Adaptive quasi-likelihood estimate in generalized linear models
CHEN Xia; CHEN Xiru
2005-01-01
This paper gives a thorough theoretical treatment on the adaptive quasilikelihood estimate of the parameters in the generalized linear models. The unknown covariance matrix of the response variable is estimated by the sample. It is shown that the adaptive estimator defined in this paper is asymptotically most efficient in the sense that it is asymptotic normal, and the covariance matrix of the limit distribution coincides with the one for the quasi-likelihood estimator for the case that the covariance matrix of the response variable is completely known.
Biohybrid control of general linear systems using the adaptive filter model of cerebellum
Emma D. Wilson
2015-07-01
Full Text Available 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 cancellation of reafferent noise. It has also been successfully applied to problems in robotics such as adaptive camera stabilisation and sensor noise cancellation. 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 stabilises 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.
Generalized Linear Orthomorphisms
Haiqing Han
2013-05-01
Full Text Available In this study, we generalize the conception of orthomorphisms and obtain a counting formula on the generalized linear orthomorphisms over the Galois field with the arbitrary prime number p as the characteristic. Thus the partial generation algorithm of generalized linear orthomorphisms is achieved. The counting formula of the linear orthomorphisms over the finite field with characteristic 2 is the special case in our results. Furthermore, the generalized linear orthomorphisms generated and discussed in this study can gain the maximum branch number when they are designed as P-permutations.
Adaptive functional linear regression
Comte, Fabienne
2011-01-01
We consider the estimation of the slope function in functional linear regression, where scalar responses are modeled in dependence of random functions. Cardot and Johannes [2010] have shown that a thresholded projection estimator can attain up to a constant minimax-rates of convergence in a general framework which allows to cover the prediction problem with respect to the mean squared prediction error as well as the estimation of the slope function and its derivatives. This estimation procedure, however, requires an optimal choice of a tuning parameter with regard to certain characteristics of the slope function and the covariance operator associated with the functional regressor. As this information is usually inaccessible in practice, we investigate a fully data-driven choice of the tuning parameter which combines model selection and Lepski's method. It is inspired by the recent work of Goldenshluger and Lepski [2011]. The tuning parameter is selected as minimizer of a stochastic penalized contrast function...
Adaptive Local Linear Quantile Regression
Yu-nan Su; Mao-zai Tian
2011-01-01
In this paper we propose a new method of local linear adaptive smoothing for nonparametric conditional quantile regression. Some theoretical properties of the procedure are investigated. Then we demonstrate the performance of the method on a simulated example and compare it with other methods. The simulation results demonstrate a reasonable performance of our method proposed especially in situations when the underlying image is piecewise linear or can be approximated by such images. Generally speaking, our method outperforms most other existing methods in the sense of the mean square estimation (MSE) and mean absolute estimation (MAE) criteria. The procedure is very stable with respect to increasing noise level and the algorithm can be easily applied to higher dimensional situations.
Adaptive Sticky Generalized Metropolis
Fabrizio Leisen; Roberto Casarin; David Luengo; Luca Martino
2013-01-01
We introduce a new class of adaptive Metropolis algorithms called adaptive sticky algorithms for efficient general-purpose simulation from a target probability distribution. The transition of the Metropolis chain is based on a multiple-try scheme and the different proposals are generated by adaptive nonparametric distributions. Our adaptation strategy uses the interpolation of support points from the past history of the chain as in the adaptive rejection Metropolis. The algorithm efficiency i...
General linear chirplet transform
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
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.
Foundations of linear and generalized linear models
Agresti, Alan
2015-01-01
A valuable overview of the most important ideas and results in statistical analysis Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linear statistical models. The book presents a broad, in-depth overview of the most commonly used statistical models by discussing the theory underlying the models, R software applications, and examples with crafted models to elucidate key ideas and promote practical model building. The book begins by illustrating the fundamentals of linear models,
Introduction to general and generalized linear models
Madsen, Henrik
2010-01-01
IntroductionExamples of types of data Motivating examples A first view on the modelsThe Likelihood PrincipleIntroduction Point estimation theory The likelihood function The score function The information matrix Alternative parameterizations of the likelihood The maximum likelihood estimate (MLE) Distribution of the ML estimator Generalized loss-function and deviance Quadratic approximation of the log-likelihood Likelihood ratio tests Successive testing in hypothesis chains Dealing with nuisance parameters General Linear ModelsIntroduction The multivariate normal distribution General linear mod
Generalized, Linear, and Mixed Models
McCulloch, Charles E; Neuhaus, John M
2011-01-01
An accessible and self-contained introduction to statistical models-now in a modernized new editionGeneralized, Linear, and Mixed Models, Second Edition provides an up-to-date treatment of the essential techniques for developing and applying a wide variety of statistical models. The book presents thorough and unified coverage of the theory behind generalized, linear, and mixed models and highlights their similarities and differences in various construction, application, and computational aspects.A clear introduction to the basic ideas of fixed effects models, random effects models, and mixed m
Adaptive feedback linearization of nonlinear SISO systems
Gonzales, R. I.; Duarte-Mermoud, M. A.; Zagalak, Petr
New Haven : Yale University, 2003, s. 160-169. [Workshop on Adaptive and Learning Systems /12./. Yale (US), 28.05.2003-30.05.2003] R&D Projects: GA ČR GA102/02/0204 Institutional research plan: CEZ:AV0Z1075907 Keywords : adaptive linearization * nonlinear systems * feedback linearization Subject RIV: BC - Control Systems Theory
Generalization of Prism Adaptation
Redding, Gordon M.; Wallace, Benjamin
2006-01-01
Prism exposure produces 2 kinds of adaptive response. Recalibration is ordinary strategic remapping of spatially coded movement commands to rapidly reduce performance error. Realignment is the extraordinary process of transforming spatial maps to bring the origins of coordinate systems into correspondence. Realignment occurs when spatial…
Actuarial statistics with generalized linear mixed models
K. Antonio; J. Beirlant
2007-01-01
Over the last decade the use of generalized linear models (GLMs) in actuarial statistics has received a lot of attention, starting from the actuarial illustrations in the standard text by McCullagh and Nelder [McCullagh, P., Nelder, J.A., 1989. Generalized linear models. In: Monographs on Statistics
Adaptive dynamic programming for linear impulse systems
Xiao-hua WANG; Juan-juan YU; Yao HUANG; Hua WANG; Zhong-hua MIAO
2014-01-01
We investigate the optimization of linear impulse systems with the reinforcement learning based adaptive dynamic programming (ADP) method. For linear impulse systems, the optimal objective function is shown to be a quadric form of the pre-impulse states. The ADP method provides solutions that iteratively converge to the optimal objective function. If an initial guess of the pre-impulse objective function is selected as a quadratic form of the pre-impulse states, the objective function iteratively converges to the optimal one through ADP. Though direct use of the quadratic objective function of the states within the ADP method is theoretically possible, the numerical singularity problem may occur due to the matrix inversion therein when the system dimensionality increases. A neural network based ADP method can circumvent this problem. A neural network with polynomial activation functions is selected to approximate the pre-impulse objective function and trained iteratively using the ADP method to achieve optimal control. After a successful training, optimal impulse control can be derived. Simulations are presented for illustrative purposes.
STABILITY OF GENERAL LINEAR METHODS FOR SYSTEMS OF FUNCTIONAL-DIFFERENTIAL AND FUNCTIONAL EQUATIONS
Si-qing Gan; Wei-min Zheng
2005-01-01
This paper is concerned with the numerical solution of functional-differential and functional equations which include functional-differential equations of neutral type as special cases. The adaptation of general linear methods is considered. It is proved that A-stable general linear methods can inherit the asymptotic stability of underlying linear systems.Some general results of numerical stability are also given.
Generalized Cross-Gramian for Linear Systems
Shaker, Hamid Reza
2012-01-01
The cross-gramian is a well-known matrix with embedded controllability and observability information. The cross-gramian is related to the Hankel operator and the Hankel singular values of a linear square system and it has several interesting properties. These properties make the cross......-gramian popular in several applications including model reduction, control configuration selection and sensitivity analysis. The ordinary cross-gramian which has been defined in the literature is the solution of a Sylvester equation. This Sylvester equation is not always solvable and therefore for some linear...... square symmetric systems, the ordinary cross-gramian does not exist. To cope with this problem, a new generalized cross-gramian is introduced in this paper. In contrast to the ordinary cross-gramian, the generalized cross-gramian can be easily obtained for general linear systems and therefore can be used...
Linear generalized synchronization of chaotic systems with uncertain parameters
Jia Zhen
2008-01-01
A more general form of projective synchronization,so called linear generalized synchronization(LGS)is proposed,which includes the generalized projective synchronization(GPS)and the hybrid projective synchronization(HPS)as its special cases.Based on the adaptive technique and Lyapunov stability theory,a general method for achieving the LGS between two chaotic or hyperchaotic systems with uncertain parameters in any scaling matrix is presented.Some numerical simulations are provided to show the effectiveness and feasibility of the proposed synchronization method.
Adaptive position controller for double armature brushless dc linear motor
Demirci, R. [Abant Izzet Baysal Univ., Technical Education Faculty, Electrical Dept., Dunez (Turkey); Dursun, M. [Gazi University, Technical Education Faculty, Electrical Dept., Ankara (Turkey)
2000-08-01
An adaptive position controller has been proposed for double armature brushless DC linear motor. The proposed position control system comprises an inner model reference adaptive velocity control loop and an outer position control loop. The parameters of the adaptive controller have been adjusted by using modified gradient type parameter adaptation algorithm. (orig.)
Detailed Decompositions in Generalized Linear Models
Kaiser, Boris
2013-01-01
We propose a new approach for performing detailed decompositions of average outcome differentials, which can be applied to all types of generalized linear models. A simulation exercise demonstrates that our method produces more convincing results than existing methods. An empirical application to the immigrant-native wage differential in Switzerland is presented.
Lower bounds for adaptive linearity tests
Lovett, Shachar
2008-01-01
Linearity tests are randomized algorithms which have oracle access to the truth table of some function f, and are supposed to distinguish between linear functions and functions which are far from linear. Linearity tests were first introduced by (Blum, Luby and Rubenfeld, 1993), and were later used in the PCP theorem, among other applications. The quality of a linearity test is described by its correctness c - the probability it accepts linear functions, its soundness s - the probability it accepts functions far from linear, and its query complexity q - the number of queries it makes. Linearity tests were studied in order to decrease the soundness of linearity tests, while keeping the query complexity small (for one reason, to improve PCP constructions). Samorodnitsky and Trevisan (Samorodnitsky and Trevisan 2000) constructed the Complete Graph Test, and prove that no Hyper Graph Test can perform better than the Complete Graph Test. Later in (Samorodnitsky and Trevisan 2006) they prove, among other results, th...
Generalized Adaptive Artificial Neural Networks
Tawel, Raoul
1993-01-01
Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.
Generalized non-linear Schroedinger hierarchy
The importance in studying the completely integrable models have became evident in the last years due to the fact that those models present an algebraic structure extremely rich, providing the natural scenery for solitons description. Those models can be described through non-linear differential equations, pseudo-linear operators (Lax formulation), or a matrix formulation. The integrability implies in the existence of a conservation law associated to each of degree of freedom. Each conserved charge Qi can be associated to a Hamiltonian, defining a time evolution related to to a time ti through the Hamilton equation ∂A/∂ti=[A,Qi]. Particularly, for a two-dimensions field theory, infinite degree of freedom exist, and consequently infinite conservation laws describing the time evolution in space of infinite times. The Hamilton equation defines a hierarchy of models which present a infinite set of conservation laws. This paper studies the generalized non-linear Schroedinger hierarchy
General Linearized Polynomial Interpolation and Its Applications
Xie, Hongmei; Suter, Bruce W
2011-01-01
In this paper, we first propose a general interpolation algorithm in a free module of a linearized polynomial ring, and then apply this algorithm to decode several important families of codes, Gabidulin codes, KK codes and MV codes. Our decoding algorithm for Gabidulin codes is different from the polynomial reconstruction algorithm by Loidreau. When applied to decode KK codes, our interpolation algorithm is equivalent to the Sudan-style list-1 decoding algorithm proposed by K/"otter and Kschischang for KK codes. The general interpolation approach is also capable of solving the interpolation problem for the list decoding of MV codes proposed by Mahdavifar and Vardy, and has a lower complexity than solving linear equations.
GENERALIZED DERIVATIONS ON PARABOLIC SUBALGEBRAS OF GENERAL LINEAR LIE ALGEBRAS
陈正新
2014-01-01
Let P be a parabolic subalgebra of a general linear Lie algebra gl(n, F) over a field F, where n ≥ 3, F contains at least n different elements, and char(F) 6= 2. In this article, we prove that generalized derivations, quasiderivations, and product zero derivations of P coincide, and any generalized derivation of P is a sum of an inner derivation, a central quasiderivation, and a scalar multiplication map of P. We also show that any commuting automorphism of P is a central automorphism, and any commuting derivation of P is a central derivation.
Adaptive Output Regulation of Unknown Linear Systems with Unknown Exosystems
Mizumoto, Ikuro; Iwai, Zenta
2009-01-01
In this paper, the adaptive regulation problem for unknown controlled systems with unknown exosystems was considered. An adaptive output feedback controller with an adaptive internal model was proposed for single input/single output linear minimum phase systems. In the proposed method, a controller with an adaptive internal model was designed through an expanded backstepping strategy of only one step with a parallel feedforward compensator (PFC).
A HYBRID ADAPTIVE ALGORITHM FOR LINEAR OPTIMIZATION
MAZIAR SALAHI; TAMÁS TERLAKY
2009-01-01
Recently, using the framework of self-regularity, Salahi in his Ph.D. thesis proposed an adaptive single step algorithm which takes advantage of the current iterate information to find an appropriate barrier parameter rather than using a fixed fraction of the current duality gap. However, his algorithm might do at most one bad step after each good step in order to keep the iterate in a certain neighborhood of the central path. In this paper, using the same framework, we propose a hybrid adapt...
ADAPTIVE GENERALIZED PREDICTIVE CONTROL OF SWITCHED SYSTEMS
WANG Yi-jing; WANG Long
2005-01-01
The problem of adaptive generalized predictive control which consists of output prediction errors for a class of switched systems is studied. The switching law is determined by the output predictive errors of a finite number of subsystems. For the single subsystem and multiple subsystems cases, it is proved that the given direct algorithm of generalized predictive control guarantees the global convergence of the system. This algorithm overcomes the inherent drawbacks of the slow convergence and large transient errors for the conventional adaptive control.
Indirect techniques for adaptive input-output linearization of non-linear systems
Teel, Andrew; Kadiyala, Raja; Kokotovic, Peter; Sastry, Shankar
1991-01-01
A technique of indirect adaptive control based on certainty equivalence for input output linearization of nonlinear systems is proven convergent. It does not suffer from the overparameterization drawbacks of the direct adaptive control techniques on the same plant. This paper also contains a semiindirect adaptive controller which has several attractive features of both the direct and indirect schemes.
Adaptive H∞ synchronization of chaotic systems via linear and nonlinear feedback control
Adaptive H∞ synchronization of chaotic systems via linear and nonlinear feedback control is investigated. The chaotic systems are redesigned by using the generalized Hamiltonian systems and observer approach. Based on Lyapunov's stability theory, linear and nonlinear feedback control of adaptive H∞ synchronization is established in order to not only guarantee stable synchronization of both master and slave systems but also reduce the effect of external disturbance on an H∞-norm constraint. Adaptive H∞ synchronization of chaotic systems via three kinds of control is investigated with applications to Lorenz and Chen systems. Numerical simulations are also given to identify the effectiveness of the theoretical analysis. (general)
Non-linear Fields in Generalized Cosmologies
Fasiello, Matteo
2016-01-01
The perturbative approach to structure formation has recently received a lot of attention in the literature. In such setups the final predictions for observables like the power spectrum is often derived under additional approximations such as a simplified time dependence. Here we provide all-order perturbative integral solutions for density and velocity fields in generalized cosmologies. We go beyond the standard results based on extending the EdS-like approximations. As an illustrative example, we apply our findings to the calculation of the one-loop power spectrum. We find corrections close to $1\\%$ in the mildly non-linear regime of $\\Lambda$CDM cosmologies for the density power spectrum, while in the case of the density-momentum power spectrum effects can reach up to $1.5\\%$ for $k\\sim 0.2h/$Mpc.
A new adaptive scheme for the adaptive linearizing control of bioprocesses
Ferreira, E. C.; Azevedo, S. Feyo de
1996-01-01
This work deals with the development of model-based adaptive control algorithms for bioprocess operation. Non-linear adaptive control laws are proposed for single input single output regulation. Parameters are continuously adapted following a new adaptive scheme which ensures second-order dynamics of the parameter error system. A computational study is presented of the application of this theory to baker’s yeast fermentation. Results put in evidence the efficient performance both of ...
Rapid, generalized adaptation to asynchronous audiovisual speech.
Van der Burg, Erik; Goodbourn, Patrick T
2015-04-01
The brain is adaptive. The speed of propagation through air, and of low-level sensory processing, differs markedly between auditory and visual stimuli; yet the brain can adapt to compensate for the resulting cross-modal delays. Studies investigating temporal recalibration to audiovisual speech have used prolonged adaptation procedures, suggesting that adaptation is sluggish. Here, we show that adaptation to asynchronous audiovisual speech occurs rapidly. Participants viewed a brief clip of an actor pronouncing a single syllable. The voice was either advanced or delayed relative to the corresponding lip movements, and participants were asked to make a synchrony judgement. Although we did not use an explicit adaptation procedure, we demonstrate rapid recalibration based on a single audiovisual event. We find that the point of subjective simultaneity on each trial is highly contingent upon the modality order of the preceding trial. We find compelling evidence that rapid recalibration generalizes across different stimuli, and different actors. Finally, we demonstrate that rapid recalibration occurs even when auditory and visual events clearly belong to different actors. These results suggest that rapid temporal recalibration to audiovisual speech is primarily mediated by basic temporal factors, rather than higher-order factors such as perceived simultaneity and source identity. PMID:25716790
Linear Perturbation Adaptive Control of Hydraulically Driven Manipulators
Andersen, T.O.; Hansen, M.R.; Conrad, Finn
2004-01-01
A method for synthesis of a robust adaptive scheme for a hydraulically driven manipulator, that takes full advantage of any known system dynamics to simplify the adaptive control problem for the unknown portion of the dynamics is presented. The control method is based on adaptive perturbation...... control.Using the Lyapunov approach, under slowly time-varying assumptions, it is shown that the tracking error and the parameter error remain bounded. This bound is a function of the ideal parameters and a bounded disturbance. The control algorithm decouples and linearizes the manipulator so that each...... joint behaves as an independent second-order system with fixed dynamics....
