Linear encoder composed of main and index scales has an extensive application in the field of modern precision measurement. The main scale is the key component of linear encoder as measuring basis. In this article, the continuous roller imprint technology is applied to the manufacturing of the main scale, this method can realize the high efficiency and low cost manufacturing of the ultra-long main scale. By means of the plastic deformation of the soft metal film substrate, the grating microstructure on the surface of the cylinder mold is replicated to the soft metal film substrate directly. Through the high precision control of continuous rotational motion of the mold, ultra-long high precision grating microstructure is obtained. This paper mainly discusses the manufacturing process of the high precision cylinder mold and the effects of the roller imprint pressure and roller rotation speed on the imprint replication quality. The above process parameters were optimized to manufacture the high quality main scale. At last, the reading test of a linear encoder contains the main scale made by the above method was conducted to evaluate its measurement accuracy, the result demonstrated the feasibility of the continuous roller imprint method.
A Linear Variable-[theta] Model for Measuring Individual Differences in Response Precision
ERIC Educational Resources Information Center
Ferrando, Pere J.
2011-01-01
Models for measuring individual response precision have been proposed for binary and graded responses. However, more continuous formats are quite common in personality measurement and are usually analyzed with the linear factor analysis model. This study extends the general Gaussian person-fluctuation model to the continuous-response case and…
Distributed static linear Gaussian models using consensus.
Belanovic, Pavle; Valcarcel Macua, Sergio; Zazo, Santiago
2012-10-01
Algorithms for distributed agreement are a powerful means for formulating distributed versions of existing centralized algorithms. We present a toolkit for this task and show how it can be used systematically to design fully distributed algorithms for static linear Gaussian models, including principal component analysis, factor analysis, and probabilistic principal component analysis. These algorithms do not rely on a fusion center, require only low-volume local (1-hop neighborhood) communications, and are thus efficient, scalable, and robust. We show how they are also guaranteed to asymptotically converge to the same solution as the corresponding existing centralized algorithms. Finally, we illustrate the functioning of our algorithms on two examples, and examine the inherent cost-performance trade-off.
Numerical linearized MHD model of flapping oscillations
NASA Astrophysics Data System (ADS)
Korovinskiy, D. B.; Ivanov, I. B.; Semenov, V. S.; Erkaev, N. V.; Kiehas, S. A.
2016-06-01
Kink-like magnetotail flapping oscillations in a Harris-like current sheet with earthward growing normal magnetic field component Bz are studied by means of time-dependent 2D linearized MHD numerical simulations. The dispersion relation and two-dimensional eigenfunctions are obtained. The results are compared with analytical estimates of the double-gradient model, which are found to be reliable for configurations with small Bz up to values ˜ 0.05 of the lobe magnetic field. Coupled with previous results, present simulations confirm that the earthward/tailward growth direction of the Bz component acts as a switch between stable/unstable regimes of the flapping mode, while the mode dispersion curve is the same in both cases. It is confirmed that flapping oscillations may be triggered by a simple Gaussian initial perturbation of the Vz velocity.
Testing Linear Models for Ability Parameters in Item Response Models
ERIC Educational Resources Information Center
Glas, Cees A. W.; Hendrawan, Irene
2005-01-01
Methods for testing hypotheses concerning the regression parameters in linear models for the latent person parameters in item response models are presented. Three tests are outlined: A likelihood ratio test, a Lagrange multiplier test and a Wald test. The tests are derived in a marginal maximum likelihood framework. They are explicitly formulated…
A Rasch Model for Continuous Ratings.
ERIC Educational Resources Information Center
Muller, Hans
1987-01-01
A unidimensional latent trait model for continuous ratings extends Andrich's rating formulation which assumes the response process at latent thresholds is based on the dichotomous Rasch model. The separability of the structural and incidental parameters is demonstrated and a procedure for estimating the parameters is outlined. (Author/GDC)
ERIC Educational Resources Information Center
Kane, Michael T.; Mroch, Andrew A.; Suh, Youngsuk; Ripkey, Douglas R.
2009-01-01
This paper analyzes five linear equating models for the "nonequivalent groups with anchor test" (NEAT) design with internal anchors (i.e., the anchor test is part of the full test). The analysis employs a two-dimensional framework. The first dimension contrasts two general approaches to developing the equating relationship. Under a "parameter…
From linear to generalized linear mixed models: A case study in repeated measures
Technology Transfer Automated Retrieval System (TEKTRAN)
Compared to traditional linear mixed models, generalized linear mixed models (GLMMs) can offer better correspondence between response variables and explanatory models, yielding more efficient estimates and tests in the analysis of data from designed experiments. Using proportion data from a designed...
Linear functional minimization for inverse modeling
Barajas-Solano, David A.; Wohlberg, Brendt Egon; Vesselinov, Velimir Valentinov; Tartakovsky, Daniel M.
2015-06-01
In this paper, we present a novel inverse modeling strategy to estimate spatially distributed parameters of nonlinear models. The maximum a posteriori (MAP) estimators of these parameters are based on a likelihood functional, which contains spatially discrete measurements of the system parameters and spatiotemporally discrete measurements of the transient system states. The piecewise continuity prior for the parameters is expressed via Total Variation (TV) regularization. The MAP estimator is computed by minimizing a nonquadratic objective equipped with the TV operator. We apply this inversion algorithm to estimate hydraulic conductivity of a synthetic confined aquifer from measurements of conductivity and hydraulic head. The synthetic conductivity field is composed of a low-conductivity heterogeneous intrusion into a high-conductivity heterogeneous medium. Our algorithm accurately reconstructs the location, orientation, and extent of the intrusion from the steady-state data only. Finally, addition of transient measurements of hydraulic head improves the parameter estimation, accurately reconstructing the conductivity field in the vicinity of observation locations.
Linear theory for filtering nonlinear multiscale systems with model error
Berry, Tyrus; Harlim, John
2014-01-01
In this paper, we study filtering of multiscale dynamical systems with model error arising from limitations in resolving the smaller scale processes. In particular, the analysis assumes the availability of continuous-time noisy observations of all components of the slow variables. Mathematically, this paper presents new results on higher order asymptotic expansion of the first two moments of a conditional measure. In particular, we are interested in the application of filtering multiscale problems in which the conditional distribution is defined over the slow variables, given noisy observation of the slow variables alone. From the mathematical analysis, we learn that for a continuous time linear model with Gaussian noise, there exists a unique choice of parameters in a linear reduced model for the slow variables which gives the optimal filtering when only the slow variables are observed. Moreover, these parameters simultaneously give the optimal equilibrium statistical estimates of the underlying system, and as a consequence they can be estimated offline from the equilibrium statistics of the true signal. By examining a nonlinear test model, we show that the linear theory extends in this non-Gaussian, nonlinear configuration as long as we know the optimal stochastic parametrization and the correct observation model. However, when the stochastic parametrization model is inappropriate, parameters chosen for good filter performance may give poor equilibrium statistical estimates and vice versa; this finding is based on analytical and numerical results on our nonlinear test model and the two-layer Lorenz-96 model. Finally, even when the correct stochastic ansatz is given, it is imperative to estimate the parameters simultaneously and to account for the nonlinear feedback of the stochastic parameters into the reduced filter estimates. In numerical experiments on the two-layer Lorenz-96 model, we find that the parameters estimated online, as part of a filtering procedure
Non linear behaviour of cell tensegrity models
NASA Astrophysics Data System (ADS)
Alippi, A.; Bettucci, A.; Biagioni, A.; Conclusio, D.; D'Orazio, A.; Germano, M.; Passeri, D.
2012-05-01
Tensegrity models for the cytoskeleton structure of living cells is largely used nowadays for interpreting the biochemical response of living tissues to mechanical stresses. Microtubules, microfilaments and filaments are the microscopic cell counterparts of struts (microtubules) and cables (microfilaments and filaments) in the macroscopic world: the formers oppose to compression, the latters to tension, thus yielding an overall structure, light and highly deformable. Specific cell surface receptors, such as integrins, act as the coupling elements that transmit the outside mechanical stress state into the cell body. Reversible finite deformations of tensegrity structures have been widely demonstrated experimentally and in a number of living cell simulations. In the present paper, the bistability behaviour of two general models, the linear bar oscillator and the icosahedron, is studied, as they are both obtained from mathematical simulation, the former, and from larger scale experiments, the latter. The discontinuity in the frequency response of the oscillation amplitude and the lateral bending of the resonance curves are put in evidence, as it grows larger as the driving amplitude increases, respectively.
NASA Astrophysics Data System (ADS)
Guo, Ying; Lv, Geli; Zeng, Guihua
2015-11-01
We show that the tolerable excess noise can be dynamically balanced in source preparation while inserting a tunable linear optics cloning machine (LOCM) for balancing the secret key rate and the maximal transmission distance of continuous-variable quantum key distribution (CVQKD). The intensities of source noise are sensitive to the tunable LOCM and can be stabilized to the suitable values to eliminate the impact of channel noise and defeat the potential attacks even in the case of the degenerated linear optics amplifier (LOA). The LOCM-additional noise can be elegantly employed by the reference partner of reconciliation to regulate the secret key rate and the transmission distance. Simulation results show that there is a considerable improvement in the secret key rate of the LOCM-based CVQKD while providing a tunable LOCM for source preparation with the specified parameters in suitable ranges.
Controlling Continuous-Variable Quantum Key Distribution with Tuned Linear Optics Cloning Machines
NASA Astrophysics Data System (ADS)
Guo, Ying; Qiu, Deli; Huang, Peng; Zeng, Guihua
2015-09-01
We show that the tolerable excess noise can be elegantly controlled while inserting a tunable linear optics cloning machine (LOCM) for continuous-variable key distribution (CVQKD). The LOCM-tuned noise can be stabilized to an optimal value by the reference partner of reconciliation to guarantee the high secret key rate. Simulation results show that there is a considerable improvement of the performance for the LOCM-based CVQKD protocol in terms of the secret rate while making a fine balance between the secret key rate and the transmission distance with the dynamically tuned parameters in suitable ranges.
A FORTRAN program for the analysis of linear continuous and sample-data systems
NASA Technical Reports Server (NTRS)
Edwards, J. W.
1976-01-01
A FORTRAN digital computer program which performs the general analysis of linearized control systems is described. State variable techniques are used to analyze continuous, discrete, and sampled data systems. Analysis options include the calculation of system eigenvalues, transfer functions, root loci, root contours, frequency responses, power spectra, and transient responses for open- and closed-loop systems. A flexible data input format allows the user to define systems in a variety of representations. Data may be entered by inputing explicit data matrices or matrices constructed in user written subroutines, by specifying transfer function block diagrams, or by using a combination of these methods.
Linearized Functional Minimization for Inverse Modeling
Wohlberg, Brendt; Tartakovsky, Daniel M.; Dentz, Marco
2012-06-21
Heterogeneous aquifers typically consist of multiple lithofacies, whose spatial arrangement significantly affects flow and transport. The estimation of these lithofacies is complicated by the scarcity of data and by the lack of a clear correlation between identifiable geologic indicators and attributes. We introduce a new inverse-modeling approach to estimate both the spatial extent of hydrofacies and their properties from sparse measurements of hydraulic conductivity and hydraulic head. Our approach is to minimize a functional defined on the vectors of values of hydraulic conductivity and hydraulic head fields defined on regular grids at a user-determined resolution. This functional is constructed to (i) enforce the relationship between conductivity and heads provided by the groundwater flow equation, (ii) penalize deviations of the reconstructed fields from measurements where they are available, and (iii) penalize reconstructed fields that are not piece-wise smooth. We develop an iterative solver for this functional that exploits a local linearization of the mapping from conductivity to head. This approach provides a computationally efficient algorithm that rapidly converges to a solution. A series of numerical experiments demonstrates the robustness of our approach.
Permutation inference for the general linear model
Winkler, Anderson M.; Ridgway, Gerard R.; Webster, Matthew A.; Smith, Stephen M.; Nichols, Thomas E.
2014-01-01
Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios. We also demonstrate how the inference on glm parameters, originally intended for independent data, can be used in certain special but useful cases in which independence is violated. Detailed examples of common neuroimaging applications are provided, as well as a complete algorithm – the “randomise” algorithm – for permutation inference with the glm. PMID:24530839
Misaligned Image Integration With Local Linear Model.
Baba, Tatsuya; Matsuoka, Ryo; Shirai, Keiichiro; Okuda, Masahiro
2016-05-01
We present a new image integration technique for a flash and long-exposure image pair to capture a dark scene without incurring blurring or noisy artifacts. Most existing methods require well-aligned images for the integration, which is often a burdensome restriction in practical use. We address this issue by locally transferring the colors of the flash images using a small fraction of the corresponding pixels in the long-exposure images. We formulate the image integration as a convex optimization problem with the local linear model. The proposed method makes it possible to integrate the color of the long-exposure image with the detail of the flash image without causing any harmful effects to its contrast, where we do not need perfect alignment between the images by virtue of our new integration principle. We show that our method successfully outperforms the state of the art in the image integration and reference-based color transfer for challenging misaligned data sets.
A Linear Programming Model for Assigning Students to Attendance Centers.
ERIC Educational Resources Information Center
Ontjes, Robert L.
A linear programing model and procedures for optimal assignment of students to attendance centers are presented. An example of the use of linear programing for the assignment of students to attendance centers in a particular school district is given. (CK)
Linear functional minimization for inverse modeling
Barajas-Solano, David A.; Wohlberg, Brendt Egon; Vesselinov, Velimir Valentinov; Tartakovsky, Daniel M.
2015-06-01
In this paper, we present a novel inverse modeling strategy to estimate spatially distributed parameters of nonlinear models. The maximum a posteriori (MAP) estimators of these parameters are based on a likelihood functional, which contains spatially discrete measurements of the system parameters and spatiotemporally discrete measurements of the transient system states. The piecewise continuity prior for the parameters is expressed via Total Variation (TV) regularization. The MAP estimator is computed by minimizing a nonquadratic objective equipped with the TV operator. We apply this inversion algorithm to estimate hydraulic conductivity of a synthetic confined aquifer from measurements of conductivity and hydraulicmore » head. The synthetic conductivity field is composed of a low-conductivity heterogeneous intrusion into a high-conductivity heterogeneous medium. Our algorithm accurately reconstructs the location, orientation, and extent of the intrusion from the steady-state data only. Finally, addition of transient measurements of hydraulic head improves the parameter estimation, accurately reconstructing the conductivity field in the vicinity of observation locations.« less
Continuous utility factor in segregation models
NASA Astrophysics Data System (ADS)
Roy, Parna; Sen, Parongama
2016-02-01
We consider the constrained Schelling model of social segregation in which the utility factor of agents strictly increases and nonlocal jumps of the agents are allowed. In the present study, the utility factor u is defined in a way such that it can take continuous values and depends on the tolerance threshold as well as the fraction of unlike neighbors. Two models are proposed: in model A the jump probability is determined by the sign of u only, which makes it equivalent to the discrete model. In model B the actual values of u are considered. Model A and model B are shown to differ drastically as far as segregation behavior and phase transitions are concerned. In model A, although segregation can be achieved, the cluster sizes are rather small. Also, a frozen state is obtained in which steady states comprise many unsatisfied agents. In model B, segregated states with much larger cluster sizes are obtained. The correlation function is calculated to show quantitatively that larger clusters occur in model B. Moreover for model B, no frozen states exist even for very low dilution and small tolerance parameter. This is in contrast to the unconstrained discrete model considered earlier where agents can move even when utility remains the same. In addition, we also consider a few other dynamical aspects which have not been studied in segregation models earlier.
Stochastic string models with continuous semimartingales
NASA Astrophysics Data System (ADS)
Bueno-Guerrero, Alberto; Moreno, Manuel; Navas, Javier F.
2015-09-01
This paper reformulates the stochastic string model of Santa-Clara and Sornette using stochastic calculus with continuous semimartingales. We present some new results, such as: (a) the dynamics of the short-term interest rate, (b) the PDE that must be satisfied by the bond price, and (c) an analytic expression for the price of a European bond call option. Additionally, we clarify some important features of the stochastic string model and show its relevance to price derivatives and the equivalence with an infinite dimensional HJM model to price European options.
Some Statistics for Assessing Person-Fit Based on Continuous-Response Models
ERIC Educational Resources Information Center
Ferrando, Pere Joan
2010-01-01
This article proposes several statistics for assessing individual fit based on two unidimensional models for continuous responses: linear factor analysis and Samejima's continuous response model. Both models are approached using a common framework based on underlying response variables and are formulated at the individual level as fixed regression…
Analysis of linear trade models and relation to scale economies.
Gomory, R E; Baumol, W J
1997-09-01
We discuss linear Ricardo models with a range of parameters. We show that the exact boundary of the region of equilibria of these models is obtained by solving a simple integer programming problem. We show that there is also an exact correspondence between many of the equilibria resulting from families of linear models and the multiple equilibria of economies of scale models.
Continuous-time discrete-space models for animal movement
Hanks, Ephraim M.; Hooten, Mevin B.; Alldredge, Mat W.
2015-01-01
The processes influencing animal movement and resource selection are complex and varied. Past efforts to model behavioral changes over time used Bayesian statistical models with variable parameter space, such as reversible-jump Markov chain Monte Carlo approaches, which are computationally demanding and inaccessible to many practitioners. We present a continuous-time discrete-space (CTDS) model of animal movement that can be fit using standard generalized linear modeling (GLM) methods. This CTDS approach allows for the joint modeling of location-based as well as directional drivers of movement. Changing behavior over time is modeled using a varying-coefficient framework which maintains the computational simplicity of a GLM approach, and variable selection is accomplished using a group lasso penalty. We apply our approach to a study of two mountain lions (Puma concolor) in Colorado, USA.
Odefy -- From discrete to continuous models
2010-01-01
Background Phenomenological information about regulatory interactions is frequently available and can be readily converted to Boolean models. Fully quantitative models, on the other hand, provide detailed insights into the precise dynamics of the underlying system. In order to connect discrete and continuous modeling approaches, methods for the conversion of Boolean systems into systems of ordinary differential equations have been developed recently. As biological interaction networks have steadily grown in size and complexity, a fully automated framework for the conversion process is desirable. Results We present Odefy, a MATLAB- and Octave-compatible toolbox for the automated transformation of Boolean models into systems of ordinary differential equations. Models can be created from sets of Boolean equations or graph representations of Boolean networks. Alternatively, the user can import Boolean models from the CellNetAnalyzer toolbox, GINSim and the PBN toolbox. The Boolean models are transformed to systems of ordinary differential equations by multivariate polynomial interpolation and optional application of sigmoidal Hill functions. Our toolbox contains basic simulation and visualization functionalities for both, the Boolean as well as the continuous models. For further analyses, models can be exported to SQUAD, GNA, MATLAB script files, the SB toolbox, SBML and R script files. Odefy contains a user-friendly graphical user interface for convenient access to the simulation and exporting functionalities. We illustrate the validity of our transformation approach as well as the usage and benefit of the Odefy toolbox for two biological systems: a mutual inhibitory switch known from stem cell differentiation and a regulatory network giving rise to a specific spatial expression pattern at the mid-hindbrain boundary. Conclusions Odefy provides an easy-to-use toolbox for the automatic conversion of Boolean models to systems of ordinary differential equations. It can be
Fault diagnosis based on continuous simulation models
NASA Technical Reports Server (NTRS)
Feyock, Stefan
1987-01-01
The results are described of an investigation of techniques for using continuous simulation models as basis for reasoning about physical systems, with emphasis on the diagnosis of system faults. It is assumed that a continuous simulation model of the properly operating system is available. Malfunctions are diagnosed by posing the question: how can we make the model behave like that. The adjustments that must be made to the model to produce the observed behavior usually provide definitive clues to the nature of the malfunction. A novel application of Dijkstra's weakest precondition predicate transformer is used to derive the preconditions for producing the required model behavior. To minimize the size of the search space, an envisionment generator based on interval mathematics was developed. In addition to its intended application, the ability to generate qualitative state spaces automatically from quantitative simulations proved to be a fruitful avenue of investigation in its own right. Implementations of the Dijkstra transform and the envisionment generator are reproduced in the Appendix.
Mathematical Models of Continuous Flow Electrophoresis
NASA Technical Reports Server (NTRS)
Saville, D. A.; Snyder, R. S.
1985-01-01
Development of high resolution continuous flow electrophoresis devices ultimately requires comprehensive understanding of the ways various phenomena and processes facilitate or hinder separation. A comprehensive model of the actual three dimensional flow, temperature and electric fields was developed to provide guidance in the design of electrophoresis chambers for specific tasks and means of interpreting test data on a given chamber. Part of the process of model development includes experimental and theoretical studies of hydrodynamic stability. This is necessary to understand the origin of mixing flows observed with wide gap gravitational effects. To insure that the model accurately reflects the flow field and particle motion requires extensive experimental work. Another part of the investigation is concerned with the behavior of concentrated sample suspensions with regard to sample stream stability particle-particle interactions which might affect separation in an electric field, especially at high field strengths. Mathematical models will be developed and tested to establish the roles of the various interactions.
Tried and True: Springing into Linear Models
ERIC Educational Resources Information Center
Darling, Gerald
2012-01-01
In eighth grade, students usually learn about forces in science class and linear relationships in math class, crucial topics that form the foundation for further study in science and engineering. An activity that links these two fundamental concepts involves measuring the distance a spring stretches as a function of how much weight is suspended…
Three-Dimensional Modeling in Linear Regression.
ERIC Educational Resources Information Center
Herman, James D.
Linear regression examines the relationship between one or more independent (predictor) variables and a dependent variable. By using a particular formula, regression determines the weights needed to minimize the error term for a given set of predictors. With one predictor variable, the relationship between the predictor and the dependent variable…
Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis
ERIC Educational Resources Information Center
Luo, Wen; Azen, Razia
2013-01-01
Dominance analysis (DA) is a method used to evaluate the relative importance of predictors that was originally proposed for linear regression models. This article proposes an extension of DA that allows researchers to determine the relative importance of predictors in hierarchical linear models (HLM). Commonly used measures of model adequacy in…
Polygenic Modeling with Bayesian Sparse Linear Mixed Models
Zhou, Xiang; Carbonetto, Peter; Stephens, Matthew
2013-01-01
Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies. These two approaches make very different assumptions, so are expected to perform well in different situations. However, in practice, for a given dataset one typically does not know which assumptions will be more accurate. Motivated by this, we consider a hybrid of the two, which we refer to as a “Bayesian sparse linear mixed model” (BSLMM) that includes both these models as special cases. We address several key computational and statistical issues that arise when applying BSLMM, including appropriate prior specification for the hyper-parameters and a novel Markov chain Monte Carlo algorithm for posterior inference. We apply BSLMM and compare it with other methods for two polygenic modeling applications: estimating the proportion of variance in phenotypes explained (PVE) by available genotypes, and phenotype (or breeding value) prediction. For PVE estimation, we demonstrate that BSLMM combines the advantages of both standard LMMs and sparse regression modeling. For phenotype prediction it considerably outperforms either of the other two methods, as well as several other large-scale regression methods previously suggested for this problem. Software implementing our method is freely available from http://stephenslab.uchicago.edu/software.html. PMID:23408905
NASA Astrophysics Data System (ADS)
Zhou, Jun; Lu, Xinbiao; Qian, Huimin
2016-09-01
The paper reports interesting but unnoticed facts about irreducibility (resp., reducibility) of Flouqet factorisations and their harmonic implication in term of controllability in finite-dimensional linear continuous-time periodic (FDLCP) systems. Reducibility and irreducibility are attributed to matrix logarithm algorithms during computing Floquet factorisations in FDLCP systems, which are a pair of essential features but remain unnoticed in the Floquet theory so far. The study reveals that reducible Floquet factorisations may bring in harmonic waves variance into the Fourier analysis of FDLCP systems that in turn may alter our interpretation of controllability when the Floquet factors are used separately during controllability testing; namely, controllability interpretation discrepancy (or simply, controllability discrepancy) may occur and must be examined whenever reducible Floquet factorisations are involved. On the contrary, when irreducible Floquet factorisations are employed, controllability interpretation discrepancy can be avoided. Examples are included to illustrate such observations.
Liu, Jian; Miller, William H.
2008-08-01
The maximum entropy analytic continuation (MEAC) method is used to extend the range of accuracy of the linearized semiclassical initial value representation (LSC-IVR)/classical Wigner approximation for real time correlation functions. The LSC-IVR provides a very effective 'prior' for the MEAC procedure since it is very good for short times, exact for all time and temperature for harmonic potentials (even for correlation functions of nonlinear operators), and becomes exact in the classical high temperature limit. This combined MEAC+LSC/IVR approach is applied here to two highly nonlinear dynamical systems, a pure quartic potential in one dimensional and liquid para-hydrogen at two thermal state points (25K and 14K under nearly zero external pressure). The former example shows the MEAC procedure to be a very significant enhancement of the LSC-IVR, for correlation functions of both linear and nonlinear operators, and especially at low temperature where semiclassical approximations are least accurate. For liquid para-hydrogen, the LSC-IVR is seen already to be excellent at T = 25K, but the MEAC procedure produces a significant correction at the lower temperature (T = 14K). Comparisons are also made to how the MEAC procedure is able to provide corrections for other trajectory-based dynamical approximations when used as priors.
Linear motor drive system for continuous-path closed-loop position control of an object
Barkman, William E.
1980-01-01
A precision numerical controlled servo-positioning system is provided for continuous closed-loop position control of a machine slide or platform driven by a linear-induction motor. The system utilizes filtered velocity feedback to provide system stability required to operate with a system gain of 100 inches/minute/0.001 inch of following error. The filtered velocity feedback signal is derived from the position output signals of a laser interferometer utilized to monitor the movement of the slide. Air-bearing slides mounted to a stable support are utilized to minimize friction and small irregularities in the slideway which would tend to introduce positioning errors. A microprocessor is programmed to read command and feedback information and converts this information into the system following error signal. This error signal is summed with the negative filtered velocity feedback signal at the input of a servo amplifier whose output serves as the drive power signal to the linear motor position control coil.
Vuori, Kaarina; Strandén, Ismo; Sevón-Aimonen, Marja-Liisa; Mäntysaari, Esa A
2006-01-01
A method based on Taylor series expansion for estimation of location parameters and variance components of non-linear mixed effects models was considered. An attractive property of the method is the opportunity for an easily implemented algorithm. Estimation of non-linear mixed effects models can be done by common methods for linear mixed effects models, and thus existing programs can be used after small modifications. The applicability of this algorithm in animal breeding was studied with simulation using a Gompertz function growth model in pigs. Two growth data sets were analyzed: a full set containing observations from the entire growing period, and a truncated time trajectory set containing animals slaughtered prematurely, which is common in pig breeding. The results from the 50 simulation replicates with full data set indicate that the linearization approach was capable of estimating the original parameters satisfactorily. However, estimation of the parameters related to adult weight becomes unstable in the case of a truncated data set.
Continuous Time Model for the Dst Index
NASA Astrophysics Data System (ADS)
Zhu, D.; Balikhin, M. A.; Billings, S. A.; Wing, S.
2004-12-01
We have used the NARMAX based system identification approach to deduce, directly from measurements of upstream magnetic field and solar wind bulk velocity, a continuous time differential relationship that governs the evolution of the Dst index. This relation has been used to deduce the analytical dependence of the ring current decay rate upon values of VBs and Dst. It is shown that this depends upon a number of previous values of the solar wind parameters and Dst. Therefore the assumption often used previously in empirical models that the magnetosphere is a first order Markov's system is not valid. We show that the continuous time relation derived can be used for a reliable forecast of the Dst index.
Modeling of linear time-varying systems by linear time-invariant systems of lower order.
NASA Technical Reports Server (NTRS)
Nosrati, H.; Meadows, H. E.
1973-01-01
A method for modeling linear time-varying differential systems by linear time-invariant systems of lower order is proposed, extending the results obtained by Bierman (1972) by resolving such qualities as the model stability, various possible models of differing dimensions, and the uniqueness or nonuniqueness of the model coefficient matrix. In addition to the advantages cited by Heffes and Sarachik (1969) and Bierman, often by modeling a subsystem of a larger system it is possible to analyze the overall system behavior more easily, with resulting savings in computation time.
Development of a Linear Stirling Model with Varying Heat Inputs
NASA Technical Reports Server (NTRS)
Regan, Timothy F.; Lewandowski, Edward J.
2007-01-01
The linear model of the Stirling system developed by NASA Glenn Research Center (GRC) has been extended to include a user-specified heat input. Previously developed linear models were limited to the Stirling convertor and electrical load. They represented the thermodynamic cycle with pressure factors that remained constant. The numerical values of the pressure factors were generated by linearizing GRC s non-linear System Dynamic Model (SDM) of the convertor at a chosen operating point. The pressure factors were fixed for that operating point, thus, the model lost accuracy if a transition to a different operating point were simulated. Although the previous linear model was used in developing controllers that manipulated current, voltage, and piston position, it could not be used in the development of control algorithms that regulated hot-end temperature. This basic model was extended to include the thermal dynamics associated with a hot-end temperature that varies over time in response to external changes as well as to changes in the Stirling cycle. The linear model described herein includes not only dynamics of the piston, displacer, gas, and electrical circuit, but also the transient effects of the heater head thermal inertia. The linear version algebraically couples two separate linear dynamic models, one model of the Stirling convertor and one model of the thermal system, through the pressure factors. The thermal system model includes heat flow of heat transfer fluid, insulation loss, and temperature drops from the heat source to the Stirling convertor expansion space. The linear model was compared to a nonlinear model, and performance was very similar. The resulting linear model can be implemented in a variety of computing environments, and is suitable for analysis with classical and state space controls analysis techniques.
An analytically linearized helicopter model with improved modeling accuracy
NASA Technical Reports Server (NTRS)
Jensen, Patrick T.; Curtiss, H. C., Jr.; Mckillip, Robert M., Jr.
1991-01-01
An analytically linearized model for helicopter flight response including rotor blade dynamics and dynamic inflow, that was recently developed, was studied with the objective of increasing the understanding, the ease of use, and the accuracy of the model. The mathematical model is described along with a description of the UH-60A Black Hawk helicopter and flight test used to validate the model. To aid in utilization of the model for sensitivity analysis, a new, faster, and more efficient implementation of the model was developed. It is shown that several errors in the mathematical modeling of the system caused a reduction in accuracy. These errors in rotor force resolution, trim force and moment calculation, and rotor inertia terms were corrected along with improvements to the programming style and documentation. Use of a trim input file to drive the model is examined. Trim file errors in blade twist, control input phase angle, coning and lag angles, main and tail rotor pitch, and uniform induced velocity, were corrected. Finally, through direct comparison of the original and corrected model responses to flight test data, the effect of the corrections on overall model output is shown.
Heat treatment modelling using strongly continuous semigroups.
Malek, Alaeddin; Abbasi, Ghasem
2015-07-01
In this paper, mathematical simulation of bioheat transfer phenomenon within the living tissue is studied using the thermal wave model. Three different sources that have therapeutic applications in laser surgery, cornea laser heating and cancer hyperthermia are used. Spatial and transient heating source, on the skin surface and inside biological body, are considered by using step heating, sinusoidal and constant heating. Mathematical simulations describe a non-Fourier process. Exact solution for the corresponding non-Fourier bioheat transfer model that has time lag in its heat flux is proposed using strongly continuous semigroup theory in conjunction with variational methods. The abstract differential equation, infinitesimal generator and corresponding strongly continuous semigroup are proposed. It is proved that related semigroup is a contraction semigroup and is exponentially stable. Mathematical simulations are done for skin burning and thermal therapy in 10 different models and the related solutions are depicted. Unlike numerical solutions, which suffer from uncertain physical results, proposed analytical solutions do not have unwanted numerical oscillations.
Heat treatment modelling using strongly continuous semigroups.
Malek, Alaeddin; Abbasi, Ghasem
2015-07-01
In this paper, mathematical simulation of bioheat transfer phenomenon within the living tissue is studied using the thermal wave model. Three different sources that have therapeutic applications in laser surgery, cornea laser heating and cancer hyperthermia are used. Spatial and transient heating source, on the skin surface and inside biological body, are considered by using step heating, sinusoidal and constant heating. Mathematical simulations describe a non-Fourier process. Exact solution for the corresponding non-Fourier bioheat transfer model that has time lag in its heat flux is proposed using strongly continuous semigroup theory in conjunction with variational methods. The abstract differential equation, infinitesimal generator and corresponding strongly continuous semigroup are proposed. It is proved that related semigroup is a contraction semigroup and is exponentially stable. Mathematical simulations are done for skin burning and thermal therapy in 10 different models and the related solutions are depicted. Unlike numerical solutions, which suffer from uncertain physical results, proposed analytical solutions do not have unwanted numerical oscillations. PMID:25912988
ERIC Educational Resources Information Center
Wang, Tianyou
2008-01-01
Von Davier, Holland, and Thayer (2004) laid out a five-step framework of test equating that can be applied to various data collection designs and equating methods. In the continuization step, they presented an adjusted Gaussian kernel method that preserves the first two moments. This article proposes an alternative continuization method that…
A linear model of population dynamics
NASA Astrophysics Data System (ADS)
Lushnikov, A. A.; Kagan, A. I.
2016-08-01
The Malthus process of population growth is reformulated in terms of the probability w(n,t) to find exactly n individuals at time t assuming that both the birth and the death rates are linear functions of the population size. The master equation for w(n,t) is solved exactly. It is shown that w(n,t) strongly deviates from the Poisson distribution and is expressed in terms either of Laguerre’s polynomials or a modified Bessel function. The latter expression allows for considerable simplifications of the asymptotic analysis of w(n,t).
Modeling interdependent animal movement in continuous time.
Niu, Mu; Blackwell, Paul G; Skarin, Anna
2016-06-01
This article presents a new approach to modeling group animal movement in continuous time. The movement of a group of animals is modeled as a multivariate Ornstein Uhlenbeck diffusion process in a high-dimensional space. Each individual of the group is attracted to a leading point which is generally unobserved, and the movement of the leading point is also an Ornstein Uhlenbeck process attracted to an unknown attractor. The Ornstein Uhlenbeck bridge is applied to reconstruct the location of the leading point. All movement parameters are estimated using Markov chain Monte Carlo sampling, specifically a Metropolis Hastings algorithm. We apply the method to a small group of simultaneously tracked reindeer, Rangifer tarandus tarandus, showing that the method detects dependency in movement between individuals. PMID:26812666
Latent log-linear models for handwritten digit classification.
Deselaers, Thomas; Gass, Tobias; Heigold, Georg; Ney, Hermann
2012-06-01
We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.
The identification of linear and non-linear models of a turbocharged automotive diesel engine
NASA Astrophysics Data System (ADS)
Billings, S. A.; Chen, S.; Backhouse, R. J.
1989-04-01
The identification results obtained from a study on a Leyland TL11 turbocharged, direct injection diesel engine are presented. Two sets of data corresponding to low and high engine speed tests, which were recorded from experimental trials on the engine, are analysed. The identification of both linear and non-linear difference equation models are described to represent the relationship between the fuel rack position (input) and the engine speed (output).
Non-linear transformer modeling and simulation
Archer, W.E.; Deveney, M.F.; Nagel, R.L.
1994-08-01
Transformers models for simulation with Pspice and Analogy`s Saber are being developed using experimental B-H Loop and network analyzer measurements. The models are evaluated for accuracy and convergence using several test circuits. Results are presented which demonstrate the effects on circuit performance from magnetic core losses eddy currents and mechanical stress on the magnetic cores.
A Second-Order Conditionally Linear Mixed Effects Model with Observed and Latent Variable Covariates
ERIC Educational Resources Information Center
Harring, Jeffrey R.; Kohli, Nidhi; Silverman, Rebecca D.; Speece, Deborah L.
2012-01-01
A conditionally linear mixed effects model is an appropriate framework for investigating nonlinear change in a continuous latent variable that is repeatedly measured over time. The efficacy of the model is that it allows parameters that enter the specified nonlinear time-response function to be stochastic, whereas those parameters that enter in a…
Temporal-mode continuous-variable cluster states using linear optics
Menicucci, Nicolas C.
2011-06-15
An extensible experimental design for optical continuous-variable cluster states of arbitrary size using four offline (vacuum) squeezers and six beam splitters is presented. This method has all the advantages of a temporal-mode encoding [Phys. Rev. Lett. 104, 250503 (2010)], including finite requirements for coherence and stability even as the computation length increases indefinitely, with none of the difficulty of inline squeezing. The extensibility stems from a construction based on Gaussian projected entangled pair states. The potential for use of this design within a fully fault-tolerant model is discussed.
Stochastic linear hybrid systems: Modeling, estimation, and application
NASA Astrophysics Data System (ADS)
Seah, Chze Eng
Hybrid systems are dynamical systems which have interacting continuous state and discrete state (or mode). Accurate modeling and state estimation of hybrid systems are important in many applications. We propose a hybrid system model, known as the Stochastic Linear Hybrid System (SLHS), to describe hybrid systems with stochastic linear system dynamics in each mode and stochastic continuous-state-dependent mode transitions. We then develop a hybrid estimation algorithm, called the State-Dependent-Transition Hybrid Estimation (SDTHE) algorithm, to estimate the continuous state and discrete state of the SLHS from noisy measurements. It is shown that the SDTHE algorithm is more accurate or more computationally efficient than existing hybrid estimation algorithms. Next, we develop a performance analysis algorithm to evaluate the performance of the SDTHE algorithm in a given operating scenario. We also investigate sufficient conditions for the stability of the SDTHE algorithm. The proposed SLHS model and SDTHE algorithm are illustrated to be useful in several applications. In Air Traffic Control (ATC), to facilitate implementations of new efficient operational concepts, accurate modeling and estimation of aircraft trajectories are needed. In ATC, an aircraft's trajectory can be divided into a number of flight modes. Furthermore, as the aircraft is required to follow a given flight plan or clearance, its flight mode transitions are dependent of its continuous state. However, the flight mode transitions are also stochastic due to navigation uncertainties or unknown pilot intents. Thus, we develop an aircraft dynamics model in ATC based on the SLHS. The SDTHE algorithm is then used in aircraft tracking applications to estimate the positions/velocities of aircraft and their flight modes accurately. Next, we develop an aircraft conformance monitoring algorithm to detect any deviations of aircraft trajectories in ATC that might compromise safety. In this application, the SLHS
Linear programming model for optimum resource allocation in rural systems
Devadas, V.
1997-07-01
The article presents a model for optimum resource allocation in a rural system. Making use of linear programming, the objective function of the linear programming model is to maximize the revenue of the rural system, and optimum resource allocation is made subject to a number of energy- and nonenergy-related constraints relevant to the rural system. The model also quantifies the major yields as well as the by-products of different sectors of the rural economic system.
Modelling, system identification, and control of an evaporative continuous crystallizer
NASA Astrophysics Data System (ADS)
Dewolf, Sjoerd
Research on physical modeling, simulation, the derivation of state space models, suitable input and output variables for control of the crystal size distribution (CSD), and the assessment of experimentally validated models for the dynamics of crytallizers is presented. A 20 liter (L) and a 970 L crystallizer and the measurement system available for online CSD measurements of the 970 L crystallizer are discussed. A general dynamic model for evaporative and nonevaporative continuous crystallizers with fines removal, clear liquor advance and product classification is presented. The model includes nonisothermal operation, size dependent crystal growth rates, growth rate dispersion, and attrition. A simulation technique for the physical model, which was chosen for its close relation to the derivation of state space models and its capability to provide CSD information at fixed time intervals as required for digital control, is discussed. The derivation of linear state space models for the dynamics of crystallizers is investigated. Multivariable control of the 970 L crystallizer is investigated to obtain knowledge of input and output control variables. An experimental model for the 970 L crystallizer is assessed and the necessity for a system identification approach is explained.
Non-linear protocell models: synchronization and chaos
NASA Astrophysics Data System (ADS)
Filisetti, A.; Serra, R.; Carletti, T.; Villani, M.; Poli, I.
2010-09-01
We consider generic protocells models allowing linear and non-linear kinetics for the main involved chemical reactions. We are interested in understanding if and how the protocell division and the metabolism do synchronise to give rise to sustainable evolution of the protocell.
Linear and Nonlinear Thinking: A Multidimensional Model and Measure
ERIC Educational Resources Information Center
Groves, Kevin S.; Vance, Charles M.
2015-01-01
Building upon previously developed and more general dual-process models, this paper provides empirical support for a multidimensional thinking style construct comprised of linear thinking and multiple dimensions of nonlinear thinking. A self-report assessment instrument (Linear/Nonlinear Thinking Style Profile; LNTSP) is presented and…
Derivation and definition of a linear aircraft model
NASA Technical Reports Server (NTRS)
Duke, Eugene L.; Antoniewicz, Robert F.; Krambeer, Keith D.
1988-01-01
A linear aircraft model for a rigid aircraft of constant mass flying over a flat, nonrotating earth is derived and defined. The derivation makes no assumptions of reference trajectory or vehicle symmetry. The linear system equations are derived and evaluated along a general trajectory and include both aircraft dynamics and observation variables.
Linear mixed-effects modeling approach to FMRI group analysis
Chen, Gang; Saad, Ziad S.; Britton, Jennifer C.; Pine, Daniel S.; Cox, Robert W.
2013-01-01
Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance–covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance–covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the
Nonlinear Submodels Of Orthogonal Linear Models
ERIC Educational Resources Information Center
Bechtel, Gordon G.
1973-01-01
It is the purpose of this paper to suggest the orthogonal analysis of variance as a device for simplifying either the analytic or iterative problem of finding LS (least squares) estimates for the parameters of particular nonlinear models. (Author/RK)
Bond models in linear and nonlinear optics
NASA Astrophysics Data System (ADS)
Aspnes, D. E.
2015-08-01
Bond models, also known as polarizable-point or mechanical models, have a long history in optics, starting with the Clausius-Mossotti relation but more accurately originating with Ewald's largely forgotten work in 1912. These models describe macroscopic phenomena such as dielectric functions and nonlinear-optical (NLO) susceptibilities in terms of the physics that takes place in real space, in real time, on the atomic scale. Their strengths lie in the insights that they provide and the questions that they raise, aspects that are often obscured by quantum-mechanical treatments. Statics versions were used extensively in the late 1960's and early 1970's to correlate NLO susceptibilities among bulk materials. Interest in NLO applications revived with the 2002 work of Powell et al., who showed that a fully anisotropic version reduced by more than a factor of 2 the relatively large number of parameters necessary to describe secondharmonic- generation (SHG) data for Si(111)/SiO2 interfaces. Attention now is focused on the exact physical meaning of these parameters, and to the extent that they represent actual physical quantities.
Failure of Tube Models to Predict the Linear Rheology of Star/Linear Blends
NASA Astrophysics Data System (ADS)
Hall, Ryan; Desai, Priyanka; Kang, Beomgoo; Katzarova, Maria; Huang, Qifan; Lee, Sanghoon; Chang, Taihyun; Venerus, David; Mays, Jimmy; Schieber, Jay; Larson, Ronald
We compare predictions of two of the most advanced versions of the tube model, namely the Hierarchical model by Wang et al. (J. Rheol. 54:223, 2010) and the BOB (branch-on-branch) model by Das et al. (J. Rheol. 50:207-234, 2006), against linear viscoelastic data on blends of monodisperse star and monodisperse linear polybutadiene polymers. The star was carefully synthesized/characterized by temperature gradient interaction chromatography, and rheological data in the high frequency region were obtained through time-temperature superposition. We found massive failures of both the Hierarchical and BOB models to predict the terminal relaxation behavior of the star/linear blends, despite their success in predicting the rheology of the pure star and pure linear. This failure occurred regardless of the choices made concerning constraint release, such as assuming arm retraction in fat or skinny tubes, or allowing for disentanglement relaxation to cut off the constraint release Rouse process at long times. The failures call into question whether constraint release can be described as a combination of constraint release Rouse processes and dynamic tube dilation within a canonical tube model of entanglement interactions.
A review of some extensions to generalized linear models.
Lindsey, J K
Although generalized linear models are reasonably well known, they are not as widely used in medical statistics as might be appropriate, with the exception of logistic, log-linear, and some survival models. At the same time, the generalized linear modelling methodology is decidedly outdated in that more powerful methods, involving wider classes of distributions, non-linear regression, censoring and dependence among responses, are required. Limitations of the generalized linear modelling approach include the need for the iterated weighted least squares (IWLS) procedure for estimation and deviances for inferences; these restrict the class of models that can be used and do not allow direct comparisons among models from different distributions. Powerful non-linear optimization routines are now available and comparisons can more fruitfully be made using the complete likelihood function. The link function is an artefact, necessary for IWLS to function with linear models, but that disappears once the class is extended to truly non-linear models. Restricting comparisons of responses under different treatments to differences in means can be extremely misleading if the shape of the distribution is changing. This may involve changes in dispersion, or of other shape-related parameters such as the skewness in a stable distribution, with the treatments or covariates. Any exact likelihood function, defined as the probability of the observed data, takes into account the fact that all observable data are interval censored, thus directly encompassing the various types of censoring possible with duration-type data. In most situations this can now be as easily used as the traditional approximate likelihood based on densities. Finally, methods are required for incorporating dependencies among responses in models including conditioning on previous history and on random effects. One important procedure for constructing such likelihoods is based on Kalman filtering. PMID:10474135
Analyzing Quasi-Experiments: Some Implications of Assuming Continuous Growth Models.
ERIC Educational Resources Information Center
Blumberg, Carol Joyce; Porter, Andrew C.
1983-01-01
The general class of continuous growth models are described and examples representative of growth models suggested for various types of academic and/or physical growth are given. The fan spread hypothesis is discussed in relationship to natural growth models, as well as differential linear growth. (PN)
The General Linear Model and Direct Standardization: A Comparison.
ERIC Educational Resources Information Center
Little, Roderick J. A.; Pullum, Thomas W.
1979-01-01
Two methods of analyzing nonorthogonal (uneven cell sizes) cross-classified data sets are compared. The methods are direct standardization and the general linear model. The authors illustrate when direct standardization may be a desirable method of analysis. (JKS)
Dilatonic non-linear sigma models and Ricci flow extensions
NASA Astrophysics Data System (ADS)
Carfora, M.; Marzuoli, A.
2016-09-01
We review our recent work describing, in terms of the Wasserstein geometry over the space of probability measures, the embedding of the Ricci flow in the renormalization group flow for dilatonic non-linear sigma models.
Identifying approximate linear models for simple nonlinear systems
NASA Technical Reports Server (NTRS)
Horta, L. G.; Juang, J.-N.
1985-01-01
This paper addresses the identification (realization) of approximate linear models from response data for certain nonlinear dynamic systems. Response characteristics for several typical nonlinear joints are analyzed mathematically and represented by series expansions. The parameters of the series expansion are then compared with the modal parameters of a linear model identified by the Eigensystem Realization Algorithm. The agreement of the identified model and the analytically derived representation is excellent for the cases studied. Also laboratory data from a model which exhibited stiffening behavior was analyzed using the Eigensystem Realization algorithm and Fast Fourier Transform. The laboratory experiment demonstrated the ability of the technique to recover the model characteristics using real data.
