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).
General linear chirplet transform
NASA Astrophysics Data System (ADS)
Yu, Gang; Zhou, Yiqi
2016-03-01
Time-frequency (TF) analysis (TFA) method is an effective tool to characterize the time-varying feature of a signal, which has drawn many attentions in a fairly long period. With the development of TFA, many advanced methods are proposed, which can provide more precise TF results. However, some restrictions are introduced inevitably. In this paper, we introduce a novel TFA method, termed as general linear chirplet transform (GLCT), which can overcome some limitations existed in current TFA methods. In numerical and experimental validations, by comparing with current TFA methods, some advantages of GLCT are demonstrated, which consist of well-characterizing the signal of multi-component with distinct non-linear features, being independent to the mathematical model and initial TFA method, allowing for the reconstruction of the interested component, and being non-sensitivity to noise.
Generalized Linear Covariance Analysis
NASA Technical Reports Server (NTRS)
Carpenter, James R.; Markley, F. Landis
2014-01-01
This talk presents a comprehensive approach to filter modeling for generalized covariance analysis of both batch least-squares and sequential estimators. We review and extend in two directions the results of prior work that allowed for partitioning of the state space into solve-for'' and consider'' parameters, accounted for differences between the formal values and the true values of the measurement noise, process noise, and textita priori solve-for and consider covariances, and explicitly partitioned the errors into subspaces containing only the influence of the measurement noise, process noise, and solve-for and consider covariances. In this work, we explicitly add sensitivity analysis to this prior work, and relax an implicit assumption that the batch estimator's epoch time occurs prior to the definitive span. We also apply the method to an integrated orbit and attitude problem, in which gyro and accelerometer errors, though not estimated, influence the orbit determination performance. We illustrate our results using two graphical presentations, which we call the variance sandpile'' and the sensitivity mosaic,'' and we compare the linear covariance results to confidence intervals associated with ensemble statistics from a Monte Carlo analysis.
Generalized Linear Covariance Analysis
NASA Technical Reports Server (NTRS)
Carpenter, J. Russell; Markley, F. Landis
2008-01-01
We review and extend in two directions the results of prior work on generalized covariance analysis methods. This prior work allowed for partitioning of the state space into "solve-for" and "consider" parameters, allowed for differences between the formal values and the true values of the measurement noise, process noise, and a priori solve-for and consider covariances, and explicitly partitioned the errors into subspaces containing only the influence of the measurement noise, process noise, and a priori solve-for and consider covariances. In this work, we explicitly add sensitivity analysis to this prior work, and relax an implicit assumption that the batch estimator s anchor time occurs prior to the definitive span. We also apply the method to an integrated orbit and attitude problem, in which gyro and accelerometer errors, though not estimated, influence the orbit determination performance. We illustrate our results using two graphical presentations, which we call the "variance sandpile" and the "sensitivity mosaic," and we compare the linear covariance results to confidence intervals associated with ensemble statistics from a Monte Carlo analysis.
Quantization of general linear electrodynamics
Rivera, Sergio; Schuller, Frederic P.
2011-03-15
General linear electrodynamics allow for an arbitrary linear constitutive relation between the field strength 2-form and induction 2-form density if crucial hyperbolicity and energy conditions are satisfied, which render the theory predictive and physically interpretable. Taking into account the higher-order polynomial dispersion relation and associated causal structure of general linear electrodynamics, we carefully develop its Hamiltonian formulation from first principles. Canonical quantization of the resulting constrained system then results in a quantum vacuum which is sensitive to the constitutive tensor of the classical theory. As an application we calculate the Casimir effect in a birefringent linear optical medium.
NASA Astrophysics Data System (ADS)
Ripamonti, Francesco; Orsini, Lorenzo; Resta, Ferruccio
2015-04-01
Non-linear behavior is present in many mechanical system operating conditions. In these cases, a common engineering practice is to linearize the equation of motion around a particular operating point, and to design a linear controller. The main disadvantage is that the stability properties and validity of the controller are local. In order to improve the controller performance, non-linear control techniques represent a very attractive solution for many smart structures. The aim of this paper is to compare non-linear model-based and non-model-based control techniques. In particular the model-based sliding-mode-control (SMC) technique is considered because of its easy implementation and the strong robustness of the controller even under heavy model uncertainties. Among the non-model-based control techniques, the fuzzy control (FC), allowing designing the controller according to if-then rules, has been considered. It defines the controller without a system reference model, offering many advantages such as an intrinsic robustness. These techniques have been tested on the pendulum nonlinear system.
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
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…
Multiconlitron: a general piecewise linear classifier.
Yujian, Li; Bo, Liu; Xinwu, Yang; Yaozong, Fu; Houjun, Li
2011-02-01
Based on the "convexly separable" concept, we present a solid geometric theory and a new general framework to design piecewise linear classifiers for two arbitrarily complicated nonintersecting classes by using a "multiconlitron," which is a union of multiple conlitrons that comprise a set of hyperplanes or linear functions surrounding a convex region for separating two convexly separable datasets. We propose a new iterative algorithm called the cross distance minimization algorithm (CDMA) to compute hard margin non-kernel support vector machines (SVMs) via the nearest point pair between two convex polytopes. Using CDMA, we derive two new algorithms, i.e., the support conlitron algorithm (SCA) and the support multiconlitron algorithm (SMA) to construct support conlitrons and support multiconlitrons, respectively, which are unique and can separate two classes by a maximum margin as in an SVM. Comparative experiments show that SMA can outperform linear SVM on many of the selected databases and provide similar results to radial basis function SVM on some of them, while SCA performs better than linear SVM on three out of four applicable databases. Other experiments show that SMA and SCA may be further improved to draw more potential in the new research direction of piecewise linear learning. PMID:21138800
Reduced-Order Models Based on Linear and Nonlinear Aerodynamic Impulse Responses
NASA Technical Reports Server (NTRS)
Silva, Walter A.
1999-01-01
This paper discusses a method for the identification and application of reduced-order models based on linear and nonlinear aerodynamic impulse responses. The Volterra theory of nonlinear systems and an appropriate kernel identification technique are described. Insight into the nature of kernels is provided by applying the method to the nonlinear Riccati equation in a non-aerodynamic application. The method is then applied to a nonlinear aerodynamic model of RAE 2822 supercritical airfoil undergoing plunge motions using the CFL3D Navier-Stokes flow solver with the Spalart-Allmaras turbulence model. Results demonstrate the computational efficiency of the technique.
Reduced Order Models Based on Linear and Nonlinear Aerodynamic Impulse Responses
NASA Technical Reports Server (NTRS)
Silva, Walter A.
1999-01-01
This paper discusses a method for the identification and application of reduced-order models based on linear and nonlinear aerodynamic impulse responses. The Volterra theory of nonlinear systems and an appropriate kernel identification technique are described. Insight into the nature of kernels is provided by applying the method to the nonlinear Riccati equation in a non-aerodynamic application. The method is then applied to a nonlinear aerodynamic model of an RAE 2822 supercritical airfoil undergoing plunge motions using the CFL3D Navier-Stokes flow solver with the Spalart-Allmaras turbulence model. Results demonstrate the computational efficiency of the technique.
A General Framework for Multiphysics Modeling Based on Numerical Averaging
NASA Astrophysics Data System (ADS)
Lunati, I.; Tomin, P.
2014-12-01
In the last years, multiphysics (hybrid) modeling has attracted increasing attention as a tool to bridge the gap between pore-scale processes and a continuum description at the meter-scale (laboratory scale). This approach is particularly appealing for complex nonlinear processes, such as multiphase flow, reactive transport, density-driven instabilities, and geomechanical coupling. We present a general framework that can be applied to all these classes of problems. The method is based on ideas from the Multiscale Finite-Volume method (MsFV), which has been originally developed for Darcy-scale application. Recently, we have reformulated MsFV starting with a local-global splitting, which allows us to retain the original degree of coupling for the local problems and to use spatiotemporal adaptive strategies. The new framework is based on the simple idea that different characteristic temporal scales are inherited from different spatial scales, and the global and the local problems are solved with different temporal resolutions. The global (coarse-scale) problem is constructed based on a numerical volume-averaging paradigm and a continuum (Darcy-scale) description is obtained by introducing additional simplifications (e.g., by assuming that pressure is the only independent variable at the coarse scale, we recover an extended Darcy's law). We demonstrate that it is possible to adaptively and dynamically couple the Darcy-scale and the pore-scale descriptions of multiphase flow in a single conceptual and computational framework. Pore-scale problems are solved only in the active front region where fluid distribution changes with time. In the rest of the domain, only a coarse description is employed. This framework can be applied to other important problems such as reactive transport and crack propagation. As it is based on a numerical upscaling paradigm, our method can be used to explore the limits of validity of macroscopic models and to illuminate the meaning of
Identification of general linear mechanical systems
NASA Technical Reports Server (NTRS)
Sirlin, S. W.; Longman, R. W.; Juang, J. N.
1983-01-01
Previous work in identification theory has been concerned with the general first order time derivative form. Linear mechanical systems, a large and important class, naturally have a second order form. This paper utilizes this additional structural information for the purpose of identification. A realization is obtained from input-output data, and then knowledge of the system input, output, and inertia matrices is used to determine a set of linear equations whereby we identify the remaining unknown system matrices. Necessary and sufficient conditions on the number, type and placement of sensors and actuators are given which guarantee identificability, and less stringent conditions are given which guarantee generic identifiability. Both a priori identifiability and a posteriori identifiability are considered, i.e., identifiability being insured prior to obtaining data, and identifiability being assured with a given data set.
On the order of general linear methods.
Constantinescu, E. M.; Mathematics and Computer Science
2009-09-01
General linear (GL) methods are numerical algorithms used to solve ODEs. The standard order conditions analysis involves the GL matrix itself and a starting procedure; however, a finishing method (F) is required to extract the actual ODE solution. The standard order analysis and stability are sufficient for the convergence of any GL method. Nonetheless, using a simple GL scheme, we show that the order definition may be too restrictive. Specifically, the order for GL methods with low order intermediate components may be underestimated. In this note we explore the order conditions for GL schemes and propose a new definition for characterizing the order of GL methods, which is focused on the final result--the outcome of F--and can provide more effective algebraic order conditions.
Development of a CFD-compatible transition model based on linear stability theory
NASA Astrophysics Data System (ADS)
Coder, James G.
A new laminar-turbulent transition model for low-turbulence external aerodynamic applications is presented that incorporates linear stability theory in a manner compatible with modern computational fluid dynamics solvers. The model uses a new transport equation that describes the growth of the maximum Tollmien-Schlichting instability amplitude in the presence of a boundary layer. To avoid the need for integration paths and non-local operations, a locally defined non-dimensional pressure-gradient parameter is used that serves as an estimator of the integral boundary-layer properties. The model has been implemented into the OVERFLOW 2.2f solver and interacts with the Spalart-Allmaras and Menter SST eddy-viscosity turbulence models. Comparisons of predictions using the new transition model with high-quality wind-tunnel measurements of airfoil section characteristics validate the predictive qualities of the model. Predictions for three-dimensional aircraft and wing geometries show the correct qualitative behavior even though limited experimental data are available. These cases also demonstrate that the model is well-behaved about general aeronautical configurations. These cases confirm that the new transition model is an improvement over the current state of the art in computational fluid dynamics transition modeling by providing more accurate solutions at approximately half the added computational expense.
On generalized hamming weighs for Galois ring linear codes
Ashikhmin, A.
1997-08-01
The definition of generalized Hamming weights (GHW) for linear codes over Galois rings is discussed. The properties of GHW for Galois ring linear codes are stated. Upper and existence bounds for GHW of Z{sub 4}-linear codes and a lower bound for GHW of the Kerdock code over Z{sub 4} are derived. GHW of some Z{sub 4}-linear codes are determined.
Generalized Multicarrier CDMA: Unification and Linear Equalization
NASA Astrophysics Data System (ADS)
Giannakis, Georgios B.; Anghel, Paul A.; Wang, Zhengdao
2005-12-01
Relying on block-symbol spreading and judicious design of user codes, this paper builds on the generalized multicarrier (GMC) quasisynchronous CDMA system that is capable of multiuser interference (MUI) elimination and intersymbol interference (ISI) suppression with guaranteed symbol recovery, regardless of the wireless frequency-selective channels. GMC-CDMA affords an all-digital unifying framework, which encompasses single-carrier and several multicarrier (MC) CDMA systems. Besides the unifying framework, it is shown that GMC-CDMA offers flexibility both in full load (maximum number of users allowed by the available bandwidth) and in reduced load settings. A novel blind channel estimation algorithm is also derived. Analytical evaluation and simulations illustrate the superior error performance and flexibility of uncoded GMC-CDMA over competing MC-CDMA alternatives especially in the presence of uplink multipath channels.
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…
Chu, Dezhang; Lawson, Gareth L; Wiebe, Peter H
2016-05-01
The linear inversion commonly used in fisheries and zooplankton acoustics assumes a constant inversion kernel and ignores the uncertainties associated with the shape and behavior of the scattering targets, as well as other relevant animal parameters. Here, errors of the linear inversion due to uncertainty associated with the inversion kernel are quantified. A scattering model-based nonlinear inversion method is presented that takes into account the nonlinearity of the inverse problem and is able to estimate simultaneously animal abundance and the parameters associated with the scattering model inherent to the kernel. It uses sophisticated scattering models to estimate first, the abundance, and second, the relevant shape and behavioral parameters of the target organisms. Numerical simulations demonstrate that the abundance, size, and behavior (tilt angle) parameters of marine animals (fish or zooplankton) can be accurately inferred from the inversion by using multi-frequency acoustic data. The influence of the singularity and uncertainty in the inversion kernel on the inversion results can be mitigated by examining the singular values for linear inverse problems and employing a non-linear inversion involving a scattering model-based kernel. PMID:27250181
Linear stability of general magnetically insulated electron flow
NASA Astrophysics Data System (ADS)
Swegle, J. A.; Mendel, C. W., Jr.; Seidel, D. B.; Quintenz, J. P.
1984-03-01
A linear stability theory for magnetically insulated systems was formulated by linearizing the general 3-D, time dependent theory of Mendel, Seidel, and Slut. It is found that, case of electron trajectories which are nearly laminar, with only small transverse motion, several suggestive simplifications occur in the eigenvalue equations.
Linear stability of general magnetically insulated electron flow
Swegle, J.A.; Mendel, C.W. Jr.; Seidel, D.B.; Quintenz, J.P.
1984-01-01
We have formulated a linear stability theory for magnetically insulated systems by linearizing the general 3-D, time-dependent theory of Mendel, Seidel, and Slutz. In the physically interesting case of electron trajectories which are nearly laminar, with only small transverse motion, we have found that several suggestive simplifications occur in the eigenvalue equations.
A novel crowd flow model based on linear fractional stable motion
NASA Astrophysics Data System (ADS)
Wei, Juan; Zhang, Hong; Wu, Zhenya; He, Junlin; Guo, Yangyong
2016-03-01
For the evacuation dynamics in indoor space, a novel crowd flow model is put forward based on Linear Fractional Stable Motion. Based on position attraction and queuing time, the calculation formula of movement probability is defined and the queuing time is depicted according to linear fractal stable movement. At last, an experiment and simulation platform can be used for performance analysis, studying deeply the relation among system evacuation time, crowd density and exit flow rate. It is concluded that the evacuation time and the exit flow rate have positive correlations with the crowd density, and when the exit width reaches to the threshold value, it will not effectively decrease the evacuation time by further increasing the exit width.
Log-linear model based behavior selection method for artificial fish swarm algorithm.
Huang, Zhehuang; Chen, Yidong
2015-01-01
Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895
Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm
Huang, Zhehuang; Chen, Yidong
2015-01-01
Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895
Linear equations in general purpose codes for stiff ODEs
Shampine, L. F.
1980-02-01
It is noted that it is possible to improve significantly the handling of linear problems in a general-purpose code with very little trouble to the user or change to the code. In such situations analytical evaluation of the Jacobian is a lot cheaper than numerical differencing. A slight change in the point at which the Jacobian is evaluated results in a more accurate Jacobian in linear problems. (RWR)
Generalized coarse-grained model based on point multipole and Gay-Berne potentials
NASA Astrophysics Data System (ADS)
Golubkov, Pavel A.; Ren, Pengyu
2006-08-01
This paper presents a general coarse-grained molecular mechanics model based on electric point multipole expansion and Gay-Berne [J. Chem. Phys. 74, 3316 (1981)] potential. Coarse graining of van der Waals potential is achieved by treating molecules as soft uniaxial ellipsoids interacting via a generalized anisotropic Gay-Berne function. The charge distribution is represented by point multipole expansion, including point charge, dipole, and quadrupole moments placed at the center of mass. The Gay-Berne and point multipole potentials are combined in the local reference frame defined by the inertial frame of the all-atom counterpart. The coarse-grained model has been applied to rigid-body molecular dynamics simulations of molecular liquids including benzene and methanol. The computational efficiency is improved by several orders of magnitude, while the results are in reasonable agreement with all-atom models and experimental data. We also discuss the implications of using point multipole for polar molecules capable of hydrogen bonding and the applicability of this model to a broad range of molecular systems including highly charged biopolymers.
Generalized coarse-grained model based on point multipole and Gay-Berne potentials.
Golubkov, Pavel A; Ren, Pengyu
2006-08-14
This paper presents a general coarse-grained molecular mechanics model based on electric point multipole expansion and Gay-Berne [J. Chem. Phys. 74, 3316 (1981)] potential. Coarse graining of van der Waals potential is achieved by treating molecules as soft uniaxial ellipsoids interacting via a generalized anisotropic Gay-Berne function. The charge distribution is represented by point multipole expansion, including point charge, dipole, and quadrupole moments placed at the center of mass. The Gay-Berne and point multipole potentials are combined in the local reference frame defined by the inertial frame of the all-atom counterpart. The coarse-grained model has been applied to rigid-body molecular dynamics simulations of molecular liquids including benzene and methanol. The computational efficiency is improved by several orders of magnitude, while the results are in reasonable agreement with all-atom models and experimental data. We also discuss the implications of using point multipole for polar molecules capable of hydrogen bonding and the applicability of this model to a broad range of molecular systems including highly charged biopolymers. PMID:16942269
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
Beam envelope calculations in general linear coupled lattices
NASA Astrophysics Data System (ADS)
Chung, Moses; Qin, Hong; Groening, Lars; Davidson, Ronald C.; Xiao, Chen
2015-01-01
The envelope equations and Twiss parameters (β and α) provide important bases for uncoupled linear beam dynamics. For sophisticated beam manipulations, however, coupling elements between two transverse planes are intentionally introduced. The recently developed generalized Courant-Snyder theory offers an effective way of describing the linear beam dynamics in such coupled systems with a remarkably similar mathematical structure to the original Courant-Snyder theory. In this work, we present numerical solutions to the symmetrized matrix envelope equation for β which removes the gauge freedom in the matrix envelope equation for w. Furthermore, we construct the transfer and beam matrices in terms of the generalized Twiss parameters, which enables calculation of the beam envelopes in arbitrary linear coupled systems.
Beam envelope calculations in general linear coupled lattices
Chung, Moses; Qin, Hong; Groening, Lars; Xiao, Chen; Davidson, Ronald C.
2015-01-15
The envelope equations and Twiss parameters (β and α) provide important bases for uncoupled linear beam dynamics. For sophisticated beam manipulations, however, coupling elements between two transverse planes are intentionally introduced. The recently developed generalized Courant-Snyder theory offers an effective way of describing the linear beam dynamics in such coupled systems with a remarkably similar mathematical structure to the original Courant-Snyder theory. In this work, we present numerical solutions to the symmetrized matrix envelope equation for β which removes the gauge freedom in the matrix envelope equation for w. Furthermore, we construct the transfer and beam matrices in terms of the generalized Twiss parameters, which enables calculation of the beam envelopes in arbitrary linear coupled systems.
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,…
Generalized in vitro-in vivo relationship (IVIVR) model based on artificial neural networks
Mendyk, Aleksander; Tuszyński, Paweł K; Polak, Sebastian; Jachowicz, Renata
2013-01-01
Background The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR) model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC)/IVIVR. Methods Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients) and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique. Results The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations. Conclusion It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2–4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures. PMID:23569360
The generalized sidelobe canceller based on quaternion widely linear processing.
Tao, Jian-wu; Chang, Wen-xiu
2014-01-01
We investigate the problem of quaternion beamforming based on widely linear processing. First, a quaternion model of linear symmetric array with two-component electromagnetic (EM) vector sensors is presented. Based on array's quaternion model, we propose the general expression of a quaternion semiwidely linear (QSWL) beamformer. Unlike the complex widely linear beamformer, the QSWL beamformer is based on the simultaneous operation on the quaternion vector, which is composed of two jointly proper complex vectors, and its involution counterpart. Second, we propose a useful implementation of QSWL beamformer, that is, QSWL generalized sidelobe canceller (GSC), and derive the simple expressions of the weight vectors. The QSWL GSC consists of two-stage beamformers. By designing the weight vectors of two-stage beamformers, the interference is completely canceled in the output of QSWL GSC and the desired signal is not distorted. We derive the array's gain expression and analyze the performance of the QSWL GSC in the presence of one type of interference. The advantage of QSWL GSC is that the main beam can always point to the desired signal's direction and the robustness to DOA mismatch is improved. Finally, simulations are used to verify the performance of the proposed QSWL GSC. PMID:24955425
Capsule deformation and orientation in general linear flows
NASA Astrophysics Data System (ADS)
Szatmary, Alex; Eggleton, Charles
2010-11-01
We considered the response of spherical and non-spherical capsules to general flows. (A capsule is an elastic membrane enclosing a fluid, immersed in fluid.) First, we established that nonspherical capsules align with the imposed irrotational linear flow; this means that initial orientation does not affect steady-state capsule deformation, so this steady-state deformation can be determined entirely by the capillary number and the type of flow. The type of flow is characterized by r: r=0 for axisymmetric flows, and r=1 for planar flows; intermediate values of r are combinations of planar and axisymmetric flow. By varying the capillary number and r, all irrotational linear Stokes flows can be generated. For the same capillary number, planar flows lead to more deformation than uniaxial or biaxial extensional flows. Deformation varies monotonically with r, so one can determine bounds on capsule deformation in general flow by only looking at uniaxial, biaxial, and planar flow. These results are applicable to spheres in all linear flows and to ellipsoids in irrotational linear flow.
The Generalized Sidelobe Canceller Based on Quaternion Widely Linear Processing
Tao, Jian-wu; Chang, Wen-xiu
2014-01-01
We investigate the problem of quaternion beamforming based on widely linear processing. First, a quaternion model of linear symmetric array with two-component electromagnetic (EM) vector sensors is presented. Based on array's quaternion model, we propose the general expression of a quaternion semiwidely linear (QSWL) beamformer. Unlike the complex widely linear beamformer, the QSWL beamformer is based on the simultaneous operation on the quaternion vector, which is composed of two jointly proper complex vectors, and its involution counterpart. Second, we propose a useful implementation of QSWL beamformer, that is, QSWL generalized sidelobe canceller (GSC), and derive the simple expressions of the weight vectors. The QSWL GSC consists of two-stage beamformers. By designing the weight vectors of two-stage beamformers, the interference is completely canceled in the output of QSWL GSC and the desired signal is not distorted. We derive the array's gain expression and analyze the performance of the QSWL GSC in the presence of one type of interference. The advantage of QSWL GSC is that the main beam can always point to the desired signal's direction and the robustness to DOA mismatch is improved. Finally, simulations are used to verify the performance of the proposed QSWL GSC. PMID:24955425
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.
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.
Clutter locus equation for more general linear array orientation
NASA Astrophysics Data System (ADS)
Bickel, Douglas L.
2011-06-01
The clutter locus is an important concept in space-time adaptive processing (STAP) for ground moving target indicator (GMTI) radar systems. The clutter locus defines the expected ground clutter location in the angle-Doppler domain. Typically in literature, the clutter locus is presented as a line, or even a set of ellipsoids, under certain assumptions about the geometry of the array. Most often, the array is assumed to be in the horizontal plane containing the velocity vector. This paper will give a more general 3-dimensional interpretation of the clutter locus for a general linear array orientation.
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
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
Linear spin-2 fields in most general backgrounds
NASA Astrophysics Data System (ADS)
Bernard, Laura; Deffayet, Cédric; Schmidt-May, Angnis; von Strauss, Mikael
2016-04-01
We derive the full perturbative equations of motion for the most general background solutions in ghost-free bimetric theory in its metric formulation. Clever field redefinitions at the level of fluctuations enable us to circumvent the problem of varying a square-root matrix appearing in the theory. This greatly simplifies the expressions for the linear variation of the bimetric interaction terms. We show that these field redefinitions exist and are uniquely invertible if and only if the variation of the square-root matrix itself has a unique solution, which is a requirement for the linearized theory to be well defined. As an application of our results we examine the constraint structure of ghost-free bimetric theory at the level of linear equations of motion for the first time. We identify a scalar combination of equations which is responsible for the absence of the Boulware-Deser ghost mode in the theory. The bimetric scalar constraint is in general not manifestly covariant in its nature. However, in the massive gravity limit the constraint assumes a covariant form when one of the interaction parameters is set to zero. For that case our analysis provides an alternative and almost trivial proof of the absence of the Boulware-Deser ghost. Our findings generalize previous results in the metric formulation of massive gravity and also agree with studies of its vielbein version.
NASA Astrophysics Data System (ADS)
Nordtvedt, K.
2015-11-01
A local system of bodies in General Relativity whose exterior metric field asymptotically approaches the Minkowski metric effaces any effects of the matter distribution exterior to its Minkowski boundary condition. To enforce to all orders this property of gravity which appears to hold in nature, a method using linear algebraic scaling equations is developed which generates by an iterative process an N-body Lagrangian expansion for gravity's motion-independent potentials which fulfills exterior effacement along with needed metric potential expansions. Then additional properties of gravity - interior effacement and Lorentz time dilation and spatial contraction - produce additional iterative, linear algebraic equations for obtaining the full non-linear and motion-dependent N-body gravity Lagrangian potentials as well.
ERIC Educational Resources Information Center
Levy, Roy; Xu, Yuning; Yel, Nedim; Svetina, Dubravka
2015-01-01
The standardized generalized dimensionality discrepancy measure and the standardized model-based covariance are introduced as tools to critique dimensionality assumptions in multidimensional item response models. These tools are grounded in a covariance theory perspective and associated connections between dimensionality and local independence.…
Comparative Study of Algorithms for Automated Generalization of Linear Objects
NASA Astrophysics Data System (ADS)
Azimjon, S.; Gupta, P. K.; Sukhmani, R. S. G. S.
2014-11-01
Automated generalization, rooted from conventional cartography, has become an increasing concern in both geographic information system (GIS) and mapping fields. All geographic phenomenon and the processes are bound to the scale, as it is impossible for human being to observe the Earth and the processes in it without decreasing its scale. To get optimal results, cartographers and map-making agencies develop set of rules and constraints, however these rules are under consideration and topic for many researches up until recent days. Reducing map generating time and giving objectivity is possible by developing automated map generalization algorithms (McMaster and Shea, 1988). Modification of the scale traditionally is a manual process, which requires knowledge of the expert cartographer, and it depends on the experience of the user, which makes the process very subjective as every user may generate different map with same requirements. However, automating generalization based on the cartographic rules and constrains can give consistent result. Also, developing automated system for map generation is the demand of this rapid changing world. The research that we have conveyed considers only generalization of the roads, as it is one of the indispensable parts of a map. Dehradun city, Uttarakhand state of India was selected as a study area. The study carried out comparative study of the generalization software sets, operations and algorithms available currently, also considers advantages and drawbacks of the existing software used worldwide. Research concludes with the development of road network generalization tool and with the final generalized road map of the study area, which explores the use of open source python programming language and attempts to compare different road network generalization algorithms. Thus, the paper discusses the alternative solutions for automated generalization of linear objects using GIS-technologies. Research made on automated of road network
Parametrizing linear generalized Langevin dynamics from explicit molecular dynamics simulations
Gottwald, Fabian; Karsten, Sven; Ivanov, Sergei D. Kühn, Oliver
2015-06-28
Fundamental understanding of complex dynamics in many-particle systems on the atomistic level is of utmost importance. Often the systems of interest are of macroscopic size but can be partitioned into a few important degrees of freedom which are treated most accurately and others which constitute a thermal bath. Particular attention in this respect attracts the linear generalized Langevin equation, which can be rigorously derived by means of a linear projection technique. Within this framework, a complicated interaction with the bath can be reduced to a single memory kernel. This memory kernel in turn is parametrized for a particular system studied, usually by means of time-domain methods based on explicit molecular dynamics data. Here, we discuss that this task is more naturally achieved in frequency domain and develop a Fourier-based parametrization method that outperforms its time-domain analogues. Very surprisingly, the widely used rigid bond method turns out to be inappropriate in general. Importantly, we show that the rigid bond approach leads to a systematic overestimation of relaxation times, unless the system under study consists of a harmonic bath bi-linearly coupled to the relevant degrees of freedom.
Hand, M. M.
1999-07-30
Variable-speed, horizontal axis wind turbines use blade-pitch control to meet specified objectives for three regions of operation. This paper focuses on controller design for the constant power production regime. A simple, rigid, non-linear turbine model was used to systematically perform trade-off studies between two performance metrics. Minimization of both the deviation of the rotor speed from the desired speed and the motion of the actuator is desired. The robust nature of the proportional-integral-derivative (PID) controller is illustrated, and optimal operating conditions are determined. Because numerous simulation runs may be completed in a short time, the relationship of the two opposing metrics is easily visualized. Traditional controller design generally consists of linearizing a model about an operating point. This step was taken for two different operating points, and the systematic design approach was used. A comparison of the optimal regions selected using the n on-linear model and the two linear models shows similarities. The linearization point selection does, however, affect the turbine performance slightly. Exploitation of the simplicity of the model allows surfaces consisting of operation under a wide range of gain values to be created. This methodology provides a means of visually observing turbine performance based upon the two metrics chosen for this study. Design of a PID controller is simplified, and it is possible to ascertain the best possible combination of controller parameters. The wide, flat surfaces indicate that a PID controller is very robust in this variable-speed wind turbine application.
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.
Generalized space and linear momentum operators in quantum mechanics
Costa, Bruno G. da
2014-06-15
We propose a modification of a recently introduced generalized translation operator, by including a q-exponential factor, which implies in the definition of a Hermitian deformed linear momentum operator p{sup ^}{sub q}, and its canonically conjugate deformed position operator x{sup ^}{sub q}. A canonical transformation leads the Hamiltonian of a position-dependent mass particle to another Hamiltonian of a particle with constant mass in a conservative force field of a deformed phase space. The equation of motion for the classical phase space may be expressed in terms of the generalized dual q-derivative. A position-dependent mass confined in an infinite square potential well is shown as an instance. Uncertainty and correspondence principles are analyzed.
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.
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
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.
The left invariant metric in the general linear group
NASA Astrophysics Data System (ADS)
Andruchow, E.; Larotonda, G.; Recht, L.; Varela, A.
2014-12-01
Left invariant metrics induced by the p-norms of the trace in the matrix algebra are studied on the general linear group. By means of the Euler-Lagrange equations, existence and uniqueness of extremal paths for the length functional are established, and regularity properties of these extremal paths are obtained. Minimizing paths in the group are shown to have a velocity with constant singular values and multiplicity. In several special cases, these geodesic paths are computed explicitly. In particular the Riemannian geodesics, corresponding to the case p = 2, are characterized as the product of two one-parameter groups. It is also shown that geodesics are one-parameter groups if and only if the initial velocity is a normal matrix. These results are further extended to the context of compact operators with p-summable spectrum, where a differential equation for the spectral projections of the velocity vector of an extremal path is obtained.
Using parallel banded linear system solvers in generalized eigenvalue problems
NASA Technical Reports Server (NTRS)
Zhang, Hong; Moss, William F.
1993-01-01
Subspace iteration is a reliable and cost effective method for solving positive definite banded symmetric generalized eigenproblems, especially in the case of large scale problems. This paper discusses an algorithm that makes use of two parallel banded solvers in subspace iteration. A shift is introduced to decompose the banded linear systems into relatively independent subsystems and to accelerate the iterations. With this shift, an eigenproblem is mapped efficiently into the memories of a multiprocessor and a high speed-up is obtained for parallel implementations. An optimal shift is a shift that balances total computation and communication costs. Under certain conditions, we show how to estimate an optimal shift analytically using the decay rate for the inverse of a banded matrix, and how to improve this estimate. Computational results on iPSC/2 and iPSC/860 multiprocessors are presented.
General Linear Rf-Current Drive Calculation in Toroidal Plasma
NASA Astrophysics Data System (ADS)
Smirnov, A. P.; Harvey, R. W.; Prater, R.
2009-04-01
A new general linear calculation of RF current drive has been implemented in the GENRAY all-frequencies RF ray tracing code. This is referred to as the ADJ-QL package, and is based on the Karney, et al. [1] relativistic Green function calculator, ADJ, generalized to non-circular plasmas in toroidal geometry, and coupled with full, bounce-averaged momentum-space RF quasilinear flux [2] expressions calculated at each point along the RF ray trajectories. This approach includes momentum conservation, polarization effects and the influence of trapped electrons. It is assumed that the electron distribution function remains close to a relativistic Maxwellian function. Within the bounds of these assumptions, small banana width, toroidal geometry and low collisionality, the calculation is applicable for all-frequencies RF electron current drive including electron cyclotron, lower hybrid, fast waves and electron Bernstein waves. GENRAY ADJ-QL calculations of the relativistic momentum-conserving current drive have been applied in several cases: benchmarking of electron cyclotron current drive in ITER against other code results; and electron Bernstein and high harmonic fast wave current drive in NSTX. The impacts of momentum conservation on the current drive are also shown for these cases.
Generalized linear joint PP-PS inversion based on two constraints
NASA Astrophysics Data System (ADS)
Fang, Yuan; Zhang, Feng-Qi; Wang, Yan-Chun
2016-03-01
Conventional joint PP—PS inversion is based on approximations of the Zoeppritz equations and assumes constant VP/VS; therefore, the inversion precision and stability cannot satisfy current exploration requirements. We propose a joint PP—PS inversion method based on the exact Zoeppritz equations that combines Bayesian statistics and generalized linear inversion. A forward model based on the exact Zoeppritz equations is built to minimize the error of the approximations in the large-angle data, the prior distribution of the model parameters is added as a regularization item to decrease the ill-posed nature of the inversion, low-frequency constraints are introduced to stabilize the low-frequency data and improve robustness, and a fast algorithm is used to solve the objective function while minimizing the computational load. The proposed method has superior antinoising properties and well reproduces real data.
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
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.
Enhancing Retrieval with Hyperlinks: A General Model Based on Propositional Argumentation Systems.
ERIC Educational Resources Information Center
Picard, Justin; Savoy, Jacques
2003-01-01
Discusses the use of hyperlinks for improving information retrieval on the World Wide Web and proposes a general model for using hyperlinks based on Probabilistic Argumentation Systems. Topics include propositional logic, knowledge, and uncertainty; assumptions; using hyperlinks to modify document score and rank; and estimating the popularity of a…
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
Accelerated Hazards Model based on Parametric Families Generalized with Bernstein Polynomials
Chen, Yuhui; Hanson, Timothy; Zhang, Jiajia
2015-01-01
Summary A transformed Bernstein polynomial that is centered at standard parametric families, such as Weibull or log-logistic, is proposed for use in the accelerated hazards model. This class provides a convenient way towards creating a Bayesian non-parametric prior for smooth densities, blending the merits of parametric and non-parametric methods, that is amenable to standard estimation approaches. For example optimization methods in SAS or R can yield the posterior mode and asymptotic covariance matrix. This novel nonparametric prior is employed in the accelerated hazards model, which is further generalized to time-dependent covariates. The proposed approach fares considerably better than previous approaches in simulations; data on the effectiveness of biodegradable carmustine polymers on recurrent brain malignant gliomas is investigated. PMID:24261450
Park, Chan-Gyung; Hwang, Jai-chan; Park, Jaehong; Noh, Hyerim
2010-03-15
We study a generalized version of Chaplygin gas as unified model of dark matter and dark energy. Using realistic theoretical models and the currently available observational data from the age of the universe, the expansion history based on the type Ia supernovae, the matter power spectrum, the cosmic microwave background radiation anisotropy power spectra, and the perturbation growth factor we put the unified model under observational test. As the model has only two free parameters in the flat Friedmann background [{Lambda}CDM (cold dark matter) model has only one free parameter] we show that the model is already tightly constrained by currently available observations. The only parameter space extremely close to the {Lambda}CDM model is allowed in this unified model.
NASA Astrophysics Data System (ADS)
Sakaris, Christos S.; Sakellariou, John S.; Fassois, Spilios D.
2015-07-01
A Generalized Functional Model Based Method for vibration-based damage precise localization on structures consisting of 1D, 2D, or 3D elements is introduced. The method generalizes previous versions applicable to structures consisting of 1D elements, thus allowing for 2D and 3D elements as well. It is based on scalar (single sensor) or vector (multiple sensor) Functional Models which - in the inspection phase - incorporate the mathematical form of the specific structural topology. Precise localization is then based on coordinate estimation within this model structure, and confidence bounds are also obtained. The effectiveness of the method is demonstrated through experiments on a 3D truss structure where damage corresponds to single bolt loosening. Both the scalar and vector versions of the method are shown to be effective even within a very limited, low frequency, bandwidth of 3-59 Hz. The improvement achieved through the use of multiple sensors is also demonstrated.
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
Fermion unification model based on the intrinsic SU(8) symmetry of a generalized Dirac equation
NASA Astrophysics Data System (ADS)
Marsch, Eckart; Narita, Yasuhito
2015-10-01
A natural generalization of the original Dirac spinor into a multi-component spinor is achieved, which corresponds to the single lepton and the three quarks of the first family of the standard model of elementary particle physics. Different fermions result from similarity transformations of the Dirac equation, but apparently there can be no more fermions according to the maximal multiplicity revealed in this study. Rotations in the fermion state space are achieved by the unitary generators of the U(1) and the SU(3) groups, corresponding to quantum electrodynamics (QED based on electric charge) and chromodynamics (QCD based on colour charge). In addition to hypercharge the dual degree of freedom of hyperspin emerges, which occurs due to the duplicity implied by the two related (Weyl and Dirac) representations of the Dirac equation. This yields the SU(2) symmetry of the weak interaction, which can be married to U(1) to generate the unified electroweak interaction as in the standard model. Therefore, the symmetry group encompassing all the three groups mentioned above is SU(8), which can accommodate and unify the observed eight basic stable fermions.
Connections between Generalizing and Justifying: Students' Reasoning with Linear Relationships
ERIC Educational Resources Information Center
Ellis, Amy B.
2007-01-01
Research investigating algebra students' abilities to generalize and justify suggests that they experience difficulty in creating and using appropriate generalizations and proofs. Although the field has documented students' errors, less is known about what students do understand to be general and convincing. This study examines the ways in which…
NASA Technical Reports Server (NTRS)
McCormick, S.; Ruge, John W.
1998-01-01
This work represents a part of a project to develop an atmospheric general circulation model based on the semi-Lagrangian advection of potential vorticity (PC) with divergence as the companion prognostic variable.
Wang, Shuo; Cao, Yang
2015-01-01
Random effect in cellular systems is an important topic in systems biology and often simulated with Gillespie’s stochastic simulation algorithm (SSA). Abridgment refers to model reduction that approximates a group of reactions by a smaller group with fewer species and reactions. This paper presents a theoretical analysis, based on comparison of the first exit time, for the abridgment on a linear chain reaction model motivated by systems with multiple phosphorylation sites. The analysis shows that if the relaxation time of the fast subsystem is much smaller than the mean firing time of the slow reactions, the abridgment can be applied with little error. This analysis is further verified with numerical experiments for models of bistable switch and oscillations in which linear chain system plays a critical role. PMID:26263559
Generalizing a Categorization of Students' Interpretations of Linear Kinematics Graphs
ERIC Educational Resources Information Center
Bollen, Laurens; De Cock, Mieke; Zuza, Kristina; Guisasola, Jenaro; van Kampen, Paul
2016-01-01
We have investigated whether and how a categorization of responses to questions on linear distance-time graphs, based on a study of Irish students enrolled in an algebra-based course, could be adopted and adapted to responses from students enrolled in calculus-based physics courses at universities in Flanders, Belgium (KU Leuven) and the Basque…
A General Linear Method for Equating with Small Samples
ERIC Educational Resources Information Center
Albano, Anthony D.
2015-01-01
Research on equating with small samples has shown that methods with stronger assumptions and fewer statistical estimates can lead to decreased error in the estimated equating function. This article introduces a new approach to linear observed-score equating, one which provides flexible control over how form difficulty is assumed versus estimated…
Guisan, A.; Edwards, T.C., Jr.; Hastie, T.
2002-01-01
An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001. We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling. ?? 2002 Elsevier Science B.V. All rights reserved.
Generalizing a categorization of students' interpretations of linear kinematics graphs
NASA Astrophysics Data System (ADS)
Bollen, Laurens; De Cock, Mieke; Zuza, Kristina; Guisasola, Jenaro; van Kampen, Paul
2016-06-01
We have investigated whether and how a categorization of responses to questions on linear distance-time graphs, based on a study of Irish students enrolled in an algebra-based course, could be adopted and adapted to responses from students enrolled in calculus-based physics courses at universities in Flanders, Belgium (KU Leuven) and the Basque Country, Spain (University of the Basque Country). We discuss how we adapted the categorization to accommodate a much more diverse student cohort and explain how the prior knowledge of students may account for many differences in the prevalence of approaches and success rates. Although calculus-based physics students make fewer mistakes than algebra-based physics students, they encounter similar difficulties that are often related to incorrectly dividing two coordinates. We verified that a qualitative understanding of kinematics is an important but not sufficient condition for students to determine a correct value for the speed. When comparing responses to questions on linear distance-time graphs with responses to isomorphic questions on linear water level versus time graphs, we observed that the context of a question influences the approach students use. Neither qualitative understanding nor an ability to find the slope of a context-free graph proved to be a reliable predictor for the approach students use when they determine the instantaneous speed.
Burant, Aniela; Thompson, Christopher; Lowry, Gregory V; Karamalidis, Athanasios K
2016-05-17
Partitioning coefficients of organic compounds between water and supercritical CO2 (sc-CO2) are necessary to assess the risk of migration of these chemicals from subsurface CO2 storage sites. Despite the large number of potential organic contaminants, the current data set of published water-sc-CO2 partitioning coefficients is very limited. Here, the partitioning coefficients of thiophene, pyrrole, and anisole were measured in situ over a range of temperatures and pressures using a novel pressurized batch-reactor system with dual spectroscopic detectors: a near-infrared spectrometer for measuring the organic analyte in the CO2 phase and a UV detector for quantifying the analyte in the aqueous phase. Our measured partitioning coefficients followed expected trends based on volatility and aqueous solubility. The partitioning coefficients and literature data were then used to update a published poly parameter linear free-energy relationship and to develop five new linear free-energy relationships for predicting water-sc-CO2 partitioning coefficients. A total of four of the models targeted a single class of organic compounds. Unlike models that utilize Abraham solvation parameters, the new relationships use vapor pressure and aqueous solubility of the organic compound at 25 °C and CO2 density to predict partitioning coefficients over a range of temperature and pressure conditions. The compound class models provide better estimates of partitioning behavior for compounds in that class than does the model built for the entire data set. PMID:27081725
NASA Astrophysics Data System (ADS)
Hakkarainen, Elina; Tähtinen, Matti
2016-05-01
Demonstrations of direct steam generation (DSG) in linear Fresnel collectors (LFC) have given promising results related to higher steam parameters compared to the current state-of-the-art parabolic trough collector (PTC) technology using oil as heat transfer fluid (HTF). However, DSG technology lacks feasible solution for long-term thermal energy storage (TES) system. This option is important for CSP technology in order to offer dispatchable power. Recently, molten salts have been proposed to be used as HTF and directly as storage medium in both line-focusing solar fields, offering storage capacity of several hours. This direct molten salt (DMS) storage concept has already gained operational experience in solar tower power plant, and it is under demonstration phase both in the case of LFC and PTC systems. Dynamic simulation programs offer a valuable effort for design and optimization of solar power plants. In this work, APROS dynamic simulation program is used to model a DMS linear Fresnel solar field with two-tank TES system, and example simulation results are presented in order to verify the functionality of the model and capability of APROS for CSP modelling and simulation.
Ureba, A.; Salguero, F. J.; Barbeiro, A. R.; Jimenez-Ortega, E.; Baeza, J. A.; Leal, A.; Miras, H.; Linares, R.; Perucha, M.
2014-08-15
Purpose: The authors present a hybrid direct multileaf collimator (MLC) aperture optimization model exclusively based on sequencing of patient imaging data to be implemented on a Monte Carlo treatment planning system (MC-TPS) to allow the explicit radiation transport simulation of advanced radiotherapy treatments with optimal results in efficient times for clinical practice. Methods: The planning system (called CARMEN) is a full MC-TPS, controlled through aMATLAB interface, which is based on the sequencing of a novel map, called “biophysical” map, which is generated from enhanced image data of patients to achieve a set of segments actually deliverable. In order to reduce the required computation time, the conventional fluence map has been replaced by the biophysical map which is sequenced to provide direct apertures that will later be weighted by means of an optimization algorithm based on linear programming. A ray-casting algorithm throughout the patient CT assembles information about the found structures, the mass thickness crossed, as well as PET values. Data are recorded to generate a biophysical map for each gantry angle. These maps are the input files for a home-made sequencer developed to take into account the interactions of photons and electrons with the MLC. For each linac (Axesse of Elekta and Primus of Siemens) and energy beam studied (6, 9, 12, 15 MeV and 6 MV), phase space files were simulated with the EGSnrc/BEAMnrc code. The dose calculation in patient was carried out with the BEAMDOSE code. This code is a modified version of EGSnrc/DOSXYZnrc able to calculate the beamlet dose in order to combine them with different weights during the optimization process. Results: Three complex radiotherapy treatments were selected to check the reliability of CARMEN in situations where the MC calculation can offer an added value: A head-and-neck case (Case I) with three targets delineated on PET/CT images and a demanding dose-escalation; a partial breast
NASA Technical Reports Server (NTRS)
Stevens, P. K.
1981-01-01
This paper presents a generalization of the Nyquist stability criterion to include general multivariable linear stationary systems subject to linear static and dynamic feedback. At the same time, a unifying proof is given for all known versions of the Nyquist criterion for finite dimensional systems.
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)…
Arakawa, Akio; Konor, C.S.
1997-12-31
There are great conceptual advantages in the use of an isentropic vertical coordinate in atmospheric models. Design of such a model, however, requires to overcome computational problems due to intersection of coordinate surfaces with the earth`s surface. Under this project, the authors have completed the development of a model based on a generalized vertical coordinate, {zeta} = F({Theta}, p, p{sub s}), in which an isentropic coordinate can be combined with a terrain-following {sigma}-coordinate a smooth transition between the two. One of the key issues in developing such a model is to satisfy the consistency between the predictions of pressure and potential temperature. In the model, the consistency is satisfied by the use of an equation that determines the vertical mass flux. A procedure to properly choose {zeta} = F({Theta}, p, p{sub s}) is also developed, which guarantees that {zeta} is a monotonic function of height even when unstable stratification occurs. There are two versions of the model constructed in parallel: one is the middle-latitude {beta}-plane version and the other is the global version. Both of these versions include moisture prediction, relaxed large-scale condensation and relaxed moist-convective adjustment schemes. A well-mixed planetary boundary layer (PBL) is also added.
Computer analysis of general linear networks using digraphs.
NASA Technical Reports Server (NTRS)
Mcclenahan, J. O.; Chan, S.-P.
1972-01-01
Investigation of the application of digraphs in analyzing general electronic networks, and development of a computer program based on a particular digraph method developed by Chen. The Chen digraph method is a topological method for solution of networks and serves as a shortcut when hand calculations are required. The advantage offered by this method of analysis is that the results are in symbolic form. It is limited, however, by the size of network that may be handled. Usually hand calculations become too tedious for networks larger than about five nodes, depending on how many elements the network contains. Direct determinant expansion for a five-node network is a very tedious process also.
NASA Astrophysics Data System (ADS)
Prieto Sierra, C.; García Alonso, E.; Mínguez Solana, R.; Medina Santamaría, R.
2013-07-01
This paper explores a new approach to lumped hydrological modelling based on general laws of growth, in particular using the classic logistic equation proposed by Verhulst. By identifying homologies between the growth of a generic system and the evolution of the flow at the outlet of a river basin, and adopting some complementary hypotheses, a compact model with 3 parameters, extensible to 4 or 5, is obtained. The model assumes that a hydrological system, under persistent conditions of precipitation, potential evapotranspiration and land uses, tends to reach an equilibrium discharge that can be expressed as a function of a dynamic aridity index, including a free parameter reflecting the basin properties. The rate at which the system approaches such equilibrium discharge, which is constantly changing and generally not attainable, is another parameter of the model; finally, a time lag is introduced to reflect a characteristic delay between the input (precipitation) and output (discharge) in the system behaviour. To test the suitability of the proposed model, 5 previously studied river basins in the UK, with different characteristics, have been analysed at a daily scale, and the results compared with those of the model IHACRES (Identification of unit Hydrographs and Component flows from Rainfall, Evaporation and Streamflow data). It is found that the logistic equilibrium model with 3 parameters properly reproduces the hydrological behaviour of such basins, improving the IHACRES in four of them; moreover, the model parameters are relatively stable over different periods of calibration and evaluation. Adding more parameters to the basic structure, the fits only improve slightly in some of the analysed series, but potentially increasing equifinality effects. The results obtained indicate that growth equations, with possible variations, can be useful and parsimonious tools for hydrological modelling, at least in certain types of watersheds.
Use of generalized linear models and digital data in a forest inventory of Northern Utah
Moisen, G.G.; Edwards, T.C., Jr.
1999-01-01
Forest inventories, like those conducted by the Forest Service's Forest Inventory and Analysis Program (FIA) in the Rocky Mountain Region, are under increased pressure to produce better information at reduced costs. Here we describe our efforts in Utah to merge satellite-based information with forest inventory data for the purposes of reducing the costs of estimates of forest population totals and providing spatial depiction of forest resources. We illustrate how generalized linear models can be used to construct approximately unbiased and efficient estimates of population totals while providing a mechanism for prediction in space for mapping of forest structure. We model forest type and timber volume of five tree species groups as functions of a variety of predictor variables in the northern Utah mountains. Predictor variables include elevation, aspect, slope, geographic coordinates, as well as vegetation cover types based on satellite data from both the Advanced Very High Resolution Radiometer (AVHRR) and Thematic Mapper (TM) platforms. We examine the relative precision of estimates of area by forest type and mean cubic-foot volumes under six different models, including the traditional double sampling for stratification strategy. Only very small gains in precision were realized through the use of expensive photointerpreted or TM-based data for stratification, while models based on topography and spatial coordinates alone were competitive. We also compare the predictive capability of the models through various map accuracy measures. The models including the TM-based vegetation performed best overall, while topography and spatial coordinates alone provided substantial information at very low cost.