Linear Perturbation Adaptive Control of Hydraulically Driven Manipulators
Andersen, T.O.; Hansen, M.R.; Conrad, Finn
control.Using the Lyapunov approach, under slowly time-varying assumptions, it is shown that the tracking error and the parameter error remain bounded. This bound is a function of the ideal parameters and a bounded disturbance. The control algorithm decouples and linearizes the manipulator so that each......A method for synthesis of a robust adaptive scheme for a hydraulically driven manipulator, that takes full advantage of any known system dynamics to simplify the adaptive control problem for the unknown portion of the dynamics is presented. The control method is based on adaptive perturbation...
Bootstraping the general linear hypothesis test
Delicado, Pedro; Río, Manuel del, 1690-1766
1993-01-01
We discuss the use of bootstrap methodology in hypothesis testing, focusing on the classical F-test for linear hypotheses in the linear model. A modification of the F-statistics which allows for resampling under the null hypothesis is proposed. This approach is specifically considered in the one-way analysis of variance model. A simulation study illustrating the behaviour of our proposal is presented.
Self-characterization of linear and nonlinear adaptive optics systems.
Hampton, Peter J; Conan, Rodolphe; Keskin, Onur; Bradley, Colin; Agathoklis, Pan
2008-01-10
We present methods used to determine the linear or nonlinear static response and the linear dynamic response of an adaptive optics (AO) system. This AO system consists of a nonlinear microelectromechanical systems deformable mirror (DM), a linear tip-tilt mirror (TTM), a control computer, and a Shack-Hartmann wavefront sensor. The system is modeled using a single-input-single-output structure to determine the one-dimensional transfer function of the dynamic response of the chain of system hardware. An AO system has been shown to be able to characterize its own response without additional instrumentation. Experimentally determined models are given for a TTM and a DM. PMID:18188192
Discrete Time Optimal Adaptive Control for Linear Stochastic Systems
JIANG Rui; LUO Guiming
2007-01-01
The least-squares(LS)algorithm has been used for system modeling for a long time. Without any excitation conditions, only the convergence rate of the common LS algorithm can be obtained. This paper analyzed the weighted least-squares(WLS)algorithm and described the good properties of the WLS algorithm. The WLS algorithm was then used for daptive control of linear stochastic systems to show that the linear closed-loop system was globally stable and that the system identification was consistent. Compared to the past optimal adaptive controller,this controller does not impose restricted conditions on the coefficients of the system, such as knowing the first coefficient before the controller. Without any persistent excitation conditions, the analysis shows that, with the regulation of the adaptive control, the closed-loop system was globally stable and the adaptive controller converged to the one-step-ahead optimal controller in some sense.
Coded Adaptive Linear Precoded Discrete Multitone Over PLC Channel
Muhammad, Fahad Syed; Hélard, Jean-François; Crussière, Matthieu
2008-01-01
Discrete multitone modulation (DMT) systems exploit the capabilities of orthogonal subcarriers to cope efficiently with narrowband interference, high frequency attenuations and multipath fadings with the help of simple equalization filters. Adaptive linear precoded discrete multitone (LP-DMT) system is based on classical DMT, combined with a linear precoding component. In this paper, we investigate the bit and energy allocation algorithm of an adaptive LP-DMT system taking into account the channel coding scheme. A coded adaptive LPDMT system is presented in the power line communication (PLC) context with a loading algorithm which accommodates the channel coding gains in bit and energy calculations. The performance of a concatenated channel coding scheme, consisting of an inner Wei's 4-dimensional 16-states trellis code and an outer Reed-Solomon code, in combination with the proposed algorithm is analyzed. Theoretical coding gains are derived and simulation results are presented for a fixed target bit error ra...
Linear Superposition of Minimal Surfaces: Generalized Helicoids and Minimal Cones
Hoppe, Jens
2016-01-01
Observing a linear superposition principle, a family of new minimal hypersurfaces in Euclidean space is found, as well as that linear combinations of generalized helicoids induce new algebraic minimal cones of arbitrarily high degree.
General linear methods for integrated circuit design
Voigtmann, Steffen
2006-01-01
Bei der Modellierung elektrischer Schaltungen ergeben sich Algebro-Differentialgleichungen (DAEs) mit proper formuliertem Hauptterm. Diese Gleichungen müssen z.B. bei der transienten Schaltungssimulation numerisch gelöst werden. Bei den klassischen Ansätzen der Linearen Mehrschrittverfahren oder der Runge-Kutta Verfahren ergeben sich Nachteile, die durch Verwendung von Allgemeinen Linearen Verfahren vermieden werden können. Sowohl Lineare Mehrschrittverfahren als auch Runge-Kutta Ver...
Adaptive feedback linearization applied to steering of ships
Thor I. Fossen
1993-10-01
Full Text Available This paper describes the application of feedback linearization to automatic steering of ships. The flexibility of the design procedure allows the autopilot to be optimized for both course-keeping and course-changing manoeuvres. Direct adaptive versions of both the course-keeping and turning controller are derived. The advantages of the adaptive controllers are improved performance and reduced fuel consumption. The application of nonlinear control theory also allows the designer in a systematic manner to compensate for nonlinearities in the control design.
Generalized local homology and cohomology for linearly compact modules
We study generalized local homology for linearly compact modules. By duality, we get some properties of generalized local cohomology modules and extend well-known properties of local cohomology of A. Grothendieck. (author)
Adaptive spectral identification techniques in presence of undetected non linearities
Cella, G; Guidi, G M
2002-01-01
The standard procedure for detection of gravitational wave coalescing binaries signals is based on Wiener filtering with an appropriate bank of template filters. This is the optimal procedure in the hypothesis of addictive Gaussian and stationary noise. We study the possibility of improving the detection efficiency with a class of adaptive spectral identification techniques, analyzing their effect in presence of non stationarities and undetected non linearities in the noise
Flexible Satellite Attitude Control via Adaptive Fuzzy Linearization
GUAN Ping; LIU Xiang-dong; CHEN Jia-bin; LIU Xiao-he
2005-01-01
The adaptive fuzzy control is combined with input-output linearization control to constitute the hybrid controller. The control method is then applied to the attitude maneuver control of the flexible satellite.The basic control structure is given. The rules of the controller parameter selection, which guarantee the attitude stabilization of the satellite with parameter uncertainties, have been analyzed. Simulation results show that the precise attitude control is accomplished in spite of the uncertainty in the system.
Adaptive Gaussian Mixture Filter Based on Statistical Linearization
Huber, Marco F.
2012-01-01
Gaussian mixtures are a common density representation in nonlinear, non-Gaussian Bayesian state estimation. Selecting an appropriate number of Gaussian components, however, is difficult as one has to trade of computational complexity against estimation accuracy. In this paper, an adaptive Gaussian mixture filter based on statistical linearization is proposed. Depending on the nonlinearity of the considered estimation problem, this filter dynamically increases the number of components via spli...
QUADRATIC INVARIANTS AND SYMPLECTIC STRUCTURE OF GENERAL LINEAR METHODS
Ai-guo Xiao; Shou-fu Li; Min Yang
2001-01-01
In this paper, we present some invariants and conservation laws of general linear methods applied to differential equation systems. We show that the quadratic invariants and symplecticity of the systems can be extended to general linear methods by a tensor product, and show that general linear methods with the matrix M=0 inherit in an extended sense the quadratic invariants possessed by the differential equation systems being integrated and preserve in an extended sense the symplectic structure of the phase space in the integration of Hamiltonian systems. These unify and extend existing relevant results on Runge-Kutta methods, linear multistep methods and one-leg methods. Finally, as special cases of general linear methods, we examine multistep Runge-Kutta methods, one-leg methods and linear two-step methods in detail.
Linear generalized synchronization of continuous-time chaotic systems
This paper develops a general approach for constructing a response system to implement linear generalized synchronization (GS) with the drive continuous-time chaotic system. Some sufficient conditions of global asymptotic linear GS between the drive and response continuous-time chaotic systems are attained from rigorously modern control theory. Finally, we take Chua's circuit as an example for illustration and verification
Linear generalized synchronization of continuous-time chaotic systems
Lu Jun
2003-01-01
This paper develops a general approach for constructing a response system to implement linear generalized synchronization (GS) with the drive continuous-time chaotic system. Some sufficient conditions of global asymptotic linear GS between the drive and response continuous-time chaotic systems are attained from rigorously modern control theory. Finally, we take Chua's circuit as an example for illustration and verification.
Adaptive set-point tracking of the Lorenz chaotic system using non-linear feedback
In this paper, an adaptive control method for set-point tracking of the Lorenz chaotic system by using non-linear feedback is proposed. The design procedure of the proposed controller is accomplished in two steps. At the first step, using Lyapunov's direct method, a non-linear state feedback is selected so that without any need to apply identification techniques, in despite of the uncertain parameters existence in the system state equations, the asymptotic stability of the general Lorenz system is guaranteed in a stochastic point of the manifold containing general system equilibrium points. At the second step, a linear state feedback with adaptive gain is added to the prior controller to eliminate the tracking error. In order to guarantee the system asymptotic stability at desired set-point, the indirect Lyapunov's method is used. Finally, to show the effectiveness of the proposed methodology, the simulation results of different experiments including system parameters changes and set-point variation are provided.
An adaptive genetic algorithm for solving bilevel linear programming problem
无
2007-01-01
Bilevel linear programming, which consists of the objective functions of the upper level and lower level, is a useful tool for modeling decentralized decision problems.Various methods are proposed for solving this problem. Of all the algorithms, the genetic algorithm is an alternative to conventional approaches to find the solution of the bilevel linear programming. In this paper, we describe an adaptive genetic algorithm for solving the bilevel linear programming problem to overcome the difficulty of determining the probabilities of crossover and mutation. In addition, some techniques are adopted not only to deal with the difficulty that most of the chromosomes may be infeasible in solving constrained optimization problem with genetic algorithm but also to improve the efficiency of the algorithm. The performance of this proposed algorithm is illustrated by the examples from references.
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…
Rapid, generalized adaptation to asynchronous audiovisual speech
Van der Burg, Erik; Goodbourn, Patrick T.
2015-01-01
The brain is adaptive. The speed of propagation through air, and of low-level sensory processing, differs markedly between auditory and visual stimuli; yet the brain can adapt to compensate for the resulting cross-modal delays. Studies investigating temporal recalibration to audiovisual speech have used prolonged adaptation procedures, suggesting that adaptation is sluggish. Here, we show that adaptation to asynchronous audiovisual speech occurs rapidly. Participants viewed a brief clip of an...
Linearly and Quadratically Separable Classifiers Using Adaptive Approach
Mohamed Abdel-Kawy Mohamed Ali Soliman; Rasha M. Abo-Bakr
2011-01-01
This paper presents a fast adaptive iterative algorithm to solve linearly separable classification problems in Rn.In each iteration,a subset of the sampling data (n-points,where n is the number of features) is adaptively chosen and a hyperplane is constructed such that it separates the chosen n-points at a margin e and best classifies the remaining points.The classification problem is formulated and the details of the algorithm are presented.Further,the algorithm is extended to solving quadratically separable classification problems.The basic idea is based on mapping the physical space to another larger one where the problem becomes linearly separable.Numerical illustrations show that few iteration steps are sufficient for convergence when classes are linearly separable.For nonlinearly separable data,given a specified maximum number of iteration steps,the algorithm returns the best hyperplane that minimizes the number of misclassified points occurring through these steps.Comparisons with other machine learning algorithms on practical and benchmark datasets are also presented,showing the performance of the proposed algorithm.
Generalized Linear Models with Applications in Engineering and the Sciences
Myers, Raymond H; Vining, G Geoffrey; Robinson, Timothy J
2012-01-01
Praise for the First Edition "The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities."-Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Ma
Adaptive Semi-linear Inversion of Strong Gravitational Lens Imaging
Nightingale, James
2014-01-01
We present a new pixelized method for the inversion of gravitationally lensed extended source images which we term adaptive semi-linear inversion (SLI). At the heart of the method is an h-means clustering algorithm which is used to derive a source plane pixelization that adapts to the lens model magnification. The distinguishing feature of adaptive SLI is that every pixelization is derived from a random initialization, ensuring that data discretization is performed in a completely different and unique way for every lens model parameter set. We compare standard SLI on a fixed source pixel grid with the new method and demonstrate the shortcomings of the former when modeling singular power law ellipsoid (SPLE) lens profiles. In particular, we demonstrate the superior reliability and efficiency of adaptive SLI which, by design, fixes the number of degrees of freedom (NDOF) of the optimization and thereby removes biases present with other methods that allow the NDOF to vary. In addition, we highlight the importanc...
Adaptive Unified Biased Estimators of Parameters in Linear Model
Hu Yang; Li-xing Zhu
2004-01-01
To tackle multi collinearity or ill-conditioned design matrices in linear models,adaptive biased estimators such as the time-honored Stein estimator,the ridge and the principal component estimators have been studied intensively.To study when a biased estimator uniformly outperforms the least squares estimator,some suficient conditions are proposed in the literature.In this paper,we propose a unified framework to formulate a class of adaptive biased estimators.This class includes all existing biased estimators and some new ones.A suficient condition for outperforming the least squares estimator is proposed.In terms of selecting parameters in the condition,we can obtain all double-type conditions in the literature.
Adaptive distributed parameter and input estimation in linear parabolic PDEs
Mechhoud, Sarra
2016-01-01
In this paper, we discuss the on-line estimation of distributed source term, diffusion, and reaction coefficients of a linear parabolic partial differential equation using both distributed and interior-point measurements. First, new sufficient identifiability conditions of the input and the parameter simultaneous estimation are stated. Then, by means of Lyapunov-based design, an adaptive estimator is derived in the infinite-dimensional framework. It consists of a state observer and gradient-based parameter and input adaptation laws. The parameter convergence depends on the plant signal richness assumption, whereas the state convergence is established using a Lyapunov approach. The results of the paper are illustrated by simulation on tokamak plasma heat transport model using simulated data.
Generalizing a Categorization of Students' Interpretations of Linear Kinematics Graphs
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 New Method for Solving General Dual Fuzzy Linear Systems
M. Otadi
2013-09-01
Full Text Available . According to fuzzy arithmetic, general dual fuzzy linear system (GDFLS cannot be replaced by a fuzzy linear system (FLS. In this paper, we use new notation of fuzzy numbers and convert a GDFLS to two linear systems in crisp case, then we discuss complexity of the proposed method. Conditions for the existence of a unique fuzzy solution to n × n GDFLS are derived
Minimal solution of general dual fuzzy linear systems
Fuzzy linear systems of equations, play a major role in several applications in various area such as engineering, physics and economics. In this paper, we investigate the existence of a minimal solution of general dual fuzzy linear equation systems. Two necessary and sufficient conditions for the minimal solution existence are given. Also, some examples in engineering and economic are considered
Testing Parametric versus Semiparametric Modelling in Generalized Linear Models
Härdle, W.K.; Mammen, E.; Müller, M.D.
1996-01-01
We consider a generalized partially linear model E(Y|X,T) = G{X'b + m(T)} where G is a known function, b is an unknown parameter vector, and m is an unknown function.The paper introduces a test statistic which allows to decide between a parametric and a semiparametric model: (i) m is linear, i.e. m(
Linear generalized synchronization of continuous-time chaotic systems
Lu Junguo E-mail: jglu@sjtu.edu.cn; Xi Yugeng
2003-08-01
This paper develops a general approach for constructing a response system to implement linear generalized synchronization (GS) with the drive continuous-time chaotic system. Some sufficient conditions of global asymptotic linear GS between the drive and response continuous-time chaotic systems are attained from rigorously modern control theory. Finally, we take Chua's circuit as an example for illustration and verification.
Adaptive linear rank tests for eQTL studies.
Szymczak, Silke; Scheinhardt, Markus O; Zeller, Tanja; Wild, Philipp S; Blankenberg, Stefan; Ziegler, Andreas
2013-02-10
Expression quantitative trait loci (eQTL) studies are performed to identify single-nucleotide polymorphisms that modify average expression values of genes, proteins, or metabolites, depending on the genotype. As expression values are often not normally distributed, statistical methods for eQTL studies should be valid and powerful in these situations. Adaptive tests are promising alternatives to standard approaches, such as the analysis of variance or the Kruskal-Wallis test. In a two-stage procedure, skewness and tail length of the distributions are estimated and used to select one of several linear rank tests. In this study, we compare two adaptive tests that were proposed in the literature using extensive Monte Carlo simulations of a wide range of different symmetric and skewed distributions. We derive a new adaptive test that combines the advantages of both literature-based approaches. The new test does not require the user to specify a distribution. It is slightly less powerful than the locally most powerful rank test for the correct distribution and at least as powerful as the maximin efficiency robust rank test. We illustrate the application of all tests using two examples from different eQTL studies. PMID:22933317
Adaptive fuzzy bilinear observer based synchronization design for generalized Lorenz system
This Letter proposes an adaptive fuzzy bilinear observer (FBO) based synchronization design for generalized Lorenz system (GLS). The GLS can be described to TS fuzzy bilinear generalized Lorenz model (FBGLM) with their states immeasurable and their parameters unknown. We design an adaptive FBO based on TS FBGLM for synchronization. Lyapunov theory is employed to guarantee the stability of error dynamic system via linear matrix equalities (LMIs) and to derive the adaptive laws to estimate unknown parameters. Numerical example is given to demonstrate the validity of our proposed adaptive FBO approach for synchronization.
Adaptation in Cones: A General Model
Dawis, Stevan M.; Purple, Richard L.