Optimization Research of Generation Investment Based on Linear Programming Model
NASA Astrophysics Data System (ADS)
Wu, Juan; Ge, Xueqian
Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.
ERIC Educational Resources Information Center
Esteley, Cristina; Villarreal, Monica; Alagia, Humberto
2004-01-01
This research report presents a study of the work of agronomy majors in which an extension of linear models to non-linear contexts can be observed. By linear models we mean the model y=a.x+b, some particular representations of direct proportionality and the diagram for the rule of three. Its presence and persistence in different types of problems…
Linear models for river flow routing on large catchments
NASA Astrophysics Data System (ADS)
Liang, G. C.; Nash, J. E.
1988-11-01
Following a brief review of the place of linear input-output models in applied hydrology, the algebraic analysis of multiple input single output linear systems is presented and applied in the context of flood routing on the Changjiang (Yangtze River) in China. Two different stretches of the river and one tributary catchment are chosen and the outflows are forecast in terms of observed flows at the upper ends of the river reaches and rainfall on the intervening catchment. It is shown that very high accuracy indeed can be obtained with multiple input linear models whether applied to the total flows and rainfall or to the departures in these quantities from their seasonal expectations. The paper concludes with some analysis of the residual errors which could provide the basis of an updating procedure if the linear models were used for forecasting purposes.
Continuing research on the classical spiraling photon model
NASA Astrophysics Data System (ADS)
Li, Hongrui
2014-11-01
Based no the classical spiraling photon model proposed by Hongrui Li, the laws of reflection, refraction of a single photon can be derived. Moreover, the polarization, total reflection, evanescent wave and Goos-Hanchen shift of a single photon can be elucidated. However, this photon model is still unfinished. Especially, the spiraling diameter of a photon is not definite. In this paper, the continuous research works on this new theory are reported. According to the facts that the diffraction limit of light and the smallest diameter of the focal spot of lenses are all equal to the wavelength λ of the light, we can get that the spiraling diameter of a photon equals to the wavelength λ, so we gain that the angle between the linear velocity of the spiraling photon υ and the component of the linear velocity in the forward direction υb is 45°, and the energy of a classical spiraling photon E = (1/2)mυ2 = (1/2)m2c2 = mc2. This coincides with Einstein's mass-energy relation. While it is obtained that the velocity of the evanescent wave in the vacuum is slower than the velocity of light in glass in straight line. In such a way, the optical fiber can slow the light down. In addition, the force analysis of a single photon in optical tweezers system is discussed. And the reason that the laser beam can capture the particle slightly downstream from the focal point can be explained.
Generation of linear dynamic models from a digital nonlinear simulation
NASA Technical Reports Server (NTRS)
Daniele, C. J.; Krosel, S. M.
1979-01-01
The results and methodology used to derive linear models from a nonlinear simulation are presented. It is shown that averaged positive and negative perturbations in the state variables can reduce numerical errors in finite difference, partial derivative approximations and, in the control inputs, can better approximate the system response in both directions about the operating point. Both explicit and implicit formulations are addressed. Linear models are derived for the F 100 engine, and comparisons of transients are made with the nonlinear simulation. The problem of startup transients in the nonlinear simulation in making these comparisons is addressed. Also, reduction of the linear models is investigated using the modal and normal techniques. Reduced-order models of the F 100 are derived and compared with the full-state models.
Variance Function Partially Linear Single-Index Models1
LIAN, HENG; LIANG, HUA; CARROLL, RAYMOND J.
2014-01-01
We consider heteroscedastic regression models where the mean function is a partially linear single index model and the variance function depends upon a generalized partially linear single index model. We do not insist that the variance function depend only upon the mean function, as happens in the classical generalized partially linear single index model. We develop efficient and practical estimation methods for the variance function and for the mean function. Asymptotic theory for the parametric and nonparametric parts of the model is developed. Simulations illustrate the results. An empirical example involving ozone levels is used to further illustrate the results, and is shown to be a case where the variance function does not depend upon the mean function. PMID:25642139
Non-linear Growth Models in Mplus and SAS
Grimm, Kevin J.; Ram, Nilam
2013-01-01
Non-linear growth curves or growth curves that follow a specified non-linear function in time enable researchers to model complex developmental patterns with parameters that are easily interpretable. In this paper we describe how a variety of sigmoid curves can be fit using the Mplus structural modeling program and the non-linear mixed-effects modeling procedure NLMIXED in SAS. Using longitudinal achievement data collected as part of a study examining the effects of preschool instruction on academic gain we illustrate the procedures for fitting growth models of logistic, Gompertz, and Richards functions. Brief notes regarding the practical benefits, limitations, and choices faced in the fitting and estimation of such models are included. PMID:23882134
Complex dynamics in the Oregonator model with linear delayed feedback
NASA Astrophysics Data System (ADS)
Sriram, K.; Bernard, S.
2008-06-01
The Belousov-Zhabotinsky (BZ) reaction can display a rich dynamics when a delayed feedback is applied. We used the Oregonator model of the oscillating BZ reaction to explore the dynamics brought about by a linear delayed feedback. The time-delayed feedback can generate a succession of complex dynamics: period-doubling bifurcation route to chaos; amplitude death; fat, wrinkled, fractal, and broken tori; and mixed-mode oscillations. We observed that this dynamics arises due to a delay-driven transition, or toggling of the system between large and small amplitude oscillations, through a canard bifurcation. We used a combination of numerical bifurcation continuation techniques and other numerical methods to explore the dynamics in the strength of feedback-delay space. We observed that the period-doubling and quasiperiodic route to chaos span a low-dimensional subspace, perhaps due to the trapping of the trajectories in the small amplitude regime near the canard; and the trapped chaotic trajectories get ejected from the small amplitude regime due to a crowding effect to generate chaotic-excitable spikes. We also qualitatively explained the observed dynamics by projecting a three-dimensional phase portrait of the delayed dynamics on the two-dimensional nullclines. This is the first instance in which it is shown that the interaction of delay and canard can bring about complex dynamics.
A Linearization Approach for Rational Nonlinear Models in Mathematical Physics
NASA Astrophysics Data System (ADS)
Robert, A. Van Gorder
2012-04-01
In this paper, a novel method for linearization of rational second order nonlinear models is discussed. In particular, we discuss an application of the δ expansion method (created to deal with problems in Quantum Field Theory) which will enable both the linearization and perturbation expansion of such equations. Such a method allows for one to quickly obtain the order zero perturbation theory in terms of certain special functions which are governed by linear equations. Higher order perturbation theories can then be obtained in terms of such special functions. One benefit to such a method is that it may be applied even to models without small physical parameters, as the perturbation is given in terms of the degree of nonlinearity, rather than any physical parameter. As an application, we discuss a method of linearizing the six Painlevé equations by an application of the method. In addition to highlighting the benefits of the method, we discuss certain shortcomings of the method.
Computer modeling of batteries from non-linear circuit elements
NASA Technical Reports Server (NTRS)
Waaben, S.; Federico, J.; Moskowitz, I.
1983-01-01
A simple non-linear circuit model for battery behavior is given. It is based on time-dependent features of the well-known PIN change storage diode, whose behavior is described by equations similar to those associated with electrochemical cells. The circuit simulation computer program ADVICE was used to predict non-linear response from a topological description of the battery analog built from advice components. By a reasonable choice of one set of parameters, the circuit accurately simulates a wide spectrum of measured non-linear battery responses to within a few millivolts.
Qu, Long; Guennel, Tobias; Marshall, Scott L
2013-12-01
Following the rapid development of genome-scale genotyping technologies, genetic association mapping has become a popular tool to detect genomic regions responsible for certain (disease) phenotypes, especially in early-phase pharmacogenomic studies with limited sample size. In response to such applications, a good association test needs to be (1) applicable to a wide range of possible genetic models, including, but not limited to, the presence of gene-by-environment or gene-by-gene interactions and non-linearity of a group of marker effects, (2) accurate in small samples, fast to compute on the genomic scale, and amenable to large scale multiple testing corrections, and (3) reasonably powerful to locate causal genomic regions. The kernel machine method represented in linear mixed models provides a viable solution by transforming the problem into testing the nullity of variance components. In this study, we consider score-based tests by choosing a statistic linear in the score function. When the model under the null hypothesis has only one error variance parameter, our test is exact in finite samples. When the null model has more than one variance parameter, we develop a new moment-based approximation that performs well in simulations. Through simulations and analysis of real data, we demonstrate that the new test possesses most of the aforementioned characteristics, especially when compared to existing quadratic score tests or restricted likelihood ratio tests. PMID:24328714
Confirming the Lanchestrian linear-logarithmic model of attrition
Hartley, D.S. III.
1990-12-01
This paper is the fourth in a series of reports on the breakthrough research in historical validation of attrition in conflict. Significant defense policy decisions, including weapons acquisition and arms reduction, are based in part on models of conflict. Most of these models are driven by their attrition algorithms, usually forms of the Lanchester square and linear laws. None of these algorithms have been validated. The results of this paper confirm the results of earlier papers, using a large database of historical results. The homogeneous linear-logarithmic Lanchestrian attrition model is validated to the extent possible with current initial and final force size data and is consistent with the Iwo Jima data. A particular differential linear-logarithmic model is described that fits the data very well. A version of Helmbold's victory predicting parameter is also confirmed, with an associated probability function. 37 refs., 73 figs., 68 tabs.
Non-Linear Finite Element Modeling of THUNDER Piezoelectric Actuators
NASA Technical Reports Server (NTRS)
Taleghani, Barmac K.; Campbell, Joel F.
1999-01-01
A NASTRAN non-linear finite element model has been developed for predicting the dome heights of THUNDER (THin Layer UNimorph Ferroelectric DrivER) piezoelectric actuators. To analytically validate the finite element model, a comparison was made with a non-linear plate solution using Von Karmen's approximation. A 500 volt input was used to examine the actuator deformation. The NASTRAN finite element model was also compared with experimental results. Four groups of specimens were fabricated and tested. Four different input voltages, which included 120, 160, 200, and 240 Vp-p with a 0 volts offset, were used for this comparison.
Inverse Modelling Problems in Linear Algebra Undergraduate Courses
ERIC Educational Resources Information Center
Martinez-Luaces, Victor E.
2013-01-01
This paper will offer an analysis from a theoretical point of view of mathematical modelling, applications and inverse problems of both causation and specification types. Inverse modelling problems give the opportunity to establish connections between theory and practice and to show this fact, a simple linear algebra example in two different…
Optical linear algebra processors: noise and error-source modeling.
Casasent, D; Ghosh, A
1985-06-01
The modeling of system and component noise and error sources in optical linear algebra processors (OLAP's) are considered, with attention to the frequency-multiplexed OLAP. General expressions are obtained for the output produced as a function of various component errors and noise. A digital simulator for this model is discussed.
MULTIVARIATE LINEAR MIXED MODELS FOR MULTIPLE OUTCOMES. (R824757)
We propose a multivariate linear mixed (MLMM) for the analysis of multiple outcomes, which generalizes the latent variable model of Sammel and Ryan. The proposed model assumes a flexible correlation structure among the multiple outcomes, and allows a global test of the impact of ...
Locally Dependent Linear Logistic Test Model with Person Covariates
ERIC Educational Resources Information Center
Ip, Edward H.; Smits, Dirk J. M.; De Boeck, Paul
2009-01-01
The article proposes a family of item-response models that allow the separate and independent specification of three orthogonal components: item attribute, person covariate, and local item dependence. Special interest lies in extending the linear logistic test model, which is commonly used to measure item attributes, to tests with embedded item…
Optical linear algebra processors - Noise and error-source modeling
NASA Technical Reports Server (NTRS)
Casasent, D.; Ghosh, A.
1985-01-01
The modeling of system and component noise and error sources in optical linear algebra processors (OLAPs) are considered, with attention to the frequency-multiplexed OLAP. General expressions are obtained for the output produced as a function of various component errors and noise. A digital simulator for this model is discussed.
A Model for Continuing Pharmacy Education
Newlon, Carey; Dickerhofe, Jeannine
2009-01-01
Objective To develop and implement a continuing pharmacy education (CPE) program at Kaiser Permanente Colorado (KPCO) Design To address the continuing education needs of its diverse pharmacy staff, an internal continuing pharmacy education (CPE) program was developed. The pharmacy department became an accredited provider by the Accreditation Council for Pharmacy Education (ACPE). Live, interactive, and evidence-based CPE programs, presented by highly qualified internal staff members, utilized videoconferencing and a Web-based learning management system. Cross-accreditation of medical and pharmacy educational programs was offered to KPCO staff members. Assessment Annual needs assessments were conducted to ensure the provision of relevant educational topics and to assess learning needs. To demonstrate outcomes of the CPE programs, 2 methods were utilized: objective effectiveness assessment and knowledge acquisition assessment. This program met the objectives for CPE activities a large majority of the time (usually over 90%), demonstrated statistically significant (p < 0.05) improvement in knowledge from before to after the CPE activity in 11 of 13 questions asked, and minimized the cost to acquire CPE credit for both the pharmacy department and its staff members. Conclusion The KPCO continuing pharmacy education program has developed a high quality and cost-favorable system that has resulted in significant improvements in attendee knowledge. PMID:19777102
Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables
ERIC Educational Resources Information Center
Henson, Robert A.; Templin, Jonathan L.; Willse, John T.
2009-01-01
This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…
PID controller design for trailer suspension based on linear model
NASA Astrophysics Data System (ADS)
Kushairi, S.; Omar, A. R.; Schmidt, R.; Isa, A. A. Mat; Hudha, K.; Azizan, M. A.
2015-05-01
A quarter of an active trailer suspension system having the characteristics of a double wishbone type was modeled as a complex multi-body dynamic system in MSC.ADAMS. Due to the complexity of the model, a linearized version is considered in this paper. A model reduction technique is applied to the linear model, resulting in a reduced-order model. Based on this simplified model, a Proportional-Integral-Derivative (PID) controller was designed in MATLAB/Simulink environment; primarily to reduce excessive roll motions and thus improving the ride comfort. Simulation results show that the output signal closely imitates the input signal in multiple cases - demonstrating the effectiveness of the controller.
The minimal linear σ model for the Goldstone Higgs
NASA Astrophysics Data System (ADS)
Feruglio, F.; Gavela, M. B.; Kanshin, K.; Machado, P. A. N.; Rigolin, S.; Saa, S.
2016-06-01
In the context of the minimal SO(5) linear σ-model, a complete renormalizable Lagrangian -including gauge bosons and fermions- is considered, with the symmetry softly broken to SO(4). The scalar sector describes both the electroweak Higgs doublet and the singlet σ. Varying the σ mass would allow to sweep from the regime of perturbative ultraviolet completion to the non-linear one assumed in models in which the Higgs particle is a low-energy remnant of some strong dynamics. We analyze the phenomenological implications and constraints from precision observables and LHC data. Furthermore, we derive the d ≤ 6 effective Lagrangian in the limit of heavy exotic fermions.
Functional Linear Models for Association Analysis of Quantitative Traits
Fan, Ruzong; Wang, Yifan; Mills, James L.; Wilson, Alexander F.; Bailey-Wilson, Joan E.; Xiong, Momiao
2014-01-01
Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study. PMID:24130119
Functional linear models for association analysis of quantitative traits.
Fan, Ruzong; Wang, Yifan; Mills, James L; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao
2013-11-01
Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study.
Direct use of linear time-domain aerodynamics in aeroservoelastic analysis: Aerodynamic model
NASA Technical Reports Server (NTRS)
Woods, J. A.; Gilbert, Michael G.
1990-01-01
The work presented here is the first part of a continuing effort to expand existing capabilities in aeroelasticity by developing the methodology which is necessary to utilize unsteady time-domain aerodynamics directly in aeroservoelastic design and analysis. The ultimate objective is to define a fully integrated state-space model of an aeroelastic vehicle's aerodynamics, structure and controls which may be used to efficiently determine the vehicle's aeroservoelastic stability. Here, the current status of developing a state-space model for linear or near-linear time-domain indicial aerodynamic forces is presented.
Piecewise linear and Boolean models of chemical reaction networks.
Veliz-Cuba, Alan; Kumar, Ajit; Josić, Krešimir
2014-12-01
Models of biochemical networks are frequently complex and high-dimensional. Reduction methods that preserve important dynamical properties are therefore essential for their study. Interactions in biochemical networks are frequently modeled using Hill functions ([Formula: see text]). Reduced ODEs and Boolean approximations of such model networks have been studied extensively when the exponent [Formula: see text] is large. However, while the case of small constant [Formula: see text] appears in practice, it is not well understood. We provide a mathematical analysis of this limit and show that a reduction to a set of piecewise linear ODEs and Boolean networks can be mathematically justified. The piecewise linear systems have closed-form solutions that closely track those of the fully nonlinear model. The simpler, Boolean network can be used to study the qualitative behavior of the original system. We justify the reduction using geometric singular perturbation theory and compact convergence, and illustrate the results in network models of a toggle switch and an oscillator.
Chen, Haixia; Zhang, Jing
2007-02-15
We propose a scheme for continuous-variable quantum cloning of coherent states with phase-conjugate input modes using linear optics. The quantum cloning machine yields M identical optimal clones from N replicas of a coherent state and N replicas of its phase conjugate. This scheme can be straightforwardly implemented with the setups accessible at present since its optical implementation only employs simple linear optical elements and homodyne detection. Compared with the original scheme for continuous-variable quantum cloning with phase-conjugate input modes proposed by Cerf and Iblisdir [Phys. Rev. Lett. 87, 247903 (2001)], which utilized a nondegenerate optical parametric amplifier, our scheme loses the output of phase-conjugate clones and is regarded as irreversible quantum cloning.
NASA Astrophysics Data System (ADS)
Lee, Dong-Jin; Lee, Sun-Kyu
2015-01-01
This paper presents a design and control system for an XY stage driven by an ultrasonic linear motor. In this study, a hybrid bolt-clamped Langevin-type ultrasonic linear motor was manufactured and then operated at the resonance frequency of the third longitudinal and the sixth lateral modes. These two modes were matched through the preload adjustment and precisely tuned by the frequency matching method based on the impedance matching method with consideration of the different moving weights. The XY stage was evaluated in terms of position and circular motion. To achieve both fine and stable motion, the controller consisted of a nominal characteristics trajectory following (NCTF) control for continuous motion, dead zone compensation, and a switching controller based on the different NCTFs for the macro- and micro-dynamics regimes. The experimental results showed that the developed stage enables positioning and continuous motion with nanometer-level accuracy.
Lee, Dong-Jin; Lee, Sun-Kyu
2015-01-01
This paper presents a design and control system for an XY stage driven by an ultrasonic linear motor. In this study, a hybrid bolt-clamped Langevin-type ultrasonic linear motor was manufactured and then operated at the resonance frequency of the third longitudinal and the sixth lateral modes. These two modes were matched through the preload adjustment and precisely tuned by the frequency matching method based on the impedance matching method with consideration of the different moving weights. The XY stage was evaluated in terms of position and circular motion. To achieve both fine and stable motion, the controller consisted of a nominal characteristics trajectory following (NCTF) control for continuous motion, dead zone compensation, and a switching controller based on the different NCTFs for the macro- and micro-dynamics regimes. The experimental results showed that the developed stage enables positioning and continuous motion with nanometer-level accuracy.
Lee, Dong-Jin; Lee, Sun-Kyu
2015-01-15
This paper presents a design and control system for an XY stage driven by an ultrasonic linear motor. In this study, a hybrid bolt-clamped Langevin-type ultrasonic linear motor was manufactured and then operated at the resonance frequency of the third longitudinal and the sixth lateral modes. These two modes were matched through the preload adjustment and precisely tuned by the frequency matching method based on the impedance matching method with consideration of the different moving weights. The XY stage was evaluated in terms of position and circular motion. To achieve both fine and stable motion, the controller consisted of a nominal characteristics trajectory following (NCTF) control for continuous motion, dead zone compensation, and a switching controller based on the different NCTFs for the macro- and micro-dynamics regimes. The experimental results showed that the developed stage enables positioning and continuous motion with nanometer-level accuracy.
Modeling continuous covariates with a "spike" at zero: Bivariate approaches.
Jenkner, Carolin; Lorenz, Eva; Becher, Heiko; Sauerbrei, Willi
2016-07-01
In epidemiology and clinical research, predictors often take value zero for a large amount of observations while the distribution of the remaining observations is continuous. These predictors are called variables with a spike at zero. Examples include smoking or alcohol consumption. Recently, an extension of the fractional polynomial (FP) procedure, a technique for modeling nonlinear relationships, was proposed to deal with such situations. To indicate whether or not a value is zero, a binary variable is added to the model. In a two stage procedure, called FP-spike, the necessity of the binary variable and/or the continuous FP function for the positive part are assessed for a suitable fit. In univariate analyses, the FP-spike procedure usually leads to functional relationships that are easy to interpret. This paper introduces four approaches for dealing with two variables with a spike at zero (SAZ). The methods depend on the bivariate distribution of zero and nonzero values. Bi-Sep is the simplest of the four bivariate approaches. It uses the univariate FP-spike procedure separately for the two SAZ variables. In Bi-D3, Bi-D1, and Bi-Sub, proportions of zeros in both variables are considered simultaneously in the binary indicators. Therefore, these strategies can account for correlated variables. The methods can be used for arbitrary distributions of the covariates. For illustration and comparison of results, data from a case-control study on laryngeal cancer, with smoking and alcohol intake as two SAZ variables, is considered. In addition, a possible extension to three or more SAZ variables is outlined. A combination of log-linear models for the analysis of the correlation in combination with the bivariate approaches is proposed. PMID:27072783
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.
Non-linear calibration models for near infrared spectroscopy.
Ni, Wangdong; Nørgaard, Lars; Mørup, Morten
2014-02-27
Different calibration techniques are available for spectroscopic applications that show nonlinear behavior. This comprehensive comparative study presents a comparison of different nonlinear calibration techniques: kernel PLS (KPLS), support vector machines (SVM), least-squares SVM (LS-SVM), relevance vector machines (RVM), Gaussian process regression (GPR), artificial neural network (ANN), and Bayesian ANN (BANN). In this comparison, partial least squares (PLS) regression is used as a linear benchmark, while the relationship of the methods is considered in terms of traditional calibration by ridge regression (RR). The performance of the different methods is demonstrated by their practical applications using three real-life near infrared (NIR) data sets. Different aspects of the various approaches including computational time, model interpretability, potential over-fitting using the non-linear models on linear problems, robustness to small or medium sample sets, and robustness to pre-processing, are discussed. The results suggest that GPR and BANN are powerful and promising methods for handling linear as well as nonlinear systems, even when the data sets are moderately small. The LS-SVM is also attractive due to its good predictive performance for both linear and nonlinear calibrations.
Johnson-Neyman Type Technique in Hierarchical Linear Models
ERIC Educational Resources Information Center
Miyazaki, Yasuo; Maier, Kimberly S.
2005-01-01
In hierarchical linear models we often find that group indicator variables at the cluster level are significant predictors for the regression slopes. When this is the case, the average relationship between the outcome and a key independent variable are different from group to group. In these settings, a question such as "what range of the…
Linear network representation of multistate models of transport.
Sandblom, J; Ring, A; Eisenman, G
1982-05-01
By introducing external driving forces in rate-theory models of transport we show how the Eyring rate equations can be transformed into Ohm's law with potentials that obey Kirchhoff's second law. From such a formalism the state diagram of a multioccupancy multicomponent system can be directly converted into linear network with resistors connecting nodal (branch) points and with capacitances connecting each nodal point with a reference point. The external forces appear as emf or current generators in the network. This theory allows the algebraic methods of linear network theory to be used in solving the flux equations for multistate models and is particularly useful for making proper simplifying approximation in models of complex membrane structure. Some general properties of linear network representation are also deduced. It is shown, for instance, that Maxwell's reciprocity relationships of linear networks lead directly to Onsager's relationships in the near equilibrium region. Finally, as an example of the procedure, the equivalent circuit method is used to solve the equations for a few transport models.
Asymptotic behavior of coupled linear systems modeling suspension bridges
NASA Astrophysics Data System (ADS)
Dell'Oro, Filippo; Giorgi, Claudio; Pata, Vittorino
2015-06-01
We consider the coupled linear system describing the vibrations of a string-beam system related to the well-known Lazer-McKenna suspension bridge model. For ɛ > 0 and k > 0, the decay properties of the solution semigroup are discussed in dependence of the nonnegative parameters γ and h, which are responsible for the damping effects.
A Methodology and Linear Model for System Planning and Evaluation.
ERIC Educational Resources Information Center
Meyer, Richard W.
1982-01-01
The two-phase effort at Clemson University to design a comprehensive library automation program is reported. Phase one was based on a version of IBM's business system planning methodology, and the second was based on a linear model designed to compare existing program systems to the phase one design. (MLW)
A SEMIPARAMETRIC BAYESIAN MODEL FOR CIRCULAR-LINEAR REGRESSION
We present a Bayesian approach to regress a circular variable on a linear predictor. The regression coefficients are assumed to have a nonparametric distribution with a Dirichlet process prior. The semiparametric Bayesian approach gives added flexibility to the model and is usefu...
Confidence Intervals for Assessing Heterogeneity in Generalized Linear Mixed Models
ERIC Educational Resources Information Center
Wagler, Amy E.
2014-01-01
Generalized linear mixed models are frequently applied to data with clustered categorical outcomes. The effect of clustering on the response is often difficult to practically assess partly because it is reported on a scale on which comparisons with regression parameters are difficult to make. This article proposes confidence intervals for…
Canonical Correlation Analysis as the General Linear Model.
ERIC Educational Resources Information Center
Vidal, Sherry
The concept of the general linear model (GLM) is illustrated and how canonical correlation analysis is the GLM is explained, using a heuristic data set to demonstrate how canonical correlation analysis subsumes various multivariate and univariate methods. The paper shows how each of these analyses produces a synthetic variable, like the Yhat…
Mathematical modelling and linear stability analysis of laser fusion cutting
NASA Astrophysics Data System (ADS)
Hermanns, Torsten; Schulz, Wolfgang; Vossen, Georg; Thombansen, Ulrich
2016-06-01
A model for laser fusion cutting is presented and investigated by linear stability analysis in order to study the tendency for dynamic behavior and subsequent ripple formation. The result is a so called stability function that describes the correlation of the setting values of the process and the process' amount of dynamic behavior.
Identifiability Results for Several Classes of Linear Compartment Models.
Meshkat, Nicolette; Sullivant, Seth; Eisenberg, Marisa
2015-08-01
Identifiability concerns finding which unknown parameters of a model can be estimated, uniquely or otherwise, from given input-output data. If some subset of the parameters of a model cannot be determined given input-output data, then we say the model is unidentifiable. In this work, we study linear compartment models, which are a class of biological models commonly used in pharmacokinetics, physiology, and ecology. In past work, we used commutative algebra and graph theory to identify a class of linear compartment models that we call identifiable cycle models, which are unidentifiable but have the simplest possible identifiable functions (so-called monomial cycles). Here we show how to modify identifiable cycle models by adding inputs, adding outputs, or removing leaks, in such a way that we obtain an identifiable model. We also prove a constructive result on how to combine identifiable models, each corresponding to strongly connected graphs, into a larger identifiable model. We apply these theoretical results to several real-world biological models from physiology, cell biology, and ecology. PMID:26337290
STATISTICAL BASED NON-LINEAR MODEL UPDATING USING FEATURE EXTRACTION
Schultz, J.F.; Hemez, F.M.
2000-10-01
This research presents a new method to improve analytical model fidelity for non-linear systems. The approach investigates several mechanisms to assist the analyst in updating an analytical model based on experimental data and statistical analysis of parameter effects. The first is a new approach at data reduction called feature extraction. This is an expansion of the update metrics to include specific phenomena or character of the response that is critical to model application. This is an extension of the classical linear updating paradigm of utilizing the eigen-parameters or FRFs to include such devices as peak acceleration, time of arrival or standard deviation of model error. The next expansion of the updating process is the inclusion of statistical based parameter analysis to quantify the effects of uncertain or significant effect parameters in the construction of a meta-model. This provides indicators of the statistical variation associated with parameters as well as confidence intervals on the coefficients of the resulting meta-model, Also included in this method is the investigation of linear parameter effect screening using a partial factorial variable array for simulation. This is intended to aid the analyst in eliminating from the investigation the parameters that do not have a significant variation effect on the feature metric, Finally an investigation of the model to replicate the measured response variation is examined.
Estimating classification images with generalized linear and additive models.
Knoblauch, Kenneth; Maloney, Laurence T
2008-12-22
Conventional approaches to modeling classification image data can be described in terms of a standard linear model (LM). We show how the problem can be characterized as a Generalized Linear Model (GLM) with a Bernoulli distribution. We demonstrate via simulation that this approach is more accurate in estimating the underlying template in the absence of internal noise. With increasing internal noise, however, the advantage of the GLM over the LM decreases and GLM is no more accurate than LM. We then introduce the Generalized Additive Model (GAM), an extension of GLM that can be used to estimate smooth classification images adaptively. We show that this approach is more robust to the presence of internal noise, and finally, we demonstrate that GAM is readily adapted to estimation of higher order (nonlinear) classification images and to testing their significance.
Exploring pseudoscalar meson scattering in linear sigma models
NASA Astrophysics Data System (ADS)
Black (Speaker), Deirdre; Fariborz, Amir H.; Moussa, Sherif; Nasri, Salah; Schechter, Joseph
2001-11-01
The three flavor linear sigma model is studied as a toy model for understanding the role of possible light scalar mesons in the ππ, πK and πɛ elastic scattering channels. We unitarize tree level amplitudes using the K-matrix prescription and, with a sufficiently general model, obtain reasonable fits to the experimental data. The effect of unitarization is very important and leads to the emergence of a nonet of light scalars, with masses below 1 GeV. We compare with a scattering treatment using a more general non-linear sigma model approach and also comment briefly upon how our results fit in with the scalar meson puzzle. .
On the fold-Hopf bifurcation for continuous piecewise linear differential systems with symmetry.
Llibre, Jaume; Ponce, Enrique; Ros, Javier; Torres, Francisco
2010-09-01
In this paper a partial unfolding for an analog to the fold-Hopf bifurcation in three-dimensional symmetric piecewise linear differential systems is obtained. A particular biparametric family of such systems is studied starting from a very degenerate configuration of nonhyperbolic periodic orbits and looking for the possible bifurcation of limit cycles. It is proved that four limit cycles can coexist after perturbation of the original configuration, and other two limit cycles are conjectured. It is shown that the described bifurcation scenario appears for appropriate values of parameters in the celebrated Chua's oscillator.
Linear systems, compartmental modeling, and estimability issues in IAQ studies
Evans, W.C.
1996-12-31
Many IAQ models are expressed as coupled systems of linear, ordinary differential equations. In this paper, the linear-systems or state-variable format for these systems will be reviewed, and some useful information will be presented which can be obtained from this formulation without explicitly solving the differential equation system. Much information concerning linear systems analysis is available in the literature of various disciplines, particularly biomathematics, wherein there is a specialization called compartmental modeling. It is important to recognize that there exists a great deal of directly usable mathematical information which can immediately be applied to IAQ modeling problems. In compartmental modeling, an issue called identifiability has long been recognized as a potential problem with experiments that are intended to extract information about a linear system`s parameters from observations of that system`s response to a forcing function. It can happen that the system`s parameters cannot be uniquely estimated from an experiment, no matter how good (noise-free) the measurements are. With a linear-systems formulation of the experimental configuration, this condition can be detected before the experiment is conducted. A related issue is termed redundancy, which refers to the inability to obtain unique parameter estimates from the data, even if the experiment is identifiable. This problem occurs for sums-of-exponentials models, fitted via nonlinear estimation to the observations. Taken together, identifiability and redundancy can be termed estimability. These difficulties can affect chamber testing in particular, since this is the context where they are attempting to estimate system parameters from observations. This paper will present an overview of these issues, with selected examples.
Inverse modelling problems in linear algebra undergraduate courses
NASA Astrophysics Data System (ADS)
Martinez-Luaces, Victor E.
2013-10-01
This paper will offer an analysis from a theoretical point of view of mathematical modelling, applications and inverse problems of both causation and specification types. Inverse modelling problems give the opportunity to establish connections between theory and practice and to show this fact, a simple linear algebra example in two different presentations will be discussed. Finally, several results will be presented and some conclusions proposed.
Linear Sigma Model Toolshed for D-brane Physics
Hellerman, Simeon
2001-08-23
Building on earlier work, we construct linear sigma models for strings on curved spaces in the presence of branes. Our models include an extremely general class of brane-worldvolume gauge field configurations. We explain in an accessible manner the mathematical ideas which suggest appropriate worldsheet interactions for generating a given open string background. This construction provides an explanation for the appearance of the derived category in D-brane physic complementary to that of recent work of Douglas.
Linear modeling of steady-state behavioral dynamics.
Palya, William L; Walter, Donald; Kessel, Robert; Lucke, Robert
2002-01-01
The observed steady-state behavioral dynamics supported by unsignaled periods of reinforcement within repeating 2,000-s trials were modeled with a linear transfer function. These experiments employed improved schedule forms and analytical methods to improve the precision of the measured transfer function, compared to previous work. The refinements include both the use of multiple reinforcement periods that improve spectral coverage and averaging of independently determined transfer functions. A linear analysis was then used to predict behavior observed for three different test schedules. The fidelity of these predictions was determined. PMID:11831782
Continuity of Care: The Transitional Care Model.
Hirschman, Karen B; Shaid, Elizabeth; McCauley, Kathleen; Pauly, Mark V; Naylor, Mary D
2015-09-30
Older adults with multiple chronic conditions complicated by other risk factors, such as deficits in activities of daily living or social barriers, experience multiple challenges in managing their healthcare needs, especially during episodes of acute illness. Identifying effective strategies to improve care transitions and outcomes for this population is essential. One rigorously tested model that has consistently demonstrated effectiveness in addressing the needs of this complex population while reducing healthcare costs is the Transitional Care Model (TCM). The TCM is a nurse-led intervention targeting older adults at risk for poor outcomes as they move across healthcare settings and between clinicians. This article provides a detailed summary of the evidence base for the TCM and the model's nine core components. We also discuss measuring the TCM's core components and the overall impact of this evidence-based care management approach.
Linear Time Invariant Models for Integrated Flight and Rotor Control
NASA Astrophysics Data System (ADS)
Olcer, Fahri Ersel
2011-12-01
Recent developments on individual blade control (IBC) and physics based reduced order models of various on-blade control (OBC) actuation concepts are opening up opportunities to explore innovative rotor control strategies for improved rotor aerodynamic performance, reduced vibration and BVI noise, and improved rotor stability, etc. Further, recent developments in computationally efficient algorithms for the extraction of Linear Time Invariant (LTI) models are providing a convenient framework for exploring integrated flight and rotor control, while accounting for the important couplings that exist between body and low frequency rotor response and high frequency rotor response. Formulation of linear time invariant (LTI) models of a nonlinear system about a periodic equilibrium using the harmonic domain representation of LTI model states has been studied in the literature. This thesis presents an alternative method and a computationally efficient scheme for implementation of the developed method for extraction of linear time invariant (LTI) models from a helicopter nonlinear model in forward flight. The fidelity of the extracted LTI models is evaluated using response comparisons between the extracted LTI models and the nonlinear model in both time and frequency domains. Moreover, the fidelity of stability properties is studied through the eigenvalue and eigenvector comparisons between LTI and LTP models by making use of the Floquet Transition Matrix. For time domain evaluations, individual blade control (IBC) and On-Blade Control (OBC) inputs that have been tried in the literature for vibration and noise control studies are used. For frequency domain evaluations, frequency sweep inputs are used to obtain frequency responses of fixed system hub loads to a single blade IBC input. The evaluation results demonstrate the fidelity of the extracted LTI models, and thus, establish the validity of the LTI model extraction process for use in integrated flight and rotor control
Glocker, Ben; Paragios, Nikos; Komodakis, Nikos; Tziritas, Georgios; Navab, Nassir
2007-01-01
In this paper we propose a novel non-rigid volume registration based on discrete labeling and linear programming. The proposed framework reformulates registration as a minimal path extraction in a weighted graph. The space of solutions is represented using a set of a labels which are assigned to predefined displacements. The graph topology corresponds to a superimposed regular grid onto the volume. Links between neighborhood control points introduce smoothness, while links between the graph nodes and the labels (end-nodes) measure the cost induced to the objective function through the selection of a particular deformation for a given control point once projected to the entire volume domain, Higher order polynomials are used to express the volume deformation from the ones of the control points. Efficient linear programming that can guarantee the optimal solution up to (a user-defined) bound is considered to recover the optimal registration parameters. Therefore, the method is gradient free, can encode various similarity metrics (simple changes on the graph construction), can guarantee a globally sub-optimal solution and is computational tractable. Experimental validation using simulated data with known deformation, as well as manually segmented data demonstrate the extreme potentials of our approach. PMID:17633717
Identification of parameters of discrete-continuous models
Cekus, Dawid Warys, Pawel
2015-03-10
In the paper, the parameters of a discrete-continuous model have been identified on the basis of experimental investigations and formulation of optimization problem. The discrete-continuous model represents a cantilever stepped Timoshenko beam. The mathematical model has been formulated and solved according to the Lagrange multiplier formalism. Optimization has been based on the genetic algorithm. The presented proceeding’s stages make the identification of any parameters of discrete-continuous systems possible.
Dust grain coagulation modelling : From discrete to continuous
NASA Astrophysics Data System (ADS)
Paruta, P.; Hendrix, T.; Keppens, R.
2016-07-01
In molecular clouds, stars are formed from a mixture of gas, plasma and dust particles. The dynamics of this formation is still actively investigated and a study of dust coagulation can help to shed light on this process. Starting from a pre-existing discrete coagulation model, this work aims to mathematically explore its properties and its suitability for numerical validation. The crucial step is in our reinterpretation from its original discrete to a well-defined continuous form, which results in the well-known Smoluchowski coagulation equation. This opens up the possibility of exploiting previous results in order to prove the existence and uniqueness of a mass conserving solution for the evolution of dust grain size distribution. Ultimately, to allow for a more flexible numerical implementation, the problem is rewritten as a non-linear hyperbolic integro-differential equation and solved using a finite volume discretisation. It is demonstrated that there is an exact numerical agreement with the initial discrete model, with improved accuracy. This is of interest for further work on dynamically coupled gas with dust simulations.
Gene Golub; Kwok Ko
2009-03-30
The solutions of sparse eigenvalue problems and linear systems constitute one of the key computational kernels in the discretization of partial differential equations for the modeling of linear accelerators. The computational challenges faced by existing techniques for solving those sparse eigenvalue problems and linear systems call for continuing research to improve on the algorithms so that ever increasing problem size as required by the physics application can be tackled. Under the support of this award, the filter algorithm for solving large sparse eigenvalue problems was developed at Stanford to address the computational difficulties in the previous methods with the goal to enable accelerator simulations on then the world largest unclassified supercomputer at NERSC for this class of problems. Specifically, a new method, the Hemitian skew-Hemitian splitting method, was proposed and researched as an improved method for solving linear systems with non-Hermitian positive definite and semidefinite matrices.
Current Density and Continuity in Discretized Models
ERIC Educational Resources Information Center
Boykin, Timothy B.; Luisier, Mathieu; Klimeck, Gerhard
2010-01-01
Discrete approaches have long been used in numerical modelling of physical systems in both research and teaching. Discrete versions of the Schrodinger equation employing either one or several basis functions per mesh point are often used by senior undergraduates and beginning graduate students in computational physics projects. In studying…
Mathematical Modelling of Continuous Biotechnological Processes
ERIC Educational Resources Information Center
Pencheva, T.; Hristozov, I.; Shannon, A. G.
2003-01-01
Biotechnological processes (BTP) are characterized by a complicated structure of organization and interdependent characteristics. Partial differential equations or systems of partial differential equations are used for their behavioural description as objects with distributed parameters. Modelling of substrate without regard to dispersion…
General mirror pairs for gauged linear sigma models
NASA Astrophysics Data System (ADS)
Aspinwall, Paul S.; Plesser, M. Ronen
2015-11-01
We carefully analyze the conditions for an abelian gauged linear σ-model to exhibit nontrivial IR behavior described by a nonsingular superconformal field theory determining a superstring vacuum. This is done without reference to a geometric phase, by associating singular behavior to a noncompact space of (semi-)classical vacua. We find that models determined by reflexive combinatorial data are nonsingular for generic values of their parameters. This condition has the pleasant feature that the mirror of a nonsingular gauged linear σ-model is another such model, but it is clearly too strong and we provide an example of a non-reflexive mirror pair. We discuss a weaker condition inspired by considering extremal transitions, which is also mirror symmetric and which we conjecture to be sufficient. We apply these ideas to extremal transitions and to understanding the way in which both Berglund-Hübsch mirror symmetry and the Vafa-Witten mirror orbifold with discrete torsion can be seen as special cases of the general combinatorial duality of gauged linear σ-models. In the former case we encounter an example showing that our weaker condition is still not necessary.
Modeling error analysis of stationary linear discrete-time filters
NASA Technical Reports Server (NTRS)
Patel, R.; Toda, M.
1977-01-01
The performance of Kalman-type, linear, discrete-time filters in the presence of modeling errors is considered. The discussion is limited to stationary performance, and bounds are obtained for the performance index, the mean-squared error of estimates for suboptimal and optimal (Kalman) filters. The computation of these bounds requires information on only the model matrices and the range of errors for these matrices. Consequently, a design can easily compare the performance of a suboptimal filter with that of the optimal filter, when only the range of errors in the elements of the model matrices is available.
MAGDM linear-programming models with distinct uncertain preference structures.
Xu, Zeshui S; Chen, Jian
2008-10-01
Group decision making with preference information on alternatives is an interesting and important research topic which has been receiving more and more attention in recent years. The purpose of this paper is to investigate multiple-attribute group decision-making (MAGDM) problems with distinct uncertain preference structures. We develop some linear-programming models for dealing with the MAGDM problems, where the information about attribute weights is incomplete, and the decision makers have their preferences on alternatives. The provided preference information can be represented in the following three distinct uncertain preference structures: 1) interval utility values; 2) interval fuzzy preference relations; and 3) interval multiplicative preference relations. We first establish some linear-programming models based on decision matrix and each of the distinct uncertain preference structures and, then, develop some linear-programming models to integrate all three structures of subjective uncertain preference information provided by the decision makers and the objective information depicted in the decision matrix. Furthermore, we propose a simple and straightforward approach in ranking and selecting the given alternatives. It is worth pointing out that the developed models can also be used to deal with the situations where the three distinct uncertain preference structures are reduced to the traditional ones, i.e., utility values, fuzzy preference relations, and multiplicative preference relations. Finally, we use a practical example to illustrate in detail the calculation process of the developed approach.
Electromagnetic analysis of diffractive lens with C method and local linear grating model
NASA Astrophysics Data System (ADS)
Xiao, Kai; Liu, Ying; Fu, Shaojun
2005-02-01
The electromagnetic theory should be applied to determine the diffraction efficiency of structures whose minimum line width is comparable with wavelength or the grooves are too deep, where scalar theory is no longer useful. The coordinate transformation method (the C method) is a very efficient method for obtaining continuous surface-relief grating efficiency for both TE and TM polarization. The local linear grating model (LLGM) models 2-D circular diffractive lens with combination of a series of local linear gratings. We synthesized and analyzed circular diffractive lens with a continuous profile not as previous authors who always use multi-lever structures. The result is compared with that of scalar theory and analysis using LLGM and rigorous coupled-wave theory. This optimization can be used as a complement of the scalar design of diffractive lens.
LINEAR MODELS FOR MANAGING SOURCES OF GROUNDWATER POLLUTION.
Gorelick, Steven M.; Gustafson, Sven-Ake; ,
1984-01-01
Mathematical models for the problem of maintaining a specified groundwater quality while permitting solute waste disposal at various facilities distributed over space are discussed. The pollutants are assumed to be chemically inert and their concentrations in the groundwater are governed by linear equations for advection and diffusion. The aim is to determine a disposal policy which maximises the total amount of pollutants released during a fixed time T while meeting the condition that the concentration everywhere is below prescribed levels.
Credibility analysis of risk classes by generalized linear model
NASA Astrophysics Data System (ADS)
Erdemir, Ovgucan Karadag; Sucu, Meral
2016-06-01
In this paper generalized linear model (GLM) and credibility theory which are frequently used in nonlife insurance pricing are combined for reliability analysis. Using full credibility standard, GLM is associated with limited fluctuation credibility approach. Comparison criteria such as asymptotic variance and credibility probability are used to analyze the credibility of risk classes. An application is performed by using one-year claim frequency data of a Turkish insurance company and results of credible risk classes are interpreted.
Using Quartile-Quartile Lines as Linear Models
ERIC Educational Resources Information Center
Gordon, Sheldon P.
2015-01-01
This article introduces the notion of the quartile-quartile line as an alternative to the regression line and the median-median line to produce a linear model based on a set of data. It is based on using the first and third quartiles of a set of (x, y) data. Dynamic spreadsheets are used as exploratory tools to compare the different approaches and…
NON-LINEAR MODELING OF THE RHIC INTERACTION REGIONS.
TOMAS,R.FISCHER,W.JAIN,A.LUO,Y.PILAT,F.
2004-07-05
For RHIC's collision lattices the dominant sources of transverse non-linearities are located in the interaction regions. The field quality is available for most of the magnets in the interaction regions from the magnetic measurements, or from extrapolations of these measurements. We discuss the implementation of these measurements in the MADX models of the Blue and the Yellow rings and their impact on beam stability.
Linearized reduced-order models for subsurface flow simulation
NASA Astrophysics Data System (ADS)
Cardoso, M. A.; Durlofsky, L. J.
2010-02-01
A trajectory piecewise linearization (TPWL) procedure for the reduced-order modeling of two-phase flow in subsurface formations is developed and applied. The method represents new pressure and saturation states using linear expansions around states previously simulated and saved during a series of preprocessing training runs. The linearized representation is projected into a low-dimensional space, with the projection matrix constructed through proper orthogonal decomposition of the states determined during the training runs. The TPWL model is applied to two example problems, containing 24,000 and 79,200 grid blocks, which are characterized by heterogeneous permeability descriptions. Extensive test simulations are performed for both models. It is shown that the TPWL model provides accurate results when the controls (bottom hole pressures of the production wells in this case) applied in test simulations are within the general range of the controls applied in the training runs, even though the well pressure schedules for the test runs can differ significantly from those of the training runs. This indicates that the TPWL model displays a reasonable degree of robustness. Runtime speedups using the procedure are very significant-a factor of 100-2000 (depending on model size and whether or not mass balance error is computed at every time step) for the cases considered. The preprocessing overhead required by the TPWL procedure is the equivalent of about four high-fidelity simulations. Finally, the TPWL procedure is applied to a computationally demanding multiobjective optimization problem, for which the Pareto front is determined. Limited high-fidelity simulations demonstrate the accuracy and applicability of TPWL for this optimization. Future work should focus on error estimation and on stabilizing the method for large models with significant density differences between phases.
