NASA Astrophysics Data System (ADS)
Kim, Tae-Jeong; Kim, Ki-Young; Shin, Dong-Hoon; Kwon, Hyun-Han
2015-04-01
It has been widely acknowledged that the appropriate simulation of natural streamflow at ungauged sites is one of the fundamental challenges to hydrology community. In particular, the key to reliable runoff simulation in ungauged basins is a reliable rainfall-runoff model and a parameter estimation. In general, parameter estimation in rainfall-runoff models is a complex issue due to an insufficient hydrologic data. This study aims to regionalize the parameters of the continuous rainfall-runoff model in conjunction with Bayesian statistical techniques to facilitate uncertainty analysis. First, this study uses the Bayesian Markov Chain Monte Carlo scheme for the Sacramento rainfall-runoff model that has been widely used around the world. The Sacramento model is calibrated against daily runoff observation, and thirteen parameters of the model are optimized as well as posterior distributor distributions for each parameter are derived. Second, we applied Bayesian generalized linear regression model to set of the parameters with basin characteristics (e.g. area and slope), to obtain a functional relationship between pairs of variables. The proposed model was validated in two gauged watersheds in accordance with the efficiency criteria such as the Nash-Sutcliffe efficiency, coefficient of efficiency, index of agreement and coefficient of correlation. The future study will be further focused on uncertainty analysis to fully incorporate propagation of the uncertainty into the regionalization framework. KEYWORDS: Ungauge, Parameter, Sacramento, Generalized linear model, Regionalization Acknowledgement This research was supported by a Grant (13SCIPA01) from Smart Civil Infrastructure Research Program funded by the Ministry of Land, Infrastructure and Transport (MOLIT) of Korea government and the Korea Agency for Infrastructure Technology Advancement (KAIA).
NOTE: Estimation of renal scintigraphy parameters using a linear piecewise-continuous model
NASA Astrophysics Data System (ADS)
Zhang, Jeff L.; Zhang, L.; Koh, T. S.; Shuter, B.
2003-06-01
Instead of performing a numerical deconvolution, we propose to use a linear piecewise-continuous model of the renal impulse response function for parametric fitting of renal scintigraphy data, to obtain clinically useful renal parameters. The strengths of the present model are its simplicity and speed of computation, while not compromising on accuracy. Preliminary patient case studies show that the estimated parameters are in good agreement with a more elaborate model.
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
NASA Technical Reports Server (NTRS)
Guo, Tong-Yi; Hwang, Chyi; Shieh, Leang-San
1994-01-01
This paper deals with the multipoint Cauer matrix continued-fraction expansion (MCFE) for model reduction of linear multi-input multi-output (MIMO) systems with various numbers of inputs and outputs. A salient feature of the proposed MCFE approach to model reduction of MIMO systems with square transfer matrices is its equivalence to the matrix Pade approximation approach. The Cauer second form of the ordinary MCFE for a square transfer function matrix is generalized in this paper to a multipoint and nonsquare-matrix version. An interesting connection of the multipoint Cauer MCFE method to the multipoint matrix Pade approximation method is established. Also, algorithms for obtaining the reduced-degree matrix-fraction descriptions and reduced-dimensional state-space models from a transfer function matrix via the multipoint Cauer MCFE algorithm are presented. Practical advantages of using the multipoint Cauer MCFE are discussed and a numerical example is provided to illustrate the algorithms.
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…
Hernández-Lloreda, María Victoria; Colmenares, Fernando; Martínez-Arias, Rosario
2004-09-01
In behavioral science, developmental discontinuities are thought to arise when the association between an outcome measure and the underlying process changes over time. Sudden changes in behavior across time are often taken to indicate that a reorganization in the outcome-process relationship may have occurred. The authors proposed in this article the use of piecewise hierarchical linear growth modeling as a statistical methodology to search for discontinuities in behavioral development and illustrated its possibilities by applying 2-piece hierarchical linear models to the study of developmental trajectories of baboon (Papio hamadryas) mothers' behavior during their infants' 1st year of life. The authors provided empirical evidence that piecewise growth modeling can be used to determine whether abrupt changes in development trajectories are tied to changes in the underlying process. PMID:15482059
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.
Wang, Ching-Yun; Dieu Tapsoba, Jean De; Duggan, Catherine; Campbell, Kristin L; McTiernan, Anne
2016-05-10
In many biomedical studies, covariates of interest may be measured with errors. However, frequently in a regression analysis, the quantiles of the exposure variable are often used as the covariates in the regression analysis. Because of measurement errors in the continuous exposure variable, there could be misclassification in the quantiles for the exposure variable. Misclassification in the quantiles could lead to bias estimation in the association between the exposure variable and the outcome variable. Adjustment for misclassification will be challenging when the gold standard variables are not available. In this paper, we develop two regression calibration estimators to reduce bias in effect estimation. The first estimator is normal likelihood-based. The second estimator is linearization-based, and it provides a simple and practical correction. Finite sample performance is examined via a simulation study. We apply the methods to a four-arm randomized clinical trial that tested exercise and weight loss interventions in women aged 50-75years. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26593772
Gonçalves, Nuno R; Whelan, Robert; Foxe, John J; Lalor, Edmund C
2014-08-15
Noninvasive investigation of human sensory processing with high temporal resolution typically involves repeatedly presenting discrete stimuli and extracting an average event-related response from scalp recorded neuroelectric or neuromagnetic signals. While this approach is and has been extremely useful, it suffers from two drawbacks: a lack of naturalness in terms of the stimulus and a lack of precision in terms of the cortical response generators. Here we show that a linear modeling approach that exploits functional specialization in sensory systems can be used to rapidly obtain spatiotemporally precise responses to complex sensory stimuli using electroencephalography (EEG). We demonstrate the method by example through the controlled modulation of the contrast and coherent motion of visual stimuli. Regressing the data against these modulation signals produces spatially focal, highly temporally resolved response measures that are suggestive of specific activation of visual areas V1 and V6, respectively, based on their onset latency, their topographic distribution and the estimated location of their sources. We discuss our approach by comparing it with fMRI/MRI informed source analysis methods and, in doing so, we provide novel information on the timing of coherent motion processing in human V6. Generalizing such an approach has the potential to facilitate the rapid, inexpensive spatiotemporal localization of higher perceptual functions in behaving humans. PMID:24736185
Linear models: permutation methods
Cade, B.S.
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]...
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…
Villante, F. L.; Ricci, B.
2010-05-01
We present a new approach to studying the properties of the Sun. We consider small variations of the physical and chemical properties of the Sun with respect to standard solar model predictions and we linearize the structure equations to relate them to the properties of the solar plasma. By assuming that the (variation of) present solar composition can be estimated from the (variation of) nuclear reaction rates and elemental diffusion efficiency in the present Sun, we obtain a linear system of ordinary differential equations which can be used to calculate the response of the Sun to an arbitrary modification of the input parameters (opacity, cross sections, etc.). This new approach is intended to be a complement to the traditional methods for solar model (SM) calculation and allows us to investigate in a more efficient and transparent way the role of parameters and assumptions in SM construction. We verify that these linear solar models recover the predictions of the traditional SMs with a high level of accuracy.
NASA Technical Reports Server (NTRS)
Cellier, Francois E.
1991-01-01
A comprehensive and systematic introduction is presented for the concepts associated with 'modeling', involving the transition from a physical system down to an abstract description of that system in the form of a set of differential and/or difference equations, and basing its treatment of modeling on the mathematics of dynamical systems. Attention is given to the principles of passive electrical circuit modeling, planar mechanical systems modeling, hierarchical modular modeling of continuous systems, and bond-graph modeling. Also discussed are modeling in equilibrium thermodynamics, population dynamics, and system dynamics, inductive reasoning, artificial neural networks, and automated model synthesis.
Non-linear Models for Longitudinal Data
Serroyen, Jan; Molenberghs, Geert; Verbeke, Geert; Davidian, Marie
2009-01-01
While marginal models, random-effects models, and conditional models are routinely considered to be the three main modeling families for continuous and discrete repeated measures with linear and generalized linear mean structures, respectively, it is less common to consider non-linear models, let alone frame them within the above taxonomy. In the latter situation, indeed, when considered at all, the focus is often exclusively on random-effects models. In this paper, we consider all three families, exemplify their great flexibility and relative ease of use, and apply them to a simple but illustrative set of data on tree circumference growth of orange trees. PMID:20160890
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
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
Foster, Guy M.; Graham, Jennifer L.
2016-01-01
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
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.
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.
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.
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.
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…
A spatially continuous magnetization model for Mars
NASA Astrophysics Data System (ADS)
Whaler, K. A.; Purucker, M. E.
2005-09-01
Using a three-component magnetic field data set at over 100,000 satellite points previously compiled for spherical harmonic analysis, we have produced a continuously varying magnetization model for Mars. The magnetized layer was assumed to be 40 km thick, an average value based on previous studies of the topography and gravity field. The severe nonuniqueness in magnetization modeling is addressed by seeking the model with minimum root-mean-square (RMS) magnetization for a given fit to the data, with the trade-off between RMS magnetization and fit controlled by a damping parameter. Our preferred model has magnetization amplitudes up to 20 A/m. It is expressed as a linear combination of the Green's functions relating each observation to magnetization at the point of interest within the crust, leading to a linear system of equations of dimension the number of data points. Although this is impractically large for direct solution, most of the matrix elements relating data to model parameters are negligibly small. We therefore apply methods applicable to sparse systems, allowing us to preserve the resolution of the original data set. Thus we produce more detailed models than any previously published, although they share many similarities. We find that tectonism in the Valles Marineris region has a magnetic signature, and we show that volcanism south of the dichotomy boundary has both a magnetic and gravity signature. The method can also be used to downward continue magnetic data, and a comparison with other leveling techniques at Mars' surface is favorable.
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)
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.
NASA Astrophysics Data System (ADS)
Peeling, S. M.; Ponting, K. M.
1991-12-01
Linear discriminant analysis is used to generate speech data transformations. This transformed data is then used within the Airborne Reconnaissance Mission (ARM) continuous speech recognition system. The aim of the ARM project is accurate recognition of continuously spoken airborne reconnaissance reports using a speech recognition system based on phoneme level hidden Markov models. A fuller description of a linear discriminant analysis, which is applied to speaker dependent data in the ARM system, is given. Preliminary results are presented from experiments using transformed data alone and also in conjunction with one, or both, of the word transition penalties and variable frame rate analysis. Speaker dependent results are reported which are significantly better then the best obtained previously.
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
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.
Quantifying and visualizing variations in sets of images using continuous linear optimal transport
NASA Astrophysics Data System (ADS)
Kolouri, Soheil; Rohde, Gustavo K.
2014-03-01
Modern advancements in imaging devices have enabled us to explore the subcellular structure of living organisms and extract vast amounts of information. However, interpreting the biological information mined in the captured images is not a trivial task. Utilizing predetermined numerical features is usually the only hope for quantifying this information. Nonetheless, direct visual or biological interpretation of results obtained from these selected features is non-intuitive and difficult. In this paper, we describe an automatic method for modeling visual variations in a set of images, which allows for direct visual interpretation of the most significant differences, without the need for predefined features. The method is based on a linearized version of the continuous optimal transport (OT) metric, which provides a natural linear embedding for the image data set, in which linear combination of images leads to a visually meaningful image. This enables us to apply linear geometric data analysis techniques such as principal component analysis and linear discriminant analysis in the linearly embedded space and visualize the most prominent modes, as well as the most discriminant modes of variations, in the dataset. Using the continuous OT framework, we are able to analyze variations in shape and texture in a set of images utilizing each image at full resolution, that otherwise cannot be done by existing methods. The proposed method is applied to a set of nuclei images segmented from Feulgen stained liver tissues in order to investigate the major visual differences in chromatin distribution of Fetal-Type Hepatoblastoma (FHB) cells compared to the normal cells.
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
Dissipative Continuous Spontaneous Localization (CSL) model
NASA Astrophysics Data System (ADS)
Smirne, Andrea; Bassi, Angelo
2015-08-01
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.
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
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.
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)
Continuing evaluation of bipolar linear devices for total dose bias dependency and ELDRS effects
NASA Technical Reports Server (NTRS)
McClure, Steven S.; Gorelick, Jerry L.; Yui, Candice; Rax, Bernard G.; Wiedeman, Michael D.
2003-01-01
We present results of continuing efforts to evaluate total dose bias dependency and ELDRS effects in bipolar linear microcircuits. Several devices were evaluated, each exhibiting moderate to significant bias and/or dose rate dependency.
On Estimation of Partially Linear Transformation Models.
Lu, Wenbin; Zhang, Hao Helen
2010-06-01
We study a general class of partially linear transformation models, which extend linear transformation models by incorporating nonlinear covariate effects in survival data analysis. A new martingale-based estimating equation approach, consisting of both global and kernel-weighted local estimation equations, is developed for estimating the parametric and nonparametric covariate effects in a unified manner. We show that with a proper choice of the kernel bandwidth parameter, one can obtain the consistent and asymptotically normal parameter estimates for the linear effects. Asymptotic properties of the estimated nonlinear effects are established as well. We further suggest a simple resampling method to estimate the asymptotic variance of the linear estimates and show its effectiveness. To facilitate the implementation of the new procedure, an iterative algorithm is developed. Numerical examples are given to illustrate the finite-sample performance of the procedure. PMID:20802823
On Estimation of Partially Linear Transformation Models
Lu, Wenbin; Zhang, Hao Helen
2010-01-01
We study a general class of partially linear transformation models, which extend linear transformation models by incorporating nonlinear covariate effects in survival data analysis. A new martingale-based estimating equation approach, consisting of both global and kernel-weighted local estimation equations, is developed for estimating the parametric and nonparametric covariate effects in a unified manner. We show that with a proper choice of the kernel bandwidth parameter, one can obtain the consistent and asymptotically normal parameter estimates for the linear effects. Asymptotic properties of the estimated nonlinear effects are established as well. We further suggest a simple resampling method to estimate the asymptotic variance of the linear estimates and show its effectiveness. To facilitate the implementation of the new procedure, an iterative algorithm is developed. Numerical examples are given to illustrate the finite-sample performance of the procedure. PMID:20802823
Numerical Based Linear Model for Dipole Magnets
Li,Y.; Krinsky, S.; Rehak, M.
2009-05-04
In this paper, we discuss an algorithm for constructing a numerical linear optics model for dipole magnets from a 3D field map. The difference between the numerical model and K. Brown's analytic approach is investigated and clarified. It was found that the optics distortion due to the dipoles' fringe focusing must be properly taken into account to accurately determine the chromaticities. In NSLS-II, there are normal dipoles with 35-mm gap and dipoles for infrared sources with 90-mm gap. This linear model of the dipole magnets is applied to the NSLS-II lattice design to match optics parameters between the DBA cells having dipoles with different gaps.
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…
NASA Astrophysics Data System (ADS)
Brake, M. R.
2011-06-01
The analysis of continuous systems with piecewise-linear constraints in their domains have previously been limited to either numerical approaches, or analytical methods that are constrained in the parameter space, boundary conditions, or order of the system. The present analysis develops a robust method for studying continuous systems with arbitrary boundary conditions and discrete piecewise-linear constraints. A superposition method is used to generate homogeneous boundary conditions, and modal analysis is used to find the displacement of the system in each state of the piecewise-linear constraint. In order to develop a mapping across each slope discontinuity in the piecewise-linear force-deflection profile, a variational calculus approach is taken that minimizes the L 2 energy norm between the previous and current states. An approach for calculating the finite-time Lyapunov exponents is presented in order to determine chaotic regimes. To illustrate this method, two examples are presented: a pinned-pinned beam with a deadband constraint, and a leaf spring coupled with a connector pin immersed in a viscous fluid. The pinned-pinned beam example illustrates the method for a non-operator based analysis. Results are used to show that the present method does not necessitate the need of a large number of basis functions to adequately map the displacement and velocity of the system across states. In the second example, the leaf spring is modeled as a clamped-free beam. The interaction between the beam and the connector pin is modeled with a preload and a penalty stiffness. Several experiments are conducted in order to validate aspects of the leaf spring model. From the results of the convergence and parameter studies, a high correlation between the finite-time Lyapunov exponents and the contact time per period of the excitation is observed. The parameter studies also indicate that when the system's parameters are changed in order to reduce the magnitude of the impact
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.
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
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…
NASA Astrophysics Data System (ADS)
Schmitt, F. G.
2014-12-01
Multiplicative cascade models, when densified (continuous scale invariance) correspond to the exponential of a linear process. Hence this cannot generate zero values. Such framework is not complete and not purely multiplicative. We present here a stochastic framework which stays in the multiplicative realm and can be used to generate zero values. The multiplicative continuous model for multifractal fields with zero values is built using infinitely multiplicative random variables, the multiplicative analog to infinitely divisible distributions for addition. It also needs stochastic multiplicative measures and multiplicative stochastic integrals. The model hence generates continuous multiplicative cascades. The model produced possesses as special case a continuous generalization of the classical discrete beta-model. Applications are numerous in many fields of applied sciences, including smallscale rainfall, soil sciences. The theory is first proposed, then simulation algorithm is presented and simulations are shown in 1D and in 2D. Figure: a continuous lognormal multifractal with zero values (512x512).
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.
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.
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.)
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.
Nonlinear damping and quasi-linear modelling.
Elliott, S J; Ghandchi Tehrani, M; Langley, R S
2015-09-28
The mechanism of energy dissipation in mechanical systems is often nonlinear. Even though there may be other forms of nonlinearity in the dynamics, nonlinear damping is the dominant source of nonlinearity in a number of practical systems. The analysis of such systems is simplified by the fact that they show no jump or bifurcation behaviour, and indeed can often be well represented by an equivalent linear system, whose damping parameters depend on the form and amplitude of the excitation, in a 'quasi-linear' model. The diverse sources of nonlinear damping are first reviewed in this paper, before some example systems are analysed, initially for sinusoidal and then for random excitation. For simplicity, it is assumed that the system is stable and that the nonlinear damping force depends on the nth power of the velocity. For sinusoidal excitation, it is shown that the response is often also almost sinusoidal, and methods for calculating the amplitude are described based on the harmonic balance method, which is closely related to the describing function method used in control engineering. For random excitation, several methods of analysis are shown to be equivalent. In general, iterative methods need to be used to calculate the equivalent linear damper, since its value depends on the system's response, which itself depends on the value of the equivalent linear damper. The power dissipation of the equivalent linear damper, for both sinusoidal and random cases, matches that dissipated by the nonlinear damper, providing both a firm theoretical basis for this modelling approach and clear physical insight. Finally, practical examples of nonlinear damping are discussed: in microspeakers, vibration isolation, energy harvesting and the mechanical response of the cochlea. PMID:26303921
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.
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.
Continuous representation for shell models of turbulence
NASA Astrophysics Data System (ADS)
Mailybaev, Alexei A.
2015-07-01
In this work we construct and analyze continuous hydrodynamic models in one space dimension, which are induced by shell models of turbulence. After Fourier transformation, such continuous models split into an infinite number of uncoupled subsystems, which are all identical to the same shell model. The two shell models, which allow such a construction, are considered: the dyadic (Desnyansky-Novikov) model with the intershell ratio λ = 23/2 and the Sabra model of turbulence with λ = \\sqrt{2+\\sqrt{5}} ≈ 2.058 . The continuous models allow for understanding of various properties of shell model solutions and provide their interpretation in physical space. We show that the asymptotic solutions of the dyadic model with Kolmogorov scaling correspond to the shocks (discontinuities) for the induced continuous solutions in physical space, and the finite-time blowup together with its viscous regularization follow the scenario similar to the Burgers equation. For the Sabra model, we provide the physical space representation for blowup solutions and intermittent turbulent dynamics.
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…
NASA Astrophysics Data System (ADS)
Zhang, Yichen; Yu, Song; Guo, Hong
2015-11-01
We propose a modified no-switching continuous-variable quantum key distribution protocol by employing a practical noiseless linear amplifier at the receiver to increase the maximal transmission distance and tolerable excess noise. A security analysis is presented to derive the secure bound of the protocol in presence of a Gaussian noisy lossy channel. Simulation results show that the modified protocol can not only transmit longer distance and tolerate more channel excess noise than the original protocol, but also distribute more secure keys in the enhanced region where we define a critical point to separate the enhanced and degenerative region. This critical point presents the condition of using a practical noiseless linear amplifier in the no-switching continuous-variable quantum cryptography, which is meaningful and instructive to implement a practical experiment.
Information metric from a linear sigma model.
Miyamoto, U; Yahikozawa, S
2012-05-01
The idea that a space-time metric emerges as a Fisher-Rao "information metric" of instanton moduli space has been examined in several field theories, such as the Yang-Mills theories and nonlinear σ models. In this paper, we report that the flat Euclidean or Minkowskian metric, rather than an anti-de Sitter metric that generically emerges from instanton moduli spaces, can be obtained as the Fisher-Rao metric from a nontrivial solution of the massive Klein-Gordon field (a linear σ model). This realization of the flat space from the simple field theory would be useful to investigate the ideas that relate the space-time geometry with the information geometry. PMID:23004729
Meshless analysis of shear deformable shells: the linear model
NASA Astrophysics Data System (ADS)
Costa, Jorge C.; Tiago, Carlos M.; Pimenta, Paulo M.
2013-10-01
This work develops a kinematically linear shell model departing from a consistent nonlinear theory. The initial geometry is mapped from a flat reference configuration by a stress-free finite deformation, after which, the actual shell motion takes place. The model maintains the features of a complete stress-resultant theory with Reissner-Mindlin kinematics based on an inextensible director. A hybrid displacement variational formulation is presented, where the domain displacements and kinematic boundary reactions are independently approximated. The resort to a flat reference configuration allows the discretization using 2-D Multiple Fixed Least-Squares (MFLS) on the domain. The consistent definition of stress resultants and consequent plane stress assumption led to a neat formulation for the analysis of shells. The consistent linear approximation, combined with MFLS, made possible efficient computations with a desired continuity degree, leading to smooth results for the displacement, strain and stress fields, as shown by several numerical examples.
Modeling patterns in data using linear and related models
Engelhardt, M.E.
1996-06-01
This report considers the use of linear models for analyzing data related to reliability and safety issues of the type usually associated with nuclear power plants. The report discusses some of the general results of linear regression analysis, such as the model assumptions and properties of the estimators of the parameters. The results are motivated with examples of operational data. Results about the important case of a linear regression model with one covariate are covered in detail. This case includes analysis of time trends. The analysis is applied with two different sets of time trend data. Diagnostic procedures and tests for the adequacy of the model are discussed. Some related methods such as weighted regression and nonlinear models are also considered. A discussion of the general linear model is also included. Appendix A gives some basic SAS programs and outputs for some of the analyses discussed in the body of the report. Appendix B is a review of some of the matrix theoretic results which are useful in the development of linear models.
A unifying review of linear gaussian models.
Roweis, S; Ghahramani, Z
1999-02-15
Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate observations and derivations made by many previous authors and introducing a new way of linking discrete and continuous state models using a simple nonlinearity. Through the use of other nonlinearities, we show how independent component analysis is also a variation of the same basic generative model. We show that factor analysis and mixtures of gaussians can be implemented in autoencoder neural networks and learned using squared error plus the same regularization term. We introduce a new model for static data, known as sensible principal component analysis, as well as a novel concept of spatially adaptive observation noise. We also review some of the literature involving global and local mixtures of the basic models and provide pseudocode for inference and learning for all the basic models. PMID:9950734
Averaging models for linear piezostructural systems
NASA Astrophysics Data System (ADS)
Kim, W.; Kurdila, A. J.; Stepanyan, V.; Inman, D. J.; Vignola, J.