NASA Astrophysics Data System (ADS)
Scafetta, Nicola
2013-11-01
Power spectra of global surface temperature (GST) records (available since 1850) reveal major periodicities at about 9.1, 10-11, 19-22 and 59-62 years. Equivalent oscillations are found in numerous multisecular paleoclimatic records. The Coupled Model Intercomparison Project 5 (CMIP5) general circulation models (GCMs), to be used in the IPCC Fifth Assessment Report (AR5, 2013), are analyzed and found not able to reconstruct this variability. In particular, from 2000 to 2013.5 a GST plateau is observed while the GCMs predicted a warming rate of about 2 °C/century. In contrast, the hypothesis that the climate is regulated by specific natural oscillations more accurately fits the GST records at multiple time scales. For example, a quasi 60-year natural oscillation simultaneously explains the 1850-1880, 1910-1940 and 1970-2000 warming periods, the 1880-1910 and 1940-1970 cooling periods and the post 2000 GST plateau. This hypothesis implies that about 50% of the ~ 0.5 °C global surface warming observed from 1970 to 2000 was due to natural oscillations of the climate system, not to anthropogenic forcing as modeled by the CMIP3 and CMIP5 GCMs. Consequently, the climate sensitivity to CO2 doubling should be reduced by half, for example from the 2.0-4.5 °C range (as claimed by the IPCC, 2007) to 1.0-2.3 °C with a likely median of ~ 1.5 °C instead of ~ 3.0 °C. Also modern paleoclimatic temperature reconstructions showing a larger preindustrial variability than the hockey-stick shaped temperature reconstructions developed in early 2000 imply a weaker anthropogenic effect and a stronger solar contribution to climatic changes. The observed natural oscillations could be driven by astronomical forcings. The ~ 9.1 year oscillation appears to be a combination of long soli-lunar tidal oscillations, while quasi 10-11, 20 and 60 year oscillations are typically found among major solar and heliospheric oscillations driven mostly by Jupiter and Saturn movements. Solar models based
NASA Astrophysics Data System (ADS)
Irmak, Suat; Mutiibwa, Denis
2010-08-01
The 1-D and single layer combination-based energy balance Penman-Monteith (PM) model has limitations in practical application due to the lack of canopy resistance (rc) data for different vegetation surfaces. rc could be estimated by inversion of the PM model if the actual evapotranspiration (ETa) rate is known, but this approach has its own set of issues. Instead, an empirical method of estimating rc is suggested in this study. We investigated the relationships between primary micrometeorological parameters and rc and developed seven models to estimate rc for a nonstressed maize canopy on an hourly time step using a generalized-linear modeling approach. The most complex rc model uses net radiation (Rn), air temperature (Ta), vapor pressure deficit (VPD), relative humidity (RH), wind speed at 3 m (u3), aerodynamic resistance (ra), leaf area index (LAI), and solar zenith angle (Θ). The simplest model requires Rn, Ta, and RH. We present the practical implementation of all models via experimental validation using scaled up rc data obtained from the dynamic diffusion porometer-measured leaf stomatal resistance through an extensive field campaign in 2006. For further validation, we estimated ETa by solving the PM model using the modeled rc from all seven models and compared the PM ETa estimates with the Bowen ratio energy balance system (BREBS)-measured ETa for an independent data set in 2005. The relationships between hourly rc versus Ta, RH, VPD, Rn, incoming shortwave radiation (Rs), u3, wind direction, LAI, Θ, and ra were presented and discussed. We demonstrated the negative impact of exclusion of LAI when modeling rc, whereas exclusion of ra and Θ did not impact the performance of the rc models. Compared to the calibration results, the validation root mean square difference between observed and modeled rc increased by 5 s m-1 for all rc models developed, ranging from 9.9 s m-1 for the most complex model to 22.8 s m-1 for the simplest model, as compared with the
Zhao, Yingfeng; Liu, Sanyang
2016-01-01
We present a practical branch and bound algorithm for globally solving generalized linear multiplicative programming problem with multiplicative constraints. To solve the problem, a relaxation programming problem which is equivalent to a linear programming is proposed by utilizing a new two-phase relaxation technique. In the algorithm, lower and upper bounds are simultaneously obtained by solving some linear relaxation programming problems. Global convergence has been proved and results of some sample examples and a small random experiment show that the proposed algorithm is feasible and efficient. PMID:27547676
NASA Technical Reports Server (NTRS)
Rankin, C. C.
1988-01-01
A consistent linearization is provided for the element-dependent corotational formulation, providing the proper first and second variation of the strain energy. As a result, the warping problem that has plagued flat elements has been overcome, with beneficial effects carried over to linear solutions. True Newton quadratic convergence has been restored to the Structural Analysis of General Shells (STAGS) code for conservative loading using the full corotational implementation. Some implications for general finite element analysis are discussed, including what effect the automatic frame invariance provided by this work might have on the development of new, improved elements.
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…
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…
ERIC Educational Resources Information Center
Henson, Robin K.
In General Linear Model (GLM) analyses, it is important to interpret structure coefficients, along with standardized weights, when evaluating variable contribution to observed effects. Although often used in canonical correlation analysis, structure coefficients are less frequently used in multiple regression and several other multivariate…
Estimation of Complex Generalized Linear Mixed Models for Measurement and Growth
ERIC Educational Resources Information Center
Jeon, Minjeong
2012-01-01
Maximum likelihood (ML) estimation of generalized linear mixed models (GLMMs) is technically challenging because of the intractable likelihoods that involve high dimensional integrations over random effects. The problem is magnified when the random effects have a crossed design and thus the data cannot be reduced to small independent clusters. A…
Implementing general quantum measurements on linear optical and solid-state qubits
NASA Astrophysics Data System (ADS)
Ota, Yukihiro; Ashhab, Sahel; Nori, Franco
2013-03-01
We show a systematic construction for implementing general measurements on a single qubit, including both strong (or projection) and weak measurements. We mainly focus on linear optical qubits. The present approach is composed of simple and feasible elements, i.e., beam splitters, wave plates, and polarizing beam splitters. We show how the parameters characterizing the measurement operators are controlled by the linear optical elements. We also propose a method for the implementation of general measurements in solid-state qubits. Furthermore, we show an interesting application of the general measurements, i.e., entanglement amplification. YO is partially supported by the SPDR Program, RIKEN. SA and FN acknowledge ARO, NSF grant No. 0726909, JSPS-RFBR contract No. 12-02-92100, Grant-in-Aid for Scientific Research (S), MEXT Kakenhi on Quantum Cybernetics, and the JSPS via its FIRST program.
Zhou, Shaohua Kevin; Aggarwal, Gaurav; Chellappa, Rama; Jacobs, David W
2007-02-01
Traditional photometric stereo algorithms employ a Lambertian reflectance model with a varying albedo field and involve the appearance of only one object. In this paper, we generalize photometric stereo algorithms to handle all appearances of all objects in a class, in particular the human face class, by making use of the linear Lambertian property. A linear Lambertian object is one which is linearly spanned by a set of basis objects and has a Lambertian surface. The linear property leads to a rank constraint and, consequently, a factorization of an observation matrix that consists of exemplar images of different objects (e.g., faces of different subjects) under different, unknown illuminations. Integrability and symmetry constraints are used to fully recover the subspace bases using a novel linearized algorithm that takes the varying albedo field into account. The effectiveness of the linear Lambertian property is further investigated by using it for the problem of illumination-invariant face recognition using just one image. Attached shadows are incorporated in the model by a careful treatment of the inherent nonlinearity in Lambert's law. This enables us to extend our algorithm to perform face recognition in the presence of multiple illumination sources. Experimental results using standard data sets are presented. PMID:17170477
Shen, Mouquan; Park, Ju H
2016-07-01
This paper addresses the H∞ filtering of continuous Markov jump linear systems with general transition probabilities and output quantization. S-procedure is employed to handle the adverse influence of the quantization and a new approach is developed to conquer the nonlinearity induced by uncertain and unknown transition probabilities. Then, sufficient conditions are presented to ensure the filtering error system to be stochastically stable with the prescribed performance requirement. Without specified structure imposed on introduced slack variables, a flexible filter design method is established in terms of linear matrix inequalities. The effectiveness of the proposed method is validated by a numerical example. PMID:27129765
Non-linear regime of the Generalized Minimal Massive Gravity in critical points
NASA Astrophysics Data System (ADS)
Setare, M. R.; Adami, H.
2016-03-01
The Generalized Minimal Massive Gravity (GMMG) theory is realized by adding the CS deformation term, the higher derivative deformation term, and an extra term to pure Einstein gravity with a negative cosmological constant. In the present paper we obtain exact solutions to the GMMG field equations in the non-linear regime of the model. GMMG model about AdS_3 space is conjectured to be dual to a 2-dimensional CFT. We study the theory in critical points corresponding to the central charges c_-=0 or c_+=0, in the non-linear regime. We show that AdS_3 wave solutions are present, and have logarithmic form in critical points. Then we study the AdS_3 non-linear deformation solution. Furthermore we obtain logarithmic deformation of extremal BTZ black hole. After that using Abbott-Deser-Tekin method we calculate the energy and angular momentum of these types of black hole solutions.
Linear and nonlinear associations between general intelligence and personality in Project TALENT.
Major, Jason T; Johnson, Wendy; Deary, Ian J
2014-04-01
Research on the relations of personality traits to intelligence has primarily been concerned with linear associations. Yet, there are no a priori reasons why linear relations should be expected over nonlinear ones, which represent a much larger set of all possible associations. Using 2 techniques, quadratic and generalized additive models, we tested for linear and nonlinear associations of general intelligence (g) with 10 personality scales from Project TALENT (PT), a nationally representative sample of approximately 400,000 American high school students from 1960, divided into 4 grade samples (Flanagan et al., 1962). We departed from previous studies, including one with PT (Reeve, Meyer, & Bonaccio, 2006), by modeling latent quadratic effects directly, controlling the influence of the common factor in the personality scales, and assuming a direction of effect from g to personality. On the basis of the literature, we made 17 directional hypotheses for the linear and quadratic associations. Of these, 53% were supported in all 4 male grades and 58% in all 4 female grades. Quadratic associations explained substantive variance above and beyond linear effects (mean R² between 1.8% and 3.6%) for Sociability, Maturity, Vigor, and Leadership in males and Sociability, Maturity, and Tidiness in females; linear associations were predominant for other traits. We discuss how suited current theories of the personality-intelligence interface are to explain these associations, and how research on intellectually gifted samples may provide a unique way of understanding them. We conclude that nonlinear models can provide incremental detail regarding personality and intelligence associations. PMID:24660993
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
Linear relations in microbial reaction systems: a general overview of their origin, form, and use.
Noorman, H J; Heijnen, J J; Ch A M Luyben, K
1991-09-01
In microbial reaction systems, there are a number of linear relations among net conversion rates. These can be very useful in the analysis of experimental data. This article provides a general approach for the formation and application of the linear relations. Two type of system descriptions, one considering the biomass as a black box and the other based on metabolic pathways, are encountered. These are defined in a linear vector and matrix algebra framework. A correct a priori description can be obtained by three useful tests: the independency, consistency, and observability tests. The independency are different. The black box approach provides only conservations relations. They are derived from element, electrical charge, energy, and Gibbs energy balances. The metabolic approach provides, in addition to the conservation relations, metabolic and reaction relations. These result from component, energy, and Gibbs energy balances. Thus it is more attractive to use the metabolic description than the black box approach. A number of different types of linear relations given in the literature are reviewed. They are classified according to the different categories that result from the black box or the metabolic system description. Validation of hypotheses related to metabolic pathways can be supported by experimental validation of the linear metabolic relations. However, definite proof from biochemical evidence remains indispensable. PMID:18604879
Zhao, Ningning; Basarab, Adrian; Kouame, Denis; Tourneret, Jean-Yves
2016-08-01
This paper proposes a joint segmentation and deconvolution Bayesian method for medical ultrasound (US) images. Contrary to piecewise homogeneous images, US images exhibit heavy characteristic speckle patterns correlated with the tissue structures. The generalized Gaussian distribution (GGD) has been shown to be one of the most relevant distributions for characterizing the speckle in US images. Thus, we propose a GGD-Potts model defined by a label map coupling US image segmentation and deconvolution. The Bayesian estimators of the unknown model parameters, including the US image, the label map, and all the hyperparameters are difficult to be expressed in a closed form. Thus, we investigate a Gibbs sampler to generate samples distributed according to the posterior of interest. These generated samples are finally used to compute the Bayesian estimators of the unknown parameters. The performance of the proposed Bayesian model is compared with the existing approaches via several experiments conducted on realistic synthetic data and in vivo US images. PMID:27187959
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
Chen, Xueli; Gao, Xinbo; Qu, Xiaochao; Chen, Duofang; Ma, Xiaopeng; Liang, Jimin; Tian, Jie
2010-10-10
The camera lens diaphragm is an important component in a noncontact optical imaging system and has a crucial influence on the images registered on the CCD camera. However, this influence has not been taken into account in the existing free-space photon transport models. To model the photon transport process more accurately, a generalized free-space photon transport model is proposed. It combines Lambertian source theory with analysis of the influence of the camera lens diaphragm to simulate photon transport process in free space. In addition, the radiance theorem is also adopted to establish the energy relationship between the virtual detector and the CCD camera. The accuracy and feasibility of the proposed model is validated with a Monte-Carlo-based free-space photon transport model and physical phantom experiment. A comparison study with our previous hybrid radiosity-radiance theorem based model demonstrates the improvement performance and potential of the proposed model for simulating photon transport process in free space. PMID:20935713
Unified Einstein-Virasoro Master Equation in the General Non-Linear Sigma Model
Boer, J. de; Halpern, M.B.
1996-06-05
The Virasoro master equation (VME) describes the general affine-Virasoro construction $T=L^abJ_aJ_b+iD^a \\dif J_a$ in the operator algebra of the WZW model, where $L^ab$ is the inverse inertia tensor and $D^a $ is the improvement vector. In this paper, we generalize this construction to find the general (one-loop) Virasoro construction in the operator algebra of the general non-linear sigma model. The result is a unified Einstein-Virasoro master equation which couples the spacetime spin-two field $L^ab$ to the background fields of the sigma model. For a particular solution $L_G^ab$, the unified system reduces to the canonical stress tensors and conventional Einstein equations of the sigma model, and the system reduces to the general affine-Virasoro construction and the VME when the sigma model is taken to be the WZW action. More generally, the unified system describes a space of conformal field theories which is presumably much larger than the sum of the general affine-Virasoro construction and the sigma model with its canonical stress tensors. We also discuss a number of algebraic and geometrical properties of the system, including its relation to an unsolved problem in the theory of $G$-structures on manifolds with torsion.
Numerical study of fourth-order linearized compact schemes for generalized NLS equations
NASA Astrophysics Data System (ADS)
Liao, Hong-lin; Shi, Han-sheng; Zhao, Ying
2014-08-01
The fourth-order compact approximation for the spatial second-derivative and several linearized approaches, including the time-lagging method of Zhang et al. (1995), the local-extrapolation technique of Chang et al. (1999) and the recent scheme of Dahlby et al. (2009), are considered in constructing fourth-order linearized compact difference (FLCD) schemes for generalized NLS equations. By applying a new time-lagging linearized approach, we propose a symmetric fourth-order linearized compact difference (SFLCD) scheme, which is shown to be more robust in long-time simulations of plane wave, breather, periodic traveling-wave and solitary wave solutions. Numerical experiments suggest that the SFLCD scheme is a little more accurate than some other FLCD schemes and the split-step compact difference scheme of Dehghan and Taleei (2010). Compared with the time-splitting pseudospectral method of Bao et al. (2003), our SFLCD method is more suitable for oscillating solutions or the problems with a rapidly varying potential.
Normality of raw data in general linear models: The most widespread myth in statistics
Kery, M.; Hatfield, J.S.
2003-01-01
In years of statistical consulting for ecologists and wildlife biologists, by far the most common misconception we have come across has been the one about normality in general linear models. These comprise a very large part of the statistical models used in ecology and include t tests, simple and multiple linear regression, polynomial regression, and analysis of variance (ANOVA) and covariance (ANCOVA). There is a widely held belief that the normality assumption pertains to the raw data rather than to the model residuals. We suspect that this error may also occur in countless published studies, whenever the normality assumption is tested prior to analysis. This may lead to the use of nonparametric alternatives (if there are any), when parametric tests would indeed be appropriate, or to use of transformations of raw data, which may introduce hidden assumptions such as multiplicative effects on the natural scale in the case of log-transformed data. Our aim here is to dispel this myth. We very briefly describe relevant theory for two cases of general linear models to show that the residuals need to be normally distributed if tests requiring normality are to be used, such as t and F tests. We then give two examples demonstrating that the distribution of the response variable may be nonnormal, and yet the residuals are well behaved. We do not go into the issue of how to test normality; instead we display the distributions of response variables and residuals graphically.
A general theory of linear cosmological perturbations: scalar-tensor and vector-tensor theories
NASA Astrophysics Data System (ADS)
Lagos, Macarena; Baker, Tessa; Ferreira, Pedro G.; Noller, Johannes
2016-08-01
We present a method for parametrizing linear cosmological perturbations of theories of gravity, around homogeneous and isotropic backgrounds. The method is sufficiently general and systematic that it can be applied to theories with any degrees of freedom (DoFs) and arbitrary gauge symmetries. In this paper, we focus on scalar-tensor and vector-tensor theories, invariant under linear coordinate transformations. In the case of scalar-tensor theories, we use our framework to recover the simple parametrizations of linearized Horndeski and ``Beyond Horndeski'' theories, and also find higher-derivative corrections. In the case of vector-tensor theories, we first construct the most general quadratic action for perturbations that leads to second-order equations of motion, which propagates two scalar DoFs. Then we specialize to the case in which the vector field is time-like (à la Einstein-Aether gravity), where the theory only propagates one scalar DoF. As a result, we identify the complete forms of the quadratic actions for perturbations, and the number of free parameters that need to be defined, to cosmologically characterize these two broad classes of theories.
Generalized stochastic resonance in a linear fractional system with a random delay
NASA Astrophysics Data System (ADS)
Gao, Shi-Long
2012-12-01
The generalized stochastic resonance (GSR) phenomena in a linear fractional random-delayed system driven by a weak periodic signal and an additive noise are considered in this paper. A random delay is considered for a linear fractional Langevin equation to describe the intercellular signal transmission and material exchange processes in ion channels. By virtue of the small delay approximation and Laplace transformation, the analytical expression for the amplitude of the first-order steady state moment is obtained. The simulation results show that the amplitude curves as functions of different system parameters behave non-monotonically and exhibit typical characteristics of GSR phenomena. Furthermore, a physical explanation for all the GSR phenomena is given and the cooperative effects of random delay and the fractional memory are also discussed.
The generalized Dirichlet-Neumann map for linear elliptic PDEs and its numerical implementation
NASA Astrophysics Data System (ADS)
Sifalakis, A. G.; Fokas, A. S.; Fulton, S. R.; Saridakis, Y. G.
2008-09-01
A new approach for analyzing boundary value problems for linear and for integrable nonlinear PDEs was introduced in Fokas [A unified transform method for solving linear and certain nonlinear PDEs, Proc. Roy. Soc. London Ser. A 53 (1997) 1411-1443]. For linear elliptic PDEs, an important aspect of this approach is the characterization of a generalized Dirichlet to Neumann map: given the derivative of the solution along a direction of an arbitrary angle to the boundary, the derivative of the solution perpendicularly to this direction is computed without solving on the interior of the domain. This is based on the analysis of the so-called global relation, an equation which couples known and unknown components of the derivative on the boundary and which is valid for all values of a complex parameter k. A collocation-type numerical method for solving the global relation for the Laplace equation in an arbitrary bounded convex polygon was introduced in Fulton et al. [An analytical method for linear elliptic PDEs and its numerical implementation, J. Comput. Appl. Math. 167 (2004) 465-483]. Here, by choosing a different set of the "collocation points" (values for k), we present a significant improvement of the results in Fulton et al. [An analytical method for linear elliptic PDEs and its numerical implementation, J. Comput. Appl. Math. 167 (2004) 465-483]. The new collocation points lead to well-conditioned collocation methods. Their combination with sine basis functions leads to a collocation matrix whose diagonal blocks are point diagonal matrices yielding efficient implementation of iterative methods; numerical experimentation suggests quadratic convergence. The choice of Chebyshev basis functions leads to higher order convergence, which for regular polygons appear to be exponential.
Random generalized linear model: a highly accurate and interpretable ensemble predictor
2013-01-01
Background Ensemble predictors such as the random forest are known to have superior accuracy but their black-box predictions are difficult to interpret. In contrast, a generalized linear model (GLM) is very interpretable especially when forward feature selection is used to construct the model. However, forward feature selection tends to overfit the data and leads to low predictive accuracy. Therefore, it remains an important research goal to combine the advantages of ensemble predictors (high accuracy) with the advantages of forward regression modeling (interpretability). To address this goal several articles have explored GLM based ensemble predictors. Since limited evaluations suggested that these ensemble predictors were less accurate than alternative predictors, they have found little attention in the literature. Results Comprehensive evaluations involving hundreds of genomic data sets, the UCI machine learning benchmark data, and simulations are used to give GLM based ensemble predictors a new and careful look. A novel bootstrap aggregated (bagged) GLM predictor that incorporates several elements of randomness and instability (random subspace method, optional interaction terms, forward variable selection) often outperforms a host of alternative prediction methods including random forests and penalized regression models (ridge regression, elastic net, lasso). This random generalized linear model (RGLM) predictor provides variable importance measures that can be used to define a “thinned” ensemble predictor (involving few features) that retains excellent predictive accuracy. Conclusion RGLM is a state of the art predictor that shares the advantages of a random forest (excellent predictive accuracy, feature importance measures, out-of-bag estimates of accuracy) with those of a forward selected generalized linear model (interpretability). These methods are implemented in the freely available R software package randomGLM. PMID:23323760
Robust root clustering for linear uncertain systems using generalized Lyapunov theory
NASA Technical Reports Server (NTRS)
Yedavalli, R. K.
1993-01-01
Consideration is given to the problem of matrix root clustering in subregions of a complex plane for linear state space models with real parameter uncertainty. The nominal matrix root clustering theory of Gutman & Jury (1981) using the generalized Liapunov equation is extended to the perturbed matrix case, and bounds are derived on the perturbation to maintain root clustering inside a given region. The theory makes it possible to obtain an explicit relationship between the parameters of the root clustering region and the uncertainty range of the parameter space.
Standard errors for EM estimates in generalized linear models with random effects.
Friedl, H; Kauermann, G
2000-09-01
A procedure is derived for computing standard errors of EM estimates in generalized linear models with random effects. Quadrature formulas are used to approximate the integrals in the EM algorithm, where two different approaches are pursued, i.e., Gauss-Hermite quadrature in the case of Gaussian random effects and nonparametric maximum likelihood estimation for an unspecified random effect distribution. An approximation of the expected Fisher information matrix is derived from an expansion of the EM estimating equations. This allows for inferential arguments based on EM estimates, as demonstrated by an example and simulations. PMID:10985213
Flexible analysis of digital PCR experiments using generalized linear mixed models.
Vynck, Matthijs; Vandesompele, Jo; Nijs, Nele; Menten, Björn; De Ganck, Ariane; Thas, Olivier
2016-09-01
The use of digital PCR for quantification of nucleic acids is rapidly growing. A major drawback remains the lack of flexible data analysis tools. Published analysis approaches are either tailored to specific problem settings or fail to take into account sources of variability. We propose the generalized linear mixed models framework as a flexible tool for analyzing a wide range of experiments. We also introduce a method for estimating reference gene stability to improve accuracy and precision of copy number and relative expression estimates. We demonstrate the usefulness of the methodology on a complex experimental setup. PMID:27551671
NASA Astrophysics Data System (ADS)
Sakaris, C. S.; Sakellariou, J. S.; Fassois, S. D.
2016-06-01
This study focuses on the problem of vibration-based damage precise localization via data-based, time series type, methods for structures consisting of 1D, 2D, or 3D elements. A Generalized Functional Model Based method is postulated based on an expanded Vector-dependent Functionally Pooled ARX (VFP-ARX) model form, capable of accounting for an arbitrary structural topology. The FP model's operating parameter vector elements are properly constrained to reflect any given topology. Damage localization is based on operating parameter vector estimation within the specified topology, so that the location estimate and its uncertainty bounds are statistically optimal. The method's effectiveness is experimentally demonstrated through damage precise localization on a laboratory spatial truss structure using various damage scenarios and a single pair of random excitation - vibration response signals in a low and limited frequency bandwidth.
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
Digit Span is (mostly) related linearly to general intelligence: Every extra bit of span counts.
Gignac, Gilles E; Weiss, Lawrence G
2015-12-01
Historically, Digit Span has been regarded as a relatively poor indicator of general intellectual functioning (g). In fact, Wechsler (1958) contended that beyond an average level of Digit Span performance, there was little benefit to possessing a greater memory span. Although Wechsler's position does not appear to have ever been tested empirically, it does appear to have become clinical lore. Consequently, the purpose of this investigation was to test Wechsler's contention on the Wechsler Adult Intelligence Scale-Fourth Edition normative sample (N = 1,800; ages: 16 - 69). Based on linear and nonlinear contrast analyses of means, as well as linear and nonlinear bifactor model analyses, all 3 Digit Span indicators (LDSF, LDSB, and LDSS) were found to exhibit primarily linear associations with FSIQ/g. Thus, the commonly held position that Digit Span performance beyond an average level is not indicative of greater intellectual functioning was not supported. The results are discussed in light of the increasing evidence across multiple domains that memory span plays an important role in intellectual functioning. PMID:25774642
Thermodynamic bounds and general properties of optimal efficiency and power in linear responses.
Jiang, Jian-Hua
2014-10-01
We study the optimal exergy efficiency and power for thermodynamic systems with an Onsager-type "current-force" relationship describing the linear response to external influences. We derive, in analytic forms, the maximum efficiency and optimal efficiency for maximum power for a thermodynamic machine described by a N×N symmetric Onsager matrix with arbitrary integer N. The figure of merit is expressed in terms of the largest eigenvalue of the "coupling matrix" which is solely determined by the Onsager matrix. Some simple but general relationships between the power and efficiency at the conditions for (i) maximum efficiency and (ii) optimal efficiency for maximum power are obtained. We show how the second law of thermodynamics bounds the optimal efficiency and the Onsager matrix and relate those bounds together. The maximum power theorem (Jacobi's Law) is generalized to all thermodynamic machines with a symmetric Onsager matrix in the linear-response regime. We also discuss systems with an asymmetric Onsager matrix (such as systems under magnetic field) for a particular situation and we show that the reversible limit of efficiency can be reached at finite output power. Cooperative effects are found to improve the figure of merit significantly in systems with multiply cross-correlated responses. Application to example systems demonstrates that the theory is helpful in guiding the search for high performance materials and structures in energy researches. PMID:25375457
Gao, Xieping; Li, Bodong; Xiao, Fen
2013-12-01
Multidimensional linear phase perfect reconstruction filter bank (MDLPPRFB) can be designed and implemented via lattice structure. The lattice structure for the MDLPPRFB with filter support N(MΞ) has been published by Muramatsu , where M is the decimation matrix, Ξ is a positive integer diagonal matrix, and N(N) denotes the set of integer vectors in the fundamental parallelepiped of the matrix N. Obviously, if Ξ is chosen to be other positive diagonal matrices instead of only positive integer ones, the corresponding lattice structure would provide more choices of filter banks, offering better trade-off between filter support and filter performance. We call such resulted filter bank as generalized-support MDLPPRFB (GSMDLPPRFB). The lattice structure for GSMDLPPRFB, however, cannot be designed by simply generalizing the process that Muramatsu employed. Furthermore, the related theories to assist the design also become different from those used by Muramatsu . Such issues will be addressed in this paper. To guide the design of GSMDLPPRFB, the necessary and sufficient conditions are established for a generalized-support multidimensional filter bank to be linear-phase. To determine the cases we can find a GSMDLPPRFB, the necessary conditions about the existence of it are proposed to be related with filter support and symmetry polarity (i.e., the number of symmetric filters ns and antisymmetric filters na). Based on a process (different from the one Muramatsu used) that combines several polyphase matrices to construct the starting block, one of the core building blocks of lattice structure, the lattice structure for GSMDLPPRFB is developed and shown to be minimal. Additionally, the result in this paper includes Muramatsu's as a special case. PMID:23974625
Model based manipulator control
NASA Technical Reports Server (NTRS)
Petrosky, Lyman J.; Oppenheim, Irving J.
1989-01-01
The feasibility of using model based control (MBC) for robotic manipulators was investigated. A double inverted pendulum system was constructed as the experimental system for a general study of dynamically stable manipulation. The original interest in dynamically stable systems was driven by the objective of high vertical reach (balancing), and the planning of inertially favorable trajectories for force and payload demands. The model-based control approach is described and the results of experimental tests are summarized. Results directly demonstrate that MBC can provide stable control at all speeds of operation and support operations requiring dynamic stability such as balancing. The application of MBC to systems with flexible links is also discussed.
The heritability of general cognitive ability increases linearly from childhood to young adulthood.
Haworth, C M A; Wright, M J; Luciano, M; Martin, N G; de Geus, E J C; van Beijsterveldt, C E M; Bartels, M; Posthuma, D; Boomsma, D I; Davis, O S P; Kovas, Y; Corley, R P; Defries, J C; Hewitt, J K; Olson, R K; Rhea, S-A; Wadsworth, S J; Iacono, W G; McGue, M; Thompson, L A; Hart, S A; Petrill, S A; Lubinski, D; Plomin, R
2010-11-01
Although common sense suggests that environmental influences increasingly account for individual differences in behavior as experiences accumulate during the course of life, this hypothesis has not previously been tested, in part because of the large sample sizes needed for an adequately powered analysis. Here we show for general cognitive ability that, to the contrary, genetic influence increases with age. The heritability of general cognitive ability increases significantly and linearly from 41% in childhood (9 years) to 55% in adolescence (12 years) and to 66% in young adulthood (17 years) in a sample of 11 000 pairs of twins from four countries, a larger sample than all previous studies combined. In addition to its far-reaching implications for neuroscience and molecular genetics, this finding suggests new ways of thinking about the interface between nature and nurture during the school years. Why, despite life's 'slings and arrows of outrageous fortune', do genetically driven differences increasingly account for differences in general cognitive ability? We suggest that the answer lies with genotype-environment correlation: as children grow up, they increasingly select, modify and even create their own experiences in part based on their genetic propensities. PMID:19488046
A General Linear Relaxometry Model of R1 Using Imaging Data
Callaghan, Martina F; Helms, Gunther; Lutti, Antoine; Mohammadi, Siawoosh; Weiskopf, Nikolaus
2015-01-01
Purpose The longitudinal relaxation rate (R1) measured in vivo depends on the local microstructural properties of the tissue, such as macromolecular, iron, and water content. Here, we use whole brain multiparametric in vivo data and a general linear relaxometry model to describe the dependence of R1 on these components. We explore a) the validity of having a single fixed set of model coefficients for the whole brain and b) the stability of the model coefficients in a large cohort. Methods Maps of magnetization transfer (MT) and effective transverse relaxation rate (R2*) were used as surrogates for macromolecular and iron content, respectively. Spatial variations in these parameters reflected variations in underlying tissue microstructure. A linear model was applied to the whole brain, including gray/white matter and deep brain structures, to determine the global model coefficients. Synthetic R1 values were then calculated using these coefficients and compared with the measured R1 maps. Results The model's validity was demonstrated by correspondence between the synthetic and measured R1 values and by high stability of the model coefficients across a large cohort. Conclusion A single set of global coefficients can be used to relate R1, MT, and R2* across the whole brain. Our population study demonstrates the robustness and stability of the model. Magn Reson Med, 2014. © 2014 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. Magn Reson Med 73:1309–1314, 2015. © 2014 Wiley Periodicals, Inc. PMID:24700606
Linear stability of a generalized multi-anticipative car following model with time delays
NASA Astrophysics Data System (ADS)
Ngoduy, D.
2015-05-01
In traffic flow, the multi-anticipative driving behavior describes the reaction of a vehicle to the driving behavior of many vehicles in front where as the time delay is defined as a physiological parameter reflecting the period of time between perceiving a stimulus of leading vehicles and performing a relevant action such as acceleration or deceleration. A lot of effort has been undertaken to understand the effects of either multi-anticipative driving behavior or time delays on traffic flow dynamics. This paper is a first attempt to analytically investigate the dynamics of a generalized class of car-following models with multi-anticipative driving behavior and different time delays associated with such multi-anticipations. To this end, this paper puts forwards to deriving the (long-wavelength) linear stability condition of such a car-following model and study how the combination of different choices of multi-anticipations and time delays affects the instabilities of traffic flow with respect to a small perturbation. It is found that the effect of delays and multi-anticipations are model-dependent, that is, the destabilization effect of delays is suppressed by the stabilization effect of multi-anticipations. Moreover, the weight factor reflecting the distribution of the driver's sensing to the relative gaps of leading vehicles is less sensitive to the linear stability condition of traffic flow than the weight factor for the relative speed of those leading vehicles.
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
Solving the Linear Balance Equation on the Globe as a Generalized Inverse Problem
NASA Technical Reports Server (NTRS)
Lu, Huei-Iin; Robertson, Franklin R.
1999-01-01
A generalized (pseudo) inverse technique was developed to facilitate a better understanding of the numerical effects of tropical singularities inherent in the spectral linear balance equation (LBE). Depending upon the truncation, various levels of determinancy are manifest. The traditional fully-determined (FD) systems give rise to a strong response, while the under-determined (UD) systems yield a weak response to the tropical singularities. The over-determined (OD) systems result in a modest response and a large residual in the tropics. The FD and OD systems can be alternatively solved by the iterative method. Differences in the solutions of an UD system exist between the inverse technique and the iterative method owing to the non- uniqueness of the problem. A realistic balanced wind was obtained by solving the principal components of the spectral LBE in terms of vorticity in an intermediate resolution. Improved solutions were achieved by including the singular-component solutions which best fit the observed wind data.
Generalized Linear Models for Identifying Predictors of the Evolutionary Diffusion of Viruses
Beard, Rachel; Magee, Daniel; Suchard, Marc A.; Lemey, Philippe; Scotch, Matthew
2014-01-01
Bioinformatics and phylogeography models use viral sequence data to analyze spread of epidemics and pandemics. However, few of these models have included analytical methods for testing whether certain predictors such as population density, rates of disease migration, and climate are drivers of spatial spread. Understanding the specific factors that drive spatial diffusion of viruses is critical for targeting public health interventions and curbing spread. In this paper we describe the application and evaluation of a model that integrates demographic and environmental predictors with molecular sequence data. The approach parameterizes evolutionary spread of RNA viruses as a generalized linear model (GLM) within a Bayesian inference framework using Markov chain Monte Carlo (MCMC). We evaluate this approach by reconstructing the spread of H5N1 in Egypt while assessing the impact of individual predictors on evolutionary diffusion of the virus. PMID:25717395
Computing Confidence Bounds for Power and Sample Size of the General Linear Univariate Model
Taylor, Douglas J.; Muller, Keith E.
2013-01-01
The power of a test, the probability of rejecting the null hypothesis in favor of an alternative, may be computed using estimates of one or more distributional parameters. Statisticians frequently fix mean values and calculate power or sample size using a variance estimate from an existing study. Hence computed power becomes a random variable for a fixed sample size. Likewise, the sample size necessary to achieve a fixed power varies randomly. Standard statistical practice requires reporting uncertainty associated with such point estimates. Previous authors studied an asymptotically unbiased method of obtaining confidence intervals for noncentrality and power of the general linear univariate model in this setting. We provide exact confidence intervals for noncentrality, power, and sample size. Such confidence intervals, particularly one-sided intervals, help in planning a future study and in evaluating existing studies. PMID:24039272
A Bayesian approach for inducing sparsity in generalized linear models with multi-category response
2015-01-01
Background The dimension and complexity of high-throughput gene expression data create many challenges for downstream analysis. Several approaches exist to reduce the number of variables with respect to small sample sizes. In this study, we utilized the Generalized Double Pareto (GDP) prior to induce sparsity in a Bayesian Generalized Linear Model (GLM) setting. The approach was evaluated using a publicly available microarray dataset containing 99 samples corresponding to four different prostate cancer subtypes. Results A hierarchical Sparse Bayesian GLM using GDP prior (SBGG) was developed to take into account the progressive nature of the response variable. We obtained an average overall classification accuracy between 82.5% and 94%, which was higher than Support Vector Machine, Random Forest or a Sparse Bayesian GLM using double exponential priors. Additionally, SBGG outperforms the other 3 methods in correctly identifying pre-metastatic stages of cancer progression, which can prove extremely valuable for therapeutic and diagnostic purposes. Importantly, using Geneset Cohesion Analysis Tool, we found that the top 100 genes produced by SBGG had an average functional cohesion p-value of 2.0E-4 compared to 0.007 to 0.131 produced by the other methods. Conclusions Using GDP in a Bayesian GLM model applied to cancer progression data results in better subclass prediction. In particular, the method identifies pre-metastatic stages of prostate cancer with substantially better accuracy and produces more functionally relevant gene sets. PMID:26423345
Extracting H I cosmological signal with generalized needlet internal linear combination
NASA Astrophysics Data System (ADS)
Olivari, L. C.; Remazeilles, M.; Dickinson, C.
2016-03-01
H I intensity mapping is a new observational technique to map fluctuations in the large-scale structure of matter using the 21 cm emission line of atomic hydrogen (H I). Sensitive H I intensity mapping experiments have the potential to detect Baryon Acoustic Oscillations at low redshifts (z ≲ 1) in order to constrain the properties of dark energy. Observations of the H I signal will be contaminated by instrumental noise and, more significantly, by astrophysical foregrounds, such as Galactic synchrotron emission, which is at least four orders of magnitude brighter than the H I signal. Foreground cleaning is recognized as one of the key challenges for future radio astronomy surveys. We study the ability of the Generalized Needlet Internal Linear Combination (GNILC) method to subtract radio foregrounds and to recover the cosmological H I signal for a general H I intensity mapping experiment. The GNILC method is a new technique that uses both frequency and spatial information to separate the components of the observed data. Our results show that the method is robust to the complexity of the foregrounds. For simulated radio observations including H I emission, Galactic synchrotron, Galactic free-free, radio sources, and 0.05 mK thermal noise, we find that the GNILC method can reconstruct the H I power spectrum for multipoles 30 < ℓ < 150 with 6 per cent accuracy on 50 per cent of the sky for a redshift z ˜ 0.25.
Unification of the general non-linear sigma model and the Virasoro master equation
Boer, J. de; Halpern, M.B. |
1997-06-01
The Virasoro master equation describes a large set of conformal field theories known as the affine-Virasoro constructions, in the operator algebra (affinie Lie algebra) of the WZW model, while the einstein equations of the general non-linear sigma model describe another large set of conformal field theories. This talk summarizes recent work which unifies these two sets of conformal field theories, together with a presumable large class of new conformal field theories. The basic idea is to consider spin-two operators of the form L{sub ij}{partial_derivative}x{sup i}{partial_derivative}x{sup j} in the background of a general sigma model. The requirement that these operators satisfy the Virasoro algebra leads to a set of equations called the unified Einstein-Virasoro master equation, in which the spin-two spacetime field L{sub ij} cuples to the usual spacetime fields of the sigma model. The one-loop form of this unified system is presented, and some of its algebraic and geometric properties are discussed.
SPARSE GENERALIZED FUNCTIONAL LINEAR MODEL FOR PREDICTING REMISSION STATUS OF DEPRESSION PATIENTS
Liu, Yashu; Nie, Zhi; Zhou, Jiayu; Farnum, Michael; Narayan, Vaibhav A; Wittenberg, Gayle; Ye, Jieping
2014-01-01
Complex diseases such as major depression affect people over time in complicated patterns. Longitudinal data analysis is thus crucial for understanding and prognosis of such diseases and has received considerable attention in the biomedical research community. Traditional classification and regression methods have been commonly applied in a simple (controlled) clinical setting with a small number of time points. However, these methods cannot be easily extended to the more general setting for longitudinal analysis, as they are not inherently built for time-dependent data. Functional regression, in contrast, is capable of identifying the relationship between features and outcomes along with time information by assuming features and/or outcomes as random functions over time rather than independent random variables. In this paper, we propose a novel sparse generalized functional linear model for the prediction of treatment remission status of the depression participants with longitudinal features. Compared to traditional functional regression models, our model enables high-dimensional learning, smoothness of functional coefficients, longitudinal feature selection and interpretable estimation of functional coefficients. Extensive experiments have been conducted on the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) data set and the results show that the proposed sparse functional regression method achieves significantly higher prediction power than existing approaches. PMID:24297562
Generalized linear transport theory in dilute neutral gases and dispersion relation of sound waves.
Bendib, A; Bendib-Kalache, K; Gombert, M M; Imadouchene, N
2006-10-01
The transport processes in dilute neutral gases are studied by using the kinetic equation with a collision relaxation model that meets all conservation requirements. The kinetic equation is solved keeping the whole anisotropic part of the distribution function with the use of the continued fractions. The conservative laws of the collision operator are taken into account with the projection operator techniques. The generalized heat flux and stress tensor are calculated in the linear approximation, as functions of the lower moments, i.e., the density, the flow velocity and the temperature. The results obtained are valid for arbitrary collision frequency nu with the respect to kv(t) and the characteristic frequency omega, where k(-1) is the characteristic length scale of the system and v(t) is the thermal velocity. The transport coefficients constitute accurate closure relations for the generalized hydrodynamic equations. An application to the dispersion and the attenuation of sound waves in the whole collisionality regime is presented. The results obtained are in very good agreement with the experimental data. PMID:17155048
NASA Astrophysics Data System (ADS)
Jellison, Gerald E., Jr.; Griffiths, C. Owen; Holcomb, David E.; Rouleau, Christopher M.
2002-09-01
The two-modulator generalized ellipsometer (2-MGE) is a spectroscopic polarization-sensitive optical instrument that is sensitive to both standard ellipsometric parameters from isotropic samples as well as cross polarization terms arising from anisotropic samples. In reflection mode, teh 2-MGE has been used to measure the complex dielectric functions of several uniaxial crystals, including TiO2, ZnO, and BiI3. The 2-MGE can also be used in the transmission mode, in which the complete Mueller matrix of a sample can be determined (using 4 zone measurements). If the sample is a linear diattenuator and retarder, then only a single zone is required to determine the sample retardation, diattenuation, the principal axis direction, and the depolarization. These measurements have been performed in two different modes: 1) Spectroscopic, where the current wavelength limits are 260 to 850 nm, and 2) Spatially resolved (Current resolution ~30-50 microns) at a single wavelength. The latter mode results in retardation, linear diattenuation, and principal axis direction "maps" of the sample. Two examples are examined in this paper. First, a simple Polaroid film polarizer is measured, where it is seen that the device behaves nearly ideally in its design wavelength range (visible), but acts more as a retarder in the infrared. Second, congruently grown LiNbO3 is examined under bias. These results show that there are significant variations in the electric field-Pockels coefficient product within the material. Spectroscopic measurements are used to determine the dispersion of the r22 Pockels coefficient.
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.
Shin, Yongyun; Raudenbush, Stephen W
2013-01-01
This article extends single-level missing data methods to efficient estimation of a Q-level nested hierarchical general linear model given ignorable missing data with a general missing pattern at any of the Q levels. The key idea is to reexpress a desired hierarchical model as the joint distribution of all variables including the outcome that are subject to missingness, conditional on all of the covariates that are completely observed and to estimate the joint model under normal theory. The unconstrained joint model, however, identifies extraneous parameters that are not of interest in subsequent analysis of the hierarchical model and that rapidly multiply as the number of levels, the number of variables subject to missingness, and the number of random coefficients grow. Therefore, the joint model may be extremely high dimensional and difficult to estimate well unless constraints are imposed to avoid the proliferation of extraneous covariance components at each level. Furthermore, the over-identified hierarchical model may produce considerably biased inferences. The challenge is to represent the constraints within the framework of the Q-level model in a way that is uniform without regard to Q; in a way that facilitates efficient computation for any number of Q levels; and also in a way that produces unbiased and efficient analysis of the hierarchical model. Our approach yields Q-step recursive estimation and imputation procedures whose qth-step computation involves only level-q data given higher-level computation components. We illustrate the approach with a study of the growth in body mass index analyzing a national sample of elementary school children. PMID:24077621
Generalized Jeans' Escape of Pick-Up Ions in Quasi-Linear Relaxation
NASA Technical Reports Server (NTRS)
Moore, T. E.; Khazanov, G. V.
2011-01-01
Jeans escape is a well-validated formulation of upper atmospheric escape that we have generalized to estimate plasma escape from ionospheres. It involves the computation of the parts of particle velocity space that are unbound by the gravitational potential at the exobase, followed by a calculation of the flux carried by such unbound particles as they escape from the potential well. To generalize this approach for ions, we superposed an electrostatic ambipolar potential and a centrifugal potential, for motions across and along a divergent magnetic field. We then considered how the presence of superthermal electrons, produced by precipitating auroral primary electrons, controls the ambipolar potential. We also showed that the centrifugal potential plays a small role in controlling the mass escape flux from the terrestrial ionosphere. We then applied the transverse ion velocity distribution produced when ions, picked up by supersonic (i.e., auroral) ionospheric convection, relax via quasi-linear diffusion, as estimated for cometary comas [1]. The results provide a theoretical basis for observed ion escape response to electromagnetic and kinetic energy sources. They also suggest that super-sonic but sub-Alfvenic flow, with ion pick-up, is a unique and important regime of ion-neutral coupling, in which plasma wave-particle interactions are driven by ion-neutral collisions at densities for which the collision frequency falls near or below the gyro-frequency. As another possible illustration of this process, the heliopause ribbon discovered by the IBEX mission involves interactions between the solar wind ions and the interstellar neutral gas, in a regime that may be analogous [2].
NASA Astrophysics Data System (ADS)
Elliott, J.; de Souza, R. S.; Krone-Martins, A.; Cameron, E.; Ishida, E. E. O.; Hilbe, J.
2015-04-01
Machine learning techniques offer a precious tool box for use within astronomy to solve problems involving so-called big data. They provide a means to make accurate predictions about a particular system without prior knowledge of the underlying physical processes of the data. In this article, and the companion papers of this series, we present the set of Generalized Linear Models (GLMs) as a fast alternative method for tackling general astronomical problems, including the ones related to the machine learning paradigm. To demonstrate the applicability of GLMs to inherently positive and continuous physical observables, we explore their use in estimating the photometric redshifts of galaxies from their multi-wavelength photometry. Using the gamma family with a log link function we predict redshifts from the PHoto-z Accuracy Testing simulated catalogue and a subset of the Sloan Digital Sky Survey from Data Release 10. We obtain fits that result in catastrophic outlier rates as low as ∼1% for simulated and ∼2% for real data. Moreover, we can easily obtain such levels of precision within a matter of seconds on a normal desktop computer and with training sets that contain merely thousands of galaxies. Our software is made publicly available as a user-friendly package developed in Python, R and via an interactive web application. This software allows users to apply a set of GLMs to their own photometric catalogues and generates publication quality plots with minimum effort. By facilitating their ease of use to the astronomical community, this paper series aims to make GLMs widely known and to encourage their implementation in future large-scale projects, such as the Large Synoptic Survey Telescope.
MGMRES: A generalization of GMRES for solving large sparse nonsymmetric linear systems
Young, D.M.; Chen, J.Y.
1994-12-31
The authors are concerned with the solution of the linear system (1): Au = b, where A is a real square nonsingular matrix which is large, sparse and non-symmetric. They consider the use of Krylov subspace methods. They first choose an initial approximation u{sup (0)} to the solution {bar u} = A{sup {minus}1}B of (1). They also choose an auxiliary matrix Z which is nonsingular. For n = 1,2,{hor_ellipsis} they determine u{sup (n)} such that u{sup (n)} {minus} u{sup (0)}{epsilon}K{sub n}(r{sup (0)},A) where K{sub n}(r{sup (0)},A) is the (Krylov) subspace spanned by the Krylov vectors r{sup (0)}, Ar{sup (0)}, {hor_ellipsis}, A{sup n{minus}1}r{sup 0} and where r{sup (0)} = b{minus}Au{sup (0)}. If ZA is SPD they also require that (u{sup (n)}{minus}{bar u}, ZA(u{sup (n)}{minus}{bar u})) be minimized. If, on the other hand, ZA is not SPD, then they require that the Galerkin condition, (Zr{sup n}, v) = 0, be satisfied for all v{epsilon}K{sub n}(r{sup (0)}, A) where r{sup n} = b{minus}Au{sup (n)}. In this paper the authors consider a generalization of GMRES. This generalized method, which they refer to as `MGMRES`, is very similar to GMRES except that they let Z = A{sup T}Y where Y is a nonsingular matrix which is symmetric by not necessarily SPD.
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
Garcia, J M; Teodoro, F; Cerdeira, R; Coelho, L M R; Kumar, Prashant; Carvalho, M G
2016-09-01
A methodology to predict PM10 concentrations in urban outdoor environments is developed based on the generalized linear models (GLMs). The methodology is based on the relationship developed between atmospheric concentrations of air pollutants (i.e. CO, NO2, NOx, VOCs, SO2) and meteorological variables (i.e. ambient temperature, relative humidity (RH) and wind speed) for a city (Barreiro) of Portugal. The model uses air pollution and meteorological data from the Portuguese monitoring air quality station networks. The developed GLM considers PM10 concentrations as a dependent variable, and both the gaseous pollutants and meteorological variables as explanatory independent variables. A logarithmic link function was considered with a Poisson probability distribution. Particular attention was given to cases with air temperatures both below and above 25°C. The best performance for modelled results against the measured data was achieved for the model with values of air temperature above 25°C compared with the model considering all ranges of air temperatures and with the model considering only temperature below 25°C. The model was also tested with similar data from another Portuguese city, Oporto, and results found to behave similarly. It is concluded that this model and the methodology could be adopted for other cities to predict PM10 concentrations when these data are not available by measurements from air quality monitoring stations or other acquisition means. PMID:26839052
Maximal freedom at minimum cost: linear large-scale structure in general modifications of gravity
Bellini, Emilio; Sawicki, Ignacy E-mail: ignacy.sawicki@outlook.com
2014-07-01
We present a turnkey solution, ready for implementation in numerical codes, for the study of linear structure formation in general scalar-tensor models involving a single universally coupled scalar field. We show that the totality of cosmological information on the gravitational sector can be compressed — without any redundancy — into five independent and arbitrary functions of time only and one constant. These describe physical properties of the universe: the observable background expansion history, fractional matter density today, and four functions of time describing the properties of the dark energy. We show that two of those dark-energy property functions control the existence of anisotropic stress, the other two — dark-energy clustering, both of which are can be scale-dependent. All these properties can in principle be measured, but no information on the underlying theory of acceleration beyond this can be obtained. We present a translation between popular models of late-time acceleration (e.g. perfect fluids, f(R), kinetic gravity braiding, galileons), as well as the effective field theory framework, and our formulation. In this way, implementing this formulation numerically would give a single tool which could consistently test the majority of models of late-time acceleration heretofore proposed.
Master equation solutions in the linear regime of characteristic formulation of general relativity
NASA Astrophysics Data System (ADS)
Cedeño M., C. E.; de Araujo, J. C. N.
2015-12-01
From the field equations in the linear regime of the characteristic formulation of general relativity, Bishop, for a Schwarzschild's background, and Mädler, for a Minkowski's background, were able to show that it is possible to derive a fourth order ordinary differential equation, called master equation, for the J metric variable of the Bondi-Sachs metric. Once β , another Bondi-Sachs potential, is obtained from the field equations, and J is obtained from the master equation, the other metric variables are solved integrating directly the rest of the field equations. In the past, the master equation was solved for the first multipolar terms, for both the Minkowski's and Schwarzschild's backgrounds. Also, Mädler recently reported a generalisation of the exact solutions to the linearised field equations when a Minkowski's background is considered, expressing the master equation family of solutions for the vacuum in terms of Bessel's functions of the first and the second kind. Here, we report new solutions to the master equation for any multipolar moment l , with and without matter sources in terms only of the first kind Bessel's functions for the Minkowski, and in terms of the Confluent Heun's functions (Generalised Hypergeometric) for radiative (nonradiative) case in the Schwarzschild's background. We particularize our families of solutions for the known cases for l =2 reported previously in the literature and find complete agreement, showing the robustness of our results.