Granita; Bahar, A.
2015-03-09
This paper discusses on linear birth and death with immigration and emigration (BIDE) process to stochastic differential equation (SDE) model. Forward Kolmogorov equation in continuous time Markov chain (CTMC) with a central-difference approximation was used to find Fokker-Planckequation corresponding to a diffusion process having the stochastic differential equation of BIDE process. The exact solution, mean and variance function of BIDE process was found.
NASA Astrophysics Data System (ADS)
Granita, Bahar, A.
2015-03-01
This paper discusses on linear birth and death with immigration and emigration (BIDE) process to stochastic differential equation (SDE) model. Forward Kolmogorov equation in continuous time Markov chain (CTMC) with a central-difference approximation was used to find Fokker-Planckequation corresponding to a diffusion process having the stochastic differential equation of BIDE process. The exact solution, mean and variance function of BIDE process was found.
ERIC Educational Resources Information Center
Esteley, Cristina B.; Villarreal, Monica E.; Alagia, Humberto R.
2010-01-01
Over the past several years, we have been exploring and researching a phenomenon that occurs among undergraduate students that we called extension of linear models to non-linear contexts or overgeneralization of linear models. This phenomenon appears when some students use linear representations in situations that are non-linear. In a first phase,…
A new high-order accurate continuous Galerkin method for linear elastodynamics problems
NASA Astrophysics Data System (ADS)
Idesman, Alexander V.
2007-07-01
A new high-order accurate time-continuous Galerkin (TCG) method for elastodynamics is suggested. The accuracy of the new implicit TCG method is increased by a factor of two in comparison to that of the standard TCG method and is one order higher than the accuracy of the standard time-discontinuous Galerkin (TDG) method at the same number of degrees of freedom. The new method is unconditionally stable and has controllable numerical dissipation at high frequencies. An iterative predictor/multi-corrector solver that includes the factorization of the effective mass matrix of the same dimension as that of the mass matrix for the second-order methods is developed for the new TCG method. A new strategy combining numerical methods with small and large numerical dissipation is developed for elastodynamics. Simple numerical tests show a significant reduction in the computation time (by 5 25 times) for the new TCG method in comparison to that for second-order methods, and the suppression of spurious high-frequency oscillations.
Sahin, Rubina; Tapadia, Kavita
2015-01-01
The three widely used isotherms Langmuir, Freundlich and Temkin were examined in an experiment using fluoride (F⁻) ion adsorption on a geo-material (limonite) at four different temperatures by linear and non-linear models. Comparison of linear and non-linear regression models were given in selecting the optimum isotherm for the experimental results. The coefficient of determination, r², was used to select the best theoretical isotherm. The four Langmuir linear equations (1, 2, 3, and 4) are discussed. Langmuir isotherm parameters obtained from the four Langmuir linear equations using the linear model differed but they were the same when using the nonlinear model. Langmuir-2 isotherm is one of the linear forms, and it had the highest coefficient of determination (r² = 0.99) compared to the other Langmuir linear equations (1, 3 and 4) in linear form, whereas, for non-linear, Langmuir-4 fitted best among all the isotherms because it had the highest coefficient of determination (r² = 0.99). The results showed that the non-linear model may be a better way to obtain the parameters. In the present work, the thermodynamic parameters show that the absorption of fluoride onto limonite is both spontaneous (ΔG < 0) and endothermic (ΔH > 0). Scanning electron microscope and X-ray diffraction images also confirm the adsorption of F⁻ ion onto limonite. The isotherm and kinetic study reveals that limonite can be used as an adsorbent for fluoride removal. In future we can develop new technology for fluoride removal in large scale by using limonite which is cost-effective, eco-friendly and is easily available in the study area.
Sahin, Rubina; Tapadia, Kavita
2015-01-01
The three widely used isotherms Langmuir, Freundlich and Temkin were examined in an experiment using fluoride (F⁻) ion adsorption on a geo-material (limonite) at four different temperatures by linear and non-linear models. Comparison of linear and non-linear regression models were given in selecting the optimum isotherm for the experimental results. The coefficient of determination, r², was used to select the best theoretical isotherm. The four Langmuir linear equations (1, 2, 3, and 4) are discussed. Langmuir isotherm parameters obtained from the four Langmuir linear equations using the linear model differed but they were the same when using the nonlinear model. Langmuir-2 isotherm is one of the linear forms, and it had the highest coefficient of determination (r² = 0.99) compared to the other Langmuir linear equations (1, 3 and 4) in linear form, whereas, for non-linear, Langmuir-4 fitted best among all the isotherms because it had the highest coefficient of determination (r² = 0.99). The results showed that the non-linear model may be a better way to obtain the parameters. In the present work, the thermodynamic parameters show that the absorption of fluoride onto limonite is both spontaneous (ΔG < 0) and endothermic (ΔH > 0). Scanning electron microscope and X-ray diffraction images also confirm the adsorption of F⁻ ion onto limonite. The isotherm and kinetic study reveals that limonite can be used as an adsorbent for fluoride removal. In future we can develop new technology for fluoride removal in large scale by using limonite which is cost-effective, eco-friendly and is easily available in the study area. PMID:26676015
[Linear mixed modeling of branch biomass for Korean pine plantation].
Dong, Li-Hu; Li, Feng-Ri; Jia, Wei-Wei
2013-12-01
Based on the measurement of 3643 branch biomass samples of 60 Korean pine (Pinus koraiensis) trees from Mengjiagang Forest Farm, Heilongjiang Province, all subset regressions techniques were used to develop the branch biomass model (branch, foliage, and total biomass models). The optimal base model of branch biomass was developed as lnw = k1 + k2 lnL(b) + k3 lnD(b). Then, linear mixed models were developed based on PROC MIXED of SAS 9.3 software, and evaluated with AIC, BIC, Log Likelihood and Likelihood ratio tests. The results showed that the foliage and total biomass models with parameters k1, k2 and k3 as mixed effects showed the best performance. The branch biomass model with parameters k5 and k2 as mixed effects showed the best performance. Finally, we evaluated the optimal base model and the mixed model of branch biomass. Model validation confirmed that the mixed model was better than the optimal base model. The mixed model with random parameters could not only provide more accurate and precise prediction, but also showed the individual difference based on variance-covariance structure.
On the Development of Parameterized Linear Analytical Longitudinal Airship Models
NASA Technical Reports Server (NTRS)
Kulczycki, Eric A.; Johnson, Joseph R.; Bayard, David S.; Elfes, Alberto; Quadrelli, Marco B.
2008-01-01
In order to explore Titan, a moon of Saturn, airships must be able to traverse the atmosphere autonomously. To achieve this, an accurate model and accurate control of the vehicle must be developed so that it is understood how the airship will react to specific sets of control inputs. This paper explains how longitudinal aircraft stability derivatives can be used with airship parameters to create a linear model of the airship solely by combining geometric and aerodynamic airship data. This method does not require system identification of the vehicle. All of the required data can be derived from computational fluid dynamics and wind tunnel testing. This alternate method of developing dynamic airship models will reduce time and cost. Results are compared to other stable airship dynamic models to validate the methods. Future work will address a lateral airship model using the same methods.
Modelling human balance using switched systems with linear feedback control.
Kowalczyk, Piotr; Glendinning, Paul; Brown, Martin; Medrano-Cerda, Gustavo; Dallali, Houman; Shapiro, Jonathan
2012-02-01
We are interested in understanding the mechanisms behind and the character of the sway motion of healthy human subjects during quiet standing. We assume that a human body can be modelled as a single-link inverted pendulum, and the balance is achieved using linear feedback control. Using these assumptions, we derive a switched model which we then investigate. Stable periodic motions (limit cycles) about an upright position are found. The existence of these limit cycles is studied as a function of system parameters. The exploration of the parameter space leads to the detection of multi-stability and homoclinic bifurcations. PMID:21697168
Bayesian partial linear model for skewed longitudinal data.
Tang, Yuanyuan; Sinha, Debajyoti; Pati, Debdeep; Lipsitz, Stuart; Lipshultz, Steven
2015-07-01
Unlike majority of current statistical models and methods focusing on mean response for highly skewed longitudinal data, we present a novel model for such data accommodating a partially linear median regression function, a skewed error distribution and within subject association structures. We provide theoretical justifications for our methods including asymptotic properties of the posterior and associated semiparametric Bayesian estimators. We also provide simulation studies to investigate the finite sample properties of our methods. Several advantages of our method compared with existing methods are demonstrated via analysis of a cardiotoxicity study of children of HIV-infected mothers.
Modelling human balance using switched systems with linear feedback control.
Kowalczyk, Piotr; Glendinning, Paul; Brown, Martin; Medrano-Cerda, Gustavo; Dallali, Houman; Shapiro, Jonathan
2012-02-01
We are interested in understanding the mechanisms behind and the character of the sway motion of healthy human subjects during quiet standing. We assume that a human body can be modelled as a single-link inverted pendulum, and the balance is achieved using linear feedback control. Using these assumptions, we derive a switched model which we then investigate. Stable periodic motions (limit cycles) about an upright position are found. The existence of these limit cycles is studied as a function of system parameters. The exploration of the parameter space leads to the detection of multi-stability and homoclinic bifurcations.
Models for cultural inheritance: a general linear model.
Feldman, M W; Cavalli-Sforza, L L
1975-07-01
A theory of cultural evolution is proposed based on a general linear mode of cultural transmission. The trait of an individual is assumed to depend on the values of the same trait in other individuals of the same, the previous or earlier generation. The transmission matrix W has as its elements the proportional contributions of each individual (i) of one generation to each individual (j) of another. In addition, there is random variation (copy error or innovation) for each individual. Means and variances of a group of N individuals change with time and will stabilize asymptotically if the matrix W is irreducible and aperiodic. The rate of convergence is geometric and is governed by the largest non-unit eigenvalue of W. Groups fragment and evolve independently if W is reducible. The means of independent groups vary at random at a predicted rate, a phenomenon termed "random cultural drift". Variances within a group tend to be small, assuming cultural homogeneity. Transmission matrices of the teacher/leader type, and of parental type have been specifically considered, as well as social hierarchies. Various limitations, extensions, and some chances of application are discussed.
Linear stability analysis of swirling turbulent flows with turbulence models
NASA Astrophysics Data System (ADS)
Gupta, Vikrant; Juniper, Matthew
2013-11-01
In this paper, we consider the growth of large scale coherent structures in turbulent flows by performing linear stability analysis around a mean flow. Turbulent flows are characterized by fine-scale stochastic perturbations. The momentum transfer caused by these perturbations affects the development of larger structures. Therefore, in a linear stability analysis, it is important to include the perturbations' influence. One way to do this is to include a turbulence model in the stability analysis. This is done in the literature by using eddy viscosity models (EVMs), which are first order turbulence models. We extend this approach by using second order turbulence models, in this case explicit algebraic Reynolds stress models (EARSMs). EARSMs are more versatile than EVMs, in that they can be applied to a wider range of flows, and could also be more accurate. We verify our EARSM-based analysis by applying it to a channel flow and then comparing the results with those from an EVM-based analysis. We then apply the EARSM-based stability analysis to swirling pipe flows and Taylor-Couette flows, which demonstrates the main benefit of EARSM-based analysis. This project is supported by EPSRC and Rolls-Royce through a Dorothy Hodgkin Research Fellowship.
Non-linear model for compression tests on articular cartilage.
Grillo, Alfio; Guaily, Amr; Giverso, Chiara; Federico, Salvatore
2015-07-01
Hydrated soft tissues, such as articular cartilage, are often modeled as biphasic systems with individually incompressible solid and fluid phases, and biphasic models are employed to fit experimental data in order to determine the mechanical and hydraulic properties of the tissues. Two of the most common experimental setups are confined and unconfined compression. Analytical solutions exist for the unconfined case with the linear, isotropic, homogeneous model of articular cartilage, and for the confined case with the non-linear, isotropic, homogeneous model. The aim of this contribution is to provide an easily implementable numerical tool to determine a solution to the governing differential equations of (homogeneous and isotropic) unconfined and (inhomogeneous and isotropic) confined compression under large deformations. The large-deformation governing equations are reduced to equivalent diffusive equations, which are then solved by means of finite difference (FD) methods. The solution strategy proposed here could be used to generate benchmark tests for validating complex user-defined material models within finite element (FE) implementations, and for determining the tissue's mechanical and hydraulic properties from experimental data.
Application of linear gauss pseudospectral method in model predictive control
NASA Astrophysics Data System (ADS)
Yang, Liang; Zhou, Hao; Chen, Wanchun
2014-03-01
This paper presents a model predictive control(MPC) method aimed at solving the nonlinear optimal control problem with hard terminal constraints and quadratic performance index. The method combines the philosophies of the nonlinear approximation model predictive control, linear quadrature optimal control and Gauss Pseudospectral method. The current control is obtained by successively solving linear algebraic equations transferred from the original problem via linearization and the Gauss Pseudospectral method. It is not only of high computational efficiency since it does not need to solve nonlinear programming problem, but also of high accuracy though there are a few discrete points. Therefore, this method is suitable for on-board applications. A design of terminal impact with a specified direction is carried out to evaluate the performance of this method. Augmented PN guidance law in the three-dimensional coordinate system is applied to produce the initial guess. And various cases for target with straight-line movements are employed to demonstrate the applicability in different impact angles. Moreover, performance of the proposed method is also assessed by comparison with other guidance laws. Simulation results indicate that this method is not only of high computational efficiency and accuracy, but also applicable in the framework of guidance design.
Wavefront Sensing for WFIRST with a Linear Optical Model
NASA Technical Reports Server (NTRS)
Jurling, Alden S.; Content, David A.
2012-01-01
In this paper we develop methods to use a linear optical model to capture the field dependence of wavefront aberrations in a nonlinear optimization-based phase retrieval algorithm for image-based wavefront sensing. The linear optical model is generated from a ray trace model of the system and allows the system state to be described in terms of mechanical alignment parameters rather than wavefront coefficients. This approach allows joint optimization over images taken at different field points and does not require separate convergence of phase retrieval at individual field points. Because the algorithm exploits field diversity, multiple defocused images per field point are not required for robustness. Furthermore, because it is possible to simultaneously fit images of many stars over the field, it is not necessary to use a fixed defocus to achieve adequate signal-to-noise ratio despite having images with high dynamic range. This allows high performance wavefront sensing using in-focus science data. We applied this technique in a simulation model based on the Wide Field Infrared Survey Telescope (WFIRST) Intermediate Design Reference Mission (IDRM) imager using a linear optical model with 25 field points. We demonstrate sub-thousandth-wave wavefront sensing accuracy in the presence of noise and moderate undersampling for both monochromatic and polychromatic images using 25 high-SNR target stars. Using these high-quality wavefront sensing results, we are able to generate upsampled point-spread functions (PSFs) and use them to determine PSF ellipticity to high accuracy in order to reduce the systematic impact of aberrations on the accuracy of galactic ellipticity determination for weak-lensing science.
An error bound for a discrete reduced order model of a linear multivariable system
NASA Technical Reports Server (NTRS)
Al-Saggaf, Ubaid M.; Franklin, Gene F.
1987-01-01
The design of feasible controllers for high dimension multivariable systems can be greatly aided by a method of model reduction. In order for the design based on the order reduction to include a guarantee of stability, it is sufficient to have a bound on the model error. Previous work has provided such a bound for continuous-time systems for algorithms based on balancing. In this note an L-infinity bound is derived for model error for a method of order reduction of discrete linear multivariable systems based on balancing.
Learning Petri net models of non-linear gene interactions.
Mayo, Michael
2005-10-01
Understanding how an individual's genetic make-up influences their risk of disease is a problem of paramount importance. Although machine-learning techniques are able to uncover the relationships between genotype and disease, the problem of automatically building the best biochemical model or "explanation" of the relationship has received less attention. In this paper, I describe a method based on random hill climbing that automatically builds Petri net models of non-linear (or multi-factorial) disease-causing gene-gene interactions. Petri nets are a suitable formalism for this problem, because they are used to model concurrent, dynamic processes analogous to biochemical reaction networks. I show that this method is routinely able to identify perfect Petri net models for three disease-causing gene-gene interactions recently reported in the literature.
Repopulation Kinetics and the Linear-Quadratic Model
NASA Astrophysics Data System (ADS)
O'Rourke, S. F. C.; McAneney, H.; Starrett, C.; O'Sullivan, J. M.
2009-08-01
The standard Linear-Quadratic (LQ) survival model for radiotherapy is used to investigate different schedules of radiation treatment planning for advanced head and neck cancer. We explore how these treament protocols may be affected by different tumour repopulation kinetics between treatments. The laws for tumour cell repopulation include the logistic and Gompertz models and this extends the work of Wheldon et al. [1], which was concerned with the case of exponential repopulation between treatments. Treatment schedules investigated include standarized and accelerated fractionation. Calculations based on the present work show, that even with growth laws scaled to ensure that the repopulation kinetics for advanced head and neck cancer are comparable, considerable variation in the survival fraction to orders of magnitude emerged. Calculations show that application of the Gompertz model results in a significantly poorer prognosis for tumour eradication. Gaps in treatment also highlight the differences in the LQ model with the effect of repopulation kinetics included.
Diagnostic Measures for Generalized Linear Models with Missing Covariates
ZHU, HONGTU; IBRAHIM, JOSEPH G.; SHI, XIAOYAN
2009-01-01
In this paper, we carry out an in-depth investigation of diagnostic measures for assessing the influence of observations and model misspecification in the presence of missing covariate data for generalized linear models. Our diagnostic measures include case-deletion measures and conditional residuals. We use the conditional residuals to construct goodness-of-fit statistics for testing possible misspecifications in model assumptions, including the sampling distribution. We develop specific strategies for incorporating missing data into goodness-of-fit statistics in order to increase the power of detecting model misspecification. A resampling method is proposed to approximate the p-value of the goodness-of-fit statistics. Simulation studies are conducted to evaluate our methods and a real data set is analysed to illustrate the use of our various diagnostic measures. PMID:20037674
Prediction of mean arterial blood pressure with linear stochastic models.
Genc, Sahika
2011-01-01
A model-based approach that integrates known portion of the cardiovascular system and unknown portion through a parameter estimation to predict evolution of the mean arterial pressure is considered. The unknown portion corresponds to the neural portion that acts like a controller that takes corrective actions to regulate the arterial blood pressure at a constant level. The input to the neural part is the arterial pressure and output is the sympathetic nerve activity. In this model, heart rate is considered a proxy for sympathetic nerve activity. The neural portion is modeled as a linear discrete-time system with random coefficients. The performance of the model is tested on a case study of acute hypotensive episodes (AHEs) on PhysioNet data. TPRs and FPRs improve as more data becomes available during estimation period.
Generalised Linear Modelling of daily rainfall in Southern England
NASA Astrophysics Data System (ADS)
Yang, C.; Chandler, R. E.; Isham, V. S.
2003-04-01
Recently published research has demonstrated the use of Generalised Linear Models (GLMs) for interpreting historical records of rainfall and other climate variables. Here, we present a case study illustrating the GLM approach to daily rainfall modelling, for a river catchment in the south of England. The area of interest is around 40km x 50km in size; data from 34 gauges are available, with record lengths ranging from 5 to 96 years. An initial modelling exercise revealed apparent spatial inconsistencies among the gauges, similar to those reported in other studies. However, it was subsequently found that these were mainly due to small rainfall values, and could be removed by thresholding the data prior to modelling. The capacity of GLMs for simulating realistic multi-site daily rainfall sequences is also demonstrated: a wide range of properties of observed rainfall sequences can be reproduced well using GLM simulations.
A positional discriminability model of linear-order judgments.
Holyoak, K J; Patterson, K K
1981-12-01
The process of judging the relative order of stimuli in a visual array was investigated in three experiments. In the basic paradigm, a linear array of six colored lines was presented briefly, and subject decided which of two target lines was the leftmost or rightmost (Experiment 1). The target lines appeared in all possible combinations of serial positions and reaction time (RT) was measured. Distance and semantic congruity effects were obtained, as well as a bowed serial position function. The RT pattern resembled that observed in comparable studies with memorized linear orderings. The serial position function was flattened when the background lines were homogeneously dissimilar to the target lines (Experiment 2). Both a distance effect and bowed serial position functions were obtained when subjects judged which of two target lines was below a black bar cue (Experiment 3). The results favored and analog positional discriminability model over a serial ends-inward scanning model. The positional discriminability model was proposed as a "core model" for the processes involved in judging relative order or magnitude in the domains of memory and perception.
ERIC Educational Resources Information Center
Cheong, Yuk Fai; Kamata, Akihito
2013-01-01
In this article, we discuss and illustrate two centering and anchoring options available in differential item functioning (DIF) detection studies based on the hierarchical generalized linear and generalized linear mixed modeling frameworks. We compared and contrasted the assumptions of the two options, and examined the properties of their DIF…
Linear Modeling and Evaluation of Controls on Flow Response in Western Post-Fire Watersheds
NASA Astrophysics Data System (ADS)
Saxe, S.; Hogue, T. S.; Hay, L.
2015-12-01
This research investigates the impact of wildfires on watershed flow regimes throughout the western United States, specifically focusing on evaluation of fire events within specified subregions and determination of the impact of climate and geophysical variables in post-fire flow response. Fire events were collected through federal and state-level databases and streamflow data were collected from U.S. Geological Survey stream gages. 263 watersheds were identified with at least 10 years of continuous pre-fire daily streamflow records and 5 years of continuous post-fire daily flow records. For each watershed, percent changes in runoff ratio (RO), annual seven day low-flows (7Q2) and annual seven day high-flows (7Q10) were calculated from pre- to post-fire. Numerous independent variables were identified for each watershed and fire event, including topographic, land cover, climate, burn severity, and soils data. The national watersheds were divided into five regions through K-clustering and a lasso linear regression model, applying the Leave-One-Out calibration method, was calculated for each region. Nash-Sutcliffe Efficiency (NSE) was used to determine the accuracy of the resulting models. The regions encompassing the United States along and west of the Rocky Mountains, excluding the coastal watersheds, produced the most accurate linear models. The Pacific coast region models produced poor and inconsistent results, indicating that the regions need to be further subdivided. Presently, RO and HF response variables appear to be more easily modeled than LF. Results of linear regression modeling showed varying importance of watershed and fire event variables, with conflicting correlation between land cover types and soil types by region. The addition of further independent variables and constriction of current variables based on correlation indicators is ongoing and should allow for more accurate linear regression modeling.
Monthly pan evaporation modeling using linear genetic programming
NASA Astrophysics Data System (ADS)
Guven, Aytac; Kisi, Ozgur
2013-10-01
This study compares the accuracy of linear genetic programming (LGP), fuzzy genetic (FG), adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and Stephens-Stewart (SS) methods in modeling pan evaporations. Monthly climatic data including solar radiation, air temperature, relative humidity, wind speed and pan evaporation from Antalya and Mersin stations, in Turkey are used in the study. The study composed of two parts. First part of the study focuses the comparison of LGP models with those of the FG, ANFIS, ANN and SS models in estimating pan evaporations of Antalya and Mersin stations, separately. From the comparison results, the LGP models are found to be better than the other models. Comparison of LGP models with the other models in estimating pan evaporations of the Mersin Station by using both stations' inputs is focused in the second part of the study. The results indicate that the LGP models better accuracy than the FG, ANFIS, ANN and SS models. It is seen that the pan evaporations can be successfully estimated by the LGP method.
Lee, Hsuan-Hsuan; Hsu, Chin-Chen; Lin, Yen-Ling; Chen, Carton W
2011-09-01
Gram-positive bacteria of the genus Streptomyces possess linear chromosomes and linear plasmids capped by terminal proteins covalently bound to the 5' ends of the DNA. The linearity of Streptomyces chromosomes raises the question of how they are transferred during conjugation, particularly when the mobilizing plasmids are also linear. The classical rolling circle replication model for transfer of circular plasmids and chromosomes from an internal origin cannot be applied to this situation. Instead it has been proposed that linear Streptomyces plasmids mobilize themselves and the linear chromosomes from their telomeres using terminal-protein-primed DNA synthesis. In support of this 'end first' model, we found that artificially circularized Streptomyces chromosomes could not be mobilized by linear plasmids (SLP2 and SCP1), while linear chromosomes could. In comparison, a circular plasmid (pIJ303) could mobilize both circular and linear chromosomes at the same efficiencies. Interestingly, artificially circularized SLP2 exhibited partial self-transfer capability, indicating that, being a composite replicon, it may have acquired the additional internal origin of transfer from an ancestral circular plasmid during evolution.
GENERALIZED PARTIALLY LINEAR MIXED-EFFECTS MODELS INCORPORATING MISMEASURED COVARIATES
Liang, Hua
2009-01-01
In this article we consider a semiparametric generalized mixed-effects model, and propose combining local linear regression, and penalized quasilikelihood and local quasilikelihood techniques to estimate both population and individual parameters and nonparametric curves. The proposed estimators take into account the local correlation structure of the longitudinal data. We establish normality for the estimators of the parameter and asymptotic expansion for the estimators of the nonparametric part. For practical implementation, we propose an appropriate algorithm. We also consider the measurement error problem in covariates in our model, and suggest a strategy for adjusting the effects of measurement errors. We apply the proposed models and methods to study the relation between virologic and immunologic responses in AIDS clinical trials, in which virologic response is classified into binary variables. A dataset from an AIDS clinical study is analyzed. PMID:20160899
Linear mixing model applied to AVHRR LAC data
NASA Technical Reports Server (NTRS)
Holben, Brent N.; Shimabukuro, Yosio E.
1993-01-01
A linear mixing model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The reflective component of the 3.55 - 3.93 microns channel was extracted and used with the two reflective channels 0.58 - 0.68 microns and 0.725 - 1.1 microns to run a Constraine Least Squares model to generate vegetation, soil, and shade fraction images for an area in the Western region of Brazil. The Landsat Thematic Mapper data covering the Emas National park region was used for estimating the spectral response of the mixture components and for evaluating the mixing model results. The fraction images were compared with an unsupervised classification derived from Landsat TM data acquired on the same day. The relationship between the fraction images and normalized difference vegetation index images show the potential of the unmixing techniques when using coarse resolution data for global studies.
Adaptive Error Estimation in Linearized Ocean General Circulation Models
NASA Technical Reports Server (NTRS)
Chechelnitsky, Michael Y.
1999-01-01
Data assimilation methods are routinely used in oceanography. The statistics of the model and measurement errors need to be specified a priori. This study addresses the problem of estimating model and measurement error statistics from observations. We start by testing innovation based methods of adaptive error estimation with low-dimensional models in the North Pacific (5-60 deg N, 132-252 deg E) to TOPEX/POSEIDON (TIP) sea level anomaly data, acoustic tomography data from the ATOC project, and the MIT General Circulation Model (GCM). A reduced state linear model that describes large scale internal (baroclinic) error dynamics is used. The methods are shown to be sensitive to the initial guess for the error statistics and the type of observations. A new off-line approach is developed, the covariance matching approach (CMA), where covariance matrices of model-data residuals are "matched" to their theoretical expectations using familiar least squares methods. This method uses observations directly instead of the innovations sequence and is shown to be related to the MT method and the method of Fu et al. (1993). Twin experiments using the same linearized MIT GCM suggest that altimetric data are ill-suited to the estimation of internal GCM errors, but that such estimates can in theory be obtained using acoustic data. The CMA is then applied to T/P sea level anomaly data and a linearization of a global GFDL GCM which uses two vertical modes. We show that the CMA method can be used with a global model and a global data set, and that the estimates of the error statistics are robust. We show that the fraction of the GCM-T/P residual variance explained by the model error is larger than that derived in Fukumori et al.(1999) with the method of Fu et al.(1993). Most of the model error is explained by the barotropic mode. However, we find that impact of the change in the error statistics on the data assimilation estimates is very small. This is explained by the large
Linear Response Screening Models for Dense, Strongly-Coupled Plasmas
NASA Astrophysics Data System (ADS)
Stanton, Liam; Murillo, Michael; Benage, John; Graziani, Frank
2011-10-01
Needs for accurate EOS and transport models of warm/hot dense matter have increased with the advent of new experiments that are able to more accurately probe these areas of phase-space. Molecular dynamics (MD) methods are often used for this, as they are apt for strongly-coupled systems. Unfortunately, the traditional Coulomb and Yukawa pair-potentials begin to fail at lower temperatures as degeneracy effects of the electron gas arise, and a more sophisticated treatment is required. We present a class of effective ion-ion interactions derived within the framework of linear response, which go beyond screening in the long-wavelength limit. These new potentials not only improve the accuracy of screening effects without contributing to the computational complexity of the model, but they also add physics entirely missing from Yukawa models (such as the onset of Friedel oscillations). Prepared by LLNL under Contract DE-AC52-07NA27344. LLNL-ABS-490713.
Accelerating transient simulation of linear reduced order models.
Thornquist, Heidi K.; Mei, Ting; Keiter, Eric Richard; Bond, Brad
2011-10-01
Model order reduction (MOR) techniques have been used to facilitate the analysis of dynamical systems for many years. Although existing model reduction techniques are capable of providing huge speedups in the frequency domain analysis (i.e. AC response) of linear systems, such speedups are often not obtained when performing transient analysis on the systems, particularly when coupled with other circuit components. Reduced system size, which is the ostensible goal of MOR methods, is often insufficient to improve transient simulation speed on realistic circuit problems. It can be shown that making the correct reduced order model (ROM) implementation choices is crucial to the practical application of MOR methods. In this report we investigate methods for accelerating the simulation of circuits containing ROM blocks using the circuit simulator Xyce.
Tommasi, C.; May, C.
2010-09-30
The DKL-optimality criterion has been recently proposed for the dual problem of model discrimination and parameter estimation, for the case of two rival models. A sequential version of the DKL-optimality criterion is herein proposed in order to discriminate and efficiently estimate more than two nested non-linear models. Our sequential method is inspired by the procedure of Biswas and Chaudhuri (2002), which is however useful only in the set up of nested linear models.
Decoding coalescent hidden Markov models in linear time
Harris, Kelley; Sheehan, Sara; Kamm, John A.; Song, Yun S.
2014-01-01
In many areas of computational biology, hidden Markov models (HMMs) have been used to model local genomic features. In particular, coalescent HMMs have been used to infer ancient population sizes, migration rates, divergence times, and other parameters such as mutation and recombination rates. As more loci, sequences, and hidden states are added to the model, however, the runtime of coalescent HMMs can quickly become prohibitive. Here we present a new algorithm for reducing the runtime of coalescent HMMs from quadratic in the number of hidden time states to linear, without making any additional approximations. Our algorithm can be incorporated into various coalescent HMMs, including the popular method PSMC for inferring variable effective population sizes. Here we implement this algorithm to speed up our demographic inference method diCal, which is equivalent to PSMC when applied to a sample of two haplotypes. We demonstrate that the linear-time method can reconstruct a population size change history more accurately than the quadratic-time method, given similar computation resources. We also apply the method to data from the 1000 Genomes project, inferring a high-resolution history of size changes in the European population. PMID:25340178
Markov-random-field modeling for linear seismic tomography.
Kuwatani, Tatsu; Nagata, Kenji; Okada, Masato; Toriumi, Mitsuhiro
2014-10-01
We apply the Markov-random-field model to linear seismic tomography and propose a method to estimate the hyperparameters for the smoothness and the magnitude of the noise. Optimal hyperparameters can be determined analytically by minimizing the free energy function, which is defined by marginalizing the evaluation function. In synthetic inversion tests under various settings, the assumed velocity structures are successfully reconstructed, which shows the effectiveness and robustness of the proposed method. The proposed mathematical framework can be applied to inversion problems in various fields in the natural sciences.
Markov-random-field modeling for linear seismic tomography
NASA Astrophysics Data System (ADS)
Kuwatani, Tatsu; Nagata, Kenji; Okada, Masato; Toriumi, Mitsuhiro
2014-10-01
We apply the Markov-random-field model to linear seismic tomography and propose a method to estimate the hyperparameters for the smoothness and the magnitude of the noise. Optimal hyperparameters can be determined analytically by minimizing the free energy function, which is defined by marginalizing the evaluation function. In synthetic inversion tests under various settings, the assumed velocity structures are successfully reconstructed, which shows the effectiveness and robustness of the proposed method. The proposed mathematical framework can be applied to inversion problems in various fields in the natural sciences.
Time series models based on generalized linear models: some further results.
Li, W K
1994-06-01
This paper considers the problem of extending the classical moving average models to time series with conditional distributions given by generalized linear models. These models have the advantage of easy construction and estimation. Statistical modelling techniques are also proposed. Some simulation results and an illustrative example are reported to illustrate the methodology. The models will have potential applications in longitudinal data analysis. PMID:8068850
Stratospheric ozone time series analysis using dynamical linear models
NASA Astrophysics Data System (ADS)
Laine, Marko; Kyrölä, Erkki
2013-04-01
We describe a hierarchical statistical state space model for ozone profile time series. The time series are from satellite measurements by the SAGE II and GOMOS instruments spanning years 1984-2012. The original data sets are combined and gridded monthly using 10 degree latitude bands, and covering 20-60 km with 1 km vertical spacing. Model components include level, trend, seasonal effect with solar activity, and quasi biennial oscillations as proxy variables. A typical feature of an atmospheric time series is that they are not stationary but exhibit both slowly varying and abrupt changes in the distributional properties. These are caused by external forcing such as changes in the solar activity or volcanic eruptions. Further, the data sampling is often nonuniform, there are data gaps, and the uncertainty of the observations can vary. When observations are combined from various sources there will be instrument and retrieval method related biases. The differences in sampling lead also to uncertainties. Standard classical ARIMA type of statistical time series methods are mostly useless for atmospheric data. A more general approach makes use of dynamical linear models and Kalman filter type of sequential algorithms. These state space models assume a linear relationship between the unknown state of the system and the observations and for the process evolution of the hidden states. They are still flexible enough to model both smooth trends and sudden changes. The above mentioned methodological challenges are discussed, together with analysis of change points in trends related to recovery of stratospheric ozone. This work is part of the ESA SPIN and ozone CCI projects.
A wavelet-linear genetic programming model for sodium (Na+) concentration forecasting in rivers
NASA Astrophysics Data System (ADS)
Ravansalar, Masoud; Rajaee, Taher; Zounemat-Kermani, Mohammad
2016-06-01
The prediction of water quality parameters in water resources such as rivers is of importance issue that needs to be considered in better management of irrigation systems and water supplies. In this respect, this study proposes a new hybrid wavelet-linear genetic programming (WLGP) model for prediction of monthly sodium (Na+) concentration. The 23-year monthly data used in this study, were measured from the Asi River at the Demirköprü gauging station located in Antakya, Turkey. At first, the measured discharge (Q) and Na+ datasets are initially decomposed into several sub-series using discrete wavelet transform (DWT). Then, these new sub-series are imposed to the ad hoc linear genetic programming (LGP) model as input patterns to predict monthly Na+ one month ahead. The results of the new proposed WLGP model are compared with LGP, WANN and ANN models. Comparison of the models represents the superiority of the WLGP model over the LGP, WANN and ANN models such that the Nash-Sutcliffe efficiencies (NSE) for WLGP, WANN, LGP and ANN models were 0.984, 0.904, 0.484 and 0.351, respectively. The achieved results even points to the superiority of the single LGP model than the ANN model. Continuously, the capability of the proposed WLGP model in terms of prediction of the Na+ peak values is also presented in this study.
Non-Linear Slosh Damping Model Development and Validation
NASA Technical Reports Server (NTRS)
Yang, H. Q.; West, Jeff
2015-01-01
Propellant tank slosh dynamics are typically represented by a mechanical model of spring mass damper. This mechanical model is then included in the equation of motion of the entire vehicle for Guidance, Navigation and Control (GN&C) analysis. For a partially-filled smooth wall propellant tank, the critical damping based on classical empirical correlation is as low as 0.05%. Due to this low value of damping, propellant slosh is potential sources of disturbance critical to the stability of launch and space vehicles. It is postulated that the commonly quoted slosh damping is valid only under the linear regime where the slosh amplitude is small. With the increase of slosh amplitude, the critical damping value should also increase. If this nonlinearity can be verified and validated, the slosh stability margin can be significantly improved, and the level of conservatism maintained in the GN&C analysis can be lessened. The purpose of this study is to explore and to quantify the dependence of slosh damping with slosh amplitude. Accurately predicting the extremely low damping value of a smooth wall tank is very challenging for any Computational Fluid Dynamics (CFD) tool. One must resolve thin boundary layers near the wall and limit numerical damping to minimum. This computational study demonstrates that with proper grid resolution, CFD can indeed accurately predict the low damping physics from smooth walls under the linear regime. Comparisons of extracted damping values with experimental data for different tank sizes show very good agreements. Numerical simulations confirm that slosh damping is indeed a function of slosh amplitude. When slosh amplitude is low, the damping ratio is essentially constant, which is consistent with the empirical correlation. Once the amplitude reaches a critical value, the damping ratio becomes a linearly increasing function of the slosh amplitude. A follow-on experiment validated the developed nonlinear damping relationship. This discovery can
Acoustic FMRI noise: linear time-invariant system model.
Rizzo Sierra, Carlos V; Versluis, Maarten J; Hoogduin, Johannes M; Duifhuis, Hendrikus Diek
2008-09-01
Functional magnetic resonance imaging (fMRI) enables sites of brain activation to be localized in human subjects. For auditory system studies, however, the acoustic noise generated by the scanner tends to interfere with the assessments of this activation. Understanding and modeling fMRI acoustic noise is a useful step to its reduction. To study acoustic noise, the MR scanner is modeled as a linear electroacoustical system generating sound pressure signals proportional to the time derivative of the input gradient currents. The transfer function of one MR scanner is determined for two different input specifications: 1) by using the gradient waveform calculated by the scanner software and 2) by using a recording of the gradient current. Up to 4 kHz, the first method is shown as reliable as the second one, and its use is encouraged when direct measurements of gradient currents are not possible. Additionally, the linear order and average damping properties of the gradient coil system are determined by impulse response analysis. Since fMRI is often based on echo planar imaging (EPI) sequences, a useful validation of the transfer function prediction ability can be obtained by calculating the acoustic output for the EPI sequence. We found a predicted sound pressure level (SPL) for the EPI sequence of 104 dB SPL compared to a measured value of 102 dB SPL. As yet, the predicted EPI pressure waveform shows similarity as well as some differences with the directly measured EPI pressure waveform.
Linear versus quadratic portfolio optimization model with transaction cost
NASA Astrophysics Data System (ADS)
Razak, Norhidayah Bt Ab; Kamil, Karmila Hanim; Elias, Siti Masitah
2014-06-01
Optimization model is introduced to become one of the decision making tools in investment. Hence, it is always a big challenge for investors to select the best model that could fulfill their goal in investment with respect to risk and return. In this paper we aims to discuss and compare the portfolio allocation and performance generated by quadratic and linear portfolio optimization models namely of Markowitz and Maximin model respectively. The application of these models has been proven to be significant and popular among others. However transaction cost has been debated as one of the important aspects that should be considered for portfolio reallocation as portfolio return could be significantly reduced when transaction cost is taken into consideration. Therefore, recognizing the importance to consider transaction cost value when calculating portfolio' return, we formulate this paper by using data from Shariah compliant securities listed in Bursa Malaysia. It is expected that, results from this paper will effectively justify the advantage of one model to another and shed some lights in quest to find the best decision making tools in investment for individual investors.
Some generalisations of linear-graph modelling for dynamic systems
NASA Astrophysics Data System (ADS)
de Silva, Clarence W.; Pourazadi, Shahram
2013-11-01
Proper modelling of a dynamic system can benefit analysis, simulation, design, evaluation and control of the system. The linear-graph (LG) approach is suitable for modelling lumped-parameter dynamic systems. By using the concepts of graph trees, it provides a graphical representation of the system, with a direct correspondence to the physical component topology. This paper systematically extends the application of LGs to multi-domain (mixed-domain or multi-physics) dynamic systems by presenting a unified way to represent different domains - mechanical, electrical, thermal and fluid. Preservation of the structural correspondence across domains is a particular advantage of LGs when modelling mixed-domain systems. The generalisation of Thevenin and Norton equivalent circuits to mixed-domain systems, using LGs, is presented. The structure of an LG model may follow a specific pattern. Vector LGs are introduced to take advantage of such patterns, giving a general LG representation for them. Through these vector LGs, the model representation becomes simpler and rather compact, both topologically and parametrically. A new single LG element is defined to facilitate the modelling of distributed-parameter (DP) systems. Examples are presented using multi-domain systems (a motion-control system and a flow-controlled pump), a multi-body mechanical system (robot manipulator) and DP systems (structural rods) to illustrate the application and advantages of the methodologies developed in the paper.
Linear models of acoustic waves in sunspot umbrae
NASA Technical Reports Server (NTRS)
Gurman, J. B.; Leibacher, J. W.
1984-01-01
The two-dimensional, linear hydrodynamics of quiet solar and umbral model atmospheres in a plane-parallel, adiabatic approximation are investigated. The 5.5-8.5 mHz oscillations observed in umbral chromospheres and transition regions are interpreted as acoustic waves propagating parallel, or nearly parallel, to the temperature gradient. These waves are not totally internally reflected by the steep temperature gradient and, thus, are not trapped. Partial reflections, however, are effective in modulating the transmission as a function of frequency. The resonant transmission mechanism of Zugzda, Locans, and Staude (1983) is found to produce a spectrum of resonances in the transmission of acoustic waves in any atmosphere with a temperature minimum. Since the observed umbral oscillations display power in only a narrow range of frequencies, characteristics of the umbral models, wave propagation, and observations that would tend to suppress the higher frequency resonances are examined.
Relating Cohesive Zone Model to Linear Elastic Fracture Mechanics
NASA Technical Reports Server (NTRS)
Wang, John T.
2010-01-01
The conditions required for a cohesive zone model (CZM) to predict a failure load of a cracked structure similar to that obtained by a linear elastic fracture mechanics (LEFM) analysis are investigated in this paper. This study clarifies why many different phenomenological cohesive laws can produce similar fracture predictions. Analytical results for five cohesive zone models are obtained, using five different cohesive laws that have the same cohesive work rate (CWR-area under the traction-separation curve) but different maximum tractions. The effect of the maximum traction on the predicted cohesive zone length and the remote applied load at fracture is presented. Similar to the small scale yielding condition for an LEFM analysis to be valid. the cohesive zone length also needs to be much smaller than the crack length. This is a necessary condition for a CZM to obtain a fracture prediction equivalent to an LEFM result.
Linear mixing model applied to coarse resolution satellite data
NASA Technical Reports Server (NTRS)
Holben, Brent N.; Shimabukuro, Yosio E.
1992-01-01
A linear mixing model typically applied to high resolution data such as Airborne Visible/Infrared Imaging Spectrometer, Thematic Mapper, and Multispectral Scanner System is applied to the NOAA Advanced Very High Resolution Radiometer coarse resolution satellite data. The reflective portion extracted from the middle IR channel 3 (3.55 - 3.93 microns) is used with channels 1 (0.58 - 0.68 microns) and 2 (0.725 - 1.1 microns) to run the Constrained Least Squares model to generate fraction images for an area in the west central region of Brazil. The derived fraction images are compared with an unsupervised classification and the fraction images derived from Landsat TM data acquired in the same day. In addition, the relationship betweeen these fraction images and the well known NDVI images are presented. The results show the great potential of the unmixing techniques for applying to coarse resolution data for global studies.
Vazquez-Leal, H; Jimenez-Fernandez, V M; Benhammouda, B; Filobello-Nino, U; Sarmiento-Reyes, A; Ramirez-Pinero, A; Marin-Hernandez, A; Huerta-Chua, J
2014-01-01
We present a homotopy continuation method (HCM) for finding multiple operating points of nonlinear circuits composed of devices modelled by using piecewise linear (PWL) representations. We propose an adaptation of the modified spheres path tracking algorithm to trace the homotopy trajectories of PWL circuits. In order to assess the benefits of this proposal, four nonlinear circuits composed of piecewise linear modelled devices are analysed to determine their multiple operating points. The results show that HCM can find multiple solutions within a single homotopy trajectory. Furthermore, we take advantage of the fact that homotopy trajectories are PWL curves meant to replace the multidimensional interpolation and fine tuning stages of the path tracking algorithm with a simple and highly accurate procedure based on the parametric straight line equation.
Vazquez-Leal, H.; Jimenez-Fernandez, V. M.; Benhammouda, B.; Filobello-Nino, U.; Sarmiento-Reyes, A.; Ramirez-Pinero, A.; Marin-Hernandez, A.; Huerta-Chua, J.
2014-01-01
We present a homotopy continuation method (HCM) for finding multiple operating points of nonlinear circuits composed of devices modelled by using piecewise linear (PWL) representations. We propose an adaptation of the modified spheres path tracking algorithm to trace the homotopy trajectories of PWL circuits. In order to assess the benefits of this proposal, four nonlinear circuits composed of piecewise linear modelled devices are analysed to determine their multiple operating points. The results show that HCM can find multiple solutions within a single homotopy trajectory. Furthermore, we take advantage of the fact that homotopy trajectories are PWL curves meant to replace the multidimensional interpolation and fine tuning stages of the path tracking algorithm with a simple and highly accurate procedure based on the parametric straight line equation. PMID:25184157
Vazquez-Leal, H; Jimenez-Fernandez, V M; Benhammouda, B; Filobello-Nino, U; Sarmiento-Reyes, A; Ramirez-Pinero, A; Marin-Hernandez, A; Huerta-Chua, J
2014-01-01
We present a homotopy continuation method (HCM) for finding multiple operating points of nonlinear circuits composed of devices modelled by using piecewise linear (PWL) representations. We propose an adaptation of the modified spheres path tracking algorithm to trace the homotopy trajectories of PWL circuits. In order to assess the benefits of this proposal, four nonlinear circuits composed of piecewise linear modelled devices are analysed to determine their multiple operating points. The results show that HCM can find multiple solutions within a single homotopy trajectory. Furthermore, we take advantage of the fact that homotopy trajectories are PWL curves meant to replace the multidimensional interpolation and fine tuning stages of the path tracking algorithm with a simple and highly accurate procedure based on the parametric straight line equation. PMID:25184157
Linear-Nonlinear-Poisson models of primate choice dynamics.