2009-03-01
In this paper, we consider a linear piezoelectric structure which employs a fast-switched, capacitively shunted subsystem to yield a tunable vibration absorber or energy harvester. The dynamics of the system is modeled as a hybrid system, where the switching law is considered as a control input and the ambient vibration is regarded as an external disturbance. It is shown that under mild assumptions of existence and uniqueness of the solution of this hybrid system, averaging theory can be applied, provided that the original system dynamics is periodic. The resulting averaged system is controlled by the duty cycle of a driven pulse-width modulated signal. The response of the averaged system approximates the performance of the original fast-switched linear piezoelectric system. It is analytically shown that the averaging approximation can be used to predict the electromechanically coupled system modal response as a function of the duty cycle of the input switching signal. This prediction is experimentally validated for the system consisting of a piezoelectric bimorph connected to an electromagnetic exciter. Experimental results show that the analytical predictions are observed in practice over a fixed "effective range" of switching frequencies. The same experiments show that the response of the switched system is insensitive to an increase in switching frequency above the effective frequency range.
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…
Robust coarticulatory modeling for continuous speech recognition
NASA Astrophysics Data System (ADS)
Schwartz, R.; Chow, Y. L.; Dunham, M. O.; Kimball, O.; Krasner, M.; Kubala, F.; Makhoul, J.; Price, P.; Roucos, S.
1986-10-01
The purpose of this project is to perform research into algorithms for the automatic recognition of individual sounds or phonemes in continuous speech. The algorithms developed should be appropriate for understanding large-vocabulary continuous speech input and are to be made available to the Strategic Computing Program for incorporation in a complete word recognition system. This report describes process to date in developing phonetic models that are appropriate for continuous speech recognition. In continuous speech, the acoustic realization of each phoneme depends heavily on the preceding and following phonemes: a process known as coarticulation. Thus, while there are relatively few phonemes in English (on the order of fifty or so), the number of possible different accoustic realizations is in the thousands. Therefore, to develop high-accuracy recognition algorithms, one may need to develop literally thousands of relatively distance phonetic models to represent the various phonetic context adequately. Developing a large number of models usually necessitates having a large amount of speech to provide reliable estimates of the model parameters. The major contributions of this work are the development of: (1) A simple but powerful formalism for modeling phonemes in context; (2) Robust training methods for the reliable estimation of model parameters by utilizing the available speech training data in a maximally effective way; and (3) Efficient search strategies for phonetic recognition while maintaining high recognition accuracy.
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…
Downward continuation methods for gravimetric geoid modelling
NASA Astrophysics Data System (ADS)
Huang, J.; Véronneau, M.
2003-04-01
The determination of a gravimetric geoid model based on the Stokes integral requires that gravity anomalies must be on the geoid and that the anomalous potential must be harmonic above the geoid. To fulfill these requirements, gravity observations (or gravity anomalies) collected on the surface of the Earth need to be reduced to the geoid by a) removing the masses above the geoid and compensating for this removal; and b) continuing the gravity anomalies downward to the geoid. A well-known case is the determination of the Helmert gravity anomalies on the geoid in terms of Helmert's 2nd condensation method (e.g. Martinec et al. 1993). There are two procedures to follow for the evaluation of the Helmert gravity anomalies on the geoid: a) the Helmert gravity anomalies are evaluated on the irregular Earth surface, then are continued downward to the geoid, i.e., masses above the geoid are removed and restored as a condensed layer on the geoid prior to the downward continuation; b) alternatively the refined Bouguer anomalies on the surface of the Earth are downward-continued to the geoid, then the Helmert gravity anomalies are evaluated on the geoid, i.e., the condensed masses are restored after the downward continuation. Both procedures are theoretically equivalent (Huang et al. 2002a). In theory, the inclusion of the downward continuation should improve the geoid regardless of the approach chosen. However, different authors arrive at contradictory conclusions pertaining to its applications (e.g. Pavlis 1998; Ardalan 2000; Jekili and Serpas 2002; Véronneau and Huang 2002). This raises an open question as to how researchers should evaluate the downward continuation in order to improve geoid modeling. In this research, the two procedures described above are used to determine the geoid in Canada. The downward continuations are evaluated using the Poisson and Moritz methods (e.g. Moritz 1980; Sideris 1988; Vanícek et al. 1996; Martinec 1996; Sjöberg 1998; Nahavandchi 2000
Piecewise Linear-Linear Latent Growth Mixture Models with Unknown Knots
ERIC Educational Resources Information Center
Kohli, Nidhi; Harring, Jeffrey R.; Hancock, Gregory R.
2013-01-01
Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear-linear latent growth mixture model (LGMM) for describing segmented change of…
Linear functional minimization for inverse modeling
NASA Astrophysics Data System (ADS)
Barajas-Solano, D. A.; Wohlberg, B. E.; Vesselinov, V. V.; Tartakovsky, D. M.
2015-06-01
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. Addition of transient measurements of hydraulic head improves the parameter estimation, accurately reconstructing the conductivity field in the vicinity of observation locations.
NASA Astrophysics Data System (ADS)
You, Dae-Joon; Lee, Sung-Ho; Jang, Seok-Myeong
2008-04-01
In the case of the manufactured linear permanent magnet synchronous machines (PMLSMs), dynamic range evaluation for system efficiency and performance limits is difficult to accomplish because of the moving length restriction with mover and the absence of interface between the design field and control field. To solve this problem, this paper presents a dynamic analysis based on design parameters by magnetic field analysis of the linear PM machine. And then, maximum operating range of the system is estimated considering the control method of a fixed dc-link voltage of the inverter. This analysis is verified from the dynamic experiments through continuous progressive motion of the manufactured disk-type PMLSM by current control.
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.
Two fusion predictors for continuous-time linear systems with different types of observations
NASA Astrophysics Data System (ADS)
Song, Haryong; Lee, Kyung Min; Shin, Vladimir
2012-01-01
This article proposes new fusion predictors for continuous-time linear systems with different types of observations. The fusion predictors are formed by the summation of the local Kalman estimators (filters and predictors) with matrix weights depending only on time instants. Both fusion predictors represent the optimal linear combination of an arbitrary number of local Kalman estimators and each is fused by the minimum mean square error criterion. As a consequence of the parallel structure of the proposed predictors, parallel computers can be used for their design. This article also establishes the relationship between fusion predictors. High accuracy and computational efficiency of the fusion predictors are demonstrated through several examples, including the damper harmonic oscillator motion with a multisensory environment.
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)
Benavides, A.; Everett, M. E.
2007-03-01
This work adopts a continuation approach, based on path tracking in model space, to solve the non-linear least-squares problem for discrimination of unexploded ordnance (UXO) using multi-receiver electromagnetic induction (EMI) data. The forward model corresponds to a stretched-exponential decay of eddy currents induced in a magnetic spheroid. We formulate an over-determined, or under-parameterized, inverse problem. An example using synthetic multi-receiver EMI responses illustrates the efficiency of the method. The fast inversion of actual field multi-receiver EMI responses of inert, buried ordnances is also shown. Software based on the continuation method could be installed within a multi-receiver EMI sensor and used for near-real-time UXO decision-making purposes without the need for a highly-trained operator.
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.
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.
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.
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.
Modeling of continuous strip production by rheocasting
Matsumiya, T.; Flemings, M.C.
1981-03-01
A process was experimentally and mathematically modeled for continuous and direct production of metal strip from its molten state by the use of Rheocasting. The process comprises 1) continuous production of a Rheocast semisolid alloy, and 2) direct shaping of the semisolid into strip. Sn-15 pct Pb was used as the modeling alloy. Crack formation and surface quality of the strip produced depend on fraction solid and deformation force. Continuous, sound strip could be obtained with good surface quality when fraction solid was between 0.50 and 0.70 and deformation force did not exceed a given maximum. Sheet thickness depends on deformation force, fraction solid, rotor rate of Rheocaster and production line speed. At constant deformation force, sheet thickness increases as fraction solid increases, rotor rate decreases and line speed is reduced. Sheet thickness is larger in the center than in the edge, but the difference is reduced by applying edgers. Some segregation of lead toward the edges is observed, ad the segregation increases as amount of deformation is increased. A mathematical model for heat flow, solidification and deformation was constructed. The model predicts the point of completion of solidification in the strip and sheet thickness as a function of deformation force and line speed. Calculations are in good agreement with experimental results.
Continuous multifractal models with zero values: a continuous \\beta -multifractal model
NASA Astrophysics Data System (ADS)
Schmitt, F. G.
2014-02-01
In this paper we propose for the first time a multiplicative continuous model for generating multifractal fields with zero values, as a continuous generalization of the intermittent lognormal \\beta -model proposed by Over and Gupta (1996). It is built using infinitely multiplicative random variables, the multiplicative analog to infinitely divisible distributions for addition. The model also needs stochastic multiplicative measures and multiplicative stochastic integrals. It possesses as a special case a continuous generalization of the classical discrete \\beta -model. Applications are numerous in many fields of applied science, including small-scale rainfall and soil science.
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.
NASA Astrophysics Data System (ADS)
Batt, Gregory S.; Gibert, James M.; Daqaq, Mohammed
2015-08-01
In this paper, the free and forced vibration response of a linearized, distributed-parameter model of a viscoelastic rod with an applied tip-mass is investigated. A nonlinear model is developed from constitutive relations and is linearized about a static equilibrium position for analysis. A classical Maxwell-Weichert model, represented via a Prony series, is used to model the viscoelastic system. The exact solution to both the free and forced vibration problem is derived and used to study the behavior of an idealized packaging system containing Nova Chemicals' Arcel® foam. It is observed that, although three Prony series terms are deemed sufficient to fit the static test data, convergence of the dynamic response and study of the storage and loss modulii necessitate the use of additional Prony series terms. It is also shown that the model is able to predict the modal frequencies and the primary resonance response at low acceleration excitation, both with reasonable accuracy given the non-homogeneity and density variation observed in the specimens. Higher acceleration inputs result in softening nonlinear responses highlighting the need for a nonlinear elastic model that extends beyond the scope of this work. Solution analysis and experimental data indicate little material vibration energy dissipation close to the first modal frequency of the mass/rod system.
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.
Equivalent linear damping characterization in linear and nonlinear force-stiffness muscle models.
Ovesy, Marzieh; Nazari, Mohammad Ali; Mahdavian, Mohammad
2016-02-01
In the current research, the muscle equivalent linear damping coefficient which is introduced as the force-velocity relation in a muscle model and the corresponding time constant are investigated. In order to reach this goal, a 1D skeletal muscle model was used. Two characterizations of this model using a linear force-stiffness relationship (Hill-type model) and a nonlinear one have been implemented. The OpenSim platform was used for verification of the model. The isometric activation has been used for the simulation. The equivalent linear damping and the time constant of each model were extracted by using the results obtained from the simulation. The results provide a better insight into the characteristics of each model. It is found that the nonlinear models had a response rate closer to the reality compared to the Hill-type models. PMID:26837750
Solidification modeling of continuous casting process
NASA Astrophysics Data System (ADS)
Lerner, V. S.; Lerner, Y. S.
2005-04-01
The aim of the present work was to utilize a new systematic mathematical-informational approach based on informational macrodynamics (IMD) to model and optimize the casting process, taking as an example horizontal continuous casting (HCC). The IMD model takes into account the interrelated thermal, diffusion, kinetic, hydrodynamic, and mechanical effects that are essential for the given casting process. The optimum technological process parameters are determined by the simultaneous solution of problems of identification and optimal control. The control functions of the synthesized optimal model are found from the extremum of the entropy functional having a particular sense of an integrated assessment of the continuous cast bar physicochemical properties. For the physical system considered, the IMD structures of the optimal model are connected with controllable equations of nonequilibrium thermodynamics. This approach was applied to the HCC of ductile iron, and the results were compared with experimental data and numerical simulation. Good agreement was confirmed between the predicted and practical data, as well as between new and traditional methods.
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. PMID:23226984
Recent Updates to the GEOS-5 Linear Model
NASA Technical Reports Server (NTRS)
Holdaway, Dan; Kim, Jong G.; Errico, Ron; Gelaro, Ronald; Mahajan, Rahul
2014-01-01
Global Modeling and Assimilation Office (GMAO) is close to having a working 4DVAR system and has developed a linearized version of GEOS-5.This talk outlines a series of improvements made to the linearized dynamics, physics and trajectory.Of particular interest is the development of linearized cloud microphysics, which provides the framework for 'all-sky' data assimilation.
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.
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.
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.
Rakheja, S; Gurram, R; Gouw, G J
1993-10-01
Hand-arm vibration (HAV) models serve as an effective tool to assess the vibration characteristics of the hand-tool system and to evaluate the attenuation performance of vibration isolation mechanisms. This paper describes a methodology to identify the parameters of HAV models, whether linear or nonlinear, using mechanical impedance data and a nonlinear programming based optimization technique. Three- and four-degrees-of-freedom (DOF) linear, piecewise linear and nonlinear HAV models are formulated and analyzed to yield impedance characteristics in the 5-1000 Hz frequency range. A local equivalent linearization algorithm, based upon the principle of energy similarity, is implemented to simulate the nonlinear HAV models. Optimization methods are employed to identify the model parameters, such that the magnitude and phase errors between the computed and measured impedance characteristics are minimum in the entire frequency range. The effectiveness of the proposed method is demonstrated through derivations of models that correlate with the measured X-axis impedance characteristics of the hand-arm system, proposed by ISO. The results of the study show that a linear model cannot predict the impedance characteristics in the entire frequency range, while a piecewise linear model yields an accurate estimation. PMID:8253830
Linear and Nonlinear Models of Agenda Setting in Television.
ERIC Educational Resources Information Center
Brosius, Hans-Bernd; Kepplinger, Hans Mathias
1992-01-01
A content analysis of major German television news shows and 53 weekly surveys on 16 issues were used to compare linear and nonlinear models as ways to describe the relationship between media coverage and the public agenda. Results indicate that nonlinear models are in some cases superior to linear models in terms of explained variance. (34…
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…
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
Rabbit models for continuous curvilinear capsulorhexis instruction
Ruggiero, Jason; Keller, Christopher; Porco, Travis; Naseri, Ayman; Sretavan, David W.
2012-01-01
PURPOSE To develop a rabbit model for continuous curvilinear capsulorhexis (CCC) instruction. SETTING University of California San Francisco, San Francisco, California, USA. DESIGN Experimental study. METHODS Isolated rabbit lenses were immersed in 2% to 8% paraformaldehyde (PFA) fixative from 15 minutes to 6 hours. Rabbit eyes were treated by substituting aqueous with 2% to 4% PFA for 30 minutes to 6 hours, followed by washes with a balanced salt solution. Treated lenses and eyes were held in purpose-designed holders using vacuum. A panel of 6 cataract surgeons with 5 to 15 years of experience performed CCC on treated lenses and eyes and responded to a questionnaire regarding the utility of these models for resident teaching using a 5-item Likert scale. RESULTS The expert panel found that rabbit lenses treated with increasing amounts of fixative simulated CCC on human lens capsules from the third to the seventh decade of life. The panel also found fixative-treated rabbit eyes to simulate some of the experience of CCC within the human anterior chamber but noted a shallower anterior chamber depth, variation in pupil size, and corneal clouding under some treatment conditions. CONCLUSIONS Experienced cataract surgeons who performed CCC on these rabbit models strongly agreed that isolated rabbit lenses treated with fixative provide a realistic simulation of CCC in human patients and that both models were useful tools for capsulorhexis instruction. Results indicate that rabbit lenses treated with 8% PFA for 15 minutes is a model with good fidelity for CCC training. PMID:22727296
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.
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.
Descriptive Linear modeling of steady-state visual evoked response
NASA Technical Reports Server (NTRS)
Levison, W. H.; Junker, A. M.; Kenner, K.
1986-01-01
A study is being conducted to explore use of the steady state visual-evoke electrocortical response as an indicator of cognitive task loading. Application of linear descriptive modeling to steady state Visual Evoked Response (VER) data is summarized. Two aspects of linear modeling are reviewed: (1) unwrapping the phase-shift portion of the frequency response, and (2) parsimonious characterization of task-loading effects in terms of changes in model parameters. Model-based phase unwrapping appears to be most reliable in applications, such as manual control, where theoretical models are available. Linear descriptive modeling of the VER has not yet been shown to provide consistent and readily interpretable results.
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…
Topology changing transitions in supersymmetric linear σ-models
NASA Astrophysics Data System (ADS)
Ryang, Shijong
1995-02-01
We analyze the two-dimensional supersymmetric linear σ-model with U(1) gauge symmetries that includes a Calabi-Yau phase and a possible Landau-Ginzburg phase. We demonstrate the topology changing transitions among the generic vacua of various linear σ-models. In the supersymmetric transition the determinantal contraction naturally arises.
Response of a rotorcraft model with damping non-linearities
NASA Astrophysics Data System (ADS)
Tongue, B. H.
1985-11-01
The linearized equations of motion of a helicopter in contact with the ground have solutions which can be linearly stable or unstable, depending on the system parameters. The present study includes physical non-linearities in the helicopter model. This allows one to determine if a steady-state response exists and, if so, what the frequency and amplitude of the oscillations will be. In this way, one can determine how serious the linearly unstable operating regime is and whether destructive oscillations are possible when the system is in the linearly stable regime. The present analysis applies to helicopters having fully articulated rotors.
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).
Employment of CB models for non-linear dynamic analysis
NASA Technical Reports Server (NTRS)
Klein, M. R. M.; Deloo, P.; Fournier-Sicre, A.
1990-01-01
The non-linear dynamic analysis of large structures is always very time, effort and CPU consuming. Whenever possible the reduction of the size of the mathematical model involved is of main importance to speed up the computational procedures. Such reduction can be performed for the part of the structure which perform linearly. Most of the time, the classical Guyan reduction process is used. For non-linear dynamic process where the non-linearity is present at interfaces between different structures, Craig-Bampton models can provide a very rich information, and allow easy selection of the relevant modes with respect to the phenomenon driving the non-linearity. The paper presents the employment of Craig-Bampton models combined with Newmark direct integration for solving non-linear friction problems appearing at the interface between the Hubble Space Telescope and its solar arrays during in-orbit maneuvers. Theory, implementation in the FEM code ASKA, and practical results are shown.
Rost, D; Assaad, F; Blümer, N
2013-05-01
We present an algorithm for solving the self-consistency equations of the dynamical mean-field theory (DMFT) with high precision and efficiency at low temperatures. In each DMFT iteration, the impurity problem is mapped to an auxiliary Hamiltonian, for which the Green function is computed by combining determinantal quantum Monte Carlo (BSS-QMC) calculations with a multigrid extrapolation procedure. The method is numerically exact, i.e., yields results which are free of significant Trotter errors, but retains the BSS advantage, compared to direct QMC impurity solvers, of linear (instead of cubic) scaling with the inverse temperature. The new algorithm is applied to the half-filled Hubbard model close to the Mott transition; detailed comparisons with exact diagonalization, Hirsch-Fye QMC, and continuous-time QMC are provided. PMID:23767655
NASA Astrophysics Data System (ADS)
De la sen, M.
2006-03-01
This paper deals with the pole-placement type robust adaptive control of continuous linear systems in the presence of bounded noise and a common class of unmodeled dynamics with the use of multiple estimation schemes working in parallel. The multiestimation scheme consisting of the above set of various single estimation schemes is a tool used to minimize the plant identification error by building an estimate which is a convex combination of the estimates at all time. The weighting functions of the individual estimates are provided at each time by a suboptimization scheme for a quadratic loss function of a possibly filtered tracking error and/or control input. The robust stability of the overall adaptive scheme is ensured by an adaptation relative dead zone which takes into account the contribution of the unmodeled dynamics and bounded noise. The basic results are derived for two different estimation strategies which have either a shared regressor with the plant or individual regressors for the input contribution and its relevant time-derivatives. In this second case, the plant input is obtained through a similar convex combination rule as the one used for the estimators in the first approach. An extension of the basic strategies is also pointed out including a combined use of the suboptimization scheme with a supervisor of past measures for the on-line calculation of the estimator weights in the convex combination.
Humans use internal models to estimate gravity and linear acceleration.
Merfeld, D M; Zupan, L; Peterka, R J
1999-04-15
Because sensory systems often provide ambiguous information, neural processes must exist to resolve these ambiguities. It is likely that similar neural processes are used by different sensory systems. For example, many tasks require neural processing to distinguish linear acceleration from gravity, but Einstein's equivalence principle states that all linear accelerometers must measure both linear acceleration and gravity. Here we investigate whether the brain uses internal models, defined as neural systems that mimic physical principles, to help estimate linear acceleration and gravity. Internal models may be used in motor contro, sensorimotor integration and sensory processing, but direct experimental evidence for such models is limited. To determine how humans process ambiguous gravity and linear acceleration cues, subjects were tilted after being rotated at a constant velocity about an Earth-vertical axis. We show that the eye movements evoked by this post-rotational tilt include a response component that compensates for the estimated linear acceleration even when no actual linear acceleration occurs. These measured responses are consistent with our internal model predictions that the nervous system can develop a non-zero estimate of linear acceleration even when no true linear acceleration is present. PMID:10217143
Linear radiation model for phase of thermal emission spectroscopy
NASA Astrophysics Data System (ADS)
Bennett, Ted D.; Yu, Fengling
2005-11-01
A linear radiation model is developed that overcomes the analytical complexity in phase of thermal emission spectroscopy. It is shown that the linear radiation model can result in a simple algebraic relation between the phase of thermal emission and four coating properties, enabling these properties to be determined by nonlinear regression analysis of experimental measurements. Suitability of the linear radiation model to various measurement conditions is explored, and the model is applied to the phase of thermal emission measurements performed on a thermal barrier coating.
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…
On-line control models for the Stanford Linear Collider
Sheppard, J.C.; Helm, R.H.; Lee, M.J.; Woodley, M.D.
1983-03-01
Models for computer control of the SLAC three-kilometer linear accelerator and damping rings have been developed as part of the control system for the Stanford Linear Collider. Some of these models have been tested experimentally and implemented in the control program for routine linac operations. This paper will describe the development and implementation of these models, as well as some of the operational results.
Neural network modelling of non-linear hydrological relationships
NASA Astrophysics Data System (ADS)
Abrahart, R. J.; See, L. M.
2007-09-01
Two recent studies have suggested that neural network modelling offers no worthwhile improvements in comparison to the application of weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. The potential of an artificial neural network to perform simple non-linear hydrological transformations under controlled conditions is examined in this paper. Eight neural network models were developed: four full or partial emulations of a recognised non-linear hydrological rainfall-runoff model; four solutions developed on an identical set of inputs and a calculated runoff coefficient output. The use of different input combinations enabled the competencies of solutions developed on a reduced number of parameters to be assessed. The selected hydrological model had a limited number of inputs and contained no temporal component. The modelling process was based on a set of random inputs that had a uniform distribution and spanned a modest range of possibilities. The initial cloning operations permitted a direct comparison to be performed with the equation-based relationship. It also provided more general information about the power of a neural network to replicate mathematical equations and model modest non-linear relationships. The second group of experiments explored a different relationship that is of hydrological interest; the target surface contained a stronger set of non-linear properties and was more challenging. Linear modelling comparisons were performed against traditional least squares multiple linear regression solutions developed on identical datasets. The reported results demonstrate that neural networks are capable of modelling non-linear hydrological processes and are therefore appropriate tools for hydrological modelling.
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.