The overlooked potential of Generalized Linear Models in astronomy, I: Binomial regression
NASA Astrophysics Data System (ADS)
de Souza, R. S.; Cameron, E.; Killedar, M.; Hilbe, J.; Vilalta, R.; Maio, U.; Biffi, V.; Ciardi, B.; Riggs, J. D.
2015-09-01
Revealing hidden patterns in astronomical data is often the path to fundamental scientific breakthroughs; meanwhile the complexity of scientific enquiry increases as more subtle relationships are sought. Contemporary data analysis problems often elude the capabilities of classical statistical techniques, suggesting the use of cutting edge statistical methods. In this light, astronomers have overlooked a whole family of statistical techniques for exploratory data analysis and robust regression, the so-called Generalized Linear Models (GLMs). In this paper-the first in a series aimed at illustrating the power of these methods in astronomical applications-we elucidate the potential of a particular class of GLMs for handling binary/binomial data, the so-called logit and probit regression techniques, from both a maximum likelihood and a Bayesian perspective. As a case in point, we present the use of these GLMs to explore the conditions of star formation activity and metal enrichment in primordial minihaloes from cosmological hydro-simulations including detailed chemistry, gas physics, and stellar feedback. We predict that for a dark mini-halo with metallicity ≈ 1.3 × 10-4Z⨀, an increase of 1.2 × 10-2 in the gas molecular fraction, increases the probability of star formation occurrence by a factor of 75%. Finally, we highlight the use of receiver operating characteristic curves as a diagnostic for binary classifiers, and ultimately we use these to demonstrate the competitive predictive performance of GLMs against the popular technique of artificial neural networks.
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 general parallel sparse-blocked matrix multiply for linear scaling SCF theory
NASA Astrophysics Data System (ADS)
Challacombe, Matt
2000-06-01
A general approach to the parallel sparse-blocked matrix-matrix multiply is developed in the context of linear scaling self-consistent-field (SCF) theory. The data-parallel message passing method uses non-blocking communication to overlap computation and communication. The space filling curve heuristic is used to achieve data locality for sparse matrix elements that decay with “separation”. Load balance is achieved by solving the bin packing problem for blocks with variable size.With this new method as the kernel, parallel performance of the simplified density matrix minimization (SDMM) for solution of the SCF equations is investigated for RHF/6-31G ∗∗ water clusters and RHF/3-21G estane globules. Sustained rates above 5.7 GFLOPS for the SDMM have been achieved for (H 2 O) 200 with 95 Origin 2000 processors. Scalability is found to be limited by load imbalance, which increases with decreasing granularity, due primarily to the inhomogeneous distribution of variable block sizes.
Sensitivity Analysis of Linear Elastic Cracked Structures Using Generalized Finite Element Method
NASA Astrophysics Data System (ADS)
Pal, Mahendra Kumar; Rajagopal, Amirtham
2014-09-01
In this work, a sensitivity analysis of linear elastic cracked structures using two-scale Generalized Finite Element Method (GFEM) is presented. The method is based on computation of material derivatives, mutual potential energies, and direct differentiation. In a computational setting, the discrete form of the mutual potential energy release rate is simple and easy to calculate, as it only requires the multiplication of the displacement vectors and stiffness sensitivity matrices. By judiciously choosing the velocity field, the method only requires displacement response in a sub-domain close to the crack tip, thus making the method computationally efficient. The method thus requires an exact computation of displacement response in a sub-domain close to the crack tip. To this end, in this study we have used a two-scale GFEM for sensitivity analysis. GFEM is based on the enrichment of the classical finite element approximation. These enrichment functions incorporate the discontinuity response in the domain. Three numerical examples which comprise mode-I and mixed mode deformations are presented to evaluate the accuracy of the fracture parameters calculated by the proposed method.
Fast inference in generalized linear models via expected log-likelihoods
Ramirez, Alexandro D.; Paninski, Liam
2015-01-01
Generalized linear models play an essential role in a wide variety of statistical applications. This paper discusses an approximation of the likelihood in these models that can greatly facilitate computation. The basic idea is to replace a sum that appears in the exact log-likelihood by an expectation over the model covariates; the resulting “expected log-likelihood” can in many cases be computed significantly faster than the exact log-likelihood. In many neuroscience experiments the distribution over model covariates is controlled by the experimenter and the expected log-likelihood approximation becomes particularly useful; for example, estimators based on maximizing this expected log-likelihood (or a penalized version thereof) can often be obtained with orders of magnitude computational savings compared to the exact maximum likelihood estimators. A risk analysis establishes that these maximum EL estimators often come with little cost in accuracy (and in some cases even improved accuracy) compared to standard maximum likelihood estimates. Finally, we find that these methods can significantly decrease the computation time of marginal likelihood calculations for model selection and of Markov chain Monte Carlo methods for sampling from the posterior parameter distribution. We illustrate our results by applying these methods to a computationally-challenging dataset of neural spike trains obtained via large-scale multi-electrode recordings in the primate retina. PMID:23832289
Yock, Adam D. Kudchadker, Rajat J.; Rao, Arvind; Dong, Lei; Beadle, Beth M.; Garden, Adam S.; Court, Laurence E.
2014-05-15
Purpose: The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Methods: Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. Results: In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: −11.6%–23.8%) and 14.6% (range: −7.3%–27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: −6.8%–40.3%) and 13.1% (range: −1.5%–52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: −11.1%–20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. Conclusions: A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography
Generalized functional linear models for gene-based case-control association studies.
Fan, Ruzong; Wang, Yifan; Mills, James L; Carter, Tonia C; Lobach, Iryna; Wilson, Alexander F; Bailey-Wilson, Joan E; Weeks, Daniel E; Xiong, Momiao
2014-11-01
By using functional data analysis techniques, we developed generalized functional linear models for testing association between a dichotomous trait and multiple genetic variants in a genetic region while adjusting for covariates. Both fixed and mixed effect models are developed and compared. Extensive simulations show that Rao's efficient score tests of the fixed effect models are very conservative since they generate lower type I errors than nominal levels, and global tests of the mixed effect models generate accurate type I errors. Furthermore, we found that the Rao's efficient score test statistics of the fixed effect models have higher power than the sequence kernel association test (SKAT) and its optimal unified version (SKAT-O) in most cases when the causal variants are both rare and common. When the causal variants are all rare (i.e., minor allele frequencies less than 0.03), the Rao's efficient score test statistics and the global tests have similar or slightly lower power than SKAT and SKAT-O. In practice, it is not known whether rare variants or common variants in a gene region are disease related. All we can assume is that a combination of rare and common variants influences disease susceptibility. Thus, the improved performance of our models when the causal variants are both rare and common shows that the proposed models can be very useful in dissecting complex traits. We compare the performance of our methods with SKAT and SKAT-O on real neural tube defects and Hirschsprung's disease datasets. The Rao's efficient score test statistics and the global tests are more sensitive than SKAT and SKAT-O in the real data analysis. Our methods can be used in either gene-disease genome-wide/exome-wide association studies or candidate gene analyses. PMID:25203683
An assessment of estimation methods for generalized linear mixed models with binary outcomes
Capanu, Marinela; Gönen, Mithat; Begg, Colin B.
2013-01-01
Two main classes of methodology have been developed for addressing the analytical intractability of generalized linear mixed models (GLMMs): likelihood-based methods and Bayesian methods. Likelihood-based methods such as the penalized quasi-likelihood approach have been shown to produce biased estimates especially for binary clustered data with small clusters sizes. More recent methods using adaptive Gaussian quadrature perform well but can be overwhelmed by problems with large numbers of random effects, and efficient algorithms to better handle these situations have not yet been integrated in standard statistical packages. Bayesian methods, though they have good frequentist properties when the model is correct, are known to be computationally intensive and also require specialized code, limiting their use in practice. In this article we introduce a modification of the hybrid approach of Capanu and Begg [1] as a bridge between the likelihood-based and Bayesian approaches by employing Bayesian estimation for the variance components followed by Laplacian estimation for the regression coefficients. We investigate its performance as well as that of several likelihood-based methods in the setting of GLMMs with binary outcomes. We apply the methods to three datasets and conduct simulations to illustrate their properties. Simulation results indicate that for moderate to large numbers of observations per random effect, adaptive Gaussian quadrature and the Laplacian approximation are very accurate, with adaptive Gaussian quadrature preferable as the number of observations per random effect increases. The hybrid approach is overall similar to the Laplace method, and it can be superior for data with very sparse random effects. PMID:23839712
NASA Technical Reports Server (NTRS)
Holdaway, Daniel; Kent, James
2015-01-01
The linearity of a selection of common advection schemes is tested and examined with a view to their use in the tangent linear and adjoint versions of an atmospheric general circulation model. The schemes are tested within a simple offline one-dimensional periodic domain as well as using a simplified and complete configuration of the linearised version of NASA's Goddard Earth Observing System version 5 (GEOS-5). All schemes which prevent the development of negative values and preserve the shape of the solution are confirmed to have nonlinear behaviour. The piecewise parabolic method (PPM) with certain flux limiters, including that used by default in GEOS-5, is found to support linear growth near the shocks. This property can cause the rapid development of unrealistically large perturbations within the tangent linear and adjoint models. It is shown that these schemes with flux limiters should not be used within the linearised version of a transport scheme. The results from tests using GEOS-5 show that the current default scheme (a version of PPM) is not suitable for the tangent linear and adjoint model, and that using a linear third-order scheme for the linearised model produces better behaviour. Using the third-order scheme for the linearised model improves the correlations between the linear and non-linear perturbation trajectories for cloud liquid water and cloud liquid ice in GEOS-5.
General methods for determining the linear stability of coronal magnetic fields
NASA Technical Reports Server (NTRS)
Craig, I. J. D.; Sneyd, A. D.; Mcclymont, A. N.
1988-01-01
A time integration of a linearized plasma equation of motion has been performed to calculate the ideal linear stability of arbitrary three-dimensional magnetic fields. The convergence rates of the explicit and implicit power methods employed are speeded up by using sequences of cyclic shifts. Growth rates are obtained for Gold-Hoyle force-free equilibria, and the corkscrew-kink instability is found to be very weak.
NASA Astrophysics Data System (ADS)
Begley, Matthew R.; Creton, Costantino; McMeeking, Robert M.
2015-11-01
A general asymptotic plane strain crack tip stress field is constructed for linear versions of neo-Hookean materials, which spans a wide variety of special cases including incompressible Mooney elastomers, the compressible Blatz-Ko elastomer, several cases of the Ogden constitutive law and a new result for a compressible linear neo-Hookean material. The nominal stress field has dominant terms that have a square root singularity with respect to the distance of material points from the crack tip in the undeformed reference configuration. At second order, there is a uniform tension parallel to the crack. The associated displacement field in plane strain at leading order has dependence proportional to the square root of the same coordinate. The relationship between the amplitude of the crack tip singularity (a stress intensity factor) and the plane strain energy release rate is outlined for the general linear material, with simplified relationships presented for notable special cases.
Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models.
Fan, Ruzong; Wang, Yifan; Chiu, Chi-Yang; Chen, Wei; Ren, Haobo; Li, Yun; Boehnke, Michael; Amos, Christopher I; Moore, Jason H; Xiong, Momiao
2016-02-01
We developed generalized functional linear models (GFLMs) to perform a meta-analysis of multiple case-control studies to evaluate the relationship of genetic data to dichotomous traits adjusting for covariates. Unlike the previously developed meta-analysis for sequence kernel association tests (MetaSKATs), which are based on mixed-effect models to make the contributions of major gene loci random, GFLMs are fixed models; i.e., genetic effects of multiple genetic variants are fixed. Based on GFLMs, we developed chi-squared-distributed Rao's efficient score test and likelihood-ratio test (LRT) statistics to test for an association between a complex dichotomous trait and multiple genetic variants. We then performed extensive simulations to evaluate the empirical type I error rates and power performance of the proposed tests. The Rao's efficient score test statistics of GFLMs are very conservative and have higher power than MetaSKATs when some causal variants are rare and some are common. When the causal variants are all rare [i.e., minor allele frequencies (MAF) < 0.03], the Rao's efficient score test statistics have similar or slightly lower power than MetaSKATs. The LRT statistics generate accurate type I error rates for homogeneous genetic-effect models and may inflate type I error rates for heterogeneous genetic-effect models owing to the large numbers of degrees of freedom and have similar or slightly higher power than the Rao's efficient score test statistics. GFLMs were applied to analyze genetic data of 22 gene regions of type 2 diabetes data from a meta-analysis of eight European studies and detected significant association for 18 genes (P < 3.10 × 10(-6)), tentative association for 2 genes (HHEX and HMGA2; P ≈ 10(-5)), and no association for 2 genes, while MetaSKATs detected none. In addition, the traditional additive-effect model detects association at gene HHEX. GFLMs and related tests can analyze rare or common variants or a combination of the two and
Chen, Gang; Adleman, Nancy E; Saad, Ziad S; Leibenluft, Ellen; Cox, Robert W
2014-10-01
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. To validate the MVM methodology, we performed simulations to assess the controllability for false positives and power achievement. A real FMRI dataset was analyzed to demonstrate the capability of the MVM approach. The methodology has been implemented into an open source program 3dMVM in AFNI, and all the statistical tests can be performed through symbolic coding with variable names instead of the tedious process of dummy coding. Our data indicates that the severity of sphericity violation varies substantially across brain regions. The differences among various modeling methodologies were addressed through direct comparisons between the MVM approach and some of the GLM implementations in
Shirokov, M. E.
2013-11-15
The method of complementary channel for analysis of reversibility (sufficiency) of a quantum channel with respect to families of input states (pure states for the most part) are considered and applied to Bosonic linear (quasi-free) channels, in particular, to Bosonic Gaussian channels. The obtained reversibility conditions for Bosonic linear channels have clear physical interpretation and their sufficiency is also shown by explicit construction of reversing channels. The method of complementary channel gives possibility to prove necessity of these conditions and to describe all reversed families of pure states in the Schrodinger representation. Some applications in quantum information theory are considered. Conditions for existence of discrete classical-quantum subchannels and of completely depolarizing subchannels of a Bosonic linear channel are presented.
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…
NASA Technical Reports Server (NTRS)
Chahine, M. T.
1977-01-01
A mapping transformation is derived for the inverse solution of nonlinear and linear integral equations of the types encountered in remote sounding studies. The method is applied to the solution of specific problems for the determination of the thermal and composition structure of planetary atmospheres from a knowledge of their upwelling radiance.
Linear and Nonlinear Optical Properties in Spherical Quantum Dots: Generalized Hulthén Potential
NASA Astrophysics Data System (ADS)
Onyeaju, M. C.; Idiodi, J. O. A.; Ikot, A. N.; Solaimani, M.; Hassanabadi, H.
2016-05-01
In this work, we studied the optical properties of spherical quantum dots confined in Hulthén potential with the appropriate centrifugal term included. The approximate solution of the bound state and wave functions were obtained from the Schrödinger wave equation by applying the factorization method. Also, we have used the density matrix formalism to investigate the linear and third-order nonlinear absorption coefficient and refractive index changes.
NASA Astrophysics Data System (ADS)
Rukolaine, Sergey A.
2016-05-01
In classical kinetic models a particle free path distribution is exponential, but this is more likely to be an exception than a rule. In this paper we derive a generalized linear Boltzmann equation (GLBE) for a general free path distribution in the framework of Alt's model. In the case that the free path distribution has at least first and second finite moments we construct an asymptotic solution to the initial value problem for the GLBE for small mean free paths. In the special case of the one-speed transport problem the asymptotic solution results in a diffusion approximation to the GLBE.
Use of a generalized linear model to evaluate range forage production estimates
NASA Astrophysics Data System (ADS)
Mitchell, John E.; Joyce, Linda A.
1986-05-01
Interdisciplinary teams have been used in federal land planning and in the private sector to reach consensus on the environmental impact of management. When a large data base is constructed, verifiability of the accuracy of the coded estimates and the underlying assumptions becomes a problem. A mechanism is provided by the use of a linear statistical model to evaluate production coefficients in terms of errors in coding and underlying assumptions. The technique can be used to evaluate other intuitive models depicting natural resource production in relation to prescribed variables, such as site factors or secondary succession.
A General Method for Solving Systems of Non-Linear Equations
NASA Technical Reports Server (NTRS)
Nachtsheim, Philip R.; Deiss, Ron (Technical Monitor)
1995-01-01
The method of steepest descent is modified so that accelerated convergence is achieved near a root. It is assumed that the function of interest can be approximated near a root by a quadratic form. An eigenvector of the quadratic form is found by evaluating the function and its gradient at an arbitrary point and another suitably selected point. The terminal point of the eigenvector is chosen to lie on the line segment joining the two points. The terminal point found lies on an axis of the quadratic form. The selection of a suitable step size at this point leads directly to the root in the direction of steepest descent in a single step. Newton's root finding method not infrequently diverges if the starting point is far from the root. However, the current method in these regions merely reverts to the method of steepest descent with an adaptive step size. The current method's performance should match that of the Levenberg-Marquardt root finding method since they both share the ability to converge from a starting point far from the root and both exhibit quadratic convergence near a root. The Levenberg-Marquardt method requires storage for coefficients of linear equations. The current method which does not require the solution of linear equations requires more time for additional function and gradient evaluations. The classic trade off of time for space separates the two methods.
A substructure coupling procedure applicable to general linear time-invariant dynamic systems
NASA Technical Reports Server (NTRS)
Howsman, T. G.; Craig, R. R., Jr.
1984-01-01
A substructure synthesis procedure applicable to structural systems containing general nonconservative terms is presented. In their final form, the nonself-adjoint substructure equations of motion are cast in state vector form through the use of a variational principle. A reduced-order mode for each substructure is implemented by representing the substructure as a combination of a small number of Ritz vectors. For the method presented, the substructure Ritz vectors are identified as a truncated set of substructure eigenmodes, which are typically complex, along with a set of generalized real attachment modes. The formation of the generalized attachment modes does not require any knowledge of the substructure flexible modes; hence, only the eigenmodes used explicitly as Ritz vectors need to be extracted from the substructure eigenproblem. An example problem is presented to illustrate the method.
NASA Technical Reports Server (NTRS)
Nemeth, Michael P.; Schultz, Marc R.
2012-01-01
A detailed exact solution is presented for laminated-composite circular cylinders with general wall construction and that undergo axisymmetric deformations. The overall solution is formulated in a general, systematic way and is based on the solution of a single fourth-order, nonhomogeneous ordinary differential equation with constant coefficients in which the radial displacement is the dependent variable. Moreover, the effects of general anisotropy are included and positive-definiteness of the strain energy is used to define uniquely the form of the basis functions spanning the solution space of the ordinary differential equation. Loading conditions are considered that include axisymmetric edge loads, surface tractions, and temperature fields. Likewise, all possible axisymmetric boundary conditions are considered. Results are presented for five examples that demonstrate a wide range of behavior for specially orthotropic and fully anisotropic cylinders.
A substructure coupling procedure applicable to general linear time-invariant dynamic systems
NASA Technical Reports Server (NTRS)
Howsman, T. G.; Craig, R. R., Jr.
1984-01-01
A substructure synthesis procedure applicable to structural systems containing general nonconservative terms is presented. In their final form, the non-self-adjoint substructure equations of motion are cast in state vector form through the use of a variational principle. A reduced-order model for each substructure is implemented by representing the substructure as a combination of a small number of Ritz vectors. For the method presented, the substructure Ritz vectors are identified as a truncated set of substructure eigenmodes, which are typically complex, along with a set of generalized real attachment modes. The formation of the generalized attachment modes does not require any knowledge of the substructure flexible modes; hence, only the eigenmodes used explicitly as Ritz vectors need to be extracted from the substructure eigenproblem. An example problem is presented to illustrate the method.
Shevenell, L.A.; Beauchamp, J.J.
1994-11-01
Several waste disposal sites are located on or adjacent to the karstic Maynardville Limestone (Cmn) and the Copper Ridge Dolomite (Ccr) at the Oak Ridge Y-12 Plant. These formations receive contaminants in groundwaters from nearby disposal sites, which can be transported quite rapidly due to the karst flow system. In order to evaluate transport processes through the karst aquifer, the solutional aspects of the formations must be characterized. As one component of this characterization effort, statistical analyses were conducted on the data related to cavities in order to determine if a suitable model could be identified that is capable of predicting the probability of cavity size or distribution in locations for which drilling data are not available. Existing data on the locations (East, North coordinates), depths (and elevations), and sizes of known conduits and other water zones were used in the analyses. Two different models were constructed in the attempt to predict the distribution of cavities in the vicinity of the Y-12 Plant: General Linear Models (GLM), and Logistic Regression Models (LOG). Each of the models attempted was very sensitive to the data set used. Models based on subsets of the full data set were found to do an inadequate job of predicting the behavior of the full data set. The fact that the Ccr and Cmn data sets differ significantly is not surprising considering the hydrogeology of the two formations differs. Flow in the Cmn is generally at elevations between 600 and 950 ft and is dominantly strike parallel through submerged, partially mud-filled cavities with sizes up to 40 ft, but more typically less than 5 ft. Recognized flow in the Ccr is generally above 950 ft elevation, with flow both parallel and perpendicular to geologic strike through conduits, which tend to be large than those on the Cnm, and are often not fully saturated at the shallower depths.
Quasi-Linear Parameter Varying Representation of General Aircraft Dynamics Over Non-Trim Region
NASA Technical Reports Server (NTRS)
Shin, Jong-Yeob
2007-01-01
For applying linear parameter varying (LPV) control synthesis and analysis to a nonlinear system, it is required that a nonlinear system be represented in the form of an LPV model. In this paper, a new representation method is developed to construct an LPV model from a nonlinear mathematical model without the restriction that an operating point must be in the neighborhood of equilibrium points. An LPV model constructed by the new method preserves local stabilities of the original nonlinear system at "frozen" scheduling parameters and also represents the original nonlinear dynamics of a system over a non-trim region. An LPV model of the motion of FASER (Free-flying Aircraft for Subscale Experimental Research) is constructed by the new method.
Iterative solution of general sparse linear systems on clusters of workstations
Lo, Gen-Ching; Saad, Y.
1996-12-31
Solving sparse irregularly structured linear systems on parallel platforms poses several challenges. First, sparsity makes it difficult to exploit data locality, whether in a distributed or shared memory environment. A second, perhaps more serious challenge, is to find efficient ways to precondition the system. Preconditioning techniques which have a large degree of parallelism, such as multicolor SSOR, often have a slower rate of convergence than their sequential counterparts. Finally, a number of other computational kernels such as inner products could ruin any gains gained from parallel speed-ups, and this is especially true on workstation clusters where start-up times may be high. In this paper we discuss these issues and report on our experience with PSPARSLIB, an on-going project for building a library of parallel iterative sparse matrix solvers.
Enhanced multi-level block ILU preconditioning strategies for general sparse linear systems
NASA Astrophysics Data System (ADS)
Saad, Yousef; Zhang, Jun
2001-05-01
This paper introduces several strategies to deal with pivot blocks in multi-level block incomplete LU factorization (BILUM) preconditioning techniques. These techniques are aimed at increasing the robustness and controlling the amount of fill-ins of BILUM for solving large sparse linear systems when large-size blocks are used to form block-independent set. Techniques proposed in this paper include double-dropping strategies, approximate singular-value decomposition, variable size blocks and use of an arrowhead block submatrix. We point out the advantages and disadvantages of these strategies and discuss their efficient implementations. Numerical experiments are conducted to show the usefulness of the new techniques in dealing with hard-to-solve problems arising from computational fluid dynamics. In addition, we discuss the relation between multi-level ILU preconditioning methods and algebraic multi-level methods.
NASA Technical Reports Server (NTRS)
Kaul, Upender K.
2005-01-01
A three-dimensional numerical solver based on finite-difference solution of three-dimensional elastodynamic equations in generalized curvilinear coordinates has been developed and used to generate data such as radial and tangential stresses over various gear component geometries under rotation. The geometries considered are an annulus, a thin annular disk, and a thin solid disk. The solution is based on first principles and does not involve lumped parameter or distributed parameter systems approach. The elastodynamic equations in the velocity-stress formulation that are considered here have been used in the solution of problems of geophysics where non-rotating Cartesian grids are considered. For arbitrary geometries, these equations along with the appropriate boundary conditions have been cast in generalized curvilinear coordinates in the present study.
Generalized linear stability of non-inertial rimming flow in a rotating horizontal cylinder.
Aggarwal, Himanshu; Tiwari, Naveen
2015-10-01
The stability of a thin film of viscous liquid inside a horizontally rotating cylinder is studied using modal and non-modal analysis. The equation governing the film thickness is derived within lubrication approximation and up to first order in aspect ratio (average film thickness to radius of the cylinder). Effect of gravity, viscous stress and capillary pressure are considered in the model. Steady base profiles are computed in the parameter space of interest that are uniform in the axial direction. A linear stability analysis is performed on these base profiles to study their stability to axial perturbations. The destabilizing behavior of aspect ratio and surface tension is demonstrated which is attributed to capillary instability. The transient growth that gives maximum amplification of any initial disturbance and the pseudospectra of the stability operator are computed. These computations reveal weak effect of non-normality of the operator and the results of eigenvalue analysis are recovered after a brief transient period. Results from nonlinear simulations are also presented which also confirm the validity of the modal analysis for the flow considered in this study. PMID:26496740
NASA Technical Reports Server (NTRS)
Noah, S. T.; Kim, Y. B.
1991-01-01
A general approach is developed for determining the periodic solutions and their stability of nonlinear oscillators with piecewise-smooth characteristics. A modified harmonic balance/Fourier transform procedure is devised for the analysis. The procedure avoids certain numerical differentiation employed previously in determining the periodic solutions, therefore enhancing the reliability and efficiency of the method. Stability of the solutions is determined via perturbations of their state variables. The method is applied to a forced oscillator interacting with a stop of finite stiffness. Flip and fold bifurcations are found to occur. This led to the identification of parameter ranges in which chaotic response occurred.
NASA Astrophysics Data System (ADS)
Sokolov, I. M.
2006-06-01
The work by Barbi, Bologna, and Grigolini [Phys. Rev. Lett. 95, 220601 (2005)] discusses a response to alternating external field of a non-Markovian two-state system, where the waiting time between the two attempted changes of state follows a power law. It introduced a new instrument for description of such situations based on a stochastic master equation with reset. In the present Brief Report we provide an alternative description of the situation within the framework of a generalized master equation. The results of our analytical approach are corroborated by direct numerical simulations of the system.
NASA Astrophysics Data System (ADS)
Wang, Lu; Wu, Li-Wei; Wei, Le; Gao, Juan; Sun, Cui-Li; Chai, Pei; Li, Dao-Wu
2014-02-01
The accuracy of attenuation correction in positron emission tomography scanners depends mainly on deriving the reliable 511-keV linear attenuation coefficient distribution in the scanned objects. In the PET/CT system, the linear attenuation distribution is usually obtained from the intensities of the CT image. However, the intensities of the CT image relate to the attenuation of photons in an energy range of 40 keV-140 keV. Before implementing PET attenuation correction, the intensities of CT images must be transformed into the PET 511-keV linear attenuation coefficients. However, the CT scan parameters can affect the effective energy of CT X-ray photons and thus affect the intensities of the CT image. Therefore, for PET/CT attenuation correction, it is crucial to determine the conversion curve with a given set of CT scan parameters and convert the CT image into a PET linear attenuation coefficient distribution. A generalized method is proposed for converting a CT image into a PET linear attenuation coefficient distribution. Instead of some parameter-dependent phantom calibration experiments, the conversion curve is calculated directly by employing the consistency conditions to yield the most consistent attenuation map with the measured PET data. The method is evaluated with phantom experiments and small animal experiments. In phantom studies, the estimated conversion curve fits the true attenuation coefficients accurately, and accurate PET attenuation maps are obtained by the estimated conversion curves and provide nearly the same correction results as the true attenuation map. In small animal studies, a more complicated attenuation distribution of the mouse is obtained successfully to remove the attenuation artifact and improve the PET image contrast efficiently.
MacNab, Ying C
2016-09-20
We present a general coregionalization framework for developing coregionalized multivariate Gaussian conditional autoregressive (cMCAR) models for Bayesian analysis of multivariate lattice data in general and multivariate disease mapping data in particular. This framework is inclusive of cMCARs that facilitate flexible modelling of spatially structured symmetric or asymmetric cross-variable local interactions, allowing a wide range of separable or non-separable covariance structures, and symmetric or asymmetric cross-covariances, to be modelled. We present a brief overview of established univariate Gaussian conditional autoregressive (CAR) models for univariate lattice data and develop coregionalized multivariate extensions. Classes of cMCARs are presented by formulating precision structures. The resulting conditional properties of the multivariate spatial models are established, which cast new light on cMCARs with richly structured covariances and cross-covariances of different spatial ranges. The related methods are illustrated via an in-depth Bayesian analysis of a Minnesota county-level cancer data set. We also bring a new dimension to the traditional enterprize of Bayesian disease mapping: estimating and mapping covariances and cross-covariances of the underlying disease risks. Maps of covariances and cross-covariances bring to light spatial characterizations of the cMCARs and inform on spatial risk associations between areas and diseases. Copyright © 2016 John Wiley & Sons, Ltd. PMID:27091685
NASA Astrophysics Data System (ADS)
Cedeño M, C. E.; de Araujo, J. C. N.
2016-05-01
A study of binary systems composed of two point particles with different masses in the linear regime of the characteristic formulation of general relativity with a Minkowski background is provided. The present paper generalizes a previous study by Bishop et al. The boundary conditions at the world tubes generated by the particles's orbits are explored, where the metric variables are decomposed in spin-weighted spherical harmonics. The power lost by the emission of gravitational waves is computed using the Bondi News function. The power found is the well-known result obtained by Peters and Mathews using a different approach. This agreement validates the approach considered here. Several multipole term contributions to the gravitational radiation field are also shown.
Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum
Wilson, Emma D.; Assaf, Tareq; Pearson, Martin J.; Rossiter, Jonathan M.; Dean, Paul; Anderson, Sean R.; Porrill, John
2015-01-01
The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks. PMID:26257638
A generalized linear mixed model for longitudinal binary data with a marginal logit link function
Parzen, Michael; Ghosh, Souparno; Lipsitz, Stuart; Sinha, Debajyoti; Fitzmaurice, Garrett M.; Mallick, Bani K.; Ibrahim, Joseph G.
2010-01-01
Summary Longitudinal studies of a binary outcome are common in the health, social, and behavioral sciences. In general, a feature of random effects logistic regression models for longitudinal binary data is that the marginal functional form, when integrated over the distribution of the random effects, is no longer of logistic form. Recently, Wang and Louis (2003) proposed a random intercept model in the clustered binary data setting where the marginal model has a logistic form. An acknowledged limitation of their model is that it allows only a single random effect that varies from cluster to cluster. In this paper, we propose a modification of their model to handle longitudinal data, allowing separate, but correlated, random intercepts at each measurement occasion. The proposed model allows for a flexible correlation structure among the random intercepts, where the correlations can be interpreted in terms of Kendall’s τ. For example, the marginal correlations among the repeated binary outcomes can decline with increasing time separation, while the model retains the property of having matching conditional and marginal logit link functions. Finally, the proposed method is used to analyze data from a longitudinal study designed to monitor cardiac abnormalities in children born to HIV-infected women. PMID:21532998
Generalized Uncertainty Quantification for Linear Inverse Problems in X-ray Imaging
Fowler, Michael James
2014-04-25
In industrial and engineering applications, X-ray radiography has attained wide use as a data collection protocol for the assessment of material properties in cases where direct observation is not possible. The direct measurement of nuclear materials, particularly when they are under explosive or implosive loading, is not feasible, and radiography can serve as a useful tool for obtaining indirect measurements. In such experiments, high energy X-rays are pulsed through a scene containing material of interest, and a detector records a radiograph by measuring the radiation that is not attenuated in the scene. One approach to the analysis of these radiographs is to model the imaging system as an operator that acts upon the object being imaged to produce a radiograph. In this model, the goal is to solve an inverse problem to reconstruct the values of interest in the object, which are typically material properties such as density or areal density. The primary objective in this work is to provide quantitative solutions with uncertainty estimates for three separate applications in X-ray radiography: deconvolution, Abel inversion, and radiation spot shape reconstruction. For each problem, we introduce a new hierarchical Bayesian model for determining a posterior distribution on the unknowns and develop efficient Markov chain Monte Carlo (MCMC) methods for sampling from the posterior. A Poisson likelihood, based on a noise model for photon counts at the detector, is combined with a prior tailored to each application: an edge-localizing prior for deconvolution; a smoothing prior with non-negativity constraints for spot reconstruction; and a full covariance sampling prior based on a Wishart hyperprior for Abel inversion. After developing our methods in a general setting, we demonstrate each model on both synthetically generated datasets, including those from a well known radiation transport code, and real high energy radiographs taken at two U. S. Department of Energy
Benedetti, Andrea; Platt, Robert; Atherton, Juli
2014-01-01
Background Over time, adaptive Gaussian Hermite quadrature (QUAD) has become the preferred method for estimating generalized linear mixed models with binary outcomes. However, penalized quasi-likelihood (PQL) is still used frequently. In this work, we systematically evaluated whether matching results from PQL and QUAD indicate less bias in estimated regression coefficients and variance parameters via simulation. Methods We performed a simulation study in which we varied the size of the data set, probability of the outcome, variance of the random effect, number of clusters and number of subjects per cluster, etc. We estimated bias in the regression coefficients, odds ratios and variance parameters as estimated via PQL and QUAD. We ascertained if similarity of estimated regression coefficients, odds ratios and variance parameters predicted less bias. Results Overall, we found that the absolute percent bias of the odds ratio estimated via PQL or QUAD increased as the PQL- and QUAD-estimated odds ratios became more discrepant, though results varied markedly depending on the characteristics of the dataset Conclusions Given how markedly results varied depending on data set characteristics, specifying a rule above which indicated biased results proved impossible. This work suggests that comparing results from generalized linear mixed models estimated via PQL and QUAD is a worthwhile exercise for regression coefficients and variance components obtained via QUAD, in situations where PQL is known to give reasonable results. PMID:24416249
Iwasaki, Yuichi; Brinkman, Stephen F
2015-04-01
Increased concerns about the toxicity of chemical mixtures have led to greater emphasis on analyzing the interactions among the mixture components based on observed effects. The authors applied a generalized linear mixed model (GLMM) to analyze survival of brown trout (Salmo trutta) acutely exposed to metal mixtures that contained copper and zinc. Compared with dominant conventional approaches based on an assumption of concentration addition and the concentration of a chemical that causes x% effect (ECx), the GLMM approach has 2 major advantages. First, binary response variables such as survival can be modeled without any transformations, and thus sample size can be taken into consideration. Second, the importance of the chemical interaction can be tested in a simple statistical manner. Through this application, the authors investigated whether the estimated concentration of the 2 metals binding to humic acid, which is assumed to be a proxy of nonspecific biotic ligand sites, provided a better prediction of survival effects than dissolved and free-ion concentrations of metals. The results suggest that the estimated concentration of metals binding to humic acid is a better predictor of survival effects, and thus the metal competition at the ligands could be an important mechanism responsible for effects of metal mixtures. Application of the GLMM (and the generalized linear model) presents an alternative or complementary approach to analyzing mixture toxicity. PMID:25524054
Kitaev models based on unitary quantum groupoids
Chang, Liang
2014-04-15
We establish a generalization of Kitaev models based on unitary quantum groupoids. In particular, when inputting a Kitaev-Kong quantum groupoid H{sub C}, we show that the ground state manifold of the generalized model is canonically isomorphic to that of the Levin-Wen model based on a unitary fusion category C. Therefore, the generalized Kitaev models provide realizations of the target space of the Turaev-Viro topological quantum field theory based on C.
Shirazi, Mohammadali; Lord, Dominique; Dhavala, Soma Sekhar; Geedipally, Srinivas Reddy
2016-06-01
Crash data can often be characterized by over-dispersion, heavy (long) tail and many observations with the value zero. Over the last few years, a small number of researchers have started developing and applying novel and innovative multi-parameter models to analyze such data. These multi-parameter models have been proposed for overcoming the limitations of the traditional negative binomial (NB) model, which cannot handle this kind of data efficiently. The research documented in this paper continues the work related to multi-parameter models. The objective of this paper is to document the development and application of a flexible NB generalized linear model with randomly distributed mixed effects characterized by the Dirichlet process (NB-DP) to model crash data. The objective of the study was accomplished using two datasets. The new model was compared to the NB and the recently introduced model based on the mixture of the NB and Lindley (NB-L) distributions. Overall, the research study shows that the NB-DP model offers a better performance than the NB model once data are over-dispersed and have a heavy tail. The NB-DP performed better than the NB-L when the dataset has a heavy tail, but a smaller percentage of zeros. However, both models performed similarly when the dataset contained a large amount of zeros. In addition to a greater flexibility, the NB-DP provides a clustering by-product that allows the safety analyst to better understand the characteristics of the data, such as the identification of outliers and sources of dispersion. PMID:26945472
Wang, Tao; He, Peng; Ahn, Kwang Woo; Wang, Xujing; Ghosh, Soumitra; Laud, Purushottam
2015-01-01
The generalized linear mixed model (GLMM) is a useful tool for modeling genetic correlation among family data in genetic association studies. However, when dealing with families of varied sizes and diverse genetic relatedness, the GLMM has a special correlation structure which often makes it difficult to be specified using standard statistical software. In this study, we propose a Cholesky decomposition based re-formulation of the GLMM so that the re-formulated GLMM can be specified conveniently via “proc nlmixed” and “proc glimmix” in SAS, or OpenBUGS via R package BRugs. Performances of these procedures in fitting the re-formulated GLMM are examined through simulation studies. We also apply this re-formulated GLMM to analyze a real data set from Type 1 Diabetes Genetics Consortium (T1DGC). PMID:25873936
Wang, Chi; Dominici, Francesca; Parmigiani, Giovanni; Zigler, Corwin Matthew
2015-01-01
Summary Confounder selection and adjustment are essential elements of assessing the causal effect of an exposure or treatment in observational studies. Building upon work by Wang et al. (2012) and Lefebvre et al. (2014), we propose and evaluate a Bayesian method to estimate average causal effects in studies with a large number of potential confounders, relatively few observations, likely interactions between confounders and the exposure of interest, and uncertainty on which confounders and interaction terms should be included. Our method is applicable across all exposures and outcomes that can be handled through generalized linear models. In this general setting, estimation of the average causal effect is different from estimation of the exposure coefficient in the outcome model due to non-collapsibility. We implement a Bayesian bootstrap procedure to integrate over the distribution of potential confounders and to estimate the causal effect. Our method permits estimation of both the overall population causal effect and effects in specified subpopulations, providing clear characterization of heterogeneous exposure effects that may vary considerably across different covariate profiles. Simulation studies demonstrate that the proposed method performs well in small sample size situations with 100 to 150 observations and 50 covariates. The method is applied to data on 15060 US Medicare beneficiaries diagnosed with a malignant brain tumor between 2000 and 2009 to evaluate whether surgery reduces hospital readmissions within thirty days of diagnosis. PMID:25899155
NASA Astrophysics Data System (ADS)
Choi, Hon-Chit; Wen, Lingfeng; Eberl, Stefan; Feng, Dagan
2006-03-01
Dynamic Single Photon Emission Computed Tomography (SPECT) has the potential to quantitatively estimate physiological parameters by fitting compartment models to the tracer kinetics. The generalized linear least square method (GLLS) is an efficient method to estimate unbiased kinetic parameters and parametric images. However, due to the low sensitivity of SPECT, noisy data can cause voxel-wise parameter estimation by GLLS to fail. Fuzzy C-Mean (FCM) clustering and modified FCM, which also utilizes information from the immediate neighboring voxels, are proposed to improve the voxel-wise parameter estimation of GLLS. Monte Carlo simulations were performed to generate dynamic SPECT data with different noise levels and processed by general and modified FCM clustering. Parametric images were estimated by Logan and Yokoi graphical analysis and GLLS. The influx rate (K I), volume of distribution (V d) were estimated for the cerebellum, thalamus and frontal cortex. Our results show that (1) FCM reduces the bias and improves the reliability of parameter estimates for noisy data, (2) GLLS provides estimates of micro parameters (K I-k 4) as well as macro parameters, such as volume of distribution (Vd) and binding potential (BP I & BP II) and (3) FCM clustering incorporating neighboring voxel information does not improve the parameter estimates, but improves noise in the parametric images. These findings indicated that it is desirable for pre-segmentation with traditional FCM clustering to generate voxel-wise parametric images with GLLS from dynamic SPECT data.
NASA Astrophysics Data System (ADS)
Lebovka, Nikolai I.; Tarasevich, Yuri Yu.; Dubinin, Dmitri O.; Laptev, Valeri V.; Vygornitskii, Nikolai V.
2015-12-01
The jamming and percolation for two generalized models of random sequential adsorption (RSA) of linear k -mers (particles occupying k adjacent sites) on a square lattice are studied by means of Monte Carlo simulation. The classical RSA model assumes the absence of overlapping of the new incoming particle with the previously deposited ones. The first model is a generalized variant of the RSA model for both k -mers and a lattice with defects. Some of the occupying k adjacent sites are considered as insulating and some of the lattice sites are occupied by defects (impurities). For this model even a small concentration of defects can inhibit percolation for relatively long k -mers. The second model is the cooperative sequential adsorption one where, for each new k -mer, only a restricted number of lateral contacts z with previously deposited k -mers is allowed. Deposition occurs in the case when z ≤(1 -d ) zm where zm=2 (k +1 ) is the maximum numbers of the contacts of k -mer, and d is the fraction of forbidden contacts. Percolation is observed only at some interval kmin≤k ≤kmax where the values kmin and kmax depend upon the fraction of forbidden contacts d . The value kmax decreases as d increases. A logarithmic dependence of the type log10(kmax) =a +b d , where a =4.04 ±0.22 ,b =-4.93 ±0.57 , is obtained.
NASA Astrophysics Data System (ADS)
Cariolle, D.; Teyssèdre, H.
2007-01-01
This article describes the validation of a linear parameterization of the ozone photochemistry for use in upper tropospheric and stratospheric studies. The present work extends a previously developed scheme by improving the 2D model used to derive the coefficients of the parameterization. The chemical reaction rates are updated from a compilation that includes recent laboratory works. Furthermore, the polar ozone destruction due to heterogeneous reactions at the surface of the polar stratospheric clouds is taken into account as a function of the stratospheric temperature and the total chlorine content. Two versions of the parameterization are tested. The first one only requires the resolution of a continuity equation for the time evolution of the ozone mixing ratio, the second one uses one additional equation for a cold tracer. The parameterization has been introduced into the chemical transport model MOCAGE. The model is integrated with wind and temperature fields from the ECMWF operational analyses over the period 2000-2004. Overall, the results show a very good agreement between the modelled ozone distribution and the Total Ozone Mapping Spectrometer (TOMS) satellite data and the "in-situ" vertical soundings. During the course of the integration the model does not show any drift and the biases are generally small. The model also reproduces fairly well the polar ozone variability, with notably the formation of "ozone holes" in the southern hemisphere with amplitudes and seasonal evolutions that follow the dynamics and time evolution of the polar vortex. The introduction of the cold tracer further improves the model simulation by allowing additional ozone destruction inside air masses exported from the high to the mid-latitudes, and by maintaining low ozone contents inside the polar vortex of the southern hemisphere over longer periods in spring time. It is concluded that for the study of climatic scenarios or the assimilation of ozone data, the present
Lebovka, Nikolai I; Tarasevich, Yuri Yu; Dubinin, Dmitri O; Laptev, Valeri V; Vygornitskii, Nikolai V
2015-12-01
The jamming and percolation for two generalized models of random sequential adsorption (RSA) of linear k-mers (particles occupying k adjacent sites) on a square lattice are studied by means of Monte Carlo simulation. The classical RSA model assumes the absence of overlapping of the new incoming particle with the previously deposited ones. The first model is a generalized variant of the RSA model for both k-mers and a lattice with defects. Some of the occupying k adjacent sites are considered as insulating and some of the lattice sites are occupied by defects (impurities). For this model even a small concentration of defects can inhibit percolation for relatively long k-mers. The second model is the cooperative sequential adsorption one where, for each new k-mer, only a restricted number of lateral contacts z with previously deposited k-mers is allowed. Deposition occurs in the case when z≤(1-d)z(m) where z(m)=2(k+1) is the maximum numbers of the contacts of k-mer, and d is the fraction of forbidden contacts. Percolation is observed only at some interval k(min)≤k≤k(max) where the values k(min) and k(max) depend upon the fraction of forbidden contacts d. The value k(max) decreases as d increases. A logarithmic dependence of the type log(10)(k(max))=a+bd, where a=4.04±0.22,b=-4.93±0.57, is obtained. PMID:26764641
NASA Astrophysics Data System (ADS)
de Souza, R. S.; Hilbe, J. M.; Buelens, B.; Riggs, J. D.; Cameron, E.; Ishida, E. E. O.; Chies-Santos, A. L.; Killedar, M.
2015-10-01
In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy's globular cluster (GC) population (NGC) is a prolonged puzzle in the astronomical literature. It falls in the category of count data analysis, yet it is usually modelled as if it were a continuous response variable. We have developed a Bayesian negative binomial regression model to study the connection between NGC and the following galaxy properties: central black hole mass, dynamical bulge mass, bulge velocity dispersion and absolute visual magnitude. The methodology introduced herein naturally accounts for heteroscedasticity, intrinsic scatter, errors in measurements in both axes (either discrete or continuous) and allows modelling the population of GCs on their natural scale as a non-negative integer variable. Prediction intervals of 99 per cent around the trend for expected NGC comfortably envelope the data, notably including the Milky Way, which has hitherto been considered a problematic outlier. Finally, we demonstrate how random intercept models can incorporate information of each particular galaxy morphological type. Bayesian variable selection methodology allows for automatically identifying galaxy types with different productions of GCs, suggesting that on average S0 galaxies have a GC population 35 per cent smaller than other types with similar brightness.
Tian, Fenghua; Liu, Hanli
2014-01-15
One of the main challenges in functional diffuse optical tomography (DOT) is to accurately recover the depth of brain activation, which is even more essential when differentiating true brain signals from task-evoked artifacts in the scalp. Recently, we developed a depth-compensated algorithm (DCA) to minimize the depth localization error in DOT. However, the semi-infinite model that was used in DCA deviated significantly from the realistic human head anatomy. In the present work, we incorporated depth-compensated DOT (DC-DOT) with a standard anatomical atlas of human head. Computer simulations and human measurements of sensorimotor activation were conducted to examine and prove the depth specificity and quantification accuracy of brain atlas-based DC-DOT. In addition, node-wise statistical analysis based on the general linear model (GLM) was also implemented and performed in this study, showing the robustness of DC-DOT that can accurately identify brain activation at the correct depth for functional brain imaging, even when co-existing with superficial artifacts. PMID:23859922
Pernet, Cyril R.
2014-01-01
This tutorial presents several misconceptions related to the use the General Linear Model (GLM) in functional Magnetic Resonance Imaging (fMRI). The goal is not to present mathematical proofs but to educate using examples and computer code (in Matlab). In particular, I address issues related to (1) model parameterization (modeling baseline or null events) and scaling of the design matrix; (2) hemodynamic modeling using basis functions, and (3) computing percentage signal change. Using a simple controlled block design and an alternating block design, I first show why “baseline” should not be modeled (model over-parameterization), and how this affects effect sizes. I also show that, depending on what is tested; over-parameterization does not necessarily impact upon statistical results. Next, using a simple periodic vs. random event related design, I show how the hemodynamic model (hemodynamic function only or using derivatives) can affects parameter estimates, as well as detail the role of orthogonalization. I then relate the above results to the computation of percentage signal change. Finally, I discuss how these issues affect group analyses and give some recommendations. PMID:24478622
Xiao, Xun; Geyer, Veikko F; Bowne-Anderson, Hugo; Howard, Jonathon; Sbalzarini, Ivo F
2016-08-01
Biological filaments, such as actin filaments, microtubules, and cilia, are often imaged using different light-microscopy techniques. Reconstructing the filament curve from the acquired images constitutes the filament segmentation problem. Since filaments have lower dimensionality than the image itself, there is an inherent trade-off between tracing the filament with sub-pixel accuracy and avoiding noise artifacts. Here, we present a globally optimal filament segmentation method based on B-spline vector level-sets and a generalized linear model for the pixel intensity statistics. We show that the resulting optimization problem is convex and can hence be solved with global optimality. We introduce a simple and efficient algorithm to compute such optimal filament segmentations, and provide an open-source implementation as an ImageJ/Fiji plugin. We further derive an information-theoretic lower bound on the filament segmentation error, quantifying how well an algorithm could possibly do given the information in the image. We show that our algorithm asymptotically reaches this bound in the spline coefficients. We validate our method in comprehensive benchmarks, compare with other methods, and show applications from fluorescence, phase-contrast, and dark-field microscopy. PMID:27104582
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
NASA Astrophysics Data System (ADS)
Asong, Zilefac E.; Khaliq, M. N.; Wheater, H. S.
2016-02-01
Based on the Generalized Linear Model (GLM) framework, a multisite stochastic modelling approach is developed using daily observations of precipitation and minimum and maximum temperatures from 120 sites located across the Canadian Prairie Provinces: Alberta, Saskatchewan and Manitoba. Temperature is modeled using a two-stage normal-heteroscedastic model by fitting mean and variance components separately. Likewise, precipitation occurrence and conditional precipitation intensity processes are modeled separately. The relationship between precipitation and temperature is accounted for by using transformations of precipitation as covariates to predict temperature fields. Large scale atmospheric covariates from the National Center for Environmental Prediction Reanalysis-I, teleconnection indices, geographical site attributes, and observed precipitation and temperature records are used to calibrate these models for the 1971-2000 period. Validation of the developed models is performed on both pre- and post-calibration period data. Results of the study indicate that the developed models are able to capture spatiotemporal characteristics of observed precipitation and temperature fields, such as inter-site and inter-variable correlation structure, and systematic regional variations present in observed sequences. A number of simulated weather statistics ranging from seasonal means to characteristics of temperature and precipitation extremes and some of the commonly used climate indices are also found to be in close agreement with those derived from observed data. This GLM-based modelling approach will be developed further for multisite statistical downscaling of Global Climate Model outputs to explore climate variability and change in this region of Canada.
NASA Astrophysics Data System (ADS)
Asong, Z. E.; Khaliq, M. N.; Wheater, H. S.
2016-08-01
In this study, a multisite multivariate statistical downscaling approach based on the Generalized Linear Model (GLM) framework is developed to downscale daily observations of precipitation and minimum and maximum temperatures from 120 sites located across the Canadian Prairie Provinces: Alberta, Saskatchewan and Manitoba. First, large scale atmospheric covariates from the National Center for Environmental Prediction (NCEP) Reanalysis-I, teleconnection indices, geographical site attributes, and observed precipitation and temperature records are used to calibrate GLMs for the 1971-2000 period. Then the calibrated models are used to generate daily sequences of precipitation and temperature for the 1962-2005 historical (conditioned on NCEP predictors), and future period (2006-2100) using outputs from five CMIP5 (Coupled Model Intercomparison Project Phase-5) Earth System Models corresponding to Representative Concentration Pathway (RCP): RCP2.6, RCP4.5, and RCP8.5 scenarios. The results indicate that the fitted GLMs are able to capture spatiotemporal characteristics of observed precipitation and temperature fields. According to the downscaled future climate, mean precipitation is projected to increase in summer and decrease in winter while minimum temperature is expected to warm faster than the maximum temperature. Climate extremes are projected to intensify with increased radiative forcing.