Corrado, Greg S; Sugrue, Leo P; Seung, H Sebastian; Newsome, William T
2005-11-01
The equilibrium phenomenon of matching behavior traditionally has been studied in stationary environments. Here we attempt to uncover the local mechanism of choice that gives rise to matching by studying behavior in a highly dynamic foraging environment. In our experiments, 2 rhesus monkeys (Macacca mulatta) foraged for juice rewards by making eye movements to one of two colored icons presented on a computer monitor, each rewarded on dynamic variable-interval schedules. Using a generalization of Wiener kernel analysis, we recover a compact mechanistic description of the impact of past reward on future choice in the form of a Linear-Nonlinear-Poisson model. We validate this model through rigorous predictive and generative testing. Compared to our earlier work with this same data set, this model proves to be a better description of choice behavior and is more tightly correlated with putative neural value signals. Refinements over previous models include hyperbolic (as opposed to exponential) temporal discounting of past rewards, and differential (as opposed to fractional) comparisons of option value. Through numerical simulation we find that within this class of strategies, the model parameters employed by animals are very close to those that maximize reward harvesting efficiency.
Electroweak corrections and unitarity in linear moose models
Chivukula, R. Sekhar; Simmons, Elizabeth H.; He, H.-J.; Kurachi, Masafumi; Tanabashi, Masaharu
2005-02-01
We calculate the form of the corrections to the electroweak interactions in the class of Higgsless models which can be deconstructed to a chain of SU(2) gauge groups adjacent to a chain of U(1) gauge groups, and with the fermions coupled to any single SU(2) group and to any single U(1) group along the chain. The primary advantage of our technique is that the size of corrections to electroweak processes can be directly related to the spectrum of vector bosons ('KK modes'). In Higgsless models, this spectrum is constrained by unitarity. Our methods also allow for arbitrary background 5D geometry, spatially dependent gauge-couplings, and brane kinetic energy terms. We find that, due to the size of corrections to electroweak processes in any unitary theory, Higgsless models with localized fermions are disfavored by precision electroweak data. Although we stress our results as they apply to continuum Higgsless 5D models, they apply to any linear moose model including those with only a few extra vector bosons. Our calculations of electroweak corrections also apply directly to the electroweak gauge sector of 5D theories with a bulk scalar Higgs boson; the constraints arising from unitarity do not apply in this case.
NASA Astrophysics Data System (ADS)
Santos, I. F.; Saracho, C. M.; Smith, J. T.; Eiland, J.
2004-04-01
This work makes a theoretical and experimental contribution to the problem of rotor-blades dynamic interaction. A validation procedure of mathematical models is carried out with the help of a simple test rig, built by a mass-spring system attached to four flexible rotating blades. With this test rig, it is possible to highlight some dynamic effects and experimentally simulate the structural behaviour of a windmill in two dimensions (2-D model). Only lateral displacement of the rotor in the horizontal direction is taken into account. Gyroscopic effect due to angular vibrations of the rotor is eliminated in the test rig by attaching the rigid rotor to a flexible foundation. The blades are modelled as Euler-Bernoulli beams. Using three different approaches to describe the beam deformation one achieves: (a) a linear model; (b) a linear beam model with second order terms; (c) a fully non-linear model. Tip masses at the end of the blades emphasize the coupling between the dynamic and elastic terms. The shape functions are chosen in order to reduce the mathematical model, so that only the first bending mode of the beam is taken into account. The resulting equations of motion have five degrees of freedom and illustrate linear, non-linear and time-dependent terms in a very transparent way. Although neither gyroscopic effect due to rotor angular vibrations nor higher blade mode shapes are considered in the analysis, the equations of motion of the rotor-blades system are still general enough for the purpose of the work: validation of different linear and non-linear models with time dependent (periodic) coefficients. Experiments are carried out in the time and frequency domains while the rotor operates with different constant angular velocities.
Linearization of the full activated sludge model No 1 for interaction analysis.
Benhalla, Abdelhay; Houssou, Mohamed; Charif, Moussa
2010-08-01
This paper deals with the linearization of the full activated sludge model No 1 (ASM1) in the scope of interaction analysis. For consistency, the linearization procedure is developed and validated within the BSM1 simulation benchmark framework. It is based on reaction rate approximation by linear combinations of states. The linear rate models are identified and incorporated in the mass balance equations, yielding a linear locally equivalent to the ASM1 model. Linear models for anoxic and aerated compartments are proposed. It is observed that the presented models track very closely the nonlinear ASM1 responses to various influent data. The key feature of this linearization strategy is that the gotten linear version of the ASM1 model is linear time invariant (LTI) and that it conserves the states biological interpretation and the original ASM1 dimension. It allows, therefore, application of interaction analysis methods and makes it possible to determine motivated control configurations for the ASM1 model. PMID:20131068
Revisiting "Discrepancy Analysis in Continuing Medical Education: A Conceptual Model"
ERIC Educational Resources Information Center
Fox, Robert D.
2011-01-01
Based upon a review and analysis of selected literature, the author presents a conceptual model of discrepancy analysis evaluation for planning, implementing, and assessing the impact of continuing medical education (CME). The model is described in terms of its value as a means of diagnosing errors in the development and implementation of CME. The…
Discrepancy Analysis in Continuing Medical Education: A Conceptual Model.
ERIC Educational Resources Information Center
Fox, Robert D.
1983-01-01
Presents a conceptual model of discrepancy analysis evaluation for planning, evaluating, and assessing the impact of continuing medical education (CME). Describes the model in terms of its value as a means of diagnosing errors in the development and implementation of CME. (JOW)
A Quasispecies Continuous Contact Model in a Critical Regime
NASA Astrophysics Data System (ADS)
Kondratiev, Yuri; Pirogov, Sergey; Zhizhina, Elena
2016-04-01
We study a new non-equilibrium dynamical model: a marked continuous contact model in d-dimensional space (d ge 3). We prove that for certain values of rates (the critical regime) this system has the one-parameter family of invariant measures labelled by the spatial density of particles. Then we prove that the process starting from the marked Poisson measure converges to one of these invariant measures. In contrast with the continuous contact model studied earlier in Kondratiev (Infin Dimens Anal Quantum Probab Relat Top 11(2):231-258, 2008), now the spatial particle density is not a conserved quantity.
Genetic demixing and evolution in linear stepping stone models
NASA Astrophysics Data System (ADS)
Korolev, K. S.; Avlund, Mikkel; Hallatschek, Oskar; Nelson, David R.
2010-04-01
Results for mutation, selection, genetic drift, and migration in a one-dimensional continuous population are reviewed and extended. The population is described by a continuous limit of the stepping stone model, which leads to the stochastic Fisher-Kolmogorov-Petrovsky-Piscounov equation with additional terms describing mutations. Although the stepping stone model was first proposed for population genetics, it is closely related to “voter models” of interest in nonequilibrium statistical mechanics. The stepping stone model can also be regarded as an approximation to the dynamics of a thin layer of actively growing pioneers at the frontier of a colony of micro-organisms undergoing a range expansion on a Petri dish. The population tends to segregate into monoallelic domains. This segregation slows down genetic drift and selection because these two evolutionary forces can only act at the boundaries between the domains; the effects of mutation, however, are not significantly affected by the segregation. Although fixation in the neutral well-mixed (or “zero-dimensional”) model occurs exponentially in time, it occurs only algebraically fast in the one-dimensional model. An unusual sublinear increase is also found in the variance of the spatially averaged allele frequency with time. If selection is weak, selective sweeps occur exponentially fast in both well-mixed and one-dimensional populations, but the time constants are different. The relatively unexplored problem of evolutionary dynamics at the edge of an expanding circular colony is studied as well. Also reviewed are how the observed patterns of genetic diversity can be used for statistical inference and the differences are highlighted between the well-mixed and one-dimensional models. Although the focus is on two alleles or variants, q -allele Potts-like models of gene segregation are considered as well. Most of the analytical results are checked with simulations and could be tested against recent spatial
Selection between Linear Factor Models and Latent Profile Models Using Conditional Covariances
ERIC Educational Resources Information Center
Halpin, Peter F.; Maraun, Michael D.
2010-01-01
A method for selecting between K-dimensional linear factor models and (K + 1)-class latent profile models is proposed. In particular, it is shown that the conditional covariances of observed variables are constant under factor models but nonlinear functions of the conditioning variable under latent profile models. The performance of a convenient…
Models for the analysis of repeated continuous outcome measures in clinical trials.
De Livera, Alysha M; Zaloumis, Sophie; Simpson, Julie A
2014-02-01
Repeated continuous outcome measures are common in clinical trials. In this tutorial style paper, using data collected from a trial evaluating an intervention for managing asthma and chronic obstructive pulmonary disease, we demonstrate ways of statistically analysing such data to answer frequently encountered clinical research questions. We illustrate the use of linear mixed effects modelling in doing so and discuss its advantages over several other commonly used approaches. The methods described in this paper can easily be carried out using standard statistical software.
Huang, A.B.; Yortsos, Y.C.
1984-09-01
This paper reports on the continuation of previous work in the linear stability of immiscible, two-phase flow displacement processes in porous media that includes continuously changing mobility and capillary effects. In Part I simple basic-flow profiles that allow exact solutions to be obtained were investigated. First, the stability of non-capillary flows corresponding to a straight line fractional flow is examined. Next, the stability of capillary flows for general basic flow profiles is examined. For values of the viscosity ratio above the critical, the numerical results show that the displacement is unstable to small disturbances of wavelength larger than a critical value, and stable otherwise. This effect is attributed to the stabilizing action of capillarity. Values of wavelength corresponding to the highest rate of growth are numerically determined. It is found that stability is enhanced at lower values of the capillary number and the injection rate. Finally, a limited sensitivity study of the effect on stability of the functional forms of relative permeability and capillary pressure is carried out.
Feedbacks, climate sensitivity, and the limits of linear models
NASA Astrophysics Data System (ADS)
Rugenstein, M.; Knutti, R.
2015-12-01
The term "feedback" is used ubiquitously in climate research, but implies varied meanings in different contexts. From a specific process that locally affects a quantity, to a formal framework that attempts to determine a global response to a forcing, researchers use this term to separate, simplify, and quantify parts of the complex Earth system. We combine large (>120 member) ensemble GCM and EMIC step forcing simulations over a broad range of forcing levels with a historical and educational perspective to organize existing ideas around feedbacks and linear forcing-feedback models. With a new method overcoming internal variability and initial condition problems we quantify the non-constancy of the climate feedback parameter. Our results suggest a strong state- and forcing-dependency of feedbacks, which is not considered appropriately in many studies. A non-constant feedback factor likely explains some of the differences in estimates of equilibrium climate sensitivity from different methods and types of data. We discuss implications for the definition of the forcing term and its various adjustments. Clarifying the value and applicability of the linear forcing feedback framework and a better quantification of feedbacks on various timescales and spatial scales remains a high priority in order to better understand past and predict future changes in the climate system.
Forecasting Groundwater Temperature with Linear Regression Models Using Historical Data.
Figura, Simon; Livingstone, David M; Kipfer, Rolf
2015-01-01
Although temperature is an important determinant of many biogeochemical processes in groundwater, very few studies have attempted to forecast the response of groundwater temperature to future climate warming. Using a composite linear regression model based on the lagged relationship between historical groundwater and regional air temperature data, empirical forecasts were made of groundwater temperature in several aquifers in Switzerland up to the end of the current century. The model was fed with regional air temperature projections calculated for greenhouse-gas emissions scenarios A2, A1B, and RCP3PD. Model evaluation revealed that the approach taken is adequate only when the data used to calibrate the models are sufficiently long and contain sufficient variability. These conditions were satisfied for three aquifers, all fed by riverbank infiltration. The forecasts suggest that with respect to the reference period 1980 to 2009, groundwater temperature in these aquifers will most likely increase by 1.1 to 3.8 K by the end of the current century, depending on the greenhouse-gas emissions scenario employed. PMID:25412761
Forecasting Groundwater Temperature with Linear Regression Models Using Historical Data.
Figura, Simon; Livingstone, David M; Kipfer, Rolf
2015-01-01
Although temperature is an important determinant of many biogeochemical processes in groundwater, very few studies have attempted to forecast the response of groundwater temperature to future climate warming. Using a composite linear regression model based on the lagged relationship between historical groundwater and regional air temperature data, empirical forecasts were made of groundwater temperature in several aquifers in Switzerland up to the end of the current century. The model was fed with regional air temperature projections calculated for greenhouse-gas emissions scenarios A2, A1B, and RCP3PD. Model evaluation revealed that the approach taken is adequate only when the data used to calibrate the models are sufficiently long and contain sufficient variability. These conditions were satisfied for three aquifers, all fed by riverbank infiltration. The forecasts suggest that with respect to the reference period 1980 to 2009, groundwater temperature in these aquifers will most likely increase by 1.1 to 3.8 K by the end of the current century, depending on the greenhouse-gas emissions scenario employed.
A linear geospatial streamflow modeling system for data sparse environments
Asante, Kwabena O.; Arlan, Guleid A.; Pervez, Md Shahriar; Rowland, James
2008-01-01
In many river basins around the world, inaccessibility of flow data is a major obstacle to water resource studies and operational monitoring. This paper describes a geospatial streamflow modeling system which is parameterized with global terrain, soils and land cover data and run operationally with satellite‐derived precipitation and evapotranspiration datasets. Simple linear methods transfer water through the subsurface, overland and river flow phases, and the resulting flows are expressed in terms of standard deviations from mean annual flow. In sample applications, the modeling system was used to simulate flow variations in the Congo, Niger, Nile, Zambezi, Orange and Lake Chad basins between 1998 and 2005, and the resulting flows were compared with mean monthly values from the open‐access Global River Discharge Database. While the uncalibrated model cannot predict the absolute magnitude of flow, it can quantify flow anomalies in terms of relative departures from mean flow. Most of the severe flood events identified in the flow anomalies were independently verified by the Dartmouth Flood Observatory (DFO) and the Emergency Disaster Database (EM‐DAT). Despite its limitations, the modeling system is valuable for rapid characterization of the relative magnitude of flood hazards and seasonal flow changes in data sparse settings.
Optimization in generalized linear models: A case study
NASA Astrophysics Data System (ADS)
Silva, Eliana Costa e.; Correia, Aldina; Lopes, Isabel Cristina
2016-06-01
The maximum likelihood method is usually chosen to estimate the regression parameters of Generalized Linear Models (GLM) and also for hypothesis testing and goodness of fit tests. The classical method for estimating GLM parameters is the Fisher scores. In this work we propose to compute the estimates of the parameters with two alternative methods: a derivative-based optimization method, namely the BFGS method which is one of the most popular of the quasi-Newton algorithms, and the PSwarm derivative-free optimization method that combines features of a pattern search optimization method with a global Particle Swarm scheme. As a case study we use a dataset of biological parameters (phytoplankton) and chemical and environmental parameters of the water column of a Portuguese reservoir. The results show that, for this dataset, BFGS and PSwarm methods provided a better fit, than Fisher scores method, and can be good alternatives for finding the estimates for the parameters of a GLM.
Teraguchi, Shunsuke; Kumagai, Yutaro; Vandenbon, Alexis; Akira, Shizuo; Standley, Daron M
2011-12-01
We have developed a coarse-grained formulation for modeling the dynamic behavior of cells quantitatively, based on stochasticity and heterogeneity, rather than on biochemical reactions. We treat each reaction as a continuous-time stochastic process, while reducing each biochemical quantity to a binary value at the level of individual cells. The system can be analytically represented by a finite set of ordinary linear differential equations, which provides a continuous time course prediction of each molecular state. Here we introduce our formalism and demonstrate it with several examples.
Discrete threshold versus continuous strength models of perceptual recognition.
Paap, K R; Chun, E; Vonnahme, P
1999-12-01
Two experiments were designed to test discrete-threshold models of letter and word recognition against models that assume that decision criteria are applied to measures of continuous strength. Although our goal is to adjudicate this matter with respect to broad classes of models, some of the specific predictions for discrete-threshold are generated from Grainger and Jacobs' (1994) Dual-Readout Model (DROM) and some of the predictions for continuous strength are generated from a revised version of the Activation-Verification Model (Paap, Newsome, McDonald, & Schvaneveldt, 1982). Experiment 1 uses a two-alternative forced-choice task that is followed by an assessment of confidence and then a whole report if a word is recognized. Factors are manipulated to assess the presence or magnitude of a neighbourhood-frequency effect, a lexical-bias effect, a word-superiority effect, and a pseudoword advantage. Several discrepancies between DROM's predictions and the obtained data are noted. Both types of models were also used to predict the distribution of responses across the levels of confidence for each individual participant. The predictions based on continuous strength were superior. Experiment 2 used a same-different task and confidence ratings to enable the generation of receiver operating characteristics (ROCs). The shapes of the ROCs are more consistent with the continuous strength assumption than with a discrete threshold. PMID:10646200
Flexible modeling of the effects of continuous prognostic factors in relative survival.
Mahboubi, Amel; Abrahamowicz, Michal; Giorgi, Roch; Binquet, Christine; Bonithon-Kopp, Claire; Quantin, Catherine
2011-05-30
Relative survival methods permit separating the effects of prognostic factors on disease-related 'excess mortality' from their effects on other-causes 'natural mortality', even when individual causes of death are unknown. As in conventional 'crude' survival, accurate assessment of prognostic factors requires testing and possibly modeling of non-proportional effects and, for continuous covariates, of non-linear relationships with the hazard. We propose a flexible extension of the additive-hazards relative survival model, in which the observed all-causes mortality hazard is represented by a sum of disease-related 'excess' and natural mortality hazards. In our flexible model, the three functions representing (i) the baseline hazard for 'excess' mortality, (ii) the time-dependent effects, and (iii) for continuous covariates, non-linear effects, on the logarithm of this hazard, are all modeled by low-dimension cubic regression splines. Non-parametric likelihood ratio tests are proposed to test the time-dependent and non-linear effects. The accuracy of the estimated functions is evaluated in multivariable simulations. To illustrate the new insights offered by the proposed model, we apply it to re-assess the effects of patient age and of secular trends on disease-related mortality in colon cancer.
Preconditioning the bidomain model with almost linear complexity
NASA Astrophysics Data System (ADS)
Pierre, Charles
2012-01-01
The bidomain model is widely used in electro-cardiology to simulate spreading of excitation in the myocardium and electrocardiograms. It consists of a system of two parabolic reaction diffusion equations coupled with an ODE system. Its discretisation displays an ill-conditioned system matrix to be inverted at each time step: simulations based on the bidomain model therefore are associated with high computational costs. In this paper we propose a preconditioning for the bidomain model either for an isolated heart or in an extended framework including a coupling with the surrounding tissues (the torso). The preconditioning is based on a formulation of the discrete problem that is shown to be symmetric positive semi-definite. A block LU decomposition of the system together with a heuristic approximation (referred to as the monodomain approximation) are the key ingredients for the preconditioning definition. Numerical results are provided for two test cases: a 2D test case on a realistic slice of the thorax based on a segmented heart medical image geometry, a 3D test case involving a small cubic slab of tissue with orthotropic anisotropy. The analysis of the resulting computational cost (both in terms of CPU time and of iteration number) shows an almost linear complexity with the problem size, i.e. of type nlog α( n) (for some constant α) which is optimal complexity for such problems.
A linear city model with asymmetric consumer distribution.
Azar, Ofer H
2015-01-01
The article analyzes a linear-city model where the consumer distribution can be asymmetric, which is important because in real markets this distribution is often asymmetric. The model yields equilibrium price differences, even though the firms' costs are equal and their locations are symmetric (at the two endpoints of the city). The equilibrium price difference is proportional to the transportation cost parameter and does not depend on the good's cost. The firms' markups are also proportional to the transportation cost. The two firms' prices will be equal in equilibrium if and only if half of the consumers are located to the left of the city's midpoint, even if other characteristics of the consumer distribution are highly asymmetric. An extension analyzes what happens when the firms have different costs and how the two sources of asymmetry - the consumer distribution and the cost per unit - interact together. The model can be useful as a tool for further development by other researchers interested in applying this simple yet flexible framework for the analysis of various topics.
A Linear City Model with Asymmetric Consumer Distribution
Azar, Ofer H.
2015-01-01
The article analyzes a linear-city model where the consumer distribution can be asymmetric, which is important because in real markets this distribution is often asymmetric. The model yields equilibrium price differences, even though the firms’ costs are equal and their locations are symmetric (at the two endpoints of the city). The equilibrium price difference is proportional to the transportation cost parameter and does not depend on the good's cost. The firms' markups are also proportional to the transportation cost. The two firms’ prices will be equal in equilibrium if and only if half of the consumers are located to the left of the city’s midpoint, even if other characteristics of the consumer distribution are highly asymmetric. An extension analyzes what happens when the firms have different costs and how the two sources of asymmetry – the consumer distribution and the cost per unit – interact together. The model can be useful as a tool for further development by other researchers interested in applying this simple yet flexible framework for the analysis of various topics. PMID:26034984
Bayesian Inference for Generalized Linear Models for Spiking Neurons
Gerwinn, Sebastian; Macke, Jakob H.; Bethge, Matthias
2010-01-01
Generalized Linear Models (GLMs) are commonly used statistical methods for modelling the relationship between neural population activity and presented stimuli. When the dimension of the parameter space is large, strong regularization has to be used in order to fit GLMs to datasets of realistic size without overfitting. By imposing properly chosen priors over parameters, Bayesian inference provides an effective and principled approach for achieving regularization. Here we show how the posterior distribution over model parameters of GLMs can be approximated by a Gaussian using the Expectation Propagation algorithm. In this way, we obtain an estimate of the posterior mean and posterior covariance, allowing us to calculate Bayesian confidence intervals that characterize the uncertainty about the optimal solution. From the posterior we also obtain a different point estimate, namely the posterior mean as opposed to the commonly used maximum a posteriori estimate. We systematically compare the different inference techniques on simulated as well as on multi-electrode recordings of retinal ganglion cells, and explore the effects of the chosen prior and the performance measure used. We find that good performance can be achieved by choosing an Laplace prior together with the posterior mean estimate. PMID:20577627
Comparison of Linear and Non-Linear Regression Models to Estimate Leaf Area Index of Dryland Shrubs.
NASA Astrophysics Data System (ADS)
Dashti, H.; Glenn, N. F.; Ilangakoon, N. T.; Mitchell, J.; Dhakal, S.; Spaete, L.
2015-12-01
Leaf area index (LAI) is a key parameter in global ecosystem studies. LAI is considered a forcing variable in land surface processing models since ecosystem dynamics are highly correlated to LAI. In response to environmental limitations, plants in semiarid ecosystems have smaller leaf area, making accurate estimation of LAI by remote sensing a challenging issue. Optical remote sensing (400-2500 nm) techniques to estimate LAI are based either on radiative transfer models (RTMs) or statistical approaches. Considering the complex radiation field of dry ecosystems, simple 1-D RTMs lead to poor results, and on the other hand, inversion of more complex 3-D RTMs is a demanding task which requires the specification of many variables. A good alternative to physical approaches is using methods based on statistics. Similar to many natural phenomena, there is a non-linear relationship between LAI and top of canopy electromagnetic waves reflected to optical sensors. Non-linear regression models can better capture this relationship. However, considering the problem of a few numbers of observations in comparison to the feature space (n
models will not necessarily outperform the more simple linear models. In this study linear versus non-linear regression techniques were investigated to estimate LAI. Our study area is located in southwestern Idaho, Great Basin. Sagebrush (Artemisia tridentata spp) serves a critical role in maintaining the structure of this ecosystem. Using a leaf area meter (Accupar LP-80), LAI values were measured in the field. Linear Partial Least Square regression and non-linear, tree based Random Forest regression have been implemented to estimate the LAI of sagebrush from hyperspectral data (AVIRIS-ng) collected in late summer 2014. Cross validation of results indicate that PLS can provide comparable results to Random Forest.
a Linear Model for Meandering Rivers with Arbitrarily Varying Width
NASA Astrophysics Data System (ADS)
Frascati, A.; Lanzoni, S.
2011-12-01
Alluvial rivers usually exhibit quite complex planforms, characterized by a wide variety of alternating bends, that have attracted the interest of a large number of researchers. Much less attention has been paid to another striking feature observed in alluvial rivers, namely the relatively regular spatial variations attained by the channel width. Actively meandering channels, in fact, generally undergo spatial oscillations systematically correlated with channel curvature, with cross sections wider at bends than at crossings. Some other streams have been observed to exhibit irregular width variations. Conversely, rivers flowing in highly vegetated flood plains, i.e. canaliform rivers, may exhibit an opposite behavior, owing to the combined effects of bank erodibility and floodplain depositional processes which, in turn, are strictly linked to vegetation cover. Similarly to streamline curvatures induced by bends, the presence of along channel width variations may have remarkable effects on the flow field and sediment dynamics and, thereby, on the equilibrium river bed configuration. In particular, spatial distribution of channel curvature typically determines the formation of a rhythmic bar-pool pattern in the channel bed strictly associated with the development of river meanders. Channel width variations are on the contrary characterized by a sequence of narrowing, yielding a central scour, alternated to the downstream development of a widening associated with the formation of a central bar. Here we present a morphodynamic model that predict at a linear level the spatial distribution of the flow field and the equilibrium bed configuration of an alluvial river characterized by arbitrary along channel distributions of both the channel axis curvature and the channel width. The mathematical model is averaged over the depth and describes the steady, non-uniform flow and sediment transport in sinuous channels with a noncohesive bed. The governing two-dimensional equations
NASA Astrophysics Data System (ADS)
Ladefoged, Claes N.; Benoit, Didier; Law, Ian; Holm, Søren; Kjær, Andreas; Højgaard, Liselotte; Hansen, Adam E.; Andersen, Flemming L.
2015-10-01
The reconstruction of PET brain data in a PET/MR hybrid scanner is challenging in the absence of transmission sources, where MR images are used for MR-based attenuation correction (MR-AC). The main challenge of MR-AC is to separate bone and air, as neither have a signal in traditional MR images, and to assign the correct linear attenuation coefficient to bone. The ultra-short echo time (UTE) MR sequence was proposed as a basis for MR-AC as this sequence shows a small signal in bone. The purpose of this study was to develop a new clinically feasible MR-AC method with patient specific continuous-valued linear attenuation coefficients in bone that provides accurate reconstructed PET image data. A total of 164 [18F]FDG PET/MR patients were included in this study, of which 10 were used for training. MR-AC was based on either standard CT (reference), UTE or our method (RESOLUTE). The reconstructed PET images were evaluated in the whole brain, as well as regionally in the brain using a ROI-based analysis. Our method segments air, brain, cerebral spinal fluid, and soft tissue voxels on the unprocessed UTE TE images, and uses a mapping of R2* values to CT Hounsfield Units (HU) to measure the density in bone voxels. The average error of our method in the brain was 0.1% and less than 1.2% in any region of the brain. On average 95% of the brain was within ±10% of PETCT, compared to 72% when using UTE. The proposed method is clinically feasible, reducing both the global and local errors on the reconstructed PET images, as well as limiting the number and extent of the outliers.
Ladefoged, Claes N; Benoit, Didier; Law, Ian; Holm, Søren; Kjær, Andreas; Højgaard, Liselotte; Hansen, Adam E; Andersen, Flemming L
2015-10-21
The reconstruction of PET brain data in a PET/MR hybrid scanner is challenging in the absence of transmission sources, where MR images are used for MR-based attenuation correction (MR-AC). The main challenge of MR-AC is to separate bone and air, as neither have a signal in traditional MR images, and to assign the correct linear attenuation coefficient to bone. The ultra-short echo time (UTE) MR sequence was proposed as a basis for MR-AC as this sequence shows a small signal in bone. The purpose of this study was to develop a new clinically feasible MR-AC method with patient specific continuous-valued linear attenuation coefficients in bone that provides accurate reconstructed PET image data. A total of 164 [(18)F]FDG PET/MR patients were included in this study, of which 10 were used for training. MR-AC was based on either standard CT (reference), UTE or our method (RESOLUTE). The reconstructed PET images were evaluated in the whole brain, as well as regionally in the brain using a ROI-based analysis. Our method segments air, brain, cerebral spinal fluid, and soft tissue voxels on the unprocessed UTE TE images, and uses a mapping of R(*)2 values to CT Hounsfield Units (HU) to measure the density in bone voxels. The average error of our method in the brain was 0.1% and less than 1.2% in any region of the brain. On average 95% of the brain was within ±10% of PETCT, compared to 72% when using UTE. The proposed method is clinically feasible, reducing both the global and local errors on the reconstructed PET images, as well as limiting the number and extent of the outliers.
On the Relation between the Linear Factor Model and the Latent Profile Model
ERIC Educational Resources Information Center
Halpin, Peter F.; Dolan, Conor V.; Grasman, Raoul P. P. P.; De Boeck, Paul
2011-01-01
The relationship between linear factor models and latent profile models is addressed within the context of maximum likelihood estimation based on the joint distribution of the manifest variables. Although the two models are well known to imply equivalent covariance decompositions, in general they do not yield equivalent estimates of the…
A stochastic regression model for general trend analysis of longitudinal continuous data.
Chao, Wei-Hsiung; Chen, Su-Hua
2009-08-01
A predictive continuous time model is developed for continuous panel data to assess the effect of time-varying covariates on the general direction of the movement of a continuous response that fluctuates over time. This is accomplished by reparameterizing the infinitesimal mean of an Ornstein-Uhlenbeck processes in terms of its equilibrium mean and a drift parameter, which assesses the rate that the process reverts to its equilibrium mean. The equilibrium mean is modeled as a linear predictor of covariates. This model can be viewed as a continuous time first-order autoregressive regression model with time-varying lag effects of covariates and the response, which is more appropriate for unequally spaced panel data than its discrete time analog. Both maximum likelihood and quasi-likelihood approaches are considered for estimating the model parameters and their performances are compared through simulation studies. The simpler quasi-likelihood approach is suggested because it yields an estimator that is of high efficiency relative to the maximum likelihood estimator and it yields a variance estimator that is robust to the diffusion assumption of the model. To illustrate the proposed model, an application to diastolic blood pressure data from a follow-up study on cardiovascular diseases is presented. Missing observations are handled naturally with this model.
Development and validation of a general purpose linearization program for rigid aircraft models
NASA Technical Reports Server (NTRS)
Duke, E. L.; Antoniewicz, R. F.
1985-01-01
A FORTRAN program that provides the user with a powerful and flexible tool for the linearization of aircraft models is discussed. The program LINEAR numerically determines a linear systems model using nonlinear equations of motion and a user-supplied, nonlinear aerodynamic model. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model. Also, included in the report is a comparison of linear and nonlinear models for a high performance aircraft.
Development and validation of a general purpose linearization program for rigid aircraft models
NASA Technical Reports Server (NTRS)
Duke, E. L.; Antoniewicz, R. F.
1985-01-01
This paper discusses a FORTRAN program that provides the user with a powerful and flexible tool for the linearization of aircraft models. The program LINEAR numerically determines a linear systems model using nonlinear equations of motion and a user-supplied, nonlinear aerodynamic model. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model. Also, included in the report is a comparison of linear and nonlinear models for a high-performance aircraft.
Misuse of Linear Models: Understanding Community Linkages in Family Adaptation to Unexpected Death.
ERIC Educational Resources Information Center
Murray, Colleen I.; Galligan, Richard J.
In research, the use of linear additive methods is expedient when prediction is the goal; however, when understanding is the goal, an exploration of non-linear multiplicative procedures is more appropriate. To compare the results of linear and non-linear models, data obtained from a survey of 35 bereaved mothers were compared using scattergrams,…
Performance Models for the Spike Banded Linear System Solver
Manguoglu, Murat; Saied, Faisal; Sameh, Ahmed; Grama, Ananth
2011-01-01
With availability of large-scale parallel platforms comprised of tens-of-thousands of processors and beyond, there is significant impetus for the development of scalable parallel sparse linear system solvers and preconditioners. An integral part of this design process is the development of performance models capable of predicting performance and providing accurate cost models for the solvers and preconditioners. There has been some work in the past on characterizing performance of the iterative solvers themselves. In this paper, we investigate the problem of characterizing performance and scalability of banded preconditioners. Recent work has demonstrated the superior convergence properties and robustness of banded preconditioners,more » compared to state-of-the-art ILU family of preconditioners as well as algebraic multigrid preconditioners. Furthermore, when used in conjunction with efficient banded solvers, banded preconditioners are capable of significantly faster time-to-solution. Our banded solver, the Truncated Spike algorithm is specifically designed for parallel performance and tolerance to deep memory hierarchies. Its regular structure is also highly amenable to accurate performance characterization. Using these characteristics, we derive the following results in this paper: (i) we develop parallel formulations of the Truncated Spike solver, (ii) we develop a highly accurate pseudo-analytical parallel performance model for our solver, (iii) we show excellent predication capabilities of our model – based on which we argue the high scalability of our solver. Our pseudo-analytical performance model is based on analytical performance characterization of each phase of our solver. These analytical models are then parameterized using actual runtime information on target platforms. An important consequence of our performance models is that they reveal underlying performance bottlenecks in both serial and parallel formulations. All of our results are validated
Fourth standard model family neutrino at future linear colliders
Ciftci, A.K.; Ciftci, R.; Sultansoy, S.
2005-09-01
It is known that flavor democracy favors the existence of the fourth standard model (SM) family. In order to give nonzero masses for the first three-family fermions flavor democracy has to be slightly broken. A parametrization for democracy breaking, which gives the correct values for fundamental fermion masses and, at the same time, predicts quark and lepton Cabibbo-Kobayashi-Maskawa (CKM) matrices in a good agreement with the experimental data, is proposed. The pair productions of the fourth SM family Dirac ({nu}{sub 4}) and Majorana (N{sub 1}) neutrinos at future linear colliders with {radical}(s)=500 GeV, 1 TeV, and 3 TeV are considered. The cross section for the process e{sup +}e{sup -}{yields}{nu}{sub 4}{nu}{sub 4}(N{sub 1}N{sub 1}) and the branching ratios for possible decay modes of the both neutrinos are determined. The decays of the fourth family neutrinos into muon channels ({nu}{sub 4}(N{sub 1}){yields}{mu}{sup {+-}}W{sup {+-}}) provide cleanest signature at e{sup +}e{sup -} colliders. Meanwhile, in our parametrization this channel is dominant. W bosons produced in decays of the fourth family neutrinos will be seen in detector as either di-jets or isolated leptons. As an example, we consider the production of 200 GeV mass fourth family neutrinos at {radical}(s)=500 GeV linear colliders by taking into account di-muon plus four jet events as signatures.
Sensitivity Analysis of Parameters in Linear-Quadratic Radiobiologic Modeling
Fowler, Jack F.
2009-04-01
Purpose: Radiobiologic modeling is increasingly used to estimate the effects of altered treatment plans, especially for dose escalation. The present article shows how much the linear-quadratic (LQ) (calculated biologically equivalent dose [BED] varies when individual parameters of the LQ formula are varied by {+-}20% and by 1%. Methods: Equivalent total doses (EQD2 = normalized total doses (NTD) in 2-Gy fractions for tumor control, acute mucosal reactions, and late complications were calculated using the linear- quadratic formula with overall time: BED = nd (1 + d/ [{alpha}/{beta}]) - log{sub e}2 (T - Tk) / {alpha}Tp, where BED is BED = total dose x relative effectiveness (RE = nd (1 + d/ [{alpha}/{beta}]). Each of the five biologic parameters in turn was altered by {+-}10%, and the altered EQD2s tabulated; the difference was finally divided by 20. EQD2 or NTD is obtained by dividing BED by the RE for 2-Gy fractions, using the appropriate {alpha}/{beta} ratio. Results: Variations in tumor and acute mucosal EQD ranged from 0.1% to 0.45% per 1% change in each parameter for conventional schedules, the largest variation being caused by overall time. Variations in 'late' EQD were 0.4% to 0.6% per 1% change in the only biologic parameter, the {alpha}/{beta} ratio. For stereotactic body radiotherapy schedules, variations were larger, up to 0.6 to 0.9 for tumor and 1.6% to 1.9% for late, per 1% change in parameter. Conclusions: Robustness occurs similar to that of equivalent uniform dose (EUD), for the same reasons. Total dose, dose per fraction, and dose-rate cause their major effects, as well known.
Gradient-based adaptation of continuous dynamic model structures
NASA Astrophysics Data System (ADS)
La Cava, William G.; Danai, Kourosh
2016-01-01
A gradient-based method of symbolic adaptation is introduced for a class of continuous dynamic models. The proposed model structure adaptation method starts with the first-principles model of the system and adapts its structure after adjusting its individual components in symbolic form. A key contribution of this work is its introduction of the model's parameter sensitivity as the measure of symbolic changes to the model. This measure, which is essential to defining the structural sensitivity of the model, not only accommodates algebraic evaluation of candidate models in lieu of more computationally expensive simulation-based evaluation, but also makes possible the implementation of gradient-based optimisation in symbolic adaptation. The proposed method is applied to models of several virtual and real-world systems that demonstrate its potential utility.
A Spatially Continuous Model of Carbohydrate Digestion and Transport Processes in the Colon.
Moorthy, Arun S; Brooks, Stephen P J; Kalmokoff, Martin; Eberl, Hermann J
2015-01-01
A spatially continuous mathematical model of transport processes, anaerobic digestion and microbial complexity as would be expected in the human colon is presented. The model is a system of first-order partial differential equations with context determined number of dependent variables, and stiff, non-linear source terms. Numerical simulation of the model is used to elucidate information about the colon-microbiota complex. It is found that the composition of materials on outflow of the model does not well-describe the composition of material in other model locations, and inferences using outflow data varies according to model reactor representation. Additionally, increased microbial complexity allows the total microbial community to withstand major system perturbations in diet and community structure. However, distribution of strains and functional groups within the microbial community can be modified depending on perturbation length and microbial kinetic parameters. Preliminary model extensions and potential investigative opportunities using the computational model are discussed. PMID:26680208
A Spatially Continuous Model of Carbohydrate Digestion and Transport Processes in the Colon
Moorthy, Arun S.; Brooks, Stephen P. J.; Kalmokoff, Martin; Eberl, Hermann J.
2015-01-01
A spatially continuous mathematical model of transport processes, anaerobic digestion and microbial complexity as would be expected in the human colon is presented. The model is a system of first-order partial differential equations with context determined number of dependent variables, and stiff, non-linear source terms. Numerical simulation of the model is used to elucidate information about the colon-microbiota complex. It is found that the composition of materials on outflow of the model does not well-describe the composition of material in other model locations, and inferences using outflow data varies according to model reactor representation. Additionally, increased microbial complexity allows the total microbial community to withstand major system perturbations in diet and community structure. However, distribution of strains and functional groups within the microbial community can be modified depending on perturbation length and microbial kinetic parameters. Preliminary model extensions and potential investigative opportunities using the computational model are discussed. PMID:26680208
Linear System Models for Ultrasonic Imaging: Application to Signal Statistics
Zemp, Roger J.; Abbey, Craig K.; Insana, Michael F.
2009-01-01
Linear equations for modeling echo signals from shift-variant systems forming ultrasonic B-mode, Doppler, and strain images are analyzed and extended. The approach is based on a solution to the homogeneous wave equation for random inhomogeneous media. When the system is shift-variant, the spatial sensitivity function—defined as a spatial weighting function that determines the scattering volume for a fixed point of time—has advantages over the point-spread function traditionally used to analyze ultrasound systems. Spatial sensitivity functions are necessary for determining statistical moments in the context of rigorous image quality assessment, and they are time-reversed copies of point-spread functions for shift variant systems. A criterion is proposed to assess the validity of a local shift-invariance assumption. The analysis reveals realistic situations in which in-phase signals are correlated to the corresponding quadrature signals, which has strong implications for assessing lesion detectability. Also revealed is an opportunity to enhance near- and far-field spatial resolution by matched filtering unfocused beams. The analysis connects several well-known approaches to modeling ultrasonic echo signals. PMID:12839176
Estimating population trends with a linear model: technical comments
Sauer, J.R.; Link, W.A.; Royle, J. Andrew
2004-01-01
Controversy has sometimes arisen over whether there is a need to accommodate the limitations of survey design in estimating population change from the count data collected in bird surveys. Analyses of surveys such as the North American Breeding Bird Survey (BBS) can be quite complex; it is natural to ask if the complexity is necessary, or whether the statisticians have run amok. Bart et al. (2003) propose a very simple analysis involving nothing more complicated than simple linear regression, and contrast their approach with model-based procedures. We review the assumptions implicit to their proposed method, and document that these assumptions are unlikely to be valid for surveys such as the BBS. One fundamental limitation of a purely design-based approach is the absence of controls for factors that influence detection of birds at survey sites. We show that failure to model observer effects in survey data leads to substantial bias in estimation of population trends from BBS data for the 20 species that Bart et al. (2003) used as the basis of their simulations. Finally, we note that the simulations presented in Bart et al. (2003) do not provide a useful evaluation of their proposed method, nor do they provide a valid comparison to the estimating- equations alternative they consider.
[Advantages of midwife-led continuity model of care].
Prins, Marianne; van Dillen, Jeroen; de Jonge, Ank
2014-01-01
In the Dutch maternity care system women at low risk of complications in pregnancy and birth are distinguished from women at an increased risk. Primary care midwives are responsible for the care in the low-risk group, whereas obstetricians are responsible for care when the risk is increased. Most professionals and stakeholders agree that more continuity of care is warranted but there is no consensus on the ideal organization of care. A midwife-led continuity model of care has been shown to offer several health benefits compared with other models, such as 'shared care'. We argue that this model would be appropriate for the Netherlands. Midwives should provide care where possible and obstetricians where necessary in order to use the expertise of both professions most effectively. This requires an extension of the scope of practice for primary care midwives. This model requires good cooperation between midwives and obstetricians. PMID:25017977
Understanding cardiac alternans: A piecewise linear modeling framework
NASA Astrophysics Data System (ADS)
Thul, R.; Coombes, S.
2010-12-01
Cardiac alternans is a beat-to-beat alternation in action potential duration (APD) and intracellular calcium (Ca2+) cycling seen in cardiac myocytes under rapid pacing that is believed to be a precursor to fibrillation. The cellular mechanisms of these rhythms and the coupling between cellular Ca2+ and voltage dynamics have been extensively studied leading to the development of a class of physiologically detailed models. These have been shown numerically to reproduce many of the features of myocyte response to pacing, including alternans, and have been analyzed mathematically using various approximation techniques that allow for the formulation of a low dimensional map to describe the evolution of APDs. The seminal work by Shiferaw and Karma is of particular interest in this regard [Shiferaw, Y. and Karma, A., "Turing instability mediated by voltage and calcium diffusion in paced cardiac cells," Proc. Natl. Acad. Sci. U.S.A. 103, 5670-5675 (2006)]. Here, we establish that the key dynamical behaviors of the Shiferaw-Karma model are arranged around a set of switches. These are shown to be the main elements for organizing the nonlinear behavior of the model. Exploiting this observation, we show that a piecewise linear caricature of the Shiferaw-Karma model, with a set of appropriate switching manifolds, can be constructed that preserves the physiological interpretation of the original model while being amenable to a systematic mathematical analysis. In illustration of this point, we formulate the dynamics of Ca2+ cycling (in response to pacing) and compute the properties of periodic orbits in terms of a stroboscopic map that can be constructed without approximation. Using this, we show that alternans emerge via a period-doubling instability and track this bifurcation in terms of physiologically important parameters. We also show that when coupled to a spatially extended model for Ca2+ transport, the model supports spatially varying patterns of alternans. We analyze
The Corporate University Model for Continuous Learning, Training and Development.
ERIC Educational Resources Information Center
El-Tannir, Akram A.
2002-01-01
Corporate universities typically convey corporate culture and provide systematic curriculum aimed at achieving strategic objectives. Virtual access and company-specific content combine to provide opportunities for continuous and active learning, a model that is becoming pervasive. (Contains 17 references.) (SK)
Models of Continuing Professional Development: A Framework for Analysis
ERIC Educational Resources Information Center
Kennedy, Aileen
2014-01-01
The area of teachers' continuing professional development (CPD) is of growing interest internationally. However, while an increasing range of literature focuses on particular aspects of CPD, there is a paucity of literature addressing the spectrum of CPD models in a comparative manner. This article therefore considers a wide range of…
Promoting Continuous Quality Improvement in Online Teaching: The META Model
ERIC Educational Resources Information Center
Dittmar, Eileen; McCracken, Holly
2012-01-01
Experienced e-learning faculty members share strategies for implementing a comprehensive postsecondary faculty development program essential to continuous improvement of instructional skills. The high-impact META Model (centered around Mentoring, Engagement, Technology, and Assessment) promotes information sharing and content creation, and fosters…
A Planning System for Continuing Education Divisions: A Model.
ERIC Educational Resources Information Center
Bazik, Martha S.
1985-01-01
Details steps in a continuing education division planning model; i.e., define the planning group, develop a planning attitude, analyze internal and external environments, develop a mechanism for forecasting trends, hold planning sessions for determining strategic focus and operational plans, establish a timetable, hold follow-up/evaluation…
The Continuous Improvement Model: A K-12 Literacy Focus
ERIC Educational Resources Information Center
Brown, Jennifer V.
2013-01-01
The purpose of the study was to determine if the eight steps of the Continuous Improvement Model (CIM) provided a framework to raise achievement and to focus educators in identifying high-yield literacy strategies. This study sought to determine if an examination of the assessment data in reading revealed differences among schools that fully,…
Shortlist B: A Bayesian Model of Continuous Speech Recognition
ERIC Educational Resources Information Center
Norris, Dennis; McQueen, James M.
2008-01-01
A Bayesian model of continuous speech recognition is presented. It is based on Shortlist (D. Norris, 1994; D. Norris, J. M. McQueen, A. Cutler, & S. Butterfield, 1997) and shares many of its key assumptions: parallel competitive evaluation of multiple lexical hypotheses, phonologically abstract prelexical and lexical representations, a feedforward…
Stable clustering, the halo model and non-linear cosmological power spectra
NASA Astrophysics Data System (ADS)
Smith, R. E.; Peacock, J. A.; Jenkins, A.; White, S. D. M.; Frenk, C. S.; Pearce, F. R.; Thomas, P. A.; Efstathiou, G.; Couchman, H. M. P.
2003-06-01
We present the results of a large library of cosmological N-body simulations, using power-law initial spectra. The non-linear evolution of the matter power spectra is compared with the predictions of existing analytic scaling formulae based on the work of Hamilton et al. The scaling approach has assumed that highly non-linear structures obey `stable clustering' and are frozen in proper coordinates. Our results show that, when transformed under the self-similarity scaling, the scale-free spectra define a non-linear locus that is clearly shallower than would be required under stable clustering. Furthermore, the small-scale non-linear power increases as both the power spectrum index n and the density parameter Ω decrease, and this evolution is not well accounted for by the previous scaling formulae. This breakdown of stable clustering can be understood as resulting from the modification of dark matter haloes by continuing mergers. These effects are naturally included in the analytic `halo model' for non-linear structure; we use this approach to fit both our scale-free results and also our previous cold dark matter data. This method is more accurate than the commonly used Peacock-Dodds formula and should be applicable to more general power spectra. Code to evaluate non-linear power spectra using this method is available from
Optimal composite scores for longitudinal clinical trials under the linear mixed effects model.