1982-01-01
Three features appear to characterize steady-state light adaptation in vertebrate cone photoreceptors: (a) the shape of the “log intensity-response” curve at different levels of adaptation is the same, the only change with adaptation is in the position of the point on the curve about which the cones operate; (b) at high adapting intensities the operating point becomes fixed in position; (c) this fixed position is at the steepest point of the log intensity-response curve. These three features ...
Faraway, Julian J
2005-01-01
Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...
Linearly Bounded Liars, Adaptive Covering Codes, and Deterministic Random Walks
Cooper, Joshua N
2009-01-01
We analyze a deterministic form of the random walk on the integer line called the {\\em liar machine}, similar to the rotor-router model, finding asymptotically tight pointwise and interval discrepancy bounds versus random walk. This provides an improvement in the best-known winning strategies in the binary symmetric pathological liar game with a linear fraction of responses allowed to be lies. Equivalently, this proves the existence of adaptive binary block covering codes with block length $n$, covering radius $\\leq fn$ for $f\\in(0,1/2)$, and cardinality $O(\\sqrt{\\log \\log n}/(1-2f))$ times the sphere bound $2^n/\\binom{n}{\\leq \\lfloor fn\\rfloor}$.
Adaptive discontinuous Galerkin methods for non-linear reactive flows
Uzunca, Murat
2016-01-01
The focus of this monograph is the development of space-time adaptive methods to solve the convection/reaction dominated non-stationary semi-linear advection diffusion reaction (ADR) equations with internal/boundary layers in an accurate and efficient way. After introducing the ADR equations and discontinuous Galerkin discretization, robust residual-based a posteriori error estimators in space and time are derived. The elliptic reconstruction technique is then utilized to derive the a posteriori error bounds for the fully discrete system and to obtain optimal orders of convergence. As coupled surface and subsurface flow over large space and time scales is described by (ADR) equation the methods described in this book are of high importance in many areas of Geosciences including oil and gas recovery, groundwater contamination and sustainable use of groundwater resources, storing greenhouse gases or radioactive waste in the subsurface.
Testing for one Generalized Linear Single Order Parameter
Ellegaard, Niels Langager; Christensen, Tage Emil; Dyre, Jeppe;
We examine a linear single order parameter model for thermoviscoelastic relaxation in viscous liquids, allowing for a distribution of relaxation times. In this model the relaxation of volume and entalpy is completely described by the relaxation of one internal order parameter. In contrast to prior...... work the order parameter may be chosen to have a non-exponential relaxation. The model predictions contradict the general consensus of the properties of viscous liquids in two ways: (i) The model predicts that following a linear isobaric temperature step, the normalized volume and entalpy relaxation...... functions are identical. This assumption conflicts with some (but not all) reports, utilizing the Tool-Narayanaswamy formalism to extrapolate from non-linear measurements to the linear regime. (ii) The model predicts that the theoretical "linear Prigogine-Defay" ratio is one. This ratio has never been...
Estimation and variable selection for generalized additive partial linear models
Wang, Li
2011-08-01
We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus results in gains in computational simplicity. We further develop a class of variable selection procedures for the linear parameters by employing a nonconcave penalized quasi-likelihood, which is shown to have an asymptotic oracle property. Monte Carlo simulations and an empirical example are presented for illustration. © Institute of Mathematical Statistics, 2011.
An implicit spectral formula for generalized linear Schroedinger equations
We generalize the semiclassical Bohr–Sommerfeld quantization rule to an exact, implicit spectral formula for linear, generalized Schroedinger equations admitting a discrete spectrum. Special cases include the position-dependent mass Schroedinger equation or the Schroedinger equation for weighted energy. Requiring knowledge of the potential and the solution associated with the lowest spectral value, our formula predicts the complete spectrum in its exact form. (author)
The linear model and hypothesis a general unifying theory
Seber, George
2015-01-01
This book provides a concise and integrated overview of hypothesis testing in four important subject areas, namely linear and nonlinear models, multivariate analysis, and large sample theory. The approach used is a geometrical one based on the concept of projections and their associated idempotent matrices, thus largely avoiding the need to involve matrix ranks. It is shown that all the hypotheses encountered are either linear or asymptotically linear, and that all the underlying models used are either exactly or asymptotically linear normal models. This equivalence can be used, for example, to extend the concept of orthogonality in the analysis of variance to other models, and to show that the asymptotic equivalence of the likelihood ratio, Wald, and Score (Lagrange Multiplier) hypothesis tests generally applies.
Beam envelope calculations in general linear coupled lattices
Chung, Moses, E-mail: mchung@unist.ac.kr [Department of Physics, Ulsan National Institute of Science and Technology, Ulsan 689-798 (Korea, Republic of); Qin, Hong [Plasma Physics Laboratory, Princeton University, Princeton, New Jersey 08543 (United States); Department of Modern Physics, University of Science and Technology of China, Hefei, Anhui 230026 (China); Groening, Lars; Xiao, Chen [GSI Helmholtzzentrum für Schwerionenforschung GmbH, Planckstrasse 1, D-64291 Darmstadt (Germany); Davidson, Ronald C. [Plasma Physics Laboratory, Princeton University, Princeton, New Jersey 08543 (United States)
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.
Neural Generalized Predictive Control of a non-linear Process
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole; Poulsen, Niels Kjølstad
1998-01-01
qualities. The controller is a non-linear version of the well-known generalized predictive controller developed in linear control theory. It involves minimization of a cost function which in the present case has to be done numerically. Therefore, we develop the numerical algorithms necessary in substantial......The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability...... detail and discuss the implementation difficulties. The neural generalized predictive controller is tested on a pneumatic servo sys-tem....
A random effects generalized linear model for reliability compositive evaluation
无
2009-01-01
This paper first proposes a random effects generalized linear model to evaluate the storage life of one kind of high reliable and small sample-sized products by combining multi-sources information of products coming from the same population but stored at different environments. The relevant algorithms are also provided. Simulation results manifest the soundness and effectiveness of the proposed model.
Witten's perturbation on strata with general adapted metrics
López, Jesús A. Álvarez; Calaza, Manuel
2015-01-01
Let $M$ be a stratum of a compact stratified space $A$. It is equipped with a general adapted metric $g$, which is slightly more general than the adapted metrics of Nagase and Brasselet-Hector-Saralegi. In particular, $g$ has a general type, which is an extension of the type of an adapted metric. A restriction on this general type is assumed, and then $g$ is called good. We consider the maximum/minimun ideal boundary condition, $d_{\\text{max/min}}$, of the compactly supported de~Rham complex ...
A twisted generalization of linear Poisson brackets of hydrodynamic type
We study a twisted generalization of linear Poisson brackets of hydrodynamic type, in which the Lie bracket is replaced by a Hom-Lie bracket. With some natural additional conditions, such structures correspond to the Hom-Novikov algebras introduced by Yau, which is the twisted version of Balinsky-Novikov's approach of constructing a Lie algebra from a Novikov algebra. Certain central extensions of this twisted generalization of linear Poisson brackets of hydrodynamic type are obtained from the bilinear forms on their corresponding Hom-Novikov algebras satisfying some invariance conditions. Finally, we give some examples of the infinite-dimensional Hom-Lie algebras constructed from the Hom-Novikov algebras. In particular, there is an interesting twisted generalization of the Virasoro algebra.
A new heuristic algorithm for general integer linear programming problems
GAO Pei-wang; CAI Ying
2006-01-01
A new heuristic algorithm is proposed for solving general integer linear programming problems.In the algorithm,the objective function hyperplane is used as a cutting plane,and then by introducing a special set of assistant sets,an efficient heuristic search for the solution to the integer linear program is carried out in the sets on the objective function hyperplane.A simple numerical example shows that the algorithm is efficient for some problems,and therefore,of practical interest.
Regularization Paths for Generalized Linear Models via Coordinate Descent
Jerome Friedman
2010-02-01
Full Text Available We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include ℓ1 (the lasso, ℓ2 (ridge regression and mixtures of the two (the elastic net. The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.
Local Linear Embedding Algorithm with Adaptively Determining Neighborhood
Zhenduo Wang
2014-06-01
Full Text Available Local linear embedding is a kind of very competitive nonlinear dimensionality reduction technique with good representational capacity for a broader range of manifolds and high computational efficiency. However, it is based on the assumption that the whole data manifolds are evenly distributed so that it determines the neighborhood for all points with the same neighborhood size. Accordingly, it fails to nicely deal with most real problems that are unevenly distributed. This paper presents a new approach that takes the general conceptual framework of Hessian locally linear embedding so as to guarantee its correctness in the setting of local isometry for an open connected subset, but dynamically determines the local neighborhood size for each point. This approach estimates the approximate geodesic distance between any two points by the shortest path in the local neighborhood graph, and then determines the neighborhood size for each point by using the relationship between its local estimated geodesic distance matrix and local Euclidean distance matrix. This approach has clear geometry intuition as well as the better performance and stability. It deals with the sparsely sampled or noise contaminated data sets that are often unevenly distributed. The conducted experiments on benchmark data sets validate the proposed approach
Computation of Optimal Monotonicity Preserving General Linear Methods
Ketcheson, David I.
2009-07-01
Monotonicity preserving numerical methods for ordinary differential equations prevent the growth of propagated errors and preserve convex boundedness properties of the solution. We formulate the problem of finding optimal monotonicity preserving general linear methods for linear autonomous equations, and propose an efficient algorithm for its solution. This algorithm reliably finds optimal methods even among classes involving very high order accuracy and that use many steps and/or stages. The optimality of some recently proposed methods is verified, and many more efficient methods are found. We use similar algorithms to find optimal strong stability preserving linear multistep methods of both explicit and implicit type, including methods for hyperbolic PDEs that use downwind-biased operators.
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
The Generalized Sidelobe Canceller Based on Quaternion Widely Linear Processing
Jian-wu Tao
2014-01-01
Full Text Available 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.
A Non-Gaussian Spatial Generalized Linear Latent Variable Model
Irincheeva, Irina
2012-08-03
We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.
Probability method for cryptanalysis of general multivariate modular linear equation
ZHOU HaiJian; LUO Ping; WANG DaoShun; DAI YiQi
2009-01-01
Finding the solution to a general multivariate modular linear equation plays an important role in crypt-analysis field. Earlier results show that obtaining a relatively short solution is possible in polynomial time. However, one problem arises here that if the equation has a short solution in given bounded range, the results outputted by earlier algorithms are often not the ones we are interested in. In this paper, we present a probability method based on lattice basis reduction to solve the problem. For a general multivariate modular linear equation with short solution in the given bounded range, the new method outputs this short solution in polynomial time, with a high probability. When the number of unknowns is not too large (smaller than 68), the probability is approximating 1. Experimental results show that Knapsack systems and Lu-Lee type systems are easily broken in polynomial time with this new method.
Parametrizing linear generalized Langevin dynamics from explicit molecular dynamics simulations
Gottwald, Fabian; Ivanov, Sergei D; Kühn, Oliver
2015-01-01
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 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 (GLE), which can be rigorously derived by means of a linear projection (LP) 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 most 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. Importa...
Optimal Scaling of Interaction Effects in Generalized Linear Models
2007-01-01
Multiplicative interaction models, such as Goodman's RC(M) association models, can be a useful tool for analyzing the content of interaction effects. However, most models for interaction effects are only suitable for data sets with two or three predictor variables. Here, we discuss an optimal scaling model for analyzing the content of interaction effects in generalized linear models with any number of categorical predictor variables. This model, which we call the optimal scaling of interactio...
Optimal Scaling of Interaction Effects in Generalized Linear Models
van Rosmalen, Joost; Koning, Alex; Groenen, Patrick
2007-01-01
textabstractMultiplicative interaction models, such as Goodman's RC(M) association models, can be a useful tool for analyzing the content of interaction effects. However, most models for interaction effects are only suitable for data sets with two or three predictor variables. Here, we discuss an optimal scaling model for analyzing the content of interaction effects in generalized linear models with any number of categorical predictor variables. This model, which we call the optimal scaling o...
A general method for enclosing solutions of interval linear equations
Rohn, Jiří
2012-01-01
Roč. 6, č. 4 (2012), s. 709-717. ISSN 1862-4472 R&D Projects: GA ČR GA201/09/1957; GA ČR GC201/08/J020 Institutional research plan: CEZ:AV0Z10300504 Keywords : interval linear equations * solution set * enclosure * absolute value inequality Subject RIV: BA - General Mathematics Impact factor: 1.654, year: 2012
A generalized linear Hubble law for an inhomogeneous barotropic Universe
Pascual-Sánchez, J. -F.
2000-01-01
In this work, I present a generalized linear Hubble law for a barotropic spherically symmetric inhomogeneous spacetime, which is in principle compatible with the acceleration of the cosmic expansion obtained as a result of high redshift Supernovae data. The new Hubble function, defined by this law, has two additional terms besides an expansion one, similar to the usual volume expansion one of the FLRW models, but now due to an angular expansion. The first additional term is dipolar and is a c...
Polymorphic Uncertain Linear Programming for Generalized Production Planning Problems
Xinbo Zhang; Feng Zhang; Xiaohong Chen; Zhong Wan
2014-01-01
A polymorphic uncertain linear programming (PULP) model is constructed to formulate a class of generalized production planning problems. In accordance with the practical environment, some factors such as the consumption of raw material, the limitation of resource and the demand of product are incorporated into the model as parameters of interval and fuzzy subsets, respectively. Based on the theory of fuzzy interval program and the modified possibility degree for the order of interval numbers,...
General treatment of a non-linear gauge condition
A non linear gauge condition is presented in the frame of a non abelian gauge theory broken with the Higgs mechanism. It is shown that this condition already introduced for the standard SU(2) x U(1) model can be generalized for any gauge model with the same type of simplification, namely the suppression of any coupling of the form: massless gauge boson, massive gauge boson, unphysical Higgs
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
Linear spin-2 fields in most general backgrounds
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.
Adaptive Linear Parameter Varying Control for Aeroservoelastic Suppression Project
National Aeronautics and Space Administration — Adaptive control offers an opportunity to fulfill present and future aircraft safety objectives though automated vehicle recovery while maintaining performance and...
Adaptive Linear Parameter Varying Control for Aeroservoelastic Suppression Project
National Aeronautics and Space Administration — Adaptive control offers an opportunity to fulfill aircraft safety objectives though automated vehicle recovery while maintaining performance and stability...
Comparative Study of Algorithms for Automated Generalization of Linear Objects
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
Speaker adaptation of HMMs using evolutionary strategy-based linear regression
Selouani, Sid-Ahmed; O'Shaughnessy, Douglas
2002-05-01
A new framework for speaker adaptation of continuous-density hidden Markov models (HMMs) is introduced. It aims to improve the robustness of speech recognizers by adapting HMM parameters to new conditions (e.g., from new speakers). It describes an optimization technique using an evolutionary strategy for linear regression-based spectral transformation. In classical iterative maximum likelihood linear regression (MLLR), a global transform matrix is estimated to make a general model better match particular target conditions. To permit adaptation on a small amount of data, a regression tree classification is performed. However, an important drawback of MLLR is that the number of regression classes is fixed. The new approach allows the degree of freedom of the global transform to be implicitly variable, as the evolutionary optimization permits the survival of only active classes. The fitness function is evaluated by the phoneme correctness through the evolution steps. The implementation requirements such as chromosome representation, selection function, genetic operators, and evaluation function have been chosen in order to lend more reliability to the global transformation matrix. Triphone experiments used the TIMIT and ARPA-RM1 databases. For new speakers, the new technique achieves 8 percent fewer word errors than the basic MLLR method.
Parametrizing linear generalized Langevin dynamics from explicit molecular dynamics simulations
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
Confidence Intervals of Variance Functions in Generalized Linear Model
Yong Zhou; Dao-ji Li
2006-01-01
In this paper we introduce an appealing nonparametric method for estimating variance and conditional variance functions in generalized linear models (GLMs), when designs are fixed points and random variables respectively. Bias-corrected confidence bands are proposed for the (conditional) variance by local linear smoothers. Nonparametric techniques are developed in deriving the bias-corrected confidence intervals of the (conditional) variance. The asymptotic distribution of the proposed estimator is established and show that the bias-corrected confidence bands asymptotically have the correct coverage properties. A small simulation is performed when unknown regression parameter is estimated by nonparametric quasi-likelihood. The results are also applicable to nonparametric autoregressive times series model with heteroscedastic conditional variance.
Adaptive Kronrod-Patterson integration of non-linear finite-element matrices
Janssen, Hans
2010-01-01
inappropriate discretization. In response, this article develops adaptive integration, based on nested Kronrod-Patterson-Gauss integration schemes: basically, the integration order is adapted to the locally observed grade of non-linearity. Adaptive integration is developed based on a standard infiltration...
Yulong Lei; Ke Liu; Yuanxia Zhang; Yao Fu; Hongbo Liu; Ge Lin; Hui Tang
2015-01-01
Recognizing various driving conditions in real time and adjusting control strategy accordingly in automatic transmission vehicles are important to improve their adaptability to the external environment. This study defines a generalized load concept which can comprehensively reflect driving condition information. The principle of a gearshift strategy based on generalized load is deduced theoretically, adopting linear interpolation between the shift lines on flat and on the largest gradient roa...
On unipotent Specht modules of finite general linear groups
Brandt, Marco
2004-01-01
Many outstanding problems in representation theory can be solved with a proper understanding of the irreducible unipotent modules for the finite general linear group GL_n(q). For each partition lambda of n there is a Specht module S^lambda for GL_n(q), defined over a field F in terms of the intersection of the kernels of certain homomorphisms. If F is a field of characteristic zero, then S^lambda is irreducible and {S^lambda | lambda is a partition of n} is a complete set of pairwise non-...
A Graphical User Interface to Generalized Linear Models in MATLAB
Peter Dunn
1999-07-01
Full Text Available Generalized linear models unite a wide variety of statistical models in a common theoretical framework. This paper discusses GLMLAB-software that enables such models to be fitted in the popular mathematical package MATLAB. It provides a graphical user interface to the powerful MATLAB computational engine to produce a program that is easy to use but with many features, including offsets, prior weights and user-defined distributions and link functions. MATLAB's graphical capacities are also utilized in providing a number of simple residual diagnostic plots.