Mendes, T M; Guimarães-Okamoto, P T C; Machado-de-Avila, R A; Oliveira, D; Melo, M M; Lobato, Z I; Kalapothakis, E; Chávez-Olórtegui, C
2015-06-01
This communication describes the general characteristics of the venom from the Brazilian scorpion Tityus fasciolatus, which is an endemic species found in the central Brazil (States of Goiás and Minas Gerais), being responsible for sting accidents in this area. The soluble venom obtained from this scorpion is toxic to mice being the LD50 is 2.984 mg/kg (subcutaneally). SDS-PAGE of the soluble venom resulted in 10 fractions ranged in size from 6 to 10-80 kDa. Sheep were employed for anti-T. fasciolatus venom serum production. Western blotting analysis showed that most of these venom proteins are immunogenic. T. fasciolatus anti-venom revealed consistent cross-reactivity with venom antigens from Tityus serrulatus. Using known primers for T. serrulatus toxins, we have identified three toxins sequences from T. fasciolatus venom. Linear epitopes of these toxins were localized and fifty-five overlapping pentadecapeptides covering complete amino acid sequence of the three toxins were synthesized in cellulose membrane (spot-synthesis technique). The epitopes were located on the 3D structures and some important residues for structure/function were identified. PMID:25817000
NASA Astrophysics Data System (ADS)
Widyaningsih, Yekti; Saefuddin, Asep; Notodiputro, Khairil A.; Wigena, Aji H.
2012-05-01
The objective of this research is to build a nested generalized linear mixed model using an ordinal response variable with some covariates. There are three main jobs in this paper, i.e. parameters estimation procedure, simulation, and implementation of the model for the real data. At the part of parameters estimation procedure, concepts of threshold, nested random effect, and computational algorithm are described. The simulations data are built for 3 conditions to know the effect of different parameter values of random effect distributions. The last job is the implementation of the model for the data about poverty in 9 districts of Java Island. The districts are Kuningan, Karawang, and Majalengka chose randomly in West Java; Temanggung, Boyolali, and Cilacap from Central Java; and Blitar, Ngawi, and Jember from East Java. The covariates in this model are province, number of bad nutrition cases, number of farmer families, and number of health personnel. In this modeling, all covariates are grouped as ordinal scale. Unit observation in this research is sub-district (kecamatan) nested in district, and districts (kabupaten) are nested in province. For the result of simulation, ARB (Absolute Relative Bias) and RRMSE (Relative Root of mean square errors) scale is used. They show that prov parameters have the highest bias, but more stable RRMSE in all conditions. The simulation design needs to be improved by adding other condition, such as higher correlation between covariates. Furthermore, as the result of the model implementation for the data, only number of farmer family and number of medical personnel have significant contributions to the level of poverty in Central Java and East Java province, and only district 2 (Karawang) of province 1 (West Java) has different random effect from the others. The source of the data is PODES (Potensi Desa) 2008 from BPS (Badan Pusat Statistik).
NASA Astrophysics Data System (ADS)
Cariolle, D.; Teyssèdre, H.
2007-05-01
This article describes the validation of a linear parameterization of the ozone photochemistry for use in upper tropospheric and stratospheric studies. The present work extends a previously developed scheme by improving the 2-D model used to derive the coefficients of the parameterization. The chemical reaction rates are updated from a compilation that includes recent laboratory work. Furthermore, the polar ozone destruction due to heterogeneous reactions at the surface of the polar stratospheric clouds is taken into account as a function of the stratospheric temperature and the total chlorine content. Two versions of the parameterization are tested. The first one only requires the solution of a continuity equation for the time evolution of the ozone mixing ratio, the second one uses one additional equation for a cold tracer. The parameterization has been introduced into the chemical transport model MOCAGE. The model is integrated with wind and temperature fields from the ECMWF operational analyses over the period 2000-2004. Overall, the results from the two versions show a very good agreement between the modelled ozone distribution and the Total Ozone Mapping Spectrometer (TOMS) satellite data and the "in-situ" vertical soundings. During the course of the integration the model does not show any drift and the biases are generally small, of the order of 10%. The model also reproduces fairly well the polar ozone variability, notably the formation of "ozone holes" in the Southern Hemisphere with amplitudes and a seasonal evolution that follow the dynamics and time evolution of the polar vortex. The introduction of the cold tracer further improves the model simulation by allowing additional ozone destruction inside air masses exported from the high to the mid-latitudes, and by maintaining low ozone content inside the polar vortex of the Southern Hemisphere over longer periods in spring time. It is concluded that for the study of climate scenarios or the assimilation of
Improving model-based diagnosis through algebraic analysis: The Petri net challenge
Portinale, L.
1996-12-31
The present paper describes the empirical evaluation of a linear algebra approach to model-based diagnosis, in case the behavioral model of the device under examination is described through a Petri net model. In particular, we show that algebraic analysis based on P-invariants of the net model, can significantly improve the performance of a model-based diagnostic system, while keeping the integrity of a general framework defined from a formal logical theory. A system called INVADS is described and experimental results, performed on a car fault domain and involving the comparison of different implementations of P-invariant based diagnosis, are then discussed.
NASA Astrophysics Data System (ADS)
Nickel, Stefan; Hertel, Anne; Pesch, Roland; Schröder, Winfried; Steinnes, Eiliv; Uggerud, Hilde Thelle
2014-12-01
Objective. This study explores the statistical relations between the accumulation of heavy metals in moss and natural surface soil and potential influencing factors such as atmospheric deposition by use of multivariate regression-kriging and generalized linear models. Based on data collected in 1995, 2000, 2005 and 2010 throughout Norway the statistical correlation of a set of potential predictors (elevation, precipitation, density of different land uses, population density, physical properties of soil) with concentrations of cadmium (Cd), mercury and lead in moss and natural surface soil (response variables), respectively, were evaluated. Spatio-temporal trends were estimated by applying generalized linear models and geostatistics on spatial data covering Norway. The resulting maps were used to investigate to what extent the HM concentrations in moss and natural surface soil are correlated. Results. From a set of ten potential predictor variables the modelled atmospheric deposition showed the highest correlation with heavy metals concentrations in moss and natural surface soil. Density of various land uses in a 5 km radius reveal significant correlations with lead and cadmium concentration in moss and mercury concentration in natural surface soil. Elevation also appeared as a relevant factor for accumulation of lead and mercury in moss and cadmium in natural surface soil respectively. Precipitation was found to be a significant factor for cadmium in moss and mercury in natural surface soil. The integrated use of multivariate generalized linear models and kriging interpolation enabled creating heavy metals maps at a high level of spatial resolution. The spatial patterns of cadmium and lead concentrations in moss and natural surface soil in 1995 and 2005 are similar. The heavy metals concentrations in moss and natural surface soil are correlated significantly with high coefficients for lead, medium for cadmium and moderate for mercury. From 1995 up to 2010 the
NASA Technical Reports Server (NTRS)
Namburu, R. R.; Tamma, K. K.
1991-01-01
The applicability and evaluation of a generalized gamma(T) family of flux-based representations are examined for two different thermal analysis formulations for structures and materials which exhibit no phase change effects. The so-called H-theta and theta forms are demonstrated for numerous test models and linear and higher-order elements. The results show that the theta form with flux-based representations is generally superior to traditional approaches.
ERIC Educational Resources Information Center
Bashaw, W. L., Ed.; Findley, Warren G., Ed.
This volume contains the five major addresses and subsequent discussion from the Symposium on the General Linear Models Approach to the Analysis of Experimental Data in Educational Research, which was held in 1967 in Athens, Georgia. The symposium was designed to produce systematic information, including new methodology, for dissemination to the…
ERIC Educational Resources Information Center
Dimitrov, Dimiter M.; Raykov, Tenko; AL-Qataee, Abdullah Ali
2015-01-01
This article is concerned with developing a measure of general academic ability (GAA) for high school graduates who apply to colleges, as well as with the identification of optimal weights of the GAA indicators in a linear combination that yields a composite score with maximal reliability and maximal predictive validity, employing the framework of…
Model-Based Prognostics of Hybrid Systems
NASA Technical Reports Server (NTRS)
Daigle, Matthew; Roychoudhury, Indranil; Bregon, Anibal
2015-01-01
Model-based prognostics has become a popular approach to solving the prognostics problem. However, almost all work has focused on prognostics of systems with continuous dynamics. In this paper, we extend the model-based prognostics framework to hybrid systems models that combine both continuous and discrete dynamics. In general, most systems are hybrid in nature, including those that combine physical processes with software. We generalize the model-based prognostics formulation to hybrid systems, and describe the challenges involved. We present a general approach for modeling hybrid systems, and overview methods for solving estimation and prediction in hybrid systems. As a case study, we consider the problem of conflict (i.e., loss of separation) prediction in the National Airspace System, in which the aircraft models are hybrid dynamical systems.
NASA Technical Reports Server (NTRS)
Frisch, Harold P.
2007-01-01
Engineers, who design systems using text specification documents, focus their work upon the completed system to meet Performance, time and budget goals. Consistency and integrity is difficult to maintain within text documents for a single complex system and more difficult to maintain as several systems are combined into higher-level systems, are maintained over decades, and evolve technically and in performance through updates. This system design approach frequently results in major changes during the system integration and test phase, and in time and budget overruns. Engineers who build system specification documents within a model-based systems environment go a step further and aggregate all of the data. They interrelate all of the data to insure consistency and integrity. After the model is constructed, the various system specification documents are prepared, all from the same database. The consistency and integrity of the model is assured, therefore the consistency and integrity of the various specification documents is insured. This article attempts to define model-based systems relative to such an environment. The intent is to expose the complexity of the enabling problem by outlining what is needed, why it is needed and how needs are being addressed by international standards writing teams.
Lewis, Robert Michael (College of William and Mary, Williamsburg, VA); Torczon, Virginia Joanne (College of William and Mary, Williamsburg, VA); Kolda, Tamara Gibson
2006-08-01
We consider the solution of nonlinear programs in the case where derivatives of the objective function and nonlinear constraints are unavailable. To solve such problems, we propose an adaptation of a method due to Conn, Gould, Sartenaer, and Toint that proceeds by approximately minimizing a succession of linearly constrained augmented Lagrangians. Our modification is to use a derivative-free generating set direct search algorithm to solve the linearly constrained subproblems. The stopping criterion proposed by Conn, Gould, Sartenaer and Toint for the approximate solution of the subproblems requires explicit knowledge of derivatives. Such information is presumed absent in the generating set search method we employ. Instead, we show that stationarity results for linearly constrained generating set search methods provide a derivative-free stopping criterion, based on a step-length control parameter, that is sufficient to preserve the convergence properties of the original augmented Lagrangian algorithm.
Cadmium-hazard mapping using a general linear regression model (Irr-Cad) for rapid risk assessment.
Simmons, Robert W; Noble, Andrew D; Pongsakul, P; Sukreeyapongse, O; Chinabut, N
2009-02-01
Research undertaken over the last 40 years has identified the irrefutable relationship between the long-term consumption of cadmium (Cd)-contaminated rice and human Cd disease. In order to protect public health and livelihood security, the ability to accurately and rapidly determine spatial Cd contamination is of high priority. During 2001-2004, a General Linear Regression Model Irr-Cad was developed to predict the spatial distribution of soil Cd in a Cd/Zn co-contaminated cascading irrigated rice-based system in Mae Sot District, Tak Province, Thailand (Longitude E 98 degrees 59'-E 98 degrees 63' and Latitude N 16 degrees 67'-16 degrees 66'). The results indicate that Irr-Cad accounted for 98% of the variance in mean Field Order total soil Cd. Preliminary validation indicated that Irr-Cad 'predicted' mean Field Order total soil Cd, was significantly (p < 0.001) correlated (R (2) = 0.92) with 'observed' mean Field Order total soil Cd values. Field Order is determined by a given field's proximity to primary outlets from in-field irrigation channels and subsequent inter-field irrigation flows. This in turn determines Field Order in Irrigation Sequence (Field Order(IS)). Mean Field Order total soil Cd represents the mean total soil Cd (aqua regia-digested) for a given Field Order(IS). In 2004-2005, Irr-Cad was utilized to evaluate the spatial distribution of total soil Cd in a 'high-risk' area of Mae Sot District. Secondary validation on six randomly selected field groups verified that Irr-Cad predicted mean Field Order total soil Cd and was significantly (p < 0.001) correlated with the observed mean Field Order total soil Cd with R (2) values ranging from 0.89 to 0.97. The practical applicability of Irr-Cad is in its minimal input requirements, namely the classification of fields in terms of Field Order(IS), strategic sampling of all primary fields and laboratory based determination of total soil Cd (T-Cd(P)) and the use of a weighed coefficient for Cd (Coeff
NASA Technical Reports Server (NTRS)
Utku, S.
1969-01-01
A general purpose digital computer program for the in-core solution of linear equilibrium problems of structural mechanics is documented. The program requires minimum input for the description of the problem. The solution is obtained by means of the displacement method and the finite element technique. Almost any geometry and structure may be handled because of the availability of linear, triangular, quadrilateral, tetrahedral, hexahedral, conical, triangular torus, and quadrilateral torus elements. The assumption of piecewise linear deflection distribution insures monotonic convergence of the deflections from the stiffer side with decreasing mesh size. The stresses are provided by the best-fit strain tensors in the least squares at the mesh points where the deflections are given. The selection of local coordinate systems whenever necessary is automatic. The core memory is used by means of dynamic memory allocation, an optional mesh-point relabelling scheme and imposition of the boundary conditions during the assembly time.
NASA Astrophysics Data System (ADS)
Sharma, S.; Narayan, A.
2001-06-01
The non-linear oscillation of inter-connected satellites system about its equilibrium position in the neighabourhood of main resonance ??=3D 1, under the combined effects of the solar radiation pressure and the dissipative forces of general nature has been discussed. It is found that the oscillation of the system gets disturbed when the frequency of the natural oscillation approaches the resonance frequency.
NASA Technical Reports Server (NTRS)
Rowe, Sidney E.
2010-01-01
In September 2007, the Engineering Directorate at the Marshall Space Flight Center (MSFC) created the Design System Focus Team (DSFT). MSFC was responsible for the in-house design and development of the Ares 1 Upper Stage and the Engineering Directorate was preparing to deploy a new electronic Configuration Management and Data Management System with the Design Data Management System (DDMS) based upon a Commercial Off The Shelf (COTS) Product Data Management (PDM) System. The DSFT was to establish standardized CAD practices and a new data life cycle for design data. Of special interest here, the design teams were to implement Model Based Definition (MBD) in support of the Upper Stage manufacturing contract. It is noted that this MBD does use partially dimensioned drawings for auxiliary information to the model. The design data lifecycle implemented several new release states to be used prior to formal release that allowed the models to move through a flow of progressive maturity. The DSFT identified some 17 Lessons Learned as outcomes of the standards development, pathfinder deployments and initial application to the Upper Stage design completion. Some of the high value examples are reviewed.
Kim, Hyunwoo J.; Adluru, Nagesh; Collins, Maxwell D.; Chung, Moo K.; Bendlin, Barbara B.; Johnson, Sterling C.; Davidson, Richard J.; Singh, Vikas
2014-01-01
Linear regression is a parametric model which is ubiquitous in scientific analysis. The classical setup where the observations and responses, i.e., (xi, yi) pairs, are Euclidean is well studied. The setting where yi is manifold valued is a topic of much interest, motivated by applications in shape analysis, topic modeling, and medical imaging. Recent work gives strategies for max-margin classifiers, principal components analysis, and dictionary learning on certain types of manifolds. For parametric regression specifically, results within the last year provide mechanisms to regress one real-valued parameter, xi ∈ R, against a manifold-valued variable, yi ∈ . We seek to substantially extend the operating range of such methods by deriving schemes for multivariate multiple linear regression —a manifold-valued dependent variable against multiple independent variables, i.e., f : Rn → . Our variational algorithm efficiently solves for multiple geodesic bases on the manifold concurrently via gradient updates. This allows us to answer questions such as: what is the relationship of the measurement at voxel y to disease when conditioned on age and gender. We show applications to statistical analysis of diffusion weighted images, which give rise to regression tasks on the manifold GL(n)/O(n) for diffusion tensor images (DTI) and the Hilbert unit sphere for orientation distribution functions (ODF) from high angular resolution acquisition. The companion open-source code is available on nitrc.org/projects/riem_mglm. PMID:25580070
Wu, Jian; Wen, Qiuting
2008-01-01
Optical positioning system is an important part in the computer aided surgery system. Under the previous research of the three linear CCD positioning system prototype, this paper proposed a new way to implement three-dimensional coordinates reconstruction of a marker in the digital signal processor while not in a computer as before. And the experiments were designed to calculate the markers' three dimensional coordinates in the DSP chip and the computer respectively, the results of the three dimensional coordinates' reconstruction showed that the calculation precision in DSP chip and the computer had no difference within 0.01mm error limit. Furthermore, the method that the three dimensional coordinates' reconstruction implemented in the DSP chip can improve the stability of the optical positioning system, and to the greatest extent to increase the calculation independent of hardware, while not depend on computer processing as before. PMID:19163161
NASA Astrophysics Data System (ADS)
Tsuboi, Zengo
2013-05-01
In [1] (Z. Tsuboi, Nucl. Phys. B 826 (2010) 399, arxiv:arXiv:0906.2039), we proposed Wronskian-like solutions of the T-system for [ M , N ]-hook of the general linear superalgebra gl (M | N). We have generalized these Wronskian-like solutions to the ones for the general T-hook, which is a union of [M1 ,N1 ]-hook and [M2 ,N2 ]-hook (M =M1 +M2, N =N1 +N2). These solutions are related to Weyl-type supercharacter formulas of infinite dimensional unitarizable modules of gl (M | N). Our solutions also include a Wronskian-like solution discussed in [2] (N. Gromov, V. Kazakov, S. Leurent, Z. Tsuboi, JHEP 1101 (2011) 155, arxiv:arXiv:1010.2720) in relation to the AdS5 /CFT4 spectral problem.
Principles of models based engineering
Dolin, R.M.; Hefele, J.
1996-11-01
This report describes a Models Based Engineering (MBE) philosophy and implementation strategy that has been developed at Los Alamos National Laboratory`s Center for Advanced Engineering Technology. A major theme in this discussion is that models based engineering is an information management technology enabling the development of information driven engineering. Unlike other information management technologies, models based engineering encompasses the breadth of engineering information, from design intent through product definition to consumer application.
NASA Astrophysics Data System (ADS)
Ramadan, Omar
2015-09-01
In this paper, systematic wave-equation finite difference time domain (WE-FDTD) formulations are presented for modeling electromagnetic wave-propagation in linear and nonlinear dispersive materials. In the proposed formulations, the complex conjugate pole residue (CCPR) pairs model is adopted in deriving a unified dispersive WE-FDTD algorithm that allows modeling different dispersive materials, such as Debye, Drude and Lorentz, in the same manner with the minimal additional auxiliary variables. Moreover, the proposed formulations are incorporated with the wave-equation perfectly matched layer (WE-PML) to construct a material independent mesh truncating technique that can be used for modeling general frequency-dependent open region problems. Several numerical examples involving linear and nonlinear dispersive materials are included to show the validity of the proposed formulations.
NASA Technical Reports Server (NTRS)
Ustino, Eugene A.
2006-01-01
This slide presentation reviews the observable radiances as functions of atmospheric parameters and of surface parameters; the mathematics of atmospheric weighting functions (WFs) and surface partial derivatives (PDs) are presented; and the equation of the forward radiative transfer (RT) problem is presented. For non-scattering atmospheres this can be done analytically, and all WFs and PDs can be computed analytically using the direct linearization approach. For scattering atmospheres, in general case, the solution of the forward RT problem can be obtained only numerically, but we need only two numerical solutions: one of the forward RT problem and one of the adjoint RT problem to compute all WFs and PDs we can think of. In this presentation we discuss applications of both the linearization and adjoint approaches
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
NASA Technical Reports Server (NTRS)
Tal-Ezer, Hillel
1987-01-01
During the process of solving a mathematical model numerically, there is often a need to operate on a vector v by an operator which can be expressed as f(A) while A is NxN matrix (ex: exp(A), sin(A), A sup -1). Except for very simple matrices, it is impractical to construct the matrix f(A) explicitly. Usually an approximation to it is used. In the present research, an algorithm is developed which uses a polynomial approximation to f(A). It is reduced to a problem of approximating f(z) by a polynomial in z while z belongs to the domain D in the complex plane which includes all the eigenvalues of A. This problem of approximation is approached by interpolating the function f(z) in a certain set of points which is known to have some maximal properties. The approximation thus achieved is almost best. Implementing the algorithm to some practical problem is described. Since a solution to a linear system Ax = b is x= A sup -1 b, an iterative solution to it can be regarded as a polynomial approximation to f(A) = A sup -1. Implementing the algorithm in this case is also described.
Beresten, S F; Rubikaite, B I; Kisselev, L L
1988-10-26
A method is proposed which permits the localization of antigenic determinants of a linear type on the polypeptide chain of a protein molecule of unknown primary structure. An antigen modified with maleic anhydride at the amino-terminal groups and at the epsilon-NH2 groups of lysine residues was subjected to partial enzymic digestion, so that the antigenic protein had, on average, less than one cleavage site per polypeptide chain. The resultant ends were labeled with 125I-labeled Bolton and Hunter reagent and the maleic group removed. The detection of the two larger labeled fragments (a longer one which still could bind to a monoclonal antibody and a shorter one which was incapable of binding) made it possible to determine the distance from the antigenic determinant to the C-terminus of the polypeptide chain. The position of the antigenic determinant could be established in more detail using partial chemical degradation of the original antigen using information about the maximal length of a fragment which has lost its ability to interact with the monoclonal antibody. The method has been applied to bovine tryptophanyl-tRNA synthetase (EC 6.1.1.2). PMID:2459255
Lipparini, Filippo; Scalmani, Giovanni; Frisch, Michael J.; Lagardère, Louis; Stamm, Benjamin; Cancès, Eric; Maday, Yvon; Piquemal, Jean-Philip; Mennucci, Benedetta
2014-11-14
We present the general theory and implementation of the Conductor-like Screening Model according to the recently developed ddCOSMO paradigm. The various quantities needed to apply ddCOSMO at different levels of theory, including quantum mechanical descriptions, are discussed in detail, with a particular focus on how to compute the integrals needed to evaluate the ddCOSMO solvation energy and its derivatives. The overall computational cost of a ddCOSMO computation is then analyzed and decomposed in the various steps: the different relative weights of such contributions are then discussed for both ddCOSMO and the fastest available alternative discretization to the COSMO equations. Finally, the scaling of the cost of the various steps with respect to the size of the solute is analyzed and discussed, showing how ddCOSMO opens significantly new possibilities when cheap or hybrid molecular mechanics/quantum mechanics methods are used to describe the solute.
Lipparini, Filippo; Scalmani, Giovanni; Lagardère, Louis; Stamm, Benjamin; Cancès, Eric; Maday, Yvon; Piquemal, Jean-Philip; Frisch, Michael J; Mennucci, Benedetta
2014-11-14
We present the general theory and implementation of the Conductor-like Screening Model according to the recently developed ddCOSMO paradigm. The various quantities needed to apply ddCOSMO at different levels of theory, including quantum mechanical descriptions, are discussed in detail, with a particular focus on how to compute the integrals needed to evaluate the ddCOSMO solvation energy and its derivatives. The overall computational cost of a ddCOSMO computation is then analyzed and decomposed in the various steps: the different relative weights of such contributions are then discussed for both ddCOSMO and the fastest available alternative discretization to the COSMO equations. Finally, the scaling of the cost of the various steps with respect to the size of the solute is analyzed and discussed, showing how ddCOSMO opens significantly new possibilities when cheap or hybrid molecular mechanics/quantum mechanics methods are used to describe the solute. PMID:25399133
NASA Astrophysics Data System (ADS)
Harko, T.; Mak, M. K.
2016-09-01
Obtaining exact solutions of the spherically symmetric general relativistic gravitational field equations describing the interior structure of an isotropic fluid sphere is a long standing problem in theoretical and mathematical physics. The usual approach to this problem consists mainly in the numerical investigation of the Tolman-Oppenheimer-Volkoff and of the mass continuity equations, which describes the hydrostatic stability of the dense stars. In the present paper we introduce an alternative approach for the study of the relativistic fluid sphere, based on the relativistic mass equation, obtained by eliminating the energy density in the Tolman-Oppenheimer-Volkoff equation. Despite its apparent complexity, the relativistic mass equation can be solved exactly by using a power series representation for the mass, and the Cauchy convolution for infinite power series. We obtain exact series solutions for general relativistic dense astrophysical objects described by the linear barotropic and the polytropic equations of state, respectively. For the polytropic case we obtain the exact power series solution corresponding to arbitrary values of the polytropic index n. The explicit form of the solution is presented for the polytropic index n=1, and for the indexes n=1/2 and n=1/5, respectively. The case of n=3 is also considered. In each case the exact power series solution is compared with the exact numerical solutions, which are reproduced by the power series solutions truncated to seven terms only. The power series representations of the geometric and physical properties of the linear barotropic and polytropic stars are also obtained.
Model based control of polymer composite manufacturing processes
NASA Astrophysics Data System (ADS)
Potaraju, Sairam
2000-10-01
The objective of this research is to develop tools that help process engineers design, analyze and control polymeric composite manufacturing processes to achieve higher productivity and cost reduction. Current techniques for process design and control of composite manufacturing suffer from the paucity of good process models that can accurately represent these non-linear systems. Existing models developed by researchers in the past are designed to be process and operation specific, hence generating new simulation models is time consuming and requires significant effort. To address this issue, an Object Oriented Design (OOD) approach is used to develop a component-based model building framework. Process models for two commonly used industrial processes (Injected Pultrusion and Autoclave Curing) are developed using this framework to demonstrate the flexibility. Steady state and dynamic validation of this simulator is performed using a bench scale injected pultrusion process. This simulator could not be implemented online for control due to computational constraints. Models that are fast enough for online implementation, with nearly the same degree of accuracy are developed using a two-tier scheme. First, lower dimensional models that captures essential resin flow, heat transfer and cure kinetics important from a process monitoring and control standpoint are formulated. The second step is to reduce these low dimensional models to Reduced Order Models (ROM) suited for online model based estimation, control and optimization. Model reduction is carried out using Proper Orthogonal Decomposition (POD) technique in conjunction with a Galerkin formulation procedure. Subsequently, a nonlinear model-based estimation and inferential control scheme based on the ROM is implemented. In particular, this research work contributes in the following general areas: (1) Design and implementation of versatile frameworks for modeling and simulation of manufacturing processes using object
Argumentation in Science Education: A Model-Based Framework
ERIC Educational Resources Information Center
Bottcher, Florian; Meisert, Anke
2011-01-01
The goal of this article is threefold: First, the theoretical background for a model-based framework of argumentation to describe and evaluate argumentative processes in science education is presented. Based on the general model-based perspective in cognitive science and the philosophy of science, it is proposed to understand arguments as reasons…
Piccardo, Matteo; Bloino, Julien; Barone, Vincenzo
2015-01-01
Models going beyond the rigid-rotor and the harmonic oscillator levels are mandatory for providing accurate theoretical predictions for several spectroscopic properties. Different strategies have been devised for this purpose. Among them, the treatment by perturbation theory of the molecular Hamiltonian after its expansion in power series of products of vibrational and rotational operators, also referred to as vibrational perturbation theory (VPT), is particularly appealing for its computational efficiency to treat medium-to-large systems. Moreover, generalized (GVPT) strategies combining the use of perturbative and variational formalisms can be adopted to further improve the accuracy of the results, with the first approach used for weakly coupled terms, and the second one to handle tightly coupled ones. In this context, the GVPT formulation for asymmetric, symmetric, and linear tops is revisited and fully generalized to both minima and first-order saddle points of the molecular potential energy surface. The computational strategies and approximations that can be adopted in dealing with GVPT computations are pointed out, with a particular attention devoted to the treatment of symmetry and degeneracies. A number of tests and applications are discussed, to show the possibilities of the developments, as regards both the variety of treatable systems and eligible methods. © 2015 Wiley Periodicals, Inc. PMID:26345131
Efficient Model-Based Diagnosis Engine
NASA Technical Reports Server (NTRS)
Fijany, Amir; Vatan, Farrokh; Barrett, Anthony; James, Mark; Mackey, Ryan; Williams, Colin
2009-01-01
An efficient diagnosis engine - a combination of mathematical models and algorithms - has been developed for identifying faulty components in a possibly complex engineering system. This model-based diagnosis engine embodies a twofold approach to reducing, relative to prior model-based diagnosis engines, the amount of computation needed to perform a thorough, accurate diagnosis. The first part of the approach involves a reconstruction of the general diagnostic engine to reduce the complexity of the mathematical-model calculations and of the software needed to perform them. The second part of the approach involves algorithms for computing a minimal diagnosis (the term "minimal diagnosis" is defined below). A somewhat lengthy background discussion is prerequisite to a meaningful summary of the innovative aspects of the present efficient model-based diagnosis engine. In model-based diagnosis, the function of each component and the relationships among all the components of the engineering system to be diagnosed are represented as a logical system denoted the system description (SD). Hence, the expected normal behavior of the engineering system is the set of logical consequences of the SD. Faulty components lead to inconsistencies between the observed behaviors of the system and the SD (see figure). Diagnosis - the task of finding faulty components - is reduced to finding those components, the abnormalities of which could explain all the inconsistencies. The solution of the diagnosis problem should be a minimal diagnosis, which is a minimal set of faulty components. A minimal diagnosis stands in contradistinction to the trivial solution, in which all components are deemed to be faulty, and which, therefore, always explains all inconsistencies.
NASA Astrophysics Data System (ADS)
Harko, Tiberiu; Liang, Shi-Dong
2016-06-01
We investigate the connection between the linear harmonic oscillator equation and some classes of second order nonlinear ordinary differential equations of Li\\'enard and generalized Li\\'enard type, which physically describe important oscillator systems. By using a method inspired by quantum mechanics, and which consist on the deformation of the phase space coordinates of the harmonic oscillator, we generalize the equation of motion of the classical linear harmonic oscillator to several classes of strongly non-linear differential equations. The first integrals, and a number of exact solutions of the corresponding equations are explicitly obtained. The devised method can be further generalized to derive explicit general solutions of nonlinear second order differential equations unrelated to the harmonic oscillator. Applications of the obtained results for the study of the travelling wave solutions of the reaction-convection-diffusion equations, and of the large amplitude free vibrations of a uniform cantilever beam are also presented.
Bacheler, N.M.; Hightower, J.E.; Burdick, S.M.; Paramore, L.M.; Buckel, J.A.; Pollock, K.H.
2010-01-01
Estimating the selectivity patterns of various fishing gears is a critical component of fisheries stock assessment due to the difficulty in obtaining representative samples from most gears. We used short-term recoveries (n = 3587) of tagged red drum Sciaenops ocellatus to directly estimate age- and length-based selectivity patterns using generalized linear models. The most parsimonious models were selected using AIC, and standard deviations were estimated using simulations. Selectivity of red drum was dependent upon the regulation period in which the fish was caught, the gear used to catch the fish (i.e., hook-and-line, gill nets, pound nets), and the fate of the fish upon recovery (i.e., harvested or released); models including all first-order interactions between main effects outperformed models without interactions. Selectivity of harvested fish was generally dome-shaped and shifted toward larger, older fish in response to regulation changes. Selectivity of caught-and-released red drum was highest on the youngest and smallest fish in the early and middle regulation periods, but increased on larger, legal-sized fish in the late regulation period. These results suggest that catch-and-release mortality has consistently been high for small, young red drum, but has recently become more common in larger, older fish. This method of estimating selectivity from short-term tag recoveries is valuable because it is simpler than full tag-return models, and may be more robust because yearly fishing and natural mortality rates do not need to be modeled and estimated. ?? 2009 Elsevier B.V.
Burdick, Summer M.; Hightower, Joseph E.; Bacheler, Nathan M.; Paramore, Lee M.; Buckel, Jeffrey A.; Pollock, Kenneth H.
2010-01-01
Estimating the selectivity patterns of various fishing gears is a critical component of fisheries stock assessment due to the difficulty in obtaining representative samples from most gears. We used short-term recoveries (n = 3587) of tagged red drum Sciaenops ocellatus to directly estimate age- and length-based selectivity patterns using generalized linear models. The most parsimonious models were selected using AIC, and standard deviations were estimated using simulations. Selectivity of red drum was dependent upon the regulation period in which the fish was caught, the gear used to catch the fish (i.e., hook-and-line, gill nets, pound nets), and the fate of the fish upon recovery (i.e., harvested or released); models including all first-order interactions between main effects outperformed models without interactions. Selectivity of harvested fish was generally dome-shaped and shifted toward larger, older fish in response to regulation changes. Selectivity of caught-and-released red drum was highest on the youngest and smallest fish in the early and middle regulation periods, but increased on larger, legal-sized fish in the late regulation period. These results suggest that catch-and-release mortality has consistently been high for small, young red drum, but has recently become more common in larger, older fish. This method of estimating selectivity from short-term tag recoveries is valuable because it is simpler than full tag-return models, and may be more robust because yearly fishing and natural mortality rates do not need to be modeled and estimated.
NASA Astrophysics Data System (ADS)
Pulquério, Mário; Garrett, Pedro; Santos, Filipe Duarte; Cruz, Maria João
2015-04-01
Portugal is on a climate change hot spot region, where precipitation is expected to decrease with important impacts regarding future water availability. As one of the European countries affected more by droughts in the last decades, it is important to assess how future precipitation regimes will change in order to study its impacts on water resources. Due to the coarse scale of global circulation models, it is often needed to downscale climate variables to the regional or local scale using statistical and/or dynamical techniques. In this study, we tested the use of a generalized linear model, as implemented in the program GLIMCLIM, to downscale precipitation for the center of Portugal where the Tagus basin is located. An analysis of the method performance is done as well as an evaluation of future precipitation trends and extremes for the twenty-first century. Additionally, we perform the first analysis of the evolution of droughts in climate change scenarios by the Standardized Precipitation Index in the study area. Results show that GLIMCLIM is able to capture the precipitation's interannual variation and seasonality correctly. However, summer precipitation is considerably overestimated. Additionally, precipitation extremes are in general well recovered, but high daily rainfall may be overestimated, and dry spell lengths are not correctly recovered by the model. Downscaled projections show a reduction in precipitation between 19 and 28 % at the end of the century. Results indicate that precipitation extremes will decrease and the magnitude of droughts can increase up to three times in relation to the 1961-1990 period which can have strong ecological, social, and economic impacts.
Yue, Yu Ryan; Wang, Xiao-Feng
2016-05-10
This paper is motivated from a retrospective study of the impact of vitamin D deficiency on the clinical outcomes for critically ill patients in multi-center critical care units. The primary predictors of interest, vitamin D2 and D3 levels, are censored at a known detection limit. Within the context of generalized linear mixed models, we investigate statistical methods to handle multiple censored predictors in the presence of auxiliary variables. A Bayesian joint modeling approach is proposed to fit the complex heterogeneous multi-center data, in which the data information is fully used to estimate parameters of interest. Efficient Monte Carlo Markov chain algorithms are specifically developed depending on the nature of the response. Simulation studies demonstrate the outperformance of the proposed Bayesian approach over other existing methods. An application to the data set from the vitamin D deficiency study is presented. Possible extensions of the method regarding the absence of auxiliary variables, semiparametric models, as well as the type of censoring are also discussed. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26643287
Model-based tomographic reconstruction
Chambers, David H.; Lehman, Sean K.; Goodman, Dennis M.
2012-06-26
A model-based approach to estimating wall positions for a building is developed and tested using simulated data. It borrows two techniques from geophysical inversion problems, layer stripping and stacking, and combines them with a model-based estimation algorithm that minimizes the mean-square error between the predicted signal and the data. The technique is designed to process multiple looks from an ultra wideband radar array. The processed signal is time-gated and each section processed to detect the presence of a wall and estimate its position, thickness, and material parameters. The floor plan of a building is determined by moving the array around the outside of the building. In this paper we describe how the stacking and layer stripping algorithms are combined and show the results from a simple numerical example of three parallel walls.
Bishop, Christopher M.
2013-01-01
Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612
NASA Technical Reports Server (NTRS)
Joshi, Anjali; Heimdahl, Mats P. E.; Miller, Steven P.; Whalen, Mike W.
2006-01-01
System safety analysis techniques are well established and are used extensively during the design of safety-critical systems. Despite this, most of the techniques are highly subjective and dependent on the skill of the practitioner. Since these analyses are usually based on an informal system model, it is unlikely that they will be complete, consistent, and error free. In fact, the lack of precise models of the system architecture and its failure modes often forces the safety analysts to devote much of their effort to gathering architectural details about the system behavior from several sources and embedding this information in the safety artifacts such as the fault trees. This report describes Model-Based Safety Analysis, an approach in which the system and safety engineers share a common system model created using a model-based development process. By extending the system model with a fault model as well as relevant portions of the physical system to be controlled, automated support can be provided for much of the safety analysis. We believe that by using a common model for both system and safety engineering and automating parts of the safety analysis, we can both reduce the cost and improve the quality of the safety analysis. Here we present our vision of model-based safety analysis and discuss the advantages and challenges in making this approach practical.
Bishop, Christopher M
2013-02-13
Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612
Qualitative model-based diagnosis using possibility theory
NASA Technical Reports Server (NTRS)
Joslyn, Cliff
1994-01-01
The potential for the use of possibility in the qualitative model-based diagnosis of spacecraft systems is described. The first sections of the paper briefly introduce the Model-Based Diagnostic (MBD) approach to spacecraft fault diagnosis; Qualitative Modeling (QM) methodologies; and the concepts of possibilistic modeling in the context of Generalized Information Theory (GIT). Then the necessary conditions for the applicability of possibilistic methods to qualitative MBD, and a number of potential directions for such an application, are described.
Model-based reconfiguration: Diagnosis and recovery
NASA Technical Reports Server (NTRS)
Crow, Judy; Rushby, John
1994-01-01
We extend Reiter's general theory of model-based diagnosis to a theory of fault detection, identification, and reconfiguration (FDIR). The generality of Reiter's theory readily supports an extension in which the problem of reconfiguration is viewed as a close analog of the problem of diagnosis. Using a reconfiguration predicate 'rcfg' analogous to the abnormality predicate 'ab,' we derive a strategy for reconfiguration by transforming the corresponding strategy for diagnosis. There are two obvious benefits of this approach: algorithms for diagnosis can be exploited as algorithms for reconfiguration and we have a theoretical framework for an integrated approach to FDIR. As a first step toward realizing these benefits we show that a class of diagnosis engines can be used for reconfiguration and we discuss algorithms for integrated FDIR. We argue that integrating recovery and diagnosis is an essential next step if this technology is to be useful for practical applications.
Vilar, Lara; Gómez, Israel; Martínez-Vega, Javier; Echavarría, Pilar; Riaño, David; Martín, M. Pilar
2016-01-01
The socio-economic factors are of key importance during all phases of wildfire management that include prevention, suppression and restoration. However, modeling these factors, at the proper spatial and temporal scale to understand fire regimes is still challenging. This study analyses socio-economic drivers of wildfire occurrence in central Spain. This site represents a good example of how human activities play a key role over wildfires in the European Mediterranean basin. Generalized Linear Models (GLM) and machine learning Maximum Entropy models (Maxent) predicted wildfire occurrence in the 1980s and also in the 2000s to identify changes between each period in the socio-economic drivers affecting wildfire occurrence. GLM base their estimation on wildfire presence-absence observations whereas Maxent on wildfire presence-only. According to indicators like sensitivity or commission error Maxent outperformed GLM in both periods. It achieved a sensitivity of 38.9% and a commission error of 43.9% for the 1980s, and 67.3% and 17.9% for the 2000s. Instead, GLM obtained 23.33, 64.97, 9.41 and 18.34%, respectively. However GLM performed steadier than Maxent in terms of the overall fit. Both models explained wildfires from predictors such as population density and Wildland Urban Interface (WUI), but differed in their relative contribution. As a result of the urban sprawl and an abandonment of rural areas, predictors like WUI and distance to roads increased their contribution to both models in the 2000s, whereas Forest-Grassland Interface (FGI) influence decreased. This study demonstrates that human component can be modelled with a spatio-temporal dimension to integrate it into wildfire risk assessment. PMID:27557113
Vilar, Lara; Gómez, Israel; Martínez-Vega, Javier; Echavarría, Pilar; Riaño, David; Martín, M Pilar
2016-01-01
The socio-economic factors are of key importance during all phases of wildfire management that include prevention, suppression and restoration. However, modeling these factors, at the proper spatial and temporal scale to understand fire regimes is still challenging. This study analyses socio-economic drivers of wildfire occurrence in central Spain. This site represents a good example of how human activities play a key role over wildfires in the European Mediterranean basin. Generalized Linear Models (GLM) and machine learning Maximum Entropy models (Maxent) predicted wildfire occurrence in the 1980s and also in the 2000s to identify changes between each period in the socio-economic drivers affecting wildfire occurrence. GLM base their estimation on wildfire presence-absence observations whereas Maxent on wildfire presence-only. According to indicators like sensitivity or commission error Maxent outperformed GLM in both periods. It achieved a sensitivity of 38.9% and a commission error of 43.9% for the 1980s, and 67.3% and 17.9% for the 2000s. Instead, GLM obtained 23.33, 64.97, 9.41 and 18.34%, respectively. However GLM performed steadier than Maxent in terms of the overall fit. Both models explained wildfires from predictors such as population density and Wildland Urban Interface (WUI), but differed in their relative contribution. As a result of the urban sprawl and an abandonment of rural areas, predictors like WUI and distance to roads increased their contribution to both models in the 2000s, whereas Forest-Grassland Interface (FGI) influence decreased. This study demonstrates that human component can be modelled with a spatio-temporal dimension to integrate it into wildfire risk assessment. PMID:27557113
Chakraborty, Hrishikesh; Helms, Ronald W; Sen, Pranab K; Cohen, Myron S
2003-05-15
Estimating the correlation coefficient between two outcome variables is one of the most important aspects of epidemiological and clinical research. A simple Pearson's correlation coefficient method is usually employed when there are complete independent data points for both outcome variables. However, researchers often deal with correlated observations in a longitudinal setting with missing values where a simple Pearson's correlation coefficient method cannot be used. General linear mixed models (GLMM) techniques were used to estimate correlation coefficients in a longitudinal data set with missing values. A random regression mixed model with unstructured covariance matrix was employed to estimate correlation coefficients between concentrations of HIV-1 RNA in blood and seminal plasma. The effects of CD4 count and antiretroviral therapy were also examined. We used data sets from three different centres (650 samples from 238 patients) where blood and seminal plasma HIV-1 RNA concentrations were collected from patients; 137 samples from 90 different patients without antiviral therapy and 513 samples from 148 patients receiving therapy were considered for analysis. We found no significant correlation between blood and semen HIV-1 RNA concentration in the absence of antiviral therapy. However, a moderate correlation between blood and semen HIV-1 RNA was observed among subjects with lower CD4 counts receiving therapy. Our findings confirm and extend the idea that the concentrations of HIV-1 in semen often differ from the HIV-1 concentration in blood. Antiretroviral therapy administered to subjects with low CD4 counts result in sufficient concomitant reduction of HIV-1 in blood and semen so as to improve the correlation between these compartments. These results have important implications for studies related to the sexual transmission of HIV, and development of HIV prevention strategies. PMID:12704609
Model-based reasoning: Troubleshooting
NASA Astrophysics Data System (ADS)
Davis, Randall; Hamscher, Walter C.
1988-07-01
To determine why something has stopped working, its useful to know how it was supposed to work in the first place. That simple observation underlies some of the considerable interest generated in recent years on the topic of model-based reasoning, particularly its application to diagnosis and troubleshooting. This paper surveys the current state of the art, reviewing areas that are well understood and exploring areas that present challenging research topics. It views the fundamental paradigm as the interaction of prediction and observation, and explores it by examining three fundamental subproblems: generating hypotheses by reasoning from a symptom to a collection of components whose misbehavior may plausibly have caused that symptom; testing each hypothesis to see whether it can account for all available observations of device behavior; then discriminating among the ones that survive testing. We analyze each of these independently at the knowledge level i.e., attempting to understand what reasoning capabilities arise from the different varieties of knowledge available to the program. We find that while a wide range of apparently diverse model-based systems have been built for diagnosis and troubleshooting, they are for the most part variations on the central theme outlined here. Their diversity lies primarily in the varying amounts of kinds of knowledge they bring to bear at each stage of the process; the underlying paradigm is fundamentally the same.
Leite-Martins, Liliana R; Mahú, Maria I M; Costa, Ana L; Mendes, Angelo; Lopes, Elisabete; Mendonça, Denisa M V; Niza-Ribeiro, João J R; de Matos, Augusto J F; da Costa, Paulo Martins
2014-11-01
Antimicrobial resistance (AMR) is a growing global public health problem, which is caused by the use of antimicrobials in both human and animal medical practice. The objectives of the present cross-sectional study were as follows: (1) to determine the prevalence of resistance in Escherichia coli isolated from the feces of pets from the Porto region of Portugal against 19 antimicrobial agents and (2) to assess the individual, clinical and environmental characteristics associated with each pet as risk markers for the AMR of the E. coli isolates. From September 2009 to May 2012, rectal swabs were collected from pets selected using a systematic random procedure from the ordinary population of animals attending the Veterinary Hospital of Porto University. A total of 78 dogs and 22 cats were sampled with the objective of isolating E. coli. The animals' owners, who allowed the collection of fecal samples from their pets, answered a questionnaire to collect information about the markers that could influence the AMR of the enteric E. coli. Chromocult tryptone bile X-glucuronide agar was used for E. coli isolation, and the disk diffusion method was used to determine the antimicrobial susceptibility. The data were analyzed using a multilevel, univariable and multivariable generalized linear mixed model (GLMM). Several (49.7%) of the 396 isolates obtained in this study were multidrug-resistant. The E. coli isolates exhibited resistance to the antimicrobial agent's ampicillin (51.3%), cephalothin (46.7%), tetracycline (45.2%) and streptomycin (43.4%). Previous quinolone treatment was the main risk marker for the presence of AMR for 12 (ampicillin, cephalothin, ceftazidime, cefotaxime, nalidixic acid, ciprofloxacin, gentamicin, tetracycline, streptomycin, chloramphenicol, trimethoprim-sulfamethoxazole and aztreonam) of the 15 antimicrobials assessed. Coprophagic habits were also positively associated with an increased risk of AMR for six drugs, ampicillin, amoxicillin
Model Based Reconstruction of UT Array Data
NASA Astrophysics Data System (ADS)
Calmon, P.; Iakovleva, E.; Fidahoussen, A.; Ribay, G.; Chatillon, S.
2008-02-01
Beyond the detection of defects, their characterization (identification, positioning, sizing) is one goal of great importance often assigned to the analysis of NDT data. The first step of such analysis in the case of ultrasonic testing amounts to image in the part the detected echoes. This operation is in general achieved by considering time of flights and by applying simplified algorithms which are often valid only on canonical situations. In this communication we present an overview of different imaging techniques studied at CEA LIST and based on the exploitation of direct models which enable to address complex configurations and are available in the CIVA software plat-form. We discuss in particular ray-model based algorithms, algorithms derived from classical synthetic focusing and processing of the full inter-element matrix (MUSIC algorithm).
Sequential Bayesian Detection: A Model-Based Approach
Sullivan, E J; Candy, J V
2007-08-13
Sequential detection theory has been known for a long time evolving in the late 1940's by Wald and followed by Middleton's classic exposition in the 1960's coupled with the concurrent enabling technology of digital computer systems and the development of sequential processors. Its development, when coupled to modern sequential model-based processors, offers a reasonable way to attack physics-based problems. In this chapter, the fundamentals of the sequential detection are reviewed from the Neyman-Pearson theoretical perspective and formulated for both linear and nonlinear (approximate) Gauss-Markov, state-space representations. We review the development of modern sequential detectors and incorporate the sequential model-based processors as an integral part of their solution. Motivated by a wealth of physics-based detection problems, we show how both linear and nonlinear processors can seamlessly be embedded into the sequential detection framework to provide a powerful approach to solving non-stationary detection problems.
Sequential Bayesian Detection: A Model-Based Approach
Candy, J V
2008-12-08
Sequential detection theory has been known for a long time evolving in the late 1940's by Wald and followed by Middleton's classic exposition in the 1960's coupled with the concurrent enabling technology of digital computer systems and the development of sequential processors. Its development, when coupled to modern sequential model-based processors, offers a reasonable way to attack physics-based problems. In this chapter, the fundamentals of the sequential detection are reviewed from the Neyman-Pearson theoretical perspective and formulated for both linear and nonlinear (approximate) Gauss-Markov, state-space representations. We review the development of modern sequential detectors and incorporate the sequential model-based processors as an integral part of their solution. Motivated by a wealth of physics-based detection problems, we show how both linear and nonlinear processors can seamlessly be embedded into the sequential detection framework to provide a powerful approach to solving non-stationary detection problems.
Model based control of dynamic atomic force microscope
Lee, Chibum; Salapaka, Srinivasa M.
2015-04-15
A model-based robust control approach is proposed that significantly improves imaging bandwidth for the dynamic mode atomic force microscopy. A model for cantilever oscillation amplitude and phase dynamics is derived and used for the control design. In particular, the control design is based on a linearized model and robust H{sub ∞} control theory. This design yields a significant improvement when compared to the conventional proportional-integral designs and verified by experiments.
Model based control of dynamic atomic force microscope.
Lee, Chibum; Salapaka, Srinivasa M
2015-04-01
A model-based robust control approach is proposed that significantly improves imaging bandwidth for the dynamic mode atomic force microscopy. A model for cantilever oscillation amplitude and phase dynamics is derived and used for the control design. In particular, the control design is based on a linearized model and robust H(∞) control theory. This design yields a significant improvement when compared to the conventional proportional-integral designs and verified by experiments. PMID:25933864
NASA Astrophysics Data System (ADS)
Hibbard, Bill
2012-05-01
Orseau and Ring, as well as Dewey, have recently described problems, including self-delusion, with the behavior of agents using various definitions of utility functions. An agent's utility function is defined in terms of the agent's history of interactions with its environment. This paper argues, via two examples, that the behavior problems can be avoided by formulating the utility function in two steps: 1) inferring a model of the environment from interactions, and 2) computing utility as a function of the environment model. Basing a utility function on a model that the agent must learn implies that the utility function must initially be expressed in terms of specifications to be matched to structures in the learned model. These specifications constitute prior assumptions about the environment so this approach will not work with arbitrary environments. But the approach should work for agents designed by humans to act in the physical world. The paper also addresses the issue of self-modifying agents and shows that if provided with the possibility to modify their utility functions agents will not choose to do so, under some usual assumptions.
Applying knowledge compilation techniques to model-based reasoning
NASA Technical Reports Server (NTRS)
Keller, Richard M.
1991-01-01
Researchers in the area of knowledge compilation are developing general purpose techniques for improving the efficiency of knowledge-based systems. In this article, an attempt is made to define knowledge compilation, to characterize several classes of knowledge compilation techniques, and to illustrate how some of these techniques can be applied to improve the performance of model-based reasoning systems.
NASA Astrophysics Data System (ADS)
Sidorin, Anatoly
2010-01-01
In linear accelerators the particles are accelerated by either electrostatic fields or oscillating Radio Frequency (RF) fields. Accordingly the linear accelerators are divided in three large groups: electrostatic, induction and RF accelerators. Overview of the different types of accelerators is given. Stability of longitudinal and transverse motion in the RF linear accelerators is briefly discussed. The methods of beam focusing in linacs are described.
Sidorin, Anatoly
2010-01-05
In linear accelerators the particles are accelerated by either electrostatic fields or oscillating Radio Frequency (RF) fields. Accordingly the linear accelerators are divided in three large groups: electrostatic, induction and RF accelerators. Overview of the different types of accelerators is given. Stability of longitudinal and transverse motion in the RF linear accelerators is briefly discussed. The methods of beam focusing in linacs are described.