Ard, M Colin; Raghavan, Nandini; Edland, Steven D
2015-01-01
Clinical trials of chronic, progressive conditions use rate of change on continuous measures as the primary outcome measure, with slowing of progression on the measure as evidence of clinical efficacy. For clinical trials with a single prespecified primary endpoint, it is important to choose an endpoint with the best signal-to-noise properties to optimize statistical power to detect a treatment effect. Composite endpoints composed of a linear weighted average of candidate outcome measures have also been proposed. Composites constructed as simple sums or averages of component tests, as well as composites constructed using weights derived from more sophisticated approaches, can be suboptimal, in some cases performing worse than individual outcome measures. We extend recent research on the construction of efficient linearly weighted composites by establishing the often overlooked connection between trial design and composite performance under linear mixed effects model assumptions and derive a formula for calculating composites that are optimal for longitudinal clinical trials of known, arbitrary design. Using data from a completed trial, we provide example calculations showing that the optimally weighted linear combination of scales can improve the efficiency of trials by almost 20% compared with the most efficient of the individual component scales. Additional simulations and analytical results demonstrate the potential losses in efficiency that can result from alternative published approaches to composite construction and explore the impact of weight estimation on composite performance.
Eaves, B.C.; Rothblum, U.G.
1990-08-01
A discounted-cost, continuous-time, infinite-horizon version of a flexible manufacturing and operator scheduling model is solved. The solution procedure is to convexify the discrete operator-assignment constraints to obtain a linear program, and then to regain the discreteness and obtain an approximate manufacturing schedule by deconvexification of the solution of the linear program over time. The strong features of the model are the accommodation of linear inequality relations among the manufacturing activities and the discrete manufacturing scheduling, whereas the weak features are intra-period relaxation of inventory availability constraints, and the absence of inventory costs, setup times, and setup charges.
NASA Astrophysics Data System (ADS)
Chen, Tao; Wu, Jun; Xu, Weiming; He, Zhiping; Qian, Liqun; Shu, Rong
2016-07-01
We have experimentally demonstrated a high power linearly polarized, dual wavelength frequency-modulated continuous-wave (FMCW) fiber laser with master-oscillator power-amplifier (MOPA) configuration, which is specially designed for simultaneous coherent distance and speed measurements. Two single longitudinal mode laser diodes working at 1550.12 and 1554.13 nm are employed as the seeds of the fiber MOPA. The wavelengths of the seeds are externally modulated by two acousto-optic frequency shifters (AOFSes) with a symmetrical sawtooth wave from 330–460 MHz in the frequency domain. The modulation periodicities for the two seeds are 26 and 26.3 μs, respectively, by which the distance ambiguity can be eliminated and therefore the detection range can be extended to a great extent. The seeds are then amplified independently to reduce their power differences during frequency modulation. After being coupled and boosted with three successive fiber amplifiers, an output power of 12.1 W is recorded from the FMCW laser with a power instability <0.14% over 1.5 h. The measured PER and full divergence angle of the laser are >18 dB and <25 μrad, respectively, indicating its excellent performance for field measurements.
NASA Astrophysics Data System (ADS)
Chen, Tao; Wu, Jun; Xu, Weiming; He, Zhiping; Qian, Liqun; Shu, Rong
2016-07-01
We have experimentally demonstrated a high power linearly polarized, dual wavelength frequency-modulated continuous-wave (FMCW) fiber laser with master-oscillator power-amplifier (MOPA) configuration, which is specially designed for simultaneous coherent distance and speed measurements. Two single longitudinal mode laser diodes working at 1550.12 and 1554.13 nm are employed as the seeds of the fiber MOPA. The wavelengths of the seeds are externally modulated by two acousto-optic frequency shifters (AOFSes) with a symmetrical sawtooth wave from 330-460 MHz in the frequency domain. The modulation periodicities for the two seeds are 26 and 26.3 μs, respectively, by which the distance ambiguity can be eliminated and therefore the detection range can be extended to a great extent. The seeds are then amplified independently to reduce their power differences during frequency modulation. After being coupled and boosted with three successive fiber amplifiers, an output power of 12.1 W is recorded from the FMCW laser with a power instability <0.14% over 1.5 h. The measured PER and full divergence angle of the laser are >18 dB and <25 μrad, respectively, indicating its excellent performance for field measurements.
Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.
Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko
2016-03-01
In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. PMID:26774211
Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.
Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko
2016-03-01
In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique.
Modeling Seismoacoustic Propagation from the Nonlinear to Linear Regimes
NASA Astrophysics Data System (ADS)
Chael, E. P.; Preston, L. A.
2015-12-01
Explosions at shallow depth-of-burial can cause nonlinear material response, such as fracturing and spalling, up to the ground surface above the shot point. These motions at the surface affect the generation of acoustic waves into the atmosphere, as well as the surface-reflected compressional and shear waves. Standard source scaling models for explosions do not account for such nonlinear interactions above the shot, while some recent studies introduce a non-isotropic addition to the moment tensor to represent them (e.g., Patton and Taylor, 2011). We are using Sandia's CTH shock physics code to model the material response in the vicinity of underground explosions, up to the overlying ground surface. Across a boundary where the motions have decayed to nearly linear behavior, we couple the signals from CTH into a linear finite-difference (FD) seismoacoustic code to efficiently propagate the wavefields to greater distances. If we assume only one-way transmission of energy through the boundary, then the particle velocities there suffice as inputs for the FD code, simplifying the specification of the boundary condition. The FD algorithm we use applies the wave equations for velocity in an elastic medium and pressure in an acoustic one, and matches the normal traction and displacement across the interface. Initially we are developing and testing a 2D, axisymmetric seismoacoustic routine; CTH can use this geometry in the source region as well. The Source Physics Experiment (SPE) in Nevada has collected seismic and acoustic data on numerous explosions at different scaled depths, providing an excellent testbed for investigating explosion phenomena (Snelson et al., 2013). We present simulations for shots SPE-4' and SPE-5, illustrating the importance of nonlinear behavior up to the ground surface. Our goal is to develop the capability for accurately predicting the relative signal strengths in the air and ground for a given combination of source yield and depth. Sandia National
Continued development of modeling tools and theory for RF heating
1998-12-01
Mission Research Corporation (MRC) is pleased to present the Department of Energy (DOE) with its renewal proposal to the Continued Development of Modeling Tools and Theory for RF Heating program. The objective of the program is to continue and extend the earlier work done by the proposed principal investigator in the field of modeling (Radio Frequency) RF heating experiments in the large tokamak fusion experiments, particularly the Tokamak Fusion Test Reactor (TFTR) device located at Princeton Plasma Physics Laboratory (PPPL). An integral part of this work is the investigation and, in some cases, resolution of theoretical issues which pertain to accurate modeling. MRC is nearing the successful completion of the specified tasks of the Continued Development of Modeling Tools and Theory for RF Heating project. The following tasks are either completed or nearing completion. (1) Anisotropic temperature and rotation upgrades; (2) Modeling for relativistic ECRH; (3) Further documentation of SHOOT and SPRUCE. As a result of the progress achieved under this project, MRC has been urged to continue this effort. Specifically, during the performance of this project two topics were identified by PPPL personnel as new applications of the existing RF modeling tools. These two topics concern (a) future fast-wave current drive experiments on the large tokamaks including TFTR and (c) the interpretation of existing and future RF probe data from TFTR. To address each of these topics requires some modification or enhancement of the existing modeling tools, and the first topic requires resolution of certain theoretical issues to produce self-consistent results. This work falls within the scope of the original project and is more suited to the project`s renewal than to the initiation of a new project.
Scaling in a Continuous Time Model for Biological Aging
NASA Astrophysics Data System (ADS)
de Almeida, R. M. C.; Thomas, G. L.
In this paper, we consider a generalization to the asexual version of Penna model for biological aging, where we take a continuous time limit. The genotype associated to each individual is an interval of real numbers over which Dirac δ-functions are defined, representing genetically programmed diseases to be switched on at defined ages of the individual life. We discuss two different continuous limits for the evolution equation and two different mutation protocols, to be implemented during reproduction. Exact stationary solutions are obtained and scaling properties are discussed.
Continuity of the robustness of contextuality of empirical models
NASA Astrophysics Data System (ADS)
Meng, HuiXian; Cao, HuaiXin; Wang, WenHua; Chen, Liang; Fan, Yajing
2016-10-01
Recently, the robustness of contextuality (RoC) of an empirical model was discussed in [Sci. China-Phys. Mech. Astron. 59, 640303 (2016)], many important properties of the RoC have been proved except for its boundedness and continuity. The aim of this paper is to find an upper bound for the RoC over all of empirical models and prove that the RoC is a continuous function on the set of all empirical models. Lastly, a relationship between the RoC and the extent of violating the noncontextual inequalities is established for an n-cycle contextual box. This relationship implies that the RoC can be used to quantify the contextuality of n-cycle boxes.
A queueing theory based model for business continuity in hospitals.
Miniati, R; Cecconi, G; Dori, F; Frosini, F; Iadanza, E; Biffi Gentili, G; Niccolini, F; Gusinu, R
2013-01-01
Clinical activities can be seen as results of precise and defined events' succession where every single phase is characterized by a waiting time which includes working duration and possible delay. Technology makes part of this process. For a proper business continuity management, planning the minimum number of devices according to the working load only is not enough. A risk analysis on the whole process should be carried out in order to define which interventions and extra purchase have to be made. Markov models and reliability engineering approaches can be used for evaluating the possible interventions and to protect the whole system from technology failures. The following paper reports a case study on the application of the proposed integrated model, including risk analysis approach and queuing theory model, for defining the proper number of device which are essential to guarantee medical activity and comply the business continuity management requirements in hospitals. PMID:24109839
A queueing theory based model for business continuity in hospitals.
Miniati, R; Cecconi, G; Dori, F; Frosini, F; Iadanza, E; Biffi Gentili, G; Niccolini, F; Gusinu, R
2013-01-01
Clinical activities can be seen as results of precise and defined events' succession where every single phase is characterized by a waiting time which includes working duration and possible delay. Technology makes part of this process. For a proper business continuity management, planning the minimum number of devices according to the working load only is not enough. A risk analysis on the whole process should be carried out in order to define which interventions and extra purchase have to be made. Markov models and reliability engineering approaches can be used for evaluating the possible interventions and to protect the whole system from technology failures. The following paper reports a case study on the application of the proposed integrated model, including risk analysis approach and queuing theory model, for defining the proper number of device which are essential to guarantee medical activity and comply the business continuity management requirements in hospitals.
A continuous-time neural model for sequential action.
Kachergis, George; Wyatte, Dean; O'Reilly, Randall C; de Kleijn, Roy; Hommel, Bernhard
2014-11-01
Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions.
A continuous-time neural model for sequential action
Kachergis, George; Wyatte, Dean; O'Reilly, Randall C.; de Kleijn, Roy; Hommel, Bernhard
2014-01-01
Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions. PMID:25267830
Functional linear models to test for differences in prairie wetland hydraulic gradients
Greenwood, Mark C.; Sojda, Richard S.; Preston, Todd M.; Swayne, David A.; Yang, Wanhong; Voinov, A.A.; Rizzoli, A.; Filatova, T.
2010-01-01
Functional data analysis provides a framework for analyzing multiple time series measured frequently in time, treating each series as a continuous function of time. Functional linear models are used to test for effects on hydraulic gradient functional responses collected from three types of land use in Northeastern Montana at fourteen locations. Penalized regression-splines are used to estimate the underlying continuous functions based on the discretely recorded (over time) gradient measurements. Permutation methods are used to assess the statistical significance of effects. A method for accommodating missing observations in each time series is described. Hydraulic gradients may be an initial and fundamental ecosystem process that responds to climate change. We suggest other potential uses of these methods for detecting evidence of climate change.
Linear multivariate evaluation models for spatial perception of soundscape.
Deng, Zhiyong; Kang, Jian; Wang, Daiwei; Liu, Aili; Kang, Joe Zhengyu
2015-11-01
Soundscape is a sound environment that emphasizes the awareness of auditory perception and social or cultural understandings. The case of spatial perception is significant to soundscape. However, previous studies on the auditory spatial perception of the soundscape environment have been limited. Based on 21 native binaural-recorded soundscape samples and a set of auditory experiments for subjective spatial perception (SSP), a study of the analysis among semantic parameters, the inter-aural-cross-correlation coefficient (IACC), A-weighted-equal sound-pressure-level (L(eq)), dynamic (D), and SSP is introduced to verify the independent effect of each parameter and to re-determine some of their possible relationships. The results show that the more noisiness the audience perceived, the worse spatial awareness they received, while the closer and more directional the sound source image variations, dynamics, and numbers of sound sources in the soundscape are, the better the spatial awareness would be. Thus, the sensations of roughness, sound intensity, transient dynamic, and the values of Leq and IACC have a suitable range for better spatial perception. A better spatial awareness seems to promote the preference slightly for the audience. Finally, setting SSPs as functions of the semantic parameters and Leq-D-IACC, two linear multivariate evaluation models of subjective spatial perception are proposed.
Linear effects models of signaling pathways from combinatorial perturbation data
Szczurek, Ewa; Beerenwinkel, Niko
2016-01-01
Motivation: Perturbations constitute the central means to study signaling pathways. Interrupting components of the pathway and analyzing observed effects of those interruptions can give insight into unknown connections within the signaling pathway itself, as well as the link from the pathway to the effects. Different pathway components may have different individual contributions to the measured perturbation effects, such as gene expression changes. Those effects will be observed in combination when the pathway components are perturbed. Extant approaches focus either on the reconstruction of pathway structure or on resolving how the pathway components control the downstream effects. Results: Here, we propose a linear effects model, which can be applied to solve both these problems from combinatorial perturbation data. We use simulated data to demonstrate the accuracy of learning the pathway structure as well as estimation of the individual contributions of pathway components to the perturbation effects. The practical utility of our approach is illustrated by an application to perturbations of the mitogen-activated protein kinase pathway in Saccharomyces cerevisiae. Availability and Implementation: lem is available as a R package at http://www.mimuw.edu.pl/∼szczurek/lem. Contact: szczurek@mimuw.edu.pl; niko.beerenwinkel@bsse.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307630
Amplitude relations in non-linear sigma model
NASA Astrophysics Data System (ADS)
Chen, Gang; Du, Yi-Jian
2014-01-01
In this paper, we investigate tree-level scattering amplitude relations in U( N) non-linear sigma model. We use Cayley parametrization. As was shown in the recent works [23,24], both on-shell amplitudes and off-shell currents with odd points have to vanish under Cayley parametrization. We prove the off-shell U(1) identity and fundamental BCJ relation for even-point currents. By taking the on-shell limits of the off-shell relations, we show that the color-ordered tree amplitudes with even points satisfy U(1)-decoupling identity and fundamental BCJ relation, which have the same formations within Yang-Mills theory. We further state that all the on-shell general KK, BCJ relations as well as the minimal-basis expansion are also satisfied by color-ordered tree amplitudes. As a consequence of the relations among color-ordered amplitudes, the total 2 m-point tree amplitudes satisfy DDM form of color decomposition as well as KLT relation.
Identifying genetically driven clinical phenotypes using linear mixed models
Mosley, Jonathan D.; Witte, John S.; Larkin, Emma K.; Bastarache, Lisa; Shaffer, Christian M.; Karnes, Jason H.; Stein, C. Michael; Phillips, Elizabeth; Hebbring, Scott J.; Brilliant, Murray H.; Mayer, John; Ye, Zhan; Roden, Dan M.; Denny, Joshua C.
2016-01-01
We hypothesized that generalized linear mixed models (GLMMs), which estimate the additive genetic variance underlying phenotype variability, would facilitate rapid characterization of clinical phenotypes from an electronic health record. We evaluated 1,288 phenotypes in 29,349 subjects of European ancestry with single-nucleotide polymorphism (SNP) genotyping on the Illumina Exome Beadchip. We show that genetic liability estimates are primarily driven by SNPs identified by prior genome-wide association studies and SNPs within the human leukocyte antigen (HLA) region. We identify 44 (false discovery rate q<0.05) phenotypes associated with HLA SNP variation and show that hypothyroidism is genetically correlated with Type I diabetes (rG=0.31, s.e. 0.12, P=0.003). We also report novel SNP associations for hypothyroidism near HLA-DQA1/HLA-DQB1 at rs6906021 (combined odds ratio (OR)=1.2 (95% confidence interval (CI): 1.1–1.2), P=9.8 × 10−11) and for polymyalgia rheumatica near C6orf10 at rs6910071 (OR=1.5 (95% CI: 1.3–1.6), P=1.3 × 10−10). Phenome-wide application of GLMMs identifies phenotypes with important genetic drivers, and focusing on these phenotypes can identify novel genetic associations. PMID:27109359
Modelling and Inverse-Modelling: Experiences with O.D.E. Linear Systems in Engineering Courses
ERIC Educational Resources Information Center
Martinez-Luaces, Victor
2009-01-01
In engineering careers courses, differential equations are widely used to solve problems concerned with modelling. In particular, ordinary differential equations (O.D.E.) linear systems appear regularly in Chemical Engineering, Food Technology Engineering and Environmental Engineering courses, due to the usefulness in modelling chemical kinetics,…
ERIC Educational Resources Information Center
Battauz, Michela; Bellio, Ruggero
2011-01-01
This paper proposes a structural analysis for generalized linear models when some explanatory variables are measured with error and the measurement error variance is a function of the true variables. The focus is on latent variables investigated on the basis of questionnaires and estimated using item response theory models. Latent variable…
NASA Astrophysics Data System (ADS)
Farrugia, Charles; Moestl, Christian; Leitner, Martin; Galvin, Antoinette; Lugaz, Noé; Yu, Wenyuan
2016-07-01
This work is about modeling of those small solar wind transients (STs) which have a flux rope geometry. The two models used are: (i) the linear force free solution of Lundquist in terms of Bessel functions, and (ii) the non-linear Gold-Hoyle solution describing a uniformly-twisted flux tube. The first has been used almost exclusively in modeling of both large and small flux ropes in the solar wind. The second was applied to one small transient. In recent work there have been claims that variant (ii) is more appropriate than (i) for large transients, i.e. magnetic clouds. We select by eye six flux rope STs from STEREO and Wind data, chosen purely on the basis of having a large and smooth rotation. We also choose these during solar maximum activity conditions since our current work shows that only then are these models appropriate.
Mathematical models of continuous flow electrophoresis: Electrophoresis technology
NASA Technical Reports Server (NTRS)
Saville, Dudley A.
1986-01-01
Two aspects of continuous flow electrophoresis were studied: (1) the structure of the flow field in continuous flow devices; and (2) the electrokinetic properties of suspended particles relevant to electrophoretic separations. Mathematical models were developed to describe flow structure and stability, with particular emphasis on effects due to buoyancy. To describe the fractionation of an arbitrary particulate sample by continuous flow electrophoresis, a general mathematical model was constructed. In this model, chamber dimensions, field strength, buffer composition, and other design variables can be altered at will to study their effects on resolution and throughput. All these mathematical models were implemented on a digital computer and the codes are available for general use. Experimental and theoretical work with particulate samples probed how particle mobility is related to buffer composition. It was found that ions on the surface of small particles are mobile, contrary to the widely accepted view. This influences particle mobility and suspension conductivity. A novel technique was used to measure the mobility of particles in concentrated suspensions.
A simple and continuous polycrystalline silicon thin-film transistor model for SPICE implementation
NASA Astrophysics Data System (ADS)
Pappas, I.; Hatzopoulos, A. T.; Tassis, D. H.; Arpatzanis, N.; Siskos, S.; Dimitriadis, C. A.; Kamarinos, G.
2006-09-01
A simple current-voltage model for polycrystalline silicon thin-film transistors (polysilicon TFTs) is proposed, including the sixth-order polynomial function coefficients fitted to the effective mobility versus gate voltage data, the channel length modulation, and impact ionization effects. The model possesses continuity of current in the transfer characteristics from weak to strong inversion and in the output characteristics throughout the linear and saturation regions of operation. The model parameters are used as input parameters in AIM-SPICE circuit simulator for device modeling. The model has been applied in a number of long and short channel TFTs, and the statistical distributions of the model parameters have been derived which are useful for checking the functionality of TFTs circuits with AIM-SPICE.
Kinjo, Ken; Uchibe, Eiji; Doya, Kenji
2013-01-01
Linearly solvable Markov Decision Process (LMDP) is a class of optimal control problem in which the Bellman's equation can be converted into a linear equation by an exponential transformation of the state value function (Todorov, 2009b). In an LMDP, the optimal value function and the corresponding control policy are obtained by solving an eigenvalue problem in a discrete state space or an eigenfunction problem in a continuous state using the knowledge of the system dynamics and the action, state, and terminal cost functions. In this study, we evaluate the effectiveness of the LMDP framework in real robot control, in which the dynamics of the body and the environment have to be learned from experience. We first perform a simulation study of a pole swing-up task to evaluate the effect of the accuracy of the learned dynamics model on the derived the action policy. The result shows that a crude linear approximation of the non-linear dynamics can still allow solution of the task, despite with a higher total cost. We then perform real robot experiments of a battery-catching task using our Spring Dog mobile robot platform. The state is given by the position and the size of a battery in its camera view and two neck joint angles. The action is the velocities of two wheels, while the neck joints were controlled by a visual servo controller. We test linear and bilinear dynamic models in tasks with quadratic and Guassian state cost functions. In the quadratic cost task, the LMDP controller derived from a learned linear dynamics model performed equivalently with the optimal linear quadratic regulator (LQR). In the non-quadratic task, the LMDP controller with a linear dynamics model showed the best performance. The results demonstrate the usefulness of the LMDP framework in real robot control even when simple linear models are used for dynamics learning. PMID:23576983
Kinjo, Ken; Uchibe, Eiji; Doya, Kenji
2013-01-01
Linearly solvable Markov Decision Process (LMDP) is a class of optimal control problem in which the Bellman's equation can be converted into a linear equation by an exponential transformation of the state value function (Todorov, 2009b). In an LMDP, the optimal value function and the corresponding control policy are obtained by solving an eigenvalue problem in a discrete state space or an eigenfunction problem in a continuous state using the knowledge of the system dynamics and the action, state, and terminal cost functions. In this study, we evaluate the effectiveness of the LMDP framework in real robot control, in which the dynamics of the body and the environment have to be learned from experience. We first perform a simulation study of a pole swing-up task to evaluate the effect of the accuracy of the learned dynamics model on the derived the action policy. The result shows that a crude linear approximation of the non-linear dynamics can still allow solution of the task, despite with a higher total cost. We then perform real robot experiments of a battery-catching task using our Spring Dog mobile robot platform. The state is given by the position and the size of a battery in its camera view and two neck joint angles. The action is the velocities of two wheels, while the neck joints were controlled by a visual servo controller. We test linear and bilinear dynamic models in tasks with quadratic and Guassian state cost functions. In the quadratic cost task, the LMDP controller derived from a learned linear dynamics model performed equivalently with the optimal linear quadratic regulator (LQR). In the non-quadratic task, the LMDP controller with a linear dynamics model showed the best performance. The results demonstrate the usefulness of the LMDP framework in real robot control even when simple linear models are used for dynamics learning.
Roughness and growth in a continuous fluid invasion model
NASA Astrophysics Data System (ADS)
Hecht, Inbal; Taitelbaum, Haim
2004-10-01
We have studied interface characteristics in a continuous fluid invasion model, first introduced by Cieplak and Robbins [Phys. Rev. Lett. 60, 2042 (1988)]. In this model, the interface grows as a response to an applied quasistatic pressure, which induces various types of instabilities. We suggest a variant of the model, which differs from the original model by the order of instabilities treatment. This order represents the relative importance of the physical mechanisms involved in the system. This variant predicts the existence of a third, intermediate regime, in the behavior of the roughness exponent as a function of the wetting properties of the system. The gradual increase of the roughness exponent in this third regime can explain the scattered experimental data for the roughness exponent in the literature. The growth exponent in this model was found to be around zero, due to the initial rough interface.
Rule-based extrapolation: a continuing challenge for exemplar models.
Denton, Stephen E; Kruschke, John K; Erickson, Michael A
2008-08-01
Erickson and Kruschke (1998, 2002) demonstrated that in rule-plus-exception categorization, people generalize category knowledge by extrapolating in a rule-like fashion, even when they are presented with a novel stimulus that is most similar to a known exception. Although exemplar models have been found to be deficient in explaining rule-based extrapolation, Rodrigues and Murre (2007) offered a variation of an exemplar model that was better able to account for such performance. Here, we present the results of a new rule-plus-exception experiment that yields rule-like extrapolation similar to that of previous experiments, and yet the data are not accounted for by Rodrigues and Murre's augmented exemplar model. Further, a hybrid rule-and-exemplar model is shown to better describe the data. Thus, we maintain that rule-plus-exception categorization continues to be a challenge for exemplar-only models. PMID:18792504
Ion cloud model for a linear quadrupole ion trap.
Douglas, Don J; Konenkov, Nikolai V
2012-01-01
If large numbers of ions are stored in a linear quadrupole ion trap, space charge causes the oscillation frequencies of ions to decrease. Ions then appear at higher apparent masses when resonantly ejected for mass analysis. In principle, to calculate mass shifts requires calculating the positions of all ions, interacting with each other, at all times, with a self-consistent space charge field. Here, we propose a simpler model for the ion cloud in the case where mass shifts and frequency shifts are relatively small (ca 0.2% and 0.4%, respectively), the trapping field is much stronger (ca × 10(2)) than the space charge field and space charge only causes small perturbations to the ion motion. The self-consistent field problem need not be considered. As test ions move with times long compared to a cycle of the trapping field, the motion of individual ions can be ignored. Static positions of the ions in the cloud are used. To generate an ion cloud, trajectories of N (ca 10,000) ions are calculated for random times between 10 and 100 cycles of the trapping radio frequency field. The ions are then distributed axially randomly in a trap four times the field radius, r(0) in length. The potential and electric field from the ion cloud are calculated from the ion positions. Near the trap center (distances r< 1r(0)), the potential and electric fields from space charge are not cylindrically symmetric, but are quite symmetric for greater values of r. Trajectories of test ions, oscillation frequencies and mass shifts can then be calculated in the trapping field, including the space charge field. Mass shifts are in good agreement with experiments for reasonable values of the initial positions and speeds of the ions. Agreement with earlier analytical models for the ion cloud, based on a uniform occupation of phase space, or a thermal (Boltzmann) distribution of ions trapped in the effective potential [D. Douglas and N.V. Konenkov, Rapid Commun. Mass Spectrom. 26, 2105 (2012)] is
A log-linear multidimensional Rasch model for capture-recapture.
Pelle, E; Hessen, D J; van der Heijden, P G M
2016-02-20
In this paper, a log-linear multidimensional Rasch model is proposed for capture-recapture analysis of registration data. In the model, heterogeneity of capture probabilities is taken into account, and registrations are viewed as dichotomously scored indicators of one or more latent variables that can account for correlations among registrations. It is shown how the probability of a generic capture profile is expressed under the log-linear multidimensional Rasch model and how the parameters of the traditional log-linear model are derived from those of the log-linear multidimensional Rasch model. Finally, an application of the model to neural tube defects data is presented.
A continuous model of the dynamical systems capable to memorise multiple shapes
NASA Astrophysics Data System (ADS)
Yudashkin, Alexander
2008-10-01
This paper proposes the novel approach to the mathematical synthesis of continuous self-organising systems capable to memorise and restore own multiple shapes defined by means of functions of single spatial variable or parametric models in two-dimensional space. The model is based on the certain universal form of the integral operator with the kernel representing the system memory. The technique for memorising shapes uses the composition of singular kernels of integral operators. The whole system is described by the potential function, whose minimisation leads to the non-linear dynamics of shape reconstruction by integro-differential non-linear equations with partial derivatives. The corresponding models are proposed and analysed for both parametric and non-parametric shape definitions. Main features of the proposed model are considered, and the results of numerical simulation are shown in case of three shapes memorising and retrieval. The proposed model can be used in theory of smart materials, artificial intelligence and some other branches of non-linear sciences where the effect of multiple shapes memorising and retrieval appears as the core feature.
Carvalho, C M; Aires-Barros, M R; Cabral, J M
2001-01-20
A model was developed to describe the conversion degree in a membrane bioreactor (MBR) for the synthesis of short-chain esters as a function of the flow rate. The transesterification reaction was catalyzed by a recombinant cutinase of Fusarium solani pisi microencapsulated in reversed micelles of AOT/isooctane. The differences of product concentration in permeate and retentate together with the deactivation profiles led to an enzyme distribution evaluation that describes the experimental values attained. The model considers the bioreactor design as well as its hydrodynamics and the enzyme kinetics. The approach included the analysis of the MBR operation as a CSTR, a PFR, and a series of continuous reactors. The comparative efficiency of these reactor types is discussed. The enzyme distribution was estimated for all the cases. The best description was obtained considering a series of two CSTRs. The modeling results led to a re-evaluation of cutinase operational stability. Deactivation rates correlated very well with the hydrodynamic aspects of biocatalyst location.
Models of reduced-noise, probabilistic linear amplifiers
NASA Astrophysics Data System (ADS)
Combes, Joshua; Walk, Nathan; Lund, A. P.; Ralph, T. C.; Caves, Carlton M.
2016-05-01
We construct an amplifier that interpolates between a nondeterministic, immaculate linear amplifier and a deterministic, ideal linear amplifier and beyond to nonideal linear amplifiers. The construction involves cascading an immaculate linear amplifier that has amplitude gain g1 with a (possibly) nonideal linear amplifier that has gain g2. With respect to normally ordered moments, the device has output noise μ2(G2-1 ) where G =g1g2 is the overall amplitude gain and μ2 is a noise parameter. When μ2≥1 , our devices realize ideal (μ2=1 ) and nonideal (μ2>1 ) linear amplifiers. When 0 ≤μ2<1 , these devices work effectively only over a restricted region of phase space and with some subunity success probability p✓. We investigate the performance of our μ2 amplifiers in terms of a gain-corrected probability-fidelity product and the ratio of input to output signal-to-noise ratios corrected for success probability.
Developing ontological model of computational linear algebra - preliminary considerations
NASA Astrophysics Data System (ADS)
Wasielewska, K.; Ganzha, M.; Paprzycki, M.; Lirkov, I.
2013-10-01
The aim of this paper is to propose a method for application of ontologically represented domain knowledge to support Grid users. The work is presented in the context provided by the Agents in Grid system, which aims at development of an agent-semantic infrastructure for efficient resource management in the Grid. Decision support within the system should provide functionality beyond the existing Grid middleware, specifically, help the user to choose optimal algorithm and/or resource to solve a problem from a given domain. The system assists the user in at least two situations. First, for users without in-depth knowledge about the domain, it should help them to select the method and the resource that (together) would best fit the problem to be solved (and match the available resources). Second, if the user explicitly indicates the method and the resource configuration, it should "verify" if her choice is consistent with the expert recommendations (encapsulated in the knowledge base). Furthermore, one of the goals is to simplify the use of the selected resource to execute the job; i.e., provide a user-friendly method of submitting jobs, without required technical knowledge about the Grid middleware. To achieve the mentioned goals, an adaptable method of expert knowledge representation for the decision support system has to be implemented. The selected approach is to utilize ontologies and semantic data processing, supported by multicriterial decision making. As a starting point, an area of computational linear algebra was selected to be modeled, however, the paper presents a general approach that shall be easily extendable to other domains.
Continuation-like semantics for modeling structural process anomalies
2012-01-01
Background Biomedical ontologies usually encode knowledge that applies always or at least most of the time, that is in normal circumstances. But for some applications like phenotype ontologies it is becoming increasingly important to represent information about aberrations from a norm. These aberrations may be modifications of physiological structures, but also modifications of biological processes. Methods To facilitate precise definitions of process-related phenotypes, such as delayed eruption of the primary teeth or disrupted ocular pursuit movements, I introduce a modeling approach that draws inspiration from the use of continuations in the analysis of programming languages and apply a similar idea to ontological modeling. This approach characterises processes by describing their outcome up to a certain point and the way they will continue in the canonical case. Definitions of process types are then given in terms of their continuations and anomalous phenotypes are defined by their differences to the canonical definitions. Results The resulting model is capable of accurately representing structural process anomalies. It allows distinguishing between different anomaly kinds (delays, interruptions), gives identity criteria for interrupted processes, and explains why normal and anomalous process instances can be subsumed under a common type, thus establishing the connection between canonical and anomalous process-related phenotypes. Conclusion This paper shows how to to give semantically rich definitions of process-related phenotypes. These allow to expand the application areas of phenotype ontologies beyond literature annotation and establishment of genotype-phenotype associations to the detection of anomalies in suitably encoded datasets. PMID:23046705
Analysis of linear trade models and relation to scale economies
Gomory, Ralph E.; Baumol, William J.
1997-01-01
We discuss linear Ricardo models with a range of parameters. We show that the exact boundary of the region of equilibria of these models is obtained by solving a simple integer programming problem. We show that there is also an exact correspondence between many of the equilibria resulting from families of linear models and the multiple equilibria of economies of scale models. PMID:11038573
Validity of tests under covariate-adaptive biased coin randomization and generalized linear models.
Shao, Jun; Yu, Xinxin
2013-12-01
Some covariate-adaptive randomization methods have been used in clinical trials for a long time, but little theoretical work has been done about testing hypotheses under covariate-adaptive randomization until Shao et al. (2010) who provided a theory with detailed discussion for responses under linear models. In this article, we establish some asymptotic results for covariate-adaptive biased coin randomization under generalized linear models with possibly unknown link functions. We show that the simple t-test without using any covariate is conservative under covariate-adaptive biased coin randomization in terms of its Type I error rate, and that a valid test using the bootstrap can be constructed. This bootstrap test, utilizing covariates in the randomization scheme, is shown to be asymptotically as efficient as Wald's test correctly using covariates in the analysis. Thus, the efficiency loss due to not using covariates in the analysis can be recovered by utilizing covariates in covariate-adaptive biased coin randomization. Our theory is illustrated with two most popular types of discrete outcomes, binary responses and event counts under the Poisson model, and exponentially distributed continuous responses. We also show that an alternative simple test without using any covariate under the Poisson model has an inflated Type I error rate under simple randomization, but is valid under covariate-adaptive biased coin randomization. Effects on the validity of tests due to model misspecification is also discussed. Simulation studies about the Type I errors and powers of several tests are presented for both discrete and continuous responses. PMID:23848580
Influent Fractionation for Modeling Continuous Anaerobic Digestion Processes.
Lübken, Manfred; Kosse, Pascal; Koch, Konrad; Gehring, Tito; Wichern, Marc
2015-01-01
The first dynamic model developed to describe anaerobic digestion processes dates back to 1969. Since then, considerable improvements in identifying the underlying biochemical processes and associated microorganisms have been achieved. These have led to an increasing complexity of both model structure and the standard set of stoichiometric and kinetic parameters. Literature has always paid attention to kinetic parameter estimation, as this determines model accuracy with respect to predicting the dynamic behavior of biogas systems. As sufficient computing power is easily available nowadays, sophisticated linear and nonlinear parameter estimation techniques are applied to evaluate parameter uncertainty. However, the uncertainty of influent fractionation in these parameter optimization procedures is generally neglected. As anaerobic digestion systems are currently increasingly used to convert a broad variety of organic biomass to methane, the lack of generally accepted guidelines for input characterization adapted to the simulation model's characteristics is a considerable limitation of model application to these substrates. Directly after the introduction of the standardized Anaerobic Digestion Model No. 1 (ADM1), several publications pointed out that the model's requirement of a detailed influent characterization can hardly be fulfilled. The main shortcoming of the model application was addressed in the reliable and practical identification of the model's input state variables for particulate and soluble carbohydrates, proteins and lipids, as well as for the inerts. Several authors derived biomass characterization procedures, most of them dedicated to a particular substrate, and some of them being of general nature, but none of these approaches have resulted in a practical standard protocol so far. This review provides an overview of existing approaches that improve substrate influent characterization to be used for state of the art anaerobic digestion models. PMID
Continuous Modeling of Calcium Transport Through Biological Membranes
NASA Astrophysics Data System (ADS)
Jasielec, J. J.; Filipek, R.; Szyszkiewicz, K.; Sokalski, T.; Lewenstam, A.
2016-06-01
In this work an approach to the modeling of the biological membranes where a membrane is treated as a continuous medium is presented. The Nernst-Planck-Poisson model including Poisson equation for electric potential is used to describe transport of ions in the mitochondrial membrane—the interface which joins mitochondrial matrix with cellular cytosis. The transport of calcium ions is considered. Concentration of calcium inside the mitochondrion is not known accurately because different analytical methods give dramatically different results. We explain mathematically these differences assuming the complexing reaction inside mitochondrion and the existence of the calcium set-point (concentration of calcium in cytosis below which calcium stops entering the mitochondrion).
Continuous Modeling of Calcium Transport Through Biological Membranes
NASA Astrophysics Data System (ADS)
Jasielec, J. J.; Filipek, R.; Szyszkiewicz, K.; Sokalski, T.; Lewenstam, A.
2016-08-01
In this work an approach to the modeling of the biological membranes where a membrane is treated as a continuous medium is presented. The Nernst-Planck-Poisson model including Poisson equation for electric potential is used to describe transport of ions in the mitochondrial membrane—the interface which joins mitochondrial matrix with cellular cytosis. The transport of calcium ions is considered. Concentration of calcium inside the mitochondrion is not known accurately because different analytical methods give dramatically different results. We explain mathematically these differences assuming the complexing reaction inside mitochondrion and the existence of the calcium set-point (concentration of calcium in cytosis below which calcium stops entering the mitochondrion).
A model for a continuous-wave iodine laser
NASA Technical Reports Server (NTRS)
Hwang, In H.; Tabibi, Bagher M.
1990-01-01
A model for a continuous-wave (CW) iodine laser has been developed and compared with the experimental results obtained from a solar-simulator-pumped CW iodine laser. The agreement between the calculated laser power output and the experimental results is generally good for various laser parameters even when the model includes only prominent rate coefficients. The flow velocity dependence of the output power shows that the CW iodine laser cannot be achieved with a flow velocity below 1 m/s for the present solar-simulator-pumped CW iodine laser system.
The continuous similarity model of bulk soil-water evaporation
NASA Technical Reports Server (NTRS)
Clapp, R. B.
1983-01-01
The continuous similarity model of evaporation is described. In it, evaporation is conceptualized as a two stage process. For an initially moist soil, evaporation is first climate limited, but later it becomes soil limited. During the latter stage, the evaporation rate is termed evaporability, and mathematically it is inversely proportional to the evaporation deficit. A functional approximation of the moisture distribution within the soil column is also included in the model. The model was tested using data from four experiments conducted near Phoenix, Arizona; and there was excellent agreement between the simulated and observed evaporation. The model also predicted the time of transition to the soil limited stage reasonably well. For one of the experiments, a third stage of evaporation, when vapor diffusion predominates, was observed. The occurrence of this stage was related to the decrease in moisture at the surface of the soil. The continuous similarity model does not account for vapor flow. The results show that climate, through the potential evaporation rate, has a strong influence on the time of transition to the soil limited stage. After this transition, however, bulk evaporation is independent of climate until the effects of vapor flow within the soil predominate.
Reasoning with Vectors: A Continuous Model for Fast Robust Inference
Widdows, Dominic; Cohen, Trevor
2015-01-01
This paper describes the use of continuous vector space models for reasoning with a formal knowledge base. The practical significance of these models is that they support fast, approximate but robust inference and hypothesis generation, which is complementary to the slow, exact, but sometimes brittle behavior of more traditional deduction engines such as theorem provers. The paper explains the way logical connectives can be used in semantic vector models, and summarizes the development of Predication-based Semantic Indexing, which involves the use of Vector Symbolic Architectures to represent the concepts and relationships from a knowledge base of subject-predicate-object triples. Experiments show that the use of continuous models for formal reasoning is not only possible, but already demonstrably effective for some recognized informatics tasks, and showing promise in other traditional problem areas. Examples described in this paper include: predicting new uses for existing drugs in biomedical informatics; removing unwanted meanings from search results in information retrieval and concept navigation; type-inference from attributes; comparing words based on their orthography; and representing tabular data, including modelling numerical values. The algorithms and techniques described in this paper are all publicly released and freely available in the Semantic Vectors open-source software package.1 PMID:26582967
Segment-based acoustic models for continuous speech recognition
NASA Astrophysics Data System (ADS)
Ostendorf, Mari; Rohlicek, J. R.
1994-02-01
In work, we are interested in the problem of large vocabulary, speaker-independent continuous speech recognition, and primarily in the acoustic modeling component of this problem. In developing acoustic models for speech recognition, we have conflicting goals. On one hand, the models should be robust to inter- and intra-speaker variability, to the use of a different vocabulary in recognition than in training, and to the effects of moderately noisy environments. In order to accomplish this, we need to model gross features and global trends. On the other hand, the models must be sensitive and detailed enough to detect fine acoustic differences between similar words in a large vocabulary task. To answer these opposing demands requires improvements in acoustic modeling at several levels: the frame level (e.g. signal processing), the phoneme level (e.g. modeling feature dynamics), and the utterance level (e.g. defining a structural context for representing the intra-utterance dependence across phonemes). This project address the problem of acoustic modeling specifically focusing on modeling at the segment level and above.
Continuous vs. discrete models of nonadiabatic monolith catalysts
Groppi, G.; Tronconi, E.
1996-08-01
Monolith catalysts are widely applied for clean up of waste gases [catalytic mufflers, volatile organic compound (VOC) incinerators, reactors for selective catalytic reduction (SCR) of NO{sub x} by NH{sub 3}] in view of their unique combination of low-pressure drops and high gas-solid interfacial areas. The crucial point in continuous heat-transfer models is the evaluation of the effective thermal conductivity coefficients, which are functions both of the physical properties of the two phases and of the monolith geometry. In this work a novel expression for calculation of the radial effective conductivity is derived. The physical consistency of the steady-state continuous model implementing such an expression is then analyzed by comparison with a discrete monolith model. In spite of the just-mentioned limitations, discrete models have been partially validated in the literature against experimental temperature profiles in heated monoliths; thus, they can be regarded as a standard in evaluating the adequacy of the continuum approach. The reference problem of pure heat transfer with constant temperature of the external monolith wall is investigated for these purposes.
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.
NASA Astrophysics Data System (ADS)
Collier, W.; Milian Sanz, J.
2016-09-01
The length and flexibility of wind turbine blades are increasing over time. Typically, the dynamic response of the blades is analysed using linear models of blade deflection, enhanced by various ad-hoc non-linear correction models. For blades undergoing large deflections, the small deflection assumption inherent to linear models becomes less valid. It has previously been demonstrated that linear and nonlinear blade models can show significantly different blade response, particularly for blade torsional deflection, leading to load prediction differences. There is a need to evaluate how load predictions from these two approaches compare to measurement data from the field. In this paper, time domain simulations in turbulent wind are carried out using the aero-elastic code Bladed with linear and non-linear blade deflection models. The turbine blade load and deflection simulation results are compared to measurement data from an onshore prototype of the GE 6MW Haliade turbine, which features 73.5m long LM blades. Both linear and non-linear blade models show a good match to measurement turbine load and blade deflections. Only the blade loads differ significantly between the two models, with other turbine loads not strongly affected. The non-linear blade model gives a better match to the measured blade root flapwise damage equivalent load, suggesting that the flapwise dynamic behaviour is better captured by the non-linear blade model. Conversely, the linear blade model shows a better match to measurements in some areas such as blade edgewise damage equivalent load.
Optimized continuous pharmaceutical manufacturing via model-predictive control.
Rehrl, Jakob; Kruisz, Julia; Sacher, Stephan; Khinast, Johannes; Horn, Martin
2016-08-20
This paper demonstrates the application of model-predictive control to a feeding blending unit used in continuous pharmaceutical manufacturing. The goal of this contribution is, on the one hand, to highlight the advantages of the proposed concept compared to conventional PI-controllers, and, on the other hand, to present a step-by-step guide for controller synthesis. The derivation of the required mathematical plant model is given in detail and all the steps required to develop a model-predictive controller are shown. Compared to conventional concepts, the proposed approach allows to conveniently consider constraints (e.g. mass hold-up in the blender) and offers a straightforward, easy to tune controller setup. The concept is implemented in a simulation environment. In order to realize it on a real system, additional aspects (e.g., state estimation, measurement equipment) will have to be investigated. PMID:27317987
Optimized continuous pharmaceutical manufacturing via model-predictive control.
Rehrl, Jakob; Kruisz, Julia; Sacher, Stephan; Khinast, Johannes; Horn, Martin
2016-08-20
This paper demonstrates the application of model-predictive control to a feeding blending unit used in continuous pharmaceutical manufacturing. The goal of this contribution is, on the one hand, to highlight the advantages of the proposed concept compared to conventional PI-controllers, and, on the other hand, to present a step-by-step guide for controller synthesis. The derivation of the required mathematical plant model is given in detail and all the steps required to develop a model-predictive controller are shown. Compared to conventional concepts, the proposed approach allows to conveniently consider constraints (e.g. mass hold-up in the blender) and offers a straightforward, easy to tune controller setup. The concept is implemented in a simulation environment. In order to realize it on a real system, additional aspects (e.g., state estimation, measurement equipment) will have to be investigated.
NASA Astrophysics Data System (ADS)
Liu, Y.; Li, T.; Zhu, C.; Zhang, R.; Wu, Y.
2015-12-01
Three-dimensional (3-D) electromagnetic (EM) forward modelling and inversion continues to be an important issue for the correct interpretation of EM data.To this end,approximate solutions have been developed that allow the construction of relatively fast forward modelling and inversion schemes.We have developed an improved quasi-linear approximation which is more appropriate in solving the linear equation for greatly shortening calculation time.We achieved this by using green's function properties.Then we introduced the improved quasi-linear approximation to spectral induced polarization (SIP) to tackle the problem of the resolution and the efficiency.The localized quasi-linear (LQL) approximation theory is appropriate for multisource array-type surveys assuming that the normal field is slowly varying within the inhomogeneity domain.However,the normal field of attenuates severely which dose not satisfy the assumption of the LQL approximation.As a consenquence,the imaginary part is not accurate when LQL approximation is adopted for the simulation.The improved quasi-linear approximation provide a new approach with the same resolution of QL approximation and much less calculation time.We have also constructed three-dimensional SIP forward modeling based on improved quasi-linear approximation method.It only takes 0.8s for forward modeling when inhomogeneity domain is divided into 2000 blocks.Beyond that, we have introduced the Cole-Cole model to the algorithm and complete the three-dimensional complex resistivity conjugate gradient inversion with parameter restraint.The model trial results show that this method can obtain good inversion results in physical parameters such as zero frequency resistivity, polarization.The results demonstrate the stability and the efficiency of the improved quasi-linear approximation and the method may be a practical solution for3-D EM forward modelling and inversion of SIP.