Patilea, Valentin; Raïssi, Hamdi
2010-01-01
Linear Vector AutoRegressive (VAR) models where the innovations could be unconditionally heteroscedastic and serially dependent are considered. The volatility structure is deterministic and quite general, including breaks or trending variances as special cases. In this framework we propose Ordinary Least Squares (OLS), Generalized Least Squares (GLS) and Adaptive Least Squares (ALS) procedures. The GLS estimator requires the knowledge of the time-varying variance structure while in the ALS ap...
Adaptive Non-linear Control of Hydraulic Actuator Systems
Hansen, Poul Erik; Conrad, Finn
Presentation of two new developed adaptive non-liner controllers for hydraulic actuator systems to give stable operation and improved performance.Results from the IMCIA project supported by the Danish Technical Research Council (STVF).......Presentation of two new developed adaptive non-liner controllers for hydraulic actuator systems to give stable operation and improved performance.Results from the IMCIA project supported by the Danish Technical Research Council (STVF)....
Adaptive Non-linear Control of Hydraulic Actuator Systems
Hansen, Poul Erik; Conrad, Finn
1998-01-01
Presentation of two new developed adaptive non-liner controllers for hydraulic actuator systems to give stable operation and improved performance.Results from the IMCIA project supported by the Danish Technical Research Council (STVF).......Presentation of two new developed adaptive non-liner controllers for hydraulic actuator systems to give stable operation and improved performance.Results from the IMCIA project supported by the Danish Technical Research Council (STVF)....
Extracting HI cosmological signal with Generalized Needlet Internal Linear Combination
Olivari, L C; Dickinson, C
2015-01-01
HI 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 (HI). Sensitive radio surveys have the potential to detect Baryon Acoustic Oscillations (BAO) at low redshifts (z < 1) in order to constrain the properties of dark energy. Observations of the HI 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 HI signal. Foreground cleaning is recognised 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 HI signal for a general HI 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 r...
Linear spin-2 fields in most general backgrounds
Bernard, Laura; Schmidt-May, Angnis; von Strauss, Mikael
2015-01-01
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 linearised 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 mas...
An adaptive fuzzy sliding mode strategy is developed for the generalized projective synchronization of a fractional-order chaotic system, where the slave system is not necessarily known in advance. Based on the designed adaptive update laws and the linear feedback method, the adaptive fuzzy sliding controllers are proposed via the fuzzy design, and the strength of the designed controllers can be adaptively adjusted according to the external disturbances. Based on the Lyapunov stability theorem, the stability and the robustness of the controlled system are proved theoretically. Numerical simulations further support the theoretical results of the paper and demonstrate the efficiency of the proposed method. Moreover, it is revealed that the proposed method allows us to manipulate arbitrarily the response dynamics of the slave system by adjusting the desired scaling factor λi and the desired translating factor ηi, which may be used in a channel-independent chaotic secure communication. (general)
Kesavan.E; Rakesh kumar.S
2013-01-01
This paper suggests an idea to design an adaptive PID controller for Non-linear liquid tank System and is implemented in PLC. Online estimation of linear parameters (Time constant and Gain) brings an exact model of the process to take perfect control action. Based on these estimated values, the controller parameters will be well tuned by internal model control. Internal model control is an unremarkably used technique and provides well tuned controller in order to have a good controlling proce...
Bounded Linear Stability Analysis - A Time Delay Margin Estimation Approach for Adaptive Control
Nguyen, Nhan T.; Ishihara, Abraham K.; Krishnakumar, Kalmanje Srinlvas; Bakhtiari-Nejad, Maryam
2009-01-01
This paper presents a method for estimating time delay margin for model-reference adaptive control of systems with almost linear structured uncertainty. The bounded linear stability analysis method seeks to represent the conventional model-reference adaptive law by a locally bounded linear approximation within a small time window using the comparison lemma. The locally bounded linear approximation of the combined adaptive system is cast in a form of an input-time-delay differential equation over a small time window. The time delay margin of this system represents a local stability measure and is computed analytically by a matrix measure method, which provides a simple analytical technique for estimating an upper bound of time delay margin. Based on simulation results for a scalar model-reference adaptive control system, both the bounded linear stability method and the matrix measure method are seen to provide a reasonably accurate and yet not too conservative time delay margin estimation.
Synchronization of general complex networks via adaptive control schemes
Ping He; Chun-Guo Jing; Chang-Zhong Chen; Tao Fan; Hassan Saberi Nik
2014-03-01
In this paper, the synchronization problem of general complex networks is investigated by using adaptive control schemes. Time-delay coupling, derivative coupling, nonlinear coupling etc. exist universally in real-world complex networks. The adaptive synchronization scheme is designed for the complex network with multiple class of coupling terms. A criterion guaranteeing synchronization of such complex networks is established by employing the Lyapunov stability theorem and adaptive control schemes. Finally, an illustrative example with numerical simulation is given to show the feasibility and efficiency of theoretical results.
Bayesian Subset Modeling for High-Dimensional Generalized Linear Models
Liang, Faming
2013-06-01
This article presents a new prior setting for high-dimensional generalized linear models, which leads to a Bayesian subset regression (BSR) with the maximum a posteriori model approximately equivalent to the minimum extended Bayesian information criterion model. The consistency of the resulting posterior is established under mild conditions. Further, a variable screening procedure is proposed based on the marginal inclusion probability, which shares the same properties of sure screening and consistency with the existing sure independence screening (SIS) and iterative sure independence screening (ISIS) procedures. However, since the proposed procedure makes use of joint information from all predictors, it generally outperforms SIS and ISIS in real applications. This article also makes extensive comparisons of BSR with the popular penalized likelihood methods, including Lasso, elastic net, SIS, and ISIS. The numerical results indicate that BSR can generally outperform the penalized likelihood methods. The models selected by BSR tend to be sparser and, more importantly, of higher prediction ability. In addition, the performance of the penalized likelihood methods tends to deteriorate as the number of predictors increases, while this is not significant for BSR. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Explicit estimating equations for semiparametric generalized linear latent variable models
Ma, Yanyuan
2010-07-05
We study generalized linear latent variable models without requiring a distributional assumption of the latent variables. Using a geometric approach, we derive consistent semiparametric estimators. We demonstrate that these models have a property which is similar to that of a sufficient complete statistic, which enables us to simplify the estimating procedure and explicitly to formulate the semiparametric estimating equations. We further show that the explicit estimators have the usual root n consistency and asymptotic normality. We explain the computational implementation of our method and illustrate the numerical performance of the estimators in finite sample situations via extensive simulation studies. The advantage of our estimators over the existing likelihood approach is also shown via numerical comparison. We employ the method to analyse a real data example from economics. © 2010 Royal Statistical Society.
Optimization in generalized linear models: A case study
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.
dglars: An R Package to Estimate Sparse Generalized Linear Models
Luigi Augugliaro
2014-09-01
Full Text Available dglars is a publicly available R package that implements the method proposed in Augugliaro, Mineo, and Wit (2013, developed to study the sparse structure of a generalized linear model. This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method proposed in Efron, Hastie, Johnstone, and Tibshirani (2004. The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve: a predictor-corrector algorithm, proposed in Augugliaro et al. (2013, and a cyclic coordinate descent algorithm, proposed in Augugliaro, Mineo, and Wit (2012. The latter algorithm, as shown here, is significantly faster than the predictor-corrector algorithm. For comparison purposes, we have implemented both algorithms.
Holst, René; Jørgensen, Bent
2015-01-01
The paper proposes a versatile class of multiplicative generalized linear longitudinal mixed models (GLLMM) with additive dispersion components, based on explicit modelling of the covariance structure. The class incorporates a longitudinal structure into the random effects models and retains a ma...... multidimensional integral of the conventional GLMM likelihood and allows an extension of the robust empirical sandwich estimator for use with both association and regression parameters. The method is applied to a set of otholit data, used for age determination of fish.......The paper proposes a versatile class of multiplicative generalized linear longitudinal mixed models (GLLMM) with additive dispersion components, based on explicit modelling of the covariance structure. The class incorporates a longitudinal structure into the random effects models and retains a...... marginal as well as a conditional interpretation. The estimation procedure is based on a computationally efficient quasi-score method for the regression parameters combined with a REML-like bias-corrected Pearson estimating function for the dispersion and correlation parameters. This avoids the...
Adaptive Generation and Diagnostics of Linear Few-Cycle Light Bullets
Martin Bock
2013-02-01
Full Text Available Recently we introduced the class of highly localized wavepackets (HLWs as a generalization of optical Bessel-like needle beams. Here we report on the progress in this field. In contrast to pulsed Bessel beams and Airy beams, ultrashort-pulsed HLWs propagate with high stability in both spatial and temporal domain, are nearly paraxial (supercollimated, have fringe-less spatial profiles and thus represent the best possible approximation to linear “light bullets”. Like Bessel beams and Airy beams, HLWs show self-reconstructing behavior. Adaptive HLWs can be shaped by ultraflat three-dimensional phase profiles (generalized axicons which are programmed via calibrated grayscale maps of liquid-crystal-on-silicon spatial light modulators (LCoS-SLMs. Light bullets of even higher complexity can either be freely formed from quasi-continuous phase maps or discretely composed from addressable arrays of identical nondiffracting beams. The characterization of few-cycle light bullets requires spatially resolved measuring techniques. In our experiments, wavefront, pulse and phase were detected with a Shack-Hartmann wavefront sensor, 2D-autocorrelation and spectral phase interferometry for direct electric-field reconstruction (SPIDER. The combination of the unique propagation properties of light bullets with the flexibility of adaptive optics opens new prospects for applications of structured light like optical tweezers, microscopy, data transfer and storage, laser fusion, plasmon control or nonlinear spectroscopy.
Dyja, Robert; van der Zee, Kristoffer G
2016-01-01
We present an adaptive methodology for the solution of (linear and) non-linear time dependent problems that is especially tailored for massively parallel computations. The basic concept is to solve for large blocks of space-time unknowns instead of marching sequentially in time. The methodology is a combination of a computationally efficient implementation of a parallel-in-space-time finite element solver coupled with a posteriori space-time error estimates and a parallel mesh generator. This methodology enables, in principle, simultaneous adaptivity in both space and time (within the block) domains. We explore this basic concept in the context of a variety of time-steppers including $\\Theta$-schemes and Backward Differentiate Formulas. We specifically illustrate this framework with applications involving time dependent linear, quasi-linear and semi-linear diffusion equations. We focus on investigating how the coupled space-time refinement indicators for this class of problems affect spatial adaptivity. Final...
Non-linear and adaptive control of a refrigeration system
Rasmussen, Henrik; Larsen, Lars F. S.
2011-01-01
capacities ensures a high energy efficiency. The level of liquid filling is indirectly measured by the superheat. Introduction of variable speed compressors and electronic expansion valves enables the use of more sophisticated control algorithms, giving a higher degree of performance and just as important...... and used in a backstepping design of a nonlinear adaptive controller. The stability of the proposed method is validated theoretically by Lyapunov analysis and experimental results show the performance of the system for a wide range of operating points. The method is compared to a conventional method based...
Generalized projective synchronization of chaotic systems via adaptive learning control
In this paper, a learning control approach is applied to the generalized projective synchronisation (GPS) of different chaotic systems with unknown periodically time-varying parameters. Using the Lyapunov–Krasovskii functional stability theory, a differential-difference mixed parametric learning law and an adaptive learning control law are constructed to make the states of two different chaotic systems asymptotically synchronised. The scheme is successfully applied to the generalized projective synchronisation between the Lorenz system and Chen system. Moreover, numerical simulations results are used to verify the effectiveness of the proposed scheme. (general)
Lee, Chang-Hun; Seo, Min-Guk; Tahk, Min-Jea; Lee, Jin-Ik; Jun, Byung-Eul
2012-01-01
A straight forward application of feedback linearization to the missile autopilot design for acceleration control may be limited due to the nonminimum characteristics and the model uncertainties. As a remedy, this paper presents a cascade structure of an acceleration controller based on approximate feedback linearization methodology with a time-delay adaptation scheme. The inner loop controller is constructed by applying feedback linearization to the approximate system which is a minimum phas...
Generalized Functional Linear Models With Semiparametric Single-Index Interactions
Li, Yehua
2010-06-01
We introduce a new class of functional generalized linear models, where the response is a scalar and some of the covariates are functional. We assume that the response depends on multiple covariates, a finite number of latent features in the functional predictor, and interaction between the two. To achieve parsimony, the interaction between the multiple covariates and the functional predictor is modeled semiparametrically with a single-index structure. We propose a two step estimation procedure based on local estimating equations, and investigate two situations: (a) when the basis functions are pre-determined, e.g., Fourier or wavelet basis functions and the functional features of interest are known; and (b) when the basis functions are data driven, such as with functional principal components. Asymptotic properties are developed. Notably, we show that when the functional features are data driven, the parameter estimates have an increased asymptotic variance, due to the estimation error of the basis functions. Our methods are illustrated with a simulation study and applied to an empirical data set, where a previously unknown interaction is detected. Technical proofs of our theoretical results are provided in the online supplemental materials.
Multivariate statistical modelling based on generalized linear models
Fahrmeir, Ludwig
1994-01-01
This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of m...
Adaptive Input-Output Linearization Technique for Robust Speed Control of Brush less DC Motor
Kim, Kyeong Hwa; Baik, In Cheol; Kim, Hyun Soo; Youn, Myung Joong [Korea Advance Institute of Science and Technology, Taejon (Korea, Republic of)
1997-06-01
An adaptive input-output linearization technique for a robust speed control of a brush less DC (BLDC) motor is presented. By using this technique, the nonlinear motor model can be effectively linearized in Brunovski canonical form, and the desired speed dynamics can be obtained based on the linearized model. This control technique, however, gives an undesirable output performance under the mismatch of the system parameters and load conditions caused by the incomplete linearization. For the robust output response, the controller parameters will be estimated by a model reference adaptive technique where the disturbance torque and flux linkage are estimated. The adaptation laws are derived by the Popov`s hyper stability theory and positivity concept. The proposed control scheme is implemented on a BLDC motor using the software of DSP TMS320C30 and the effectiveness is verified through the comparative simulations and experiments. (author). 14 refs., 12 figs., 1 tab.
Generalization in adaptation to stable and unstable dynamics.
Abdelhamid Kadiallah
Full Text Available Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization.
Domingues M. O.
2013-12-01
Full Text Available We present a new adaptive multiresoltion method for the numerical simulation of ideal magnetohydrodynamics. The governing equations, i.e., the compressible Euler equations coupled with the Maxwell equations are discretized using a finite volume scheme on a two-dimensional Cartesian mesh. Adaptivity in space is obtained via Harten’s cell average multiresolution analysis, which allows the reliable introduction of a locally refined mesh while controlling the error. The explicit time discretization uses a compact Runge–Kutta method for local time stepping and an embedded Runge-Kutta scheme for automatic time step control. An extended generalized Lagrangian multiplier approach with the mixed hyperbolic-parabolic correction type is used to control the incompressibility of the magnetic field. Applications to a two-dimensional problem illustrate the properties of the method. Memory savings and numerical divergences of magnetic field are reported and the accuracy of the adaptive computations is assessed by comparing with the available exact solution.
Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks
Kanevski, Mikhail
2015-04-01
The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press
Adaptive fault-tolerant control of linear systems with actuator saturation and L2-disturbances
Wei GUAN; Guanghong YANG
2009-01-01
This paper studies the problem of designing adaptive fault-tolerant H-infinity controllers for linear timeinvariant systems with actuator saturation. The disturbance tolerance ability of the closed-loop system is measured by an optimal index. The notion of an adaptive H-infinity performance index is proposed to describe the disturbance attenuation performances of closed-loop systems. New methods for designing indirect adaptive fault-tolerant controllers via state feedback are presented for actuator fault compensations. Based on the on-line estimation of eventual faults, the adaptive fault-tolerant controller parameters are updated automatically to compensate for the fault effects on systems. The designs are developed in the framework of the linear matrix inequality (LMI) approach, which can guarantee the disturbance tolerance ability and adaptive H-infinity performances of closed-loop systems in the cases of actuator saturation and actuator failures. An example is given to illustrate the efficiency of the design method.
Serial and parallel dynamic adaptation of general hybrid meshes
Kavouklis, Christos
The Navier-Stokes equations are a standard mathematical representation of viscous fluid flow. Their numerical solution in three dimensions remains a computationally intensive and challenging task, despite recent advances in computer speed and memory. A strategy to increase accuracy of Navier-Stokes simulations, while maintaining computing resources to a minimum, is local refinement of the associated computational mesh in regions of large solution gradients and coarsening in regions where the solution does not vary appreciably. In this work we consider adaptation of general hybrid meshes for Computational Fluid Dynamics (CFD) applications. Hybrid meshes are composed of four types of elements; hexahedra, prisms, pyramids and tetrahedra, and have been proven a promising technology in accurately resolving fluid flow for complex geometries. The first part of this dissertation is concerned with the design and implementation of a serial scheme for the adaptation of general three dimensional hybrid meshes. We have defined 29 refinement types, for all four kinds of elements. The core of the present adaptation scheme is an iterative algorithm that flags mesh edges for refinement, so that the adapted mesh is conformal. Of primary importance is considered the design of a suitable dynamic data structure that facilitates refinement and coarsening operations and furthermore minimizes memory requirements. A special dynamic list is defined for mesh elements, in contrast with the usual tree structures. It contains only elements of the current adaptation step and minimal information that is utilized to reconstruct parent elements when the mesh is coarsened. In the second part of this work, a new parallel dynamic mesh adaptation and load balancing algorithm for general hybrid meshes is presented. Partitioning of a hybrid mesh reduces to partitioning of the corresponding dual graph. Communication among processors is based on the faces of the interpartition boundary. The distributed
丛玉豪
2001-01-01
The stability analysis of linear multistep methods for the numerical solutions of the systems of generalized neutral delay differential equations is discussed. The stability behaviour of linear multistep methods was analysed for the solution of the generalized system of linear neutral test equations. After the establishment of a sufficient condition for asymptotic stability of the solutions of the generalized system, it is shown that a linear multistep method is NGPG-stable if and only if it is A-stable.