Model-based ocean acoustic passive localization. Revision 1
Candy, J.V.; Sullivan, E.J.
1994-06-01
A model-based approach is developed (theoretically) to solve the passive localization problem. Here the authors investigate the design of a model-based identifier for a shallow water ocean acoustic problem characterized by a normal-mode model. In this problem they show how the processor can be structured to estimate the vertical wave numbers directly from measured pressure-field and sound speed measurements thereby eliminating the need for synthetic aperture processing or even a propagation model solution. Finally, they investigate various special cases of the source localization problem, designing a model-based localizer for each and evaluating the underlying structure with the expectation of gaining more and more insight into the general problem.
ERIC Educational Resources Information Center
Walkiewicz, T. A.; Newby, N. D., Jr.
1972-01-01
A discussion of linear collisions between two or three objects is related to a junior-level course in analytical mechanics. The theoretical discussion uses a geometrical approach that treats elastic and inelastic collisions from a unified point of view. Experiments with a linear air track are described. (Author/TS)
Model-based phase-shifting interferometer
NASA Astrophysics Data System (ADS)
Liu, Dong; Zhang, Lei; Shi, Tu; Yang, Yongying; Chong, Shiyao; Miao, Liang; Huang, Wei; Shen, Yibing; Bai, Jian
2015-10-01
A model-based phase-shifting interferometer (MPI) is developed, in which a novel calculation technique is proposed instead of the traditional complicated system structure, to achieve versatile, high precision and quantitative surface tests. In the MPI, the partial null lens (PNL) is employed to implement the non-null test. With some alternative PNLs, similar as the transmission spheres in ZYGO interferometers, the MPI provides a flexible test for general spherical and aspherical surfaces. Based on modern computer modeling technique, a reverse iterative optimizing construction (ROR) method is employed for the retrace error correction of non-null test, as well as figure error reconstruction. A self-compiled ray-tracing program is set up for the accurate system modeling and reverse ray tracing. The surface figure error then can be easily extracted from the wavefront data in forms of Zernike polynomials by the ROR method. Experiments of the spherical and aspherical tests are presented to validate the flexibility and accuracy. The test results are compared with those of Zygo interferometer (null tests), which demonstrates the high accuracy of the MPI. With such accuracy and flexibility, the MPI would possess large potential in modern optical shop testing.
Petit, Andrew S.; Subotnik, Joseph E.
2014-07-07
In this paper, we develop a surface hopping approach for calculating linear absorption spectra using ensembles of classical trajectories propagated on both the ground and excited potential energy surfaces. We demonstrate that our method allows the dipole-dipole correlation function to be determined exactly for the model problem of two shifted, uncoupled harmonic potentials with the same harmonic frequency. For systems where nonadiabatic dynamics and electronic relaxation are present, preliminary results show that our method produces spectra in better agreement with the results of exact quantum dynamics calculations than spectra obtained using the standard ground-state Kubo formalism. As such, our proposed surface hopping approach should find immediate use for modeling condensed phase spectra, especially for expensive calculations using ab initio potential energy surfaces.
Comparison of chiller models for use in model-based fault detection
Sreedharan, Priya; Haves, Philip
2001-06-07
Selecting the model is an important and essential step in model based fault detection and diagnosis (FDD). Factors that are considered in evaluating a model include accuracy, training data requirements, calibration effort, generality, and computational requirements. The objective of this study was to evaluate different modeling approaches for their applicability to model based FDD of vapor compression chillers. Three different models were studied: the Gordon and Ng Universal Chiller model (2nd generation) and a modified version of the ASHRAE Primary Toolkit model, which are both based on first principles, and the DOE-2 chiller model, as implemented in CoolTools{trademark}, which is empirical. The models were compared in terms of their ability to reproduce the observed performance of an older, centrifugal chiller operating in a commercial office building and a newer centrifugal chiller in a laboratory. All three models displayed similar levels of accuracy. Of the first principles models, the Gordon-Ng model has the advantage of being linear in the parameters, which allows more robust parameter estimation methods to be used and facilitates estimation of the uncertainty in the parameter values. The ASHRAE Toolkit Model may have advantages when refrigerant temperature measurements are also available. The DOE-2 model can be expected to have advantages when very limited data are available to calibrate the model, as long as one of the previously identified models in the CoolTools library matches the performance of the chiller in question.
Christofilos, N.C.; Polk, I.J.
1959-02-17
Improvements in linear particle accelerators are described. A drift tube system for a linear ion accelerator reduces gap capacity between adjacent drift tube ends. This is accomplished by reducing the ratio of the diameter of the drift tube to the diameter of the resonant cavity. Concentration of magnetic field intensity at the longitudinal midpoint of the external sunface of each drift tube is reduced by increasing the external drift tube diameter at the longitudinal center region.
González-Díaz, Humberto; Arrasate, Sonia; Gómez-SanJuan, Asier; Sotomayor, Nuria; Lete, Esther; Besada-Porto, Lina; Ruso, Juan M
2013-01-01
In general perturbation methods starts with a known exact solution of a problem and add "small" variation terms in order to approach to a solution for a related problem without known exact solution. Perturbation theory has been widely used in almost all areas of science. Bhor's quantum model, Heisenberg's matrix mechanincs, Feyman diagrams, and Poincare's chaos model or "butterfly effect" in complex systems are examples of perturbation theories. On the other hand, the study of Quantitative Structure-Property Relationships (QSPR) in molecular complex systems is an ideal area for the application of perturbation theory. There are several problems with exact experimental solutions (new chemical reactions, physicochemical properties, drug activity and distribution, metabolic networks, etc.) in public databases like CHEMBL. However, in all these cases, we have an even larger list of related problems without known solutions. We need to know the change in all these properties after a perturbation of initial boundary conditions. It means, when we test large sets of similar, but different, compounds and/or chemical reactions under the slightly different conditions (temperature, time, solvents, enzymes, assays, protein targets, tissues, partition systems, organisms, etc.). However, to the best of our knowledge, there is no QSPR general-purpose perturbation theory to solve this problem. In this work, firstly we review general aspects and applications of both perturbation theory and QSPR models. Secondly, we formulate a general-purpose perturbation theory for multiple-boundary QSPR problems. Last, we develop three new QSPR-Perturbation theory models. The first model classify correctly >100,000 pairs of intra-molecular carbolithiations with 75-95% of Accuracy (Ac), Sensitivity (Sn), and Specificity (Sp). The model predicts probabilities of variations in the yield and enantiomeric excess of reactions due to at least one perturbation in boundary conditions (solvent, temperature
Cognitive control predicts use of model-based reinforcement learning.
Otto, A Ross; Skatova, Anya; Madlon-Kay, Seth; Daw, Nathaniel D
2015-02-01
Accounts of decision-making and its neural substrates have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental work suggest that this classic distinction between behaviorally and neurally dissociable systems for habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning (RL), called model-free and model-based RL, but the cognitive or computational processes by which one system may dominate over the other in the control of behavior is a matter of ongoing investigation. To elucidate this question, we leverage the theoretical framework of cognitive control, demonstrating that individual differences in utilization of goal-related contextual information--in the service of overcoming habitual, stimulus-driven responses--in established cognitive control paradigms predict model-based behavior in a separate, sequential choice task. The behavioral correspondence between cognitive control and model-based RL compellingly suggests that a common set of processes may underpin the two behaviors. In particular, computational mechanisms originally proposed to underlie controlled behavior may be applicable to understanding the interactions between model-based and model-free choice behavior. PMID:25170791
Expediting model-based optoacoustic reconstructions with tomographic symmetries
Lutzweiler, Christian; Deán-Ben, Xosé Luís; Razansky, Daniel
2014-01-15
Purpose: Image quantification in optoacoustic tomography implies the use of accurate forward models of excitation, propagation, and detection of optoacoustic signals while inversions with high spatial resolution usually involve very large matrices, leading to unreasonably long computation times. The development of fast and memory efficient model-based approaches represents then an important challenge to advance on the quantitative and dynamic imaging capabilities of tomographic optoacoustic imaging. Methods: Herein, a method for simplification and acceleration of model-based inversions, relying on inherent symmetries present in common tomographic acquisition geometries, has been introduced. The method is showcased for the case of cylindrical symmetries by using polar image discretization of the time-domain optoacoustic forward model combined with efficient storage and inversion strategies. Results: The suggested methodology is shown to render fast and accurate model-based inversions in both numerical simulations andpost mortem small animal experiments. In case of a full-view detection scheme, the memory requirements are reduced by one order of magnitude while high-resolution reconstructions are achieved at video rate. Conclusions: By considering the rotational symmetry present in many tomographic optoacoustic imaging systems, the proposed methodology allows exploiting the advantages of model-based algorithms with feasible computational requirements and fast reconstruction times, so that its convenience and general applicability in optoacoustic imaging systems with tomographic symmetries is anticipated.
Generalized Predictive and Neural Generalized Predictive Control of Aerospace Systems
NASA Technical Reports Server (NTRS)
Kelkar, Atul G.
2000-01-01
The research work presented in this thesis addresses the problem of robust control of uncertain linear and nonlinear systems using Neural network-based Generalized Predictive Control (NGPC) methodology. A brief overview of predictive control and its comparison with Linear Quadratic (LQ) control is given to emphasize advantages and drawbacks of predictive control methods. It is shown that the Generalized Predictive Control (GPC) methodology overcomes the drawbacks associated with traditional LQ control as well as conventional predictive control methods. It is shown that in spite of the model-based nature of GPC it has good robustness properties being special case of receding horizon control. The conditions for choosing tuning parameters for GPC to ensure closed-loop stability are derived. A neural network-based GPC architecture is proposed for the control of linear and nonlinear uncertain systems. A methodology to account for parametric uncertainty in the system is proposed using on-line training capability of multi-layer neural network. Several simulation examples and results from real-time experiments are given to demonstrate the effectiveness of the proposed methodology.
Automated extraction of knowledge for model-based diagnostics
NASA Technical Reports Server (NTRS)
Gonzalez, Avelino J.; Myler, Harley R.; Towhidnejad, Massood; Mckenzie, Frederic D.; Kladke, Robin R.
1990-01-01
The concept of accessing computer aided design (CAD) design databases and extracting a process model automatically is investigated as a possible source for the generation of knowledge bases for model-based reasoning systems. The resulting system, referred to as automated knowledge generation (AKG), uses an object-oriented programming structure and constraint techniques as well as internal database of component descriptions to generate a frame-based structure that describes the model. The procedure has been designed to be general enough to be easily coupled to CAD systems that feature a database capable of providing label and connectivity data from the drawn system. The AKG system is capable of defining knowledge bases in formats required by various model-based reasoning tools.
Testing Strategies for Model-Based Development
NASA Technical Reports Server (NTRS)
Heimdahl, Mats P. E.; Whalen, Mike; Rajan, Ajitha; Miller, Steven P.
2006-01-01
This report presents an approach for testing artifacts generated in a model-based development process. This approach divides the traditional testing process into two parts: requirements-based testing (validation testing) which determines whether the model implements the high-level requirements and model-based testing (conformance testing) which determines whether the code generated from a model is behaviorally equivalent to the model. The goals of the two processes differ significantly and this report explores suitable testing metrics and automation strategies for each. To support requirements-based testing, we define novel objective requirements coverage metrics similar to existing specification and code coverage metrics. For model-based testing, we briefly describe automation strategies and examine the fault-finding capability of different structural coverage metrics using tests automatically generated from the model.
Model-based satellite acquisition and tracking
NASA Technical Reports Server (NTRS)
Casasent, David; Lee, Andrew J.
1988-01-01
A model-based optical processor is introduced for the acquisition and tracking of a satellite in close proximity to an imaging sensor of a space robot. The type of satellite is known in advance, and a model of the satellite (which exists from its design) is used in this task. The model base is used to generate multiple smart filters of the various parts of the satellite, which are used in a symbolic multi-filter optical correlator. The output from the correlator is then treated as a symbolic description of the object, which is operated upon by an optical inference processor to determine the position and orientation of the satellite and to track it as a function of time. The knowledge and model base also serves to generate the rules used by the inference machine. The inference machine allows for feedback to optical correlators or feature extractors to locate the individual parts of the satellite and their orientations.
Multimode model based defect characterization in composites
NASA Astrophysics Data System (ADS)
Roberts, R.; Holland, S.; Gregory, E.
2016-02-01
A newly-initiated research program for model-based defect characterization in CFRP composites is summarized. The work utilizes computational models of the interaction of NDE probing energy fields (ultrasound and thermography), to determine 1) the measured signal dependence on material and defect properties (forward problem), and 2) an assessment of performance-critical defect properties from analysis of measured NDE signals (inverse problem). Work is reported on model implementation for inspection of CFRP laminates containing delamination and porosity. Forward predictions of measurement response are presented, as well as examples of model-based inversion of measured data for the estimation of defect parameters.
Model-based internal wave processing
Candy, J.V.; Chambers, D.H.
1995-06-09
A model-based approach is proposed to solve the oceanic internal wave signal processing problem that is based on state-space representations of the normal-mode vertical velocity and plane wave horizontal velocity propagation models. It is shown that these representations can be utilized to spatially propagate the modal (dept) vertical velocity functions given the basic parameters (wave numbers, Brunt-Vaisala frequency profile etc.) developed from the solution of the associated boundary value problem as well as the horizontal velocity components. Based on this framework, investigations are made of model-based solutions to the signal enhancement problem for internal waves.
Reduced-order-model based feedback control of the Modified Hasegawa-Wakatani equations
NASA Astrophysics Data System (ADS)
Goumiri, Imene; Rowley, Clarence; Ma, Zhanhua; Gates, David; Parker, Jeffrey; Krommes, John
2012-10-01
In this study, we demonstrate the development of model-based feedback control for stabilization of an unstable equilibrium obtained in the Modified Hasegawa-Wakatani (MHW) equations, a classic model in plasma turbulence. First, a balanced truncation is applied; a model reduction technique that has been proved successful in flow control design problems, to obtain a low dimensional model of the linearized MHW equation. A model-based feedback controller is then designed for the reduced order model using linear quadratic regulators (LQR) then a linear quadratic gaussian (LQG) control. The controllers are then applied on the original linearized and nonlinear MHW equations to stabilize the equilibrium and suppress the transition to drift-wave induced turbulences.
Solitary pulses in linearly coupled Ginzburg-Landau equations.
Malomed, Boris A
2007-09-01
This article presents a brief review of dynamical models based on systems of linearly coupled complex Ginzburg-Landau (CGL) equations. In the simplest case, the system features linear gain, cubic nonlinearity (possibly combined with cubic loss), and group-velocity dispersion (GVD) in one equation, while the other equation is linear, featuring only intrinsic linear loss. The system models a dual-core fiber laser, with a parallel-coupled active core and an additional stabilizing passive (lossy) one. The model gives rise to exact analytical solutions for stationary solitary pulses (SPs). The article presents basic results concerning stability of the SPs; interactions between pulses are also considered, as are dark solitons (holes). In the case of the anomalous GVD, an unstable stationary SP may transform itself, via the Hopf bifurcation, into a stable localized breather. Various generalizations of the basic system are briefly reviewed too, including a model with quadratic (second-harmonic-generating) nonlinearity and a recently introduced model of a different but related type, based on linearly coupled CGL equations with cubic-quintic nonlinearity. The latter system features spontaneous symmetry breaking of stationary SPs, and also the formation of stable breathers. PMID:17903024
Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS
Barker, Jeffrey W.; Aarabi, Ardalan; Huppert, Theodore J.
2013-01-01
Systemic physiology and motion-induced artifacts represent two major sources of confounding noise in functional near infrared spectroscopy (fNIRS) imaging that can reduce the performance of analyses and inflate false positive rates (i.e., type I errors) of detecting evoked hemodynamic responses. In this work, we demonstrated a general algorithm for solving the general linear model (GLM) for both deconvolution (finite impulse response) and canonical regression models based on designing optimal pre-whitening filters using autoregressive models and employing iteratively reweighted least squares. We evaluated the performance of the new method by performing receiver operating characteristic (ROC) analyses using synthetic data, in which serial correlations, motion artifacts, and evoked responses were controlled via simulations, as well as using experimental data from children (3–5 years old) as a source baseline physiological noise and motion artifacts. The new method outperformed ordinary least squares (OLS) with no motion correction, wavelet based motion correction, or spline interpolation based motion correction in the presence of physiological and motion related noise. In the experimental data, false positive rates were as high as 37% when the estimated p-value was 0.05 for the OLS methods. The false positive rate was reduced to 5–9% with the proposed method. Overall, the method improves control of type I errors and increases performance when motion artifacts are present. PMID:24009999
Linear quantum feedback networks
NASA Astrophysics Data System (ADS)
Gough, J. E.; Gohm, R.; Yanagisawa, M.
2008-12-01
The mathematical theory of quantum feedback networks has recently been developed [J. Gough and M. R. James, e-print arXiv:0804.3442v2] for general open quantum dynamical systems interacting with bosonic input fields. In this article we show, for the special case of linear dynamical Markovian systems with instantaneous feedback connections, that the transfer functions can be deduced and agree with the algebraic rules obtained in the nonlinear case. Using these rules, we derive the transfer functions for linear quantum systems in series, in cascade, and in feedback arrangements mediated by beam splitter devices.
Colgate, S.A.
1958-05-27
An improvement is presented in linear accelerators for charged particles with respect to the stable focusing of the particle beam. The improvement consists of providing a radial electric field transverse to the accelerating electric fields and angularly introducing the beam of particles in the field. The results of the foregoing is to achieve a beam which spirals about the axis of the acceleration path. The combination of the electric fields and angular motion of the particles cooperate to provide a stable and focused particle beam.
Linear Programming Problems for Generalized Uncertainty
ERIC Educational Resources Information Center
Thipwiwatpotjana, Phantipa
2010-01-01
Uncertainty occurs when there is more than one realization that can represent an information. This dissertation concerns merely discrete realizations of an uncertainty. Different interpretations of an uncertainty and their relationships are addressed when the uncertainty is not a probability of each realization. A well known model that can handle…
Model-Based Inquiries in Chemistry
ERIC Educational Resources Information Center
Khan, Samia
2007-01-01
In this paper, instructional strategies for sustaining model-based inquiry in an undergraduate chemistry class were analyzed through data collected from classroom observations, a student survey, and in-depth problem-solving sessions with the instructor and students. Analysis of teacher-student interactions revealed a cyclical pattern in which…
What's Missing in Model-Based Teaching
ERIC Educational Resources Information Center
Khan, Samia
2011-01-01
In this study, the author investigated how four science teachers employed model-based teaching (MBT) over a 1-year period. The purpose of the research was to develop a baseline of the fundamental and specific dimensions of MBT that are present and absent in science teaching. Teacher interviews, classroom observations, and pre and post-student…
Sandboxes for Model-Based Inquiry
ERIC Educational Resources Information Center
Brady, Corey; Holbert, Nathan; Soylu, Firat; Novak, Michael; Wilensky, Uri
2015-01-01
In this article, we introduce a class of constructionist learning environments that we call "Emergent Systems Sandboxes" ("ESSs"), which have served as a centerpiece of our recent work in developing curriculum to support scalable model-based learning in classroom settings. ESSs are a carefully specified form of virtual…
Comparison of co-expression measures: mutual information, correlation, and model based indices
2012-01-01
Background Co-expression measures are often used to define networks among genes. Mutual information (MI) is often used as a generalized correlation measure. It is not clear how much MI adds beyond standard (robust) correlation measures or regression model based association measures. Further, it is important to assess what transformations of these and other co-expression measures lead to biologically meaningful modules (clusters of genes). Results We provide a comprehensive comparison between mutual information and several correlation measures in 8 empirical data sets and in simulations. We also study different approaches for transforming an adjacency matrix, e.g. using the topological overlap measure. Overall, we confirm close relationships between MI and correlation in all data sets which reflects the fact that most gene pairs satisfy linear or monotonic relationships. We discuss rare situations when the two measures disagree. We also compare correlation and MI based approaches when it comes to defining co-expression network modules. We show that a robust measure of correlation (the biweight midcorrelation transformed via the topological overlap transformation) leads to modules that are superior to MI based modules and maximal information coefficient (MIC) based modules in terms of gene ontology enrichment. We present a function that relates correlation to mutual information which can be used to approximate the mutual information from the corresponding correlation coefficient. We propose the use of polynomial or spline regression models as an alternative to MI for capturing non-linear relationships between quantitative variables. Conclusion The biweight midcorrelation outperforms MI in terms of elucidating gene pairwise relationships. Coupled with the topological overlap matrix transformation, it often leads to more significantly enriched co-expression modules. Spline and polynomial networks form attractive alternatives to MI in case of non-linear relationships
NASA Technical Reports Server (NTRS)
2006-01-01
[figure removed for brevity, see original site] Context image for PIA03667 Linear Clouds
These clouds are located near the edge of the south polar region. The cloud tops are the puffy white features in the bottom half of the image.
Image information: VIS instrument. Latitude -80.1N, Longitude 52.1E. 17 meter/pixel resolution.
Note: this THEMIS visual image has not been radiometrically nor geometrically calibrated for this preliminary release. An empirical correction has been performed to remove instrumental effects. A linear shift has been applied in the cross-track and down-track direction to approximate spacecraft and planetary motion. Fully calibrated and geometrically projected images will be released through the Planetary Data System in accordance with Project policies at a later time.
NASA's Jet Propulsion Laboratory manages the 2001 Mars Odyssey mission for NASA's Office of Space Science, Washington, D.C. The Thermal Emission Imaging System (THEMIS) was developed by Arizona State University, Tempe, in collaboration with Raytheon Santa Barbara Remote Sensing. The THEMIS investigation is led by Dr. Philip Christensen at Arizona State University. Lockheed Martin Astronautics, Denver, is the prime contractor for the Odyssey project, and developed and built the orbiter. Mission operations are conducted jointly from Lockheed Martin and from JPL, a division of the California Institute of Technology in Pasadena.
A Model-Based Prognostics Approach Applied to Pneumatic Valves
NASA Technical Reports Server (NTRS)
Daigle, Matthew J.; Goebel, Kai
2011-01-01
Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.
An Application of Explanatory Item Response Modeling for Model-Based Proficiency Scaling
ERIC Educational Resources Information Center
Hartig, Johannes; Frey, Andreas; Nold, Gunter; Klieme, Eckhard
2012-01-01
The article compares three different methods to estimate effects of task characteristics and to use these estimates for model-based proficiency scaling: prediction of item difficulties from the Rasch model, the linear logistic test model (LLTM), and an LLTM including random item effects (LLTM+e). The methods are applied to empirical data from a…
ERIC Educational Resources Information Center
Joseph, Dan; Hartman, Gregory; Gibson, Caleb
2011-01-01
In this article we explore the consequences of modifying the common definition of a parabola by considering the locus of all points equidistant from a focus and (not necessarily linear) directrix. The resulting derived curves, which we call "generalized parabolas," are often quite beautiful and possess many interesting properties. We show that…
Holleczek, Bernd; Brenner, Hermann
2013-05-01
Period analysis is increasingly employed in analyses of long-term survival of patients with chronic diseases such as cancer, as it derives more up-to-date survival estimates than traditional cohort based approaches. It has recently been extended with regression modelling using generalized linear models, which increases the precision of the survival estimates and enables to assess and account for effects of additional covariates. This paper provides a detailed presentation how model based period analysis may be used to derive population-based absolute and relative survival estimates using the freely available R language and statistical environment and already available R programs for period analysis. After an introduction of the underlying regression model and a description of the software tools we provide a step-by-step implementation of two regression models in R and illustrate how estimates and a test for trend over time in relative survival may be derived using data from a population based cancer registry. PMID:23116692
Beckmann, Christian F.; Jenkinson, Mark; Woolrich, Mark W.; Behrens, Timothy E.J.; Flitney, David E.; Devlin, Joseph T.; Smith, Stephen M.
2009-01-01
This article presents results obtained from applying various tools from FSL (FMRIB Software Library) to data from the repetition priming experiment used for the HBM’05 Functional Image Analysis Contest. We present analyses from the model-based General Linear Model (GLM) tool (FEAT) and from the model-free independent component analysis tool (MELODIC). We also discuss the application of tools for the correction of image distortions prior to the statistical analysis and the utility of recent advances in functional magnetic resonance imaging (FMRI) time series modeling and inference such as the use of optimal constrained HRF basis function modeling and mixture modeling inference. The combination of hemodynamic response function (HRF) and mixture modeling, in particular, revealed that both sentence content and speaker voice priming effects occurred bilaterally along the length of the superior temporal sulcus (STS). These results suggest that both are processed in a single underlying system without any significant asymmetries for content vs. voice processing. PMID:16565953
Optical Scanner for Linear Arrays
NASA Technical Reports Server (NTRS)
Finkel, M. W.
1986-01-01
Optical scanner instantaneously reads contiguous lines forming scene or target in object plane. Reading active or passive and scans, continuous or discrete. Scans essentially linear with scan angle and symmetric about axial ray. Nominal focal error, resulting from curvature of scan, well within Rayleigh limit. Scanner specifically designed to be fully compatible with general requirements of linear arrays.
A probabilistic graphical model based stochastic input model construction
Wan, Jiang; Zabaras, Nicholas
2014-09-01
Model reduction techniques have been widely used in modeling of high-dimensional stochastic input in uncertainty quantification tasks. However, the probabilistic modeling of random variables projected into reduced-order spaces presents a number of computational challenges. Due to the curse of dimensionality, the underlying dependence relationships between these random variables are difficult to capture. In this work, a probabilistic graphical model based approach is employed to learn the dependence by running a number of conditional independence tests using observation data. Thus a probabilistic model of the joint PDF is obtained and the PDF is factorized into a set of conditional distributions based on the dependence structure of the variables. The estimation of the joint PDF from data is then transformed to estimating conditional distributions under reduced dimensions. To improve the computational efficiency, a polynomial chaos expansion is further applied to represent the random field in terms of a set of standard random variables. This technique is combined with both linear and nonlinear model reduction methods. Numerical examples are presented to demonstrate the accuracy and efficiency of the probabilistic graphical model based stochastic input models. - Highlights: • Data-driven stochastic input models without the assumption of independence of the reduced random variables. • The problem is transformed to a Bayesian network structure learning problem. • Examples are given in flows in random media.
Systems Engineering Interfaces: A Model Based Approach
NASA Technical Reports Server (NTRS)
Fosse, Elyse; Delp, Christopher
2013-01-01
Currently: Ops Rev developed and maintains a framework that includes interface-specific language, patterns, and Viewpoints. Ops Rev implements the framework to design MOS 2.0 and its 5 Mission Services. Implementation de-couples interfaces and instances of interaction Future: A Mission MOSE implements the approach and uses the model based artifacts for reviews. The framework extends further into the ground data layers and provides a unified methodology.
Reduced-order model based feedback control of the modified Hasegawa-Wakatani model
NASA Astrophysics Data System (ADS)
Goumiri, I. R.; Rowley, C. W.; Ma, Z.; Gates, D. A.; Krommes, J. A.; Parker, J. B.
2013-04-01
In this work, the development of model-based feedback control that stabilizes an unstable equilibrium is obtained for the Modified Hasegawa-Wakatani (MHW) equations, a classic model in plasma turbulence. First, a balanced truncation (a model reduction technique that has proven successful in flow control design problems) is applied to obtain a low dimensional model of the linearized MHW equation. Then, a model-based feedback controller is designed for the reduced order model using linear quadratic regulators. Finally, a linear quadratic Gaussian controller which is more resistant to disturbances is deduced. The controller is applied on the non-reduced, nonlinear MHW equations to stabilize the equilibrium and suppress the transition to drift-wave induced turbulence.
Reduced-order model based feedback control of the modified Hasegawa-Wakatani model
Goumiri, I. R.; Rowley, C. W.; Ma, Z.; Gates, D. A.; Krommes, J. A.; Parker, J. B.
2013-04-15
In this work, the development of model-based feedback control that stabilizes an unstable equilibrium is obtained for the Modified Hasegawa-Wakatani (MHW) equations, a classic model in plasma turbulence. First, a balanced truncation (a model reduction technique that has proven successful in flow control design problems) is applied to obtain a low dimensional model of the linearized MHW equation. Then, a model-based feedback controller is designed for the reduced order model using linear quadratic regulators. Finally, a linear quadratic Gaussian controller which is more resistant to disturbances is deduced. The controller is applied on the non-reduced, nonlinear MHW equations to stabilize the equilibrium and suppress the transition to drift-wave induced turbulence.
MODEL-BASED IMAGE RECONSTRUCTION FOR MRI
Fessler, Jeffrey A.
2010-01-01
Magnetic resonance imaging (MRI) is a sophisticated and versatile medical imaging modality. Traditionally, MR images are reconstructed from the raw measurements by a simple inverse 2D or 3D fast Fourier transform (FFT). However, there are a growing number of MRI applications where a simple inverse FFT is inadequate, e.g., due to non-Cartesian sampling patterns, non-Fourier physical effects, nonlinear magnetic fields, or deliberate under-sampling to reduce scan times. Such considerations have led to increasing interest in methods for model-based image reconstruction in MRI. PMID:21135916
Model-based multiple patterning layout decomposition
NASA Astrophysics Data System (ADS)
Guo, Daifeng; Tian, Haitong; Du, Yuelin; Wong, Martin D. F.
2015-10-01
As one of the most promising next generation lithography technologies, multiple patterning lithography (MPL) plays an important role in the attempts to keep in pace with 10 nm technology node and beyond. With feature size keeps shrinking, it has become impossible to print dense layouts within one single exposure. As a result, MPL such as double patterning lithography (DPL) and triple patterning lithography (TPL) has been widely adopted. There is a large volume of literature on DPL/TPL layout decomposition, and the current approach is to formulate the problem as a classical graph-coloring problem: Layout features (polygons) are represented by vertices in a graph G and there is an edge between two vertices if and only if the distance between the two corresponding features are less than a minimum distance threshold value dmin. The problem is to color the vertices of G using k colors (k = 2 for DPL, k = 3 for TPL) such that no two vertices connected by an edge are given the same color. This is a rule-based approach, which impose a geometric distance as a minimum constraint to simply decompose polygons within the distance into different masks. It is not desired in practice because this criteria cannot completely capture the behavior of the optics. For example, it lacks of sufficient information such as the optical source characteristics and the effects between the polygons outside the minimum distance. To remedy the deficiency, a model-based layout decomposition approach to make the decomposition criteria base on simulation results was first introduced at SPIE 2013.1 However, the algorithm1 is based on simplified assumption on the optical simulation model and therefore its usage on real layouts is limited. Recently AMSL2 also proposed a model-based approach to layout decomposition by iteratively simulating the layout, which requires excessive computational resource and may lead to sub-optimal solutions. The approach2 also potentially generates too many stiches. In this
Vector space model based on semantic relatedness
NASA Astrophysics Data System (ADS)
Bondarchuk, Dmitry; Timofeeva, Galina
2015-11-01
Most of data-mining methods are based on the vector space model of knowledge representation. The vector space model uses the frequency of a term in order to determine its relevance in a document. Terms can be similar by semantic meaning but be lexicographically different ones, so the classification based on the frequency of terms does not give desired results in some subject areas such as the vacancies selection. The modified vector space model based on the semantic relatedness is suggested for data-mining in this area. Evaluation results show that the proposed algorithm is better then one based on the standard vector space model.
Model-based Tomographic Reconstruction Literature Search
Chambers, D H; Lehman, S K
2005-11-30
In the process of preparing a proposal for internal research funding, a literature search was conducted on the subject of model-based tomographic reconstruction (MBTR). The purpose of the search was to ensure that the proposed research would not replicate any previous work. We found that the overwhelming majority of work on MBTR which used parameterized models of the object was theoretical in nature. Only three researchers had applied the technique to actual data. In this note, we summarize the findings of the literature search.
Model-based vision using geometric hashing
NASA Astrophysics Data System (ADS)
Akerman, Alexander, III; Patton, Ronald
1991-04-01
The Geometric Hashing technique developed by the NYU Courant Institute has been applied to various automatic target recognition applications. In particular, I-MATH has extended the hashing algorithm to perform automatic target recognition ofsynthetic aperture radar (SAR) imagery. For this application, the hashing is performed upon the geometric locations of dominant scatterers. In addition to being a robust model-based matching algorithm -- invariant under translation, scale, and 3D rotations of the target -- hashing is of particular utility because it can still perform effective matching when the target is partially obscured. Moreover, hashing is very amenable to a SIMD parallel processing architecture, and thus potentially realtime implementable.
Note: Model-based identification method of a cable-driven wearable device for arm rehabilitation
NASA Astrophysics Data System (ADS)
Cui, Xiang; Chen, Weihai; Zhang, Jianbin; Wang, Jianhua
2015-09-01
Cable-driven exoskeletons have used active cables to actuate the system and are worn on subjects to provide motion assistance. However, this kind of wearable devices usually contains uncertain kinematic parameters. In this paper, a model-based identification method has been proposed for a cable-driven arm exoskeleton to estimate its uncertainties. The identification method is based on the linearized error model derived from the kinematics of the exoskeleton. Experiment has been conducted to demonstrate the feasibility of the proposed model-based method in practical application.
Model-Based Fault Tolerant Control
NASA Technical Reports Server (NTRS)
Kumar, Aditya; Viassolo, Daniel
2008-01-01
The Model Based Fault Tolerant Control (MBFTC) task was conducted under the NASA Aviation Safety and Security Program. The goal of MBFTC is to develop and demonstrate real-time strategies to diagnose and accommodate anomalous aircraft engine events such as sensor faults, actuator faults, or turbine gas-path component damage that can lead to in-flight shutdowns, aborted take offs, asymmetric thrust/loss of thrust control, or engine surge/stall events. A suite of model-based fault detection algorithms were developed and evaluated. Based on the performance and maturity of the developed algorithms two approaches were selected for further analysis: (i) multiple-hypothesis testing, and (ii) neural networks; both used residuals from an Extended Kalman Filter to detect the occurrence of the selected faults. A simple fusion algorithm was implemented to combine the results from each algorithm to obtain an overall estimate of the identified fault type and magnitude. The identification of the fault type and magnitude enabled the use of an online fault accommodation strategy to correct for the adverse impact of these faults on engine operability thereby enabling continued engine operation in the presence of these faults. The performance of the fault detection and accommodation algorithm was extensively tested in a simulation environment.
Sandboxes for Model-Based Inquiry
NASA Astrophysics Data System (ADS)
Brady, Corey; Holbert, Nathan; Soylu, Firat; Novak, Michael; Wilensky, Uri
2015-04-01
In this article, we introduce a class of constructionist learning environments that we call Emergent Systems Sandboxes ( ESSs), which have served as a centerpiece of our recent work in developing curriculum to support scalable model-based learning in classroom settings. ESSs are a carefully specified form of virtual construction environment that support students in creating, exploring, and sharing computational models of dynamic systems that exhibit emergent phenomena. They provide learners with "entity"-level construction primitives that reflect an underlying scientific model. These primitives can be directly "painted" into a sandbox space, where they can then be combined, arranged, and manipulated to construct complex systems and explore the emergent properties of those systems. We argue that ESSs offer a means of addressing some of the key barriers to adopting rich, constructionist model-based inquiry approaches in science classrooms at scale. Situating the ESS in a large-scale science modeling curriculum we are implementing across the USA, we describe how the unique "entity-level" primitive design of an ESS facilitates knowledge system refinement at both an individual and social level, we describe how it supports flexible modeling practices by providing both continuous and discrete modes of executability, and we illustrate how it offers students a variety of opportunities for validating their qualitative understandings of emergent systems as they develop.
Model-based damage evaluation of layered CFRP structures
NASA Astrophysics Data System (ADS)
Munoz, Rafael; Bochud, Nicolas; Rus, Guillermo; Peralta, Laura; Melchor, Juan; Chiachío, Juan; Chiachío, Manuel; Bond, Leonard J.
2015-03-01
An ultrasonic evaluation technique for damage identification of layered CFRP structures is presented. This approach relies on a model-based estimation procedure that combines experimental data and simulation of ultrasonic damage-propagation interactions. The CFPR structure, a [0/90]4s lay-up, has been tested in an immersion through transmission experiment, where a scan has been performed on a damaged specimen. Most ultrasonic techniques in industrial practice consider only a few features of the received signals, namely, time of flight, amplitude, attenuation, frequency contents, and so forth. In this case, once signals are captured, an algorithm is used to reconstruct the complete signal waveform and extract the unknown damage parameters by means of modeling procedures. A linear version of the data processing has been performed, where only Young modulus has been monitored and, in a second nonlinear version, the first order nonlinear coefficient β was incorporated to test the possibility of detection of early damage. The aforementioned physical simulation models are solved by the Transfer Matrix formalism, which has been extended from linear to nonlinear harmonic generation technique. The damage parameter search strategy is based on minimizing the mismatch between the captured and simulated signals in the time domain in an automated way using Genetic Algorithms. Processing all scanned locations, a C-scan of the parameter of each layer can be reconstructed, obtaining the information describing the state of each layer and each interface. Damage can be located and quantified in terms of changes in the selected parameter with a measurable extension. In the case of the nonlinear coefficient of first order, evidence of higher sensitivity to damage than imaging the linearly estimated Young Modulus is provided.
Unifying Model-Based and Reactive Programming within a Model-Based Executive
NASA Technical Reports Server (NTRS)
Williams, Brian C.; Gupta, Vineet; Norvig, Peter (Technical Monitor)
1999-01-01
Real-time, model-based, deduction has recently emerged as a vital component in AI's tool box for developing highly autonomous reactive systems. Yet one of the current hurdles towards developing model-based reactive systems is the number of methods simultaneously employed, and their corresponding melange of programming and modeling languages. This paper offers an important step towards unification. We introduce RMPL, a rich modeling language that combines probabilistic, constraint-based modeling with reactive programming constructs, while offering a simple semantics in terms of hidden state Markov processes. We introduce probabilistic, hierarchical constraint automata (PHCA), which allow Markov processes to be expressed in a compact representation that preserves the modularity of RMPL programs. Finally, a model-based executive, called Reactive Burton is described that exploits this compact encoding to perform efficIent simulation, belief state update and control sequence generation.
Ocean acoustic signal processing: A model-based approach
Candy, J.V. ); Sullivan, E.J. )
1992-12-01
A model-based approach is proposed to solve the ocean acoustic signal processing problem that is based on a state-space representation of the normal-mode propagation model. It is shown that this representation can be utilized to spatially propagate both modal (depth) and range functions given the basic parameters (wave numbers, etc.) developed from the solution of the associated boundary value problem. This model is then generalized to the stochastic case where an approximate Gauss--Markov model evolves. The Gauss--Markov representation, in principle, allows the inclusion of stochastic phenomena such as noise and modeling errors in a consistent manner. Based on this framework, investigations are made of model-based solutions to the signal enhancement, detection and related parameter estimation problems. In particular, a modal/pressure field processor is designed that allows {ital in} {ital situ} recursive estimation of the sound velocity profile. Finally, it is shown that the associated residual or so-called innovation sequence that ensues from the recursive nature of this formulation can be employed to monitor the model's fit to the data and also form the basis of a sequential detector.
MODEL-BASED CLUSTERING OF LARGE NETWORKS1
Vu, Duy Q.; Hunter, David R.; Schweinberger, Michael
2015-01-01
We describe a network clustering framework, based on finite mixture models, that can be applied to discrete-valued networks with hundreds of thousands of nodes and billions of edge variables. Relative to other recent model-based clustering work for networks, we introduce a more flexible modeling framework, improve the variational-approximation estimation algorithm, discuss and implement standard error estimation via a parametric bootstrap approach, and apply these methods to much larger data sets than those seen elsewhere in the literature. The more flexible framework is achieved through introducing novel parameterizations of the model, giving varying degrees of parsimony, using exponential family models whose structure may be exploited in various theoretical and algorithmic ways. The algorithms are based on variational generalized EM algorithms, where the E-steps are augmented by a minorization-maximization (MM) idea. The bootstrapped standard error estimates are based on an efficient Monte Carlo network simulation idea. Last, we demonstrate the usefulness of the model-based clustering framework by applying it to a discrete-valued network with more than 131,000 nodes and 17 billion edge variables. PMID:26605002
Model-based approach to real-time target detection
NASA Astrophysics Data System (ADS)
Hackett, Jay K.; Gold, Ed V.; Long, Daniel T.; Cloud, Eugene L.; Duvoisin, Herbert A.
1992-09-01
Land mine detection and extraction from infra-red (IR) scenes using real-time parallel processing is of significant interest to ground based infantry. The mine detection algorithms consist of several sub-processes to progress from raw input IR imagery to feature based mine nominations. Image enhancement is first applied; this consists of noise and sensor artifact removal. Edge grouping is used to determine the boundary of the objects. The generalized Hough Transform tuned to the land mine signature acts as a model based matched nomination filter. Once the object is found, the model is used to guide the labeling of each pixel as background, object, or object boundary. Using these labels to identify object regions, feature primitives are extracted in a high speed parallel processor. A feature based screener then compares each object's feature primitives to acceptable values and rejects all objects that do not resemble mines. This operation greatly reduces the number of objects that must be passed from a real-time parallel processor to the classifier. We will discuss details of this model- based approach, including results from actual IR field test imagery.
Model-based patterns in prostate cancer mortality worldwide
Fontes, F; Severo, M; Castro, C; Lourenço, S; Gomes, S; Botelho, F; La Vecchia, C; Lunet, N
2013-01-01
Background: Prostate cancer mortality has been decreasing in several high income countries and previous studies analysed the trends mostly according to geographical criteria. We aimed to identify patterns in the time trends of prostate cancer mortality across countries using a model-based approach. Methods: Model-based clustering was used to identify patterns of variation in prostate cancer mortality (1980–2010) across 37 European, five non-European high-income countries and four leading emerging economies. We characterised the patterns observed regarding the geographical distribution and gross national income of the countries, as well as the trends observed in mortality/incidence ratios. Results: We identified three clusters of countries with similar variation in prostate cancer mortality: pattern 1 (‘no mortality decline'), characterised by a continued increase throughout the whole period; patterns 2 (‘later mortality decline') and 3 (‘earlier mortality decline') depict mortality declines, starting in the late and early 1990s, respectively. These clusters are also homogeneous regarding the variation in the prostate cancer mortality/incidence ratios, while are heterogeneous with reference to the geographical region of the countries and distribution of the gross national income. Conclusion: We provide a general model for the description and interpretation of the trends in prostate cancer mortality worldwide, based on three main patterns. PMID:23660943
Improved shape-signature and matching methods for model-based robotic vision
NASA Technical Reports Server (NTRS)
Schwartz, J. T.; Wolfson, H. J.
1987-01-01
Researchers describe new techniques for curve matching and model-based object recognition, which are based on the notion of shape-signature. The signature which researchers use is an approximation of pointwise curvature. Described here is curve matching algorithm which generalizes a previous algorithm which was developed using this signature, allowing improvement and generalization of a previous model-based object recognition scheme. The results and the experiments described relate to 2-D images. However, natural extensions to the 3-D case exist and are being developed.
Neural mass model-based tracking of anesthetic brain states.
Kuhlmann, Levin; Freestone, Dean R; Manton, Jonathan H; Heyse, Bjorn; Vereecke, Hugo E M; Lipping, Tarmo; Struys, Michel M R F; Liley, David T J
2016-06-01
Neural mass model-based tracking of brain states from electroencephalographic signals holds the promise of simultaneously tracking brain states while inferring underlying physiological changes in various neuroscientific and clinical applications. Here, neural mass model-based tracking of brain states using the unscented Kalman filter applied to estimate parameters of the Jansen-Rit cortical population model is evaluated through the application of propofol-based anesthetic state monitoring. In particular, 15 subjects underwent propofol anesthesia induction from awake to anesthetised while behavioral responsiveness was monitored and frontal electroencephalographic signals were recorded. The unscented Kalman filter Jansen-Rit model approach applied to frontal electroencephalography achieved reasonable testing performance for classification of the anesthetic brain state (sensitivity: 0.51; chance sensitivity: 0.17; nearest neighbor sensitivity 0.75) when compared to approaches based on linear (autoregressive moving average) modeling (sensitivity 0.58; nearest neighbor sensitivity: 0.91) and a high performing standard depth of anesthesia monitoring measure, Higuchi Fractal Dimension (sensitivity: 0.50; nearest neighbor sensitivity: 0.88). Moreover, it was found that the unscented Kalman filter based parameter estimates of the inhibitory postsynaptic potential amplitude varied in the physiologically expected direction with increases in propofol concentration, while the estimates of the inhibitory postsynaptic potential rate constant did not. These results combined with analysis of monotonicity of parameter estimates, error analysis of parameter estimates, and observability analysis of the Jansen-Rit model, along with considerations of extensions of the Jansen-Rit model, suggests that the Jansen-Rit model combined with unscented Kalman filtering provides a valuable reference point for future real-time brain state tracking studies. This is especially true for studies of
Concept Modeling-based Drug Repositioning
Patchala, Jagadeesh; Jegga, Anil G
2015-01-01
Our hypothesis is that drugs and diseases sharing similar biomedical and genomic concepts are likely to be related, and thus repositioning opportunities can be identified by ranking drugs based on the incidence of shared similar concepts with diseases and vice versa. To test this, we constructed a probabilistic topic model based on the Unified Medical Language System (UMLS) concepts that appear in the disease and drug related abstracts in MEDLINE. The resulting probabilistic topic associations were used to measure the similarity between disease and drugs. The success of the proposed model is evaluated using a set of repositioned drugs, and comparing a drug’s ranking based on its similarity to the original and new indication. We then applied the model to rare disorders and compared them to all approved drugs to facilitate “systematically serendipitous” discovery of relationships between rare diseases and existing drugs, some of which could be potential repositioning candidates. PMID:26306277
Model-based reasoning in SSF ECLSS
NASA Technical Reports Server (NTRS)
Miller, J. K.; Williams, George P. W., Jr.
1992-01-01
The interacting processes and reconfigurable subsystems of the Space Station Freedom Environmental Control and Life Support System (ECLSS) present a tremendous technical challenge to Freedom's crew and ground support. ECLSS operation and problem analysis is time-consuming for crew members and difficult for current computerized control, monitoring, and diagnostic software. These challenges can be at least partially mitigated by the use of advanced techniques such as Model-Based Reasoning (MBR). This paper will provide an overview of MBR as it is being applied to Space Station Freedom ECLSS. It will report on work being done to produce intelligent systems to help design, control, monitor, and diagnose Freedom's ECLSS. Specifically, work on predictive monitoring, diagnosability, and diagnosis, with emphasis on the automated diagnosis of the regenerative water recovery and air revitalization processes will be discussed.
Model-based vision for space applications
NASA Technical Reports Server (NTRS)
Chaconas, Karen; Nashman, Marilyn; Lumia, Ronald
1992-01-01
This paper describes a method for tracking moving image features by combining spatial and temporal edge information with model based feature information. The algorithm updates the two-dimensional position of object features by correlating predicted model features with current image data. The results of the correlation process are used to compute an updated model. The algorithm makes use of a high temporal sampling rate with respect to spatial changes of the image features and operates in a real-time multiprocessing environment. Preliminary results demonstrate successful tracking for image feature velocities between 1.1 and 4.5 pixels every image frame. This work has applications for docking, assembly, retrieval of floating objects and a host of other space-related tasks.
Model-Based Vision Using Relational Summaries
NASA Astrophysics Data System (ADS)
Lu, Haiyuan; Shapiro, Linda G.
1989-03-01
A CAD-to-vision system is a computer system that inputs a CAD model of an object and outputs a vision model and matching procedure by which that object can be recognized and/or its position and orientation determined. CAD-model-based systems are extremely useful for industrial vision tasks where a number of different manufactured parts must be automatically manipulated and/or inspected. Another area where vision systems based on CAD models is becoming important is in the United States space program. Since the space station and space vehicles are recent or even current designs, we can expect to have CAD models of these objects to work with. Vision tasks in space such as docking and tracking of vehicles, guided assembly tasks, and inspection of the space station itself for cracks and other problems can rely on model-directed vision techniques.
Model-based ocean acoustic passive localization
Candy, J.V.; Sullivan, E.J.
1994-01-24
The detection, localization and classification of acoustic sources (targets) in a hostile ocean environment is a difficult problem -- especially in light of the improved design of modern submarines and the continual improvement in quieting technology. Further the advent of more and more diesel-powered vessels makes the detection problem even more formidable than ever before. It has recently been recognized that the incorporation of a mathematical model that accurately represents the phenomenology under investigation can vastly improve the performance of any processor, assuming, of course, that the model is accurate. Therefore, it is necessary to incorporate more knowledge about the ocean environment into detection and localization algorithms in order to enhance the overall signal-to-noise ratios and improve performance. An alternative methodology to matched-field/matched-mode processing is the so-called model-based processor which is based on a state-space representation of the normal-mode propagation model. If state-space solutions can be accomplished, then many of the current ocean acoustic processing problems can be analyzed and solved using this framework to analyze performance results based on firm statistical and system theoretic grounds. The model-based approach, is (simply) ``incorporating mathematical models of both physical phenomenology and the measurement processes including noise into the processor to extract the desired information.`` In this application, we seek techniques to incorporate the: (1) ocean acoustic propagation model; (2) sensor array measurement model; and (3) noise models (ambient, shipping, surface and measurement) into a processor to solve the associated localization/detection problems.
Fast Algorithms for Model-Based Diagnosis
NASA Technical Reports Server (NTRS)
Fijany, Amir; Barrett, Anthony; Vatan, Farrokh; Mackey, Ryan
2005-01-01
Two improved new methods for automated diagnosis of complex engineering systems involve the use of novel algorithms that are more efficient than prior algorithms used for the same purpose. Both the recently developed algorithms and the prior algorithms in question are instances of model-based diagnosis, which is based on exploring the logical inconsistency between an observation and a description of a system to be diagnosed. As engineering systems grow more complex and increasingly autonomous in their functions, the need for automated diagnosis increases concomitantly. In model-based diagnosis, the function of each component and the interconnections among all the components of the system to be diagnosed (for example, see figure) are represented as a logical system, called the system description (SD). Hence, the expected behavior of the system is the set of logical consequences of the SD. Faulty components lead to inconsistency between the observed behaviors of the system and the SD. The task of finding the faulty components (diagnosis) reduces to finding the components, the abnormalities of which could explain all the inconsistencies. Of course, the meaningful solution should be a minimal set of faulty components (called a minimal diagnosis), because the trivial solution, in which all components are assumed to be faulty, always explains all inconsistencies. Although the prior algorithms in question implement powerful methods of diagnosis, they are not practical because they essentially require exhaustive searches among all possible combinations of faulty components and therefore entail the amounts of computation that grow exponentially with the number of components of the system.
Model-Based Signal Processing: Correlation Detection With Synthetic Seismograms
Rodgers, A; Harris, D; Pasyanos, M; Blair, S; Matt, R
2006-08-30
Recent applications of correlation methods to seismological problems illustrate the power of coherent signal processing applied to seismic waveforms. Examples of these applications include detection of low amplitude signals buried in ambient noise and cross-correlation of sets of waveforms to form event clusters and accurately measure delay times for event relocation and/or earth structure. These methods rely on the exploitation of the similarity of individual waveforms and have been successfully applied to large sets of empirical observations. However, in cases with little or no empirical event data, such as aseismic regions or exotic event types, correlation methods with observed seismograms will not be possible due to the lack of previously observed similar waveforms. This study uses model-based signals computed for three-dimensional (3D) Earth models to form the basis for correlation detection. Synthetic seismograms are computed for fully 3D models estimated from the Markov Chain Monte-Carlo (MCMC) method. MCMC uses stochastic sampling to fit multiple seismological data sets. Rather than estimate a single ''optimal'' model, MCMC results in a suite of models that sample the model space and incorporates uncertainty through variability of the models. The variability reflects our ignorance of Earth structure, due to limited resolution, data and modeling errors, and produces variability in the seismic waveform response. Model-based signals are combined using a subspace method where the synthetic signals are decomposed into an orthogonal basis by singular-value decomposition (SVD) and the observed waveforms are represented with a linear combination of a sub-set of eigenvectors (signals) associated with the most significant eigenvalues. We have demonstrated the method by modeling long-period (80-10 seconds) regional seismograms for a moderate (M{approx}5) earthquake near the China-North Korea border. Synthetic seismograms are computed with the Spectral Element Method
Linear analysis of incompressible Rayleigh-Taylor instability in solids.