Multiphysics modeling of the steel continuous casting process
NASA Astrophysics Data System (ADS)
Hibbeler, Lance C.
This work develops a macroscale, multiphysics model of the continuous casting of steel. The complete model accounts for the turbulent flow and nonuniform distribution of superheat in the molten steel, the elastic-viscoplastic thermal shrinkage of the solidifying shell, the heat transfer through the shell-mold interface with variable gap size, and the thermal distortion of the mold. These models are coupled together with carefully constructed boundary conditions with the aid of reduced-order models into a single tool to investigate behavior in the mold region, for practical applications such as predicting ideal tapers for a beam-blank mold. The thermal and mechanical behaviors of the mold are explored as part of the overall modeling effort, for funnel molds and for beam-blank molds. These models include high geometric detail and reveal temperature variations on the mold-shell interface that may be responsible for cracks in the shell. Specifically, the funnel mold has a column of mold bolts in the middle of the inside-curve region of the funnel that disturbs the uniformity of the hot face temperatures, which combined with the bending effect of the mold on the shell, can lead to longitudinal facial cracks. The shoulder region of the beam-blank mold shows a local hot spot that can be reduced with additional cooling in this region. The distorted shape of the funnel mold narrow face is validated with recent inclinometer measurements from an operating caster. The calculated hot face temperatures and distorted shapes of the mold are transferred into the multiphysics model of the solidifying shell. The boundary conditions for the first iteration of the multiphysics model come from reduced-order models of the process; one such model is derived in this work for mold heat transfer. The reduced-order model relies on the physics of the solution to the one-dimensional heat-conduction equation to maintain the relationships between inputs and outputs of the model. The geometric
The Simplest Complete Model of Choice Response Time: Linear Ballistic Accumulation
ERIC Educational Resources Information Center
Brown, Scott D.; Heathcote, Andrew
2008-01-01
We propose a linear ballistic accumulator (LBA) model of decision making and reaction time. The LBA is simpler than other models of choice response time, with independent accumulators that race towards a common response threshold. Activity in the accumulators increases in a linear and deterministic manner. The simplicity of the model allows…
Seth J. Putterman
2006-01-10
FINAL REPORT ON : NON-LINEAR WAVES IN CONTINUOUS MEDIA Doe DE FG03-87ER13686 (001312-001) Submitted January 10, 2006 by Seth J. Putterman 310-8252269 Physics Department University of California Los Angeles, CA 90095 puherman@ritva.physics.ucla.edu NON-LINEAR WAVES IN CONTINUOUS MEDIA I am happy to report that this project has been a big success. For over 10 years the DOE [Division of Materials Sciences and Engineering] has funded our research program on the overarching theme of spontaneous energy focusing phenomena. These effects occur when a nonlinear macroscopic system is excited so as to drive it far from equilibrium. The subsequent relaxation to equilibrium does not occur smoothly but instead is accompanied by the formation of structured domains where the energy density is highly concentrated. A signature example is picosecond sonoluminescence [1] wherein a smooth sound wave has its energy density focused by 12 orders of magnitude to generate a clock-like string of picosecond flashes of ultraviolet light. Our earlier work on solitons [2] demonstrated how uniform surface waves break up into stable localized structures. Our experimental work on turbulence produced photos of localized structures lying many standard deviations outside the range of gaussian statistics[3]. This effect is referred to as intermittency. Our recent work on friction finds its motivation in those theories of sonoluminescence which invoke frictional electricity. In its most common form this is the generation of a spark when we touch a doorknob after walking over a carpet. Our reading of the literature on this subject indicated that frictional electricity like sonoluminescence is not understood. So to probe triboelectrification we set up a modern version of an experiment performed by Bernoulli in 1700. Here sparking is caused by the rubbing of glass against mercury. We indeed observed flashes of light which were accompanied by events of stick-slip friction at the interface between the
A novel biomechanical model assessing continuous orthodontic archwire activation
Canales, Christopher; Larson, Matthew; Grauer, Dan; Sheats, Rose; Stevens, Clarke; Ko, Ching-Chang
2013-01-01
Objective The biomechanics of a continuous archwire inserted into multiple orthodontic brackets is poorly understood. The purpose of this research was to apply the birth-death technique to simulate insertion of an orthodontic wire and consequent transfer of forces to the dentition in an anatomically accurate model. Methods A digital model containing the maxillary dentition, periodontal ligament (PDL), and surrounding bone was constructed from human computerized tomography data. Virtual brackets were placed on four teeth (central and lateral incisors, canine and first premolar), and a steel archwire (0.019″ × 0.025″) with a 0.5 mm step bend to intrude the lateral incisor was virtually inserted into the bracket slots. Forces applied to the dentition and surrounding structures were simulated utilizing the birth-death technique. Results The goal of simulating a complete bracket-wire system on accurate anatomy including multiple teeth was achieved. Orthodontic force delivered by the wire-bracket interaction was: central incisor 19.1 N, lateral incisor 21.9 N, and canine 19.9 N. Loading the model with equivalent point forces showed a different stress distribution in the PDL. Conclusions The birth-death technique proved to be a useful biomechanical simulation method for placement of a continuous archwire in orthodontic brackets. The ability to view the stress distribution throughout proper anatomy and appliances advances understanding of orthodontic biomechanics. PMID:23374936
Linking continuous and discrete intervertebral disc models through homogenisation.
Karajan, N; Röhrle, O; Ehlers, W; Schmitt, S
2013-06-01
At present, there are two main numerical approaches that are frequently used to simulate the mechanical behaviour of the human spine. Researchers with a continuum-mechanical background often utilise the finite-element method (FEM), where the involved biological soft and hard tissues are modelled on a macroscopic (continuum) level. In contrast, groups associated with the science of human movement usually apply discrete multi-body systems (MBS). Herein, the bones are modelled as rigid bodies, which are connected by Hill-type muscles and non-linear rheological spring-dashpot models to represent tendons and cartilaginous connective tissue like intervertebral discs (IVD). A possibility to benefit from both numerical methods is to couple them and use each approach, where it is most appropriate. Herein, the basic idea is to utilise MBS in simulations of the overall body and apply the FEM only to selected regions of interest. In turn, the FEM is used as homogenisation tool, which delivers more accurate non-linear relationships describing the behaviour of the IVD in the multi-body dynamics model. The goal of this contribution is to present an approach to couple both numerical methods without the necessity to apply a gluing algorithm in the context of a co-simulation. Instead, several pre-computations of the intervertebral disc are performed offline to generate an approximation of the homogenised finite-element (FE) result. In particular, the discrete degrees of freedom (DOF) of the MBS, that is, three displacements and three rotations, are applied to the FE model of the IVD, and the resulting homogenised forces and moments are recorded. Moreover, a polynomial function is presented with the discrete DOF of the MBS as variables and the discrete forces an moments as function values. For the sake of a simple verification, the coupling method is applied to a simplified motion segment of the spine. Herein, two stiff cylindrical vertebrae with an interjacent homogeneous
Ying, Xiaoguo; Liu, Wei; Hui, Guohua
2015-01-01
In this paper, litchi freshness rapid non-destructive evaluating method using electronic nose (e-nose) and non-linear stochastic resonance (SR) was proposed. EN responses to litchi samples were continuously detected for 6 d Principal component analysis (PCA) and non-linear stochastic resonance (SR) methods were utilized to analyze EN detection data. PCA method could not totally discriminate litchi samples, while SR signal-to-noise ratio (SNR) eigen spectrum successfully discriminated all litchi samples. Litchi freshness predictive model developed using SNR eigen values shows high predictive accuracy with regression coefficients R2 = 0 .99396. PMID:25920547
Small-signal, continuous, exact model of PWM voltage regulators
NASA Astrophysics Data System (ADS)
Burkhardt, W.; Maranesi, P.; Varoli, V.
1985-02-01
The small-signal time-continuous open-loop response of buck, boost, and buck-boost pulse-width-modulation (PWM) voltage regulators using MOSFET switches in their power stages is modeled, applying a time-domain sampling theorem (Woodward, 1953) to obtain the Fourier open-loop transfer function corresponding to the comb function describing the response at the chopping instants only. The results are presented graphically along with simplified circuit diagrams of the PWM devices, and the accuracy and computational efficiency of the analytical approach are indicated.
Model Hosting for continuous updating and transparent Water Resources Management
NASA Astrophysics Data System (ADS)
Jódar, Jorge; Almolda, Xavier; Batlle, Francisco; Carrera, Jesús
2013-04-01
Numerical models have become a standard tool for water resources management. They are required for water volume bookkeeping and help in decision making. Nevertheless, numerical models are complex and they can be used only by highly qualified technicians, which are often far from the decision makers. Moreover, they need to be maintained. That is, they require updating of their state, by assimilation of measurements, natural and anthropic actions (e.g., pumping and weather data), and model parameters. Worst, their very complexity implies that are they viewed as obscure and far, which hinders transparency and governance. We propose internet model hosting as an alternative to overcome these limitations. The basic idea is to keep the model hosted in the cloud. The model is updated as new data (measurements and external forcing) becomes available, which ensures continuous maintenance, with a minimal human cost (only required to address modelling problems). Internet access facilitates model use not only by modellers, but also by people responsible for data gathering and by water managers. As a result, the model becomes an institutional tool shared by water agencies to help them not only in decision making for sustainable management of water resources, but also in generating a common discussion platform. By promoting intra-agency sharing, the model becomes the common official position of the agency, which facilitates commitment in their adopted decisions regarding water management. Moreover, by facilitating access to stakeholders and the general public, the state of the aquifer and the impacts of alternative decisions become transparent. We have developed a tool (GAC, Global Aquifer Control) to address the above requirements. The application has been developed using Cloud Computing technologies, which facilitates the above operations. That is, GAC automatically updates the numerical models with the new available measurements, and then simulates numerous management options
Results and Comparison from the SAM Linear Fresnel Technology Performance Model: Preprint
Wagner, M. J.
2012-04-01
This paper presents the new Linear Fresnel technology performance model in NREL's System Advisor Model. The model predicts the financial and technical performance of direct-steam-generation Linear Fresnel power plants, and can be used to analyze a range of system configurations. This paper presents a brief discussion of the model formulation and motivation, and provides extensive discussion of the model performance and financial results. The Linear Fresnel technology is also compared to other concentrating solar power technologies in both qualitative and quantitative measures. The Linear Fresnel model - developed in conjunction with the Electric Power Research Institute - provides users with the ability to model a variety of solar field layouts, fossil backup configurations, thermal receiver designs, and steam generation conditions. This flexibility aims to encompass current market solutions for the DSG Linear Fresnel technology, which is seeing increasing exposure in fossil plant augmentation and stand-alone power generation applications.
Linear relaxation in large two-dimensional Ising models
NASA Astrophysics Data System (ADS)
Lin, Y.; Wang, F.
2016-02-01
Critical dynamics in two-dimension Ising lattices up to 2048 ×2048 is simulated on field-programmable-gate-array- based computing devices. Linear relaxation times are measured from extremely long Monte Carlo simulations. The longest simulation has 7.1 ×1016 spin updates, which would take over 37 years to simulate on a general purpose computer. The linear relaxation time of the Ising lattices is found to follow the dynamic scaling law for correlation lengths as long as 2048. The dynamic exponent z of the system is found to be 2.179(12), which is consistent with previous studies of Ising lattices with shorter correlation lengths. It is also found that Monte Carlo simulations of critical dynamics in Ising lattices larger than 512 ×512 are very sensitive to the statistical correlations between pseudorandom numbers, making it even more difficult to study such large systems.
AN ADA LINEAR ALGEBRA PACKAGE MODELED AFTER HAL/S
NASA Technical Reports Server (NTRS)
Klumpp, A. R.
1994-01-01
This package extends the Ada programming language to include linear algebra capabilities similar to those of the HAL/S programming language. The package is designed for avionics applications such as Space Station flight software. In addition to the HAL/S built-in functions, the package incorporates the quaternion functions used in the Shuttle and Galileo projects, and routines from LINPAK that solve systems of equations involving general square matrices. Language conventions in this package follow those of HAL/S to the maximum extent practical and minimize the effort required for writing new avionics software and translating existent software into Ada. Valid numeric types in this package include scalar, vector, matrix, and quaternion declarations. (Quaternions are fourcomponent vectors used in representing motion between two coordinate frames). Single precision and double precision floating point arithmetic is available in addition to the standard double precision integer manipulation. Infix operators are used instead of function calls to define dot products, cross products, quaternion products, and mixed scalar-vector, scalar-matrix, and vector-matrix products. The package contains two generic programs: one for floating point, and one for integer. The actual component type is passed as a formal parameter to the generic linear algebra package. The procedures for solving systems of linear equations defined by general matrices include GEFA, GECO, GESL, and GIDI. The HAL/S functions include ABVAL, UNIT, TRACE, DET, INVERSE, TRANSPOSE, GET, PUT, FETCH, PLACE, and IDENTITY. This package is written in Ada (Version 1.2) for batch execution and is machine independent. The linear algebra software depends on nothing outside the Ada language except for a call to a square root function for floating point scalars (such as SQRT in the DEC VAX MATHLIB library). This program was developed in 1989, and is a copyrighted work with all copyright vested in NASA.
Modeling and simulation of continuous fiber-reinforced ceramic composites
NASA Astrophysics Data System (ADS)
Bheemreddy, Venkata
Finite element modeling framework based on cohesive damage modeling, constitutive material behavior using user-material subroutines, and extended finite element method (XFEM), are developed for studying the failure behavior of continuous fiber-reinforced ceramic matrix composites (CFCCs) by the example of a silicon carbide matrix reinforced with silicon carbide fiber (SiC/SiCf) composite. This work deals with developing comprehensive numerical models for three problems: (1) fiber/matrix interface debonding and fiber pull-out, (2) mechanical behavior of a CFCC using a representative volume element (RVE) approach, and (3) microstructure image-based modeling of a CFCC using object oriented finite element analysis (OOF). Load versus displacement behavior during a fiber pull-out event was investigated using a cohesive damage model and an artificial neural network model. Mechanical behavior of a CFCC was investigated using a statistically equivalent RVE. A three-step procedure was developed for generating a randomized fiber distribution. Elastic properties and damage behavior of a CFCC were analyzed using the developed RVE models. Scattering of strength distribution in CFCCs was taken into account using a Weibull probability law. A multi-scale modeling framework was developed for evaluating the fracture behavior of a CFCC as a function of microstructural attributes. A finite element mesh of the microstructure was generated using an OOF tool. XFEM was used to study crack propagation in the microstructure and the fracture behavior was analyzed. The work performed provides a valuable procedure for developing a multi-scale framework for comprehensive damage study of CFCCs.
Model Calculations of Continuous-Wave Laser Ionization of Krypton
Bret D. Cannon
1999-07-27
This report describes modeling of a scheme that uses continuous-wave (CW) lasers to ionize selected isotopes of krypton with high isotopic selectivity. The models predict that combining this ionization scheme with mass spectrometric measurement of the resulting ions can be the basis for ultra-sensitive methods to measure {sup 85}Kr in the presence of a 10{sup 11} excess of the stable krypton isotopes. Two experimental setups are considered in this model: the first setup is for krypton as a static gas, the second is for krypton in an atomic beam. In the static gas experiment, for a total krypton press of 10{sup {minus}4} torr and 10 W of power in the cavity, the model predicts a total krypton ion current of 4.6 x 10{sup 8} s{sup {minus}1} and for a {sup 85}Kr/Kr of 10{sup {minus}11} a {sup 85}Kr ion current of 3.5 s{sup {minus}1} or about 10,000 per hour. The atomic beam setup allowed higher isotopic selectivity; the model predicts a {sup 85}Kr ion current of 18 s{sup {minus}1} or 65,000 per hour.
Implicit electrostatic solvent model with continuous dielectric permittivity function.
Basilevsky, Mikhail V; Grigoriev, Fedor V; Nikitina, Ekaterina A; Leszczynski, Jerzy
2010-02-25
The modification of the electrostatic continuum solvent model considered in the present work is based on the exact solution of the Poisson equation, which can be constructed provided that the dielectric permittivity epsilon of the total solute and solvent system is an isotropic and continuous spatial function. This assumption allows one to formulate a numerically efficient and universal computational scheme that covers the important case of a variable epsilon function inherent to the solvent region. The obtained type of solution is unavailable for conventional dielectric continuum models such as the Onsager and Kirkwood models for spherical cavities and the polarizable continuum model (PCM) for solute cavities of general shape, which imply that epsilon is discontinuous on the boundary confining the excluded volume cavity of the solute particle. Test computations based on the present algorithm are performed for water and several nonaqueous solvents. They illustrate specific features of this approach, called the "smooth boundary continuum model" (SBCM), as compared to the PCM procedure, and suggest primary tentative results of its parametrization for different solvents. The calculation for the case of a binary solvent mixture with variable epsilon in the solvent space region demonstrates the applicability of this approach to a novel application field covered by the SBCM.
ERIC Educational Resources Information Center
Doherty-Restrepo, Jennifer L.; Hughes, Brian J.; Del Rossi, Gianluca; Pitney, William A.
2009-01-01
Objective: Although continuing education is required for athletic trainers (AT) to maintain their Board of Certification credential, little is known regarding its efficacy for advancing knowledge and improving patient care. Continuing professional education (CPE) is designed to provide professionals with important practical learning opportunities.…
Design, experimentation, and modeling of a novel continuous biodrying process
NASA Astrophysics Data System (ADS)
Navaee-Ardeh, Shahram
biodrying reactor were the type of biomass feed and the outlet relative humidity profiles. The biomass feed is mill specific and since one mill was studied for this study, the nutrition level of the biomass feed was found adequate for the microbial activity, and hence the type of biomass is a fixed parameter. The influence of outlet relative humidity profile was investigated on the overall performance and the complexity index of the continuous biodrying reactor. The best biodrying efficiency was achieved at an outlet relative humidity profile which controls the removal of unbound water at the wet-bulb temperature in the 1st and 2nd compartments of the reactor, and the removal of bound water at the dry-bulb temperature in the 3rd and 4th compartments. Through a systematic modeling approach, a 2-D model was developed to describe the transport phenomena in the continuous biodrying reactor. The results of the 2-D model were in satisfactory agreement with the experimental data. It was found that about 30% w/w of the total water removal (drying rate) takes place in the 1st and 2nd compartments mainly under a convection dominated mechanism, whereas about 70% w/w of the total water removal takes place in the 3rd and 4th compartments where a bioheat-diffusion dominated mechanism controls the transport phenomena. The 2-D model was found to be an appropriate tool for the estimation of the total water removal rate (drying rate) in the continuous biodrying reactor when compared to the 1-D model. A dimensionless analysis was performed on the 2-D model and established the preliminary criteria for the scale-up of the continuous biodrying process. Finally, a techno-economic assessment of the continuous biodrying process revealed that there is great potential for the implementation of the biodrying process in Canadian pulp and paper mills. The techno-economic results were compared to the other competitive existing drying technologies. It was proven that the continuous biodrying process
Design, experimentation, and modeling of a novel continuous biodrying process
NASA Astrophysics Data System (ADS)
Navaee-Ardeh, Shahram
biodrying reactor were the type of biomass feed and the outlet relative humidity profiles. The biomass feed is mill specific and since one mill was studied for this study, the nutrition level of the biomass feed was found adequate for the microbial activity, and hence the type of biomass is a fixed parameter. The influence of outlet relative humidity profile was investigated on the overall performance and the complexity index of the continuous biodrying reactor. The best biodrying efficiency was achieved at an outlet relative humidity profile which controls the removal of unbound water at the wet-bulb temperature in the 1st and 2nd compartments of the reactor, and the removal of bound water at the dry-bulb temperature in the 3rd and 4th compartments. Through a systematic modeling approach, a 2-D model was developed to describe the transport phenomena in the continuous biodrying reactor. The results of the 2-D model were in satisfactory agreement with the experimental data. It was found that about 30% w/w of the total water removal (drying rate) takes place in the 1st and 2nd compartments mainly under a convection dominated mechanism, whereas about 70% w/w of the total water removal takes place in the 3rd and 4th compartments where a bioheat-diffusion dominated mechanism controls the transport phenomena. The 2-D model was found to be an appropriate tool for the estimation of the total water removal rate (drying rate) in the continuous biodrying reactor when compared to the 1-D model. A dimensionless analysis was performed on the 2-D model and established the preliminary criteria for the scale-up of the continuous biodrying process. Finally, a techno-economic assessment of the continuous biodrying process revealed that there is great potential for the implementation of the biodrying process in Canadian pulp and paper mills. The techno-economic results were compared to the other competitive existing drying technologies. It was proven that the continuous biodrying process
A hybrid oscillatory interference/continuous attractor network model of grid cell firing.
Bush, Daniel; Burgess, Neil
2014-04-01
Grid cells in the rodent medial entorhinal cortex exhibit remarkably regular spatial firing patterns that tessellate all environments visited by the animal. Two theoretical mechanisms that could generate this spatially periodic activity pattern have been proposed: oscillatory interference and continuous attractor dynamics. Although a variety of evidence has been cited in support of each, some aspects of the two mechanisms are complementary, suggesting that a combined model may best account for experimental data. The oscillatory interference model proposes that the grid pattern is formed from linear interference patterns or "periodic bands" in which velocity-controlled oscillators integrate self-motion to code displacement along preferred directions. However, it also allows the use of symmetric recurrent connectivity between grid cells to provide relative stability and continuous attractor dynamics. Here, we present simulations of this type of hybrid model, demonstrate that it generates intracellular membrane potential profiles that closely match those observed in vivo, addresses several criticisms aimed at pure oscillatory interference and continuous attractor models, and provides testable predictions for future empirical studies. PMID:24695724
Linear regression models and k-means clustering for statistical analysis of fNIRS data.
Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro
2015-02-01
We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets.
Linear regression models and k-means clustering for statistical analysis of fNIRS data
Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro
2015-01-01
We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets. PMID:25780751
Optimal Scaling of Interaction Effects in Generalized Linear Models
ERIC Educational Resources Information Center
van Rosmalen, Joost; Koning, Alex J.; Groenen, Patrick J. F.
2009-01-01
Multiplicative interaction models, such as Goodman's (1981) RC(M) association models, can be a useful tool for analyzing the content of interaction effects. However, most models for interaction effects are suitable only for data sets with two or three predictor variables. Here, we discuss an optimal scaling model for analyzing the content of…
Modeling thermal sensation in a Mediterranean climate—a comparison of linear and ordinal models
NASA Astrophysics Data System (ADS)
Pantavou, Katerina; Lykoudis, Spyridon
2014-08-01
A simple thermo-physiological model of outdoor thermal sensation adjusted with psychological factors is developed aiming to predict thermal sensation in Mediterranean climates. Microclimatic measurements simultaneously with interviews on personal and psychological conditions were carried out in a square, a street canyon and a coastal location of the greater urban area of Athens, Greece. Multiple linear and ordinal regression were applied in order to estimate thermal sensation making allowance for all the recorded parameters or specific, empirically selected, subsets producing so-called extensive and empirical models, respectively. Meteorological, thermo-physiological and overall models - considering psychological factors as well - were developed. Predictions were improved when personal and psychological factors were taken into account as compared to meteorological models. The model based on ordinal regression reproduced extreme values of thermal sensation vote more adequately than the linear regression one, while the empirical model produced satisfactory results in relation to the extensive model. The effects of adaptation and expectation on thermal sensation vote were introduced in the models by means of the exposure time, season and preference related to air temperature and irradiation. The assessment of thermal sensation could be a useful criterion in decision making regarding public health, outdoor spaces planning and tourism.
Modeling the thin-slab continuous-casting mold
O'Connor, T.G. . Applied Superconductivity Group); Dantzig, J.A. . Dept. of Mechanical and Industrial Engineering)
1994-06-01
A three-dimensional mathematical model has been developed to compute the thermomechanical state in the mold of thin-slab continuous casters. The thin-slab mold differs from those used in conventional slab casters in that the upper portion of the broad side walls defines a funnel-shaped chamber which allows the nozzle to be submerged into the liquid metal. The chamber converges with distance down the mold, reducing to the rectangular cross section of the finished casting near the mold exit. The new mold, along with casting speeds up to 6 m/min, allows slabs to be cast 50--60 mm thick, compared with 150 to 350 mm in conventional continuous slab casting. However, the mold shape and high casting speed lead to higher mold temperatures and shorter mold life than are found in conventional slab casters. In this article, the author develop mathematical models of the process to determine the role of various process parameters in determining the mold life. Finite-element analysis is used to determine the temperatures in the mold and cast slab, and these data are then used in an elastic-viscoplastic analysis to investigate the deformation of the mold wall in service. Cyclic inelastic strains up to 1.75 pct are found in a region below the meniscus along the funnel edge. These large strains result from the combination of locally high temperatures coupled with geometric restraint of the mold. The deformation leads to short mold life because of thermal fatigue cracking of the mold. The computed locations and time to failure of the mold in fatigue agree very well with observations of the appearance of mold surface cracks in an operating caster. The models are also used to develop an improved mold design.
Modeling the thin-slab continuous-casting mold
NASA Astrophysics Data System (ADS)
Oconnor, Thomas G.; Dantzig, Jonathan A.
1994-06-01
A three-dimensional mathematical model has been developed to compute the thermomechanical state in the mold of thin-slab continuous casters. The thin-slab mold differs from those used in conventional slab casters in that the upper portion of the broad side walls defines a funnel-shaped chamber which allows the nozzle to be submerged into the liquid metal. The chamber converges with distance down the mold, reducing to the rectangular cross section of the finished casting near the mold exit. The new mold, along with casting speeds up to 6 m/min, allows slabs to be cast 50 60 mm thick, compared with 150 to 350 mm in conventional continuous slab casting. However, the mold shape and high casting speed lead to higher mold temperatures and shorter mold life than are found in conventional slab casters. In this article, we develop mathematical models of the process to determine the role of various process parameters in determining the mold life. Finite-element analysis is used to determine the temperatures in the mold and cast slab, and these data are then used in an elastic-viscoplastic analysis to investigate the deformation of the mold wall in service. Cyclic inelastic strains up to 1.75 Pct are found in a region below the meniscus along the funnel edge. These large strains result from the combination of locally high temperatures coupled with geometric restraint of the mold. The deformation leads to short mold life because of thermal fatigue cracking of the mold. The computed locations and time to failure of the mold in fatigue agree very well with observations of the appearance of mold surface cracks in an operating caster. The models are also used to develop an improved mold design.
Frequency Response of Synthetic Vocal Fold Models with Linear and Nonlinear Material Properties
ERIC Educational Resources Information Center
Shaw, Stephanie M.; Thomson, Scott L.; Dromey, Christopher; Smith, Simeon
2012-01-01
Purpose: The purpose of this study was to create synthetic vocal fold models with nonlinear stress-strain properties and to investigate the effect of linear versus nonlinear material properties on fundamental frequency (F[subscript 0]) during anterior-posterior stretching. Method: Three materially linear and 3 materially nonlinear models were…
Item Purification in Differential Item Functioning Using Generalized Linear Mixed Models
ERIC Educational Resources Information Center
Liu, Qian
2011-01-01
For this dissertation, four item purification procedures were implemented onto the generalized linear mixed model for differential item functioning (DIF) analysis, and the performance of these item purification procedures was investigated through a series of simulations. Among the four procedures, forward and generalized linear mixed model (GLMM)…
A continuous fiber distribution material model for human cervical tissue.
Myers, Kristin M; Hendon, Christine P; Gan, Yu; Yao, Wang; Yoshida, Kyoko; Fernandez, Michael; Vink, Joy; Wapner, Ronald J
2015-06-25
The uterine cervix during pregnancy is the vital mechanical barrier which resists compressive and tensile loads generated from a growing fetus. Premature cervical remodeling and softening is hypothesized to result in the shortening of the cervix, which is known to increase a woman׳s risk of preterm birth. To understand the role of cervical material properties in preventing preterm birth, we derive a cervical material model based on previous mechanical, biochemical and histological experiments conducted on nonpregnant and pregnant human hysterectomy cervical tissue samples. In this study we present a three-dimensional fiber composite model that captures the equilibrium material behavior of the tissue in tension and compression. Cervical tissue is modeled as a fibrous composite material, where a single family of preferentially aligned and continuously distributed collagen fibers are embedded in a compressible neo-Hookean ground substance. The total stress in the collagen solid network is calculated by integrating the fiber stresses. The shape of the fiber distribution is described by an ellipsoid where semi-principal axis lengths are fit to optical coherence tomography measurements. The composite material model is fit to averaged mechanical testing data from uni-axial compression and tension experiments, and averaged material parameters are reported for nonpregnant and term pregnant human cervical tissue. The model is then evaluated by investigating the stress and strain state of a uniform thick-walled cylinder under a compressive stress with collagen fibers preferentially aligned in the circumferential direction. This material modeling framework for the equilibrium behavior of human cervical tissue serves as a basis to determine the role of preferentially-aligned cervical collagen fibers in preventing cervical deformation during pregnancy.
Modal identification based on Gaussian continuous time autoregressive moving average model
NASA Astrophysics Data System (ADS)
Xiuli, Du; Fengquan, Wang
2010-09-01
A new time-domain modal identification method of the linear time-invariant system driven by the non-stationary Gaussian random force is presented in this paper. The proposed technique is based on the multivariate continuous time autoregressive moving average (CARMA) model. This method can identify physical parameters of a system from the response-only data. To do this, we first transform the structural dynamic equation into the CARMA model, and subsequently rewrite it in the state-space form. Second, we present the exact maximum likelihood estimators of parameters of the continuous time autoregressive (CAR) model by virtue of the Girsanov theorem, under the assumption that the uniformly modulated function is approximately equal to a constant matrix over a very short period of time. Then, based on the relation between the CAR model and the CARMA model, we present the exact maximum likelihood estimators of parameters of the CARMA model. Finally, the modal parameters are identified by the eigenvalue analysis method. Numerical results show that the method we introduced here not only has high precision and robustness, but also has very high computing efficiency. Therefore, it is suitable for real-time modal identification.
Linear moose model with pairs of degenerate gauge boson triplets
NASA Astrophysics Data System (ADS)
Casalbuoni, Roberto; Coradeschi, Francesco; de Curtis, Stefania; Dominici, Daniele
2008-05-01
The possibility of a strongly interacting electroweak symmetry breaking sector, as opposed to the weakly interacting light Higgs of the standard model, is not yet ruled out by experiments. In this paper we make an extensive study of a deconstructed model (or “moose” model) providing an effective description of such a strong symmetry breaking sector, and show its compatibility with experimental data for a wide portion of the model parameter space. The model is a direct generalization of the previously proposed D-BESS model.
Linear moose model with pairs of degenerate gauge boson triplets
Casalbuoni, Roberto; Coradeschi, Francesco; De Curtis, Stefania; Dominici, Daniele
2008-05-01
The possibility of a strongly interacting electroweak symmetry breaking sector, as opposed to the weakly interacting light Higgs of the standard model, is not yet ruled out by experiments. In this paper we make an extensive study of a deconstructed model (or ''moose'' model) providing an effective description of such a strong symmetry breaking sector, and show its compatibility with experimental data for a wide portion of the model parameter space. The model is a direct generalization of the previously proposed D-BESS model.
Development of a Linear Stirling System Model with Varying Heat Inputs
NASA Technical Reports Server (NTRS)
Regan, Timothy F.; Lewandowski, Edward J.
2007-01-01
The linear model of the Stirling system developed by NASA Glenn Research Center (GRC) has been extended to include a user-specified heat input. Previously developed linear models were limited to the Stirling convertor and electrical load. They represented the thermodynamic cycle with pressure factors that remained constant. The numerical values of the pressure factors were generated by linearizing GRC's nonlinear System Dynamic Model (SDM) of the convertor at a chosen operating point. The pressure factors were fixed for that operating point, thus, the model lost accuracy if a transition to a different operating point were simulated. Although the previous linear model was used in developing controllers that manipulated current, voltage, and piston position, it could not be used in the development of control algorithms that regulated hot-end temperature. This basic model was extended to include the thermal dynamics associated with a hot-end temperature that varies over time in response to external changes as well as to changes in the Stirling cycle. The linear model described herein includes not only dynamics of the piston, displacer, gas, and electrical circuit, but also the transient effects of the heater head thermal inertia. The linear version algebraically couples two separate linear dynamic models, one model of the Stirling convertor and one model of the thermal system, through the pressure factors. The thermal system model includes heat flow of heat transfer fluid, insulation loss, and temperature drops from the heat source to the Stirling convertor expansion space. The linear model was compared to a nonlinear model, and performance was very similar. The resulting linear model can be implemented in a variety of computing environments, and is suitable for analysis with classical and state space controls analysis techniques.
Non-linear characterisation of the physical model of an ancient masonry bridge
NASA Astrophysics Data System (ADS)
Zanotti Fragonara, L.; Ceravolo, R.; Matta, E.; Quattrone, A.; De Stefano, A.; Pecorelli, M.
2012-08-01
This paper presents the non-linear investigations carried out on a scaled model of a two-span masonry arch bridge. The model has been built in order to study the effect of the central pile settlement due to riverbank erosion. Progressive damage was induced in several steps by applying increasing settlements at the central pier. For each settlement step, harmonic shaker tests were conducted under different excitation levels, this allowing for the non-linear identification of the progressively damaged system. The shaker tests have been performed at resonance with the modal frequency of the structure, which were determined from a previous linear identification. Estimated non-linearity parameters, which result from the systematic application of restoring force based identification algorithms, can corroborate models to be used in the reassessment of existing structures. The method used for non-linear identification allows monitoring the evolution of non-linear parameters or indicators which can be used in damage and safety assessment.
Ogundimu, Emmanuel O.; Cook, Jonathan A.; Manach, Yannick Le; Altman, Douglas G.
2016-01-01
Continuous predictors are routinely encountered when developing a prognostic model. Investigators, who are often non‐statisticians, must decide how to handle continuous predictors in their models. Categorising continuous measurements into two or more categories has been widely discredited, yet is still frequently done because of its simplicity, investigator ignorance of the potential impact and of suitable alternatives, or to facilitate model uptake. We examine three broad approaches for handling continuous predictors on the performance of a prognostic model, including various methods of categorising predictors, modelling a linear relationship between the predictor and outcome and modelling a nonlinear relationship using fractional polynomials or restricted cubic splines. We compare the performance (measured by the c‐index, calibration and net benefit) of prognostic models built using each approach, evaluating them using separate data from that used to build them. We show that categorising continuous predictors produces models with poor predictive performance and poor clinical usefulness. Categorising continuous predictors is unnecessary, biologically implausible and inefficient and should not be used in prognostic model development. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:27193918
Coaction versus reciprocity in continuous-time models of cooperation.
van Doorn, G Sander; Riebli, Thomas; Taborsky, Michael
2014-09-01
Cooperating animals frequently show closely coordinated behaviours organized by a continuous flow of information between interacting partners. Such real-time coaction is not captured by the iterated prisoner's dilemma and other discrete-time reciprocal cooperation games, which inherently feature a delay in information exchange. Here, we study the evolution of cooperation when individuals can dynamically respond to each other's actions. We develop continuous-time analogues of iterated-game models and describe their dynamics in terms of two variables, the propensity of individuals to initiate cooperation (altruism) and their tendency to mirror their partner's actions (coordination). These components of cooperation stabilize at an evolutionary equilibrium or show oscillations, depending on the chosen payoff parameters. Unlike reciprocal altruism, cooperation by coaction does not require that those willing to initiate cooperation pay in advance for uncertain future benefits. Correspondingly, we show that introducing a delay to information transfer between players is equivalent to increasing the cost of cooperation. Cooperative coaction can therefore evolve much more easily than reciprocal cooperation. When delays entirely prevent coordination, we recover results from the discrete-time alternating prisoner's dilemma, indicating that coaction and reciprocity are connected by a continuum of opportunities for real-time information exchange.
Modeling discrete and continuous entities with fractions and decimals.
Rapp, Monica; Bassok, Miriam; DeWolf, Melissa; Holyoak, Keith J
2015-03-01
When people use mathematics to model real-life situations, their use of mathematical expressions is often mediated by semantic alignment (Bassok, Chase, & Martin, 1998): The entities in a problem situation evoke semantic relations (e.g., tulips and vases evoke the functionally asymmetric "contain" relation), which people align with analogous mathematical relations (e.g., the noncommutative division operation, tulips/vases). Here we investigate the possibility that semantic alignment is also involved in the comprehension and use of rational numbers (fractions and decimals). A textbook analysis and results from two experiments revealed that both mathematic educators and college students tend to align the discreteness versus continuity of the entities in word problems (e.g., marbles vs. distance) with distinct symbolic representations of rational numbers--fractions versus decimals, respectively. In addition, fractions and decimals tend to be used with nonmetric units and metric units, respectively. We discuss the importance of the ontological distinction between continuous and discrete entities to mathematical cognition, the role of symbolic notations, and possible implications of our findings for the teaching of rational numbers.
Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne
2012-01-01
In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882
NASA Astrophysics Data System (ADS)
Martino, Sara; Kolberg, Sjur
2014-05-01
Spatially distributed precipitation estimates are needed in hydrological modelling as well as in regional climate analysis. In mountainous regions, interpolating data from a rain gauge network often provides mediocre results, in particular due to high terrain-induced variability on a smaller scale than the gauge network typically resolves. The interpolation is particularly difficult at short time scales like daily or hourly precipitation. Smith and Barstad (2004) introduced a linear model for simulating orographic precipitation over a digital elevation model (DEM), driven by spatially homogeneous input of wind, temperature and humidity. The model has been used to dynamically downscale reanalysis data and future climate predictions (Crochet et al 2007; Johannesson et al.2007). The current investigation applies the orography model in conjunction with interpolation from precipitation gauges, in order to construct daily precipitation maps with a level of detail finer than the gauge network density. The experiment is carried out over the southern part of Norway, a region with high mountains, glaciers, and strong precipitation gradients. We feed the orography model with the last re-analysis data from ECMWF (ERA-Interim) over the period 1979-2012. Preliminary results using the older ERA40 reanalysis data show the simulated orographic precipitation to be in good agreement with precipitation observations accumulated over various time scales. In particular, the orographic prediction improve on lapse rate based methods to incorporate terrain effects. However, the first experiments have also revealed practical challenges in applying the model on a permanent basis for large regions. Work therefore continues to adapt the theory to operational use, to optimize pre-processing and data flow, and to identify scale limits and situations for which the model is inadequate. A cross-validation approach is used to quantify performance and compare different settings in the orography model.
Linearized one-dimensional model of optical plasmatron operation
NASA Astrophysics Data System (ADS)
Raizer, Iu. P.
1984-01-01
An analysis is made of a steady-state optical discharge in a stream of gas, sustained by a focused CW CO2 laser beam. By means of a series of simplifications the complex equations of the process are reduced to a second-order ordinary inhomogeneous differential equation with variable coefficients, which is linear in each of the characteristic regions of the optic axis. The equation is solved analytically by means of Kummer functions which are transformed into combinations of Bessel functions. The positions of the discharge boundaries are determined as a function of the laser power, the flow velocity, and the focusing angle. The regime with no gas flow is also considered.
Microgrid Reliability Modeling and Battery Scheduling Using Stochastic Linear Programming
Cardoso, Goncalo; Stadler, Michael; Siddiqui, Afzal; Marnay, Chris; DeForest, Nicholas; Barbosa-Povoa, Ana; Ferrao, Paulo
2013-05-23
This paper describes the introduction of stochastic linear programming into Operations DER-CAM, a tool used to obtain optimal operating schedules for a given microgrid under local economic and environmental conditions. This application follows previous work on optimal scheduling of a lithium-iron-phosphate battery given the output uncertainty of a 1 MW molten carbonate fuel cell. Both are in the Santa Rita Jail microgrid, located in Dublin, California. This fuel cell has proven unreliable, partially justifying the consideration of storage options. Several stochastic DER-CAM runs are executed to compare different scenarios to values obtained by a deterministic approach. Results indicate that using a stochastic approach provides a conservative yet more lucrative battery schedule. Lower expected energy bills result, given fuel cell outages, in potential savings exceeding 6percent.
Computational models of signalling networks for non-linear control.
Fuente, Luis A; Lones, Michael A; Turner, Alexander P; Stepney, Susan; Caves, Leo S; Tyrrell, Andy M
2013-05-01
Artificial signalling networks (ASNs) are a computational approach inspired by the signalling processes inside cells that decode outside environmental information. Using evolutionary algorithms to induce complex behaviours, we show how chaotic dynamics in a conservative dynamical system can be controlled. Such dynamics are of particular interest as they mimic the inherent complexity of non-linear physical systems in the real world. Considering the main biological interpretations of cellular signalling, in which complex behaviours and robust cellular responses emerge from the interaction of multiple pathways, we introduce two ASN representations: a stand-alone ASN and a coupled ASN. In particular we note how sophisticated cellular communication mechanisms can lead to effective controllers, where complicated problems can be divided into smaller and independent tasks.
Analysis of Power Model for Linear Plasma Device
NASA Astrophysics Data System (ADS)
Zhang, Weiwei; Deng, Baiquan; Zuo, Haoyi; Zheng, Xianjun; Cao, Xiaogang; Xue, Xiaoyan; Ou, Wei; Cao, Zhi; Gou, Fujun
2016-08-01
A single cathode linear plasma device has been designed and constructed to investigate the interactions between plasma and materials at the Sichuan University. In order to further investigate the Ohmic power of the device, the output heat load on the specimen and electric potential difference (between cathode and anode) have been tested under different discharge currents. This special power distribution in the radial direction of the plasma discharge channel has also been discussed and described by some improved integral equations in this paper; it can be further simplified as P ∝ α-2 in one-parameter. Besides, we have measured the power loss of the channel under different discharge currents by the calorimetric method, calculated the effective power of the device and evaluated the performances of the plasma device through the power efficiency analysis. supported by International Thermonuclear Experimental Reactor (ITER) Program (No. 2013GB114003) and National Natural Science Foundation of China (Nos. 11275135 and 11475122)
Analysis of Power Model for Linear Plasma Device
NASA Astrophysics Data System (ADS)
Zhang, Weiwei; Deng, Baiquan; Zuo, Haoyi; Zheng, Xianjun; Cao, Xiaogang; Xue, Xiaoyan; Ou, Wei; Cao, Zhi; Gou, Fujun
2016-08-01
A single cathode linear plasma device has been designed and constructed to investigate the interactions between plasma and materials at the Sichuan University. In order to further investigate the Ohmic power of the device, the output heat load on the specimen and electric potential difference (between cathode and anode) have been tested under different discharge currents. This special power distribution in the radial direction of the plasma discharge channel has also been discussed and described by some improved integral equations in this paper; it can be further simplified as P ∝ α‑2 in one-parameter. Besides, we have measured the power loss of the channel under different discharge currents by the calorimetric method, calculated the effective power of the device and evaluated the performances of the plasma device through the power efficiency analysis. supported by International Thermonuclear Experimental Reactor (ITER) Program (No. 2013GB114003) and National Natural Science Foundation of China (Nos. 11275135 and 11475122)
Evaluating a linearized Euler equations model for strong turbulence effects on sound propagation.
Ehrhardt, Loïc; Cheinet, Sylvain; Juvé, Daniel; Blanc-Benon, Philippe
2013-04-01
Sound propagation outdoors is strongly affected by atmospheric turbulence. Under strongly perturbed conditions or long propagation paths, the sound fluctuations reach their asymptotic behavior, e.g., the intensity variance progressively saturates. The present study evaluates the ability of a numerical propagation model based on the finite-difference time-domain solving of the linearized Euler equations in quantitatively reproducing the wave statistics under strong and saturated intensity fluctuations. It is the continuation of a previous study where weak intensity fluctuations were considered. The numerical propagation model is presented and tested with two-dimensional harmonic sound propagation over long paths and strong atmospheric perturbations. The results are compared to quantitative theoretical or numerical predictions available on the wave statistics, including the log-amplitude variance and the probability density functions of the complex acoustic pressure. The match is excellent for the evaluated source frequencies and all sound fluctuations strengths. Hence, this model captures these many aspects of strong atmospheric turbulence effects on sound propagation. Finally, the model results for the intensity probability density function are compared with a standard fit by a generalized gamma function.
Bayesian modeling of censored partial linear models using scale-mixtures of normal distributions
NASA Astrophysics Data System (ADS)
Castro, Luis M.; Lachos, Victor H.; Ferreira, Guillermo P.; Arellano-Valle, Reinaldo B.
2012-10-01
Regression models where the dependent variable is censored (limited) are usually considered in statistical analysis. Particularly, the case of a truncation to the left of zero and a normality assumption for the error terms is studied in detail by [1] in the well known Tobit model. In the present article, this typical censored regression model is extended by considering a partial linear model with errors belonging to the class of scale mixture of normal distributions. We achieve a fully Bayesian inference by adopting a Metropolis algorithm within a Gibbs sampler. The likelihood function is utilized to compute not only some Bayesian model selection measures but also to develop Bayesian case-deletion influence diagnostics based on the q-divergence measures. We evaluate the performances of the proposed methods with simulated data. In addition, we present an application in order to know what type of variables affect the income of housewives.
Frequency Response of Synthetic Vocal Fold Models with Linear and Nonlinear Material Properties
Shaw, Stephanie M.; Thomson, Scott L.; Dromey, Christopher; Smith, Simeon
2014-01-01
Purpose The purpose of this study was to create synthetic vocal fold models with nonlinear stress-strain properties and to investigate the effect of linear versus nonlinear material properties on fundamental frequency during anterior-posterior stretching. Method Three materially linear and three materially nonlinear models were created and stretched up to 10 mm in 1 mm increments. Phonation onset pressure (Pon) and fundamental frequency (F0) at Pon were recorded for each length. Measurements were repeated as the models were relaxed in 1 mm increments back to their resting lengths, and tensile tests were conducted to determine the stress-strain responses of linear versus nonlinear models. Results Nonlinear models demonstrated a more substantial frequency response than did linear models and a more predictable pattern of F0 increase with respect to increasing length (although range was inconsistent across models). Pon generally increased with increasing vocal fold length for nonlinear models, whereas for linear models, Pon decreased with increasing length. Conclusions Nonlinear synthetic models appear to more accurately represent the human vocal folds than linear models, especially with respect to F0 response. PMID:22271874
Continuous Dissolved Oxygen Measurements and Modelling Metabolism in Peatland Streams.