Cavity characterization for general use in linear electron accelerators
The main objective of this work is to is to develop measurement techniques for the characterization of microwave cavities used in linear electron accelerators. Methods are developed for the measurement of parameters that are essential to the design of an accelerator structure using conventional techniques of resonant cavities at low power. Disk-loaded cavities were designed and built, similar to those in most existing linear electron accelerators. As a result, the methods developed and the estimated accuracy were compared with those from other investigators. The results of this work are relevant for the design of cavities with the objective of developing linear electron accelerators. (author)
General Thoughts about Tracking for a Linear Collider Detector
Schumm, Bruce A.
2005-01-01
Basic scaling laws are used to compare the relative capabilities of gaseous and solid-state tracking. Conclusions are drawn in the context of physics processes expected to be at the focus of studies at the International Linear Collider.
Multiscale adaptive generalized estimating equations for longitudinal neuroimaging data.
Li, Yimei; Gilmore, John H; Shen, Dinggang; Styner, Martin; Lin, Weili; Zhu, Hongtu
2013-05-15
Many large-scale longitudinal imaging studies have been or are being widely conducted to better understand the progress of neuropsychiatric and neurodegenerative disorders and normal brain development. The goal of this article is to develop a multiscale adaptive generalized estimation equation (MAGEE) method for spatial and adaptive analysis of neuroimaging data from longitudinal studies. MAGEE is applicable to making statistical inference on regression coefficients in both balanced and unbalanced longitudinal designs and even in twin and familial studies, whereas standard software platforms have several major limitations in handling these complex studies. Specifically, conventional voxel-based analyses in these software platforms involve Gaussian smoothing imaging data and then independently fitting a statistical model at each voxel. However, the conventional smoothing methods suffer from the lack of spatial adaptivity to the shape and spatial extent of region of interest and the arbitrary choice of smoothing extent, while independently fitting statistical models across voxels does not account for the spatial properties of imaging observations and noise distribution. To address such drawbacks, we adapt a powerful propagation-separation (PS) procedure to sequentially incorporate the neighboring information of each voxel and develop a new novel strategy to solely update a set of parameters of interest, while fixing other nuisance parameters at their initial estimators. Simulation studies and real data analysis show that MAGEE significantly outperforms voxel-based analysis. PMID:23357075
GENERALIZED RICCATI TRANSFORMATION AND OSCILLATION FOR LINEAR DIFFERENTIAL EQUATIONS WITH DAMPING
ZhengZhaowen; LiuJingzhao
2005-01-01
Using generalized Riccati transformation, some new oscillation criteria for damped linear differential equations are established. These results improve and generalize some known oscillation criteria due to A.Wintner [8], I.V.Kamenev [10] for the undamped linear differential equations, and Sobol [3], J.S.W.Wong [1] for the damped linear differential equations.
Testing linear forms of variance components by generalized fixed-level tests
Weimann, Boris
1998-01-01
This report extends the technique of testing single variance components with generalized fixed-level tests - in situations when nuisance parameters make exact testing impossible - to the more general way of testing hypotheses on linear forms of variance components. An extension of the definition of a generalized test variable leads to a generalized fixed-level test for arbitrary linear hypotheses on variance components in balanced mixed linear models of the ANOVA-type. For point null hypothes...
Mjalli, F. S.
2006-01-01
Nonlinearity of the extraction process is addressed via the application of instantaneous linearization to control the extract and raffinate concentrations. Two feed-forward neural networks with delayed inputs and outputs were trained and validated to capture the dynamics of the extraction process. These nonlinear models were then adopted in an instantaneous linearization algorithm into two control algorithms. The self-tuning adaptive control strategy was compared to an approximate model predi...
STAR adaptation of QR algorithm. [program for solving over-determined systems of linear equations
Shah, S. N.
1981-01-01
The QR algorithm used on a serial computer and executed on the Control Data Corporation 6000 Computer was adapted to execute efficiently on the Control Data STAR-100 computer. How the scalar program was adapted for the STAR-100 and why these adaptations yielded an efficient STAR program is described. Program listings of the old scalar version and the vectorized SL/1 version are presented in the appendices. Execution times for the two versions applied to the same system of linear equations, are compared.
Intelligent control of non-linear dynamical system based on the adaptive neurocontroller
Engel, E.; Kovalev, I. V.; Kobezhicov, V.
2015-10-01
This paper presents an adaptive neuro-controller for intelligent control of non-linear dynamical system. The formed as the fuzzy selective neural net the adaptive neuro-controller on the base of system's state, creates the effective control signal under random perturbations. The validity and advantages of the proposed adaptive neuro-controller are demonstrated by numerical simulations. The simulation results show that the proposed controller scheme achieves real-time control speed and the competitive performance, as compared to PID, fuzzy logic controllers.
Nguyen, Nhan
2013-01-01
This paper presents the optimal control modification for linear uncertain plants. The Lyapunov analysis shows that the modification parameter has a limiting value depending on the nature of the uncertainty. The optimal control modification exhibits a linear asymptotic property that enables it to be analyzed in a linear time invariant framework for linear uncertain plants. The linear asymptotic property shows that the closed-loop plants in the limit possess a scaled input-output mapping. Using this property, we can derive an analytical closed-loop transfer function in the limit as the adaptive gain tends to infinity. The paper revisits the Rohrs counterexample problem that illustrates the nature of non-robustness of model-reference adaptive control in the presence of unmodeled dynamics. An analytical approach is developed to compute exactly the modification parameter for the optimal control modification that stabilizes the plant in the Rohrs counterexample. The linear asymptotic property is also used to address output feedback adaptive control for non-minimum phase plants with a relative degree 1.
Decentralized adaptive generalized predictive control for structural vibration
LU Minyue; GU Zhongquan
2005-01-01
A decentralized generalized predictive control (GPC) algorithm is developed for strongly coupled multi-input multi-output systems with parallel computation. The algorithm is applied to adaptive control of structural vibration. The key steps in this algorithm are to group the actuators and the sensors and then to pair these groups into subsystems. It is important that the on-line identification and the control law design can be a parallel process for all these subsystems. It avoids the high computation cost in ordinary predictive control,and is of great advantage especially for large-scale systems.
Non-linear sigma models with generalized geometry
In this paper it is shown that the bosonic non-linear sigma model with algebraically extended world-sheet geometry is locally equivalent to the usual theory, but differs from the latter globally in that world-sheet instantons do not destabilize axions and in that it has a different critical dimension
Alternating prism exposure causes dual adaptation and generalization to a novel displacement
Welch, Robert B.; Bridgeman, Bruce; Anand, Sulekha; Browman, Kaitlin E.
1993-01-01
In two experiments, we examined the hypothesis that repeatedly adapting and readapting to two mutually conflicting sensory environments fosters the development of a separate adaptation to each situation (dual adaptation) as well as an increased ability to adapt to a novel displacement (adaptive generalization). In the preliminary study, subjects alternated between adapting their visuomotor coordination to 30-diopter prismatic displacement and readapting to normal vision. Dual adaptation was observed by the end of 10 alternation cycles. However, an unconfounded test of adaptive generalization was prevented by an unexpected prism-adaptive shift in preexposure baselines for the dual-adapted subjects. In the primary experiment, the subjects adapted and readapted to opposite 15-diopter displacements for a total of 12 cycles. Both dual adaptation and adaptive generalization to a 30-diopter displacement were obtained. These findings may be understood in terms of serial reversal learning and 'learning to learn'.
Parallel adaptation of general three-dimensional hybrid meshes
A new parallel dynamic mesh adaptation and load balancing algorithm for general hybrid grids has been developed. The meshes considered in this work are composed of four kinds of elements; tetrahedra, prisms, hexahedra and pyramids, which poses a challenge to parallel mesh adaptation. Additional complexity imposed by the presence of multiple types of elements affects especially data migration, updates of local data structures and interpartition data structures. Efficient partition of hybrid meshes has been accomplished by transforming them to suitable graphs and using serial graph partitioning algorithms. Communication among processors is based on the faces of the interpartition boundary and the termination detection algorithm of Dijkstra is employed to ensure proper flagging of edges for refinement. An inexpensive dynamic load balancing strategy is introduced to redistribute work load among processors after adaptation. In particular, only the initial coarse mesh, with proper weighting, is balanced which yields savings in computation time and relatively simple implementation of mesh quality preservation rules, while facilitating coarsening of refined elements. Special algorithms are employed for (i) data migration and dynamic updates of the local data structures, (ii) determination of the resulting interpartition boundary and (iii) identification of the communication pattern of processors. Several representative applications are included to evaluate the method.
Adaptive neural network error control for generalized perturbation theory
This paper addresses the issue of adaptive error control within generalized perturbation theory (GPT). The strategy herein assessed considers an artificial neural network (ANN) error estimator. The underlying tool facilitating this research is the FORMOSA-P code, a pressurized water reactor (PWR) nuclear fuel management optimization package, which combines simulated annealing and nodal GPT. A number of applications exist where traditional GPT may be limited by the magnitude of perturbations, which it can accurately handle. In fact, other alternative such as supervariational techniques (i.e., n'th-order GPT) and/or multireference strategies (i.e., rodded adjoints) are being sought for boiling water reactor and rodded applications. A perhaps not-so-obvious alternative could be to employ a neural network for adaptive error control within GPT. This study presents the results of two ANN models. The first model constitutes an intensively well-trained ANN used to contrast its global core parameter (i.e., keff) prediction capability versus that of a GPT model. The second model is a similar ANN intended for adaptive GPT error correction. In other words, the latter ANN is trained on-the-fly within the scope of a standard FORMOSA-P calculation
Parallel adaptation of general three-dimensional hybrid meshes
Kavouklis, Christos; Kallinderis, Yannis
2010-05-01
A new parallel dynamic mesh adaptation and load balancing algorithm for general hybrid grids has been developed. The meshes considered in this work are composed of four kinds of elements; tetrahedra, prisms, hexahedra and pyramids, which poses a challenge to parallel mesh adaptation. Additional complexity imposed by the presence of multiple types of elements affects especially data migration, updates of local data structures and interpartition data structures. Efficient partition of hybrid meshes has been accomplished by transforming them to suitable graphs and using serial graph partitioning algorithms. Communication among processors is based on the faces of the interpartition boundary and the termination detection algorithm of Dijkstra is employed to ensure proper flagging of edges for refinement. An inexpensive dynamic load balancing strategy is introduced to redistribute work load among processors after adaptation. In particular, only the initial coarse mesh, with proper weighting, is balanced which yields savings in computation time and relatively simple implementation of mesh quality preservation rules, while facilitating coarsening of refined elements. Special algorithms are employed for (i) data migration and dynamic updates of the local data structures, (ii) determination of the resulting interpartition boundary and (iii) identification of the communication pattern of processors. Several representative applications are included to evaluate the method.
Dark energy cosmology with generalized linear equation of state
Dark energy with the usually used equation of state p = wρ, where w const 0), where the constants α and ρ0 are free parameters. This non-homogeneous linear equation of state provides the description of both hydrodynamically stable (α > 0) and unstable (α < 0) fluids. In particular, the considered cosmological model describes the hydrodynamically stable dark (and phantom) energy. The possible types of cosmological scenarios in this model are determined and classified in terms of attractors and unstable points by using phase trajectories analysis. For the dark energy case, some distinctive types of cosmological scenarios are possible: (i) the universe with the de Sitter attractor at late times, (ii) the bouncing universe, (iii) the universe with the big rip and with the anti-big rip. In the framework of a linear equation of state the universe filled with a phantom energy, w < -1, may have either the de Sitter attractor or the big rip
The Complexity of Generalized Satisfiability for Linear Temporal Logic
Bauland, Michael; Schneider, Thomas; Schnoor, Henning; Schnoor, Ilka; Vollmer, Heribert
2008-01-01
In a seminal paper from 1985, Sistla and Clarke showed that satisfiability for Linear Temporal Logic (LTL) is either NP-complete or PSPACE-complete, depending on the set of temporal operators used. If, in contrast, the set of propositional operators is restricted, the complexity may decrease. This paper undertakes a systematic study of satisfiability for LTL formulae over restricted sets of propositional and temporal operators. Since every propositional operator corresponds to a Boolean funct...
Inference of High-dimensional Autoregressive Generalized Linear Models
Hall, Eric C.; Raskutti, Garvesh; Willett, Rebecca
2016-01-01
Vector autoregressive models characterize a variety of time series in which linear combinations of current and past observations can be used to accurately predict future observations. For instance, each element of an observation vector could correspond to a different node in a network, and the parameters of an autoregressive model would correspond to the impact of the network structure on the time series evolution. Often these models are used successfully in practice to learn the structure of...
Generalized linear mixed models modern concepts, methods and applications
Stroup, Walter W
2012-01-01
PART I The Big PictureModeling BasicsWhat Is a Model?Two Model Forms: Model Equation and Probability DistributionTypes of Model EffectsWriting Models in Matrix FormSummary: Essential Elements for a Complete Statement of the ModelDesign MattersIntroductory Ideas for Translating Design and Objectives into ModelsDescribing ""Data Architecture"" to Facilitate Model SpecificationFrom Plot Plan to Linear PredictorDistribution MattersMore Complex Example: Multiple Factors with Different Units of ReplicationSetting the StageGoals for Inference with Models: OverviewBasic Tools of InferenceIssue I: Data
Generalized inner bending continua for linear fiber reinforced materials
Boutin, Claude; Soubestre, Jean
2011-01-01
This paper deals with the effective behaviour of elastic materials periodically reinforced by linear slender elastic inclusions. Assuming a small scale ratio ε between the cell size and the characteristic size of the macroscopic deformation, the macro-behaviour at the leading order is derived by the homogenization method of periodic media. Different orders of magnitude of the contrast between the shear modulus of the material μm and of the reinforcement μp are considered. A contrast μm/μp of ...
Variable Selection for Marginal Longitudinal Generalized Linear Models
Eva Cantoni; Joanna Mills Flemming; Elvezio Ronchetti
2003-01-01
Variable selection is an essential part of any statistical analysis and yet has been somewhat neglected in the context of longitudinal data analysis. In this paper we propose a generalized version of Mallows's Cp (GCp) suitable for use with both parametric and nonparametric models. GCp provides an estimate of a measure of model's adequacy for prediction. We examine its performance with popular marginal longitudinal models (fitted using GEE) and contrast results with what is typically done in ...
An adaptive hybrid stress transition quadrilateral finite element method for linear elasticity
Huang, Feiteng; Xie, Xiaoping; Zhang, Chen-Song
2014-01-01
In this paper, we discuss an adaptive hybrid stress finite element method on quadrilateral meshes for linear elasticity problems. To deal with hanging nodes arising in the adaptive mesh refinement, we propose new transition types of hybrid stress quadrilateral elements with 5 to 7 nodes. In particular, we derive a priori error estimation for the 5-node transition hybrid stress element to show that it is free from Poisson-locking, in the sense that the error bound in the a priori estimate is i...
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
The generalization of some trellis properties of linear codes to group codes
KAN HaiBin; LI XueFei; SHEN Hong
2009-01-01
In this paper, we discuss some trellis properties for codes over a finite Abelian group, which are the generalization of the corresponding trellis properties for linear codes over a field. Also, we also inves-tigate difficulties when we try to generalize a property of a tail-biting trellis for a linear code over a field to a group code.
Computer analysis of general linear networks using digraphs.
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.
LI, ZHONGKUI; Duan, Zhisheng
2014-01-01
This paper addresses the distributed consensus design problem for linear multi-agent systems with directed communication graphs and external disturbances. Both the cases with strongly connected communication graphs and leader-follower graphs containing a directed spanning tree with the leader as the root are discussed. Distributed adaptive consensus protocols based on the relative states of neighboring agents are designed, which can ensure the ultimate boundedness of the consensus error and a...
Manjula Devi Ramasamy; Kuppuswami Subbaraya Gounder
2014-01-01
Multilayer Feed Forward Neural Network (MFNN) has been successfully administered architectures for solving a wide range of supervised pattern recognition tasks. The most problematic task of MFNN is training phase which consumes very long training time on very huge training datasets. An enhanced linear adaptive skipping training algorithm for MFNN called Half of Threshold (HOT) is proposed in this research paper. The core idea of this study is to reduce the training time through random present...
Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network
Manjula Devi, R.; R. C. Suganthe; S. KUPPUSWAMI
2013-01-01
Artificial neural network has been extensively consumed training model for solving pattern recognition tasks. However, training a very huge training data set using complex neural network necessitates excessively high training time. In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted. The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do ...
Abbaszadeh, Masoud
2015-01-01
A sequential trajectory linearized adaptive model based predictive controller is designed using the DMC algorithm to control the temperature of a batch MMA polymerization process. Using the mechanistic model of the polymerization, a parametric transfer function is derived to relate the reactor temperature to the power of the heaters. Then, a multiple model predictive control approach is taken in to track a desired temperature trajectory.The coefficients of the multiple transfer functions are ...
Sample based 3D face reconstruction from a single frontal image by adaptive locally linear embedding
ZHANG Jian; ZHUANG Yue-ting
2007-01-01
In this paper, we propose a highly automatic approach for 3D photorealistic face reconstruction from a single frontal image. The key point of our work is the implementation of adaptive manifold learning approach. Beforehand, an active appearance model (AAM) is trained for automatic feature extraction and adaptive locally linear embedding (ALLE) algorithm is utilized to reduce the dimensionality of the 3D database. Then, given an input frontal face image, the corresponding weights between 3D samples and the image are synthesized adaptively according to the AAM selected facial features. Finally, geometry reconstruction is achieved by linear weighted combination of adaptively selected samples. Radial basis function (RBF) is adopted to map facial texture from the frontal image to the reconstructed face geometry. The texture of invisible regions between the face and the ears is interpolated by sampling from the frontal image. This approach has several advantages: (1) Only a single frontal face image is needed for highly automatic face reconstruction; (2) Compared with former works, our reconstruction approach provides higher accuracy; (3) Constraint based RBF texture mapping provides natural appearance for reconstructed face.
Erlicher, Silvano; Point, Nelly
2006-01-01
International audience A simple way to define the flow rules of plasticity models is the assumption of generalized normality associated with a suitable pseudo-potential function. This approach, however, is not usually employed to formulate endochronic theory and non-linear kinematic (NLK) hardening rules as well as generalized plasticity models. In this paper, generalized normality is used to give a new formulation of these classes of models. As a result, a suited pseudo-potential is intro...