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 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…
Recent History Functional Linear Models for Sparse Longitudinal Data
Kim, Kion; Şentürk, Damla; Li, Runze
2011-01-01
We consider the recent history functional linear models, relating a longitudinal response to a longitudinal predictor where the predictor process only in a sliding window into the recent past has an effect on the response value at the current time. We propose an estimation procedure for recent history functional linear models that is geared towards sparse longitudinal data, where the observation times across subjects are irregular and total number of measurements per subject is small. The proposed estimation procedure builds upon recent developments in literature for estimation of functional linear models with sparse data and utilizes connections between the recent history functional linear models and varying coefficient models. We establish uniform consistency of the proposed estimators, propose prediction of the response trajectories and derive their asymptotic distribution leading to asymptotic point-wise confidence bands. We include a real data application and simulation studies to demonstrate the efficacy of the proposed methodology. PMID:21691421
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)
Genetic parameters for racing records in trotters using linear and generalized linear models.
Suontama, M; van der Werf, J H J; Juga, J; Ojala, M
2012-09-01
Heritability and repeatability and genetic and phenotypic correlations were estimated for trotting race records with linear and generalized linear models using 510,519 records on 17,792 Finnhorses and 513,161 records on 25,536 Standardbred trotters. Heritability and repeatability were estimated for single racing time and earnings traits with linear models, and logarithmic scale was used for racing time and fourth-root scale for earnings to correct for nonnormality. Generalized linear models with a gamma distribution were applied for single racing time and with a multinomial distribution for single earnings traits. In addition, genetic parameters for annual earnings were estimated with linear models on the observed and fourth-root scales. Racing success traits of single placings, winnings, breaking stride, and disqualifications were analyzed using generalized linear models with a binomial distribution. Estimates of heritability were greatest for racing time, which ranged from 0.32 to 0.34. Estimates of heritability were low for single earnings with all distributions, ranging from 0.01 to 0.09. Annual earnings were closer to normal distribution than single earnings. Heritability estimates were moderate for annual earnings on the fourth-root scale, 0.19 for Finnhorses and 0.27 for Standardbred trotters. Heritability estimates for binomial racing success variables ranged from 0.04 to 0.12, being greatest for winnings and least for breaking stride. Genetic correlations among racing traits were high, whereas phenotypic correlations were mainly low to moderate, except correlations between racing time and earnings were high. On the basis of a moderate heritability and moderate to high repeatability for racing time and annual earnings, selection of horses for these traits is effective when based on a few repeated records. Because of high genetic correlations, direct selection for racing time and annual earnings would also result in good genetic response in racing success
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.
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.
Model checking for linear temporal logic: An efficient implementation
NASA Technical Reports Server (NTRS)
Sherman, Rivi; Pnueli, Amir
1990-01-01
This report provides evidence to support the claim that model checking for linear temporal logic (LTL) is practically efficient. Two implementations of a linear temporal logic model checker is described. One is based on transforming the model checking problem into a satisfiability problem; the other checks an LTL formula for a finite model by computing the cross-product of the finite state transition graph of the program with a structure containing all possible models for the property. An experiment was done with a set of mutual exclusion algorithms and tested safety and liveness under fairness for these algorithms.
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…
Modelling Childhood Growth Using Fractional Polynomials and Linear Splines
Tilling, Kate; Macdonald-Wallis, Corrie; Lawlor, Debbie A.; Hughes, Rachael A.; Howe, Laura D.
2014-01-01
Background There is increasing emphasis in medical research on modelling growth across the life course and identifying factors associated with growth. Here, we demonstrate multilevel models for childhood growth either as a smooth function (using fractional polynomials) or a set of connected linear phases (using linear splines). Methods We related parental social class to height from birth to 10 years of age in 5,588 girls from the Avon Longitudinal Study of Parents and Children (ALSPAC). Multilevel fractional polynomial modelling identified the best-fitting model as being of degree 2 with powers of the square root of age, and the square root of age multiplied by the log of age. The multilevel linear spline model identified knot points at 3, 12 and 36 months of age. Results Both the fractional polynomial and linear spline models show an initially fast rate of growth, which slowed over time. Both models also showed that there was a disparity in length between manual and non-manual social class infants at birth, which decreased in magnitude until approximately 1 year of age and then increased. Conclusions Multilevel fractional polynomials give a more realistic smooth function, and linear spline models are easily interpretable. Each can be used to summarise individual growth trajectories and their relationships with individual-level exposures. PMID:25413651
Phase II monitoring of auto-correlated linear profiles using linear mixed model
NASA Astrophysics Data System (ADS)
Narvand, A.; Soleimani, P.; Raissi, Sadigh
2013-05-01
In many circumstances, the quality of a process or product is best characterized by a given mathematical function between a response variable and one or more explanatory variables that is typically referred to as profile. There are some investigations to monitor auto-correlated linear and nonlinear profiles in recent years. In the present paper, we use the linear mixed models to account autocorrelation within observations which is gathered on phase II of the monitoring process. We undertake that the structure of correlated linear profiles simultaneously has both random and fixed effects. The work enhanced a Hotelling's T 2 statistic, a multivariate exponential weighted moving average (MEWMA), and a multivariate cumulative sum (MCUSUM) control charts to monitor process. We also compared their performances, in terms of average run length criterion, and designated that the proposed control charts schemes could effectively act in detecting shifts in process parameters. Finally, the results are applied on a real case study in an agricultural field.
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.
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.
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.
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
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.
Dynamic modeling of electrochemical systems using linear graph theory
NASA Astrophysics Data System (ADS)
Dao, Thanh-Son; McPhee, John
An electrochemical cell is a multidisciplinary system which involves complex chemical, electrical, and thermodynamical processes. The primary objective of this paper is to develop a linear graph-theoretical modeling for the dynamic description of electrochemical systems through the representation of the system topologies. After a brief introduction to the topic and a review of linear graphs, an approach to develop linear graphs for electrochemical systems using a circuitry representation is discussed, followed in turn by the use of the branch and chord transformation techniques to generate final dynamic equations governing the system. As an example, the application of linear graph theory to modeling a nickel metal hydride (NiMH) battery will be presented. Results show that not only the number of equations are reduced significantly, but also the linear graph model simulates faster compared to the original lumped parameter model. The approach presented in this paper can be extended to modeling complex systems such as an electric or hybrid electric vehicle where a battery pack is interconnected with other components in many different domains.
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…
ASSESSING THE ACCURACY OF THE LINEARIZED LANGMUIR MODEL
Technology Transfer Automated Retrieval System (TEKTRAN)
One of the most commonly used models for describing phosphorus (P) sorption to soils is the nonlinear Langmuir model. To avoid the difficulties in fitting the nonlinear Langmuir equation to sorption data, linearized versions are commonly used. Although concerns have been raised in the past regarding...
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.
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 ...
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.
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. PMID:24130119
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. The implications of these findings are potentially far-reaching. Two-sided daily attrition data on a large number of battles is needed to absolutely confirm these results. Such a confirmation will require that numerous computer conflict models containing square and linear law based attrition algorithms be reexamined. It is conceivable that complex mixed, heterogeneous, square plus linear law algorithms may produce the same results as a homogeneous mixed linear-logarithmic law algorithm; however, such an occurrence is by no means assured. Even without such absolute confirmation, the results of this research allow the analysis of combat data for the effects of training, weather, leadership, and other human factors, unencumbered by the force size effects.
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.
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.
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.
Multikernel linear mixed models for complex phenotype prediction.
Weissbrod, Omer; Geiger, Dan; Rosset, Saharon
2016-07-01
Linear mixed models (LMMs) and their extensions have recently become the method of choice in phenotype prediction for complex traits. However, LMM use to date has typically been limited by assuming simple genetic architectures. Here, we present multikernel linear mixed model (MKLMM), a predictive modeling framework that extends the standard LMM using multiple-kernel machine learning approaches. MKLMM can model genetic interactions and is particularly suitable for modeling complex local interactions between nearby variants. We additionally present MKLMM-Adapt, which automatically infers interaction types across multiple genomic regions. In an analysis of eight case-control data sets from the Wellcome Trust Case Control Consortium and more than a hundred mouse phenotypes, MKLMM-Adapt consistently outperforms competing methods in phenotype prediction. MKLMM is as computationally efficient as standard LMMs and does not require storage of genotypes, thus achieving state-of-the-art predictive power without compromising computational feasibility or genomic privacy. PMID:27302636
Parallel, iterative solution of sparse linear systems: Models and architectures
NASA Technical Reports Server (NTRS)
Reed, D. A.; Patrick, M. L.
1984-01-01
A model of a general class of asynchronous, iterative solution methods for linear systems is developed. In the model, the system is solved by creating several cooperating tasks that each compute a portion of the solution vector. A data transfer model predicting both the probability that data must be transferred between two tasks and the amount of data to be transferred is presented. This model is used to derive an execution time model for predicting parallel execution time and an optimal number of tasks given the dimension and sparsity of the coefficient matrix and the costs of computation, synchronization, and communication. The suitability of different parallel architectures for solving randomly sparse linear systems is discussed. Based on the complexity of task scheduling, one parallel architecture, based on a broadcast bus, is presented and analyzed.
A QUANTITATIVE PEDOLOGY APPROACH TO CONTINUOUS SOIL LANDSCAPE MODELS
Technology Transfer Automated Retrieval System (TEKTRAN)
Continuous representations of soil profiles and landscapes are needed to provide input into process based models and to move beyond the categorical paradigm of horizons and map-units. Continuous models of soil landscapes should be driven by the factors and processes of the soil genetic model. Parame...
Continuous-Discontinuous Model for Ductile Fracture
Seabra, Mariana R. R.; Cesar de Sa, Jose M. A.
2010-06-15
In this contribution, a continuum-dicontinuum model for ductile failure is presented. The degradation of material properties trough deformation is described by Continuum Damage Mechanics in a non-local integral formulation to avoid mesh dependence. In the final stage of failure, the damaged zone is replaced by a cohesive macro crack and subsequent traction-free macro crack for a more realistic representation of the phenomenon. The inclusion of the discontinuity surfaces is performed by the XFEM and Level Set Method and avoids the spurious damage growth typical of this class of models.
A linear model of stationary elevator traveling and compensation cables
NASA Astrophysics Data System (ADS)
Zhu, W. D.; Ren, H.
2013-06-01
Based on a recent asymptotic analysis of a nonlinear model of a slack cable, a computationally efficient, linear model is developed for calculating the natural frequencies, mode shapes, and dynamic responses of stationary elevator traveling and compensation cables. The linear cable model consists of two vertical cable segments connected by a half-circular lower loop. The two vertical cable segments are modeled as a string with a variable tension due to the weight of the cable. The horizontal displacements of the cable segments consist of boundary-induced displacements and relative elastic displacements, where the boundary-induced displacements are interpolated from the displacements of the two lower ends of the cable segments, and the relative elastic displacements satisfy the corresponding homogeneous boundary conditions of the cable segments. The horizontal displacement of the lower loop is interpolated from those of the two lower ends of the two cable segments, and the bending stiffness of the lower loop is modeled by a spring with a constant stiffness, which can be calculated from the nonlinear model. Given a car position, the natural frequencies and mode shapes of an elevator traveling or compensation cable are calculated using the linear model and compared with those from the nonlinear model. The calculated natural frequencies are also compared with those from a full-scale experiment. In addition, the dynamic responses of a cable under a boundary excitation are calculated and compared with those from the nonlinear model. There is a good agreement between the predictions from the linear and nonlinear models and between the measured natural frequencies from the full-scale experiment and the corresponding calculated ones.
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.
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.
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…
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…
Identification of linear system models and state estimators for controls
NASA Technical Reports Server (NTRS)
Chen, Chung-Wen
1992-01-01
The following paper is presented in viewgraph format and covers topics including: (1) linear state feedback control system; (2) Kalman filter state estimation; (3) relation between residual and stochastic part of output; (4) obtaining Kalman filter gain; (5) state estimation under unknown system model and unknown noises; and (6) relationship between filter Markov parameters and system Markov parameters.
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.
BF Models in Dual Formulations of Linearized Gravity
Bizdadea, Constantin; Cioroianu, Eugen M.; Danehkar, Ashbiz; Iordache, Marius; Saliu, Solange O.; Sararu, Silviu C.
2009-05-22
The case of couplings in D = 5 between a simple, maximal BF model and the dual formulation of linearized gravity is considered. All the possible interactions are exhausted by means of computing the 'free' local BRST cohomology in ghost number zero.
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. PMID:7093425
Use of Linear Models for Thermal Processing Acidified Foods
Technology Transfer Automated Retrieval System (TEKTRAN)
Acidified vegetable products with a pH above 3.3 must be pasteurized to assure the destruction of acid resistant pathogenic bacteria. The times and temperatures needed to assure a five log reduction by pasteurization have previously been determined using a non-linear (Weibull) model. Recently, the F...
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.
Evaluating Faculty Salary Equity Using Hierarchical Linear Modeling.
ERIC Educational Resources Information Center
Stapleton, Laura M.; Lissitz, Robert W.
This paper presents results from a comparison of the multiple regression (MR) approach to examining faculty salary equity (with clusters for the various disciplines) and hierarchical linear modeling (HLM) for the same problem. The comparison was done in two steps. First, a practical example of applying both techniques, using empirical data, is…
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…
A General Linear Model Approach to Adjusting the Cumulative GPA.
ERIC Educational Resources Information Center
Young, John W.
A general linear model (GLM), using least-squares techniques, was used to develop a criterion measure to replace freshman year grade point average (GPA) in college admission predictive validity studies. Problems with the use of GPA include those associated with the combination of grades from different courses and disciplines into a single measure,…
Linearity of Quantum Probability Measure and Hardy's Model
NASA Astrophysics Data System (ADS)
Fujikawa, Kazuo; Oh, C. H.; Zhang, Chengjie
2014-01-01
We re-examine d = 4 hidden-variables model for a system of two spin-1/2 particles in view of the concrete model of Hardy, who analyzed the criterion of entanglement without referring to inequality. The basis of our analysis is the linearity of the probability measure related to the Born probability interpretation, which excludes noncontextual hidden-variables model in d≥3. To be specific, we note the inconsistency of the noncontextual hidden-variables model in d = 4 with the linearity of the quantum mechanical probability measure in the sense <ψ|aṡσ ⊗b ṡσ|ψ>+ <ψ|a ṡσ ⊗b‧ ṡσ|ψ> = <ψ|aṡσ⊗(b + b‧)ṡσ|ψ> for noncollinear b and b‧. It is then shown that Hardy's model in d = 4 does not lead to a unique mathematical expression in the demonstration of the discrepancy of local realism (hidden-variables model) with entanglement and thus his proof is incomplete. We identify the origin of this nonuniqueness with the nonuniqueness of translating quantum mechanical expressions into expressions in hidden-variables model, which results from the failure of the above linearity of the probability measure. In contrast, if the linearity of the probability measure is strictly imposed, which tantamounts to asking that the noncontextual hidden-variables model in d = 4 gives the Clauser-Horne-Shimony-Holt (CHSH) inequality ||≤2 uniquely, it is shown that the hidden-variables model can describe only separable quantum mechanical states; this conclusion is in perfect agreement with the so-called Gisin's theorem which states that ||≤2 implies separable states.
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.
Linear parameter varying battery model identification using subspace methods
NASA Astrophysics Data System (ADS)
Hu, Y.; Yurkovich, S.
2011-03-01
The advent of hybrid and plug-in hybrid electric vehicles has created a demand for more precise battery pack management systems (BMS). Among methods used to design various components of a BMS, such as state-of-charge (SoC) estimators, model based approaches offer a good balance between accuracy, calibration effort and implementability. Because models used for these approaches are typically low in order and complexity, the traditional approach is to identify linear (or slightly nonlinear) models that are scheduled based on operating conditions. These models, formally known as linear parameter varying (LPV) models, tend to be difficult to identify because they contain a large amount of coefficients that require calibration. Consequently, the model identification process can be very laborious and time-intensive. This paper describes a comprehensive identification algorithm that uses linear-algebra-based subspace methods to identify a parameter varying state variable model that can describe the input-to-output dynamics of a battery under various operating conditions. Compared with previous methods, this approach is much faster and provides the user with information on the order of the system without placing an a priori structure on the system matrices. The entire process and various nuances are demonstrated using data collected from a lithium ion battery, and the focus is on applications for energy storage in automotive applications.
Skills Diagnosis Using IRT-Based Continuous Latent Trait Models
ERIC Educational Resources Information Center
Stout, William
2007-01-01
This article summarizes the continuous latent trait IRT approach to skills diagnosis as particularized by a representative variety of continuous latent trait models using item response functions (IRFs). First, several basic IRT-based continuous latent trait approaches are presented in some detail. Then a brief summary of estimation, model…
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 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
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
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.
Can the Non-linear Ballooning Model describe ELMs?
NASA Astrophysics Data System (ADS)
Henneberg, S. A.; Cowley, S. C.; Wilson, H. R.
2015-11-01
The explosive, filamentary plasma eruptions described by the non-linear ideal MHD ballooning model is tested quantitatively against experimental observations of ELMs in MAST. The equations describing this model were derived by Wilson and Cowley for tokamak-like geometry which includes two differential equations: the linear ballooning equation which describes the spatial distribution along the field lines and the non-linear ballooning mode envelope equation, which is a two-dimensional, non-linear differential equation which can involve fractional temporal-derivatives, but is often second-order in time and space. To employ the second differential equation for a specific geometry one has to evaluate the coefficients of the equation which is non-trivial as it involves field line averaging of slowly converging functions. We have solved this system for MAST, superimposing the solutions of both differential equations and mapping them onto a MAST plasma. Comparisons with the evolution of ELM filaments in MAST will be reported in order to test the model. The support of the EPSRC for the FCDT (Grant EP/K504178/1), of Euratom research and training programme 2014-2018 (No 633053) and of the RCUK Energy Programme [grant number EP/I501045] is gratefully acknowledged.
Models for total dose degradation of linear integrated circuits
Johnston, A.H.; Plaag, R.E.
1987-12-01
Mechanisms for total dose degradation of linear circuits are discussed, including bulk effects, oxide charge buildup and recombination at the Si-SiO/sub 2/ interface. The dependence of damage on bias, dose, particle type and energy is used in conjunction with two-dimensional modeling to identify the failure mechanism in a specific linear device type. The importance of surface recombination is demonstrated along with the absence of bias dependence. Bulk damage is shown to be important for high energy electron irradiation because of wide-base pnp transistors. This causes substantial differences in device failure between electron and cobalt-60 environments that need to be taken into account for test standards and data bases that include commercial bipolar integrated circuits. Valid test methodologies for linear device must consider the energy and particle type present in the actual environment.
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.
Yang, Xiaowei; Nie, Kun
2008-03-15
Longitudinal data sets in biomedical research often consist of large numbers of repeated measures. In many cases, the trajectories do not look globally linear or polynomial, making it difficult to summarize the data or test hypotheses using standard longitudinal data analysis based on various linear models. An alternative approach is to apply the approaches of functional data analysis, which directly target the continuous nonlinear curves underlying discretely sampled repeated measures. For the purposes of data exploration, many functional data analysis strategies have been developed based on various schemes of smoothing, but fewer options are available for making causal inferences regarding predictor-outcome relationships, a common task seen in hypothesis-driven medical studies. To compare groups of curves, two testing strategies with good power have been proposed for high-dimensional analysis of variance: the Fourier-based adaptive Neyman test and the wavelet-based thresholding test. Using a smoking cessation clinical trial data set, this paper demonstrates how to extend the strategies for hypothesis testing into the framework of functional linear regression models (FLRMs) with continuous functional responses and categorical or continuous scalar predictors. The analysis procedure consists of three steps: first, apply the Fourier or wavelet transform to the original repeated measures; then fit a multivariate linear model in the transformed domain; and finally, test the regression coefficients using either adaptive Neyman or thresholding statistics. Since a FLRM can be viewed as a natural extension of the traditional multiple linear regression model, the development of this model and computational tools should enhance the capacity of medical statistics for longitudinal data. PMID:17610294
Linear models for joint association and linkage QTL mapping
2009-01-01
Background Populational linkage disequilibrium and within-family linkage are commonly used for QTL mapping and marker assisted selection. The combination of both results in more robust and accurate locations of the QTL, but models proposed so far have been either single marker, complex in practice or well fit to a particular family structure. Results We herein present linear model theory to come up with additive effects of the QTL alleles in any member of a general pedigree, conditional to observed markers and pedigree, accounting for possible linkage disequilibrium among QTLs and markers. The model is based on association analysis in the founders; further, the additive effect of the QTLs transmitted to the descendants is a weighted (by the probabilities of transmission) average of the substitution effects of founders' haplotypes. The model allows for non-complete linkage disequilibrium QTL-markers in the founders. Two submodels are presented: a simple and easy to implement Haley-Knott type regression for half-sib families, and a general mixed (variance component) model for general pedigrees. The model can use information from all markers. The performance of the regression method is compared by simulation with a more complex IBD method by Meuwissen and Goddard. Numerical examples are provided. Conclusion The linear model theory provides a useful framework for QTL mapping with dense marker maps. Results show similar accuracies but a bias of the IBD method towards the center of the region. Computations for the linear regression model are extremely simple, in contrast with IBD methods. Extensions of the model to genomic selection and multi-QTL mapping are straightforward. PMID:19788745
Classifying linearly shielded modified gravity models in effective field theory.
Lombriser, Lucas; Taylor, Andy
2015-01-23
We study the model space generated by the time-dependent operator coefficients in the effective field theory of the cosmological background evolution and perturbations of modified gravity and dark energy models. We identify three classes of modified gravity models that reduce to Newtonian gravity on the small scales of linear theory. These general classes contain enough freedom to simultaneously admit a matching of the concordance model background expansion history. In particular, there exists a large model space that mimics the concordance model on all linear quasistatic subhorizon scales as well as in the background evolution. Such models also exist when restricting the theory space to operators introduced in Horndeski scalar-tensor gravity. We emphasize that whereas the partially shielded scenarios might be of interest to study in connection with tensions between large and small scale data, with conventional cosmological probes, the ability to distinguish the fully shielded scenarios from the concordance model on near-horizon scales will remain limited by cosmic variance. Novel tests of the large-scale structure remedying this deficiency and accounting for the full covariant nature of the alternative gravitational theories, however, might yield further insights on gravity in this regime. PMID:25658988
A non-linear model of economic production processes
NASA Astrophysics Data System (ADS)
Ponzi, A.; Yasutomi, A.; Kaneko, K.
2003-06-01
We present a new two phase model of economic production processes which is a non-linear dynamical version of von Neumann's neoclassical model of production, including a market price-setting phase as well as a production phase. The rate of an economic production process is observed, for the first time, to depend on the minimum of its input supplies. This creates highly non-linear supply and demand dynamics. By numerical simulation, production networks are shown to become unstable when the ratio of different products to total processes increases. This provides some insight into observed stability of competitive capitalist economies in comparison to monopolistic economies. Capitalist economies are also shown to have low unemployment.
Flood Nowcasting With Linear Catchment Models, Radar and Kalman Filters
NASA Astrophysics Data System (ADS)
Pegram, Geoff; Sinclair, Scott
A pilot study using real time rainfall data as input to a parsimonious linear distributed flood forecasting model is presented. The aim of the study is to deliver an operational system capable of producing flood forecasts, in real time, for the Mgeni and Mlazi catchments near the city of Durban in South Africa. The forecasts can be made at time steps which are of the order of a fraction of the catchment response time. To this end, the model is formulated in Finite Difference form in an equation similar to an Auto Regressive Moving Average (ARMA) model; it is this formulation which provides the required computational efficiency. The ARMA equation is a discretely coincident form of the State-Space equations that govern the response of an arrangement of linear reservoirs. This results in a functional relationship between the reservoir response con- stants and the ARMA coefficients, which guarantees stationarity of the ARMA model. Input to the model is a combined "Best Estimate" spatial rainfall field, derived from a combination of weather RADAR and Satellite rainfield estimates with point rain- fall given by a network of telemetering raingauges. Several strategies are employed to overcome the uncertainties associated with forecasting. Principle among these are the use of optimal (double Kalman) filtering techniques to update the model states and parameters in response to current streamflow observations and the application of short term forecasting techniques to provide future estimates of the rainfield as input to the model.