Piriz, A R; Cela, J J López; Tahir, N A
2009-10-01
The study of the linear stage of the incompressible Rayleigh-Taylor instability in elastic-plastic solids is performed by considering thick plates under a constant acceleration that is also uniform except for a small sinusoidal ripple in the horizontal plane. The analysis is carried out by using an analytical model based on the Newton second law and it is complemented with extensive two-dimensional numerical simulations. The conditions for marginal stability that determine the instability threshold are derived. Besides, the boundary for the transition from the elastic to the plastic regime is obtained and it is demonstrated that such a transition is not a sufficient condition for instability. The model yields complete analytical solutions for the perturbation amplitude evolution and reveals the main physical process that governs the instability. The theory is in general agreement with the numerical simulations and provides useful quantitative results. Implications for high-energy-density-physics experiments are also discussed. PMID:19905434
Sparse linear programming subprogram
Hanson, R.J.; Hiebert, K.L.
1981-12-01
This report describes a subprogram, SPLP(), for solving linear programming problems. The package of subprogram units comprising SPLP() is written in Fortran 77. The subprogram SPLP() is intended for problems involving at most a few thousand constraints and variables. The subprograms are written to take advantage of sparsity in the constraint matrix. A very general problem statement is accepted by SPLP(). It allows upper, lower, or no bounds on the variables. Both the primal and dual solutions are returned as output parameters. The package has many optional features. Among them is the ability to save partial results and then use them to continue the computation at a later time.
On the Performance of Stochastic Model-Based Image Segmentation
NASA Astrophysics Data System (ADS)
Lei, Tianhu; Sewchand, Wilfred
1989-11-01
A new stochastic model-based image segmentation technique for X-ray CT image has been developed and has been extended to the more general nondiffraction CT images which include MRI, SPELT, and certain type of ultrasound images [1,2]. The nondiffraction CT image is modeled by a Finite Normal Mixture. The technique utilizes the information theoretic criterion to detect the number of the region images, uses the Expectation-Maximization algorithm to estimate the parameters of the image, and uses the Bayesian classifier to segment the observed image. How does this technique over/under-estimate the number of the region images? What is the probability of errors in the segmentation of this technique? This paper addresses these two problems and is a continuation of [1,2].
Hernández Suárez, Marcos; Astray Dopazo, Gonzalo; Larios López, Dina; Espinosa, Francisco
2015-01-01
There are a large number of tomato cultivars with a wide range of morphological, chemical, nutritional and sensorial characteristics. Many factors are known to affect the nutrient content of tomato cultivars. A complete understanding of the effect of these factors would require an exhaustive experimental design, multidisciplinary scientific approach and a suitable statistical method. Some multivariate analytical techniques such as Principal Component Analysis (PCA) or Factor Analysis (FA) have been widely applied in order to search for patterns in the behaviour and reduce the dimensionality of a data set by a new set of uncorrelated latent variables. However, in some cases it is not useful to replace the original variables with these latent variables. In this study, Automatic Interaction Detection (AID) algorithm and Artificial Neural Network (ANN) models were applied as alternative to the PCA, AF and other multivariate analytical techniques in order to identify the relevant phytochemical constituents for characterization and authentication of tomatoes. To prove the feasibility of AID algorithm and ANN models to achieve the purpose of this study, both methods were applied on a data set with twenty five chemical parameters analysed on 167 tomato samples from Tenerife (Spain). Each tomato sample was defined by three factors: cultivar, agricultural practice and harvest date. General Linear Model linked to AID (GLM-AID) tree-structured was organized into 3 levels according to the number of factors. p-Coumaric acid was the compound the allowed to distinguish the tomato samples according to the day of harvest. More than one chemical parameter was necessary to distinguish among different agricultural practices and among the tomato cultivars. Several ANN models, with 25 and 10 input variables, for the prediction of cultivar, agricultural practice and harvest date, were developed. Finally, the models with 10 input variables were chosen with fit’s goodness between 44 and
Model-Based Engineering and Manufacturing CAD/CAM Benchmark.
Domm, T.C.; Underwood, R.S.
1999-10-13
The Benchmark Project was created from a desire to identify best practices and improve the overall efficiency and performance of the Y-12 Plant's systems and personnel supporting the manufacturing mission. The mission of the benchmark team was to search out industry leaders in manufacturing and evaluate their engineering practices and processes to determine direction and focus for Y-12 modernization efforts. The companies visited included several large established companies and a new, small, high-tech machining firm. As a result of this effort, changes are recommended that will enable Y-12 to become a more modern, responsive, cost-effective manufacturing facility capable of supporting the needs of the Nuclear Weapons Complex (NWC) into the 21st century. The benchmark team identified key areas of interest, both focused and general. The focus areas included Human Resources, Information Management, Manufacturing Software Tools, and Standards/Policies and Practices. Areas of general interest included Infrastructure, Computer Platforms and Networking, and Organizational Structure. The results of this benchmark showed that all companies are moving in the direction of model-based engineering and manufacturing. There was evidence that many companies are trying to grasp how to manage current and legacy data. In terms of engineering design software tools, the companies contacted were somewhere between 3-D solid modeling and surfaced wire-frame models. The manufacturing computer tools were varied, with most companies using more than one software product to generate machining data and none currently performing model-based manufacturing (MBM) from a common model. The majority of companies were closer to identifying or using a single computer-aided design (CAD) system than a single computer-aided manufacturing (CAM) system. The Internet was a technology that all companies were looking to either transport information more easily throughout the corporation or as a conduit for
NASA Technical Reports Server (NTRS)
Ferencz, Donald C.; Viterna, Larry A.
1991-01-01
ALPS is a computer program which can be used to solve general linear program (optimization) problems. ALPS was designed for those who have minimal linear programming (LP) knowledge and features a menu-driven scheme to guide the user through the process of creating and solving LP formulations. Once created, the problems can be edited and stored in standard DOS ASCII files to provide portability to various word processors or even other linear programming packages. Unlike many math-oriented LP solvers, ALPS contains an LP parser that reads through the LP formulation and reports several types of errors to the user. ALPS provides a large amount of solution data which is often useful in problem solving. In addition to pure linear programs, ALPS can solve for integer, mixed integer, and binary type problems. Pure linear programs are solved with the revised simplex method. Integer or mixed integer programs are solved initially with the revised simplex, and the completed using the branch-and-bound technique. Binary programs are solved with the method of implicit enumeration. This manual describes how to use ALPS to create, edit, and solve linear programming problems. Instructions for installing ALPS on a PC compatible computer are included in the appendices along with a general introduction to linear programming. A programmers guide is also included for assistance in modifying and maintaining the program.
NASA Technical Reports Server (NTRS)
Hazra, Rajeeb; Viles, Charles L.; Park, Stephen K.; Reichenbach, Stephen E.; Sieracki, Michael E.
1992-01-01
Consideration is given to a model-based method for estimating the spatial frequency response of a digital-imaging system (e.g., a CCD camera) that is modeled as a linear, shift-invariant image acquisition subsystem that is cascaded with a linear, shift-variant sampling subsystem. The method characterizes the 2D frequency response of the image acquisition subsystem to beyond the Nyquist frequency by accounting explicitly for insufficient sampling and the sample-scene phase. Results for simulated systems and a real CCD-based epifluorescence microscopy system are presented to demonstrate the accuracy of the method.
Comparison between a model-based and a conventional pyramid sensor reconstructor.
Korkiakoski, Visa; Vérinaud, Christophe; Le Louarn, Miska; Conan, Rodolphe
2007-08-20
A model of a non-modulated pyramid wavefront sensor (P-WFS) based on Fourier optics has been presented. Linearizations of the model represented as Jacobian matrices are used to improve the P-WFS phase estimates. It has been shown in simulations that a linear approximation of the P-WFS is sufficient in closed-loop adaptive optics. Also a method to compute model-based synthetic P-WFS command matrices is shown, and its performance is compared to the conventional calibration. It was observed that in poor visibility the new calibration is better than the conventional. PMID:17712383
Using rule-based shot dose assignment in model-based MPC applications
NASA Astrophysics Data System (ADS)
Bork, Ingo; Buck, Peter; Wang, Lin; Müller, Uwe
2014-10-01
Shrinking feature sizes and the need for tighter CD (Critical Dimension) control require the introduction of new technologies in mask making processes. One of those methods is the dose assignment of individual shots on VSB (Variable Shaped Beam) mask writers to compensate CD non-linearity effects and improve dose edge slope. Using increased dose levels only for most critical features, generally only for the smallest CDs on a mask, the change in mask write time is minimal while the increase in image quality can be significant. This paper describes a method combining rule-based shot dose assignment with model-based shot size correction. This combination proves to be very efficient in correcting mask linearity errors while also improving dose edge slope of small features. Shot dose assignment is based on tables assigning certain dose levels to a range of feature sizes. The dose to feature size assignment is derived from mask measurements in such a way that shape corrections are kept to a minimum. For example, if a 50nm drawn line on mask results in a 45nm chrome line using nominal dose, a dose level is chosen which is closest to getting the line back on target. Since CD non-linearity is different for lines, line-ends and contacts, different tables are generated for the different shape categories. The actual dose assignment is done via DRC rules in a pre-processing step before executing the shape correction in the MPC engine. Dose assignment to line ends can be restricted to critical line/space dimensions since it might not be required for all line ends. In addition, adding dose assignment to a wide range of line ends might increase shot count which is undesirable. The dose assignment algorithm is very flexible and can be adjusted based on the type of layer and the best balance between accuracy and shot count. These methods can be optimized for the number of dose levels available for specific mask writers. The MPC engine now needs to be able to handle different dose
A Kp forecast model based on neural network
NASA Astrophysics Data System (ADS)
Gong, J.; Liu, Y.; Luo, B.; Liu, S.
2013-12-01
As an important global geomagnetic disturbance index, Kp is difficult to predict, especially when Kp reaches 5 which means that the disturbance has reached the scales of geomagnetic storm and can cause spacecraft and power system anomaly. Statistical results showed that there exists high correlation between solar wind-magnetosphere coupling function and Kp index, and a linear combination of two solar wind-magnetosphere coupling terms, merging term and viscous term, proved to be good in predicting the Kp index. In this study, using the upstream solar wind parameters by the ACE satellite since 1998 and the two derived coupling terms mentioned above, a Kp forecast model based on artificial neural network is developed. For the operational need of predicting the geomagnetic disturbance as soon as possible, we construct the solar wind data and develop the model in an innovative way. For each Kp value at time t (the universal times of 8 Kp values in each day are noted as t=3, 6, 9, ..., 18, 21, 24), the model gives 6 predicted values every half an hour at t-3.5, t-3.0, t-2.5, t-2.0, t-1.5, t-1.0, based on the half-hour averaged model inputs (solar wind parameters and derived solar wind-magnetosphere coupling terms). The last predicted value at t-1.0 provides the final prediction. Evaluated with the test set data including years 1998, 2002 and 2006, the model yields the linear correlation coefficient (LC) of 0.88 and the root mean square error (RMSE) of 0.65 between the modeled and observed Kp values. Furthermore, if the nowcast Kp is available and included in the model input, the model can be improved and gives an LC of 0.90 and an RMSE of 0.62.
[Fast spectral modeling based on Voigt peaks].
Li, Jin-rong; Dai, Lian-kui
2012-03-01
Indirect hard modeling (IHM) is a recently introduced method for quantitative spectral analysis, which was applied to the analysis of nonlinear relation between mixture spectrum and component concentration. In addition, IHM is an effectual technology for the analysis of components of mixture with molecular interactions and strongly overlapping bands. Before the establishment of regression model, IHM needs to model the measured spectrum as a sum of Voigt peaks. The precision of the spectral model has immediate impact on the accuracy of the regression model. A spectrum often includes dozens or even hundreds of Voigt peaks, which mean that spectral modeling is a optimization problem with high dimensionality in fact. So, large operation overhead is needed and the solution would not be numerically unique due to the ill-condition of the optimization problem. An improved spectral modeling method is presented in the present paper, which reduces the dimensionality of optimization problem by determining the overlapped peaks in spectrum. Experimental results show that the spectral modeling based on the new method is more accurate and needs much shorter running time than conventional method. PMID:22582612
Model-Based Method for Sensor Validation
NASA Technical Reports Server (NTRS)
Vatan, Farrokh
2012-01-01
Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety. The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures. The method developed in this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).
Model-based target and background characterization
NASA Astrophysics Data System (ADS)
Mueller, Markus; Krueger, Wolfgang; Heinze, Norbert
2000-07-01
Up to now most approaches of target and background characterization (and exploitation) concentrate solely on the information given by pixels. In many cases this is a complex and unprofitable task. During the development of automatic exploitation algorithms the main goal is the optimization of certain performance parameters. These parameters are measured during test runs while applying one algorithm with one parameter set to images that constitute of image domains with very different domain characteristics (targets and various types of background clutter). Model based geocoding and registration approaches provide means for utilizing the information stored in GIS (Geographical Information Systems). The geographical information stored in the various GIS layers can define ROE (Regions of Expectations) and may allow for dedicated algorithm parametrization and development. ROI (Region of Interest) detection algorithms (in most cases MMO (Man- Made Object) detection) use implicit target and/or background models. The detection algorithms of ROIs utilize gradient direction models that have to be matched with transformed image domain data. In most cases simple threshold calculations on the match results discriminate target object signatures from the background. The geocoding approaches extract line-like structures (street signatures) from the image domain and match the graph constellation against a vector model extracted from a GIS (Geographical Information System) data base. Apart from geo-coding the algorithms can be also used for image-to-image registration (multi sensor and data fusion) and may be used for creation and validation of geographical maps.
Enhancing model based forecasting of geomagnetic storms
NASA Astrophysics Data System (ADS)
Webb, Alla G.
Modern society is increasingly dependent on the smooth operation of large scale technology supporting Earth based activities such as communication, electricity distribution, and navigation. This technology is potentially threatened by global geomagnetic storms, which are caused by the impact of plasma ejected from the Sun upon the protective magnetic field that surrounds the Earth. Forecasting the timing and magnitude of these geomagnetic storms is part of the emerging discipline of space weather. The most severe geomagnetic storms are caused by magnetic clouds, whose properties and characteristics are important variables in space weather forecasting systems. The methodology presented here is the development of a new statistical approach to characterize the physical properties (variables) of the magnetic clouds and to examine the extent to which theoretical models can be used in describing both of these physical properties, as well as their evolution in space and time. Since space weather forecasting is a complex system, a systems engineering approach is used to perform analysis, validation, and verification of the magnetic cloud models (subsystem of the forecasting system) using a model-based methodology. This research demonstrates that in order to validate magnetic cloud models, it is important to categorize the data by physical parameters such as velocity and distance travelled. This understanding will improve the modeling accuracy of magnetic clouds in space weather forecasting systems and hence increase forecasting accuracy of geomagnetic storms and their impact on earth systems.
Model based systems engineering for astronomical projects
NASA Astrophysics Data System (ADS)
Karban, R.; Andolfato, L.; Bristow, P.; Chiozzi, G.; Esselborn, M.; Schilling, M.; Schmid, C.; Sommer, H.; Zamparelli, M.
2014-08-01
Model Based Systems Engineering (MBSE) is an emerging field of systems engineering for which the System Modeling Language (SysML) is a key enabler for descriptive, prescriptive and predictive models. This paper surveys some of the capabilities, expectations and peculiarities of tools-assisted MBSE experienced in real-life astronomical projects. The examples range in depth and scope across a wide spectrum of applications (for example documentation, requirements, analysis, trade studies) and purposes (addressing a particular development need, or accompanying a project throughout many - if not all - its lifecycle phases, fostering reuse and minimizing ambiguity). From the beginnings of the Active Phasing Experiment, through VLT instrumentation, VLTI infrastructure, Telescope Control System for the E-ELT, until Wavefront Control for the E-ELT, we show how stepwise refinements of tools, processes and methods have provided tangible benefits to customary system engineering activities like requirement flow-down, design trade studies, interfaces definition, and validation, by means of a variety of approaches (like Model Checking, Simulation, Model Transformation) and methodologies (like OOSEM, State Analysis)
LRGS: Linear Regression by Gibbs Sampling
NASA Astrophysics Data System (ADS)
Mantz, Adam B.
2016-02-01
LRGS (Linear Regression by Gibbs Sampling) implements a Gibbs sampler to solve the problem of multivariate linear regression with uncertainties in all measured quantities and intrinsic scatter. LRGS extends an algorithm by Kelly (2007) that used Gibbs sampling for performing linear regression in fairly general cases in two ways: generalizing the procedure for multiple response variables, and modeling the prior distribution of covariates using a Dirichlet process.
Model-based Processing of Micro-cantilever Sensor Arrays
Tringe, J W; Clague, D S; Candy, J V; Lee, C L; Rudd, R E; Burnham, A K
2004-11-17
We develop a model-based processor (MBP) for a micro-cantilever array sensor to detect target species in solution. After discussing the generalized framework for this problem, we develop the specific model used in this study. We perform a proof-of-concept experiment, fit the model parameters to the measured data and use them to develop a Gauss-Markov simulation. We then investigate two cases of interest: (1) averaged deflection data, and (2) multi-channel data. In both cases the evaluation proceeds by first performing a model-based parameter estimation to extract the model parameters, next performing a Gauss-Markov simulation, designing the optimal MBP and finally applying it to measured experimental data. The simulation is used to evaluate the performance of the MBP in the multi-channel case and compare it to a ''smoother'' (''averager'') typically used in this application. It was shown that the MBP not only provides a significant gain ({approx} 80dB) in signal-to-noise ratio (SNR), but also consistently outperforms the smoother by 40-60 dB. Finally, we apply the processor to the smoothed experimental data and demonstrate its capability for chemical detection. The MBP performs quite well, though it includes a correctable systematic bias error. The project's primary accomplishment was the successful application of model-based processing to signals from micro-cantilever arrays: 40-60 dB improvement vs. the smoother algorithm was demonstrated. This result was achieved through the development of appropriate mathematical descriptions for the chemical and mechanical phenomena, and incorporation of these descriptions directly into the model-based signal processor. A significant challenge was the development of the framework which would maximize the usefulness of the signal processing algorithms while ensuring the accuracy of the mathematical description of the chemical-mechanical signal. Experimentally, the difficulty was to identify and characterize the non
The Impact of Acquiescence on Forced-Choice Responses: A Model-Based Analysis
ERIC Educational Resources Information Center
Ferrando, Pere J.; Anguiano-Carrasco, Cristina; Chico, Eliseo
2011-01-01
The general aim of the present study is to assess the potential usefulness of the normative Forced Choice (FC) format for reducing the impact of acquiescent responding (AR). To this end it makes two types of contributions: methodological and substantive. Methodologically, it proposes a model-based procedure, derived from a basic response…
Lithium battery aging model based on Dakin's degradation approach
NASA Astrophysics Data System (ADS)
Baghdadi, Issam; Briat, Olivier; Delétage, Jean-Yves; Gyan, Philippe; Vinassa, Jean-Michel
2016-09-01
This paper proposes and validates a calendar and power cycling aging model for two different lithium battery technologies. The model development is based on previous SIMCAL and SIMSTOCK project data. In these previous projects, the effect of the battery state of charge, temperature and current magnitude on aging was studied on a large panel of different battery chemistries. In this work, data are analyzed using Dakin's degradation approach. In fact, the logarithms of battery capacity fade and the increase in resistance evolves linearly over aging. The slopes identified from straight lines correspond to battery aging rates. Thus, a battery aging rate expression function of aging factors was deduced and found to be governed by Eyring's law. The proposed model simulates the capacity fade and resistance increase as functions of the influencing aging factors. Its expansion using Taylor series was consistent with semi-empirical models based on the square root of time, which are widely studied in the literature. Finally, the influence of the current magnitude and temperature on aging was simulated. Interestingly, the aging rate highly increases with decreasing and increasing temperature for the ranges of -5 °C-25 °C and 25 °C-60 °C, respectively.
Evaluating face trustworthiness: a model based approach
Baron, Sean G.; Oosterhof, Nikolaas N.
2008-01-01
Judgments of trustworthiness from faces determine basic approach/avoidance responses and approximate the valence evaluation of faces that runs across multiple person judgments. Here, based on trustworthiness judgments and using a computer model for face representation, we built a model for representing face trustworthiness (study 1). Using this model, we generated novel faces with an increased range of trustworthiness and used these faces as stimuli in a functional Magnetic Resonance Imaging study (study 2). Although participants did not engage in explicit evaluation of the faces, the amygdala response changed as a function of face trustworthiness. An area in the right amygdala showed a negative linear response—as the untrustworthiness of faces increased so did the amygdala response. Areas in the left and right putamen, the latter area extended into the anterior insula, showed a similar negative linear response. The response in the left amygdala was quadratic—strongest for faces on both extremes of the trustworthiness dimension. The medial prefrontal cortex and precuneus also showed a quadratic response, but their response was strongest to faces in the middle range of the trustworthiness dimension. PMID:19015102
NASA Astrophysics Data System (ADS)
Yamasaki, Tadashi; Houseman, Gregory; Hamling, Ian; Postek, Elek
2010-05-01
We have developed a new parallelized 3-D numerical code, OREGANO_VE, for the solution of the general visco-elastic problem in a rectangular block domain. The mechanical equilibrium equation is solved using the finite element method for a (non-)linear Maxwell visco-elastic rheology. Time-dependent displacement and/or traction boundary conditions can be applied. Matrix assembly is based on a tetrahedral element defined by 4 vertex nodes and 6 nodes located at the midpoints of the edges, and within which displacement is described by a quadratic interpolation function. For evaluating viscoelastic relaxation, an explicit time-stepping algorithm (Zienkiewicz and Cormeau, Int. J. Num. Meth. Eng., 8, 821-845, 1974) is employed. We test the accurate implementation of the OREGANO_VE by comparing numerical and analytic (or semi-analytic half-space) solutions to different problems in a range of applications: (1) equilibration of stress in a constant density layer after gravity is switched on at t = 0 tests the implementation of spatially variable viscosity and non-Newtonian viscosity; (2) displacement of the welded interface between two blocks of differing viscosity tests the implementation of viscosity discontinuities, (3) displacement of the upper surface of a layer under applied normal load tests the implementation of time-dependent surface tractions (4) visco-elastic response to dyke intrusion (compared with the solution in a half-space) tests the implementation of all aspects. In each case, the accuracy of the code is validated subject to use of a sufficiently small time step, providing assurance that the OREGANO_VE code can be applied to a range of visco-elastic relaxation processes in three dimensions, including post-seismic deformation and post-glacial uplift. The OREGANO_VE code includes a capability for representation of prescribed fault slip on an internal fault. The surface displacement associated with large earthquakes can be detected by some geodetic observations
Model Based Autonomy for Robust Mars Operations
NASA Technical Reports Server (NTRS)
Kurien, James A.; Nayak, P. Pandurang; Williams, Brian C.; Lau, Sonie (Technical Monitor)
1998-01-01
Space missions have historically relied upon a large ground staff, numbering in the hundreds for complex missions, to maintain routine operations. When an anomaly occurs, this small army of engineers attempts to identify and work around the problem. A piloted Mars mission, with its multiyear duration, cost pressures, half-hour communication delays and two-week blackouts cannot be closely controlled by a battalion of engineers on Earth. Flight crew involvement in routine system operations must also be minimized to maximize science return. It also may be unrealistic to require the crew have the expertise in each mission subsystem needed to diagnose a system failure and effect a timely repair, as engineers did for Apollo 13. Enter model-based autonomy, which allows complex systems to autonomously maintain operation despite failures or anomalous conditions, contributing to safe, robust, and minimally supervised operation of spacecraft, life support, In Situ Resource Utilization (ISRU) and power systems. Autonomous reasoning is central to the approach. A reasoning algorithm uses a logical or mathematical model of a system to infer how to operate the system, diagnose failures and generate appropriate behavior to repair or reconfigure the system in response. The 'plug and play' nature of the models enables low cost development of autonomy for multiple platforms. Declarative, reusable models capture relevant aspects of the behavior of simple devices (e.g. valves or thrusters). Reasoning algorithms combine device models to create a model of the system-wide interactions and behavior of a complex, unique artifact such as a spacecraft. Rather than requiring engineers to all possible interactions and failures at design time or perform analysis during the mission, the reasoning engine generates the appropriate response to the current situation, taking into account its system-wide knowledge, the current state, and even sensor failures or unexpected behavior.
NASA Technical Reports Server (NTRS)
Thormann, Wolfgang; Mosher, Richard A.
1985-01-01
The general equations which describe the electrophoretic transport of components in solution are restated using Newman's general concept of mobilities. A concise derivation of the moving boundary equation and the regulating function from the continuity equation is presented. Various other regulating principles across moving and stationary boundaries are also discussed, which permits a review of the features and interrelationships of the electrophoretic models based on electromigration only. The effect of considering an interactive (dissociating) solvent on the mathematical treatment is discussed.
A Systematic Approach for Model-Based Aircraft Engine Performance Estimation
NASA Technical Reports Server (NTRS)
Simon, Donald L.; Garg, Sanjay
2010-01-01
A requirement for effective aircraft engine performance estimation is the ability to account for engine degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. This paper presents a linear point design methodology for minimizing the degradation-induced error in model-based aircraft engine performance estimation applications. The technique specifically focuses on the underdetermined estimation problem, where there are more unknown health parameters than available sensor measurements. A condition for Kalman filter-based estimation is that the number of health parameters estimated cannot exceed the number of sensed measurements. In this paper, the estimated health parameter vector will be replaced by a reduced order tuner vector whose dimension is equivalent to the sensed measurement vector. The reduced order tuner vector is systematically selected to minimize the theoretical mean squared estimation error of a maximum a posteriori estimator formulation. This paper derives theoretical estimation errors at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the estimation accuracy achieved through conventional maximum a posteriori and Kalman filter estimation approaches. Maximum a posteriori estimation results demonstrate that reduced order tuning parameter vectors can be found that approximate the accuracy of estimating all health parameters directly. Kalman filter estimation results based on the same reduced order tuning parameter vectors demonstrate that significantly improved estimation accuracy can be achieved over the conventional approach of selecting a subset of health parameters to serve as the tuner vector. However, additional development is necessary to fully extend the methodology to Kalman filter
Model Order and Identifiability of Non-Linear Biological Systems in Stable Oscillation.
Wigren, Torbjörn
2015-01-01
The paper presents a theoretical result that clarifies when it is at all possible to determine the nonlinear dynamic equations of a biological system in stable oscillation, from measured data. As it turns out the minimal order needed for this is dependent on the minimal dimension in which the stable orbit of the system does not intersect itself. This is illustrated with a simulated fourth order Hodgkin-Huxley spiking neuron model, which is identified using a non-linear second order differential equation model. The simulated result illustrates that the underlying higher order model of the spiking neuron cannot be uniquely determined given only the periodic measured data. The result of the paper is of general validity when the dynamics of biological systems in stable oscillation is identified, and illustrates the need to carefully address non-linear identifiability aspects when validating models based on periodic data. PMID:26671817
Linearly Forced Isotropic Turbulence
NASA Technical Reports Server (NTRS)
Lundgren, T. S.
2003-01-01
Stationary isotropic turbulence is often studied numerically by adding a forcing term to the Navier-Stokes equation. This is usually done for the purpose of achieving higher Reynolds number and longer statistics than is possible for isotropic decaying turbulence. It is generally accepted that forcing the Navier-Stokes equation at low wave number does not influence the small scale statistics of the flow provided that there is wide separation between the largest and smallest scales. It will be shown, however, that the spectral width of the forcing has a noticeable effect on inertial range statistics. A case will be made here for using a broader form of forcing in order to compare computed isotropic stationary turbulence with (decaying) grid turbulence. It is shown that using a forcing function which is directly proportional to the velocity has physical meaning and gives results which are closer to both homogeneous and non-homogeneous turbulence. Section 1 presents a four part series of motivations for linear forcing. Section 2 puts linear forcing to a numerical test with a pseudospectral computation.
Energy Science and Technology Software Center (ESTSC)
2006-11-17
Software that simulates and inverts electromagnetic field data for subsurface electrical properties (electrical conductivity) of geological media. The software treats data produced by a time harmonic source field excitation arising from the following antenna geometery: loops and grounded bipoles, as well as point electric and magnetic dioples. The inversion process is carried out using a non-linear conjugate gradient optimization scheme, which minimizes the misfit between field data and model data using a least squares criteria.more » The software is an upgrade from the code NLCGCS_MP ver 1.0. The upgrade includes the following components: Incorporation of new 1 D field sourcing routines to more accurately simulate the 3D electromagnetic field for arbitrary geologic& media, treatment for generalized finite length transmitting antenna geometry (antennas with vertical and horizontal component directions). In addition, the software has been upgraded to treat transverse anisotropy in electrical conductivity.« less
Fast Censored Linear Regression
HUANG, YIJIAN
2013-01-01
Weighted log-rank estimating function has become a standard estimation method for the censored linear regression model, or the accelerated failure time model. Well established statistically, the estimator defined as a consistent root has, however, rather poor computational properties because the estimating function is neither continuous nor, in general, monotone. We propose a computationally efficient estimator through an asymptotics-guided Newton algorithm, in which censored quantile regression methods are tailored to yield an initial consistent estimate and a consistent derivative estimate of the limiting estimating function. We also develop fast interval estimation with a new proposal for sandwich variance estimation. The proposed estimator is asymptotically equivalent to the consistent root estimator and barely distinguishable in samples of practical size. However, computation time is typically reduced by two to three orders of magnitude for point estimation alone. Illustrations with clinical applications are provided. PMID:24347802
Generalized Multilevel Structural Equation Modeling
ERIC Educational Resources Information Center
Rabe-Hesketh, Sophia; Skrondal, Anders; Pickles, Andrew
2004-01-01
A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent…
Anisotropic model-based SAR processing
NASA Astrophysics Data System (ADS)
Knight, Chad; Gunther, Jake; Moon, Todd
2013-05-01
Synthetic aperture radar (SAR) collections that integrate over a wide range of aspect angles hold the potentional for improved resolution and fosters improved scene interpretability and target detection. However, in practice it is difficult to realize the potential due to the anisotropic scattering of objects in the scene. The radar cross section (RCS) of most objects changes as a function of aspect angle. The isotropic assumption is tacitly made for most common image formation algorithms (IFA). For wide aspect scenarios one way to account for anistropy would be to employ a piecewise linear model. This paper focuses on such a model but it incorporates aspect and spatial magnitude filters in the image formation process. This is advantageous when prior knowledge is available regarding the desired targets' RCS signature spatially and in aspect. The appropriate filters can be incorporated into the image formation processing so that specific targets are emphasized while other targets are suppressed. This is demonstrated on the Air Force Research Laboratory (AFRL) GOTCHA1 data set to demonstrate the utility of the proposed approach.
Reduced-Order Model Based Feedback Control For Modified Hasegawa-Wakatani Model
Goumiri, I. R.; Rowley, C. W.; Ma, Z.; Gates, D. A.; Krommes, J. A.; Parker, J. B.
2013-01-28
In this work, the development of model-based feedback control that stabilizes an unstable equilibrium is obtained for the Modi ed Hasegawa-Wakatani (MHW) equations, a classic model in plasma turbulence. First, a balanced truncation (a model reduction technique that has proven successful in ow control design problems) is applied to obtain a low dimensional model of the linearized MHW equation. Then a modelbased feedback controller is designed for the reduced order model using linear quadratic regulators (LQR). Finally, a linear quadratic gaussian (LQG) controller, which is more resistant to disturbances is deduced. The controller is applied on the non-reduced, nonlinear MHW equations to stabilize the equilibrium and suppress the transition to drift-wave induced turbulence.
A nanoscale linear-to-linear motion converter of graphene.
Dai, Chunchun; Guo, Zhengrong; Zhang, Hongwei; Chang, Tienchong
2016-08-14
Motion conversion plays an irreplaceable role in a variety of machinery. Although many macroscopic motion converters have been widely used, it remains a challenge to convert motion at the nanoscale. Here we propose a nanoscale linear-to-linear motion converter, made of a flake-substrate system of graphene, which can convert the out-of-plane motion of the substrate into the in-plane motion of the flake. The curvature gradient induced van der Waals potential gradient between the flake and the substrate provides the driving force to achieve motion conversion. The proposed motion converter may have general implications for the design of nanomachinery and nanosensors. PMID:27335206
A nanoscale linear-to-linear motion converter of graphene
NASA Astrophysics Data System (ADS)
Dai, Chunchun; Guo, Zhengrong; Zhang, Hongwei; Chang, Tienchong
2016-07-01
Motion conversion plays an irreplaceable role in a variety of machinery. Although many macroscopic motion converters have been widely used, it remains a challenge to convert motion at the nanoscale. Here we propose a nanoscale linear-to-linear motion converter, made of a flake-substrate system of graphene, which can convert the out-of-plane motion of the substrate into the in-plane motion of the flake. The curvature gradient induced van der Waals potential gradient between the flake and the substrate provides the driving force to achieve motion conversion. The proposed motion converter may have general implications for the design of nanomachinery and nanosensors.
Distributed model-based nonlinear sensor fault diagnosis in wireless sensor networks
NASA Astrophysics Data System (ADS)
Lo, Chun; Lynch, Jerome P.; Liu, Mingyan
2016-01-01
Wireless sensors operating in harsh environments have the potential to be error-prone. This paper presents a distributive model-based diagnosis algorithm that identifies nonlinear sensor faults. The diagnosis algorithm has advantages over existing fault diagnosis methods such as centralized model-based and distributive model-free methods. An algorithm is presented for detecting common non-linearity faults without using reference sensors. The study introduces a model-based fault diagnosis framework that is implemented within a pair of wireless sensors. The detection of sensor nonlinearities is shown to be equivalent to solving the largest empty rectangle (LER) problem, given a set of features extracted from an analysis of sensor outputs. A low-complexity algorithm that gives an approximate solution to the LER problem is proposed for embedment in resource constrained wireless sensors. By solving the LER problem, sensors corrupted by non-linearity faults can be isolated and identified. Extensive analysis evaluates the performance of the proposed algorithm through simulation.
Preconditioned quantum linear system algorithm.
Clader, B D; Jacobs, B C; Sprouse, C R
2013-06-21
We describe a quantum algorithm that generalizes the quantum linear system algorithm [Harrow et al., Phys. Rev. Lett. 103, 150502 (2009)] to arbitrary problem specifications. We develop a state preparation routine that can initialize generic states, show how simple ancilla measurements can be used to calculate many quantities of interest, and integrate a quantum-compatible preconditioner that greatly expands the number of problems that can achieve exponential speedup over classical linear systems solvers. To demonstrate the algorithm's applicability, we show how it can be used to compute the electromagnetic scattering cross section of an arbitrary target exponentially faster than the best classical algorithm. PMID:23829722
Propagating uncertainties in statistical model based shape prediction
NASA Astrophysics Data System (ADS)
Syrkina, Ekaterina; Blanc, Rémi; Székely, Gàbor
2011-03-01
This paper addresses the question of accuracy assessment and confidence regions estimation in statistical model based shape prediction. Shape prediction consists in estimating the shape of an organ based on a partial observation, due e.g. to a limited field of view or poorly contrasted images, and generally requires a statistical model. However, such predictions can be impaired by several sources of uncertainty, in particular the presence of noise in the observation, limited correlations between the predictors and the shape to predict, as well as limitations of the statistical shape model - in particular the number of training samples. We propose a framework which takes these into account and derives confidence regions around the predicted shape. Our method relies on the construction of two separate statistical shape models, for the predictors and for the unseen parts, and exploits the correlations between them assuming a joint Gaussian distribution. Limitations of the models are taken into account by jointly optimizing the prediction and minimizing the shape reconstruction error through cross-validation. An application to the prediction of the shape of the proximal part of the human tibia given the shape of the distal femur is proposed, as well as the evaluation of the reliability of the estimated confidence regions, using a database of 184 samples. Potential applications are reconstructive surgery, e.g. to assess whether an implant fits in a range of acceptable shapes, or functional neurosurgery when the target's position is not directly visible and needs to be inferred from nearby visible structures.
Femtosecond molecular dynamics of tautomerization in model base pairs
NASA Astrophysics Data System (ADS)
Douhal, A.; Kim, S. K.; Zewail, A. H.
1995-11-01
HYDROGEN bonds commonly lend robustness and directionality to molecular recognition processes and supramolecular structures1,2. In particular, the two or three hydrogen bonds in Watson-Crick base pairs bind the double-stranded DNA helix and determine the complementarity of the pairing. Watson and Crick pointed out3, however, that the possible tautomers of base pairs, in which hydrogen atoms become attached to the donor atom of the hydrogen bond, might disturb the genetic code, as the tautomer is capable of pairing with different partners. But the dynamics of hydrogen bonds in general, and of this tautomerization process in particular, are not well understood. Here we report observations of the femtosecond dynamics of tautomerization in model base pairs (7-azaindole dimers) containing two hydrogen bonds. Because of the femtosecond resolution of proton motions, we are able to examine the cooperativity of formation of the tautomer (in which the protons on each base are shifted sequentially to the other base), and to determine the characteristic timescales of the motions in a solvent-free environment. We find that the first step occurs on a timescale of a few hundred femtoseconds, whereas the second step, to form the full tautomer, is much slower, taking place within several picoseconds; the timescales are changed significantly by replacing hydrogen with deuterium. These results establish the molecular basis of the dynamics and the role of quantum tunnelling.
Tyre pressure monitoring using a dynamical model-based estimator
NASA Astrophysics Data System (ADS)
Reina, Giulio; Gentile, Angelo; Messina, Arcangelo
2015-04-01
In the last few years, various control systems have been investigated in the automotive field with the aim of increasing the level of safety and stability, avoid roll-over, and customise handling characteristics. One critical issue connected with their integration is the lack of state and parameter information. As an example, vehicle handling depends to a large extent on tyre inflation pressure. When inflation pressure drops, handling and comfort performance generally deteriorate. In addition, it results in an increase in fuel consumption and in a decrease in lifetime. Therefore, it is important to keep tyres within the normal inflation pressure range. This paper introduces a model-based approach to estimate online tyre inflation pressure. First, basic vertical dynamic modelling of the vehicle is discussed. Then, a parameter estimation framework for dynamic analysis is presented. Several important vehicle parameters including tyre inflation pressure can be estimated using the estimated states. This method aims to work during normal driving using information from standard sensors only. On the one hand, the driver is informed about the inflation pressure and he is warned for sudden changes. On the other hand, accurate estimation of the vehicle states is available as possible input to onboard control systems.
Models-Based Practice: Great White Hope or White Elephant?
ERIC Educational Resources Information Center
Casey, Ashley
2014-01-01
Background: Many critical curriculum theorists in physical education have advocated a model- or models-based approach to teaching in the subject. This paper explores the literature base around models-based practice (MBP) and asks if this multi-models approach to curriculum planning has the potential to be the great white hope of pedagogical change…
Model-Based Software Testing for Object-Oriented Software
ERIC Educational Resources Information Center
Biju, Soly Mathew
2008-01-01
Model-based testing is one of the best solutions for testing object-oriented software. It has a better test coverage than other testing styles. Model-based testing takes into consideration behavioural aspects of a class, which are usually unchecked in other testing methods. An increase in the complexity of software has forced the software industry…
Learning of Chemical Equilibrium through Modelling-Based Teaching
ERIC Educational Resources Information Center
Maia, Poliana Flavia; Justi, Rosaria
2009-01-01
This paper presents and discusses students' learning process of chemical equilibrium from a modelling-based approach developed from the use of the "Model of Modelling" diagram. The investigation was conducted in a regular classroom (students 14-15 years old) and aimed at discussing how modelling-based teaching can contribute to students learning…
Model-based cartilage thickness measurement in the submillimeter range
Streekstra, G. J.; Strackee, S. D.; Maas, M.; Wee, R. ter; Venema, H. W.
2007-09-15
Current methods of image-based thickness measurement in thin sheet structures utilize second derivative zero crossings to locate the layer boundaries. It is generally acknowledged that the nonzero width of the point spread function (PSF) limits the accuracy of this measurement procedure. We propose a model-based method that strongly reduces PSF-induced bias by incorporating the PSF into the thickness estimation method. We estimated the bias in thickness measurements in simulated thin sheet images as obtained from second derivative zero crossings. To gain insight into the range of sheet thickness where our method is expected to yield improved results, sheet thickness was varied between 0.15 and 1.2 mm with an assumed PSF as present in the high-resolution modes of current computed tomography (CT) scanners [full width at half maximum (FWHM) 0.5-0.8 mm]. Our model-based method was evaluated in practice by measuring layer thickness from CT images of a phantom mimicking two parallel cartilage layers in an arthrography procedure. CT arthrography images of cadaver wrists were also evaluated, and thickness estimates were compared to those obtained from high-resolution anatomical sections that served as a reference. The thickness estimates from the simulated images reveal that the method based on second derivative zero crossings shows considerable bias for layers in the submillimeter range. This bias is negligible for sheet thickness larger than 1 mm, where the size of the sheet is more than twice the FWHM of the PSF but can be as large as 0.2 mm for a 0.5 mm sheet. The results of the phantom experiments show that the bias is effectively reduced by our method. The deviations from the true thickness, due to random fluctuations induced by quantum noise in the CT images, are of the order of 3% for a standard wrist imaging protocol. In the wrist the submillimeter thickness estimates from the CT arthrography images correspond within 10% to those estimated from the anatomical
NASA Astrophysics Data System (ADS)
Keller, Brad M.; Nathan, Diane L.; Conant, Emily F.; Kontos, Despina
2012-03-01
Breast percent density (PD%), as measured mammographically, is one of the strongest known risk factors for breast cancer. While the majority of studies to date have focused on PD% assessment from digitized film mammograms, digital mammography (DM) is becoming increasingly common, and allows for direct PD% assessment at the time of imaging. This work investigates the accuracy of a generalized linear model-based (GLM) estimation of PD% from raw and postprocessed digital mammograms, utilizing image acquisition physics, patient characteristics and gray-level intensity features of the specific image. The model is trained in a leave-one-woman-out fashion on a series of 81 cases for which bilateral, mediolateral-oblique DM images were available in both raw and post-processed format. Baseline continuous and categorical density estimates were provided by a trained breast-imaging radiologist. Regression analysis is performed and Pearson's correlation, r, and Cohen's kappa, κ, are computed. The GLM PD% estimation model performed well on both processed (r=0.89, p<0.001) and raw (r=0.75, p<0.001) images. Model agreement with radiologist assigned density categories was also high for processed (κ=0.79, p<0.001) and raw (κ=0.76, p<0.001) images. Model-based prediction of breast PD% could allow for a reproducible estimation of breast density, providing a rapid risk assessment tool for clinical practice.
Belos Block Linear Solvers Package
Energy Science and Technology Software Center (ESTSC)
2004-03-01
Belos is an extensible and interoperable framework for large-scale, iterative methods for solving systems of linear equations with multiple right-hand sides. The motivation for this framework is to provide a generic interface to a collection of algorithms for solving large-scale linear systems. Belos is interoperable because both the matrix and vectors are considered to be opaque objects--only knowledge of the matrix and vectors via elementary operations is necessary. An implementation of Balos is accomplished viamore » the use of interfaces. One of the goals of Belos is to allow the user flexibility in specifying the data representation for the matrix and vectors and so leverage any existing software investment. The algorithms that will be included in package are Krylov-based linear solvers, like Block GMRES (Generalized Minimal RESidual) and Block CG (Conjugate-Gradient).« less
A Constrained Linear Estimator for Multiple Regression
ERIC Educational Resources Information Center
Davis-Stober, Clintin P.; Dana, Jason; Budescu, David V.
2010-01-01
"Improper linear models" (see Dawes, Am. Psychol. 34:571-582, "1979"), such as equal weighting, have garnered interest as alternatives to standard regression models. We analyze the general circumstances under which these models perform well by recasting a class of "improper" linear models as "proper" statistical models with a single predictor. We…
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…
NASA Astrophysics Data System (ADS)
Rodríguez, A.; Tsallis, C.
2014-12-01
We show that the N → ∞ limiting probability distributions of a recently introduced family of d-dimensional scale-invariant probabilistic models based on Leibniz-like (d + 1)-dimensional hyperpyramids (Rodríguez and Tsallis 2012 J. Math. Phys. 53 023302) are given by Dirichlet distributions for d = 1, 2, …. It was formerly proved by Rodríguez et al that, for the one-dimensional case (d = 1), the corresponding limiting distributions are q-Gaussians (\\propto e_q- β x^2 , with e_1-β x^2=e-β x^2) . The Dirichlet distributions generalize the so-called Beta distributions to higher dimensions. Consistently, we make a connection between one-dimensional q-Gaussians and Beta distributions via a linear transformation. In addition, we discuss the probabilistically admissible region of parameters q and β defining a normalizable q-Gaussian, focusing particularly on the possibility of having both bell-shaped and U-shaped q-Gaussians, the latter corresponding, in an appropriate physical interpretation, to negative temperatures.
Applications of algebraic image operators to model-based vision
NASA Technical Reports Server (NTRS)
Lerner, Bao-Ting; Morelli, Michael V.; Thomas, Hans J.
1989-01-01
A highly structured and compact algebraic representation of grey-level images is expanded. Addition and multiplication are defined for the set of all grey-level images, which can then be described as polynomials of two variables. Utilizing this new algebraic structure, an innovative, efficient edge-detection scheme is devised. A robust method for linear feature extraction is developed by combining the techniques of a Hough transform and a line follower with this new edge detection scheme. The major advantage of this feature extractor is its general, object-independent nature. Target attributes, such as line segment lengths, intersections, angles of intersection, and endpoints are derived by the feature extraction algorithm and employed during model matching. The feature extractor and model matcher are being incorporated into a distributed robot-control system.
Deserno, L; Wilbertz, T; Reiter, A; Horstmann, A; Neumann, J; Villringer, A; Heinze, H-J; Schlagenhauf, F
2015-01-01
High impulsivity is an important risk factor for addiction with evidence from endophenotype studies. In addiction, behavioral control is shifted toward the habitual end. Habitual control can be described by retrospective updating of reward expectations in ‘model-free' temporal-difference algorithms. Goal-directed control relies on the prospective consideration of actions and their outcomes, which can be captured by forward-planning ‘model-based' algorithms. So far, no studies have examined behavioral and neural signatures of model-free and model-based control in healthy high-impulsive individuals. Fifty healthy participants were drawn from the upper and lower ends of 452 individuals, completing the Barratt Impulsiveness Scale. All participants performed a sequential decision-making task during functional magnetic resonance imaging (fMRI) and underwent structural MRI. Behavioral and fMRI data were analyzed by means of computational algorithms reflecting model-free and model-based control. Both groups did not differ regarding the balance of model-free and model-based control, but high-impulsive individuals showed a subtle but significant accentuation of model-free control alone. Right lateral prefrontal model-based signatures were reduced in high-impulsive individuals. Effects of smoking, drinking, general cognition or gray matter density did not account for the findings. Irrespectively of impulsivity, gray matter density in the left dorsolateral prefrontal cortex was positively associated with model-based control. The present study supports the idea that high levels of impulsivity are accompanied by behavioral and neural signatures in favor of model-free behavioral control. Behavioral results in healthy high-impulsive individuals were qualitatively different to findings in patients with the same task. The predictive relevance of these results remains an important target for future longitudinal studies. PMID:26460483
Deserno, L; Wilbertz, T; Reiter, A; Horstmann, A; Neumann, J; Villringer, A; Heinze, H-J; Schlagenhauf, F
2015-01-01
High impulsivity is an important risk factor for addiction with evidence from endophenotype studies. In addiction, behavioral control is shifted toward the habitual end. Habitual control can be described by retrospective updating of reward expectations in 'model-free' temporal-difference algorithms. Goal-directed control relies on the prospective consideration of actions and their outcomes, which can be captured by forward-planning 'model-based' algorithms. So far, no studies have examined behavioral and neural signatures of model-free and model-based control in healthy high-impulsive individuals. Fifty healthy participants were drawn from the upper and lower ends of 452 individuals, completing the Barratt Impulsiveness Scale. All participants performed a sequential decision-making task during functional magnetic resonance imaging (fMRI) and underwent structural MRI. Behavioral and fMRI data were analyzed by means of computational algorithms reflecting model-free and model-based control. Both groups did not differ regarding the balance of model-free and model-based control, but high-impulsive individuals showed a subtle but significant accentuation of model-free control alone. Right lateral prefrontal model-based signatures were reduced in high-impulsive individuals. Effects of smoking, drinking, general cognition or gray matter density did not account for the findings. Irrespectively of impulsivity, gray matter density in the left dorsolateral prefrontal cortex was positively associated with model-based control. The present study supports the idea that high levels of impulsivity are accompanied by behavioral and neural signatures in favor of model-free behavioral control. Behavioral results in healthy high-impulsive individuals were qualitatively different to findings in patients with the same task. The predictive relevance of these results remains an important target for future longitudinal studies. PMID:26460483
NASA Technical Reports Server (NTRS)
Tuey, R. C.
1972-01-01
Computer solutions of linear programming problems are outlined. Information covers vector spaces, convex sets, and matrix algebra elements for solving simultaneous linear equations. Dual problems, reduced cost analysis, ranges, and error analysis are illustrated.
NASA Astrophysics Data System (ADS)
Young, T.
This book is intended to be used as a textbook in a one-semester course at a variety of levels. Because of self-study features incorporated, it may also be used by practicing electronic engineers as a formal and thorough introduction to the subject. The distinction between linear and digital integrated circuits is discussed, taking into account digital and linear signal characteristics, linear and digital integrated circuit characteristics, the definitions for linear and digital circuits, applications of digital and linear integrated circuits, aspects of fabrication, packaging, and classification and numbering. Operational amplifiers are considered along with linear integrated circuit (LIC) power requirements and power supplies, voltage and current regulators, linear amplifiers, linear integrated circuit oscillators, wave-shaping circuits, active filters, DA and AD converters, demodulators, comparators, instrument amplifiers, current difference amplifiers, analog circuits and devices, and aspects of troubleshooting.
Model-Based Reasoning in Humans Becomes Automatic with Training
Lübbert, Annika; Guitart-Masip, Marc; Dolan, Raymond J.
2015-01-01
Model-based and model-free reinforcement learning (RL) have been suggested as algorithmic realizations of goal-directed and habitual action strategies. Model-based RL is more flexible than model-free but requires sophisticated calculations using a learnt model of the world. This has led model-based RL to be identified with slow, deliberative processing, and model-free RL with fast, automatic processing. In support of this distinction, it has recently been shown that model-based reasoning is impaired by placing subjects under cognitive load—a hallmark of non-automaticity. Here, using the same task, we show that cognitive load does not impair model-based reasoning if subjects receive prior training on the task. This finding is replicated across two studies and a variety of analysis methods. Thus, task familiarity permits use of model-based reasoning in parallel with other cognitive demands. The ability to deploy model-based reasoning in an automatic, parallelizable fashion has widespread theoretical implications, particularly for the learning and execution of complex behaviors. It also suggests a range of important failure modes in psychiatric disorders. PMID:26379239
NASA Technical Reports Server (NTRS)
Lawson, C. L.; Krogh, F. T.; Gold, S. S.; Kincaid, D. R.; Sullivan, J.; Williams, E.; Hanson, R. J.; Haskell, K.; Dongarra, J.; Moler, C. B.