Finite element model for linear-elastic mixed mode loading using adaptive mesh strategy
无
2008-01-01
An adaptive mesh finite element model has been developed to predict the crack propagation direction as well as to calculate the stress intensity factors (SIFs), under linear-elastic assumption for mixed mode loading application. The finite element mesh is generated using the advancing front method. In order to suit the requirements of the fracture analysis, the generation of the background mesh and the construction of singular elements have been added to the developed program. The adaptive remeshing process is carried out based on the posteriori stress error norm scheme to obtain an optimal mesh. Previous works of the authors have proposed techniques for adaptive mesh generation of 2D cracked models. Facilitated by the singular elements, the displacement extrapolation technique is employed to calculate the SIF. The fracture is modeled by the splitting node approach and the trajectory follows the successive linear extensions of each crack increment. The SIFs values for two different case studies were estimated and validated by direct comparisons with other researchers work.
Design of Attitude Control System for UAV Based on Feedback Linearization and Adaptive Control
Wenya Zhou
2014-01-01
Full Text Available Attitude dynamic model of unmanned aerial vehicles (UAVs is multi-input multioutput (MIMO, strong coupling, and nonlinear. Model uncertainties and external gust disturbances should be considered during designing the attitude control system for UAVs. In this paper, feedback linearization and model reference adaptive control (MRAC are integrated to design the attitude control system for a fixed wing UAV. First of all, the complicated attitude dynamic model is decoupled into three single-input single-output (SISO channels by input-output feedback linearization. Secondly, the reference models are determined, respectively, according to the performance indexes of each channel. Subsequently, the adaptive control law is obtained using MRAC theory. In order to demonstrate the performance of attitude control system, the adaptive control law and the proportional-integral-derivative (PID control law are, respectively, used in the coupling nonlinear simulation model. Simulation results indicate that the system performance indexes including maximum overshoot, settling time (2% error range, and rise time obtained by MRAC are better than those by PID. Moreover, MRAC system has stronger robustness with respect to the model uncertainties and gust disturbance.
Auxiliary space preconditioners for linear elasticity based on generalized finite element methods
Brannick, James; Cho, Durkbin
2010-01-01
We construct and analyze a preconditioner of the linear elastiity system discretized by conforming linear finite elements in the framework of the auxiliary space method. The auxiliary space preconditioner is based on discretization of a scalar elliptic equation with Generalized Finite Element Method.
Zhang, Ruikun; Hou, Zhongsheng; Ji, Honghai; Yin, Chenkun
2016-04-01
In this paper, an adaptive iterative learning control scheme is proposed for a class of non-linearly parameterised systems with unknown time-varying parameters and input saturations. By incorporating a saturation function, a new iterative learning control mechanism is presented which includes a feedback term and a parameter updating term. Through the use of parameter separation technique, the non-linear parameters are separated from the non-linear function and then a saturated difference updating law is designed in iteration domain by combining the unknown parametric term of the local Lipschitz continuous function and the unknown time-varying gain into an unknown time-varying function. The analysis of convergence is based on a time-weighted Lyapunov-Krasovskii-like composite energy function which consists of time-weighted input, state and parameter estimation information. The proposed learning control mechanism warrants a L2[0, T] convergence of the tracking error sequence along the iteration axis. Simulation results are provided to illustrate the effectiveness of the adaptive iterative learning control scheme.
Linearized solution of isentropic motions of a perfect fluid in general relativity
Isentropic motions of a perfect fluid are studied by using comoving coordinates in the framework of general relativity without assuming any symmetry in the line element and a linearized solution is obtained by dealing with the Cauchy problem. (author)
Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation
Groll, Andreas
2011-01-01
Generalized linear mixed models are a widely used tool for modeling longitudinal data. However, their use is typically restricted to few covariates, because the presence of many predictors yields unstable estimates. The presented approach to the fitting of generalized linear mixed models includes an L1-penalty term that enforces variable selection and shrinkage simultaneously. A gradient ascent algorithm is proposed that allows to maximize the penalized loglikelihood yielding models with r...
General linearization formulae for products of continuous hypergeometric-type polynomials
Sánchez-Ruiz, Jorge; López Artés, Pedro; Martínez-Finkelshtein, Andrei; Sánchez Dehesa, Jesús
1999-01-01
The linearization of products of wavefunctions of exactly solvable potentials often reduces to the generalized linearization problem for hypergeometric polynomials (HPs) of a continuous variable, which consists of the expansion of the product of two arbitrary HPs in series of an orthogonal HP set. Here, this problem is algebraically solved directly in terms of the coefficients of the second-order differential equations satisfied by the involved polynomials. General expressions for the expansi...
Adaptive H∞ nonlinear velocity tracking using RBFNN for linear DC brushless motor
Tsai, Ching-Chih; Chan, Cheng-Kain; Li, Yi Yu
2012-01-01
This article presents an adaptive H ∞ nonlinear velocity control for a linear DC brushless motor. A simplified model of this motor with friction is briefly recalled. The friction dynamics is described by the Lu Gre model and the online tuning radial basis function neural network (RBFNN) is used to parameterise the nonlinear friction function and un-modelled errors. An adaptive nonlinear H ∞ control method is then proposed to achieve velocity tracking, by assuming that the upper bounds of the ripple force, the changeable load and the nonlinear friction can be learned by the RBFNN. The closed-loop system is proven to be uniformly bounded using the Lyapunov stability theory. The feasibility and the efficacy of the proposed control are exemplified by conducting two velocity tracking experiments.
Research of robust adaptive trajectory linearization control based on T-S fuzzy system
Jiang Changsheng; Zhang Chunyu; Zhu Liang
2008-01-01
A robust adaptive trajectory linearization control (RATLC) algorithm for a class of nonlinear systems with uncertainty and disturbance based on the T-S fuzzy system is presented. The unknown disturbance and uncertainty are estimated by the T-S fuzzy system, and a robust adaptive control law is designed by the Lyapunov theory. Irrespective of whether the dimensions of the system and the rules of the fuzzy system are large or small, there is only one parameter adjusting on line. Uniformly ultimately boundedness of all signals of the composite closed-loop system are proved by theory analysis. Finally, a numerical example is studied based on the proposed method. The simulation results demonstrate the effectiveness and robustness of the control scheme.
Yunfeng Wu
2014-01-01
Full Text Available This paper presents a novel adaptive linear and normalized combination (ALNC method that can be used to combine the component radial basis function networks (RBFNs to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error and the better fidelity (characterized by normalized correlation coefficient of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.
Maps for general open quantum systems and a theory of linear quantum error correction
We show that quantum subdynamics of an open quantum system can always be described by a linear, Hermitian map irrespective of the form of the initial total system state. Since the theory of quantum error correction was developed based on the assumption of completely positive (CP) maps, we present a generalized theory of linear quantum error correction, which applies to any linear map describing the open system evolution. In the physically relevant setting of Hermitian maps, we show that the CP-map-based version of quantum error correction theory applies without modifications. However, we show that a more general scenario is also possible, where the recovery map is Hermitian but not CP. Since non-CP maps have nonpositive matrices in their range, we provide a geometric characterization of the positivity domain of general linear maps. In particular, we show that this domain is convex and that this implies a simple algorithm for finding its boundary.
Niche adaptation by expansion and reprogramming of general transcription factors
Turkarslan, Serdar; Reiss, David J; Gibbins, Goodwin; Su, Wan Lin; Pan, Min; Bare, J Christopher; Plaisier, Christopher L.; Baliga, Nitin S
2011-01-01
The evolutionary success of an organism depends on its ability to continually adapt to changes in the patterns of constant, periodic, and transient challenges within its environment. This process of ‘niche adaptation' requires reprogramming of the organism's environmental response networks by reorganizing interactions among diverse parts including environmental sensors, signal transducers, and transcriptional and post-transcriptional regulators. Gene duplications have been discovered to be on...
Tsarev, Sergey P.
2005-01-01
We give a new procedure for generalized factorization and construction of the complete solution of strictly hyperbolic linear partial differential equations or strictly hyperbolic systems of such equations in the plane. This procedure generalizes the classical theory of Laplace transformations of second-order equations in the plane.
Univariate and multivariate general linear models theory and applications with SAS
Kim, Kevin
2006-01-01
Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data sets from a variety of disciplines, such as the social and behavioral sciences.With revised examples that include options available using SAS 9.0, this expanded edition divides theory from applications within each chapter. Following an overview of the GLM, the book introduces unrestricted GLMs to analyze multiple regr
Evaluation of Lyapunov exponent in generalized linear dynamical models of queueing networks
Krivulin, N. K.
2012-01-01
The problem of evaluation of Lyapunov exponent in queueing network analysis is considered based on models and methods of idempotent algebra. General existence conditions for Lyapunov exponent to exist in generalized linear stochastic dynamic systems are given, and examples of evaluation of the exponent for systems with matrices of particular types are presented. A method which allow one to get the exponent is proposed based on some appropriate decomposition of the system matrix. A general app...
Manjula Devi Ramasamy
2014-01-01
Full Text Available Multilayer Feed Forward Neural Network (MFNN has been successfully administered architectures for solving a wide range of supervised pattern recognition tasks. The most problematic task of MFNN is training phase which consumes very long training time on very huge training datasets. An enhanced linear adaptive skipping training algorithm for MFNN called Half of Threshold (HOT is proposed in this research paper. The core idea of this study is to reduce the training time through random presentation of training input samples without affecting the network’s accuracy. The random presentation is done by partitioning the training dataset into two distinct classes, classified and misclassified class, based on the comparison result of the calculated error measure with half of threshold value. Only the input samples in the misclassified class are presented to the next epoch for training, whereas the correctly classified class is skipped linearly which dynamically reducing the number of input samples exhibited at every single epoch without affecting the network’s accuracy. Thus decreasing the size of the training dataset linearly can reduce the total training time, thereby speeding up the training process. This HOT algorithm can be implemented with any training algorithm used for supervised pattern classification and its implementation is very simple and easy. Simulation study results proved that HOT training algorithm achieves faster training than the other standard training algorithm.
Bounded Real Lemma for Generalized Linear System with Finite Discrete Jumps
无
2006-01-01
The strict bounded real lemma for linear system with finite discrete jumps was considered. Especially,the case where D matrices in the system are not assumed to be zero was dealt. Several versions of the bounded real lemma are presented in terms of solution to Riccati differential equations or inequalities with finite discrete jumps.Both the finite and infinite horizon cases are considered. These results generalize the existed bounded real lemma for linear systems.
The Solution Structure and Error Estimation for The Generalized Linear Complementarity Problem
Tingfa Yan
2014-07-01
Full Text Available In this paper, we consider the generalized linear complementarity problem (GLCP. Firstly, we develop some equivalent reformulations of the problem under milder conditions, and then characterize the solution of the GLCP. Secondly, we also establish the global error estimation for the GLCP by weakening the assumption. These results obtained in this paper can be taken as an extension for the classical linear complementarity problems.
Linear-scaling symmetry-adapted perturbation theory with scaled dispersion
We present a linear-scaling symmetry-adapted perturbation theory (SAPT) method that is based on an atomic orbital (AO) formulation of zeroth-order SAPT (SAPT0). The non-dispersive terms are realized with linear-scaling cost using both the continuous fast multipole method (CFMM) and the linear exchange (LinK) approach for integral contractions as well as our efficient Laplace-based coupled-perturbed self-consistent field method (DL-CPSCF) for evaluating response densities. The reformulation of the dispersion term is based on our linear-scaling AO Møller-Plesset second-order perturbation theory (AO-MP2) method, that uses our recently introduced QQR-type screening [S. A. Maurer, D. S. Lambrecht, J. Kussmann, and C. Ochsenfeld, J. Chem. Phys. 138, 014101 (2013)] for preselecting numerically significant energy contributions. Similar to scaled opposite-spin MP2, we neglect the exchange-dispersion term in SAPT and introduce a scaling factor for the dispersion term, which compensates for the error and at the same time accounts for basis set incompleteness effects and intramonomer correlation. We show in extensive benchmark calculations that the new scaled-dispersion (sd-)SAPT0 approach provides reliable results for small and large interacting systems where the results with a small 6-31G** basis are roughly comparable to supermolecular MP2 calculations in a triple-zeta basis. The performance of our method is demonstrated with timings on cellulose fragments, DNA systems, and cutouts of a protein-ligand complex with up to 1100 atoms on a single computer core
An adaptive input-output feedback linearization controller for doubly-fed induction machine drives
Payam Farrokh Amir
2008-01-01
Full Text Available In this paper a nonlinear controller is presented for Doubly-Fed Induction Machine (DFIM drives. The nonlinear controller is designed based on the adaptive input-output feedback linearization control technique, using the fifth order model of induction machine in fixed stator d, q axis reference frames with stator currents and rotor flux components as state variables. The nonlinear controller can perfectly track the torque and flux reference signals in spite of stator and rotor resistance variations. Two level SVM-PWM back-to-back voltage source inverters are employed in the rotor circuit, in order to make the drive system capable of operating in the motoring and generating modes below and above the synchronous speed. Computer simulation results obtained, confirm the effectiveness and validity of the proposed control approach.
Unified Einstein-Virasoro Master Equation in the General Non-Linear Sigma Model
de Boer, J
2009-01-01
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 f...
Unified Einstein-Virasoro Master Equation in the General Non-Linear Sigma Model
de Boer, J; Halpern, M. B.
1996-01-01
The Virasoro master equation (VME) describes the general affine-Virasoro construction $T=L^{ab}J_aJ_b+iD^a \\dif J_a$ 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-t...
Erlicher, Silvano; 10.1016/j.ijsolstr.2005.03.022
2008-01-01
A simple way to define the flow rules of plasticity models is the assumption of generalized normality associated with a suitable pseudo-potential function. This approach, however, is not usually employed to formulate endochronic theory and non-linear kinematic (NLK) hardening rules as well as generalized plasticity models. In this paper, generalized normality is used to give a new formulation of these classes of models. As a result, a suited pseudo-potential is introduced for endochronic models and a non-standard description of NLK hardening and generalized plasticity models is also provided. This new formulation allows for an effective investigation of the relationships between these three classes of plasticity models.
Novel Adaptive Learning Control of Linear Systems with Completely Unknown Time Delays
Wei-Sheng Chen
2009-01-01
A novel output-feedback adaptive learning control approach is developed for a class of linear time-delay systems. Three kinds of uncertainties: time delays, number of time delays, and system parameters are all assumed to be completely unknown, which is different from the previous work. The design procedure includes two steps. First, according to the given periodic desired reference output and the allowed bound of tracking error, a suitable finite Fourier series expansion (FSE) is chosen as a practical reference output to he tracked. Second, by expressing the delayed practical reference output as a known time-varying vector multiplied by an unknown constant vector, we combine three kinds of uncertainties into an unknown constant vector and then estimate the vector by designing an adaptive law. By constructing a Lyapunov-Krasovskii functional, it is proved that the system output can asymptotically track the practical reference signal An example is provided to illustrate the effectiveness of the control scheme developed in this paper.
LaChapelle, J.
2004-01-01
A path integral is presented that solves a general class of linear second order partial differential equations with Dirichlet/Neumann boundary conditions. Elementary kernels are constructed for both Dirichlet and Neumann boundary conditions. The general solution can be specialized to solve elliptic, parabolic, and hyperbolic partial differential equations with boundary conditions. This extends the well-known path integral solution of the Schr\\"{o}dinger/diffusion equation in unbounded space. ...
General-relativistic rotation laws in rotating fluid bodies: constant linear velocity
Knopik, Jerzy; Malec, Edward
2015-01-01
New rotation laws have been recently found for general-relativistic self-gravitating stationary fluids. It was not clear whether they apply to systems rotating with a constant linear velocity. In this paper we fill this gap. The answer is positive. That means, in particular, that these systems should exhibit the recently discovered general-relativistic weak-field effects within rotating tori: the dynamic anti-dragging and the deviation from the Keplerian motion induced by the fluid selfgravity.
We study many particle systems in the context of mean field forces, concentration-dependent diffusion coefficients, generalized equilibrium distributions, and quantum statistics. Using kinetic transport theory and linear nonequilibrium thermodynamics we derive for these systems a generalized multivariate Fokker-Planck equation. It is shown that this Fokker-Planck equation describes relaxation processes, has stationary maximum entropy distributions, can have multiple stationary solutions and stationary solutions that differ from Boltzmann distributions
Linearity enhancement of TVGA based on adaptive sweep optimisation in monostatic radar receiver
Almslmany, Amir; Wang, Caiyun; Cao, Qunsheng
2016-08-01
The limited input dynamic power range of the radar receiver and the power loss due to the targets' ranges are two potential problems in the radar receivers. This paper proposes a model based on the time-varying gain amplifier (TVGA) to compensate the power loss from the targets' ranges, and using the negative impedance compensation technique to enhance the TVGA linearity based on Volterra series. The simulation has been done based on adaptive sweep optimisation (ASO) using advanced design system (ADS) and Matlab. It shows that the suppression of the third-order intermodulation products (IMR3) was carried out for two-tone test, the high-gain accuracy improved by 3 dB, and the high linearity IMR3 improved by 14 dB. The monostatic radar system was tested to detect three targets at different ranges and to compare its probability of detection with the prior models; the results show that the probability of detection has been increased for ASO/TVGA.
A general non-linear optimization algorithm for lower bound limit analysis
Krabbenhøft, Kristian; Damkilde, Lars
2003-01-01
The non-linear programming problem associated with the discrete lower bound limit analysis problem is treated by means of an algorithm where the need to linearize the yield criteria is avoided. The algorithm is an interior point method and is completely general in the sense that no particular...... load optimization problem. and finally the efficiency and accuracy of the method is demonstrated by means of examples of plate and slab structures obeying different non-linear yield criteria. Copyright (C) 2002 John Wiley Sons. Ltd....... finite element discretization or yield criterion is required. As with interior point methods for linear programming the number of iterations is affected only little by the problem size. Some practical implementation issues are discussed with reference to the special structure of the common lower bound...