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.
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…
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.
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.
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.
A general linear model for MEG beamformer imaging.
Brookes, Matthew J; Gibson, Andrew M; Hall, Stephen D; Furlong, Paul L; Barnes, Gareth R; Hillebrand, Arjan; Singh, Krish D; Holliday, Ian E; Francis, Sue T; Morris, Peter G
2004-11-01
A new general linear model (GLM) beamformer method is described for processing magnetoencephalography (MEG) data. A standard nonlinear beamformer is used to determine the time course of neuronal activation for each point in a predefined source space. A Hilbert transform gives the envelope of oscillatory activity at each location in any chosen frequency band (not necessary in the case of sustained (DC) fields), enabling the general linear model to be applied and a volumetric T statistic image to be determined. The new method is illustrated by a two-source simulation (sustained field and 20 Hz) and is shown to provide accurate localization. The method is also shown to locate accurately the increasing and decreasing gamma activities to the temporal and frontal lobes, respectively, in the case of a scintillating scotoma. The new method brings the advantages of the general linear model to the analysis of MEG data and should prove useful for the localization of changing patterns of activity across all frequency ranges including DC (sustained fields). PMID:15528094
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,…
NASA Technical Reports Server (NTRS)
Rosen, I. G.; Wang, C.
1990-01-01
The convergence of solutions to the discrete or sampled time linear quadratic regulator problem and associated Riccati equation for infinite dimensional systems to the solutions to the corresponding continuous time problem and equation, as the length of the sampling interval (the sampling rate) tends toward zero (infinity) is established. Both the finite and infinite time horizon problems are studied. In the finite time horizon case, strong continuity of the operators which define the control system and performance index together with a stability and consistency condition on the sampling scheme are required. For the infinite time horizon problem, in addition, the sampled systems must be stabilizable and detectable, uniformly with respect to the sampling rate. Classes of systems for which this condition can be verified are discussed. Results of numerical studies involving the control of a heat/diffusion equation, a hereditary of delay system, and a flexible beam are presented and discussed.
NASA Technical Reports Server (NTRS)
Rosen, I. G.; Wang, C.
1992-01-01
The convergence of solutions to the discrete- or sampled-time linear quadratic regulator problem and associated Riccati equation for infinite-dimensional systems to the solutions to the corresponding continuous time problem and equation, as the length of the sampling interval (the sampling rate) tends toward zero(infinity) is established. Both the finite-and infinite-time horizon problems are studied. In the finite-time horizon case, strong continuity of the operators that define the control system and performance index, together with a stability and consistency condition on the sampling scheme are required. For the infinite-time horizon problem, in addition, the sampled systems must be stabilizable and detectable, uniformly with respect to the sampling rate. Classes of systems for which this condition can be verified are discussed. Results of numerical studies involving the control of a heat/diffusion equation, a hereditary or delay system, and a flexible beam are presented and discussed.
CANFIS: A non-linear regression procedure to produce statistical air-quality forecast models
Burrows, W.R.; Montpetit, J.; Pudykiewicz, J.
1997-12-31
Statistical models for forecasts of environmental variables can provide a good trade-off between significance and precision in return for substantial saving of computer execution time. Recent non-linear regression techniques give significantly increased accuracy compared to traditional linear regression methods. Two are Classification and Regression Trees (CART) and the Neuro-Fuzzy Inference System (NFIS). Both can model predict and distributions, including the tails, with much better accuracy than linear regression. Given a learning data set of matched predict and predictors, CART regression produces a non-linear, tree-based, piecewise-continuous model of the predict and data. Its variance-minimizing procedure optimizes the task of predictor selection, often greatly reducing initial data dimensionality. NFIS reduces dimensionality by a procedure known as subtractive clustering but it does not of itself eliminate predictors. Over-lapping coverage in predictor space is enhanced by NFIS with a Gaussian membership function for each cluster component. Coefficients for a continuous response model based on the fuzzified cluster centers are obtained by a least-squares estimation procedure. CANFIS is a two-stage data-modeling technique that combines the strength of CART to optimize the process of selecting predictors from a large pool of potential predictors with the modeling strength of NFIS. A CANFIS model requires negligible computer time to run. CANFIS models for ground-level O{sub 3}, particulates, and other pollutants will be produced for each of about 100 Canadian sites. The air-quality models will run twice daily using a small number of predictors isolated from a large pool of upstream and local Lagrangian potential predictors.
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
A new pore-scale model for linear and non-linear heterogeneous dissolution and precipitation
NASA Astrophysics Data System (ADS)
Huber, Christian; Shafei, Babak; Parmigiani, Andrea
2014-01-01
Pore-scale processes exert a strong control on the transport of reactants in porous media at the continuum scale. As such, pore-scale numerical models can offer a more quantitative understanding of the coupling between transport and reaction and yield parameterized constitutive equations to introduce pore-scale corrections into macroscopic (continuum) reactive transport models. In the present study, we present a new pore-scale model for the advection and diffusion of reactants in porous media with complex topologies. The model is based on the lattice Boltzmann method and couples a fluid flow solver to an optimal advection-diffusion transport model. Internal solid-fluid boundaries (grain boundaries) are explicitly part of the numerical domain, which allows the algorithm to solve for surface reactions independently from the surface shape and orientation of the grains. Thus, the approach is well suited for the treatment of heterogeneous reactions in complex pore structures. We present single and multispecies reactive transport applications of the model. In the first application we study the permeability change of a porous medium associated with a given porosity change during dissolution and precipitation using linear reaction kinetics. We show that, for a given porous medium, the correlation between porosity and permeability changes depends on the transport regime (the ratio of advective to diffusive transport) and the reaction rate. Finally, we carry out simulations of multispecies reactive transport, focusing on the case of calcium carbonate dissolution/precipitation. Our results highlight the difference between pH dependent and independent reaction rates for heterogeneous reactions in complex geometries at the pore scale.
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.
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. PMID:25792623
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
NASA Astrophysics Data System (ADS)
Łuczko, J.
2002-08-01
A geometrically non-linear model of the rotating shaft is introduced, which includes Kárman non-linearity, non-linear curvature effects, large displacements and rotations as well as gyroscopic effects. Through applying Timoshenko-type assumptions, the shear effects are also included in the model. Convenient matrix descriptions are used in order to facilitate the analysis based on Galerkin and continuation methods. The model is used to analyze the phenomenon of internal resonance. The influence of some of the system's parameters on the amplitude and frequency of self-excited vibration is investigated.
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.
First class models from linear and nonlinear second class constraints
NASA Astrophysics Data System (ADS)
Dehghani, Mehdi; Mardaani, Maryam; Monemzadeh, Majid; Nejad, Salman Abarghouei
2015-10-01
Two models with linear and nonlinear second class constraints are considered and gauged by embedding in an extended phase space. These models are studied by considering a free non-relativistic particle on the hyperplane and hypersphere in the configuration space. The gauged theory of the first model is obtained by converting the very second class system to the first class one directly. In contrast, the first class system related to the free particle on the hypersphere is derived with the help of the infinite Batalin-Fradkin-Tyutin (BFT) embedding procedure. We propose a practical formula, based on the simplified BFT method, which is practical in gauging linear and some nonlinear second class systems. As a result of gauging these two models, we show that in the conversion of second class constraints to the first class ones, the minimum number of phase space degrees of freedom for both systems is a pair of phase space coordinates. This pair is made up of a coordinate and its conjugate momentum for the first model, but the corresponding Poisson structure of the embedded non-relativistic particle on hypersphere is a nontrivial one. We derive infinite correction terms for the Hamiltonian of the nonlinear constraints and an interacting gauged Hamiltonian is constructed by summing over them. At the end, we find an open algebra for three first class objects of the embedded nonlinear system.
NASA Astrophysics Data System (ADS)
Reagor, Matthew; Pfaff, Wolfgang; Heeres, Reinier; Ofek, Nissim; Chou, Kevin; Blumoff, Jacob; Leghtas, Zaki; Touzard, Steven; Sliwa, Katrina; Holland, Eric; Albert, Victor V.; Frunzio, Luigi; Devoret, Michel H.; Jiang, Liang; Schoelkopf, Robert J.
2015-03-01
Recent advances in circuit QED have shown great potential for using microwave resonators as quantum memories. In particular, it is possible to encode the state of a quantum bit in non-classical photonic states inside a high-Q linear resonator. An outstanding challenge is to perform controlled operations on such a photonic state. We demonstrate experimentally how a continuous drive on a transmon qubit coupled to a high-Q storage resonator can be used to induce non-linear dynamics of the resonator. Tailoring the drive properties allows us to cancel or enhance non-linearities in the system such that we can manipulate the state stored in the cavity. This approach can be used to either counteract undesirable evolution due to the bare Hamiltonian of the system or, ultimately, to perform logical operations on the state encoded in the cavity field. Our method provides a promising pathway towards performing universal control for quantum states stored in high-coherence resonators in the circuit QED platform.
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.
NASA Astrophysics Data System (ADS)
Zander, C.; Plastino, A. R.; Díaz-Alonso, J.
2015-11-01
We investigate time-dependent solutions for a non-linear Schrödinger equation recently proposed by Nassar and Miret-Artés (NM) to describe the continuous measurement of the position of a quantum particle (Nassar, 2013; Nassar and Miret-Artés, 2013). Here we extend these previous studies in two different directions. On the one hand, we incorporate a potential energy term in the NM equation and explore the corresponding wave packet dynamics, while in the previous works the analysis was restricted to the free-particle case. On the other hand, we investigate time-dependent solutions while previous studies focused on a stationary one. We obtain exact wave packet solutions for linear and quadratic potentials, and approximate solutions for the Morse potential. The free-particle case is also revisited from a time-dependent point of view. Our analysis of time-dependent solutions allows us to determine the stability properties of the stationary solution considered in Nassar (2013), Nassar and Miret-Artés (2013). On the basis of these results we reconsider the Bohmian approach to the NM equation, taking into account the fact that the evolution equation for the probability density ρ =| ψ | 2 is not a continuity equation. We show that the effect of the source term appearing in the evolution equation for ρ has to be explicitly taken into account when interpreting the NM equation from a Bohmian point of view.
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. PMID:22254409
Marginally specified generalized linear mixed models: a robust approach.
Mills, J E; Field, C A; Dupuis, D J
2002-12-01
Longitudinal data modeling is complicated by the necessity to deal appropriately with the correlation between observations made on the same individual. Building on an earlier nonrobust version proposed by Heagerty (1999, Biometrics 55, 688-698), our robust marginally specified generalized linear mixed model (ROBMS-GLMM) provides an effective method for dealing with such data. This model is one of the first to allow both population-averaged and individual-specific inference. As well, it adopts the flexibility and interpretability of generalized linear mixed models for introducing dependence but builds a regression structure for the marginal mean, allowing valid application with time-dependent (exogenous) and time-independent covariates. These new estimators are obtained as solutions of a robustified likelihood equation involving Huber's least favorable distribution and a collection of weights. Huber's least favorable distribution produces estimates that are resistant to certain deviations from the random effects distributional assumptions. Innovative weighting strategies enable the ROBMS-GLMM to perform well when faced with outlying observations both in the response and covariates. We illustrate the methodology with an analysis of a prospective longitudinal study of laryngoscopic endotracheal intubation, a skill that numerous health-care professionals are expected to acquire. The principal goal of our research is to achieve robust inference in longitudinal analyses. PMID:12495126
Rethinking the linear regression model for spatial ecological data.
Wagner, Helene H
2013-11-01
The linear regression model, with its numerous extensions including multivariate ordination, is fundamental to quantitative research in many disciplines. However, spatial or temporal structure in the data may invalidate the regression assumption of independent residuals. Spatial structure at any spatial scale can be modeled flexibly based on a set of uncorrelated component patterns (e.g., Moran's eigenvector maps, MEM) that is derived from the spatial relationships between sampling locations as defined in a spatial weight matrix. Spatial filtering thus addresses spatial autocorrelation in the residuals by adding such component patterns (spatial eigenvectors) as predictors to the regression model. However, space is not an ecologically meaningful predictor, and commonly used tests for selecting significant component patterns do not take into account the specific nature of these variables. This paper proposes "spatial component regression" (SCR) as a new way of integrating the linear regression model with Moran's eigenvector maps. In its unconditioned form, SCR decomposes the relationship between response and predictors by component patterns, whereas conditioned SCR provides an alternative method of spatial filtering, taking into account the statistical properties of component patterns in the design of statistical hypothesis tests. Application to the well-known multivariate mite data set illustrates how SCR may be used to condition for significant residual spatial structure and to identify additional predictors associated with residual spatial structure. Finally, I argue that all variance is spatially structured, hence spatial independence is best characterized by a lack of excess variance at any spatial scale, i.e., spatial white noise. PMID:24400490
Optimizing the Teaching-Learning Process Through a Linear Programming Model--Stage Increment Model.
ERIC Educational Resources Information Center
Belgard, Maria R.; Min, Leo Yoon-Gee
An operations research method to optimize the teaching-learning process is introduced in this paper. In particular, a linear programing model is proposed which, unlike dynamic or control theory models, allows the computer to react to the responses of a learner in seconds or less. To satisfy the assumptions of linearity, the seemingly complicated…
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.
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…
Comparison of linear and nonlinear subgrid scale model for hybrid RANS/LES modelling
NASA Astrophysics Data System (ADS)
Straka, Petr
2016-06-01
The contribution deals with application of the hybrid RANS/LES model for calculation of flow around the circular cylinder. Used hybrid RANS/LES model is based on transport equation for the kinetic energy which is shared in both RANS and LES modes. The linear and the nonlinear closure formulas are described in the paper. Numerical results are compared with the experimental data. Results show that the nonlinear model predicts development of the wake behind the cylinder better than the linear model.
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.
Tangent linear analysis of the Mosaic land surface model
NASA Astrophysics Data System (ADS)
Yang, Runhua; Cohn, Stephen E.; da Silva, Arlindo; Joiner, Joanna; Houser, Paul R.
2003-01-01
In this study, a tangent linear eigenanalysis is applied to the Mosaic land surface model (LSM) [, 1992] to examine the impacts of the model internal dynamics and physics on the land surface state variability. The tangent linear model (TLM) of the Mosaic LSM is derived numerically for two sets of basic states and two tile types of land condition, grass and bare soil. An additional TLM, for the soil moisture subsystem of this LSM, is derived analytically for the same cases to obtain explicit expressions for the eigenvalues. An eigenvalue of the TLM determines a characteristic timescale, and the corresponding eigenvector, or mode, describes a particular coupling among the perturbed states. The results show that (1) errors in initial conditions tend to decay with e-folding times given by the characteristic timescales; (2) the LSM exhibits a wide range of internal variability, modes mainly representing surface temperature and surface moisture perturbations exhibit short timescales, whereas modes mainly representing deep soil temperature perturbations and moisture transfer throughout the entire soil column exhibit much longer timescales; (3) the modes of soil moisture tend to be weakly coupled with other perturbed variables, and the mode representing the deep soil temperature perturbation has a consistent e-folding time across the experiments; (4) the key parameters include soil moisture, soil layer depth, and soil hydraulic parameters. The results agree qualitatively with previous findings. However, tangent linear eigenanalysis provides a new approach to the quantitative substantiation of those findings. Also, it reveals the evolution and the coupling of the perturbed land states that are useful for the development of land surface data assimilation schemes. One must be careful when generalizing the quantitative results since they are obtained with respect to two specific basic states and two simple land conditions. Also, the methodology employed here does not apply
Linear system identification via backward-time observer models
NASA Technical Reports Server (NTRS)
Juang, Jer-Nan; Phan, Minh
1993-01-01
This paper presents an algorithm to identify a state-space model of a linear system using a backward-time approach. The procedure consists of three basic steps. First, the Markov parameters of a backward-time observer are computed from experimental input-output data. Second, the backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) from which a backward-time state-space model is realized using the Eigensystem Realization Algorithm. Third, the obtained backward-time state space model is converted to the usual forward-time representation. Stochastic properties of this approach will be discussed. Experimental results are given to illustrate when and to what extent this concept works.
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.
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 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.
Spatial temporal disaggregation of daily rainfall from a generalized linear model
NASA Astrophysics Data System (ADS)
Segond, M.-L.; Onof, C.; Wheater, H. S.
2006-12-01
SummaryThis paper describes a methodology for continuous simulation of spatially-distributed hourly rainfall, based on observed data from a daily raingauge network. Generalized linear models (GLMs), which can represent the spatial and temporal non-stationarities of multi-site daily rainfall (Chandler, R.E., Wheater, H.S., 2002. Analysis of rainfall variability using generalised linear models: a case study from the west of Ireland. Water Resources Research, 38 (10), 1192. doi:10.1029/2001WR000906), are combined with a single-site disaggregation model based on Poisson cluster processes (Koutsoyiannis, D., Onof, C., 2001. Rainfall disaggregation using adjusting procedures on a Poisson cluster model. Journal of Hydrology 246, 109-122). The resulting sub-daily temporal profile is then applied linearly to all sites over the catchment to reproduce the spatially-varying daily totals. The method is tested for the River Lee catchment, UK, a tributary of the Thames covering an area of 1400 km 2. Twenty simulations of 12 years of hourly rainfall are generated at 20 sites and compared with the historical series. The proposed model preserves most standard statistics but has some limitations in the representation of extreme rainfall and the correlation structure. The method can be extended to sites within the modelled region not used in the model calibration.
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. PMID:25375468
The linear interaction model of personality effects in health communication.
Dutta-Bergman, Mohan Jyoti
2003-01-01
The recent growth of research in message tailoring has opened up new avenues for researchers to use personality variables for message delivery. This article builds on research on idiocentrism and self-monitoring to propose a framework for message appeal construction. Based on a scheme for appeal categorization borrowed from commercial marketing, the article suggests that low and high idiocentrics differ from each other in the way they respond to appeal types. Similarly, significant differences are demonstrated between low and high self-monitors in the realm of their response to message appeals. A linear interaction model is proposed to document the combined effects of self-monitoring and idiocentrism. PMID:12553779
Modeling of Linear Gas Tungsten Arc Welding of Stainless Steel
NASA Astrophysics Data System (ADS)
Maran, P.; Sornakumar, T.; Sundararajan, T.
2008-08-01
A heat and fluid flow model has been developed to solve the gas tungsten arc (GTA) linear welding problem for austenitic stainless steel. The moving heat source problem associated with the electrode traverse has been simplified into an equivalent two-dimensional (2-D) transient problem. The torch residence time has been calculated from the arc diameter and torch speed. The mathematical formulation considers buoyancy, electromagnetic induction, and surface tension forces. The governing equations have been solved by the finite volume method. The temperature and velocity fields have been determined. The theoretical predictions for weld bead geometry are in good agreement with experimental measurements.
Linear and generalized linear models for the detection of QTL effects on within-subject variability
Wittenburg, Dörte; Guiard, Volker; Liese, Friedrich; Reinsch, Norbert
2007-01-01
Summary Quantitative trait loci (QTLs) may affect not only the mean of a trait but also its variability. A special aspect is the variability between multiple measured traits of genotyped animals, such as the within-litter variance of piglet birth weights. The sample variance of repeated measurements is assigned as an observation for every genotyped individual. It is shown that the conditional distribution of the non-normally distributed trait can be approximated by a gamma distribution. To detect QTL effects in the daughter design, a generalized linear model with the identity link function is applied. Suitable test statistics are constructed to test the null hypothesis H0: No QTL with effect on the within-litter variance is segregating versus HA: There is a QTL with effect on the variability of birth weight within litter. Furthermore, estimates of the QTL effect and the QTL position are introduced and discussed. The efficiency of the presented tests is compared with a test based on weighted regression. The error probability of the first type as well as the power of QTL detection are discussed and compared for the different tests. PMID:18208630
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.
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.
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
NASA Astrophysics Data System (ADS)
Liang, Jianwu; Zhou, Jian; Shi, Jinjing; He, Guangqiang; Guo, Ying
2016-02-01
We characterize the efficiency of the practical continuous-variable quantum key distribution (CVQKD) while inserting the heralded noiseless linear amplifier (NLA) before detectors to increase the secret key rate and the maximum transmission distance in Gaussian channels. In the heralded NLA-based CVQKD system, the entanglement source is only placed in the middle while the two participants are unnecessary to trust their source. The intensities of source noise are sensitive to the tunable NLA with the parameter g in a suitable range and can be stabilized to the suitable constant values to eliminate the impact of channel noise and defeat the potential attacks. Simulation results show that there is a well balance between the secret key rate and the maximum transmission distance with the tunable NLA.
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…
A MCMC-Method for Models with Continuous Latent Responses.
ERIC Educational Resources Information Center
Maris, Gunter; Maris, Eric
2002-01-01
Introduces a new technique for estimating the parameters of models with continuous latent data. To streamline presentation of this Markov Chain Monte Carlo (MCMC) method, the Rasch model is used. Also introduces a new sampling-based Bayesian technique, the DA-T-Gibbs sampler. (SLD)
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.
Feedbacks, climate sensitivity and the limits of linear models.
Knutti, Reto; Rugenstein, Maria A A
2015-11-13
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 new model results with a historical and educational perspective to organize existing ideas around feedbacks and linear models. Our results suggest that the state- and forcing-dependency of feedbacks are probably not appreciated enough, and not considered appropriately in many studies. A non-constant feedback parameter likely explains some of the differences in estimates of equilibrium climate sensitivity from different methods and types of data. 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. PMID:26438287
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.
Measuring and modeling continuous quality distributions of soil organic matter
NASA Astrophysics Data System (ADS)
Bruun, S.; Gren, G. I. Ã.; Christensen, B. T.; Jensen, L. S.
2010-01-01
An understanding of the dynamics of soil organic matter (SOM) is important for our ability to develop management practices that preserve soil quality and sequester carbon. Most SOM decomposition models represent the heterogeneity of organic matter by a few discrete compartments with different turnover rates, while other models employ a continuous quality distribution. To make the multi-compartment models more mechanistic in nature, it has been argued that the compartments should be related to soil fractions actually occurring and having a functional role in the soil. In this paper, we make the case that fractionation methods that can measure continuous quality distributions should be developed, and that the temporal development of these distributions should be incorporated into SOM models. The measured continuous SOM quality distributions should hold valuable information not only for model development, but also for direct interpretation. Measuring continuous distributions requires that the measurements along the quality variable are so frequent that the distribution approaches the underlying continuum. Continuous distributions lead to possible simplifications of the model formulations, which considerably reduce the number of parameters needed to describe SOM turnover. A general framework for SOM models representing SOM across measurable quality distributions is presented and simplifications for specific situations are discussed. Finally, methods that have been used or have the potential to be used to measure continuous quality SOM distributions are reviewed. Generally, existing fractionation methods will have to be modified to allow measurement of distributions or new fractionation techniques will have to be developed. Developing the distributional models in concert with the fractionation methods to measure the distributions will be a major task. We hope the current paper will help generate the interest needed to accommodate this.
Measuring and modelling continuous quality distributions of soil organic matter
NASA Astrophysics Data System (ADS)
Bruun, S.; Gren, G. I.; Christensen, B. T.; Jensen, L. S.