Dick, Jonathan J; Soulsby, Chris; Birkel, Christian; Malcolm, Iain; Tetzlaff, Doerthe
2016-01-01
Stream water dissolved oxygen was monitored in a 3.2km2 moorland headwater catchment in the Scottish Highlands. The stream consists of three 1st order headwaters and a 2nd order main stem. The stream network is fringed by peat soils with no riparian trees, though dwarf shrubs provide shading in the lower catchment. Dissolved oxygen (DO) is regulated by the balance between atmospheric re-aeration and the metabolic processes of photosynthesis and respiration. DO was continuously measured for >1 year and the data used to calibrate a mass balance model, to estimate primary production, respiration and re-aeration for a 1st order site and in the 2nd order main stem. Results showed that the stream was always heterotrophic at both sites. Sites were most heterotrophic in the summer reflecting higher levels of stream metabolism. The 1st order stream appeared more heterotrophic which was consistent with the evident greater biomass of macrophytes in the 2nd order stream, with resulting higher primary productivity. Comparison between respiration, primary production, re-aeration and potential physical controls revealed only weak relationships. However, the most basic model parameters (e.g. the parameter linking light and photosynthesis) controlling ecosystem processes resulted in significant differences between the sites which seem related to the stream channel geometry. PMID:27556278
Continuous Dissolved Oxygen Measurements and Modelling Metabolism in Peatland Streams
Dick, Jonathan J.; Soulsby, Chris; Birkel, Christian; Malcolm, Iain; Tetzlaff, Doerthe
2016-01-01
Stream water dissolved oxygen was monitored in a 3.2km2 moorland headwater catchment in the Scottish Highlands. The stream consists of three 1st order headwaters and a 2nd order main stem. The stream network is fringed by peat soils with no riparian trees, though dwarf shrubs provide shading in the lower catchment. Dissolved oxygen (DO) is regulated by the balance between atmospheric re-aeration and the metabolic processes of photosynthesis and respiration. DO was continuously measured for >1 year and the data used to calibrate a mass balance model, to estimate primary production, respiration and re-aeration for a 1st order site and in the 2nd order main stem. Results showed that the stream was always heterotrophic at both sites. Sites were most heterotrophic in the summer reflecting higher levels of stream metabolism. The 1st order stream appeared more heterotrophic which was consistent with the evident greater biomass of macrophytes in the 2nd order stream, with resulting higher primary productivity. Comparison between respiration, primary production, re-aeration and potential physical controls revealed only weak relationships. However, the most basic model parameters (e.g. the parameter linking light and photosynthesis) controlling ecosystem processes resulted in significant differences between the sites which seem related to the stream channel geometry. PMID:27556278
Fitting host-parasitoid models with CV2 > 1 using hierarchical generalized linear models.
Perry, J N; Noh, M S; Lee, Y; Alston, R D; Norowi, H M; Powell, W; Rennolls, K
2000-01-01
The powerful general Pacala-Hassell host-parasitoid model for a patchy environment, which allows host density-dependent heterogeneity (HDD) to be distinguished from between-patch, host density-independent heterogeneity (HDI), is reformulated within the class of the generalized linear model (GLM) family. This improves accessibility through the provision of general software within well-known statistical systems, and allows a rich variety of models to be formulated. Covariates such as age class, host density and abiotic factors may be included easily. For the case where there is no HDI, the formulation is a simple GLM. When there is HDI in addition to HDD, the formulation is a hierarchical generalized linear model. Two forms of HDI model are considered, both with between-patch variability: one has binomial variation within patches and one has extra-binomial, overdispersed variation within patches. Examples are given demonstrating parameter estimation with standard errors, and hypothesis testing. For one example given, the extra-binomial component of the HDI heterogeneity in parasitism is itself shown to be strongly density dependent. PMID:11416907
NASA Astrophysics Data System (ADS)
Yang, Wu; Liu, Li; Zhou, Si-Da; Ma, Zhi-Sai
2015-10-01
This work proposes a Moving Kriging (MK) shape function modeling method for modal identification of linear time-varying (LTV) structural systems based on vector time-dependent autoregressive moving average (VTARMA) models. It aims to avoid the functional subspaces selection of the conventional functional series VTARMA (FS-VTARMA) models. Instead of the common basis functions, it constructs the time-varying coefficients on the time nodes with the MK shape functions in a compact support domain. The merit of the MK shape function is to determine its shape parameters upon vector random vibration signals adaptively. Model identification is effectively dealt with through an optimization scheme that decomposes the identification problem into two subproblems: estimating model parameters via two-stage least squares (2SLS) method and estimating shape function parameters via a discrete-continuous-variable hybrid optimization. In addition, the model order selection is achieved by the optimization scheme. This method has been validated by a Monte Carlo study of simulation case and further by an experimental test case, and the performance and potential advantages are illustrated.
NASA Technical Reports Server (NTRS)
1979-01-01
The computer program Linear SCIDNT which evaluates rotorcraft stability and control coefficients from flight or wind tunnel test data is described. It implements the maximum likelihood method to maximize the likelihood function of the parameters based on measured input/output time histories. Linear SCIDNT may be applied to systems modeled by linear constant-coefficient differential equations. This restriction in scope allows the application of several analytical results which simplify the computation and improve its efficiency over the general nonlinear case.
Linear summation of outputs in a balanced network model of motor cortex
Capaday, Charles; van Vreeswijk, Carl
2015-01-01
Given the non-linearities of the neural circuitry's elements, we would expect cortical circuits to respond non-linearly when activated. Surprisingly, when two points in the motor cortex are activated simultaneously, the EMG responses are the linear sum of the responses evoked by each of the points activated separately. Additionally, the corticospinal transfer function is close to linear, implying that the synaptic interactions in motor cortex must be effectively linear. To account for this, here we develop a model of motor cortex composed of multiple interconnected points, each comprised of reciprocally connected excitatory and inhibitory neurons. We show how non-linearities in neuronal transfer functions are eschewed by strong synaptic interactions within each point. Consequently, the simultaneous activation of multiple points results in a linear summation of their respective outputs. We also consider the effects of reduction of inhibition at a cortical point when one or more surrounding points are active. The network response in this condition is linear over an approximately two- to three-fold decrease of inhibitory feedback strength. This result supports the idea that focal disinhibition allows linear coupling of motor cortical points to generate movement related muscle activation patterns; albeit with a limitation on gain control. The model also explains why neural activity does not spread as far out as the axonal connectivity allows, whilst also explaining why distant cortical points can be, nonetheless, functionally coupled by focal disinhibition. Finally, we discuss the advantages that linear interactions at the cortical level afford to motor command synthesis. PMID:26097452
Direct-Steam Linear Fresnel Performance Model for NREL's System Advisor Model
Wagner, M. J.; Zhu, G.
2012-09-01
This paper presents the technical formulation and demonstrated model performance results of a new direct-steam-generation (DSG) model in NREL's System Advisor Model (SAM). The model predicts the annual electricity production of a wide range of system configurations within the DSG Linear Fresnel technology by modeling hourly performance of the plant in detail. The quasi-steady-state formulation allows users to investigate energy and mass flows, operating temperatures, and pressure drops for geometries and solar field configurations of interest. The model includes tools for heat loss calculation using either empirical polynomial heat loss curves as a function of steam temperature, ambient temperature, and wind velocity, or a detailed evacuated tube receiver heat loss model. Thermal losses are evaluated using a computationally efficient nodal approach, where the solar field and headers are discretized into multiple nodes where heat losses, thermal inertia, steam conditions (including pressure, temperature, enthalpy, etc.) are individually evaluated during each time step of the simulation. This paper discusses the mathematical formulation for the solar field model and describes how the solar field is integrated with the other subsystem models, including the power cycle and optional auxiliary fossil system. Model results are also presented to demonstrate plant behavior in the various operating modes.
Reconstruction of two constant coefficients in linear anisotropic diffusion model
NASA Astrophysics Data System (ADS)
Mola, Gianluca; Okazawa, Noboru; Yokota, Tomomi
2016-11-01
Let (H,< \\cdot ,\\cdot > ) be a complex Hilbert space and A:D(A)\\to H and B:D(B)\\to H be nonnegative and selfadjoint operators. We study the inverse problem consisting in the identification of the function u:[0,T]\\to H and two constants α, β \\in {{{R}}}+=(0,∞ ) (diffusion coefficients) that fulfill the initial-value problem u ‧ ( t ) + α Au ( t ) + β Bu ( t ) = 0 , t ∈ ( 0 , T ) , u ( 0 ) = x , and the additional conditions < Au ( T ) , u ( T ) > = φ and < Bu ( T ) , u ( T ) > = ψ . Under suitable assumptions on the operators A and B, and on the data x\\in H and \\varphi ,\\psi \\gt 0, we shall construct a solution and prove its uniqueness and continuous dependence on the data. Applications are considered.
Simulation of Tropical Climate with a Linear Primitive Equation Model.
NASA Astrophysics Data System (ADS)
Seager, Richard; Zebiak, Stephen E.
1995-10-01
The tropical climate simulated with a new global atmosphere model is presented. The model is purposely designed for climate studies and is still under development. It is designed to bridge the gap between very efficient but simple models of the tropical atmosphere and sophisticated but inefficient general circulation models (GCMs). In this paper the authors examine the sensitivity of the model's climate to specific formulations of convection, boundary-layer physics, and radiation.The model uses the Betts-Miller convection scheme and a parameterization of the planetary boundary layer (PBL) that combines similarity theory for computation of surface fluxes with a simple scheme for diagnosing PBL depth. Radiative cooling is specified and land surface processes are bypassed by relaxing modeled low-level values to observed quantities. Orography is ignored. The model contains six vertical layers and has a horizontal resolution of about 3° × 5.625°.The authors compare the climate simulated with two different versions of the Betts-Miller convection scheme. More realistic simulations of rainfall are obtained with the later version, which includes the effects of convective downdrafts. These, by cooling and drying the PBL, act to restrict the areas of convection while strengthening the intertropical convergence zone. The sensitivity to choice of PBL physics is less, and quite similar results were obtained when the PBL scheme was replaced with constant exchange coefficients and PBL depth. In contrast, the amount of precipitation varied strongly with the prescribed radiative cooling. The important role that shallow convection and cloud-radiation interactions play in the spatial organization of deep convection is demonstrated, by default, in an experiment using clear-sky radiative transfer.The modeled climate, as judged qualitatively by its simulation of quantities of importance to air-sea interaction and climate, such as the low-level wind field and precipitation, is in many
Auger-Méthé, Marie; Field, Chris; Albertsen, Christoffer M; Derocher, Andrew E; Lewis, Mark A; Jonsen, Ian D; Mills Flemming, Joanna
2016-01-01
State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results. PMID:27220686
Auger-Méthé, Marie; Field, Chris; Albertsen, Christoffer M; Derocher, Andrew E; Lewis, Mark A; Jonsen, Ian D; Mills Flemming, Joanna
2016-05-25
State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results.
NASA Astrophysics Data System (ADS)
García-Díaz, J. Carlos
2009-11-01
Fault detection and diagnosis is an important problem in process engineering. Process equipments are subject to malfunctions during operation. Galvanized steel is a value added product, furnishing effective performance by combining the corrosion resistance of zinc with the strength and formability of steel. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing and the increasingly stringent quality requirements in automotive industry has also demanded ongoing efforts in process control to make the process more robust. When faults occur, they change the relationship among these observed variables. This work compares different statistical regression models proposed in the literature for estimating the quality of galvanized steel coils on the basis of short time histories. Data for 26 batches were available. Five variables were selected for monitoring the process: the steel strip velocity, four bath temperatures and bath level. The entire data consisting of 48 galvanized steel coils was divided into sets. The first training data set was 25 conforming coils and the second data set was 23 nonconforming coils. Logistic regression is a modeling tool in which the dependent variable is categorical. In most applications, the dependent variable is binary. The results show that the logistic generalized linear models do provide good estimates of quality coils and can be useful for quality control in manufacturing process.
Lung cancer mortality trends in Chile and six-year projections using Bayesian dynamic linear models.
Torres-Avilés, Francisco; Moraga, Tomás; Núñez, Loreto; Icaza, Gloria
2015-09-01
The objectives were to analyze lung cancer mortality trends in Chile from 1990 to 2009, and to project the rates six years forward. Lung cancer mortality data were obtained from the Chilean Ministry of Health. To obtain mortality rates, population projections were used, based on the 2002 National Census. Rates were adjusted using the world standard population as reference. Bayesian dynamic linear models were fitted to estimate trends from 1990 to 2009 and to obtain projections for 2010-2015. During the period under study, there was a 19.9% reduction in the lung cancer mortality rate in men. In women, there was increase of 28.4%. The second-order model showed a better fit for men, and the first-order model a better fit for women. Between 2010 and 2015 the downward trend continued in men, while a trend to stabilization was projected for lung cancer mortality in women in Chile. This analytical approach could be useful implement surveillance systems for chronic non-communicable disease and to evaluate preventive strategies. PMID:26578021
Misspecification of the covariance structure in generalized linear mixed models.
Chavance, M; Escolano, S
2016-04-01
When fitting marginal models to correlated outcomes, the so-called sandwich variance is commonly used. However, this is not the case when fitting mixed models. Using two data sets, we illustrate the problems that can be encountered. We show that the differences or the ratios between the naive and sandwich standard deviations of the fixed effects estimators provide convenient means of assessing the fit of the model, as both are consistent when the covariance structure is correctly specified, but only the latter is when that structure is misspecified. When the number of statistical units is not too small, the sandwich formula correctly estimates the variance of the fixed effects estimator even if the random effects are misspecified, and it can be used in a diagnostic tool for assessing the misspecification of the random effects. A simple comparison with the naive variance is sufficient and we propose considering a ratio of the naive and sandwich standard deviation out of the [3/4; 4/3] interval as signaling a risk of erroneous inference due to a model misspecification. We strongly advocate broader use of the sandwich variance for statistical inference about the fixed effects in mixed models.
NASA Astrophysics Data System (ADS)
García, Hermes A.; Guerrero-Bolaño, Francisco J.; Obregón-Neira, Nelson
2010-05-01
Due to both mathematical tractability and efficiency on computational resources, it is very common to find in the realm of numerical modeling in hydro-engineering that regular linearization techniques have been applied to nonlinear partial differential equations properly obtained in environmental flow studies. Sometimes this simplification is also made along with omission of nonlinear terms involved in such equations which in turn diminishes the performance of any implemented approach. This is the case for example, for contaminant transport modeling in streams. Nowadays, a traditional and one of the most common used water quality model such as QUAL2k, preserves its original algorithm, which omits nonlinear terms through linearization techniques, in spite of the continuous algorithmic development and computer power enhancement. For that reason, the main objective of this research was to generate a flexible tool for non-linear water quality modeling. The solution implemented here was based on two genetic algorithms, used in a nested way in order to find two different types of solutions sets: the first set is composed by the concentrations of the physical-chemical variables used in the modeling approach (16 variables), which satisfies the non-linear equation system. The second set, is the typical solution of the inverse problem, the parameters and constants values for the model when it is applied to a particular stream. From a total of sixteen (16) variables, thirteen (13) was modeled by using non-linear coupled equation systems and three (3) was modeled in an independent way. The model used here had a requirement of fifty (50) parameters. The nested genetic algorithm used for the numerical solution of a non-linear equation system proved to serve as a flexible tool to handle with the intrinsic non-linearity that emerges from the interactions occurring between multiple variables involved in water quality studies. However because there is a strong data limitation in
Modeling taper charge with a non-linear equation
NASA Technical Reports Server (NTRS)
Mcdermott, P. P.
1985-01-01
Work aimed at modeling the charge voltage and current characteristics of nickel-cadmium cells subject to taper charge is presented. Work reported at previous NASA Battery Workshops has shown that the voltage of cells subject to constant current charge and discharge can be modeled very accurately with the equation: voltage = A + (B/(C-X)) + De to the -Ex where A, B, D, and E are fit parameters and x is amp-hr of charge removed during discharge or returned during charge. In a constant current regime, x is also equivalent to time on charge or discharge.
Analysis of the Development of the Working Alliance Using Hierarchical Linear Modeling.
ERIC Educational Resources Information Center
Kivlighan, Dennis M.; Shaughnessy, Peter
1995-01-01
Describes method of analysis of the relation between working alliance and therapeutic outcome using hierarchical linear modeling. Results revealed a significant association between linear growth function of therapist ratings of working alliance and therapeutic outcome. Discusses need to conceptualize working alliance as a temporally variant, as…
The Generalized Logit-Linear Item Response Model for Binary-Designed Items
ERIC Educational Resources Information Center
Revuelta, Javier
2008-01-01
This paper introduces the generalized logit-linear item response model (GLLIRM), which represents the item-solving process as a series of dichotomous operations or steps. The GLLIRM assumes that the probability function of the item response is a logistic function of a linear composite of basic parameters which describe the operations, and the…
Ma, Rongfei
2015-01-01
In this paper, ammonia quantitative analysis based on miniaturized Al ionization gas sensor and non-linear bistable dynamic model was proposed. Al plate anodic gas-ionization sensor was used to obtain the current-voltage (I-V) data. Measurement data was processed by non-linear bistable dynamics model. Results showed that the proposed method quantitatively determined ammonia concentrations. PMID:25975362
Computation of linear acceleration through an internal model in the macaque cerebellum.
Laurens, Jean; Meng, Hui; Angelaki, Dora E
2013-11-01
A combination of theory and behavioral findings support a role for internal models in the resolution of sensory ambiguities and sensorimotor processing. Although the cerebellum has been proposed as a candidate for implementation of internal models, concrete evidence from neural responses is lacking. Using unnatural motion stimuli, which induce incorrect self-motion perception and eye movements, we explored the neural correlates of an internal model that has been proposed to compensate for Einstein's equivalence principle and generate neural estimates of linear acceleration and gravity. We found that caudal cerebellar vermis Purkinje cells and cerebellar nuclei neurons selective for actual linear acceleration also encoded erroneous linear acceleration, as would be expected from the internal model hypothesis, even when no actual linear acceleration occurred. These findings provide strong evidence that the cerebellum might be involved in the implementation of internal models that mimic physical principles to interpret sensory signals, as previously hypothesized.
Computation of linear acceleration through an internal model in the macaque cerebellum
Laurens, Jean; Meng, Hui; Angelaki, Dora E.
2013-01-01
A combination of theory and behavioral findings has supported a role for internal models in the resolution of sensory ambiguities and sensorimotor processing. Although the cerebellum has been proposed as a candidate for implementation of internal models, concrete evidence from neural responses is lacking. Here we exploit un-natural motion stimuli, which induce incorrect self-motion perception and eye movements, to explore the neural correlates of an internal model proposed to compensate for Einstein’s equivalence principle and generate neural estimates of linear acceleration and gravity. We show that caudal cerebellar vermis Purkinje cells and cerebellar nuclei neurons selective for actual linear acceleration also encode erroneous linear acceleration, as expected from the internal model hypothesis, even when no actual linear acceleration occurs. These findings provide strong evidence that the cerebellum might be involved in the implementation of internal models that mimic physical principles to interpret sensory signals, as previously hypothesized by theorists. PMID:24077562
Item Response Theory Using Hierarchical Generalized Linear Models
ERIC Educational Resources Information Center
Ravand, Hamdollah
2015-01-01
Multilevel models (MLMs) are flexible in that they can be employed to obtain item and person parameters, test for differential item functioning (DIF) and capture both local item and person dependence. Papers on the MLM analysis of item response data have focused mostly on theoretical issues where applications have been add-ons to simulation…
Linear Model to Assess the Scale's Validity of a Test
ERIC Educational Resources Information Center
Tristan, Agustin; Vidal, Rafael
2007-01-01
Wright and Stone had proposed three features to assess the quality of the distribution of the items difficulties in a test, on the so called "most probable response map": line, stack and gap. Once a line is accepted as a design model for a test, gaps and stacks are practically eliminated, producing an evidence of the "scale validity" of the test.…
A Decomposition Method Based on a Model of Continuous Change
HORIUCHI, SHIRO; WILMOTH, JOHN R.; PLETCHER, SCOTT D.
2008-01-01
A demographic measure is often expressed as a deterministic or stochastic function of multiple variables (covariates), and a general problem (the decomposition problem) is to assess contributions of individual covariates to a difference in the demographic measure (dependent variable) between two populations. We propose a method of decomposition analysis based on an assumption that covariates change continuously along an actual or hypothetical dimension. This assumption leads to a general model that logically justifies the additivity of covariate effects and the elimination of interaction terms, even if the dependent variable itself is a nonadditive function. A comparison with earlier methods illustrates other practical advantages of the method: in addition to an absence of residuals or interaction terms, the method can easily handle a large number of covariates and does not require a logically meaningful ordering of covariates. Two empirical examples show that the method can be applied flexibly to a wide variety of decomposition problems. This study also suggests that when data are available at multiple time points over a long interval, it is more accurate to compute an aggregated decomposition based on multiple subintervals than to compute a single decomposition for the entire study period. PMID:19110897
Vibration Model Validation for Linear Collider Detector Platforms
Bertsche, Kirk; Amann, J.W.; Markiewicz, T.W.; Oriunno, M.; Weidemann, A.; White, G.; /SLAC
2012-05-16
The ILC and CLIC reference designs incorporate reinforced-concrete platforms underneath the detectors so that the two detectors can each be moved onto and off of the beamline in a Push-Pull configuration. These platforms could potentially amplify ground vibrations, which would reduce luminosity. In this paper we compare vibration models to experimental data on reinforced concrete structures, estimate the impact on luminosity, and summarize implications for the design of a reinforced concrete platform for the ILC or CLIC detectors.
Evaluation of a Linear Mixing Model to Retrieve Soil and Vegetation Temperatures of Land Targets
NASA Astrophysics Data System (ADS)
Yang, Jinxin; Jia, Li; Cui, Yaokui; Zhou, Jie; Menenti, Massimo
2014-03-01
A simple linear mixing model of heterogeneous soil-vegetation system and retrieval of component temperatures from directional remote sensing measurements by inverting this model is evaluated in this paper using observations by a thermal camera. The thermal camera was used to obtain multi-angular TIR (Thermal Infra-Red) images over vegetable and orchard canopies. A whole thermal camera image was treated as a pixel of a satellite image to evaluate the model with the two-component system, i.e. soil and vegetation. The evaluation included two parts: evaluation of the linear mixing model and evaluation of the inversion of the model to retrieve component temperatures. For evaluation of the linear mixing model, the RMSE is 0.2 K between the observed and modelled brightness temperatures, which indicates that the linear mixing model works well under most conditions. For evaluation of the model inversion, the RMSE between the model retrieved and the observed vegetation temperatures is 1.6K, correspondingly, the RMSE between the observed and retrieved soil temperatures is 2.0K. According to the evaluation of the sensitivity of retrieved component temperatures on fractional cover, the linear mixing model gives more accurate retrieval accuracies for both soil and vegetation temperatures under intermediate fractional cover conditions.
Linear system identification via backward-time observer models
NASA Technical Reports Server (NTRS)
Juang, Jer-Nan; Phan, Minh Q.
1992-01-01
Presented here is an algorithm to compute the Markov parameters of a backward-time observer for a backward-time model from experimental input and output data. The backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) for the backward-time system identification. The identified backward-time system Markov parameters are used in the Eigensystem Realization Algorithm to identify a backward-time state-space model, which can be easily converted to the usual forward-time representation. If one reverses time in the model to be identified, what were damped true system modes become modes with negative damping, growing as the reversed time increases. On the other hand, the noise modes in the identification still maintain the property that they are stable. The shift from positive damping to negative damping of the true system modes allows one to distinguish these modes from noise modes. Experimental results are given to illustrate when and to what extent this concept works.
Model Checking Linear-Time Properties of Probabilistic Systems
NASA Astrophysics Data System (ADS)
Baier, Christel; Größer, Marcus; Ciesinski, Frank
This chapter is about the verification of Markov decision processes (MDPs) which incorporate one of the fundamental models for reasoning about probabilistic and nondeterministic phenomena in reactive systems. MDPs have their roots in the field of operations research and are nowadays used in a wide variety of areas including verification, robotics, planning, controlling, reinforcement learning, economics and semantics of randomized systems. Furthermore, MDPs served as the basis for the introduction of probabilistic automata which are related to weighted automata. We describe the use of MDPs as an operational model for randomized systems, e.g., systems that employ randomized algorithms, multi-agent systems or systems with unreliable components or surroundings. In this context we outline the theory of verifying ω-regular properties of such operational models. As an integral part of this theory we use ω-automata, i.e., finite-state automata over finite alphabets that accept languages of infinite words. Additionally, basic concepts of important reduction techniques are sketched, namely partial order reduction of MDPs and quotient system reduction of the numerical problem that arises in the verification of MDPs. Furthermore we present several undecidability and decidability results for the controller synthesis problem for partially observable MDPs.
NASA Astrophysics Data System (ADS)
Rudy, Ashley C. A.; Lamoureux, Scott F.; Treitz, Paul; van Ewijk, Karin Y.
2016-07-01
To effectively assess and mitigate risk of permafrost disturbance, disturbance-prone areas can be predicted through the application of susceptibility models. In this study we developed regional susceptibility models for permafrost disturbances using a field disturbance inventory to test the transferability of the model to a broader region in the Canadian High Arctic. Resulting maps of susceptibility were then used to explore the effect of terrain variables on the occurrence of disturbances within this region. To account for a large range of landscape characteristics, the model was calibrated using two locations: Sabine Peninsula, Melville Island, NU, and Fosheim Peninsula, Ellesmere Island, NU. Spatial patterns of disturbance were predicted with a generalized linear model (GLM) and generalized additive model (GAM), each calibrated using disturbed and randomized undisturbed locations from both locations and GIS-derived terrain predictor variables including slope, potential incoming solar radiation, wetness index, topographic position index, elevation, and distance to water. Each model was validated for the Sabine and Fosheim Peninsulas using independent data sets while the transferability of the model to an independent site was assessed at Cape Bounty, Melville Island, NU. The regional GLM and GAM validated well for both calibration sites (Sabine and Fosheim) with the area under the receiver operating curves (AUROC) > 0.79. Both models were applied directly to Cape Bounty without calibration and validated equally with AUROC's of 0.76; however, each model predicted disturbed and undisturbed samples differently. Additionally, the sensitivity of the transferred model was assessed using data sets with different sample sizes. Results indicated that models based on larger sample sizes transferred more consistently and captured the variability within the terrain attributes in the respective study areas. Terrain attributes associated with the initiation of disturbances were
NASA Astrophysics Data System (ADS)
Mead, A. J.; Peacock, J. A.; Heymans, C.; Joudaki, S.; Heavens, A. F.
2015-12-01
We present an optimized variant of the halo model, designed to produce accurate matter power spectra well into the non-linear regime for a wide range of cosmological models. To do this, we introduce physically motivated free parameters into the halo-model formalism and fit these to data from high-resolution N-body simulations. For a variety of Λ cold dark matter (ΛCDM) and wCDM models, the halo-model power is accurate to ≃ 5 per cent for k ≤ 10h Mpc-1 and z ≤ 2. An advantage of our new halo model is that it can be adapted to account for the effects of baryonic feedback on the power spectrum. We demonstrate this by fitting the halo model to power spectra from the OWLS (OverWhelmingly Large Simulations) hydrodynamical simulation suite via parameters that govern halo internal structure. We are able to fit all feedback models investigated at the 5 per cent level using only two free parameters, and we place limits on the range of these halo parameters for feedback models investigated by the OWLS simulations. Accurate predictions to high k are vital for weak-lensing surveys, and these halo parameters could be considered nuisance parameters to marginalize over in future analyses to mitigate uncertainty regarding the details of feedback. Finally, we investigate how lensing observables predicted by our model compare to those from simulations and from HALOFIT for a range of k-cuts and feedback models and quantify the angular scales at which these effects become important. Code to calculate power spectra from the model presented in this paper can be found at https://github.com/alexander-mead/hmcode.
Non-linear dynamic modeling of an automobile hydraulic active suspension system
NASA Astrophysics Data System (ADS)
Mrad, R. Ben; Levitt, J. A.; Fassois, S. D.
1994-09-01
Motived by the strong need for realistically describing the dynamical behaviour of automotive systems through adequate mathematical models, a computer-stimulation-suitable non-linear quarter-car model of a hydraulic active suspension system is developed. Unlike previously available linear models characterised by idealised actuator and component behaviour, the developed model accounts for the dynamics of the main system components, including the suspension bushing, pump, accumulator, power and bypass valves, and hydraulic actuator, while also incorporating preliminary versions of the system controllers. Significant system characteristics, such as non-linear pressure-flow relationships, fluid compressibility, pump and valve non-linearities, leakages, as well as Coulomb friction, are also explicitly accounted for, and the underpinning assumptions are discussed. Simulation results obtained by exercising the model provide insight into the system behavior, illustrate the importance of the actuator/component dynamics and their associated non-linearities and reveal the inadequacy of the idealised linear models in capturing the system behaviour, demonstrate specific effects of valve leakage and fluid bulk modulus, are in qualitative agreement with experimental measurements, and stress the need for proper control law design and tuning. The developed model is particularly suitable for analysis, design, control law optimisation, and diagnostic strategies development.
As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...
Huffman and linear scanning methods with statistical language models.
Roark, Brian; Fried-Oken, Melanie; Gibbons, Chris
2015-03-01
Current scanning access methods for text generation in AAC devices are limited to relatively few options, most notably row/column variations within a matrix. We present Huffman scanning, a new method for applying statistical language models to binary-switch, static-grid typing AAC interfaces, and compare it to other scanning options under a variety of conditions. We present results for 16 adults without disabilities and one 36-year-old man with locked-in syndrome who presents with complex communication needs and uses AAC scanning devices for writing. Huffman scanning with a statistical language model yielded significant typing speedups for the 16 participants without disabilities versus any of the other methods tested, including two row/column scanning methods. A similar pattern of results was found with the individual with locked-in syndrome. Interestingly, faster typing speeds were obtained with Huffman scanning using a more leisurely scan rate than relatively fast individually calibrated scan rates. Overall, the results reported here demonstrate great promise for the usability of Huffman scanning as a faster alternative to row/column scanning.
Huffman and linear scanning methods with statistical language models.
Roark, Brian; Fried-Oken, Melanie; Gibbons, Chris
2015-03-01
Current scanning access methods for text generation in AAC devices are limited to relatively few options, most notably row/column variations within a matrix. We present Huffman scanning, a new method for applying statistical language models to binary-switch, static-grid typing AAC interfaces, and compare it to other scanning options under a variety of conditions. We present results for 16 adults without disabilities and one 36-year-old man with locked-in syndrome who presents with complex communication needs and uses AAC scanning devices for writing. Huffman scanning with a statistical language model yielded significant typing speedups for the 16 participants without disabilities versus any of the other methods tested, including two row/column scanning methods. A similar pattern of results was found with the individual with locked-in syndrome. Interestingly, faster typing speeds were obtained with Huffman scanning using a more leisurely scan rate than relatively fast individually calibrated scan rates. Overall, the results reported here demonstrate great promise for the usability of Huffman scanning as a faster alternative to row/column scanning. PMID:25672825
Non-linear sigma-models and string theories
Sen, A.
1986-10-01
The connection between sigma-models and string theories is discussed, as well as how the sigma-models can be used as tools to prove various results in string theories. Closed bosonic string theory in the light cone gauge is very briefly introduced. Then, closed bosonic string theory in the presence of massless background fields is discussed. The light cone gauge is used, and it is shown that in order to obtain a Lorentz invariant theory, the string theory in the presence of background fields must be described by a two-dimensional conformally invariant theory. The resulting constraints on the background fields are found to be the equations of motion of the string theory. The analysis is extended to the case of the heterotic string theory and the superstring theory in the presence of the massless background fields. It is then shown how to use these results to obtain nontrivial solutions to the string field equations. Another application of these results is shown, namely to prove that the effective cosmological constant after compactification vanishes as a consequence of the classical equations of motion of the string theory. 34 refs. (LEW)
Modeling contaminant migration with linear sorption in strongly heterogeneous media
Bai, M.; Roegiers, J.C.; Elsworth, D.; Inyang, H.I.
1997-11-01
A triple-porosity model is presented to evaluate transport behavior in porous media with a structure comprising a spectrum of pore sizes, represented discretely as macro-, meso-, and micropores. Characterizations are completed to provide adequate semianalytical solutions for the validation of codes representing discrete distributions of pore geometry and to adequately describe extended tailing and multicomponent solute front breakthroughs apparent in field and laboratory data. Semianalytical solutions are derived for a one dimensional flow geometry by using Laplace transforms under the assumption that solute transport in the two interactive mobile-transport regions (i.e., macro- and mesopores) is affected by exchange with immobile solutes in the micropore region. Sensitivity analyses are conducted to identify the propensity for extensive tailing in the breakthrough response, over single-porosity approaches, and the development of multiple breakthrough fronts with reverse diffusion. Both behaviors result from the strongly heterogeneous nature of the transport processes, accommodated in the multiporosity model, and are well suited to the representation of real porous and porous-fractured disordered media.
NASA Astrophysics Data System (ADS)
Smolders, K.; Volckaert, M.; Swevers, J.
2008-11-01
This paper presents a nonlinear model-based iterative learning control procedure to achieve accurate tracking control for nonlinear lumped mechanical continuous-time systems. The model structure used in this iterative learning control procedure is new and combines a linear state space model and a nonlinear feature space transformation. An intuitive two-step iterative algorithm to identify the model parameters is presented. It alternates between the estimation of the linear and the nonlinear model part. It is assumed that besides the input and output signals also the full state vector of the system is available for identification. A measurement and signal processing procedure to estimate these signals for lumped mechanical systems is presented. The iterative learning control procedure relies on the calculation of the input that generates a given model output, so-called offline model inversion. A new offline nonlinear model inversion method for continuous-time, nonlinear time-invariant, state space models based on Newton's method is presented and applied to the new model structure. This model inversion method is not restricted to minimum phase models. It requires only calculation of the first order derivatives of the state space model and is applicable to multivariable models. For periodic reference signals the method yields a compact implementation in the frequency domain. Moreover it is shown that a bandwidth can be specified up to which learning is allowed when using this inversion method in the iterative learning control procedure. Experimental results for a nonlinear single-input-single-output system corresponding to a quarter car on a hydraulic test rig are presented. It is shown that the new nonlinear approach outperforms the linear iterative learning control approach which is currently used in the automotive industry on durability test rigs.
Parallel linear dynamic models can mimic the McGurk effect in clinical populations.
Altieri, Nicholas; Yang, Cheng-Ta
2016-10-01
One of the most common examples of audiovisual speech integration is the McGurk effect. As an example, an auditory syllable /ba/ recorded over incongruent lip movements that produce "ga" typically causes listeners to hear "da". This report hypothesizes reasons why certain clinical and listeners who are hard of hearing might be more susceptible to visual influence. Conversely, we also examine why other listeners appear less susceptible to the McGurk effect (i.e., they report hearing just the auditory stimulus without being influenced by the visual). Such explanations are accompanied by a mechanistic explanation of integration phenomena including visual inhibition of auditory information, or slower rate of accumulation of inputs. First, simulations of a linear dynamic parallel interactive model were instantiated using inhibition and facilitation to examine potential mechanisms underlying integration. In a second set of simulations, we systematically manipulated the inhibition parameter values to model data obtained from listeners with autism spectrum disorder. In summary, we argue that cross-modal inhibition parameter values explain individual variability in McGurk perceptibility. Nonetheless, different mechanisms should continue to be explored in an effort to better understand current data patterns in the audiovisual integration literature. PMID:27272510
Mathematics in Marine Botany: Examples of the Modelling Process. Part II: Continuous Models.
ERIC Educational Resources Information Center
Nyman, Melvin A.; Brown, Murray T.
1996-01-01
Describes some continuous models for growth of the seaweed Macrocystis pyrifera. Uses observed growth rates over several months to derive first-order differential equations as models for growth rates of individual fronds. The nature of the solutions is analyzed and comparison between these theoretical results and documented characteristics of…
ERIC Educational Resources Information Center
Markon, Kristian E.; Krueger, Robert F.
2006-01-01
Distinguishing between discrete and continuous latent variable distributions has become increasingly important in numerous domains of behavioral science. Here, the authors explore an information-theoretic approach to latent distribution modeling, in which the ability of latent distribution models to represent statistical information in observed…
Localization of the SFT inspired nonlocal linear models and exact solutions
NASA Astrophysics Data System (ADS)
Vernov, S. Yu.
2011-05-01
A general class of gravitational models driven by a nonlocal scalar field with a linear or quadratic potential is considered. We study the action with an arbitrary analytic function ℱ(□ g ), which has both simple and double roots. The way of localization of nonlocal Einstein equations is generalized on models with linear potentials. Exact solutions in the Friedmann-Robertson-Walker and Bianchi I metrics are presented.
NASA Technical Reports Server (NTRS)
Penfield, P., Jr.; Peterson, D. F.; Steinbrecher, D. H.
1972-01-01
A nonlinear circuit model for avalanche diodes is proposed. The model was derived by assuming that the bias dependence of the elements in a known small-signal equivalent-circuit model for existing diodes arises in a manner consistent with the theory of an idealized Read-type device. The model contains a nonlinear R-L branch, a controlled source, and a linear depletion capacitance. The model is used in the nearly linear sense to predict intermodulation distortion and gain compression in avalanche diode amplifiers. Computed results for amplifiers with existing diodes are shown to be in good agreement with experiment.
2016-01-01
Background Self-contained tests estimate and test the association between a phenotype and mean expression level in a gene set defined a priori. Many self-contained gene set analysis methods have been developed but the performance of these methods for phenotypes that are continuous rather than discrete and with multiple nuisance covariates has not been well studied. Here, I use Monte Carlo simulation to evaluate the performance of both novel and previously published (and readily available via R) methods for inferring effects of a continuous predictor on mean expression in the presence of nuisance covariates. The motivating data are a high-profile dataset which was used to show opposing effects of hedonic and eudaimonic well-being (or happiness) on the mean expression level of a set of genes that has been correlated with social adversity (the CTRA gene set). The original analysis of these data used a linear model (GLS) of fixed effects with correlated error to infer effects of Hedonia and Eudaimonia on mean CTRA expression. Methods The standardized effects of Hedonia and Eudaimonia on CTRA gene set expression estimated by GLS were compared to estimates using multivariate (OLS) linear models and generalized estimating equation (GEE) models. The OLS estimates were tested using O’Brien’s OLS test, Anderson’s permutation \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}${r}_{F}^{2}$\\end{document}rF2-test, two permutation F-tests (including GlobalAncova), and a rotation z-test (Roast). The GEE estimates were tested using a Wald test with robust standard errors. The performance (Type I, II, S, and M errors) of all tests was investigated using a Monte Carlo simulation of data explicitly modeled on the re-analyzed dataset. Results GLS estimates are inconsistent between data
A new adaptive multiple modelling approach for non-linear and non-stationary systems
NASA Astrophysics Data System (ADS)
Chen, Hao; Gong, Yu; Hong, Xia
2016-07-01
This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.
Batet, Oscar; Dios, Federico; Comeron, Adolfo; Agishev, Ravil
2010-06-10
We analyze the intensity-modulation frequency-modulated continuous-wave (FMCW) technique for lidar remote sensing in the context of its application to distributed media. The goal of the technique is the reproduction of the sounded-medium profile along the emission path. A conceptual analysis is carried out to show the problems the basic version of the method presents for this application. The principal point is the appearance of a bandpass filtering effect, which seems to hinder its use in this context. A modified version of the technique is proposed to overcome this problem. A number of computer simulations confirm the ability of the modified FMCW technique to sound distributed media. PMID:20539357
Pressure-induced continuous phase transition of charge-density-wave state in a linear-chain complex
NASA Astrophysics Data System (ADS)
Kuroda, Noritaka; Sakai, Masamichi; Nishina, Yuichiro; Sasaki, Kazuo
1992-05-01
[Pt(en)2][Pt(en)2Cl2](ClO4)4 (en=ethylenediamine) is found to undergo a novel phase transition from the charge-density-wave (CDW) state to a new phase under hydrostatic pressure. The new phase appears at 3 GPa and coexists with the CDW state up to 6 GPa, at least. The volume ratio between the two coexisting phases measured by Raman scattering spectroscopy changes continuously with pressure. The result is discussed in terms of the theoretical approach to the polymorphic phase transition developed recently by Bassler, Sasaki, and Griffiths. It is suggested that kink solitons play an important role in this phase transition.
A Method for Generating Reduced-Order Linear Models of Multidimensional Supersonic Inlets
NASA Technical Reports Server (NTRS)
Chicatelli, Amy; Hartley, Tom T.
1998-01-01
Simulation of high speed propulsion systems may be divided into two categories, nonlinear and linear. The nonlinear simulations are usually based on multidimensional computational fluid dynamics (CFD) methodologies and tend to provide high resolution results that show the fine detail of the flow. Consequently, these simulations are large, numerically intensive, and run much slower than real-time. ne linear simulations are usually based on large lumping techniques that are linearized about a steady-state operating condition. These simplistic models often run at or near real-time but do not always capture the detailed dynamics of the plant. Under a grant sponsored by the NASA Lewis Research Center, Cleveland, Ohio, a new method has been developed that can be used to generate improved linear models for control design from multidimensional steady-state CFD results. This CFD-based linear modeling technique provides a small perturbation model that can be used for control applications and real-time simulations. It is important to note the utility of the modeling procedure; all that is needed to obtain a linear model of the propulsion system is the geometry and steady-state operating conditions from a multidimensional CFD simulation or experiment. This research represents a beginning step in establishing a bridge between the controls discipline and the CFD discipline so that the control engineer is able to effectively use multidimensional CFD results in control system design and analysis.
A componential model of human interaction with graphs: 1. Linear regression modeling
NASA Technical Reports Server (NTRS)
Gillan, Douglas J.; Lewis, Robert
1994-01-01
Task analyses served as the basis for developing the Mixed Arithmetic-Perceptual (MA-P) model, which proposes (1) that people interacting with common graphs to answer common questions apply a set of component processes-searching for indicators, encoding the value of indicators, performing arithmetic operations on the values, making spatial comparisons among indicators, and repsonding; and (2) that the type of graph and user's task determine the combination and order of the components applied (i.e., the processing steps). Two experiments investigated the prediction that response time will be linearly related to the number of processing steps according to the MA-P model. Subjects used line graphs, scatter plots, and stacked bar graphs to answer comparison questions and questions requiring arithmetic calculations. A one-parameter version of the model (with equal weights for all components) and a two-parameter version (with different weights for arithmetic and nonarithmetic processes) accounted for 76%-85% of individual subjects' variance in response time and 61%-68% of the variance taken across all subjects. The discussion addresses possible modifications in the MA-P model, alternative models, and design implications from the MA-P model.
Predicting musically induced emotions from physiological inputs: linear and neural network models
Russo, Frank A.; Vempala, Naresh N.; Sandstrom, Gillian M.
2013-01-01
Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of “felt” emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants—heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion. PMID:23964250
Schneider, Uwe
2009-04-15
A simple mechanistic model for predicting cancer induction after fractionated radiotherapy is developed. The model is based upon the linear-quadratic model. The inductions of carcinomas and sarcomas are modeled separately. The linear-quadratic model of cell kill is applied to normal tissues which are unintentionally irradiated during a cancer treatment with radiotherapy. Tumor induction is modeled such that each transformation process results in a tumor cell. The microscopic transformation parameter was chosen such that in the limit of low dose and acute exposure, the parameters of the linear-no-threshold model for tumor induction were approached. The differential equations describing carcinoma and sarcoma inductions can be solved analytically. Cancer induction in this model is a function of treatment dose, the cell kill parameters ({alpha},{beta}), the tumor induction variable ({mu}), and the repopulation parameter ({xi}). Carcinoma induction shows a bell shaped behavior as long as cell repopulation is small. Assuming large cell repopulation rates, a plateaulike function is approached. In contrast, sarcoma induction is negligible for low doses and increases with increasing dose up to a constant value. The proposed model describes carcinoma and sarcoma inductions after fractionated radiotherapy as an analytical function of four parameters. In the limit of low dose and for an instant irradiation it reproduces the results of the linear-no-threshold model. The obtained dose-response curves for cancer induction can be implemented with other models such as the organ-equivalent dose model to predict second cancers after radiotherapy.
ERIC Educational Resources Information Center
Yan, Jun; Aseltine, Robert H., Jr.; Harel, Ofer
2013-01-01
Comparing regression coefficients between models when one model is nested within another is of great practical interest when two explanations of a given phenomenon are specified as linear models. The statistical problem is whether the coefficients associated with a given set of covariates change significantly when other covariates are added into…
Land Use and Soil Erosion. A National Linear Programming Model. Technical Bulletin Number 1742.
ERIC Educational Resources Information Center
Huang, Wen-Yuan; And Others
This technical bulletin documents a model, the Natural Resource Linear Programming (NRLP) model, capable of measuring the effects of land use restrictions imposed as conservation measures. The primary use for the model is to examine the government expenditures required to compensate farmers for retiring potentially erodible private cropland. The…
A Hierarchical Linear Model with Factor Analysis Structure at Level 2
ERIC Educational Resources Information Center
Miyazaki, Yasuo; Frank, Kenneth A.
2006-01-01
In this article the authors develop a model that employs a factor analysis structure at Level 2 of a two-level hierarchical linear model (HLM). The model (HLM2F) imposes a structure on a deficient rank Level 2 covariance matrix [tau], and facilitates estimation of a relatively large [tau] matrix. Maximum likelihood estimators are derived via the…
Combining and connecting linear, multi-input, multi-output subsystem models
NASA Technical Reports Server (NTRS)
Duke, E. L.
1986-01-01
The mathematical background for combining and connecting linear, multi-input, multi-output subsystem models into an overall system model is provided. Several examples of subsystem configurations are examined in detail. A description of a MATRIX (sub x) command file to aid in the process of combining and connecting these subsystem models is contained.
Region-Based Association Test for Familial Data under Functional Linear Models
Axenovich, Tatiana I.
2015-01-01
Region-based association analysis is a more powerful tool for gene mapping than testing of individual genetic variants, particularly for rare genetic variants. The most powerful methods for regional mapping are based on the functional data analysis approach, which assumes that the regional genome of an individual may be considered as a continuous stochastic function that contains information about both linkage and linkage disequilibrium. Here, we extend this powerful approach, earlier applied only to independent samples, to the samples of related individuals. To this end, we additionally include a random polygene effects in functional linear model used for testing association between quantitative traits and multiple genetic variants in the region. We compare the statistical power of different methods using Genetic Analysis Workshop 17 mini-exome family data and a wide range of simulation scenarios. Our method increases the power of regional association analysis of quantitative traits compared with burden-based and kernel-based methods for the majority of the scenarios. In addition, we estimate the statistical power of our method using regions with small number of genetic variants, and show that our method retains its advantage over burden-based and kernel-based methods in this case as well. The new method is implemented as the R-function ‘famFLM’ using two types of basis functions: the B-spline and Fourier bases. We compare the properties of the new method using models that differ from each other in the type of their function basis. The models based on the Fourier basis functions have an advantage in terms of speed and power over the models that use the B-spline basis functions and those that combine B-spline and Fourier basis functions. The ‘famFLM’ function is distributed under GPLv3 license and is freely available at http://mga.bionet.nsc.ru/soft/famFLM/. PMID:26111046
Canary, Jana D; Blizzard, Leigh; Barry, Ronald P; Hosmer, David W; Quinn, Stephen J
2016-05-01
Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) . PMID:26584470
Canary, Jana D; Blizzard, Leigh; Barry, Ronald P; Hosmer, David W; Quinn, Stephen J
2016-05-01
Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) .