SELECTION OF THE LINEAR COMBINING VECTOR G OF THE GENERALIZED SELF-SHRINKING GENERATORS
Dong Lihua; Zeng Yong; Hu Yupu
2006-01-01
Given an m-sequence, the main factor influencing the least period of the Generalized Self-Shrinking (GSS) sequence is the selection of the linear combining vector G. Based on the calculation of the minimalpolynomial ofL GSS sequences and the comparison of their degrees, an algorithm for selecting the linear combining vector G is presented, which is simple to understand, to implement and to prove. By using this method,much more than 2L-1 linear combining vectors G of the desired properties will be resulted. Thus in the practical application the linear combining vector G can be chosen with great arbitrariness. Additionally, this algorithm can be extended to any finite field easily.
Robust speech perception: recognize the familiar, generalize to the similar, and adapt to the novel.
Kleinschmidt, Dave F; Jaeger, T Florian
2015-04-01
Successful speech perception requires that listeners map the acoustic signal to linguistic categories. These mappings are not only probabilistic, but change depending on the situation. For example, one talker's /p/ might be physically indistinguishable from another talker's /b/ (cf. lack of invariance). We characterize the computational problem posed by such a subjectively nonstationary world and propose that the speech perception system overcomes this challenge by (a) recognizing previously encountered situations, (b) generalizing to other situations based on previous similar experience, and (c) adapting to novel situations. We formalize this proposal in the ideal adapter framework: (a) to (c) can be understood as inference under uncertainty about the appropriate generative model for the current talker, thereby facilitating robust speech perception despite the lack of invariance. We focus on 2 critical aspects of the ideal adapter. First, in situations that clearly deviate from previous experience, listeners need to adapt. We develop a distributional (belief-updating) learning model of incremental adaptation. The model provides a good fit against known and novel phonetic adaptation data, including perceptual recalibration and selective adaptation. Second, robust speech recognition requires that listeners learn to represent the structured component of cross-situation variability in the speech signal. We discuss how these 2 aspects of the ideal adapter provide a unifying explanation for adaptation, talker-specificity, and generalization across talkers and groups of talkers (e.g., accents and dialects). The ideal adapter provides a guiding framework for future investigations into speech perception and adaptation, and more broadly language comprehension. PMID:25844873
Bayesian prediction of spatial count data using generalized linear mixed models
Christensen, Ole Fredslund; Waagepetersen, Rasmus Plenge
2002-01-01
Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, ...
ON THE SOLVABILITY OF GENERAL LINEAR METHODS FOR DISSIPATIVE DYNAMICAL SYSTEMS
Ai-guo Xiao
2000-01-01
The main purpose of the present paper is to examine the existence and local uniqueness of solutions of the implicit equations arising in the application of a weakly algebraically stable general linear methods to dissipative dynamical systems, and to extend the existing relevant results of Runge-Kutta methods by Humphries and Stuart(1994).
Large-Sample Theory for Generalized Linear Models with Non-natural Link and Random Variates
Jie-li Ding; Xi-ru Chen
2006-01-01
For generalized linear models (GLM), in the case that the regressors are stochastic and have different distributions and the observations of the responses may have different dimensionality, the asymptotic theory of the maximum likelihood estimate (MLE) of the parameters are studied under the assumption of a non-natural link function.
Modelling the score of the sport match via dynamic generalized linear models
Volf, Petr; Hejdušek, M.
Lisabon: ISI International Statistical Institute, 2007, s. 1-4. [ISI 2007. Session of the International Statistical Institute /56./. Lisboa (PT), 22.08.2007-29.08.2007] R&D Projects: GA MŠk(CZ) 1M06047 Institutional research plan: CEZ:AV0Z10750506 Keywords : generalized linear model * Poisson distribution * sport statistics Subject RIV: BB - Applied Statistics, Operational Research
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…
Rate of strong consistency of quasi maximum likelihood estimate in generalized linear models
无
2004-01-01
［1］McCullagh, P., Nelder, J. A., Generalized Linear Models, New York: Chapman and Hall, 1989.［2］Wedderbum, R. W. M., Quasi-likelihood functions, generalized linear models and Gauss-Newton method,Biometrika, 1974, 61:439-447.［3］Fahrmeir, L., Maximum likelihood estimation in misspecified generalized linear models, Statistics, 1990, 21:487-502.［4］Fahrmeir, L., Kaufmann, H., Consistency and asymptotic normality of the maximum likelihood estimator in generalized linear models, Ann. Statist., 1985, 13: 342-368.［5］Melder, J. A., Pregibon, D., An extended quasi-likelihood function, Biometrika, 1987, 74: 221-232.［6］Bennet, G., Probability inequalities for the sum of independent random variables, JASA, 1962, 57: 33-45.［7］Stout, W. F., Almost Sure Convergence, New York:Academic Press, 1974.［8］Petrov, V, V., Sums of Independent Random Variables, Berlin, New York: Springer-Verlag, 1975.
Regression Is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.
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…
A Note on an R^2 Measure for Fixed Effects in the Generalized Linear Mixed Model
Edwards, Lloyd J.
2010-01-01
Using the LRT statistic, a model R^2 is proposed for the generalized linear mixed model for assessing the association between the correlated outcomes and fixed effects. The R^2 compares the full model to a null model with all fixed effects deleted.
The theory of a general quantum system interacting with a linear dissipative system
A formalism has been developed, using Feynman's space-time formulation of nonrelativistic quantum mechanics whereby the behavior of a system of interest, which is coupled to other external quantum systems, may be calculated in terms of its own variables only. It is shown that the effect of the external systems in such a formalism can always be included in a general class of functionals (influence functionals) of the coordinates of the system only. The properties of influence functionals for general systems are examined. Then, specific forms of influence functionals representing the effect of definite and random classical forces, linear dissipative systems at finite temperatures, and combinations of these are analyzed in detail. The linear system analysis is first done for perfectly linear systems composed of combinations of harmonic oscillators, loss being introduced by continuous distributions of oscillators. Then approximately linear systems and restrictions necessary for the linear behavior are considered. Influence functionals for all linear systems are shown to have the same form in terms of their classical response functions. In addition, a fluctuation-dissipation theorem is derived relating temperature and dissipation of the linear system to a fluctuating classical potential acting on the system of interest which reduces to the Nyquist-Johnson relation for noise in the case of electric circuits. Sample calculations of transition probabilities for the spontaneous emission of an atom in free space and in a cavity are made. Finally, a theorem is proved showing that within the requirements of linearity all sources of noise or quantum fluctuation introduced by maser-type amplification devices are accounted for by a classical calculation of the characteristics of the maser
General introduction to altitude adaptation and mountain sickness
Bartsch, P.; Saltin, B.
2008-01-01
over 24-48 h to improve the oxygen-carrying capacity of the blood, and is further improved during a prolonged sojourn at altitude through an enhanced erythropoiesis and larger Hb mass, allowing for a partial or full restoration of the blood volume and arterial oxygen content. Most of these adaptations......-30% of subjects at altitudes between 2500 and 3000 m a.s.l. Pulmonary edema is rarely seen below 3000 m a.s.l. and brain edema is not seen below 4000 m a.s.l. It is possible to travel to altitudes of 2500-3000 m a.s.l., wait for 2 days, and then gradually start to train. At higher altitudes, one should...... modalities using hypoxia and altitude for improvement of performance Udgivelsesdato: 2008/8...
Generalized linear models with random effects unified analysis via H-likelihood
Lee, Youngjo; Pawitan, Yudi
2006-01-01
Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors.Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives. Complementing theory with examples, many of...
The computational capability of efficiently and accurately evaluate reactor core attributes (i.e., keff and power distributions as a function of cycle burnup) utilizing a second-order accurate advanced nodal Generalized Perturbation Theory (GPT) model has been developed. The GPT model is derived from the forward non-linear iterative Nodal Expansion Method (NEM) strategy, thereby extending its inherent savings in memory storage and high computational efficiency to also encompass GPT via the preservation of the finite-difference matrix structure. The above development was easily implemented into the existing coarse-mesh finite-difference GPT-based in-core fuel management optimization code FORMOSA-P, thus combining the proven robustness of its adaptive Simulated Annealing (SA) multiple-objective optimization algorithm with a high-fidelity NEM GPT neutronics model to produce a powerful computational tool used to generate families of near-optimum loading patterns for PWRs. (orig.)
Consensus of Continuous-Time Multiagent Systems with General Linear Dynamics and Nonuniform Sampling
Yanping Gao
2013-01-01
Full Text Available This paper studies the consensus problem of multiple agents with general linear continuous-time dynamics. It is assumed that the information transmission among agents is intermittent; namely, each agent can only obtain the information of other agents at some discrete times, where the discrete time intervals may not be equal. Some sufficient conditions for consensus in the cases of state feedback and static output feedback are established, and it is shown that if the controller gain and the upper bound of discrete time intervals satisfy certain linear matrix inequality, then consensus can be reached. Simulations are performed to validate the theoretical results.
Niche adaptation by expansion and reprogramming of general transcription factors.
Turkarslan, Serdar; Reiss, David J; Gibbins, Goodwin; Su, Wan Lin; Pan, Min; Bare, J Christopher; Plaisier, Christopher L; Baliga, Nitin S
2011-01-01
Numerous lineage-specific expansions of the transcription factor B (TFB) family in archaea suggests an important role for expanded TFBs in encoding environment-specific gene regulatory programs. Given the characteristics of hypersaline lakes, the unusually large numbers of TFBs in halophilic archaea further suggests that they might be especially important in rapid adaptation to the challenges of a dynamically changing environment. Motivated by these observations, we have investigated the implications of TFB expansions by correlating sequence variations, regulation, and physical interactions of all seven TFBs in Halobacterium salinarum NRC-1 to their fitness landscapes, functional hierarchies, and genetic interactions across 2488 experiments covering combinatorial variations in salt, pH, temperature, and Cu stress. This systems analysis has revealed an elegant scheme in which completely novel fitness landscapes are generated by gene conversion events that introduce subtle changes to the regulation or physical interactions of duplicated TFBs. Based on these insights, we have introduced a synthetically redesigned TFB and altered the regulation of existing TFBs to illustrate how archaea can rapidly generate novel phenotypes by simply reprogramming their TFB regulatory network. PMID:22108796
Generalization of Hindi OCR Using Adaptive Segmentation and Font Files
Agrawal, Mudit; Ma, Huanfeng; Doermann, David
In this chapter, we describe an adaptive Indic OCR system implemented as part of a rapidly retargetable language tool effort and extend work found in [20, 2]. The system includes script identification, character segmentation, training sample creation, and character recognition. For script identification, Hindi words are identified in bilingual or multilingual document images using features of the Devanagari script and support vector machine (SVM). Identified words are then segmented into individual characters, using a font-model-based intelligent character segmentation and recognition system. Using characteristics of structurally similar TrueType fonts, our system automatically builds a model to be used for the segmentation and recognition of the new script, independent of glyph composition. The key is a reliance on known font attributes. In our recognition system three feature extraction methods are used to demonstrate the importance of appropriate features for classification. The methods are tested on both Latin and non-Latin scripts. Results show that the character-level recognition accuracy exceeds 92% for non-Latin and 96% for Latin text on degraded documents. This work is a step toward the recognition of scripts of low-density languages which typically do not warrant the development of commercial OCR, yet often have complete TrueType font descriptions.
McTaggart Cowan, H.M.; Tsuchiya, A.; O'Cathain, A; Brazier, J. E.
2010-01-01
Economic evaluation of healthcare technologies uses values for hypothetical health states elicited from the general population rather than patients. However, they may not consider adaptation. This study explored the extent to which the general population changes their initial values, and the factors that influenced this change, after being informed about adaptation. Three rheumatoid arthritis (RA) states were used for illustration. Two respondent groups were interviewed. The Initially Uninfor...
Kun, David William
Unmanned aircraft systems (UASs) are gaining popularity in civil and commercial applications as their lightweight on-board computers become more powerful and affordable, their power storage devices improve, and the Federal Aviation Administration addresses the legal and safety concerns of integrating UASs in the national airspace. Consequently, many researchers are pursuing novel methods to control UASs in order to improve their capabilities, dependability, and safety assurance. The nonlinear control approach is a common choice as it offers several benefits for these highly nonlinear aerospace systems (e.g., the quadrotor). First, the controller design is physically intuitive and is derived from well known dynamic equations. Second, the final control law is valid in a larger region of operation, including far from the equilibrium states. And third, the procedure is largely methodical, requiring less expertise with gain tuning, which can be arduous for a novice engineer. Considering these facts, this thesis proposes a nonlinear controller design method that combines the advantages of adaptive robust control (ARC) with the powerful design tools of linear matrix inequalities (LMI). The ARC-LMI controller is designed with a discontinuous projection-based adaptation law, and guarantees a prescribed transient and steady state tracking performance for uncertain systems in the presence of matched disturbances. The norm of the tracking error is bounded by a known function that depends on the controller design parameters in a known form. Furthermore, the LMI-based part of the controller ensures the stability of the system while overcoming polytopic uncertainties, and minimizes the control effort. This can reduce the number of parameters that require adaptation, and helps to avoid control input saturation. These desirable characteristics make the ARC-LMI control algorithm well suited for the quadrotor UAS, which may have unknown parameters and may encounter external
Adaptive set-membership identification in 0(m) time for linear-in-parameters models
Deller, J. R., Jr.; Odehl, S. F.
1992-04-01
This paper describes some fundamental contributions to the theory and applicability of optimal bounding ellipsoid (OBE) algorithms for signal processing. AU reported OBE algorithms are placed in a general framework which fruitfully demonstrates the relationship between the set-membership principles and least square error identification. Within this framework, flexible measures for adding explicit adaptation capability are formulated and demonstrated through simulation. Computational complexity analysis of OBE algorithms reveals that they are of O(m2) complexity per data sample with m the number of parameters identified, in spite of their well-known propensity toward highly-selective updating. Two very different approaches are described for rendering a specific OBE algorithm, the set-membership weighted recursive least squares algorithm, of 0(m) complexity. The first approach involves an algorithmic solution in which a suboptimal test for innovation is employed. The performance is demonstrated through simulation. The second method is an architectural approach in which complexity is reduced through parallel computation.
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
Generalization of force-field adaptation in proprioceptively-deafferented subjects.
Lefumat, Hannah Z; Miall, R Chris; Cole, Jonathan D; Bringoux, Lionel; Bourdin, Christophe; Vercher, Jean-Louis; Sarlegna, Fabrice R
2016-03-11
Humans have the remarkable ability to adapt their motor behaviour to changes in body properties and/or environmental conditions, based on sensory feedback such as vision and proprioception. The role of proprioception has been highlighted for the adaptation to new upper-limb dynamics, which is known to generalize to the opposite, non-adapted limb in healthy individuals. Such interlimb transfer seems to depend on sensory feedback, and the present study assessed whether the chronic loss of proprioception precludes interlimb transfer of dynamic adaptation by testing two well-characterized proprioceptively-deafferented subjects. These had to reach toward visual targets with vision of the limb. For both deafferented subjects, we observed adaptation of the dominant arm to Coriolis forces and after-effects on non-dominant arm movements in different movement directions, thus indicating interlimb transfer. Overall, our findings show that motor learning can generalize across limbs and movement directions despite the loss of proprioceptive afferents. PMID:26826606
A cautionary note on generalized linear models for covariance of unbalanced longitudinal data
Huang, Jianhua Z.
2012-03-01
Missing data in longitudinal studies can create enormous challenges in data analysis when coupled with the positive-definiteness constraint on a covariance matrix. For complete balanced data, the Cholesky decomposition of a covariance matrix makes it possible to remove the positive-definiteness constraint and use a generalized linear model setup to jointly model the mean and covariance using covariates (Pourahmadi, 2000). However, this approach may not be directly applicable when the longitudinal data are unbalanced, as coherent regression models for the dependence across all times and subjects may not exist. Within the existing generalized linear model framework, we show how to overcome this and other challenges by embedding the covariance matrix of the observed data for each subject in a larger covariance matrix and employing the familiar EM algorithm to compute the maximum likelihood estimates of the parameters and their standard errors. We illustrate and assess the methodology using real data sets and simulations. © 2011 Elsevier B.V.
U-Quantile Processes and Generalized Linear Statistics of Dependent Data
Wendler, Martin
2010-01-01
Generalized linear statistics are a unifying class that contains U-statistics, U-quantiles, L-statistics as well as trimmed and winsorized U-statistics. For example, many commonly used estimators of scale fall into this class. GL-statistics only have been studied under independence; in this paper, we establish the central limit theorem (CLT) and the law of the iterated logarithm (LIL) for GL-statistics of sequences which are strongly mixing or L^1 near epoch dependent on an absolutely regular process. We first investigate the empirical U-process. With the help of a generalized Bahadur representation, the CLT and the LIL for the empirical U-quantile process follow. As GL-statistics are linear functionals of the U-quantile process, the CLT and the LIL for GL-statistics are straightforward corollaries.
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
Goyal, S; Goyal, G. K.
2012-01-01
This paper highlights the significance of computational intelligence models for predicting shelf life of processed cheese stored at 7-8 g.C. Linear Layer and Generalized Regression models were developed with input parameters: Soluble nitrogen, pH, Standard plate count, Yeast & mould count, Spores, and sensory score as output parameter. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were used in order to compare the prediction ability o...
R. E. Chandler; Wheater, H. S.
2002-01-01
In the early 1990s a cluster of extreme flood events occurred in the south Galway region of western Ireland, and this led to speculation of changing rainfall patterns in the area. In this paper we illustrate the use of generalized linear models (GLMs) to test for such changes and quantify their structure. GLMs, long established in the statistical literature, provide a flexible and rigorous formal framework within which to distinguish between possible climate change scenarios and are able to d...
A generalized linear Boltzmann equation for non-classical particle transport
This paper presents a derivation and initial study of a new generalized linear Boltzmann equation (GLBE), which describes particle transport for random statistically homogeneous systems in which the distribution function for chord lengths between scattering centers is non-exponential. Such problems have recently been proposed for the description of photon transport in atmospheric clouds; this paper is a first attempt to develop a Boltzmann-like equation for these and other related applications.