1982-01-01
The Basic Linear Algebra Subprograms (BLAS) library is a collection of 38 FORTRAN-callable routines for performing basic operations of numerical linear algebra. BLAS library is portable and efficient source of basic operations for designers of programs involving linear algebriac computations. BLAS library is supplied in portable FORTRAN and Assembler code versions for IBM 370, UNIVAC 1100 and CDC 6000 series computers.
Model-Based, Closed-Loop Control of PZT Creep for Cavity Ring-Down Spectroscopy
McCartt, A D; Ognibene, T J; Bench, G; Turteltaub, K W
2014-01-01
Cavity ring-down spectrometers typically employ a PZT stack to modulate the cavity transmission spectrum. While PZTs ease instrument complexity and aid measurement sensitivity, PZT hysteresis hinders the implementation of cavity-length-stabilized, data-acquisition routines. Once the cavity length is stabilized, the cavity’s free spectral range imparts extreme linearity and precision to the measured spectrum’s wavelength axis. Methods such as frequency-stabilized cavity ring-down spectroscopy have successfully mitigated PZT hysteresis, but their complexity limits commercial applications. Described herein is a single-laser, model-based, closed-loop method for cavity length control. PMID:25395738
On Solving Non-Autonomous Linear Difference Equations with Applications
ERIC Educational Resources Information Center
Abu-Saris, Raghib M.
2006-01-01
An explicit formula is established for the general solution of the homogeneous non-autonomous linear difference equation. The formula developed is then used to characterize globally periodic linear difference equations with constant coefficients.
Distributed real-time model-based diagnosis
NASA Technical Reports Server (NTRS)
Barrett, A. C.; Chung, S. H.
2003-01-01
This paper presents an approach to onboard anomaly diagnosis that combines the simplicity and real-time guarantee of a rule-based diagnosis system with the specification ease and coverage guarantees of a model-based diagnosis system.
Research on infrared imaging illumination model based on materials
NASA Astrophysics Data System (ADS)
Hu, Hai-he; Feng, Chao-yin; Guo, Chang-geng; Zheng, Hai-jing; Han, Qiang; Hu, Hai-yan
2013-09-01
In order to effectively simulate infrared features of the scene and infrared high light phenomenon, Based on the visual light illumination model, according to the optical property of all material types in the scene, the infrared imaging illumination models are proposed to fulfill different materials: to the smooth material with specular characteristic, adopting the infrared imaging illumination model based on Blinn-Phone reflection model and introducing the self emission; to the ordinary material which is similar to black body without highlight feature, ignoring the computation of its high light reflection feature, calculating simply the material's self emission and its reflection to the surrounding as its infrared imaging illumination model, the radiation energy under zero range of visibility can be obtained according to the above two models. The OpenGl rendering technology is used to construct infrared scene simulation system which can also simulate infrared electro-optical imaging system, then gets the synthetic infrared images from any angle of view of the 3D scenes. To validate the infrared imaging illumination model, two typical 3D scenes are made, and their infrared images are calculated to compare and contrast with the real collected infrared images obtained by a long wave infrared band imaging camera. There are two major points in the paper according to the experiment results: firstly, the infrared imaging illumination models are capable of producing infrared images which are very similar to those received by thermal infrared camera; secondly, the infrared imaging illumination models can simulate the infrared specular feature of relative materials and common infrared features of general materials, which shows the validation of the infrared imaging illumination models. Quantitative analysis shows that the simulation images are similar to the collected images in the aspects of main features, but their histogram distribution does not match very well, the
Rasch model based analysis of the Force Concept Inventory
NASA Astrophysics Data System (ADS)
Planinic, Maja; Ivanjek, Lana; Susac, Ana
2010-06-01
The Force Concept Inventory (FCI) is an important diagnostic instrument which is widely used in the field of physics education research. It is therefore very important to evaluate and monitor its functioning using different tools for statistical analysis. One of such tools is the stochastic Rasch model, which enables construction of linear measures for persons and items from raw test scores and which can provide important insight in the structure and functioning of the test (how item difficulties are distributed within the test, how well the items fit the model, and how well the items work together to define the underlying construct). The data for the Rasch analysis come from the large-scale research conducted in 2006-07, which investigated Croatian high school students’ conceptual understanding of mechanics on a representative sample of 1676 students (age 17-18 years). The instrument used in research was the FCI. The average FCI score for the whole sample was found to be (27.7±0.4)% , indicating that most of the students were still non-Newtonians at the end of high school, despite the fact that physics is a compulsory subject in Croatian schools. The large set of obtained data was analyzed with the Rasch measurement computer software WINSTEPS 3.66. Since the FCI is routinely used as pretest and post-test on two very different types of population (non-Newtonian and predominantly Newtonian), an additional predominantly Newtonian sample ( N=141 , average FCI score of 64.5%) of first year students enrolled in introductory physics course at University of Zagreb was also analyzed. The Rasch model based analysis suggests that the FCI has succeeded in defining a sufficiently unidimensional construct for each population. The analysis of fit of data to the model found no grossly misfitting items which would degrade measurement. Some items with larger misfit and items with significantly different difficulties in the two samples of students do require further examination
Validation of model based active control of combustion instability
Fleifil, M.; Ghoneim, Z.; Ghoniem, A.F.
1998-07-01
The demand for efficient, company and clean combustion systems have spurred research into the fundamental mechanisms governing their performance and means of interactively changing their performance characteristics. Thermoacoustic instability which is frequently observed in combustion systems with high power density, when burning close to the lean flammability limit, or using exhaust gas recirculation to meet more stringent emissions regulations, etc. Its occurrence and/or means to mitigate them passively lead to performance degradation such as reduced combustion efficiency, high local heat transfer rates, increase in the mixture equivalence ratio or system failure due to structural damage. This paper reports on their study of the origin of thermoacoustic instability, its dependence on system parameters and the means of actively controlling it. The authors have developed an analytical model of thermoacoustic instability in premixed combustors. The model combines a heat release dynamics model constructed using the kinematics of a premixed flame stabilized behind a perforated plate with the linearized conservation equations governing the system acoustics. This formulation allows model based controller design. In order to test the performance of the analytical model, a numerical solution of the partial differential equations governing the system has been carried out using the principle of harmonic separation and focusing on the dominant unstable mode. This leads to a system of ODEs governing the thermofluid variables. Analytical predictions of the frequency and growth ate of the unstable mode are shown to be in good agreement with the numerical simulations as well s with those obtained using experimental identification techniques when applied to a laboratory combustor. The authors use these results to confirm the validity of the assumptions used in formulating the analytical model. A controller based on the minimization of a cost function using the LQR technique has
Cognitive Control Predicts Use of Model-Based Reinforcement-Learning
Otto, A. Ross; Skatova, Anya; Madlon-Kay, Seth; Daw, Nathaniel D.
2015-01-01
Accounts of decision-making and its neural substrates have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental work suggest that this classic distinction between behaviorally and neurally dissociable systems for habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning (RL), called model-free and model-based RL, but the cognitive or computational processes by which one system may dominate over the other in the control of behavior is a matter of ongoing investigation. To elucidate this question, we leverage the theoretical framework of cognitive control, demonstrating that individual differences in utilization of goal-related contextual information—in the service of overcoming habitual, stimulus-driven responses—in established cognitive control paradigms predict model-based behavior in a separate, sequential choice task. The behavioral correspondence between cognitive control and model-based RL compellingly suggests that a common set of processes may underpin the two behaviors. In particular, computational mechanisms originally proposed to underlie controlled behavior may be applicable to understanding the interactions between model-based and model-free choice behavior. PMID:25170791
Reduced model-based decision-making in schizophrenia.
Culbreth, Adam J; Westbrook, Andrew; Daw, Nathaniel D; Botvinick, Matthew; Barch, Deanna M
2016-08-01
Individuals with schizophrenia have a diminished ability to use reward history to adaptively guide behavior. However, tasks traditionally used to assess such deficits often rely on multiple cognitive and neural processes, leaving etiology unresolved. In the current study, we adopted recent computational formalisms of reinforcement learning to distinguish between model-based and model-free decision-making in hopes of specifying mechanisms associated with reinforcement-learning dysfunction in schizophrenia. Under this framework, decision-making is model-free to the extent that it relies solely on prior reward history, and model-based if it relies on prospective information such as motivational state, future consequences, and the likelihood of obtaining various outcomes. Model-based and model-free decision-making was assessed in 33 schizophrenia patients and 30 controls using a 2-stage 2-alternative forced choice task previously demonstrated to discern individual differences in reliance on the 2 forms of reinforcement-learning. We show that, compared with controls, schizophrenia patients demonstrate decreased reliance on model-based decision-making. Further, parameter estimates of model-based behavior correlate positively with IQ and working memory measures, suggesting that model-based deficits seen in schizophrenia may be partially explained by higher-order cognitive deficits. These findings demonstrate specific reinforcement-learning and decision-making deficits and thereby provide valuable insights for understanding disordered behavior in schizophrenia. (PsycINFO Database Record PMID:27175984
Optimising a model-based approach to inferring fear learning from skin conductance responses
Staib, Matthias; Castegnetti, Giuseppe; Bach, Dominik R.
2015-01-01
Anticipatory sympathetic arousal is often inferred from skin conductance responses (SCR) and used to quantify fear learning. We have previously provided a model-based approach for this inference, based on a quantitative Psychophysiological Model (PsPM) formulated in non-linear dynamic equations. Here we seek to optimise the inversion of this PsPM. Using two independent fear conditioning datasets, we benchmark predictive validity as the sensitivity to separate the likely presence or absence of the unconditioned stimulus. Predictive validity is optimised across both datasets by (a) using a canonical form of the SCR shape (b) filtering the signal with a bi-directional band-pass filter with cut off frequencies 0.0159 and 5 Hz, (c) simultaneously inverting two trials (d) explicitly modelling skin conductance level changes between trials (e) the choice of the inversion algorithm (f) z-scoring estimates of anticipatory sympathetic arousal from each participant across trials. The original model-based method has higher predictive validity than conventional peak-scoring or an alternative model-based method (Ledalab), and benefits from constraining the model, optimised data preconditioning, and post-processing of ensuing parameters. PMID:26291885
Thyagarajan, T.; Ponnavaikko, M.; Shanmugam, J.; Panda, R.C.; Rao, P.G.
1998-07-01
This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with up-to-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN. 118 refs.
Optimising a model-based approach to inferring fear learning from skin conductance responses.
Staib, Matthias; Castegnetti, Giuseppe; Bach, Dominik R
2015-11-30
Anticipatory sympathetic arousal is often inferred from skin conductance responses (SCR) and used to quantify fear learning. We have previously provided a model-based approach for this inference, based on a quantitative Psychophysiological Model (PsPM) formulated in non-linear dynamic equations. Here we seek to optimise the inversion of this PsPM. Using two independent fear conditioning datasets, we benchmark predictive validity as the sensitivity to separate the likely presence or absence of the unconditioned stimulus. Predictive validity is optimised across both datasets by (a) using a canonical form of the SCR shape (b) filtering the signal with a bi-directional band-pass filter with cut off frequencies 0.0159 and 5 Hz, (c) simultaneously inverting two trials (d) explicitly modelling skin conductance level changes between trials (e) the choice of the inversion algorithm (f) z-scoring estimates of anticipatory sympathetic arousal from each participant across trials. The original model-based method has higher predictive validity than conventional peak-scoring or an alternative model-based method (Ledalab), and benefits from constraining the model, optimised data preconditioning, and post-processing of ensuing parameters. PMID:26291885
A fuzzy case based reasoning tool for model based approach to rocket engine health monitoring
NASA Technical Reports Server (NTRS)
Krovvidy, Srinivas; Nolan, Adam; Hu, Yong-Lin; Wee, William G.
1992-01-01
In this system we develop a fuzzy case based reasoner that can build a case representation for several past anomalies detected, and we develop case retrieval methods that can be used to index a relevant case when a new problem (case) is presented using fuzzy sets. The choice of fuzzy sets is justified by the uncertain data. The new problem can be solved using knowledge of the model along with the old cases. This system can then be used to generalize the knowledge from previous cases and use this generalization to refine the existing model definition. This in turn can help to detect failures using the model based algorithms.
Model-based HSF using by target point control function
NASA Astrophysics Data System (ADS)
Kim, Seongjin; Do, Munhoe; An, Yongbae; Choi, Jaeseung; Yang, Hyunjo; Yim, Donggyu
2015-03-01
As the technology node shrinks, ArF Immersion reaches the limitation of wafer patterning, furthermore weak point during the mask processing is generated easily. In order to make strong patterning result, the design house conducts lithography rule checking (LRC). Despite LRC processing, we found the weak point at the verification stage of optical proximity correction (OPC). It is called the hot spot point (HSP). In order to fix the HSP, many studies have been performed. One of the most general hot spot fixing (HSF) methods is that the modification bias which consists of "Line-Resizing" and "Space-Resizing". In addition to the general rule biasing method, resolution enhancement techniques (RET) which includes the inverse lithography technology (ILT) and model based assist feature (MBAF) have been adapted to remove the hot spot and to maximize the process window. If HSP is found during OPC verification stage, various HSF methods can be applied. However, HSF process added on regular OPC procedure makes OPC turn-around time (TAT) increased. In this paper, we introduce a new HSF method that is able to make OPC TAT shorter than the common HSF method. The new HSF method consists of two concepts. The first one is that OPC target point is controlled to fix HSP. Here, the target point should be moved to optimum position at where the edge placement error (EPE) can be 0 at critical points. Many parameters such as a model accuracy or an OPC recipe become the cause of larger EPE. The second one includes controlling of model offset error through target point adjustment. Figure 1 shows the case EPE is not 0. It means that the simulation contour was not targeted well after OPC process. On the other hand, Figure 2 shows the target point is moved -2.5nm by using target point control function. As a result, simulation contour is matched to the original layout. This function can be powerfully adapted to OPC procedure of memory and logic devices.
Wiedemann, H.
1981-11-01
Since no linear colliders have been built yet it is difficult to know at what energy the linear cost scaling of linear colliders drops below the quadratic scaling of storage rings. There is, however, no doubt that a linear collider facility for a center of mass energy above say 500 GeV is significantly cheaper than an equivalent storage ring. In order to make the linear collider principle feasible at very high energies a number of problems have to be solved. There are two kinds of problems: one which is related to the feasibility of the principle and the other kind of problems is associated with minimizing the cost of constructing and operating such a facility. This lecture series describes the problems and possible solutions. Since the real test of a principle requires the construction of a prototype I will in the last chapter describe the SLC project at the Stanford Linear Accelerator Center.
Multi-site validation of a soil organic matter model based on generally available input data
NASA Astrophysics Data System (ADS)
Franko, Uwe; Kolbe, Hartmut; Thiel, Enrico; Ließ, Ekkehard
2010-05-01
Regionalization of soil organic matter (SOM) dynamics by means of modelling is often restricted by the availability of model inputs on the used scale. Taking this into account the approach of the CANDY model (Franko et al., 1995) has been strongly simplified with a remarkable reduction of input requirements. This simplified model called Candy Carbon balance (CCB) was validated against a large number of long term datasets from arable field experiments mainly situated in Germany but also from some places in Europe altogether containing 41 sites with 65 experiments and a total number of 598 different treatments. The required input data are: - soil: clay content, soil classification - climate: rainfall and air temperature as long term averages - management: crop rotation with yields and organic amendments - SOM: at least one initial value; more observations for validation purpose During the calculation process the model makes use of some other soil parameters like silt content, bulk density, pore volume, field capacity and permanent wilting point. These data may be delivered by the user or if absent they will be calculated from pedotransfer functions. If the initial information includes not only organic Carbon (Corg) but also total Nitrogen (Nt) the SOM dynamics will be modelled for both elements. The CCB model follows the CANDY concept distributing SOM in a long term stabilized and a decomposable fraction. The long term stabilized part is calculated comparable to the CIPS model (Kuka,2005) taking the SOM in soil micro pores as highly stabilized so that its turnover can be neglected for this model application and hence will show no dynamics during simulation. Soil texture, rainfall, irrigation amount and air temperature are used to estimate the site specific biologic activity for SOM turnover in terms of Biological Active Time (BAT). Annual yield data are transformed to the amounts of organic matter input due to cropping that together with organic amendments are the sources of SOM reproduction. Model results for Corg and Nt have been validated against observed values. The overall result from a model application under practice related conditions shows an acceptable performance of the model. For Corg the mean difference between model and observation is MED=-0.010 and the root mean squared deviation is RMSD=0.158. In case of Nt the results are MED=-0.004 and RMSD=0.012. Basing on this results the CCB model is considered as applicable in advisory service for arable fields on a wide range of site conditions.
An estimation of generalized bradley-terry models based on the em algorithm.
Fujimoto, Yu; Hino, Hideitsu; Murata, Noboru
2011-06-01
The Bradley-Terry model is a statistical representation for one's preference or ranking data by using pairwise comparison results of items. For estimation of the model, several methods based on the sum of weighted Kullback-Leibler divergences have been proposed from various contexts. The purpose of this letter is to interpret an estimation mechanism of the Bradley-Terry model from the viewpoint of flatness, a fundamental notion used in information geometry. Based on this point of view, a new estimation method is proposed on a framework of the em algorithm. The proposed method is different in its objective function from that of conventional methods, especially in treating unobserved comparisons, and it is consistently interpreted in a probability simplex. An estimation method with weight adaptation is also proposed from a viewpoint of the sensitivity. Experimental results show that the proposed method works appropriately, and weight adaptation improves accuracy of the estimate. PMID:21395441
Reduced-order Model Based Feedback Control of Cavity Flows -- Changes in the Flow Characteristics
NASA Astrophysics Data System (ADS)
Samimy, Mo; Little, Jesse; Debiasi, Marco; Caraballo, Edgar; Serrani, Andrea; Yuan, Xin
2006-11-01
We have developed and experimentally implemented reduced-order model based feedback control of subsonic cavity flows. Reduced-order models were developed via Proper Orthogonal Decomposition (POD) using particle imaging velocimetry (PIV) in conjunction with the Galerkin projection of the governing Navier-Stokes equations onto the resulting spatial eigenfunctions. The stochastic estimation technique using simultaneous PIV and surface pressure measurements was used to establish correlation between the flow field and surface pressure. For the implementation of the controller, dynamic surface pressure measurements were used for the estimation of the POD modal coefficients. The reduced-order model was linearized around the equilibrium point and a linear-quadratic optimal controller was designed and implemented in the experiments. The actuator was a compression driver type and its output was channeled through a one millimeter slit spanning the entire width of the cavity leading edge. Models based on one or more flow conditions for a Mach 0.3 cavity flow were developed and used, which suppressed the cavity resonant modes. We will compare and contrast the flow characteristics for the open- and closed-loop controlled flows.
Otten, Alexander; van Vuuren, Wieke; Stienen, Arno; van Asseldonk, Edwin; Schouten, Alfred; van der Kooij, Herman
2011-01-01
Robotics used for diagnostic measurements on, e.g. stroke survivors, require actuators that are both stiff and compliant. Stiffness is required for identification purposes, and compliance to compensate for the robots dynamics, so that the subject can move freely while using the robot. A hydraulic actuator can act as a position (stiff) or a torque (compliant) actuator. The drawback of a hydraulic actuator is that it behaves nonlinear. This article examines two methods for controlling a nonlinear hydraulic actuator. The first method that is often applied uses an elastic element (i.e. spring) connected in series with the hydraulic actuator so that the torque can be measured as the deflection of the spring. This torque measurement is used for proportional integral control. The second method of control uses the inverse of the model of the actuator as a linearizing controller. Both methods are compared using simulation results. The controller designed for the series elastic hydraulic actuator is faster to implement, but only shows good performance for the working range for which the controller is designed due to the systems nonlinear behavior. The elastic element is a limiting factor when designing a position controller due to its low torsional stiffness. The model-based controller linearizes the nonlinear system and shows good performance when used for torque and position control. Implementing the model-based controller does require building and validating of the detailed model. PMID:22275654
Linear phase compressive filter
McEwan, T.E.
1995-06-06
A phase linear filter for soliton suppression is in the form of a laddered series of stages of non-commensurate low pass filters with each low pass filter having a series coupled inductance (L) and a reverse biased, voltage dependent varactor diode, to ground which acts as a variable capacitance (C). L and C values are set to levels which correspond to a linear or conventional phase linear filter. Inductance is mapped directly from that of an equivalent nonlinear transmission line and capacitance is mapped from the linear case using a large signal equivalent of a nonlinear transmission line. 2 figs.
Linear phase compressive filter
McEwan, Thomas E.
1995-01-01
A phase linear filter for soliton suppression is in the form of a laddered series of stages of non-commensurate low pass filters with each low pass filter having a series coupled inductance (L) and a reverse biased, voltage dependent varactor diode, to ground which acts as a variable capacitance (C). L and C values are set to levels which correspond to a linear or conventional phase linear filter. Inductance is mapped directly from that of an equivalent nonlinear transmission line and capacitance is mapped from the linear case using a large signal equivalent of a nonlinear transmission line.
Fault tolerant linear actuator
Tesar, Delbert
2004-09-14
In varying embodiments, the fault tolerant linear actuator of the present invention is a new and improved linear actuator with fault tolerance and positional control that may incorporate velocity summing, force summing, or a combination of the two. In one embodiment, the invention offers a velocity summing arrangement with a differential gear between two prime movers driving a cage, which then drives a linear spindle screw transmission. Other embodiments feature two prime movers driving separate linear spindle screw transmissions, one internal and one external, in a totally concentric and compact integrated module.
Particle Filtering for Model-Based Anomaly Detection in Sensor Networks
NASA Technical Reports Server (NTRS)
Solano, Wanda; Banerjee, Bikramjit; Kraemer, Landon
2012-01-01
A novel technique has been developed for anomaly detection of rocket engine test stand (RETS) data. The objective was to develop a system that postprocesses a csv file containing the sensor readings and activities (time-series) from a rocket engine test, and detects any anomalies that might have occurred during the test. The output consists of the names of the sensors that show anomalous behavior, and the start and end time of each anomaly. In order to reduce the involvement of domain experts significantly, several data-driven approaches have been proposed where models are automatically acquired from the data, thus bypassing the cost and effort of building system models. Many supervised learning methods can efficiently learn operational and fault models, given large amounts of both nominal and fault data. However, for domains such as RETS data, the amount of anomalous data that is actually available is relatively small, making most supervised learning methods rather ineffective, and in general met with limited success in anomaly detection. The fundamental problem with existing approaches is that they assume that the data are iid, i.e., independent and identically distributed, which is violated in typical RETS data. None of these techniques naturally exploit the temporal information inherent in time series data from the sensor networks. There are correlations among the sensor readings, not only at the same time, but also across time. However, these approaches have not explicitly identified and exploited such correlations. Given these limitations of model-free methods, there has been renewed interest in model-based methods, specifically graphical methods that explicitly reason temporally. The Gaussian Mixture Model (GMM) in a Linear Dynamic System approach assumes that the multi-dimensional test data is a mixture of multi-variate Gaussians, and fits a given number of Gaussian clusters with the help of the wellknown Expectation Maximization (EM) algorithm. The
Model-Based Engineering and Manufacturing CAD/CAM Benchmark
Domm, T.D.; Underwood, R.S.
1999-04-26
The Benehmark Project was created from a desire to identify best practices and improve the overall efficiency and performance of the Y-12 Plant's systems and personnel supprting the manufacturing mission. The mission of the benchmark team was to search out industry leaders in manufacturing and evaluate lheir engineering practices and processes to determine direction and focus fm Y-12 modmizadon efforts. The companies visited included several large established companies and anew, small, high-tech machining firm. As a result of this efforL changes are recommended that will enable Y-12 to become a more responsive cost-effective manufacturing facility capable of suppordng the needs of the Nuclear Weapons Complex (NW@) and Work Fw Others into the 21' century. The benchmark team identified key areas of interest, both focused and gencml. The focus arm included Human Resources, Information Management, Manufacturing Software Tools, and Standarda/ Policies and Practices. Areas of general interest included Inhstructure, Computer Platforms and Networking, and Organizational Structure. The method for obtaining the desired information in these areas centered on the creation of a benchmark questionnaire. The questionnaire was used throughout each of the visits as the basis for information gathering. The results of this benchmark showed that all companies are moving in the direction of model-based engineering and manufacturing. There was evidence that many companies are trying to grasp how to manage current and legacy data. In terms of engineering design software tools, the companies contacted were using both 3-D solid modeling and surfaced Wire-frame models. The manufacturing computer tools were varie4 with most companies using more than one software product to generate machining data and none currently performing model-based manufacturing (MBM) ftom a common medel. The majority of companies were closer to identifying or using a single computer-aided design (CAD) system than a
Model-Based Engine Control Architecture with an Extended Kalman Filter
NASA Technical Reports Server (NTRS)
Csank, Jeffrey T.; Connolly, Joseph W.
2016-01-01
This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The non-linear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.
Passive localization in ocean acoustics: A model-based approach
Candy, J.V.; Sullivan, E.J.
1995-09-01
A model-based approach is developed to solve the passive localization problem in ocean acoustics using the state-space formulation for the first time. It is shown that the inherent structure of the resulting processor consists of a parameter estimator coupled to a nonlinear optimization scheme. The parameter estimator is designed using the model-based approach in which an ocean acoustic propagation model is used in developing the model-based processor required for localization. Recall that model-based signal processing is a well-defined methodology enabling the inclusion of environmental (propagation) models, measurement (sensor arrays) models, and noise (shipping, measurement) models into a sophisticated processing algorithm. Here the parameter estimator is designed, or more appropriately the model-based identifier (MBID) for a propagation model developed from a shallow water ocean experiment. After simulation, it is then applied to a set of experimental data demonstrating the applicability of this approach. {copyright} {ital 1995} {ital Acoustical} {ital Society} {ital of} {ital America}.
Using Model-Based Reasoning for Autonomous Instrument Operation - Lessons Learned From IMAGE/LENA
NASA Technical Reports Server (NTRS)
Johnson, Michael A.; Rilee, Michael L.; Truszkowski, Walt; Bailin, Sidney C.
2001-01-01
Model-based reasoning has been applied as an autonomous control strategy on the Low Energy Neutral Atom (LENA) instrument currently flying on board the Imager for Magnetosphere-to-Aurora Global Exploration (IMAGE) spacecraft. Explicit models of instrument subsystem responses have been constructed and are used to dynamically adapt the instrument to the spacecraft's environment. These functions are cast as part of a Virtual Principal Investigator (VPI) that autonomously monitors and controls the instrument. In the VPI's current implementation, LENA's command uplink volume has been decreased significantly from its previous volume; typically, no uplinks are required for operations. This work demonstrates that a model-based approach can be used to enhance science instrument effectiveness. The components of LENA are common in space science instrumentation, and lessons learned by modeling this system may be applied to other instruments. Future work involves the extension of these methods to cover more aspects of LENA operation and the generalization to other space science instrumentation.
Powerful Electromechanical Linear Actuator
NASA Technical Reports Server (NTRS)
Cowan, John R.; Myers, William N.
1994-01-01
Powerful electromechanical linear actuator designed to replace hydraulic actuator that provides incremental linear movements to large object and holds its position against heavy loads. Electromechanical actuator cleaner and simpler, and needs less maintenance. Two principal innovative features that distinguish new actuator are use of shaft-angle resolver as source of position feedback to electronic control subsystem and antibacklash gearing arrangement.
Richter, B.
1985-12-01
A report is given on the goals and progress of the SLAC Linear Collider. The status of the machine and the detectors are discussed and an overview is given of the physics which can be done at this new facility. Some ideas on how (and why) large linear colliders of the future should be built are given.
Linear Equations: Equivalence = Success
ERIC Educational Resources Information Center
Baratta, Wendy
2011-01-01
The ability to solve linear equations sets students up for success in many areas of mathematics and other disciplines requiring formula manipulations. There are many reasons why solving linear equations is a challenging skill for students to master. One major barrier for students is the inability to interpret the equals sign as anything other than…
Linearly polarized fiber amplifier
Kliner, Dahv A.; Koplow, Jeffery P.
2004-11-30
Optically pumped rare-earth-doped polarizing fibers exhibit significantly higher gain for one linear polarization state than for the orthogonal state. Such a fiber can be used to construct a single-polarization fiber laser, amplifier, or amplified-spontaneous-emission (ASE) source without the need for additional optical components to obtain stable, linearly polarized operation.
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.
Linear regression in astronomy. II
NASA Technical Reports Server (NTRS)
Feigelson, Eric D.; Babu, Gutti J.
1992-01-01
A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.
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]...
NASA Technical Reports Server (NTRS)
Clancy, John P.
1988-01-01
The object of the invention is to provide a mechanical force actuator which is lightweight and manipulatable and utilizes linear motion for push or pull forces while maintaining a constant overall length. The mechanical force producing mechanism comprises a linear actuator mechanism and a linear motion shaft mounted parallel to one another. The linear motion shaft is connected to a stationary or fixed housing and to a movable housing where the movable housing is mechanically actuated through actuator mechanism by either manual means or motor means. The housings are adapted to releasably receive a variety of jaw or pulling elements adapted for clamping or prying action. The stationary housing is adapted to be pivotally mounted to permit an angular position of the housing to allow the tool to adapt to skewed interfaces. The actuator mechanisms is operated by a gear train to obtain linear motion of the actuator mechanism.
Model Based Mission Assurance: Emerging Opportunities for Robotic Systems
NASA Technical Reports Server (NTRS)
Evans, John W.; DiVenti, Tony
2016-01-01
The emergence of Model Based Systems Engineering (MBSE) in a Model Based Engineering framework has created new opportunities to improve effectiveness and efficiencies across the assurance functions. The MBSE environment supports not only system architecture development, but provides for support of Systems Safety, Reliability and Risk Analysis concurrently in the same framework. Linking to detailed design will further improve assurance capabilities to support failures avoidance and mitigation in flight systems. This also is leading new assurance functions including model assurance and management of uncertainty in the modeling environment. Further, the assurance cases, a structured hierarchal argument or model, are emerging as a basis for supporting a comprehensive viewpoint in which to support Model Based Mission Assurance (MBMA).
A Navigational Analysis of Linear and Non-Linear Hypermedia Interfaces.
ERIC Educational Resources Information Center
Hall, Richard H.; Balestra, Joel; Davis, Miles
The purpose of this experiment was to assess the effectiveness of a comprehensive model for the analysis of hypermap navigation patterns through a comparison of navigation patterns associated with a traditional linear interface versus a non-linear "hypermap" interface. Twenty-six general psychology university students studied material on bipolar…
When Does Model-Based Control Pay Off?
Kool, Wouter; Cushman, Fiery A; Gershman, Samuel J
2016-08-01
Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to "model-free" and "model-based" strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding. It is assumed that this trade-off between accuracy and computational demand plays an important role in the arbitration between the two strategies, but we show that the hallmark task for dissociating model-free and model-based strategies, as well as several related variants, do not embody such a trade-off. We describe five factors that reduce the effectiveness of the model-based strategy on these tasks by reducing its accuracy in estimating reward outcomes and decreasing the importance of its choices. Based on these observations, we describe a version of the task that formally and empirically obtains an accuracy-demand trade-off between model-free and model-based strategies. Moreover, we show that human participants spontaneously increase their reliance on model-based control on this task, compared to the original paradigm. Our novel task and our computational analyses may prove important in subsequent empirical investigations of how humans balance accuracy and demand. PMID:27564094
An approach to accidents modeling based on compounds road environments.
Fernandes, Ana; Neves, Jose
2013-04-01
The most common approach to study the influence of certain road features on accidents has been the consideration of uniform road segments characterized by a unique feature. However, when an accident is related to the road infrastructure, its cause is usually not a single characteristic but rather a complex combination of several characteristics. The main objective of this paper is to describe a methodology developed in order to consider the road as a complete environment by using compound road environments, overcoming the limitations inherented in considering only uniform road segments. The methodology consists of: dividing a sample of roads into segments; grouping them into quite homogeneous road environments using cluster analysis; and identifying the influence of skid resistance and texture depth on road accidents in each environment by using generalized linear models. The application of this methodology is demonstrated for eight roads. Based on real data from accidents and road characteristics, three compound road environments were established where the pavement surface properties significantly influence the occurrence of accidents. Results have showed clearly that road environments where braking maneuvers are more common or those with small radii of curvature and high speeds require higher skid resistance and texture depth as an important contribution to the accident prevention. PMID:23376544
Applications Of Algebraic Image Operators To Model-Based Vision
NASA Astrophysics Data System (ADS)
Lerner, Bao-Ting; Morelli, Michael V.; Thomas, Hans J.
1989-03-01
This paper extends our previous research on a highly structured and compact algebraic representation of grey-level images. Addition and multiplication are defined for the set of all grey-level images, which can then be described as polynomials of two variables. Utilizing this new algebraic structure, we have devised an innovative, efficient edge detection scheme.We have developed a robust method for linear feature extraction by combining the techniques of a Hough transform and a line follower with this new edge detection scheme. The major advantage of this feature extractor is its general, object-independent nature. Target attributes, such as line segment lengths, intersections, angles of intersection, and endpoints are derived by the feature extraction algorithm and employed during model matching. The feature extractor and model matcher are being incorporated into a distributed robot control system. Model matching is accomplished using both top-down and bottom-up processing: a priori sensor and world model information are used to constrain the search of the image space for features, while extracted image information is used to update the model.
Model Based Analysis and Test Generation for Flight Software
NASA Technical Reports Server (NTRS)
Pasareanu, Corina S.; Schumann, Johann M.; Mehlitz, Peter C.; Lowry, Mike R.; Karsai, Gabor; Nine, Harmon; Neema, Sandeep
2009-01-01
We describe a framework for model-based analysis and test case generation in the context of a heterogeneous model-based development paradigm that uses and combines Math- Works and UML 2.0 models and the associated code generation tools. This paradigm poses novel challenges to analysis and test case generation that, to the best of our knowledge, have not been addressed before. The framework is based on a common intermediate representation for different modeling formalisms and leverages and extends model checking and symbolic execution tools for model analysis and test case generation, respectively. We discuss the application of our framework to software models for a NASA flight mission.
Verification and Validation of Model-Based Autonomous Systems
NASA Technical Reports Server (NTRS)
Pecheur, Charles; Koga, Dennis (Technical Monitor)
2001-01-01
This paper presents a three year project (FY99 to FY01) on the verification and validation of model based autonomous systems. The topics include: 1) Project Profile; 2) Model-Based Autonomy; 3) The Livingstone MIR; 4) MPL2SMV; 5) Livingstone to SMV Translation; 6) Symbolic Model Checking; 7) From Livingstone Models to SMV Models; 8) Application In-Situ Propellant Production; 9) Closed-Loop Verification Principle; 10) Livingstone PathFinder (LPF); 11) Publications and Presentations; and 12) Future Directions. This paper is presented in viewgraph form.
A Model Based Mars Climate Database for the Mission Design
NASA Technical Reports Server (NTRS)
2005-01-01
A viewgraph presentation on a model based climate database is shown. The topics include: 1) Why a model based climate database?; 2) Mars Climate Database v3.1 Who uses it ? (approx. 60 users!); 3) The new Mars Climate database MCD v4.0; 4) MCD v4.0: what's new ? 5) Simulation of Water ice clouds; 6) Simulation of Water ice cycle; 7) A new tool for surface pressure prediction; 8) Acces to the database MCD 4.0; 9) How to access the database; and 10) New web access
Features in visual search combine linearly
Pramod, R. T.; Arun, S. P.
2014-01-01
Single features such as line orientation and length are known to guide visual search, but relatively little is known about how multiple features combine in search. To address this question, we investigated how search for targets differing in multiple features (intensity, length, orientation) from the distracters is related to searches for targets differing in each of the individual features. We tested race models (based on reaction times) and co-activation models (based on reciprocal of reaction times) for their ability to predict multiple feature searches. Multiple feature searches were best accounted for by a co-activation model in which feature information combined linearly (r = 0.95). This result agrees with the classic finding that these features are separable i.e., subjective dissimilarity ratings sum linearly. We then replicated the classical finding that the length and width of a rectangle are integral features—in other words, they combine nonlinearly in visual search. However, to our surprise, upon including aspect ratio as an additional feature, length and width combined linearly and this model outperformed all other models. Thus, length and width of a rectangle became separable when considered together with aspect ratio. This finding predicts that searches involving shapes with identical aspect ratio should be more difficult than searches where shapes differ in aspect ratio. We confirmed this prediction on a variety of shapes. We conclude that features in visual search co-activate linearly and demonstrate for the first time that aspect ratio is a novel feature that guides visual search. PMID:24715328
NASA Technical Reports Server (NTRS)
Studer, P. A. (Inventor)
1983-01-01
A linear magnetic bearing system having electromagnetic vernier flux paths in shunt relation with permanent magnets, so that the vernier flux does not traverse the permanent magnet, is described. Novelty is believed to reside in providing a linear magnetic bearing having electromagnetic flux paths that bypass high reluctance permanent magnets. Particular novelty is believed to reside in providing a linear magnetic bearing with a pair of axially spaced elements having electromagnets for establishing vernier x and y axis control. The magnetic bearing system has possible use in connection with a long life reciprocating cryogenic refrigerator that may be used on the space shuttle.
... to your desktop! more... What Is a General Dentist? Article Chapters What Is a General Dentist? General ... Reviewed: January 2012 ?xml:namespace> Related Articles: General Dentists FAGD and MAGD: What Do These Awards Mean? ...
SCHWARZ'S LEMMA IN NORMED LINEAR SPACES*
Harris, Lawrence A.
1969-01-01
In this paper we show that any Fréchet holomorphic function mapping the open unit ball of one normed linear space into the closed unit ball of another must be a linear mapping if the Fréchet derivative of the function at zero is a surjective isometry. From this fact we deduce a Banach-Stone theorem for operator algebras which generalizes that of R. V. Kadison. PMID:16591741
Problem Solving: Physics Modeling-Based Interactive Engagement
ERIC Educational Resources Information Center
Ornek, Funda
2009-01-01
The purpose of this study was to investigate how modeling-based instruction combined with an interactive-engagement teaching approach promotes students' problem solving abilities. I focused on students in a calculus-based introductory physics course, based on the matter and interactions curriculum of Chabay & Sherwood (2002) at a large state…
Product Lifecycle Management Architecture: A Model Based Systems Engineering Analysis.
Noonan, Nicholas James
2015-07-01
This report is an analysis of the Product Lifecycle Management (PLM) program. The analysis is centered on a need statement generated by a Nuclear Weapons (NW) customer. The need statement captured in this report creates an opportunity for the PLM to provide a robust service as a solution. Lifecycles for both the NW and PLM are analyzed using Model Based System Engineering (MBSE).
Impact of Model-Based Teaching on Argumentation Skills
ERIC Educational Resources Information Center
Ogan-Bekiroglu, Feral; Belek, Deniz Eren
2014-01-01
The purpose of this study was to examine effects of model-based teaching on students' argumentation skills. Experimental design guided to the research. The participants of the study were pre-service physics teachers. The argumentative intervention lasted seven weeks. Data for this research were collected via video recordings and written…
Educational Value and Models-Based Practice in Physical Education
ERIC Educational Resources Information Center
Kirk, David
2013-01-01
A models-based approach has been advocated as a means of overcoming the serious limitations of the traditional approach to physical education. One of the difficulties with this approach is that physical educators have sought to use it to achieve diverse and sometimes competing educational benefits, and these wide-ranging aspirations are rarely if…
Model-based diagnostics for Space Station Freedom
NASA Technical Reports Server (NTRS)
Fesq, Lorraine M.; Stephan, Amy; Martin, Eric R.; Lerutte, Marcel G.
1991-01-01
An innovative approach to fault management was recently demonstrated for the NASA LeRC Space Station Freedom (SSF) power system testbed. This project capitalized on research in model-based reasoning, which uses knowledge of a system's behavior to monitor its health. The fault management system (FMS) can isolate failures online, or in a post analysis mode, and requires no knowledge of failure symptoms to perform its diagnostics. An in-house tool called MARPLE was used to develop and run the FMS. MARPLE's capabilities are similar to those available from commercial expert system shells, although MARPLE is designed to build model-based as opposed to rule-based systems. These capabilities include functions for capturing behavioral knowledge, a reasoning engine that implements a model-based technique known as constraint suspension, and a tool for quickly generating new user interfaces. The prototype produced by applying MARPLE to SSF not only demonstrated that model-based reasoning is a valuable diagnostic approach, but it also suggested several new applications of MARPLE, including an integration and testing aid, and a complement to state estimation.
... is the device most commonly used for external beam radiation treatments for patients with cancer. The linear ... shape of the patient's tumor and the customized beam is directed to the patient's tumor. The beam ...
NASA Technical Reports Server (NTRS)
Callier, Frank M.; Desoer, Charles A.
1991-01-01
The aim of this book is to provide a systematic and rigorous access to the main topics of linear state-space system theory in both the continuous-time case and the discrete-time case; and the I/O description of linear systems. The main thrusts of the work are the analysis of system descriptions and derivations of their properties, LQ-optimal control, state feedback and state estimation, and MIMO unity-feedback systems.
Shetty, Shricharith; Rao, Raghavendra; Kudva, R Ranjini; Subramanian, Kumudhini
2016-01-01
Alopecia areata (AA) over scalp is known to present in various shapes and extents of hair loss. Typically it presents as circumscribed patches of alopecia with underlying skin remaining normal. We describe a rare variant of AA presenting in linear band-like form. Only four cases of linear alopecia have been reported in medical literature till today, all four being diagnosed as lupus erythematosus profundus. PMID:27625568
NASA Technical Reports Server (NTRS)
Laughlin, Darren
1995-01-01
Inertial linear actuators developed to suppress residual accelerations of nominally stationary or steadily moving platforms. Function like long-stroke version of voice coil in conventional loudspeaker, with superimposed linear variable-differential transformer. Basic concept also applicable to suppression of vibrations of terrestrial platforms. For example, laboratory table equipped with such actuators plus suitable vibration sensors and control circuits made to vibrate much less in presence of seismic, vehicular, and other environmental vibrational disturbances.
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
When Does Model-Based Control Pay Off?
2016-01-01
Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to “model-free” and “model-based” strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding. It is assumed that this trade-off between accuracy and computational demand plays an important role in the arbitration between the two strategies, but we show that the hallmark task for dissociating model-free and model-based strategies, as well as several related variants, do not embody such a trade-off. We describe five factors that reduce the effectiveness of the model-based strategy on these tasks by reducing its accuracy in estimating reward outcomes and decreasing the importance of its choices. Based on these observations, we describe a version of the task that formally and empirically obtains an accuracy-demand trade-off between model-free and model-based strategies. Moreover, we show that human participants spontaneously increase their reliance on model-based control on this task, compared to the original paradigm. Our novel task and our computational analyses may prove important in subsequent empirical investigations of how humans balance accuracy and demand. PMID:27564094
Model-based Roentgen stereophotogrammetry of orthopaedic implants.
Valstar, E R; de Jong, F W; Vrooman, H A; Rozing, P M; Reiber, J H
2001-06-01
Attaching tantalum markers to prostheses for Roentgen stereophotogrammetry (RSA) may be difficult and is sometimes even impossible. In this study, a model-based RSA method that avoids the attachment of markers to prostheses is presented and validated. This model-based RSA method uses a triangulated surface model of the implant. A projected contour of this model is calculated and this calculated model contour is matched onto the detected contour of the actual implant in the RSA radiograph. The difference between the two contours is minimized by variation of the position and orientation of the model. When a minimal difference between the contours is found, an optimal position and orientation of the model has been obtained. The method was validated by means of a phantom experiment. Three prosthesis components were used in this experiment: the femoral and tibial component of an Interax total knee prosthesis (Stryker Howmedica Osteonics Corp., Rutherfort, USA) and the femoral component of a Profix total knee prosthesis (Smith & Nephew, Memphis, USA). For the prosthesis components used in this study, the accuracy of the model-based method is lower than the accuracy of traditional RSA. For the Interax femoral and tibial components, significant dimensional tolerances were found that were probably caused by the casting process and manual polishing of the components surfaces. The largest standard deviation for any translation was 0.19mm and for any rotation it was 0.52 degrees. For the Profix femoral component that had no large dimensional tolerances, the largest standard deviation for any translation was 0.22mm and for any rotation it was 0.22 degrees. From this study we may conclude that the accuracy of the current model-based RSA method is sensitive to dimensional tolerances of the implant. Research is now being conducted to make model-based RSA less sensitive to dimensional tolerances and thereby improving its accuracy. PMID:11470108
On-line and Model-based Approaches to the Visual Control of Action
Zhao, Huaiyong; Warren, William H.
2014-01-01
Two general approaches to the visual control of action have emerged in last few decades, known as the on-line and model-based approaches. The key difference between them is whether action is controlled by current visual information or on the basis of an internal world model. In this paper, we evaluate three hypotheses: strong on-line control, strong model-based control, and a hybrid solution that combines on-line control with weak off-line strategies. We review experimental research on the control of locomotion and manual actions, which indicates that (a) an internal world model is neither sufficient nor necessary to control action at normal levels of performance; (b) current visual information is necessary and sufficient to control action at normal levels; and (c) under certain conditions (e.g. occlusion) action is controlled by less accurate, simple strategies such as heuristics, visual-motor mappings, or spatial memory. We conclude that the strong model-based hypothesis is not sustainable. Action is normally controlled on-line when current information is available, consistent with the strong on-line control hypothesis. In exceptional circumstances, action is controlled by weak, context-specific, off-line strategies. This hybrid solution is comprehensive, parsimonious, and able to account for a variety of tasks under a range of visual conditions. PMID:25454700
Principal Component Analysis of breast DCE-MRI Adjusted with a Model Based Method
Eyal, Erez.; Badikhi, Daria; Furman-Haran, Edna; Kelcz, Fredrick; Kirshenbaum, Kevin J.; Degani, Hadassa
2010-01-01
Purpose To investigate a fast, objective and standardized method for analyzing breast DCE-MRI applying principal component analysis (PCA) adjusted with a model based method. Materials and Methods 3D gradient-echo dynamic contrast-enhanced breast images of 31 malignant and 38 benign lesions, recorded on a 1.5 Tesla scanner were retrospectively analyzed by PCA and by the model based three-time-point (3TP) method. Results Intensity scaled (IS) and enhancement scaled (ES) datasets were reduced by PCA yielding a 1st IS-eigenvector that captured the signal variation between fat and fibroglandular tissue; two IS-eigenvectors and the two first ES-eigenvectors that captured contrast-enhanced changes, whereas the remaining eigenvectors captured predominantly noise changes. Rotation of the two contrast related eigenvectors led to a high congruence between the projection coefficients and the 3TP parameters. The ES-eigenvectors and the rotation angle were highly reproducible across malignant lesions enabling calculation of a general rotated eigenvector base. ROC curve analysis of the projection coefficients of the two eigenvectors indicated high sensitivity of the 1st rotated eigenvector to detect lesions (AUC>0.97) and of the 2nd rotated eigenvector to differentiate malignancy from benignancy (AUC=0.87). Conclusion PCA adjusted with a model-based method provided a fast and objective computer-aided diagnostic tool for breast DCE-MRI. PMID:19856419
Spectral-Reflectance Linear Models for Optical Color-Pattern Recognition
NASA Astrophysics Data System (ADS)
Nieves, Juan L.; Hernández-Andrés, Javier; Valero, Eva; Romero, Javier
2004-03-01
We propose a new method of color-pattern recognition by optical correlation that uses a linear description of spectral reflectance functions and the spectral power distribution of illuminants that contains few parameters. We report on a method of preprocessing color input scenes in which the spectral functions are derived from linear models based on principal-component analysis. This multichannel algorithm transforms the red-green-blue (RGB) components into a new set of components that permit a generalization of the matched filter operations that are usually applied in optical pattern recognition with more-stable results under changes in illumination in the source images. The correlation is made in the subspace spanned by the coefficients that describe all reflectances according to a suitable basis for linear representation. First we illustrate the method in a control experiment in which the scenes are captured under known conditions of illumination. The discrimination capability of the algorithm improves upon the conventional RGB multichannel decomposition used in optical correlators when scenes are captured under different illuminant conditions and is slightly better than color recognition based on uniform color spaces (e.g., the CIELab system). Then we test the coefficient method in situations in which the target is captured under a reference illuminant and the scene that contains the target under an unknown spectrally different illuminant. We show that the method prevents false alarms caused by changes in the illuminant and that only two coefficients suffice to discriminate polychromatic objects.
Model-Based Engine Control Architecture with an Extended Kalman Filter
NASA Technical Reports Server (NTRS)
Csank, Jeffrey T.; Connolly, Joseph W.
2016-01-01
This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The nonlinear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.
Model based 3D segmentation and OCT image undistortion of percutaneous implants.
Müller, Oliver; Donner, Sabine; Klinder, Tobias; Dragon, Ralf; Bartsch, Ivonne; Witte, Frank; Krüger, Alexander; Heisterkamp, Alexander; Rosenhahn, Bodo
2011-01-01
Optical Coherence Tomography (OCT) is a noninvasive imaging technique which is used here for in vivo biocompatibility studies of percutaneous implants. A prerequisite for a morphometric analysis of the OCT images is the correction of optical distortions caused by the index of refraction in the tissue. We propose a fully automatic approach for 3D segmentation of percutaneous implants using Markov random fields. Refraction correction is done by using the subcutaneous implant base as a prior for model based estimation of the refractive index using a generalized Hough transform. Experiments show the competitiveness of our algorithm towards manual segmentations done by experts. PMID:22003731
Generalized Structured Component Analysis
ERIC Educational Resources Information Center
Hwang, Heungsun; Takane, Yoshio
2004-01-01
We propose an alternative method to partial least squares for path analysis with components, called generalized structured component analysis. The proposed method replaces factors by exact linear combinations of observed variables. It employs a well-defined least squares criterion to estimate model parameters. As a result, the proposed method…
Estimators for overdetermined linear Stokes parameters
NASA Astrophysics Data System (ADS)
Furey, John
2016-05-01
The mathematics of estimating overdetermined polarization parameters is worked out within the context of the inverse modeling of linearly polarized light, and as the primary new result the general solution is presented for estimators of the linear Stokes parameters from any number of measurements. The utility of the general solution is explored in several illustrative examples including the canonical case of two orthogonal pairs. In addition to the actual utility of these estimators in Stokes analysis, the pedagogical discussion illustrates many of the considerations involved in solving the ill-posed problem of overdetermined parameter estimation. Finally, suggestions are made for using a rapidly rotating polarizer for continuously updating polarization estimates.
Linear optoacoustic underwater communication.
Blackmon, Fletcher; Estes, Lee; Fain, Gilbert
2005-06-20
The linear mechanism for optical-to-acoustic energy conversion is explored for optoacoustic communication from an in-air platform or surface vessel to a submerged vessel such as a submarine or unmanned undersea vehicle. The communication range that can be achieved is addressed. A number of conventional signals used in underwater acoustic telemetry applications are shown to be capable of being generated experimentally through the linear optoacoustic regime conversion process. These results are in agreement with simulation based on current theoretical models. A number of practical issues concerning linear optoacoustic communication are addressed that lead to a formulation of a linear-regime optoacoustic communication scheme. The use of oblique laser beam incidence at the air-water interface to obtain considerable in-air range from the laser source to the in-water receiver is addressed. Also, the effect of oblique incidence on in-water range is examined. Next, the optimum and suboptimum linear optoacoustic sound-generation techniques for selecting the optical wavelength and signaling frequency for optimizing in-water range are addressed and discussed. Optoacoustic communication techniques employing M-ary frequency shift keying and multifrequency shift keying are then compared with regard to communication parameters such as bandwidth, data rate, range coverage, and number of lasers employed. PMID:15989059
Superconducting linear actuator
NASA Technical Reports Server (NTRS)
Johnson, Bruce; Hockney, Richard
1993-01-01
Special actuators are needed to control the orientation of large structures in space-based precision pointing systems. Electromagnetic actuators that presently exist are too large in size and their bandwidth is too low. Hydraulic fluid actuation also presents problems for many space-based applications. Hydraulic oil can escape in space and contaminate the environment around the spacecraft. A research study was performed that selected an electrically-powered linear actuator that can be used to control the orientation of a large pointed structure. This research surveyed available products, analyzed the capabilities of conventional linear actuators, and designed a first-cut candidate superconducting linear actuator. The study first examined theoretical capabilities of electrical actuators and determined their problems with respect to the application and then determined if any presently available actuators or any modifications to available actuator designs would meet the required performance. The best actuator was then selected based on available design, modified design, or new design for this application. The last task was to proceed with a conceptual design. No commercially-available linear actuator or modification capable of meeting the specifications was found. A conventional moving-coil dc linear actuator would meet the specification, but the back-iron for this actuator would weigh approximately 12,000 lbs. A superconducting field coil, however, eliminates the need for back iron, resulting in an actuator weight of approximately 1000 lbs.