2009-09-01
An understanding of the dynamics of soil organic matter (SOM) is important for our ability to develop management practices that preserve soil quality and sequester carbon. Most SOM decomposition models represent the heterogeneity of organic matter by a few discrete compartments with different turnover rates, while other models employ a continuous quality distribution. To make the multi-compartment models more mechanistic in nature, it has been argued that the compartments should be related to soil fractions actually occurring and having a functional role in the soil. In this paper, we make the case that fractionation methods that can measure continuous quality distributions should be developed, and that the temporal development of these distributions should be incorporated into SOM models. The measured continuous SOM quality distributions should hold valuable information not only for model development, but also for direct interpretation. Measuring continuous distributions requires that the measurements along the quality variable are so frequent that the distribution is approaching the underlying continuum. Continuous distributions leads to possible simplifications of the model formulations, which considerably reduce the number of parameters needed to describe SOM turnover. A general framework for SOM models representing SOM across measurable quality distributions is presented and simplifications for specific situations are discussed. Finally, methods that have been used or have the potential to be used to measure continuous quality SOM distributions are reviewed. Generally, existing fractionation methods have to be modified to allow measurement of distributions or new fractionation techniques will have to be developed. Developing the distributional models in concert with the fractionation methods to measure the distributions will be a major task. We hope the current paper will help spawning the interest needed to accommodate this.
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.
Pointwise Description for the Linearized Fokker-Planck-Boltzmann Model
NASA Astrophysics Data System (ADS)
Wu, Kung-Chien
2015-09-01
In this paper, we study the pointwise (in the space variable) behavior of the linearized Fokker-Planck-Boltzmann model for nonsmooth initial perturbations. The result reveals both the fluid and kinetic aspects of this model. The fluid-like waves are constructed as the long-wave expansion in the spectrum of the Fourier modes for the space variable, and it has polynomial time decay rate. We design a Picard-type iteration for constructing the increasingly regular kinetic-like waves, which are carried by the transport equations and have exponential time decay rate. The Mixture Lemma plays an important role in constructing the kinetic-like waves, this lemma was originally introduced by Liu-Yu (Commun Pure Appl Math 57:1543-1608, 2004) for Boltzmann equation, but the Fokker-Planck term in this paper creates some technical difficulties.
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. 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
Application of fractional derivative models in linear viscoelastic problems
NASA Astrophysics Data System (ADS)
Sasso, M.; Palmieri, G.; Amodio, D.
2011-11-01
Appropriate knowledge of viscoelastic properties of polymers and elastomers is of fundamental importance for a correct modelization and analysis of structures where such materials are present, especially when dealing with dynamic and vibration problems. In this paper experimental results of a series of compression and tension tests on specimens of styrene-butadiene rubber and polypropylene plastic are presented; tests consist of creep and relaxation tests, as well as cyclic loading at different frequencies. Experimental data are then used to calibrate some linear viscoelastic models; besides the classical approach based on a combination in series or parallel of standard mechanical elements as springs and dashpots, particular emphasis is given to the application of models whose constitutive equations are based on differential equations of fractional order (Fractional Derivative Model). The two approaches are compared analyzing their capability to reproduce all the experimental data for given materials; also, the main computational issues related with these models are addressed, and the advantage of using a limited number of parameters is demonstrated.
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.
The Dantzig Selector for Censored Linear Regression Models
Li, Yi; Dicker, Lee; Zhao, Sihai Dave
2013-01-01
The Dantzig variable selector has recently emerged as a powerful tool for fitting regularized regression models. To our knowledge, most work involving the Dantzig selector has been performed with fully-observed response variables. This paper proposes a new class of adaptive Dantzig variable selectors for linear regression models when the response variable is subject to right censoring. This is motivated by a clinical study to identify genes predictive of event-free survival in newly diagnosed multiple myeloma patients. Under some mild conditions, we establish the theoretical properties of our procedures, including consistency in model selection (i.e. the right subset model will be identified with a probability tending to 1) and the optimal efficiency of estimation (i.e. the asymptotic distribution of the estimates is the same as that when the true subset model is known a priori). The practical utility of the proposed adaptive Dantzig selectors is verified via extensive simulations. We apply our new methods to the aforementioned myeloma clinical trial and identify important predictive genes. PMID:24478569
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
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…
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
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.
Direction of Effects in Multiple Linear Regression Models.
Wiedermann, Wolfgang; von Eye, Alexander
2015-01-01
Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed. PMID:26609741
Fluctuation loops in a noise-driven linear circuit model
NASA Astrophysics Data System (ADS)
Teitsworth, Stephen; Ghanta, Akhil; Neu, John
Understanding the spatio-temporal structure of most probable fluctuation pathways to rarely occurring states is a central problem in the study of noise-driven, non-equilibrium dynamical systems. When the underlying system does not possess detailed balance, the optimal fluctuation pathway to a particular state and relaxation pathway from that state may combine to form a loop-like structure in the system phase space which we call a fluctuation loop. Here, we study fluctuation loops in a linear circuit model consisting of coupled RC elements, where each element is driven by its own noise source and, generally, the effective noise strengths of different elements are not equal. Using a stochastic Hamiltonian approach, we determine the optimal fluctuation pathways, and construct corresponding fluctuation loops. Analytical results agree closely with suitably averaged simulation results based on the associated Langevin equation. To better characterize fluctuation loops, we study the time-dependent area tensor that is swept out by individual stochastic trajectories in the system phase space. At long times, the area tensor scales linearly with time, with a coefficient that precisely vanishes when the system satisfies detailed balance.
Improvement of Continuous Hydrologic Models and HMS SMA Parameters Reduction
NASA Astrophysics Data System (ADS)
Rezaeian Zadeh, Mehdi; Zia Hosseinipour, E.; Abghari, Hirad; Nikian, Ashkan; Shaeri Karimi, Sara; Moradzadeh Azar, Foad
2010-05-01
Hydrological models can help us to predict stream flows and associated runoff volumes of rainfall events within a watershed. There are many different reasons why we need to model the rainfall-runoff processes of for a watershed. However, the main reason is the limitation of hydrological measurement techniques and the costs of data collection at a fine scale. Generally, we are not able to measure all that we would like to know about a given hydrological systems. This is very particularly the case for ungauged catchments. Since the ultimate aim of prediction using models is to improve decision-making about a hydrological problem, therefore, having a robust and efficient modeling tool becomes an important factor. Among several hydrologic modeling approaches, continuous simulation has the best predictions because it can model dry and wet conditions during a long-term period. Continuous hydrologic models, unlike event based models, account for a watershed's soil moisture balance over a long-term period and are suitable for simulating daily, monthly, and seasonal streamflows. In this paper, we describe a soil moisture accounting (SMA) algorithm added to the hydrologic modeling system (HEC-HMS) computer program. As is well known in the hydrologic modeling community one of the ways for improving a model utility is the reduction of input parameters. The enhanced model developed in this study is applied to Khosrow Shirin Watershed, located in the north-west part of Fars Province in Iran, a data limited watershed. The HMS SMA algorithm divides the potential path of rainfall onto a watershed into five zones. The results showed that the output of HMS SMA is insensitive with the variation of many parameters such as soil storage and soil percolation rate. The study's objective is to remove insensitive parameters from the model input using Multi-objective sensitivity analysis. Keywords: Continuous Hydrologic Modeling, HMS SMA, Multi-objective sensitivity analysis, SMA Parameters
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.
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
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.
Simulation of non-linear coregionalization models by FFTMA
NASA Astrophysics Data System (ADS)
Liang, Min; Marcotte, Denis; Shamsipour, Pejman
2016-04-01
A fast and efficient method to simulate multivariate fields with non-linear models of coregionalization (N-LMC) is described. The method generalizes FFTMA to the multivariate simulation of the N-LMC with symmetric cross-covariances, hence the name GFFTMA. It allows us for example to use an exponential model as the direct covariance for the main variable, a Cauchy model for the secondary variable and a K-Bessel model for the cross-covariance. Each covariance and cross-covariance are Fast Fourier Transformed (FFT) to get the discrete spectral densities. Then the spectral matrix is eigen-decomposed at each frequency separately to provide the square root matrix and to enforce positive-definiteness in cases where small negative eigenvalues are found. Finally the simulated spectrum is obtained as multiplication of the root matrix and the white noise coefficients. The method is particularly fast for covariances having derivatives at the origin and/or for covariances with long range. Hence, two-variables' 2D fields of 100 million pixels with all-Gaussian or all-cubic covariances and cross-covariance are both simulated in less than 200 s. The CPU-time increases only as N log(N) (N, the number of points to simulate). Additional realizations are obtained at a low marginal cost as the eigen-decomposition step needs to be done only once for the first realization. The main limitation of the approach is its rather stringent memory requirement. Synthetic examples illustrate the simulations of N-LMC with two and three variables for different combinations of the seven available models. It shows that the theoretical models are all well reproduced. An illustrative case-study on overburden thickness simulation is provided where the secondary information consists of a latent Gaussian variable identifying the geological domain.
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.
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. PMID:26422177
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
Blended Linear Models for Reduced Compliant Mechanical Systems.
Andrews, Sheldon; Teichmann, Marek; Kry, Paul G
2016-03-01
We present a method for the simulation of compliant, articulated structures using a plausible approximate model that focuses on modeling endpoint interaction. We approximate the structure's behavior about a reference configuration, resulting in a first order reduced compliant system, or FORK (-1) S. Several levels of approximation are available depending on which parts and surfaces we would like to have interactive contact forces, allowing various levels of detail to be selected. Our approach is fast and computation of the full structure's state may be parallelized. Furthermore, we present a method for reducing error by combining multiple FORK (-1)S models at different linearization points, through twist blending and matrix interpolation. Our approach is suitable for stiff, articulate grippers, such as those used in robotic simulation, or physics-based characters under static proportional derivative control. We demonstrate that simulations with our method can deal with kinematic chains and loops with non-uniform stiffness across joints, and that it produces plausible effects due to stiffness, damping, and inertia. PMID:26829238
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 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
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.
Model Lorentz-like equation with continuous spectrum
NASA Astrophysics Data System (ADS)
Dudyński, Marek
2016-07-01
We present a new model of the Lorentz gas kinetic equation for a system where the integral collision operator has a spectrum consisting of a continuous and discrete part. The spectral gap between the two kinds of the spectrum is an adjustable parameter of the model. This allows for the analysis of the existence and property of the hydrodynamical eigenstates and the meaning of the Grad's method of moments for the transition between hard and soft interactions.
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…
A Beta Item Response Model for Continuous Bounded Responses
ERIC Educational Resources Information Center
Noel, Yvonnick; Dauvier, Bruno
2007-01-01
An item response model is proposed for the analysis of continuous response formats in an item response theory (IRT) framework. With such formats, respondents are asked to report their response as a mark on a fixed-length graphical segment whose ends are labeled with extreme responses. An interpolation process is proposed as the response mechanism…
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,…
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 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
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…
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.
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.
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.
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
Formulation of the linear model from the nonlinear simulation for the F18 HARV
NASA Technical Reports Server (NTRS)
Hall, Charles E., Jr.
1991-01-01
The F-18 HARV is a modified F-18 Aircraft which is capable of flying in the post-stall regime in order to achieve superagility. The onset of aerodynamic stall, and continued into the post-stall region, is characterized by nonlinearities in the aerodynamic coefficients. These aerodynamic coefficients are not expressed as analytic functions, but rather in the form of tabular data. The nonlinearities in the aerodynamic coefficients yield a nonlinear model of the aircraft's dynamics. Nonlinear system theory has made many advances, but this area is not sufficiently developed to allow its application to this problem, since many of the theorems are existance theorems and that the systems are composed of analytic functions. Thus, the feedback matrices and the state estimators are obtained from linear system theory techniques. It is important, in order to obtain the correct feedback matrices and state estimators, that the linear description of the nonlinear flight dynamics be as accurate as possible. A nonlinear simulation is run under the Advanced Continuous Simulation Language (ACSL). The ACSL simulation uses FORTRAN subroutines to interface to the look-up tables for the aerodynamic data. ACSL has commands to form the linear representation for the system. Other aspects of this investigation are discussed.
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.
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.
Linear Models Based on Noisy Data and the Frisch Scheme*
Ning, Lipeng; Georgiou, Tryphon T.; Tannenbaum, Allen; Boyd, Stephen P.
2016-01-01
We address the problem of identifying linear relations among variables based on noisy measurements. This is a central question in the search for structure in large data sets. Often a key assumption is that measurement errors in each variable are independent. This basic formulation has its roots in the work of Charles Spearman in 1904 and of Ragnar Frisch in the 1930s. Various topics such as errors-in-variables, factor analysis, and instrumental variables all refer to alternative viewpoints on this problem and on ways to account for the anticipated way that noise enters the data. In the present paper we begin by describing certain fundamental contributions by the founders of the field and provide alternative modern proofs to certain key results. We then go on to consider a modern viewpoint and novel numerical techniques to the problem. The central theme is expressed by the Frisch–Kalman dictum, which calls for identifying a noise contribution that allows a maximal number of simultaneous linear relations among the noise-free variables—a rank minimization problem. In the years since Frisch’s original formulation, there have been several insights, including trace minimization as a convenient heuristic to replace rank minimization. We discuss convex relaxations and theoretical bounds on the rank that, when met, provide guarantees for global optimality. A complementary point of view to this minimum-rank dictum is presented in which models are sought leading to a uniformly optimal quadratic estimation error for the error-free variables. Points of contact between these formalisms are discussed, and alternative regularization schemes are presented. PMID:27168672
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.
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.
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
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.
A model of continuous quality improvement for health service organisations.
Thornber, M
1992-01-01
Continuous Quality Improvement (or Total Quality Management) is an approach to management originally used in manufacturing and now being applied in the health services. This article describes a model of Continuous Quality Improvement which has been used in NSW public and private hospitals. The model consists of Ten Key Elements. The first driving force of this model is 'defining quality in terms of customer expectations' of quality. The second driving force emphasises that 'quality improvement is a leadership issue'. Leaders are required to: coordinate staff participation in work process analysis; train staff in the customer service orientation; lead effective meetings and negotiate with both internal and external service partners. Increased staff motivation, quality improvement and reduction in running costs are seen to be the benefits of CQI for health service organisations. PMID:10117452
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 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
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. PMID:25267830
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
CSMP (Continuous System Modeling Program) modeling of brushless DC motors
NASA Astrophysics Data System (ADS)
Thomas, S. M.
1984-09-01
Recent improvements in rare earth magnets have made it possible to construct strong, lightweight, high horsepower DC motors. This has occasioned a reassessment of electromechanical actuators as alternatives to comparable pneumatic and hydraulic systems for use in flight control actuators for tactical missiles. This thesis develops a low-order mathematical model for the simulation and analysis of brushless DC motor performance. This model is implemented in CSMP language. It is used to predict such motor performance curves as speed, current and power versus torque. Electronic commutation based on Hall effect sensor positional feedback is simulated. Steady state motor behavior is studied under both constant and variable air gap flux conditions. The variable flux takes two different forms. In the first case, the flux is varied as a simple sinusoid. In the second case, the flux is varied as the sum of a sinusoid and one of its harmonics.
Modelling continuous fumigation of Nanticoke generating station plume
NASA Astrophysics Data System (ADS)
Misra, P. K.; Onlock, S.
A shoreline fumigation model is verified with the data from two studies conducted for the Ontario Hydro generating station plumes at Nanticoke. The model reproduces the physical system of continuous fumigation reasonably well. Predictions are shown to agree with observed values within the framework of the uncertainties in various input parameters. As expected, the parameters defining the state of the onshore air mass are critical in the estimate of ground level concentrations inside the fumigation zone. Also, sampling time plays an important role, even though data from the averaging of three to four helicopter passes conform to half hourly average concentration data and model predictions very well.
Formal modeling and verification of fractional order linear systems.
Zhao, Chunna; Shi, Likun; Guan, Yong; Li, Xiaojuan; Shi, Zhiping
2016-05-01
This paper presents a formalization of a fractional order linear system in a higher-order logic (HOL) theorem proving system. Based on the formalization of the Grünwald-Letnikov (GL) definition, we formally specify and verify the linear and superposition properties of fractional order systems. The proof provides a rigor and solid underpinnings for verifying concrete fractional order linear control systems. Our implementation in HOL demonstrates the effectiveness of our approach in practical applications. PMID:27126601
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
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
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.
Modeling and Processing of Continuous 3D Elastic Wavefield Data
NASA Astrophysics Data System (ADS)
Milkereit, B.; Bohlen, T.
2001-12-01
Continuous seismic wavefields are excited by earthquake clustering, induced seismicity in reservoirs, and mining. In hydrocarbon reservoirs, for example, pore pressure changes and fluid flow (mass transfer) will cause incremental deviatoric stresses sufficient to trigger and sustain seismic activity. Here we address three aspects of seismic wavefields in three-dimensional heterogeneous media triggered by distributed sources in space and time: forward modeling, multichannel data processing, and source location imaging. A power law distribution of seismic sources (such as the Gutenberg-Richter law) is used for the modeling of viscoelastic/elastic wave propagation through a realistic earth model. 3D modeling provides new insight in the interaction of multi-source wavefields and the role of scale-dependend elastic model parameters on transmitted and reflected/back-scattered wavefields. There exists a strong correlation between the spatial properties of the compressional, shear wave and density perturbations and the lateral correlation length of the resulting reflected or transmitted seismic wavefields. Modeling is based on the implementation of 3D elastic/viscoelastic FD codes on massive parallel and/or distributed computing resources using MPI (message passing interface). For parallelization, large grid 3D earth models are decomposed into subvolume processing elements whereby each processing element is updating the wavefield within its portion of the grid. Processing of continuous seismic wavefields excited by multiple distributed sources is based on a combination of crosscorrelated or slowness-transformed array data and Kirchhoff or reverse time migration for source location or source volume imaging. The appearance of slowness in both migration and array data processing suggests the possibility of combining them into a single process. In order to place further constraints on the migration, the directivity properties of 3-component receiver arrays can be included in
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
Kinetic model of continuous-wave flow chemical lasers
NASA Astrophysics Data System (ADS)
Gao, Z.; X., E.
1982-02-01
A kinetic approach to modeling the gain in a chemical wave continuous laser when the lasing frequency is coincident with the center of the line shape is presented. Governing equations are defined for the relaxing behavior of an initially nonequilibrium distribution toward the local equilibrium Boltzmann-Maxwellian distribution. A new gain is introduced which is related to the thermal motion of the molecules and cold-reaction and premixed CW models are discussed. Coincidence of the lasing frequency with the line shape is demonstrated to result in a radiative intensity within the homogeneous broadening limit. The rate model predictions are compared with those of the kinetic model. It is found that when the broadening parameter is less than 0.2 the kinetic model more accurately describes the behavior of the CW chemical laser.
Mathematical model for multicomponent separations on the continuous annular chromatograph
Bratzler, R.L.; Begovich, J.M.
1980-12-01
A model for multicomponent separations on ion exchange columns has been adapted for use in studying the performance of the continuous annular chromatograph. The model accurately predicts solute peak positions in the column effluent and qualitatively predicts trends in solute effluent resolution as a function of increasing bandwidth of the solute feed pulse. The major virtues of the model are its simplicity in terms of the calculations involved and the fact that it incorporates the nonlinear solute-resin binding isotherms common in many ion exchange separations. Because dispersion effects are not accounted for in the model, discrepancies exist between the shapes of the effluent peaks predicted by the model and those determined experimentally.
Functional linear models to test for differences in prairie wetland hydraulic gradients
Greenwood, Mark C.; Sojda, Richard S.; Preston, Todd M.
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.
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
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
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. PMID:26627762
Markov Boundary Discovery with Ridge Regularized Linear Models
Visweswaran, Shyam
2016-01-01
Ridge regularized linear models (RRLMs), such as ridge regression and the SVM, are a popular group of methods that are used in conjunction with coefficient hypothesis testing to discover explanatory variables with a significant multivariate association to a response. However, many investigators are reluctant to draw causal interpretations of the selected variables due to the incomplete knowledge of the capabilities of RRLMs in causal inference. Under reasonable assumptions, we show that a modified form of RRLMs can get “very close” to identifying a subset of the Markov boundary by providing a worst-case bound on the space of possible solutions. The results hold for any convex loss, even when the underlying functional relationship is nonlinear, and the solution is not unique. Our approach combines ideas in Markov boundary and sufficient dimension reduction theory. Experimental results show that the modified RRLMs are competitive against state-of-the-art algorithms in discovering part of the Markov boundary from gene expression data. PMID:27170915
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…
Surrogate model reduction for linear dynamic systems based on a frequency domain modal analysis
NASA Astrophysics Data System (ADS)
Kim, T.
2015-10-01
A novel model reduction methodology for linear dynamic systems with parameter variations is presented based on a frequency domain formulation and use of the proper orthogonal decomposition. For an efficient treatment of parameter variations, the system matrices are divided into a nominal and an incremental part. It is shown that the perturbed part is modally equivalent to a new system where the incremental matrices are isolated into the forcing term. To account for the continuous changes in the parameters, the single-composite-input is invoked with a finite number of predetermined incremental matrices. The frequency-domain Karhunen-Loeve procedure is used to calculate a rich set of basis modes accounting for the variations. For demonstration, the new procedure is applied to a finite element model of the Goland wing undergoing oscillations and shown to produce extremely accurate reduced-order surrogate model for a wide range of parameter variations.
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.
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).
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.
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.
Identification and automatic segmentation of multiphasic cell growth using a linear hybrid model.
Hartmann, András; Neves, Ana Rute; Lemos, João M; Vinga, Susana
2016-09-01
This article considers a new mathematical model for the description of multiphasic cell growth. A linear hybrid model is proposed and it is shown that the two-parameter logistic model with switching parameters can be represented by a Switched affine AutoRegressive model with eXogenous inputs (SARX). The growth phases are modeled as continuous processes, while the switches between the phases are considered to be discrete events triggering a change in growth parameters. This framework provides an easily interpretable model, because the intrinsic behavior is the same along all the phases but with a different parameterization. Another advantage of the hybrid model is that it offers a simpler alternative to recent more complex nonlinear models. The growth phases and parameters from datasets of different microorganisms exhibiting multiphasic growth behavior such as Lactococcus lactis, Streptococcus pneumoniae, and Saccharomyces cerevisiae, were inferred. The segments and parameters obtained from the growth data are close to the ones determined by the experts. The fact that the model could explain the data from three different microorganisms and experiments demonstrates the strength of this modeling approach for multiphasic growth, and presumably other processes consisting of multiple phases. PMID:27424949
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
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.
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
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. PMID:26423044
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.
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.
Demographic inference under a spatially continuous coalescent model.
Joseph, T A; Hickerson, M J; Alvarado-Serrano, D F
2016-08-01
In contrast with the classical population genetics theory that models population structure as discrete panmictic units connected by migration, many populations exhibit heterogeneous spatial gradients in population connectivity across semi-continuous habitats. The historical dynamics of such spatially structured populations can be captured by a spatially explicit coalescent model recently proposed by Etheridge (2008) and Barton et al. (2010a, 2010b) and whereby allelic lineages are distributed in a two-dimensional spatial continuum and move within this continuum based on extinction and coalescent events. Though theoretically rigorous, this model, which we here refer to as the continuum model, has not yet been implemented for demographic inference. To this end, here we introduce and demonstrate a statistical pipeline that couples the coalescent simulator of Kelleher et al. (2014) that simulates genealogies under the continuum model, with an approximate Bayesian computation (ABC) framework for parameter estimation of neighborhood size (that is, the number of locally breeding individuals) and dispersal ability (that is, the distance an offspring can travel within a generation). Using empirically informed simulations and simulation-based ABC cross-validation, we first show that neighborhood size can be accurately estimated. We then apply our pipeline to the South African endemic shrub species Berkheya cuneata to use the resulting estimates of dispersal ability and neighborhood size to infer the average population density of the species. More generally, we show that spatially explicit coalescent models can be successfully integrated into model-based demographic inference. PMID:27118157
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
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
Bayesian Learning of a Language Model from Continuous Speech
NASA Astrophysics Data System (ADS)
Neubig, Graham; Mimura, Masato; Mori, Shinsuke; Kawahara, Tatsuya
We propose a novel scheme to learn a language model (LM) for automatic speech recognition (ASR) directly from continuous speech. In the proposed method, we first generate phoneme lattices using an acoustic model with no linguistic constraints, then perform training over these phoneme lattices, simultaneously learning both lexical units and an LM. As a statistical framework for this learning problem, we use non-parametric Bayesian statistics, which make it possible to balance the learned model's complexity (such as the size of the learned vocabulary) and expressive power, and provide a principled learning algorithm through the use of Gibbs sampling. Implementation is performed using weighted finite state transducers (WFSTs), which allow for the simple handling of lattice input. Experimental results on natural, adult-directed speech demonstrate that LMs built using only continuous speech are able to significantly reduce ASR phoneme error rates. The proposed technique of joint Bayesian learning of lexical units and an LM over lattices is shown to significantly contribute to this improvement.