ERIC Educational Resources Information Center
So, Tak-Shing Harry; Peng, Chao-Ying Joanne
This study compared the accuracy of predicting two-group membership obtained from K-means clustering with those derived from linear probability modeling, linear discriminant function, and logistic regression under various data properties. Multivariate normally distributed populations were simulated based on combinations of population proportions,…
ERIC Educational Resources Information Center
Armey, Michael F.; Crowther, Janis H.
2008-01-01
Research has identified a significant increase in both the incidence and prevalence of non-suicidal self-injury (NSSI). The present study sought to test both linear and non-linear cusp catastrophe models by using aversive self-awareness, which was operationalized as a composite of aversive self-relevant affect and cognitions, and dissociation as…
Iterated non-linear model predictive control based on tubes and contractive constraints.
Murillo, M; Sánchez, G; Giovanini, L
2016-05-01
This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle.
Iterated non-linear model predictive control based on tubes and contractive constraints.
Murillo, M; Sánchez, G; Giovanini, L
2016-05-01
This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle. PMID:26850752
Linear and nonlinear quantitative structure-property relationship modelling of skin permeability.
Khajeh, A; Modarress, H
2014-01-01
In this work, quantitative structure-property relationship (QSPR) models were developed to estimate skin permeability based on theoretically derived molecular descriptors and a diverse set of experimental data. The newly developed method combining modified particle swarm optimization (MPSO) and multiple linear regression (MLR) was used to select important descriptors and develop the linear model using a training set of 225 compounds. The adaptive neuro-fuzzy inference system (ANFIS) was used as an efficient nonlinear method to correlate the selected descriptors with experimental skin permeability data (log Kp). The linear and nonlinear models were assessed by internal and external validation. The obtained models with three descriptors show good predictive ability for the test set, with coefficients of determination for the MPSO-MLR and ANFIS models equal to 0.874 and 0.890, respectively. The QSPR study suggests that hydrophobicity (encoded as log P) is the most important factor in transdermal penetration. PMID:24090175
NASA Astrophysics Data System (ADS)
Katzarova, Maria; Desai, Priyanka; Kang, Beomgoo; Hall, Ryan; Huang, Qifan; Lee, Sanghoon; Chang, Taihyun; Venerus, David; Mays, Jimmy; Schieber, Jay; Larson, Ronald
The discrete slip-link model (DSM) is a single-chain mean-field model for entanglement-dominated polymer dynamics. The model has been used successfully to make predictions about the linear and nonlinear rheology of monodisperse homopolymer melts, polydisperse melts, or blends. By using recent advances in coarse-graining, hierarchical modeling, and graphics processors, the model is amenable to predictions of well-entangled branched polymers. Here, the parameters of the most coarse-grained member of the hierarchy are fit to the dynamic modulus of monodisperse linear chains and applied to symmetric 4-arm polybutadiene (PBd) star-linear blends with roughly 20 entanglements per star arm, but with no parameter adjustment. Agreement with data is quantitative. This detailed model is further used to examine assumptions and approximations typically made in tube models for blending, including factorization in the time domain. Failure of these assumptions point towards possible fixes to tube models.
Internal Physical Features of a Land Surface Model Employing a Tangent Linear Model
NASA Technical Reports Server (NTRS)
Yang, Runhua; Cohn, Stephen E.; daSilva, Arlindo; Joiner, Joanna; Houser, Paul R.
1997-01-01
The Earth's land surface, including its biomass, is an integral part of the Earth's weather and climate system. Land surface heterogeneity, such as the type and amount of vegetative covering., has a profound effect on local weather variability and therefore on regional variations of the global climate. Surface conditions affect local weather and climate through a number of mechanisms. First, they determine the re-distribution of the net radiative energy received at the surface, through the atmosphere, from the sun. A certain fraction of this energy increases the surface ground temperature, another warms the near-surface atmosphere, and the rest evaporates surface water, which in turn creates clouds and causes precipitation. Second, they determine how much rainfall and snowmelt can be stored in the soil and how much instead runs off into waterways. Finally, surface conditions influence the near-surface concentration and distribution of greenhouse gases such as carbon dioxide. The processes through which these mechanisms interact with the atmosphere can be modeled mathematically, to within some degree of uncertainty, on the basis of underlying physical principles. Such a land surface model provides predictive capability for surface variables including ground temperature, surface humidity, and soil moisture and temperature. This information is important for agriculture and industry, as well as for addressing fundamental scientific questions concerning global and local climate change. In this study we apply a methodology known as tangent linear modeling to help us understand more deeply, the behavior of the Mosaic land surface model, a model that has been developed over the past several years at NASA/GSFC. This methodology allows us to examine, directly and quantitatively, the dependence of prediction errors in land surface variables upon different vegetation conditions. The work also highlights the importance of accurate soil moisture information. Although surface
A consistent quantum model for continuous photodetection processes
NASA Astrophysics Data System (ADS)
de Oliveira, M. C.; Mizrahi, S. S.; Dodonov, V. V.
2003-06-01
We modify some aspects of the continuous photodetection theory proposed by Srinivas and Davies (SD) (1981 Opt. Acta 28 981), which describes the non-unitary evolution of a quantum field state subjected to a continuous photocount measurement. In order to remedy inconsistencies that appear in their approach, we redefine the 'annihilation' and 'creation' operators that enter in the photocount super-operators. We show that this new approach not only still satisfies all the requirements for a consistent photocount theory according to SD precepts, but also avoids some weird result appearing when previous definitions are used.
Linear and quadratic models of point process systems: contributions of patterned input to output.
Lindsay, K A; Rosenberg, J R
2012-08-01
In the 1880's Volterra characterised a nonlinear system using a functional series connecting continuous input and continuous output. Norbert Wiener, in the 1940's, circumvented problems associated with the application of Volterra series to physical problems by deriving from it a new series of terms that are mutually uncorrelated with respect to Gaussian processes. Subsequently, Brillinger, in the 1970's, introduced a point-process analogue of Volterra's series connecting point-process inputs to the instantaneous rate of point-process output. We derive here a new series from this analogue in which its terms are mutually uncorrelated with respect to Poisson processes. This new series expresses how patterned input in a spike train, represented by third-order cross-cumulants, is converted into the instantaneous rate of an output point-process. Given experimental records of suitable duration, the contribution of arbitrary patterned input to an output process can, in principle, be determined. Solutions for linear and quadratic point-process models with one and two inputs and a single output are investigated. Our theoretical results are applied to isolated muscle spindle data in which the spike trains from the primary and secondary endings from the same muscle spindle are recorded in response to stimulation of one and then two static fusimotor axons in the absence and presence of a random length change imposed on the parent muscle. For a fixed mean rate of input spikes, the analysis of the experimental data makes explicit which patterns of two input spikes contribute to an output spike.
On some spurious mode issues in shallow-water models using a linear algebra approach
NASA Astrophysics Data System (ADS)
Le Roux, D. Y.; Sène, A.; Rostand, V.; Hanert, E.
Numerical methods that are usually employed in ocean modelling are typically finite-difference, finite and spectral-element techniques. For most of these methods the coupling between the momentum and continuity equations is a delicate problem and it usually leads to spurious solutions in the representation of inertia-gravity waves. The spurious modes have a wide range of characteristics and may take the form of pressure (surface-elevation), velocity and/or Coriolis modes. The modes usually cause aliasing and an accumulation of energy in the smallest-resolvable scale, leading to noisy solutions. The Fourier analysis has proven practical and beneficial to describe the spurious solutions of several classical schemes. However it is restricted to uniform meshes on which the variables are regularly distributed. In this paper, a linear algebra approach is proposed to study the existence and the behaviour of stationary spurious modes associated with zero frequency, for some popular finite-difference and finite-element grids. The present approach is performed on uniform meshes but it applies equally well to regular as well as unstructured meshes with irregular geometry for the finite-element schemes.
Unsteady streamflow simulation using a linear implicit finite-difference model
Land, Larry F.
1978-01-01
A computer program for simulating one-dimensional subcritical, gradually varied, unsteady flow in a stream has been developed and documented. Given upstream and downstream boundary conditions and channel geometry data, roughness coefficients, stage, and discharge can be calculated anywhere within the reach as a function of time. The program uses a linear implicit finite-difference technique that discritizes the partial differential equations. Then it arranges the coefficients of the continuity and momentum equations into a pentadiagonal matrix for solution. Because it is a reasonable compromise between computational accuracy, speed and ease of use,the technique is one of the most commonly used. The upstream boundary condition is a depth hydrograph. However, options also allow the boundary condition to be discharge or water-surface elevation. The downstream boundary condition is a depth which may be constant, self-setting, or unsteady. The reach may be divided into uneven increments and the cross sections may be nonprismatic and may vary from one to the other. Tributary and lateral inflow may enter the reach. The digital model will simulate such common problems as (1) flood waves, (2) releases from dams, and (3) channels where storage is a consideration. It may also supply the needed flow information for mass-transport simulation. (Woodard-USGS)
NASA Astrophysics Data System (ADS)
Alkhalifah, Tariq; Choi, Yunseok
2012-12-01
Traveltime inversion focuses on the geometrical features of the waveform (traveltimes), which is generally smooth, and thus, tends to provide averaged (smoothed) information of the model. On other hand, general waveform inversion uses additional elements of the wavefield including amplitudes to extract higher resolution information, but this comes at the cost of introducing non-linearity to the inversion operator, complicating the convergence process. We use unwrapped phase-based objective functions in waveform inversion as a link between the two general types of inversions in a domain in which such contributions to the inversion process can be easily identified and controlled. The instantaneous traveltime is a measure of the average traveltime of the energy in a trace as a function of frequency. It unwraps the phase of wavefields yielding far less non-linearity in the objective function than that experienced with conventional wavefields, yet it still holds most of the critical wavefield information in its frequency dependency. However, it suffers from non-linearity introduced by the model (or reflectivity), as reflections from independent events in our model interact with each other. Unwrapping the phase of such a model can mitigate this non-linearity as well. Specifically, a simple modification to the inverted domain (or model), can reduce the effect of the model-induced non-linearity and, thus, make the inversion more convergent. Simple numerical examples demonstrate these assertions.
Goedert, Kelly M.; Boston, Raymond C.; Barrett, A. M.
2013-01-01
Valid research on neglect rehabilitation demands a statistical approach commensurate with the characteristics of neglect rehabilitation data: neglect arises from impairment in distinct brain networks leading to large between-subject variability in baseline symptoms and recovery trajectories. Studies enrolling medically ill, disabled patients, may suffer from missing, unbalanced data, and small sample sizes. Finally, assessment of rehabilitation requires a description of continuous recovery trajectories. Unfortunately, the statistical method currently employed in most studies of neglect treatment [repeated measures analysis of variance (ANOVA), rANOVA] does not well-address these issues. Here we review an alternative, mixed linear modeling (MLM), that is more appropriate for assessing change over time. MLM better accounts for between-subject heterogeneity in baseline neglect severity and in recovery trajectory. MLM does not require complete or balanced data, nor does it make strict assumptions regarding the data structure. Furthermore, because MLM better models between-subject heterogeneity it often results in increased power to observe treatment effects with smaller samples. After reviewing current practices in the field, and the assumptions of rANOVA, we provide an introduction to MLM. We review its assumptions, uses, advantages, and disadvantages. Using real and simulated data, we illustrate how MLM may improve the ability to detect effects of treatment over ANOVA, particularly with the small samples typical of neglect research. Furthermore, our simulation analyses result in recommendations for the design of future rehabilitation studies. Because between-subject heterogeneity is one important reason why studies of neglect treatments often yield conflicting results, employing statistical procedures that model this heterogeneity more accurately will increase the efficiency of our efforts to find treatments to improve the lives of individuals with neglect. PMID
Continuously Optimized Reliable Energy (CORE) Microgrid: Models & Tools (Fact Sheet)
Not Available
2013-07-01
This brochure describes Continuously Optimized Reliable Energy (CORE), a trademarked process NREL employs to produce conceptual microgrid designs. This systems-based process enables designs to be optimized for economic value, energy surety, and sustainability. Capabilities NREL offers in support of microgrid design are explained.
Vibration Stabilization of a Mechanical Model of a X-Band Linear Collider Final Focus Magnet
Frisch, Josef; Chang, Allison; Decker, Valentin; Doyle, Eric; Eriksson, Leif; Hendrickson, Linda; Himel, Thomas; Markiewicz, Thomas; Partridge, Richard; Seryi, Andrei; /SLAC
2006-09-28
The small beam sizes at the interaction point of a X-band linear collider require mechanical stabilization of the final focus magnets at the nanometer level. While passive systems provide adequate performance at many potential sites, active mechanical stabilization is useful if the natural or cultural ground vibration is higher than expected. A mechanical model of a room temperature linear collider final focus magnet has been constructed and actively stabilized with an accelerometer based system.
Lininger, Monica; Spybrook, Jessaca; Cheatham, Christopher C
2015-04-01
Longitudinal designs are common in the field of athletic training. For example, in the Journal of Athletic Training from 2005 through 2010, authors of 52 of the 218 original research articles used longitudinal designs. In 50 of the 52 studies, a repeated-measures analysis of variance was used to analyze the data. A possible alternative to this approach is the hierarchical linear model, which has been readily accepted in other medical fields. In this short report, we demonstrate the use of the hierarchical linear model for analyzing data from a longitudinal study in athletic training. We discuss the relevant hypotheses, model assumptions, analysis procedures, and output from the HLM 7.0 software. We also examine the advantages and disadvantages of using the hierarchical linear model with repeated measures and repeated-measures analysis of variance for longitudinal data.
Estimation of linear mixed models with a mixture of distribution for the random effects.
Proust, Cécile; Jacqmin-Gadda, Hélène
2005-05-01
The aim of this paper is to propose an algorithm to estimate linear mixed model when random effect distribution is a mixture of Gaussians. This heterogeneous linear mixed model relaxes the classical Gaussian assumption for the random effects and, when used for longitudinal data, can highlight distinct patterns of evolution. The observed likelihood is maximized using a Marquardt algorithm instead of the EM algorithm which is frequently used for mixture models. Indeed, the EM algorithm is computationally expensive and does not provide good convergence criteria nor direct estimates of the variance of the parameters. The proposed method also allows to classify subjects according to the estimated profiles by computing posterior probabilities of belonging to each component. The use of heterogeneous linear mixed model is illustrated through a study of the different patterns of cognitive evolution in the elderly. HETMIXLIN is a free Fortran90 program available on the web site: http://www.isped.u-bordeaux2.fr.
NASA Technical Reports Server (NTRS)
Taylor, B. K.; Casasent, D. P.
1989-01-01
The use of simplified error models to accurately simulate and evaluate the performance of an optical linear-algebra processor is described. The optical architecture used to perform banded matrix-vector products is reviewed, along with a linear dynamic finite-element case study. The laboratory hardware and ac-modulation technique used are presented. The individual processor error-source models and their simulator implementation are detailed. Several significant simplifications are introduced to ease the computational requirements and complexity of the simulations. The error models are verified with a laboratory implementation of the processor, and are used to evaluate its potential performance.
Analysis of an inventory model for both linearly decreasing demand and holding cost
NASA Astrophysics Data System (ADS)
Malik, A. K.; Singh, Parth Raj; Tomar, Ajay; Kumar, Satish; Yadav, S. K.
2016-03-01
This study proposes the analysis of an inventory model for linearly decreasing demand and holding cost for non-instantaneous deteriorating items. The inventory model focuses on commodities having linearly decreasing demand without shortages. The holding cost doesn't remain uniform with time due to any form of variation in the time value of money. Here we consider that the holding cost decreases with respect to time. The optimal time interval for the total profit and the optimal order quantity are determined. The developed inventory model is pointed up through a numerical example. It also includes the sensitivity analysis.
Based on linear spectral mixture model (LSMM) unmixing remote sensing image
NASA Astrophysics Data System (ADS)
Liu, Jiaodi; Cao, Weibin
2011-06-01
There are mixed pixels in remote sensing images ordinarily, this is a difficulty of the pixel classification (ie, unmixing) in remote sensing image processing.Linear spectral separation, estimating the value end of Genpo degree, for spatial modeling, through the non-constrained mixed pixel decomposition,with cotton, corn, tomatoes and soil four endmembers to decompose mixed pixels, Got four endmember abundance images and the RMS error image, the planting area of cotton and cotton-growing area of the measurement in the decomposition of mixed pixel block, and obtained unmixing accuracy. Experimental results show that: a simple linear mixed model modeling, and computation is greatly reduced, high precision, strong adaptability.
Cardoso, F F; Tempelman, R J
2012-07-01
The objectives of this work were to assess alternative linear reaction norm (RN) models for genetic evaluation of Angus cattle in Brazil. That is, we investigated the interaction between genotypes and continuous descriptors of the environmental variation to examine evidence of genotype by environment interaction (G×E) in post-weaning BW gain (PWG) and to compare the environmental sensitivity of national and imported Angus sires. Data were collected by the Brazilian Angus Improvement Program from 1974 to 2005 and consisted of 63,098 records and a pedigree file with 95,896 animals. Six models were implemented using Bayesian inference and compared using the Deviance Information Criterion (DIC). The simplest model was M(1), a traditional animal model, which showed the largest DIC and hence the poorest fit when compared with the 4 alternative RN specifications accounting for G×E. In M(2), a 2-step procedure was implemented using the contemporary group posterior means of M(1) as the environmental gradient, ranging from -92.6 to +265.5 kg. Moreover, the benefits of jointly estimating all parameters in a 1-step approach were demonstrated by M(3). Additionally, we extended M(3) to allow for residual heteroskedasticity using an exponential function (M(4)) and the best fitting (smallest DIC) environmental classification model (M(5)) specification. Finally, M(6) added just heteroskedastic residual variance to M(1). Heritabilities were less at harsh environments and increased with the improvement of production conditions for all RN models. Rank correlations among genetic merit predictions obtained by M(1) and by the best fitting RN models M(3) (homoskedastic) and M(5) (heteroskedastic) at different environmental levels ranged from 0.79 and 0.81, suggesting biological importance of G×E in Brazilian Angus PWG. These results suggest that selection progress could be optimized by adopting environment-specific genetic merit predictions. The PWG environmental sensitivity of
León, Larry F; Cai, Tianxi
2012-04-01
In this paper we develop model checking techniques for assessing functional form specifications of covariates in censored linear regression models. These procedures are based on a censored data analog to taking cumulative sums of "robust" residuals over the space of the covariate under investigation. These cumulative sums are formed by integrating certain Kaplan-Meier estimators and may be viewed as "robust" censored data analogs to the processes considered by Lin, Wei & Ying (2002). The null distributions of these stochastic processes can be approximated by the distributions of certain zero-mean Gaussian processes whose realizations can be generated by computer simulation. Each observed process can then be graphically compared with a few realizations from the Gaussian process. We also develop formal test statistics for numerical comparison. Such comparisons enable one to assess objectively whether an apparent trend seen in a residual plot reects model misspecification or natural variation. We illustrate the methods with a well known dataset. In addition, we examine the finite sample performance of the proposed test statistics in simulation experiments. In our simulation experiments, the proposed test statistics have good power of detecting misspecification while at the same time controlling the size of the test.
Aboveground biomass and carbon stocks modelling using non-linear regression model
NASA Astrophysics Data System (ADS)
Ain Mohd Zaki, Nurul; Abd Latif, Zulkiflee; Nazip Suratman, Mohd; Zainee Zainal, Mohd
2016-06-01
Aboveground biomass (AGB) is an important source of uncertainty in the carbon estimation for the tropical forest due to the variation biodiversity of species and the complex structure of tropical rain forest. Nevertheless, the tropical rainforest holds the most extensive forest in the world with the vast diversity of tree with layered canopies. With the usage of optical sensor integrate with empirical models is a common way to assess the AGB. Using the regression, the linkage between remote sensing and a biophysical parameter of the forest may be made. Therefore, this paper exemplifies the accuracy of non-linear regression equation of quadratic function to estimate the AGB and carbon stocks for the tropical lowland Dipterocarp forest of Ayer Hitam forest reserve, Selangor. The main aim of this investigation is to obtain the relationship between biophysical parameter field plots with the remotely-sensed data using nonlinear regression model. The result showed that there is a good relationship between crown projection area (CPA) and carbon stocks (CS) with Pearson Correlation (p < 0.01), the coefficient of correlation (r) is 0.671. The study concluded that the integration of Worldview-3 imagery with the canopy height model (CHM) raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the lowland Dipterocarp forest.
ERIC Educational Resources Information Center
Zhu, Xiaoshu
2013-01-01
The current study introduced a general modeling framework, multilevel mixture IRT (MMIRT) which detects and describes characteristics of population heterogeneity, while accommodating the hierarchical data structure. In addition to introducing both continuous and discrete approaches to MMIRT, the main focus of the current study was to distinguish…
Siegel, Jeffry A; Welsh, James S
2016-04-01
In the past several years, there has been a great deal of attention from the popular media focusing on the alleged carcinogenicity of low-dose radiation exposures received by patients undergoing medical imaging studies such as X-rays, computed tomography scans, and nuclear medicine scintigraphy. The media has based its reporting on the plethora of articles published in the scientific literature that claim that there is "no safe dose" of ionizing radiation, while essentially ignoring all the literature demonstrating the opposite point of view. But this reported "scientific" literature in turn bases its estimates of cancer induction on the linear no-threshold hypothesis of radiation carcinogenesis. The use of the linear no-threshold model has yielded hundreds of articles, all of which predict a definite carcinogenic effect of any dose of radiation, regardless of how small. Therefore, hospitals and professional societies have begun campaigns and policies aiming to reduce the use of certain medical imaging studies based on perceived risk:benefit ratio assumptions. However, as they are essentially all based on the linear no-threshold model of radiation carcinogenesis, the risk:benefit ratio models used to calculate the hazards of radiological imaging studies may be grossly inaccurate if the linear no-threshold hypothesis is wrong. Here, we review the myriad inadequacies of the linear no-threshold model and cast doubt on the various studies based on this overly simplistic model.
Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...
The Use of Linear Models for Determining School Workload and Activity Level.
ERIC Educational Resources Information Center
Vicino, Frank L.
This paper outlines the design and use of two linear models as decision-making tools in a school district. The problem to be solved was the allocation of resources for both clerical and custodial personnel. A solution was desired that could be quantified and documented and objectively serve the needs of the district. A clerical support model was…
Missing Data Treatments at the Second Level of Hierarchical Linear Models
ERIC Educational Resources Information Center
St. Clair, Suzanne W.
2011-01-01
The current study evaluated the performance of traditional versus modern MDTs in the estimation of fixed-effects and variance components for data missing at the second level of an hierarchical linear model (HLM) model across 24 different study conditions. Variables manipulated in the analysis included, (a) number of Level-2 variables with missing…
Avoiding Boundary Estimates in Hierarchical Linear Models through Weakly Informative Priors
ERIC Educational Resources Information Center
Chung, Yeojin; Rabe-Hesketh, Sophia; Gelman, Andrew; Dorie, Vincent; Liu, Jinchen
2012-01-01
Hierarchical or multilevel linear models are widely used for longitudinal or cross-sectional data on students nested in classes and schools, and are particularly important for estimating treatment effects in cluster-randomized trials, multi-site trials, and meta-analyses. The models can allow for variation in treatment effects, as well as…
USING LINEAR AND POLYNOMIAL MODELS TO EXAMINE THE ENVIRONMENTAL STABILITY OF VIRUSES
The article presents the development of model equations for describing the fate of viral infectivity in environmental samples. Most of the models were based upon the use of a two-step linear regression approach. The first step employs regression of log base 10 transformed viral t...
Augmenting Visual Analysis in Single-Case Research with Hierarchical Linear Modeling
ERIC Educational Resources Information Center
Davis, Dawn H.; Gagne, Phill; Fredrick, Laura D.; Alberto, Paul A.; Waugh, Rebecca E.; Haardorfer, Regine
2013-01-01
The purpose of this article is to demonstrate how hierarchical linear modeling (HLM) can be used to enhance visual analysis of single-case research (SCR) designs. First, the authors demonstrated the use of growth modeling via HLM to augment visual analysis of a sophisticated single-case study. Data were used from a delayed multiple baseline…
Regression Is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.
ERIC Educational Resources Information Center
Vidal, Sherry
Although the concept of the general linear model (GLM) has existed since the 1960s, other univariate analyses such as the t-test and the analysis of variance models have remained popular. The GLM produces an equation that minimizes the mean differences of independent variables as they are related to a dependent variable. From a computer printout…
Modeling the continuous lactic acid production process from wheat flour.
Gonzalez, Karen; Tebbani, Sihem; Lopes, Filipa; Thorigné, Aurore; Givry, Sébastien; Dumur, Didier; Pareau, Dominique
2016-01-01
A kinetic model of the simultaneous saccharification, protein hydrolysis, and fermentation (SSPHF) process for lactic acid production from wheat flour has been developed. The model describes the bacterial growth, substrate consumption, lactic acid production, and maltose hydrolysis. The model was fitted and validated with data from SSPHF experiments obtained under different dilution rates. The results of the model are in good agreement with the experimental data. Steady state concentrations of biomass, lactic acid, glucose, and maltose as function of the dilution rate were predicted by the model. This steady state analysis is further useful to determine the operating conditions that maximize lactic acid productivity.
Modelling and real-time simulation of continuous-discrete systems in mechatronics
Lindow, H.
1996-12-31
This work presents a methodology for simulation and modelling of systems with continuous - discrete dynamics. It derives hybrid discrete event models from Lagrange`s equations of motion. This method combines continuous mechanical, electrical and thermodynamical submodels on one hand with discrete event models an the other hand into a hybrid discrete event model. This straight forward software development avoids numeric overhead.
Boutonnet, Audrey; Morin, Arnaud; Petit, Pierre; Vicendo, Patricia; Poinsot, Véréna; Couderc, François
2016-03-17
Pulsed lasers are widely used in capillary electrophoresis (CE) studies to provide laser induced fluorescence (LIF) detection. Unfortunately pulsed lasers do not give linear calibration curves over a wide range of concentrations. While this does not prevent their use in CE/LIF studies, the non-linear behavior must be understood. Using 7-hydroxycoumarin (7-HC) (10-5000 nM), Tamra (10-5000 nM) and tryptophan (1-200 μM) as dyes, we observe that continuous lasers and LEDs result in linear calibration curves, while pulsed lasers give polynomial ones. The effect is seen with both visible light (530 nm) and with UV light (355 nm, 266 nm). In this work we point out the formation of byproducts induced by pulsed laser upon irradiation of 7-HC. Their separation by CE using two Zeta LIF detectors clearly shows that this process is related to the first laser detection. All of these photodegradation products can be identified by an ESI-/MS investigation and correspond to at least two 7HC dimers. By using the photodegradation model proposed by Heywood and Farnsworth (2010) and by taking into account the 7-HC results and the fact that in our system we do not have a constant concentration of fluorophore, it is possible to propose a new photochemical model of fluorescence in LIF detection. The model, like the experiment, shows that it is difficult to obtain linear quantitation curves with pulsed lasers while UV-LEDs used in continuous mode have this advantage. They are a good alternative to UV pulsed lasers. An application involving the separation and linear quantification of oligosaccharides labeled with 2-aminobezoic acid is presented using HILIC and LED (365 nm) induced fluorescence. PMID:26920784
Boutonnet, Audrey; Morin, Arnaud; Petit, Pierre; Vicendo, Patricia; Poinsot, Véréna; Couderc, François
2016-03-17
Pulsed lasers are widely used in capillary electrophoresis (CE) studies to provide laser induced fluorescence (LIF) detection. Unfortunately pulsed lasers do not give linear calibration curves over a wide range of concentrations. While this does not prevent their use in CE/LIF studies, the non-linear behavior must be understood. Using 7-hydroxycoumarin (7-HC) (10-5000 nM), Tamra (10-5000 nM) and tryptophan (1-200 μM) as dyes, we observe that continuous lasers and LEDs result in linear calibration curves, while pulsed lasers give polynomial ones. The effect is seen with both visible light (530 nm) and with UV light (355 nm, 266 nm). In this work we point out the formation of byproducts induced by pulsed laser upon irradiation of 7-HC. Their separation by CE using two Zeta LIF detectors clearly shows that this process is related to the first laser detection. All of these photodegradation products can be identified by an ESI-/MS investigation and correspond to at least two 7HC dimers. By using the photodegradation model proposed by Heywood and Farnsworth (2010) and by taking into account the 7-HC results and the fact that in our system we do not have a constant concentration of fluorophore, it is possible to propose a new photochemical model of fluorescence in LIF detection. The model, like the experiment, shows that it is difficult to obtain linear quantitation curves with pulsed lasers while UV-LEDs used in continuous mode have this advantage. They are a good alternative to UV pulsed lasers. An application involving the separation and linear quantification of oligosaccharides labeled with 2-aminobezoic acid is presented using HILIC and LED (365 nm) induced fluorescence.
Applications of a linearized land-atmosphere model to SVAT modelling and remote-sensing
NASA Astrophysics Data System (ADS)
Gentine, P.; Entekhabi, D.; Polcher, J.
2009-04-01
The present study refreshes and improves the work first introduced by Lettau (1951). A linearized land-atmosphere model, forced by times series of incoming radiation at the land-surface, is solved analytically. With this model, the profiles of temperature and heat fluxes in the soil and the Atmospheric Boundary Layer (ABL) can be expressed in terms of temporal Fourier series. Moreover the surface variables (temperature, specific humidity, surface fluxes) are also derived analytically and are expressed as functions of both surface parameters (friction velocity, vegetation height, aerodynamic resistance, stomatal conductance) and frequency of the forcing of incoming radiation. This original approach has several advantages. The model only requires very little data to perform well (time series of incoming radiation at the land-surface, mean daily specific humidity and potential temperature at any given height) and allows theoretically studying the temporal and spectral response of a coupled land-atmosphere system to any forcing of incoming radiation at the land-surface. The diurnal evolution of the ABL and the soil temperature and flux profiles will be emphasized, as well as their dependency on the frequency of the forcing. This will theoretically highlight the existence and diurnal behavior of the Surface and Mixed-Layer. Moreover, this model is shown to be helpful for the conception of remote-sensing tools and for the use of data assimilation. The spectral analysis of the coupling between the land and the atmosphere helps investigate the relevance of the sensor measurements, according to the temporal resolution of the sensor as well as the penetration depth of its electromagnetic wave. This will give insight on the appropriate variables for the data assimilation, in conjunction with land-surface models (Soil Vegetation Atmopshere Transfer models). Moreover guidelines for the collection of remotely sensed data can be obtained through this simple model. The model will
Modeling neuro-vascular coupling in rat cerebellum: characterization of deviations from linearity.
Rasmussen, Tina; Holstein-Rathlou, Niels-Henrik; Lauritzen, Martin
2009-03-01
We investigated the quantitative relation between neuronal activity and blood flow by means of a general parametric mathematical model which described the neuro-vascular system as being dynamic, linear, time-invariant, and subjected to additive noise. The model was constructed from measurements by means of system identification methods and validated across experiments. We sought to cover the system response to multiple stimulation frequencies and durations by a single model. We used the model to investigate the transport delay, the linear order, the deviations from linearity, and conditions for linearizability. We exercised the model on data from rat cerebellar cortex. In anesthetized rats, stimulation of the inferior olive caused climbing fiber activity and blood flow changes. Field potential amplitudes were used as an indicator of neuronal activity and blood flow was measured by laser-Doppler flowmetry. In one set of experiments, stimulation frequencies were in the range 2-20 Hz and the stimulation durations were 60 s and 600 s. The transport delay was estimated to be nearly zero, the linear order to be two. The deviations from linearity were consistently characterized as frequency saturation and dips in blood flow responses to stimulation for 60 s, and overgrowth of blood flow responses to stimulation for 600 s. In another set of experiments, stimulation frequencies were in the range 0.5-10 Hz and the stimulation duration was 15 s. The neuro-vascular system could be approximated by the linear model when the stimulation frequencies were restricted to the range 0.5-7 Hz. In conclusion, our model could predict blood flow responses to stimuli of low frequency and short duration. PMID:19027074
NASA Astrophysics Data System (ADS)
Magga, Zoi; Tzovolou, Dimitra N.; Theodoropoulou, Maria A.; Tsakiroglou, Christos D.
2012-03-01
The risk assessment of groundwater pollution by pesticides may be based on pesticide sorption and biodegradation kinetic parameters estimated with inverse modeling of datasets from either batch or continuous flow soil column experiments. In the present work, a chemical non-equilibrium and non-linear 2-site sorption model is incorporated into solute transport models to invert the datasets of batch and soil column experiments, and estimate the kinetic sorption parameters for two pesticides: N-phosphonomethyl glycine (glyphosate) and 2,4-dichlorophenoxy-acetic acid (2,4-D). When coupling the 2-site sorption model with the 2-region transport model, except of the kinetic sorption parameters, the soil column datasets enable us to estimate the mass-transfer coefficients associated with solute diffusion between mobile and immobile regions. In order to improve the reliability of models and kinetic parameter values, a stepwise strategy that combines batch and continuous flow tests with adequate true-to-the mechanism analytical of numerical models, and decouples the kinetics of purely reactive steps of sorption from physical mass-transfer processes is required.
A note on probabilistic models over strings: the linear algebra approach.
Bouchard-Côté, Alexandre
2013-12-01
Probabilistic models over strings have played a key role in developing methods that take into consideration indels as phylogenetically informative events. There is an extensive literature on using automata and transducers on phylogenies to do inference on these probabilistic models, in which an important theoretical question is the complexity of computing the normalization of a class of string-valued graphical models. This question has been investigated using tools from combinatorics, dynamic programming, and graph theory, and has practical applications in Bayesian phylogenetics. In this work, we revisit this theoretical question from a different point of view, based on linear algebra. The main contribution is a set of results based on this linear algebra view that facilitate the analysis and design of inference algorithms on string-valued graphical models. As an illustration, we use this method to give a new elementary proof of a known result on the complexity of inference on the "TKF91" model, a well-known probabilistic model over strings. Compared to previous work, our proving method is easier to extend to other models, since it relies on a novel weak condition, triangular transducers, which is easy to establish in practice. The linear algebra view provides a concise way of describing transducer algorithms and their compositions, opens the possibility of transferring fast linear algebra libraries (for example, based on GPUs), as well as low rank matrix approximation methods, to string-valued inference problems. PMID:24135792
A note on probabilistic models over strings: the linear algebra approach.
Bouchard-Côté, Alexandre
2013-12-01
Probabilistic models over strings have played a key role in developing methods that take into consideration indels as phylogenetically informative events. There is an extensive literature on using automata and transducers on phylogenies to do inference on these probabilistic models, in which an important theoretical question is the complexity of computing the normalization of a class of string-valued graphical models. This question has been investigated using tools from combinatorics, dynamic programming, and graph theory, and has practical applications in Bayesian phylogenetics. In this work, we revisit this theoretical question from a different point of view, based on linear algebra. The main contribution is a set of results based on this linear algebra view that facilitate the analysis and design of inference algorithms on string-valued graphical models. As an illustration, we use this method to give a new elementary proof of a known result on the complexity of inference on the "TKF91" model, a well-known probabilistic model over strings. Compared to previous work, our proving method is easier to extend to other models, since it relies on a novel weak condition, triangular transducers, which is easy to establish in practice. The linear algebra view provides a concise way of describing transducer algorithms and their compositions, opens the possibility of transferring fast linear algebra libraries (for example, based on GPUs), as well as low rank matrix approximation methods, to string-valued inference problems.
[Simulation of the branch biomass for Chinese fir plantation using the linear mixed effects model].
Xu, Hao; Zun, Yu-jun; Wang, Xin-jie; Fang, Jing; Tu, Hong-tao; Liu, Su-zhen
2015-10-01
Based on data obtained from 572 branches of 45 Chinese fir trees in Jiangle Forest Farm, Fujian Province, southeast China, prediction models for branch, foliage biomass and total branch and foliage biomass of individual tree were developed by linear mixed effects (LME) method, and tested by independent samples. The results showed that the LME models provided better performance than the multiple linear regression models for the branch, foliage and total biomass prediction of Chinese fir plantation. The LME models with different combinations of the random effects parameters had different fitting precisions. The LME models including variance structures could effectively remove the heteroscedasticity in the data and improved the precision. The LME model with the exponential function as the variance structure had better fitting precisions for the total biomass and foliage biomass models, and that with the constant plus power function as the variance structure had better performance for the branch biomass model. Model validation confirmed that the LME models with the random effects and heteroscedasticity structure could significantly improve the precision of prediction, compared to the multiple linear regression models.
Vestibular coriolis effect differences modeled with three-dimensional linear-angular interactions.
Holly, Jan E
2004-01-01
The vestibular coriolis (or "cross-coupling") effect is traditionally explained by cross-coupled angular vectors, which, however, do not explain the differences in perceptual disturbance under different acceleration conditions. For example, during head roll tilt in a rotating chair, the magnitude of perceptual disturbance is affected by a number of factors, including acceleration or deceleration of the chair rotation or a zero-g environment. Therefore, it has been suggested that linear-angular interactions play a role. The present research investigated whether these perceptual differences and others involving linear coriolis accelerations could be explained under one common framework: the laws of motion in three dimensions, which include all linear-angular interactions among all six components of motion (three angular and three linear). The results show that the three-dimensional laws of motion predict the differences in perceptual disturbance. No special properties of the vestibular system or nervous system are required. In addition, simulations were performed with angular, linear, and tilt time constants inserted into the model, giving the same predictions. Three-dimensional graphics were used to highlight the manner in which linear-angular interaction causes perceptual disturbance, and a crucial component is the Stretch Factor, which measures the "unexpected" linear component.
Robust estimation for partially linear models with large-dimensional covariates
Zhu, LiPing; Li, RunZe; Cui, HengJian
2014-01-01
We are concerned with robust estimation procedures to estimate the parameters in partially linear models with large-dimensional covariates. To enhance the interpretability, we suggest implementing a noncon-cave regularization method in the robust estimation procedure to select important covariates from the linear component. We establish the consistency for both the linear and the nonlinear components when the covariate dimension diverges at the rate of o(n), where n is the sample size. We show that the robust estimate of linear component performs asymptotically as well as its oracle counterpart which assumes the baseline function and the unimportant covariates were known a priori. With a consistent estimator of the linear component, we estimate the nonparametric component by a robust local linear regression. It is proved that the robust estimate of nonlinear component performs asymptotically as well as if the linear component were known in advance. Comprehensive simulation studies are carried out and an application is presented to examine the finite-sample performance of the proposed procedures. PMID:24955087
Vestibular coriolis effect differences modeled with three-dimensional linear-angular interactions.
Holly, Jan E
2004-01-01
The vestibular coriolis (or "cross-coupling") effect is traditionally explained by cross-coupled angular vectors, which, however, do not explain the differences in perceptual disturbance under different acceleration conditions. For example, during head roll tilt in a rotating chair, the magnitude of perceptual disturbance is affected by a number of factors, including acceleration or deceleration of the chair rotation or a zero-g environment. Therefore, it has been suggested that linear-angular interactions play a role. The present research investigated whether these perceptual differences and others involving linear coriolis accelerations could be explained under one common framework: the laws of motion in three dimensions, which include all linear-angular interactions among all six components of motion (three angular and three linear). The results show that the three-dimensional laws of motion predict the differences in perceptual disturbance. No special properties of the vestibular system or nervous system are required. In addition, simulations were performed with angular, linear, and tilt time constants inserted into the model, giving the same predictions. Three-dimensional graphics were used to highlight the manner in which linear-angular interaction causes perceptual disturbance, and a crucial component is the Stretch Factor, which measures the "unexpected" linear component. PMID:15735327
ERIC Educational Resources Information Center
Alexandrowicz, Rainer W.
2011-01-01
The linear logistic test model (LLTM) is a valuable and approved tool in educational research, as it allows for modelling cognitive components involved in a cognitive task. It allows for a rigorous assessment of fit by means of a Likelihood Ratio Test (LRT). This approach is genuine to the Rasch family of models, yet it suffers from the unsolved…
Model predictive control of non-linear systems over networks with data quantization and packet loss.
Yu, Jimin; Nan, Liangsheng; Tang, Xiaoming; Wang, Ping
2015-11-01
This paper studies the approach of model predictive control (MPC) for the non-linear systems under networked environment where both data quantization and packet loss may occur. The non-linear controlled plant in the networked control system (NCS) is represented by a Tagaki-Sugeno (T-S) model. The sensed data and control signal are quantized in both links and described as sector bound uncertainties by applying sector bound approach. Then, the quantized data are transmitted in the communication networks and may suffer from the effect of packet losses, which are modeled as Bernoulli process. A fuzzy predictive controller which guarantees the stability of the closed-loop system is obtained by solving a set of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method.
Model predictive control of non-linear systems over networks with data quantization and packet loss.
Yu, Jimin; Nan, Liangsheng; Tang, Xiaoming; Wang, Ping
2015-11-01
This paper studies the approach of model predictive control (MPC) for the non-linear systems under networked environment where both data quantization and packet loss may occur. The non-linear controlled plant in the networked control system (NCS) is represented by a Tagaki-Sugeno (T-S) model. The sensed data and control signal are quantized in both links and described as sector bound uncertainties by applying sector bound approach. Then, the quantized data are transmitted in the communication networks and may suffer from the effect of packet losses, which are modeled as Bernoulli process. A fuzzy predictive controller which guarantees the stability of the closed-loop system is obtained by solving a set of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method. PMID:26341070
Continuous streamflow simulation with the HRCDHM distributed hydrologic model
NASA Astrophysics Data System (ADS)
Carpenter, Theresa M.; Georgakakos, Konstantine P.
2004-10-01
The objective of the authors' work in the area of distributed modeling is to determine the manner with which rainfall input and model parameter uncertainty shapes the character of the flow simulation and prediction uncertainty of distributed hydrologic models. Toward this end and as a tool for the investigation, a distributed model, HRCDHM, has been formulated and tested as part of the NOAA Distributed Model Intercomparison Project (DMIP). This paper examines hourly flow simulations from HRCDHM applied with operational data obtained for the DMIP study watersheds. HRCDHM is a catchment-based, distributed input, distributed parameter hydrologic model. The hydrologic processes of infiltration/percolation, evapotranspiration, surface and subsurface flow (includes leakage to deep groundwater) are modeled along the vertical direction on a subcatchment basis in a manner similar to the Sacramento Soil Moisture Accounting model, and kinematic channel routing carries the flow through the network of subcatchments to the watershed outlet, providing capability for spatially distributed flow simulations. Subcatchment physical properties are derived from various digital terrain and land-characteristics databases through GIS processing and they are used to derive spatially distributed model parameter values. The NWS operational WSR-88D hourly radar rainfall estimates (Stage III product with pixel scale of approximately 4 km) constitute the rainfall forcing and a combination of model-derived and observed hourly surface meteorological data are used to produce the potential evapotranspiration forcing. HRCDHM was applied to and was calibrated for five watersheds for the period May 1993 through June 2000. Validation was done with data not used during the calibration period. This application shows that: (a) the HRCDHM, when forced with hourly data, is able to reproduce well the observed hourly streamflow at the outlet of each study watershed; and (b) beyond these outlet locations
Impact of using linear optimization models in dose planning for HDR brachytherapy
Holm, Aasa; Larsson, Torbjoern; Carlsson Tedgren, Aasa
2012-02-15
Purpose: Dose plans generated with optimization models hitherto used in high-dose-rate (HDR) brachytherapy have shown a tendency to yield longer dwell times than manually optimized plans. Concern has been raised for the corresponding undesired hot spots, and various methods to mitigate these have been developed. The hypotheses upon this work is based are (a) that one cause for the long dwell times is the use of objective functions comprising simple linear penalties and (b) that alternative penalties, as these are piecewise linear, would lead to reduced length of individual dwell times. Methods: The characteristics of the linear penalties and the piecewise linear penalties are analyzed mathematically. Experimental comparisons between the two types of penalties are carried out retrospectively for a set of prostate cancer patients. Results: When the two types of penalties are compared, significant changes can be seen in the dwell times, while most dose-volume parameters do not differ significantly. On average, total dwell times were reduced by 4.2%, with a reduction of maximum dwell times by 25%, when the alternative penalties were used. Conclusions: The use of linear penalties in optimization models for HDR brachytherapy is one cause for the undesired long dwell times that arise in mathematically optimized plans. By introducing alternative penalties, a significant reduction in dwell times can be achieved for HDR brachytherapy dose plans. Although various measures for mitigating the long dwell times are already available, the observation that linear penalties contribute to their appearance is of fundamental interest.
Three-dimensional finite-difference modeling of non-linear ground notion
Jones, E.M.; Olsen, K.B.
1997-08-01
We present a hybrid finite-difference technique capable of modeling non-linear soil amplification from the 3-D finite-fault radiation pattern for earthquakes in arbitrary earth models. The method is applied to model non-linear effects in the soils of the San Fernando Valley (SFV) from the 17 January 1994 M 6.7 Northridge earthquake. 0-7 Hz particle velocities are computed for an area of 17 km by 19 km immediately above the causative fault and 5 km below the surface where peak strike-parallel, strike-perpendicular, vertical, and total velocities reach values of 71 cm/s, 145 cm/s, 152 cm/s, and 180 cm/s, respectively. Selected Green`s functions and a soil model for the SFV are used to compute the approximate stress level during the earthquake, and comparison to the values for near-surface alluvium at the U.S. Nevada Test Site suggests that the non-linear regime may have been entered. We use selected values from the simulated particle velocity distribution at 5 km depth to compute the non-linear response in a soil column below a site within the Van Norman Complex in SFV, where the strongest ground motion was recorded. Since site-specific non- linear material parameters from the SFV are currently unavailable, values are taken from analyses of observed Test Site ground motions. Preliminary results show significant reduction of spectral velocities at the surface normalized to the peak source velocity due to non-linear effects when the peak velocity increases from 32 cm/s (approximately linear case) to 64 cm/s (30-92%), 93 cm/s (7-83%), and 124 cm/s (2-70%). The largest reduction occurs for frequencies above 1 Hz.