High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software
Fabregat-Traver, Diego; Sharapov, Sodbo Zh.; Hayward, Caroline; Rudan, Igor; Campbell, Harry; Aulchenko, Yurii; Bientinesi, Paolo
2014-01-01
To raise the power of genome-wide association studies (GWAS) and avoid false-positive results in structured populations, one can rely on mixed model based tests. When large samples are used, and when multiple traits are to be studied in the ’omics’ context, this approach becomes computationally challenging. Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. We implement these optimal algorithms in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations, increasing throughput while reducing memory usage and energy consumption. We show that, compared to existing libraries, our algorithms and software achieve considerable speed-ups. The OmicABEL software described in this manuscript is available under the GNU GPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL. PMID:25717363
High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software.
Fabregat-Traver, Diego; Sharapov, Sodbo Zh; Hayward, Caroline; Rudan, Igor; Campbell, Harry; Aulchenko, Yurii; Bientinesi, Paolo
2014-01-01
To raise the power of genome-wide association studies (GWAS) and avoid false-positive results in structured populations, one can rely on mixed model based tests. When large samples are used, and when multiple traits are to be studied in the 'omics' context, this approach becomes computationally challenging. Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. We implement these optimal algorithms in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations, increasing throughput while reducing memory usage and energy consumption. We show that, compared to existing libraries, our algorithms and software achieve considerable speed-ups. The OmicABEL software described in this manuscript is available under the GNU GPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL. PMID:25717363
A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data
NASA Technical Reports Server (NTRS)
Simon, Donald L.; Rinehart, Aidan Walker
2015-01-01
This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.
A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data
NASA Technical Reports Server (NTRS)
Simon, Donald L.; Rinehart, Aidan W.
2014-01-01
This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.
A new multiplicative denoising variational model based on mth root transformation.
Yun, Sangwoon; Woo, Hyenkyun
2012-05-01
In coherent imaging systems, such as the synthetic aperture radar (SAR), the observed images are contaminated by multiplicative noise. Due to the edge-preserving feature of the total variation (TV), variational models with TV regularization have attracted much interest in removing multiplicative noise. However, the fidelity term of the variational model, based on maximum a posteriori estimation, is not convex, and so, it is usually difficult to find a global solution. Hence, the logarithmic function is used to transform the nonconvex variational model to the convex one. In this paper, instead of using the log, we exploit the m th root function to relax the nonconvexity of the variational model. An algorithm based on the augmented Lagrangian function, which has been applied to solve the log transformed convex variational model, can be applied to solve our proposed model. However, this algorithm requires solving a subproblem, which does not have a closed-form solution, at each iteration. Hence, we propose to adapt the linearized proximal alternating minimization algorithm, which does not require inner iterations for solving the subproblems. In addition, the proposed method is very simple and highly parallelizable; thus, it is efficient to remove multiplicative noise in huge SAR images. The proposed model for multiplicative noise removal shows overall better performance than the convex model based on the log transformation. PMID:22287244
Model-based control of vortex shedding at low Reynolds numbers
NASA Astrophysics Data System (ADS)
Illingworth, Simon J.
2016-03-01
Model-based feedback control of vortex shedding at low Reynolds numbers is considered. The feedback signal is provided by velocity measurements in the wake, and actuation is achieved using blowing and suction on the cylinder's surface. Using two-dimensional direct numerical simulations and reduced-order modelling techniques, linear models of the wake are formed at Reynolds numbers between 45 and 110. These models are used to design feedback controllers using H_∞ loop-shaping. Complete suppression of shedding is demonstrated up to Re = 110—both for a single-sensor arrangement and for a three-sensor arrangement. The robustness of the feedback controllers is also investigated by applying them over a range of off-design Reynolds numbers, and good robustness properties are seen. It is also observed that it becomes increasingly difficult to achieve acceptable control performance—measured in a suitable way—as Reynolds number increases.
Predictive models based on sensitivity theory and their application to practical shielding problems
Bhuiyan, S.I.; Roussin, R.W.; Lucius, J.L.; Bartine, D.E.
1983-01-01
Two new calculational models based on the use of cross-section sensitivity coefficients have been devised for calculating radiation transport in relatively simple shields. The two models, one an exponential model and the other a power model, have been applied, together with the traditional linear model, to 1- and 2-m-thick concrete-slab problems in which the water content, reinforcing-steel content, or composition of the concrete was varied. Comparing the results obtained with the three models with those obtained from exact one-dimensional discrete-ordinates transport calculations indicates that the exponential model, named the BEST model (for basic exponential shielding trend), is a particularly promising predictive tool for shielding problems dominated by exponential attenuation. When applied to a deep-penetration sodium problem, the BEST model also yields better results than do calculations based on second-order sensitivity theory.
Scintillation event energy measurement via a pulse model based iterative deconvolution method
NASA Astrophysics Data System (ADS)
Deng, Zhenzhou; Xie, Qingguo; Duan, Zhiwen; Xiao, Peng
2013-11-01
This work focuses on event energy measurement, a crucial task of scintillation detection systems. We modeled the scintillation detector as a linear system and treated the energy measurement as a deconvolution problem. We proposed a pulse model based iterative deconvolution (PMID) method, which can process pileup events without detection and is adaptive for different signal pulse shapes. The proposed method was compared with digital gated integrator (DGI) and digital delay-line clipping (DDLC) using real world experimental data. For singles data, the energy resolution (ER) produced by PMID matched that of DGI. For pileups, the PMID method outperformed both DGI and DDLC in ER and counts recovery. The encouraging results suggest that the PMID method has great potentials in applications like photon-counting systems and pulse height spectrometers, in which multiple-event pileups are common.
Franklin, K W; Howell, L N; Lewis, D G; Neugebauer, C A; O'Brien, D W; Schilling, S A
2001-05-15
The purpose of this White Paper is to outline the benefits we expect to receive from Model-Based Engineering and Manufacturing (MBE/M) for the design, analysis, fabrication, and assembly of nuclear weapons for upcoming Life Extension Programs (LEPs). Industry experiences with model-based approaches and the NNSA/DP investments and experiences, discussed in this paper, indicate that model-based methods can achieve reliable refurbished weapons for the stockpile with less cost and time. In this the paper, we list both general and specific benefits of MBE/M for the upcoming LEPs and the metrics for determining the success of model-based approaches. We also present some outstanding issues and challenges to deploying and achieving long-term benefit from the MBE/M. In conclusion, we argue that successful completion of the upcoming LEPs--with very aggressive schedule and funding restrictions--will depend on electronic model-based methods. We ask for a strong commitment from LEP managers throughout the Nuclear Weapons Complex to support deployment and use of MBE/M systems to meet their program needs.
Chappell, Michael A; Woolrich, Mark W; Petersen, Esben T; Golay, Xavier; Payne, Stephen J
2013-05-01
Amongst the various implementations of arterial spin labeling MRI methods for quantifying cerebral perfusion, the QUASAR method is unique. By using a combination of labeling with and without flow suppression gradients, the QUASAR method offers the separation of macrovascular and tissue signals. This permits local arterial input functions to be defined and "model-free" analysis, using numerical deconvolution, to be used. However, it remains unclear whether arterial spin labeling data are best treated using model-free or model-based analysis. This work provides a critical comparison of these two approaches for QUASAR arterial spin labeling in the healthy brain. An existing two-component (arterial and tissue) model was extended to the mixed flow suppression scheme of QUASAR to provide an optimal model-based analysis. The model-based analysis was extended to incorporate dispersion of the labeled bolus, generally regarded as the major source of discrepancy between the two analysis approaches. Model-free and model-based analyses were compared for perfusion quantification including absolute measurements, uncertainty estimation, and spatial variation in cerebral blood flow estimates. Major sources of discrepancies between model-free and model-based analysis were attributed to the effects of dispersion and the degree to which the two methods can separate macrovascular and tissue signal. PMID:22711674
NASA Astrophysics Data System (ADS)
Liang, Ling L.; Fulmer, Gavin W.; Majerich, David M.; Clevenstine, Richard; Howanski, Raymond
2012-02-01
The purpose of this study is to examine the effects of a model-based introductory physics curriculum on conceptual learning in a Physics First (PF) Initiative. This is the first comparative study in physics education that applies the Rasch modeling approach to examine the effects of a model-based curriculum program combined with PF in the United States. Five teachers and 301 students (in grades 9 through 12) in two mid-Atlantic high schools participated in the study. The students' conceptual learning was measured by the Force Concept Inventory (FCI). It was found that the ninth-graders enrolled in the model-based program in a PF initiative achieved substantially greater conceptual understanding of the physics content than those 11th-/12th-graders enrolled in the conventional non-modeling, non-PF program (Honors strand). For the 11th-/12th-graders enrolled in the non-PF, non-honors strands, the modeling classes also outperformed the conventional non-modeling classes. The instructional activity reports by students indicated that the model-based approach was generally implemented in modeling classrooms. A closer examination of the field notes and the classroom observation profiles revealed that the greatest inconsistencies in model-based teaching practices observed were related to classroom interactions or discourse. Implications and recommendations for future studies are also discussed.
Using Model-Based Reasoning for Autonomous Instrument Operation
NASA Technical Reports Server (NTRS)
Johnson, Mike; Rilee, M.; Truszkowski, W.; Powers, Edward I. (Technical Monitor)
2000-01-01
of environmental hazards, frame the problem of constructing autonomous science instruments. we are developing a model of the Low Energy Neutral Atom instrument (LENA) that is currently flying on board the Imager for Magnetosphere-to-Aurora Global Exploration (IMAGE) spacecraft. LENA is a particle detector that uses high voltage electrostatic optics and time-of-flight mass spectrometry to image neutral atom emissions from the denser regions of the Earth's magnetosphere. As with most spacecraft borne science instruments, phenomena in addition to neutral atoms are detected by LENA. Solar radiation and energetic particles from Earth's radiation belts are of particular concern because they may help generate currents that may compromise LENA's long term performance. An explicit model of the instrument response has been constructed and is currently in use on board IMAGE to dynamically adapt LENA to the presence or absence of energetic background radiations. The components of LENA are common in space science instrumentation, and lessons learned by modelling this system may be applied to other instruments. This work demonstrates that a model-based approach can be used to enhance science instrument effectiveness. Our future work involves the extension of these methods to cover more aspects of LENA operation and the generalization to other space science instrumentation.
Model based document and report generation for systems engineering
NASA Astrophysics Data System (ADS)
Delp, C.; Lam, D.; Fosse, E.; Lee, Cin-Young
As Model Based Systems Engineering (MBSE) practices gain adoption, various approaches have been developed in order to simplify and automate the process of generating documents from models. Essentially, all of these techniques can be unified around the concept of producing different views of the model according to the needs of the intended audience. In this paper, we will describe a technique developed at JPL of applying SysML Viewpoints and Views to generate documents and reports. An architecture of model-based view and document generation will be presented, and the necessary extensions to SysML with associated rationale will be explained. A survey of examples will highlight a variety of views that can be generated, and will provide some insight into how collaboration and integration is enabled. We will also describe the basic architecture for the enterprise applications that support this approach.
Fusing Quantitative Requirements Analysis with Model-based Systems Engineering
NASA Technical Reports Server (NTRS)
Cornford, Steven L.; Feather, Martin S.; Heron, Vance A.; Jenkins, J. Steven
2006-01-01
A vision is presented for fusing quantitative requirements analysis with model-based systems engineering. This vision draws upon and combines emergent themes in the engineering milieu. "Requirements engineering" provides means to explicitly represent requirements (both functional and non-functional) as constraints and preferences on acceptable solutions, and emphasizes early-lifecycle review, analysis and verification of design and development plans. "Design by shopping" emphasizes revealing the space of options available from which to choose (without presuming that all selection criteria have previously been elicited), and provides means to make understandable the range of choices and their ramifications. "Model-based engineering" emphasizes the goal of utilizing a formal representation of all aspects of system design, from development through operations, and provides powerful tool suites that support the practical application of these principles. A first step prototype towards this vision is described, embodying the key capabilities. Illustrations, implications, further challenges and opportunities are outlined.
Model-based hierarchical reinforcement learning and human action control
Botvinick, Matthew; Weinstein, Ari
2014-01-01
Recent work has reawakened interest in goal-directed or ‘model-based’ choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision-making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model-based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model-based mechanisms might play a special and pivotal role in human decision-making, dramatically extending the scope and complexity of human behaviour. PMID:25267822
Model-based decision support in diabetes care.
Salzsieder, E; Vogt, L; Kohnert, K-D; Heinke, P; Augstein, P
2011-05-01
The model-based Karlsburg Diabetes Management System (KADIS®) has been developed as a patient-focused decision-support tool to provide evidence-based advice for physicians in their daily efforts to optimize metabolic control in diabetes care of their patients on an individualized basis. For this purpose, KADIS® was established in terms of a personalized, interactive in silico simulation procedure, implemented into a problem-related diabetes health care network and evaluated under different conditions by conducting open-label mono- and polycentric trials, and a case-control study, and last but not least, by application in routine diabetes outpatient care. The trial outcomes clearly show that the recommendations provided to the physicians by KADIS® lead to significant improvement of metabolic control. This model-based decision-support system provides an excellent tool to effectively guide physicians in personalized decision-making to achieve optimal metabolic control for their patients. PMID:20621384
Adaptive, Model-Based Monitoring and Threat Detection
NASA Astrophysics Data System (ADS)
Valdes, Alfonso; Skinner, Keith
2002-09-01
We explore the suitability of model-based probabilistic techniques, such as Bayes networks, to the field of intrusion detection and alert report correlation. We describe a network intrusion detection system (IDS) using Bayes inference, wherein the knowledge base is encoded not as rules but as conditional probability relations between observables and hypotheses of normal and malicious usage. The same high-performance Bayes inference library was employed in a component of the Mission-Based Correlation effort, using an initial knowledge base that adaptively learns the security administrator's preference for alert priority and rank. Another major effort demonstrated probabilistic techniques in heterogeneous sensor correlation. We provide results for simulated attack data, live traffic, and the CyberPanel Grand Challenge Problem. Our results establish that model-based probabilistic techniques are an important complementary capability to signature-based methods in detection and correlation.
Hierarchical model-based interferometric synthetic aperture radar image registration
NASA Astrophysics Data System (ADS)
Wang, Yang; Huang, Haifeng; Dong, Zhen; Wu, Manqing
2014-01-01
With the rapid development of spaceborne interferometric synthetic aperture radar technology, classical image registration methods are incompetent for high-efficiency and high-accuracy masses of real data processing. Based on this fact, we propose a new method. This method consists of two steps: coarse registration that is realized by cross-correlation algorithm and fine registration that is realized by hierarchical model-based algorithm. Hierarchical model-based algorithm is a high-efficiency optimization algorithm. The key features of this algorithm are a global model that constrains the overall structure of the motion estimated, a local model that is used in the estimation process, and a coarse-to-fine refinement strategy. Experimental results from different kinds of simulated and real data have confirmed that the proposed method is very fast and has high accuracy. Comparing with a conventional cross-correlation method, the proposed method provides markedly improved performance.
Model Based Document and Report Generation for Systems Engineering
NASA Technical Reports Server (NTRS)
Delp, Christopher; Lam, Doris; Fosse, Elyse; Lee, Cin-Young
2013-01-01
As Model Based Systems Engineering (MBSE) practices gain adoption, various approaches have been developed in order to simplify and automate the process of generating documents from models. Essentially, all of these techniques can be unified around the concept of producing different views of the model according to the needs of the intended audience. In this paper, we will describe a technique developed at JPL of applying SysML Viewpoints and Views to generate documents and reports. An architecture of model-based view and document generation will be presented, and the necessary extensions to SysML with associated rationale will be explained. A survey of examples will highlight a variety of views that can be generated, and will provide some insight into how collaboration and integration is enabled. We will also describe the basic architecture for the enterprise applications that support this approach.
3-D model-based tracking for UAV indoor localization.
Teulière, Céline; Marchand, Eric; Eck, Laurent
2015-05-01
This paper proposes a novel model-based tracking approach for 3-D localization. One main difficulty of standard model-based approach lies in the presence of low-level ambiguities between different edges. In this paper, given a 3-D model of the edges of the environment, we derive a multiple hypotheses tracker which retrieves the potential poses of the camera from the observations in the image. We also show how these candidate poses can be integrated into a particle filtering framework to guide the particle set toward the peaks of the distribution. Motivated by the UAV indoor localization problem where GPS signal is not available, we validate the algorithm on real image sequences from UAV flights. PMID:25099967
Model-based inversion for a shallow ocean application
Candy, J.V.; Sullivan, E.J.
1994-03-01
A model-based approach to invert or estimate the sound speed profile (SSP) from noisy pressure-field measurements is discussed. The resulting model-based processor (MBP) is based on the state-space representation of the normal-mode propagation model. Using data obtained from the well-known Hudson Canyon experiment, a noisy shallow water ocean environment, the processor is designed and the results compared to those predicted using various propagation models and data. It is shown that the MBP not only predicts the sound speed quite well, but also is able to simultaneously provide enhanced estimates of both modal and pressure-field measurements which are useful for localization and rapid ocean environmental characterization.
Identifying Model-Based Reconfiguration Goals through Functional Deficiencies
NASA Technical Reports Server (NTRS)
Benazera, Emmanuel; Trave-Massuyes, Louise
2004-01-01
Model-based diagnosis is now advanced to the point autonomous systems face some uncertain and faulty situations with success. The next step toward more autonomy is to have the system recovering itself after faults occur, a process known as model-based reconfiguration. After faults occur, given a prediction of the nominal behavior of the system and the result of the diagnosis operation, this paper details how to automatically determine the functional deficiencies of the system. These deficiencies are characterized in the case of uncertain state estimates. A methodology is then presented to determine the reconfiguration goals based on the deficiencies. Finally, a recovery process interleaves planning and model predictive control to restore the functionalities in prioritized order.
Fuzzy model-based observers for fault detection in CSTR.
Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan
2015-11-01
Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. PMID:26521723
Model based control of a rehabilitation robot for lower extremities.
Xie, Xiao-Liang; Hou, Zeng-Guang; Li, Peng-Feng; Ji, Cheng; Zhang, Feng; Tan, Min; Wang, Hongbo; Hu, Guoqing
2010-01-01
This paper mainly focuses on the trajectory tracking control of a lower extremity rehabilitation robot during passive training process of patients. Firstly, a mathematical model of the rehabilitation robot is introduced by using Lagrangian analysis. Then, a model based computed-torque control scheme is designed to control the constrained four-link robot (with patient's foot fixed on robot's end-effector) to track a predefined trajectory. Simulation results are provided to illustrate the effectiveness of the proposed model based computed-torque algorithm. In the simulation, a multi-body dynamics and motion software named ADAMS is used. The combined simulation of ADAMS and MATLAB is able to produce more realistic results of this complex integrated system. PMID:21097222
REAL-TIME MODEL-BASED ELECTRICAL POWERED WHEELCHAIR CONTROL
Wang, Hongwu; Salatin, Benjamin; Grindle, Garrett G.; Ding, Dan; Cooper, Rory A.
2009-01-01
The purpose of this study was to evaluate the effects of three different control methods on driving speed variation and wheel-slip of an electric-powered wheelchair (EPW). A kinematic model as well as 3-D dynamic model was developed to control the velocity and traction of the wheelchair. A smart wheelchair platform was designed and built with a computerized controller and encoders to record wheel speeds and to detect the slip. A model based, a proportional-integral-derivative (PID) and an open-loop controller were applied with the EPW driving on four different surfaces at three specified speeds. The speed errors, variation, rise time, settling time and slip coefficient were calculated and compared for a speed step-response input. Experimental results showed that model based control performed best on all surfaces across the speeds. PMID:19733494
MTK: An AI tool for model-based reasoning
NASA Technical Reports Server (NTRS)
Erickson, William K.; Schwartz, Mary R.
1987-01-01
A 1988 goal for the Systems Autonomy Demonstration Project Office of the NASA Ames Research Center is to apply model-based representation and reasoning techniques in a knowledge-based system that will provide monitoring, fault diagnosis, control and trend analysis of the space station Thermal Management System (TMS). A number of issues raised during the development of the first prototype system inspired the design and construction of a model-based reasoning tool called MTK, which was used in the building of the second prototype. These issues are outlined, along with examples from the thermal system to highlight the motivating factors behind them. An overview of the capabilities of MTK is given.
Designing linear systolic arrays
Kumar, V.K.P.; Tsai, Y.C. . Dept. of Electrical Engineering)
1989-12-01
The authors develop a simple mapping technique to design linear systolic arrays. The basic idea of the technique is to map the computations of a certain class of two-dimensional systolic arrays onto one-dimensional arrays. Using this technique, systolic algorithms are derived for problems such as matrix multiplication and transitive closure on linearly connected arrays of PEs with constant I/O bandwidth. Compared to known designs in the literature, the technique leads to modular systolic arrays with constant hardware in each PE, few control lines, lexicographic data input/output, and improved delay time. The unidirectional flow of control and data in this design assures implementation of the linear array in the known fault models of wafer scale integration.
NASA Technical Reports Server (NTRS)
Leviton, Douglas B. (Inventor)
1993-01-01
A Linear Motion Encoding device for measuring the linear motion of a moving object is disclosed in which a light source is mounted on the moving object and a position sensitive detector such as an array photodetector is mounted on a nearby stationary object. The light source emits a light beam directed towards the array photodetector such that a light spot is created on the array. An analog-to-digital converter, connected to the array photodetector is used for reading the position of the spot on the array photodetector. A microprocessor and memory is connected to the analog-to-digital converter to hold and manipulate data provided by the analog-to-digital converter on the position of the spot and to compute the linear displacement of the moving object based upon the data from the analog-to-digital converter.
Model-Based Detection in a Shallow Water Ocean Environment
Candy, J V
2001-07-30
A model-based detector is developed to process shallow water ocean acoustic data. The function of the detector is to adaptively monitor the environment and decide whether or not a change from normal has occurred. Here we develop a processor incorporating both a normal-mode ocean acoustic model and a vertical hydrophone array. The detector is applied to data acquired from the Hudson Canyon experiments at various ranges and its performance is evaluated.
Applying Model Based Systems Engineering to NASA's Space Communications Networks
NASA Technical Reports Server (NTRS)
Bhasin, Kul; Barnes, Patrick; Reinert, Jessica; Golden, Bert
2013-01-01
System engineering practices for complex systems and networks now require that requirement, architecture, and concept of operations product development teams, simultaneously harmonize their activities to provide timely, useful and cost-effective products. When dealing with complex systems of systems, traditional systems engineering methodology quickly falls short of achieving project objectives. This approach is encumbered by the use of a number of disparate hardware and software tools, spreadsheets and documents to grasp the concept of the network design and operation. In case of NASA's space communication networks, since the networks are geographically distributed, and so are its subject matter experts, the team is challenged to create a common language and tools to produce its products. Using Model Based Systems Engineering methods and tools allows for a unified representation of the system in a model that enables a highly related level of detail. To date, Program System Engineering (PSE) team has been able to model each network from their top-level operational activities and system functions down to the atomic level through relational modeling decomposition. These models allow for a better understanding of the relationships between NASA's stakeholders, internal organizations, and impacts to all related entities due to integration and sustainment of existing systems. Understanding the existing systems is essential to accurate and detailed study of integration options being considered. In this paper, we identify the challenges the PSE team faced in its quest to unify complex legacy space communications networks and their operational processes. We describe the initial approaches undertaken and the evolution toward model based system engineering applied to produce Space Communication and Navigation (SCaN) PSE products. We will demonstrate the practice of Model Based System Engineering applied to integrating space communication networks and the summary of its
Model-based rational strategy for chromatographic resin selection.
Nfor, Beckley K; Zuluaga, Diego S; Verheijen, Peter J T; Verhaert, Peter D E M; van der Wielen, Luuk A M; Ottens, Marcel
2011-01-01
A model-based rational strategy for the selection of chromatographic resins is presented. The main question being addressed is that of selecting the most optimal chromatographic resin from a few promising alternatives. The methodology starts with chromatographic modeling,parameters acquisition, and model validation, followed by model-based optimization of the chromatographic separation for the resins of interest. Finally, the resins are rationally evaluated based on their optimized operating conditions and performance metrics such as product purity, yield, concentration, throughput, productivity, and cost. Resin evaluation proceeds by two main approaches. In the first approach, Pareto frontiers from multi-objective optimization of conflicting objectives are overlaid for different resins, enabling direct visualization and comparison of resin performances based on the feasible solution space. The second approach involves the transformation of the resin performances into weighted resin scores, enabling the simultaneous consideration of multiple performance metrics and the setting of priorities. The proposed model-based resin selection strategy was illustrated by evaluating three mixed mode adsorbents (ADH, PPA, and HEA) for the separation of a ternary mixture of bovine serum albumin, ovalbumin, and amyloglucosidase. In order of decreasing weighted resin score or performance, the top three resins for this separation were ADH [PPA[HEA. The proposed model-based approach could be a suitable alternative to column scouting during process development, the main strengths being that minimal experimentation is required and resins are evaluated under their ideal working conditions, enabling a fair comparison. This work also demonstrates the application of column modeling and optimization to mixed mode chromatography. PMID:22238769
On Environmental Model-Based Visual Perception for Humanoids
NASA Astrophysics Data System (ADS)
Gonzalez-Aguirre, D.; Wieland, S.; Asfour, T.; Dillmann, R.
In this article an autonomous visual perception framework for humanoids is presented. This model-based framework exploits the available knowledge and the context acquired during global localization in order to overcome the limitations of pure data-driven approaches. The reasoning for perception and the properceptive components are the key elements to solve complex visual assertion queries with a proficient performance. Experimental evaluation with the humanoid robot ARMAR-IIIa is presented.
Linearly Adjustable International Portfolios
Fonseca, R. J.; Kuhn, D.; Rustem, B.
2010-09-30
We present an approach to multi-stage international portfolio optimization based on the imposition of a linear structure on the recourse decisions. Multiperiod decision problems are traditionally formulated as stochastic programs. Scenario tree based solutions however can become intractable as the number of stages increases. By restricting the space of decision policies to linear rules, we obtain a conservative tractable approximation to the original problem. Local asset prices and foreign exchange rates are modelled separately, which allows for a direct measure of their impact on the final portfolio value.
Multiple Damage Progression Paths in Model-Based Prognostics
NASA Technical Reports Server (NTRS)
Daigle, Matthew; Goebel, Kai Frank
2011-01-01
Model-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model-based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active
A Model-Based Expert System For Digital Systems Design
NASA Astrophysics Data System (ADS)
Wu, J. G.; Ho, W. P. C.; Hu, Y. H.; Yun, D. Y. Y.; Parng, T. M.
1987-05-01
In this paper, we present a model-based expert system for automatic digital systems design. The goal of digital systems design is to generate a workable and efficient design from high level specifications. The formalization of the design process is a necessity for building an efficient automatic CAD system. Our approach combines model-based, heuristic best-first search, and meta-planning techniques from AI to facilitate the design process. The design process is decomposed into three subprocesses. First, the high-level behavioral specifications are translated into sequences of primitive behavioral operations. Next, primitive operations are grouped to form intermediate-level behavioral functions. Finally, structural function modules are selected to implement these functions. Using model-based reasoning on the primitive behavioral operations level extends the solution space considered in design and provides more opportunity for minimization. Heuristic best-first search and meta-planning tech-niques control the decision-making in the latter two subprocesses to optimize the final design. They also facilitate system maintenance by separating design strategy from design knowledge.
A cloud model-based approach for water quality assessment.
Wang, Dong; Liu, Dengfeng; Ding, Hao; Singh, Vijay P; Wang, Yuankun; Zeng, Xiankui; Wu, Jichun; Wang, Lachun
2016-07-01
Water quality assessment entails essentially a multi-criteria decision-making process accounting for qualitative and quantitative uncertainties and their transformation. Considering uncertainties of randomness and fuzziness in water quality evaluation, a cloud model-based assessment approach is proposed. The cognitive cloud model, derived from information science, can realize the transformation between qualitative concept and quantitative data, based on probability and statistics and fuzzy set theory. When applying the cloud model to practical assessment, three technical issues are considered before the development of a complete cloud model-based approach: (1) bilateral boundary formula with nonlinear boundary regression for parameter estimation, (2) hybrid entropy-analytic hierarchy process technique for calculation of weights, and (3) mean of repeated simulations for determining the degree of final certainty. The cloud model-based approach is tested by evaluating the eutrophication status of 12 typical lakes and reservoirs in China and comparing with other four methods, which are Scoring Index method, Variable Fuzzy Sets method, Hybrid Fuzzy and Optimal model, and Neural Networks method. The proposed approach yields information concerning membership for each water quality status which leads to the final status. The approach is found to be representative of other alternative methods and accurate. PMID:26995351
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…
Noiseless Linear Amplification with General Local Unitary Operations
NASA Astrophysics Data System (ADS)
Song, Yang; Ning-Juan, Ruan; Yun, Su; Xu-Ling, Lin; Zhi-Qiang, Wu
2016-07-01
Not Available Supported by the National Natural Science Foundation of China under Grant Nos 11304013, 11204197, 11204379 and 11074244, the National Basic Research Program of China under Grant No 2011CBA00200, the Doctor Science Research Foundation of Ministry of Education of China under Grant No 20113402110059, and Civil Aerospace 2013669.
General Purpose Unfolding Program with Linear and Nonlinear Regularizations.
Energy Science and Technology Software Center (ESTSC)
1987-05-07
Version 00 The interpretation of several physical measurements requires the unfolding or deconvolution of the solution of Fredholm integral equations of the first kind. Examples include neutron spectroscopy with activation detectors, moderating spheres, or proton recoil measurements. LOUHI82 is designed to be applicable to a large number of physical problems and to be extended to incorporate other unfolding methods.
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…
ERIC Educational Resources Information Center
Dobbs, David E.
2013-01-01
A direct method is given for solving first-order linear recurrences with constant coefficients. The limiting value of that solution is studied as "n to infinity." This classroom note could serve as enrichment material for the typical introductory course on discrete mathematics that follows a calculus course.
Improved Electrohydraulic Linear Actuators
NASA Technical Reports Server (NTRS)
Hamtil, James
2004-01-01
A product line of improved electrohydraulic linear actuators has been developed. These actuators are designed especially for use in actuating valves in rocket-engine test facilities. They are also adaptable to many industrial uses, such as steam turbines, process control valves, dampers, motion control, etc. The advantageous features of the improved electrohydraulic linear actuators are best described with respect to shortcomings of prior electrohydraulic linear actuators that the improved ones are intended to supplant. The flow of hydraulic fluid to the two ports of the actuator cylinder is controlled by a servo valve that is controlled by a signal from a servo amplifier that, in turn, receives an analog position-command signal (a current having a value between 4 and 20 mA) from a supervisory control system of the facility. As the position command changes, the servo valve shifts, causing a greater flow of hydraulic fluid to one side of the cylinder and thereby causing the actuator piston to move to extend or retract a piston rod from the actuator body. A linear variable differential transformer (LVDT) directly linked to the piston provides a position-feedback signal, which is compared with the position-command signal in the servo amplifier. When the position-feedback and position-command signals match, the servo valve moves to its null position, in which it holds the actuator piston at a steady position.
Resistors Improve Ramp Linearity
NASA Technical Reports Server (NTRS)
Kleinberg, L. L.
1982-01-01
Simple modification to bootstrap ramp generator gives more linear output over longer sweep times. New circuit adds just two resistors, one of which is adjustable. Modification cancels nonlinearities due to variations in load on charging capacitor and due to changes in charging current as the voltage across capacitor increases.
Linear Classification Functions.
ERIC Educational Resources Information Center
Huberty, Carl J.; Smith, Jerry D.
Linear classification functions (LCFs) arise in a predictive discriminant analysis for the purpose of classifying experimental units into criterion groups. The relative contribution of the response variables to classification accuracy may be based on LCF-variable correlations for each group. It is proved that, if the raw response measures are…
NASA Technical Reports Server (NTRS)
Chandler, J. A. (Inventor)
1985-01-01
The linear motion valve is described. The valve spool employs magnetically permeable rings, spaced apart axially, which engage a sealing assembly having magnetically permeable pole pieces in magnetic relationship with a magnet. The gap between the ring and the pole pieces is sealed with a ferrofluid. Depletion of the ferrofluid is minimized.
NASA Astrophysics Data System (ADS)
McFadden, Fiona J. Stevens; Welch, Barry J.; Austin, Pual C.
2006-02-01
This paper investigates the application of multivariable model-based control to improve the regulatory control of electrolyte temperature, aluminum fluoride concentration, liquidus temperature, superheat, and electrolyte height. Also examined are therappropriateness of different control structures and the possible inclusion of recently developed sensors for alumina concentration and individual cell duct flowrate, temperature, and heat loss. For the smelter in this study, the maximum improvement possible with a multivariable model-based controller is predicted to be 30 40% reduction in standard deviation in electrolyte temperature, aluminum fluoride concentration, liquidus temperature, and superheat, and around half this for electrolyte height. Three control structures were found to be appropriate; all are different than the existing control structure, which was found to be suboptimal. Linear Quadratic Gaussian controllers were designed for each control structure and their predicted performance compared.
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
Families of Linear Recurrences for Catalan Numbers
ERIC Educational Resources Information Center
Gauthier, N.
2011-01-01
Four different families of linear recurrences are derived for Catalan numbers. The derivations rest on John Riordan's 1973 generalization of Catalan numbers to a set of polynomials. Elementary differential and integral calculus techniques are used and the results should be of interest to teachers and students of introductory courses in calculus…
DUSTYWAVE: Linear waves in gas and dust
NASA Astrophysics Data System (ADS)
Laibe, Guillaume; Price, Daniel J.
2016-02-01
Written in Fortran, DUSTYWAVE computes the exact solution for linear waves in a two-fluid mixture of gas and dust. The solutions are general with respect to both the dust-to-gas ratio and the amplitude of the drag coefficient.
NASA Astrophysics Data System (ADS)
Thomson, Mark J.; McKellar, Bruce H. J.
1991-04-01
A simple, non-linear generalization of the MSW equation is presented and its analytic solution is outlined. The orbits of the polarization vector are shown to be periodic, and to lie on a sphere. Their non-trivial flow patterns fall into two topological categories, the more complex of which can become chaotic if perturbed.
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…
Model-based synthesis of locally contingent responses to global market signals
NASA Astrophysics Data System (ADS)
Magliocca, N. R.
2015-12-01
Rural livelihoods and the land systems on which they depend are increasingly influenced by distant markets through economic globalization. Place-based analyses of land and livelihood system sustainability must then consider both proximate and distant influences on local decision-making. Thus, advancing land change theory in the context of economic globalization calls for a systematic understanding of the general processes as well as local contingencies shaping local responses to global signals. Synthesis of insights from place-based case studies of land and livelihood change is a path forward for developing such systematic knowledge. This paper introduces a model-based synthesis approach to investigating the influence of local socio-environmental and agent-level factors in mediating land-use and livelihood responses to changing global market signals. A generalized agent-based modeling framework is applied to six case-study sites that differ in environmental conditions, market access and influence, and livelihood settings. The largest modeled land conversions and livelihood transitions to market-oriented production occurred in sties with relatively productive agricultural land and/or with limited livelihood options. Experimental shifts in the distributions of agents' risk tolerances generally acted to attenuate or amplify responses to changes in global market signals. Importantly, however, responses of agents at different points in the risk tolerance distribution varied widely, with the wealth gap growing wider between agents with higher or lower risk tolerance. These results demonstrate model-based synthesis is a promising approach to overcome many of the challenges of current synthesis methods in land change science, and to identify generalized as well as locally contingent responses to global market signals.
Linear irreversible heat engines based on local equilibrium assumptions
NASA Astrophysics Data System (ADS)
Izumida, Yuki; Okuda, Koji
2015-08-01
We formulate an endoreversible finite-time Carnot cycle model based on the assumptions of local equilibrium and constant energy flux, where the efficiency and the power are expressed in terms of the thermodynamic variables of the working substance. By analyzing the entropy production rate caused by the heat transfer in each isothermal process during the cycle, and using the endoreversible condition applied to the linear response regime, we identify the thermodynamic flux and force of the present system and obtain a linear relation that connects them. We calculate the efficiency at maximum power in the linear response regime by using the linear relation, which agrees with the Curzon-Ahlborn (CA) efficiency known as the upper bound in this regime. This reason is also elucidated by rewriting our model into the form of the Onsager relations, where our model turns out to satisfy the tight-coupling condition leading to the CA efficiency.
Broadband model-based processing for shallow ocean environments
Candy, J.V.; Sullivan, E.J.
1998-07-01
Most acoustic sources found in the ocean environment are spatially complex and broadband. In the case of shallow water propagation, these source characteristics complicate the analysis of received acoustic data considerably. A common approach to the broadband problem is to decompose the received signal into a set of narrow-band lines. This then allows the problem to be treated as a multiplicity of narrow-band problems. Here a model-based approach is developed for the processing of data received on a vertical array from a broadband source where it is assumed that the propagation is governed by the normal-mode model. The goal of the processor is to provide an enhanced (filtered) version of the pressure at the array and the modal functions. Thus a pre-processor is actually developed, since one could think of several applications for these enhanced quantities such as localization, modal estimation, etc. It is well-known that in normal-mode theory a different modal structure evolves for each temporal frequency; thus it is not surprising that the model-based solution to this problem results in a scheme that requires a {open_quotes}bank{close_quotes} of narrow-band model-based processors{emdash}each with its own underlying modal structure for the narrow frequency band it operates over. The {open_quotes}optimal{close_quotes} Bayesian solution to the broadband pressure field enhancement and modal function extraction problem is developed. It is shown how this broadband processor can be implemented (using a suboptimal scheme) in pseudo real time due to its inherent parallel structure. A set of noisy broadband data is synthesized to demonstrate how to construct the processor and achieve a minimum variance (optimal Bayesian) design. It is shown that both broadband pressure-field and modal function estimates can be extracted illustrating the feasibility of this approach. {copyright} {ital 1998 Acoustical Society of America.}
The Design of Model-Based Training Programs
NASA Technical Reports Server (NTRS)
Polson, Peter; Sherry, Lance; Feary, Michael; Palmer, Everett; Alkin, Marty; McCrobie, Dan; Kelley, Jerry; Rosekind, Mark (Technical Monitor)
1997-01-01
This paper proposes a model-based training program for the skills necessary to operate advance avionics systems that incorporate advanced autopilots and fight management systems. The training model is based on a formalism, the operational procedure model, that represents the mission model, the rules, and the functions of a modem avionics system. This formalism has been defined such that it can be understood and shared by pilots, the avionics software, and design engineers. Each element of the software is defined in terms of its intent (What?), the rationale (Why?), and the resulting behavior (How?). The Advanced Computer Tutoring project at Carnegie Mellon University has developed a type of model-based, computer aided instructional technology called cognitive tutors. They summarize numerous studies showing that training times to a specified level of competence can be achieved in one third the time of conventional class room instruction. We are developing a similar model-based training program for the skills necessary to operation the avionics. The model underlying the instructional program and that simulates the effects of pilots entries and the behavior of the avionics is based on the operational procedure model. Pilots are given a series of vertical flightpath management problems. Entries that result in violations, such as failure to make a crossing restriction or violating the speed limits, result in error messages with instruction. At any time, the flightcrew can request suggestions on the appropriate set of actions. A similar and successful training program for basic skills for the FMS on the Boeing 737-300 was developed and evaluated. The results strongly support the claim that the training methodology can be adapted to the cockpit.
A Linear Bicharacteristic FDTD Method
NASA Technical Reports Server (NTRS)
Beggs, John H.
2001-01-01
The linear bicharacteristic scheme (LBS) was originally developed to improve unsteady solutions in computational acoustics and aeroacoustics [1]-[7]. It is a classical leapfrog algorithm, but is combined with upwind bias in the spatial derivatives. This approach preserves the time-reversibility of the leapfrog algorithm, which results in no dissipation, and it permits more flexibility by the ability to adopt a characteristic based method. The use of characteristic variables allows the LBS to treat the outer computational boundaries naturally using the exact compatibility equations. The LBS offers a central storage approach with lower dispersion than the Yee algorithm, plus it generalizes much easier to nonuniform grids. It has previously been applied to two and three-dimensional freespace electromagnetic propagation and scattering problems [3], [6], [7]. This paper extends the LBS to model lossy dielectric and magnetic materials. Results are presented for several one-dimensional model problems, and the FDTD algorithm is chosen as a convenient reference for comparison.
Linearized gravity with matter time
NASA Astrophysics Data System (ADS)
Ali, Masooma; Husain, Viqar; Rahmati, Shohreh; Ziprick, Jonathan
2016-05-01
We study general relativity with pressureless dust in the canonical formulation, with the dust field chosen as a matter time gauge. The resulting theory has three physical degrees of freedom in the metric field. The linearized canonical theory reveals two graviton modes and a scalar mode. We find that the graviton modes remain Lorentz covariant despite the time gauge, and that the scalar mode is ultralocal. We also discuss a modification of the theory to include a parameter in the Hamiltonian that is analogous to that in Horava-Lifshitz models. In this case the scalar mode is no longer ultralocal and it acquires a propagation speed that is dependent on the deformation parameter.
Overview of linear collider designs
Siemann, R.H.
1993-04-01
Linear collider design and development have become focused on a center-of-mass energy E{sub CM} = 0.5 TeV and a luminosity L {approximately} 5 {times} 10{sup 33} cm{sup {minus}2}sec{sup {minus}1}. There are diverse approaches to meeting these general objectives. The diversity arises from different judgements about the ease of developing new and improving existing technology, costs, extension to higher energies, experimental backgrounds and center-of-mass energy spectrum, and tolerances and beam power. The parameters of possible colliders are given in this paper. This report will focus on some of the common themes of these designs and the different between them.
Linear regression in astronomy. I
NASA Technical Reports Server (NTRS)
Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh
1990-01-01
Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.
The International Linear Collider
NASA Astrophysics Data System (ADS)
List, Benno
2014-04-01
The International Linear Collider (ILC) is a proposed e+e- linear collider with a centre-of-mass energy of 200-500 GeV, based on superconducting RF cavities. The ILC would be an ideal machine for precision studies of a light Higgs boson and the top quark, and would have a discovery potential for new particles that is complementary to that of LHC. The clean experimental conditions would allow the operation of detectors with extremely good performance; two such detectors, ILD and SiD, are currently being designed. Both make use of novel concepts for tracking and calorimetry. The Japanese High Energy Physics community has recently recommended to build the ILC in Japan.
Banks, R.M.
1986-01-14
This patent describes a linear output nitinol engine consisting of a number of integrated communicating parts. The engine has an external support framework which is described in detail. The patent further describes a wire transport mechanism, a pair of linkage levers with a loom secured to them, a number of nitinol wires strung between the looms, and a power takeoff block secured to the linkage levers. A pulley positioned in a flip-flop supporting bracket and a power takeoff modality including a tension member connected to a power output cable in order to provide linear power output transmission is described. A method for biasing the timing and the mechanism for timing the synchronization of the throw over arms and the flip-flop of the pulley are also described.
NASA Technical Reports Server (NTRS)
Goldowsky, Michael P. (Inventor)
1987-01-01
A reciprocating linear motor is formed with a pair of ring-shaped permanent magnets having opposite radial polarizations, held axially apart by a nonmagnetic yoke, which serves as an axially displaceable armature assembly. A pair of annularly wound coils having axial lengths which differ from the axial lengths of the permanent magnets are serially coupled together in mutual opposition and positioned with an outer cylindrical core in axial symmetry about the armature assembly. One embodiment includes a second pair of annularly wound coils serially coupled together in mutual opposition and an inner cylindrical core positioned in axial symmetry inside the armature radially opposite to the first pair of coils. Application of a potential difference across a serial connection of the two pairs of coils creates a current flow perpendicular to the magnetic field created by the armature magnets, thereby causing limited linear displacement of the magnets relative to the coils.
A model-based multisensor data fusion knowledge management approach
NASA Astrophysics Data System (ADS)
Straub, Jeremy
2014-06-01
A variety of approaches exist for combining data from multiple sensors. The model-based approach combines data based on its support for or refutation of elements of the model which in turn can be used to evaluate an experimental thesis. This paper presents a collection of algorithms for mapping various types of sensor data onto a thesis-based model and evaluating the truth or falsity of the thesis, based on the model. The use of this approach for autonomously arriving at findings and for prioritizing data are considered. Techniques for updating the model (instead of arriving at a true/false assertion) are also discussed.
Method for Real-Time Model Based Structural Anomaly Detection
NASA Technical Reports Server (NTRS)
Smith, Timothy A. (Inventor); Urnes, James M., Sr. (Inventor); Reichenbach, Eric Y. (Inventor)
2015-01-01
A system and methods for real-time model based vehicle structural anomaly detection are disclosed. A real-time measurement corresponding to a location on a vehicle structure during an operation of the vehicle is received, and the real-time measurement is compared to expected operation data for the location to provide a modeling error signal. A statistical significance of the modeling error signal to provide an error significance is calculated, and a persistence of the error significance is determined. A structural anomaly is indicated, if the persistence exceeds a persistence threshold value.
Model-Based Information Extraction From Synthetic Aperture Radar Signals
NASA Astrophysics Data System (ADS)
Matzner, Shari A.
2011-07-01
Synthetic aperture radar (SAR) is a remote sensing technology for imaging areas of the earth's surface. SAR has been successfully used for monitoring characteristics of the natural environment such as land cover type and tree density. With the advent of higher resolution sensors, it is now theoretically possible to extract information about individual structures such as buildings from SAR imagery. This information could be used for disaster response and security-related intelligence. SAR has an advantage over other remote sensing technologies for these applications because SAR data can be collected during the night and in rainy or cloudy conditions. This research presents a model-based method for extracting information about a building -- its height and roof slope -- from a single SAR image. Other methods require multiple images or ancillary data from specialized sensors, making them less practical. The model-based method uses simulation to match a hypothesized building to an observed SAR image. The degree to which a simulation matches the observed data is measured by mutual information. The success of this method depends on the accuracy of the simulation and on the reliability of the mutual information similarity measure. Electromagnetic theory was applied to relate a building's physical characteristics to the features present in a SAR image. This understanding was used to quantify the precision of building information contained in SAR data, and to identify the inputs needed for accurate simulation. A new SAR simulation technique was developed to meet the accuracy and efficiency requirements of model-based information extraction. Mutual information, a concept from information theory, has become a standard for measuring the similarity between medical images. Its performance in the context of matching a simulation image to a SAR image was evaluated in this research, and it was found to perform well under certain conditions. The factors that affect its performance
Temporal and contextual knowledge in model-based expert systems
NASA Technical Reports Server (NTRS)
Toth-Fejel, Tihamer; Heher, Dennis
1987-01-01
A basic paradigm that allows representation of physical systems with a focus on context and time is presented. Paragon provides the capability to quickly capture an expert's knowledge in a cognitively resonant manner. From that description, Paragon creates a simulation model in LISP, which when executed, verifies that the domain expert did not make any mistakes. The Achille's heel of rule-based systems has been the lack of a systematic methodology for testing, and Paragon's developers are certain that the model-based approach overcomes that problem. The reason this testing is now possible is that software, which is very difficult to test, has in essence been transformed into hardware.
Model-based engineering for medical-device software.
Ray, Arnab; Jetley, Raoul; Jones, Paul L; Zhang, Yi
2010-01-01
This paper demonstrates the benefits of adopting model-based design techniques for engineering medical device software. By using a patient-controlled analgesic (PCA) infusion pump as a candidate medical device, the authors show how using models to capture design information allows for i) fast and efficient construction of executable device prototypes ii) creation of a standard, reusable baseline software architecture for a particular device family, iii) formal verification of the design against safety requirements, and iv) creation of a safety framework that reduces verification costs for future versions of the device software. 1. PMID:21142522
A parametric vocal fold model based on magnetic resonance imaging.
Wu, Liang; Zhang, Zhaoyan
2016-08-01
This paper introduces a parametric three-dimensional body-cover vocal fold model based on magnetic resonance imaging (MRI) of the human larynx. Major geometric features that are observed in the MRI images but missing in current vocal fold models are discussed, and their influence on vocal fold vibration is evaluated using eigenmode analysis. Proper boundary conditions for the model are also discussed. Based on control parameters corresponding to anatomic landmarks that can be easily measured, this model can be adapted toward a subject-specific vocal fold model for voice production research and clinical applications. PMID:27586774
A model-based executive for commanding robot teams
NASA Technical Reports Server (NTRS)
Barrett, Anthony
2005-01-01
The paper presents a way to robustly command a system of systems as a single entity. Instead of modeling each component system in isolation and then manually crafting interaction protocols, this approach starts with a model of the collective population as a single system. By compiling the model into separate elements for each component system and utilizing a teamwork model for coordination, it circumvents the complexities of manually crafting robust interaction protocols. The resulting systems are both globally responsive by virtue of a team oriented interaction model and locally responsive by virtue of a distributed approach to model-based fault detection, isolation, and recovery.
Spring-Model-Based Wireless Localization in Cooperative User Environments
NASA Astrophysics Data System (ADS)
Ke, Wei; Wu, Lenan; Qi, Chenhao
To overcome the shortcomings of conventional cellular positioning, a novel cooperative location algorithm that uses the available peer-to-peer communication between the mobile terminals (MTs) is proposed. The main idea behind the proposed approach is to incorporate the long- and short-range location information to improve the estimation of the MT's coordinates. Since short-range communications among MTs are characterized by high line-of-sight (LOS) probability, an improved spring-model-based cooperative location method can be exploited to provide low-cost improvement for cellular-based location in the non-line-of-sight (NLOS) environments.
Hot blast stove process model and model-based controller
Muske, K.R.; Howse, J.W.; Hansen, G.A.; Cagliostro, D.J.; Chaubal, P.C.
1998-12-31
This paper describes the process model and model-based control techniques implemented on the hot blast stoves for the No. 7 Blast Furnace at the Inland Steel facility in East Chicago, Indiana. A detailed heat transfer model of the stoves is developed and verified using plant data. This model is used as part of a predictive control scheme to determine the minimum amount of fuel necessary to achieve the blast air requirements. The model is also used to predict maximum and minimum temperature constraint violations within the stove so that the controller can take corrective actions while still achieving the required stove performance.
Evaluating model accuracy for model-based reasoning
NASA Technical Reports Server (NTRS)
Chien, Steve; Roden, Joseph
1992-01-01
Described here is an approach to automatically assessing the accuracy of various components of a model. In this approach, actual data from the operation of a target system is used to drive statistical measures to evaluate the prediction accuracy of various portions of the model. We describe how these statistical measures of model accuracy can be used in model-based reasoning for monitoring and design. We then describe the application of these techniques to the monitoring and design of the water recovery system of the Environmental Control and Life Support System (ECLSS) of Space Station Freedom.