Oscillation threshold of a clarinet model: a numerical continuation approach.
Karkar, Sami; Vergez, Christophe; Cochelin, Bruno
2012-01-01
This paper focuses on the oscillation threshold of single reed instruments. Several characteristics such as blowing pressure at threshold, regime selection, and playing frequency are known to change radically when taking into account the reed dynamics and the flow induced by the reed motion. Previous works have shown interesting tendencies, using analytical expressions with simplified models. In the present study, a more elaborated physical model is considered. The influence of several parameters, depending on the reed properties, the design of the instrument or the control operated by the player, are studied. Previous results on the influence of the reed resonance frequency are confirmed. New results concerning the simultaneous influence of two model parameters on oscillation threshold, regime selection and playing frequency are presented and discussed. The authors use a numerical continuation approach. Numerical continuation consists in following a given solution of a set of equations when a parameter varies. Considering the instrument as a dynamical system, the oscillation threshold problem is formulated as a path following of Hopf bifurcations, generalizing the usual approach of the characteristic equation, as used in previous works. The proposed numerical approach proves to be useful for the study of musical instruments. It is complementary to analytical analysis and direct time-domain or frequency-domain simulations since it allows to derive information that is hardly reachable through simulation, without the approximations needed for analytical approach. PMID:22280691
Oscillation threshold of a clarinet model: A numerical continuation approach
NASA Astrophysics Data System (ADS)
Karkar, Sami; Vergez, Christophe; Cochelin, Bruno
This paper focuses on the oscillation threshold of single reed instruments. Several characteristics such as blowing pressure at threshold, regime selection, and playing frequency are known to change radically when taking into account the reed dynamics and the flow induced by the reed motion. Previous works have shown interesting tendencies, using analytical expressions with simplified models. In the present study, a more elaborated physical model is considered. The influence of several parameters, depending on the reed properties, the design of the instrument or the control operated by the player, are studied. Previous results on the influence of the reed resonance frequency are confirmed. New results concerning the simultaneous influence of two model parameters on oscillation threshold, regime selection and playing frequency are presented and discussed. The authors use a numerical continuation approach. Numerical continuation consists in following a given solution of a set of equations when a parameter varies. Considering the instrument as a dynamical system, the oscillation threshold problem is formulated as a path following of Hopf bifurcations, generalizing the usual approach of the characteristic equation, as used in previous works. The proposed numerical approach proves to be useful for the study of musical instruments. It is complementary to analytical analysis and direct time-domain or frequency-domain simulations since it allows to derive information that is hardly reachable through simulation, without the approximations needed for analytical approach.
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
Segment-based acoustic models for continuous speech recognition
NASA Astrophysics Data System (ADS)
Ostendorf, Mari; Rohlicek, J. R.
1993-07-01
This research aims to develop new and more accurate stochastic models for speaker-independent continuous speech recognition, by extending previous work in segment-based modeling and by introducing a new hierarchical approach to representing intra-utterance statistical dependencies. These techniques, which are more costly than traditional approaches because of the large search space associated with higher order models, are made feasible through rescoring a set of HMM-generated N-best sentence hypotheses. We expect these different modeling techniques to result in improved recognition performance over that achieved by current systems, which handle only frame-based observations and assume that these observations are independent given an underlying state sequence. In the fourth quarter of the project, we have completed the following: (1) ported our recognition system to the Wall Street Journal task, a standard task in the ARPA community; (2) developed an initial dependency-tree model of intra-utterance observation correlation; and (3) implemented baseline language model estimation software. Our initial results on the Wall Street Journal task are quite good and represent significantly improved performance over most HMM systems reporting on the Nov. 1992 5k vocabulary test set.
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
Likert pain score modeling: a Markov integer model and an autoregressive continuous model.
Plan, E L; Elshoff, J-P; Stockis, A; Sargentini-Maier, M L; Karlsson, M O
2012-05-01
Pain intensity is principally assessed using rating scales such as the 11-point Likert scale. In general, frequent pain assessments are serially correlated and underdispersed. The aim of this investigation was to develop population models adapted to fit the 11-point pain scale. Daily Likert scores were recorded over 18 weeks by 231 patients with neuropathic pain from a clinical trial placebo group. An integer model consisting of a truncated generalized Poisson (GP) distribution with Markovian transition probability inflation was implemented in NONMEM 7.1.0. It was compared to a logit-transformed autoregressive continuous model with correlated residual errors. In both models, the score baseline was estimated to be 6.2 and the placebo effect to be 19%. Developed models similarly retrieved consistent underlying features of the data and therefore correspond to platform models for drug effect detection. The integer model was complex but flexible, whereas the continuous model can more easily be developed, although requires longer runtimes. PMID:22433987
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
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.
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…
Power and Bias in Hierarchical Linear Growth Models: More Measurements of Fewer People
ERIC Educational Resources Information Center
Haardoerfer, Regine
2010-01-01
Hierarchical Linear Modeling (HLM) sample size recommendations are mostly made with traditional group-design research in mind, as HLM as been used almost exclusively in group-design studies. Single-case research can benefit from utilizing hierarchical linear growth modeling, but sample size recommendations for growth modeling with HLM are scarce…
Continuous percolation transition in suppressed random cluster growth model
NASA Astrophysics Data System (ADS)
Roy, Bappaditya; Santra, S. B.
2016-05-01
A new suppressed cluster growth model on 2D square lattice combining Hoshen-Kopelman and Leath approaches is studied here. The lattice sites are initially occupied randomly with probability (ρ). The empty perimeter sites of the clusters of occupied sites are grown with a cluster size dependent probability. The growth probability is then lowest for the largest cluster and highest for the smallest cluster. At the end of growth process all the cluster related quantities are estimated and they are found to display power law scaling as in percolation transition. However, the values of the critical exponents vary continuously with ρ, the initial seed concentration. At higher values of ρ, the model belongs the percolation universality class.
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.
Towards a continuous population model for natural language vowel shift.
Shipman, Patrick D; Faria, Sérgio H; Strickland, Christopher
2013-09-01
The Great English Vowel Shift of 16th-19th centuries and the current Northern Cities Vowel Shift are two examples of collective language processes characterized by regular phonetic changes, that is, gradual changes in vowel pronunciation over time. Here we develop a structured population approach to modeling such regular changes in the vowel systems of natural languages, taking into account learning patterns and effects such as social trends. We treat vowel pronunciation as a continuous variable in vowel space and allow for a continuous dependence of vowel pronunciation in time and age of the speaker. The theory of mixtures with continuous diversity provides a framework for the model, which extends the McKendrick-von Foerster equation to populations with age and phonetic structures. We develop the general balance equations for such populations and propose explicit expressions for the factors that impact the evolution of the vowel pronunciation distribution. For illustration, we present two examples of numerical simulations. In the first one we study a stationary solution corresponding to a state of phonetic equilibrium, in which speakers of all ages share a similar phonetic profile. We characterize the variance of the phonetic distribution in terms of a parameter measuring a ratio of phonetic attraction to dispersion. In the second example we show how vowel shift occurs upon starting with an initial condition consisting of a majority pronunciation that is affected by an immigrant minority with a different vowel pronunciation distribution. The approach developed here for vowel systems may be applied also to other learning situations and other time-dependent processes of cognition in self-interacting populations, like opinions or perceptions. PMID:23624180
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
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
A Hybrid Oscillatory Interference/Continuous Attractor Network Model of Grid Cell Firing
2014-01-01
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
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.
Traumatic Neuroma in Continuity Injury Model in Rodents
Kemp, Stephen William Peter; Khu, Kathleen Joy Ong Lopez; Kumar, Ranjan; Webb, Aubrey A.; Midha, Rajiv
2012-01-01
Abstract Traumatic neuroma in continuity (NIC) results in profound neurological deficits, and its management poses the most challenging problem to peripheral nerve surgeons today. The absence of a clinically relevant experimental model continues to handicap our ability to investigate ways of better diagnosis and treatment for these disabling injuries. Various injury techniques were tested on Lewis rat sciatic nerves. Optimal experimental injuries that consistently resulted in NIC combined both intense focal compression and traction forces. Nerves were harvested at 0, 5, 13, 21, and 65 days for histological examination. Skilled locomotion and ground reaction force (GRF) analysis were performed up to 9 weeks on the experimental (n=6) and crush-control injuries (n=5). Focal widening, disruption of endoneurium and perineurium with aberrant intra- and extrafascicular axonal regeneration and progressive fibrosis was consistently demonstrated in 14 of 14 nerves with refined experimental injuries. At 8 weeks, experimental animals displayed a significantly greater slip ratio in both skilled locomotor assessments, compared to nerve crush animals (p<0.01). GRFs of the crush- injured animals showed earlier improvement compared to the experimental animals, whose overall GRF patterns failed to recover as well as the crush group. We have demonstrated histological features and poor functional recovery consistent with NIC formation in a rat model. The injury mechanism employed combines traction and compression forces akin to the physical forces at play in clinical nerve injuries. This model may serve as a tool to help diagnose this injury earlier and to develop intervention strategies to improve patient outcomes. PMID:22011082
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.
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. PMID:25817474
Wu, Tsan-Pei; Wang, Xiao-Qun; Guo, Guang-Yu; Anders, Frithjof; Chung, Chung-Hou
2016-05-01
The quantum criticality of the two-lead two-channel pseudogap Anderson impurity model is studied. Based on the non-crossing approximation (NCA) and numerical renormalization group (NRG) approaches, we calculate both the linear and nonlinear conductance of the model at finite temperatures with a voltage bias and a power-law vanishing conduction electron density of states, [Formula: see text] (0 < r < 1) near the Fermi energy [Formula: see text]. At a fixed lead-impurity hybridization, a quantum phase transition from the two-channel Kondo (2CK) to the local moment (LM) phase is observed with increasing r from r = 0 to [Formula: see text]. Surprisingly, in the 2CK phase, different power-law scalings from the well-known [Formula: see text] or [Formula: see text] form is found. Moreover, novel power-law scalings in conductances at the 2CK-LM quantum critical point are identified. Clear distinctions are found on the critical exponents between linear and non-linear conductance at criticality. The implications of these two distinct quantum critical properties for the non-equilibrium quantum criticality in general are discussed. PMID:27045815
NASA Astrophysics Data System (ADS)
Wu, Tsan-Pei; Wang, Xiao-Qun; Guo, Guang-Yu; Anders, Frithjof; Chung, Chung-Hou
2016-05-01
The quantum criticality of the two-lead two-channel pseudogap Anderson impurity model is studied. Based on the non-crossing approximation (NCA) and numerical renormalization group (NRG) approaches, we calculate both the linear and nonlinear conductance of the model at finite temperatures with a voltage bias and a power-law vanishing conduction electron density of states, {ρ\\text{c}}(ω )\\propto |ω -{μ\\text{F}}{{|}r} (0 < r < 1) near the Fermi energy {μ\\text{F}} . At a fixed lead-impurity hybridization, a quantum phase transition from the two-channel Kondo (2CK) to the local moment (LM) phase is observed with increasing r from r = 0 to r={{r}\\text{c}}<1 . Surprisingly, in the 2CK phase, different power-law scalings from the well-known \\sqrt{T} or \\sqrt{V} form is found. Moreover, novel power-law scalings in conductances at the 2CK-LM quantum critical point are identified. Clear distinctions are found on the critical exponents between linear and non-linear conductance at criticality. The implications of these two distinct quantum critical properties for the non-equilibrium quantum criticality in general are discussed.
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.
A continuous function model for path prediction of entities
NASA Astrophysics Data System (ADS)
Nanda, S.; Pray, R.
2007-04-01
As militaries across the world continue to evolve, the roles of humans in various theatres of operation are being increasingly targeted by military planners for substitution with automation. Forward observation and direction of supporting arms to neutralize threats from dynamic adversaries is one such example. However, contemporary tracking and targeting systems are incapable of serving autonomously for they do not embody the sophisticated algorithms necessary to predict the future positions of adversaries with the accuracy offered by the cognitive and analytical abilities of human operators. The need for these systems to incorporate methods characterizing such intelligence is therefore compelling. In this paper, we present a novel technique to achieve this goal by modeling the path of an entity as a continuous polynomial function of multiple variables expressed as a Taylor series with a finite number of terms. We demonstrate the method for evaluating the coefficient of each term to define this function unambiguously for any given entity, and illustrate its use to determine the entity's position at any point in time in the future.
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. PMID:25401267
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. PMID:24727186
Centrifuge modeling of buried continuous pipelines subjected to normal faulting
NASA Astrophysics Data System (ADS)
Moradi, Majid; Rojhani, Mahdi; Galandarzadeh, Abbas; Takada, Shiro
2013-03-01
Seismic ground faulting is the greatest hazard for continuous buried pipelines. Over the years, researchers have attempted to understand pipeline behavior mostly via numerical modeling such as the finite element method. The lack of well-documented field case histories of pipeline failure from seismic ground faulting and the cost and complicated facilities needed for full-scale experimental simulation mean that a centrifuge-based method to determine the behavior of pipelines subjected to faulting is best to verify numerical approaches. This paper presents results from three centrifuge tests designed to investigate continuous buried steel pipeline behavior subjected to normal faulting. The experimental setup and procedure are described and the recorded axial and bending strains induced in a pipeline are presented and compared to those obtained via analytical methods. The influence of factors such as faulting offset, burial depth and pipe diameter on the axial and bending strains of pipes and on ground soil failure and pipeline deformation patterns are also investigated. Finally, the tensile rupture of a pipeline due to normal faulting is investigated.
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.
ERIC Educational Resources Information Center
Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.
2006-01-01
Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…
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)…
Toscano, Joseph C.; McMurray, Bob; Dennhardt, Joel; Luck, Steven. J.
2012-01-01
Speech sounds are highly variable, yet listeners readily extract information from them and transform continuous acoustic signals into meaningful categories during language comprehension. A central question is whether perceptual encoding captures continuous acoustic detail in a one-to-one fashion or whether it is affected by categories. We addressed this in an event-related potential (ERP) experiment in which listeners categorized spoken words that varied along a continuous acoustic dimension (voice onset time; VOT) in an auditory oddball task. We found that VOT effects were present through a late stage of perceptual processing (N1 component, ca. 100 ms poststimulus) and were independent of categories. In addition, effects of within-category differences in VOT were present at a post-perceptual categorization stage (P3 component, ca. 450 ms poststimulus). Thus, at perceptual levels, acoustic information is encoded continuously, independent of phonological information. Further, at phonological levels, fine-grained acoustic differences are preserved along with category information. PMID:20935168
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.
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.
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.
Some computer simulations based on the linear relative risk model
Gilbert, E.S.
1991-10-01
This report presents the results of computer simulations designed to evaluate and compare the performance of the likelihood ratio statistic and the score statistic for making inferences about the linear relative risk mode. The work was motivated by data on workers exposed to low doses of radiation, and the report includes illustration of several procedures for obtaining confidence limits for the excess relative risk coefficient based on data from three studies of nuclear workers. The computer simulations indicate that with small sample sizes and highly skewed dose distributions, asymptotic approximations to the score statistic or to the likelihood ratio statistic may not be adequate. For testing the null hypothesis that the excess relative risk is equal to zero, the asymptotic approximation to the likelihood ratio statistic was adequate, but use of the asymptotic approximation to the score statistic rejected the null hypothesis too often. Frequently the likelihood was maximized at the lower constraint, and when this occurred, the asymptotic approximations for the likelihood ratio and score statistics did not perform well in obtaining upper confidence limits. The score statistic and likelihood ratio statistics were found to perform comparably in terms of power and width of the confidence limits. It is recommended that with modest sample sizes, confidence limits be obtained using computer simulations based on the score statistic. Although nuclear worker studies are emphasized in this report, its results are relevant for any study investigating linear dose-response functions with highly skewed exposure distributions. 22 refs., 14 tabs.
Linear programming model of a meat processing plant
Shah, S.A.; Okos, M.R.; Reklaitis, G.V.
1981-01-01
A multi-period and multi-product production-planning model of an operational meat processing plant is presented. The model input is the time-varying customer demand and the output is the optimum product mix. The model results are interpreted and compared with actual data. Various production strategies are evaluated.
Estimating water quality using linear mixed models with stream discharge and turbidity
NASA Astrophysics Data System (ADS)
Lessels, J. S.; Bishop, T. F. A.
2013-08-01
Most water quality monitoring schemes rely on estimation methods as it is often far too expensive to monitor water quality properties continuously. Estimations are used to evaluate management strategies and long term trends. It is critical that the estimation methods provide accurate estimations and an accurate estimate of the associated uncertainty. Currently the most common estimation methods assume observations are sampled using a probabilistic sampling scheme, however this assumption is often not met. This paper evaluated the ability of a linear mixed model to estimate water quality concentration values based on observations collected using non-probabilistic sampling. The linear mixed models were used to predict total phosphorus and total nitrogen observations from two catchments in south east Australia. A comparison between stream discharge and turbidity as predictors is made to investigate the effectiveness of turbidity to estimate water quality. In addition to stream discharge and turbidity, several covariates were derived from stream discharge in an attempt to account for hydrological processes. To compare models and their covariates leave one out event cross validation was performed. Event cross validation evaluated predictions during periods of high stream discharge. The inclusion of temporal auto-correlation component improved the accuracy of all models for total phosphorus and total nitrogen. For both catchments the use of turbidity instead of stream discharge increased the accuracy of predictions by at least 15% for total phosphorus and total nitrogen. However, event based cross validation indicated that a combination of both turbidity and stream discharge based variables provided more accurate predictions, decreasing the event RMSE by 18% for total phosphorus and 24% for total nitrogen. In catchments characterised by long periods of base-flow and short rainfall events the addition of turbidity measurements provide more accurate predictions during base
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
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
Towards continualized task-based resolution modeling in PET imaging
NASA Astrophysics Data System (ADS)
Ashrafinia, Saeed; Karakatsanis, Nicolas; Mohy-ud-Din, Hassan; Rahmim, Arman
2014-03-01
We propose a generalized resolution modeling (RM) framework, including extensive task-based optimization, wherein we continualize the conventionally discrete framework of RM vs. no RM, to include varying degrees of RM. The proposed framework has the advantage of providing a trade-off between the enhanced contrast recovery by RM and the reduced inter-voxel correlations in the absence of RM, and to enable improved task performance. The investigated context was that of oncologic lung FDG PET imaging. Given a realistic blurring kernel of FWHM h (`true PSF'), we performed iterative EM including RM using a wide range of `modeled PSF' kernels with varying widths h. In our simulations, h = 6mm, while h varied from 0 (no RM) to 12mm, thus considering both underestimation and overestimation of the true PSF. Detection task performance was performed using prewhitened (PWMF) and nonprewhitened matched filter (NPWMF) observers. It was demonstrated that an underestimated resolution blur (h = 4mm) enhanced task performance, while slight over-estimation (h = 7mm) also achieved enhanced performance. The latter is ironically attributed to the presence of ringing artifacts. Nonetheless, in the case of the NPWMF, the increasing intervoxel correlations with increasing values of h degrade detection task performance, and underestimation of the true PSF provides the optimal task performance. The proposed framework also achieves significant improvement of reproducibility, which is critical in quantitative imaging tasks such as treatment response monitoring.
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.
Nonlinearity measure and internal model control based linearization in anti-windup design
Perev, Kamen
2013-12-18
This paper considers the problem of internal model control based linearization in anti-windup design. The nonlinearity measure concept is used for quantifying the control system degree of nonlinearity. The linearizing effect of a modified internal model control structure is presented by comparing the nonlinearity measures of the open-loop and closed-loop systems. It is shown that the linearization properties are improved by increasing the control system local feedback gain. However, it is emphasized that at the same time the stability of the system deteriorates. The conflicting goals of stability and linearization are resolved by solving the design problem in different frequency ranges.
Model Averaging Methods for Weight Trimming in Generalized Linear Regression Models
Elliott, Michael R.
2012-01-01
In sample surveys where units have unequal probabilities of inclusion, associations between the inclusion probability and the statistic of interest can induce bias in unweighted estimates. This is true even in regression models, where the estimates of the population slope may be biased if the underlying mean model is misspecified or the sampling is nonignorable. Weights equal to the inverse of the probability of inclusion are often used to counteract this bias. Highly disproportional sample designs have highly variable weights; weight trimming reduces large weights to a maximum value, reducing variability but introducing bias. Most standard approaches are ad hoc in that they do not use the data to optimize bias-variance trade-offs. This article uses Bayesian model averaging to create “data driven” weight trimming estimators. We extend previous results for linear regression models (Elliott 2008) to generalized linear regression models, developing robust models that approximate fully-weighted estimators when bias correction is of greatest importance, and approximate unweighted estimators when variance reduction is critical. PMID:23275683
Dynamic Spring Model of Rubber Bush Based on Linear Viscoelasticity
NASA Astrophysics Data System (ADS)
Fujikawa, Masaki; Sato, Masami; Kobayashi, Takaya
A set of simplified formulae is proposed for estimating the dynamic spring constants of rubber bushes used in suspension systems. These formulae are structured by extending a set of elastic solutions[Editor2] proposed before to calculate the dynamic spring constants according to the associated law (pseudo-elasticity) of the linear viscoelasticity theory. A unique feature of this method is that it helps in the easy and quick evaluation of the dynamic behavior of rubber bushes for all the six degrees of freedom (axial loading, loading normal to an axis in two directions, wrench in two directions, and torsion[Editor3]) with no direct involvement of the FEM. In order to validate this method of calculation, the results obtained for all the degrees of freedom are compared with those obtained using the FEM. It is verified that this approach is capable of qualitatively reproducing the results obtained by using the FEM analysis.
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)
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.
Time to change from a simple linear model to a complex systems model
2016-01-01
A simple linear model to test the hypothesis based on one-on-one relationship has been used to find the causative factors of diseases. However, we now know that not just one, but many factors from different systems such as chemical exposure, genes, epigenetic changes, and proteins are involved in the pathogenesis of chronic diseases such as diabetes mellitus. So, with availability of modern technologies to understand the intricate nature of relations among complex systems, we need to move forward to the future by taking complex systems model. PMID:27158003
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
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.
Semi-physical neural modeling for linear signal restoration.
Bourgois, Laurent; Roussel, Gilles; Benjelloun, Mohammed
2013-02-01
This paper deals with the design methodology of an Inverse Neural Network (INN) model. The basic idea is to carry out a semi-physical model gathering two types of information: the a priori knowledge of the deterministic rules which govern the studied system and the observation of the actual conduct of this system obtained from experimental data. This hybrid model is elaborated by being inspired by the mechanisms of a neuromimetic network whose structure is constrained by the discrete reverse-time state-space equations. In order to validate the approach, some tests are performed on two dynamic models. The first suggested model is a dynamic system characterized by an unspecified r-order Ordinary Differential Equation (ODE). The second one concerns in particular the mass balance equation for a dispersion phenomenon governed by a Partial Differential Equation (PDE) discretized on a basic mesh. The performances are numerically analyzed in terms of generalization, regularization and training effort. PMID:23275139
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
Non-linear modelling and optimal control of a hydraulically actuated seismic isolator test rig
NASA Astrophysics Data System (ADS)
Pagano, Stefano; Russo, Riccardo; Strano, Salvatore; Terzo, Mario
2013-02-01
This paper investigates the modelling, parameter identification and control of an unidirectional hydraulically actuated seismic isolator test rig. The plant is characterized by non-linearities such as the valve dead zone and frictions. A non-linear model is derived and then employed for parameter identification. The results concerning the model validation are illustrated and they fully confirm the effectiveness of the proposed model. The testing procedure of the isolation systems is based on the definition of a target displacement time history of the sliding table and, consequently, the precision of the table positioning is of primary importance. In order to minimize the test rig tracking error, a suitable control system has to be adopted. The system non-linearities highly limit the performances of the classical linear control and a non-linear one is therefore adopted. The test rig mathematical model is employed for a non-linear control design that minimizes the error between the target table position and the current one. The controller synthesis is made by taking no specimen into account. The proposed approach consists of a non-linear optimal control based on the state-dependent Riccati equation (SDRE). Numerical simulations have been performed in order to evaluate the soundness of the designed control with and without the specimen under test. The results confirm that the performances of the proposed non-linear controller are not invalidated because of the presence of the specimen.
Cost decomposition of linear systems with application to model reduction
NASA Technical Reports Server (NTRS)
Skelton, R. E.
1980-01-01
A means is provided to assess the value or 'cst' of each component of a large scale system, when the total cost is a quadratic function. Such a 'cost decomposition' of the system has several important uses. When the components represent physical subsystems which can fail, the 'component cost' is useful in failure mode analysis. When the components represent mathematical equations which may be truncated, the 'component cost' becomes a criterion for model truncation. In this latter event component costs provide a mechanism by which the specific control objectives dictate which components should be retained in the model reduction process. This information can be valuable in model reduction and decentralized control problems.
Linear and nonlinear instabilities in unified dark energy models
Avelino, P. P.; Beca, L. M. G.; Martins, C. J. A. P.
2008-03-15
We revisit the paradigm of unified dark energy discussing in detail the averaging problem in this type of scenario, highlighting the need for a full nonlinear treatment. We also address the question of if and how models with one or several dark fluids can be observationally distinguished. Simpler and physically clearer derivations of some key results, most notably on the relation between the generalized Chaplygin gas and the standard ({lambda}CDM) 'concordance' model and on a Jeans-type small-scale instability of some coupled dark energy/dark matter models are presented.
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.
A life cycle model of continuous clinical process innovation.
Savitz, L A; Kaluzny, A D; Kelly, D L
2000-01-01
The changing healthcare environment has created a sense of urgency for continuous innovation in clinical care processes. Managers and clinicians are investing unprecedented funds and energy in the development of various clinical process innovations (CPI) such as clinical pathways, electronic workstations, and various forms of information technology. While increasing attention has been paid to the development of such initiatives, our understanding of how best to disseminate and ensure their use is limited. In this first of two articles dealing with the dissemination and use of CPI in integrated delivery systems, we present a "life cycle" model of the dissemination process and suggest opportunities for managing CPI. The management of CPI requires more than just an understanding of the factors that may facilitate or impede its implementation and use. Managers require an understanding of the actual process so that they can assess the specific implementation stage at which the organization is presently operating, and design appropriate interventions that can affect the process. A future article will identify the factors that facilitate and inhibit the process and suggest some intervention strategies. PMID:11067423
Modeling results for a linear simulator of a divertor
Hooper, E.B.; Brown, M.D.; Byers, J.A.; Casper, T.A.; Cohen, B.I.; Cohen, R.H.; Jackson, M.C.; Kaiser, T.B.; Molvik, A.W.; Nevins, W.M.; Nilson, D.G.; Pearlstein, L.D.; Rognlien, T.D.
1993-06-23
A divertor simulator, IDEAL, has been proposed by S. Cohen to study the difficult power-handling requirements of the tokamak program in general and the ITER program in particular. Projections of the power density in the ITER divertor reach {approximately} 1 Gw/m{sup 2} along the magnetic fieldlines and > 10 MW/m{sup 2} on a surface inclined at a shallow angle to the fieldlines. These power densities are substantially greater than can be handled reliably on the surface, so new techniques are required to reduce the power density to a reasonable level. Although the divertor physics must be demonstrated in tokamaks, a linear device could contribute to the development because of its flexibility, the easy access to the plasma and to tested components, and long pulse operation (essentially cw). However, a decision to build a simulator requires not just the recognition of its programmatic value, but also confidence that it can meet the required parameters at an affordable cost. Accordingly, as reported here, it was decided to examine the physics of the proposed device, including kinetic effects resulting from the intense heating required to reach the plasma parameters, and to conduct an independent cost estimate. The detailed role of the simulator in a divertor program is not explored in this report.
Location-scale cumulative odds models for ordinal data: a generalized non-linear model approach.
Cox, C
1995-06-15
Proportional odds regression models for multinomial probabilities based on ordered categories have been generalized in two somewhat different directions. Models having scale as well as location parameters for adjustment of boundaries (on an unobservable, underlying continuum) between categories have been employed in the context of ROC analysis. Partial proportional odds models, having different regression adjustments for different multinomial categories, have also been proposed. This paper considers a synthesis and further generalization of these two families. With use of a number of examples, I discuss and illustrate properties of this extended family of models. Emphasis is on the computation of maximum likelihood estimates of parameters, asymptotic standard deviations, and goodness-of-fit statistics with use of non-linear regression programs in standard statistical software such as SAS. PMID:7667560
Numerical modeling of shape memory alloy linear actuator
NASA Astrophysics Data System (ADS)
Jani, Jaronie Mohd; Huang, Sunan; Leary, Martin; Subic, Aleksandar
2015-09-01
The demand for shape memory alloy (SMA) actuators in high-technology applications is increasing; however, there exist technical challenges to the commercial application of SMA actuator technologies, especially associated with actuation duration. Excessive activation duration results in actuator damage due to overheating while excessive deactivation duration is not practical for high-frequency applications. Analytical and finite difference equation models were developed in this work to predict the activation and deactivation durations and associated SMA thermomechanical behavior under variable environmental and design conditions. Relevant factors, including latent heat effect, induced stress and material property variability are accommodated. An existing constitutive model was integrated into the proposed models to generate custom SMA stress-strain curves. Strong agreement was achieved between the proposed numerical models and experimental results; confirming their applicability for predicting the behavior of SMA actuators with variable thermomechanical conditions.
Existence of vortices in a self-dual gauged linear sigma model and its singular limit
NASA Astrophysics Data System (ADS)
Kim, Namkwon
2006-03-01
We study rigorously the static (2 + 1)D gauged linear sigma model introduced by Schroers. Analysing the governing system of partial differential equations, we show the existence of energy finite vortices under the partially broken symmetry on R2 with some conditions consistent with the necessary conditions given by Yang. Also, with a special choice of representation, we show that the gauged O(3) sigma model is a singular limit of the gauged linear sigma model.
NASA Astrophysics Data System (ADS)
Yao, Yao
2012-05-01
Hydraulic fracturing technology is being widely used within the oil and gas industry for both waste injection and unconventional gas production wells. It is essential to predict the behavior of hydraulic fractures accurately based on understanding the fundamental mechanism(s). The prevailing approach for hydraulic fracture modeling continues to rely on computational methods based on Linear Elastic Fracture Mechanics (LEFM). Generally, these methods give reasonable predictions for hard rock hydraulic fracture processes, but still have inherent limitations, especially when fluid injection is performed in soft rock/sand or other non-conventional formations. These methods typically give very conservative predictions on fracture geometry and inaccurate estimation of required fracture pressure. One of the reasons the LEFM-based methods fail to give accurate predictions for these materials is that the fracture process zone ahead of the crack tip and softening effect should not be neglected in ductile rock fracture analysis. A 3D pore pressure cohesive zone model has been developed and applied to predict hydraulic fracturing under fluid injection. The cohesive zone method is a numerical tool developed to model crack initiation and growth in quasi-brittle materials considering the material softening effect. The pore pressure cohesive zone model has been applied to investigate the hydraulic fracture with different rock properties. The hydraulic fracture predictions of a three-layer water injection case have been compared using the pore pressure cohesive zone model with revised parameters, LEFM-based pseudo 3D model, a Perkins-Kern-Nordgren (PKN) model, and an analytical solution. Based on the size of the fracture process zone and its effect on crack extension in ductile rock, the fundamental mechanical difference of LEFM and cohesive fracture mechanics-based methods is discussed. An effective fracture toughness method has been proposed to consider the fracture process zone
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
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
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.
A new approach to modeling linear accelerator systems
Gillespie, G.H.; Hill, B.W.; Jameson, R.A.
1994-07-22
A novel computer code is being developed to generate system level designs of radiofrequency ion accelerators with specific applications to machines of interest to Accelerator Driven Transmutation Technologies (ADTT). The goal of the Accelerator System Model (ASM) code is to create a modeling and analysis tool that is easy to use, automates many of the initial design calculations, supports trade studies used in accessing alternate designs and yet is flexible enough to incorporate new technology concepts as they emerge. Hardware engineering parameters and beam dynamics are to be modeled at comparable levels of fidelity. Existing scaling models of accelerator subsystems were used to produce a prototype of ASM (version 1.0) working within the Shell for Particle Accelerator Related Code (SPARC) graphical user interface. A small user group has been testing and evaluating the prototype for about a year. Several enhancements and improvements are now being developed. The current version of ASM is described and examples of the modeling and analysis capabilities are illustrated. The results of an example study, for an accelerator concept typical of ADTT applications, is presented and sample displays from the computer interface are shown.
A new approach to modeling linear accelerator systems
Gillespie, George H.; Hill, Barrey W.; Jameson, Robert A.
1995-09-15
A novel computer code is being developed to generate system level designs of radiofrequency ion accelerators with specific applications to machines of interest to Accelerator Driven Transmutation Technologies (ADTT). The goal of the Accelerator System Model (ASM) code is to create a modeling and analysis tool that is easy to use, automates many of the initial design calculations, supports trade studies used in assessing alternate designs and yet is flexible enough to incorporate new technology concepts as they emerge. Hardware engineering parameters and beam dynamics are to be modeled at comparable levels of fidelity. Existing scaling models of accelerator subsystems were used to produce a prototype of ASM (version 1.0) working within the Shell for Particle Accelerator Related Code (SPARC) graphical user interface. A small user group has been testing and evaluating the prototype for about a year. Several enhancements and improvements are now being developed. The current version of ASM is described and examples of the modeling and analysis capabilities are illustrated. The results of an example parameter trade study, for an accelerator concept typical of ADTT applications, is presented and sample displays from the computer interface are shown.
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
NASA Astrophysics Data System (ADS)
Hoyos, Mauricio; Moore, Lee; Williams, P. Stephen; Zborowski, Maciej
2011-05-01
The Quadrupole Magnetic Sorter (QMS), employing an annular flow channel concentric with the aperture of a quadrupole magnet, is well established for cell and particle separations. Here we propose a magnetic particle separator comprising a linear array of cylindrical magnets, analogous to the array proposed by Klaus Halbach, mated to a substantially improved form of a parallel plate SPLITT channel, known as the step-SPLITT channel. While the magnetic force and throughput are generally lower than for the QMS, the new separator has advantages in ease of fabrication and the ability to vary the magnetic force to suit the separands. Preliminary experiments yield results consistent with prediction and show promise regarding future separations of cells of biomedical interest.
A model of asynchronous iterative algorithms for solving large, sparse, linear systems
NASA Technical Reports Server (NTRS)
Reed, D. A.; Patrick, M. L.
1984-01-01
Solving large, sparse, linear systems of equations is one of the fundamental problems in large scale scientific and engineering computation. A model of a general class of asynchronous, iterative solution methods for linear systems is developed. In the model, the system is solved by creating several cooperating tasks that each compute a portion of the solution vector. This model is then analyzed to determine the expected intertask data transfer and task computational complexity as functions of the number of tasks. Based on the analysis, recommendations for task partitioning are made. These recommendations are a function of the sparseness of the linear system, its structure (i.e., randomly sparse or banded), and dimension.
[Cancer incidence estimates for Germany via log-linear models].
Haberland, J; Bertz, J; Görsch, B; Schön, D
2001-01-01
In Germany presently no nationwide cancer registration exists. To estimate national cancer incidence, Poisson regression models were fitted to incidence/mortality ratios using age and sex specific data of the cancer registry of Saarland, Germany and were then applied to national mortality. The models estimate the absolute number of incident cases at a given point in time and moreover allow the assessment of time trends. Applied to nationwide mortality the models imply a total of 347,000 new cancer cases in Germany for 1998 with 179,000 females and 168,000 males. During the nineties the age-standardised rate (European standard) has slightly decreased for males and slightly increased for females. PMID:11561205
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
Downscaling of rainfall in Peru using Generalised Linear Models
NASA Astrophysics Data System (ADS)
Bergin, E.; Buytaert, W.; Onof, C.; Wheater, H.
2012-04-01
The assessment of water resources in the Peruvian Andes is particularly important because the Peruvian economy relies heavily on agriculture. Much of the agricultural land is situated near to the coast and relies on large quantities of water for irrigation. The simulation of synthetic rainfall series is thus important to evaluate the reliability of water supplies for current and future scenarios of climate change. In addition to water resources concerns, there is also a need to understand extreme heavy rainfall events, as there was significant flooding in Machu Picchu in 2010. The region exhibits a reduction of rainfall in 1983, associated with El Nino Southern Oscillation (SOI). NCEP Reanalysis 1 data was used to provide weather variable data. Correlations were calculated for several weather variables using raingauge data in the Andes. These were used to evaluate teleconnections and provide suggested covariates for the downscaling model. External covariates used in the model include sea level pressure and sea surface temperature over the region of the Humboldt Current. Relative humidity and temperature data over the region are also included. The SOI teleconnection is also used. Covariates are standardised using observations for 1960-1990. The GlimClim downscaling model was used to fit a stochastic daily rainfall model to 13 sites in the Peruvian Andes. Results indicate that the model is able to reproduce rainfall statistics well, despite the large area used. Although the correlation between individual rain gauges is generally quite low, all sites are affected by similar weather patterns. This is an assumption of the GlimClim downscaling model. Climate change scenarios are considered using several GCM outputs for the A1B scenario. GCM data was corrected for bias using 1960-1990 outputs from the 20C3M scenario. Rainfall statistics for current and future scenarios are compared. The region shows an overall decrease in mean rainfall but with an increase in variance.
Response Characteristics of a Linear Rotorcraft Vibration Model
NASA Technical Reports Server (NTRS)
Kunz, Donald L.
1982-01-01
A fully coupled vibration model, consisting of a rotor with only flapping degrees of freedom plus pylon and fuselage pitching motion, was used in a parametric study undertaken to investigate the response characteristics of a simplified helicopter. Among the parameters studied were uncoupled body frequency, blade stiffness, hinge offset, advance ratio, and mast height. Results from the harmonic balance solution of the equations of motion show how each of these quantities affects the response of the model. The results also indicate that there is a potential for reducing vibration response through the judicious definition of the design parameters.
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.
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…
Numerical analysis of linear buckling of wind turbine blade with different trailing bonding models
NASA Astrophysics Data System (ADS)
Zhang, J. D.; Xu, Y.
2013-12-01
The work focus on the linear buckling analysis of wind turbine blade with different trailing bonding models. Based on finite element model, it has been demonstrated that there are some differences for buckling load factor between different models. Several different models are valid for buckling analysis.
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.
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…
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.
Battery Life Estimator Manual Linear Modeling and Simulation
Jon P. Christophersen; Ira Bloom; Ed Thomas; Vince Battaglia
2009-08-01
The Battery Life Estimator (BLE) Manual has been prepared to assist developers in their efforts to estimate the calendar life of advanced batteries for automotive applications. Testing requirements and procedures are defined by the various manuals previously published under the United States Advanced Battery Consortium (USABC). The purpose of this manual is to describe and standardize a method for estimating calendar life based on statistical models and degradation data acquired from typical USABC battery testing.
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.
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.
Blanco, M; Maspoch, S; Villarroya, I; Peralta, X; González, J M; Torres, J
2001-03-01
The fact that bitumens behave as non-Newtonian fluids results in non-linear relationships between their near-infrared (NIR) spectra and the physico-chemical properties that define their consistency (viz. penetration and viscosity). Determining such properties using linear calibration techniques [e.g. partial least-squares regression (PLSR)] entails the previous transformation of the original variables by use of non-linear functions and employing the transformed variables to construct the models. Other properties of bitumens such as density and composition exhibit linear relationships with their NIR spectra. Artificial neural networks (ANNs) enable modelling of systems with a non-linear property-spectrum relationship; also, they allow one to determine several properties of a sample with a single model, so they are effective alternatives to linear calibration methods. In this work, the ability of ANNs simultaneously to determine both linear and non-linear parameters for bitumens without the need previously to transform the original variables was assessed. Based on the results, ANNs allow the simultaneous determination of several linear and non-linear physical properties typical of bitumens. PMID:11284343
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.
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
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...
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
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.
DEM Modelling of Non-linear Viscoelastic Stress Waves
NASA Astrophysics Data System (ADS)
Wang, Wenqiang; Tang, Zhiping; Horie, Yasuyuki
2001-06-01
A DEM(Discrete Element Method) simulation of nonlinear viscoelastic stress wave problems is carried out. The interaction forces among elements are described using a model in which neighbor elements are linked by a nonlinear spring and a certain number of Maxwell components in parallel. By making use of exponential relaxation moduli, it is shown that numerical computation of the convolution integral does not require storing and repeatedly calculating strain history, and can reduce the computational cost dramatically. To validate the viscoelastic DM2 code, stress wave propagation in a Maxwell rod with one end subjected to a constant stress loading is simulated. Results excellently fit those from the characteristics calculation. Satisfactory results are also obtained in the simulation of one-dimensional plane wave in a plastic bonded explosive. The code is then used to investigate the problem of meso-scale damage in this explosive under shock loading. Results not only show "compression damage", but also reveal a complex damage evolution. They demonstrate a unique capability of DEM in modeling heterogeneous materials.
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)
Separation of harmonic sounds using multipitch analysis and linear models for the overtone series
NASA Astrophysics Data System (ADS)
Virtanen, Tuomas; Klapuri, Anssi
2002-05-01
A signal processing method for the separation of concurrent harmonic sounds is described. The method is based on a two-stage approach. First, a multiple fundamental frequency estimator is applied to find initial sound parameters which are reliable, but inaccurate and static. Second, time-varying sinusoidal parameters are estimated in an iterative algorithm. The harmonic structure is retained by keeping the frequency ratio of overtones constant over time. Overlapping harmonic components are resolved using linear models for the overtone series. In practice, the models retain the spectral envelope continuity of natural sounds. Simulation experiments were carried out using generated test signals, which were random mixtures of two to six notes from recorded natural instruments. The system is able to produce meaningful results in all polyphonies, the quality of separated sounds gradually degrading along with the polyphony. Some denoising algorithms were applied to suppress nonstationary noise component, such as drums in real-world music signals. However, the usability of the system for real musical signals is still quite limited.
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
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.
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.
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.
Non-linear force-free field modeling: model techniques, boundary conditions, hares, and hounds
NASA Astrophysics Data System (ADS)
Schrijver, C. J.; De Rosa, M. L.; Metcalf, T.
2005-05-01
Understanding the conditions under which solar magnetic fields can destabilize to erupt in flares and coronal mass ejections requires a quantitative understanding of the coronal magnetic field and of the currents that it carries. The increased availability of vector magnetograms, together with EUV and X-ray coronal images, should provide adequate constraints to model the coronal field, and thus to visualize its 3D geometry and to measure the available free energy and helicity. Non-linear force-free fields (NLFFF) are likely a useful model to use when extrapolating the solar surface field upward into the coronal volume. It may even be possible to use the observed trajectories of coronal loops, evident in EUV images of the corona, as a further constraint. We present initial results of a team effort to understand the intricacies of NLFFF modeling: we discuss and evaluate comparisons of NLFFF models computed with different models and applications of boundary conditions, and look ahead to full coronal field modeling for the upcoming Solar-B and SDO missions.
The continuous molecular fields approach to building 3D-QSAR models.
Baskin, Igor I; Zhokhova, Nelly I
2013-05-01
The continuous molecular fields (CMF) approach is based on the application of continuous functions for the description of molecular fields instead of finite sets of molecular descriptors (such as interaction energies computed at grid nodes) commonly used for this purpose. These functions can be encapsulated into kernels and combined with kernel-based machine learning algorithms to provide a variety of novel methods for building classification and regression structure-activity models, visualizing chemical datasets and conducting virtual screening. In this article, the CMF approach is applied to building 3D-QSAR models for 8 datasets through the use of five types of molecular fields (the electrostatic, steric, hydrophobic, hydrogen-bond acceptor and donor ones), the linear convolution molecular kernel with the contribution of each atom approximated with a single isotropic Gaussian function, and the kernel ridge regression data analysis technique. It is shown that the CMF approach even in this simplest form provides either comparable or enhanced predictive performance in comparison with state-of-the-art 3D-QSAR methods. PMID:23719959
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.
NASA Astrophysics Data System (ADS)
Wasko, Conrad; Pui, Alexander; Sharma, Ashish; Mehrotra, Rajeshwar; Jeremiah, Erwin
2015-12-01
Low-frequency variability, in the form of the El Niño-Southern Oscillation, plays a key role in shaping local weather systems. However, current continuous stochastic rainfall models do not account for this variability in their simulations. Here a modified Random Pulse Bartlett Lewis stochastic generation model is presented for continuous rainfall simulation exhibiting low-frequency variability. Termed the Hierarchical Random Bartlett Lewis Model (HRBLM), the model features a hierarchical structure to represent a range of rainfall characteristics associated with the El Niño-Southern Oscillation with parameters conditioned to vary as functions of relevant climatic states. Long observational records of near-continuous rainfall at various locations in Australia are used to formulate and evaluate the model. The results indicate clear benefits of using the hierarchical climate-dependent structure proposed. In addition to accurately representing the wet spells characteristics and observed low-frequency variability, the model replicates the interannual variability of the antecedent rainfall preceding the extremes, which is known to be of considerable importance in design flood estimation applications.
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
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…
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
Subedi, Bidya Raj; Reese, Nancy; Powell, Randy
2015-01-01
This study explored significant predictors of student's Grade Point Average (GPA) and truancy (days absent), and also determined teacher effectiveness based on proportion of variance explained at teacher level model. We employed a two-level hierarchical linear model (HLM) with student and teacher data at level-1 and level-2 models, respectively.…
ERIC Educational Resources Information Center
Chapman, Robin S.; Hesketh, Linda J.; Kistler, Doris J.
2002-01-01
Longitudinal change in syntax comprehension and production skill, measured over six years, was modeled in 31 individuals (ages 5-20) with Down syndrome. The best fitting Hierarchical Linear Modeling model of comprehension uses age and visual and auditory short-term memory as predictors of initial status, and age for growth trajectory. (Contains…
A Graphical Method for Assessing the Identification of Linear Structural Equation Models
ERIC Educational Resources Information Center
Eusebi, Paolo
2008-01-01
A graphical method is presented for assessing the state of identifiability of the parameters in a linear structural equation model based on the associated directed graph. We do not restrict attention to recursive models. In the recent literature, methods based on graphical models have been presented as a useful tool for assessing the state of…
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.
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…
Kim, Sung-Phil; Sanchez, Justin C; Erdogmus, Deniz; Rao, Yadunandana N; Wessberg, Johan; Principe, Jose C; Nicolelis, Miguel
2003-01-01
This paper proposes a divide-and-conquer strategy for designing brain machine interfaces. A nonlinear combination of competitively trained local linear models (experts) is used to identify the mapping from neuronal activity in cortical areas associated with arm movement to the hand position of a primate. The proposed architecture and the training algorithm are described in detail and numerical performance comparisons with alternative linear and nonlinear modeling approaches, including time-delay neural networks and recursive multilayer perceptrons, are presented. This new strategy allows training the local linear models using normalized LMS and using a relatively smaller nonlinear network to efficiently combine the predictions of the linear experts. This leads to savings in computational requirements, while the performance is still similar to a large fully nonlinear network. PMID:12850045
Region-Based Association Test for Familial Data under Functional Linear Models.
Svishcheva, Gulnara R; Belonogova, Nadezhda M; 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
Correlation and simple linear regression.
Eberly, Lynn E
2007-01-01
This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression. PMID:18450049
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.
Applying a Continuous Quality Improvement Model To Assess Institutional Effectiveness.
ERIC Educational Resources Information Center
Roberts, Keith
This handbook outlines techniques and processes for improving institutional effectiveness and ensuring continuous quality improvement, based on strategic planning activities at Wisconsin's Milwaukee Area Technical College (MATC). First, institutional effectiveness is defined and 17 core indicators of effectiveness developed by the Wisconsin…
Teachers' Continuing Professional Development: Contested Concepts, Understandings and Models
ERIC Educational Resources Information Center
Fraser, Christine; Kennedy, Aileen; Reid, Lesley; Mckinney, Stephen
2007-01-01
Teachers' continuing professional development (CPD) is being given increasing importance in countries throughout the world. In Scotland, the changing professional and political context has resulted in unprecedented investment in CPD. However, analysis and evaluation of CPD policies, practice and impact is complex. In seeking to understand some of…
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. 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
Application of the Hyper-Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes.
Khazraee, S Hadi; Sáez-Castillo, Antonio Jose; Geedipally, Srinivas Reddy; Lord, Dominique
2015-05-01
The hyper-Poisson distribution can handle both over- and underdispersion, and its generalized linear model formulation allows the dispersion of the distribution to be observation-specific and dependent on model covariates. This study's objective is to examine the potential applicability of a newly proposed generalized linear model framework for the hyper-Poisson distribution in analyzing motor vehicle crash count data. The hyper-Poisson generalized linear model was first fitted to intersection crash data from Toronto, characterized by overdispersion, and then to crash data from railway-highway crossings in Korea, characterized by underdispersion. The results of this study are promising. When fitted to the Toronto data set, the goodness-of-fit measures indicated that the hyper-Poisson model with a variable dispersion parameter provided a statistical fit as good as the traditional negative binomial model. The hyper-Poisson model was also successful in handling the underdispersed data from Korea; the model performed as well as the gamma probability model and the Conway-Maxwell-Poisson model previously developed for the same data set. The advantages of the hyper-Poisson model studied in this article are noteworthy. Unlike the negative binomial model, which has difficulties in handling underdispersed data, the hyper-Poisson model can handle both over- and underdispersed crash data. Although not a major issue for the Conway-Maxwell-Poisson model, the effect of each variable on the expected mean of crashes is easily interpretable in the case of this new model. PMID:25385093
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.
Comparison of Logistic Regression and Linear Regression in Modeling Percentage Data
Zhao, Lihui; Chen, Yuhuan; Schaffner, Donald W.
2001-01-01
Percentage is widely used to describe different results in food microbiology, e.g., probability of microbial growth, percent inactivated, and percent of positive samples. Four sets of percentage data, percent-growth-positive, germination extent, probability for one cell to grow, and maximum fraction of positive tubes, were obtained from our own experiments and the literature. These data were modeled using linear and logistic regression. Five methods were used to compare the goodness of fit of the two models: percentage of predictions closer to observations, range of the differences (predicted value minus observed value), deviation of the model, linear regression between the observed and predicted values, and bias and accuracy factors. Logistic regression was a better predictor of at least 78% of the observations in all four data sets. In all cases, the deviation of logistic models was much smaller. The linear correlation between observations and logistic predictions was always stronger. Validation (accomplished using part of one data set) also demonstrated that the logistic model was more accurate in predicting new data points. Bias and accuracy factors were found to be less informative when evaluating models developed for percentage data, since neither of these indices can compare predictions at zero. Model simplification for the logistic model was demonstrated with one data set. The simplified model was as powerful in making predictions as the full linear model, and it also gave clearer insight in determining the key experimental factors. PMID:11319091
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
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…
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. PMID:26399412
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)
A three-dimensional network model describing a non-linear composite material
NASA Astrophysics Data System (ADS)
Mårtensson, E.; Gäfvert, U.
2004-01-01
A three-dimensional network model for performing non-linear time-dependent simulations of the electrical characteristics related to a composite material is presented. The considered compounds are represented by a cubic lattice and consist of conducting particles distributed in an insulating matrix. Earlier studies of the non-linear characteristics of silicon carbide (SiC) grains and of the linear frequency-dependent electrical properties of composites are combined and extended. The calculations are compared to measurements on ethylene-propylene-diene monomer rubber filled with angular SiC grains. The field-dependent conductivity measured for the unconsolidated SiC powder is used as input to the simulations. The model can manage the conductivity difference of seven decades between the constituents and the strong exponential non-linearity of the conducting particles. The network calculations replicate the experimental characteristic at high filler concentrations, where direct 'face' contacts between the filler grains dominate the behaviour. At lower concentrations, it is shown that indirect 'edge' contacts involving the polymer control the current transport also in the non-linear high field range. The general effective conductivity describing an edge connection in the linear case is no longer appropriate. Non-linear mechanisms in the polymer and the conducting grains within a field enhanced limited region around the contact need to be represented by an equivalent circuit element with a case-dependent resulting expression.
Liu, Dawei; Lin, Xihong; Ghosh, Debashis
2007-12-01
We consider a semiparametric regression model that relates a normal outcome to covariates and a genetic pathway, where the covariate effects are modeled parametrically and the pathway effect of multiple gene expressions is modeled parametrically or nonparametrically using least-squares kernel machines (LSKMs). This unified framework allows a flexible function for the joint effect of multiple genes within a pathway by specifying a kernel function and allows for the possibility that each gene expression effect might be nonlinear and the genes within the same pathway are likely to interact with each other in a complicated way. This semiparametric model also makes it possible to test for the overall genetic pathway effect. We show that the LSKM semiparametric regression can be formulated using a linear mixed model. Estimation and inference hence can proceed within the linear mixed model framework using standard mixed model software. Both the regression coefficients of the covariate effects and the LSKM estimator of the genetic pathway effect can be obtained using the best linear unbiased predictor in the corresponding linear mixed model formulation. The smoothing parameter and the kernel parameter can be estimated as variance components using restricted maximum likelihood. A score test is developed to test for the genetic pathway effect. Model/variable selection within the LSKM framework is discussed. The methods are illustrated using a prostate cancer data set and evaluated using simulations. PMID:18078480
Equilibrium Phase Behavior of the Square-Well Linear Microphase-Forming Model.
Zhuang, Yuan; Charbonneau, Patrick
2016-07-01
We have recently developed a simulation approach to calculate the equilibrium phase diagram of particle-based microphase formers. Here, this approach is used to calculate the phase behavior of the square-well linear model for different strengths and ranges of the linear long-range repulsive component. The results are compared with various theoretical predictions for microphase formation. The analysis further allows us to better understand the mechanism for microphase formation in colloidal suspensions. PMID:27117230
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.
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.
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.
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.
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.
Holmes, William R; Trueblood, Jennifer S; Heathcote, Andrew
2016-03-01
In the real world, decision making processes must be able to integrate non-stationary information that changes systematically while the decision is in progress. Although theories of decision making have traditionally been applied to paradigms with stationary information, non-stationary stimuli are now of increasing theoretical interest. We use a random-dot motion paradigm along with cognitive modeling to investigate how the decision process is updated when a stimulus changes. Participants viewed a cloud of moving dots, where the motion switched directions midway through some trials, and were asked to determine the direction of motion. Behavioral results revealed a strong delay effect: after presentation of the initial motion direction there is a substantial time delay before the changed motion information is integrated into the decision process. To further investigate the underlying changes in the decision process, we developed a Piecewise Linear Ballistic Accumulator model (PLBA). The PLBA is efficient to simulate, enabling it to be fit to participant choice and response-time distribution data in a hierarchal modeling framework using a non-parametric approximate Bayesian algorithm. Consistent with behavioral results, PLBA fits confirmed the presence of a long delay between presentation and integration of new stimulus information, but did not support increased response caution in reaction to the change. We also found the decision process was not veridical, as symmetric stimulus change had an asymmetric effect on the rate of evidence accumulation. Thus, the perceptual decision process was slow to react to, and underestimated, new contrary motion information. PMID:26760448
NASA Astrophysics Data System (ADS)
Adcock, T. A. A.; Taylor, P. H.
2016-01-01
The non-linear Schrödinger equation and its higher order extensions are routinely used for analysis of extreme ocean waves. This paper compares the evolution of individual wave-packets modelled using non-linear Schrödinger type equations with packets modelled using fully non-linear potential flow models. The modified non-linear Schrödinger Equation accurately models the relatively large scale non-linear changes to the shape of wave-groups, with a dramatic contraction of the group along the mean propagation direction and a corresponding extension of the width of the wave-crests. In addition, as extreme wave form, there is a local non-linear contraction of the wave-group around the crest which leads to a localised broadening of the wave spectrum which the bandwidth limited non-linear Schrödinger Equations struggle to capture. This limitation occurs for waves of moderate steepness and a narrow underlying spectrum.
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. PMID:25824269
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…
Effects on Predictive Ability of the Linear versus Location Models in Discriminant Analysis.
ERIC Educational Resources Information Center
Steele, Maryann E.
The Mahalanobis distance model was compared with the linear discriminant function model and found to provide very similar results, even when a number of the variables were binary. A group of college freshmen were categorized into two groups: 116 "leavers," students who did not return for the second year, and 269 "returners." Data from the…
Application of wavelet-based multiple linear regression model to rainfall forecasting in Australia
NASA Astrophysics Data System (ADS)
He, X.; Guan, H.; Zhang, X.; Simmons, C.
2013-12-01
In this study, a wavelet-based multiple linear regression model is applied to forecast monthly rainfall in Australia by using monthly historical rainfall data and climate indices as inputs. The wavelet-based model is constructed by incorporating the multi-resolution analysis (MRA) with the discrete wavelet transform and multiple linear regression (MLR) model. The standardized monthly rainfall anomaly and large-scale climate index time series are decomposed using MRA into a certain number of component subseries at different temporal scales. The hierarchical lag relationship between the rainfall anomaly and each potential predictor is identified by cross correlation analysis with a lag time of at least one month at different temporal scales. The components of predictor variables with known lag times are then screened with a stepwise linear regression algorithm to be selectively included into the final forecast model. The MRA-based rainfall forecasting method is examined with 255 stations over Australia, and compared to the traditional multiple linear regression model based on the original time series. The models are trained with data from the 1959-1995 period and then tested in the 1996-2008 period for each station. The performance is compared with observed rainfall values, and evaluated by common statistics of relative absolute error and correlation coefficient. The results show that the wavelet-based regression model provides considerably more accurate monthly rainfall forecasts for all of the selected stations over Australia than the traditional regression model.
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...
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…
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…
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...
Continuous performance measurement in flight systems. [sequential control model
NASA Technical Reports Server (NTRS)
Connelly, E. M.; Sloan, N. A.; Zeskind, R. M.
1975-01-01
The desired response of many man machine control systems can be formulated as a solution to an optimal control synthesis problem where the cost index is given and the resulting optimal trajectories correspond to the desired trajectories of the man machine system. Optimal control synthesis provides the reference criteria and the significance of error information required for performance measurement. The synthesis procedure described provides a continuous performance measure (CPM) which is independent of the mechanism generating the control action. Therefore, the technique provides a meaningful method for online evaluation of man's control capability in terms of total man machine performance.
Continuing education for medical students: a library model
Swanberg, Stephanie M.; Engwall, Keith; Mi, Misa
2015-01-01
Purpose The research assessed a three-year continuing medical education–style program for medical students in a Midwestern academic medical library. Methods A mixed methods approach of a survey and two focus groups comparing attendees versus non-attendees assessed the program. Results Eleven students participated in the focus groups. Attendance was driven by topic interest and lunch. Barriers included lack of interest, scheduling, location, and convenience. Conclusions Although attendance was a challenge, students valued opportunities to learn new skills. This study showcases a reproducible method to engage students outside the curriculum. PMID:26512222
Infinite Continuous Feature Model for Psychiatric Comorbidity Analysis.
Valera, Isabel; Ruiz, Francisco J R; Olmos, Pablo M; Blanco, Carlos; Perez-Cruz, Fernando
2016-02-01
We aim at finding the comorbidity patterns of substance abuse, mood and personality disorders using the diagnoses from the National Epidemiologic Survey on Alcohol and Related Conditions database. To this end, we propose a novel Bayesian nonparametric latent feature model for categorical observations, based on the Indian buffet process, in which the latent variables can take values between 0 and 1. The proposed model has several interesting features for modeling psychiatric disorders. First, the latent features might be off, which allows distinguishing between the subjects who suffer a condition and those who do not. Second, the active latent features take positive values, which allows modeling the extent to which the patient has that condition. We also develop a new Markov chain Monte Carlo inference algorithm for our model that makes use of a nested expectation propagation procedure. PMID:26654208
Engagement, Capacity and Continuity -- A Model for Outreach Provision
NASA Astrophysics Data System (ADS)
Noel-Storr, J.
2005-12-01
In order to discover what produces successful youth development, it is often most appropriate to start with a statement of intended outcomes, and then discover what strategies lead to those goals being fulfilled. Research has shown that to achieve the objective of producing young adults who are scientifically motivated (i.e. are either engaged in scientific or technical careers or have strong and committed interests in those areas) three key components must be in place: (i) Engagement -- opportunities providing glimpses of the excitement of science and gateways into scientific learning; (ii) Capacity -- here, the capacity of the educational system, formal and informal, to provide rich educational opportunities in scientific fields; and (iii) Continuity -- the ongoing support of science learning in and out of school, continued opportunities to pursue science and the layout of clear pathways towards scientific careers. Here I show ways in which this philosophy can be turned into a comprehensive, multi-component outreach program, in particular highlighting current efforts and planned developments within the area of one school district in Southern Arizona.
Robust model predictive control for optimal continuous drug administration.
Sopasakis, Pantelis; Patrinos, Panagiotis; Sarimveis, Haralambos
2014-10-01
In this paper the model predictive control (MPC) technology is used for tackling the optimal drug administration problem. The important advantage of MPC compared to other control technologies is that it explicitly takes into account the constraints of the system. In particular, for drug treatments of living organisms, MPC can guarantee satisfaction of the minimum toxic concentration (MTC) constraints. A whole-body physiologically-based pharmacokinetic (PBPK) model serves as the dynamic prediction model of the system after it is formulated as a discrete-time state-space model. Only plasma measurements are assumed to be measured on-line. The rest of the states (drug concentrations in other organs and tissues) are estimated in real time by designing an artificial observer. The complete system (observer and MPC controller) is able to drive the drug concentration to the desired levels at the organs of interest, while satisfying the imposed constraints, even in the presence of modelling errors, disturbances and noise. A case study on a PBPK model with 7 compartments, constraints on 5 tissues and a variable drug concentration set-point illustrates the efficiency of the methodology in drug dosing control applications. The proposed methodology is also tested in an uncertain setting and proves successful in presence of modelling errors and inaccurate measurements. PMID:24986530
Semiparametric Analysis of Heterogeneous Data Using Varying-Scale Generalized Linear Models
Xie, Minge; Simpson, Douglas G.; Carroll, Raymond J.
2009-01-01
This article describes a class of heteroscedastic generalized linear regression models in which a subset of the regression parameters are rescaled nonparametrically, and develops efficient semiparametric inferences for the parametric components of the models. Such models provide a means to adapt for heterogeneity in the data due to varying exposures, varying levels of aggregation, and so on. The class of models considered includes generalized partially linear models and nonparametrically scaled link function models as special cases. We present an algorithm to estimate the scale function nonparametrically, and obtain asymptotic distribution theory for regression parameter estimates. In particular, we establish that the asymptotic covariance of the semiparametric estimator for the parametric part of the model achieves the semiparametric lower bound. We also describe bootstrap-based goodness-of-scale test. We illustrate the methodology with simulations, published data, and data from collaborative research on ultrasound safety. PMID:19444331
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
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
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
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
Fusing Continuous-Valued Medical Labels Using a Bayesian Model.
Zhu, Tingting; Dunkley, Nic; Behar, Joachim; Clifton, David A; Clifford, Gari D
2015-12-01
With the rapid increase in volume of time series medical data available through wearable devices, there is a need to employ automated algorithms to label data. Examples of labels include interventions, changes in activity (e.g. sleep) and changes in physiology (e.g. arrhythmias). However, automated algorithms tend to be unreliable resulting in lower quality care. Expert annotations are scarce, expensive, and prone to significant inter- and intra-observer variance. To address these problems, a Bayesian Continuous-valued Label Aggregator (BCLA) is proposed to provide a reliable estimation of label aggregation while accurately infer the precision and bias of each algorithm. The BCLA was applied to QT interval (pro-arrhythmic indicator) estimation from the electrocardiogram using labels from the 2006 PhysioNet/Computing in Cardiology Challenge database. It was compared to the mean, median, and a previously proposed Expectation Maximization (EM) label aggregation approaches. While accurately predicting each labelling algorithm's bias and precision, the root-mean-square error of the BCLA was 11.78 ± 0.63 ms, significantly outperforming the best Challenge entry (15.37 ± 2.13 ms) as well as the EM, mean, and median voting strategies (14.76 ± 0.52, 17.61 ± 0.55, and 14.43 ± 0.57 ms respectively with p < 0.0001). The BCLA could therefore provide accurate estimation for medical continuous-valued label tasks in an unsupervised manner even when the ground truth is not available. PMID:26036335
Chen, Gang; Adleman, Nancy E.; Saad, Ziad S.; Leibenluft, Ellen; Cox, RobertW.
2014-01-01
All neuroimaging packages can handle group analysis with t-tests or general linear modeling (GLM). However, they are quite hamstrung when there are multiple within-subject factors or when quantitative covariates are involved in the presence of a within-subject factor. In addition, sphericity is typically assumed for the variance–covariance structure when there are more than two levels in a within-subject factor. To overcome such limitations in the traditional AN(C)OVA and GLM, we adopt a multivariate modeling (MVM) approach to analyzing neuroimaging data at the group level with the following advantages: a) there is no limit on the number of factors as long as sample sizes are deemed appropriate; b) quantitative covariates can be analyzed together with within- subject factors; c) when a within-subject factor is involved, three testing methodologies are provided: traditional univariate testing (UVT)with sphericity assumption (UVT-UC) and with correction when the assumption is violated (UVT-SC), and within-subject multivariate testing (MVT-WS); d) to correct for sphericity violation at the voxel level, we propose a hybrid testing (HT) approach that achieves equal or higher power via combining traditional sphericity correction methods (Greenhouse–Geisser and Huynh–Feldt) with MVT-WS. PMID:24954281
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.
Stability analysis, non-linear pulsations and mass loss of models for 55 Cygni (HD 198478)
NASA Astrophysics Data System (ADS)
Yadav, Abhay Pratap; Glatzel, Wolfgang
2016-04-01
55 Cygni is a variable supergiant. Recent observational studies revealed that this star pulsates in pressure, gravity and strange modes. The pulsations seem to be associated with episodes of mass loss. In this paper we present a theoretical study of stellar models with parameters close to that of 55 Cygni. A linear non-adiabatic stability analysis with respect to radial perturbations is performed and the evolution of instabilities into the non-linear regime is followed by numerical simulation. Our study indicates that the mass of 55 Cygni lies below 28 M⊙. As the final consequence of the instabilities the non-linear simulations revealed finite amplitude pulsations with periods consistent with the observations. The non-linear results also indicate a connection between pulsations and mass loss and allow for an estimate of the mean mass-loss rate. It is consistent with the observed values.
Modelling land-fast sea ice using a linear elastic model
NASA Astrophysics Data System (ADS)
Plante, Mathieu; Tremblay, Bruno
2016-04-01
Land-fast ice is an important component of the Arctic system, capping the coastal Arctic waters for most of the year and exerting a large influence on ocean-atmosphere heat exchanges. Yet, the accurate representation of land-fast ice in most large-scale sea ice models remains a challenge, part due to the difficult (and sometimes non-physical) parametrisation of ice fracture. In this study, a linear elastic model is developed to investigate the internal stresses induced by the wind forcing on the land-fast ice, modelled as a 2D elastic plate. The model simulates ice fracture by the implementation of a damage coefficient which causes a local reduction in internal stress. This results in a cascade propagation of damage, simulating the ice fracture that determines the position of the land-fast ice edge. The modelled land-fast ice cover is sensitive to the choice of failure criterion. The parametrised cohesion, tensile and compressive strength and the relationship with the land-fast ice stability is discussed. To estimate the large scale mechanical properties of land-fast ice, these results are compared to a set of land-fast ice break up events and ice bridge formations observed in the Siberian Arctic. These events are identified using brightness temperature imagery from the MODIS (Moderate Resolution Imaging Spectroradiometer) Terra and Aqua satellites, from which the position of the flaw lead is identifiable by the opening of polynyi adjacent to the land-fast ice edge. The shape of the land-fast ice before, during and after these events, along with the characteristic scale of the resulting ice floes, are compared to the model results to extrapolate the stress state that corresponds to these observations. The model setting that best reproduce the scale of the observed break up events is used to provide an estimation of the strength of the ice relative to the wind forcing. These results will then be used to investigate the relationship between the ice thickness and the
Modelling the Non-Linear Viscoelastic and Viscoplastic Behaviour of Aramid Fibre Yarns
NASA Astrophysics Data System (ADS)
Chailleux, E.; Davies, P.
A non-linear viscoelastic viscoplastic model is proposed for the tensile behaviour of aramid fibres, based on an analysis of the deformation mechanisms of these materials. This model uses the macroscopic formulation developed by Schapery together with the plasticity concept of Perzyna. A simple identification procedure for the model parameters has been developed using creep/recovery cycles at different load levels. The identification reveals that two of the four parameters of the viscoelastic model (g1 and aσ) are independent of stress level. This may be due to the simple and regular nature of the fibre structure. The model enables the parameters which characterise the non-linear reversible viscoelasticity to be identified independently from those which characterise the viscoplasticity. The model predictions are compared to experimental data for a more complex load sequence and reasonable correlation is obtained.
Bayesian Model Selection in Complex Linear Systems, as Illustrated in Genetic Association Studies
Wen, Xiaoquan
2013-01-01
Summary Motivated by examples from genetic association studies, this paper considers the model selection problem in a general complex linear model system and in a Bayesian framework. We discuss formulating model selection problems and incorporating context-dependent a priori information through different levels of prior specifications. We also derive analytic Bayes factors and their approximations to facilitate model selection and discuss their theoretical and computational properties. We demonstrate our Bayesian approach based on an implemented Markov Chain Monte Carlo (MCMC) algorithm in simulations and a real data application of mapping tissue-specific eQTLs. Our novel results on Bayes factors provide a general framework to perform efficient model comparisons in complex linear model systems. PMID:24350677