Bivariate Random Effects Meta-analysis of Diagnostic Studies Using Generalized Linear Mixed Models
Chu, Haitao; Guo, Hongfei; Zhou, Yijie
2009-01-01
Bivariate random effect models are currently one of the main methods recommended to synthesize diagnostic test accuracy studies. However, only the logit-transformation on sensitivity and specificity has been previously considered in the literature. In this paper, we consider a bivariate generalized linear mixed model to jointly model the sensitivities and specificities, and discuss the estimation of the summary receiver operating characteristic curve (ROC) and the area under the ROC curve (AU...
d'Eon, Eugene
2013-01-01
We derive new diffusion solutions to the monoenergetic generalized linear Boltzmann transport equation (GLBE) for the stationary collision density and scalar flux about an isotropic point source in an infinite $d$-dimensional absorbing medium with isotropic scattering. We consider both classical transport theory with exponentially-distributed free paths in arbitrary dimensions as well as a number of non-classical transport theories (non-exponential random flights) that describe a broader clas...
Convergence analysis for general linear methods applied to stiff delay differential equations
无
2002-01-01
For Runge-Kutta methods applied to stiff delay differential equations (DDEs), the concept of D-convergence was proposed, which is an extension to that of B-convergence in ordinary differential equations (ODEs). In this paper, D-convergence of general linear methods is discussed and the previous related results are improved. Some order results to determine D-convergence of the methods are obtained.
The general solution of a mixed integer linear programme over a cone
Williams, H. P.
2013-01-01
We give a general method of finding the optimal objective, and solution, values of a Mixed Integer Linear Programme over a Cone (MILPC) as a function of the coefficients (objective, matrix and right- hand side). In order to do this we first convert the matrix of constraint coefficients to a Normal Form (Modified Hermite Normal Form (MHNF)). Then we project out all the variables leaving an (attainable) bound on the optimal objective value. For (M)IPs, including MILPC, projection is more comple...
Feng, Jian-Ying; Zhang, Jin; Zhang, Wen-Jie; Wang, Shi-Bo; Han, Shi-Feng; Zhang, Yuan-Ming
2013-01-01
Many important phenotypic traits in plants are ordinal. However, relatively little is known about the methodologies for ordinal trait association studies. In this study, we proposed a hierarchical generalized linear mixed model for mapping quantitative trait locus (QTL) of ordinal traits in crop cultivars. In this model, all the main-effect QTL and QTL-by-environment interaction were treated as random, while population mean, environmental effect and population structure were fixed. In the est...
In this paper, we study chaos (lag) synchronization of a new LC chaotic system, which can exhibit not only a two-scroll attractor but also two double-scroll attractors for different parameter values, via three types of state feedback controls: (i) linear feedback control; (ii) adaptive feedback control; and (iii) a combination of linear feedback and adaptive feedback controls. As a consequence, ten families of new feedback control laws are designed to obtain global chaos lag synchronization for τ < 0 and global chaos synchronization for τ = 0 of the LC system. Numerical simulations are used to illustrate these theoretical results. Each family of these obtained feedback control laws, including two linear (adaptive) functions or one linear function and one adaptive function, is added to two equations of the LC system. This is simpler than the known synchronization controllers, which apply controllers to all equations of the LC system. Moreover, based on the obtained results of the LC system, we also derive the control laws for chaos (lag) synchronization of another new type of chaotic system
S. Goyal
2012-03-01
Full Text Available This paper highlights the significance of computational intelligence models for predicting shelf life of processed cheese stored at 7-8 g.C. Linear Layer and Generalized Regression models were developed with input parameters: Soluble nitrogen, pH, Standard plate count, Yeast & mould count, Spores, and sensory score as output parameter. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were used in order to compare the prediction ability of the models. The study revealed that Generalized Regression computational intelligence models are quite effective in predicting the shelf life of processed cheese stored at 7-8 g.C.
Second degree generalized Jacobi iteration method for solving system of linear equations
Tesfaye Kebede Enyew
2016-05-01
Full Text Available In this paper, a Second degree generalized Jacobi Iteration method for solving system of linear equations, $Ax=b$ and discuss about the optimal values $a_{1}$ and $b_{1}$ in terms of spectral radius about for the convergence of SDGJ method of $x^{(n+1}=b_{1}[D_{m}^{-1}(L_{m}+U_{m}x^{(n}+k_{1m}]-a_{1}x^{(n-1}.$ Few numerical examples are considered to show that the effective of the Second degree Generalized Jacobi Iteration method (SDGJ in comparison with FDJ, FDGJ, SDJ.
General Relativity coupled with Non-Linear Electrodynamics: results and limitations
Chinaglia, Stefano
2015-01-01
We discuss how to generate a black hole solution of the Einstein Equations (EE) via non-linear electrodynamics (NED). We discuss the thermodynamical properties of a general NED solution, recovering the First Law. Then we illustrate the general mechanism and discuss some specific cases, showing that finding a generating Lagrangian (for a specific solution) only requires solving an algebraic equation (we study some analytical cases). Finally, we argue that NED paradigm, though self-consistent, is not the best tool for studying regular black holes.
Pro-reductive groups attached to irreducible representations of the General Linear Supergroup
Heidersdorf, Thorsten
2013-01-01
We study tensor product decompositions of representations of the General Linear Supergroup Gl(m|n). We show that the quotient of Rep(Gl(m|n),\\epsilon)$ by the tensor ideal of negligible representations is the representation category of a pro-reductive supergroup G red. In the Gl(m|1)-case we show G red = Gl(m-1) \\times Gl(1) \\times Gl(1). In the general case we study the image of the canonical tensor functor Fmn from Deligne's interpolating category Rep (Gl m-n) to Rep(Gl(m|n),\\epsilon). We d...
Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data
Mingwu Jin
2012-01-01
Full Text Available Local canonical correlation analysis (CCA is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM, a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.
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
Grayver, Alexander V.; Kuvshinov, Alexey V.
2016-02-01
This paper presents a methodology to sample equivalence domain (ED) in non-linear PDE-constrained inverse problems. For this purpose, we first applied state-of-the-art stochastic optimization algorithm called Covariance Matrix Adaptation Evolution Strategy (CMAES) to identify low misfit regions of the model space. These regions were then randomly sampled to create an ensemble of equivalent models and quantify uncertainty. CMAES is aimed at exploring model space globally and is robust on very ill-conditioned problems. We show that the number of iterations required to converge grows at a moderate rate with respect to number of unknowns and the algorithm is embarrassingly parallel. We formulated the problem by using the generalized Gaussian distribution. This enabled us to seamlessly use arbitrary norms for residual and regularization terms. We show that various regularization norms facilitate studying different classes of equivalent solutions. We further show how performance of the standard Metropolis-Hastings Markov chain Monte Carlo (MCMC) algorithm can be substantially improved by using information CMAES provides. This methodology was tested by using individual and joint inversions of Magneotelluric, Controlled-source Electromagnetic (EM) and Global EM induction data.
The linear stability of parallel shear flows for an inviscid generalized two-dimensional (2D) fluid system, the so-called α turbulence system, is studied. This system is characterized by the relation q = −( − Δ)α/2ψ between the advected scalar q and the stream function ψ. Here, α is a real number not exceeding 3 and q is referred to as the generalized vorticity. In this study, a sufficient condition for linear stability of parallel shear flows is derived using the conservation of wave activity. A stability analysis is then performed for a sheet vortex that violates the stability condition. The instability of a sheet vortex in the 2D Euler system (α = 2) is referred to as a Kelvin–Helmholtz (KH) instability; such an instability for the generalized 2D fluid system is investigated for 0 3−α for 1 < α < 3, where k is the wavenumber of the perturbation. In contrast, for 0 < α ⩽ 1, the growth rate is infinite. In other words, a transition of the growth rate of the perturbation occurs at α = 1. A physical model for KH instability in the generalized 2D fluid system, which can explain the transition of the growth rate of the perturbation at α = 1, is proposed. (paper)
Cressman, Erin K; Henriques, Denise Y P
2015-07-01
Visuomotor learning results in changes in both motor and sensory systems (Cressman EK, Henriques DY. J Neurophysiol 102: 3505-3518, 2009), such that reaches are adapted and sense of felt hand position recalibrated after reaching with altered visual feedback of the hand. Moreover, visuomotor learning has been shown to generalize such that reach adaptation achieved at a trained target location can influence reaches to novel target directions (Krakauer JW, Pine ZM, Ghilardi MF, Ghez C. J Neurosci 20: 8916-8924, 2000). We looked to determine whether proprioceptive recalibration also generalizes to novel locations. Moreover, we looked to establish the relationship between reach adaptation and changes in sense of felt hand position by determining whether proprioceptive recalibration generalizes to novel targets in a similar manner as reach adaptation. On training trials, subjects reached to a single target with aligned or misaligned cursor-hand feedback, in which the cursor was either rotated or scaled in extent relative to hand movement. After reach training, subjects reached to the training target and novel targets (including targets from a second start position) without visual feedback to assess generalization of reach adaptation. Subjects then performed a proprioceptive estimation task, in which they indicated the position of their hand relative to visual reference markers placed at similar locations as the trained and novel reach targets. Results indicated that shifts in hand position generalized across novel locations, independent of reach adaptation. Thus these distinct sensory and motor generalization patterns suggest that reach adaptation and proprioceptive recalibration arise from independent error signals and that changes in one system cannot guide adjustments in the other. PMID:25972587
A general theory of linear cosmological perturbations: scalar-tensor and vector-tensor theories
Lagos, Macarena; Ferreira, Pedro G; Noller, Johannes
2016-01-01
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 (\\`a la Einstein-Aether gravity), where the theory only propagates one scalar DoF. As a result, we identify the complete forms of the quadratic act...
The quantum general linear supergroup, canonical bases and Kazhdan-Lusztig polynomials
无
2009-01-01
Canonical bases of the tensor powers of the natural Uq(glm|n)-module V are constructed by adapting the work of Frenkel, Khovanov and Kirrilov to the quantum supergroup setting. This result is generalized in several directions. We first construct the canonical bases of the Z2-graded symmetric algebra of V and tensor powers of this superalgebra; then construct canonical bases for the superalgebra Oq(Mm|n) of a quantum (m, n) × (m, n)-supermatrix; and finally deduce from the latter result the canonical basis of every irreducible tensor module for Uq(glm|n) by applying a quantum analogue of the Borel-Weil construction.
Zhang Ruo-Xun; Yang Shi-Ping
2008-01-01
This paper presents a general method of the generalized projective synchronization and the parameter identification between two different chaotic systems with unknown parameters.This approach is based on Lyapunov stability theory,and employs a combination of feedback control and adaptive control.With this method one can achieve the generalized projective synchronization and realize the parameter identifications between almost all chaotic (hyperchaotic) systems with unknown parameters.Numerical simulations results are presented to demonstrate the effectiveness of the method.
The exact solutions of the Cauchy problem for two equations which are slight generalization of the so-called linear vectorial Fokker-Planck equation are found using the disentangling techniques of Feynman and algebraic (operational) methods. This approach may be considered as a generalization of the Suzuki's method for solving the one-dimensional linear Pokker-Planck equation
Generalized linear sampling method for elastic-wave sensing of heterogeneous fractures
Pourahmadian, Fatemeh; Haddar, Houssem
2016-01-01
A theoretical foundation is developed for active seismic reconstruction of fractures endowed with spatially-varying interfacial condition (e.g.~partially-closed fractures, hydraulic fractures). The proposed indicator functional carries a superior localization property with no significant sensitivity to the fracture's contact condition, measurement errors, and illumination frequency. This is accomplished through the paradigm of the $F_\\sharp$-factorization technique and the recently developed Generalized Linear Sampling Method (GLSM) applied to elastodynamics. The direct scattering problem is formulated in the frequency domain where the fracture surface is illuminated by a set of incident plane waves, while monitoring the induced scattered field in the form of (elastic) far-field patterns. The analysis of the well-posedness of the forward problem leads to an admissibility condition on the fracture's (linearized) contact parameters. This in turn contributes toward establishing the applicability of the $F_\\sharp...
Zhen Jia; Guangming Deng
2012-01-01
The generalized projective synchronization (GPS) of chaotic systems with uncertain parameter noise and external disturbance is discussed. Based on the adaptive technique, a response system is constructed, and a novel adaptive controller is designed to guarantee the GPS between the drive-response systems, and to eliminate the effect of external disturbance and parameters noise on GPS. The conclusion is proved theoretically, and corresponding numerical simulations are provided to verify the eff...
Analysis of dental caries using generalized linear and count regression models
Javali M. Phil
2013-11-01
Full Text Available Generalized linear models (GLM are generalization of linear regression models, which allow fitting regression models to response data in all the sciences especially medical and dental sciences that follow a general exponential family. These are flexible and widely used class of such models that can accommodate response variables. Count data are frequently characterized by overdispersion and excess zeros. Zero-inflated count models provide a parsimonious yet powerful way to model this type of situation. Such models assume that the data are a mixture of two separate data generation processes: one generates only zeros, and the other is either a Poisson or a negative binomial data-generating process. Zero inflated count regression models such as the zero-inflated Poisson (ZIP, zero-inflated negative binomial (ZINB regression models have been used to handle dental caries count data with many zeros. We present an evaluation framework to the suitability of applying the GLM, Poisson, NB, ZIP and ZINB to dental caries data set where the count data may exhibit evidence of many zeros and over-dispersion. Estimation of the model parameters using the method of maximum likelihood is provided. Based on the Vuong test statistic and the goodness of fit measure for dental caries data, the NB and ZINB regression models perform better than other count regression models.
A General Method for Solving Systems of Non-Linear Equations
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.
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
Estimation in the General Linear Model when the Accuracy is Specified Before Data Collection
Finster, Mark
1985-01-01
An estimator $\\hat{\\beta}$ of $\\beta$ is accurate with accuracy $A$ and confidence $\\gamma, 0 < \\gamma < 1,$ if $P(\\hat{\\beta} - \\beta \\in A) \\geq \\gamma$ for all $\\beta.$ Given a sequence $Y_1, Y_2, \\cdots$ of independent vector-valued homoscedastic normally-distributed random variables generated via the general linear model $Y_i = X_i\\beta + \\varepsilon,$ the $k$-dimensional parameter $\\beta$ is accurately estimated using a sequential version of the maximum probability estimator developed b...
Thi Thao Phuong, HOANG; Duc Cam Hai, VO; Thanh Hai, ONG
2016-01-01
The paper is concerned with a low-order finite element method, namely the staggered cell-centered finite element method (SC-FEM), which has been proposed and analyzed in [1] for two-dimensional compressible and nearly incompressible linear elasticity problems. In this work, we extend the results to the three-dimensional case and focus on the creating of the meshes, including a dual mesh and its tetrahedral submesh from a general primal mesh. The displacement is approximated by piecewise trili...
Robust root clustering for linear uncertain systems using generalized Lyapunov theory
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.
Supersymmetry of Generalized Linear Schrödinger Equations in (1+1 Dimensions
Axel Schulze-Halberg
2009-10-01
Full Text Available We review recent results on how to extend the supersymmetry SUSY normalism in Quantum Mechanics to linear generalizations of the time-dependent Schrödinger equation in (1+1 dimensions. The class of equations we consider contains many known cases, such as the Schrödinger equation for position-dependent mass. By evaluating intertwining relations, we obtain explicit formulas for the interrelations between the supersymmetric partner potentials and their corresponding solutions. We review reality conditions for the partner potentials and show how our SUSY formalism can be extended to the Fokker-Planck and thenonhomogeneous Burgers equation.
A General Linear Wave Theory for Water Waves Propagating over Uneven Porous Bottoms
锁要红; 黄虎
2004-01-01
Starting from the widespread phenomena of porous bottoms in the near shore region, considering fully the diversity of bottom topography and wave number variation, and including the effect of evanescent modes, a general linear wave theory for water waves propagating over uneven porous bottoms in the near shore region is established by use of Green's second identity. This theory can be reduced to a number of the most typical mild-slope equations currently in use and provide a reliable research basis for follow-up development of nonlinear water wave theory involving porous bottoms.
A generalization of Dirac non-linear electrodynamics, and spinning charged particles
The Dirac non-linear electrodynamics is generalized by introducing two potentials (namely, the vector potential a and the pseudo-vector potential γ 5 B of the electromagnetic theory with charges and magnetic monopoles), and by imposing the pseudoscalar part of the product W W* to BE zero, with W = A + γ 5 B. Also, is demonstrated that the field equations of such a theory posses a soliton-like solution which can represent a priori a charged particle. (L.C.J.A.)
On subgroups of the general linear group over a commutative von Neumann regular ring
Let R be a commutative von Neumann regular ring. In this paper we study the lattice of subgroups of the general linear group G = GLn(R) containing the group D = Dn(R) of diagonal matrices. We show that, if R satisfies some condition, then for every such subgroup H there is a uniquely determined D-net σ of ideals such that G(σ) ≤ H ≤ N(σ), where N(σ) is the normalizer of G(σ), a D-net subgroup. (author). 7 refs
NURWAHA Deogratias; WANG Xin-hou
2008-01-01
This paper presents a comparison study of two models for predicting the strength of rotor spun cotton yarns from fiber properties. The adaptive neuro-fuzzy system inference (ANFIS) and Multiple Linear Regression models are used to predict the rotor spun yarn strength. Fiber properties and yarn count are used as inputs to train the two models and the count-strength-product (CSP) was the target. The predictive performances of the two models are estimated and compared. We found that the ANFIS has a better predictive power in comparison with linear multipleregression model. The impact of each fiber property is also illustrated.
The Adapted Ordering Method for Lie Algebras and Superalgebras and their Generalizations
Gato-Rivera, Beatriz
2007-01-01
In 1998 the Adapted Ordering Method was developed for the representation theory of the superconformal algebras in two dimensions. It allows: to determine maximal dimensions for a given type of space of singular vectors, to identify all singular vectors by only a few coefficients, to spot subsingular vectors and to set the basis for constructing embedding diagrams. In this article we present the Adapted Ordering Method for general Lie algebras and superalgebras, and their generalizations, provided they can be triangulated. We also review briefly the results obtained for the Virasoro algebra and for the N=2 and Ramond N=1 superconformal algebras.
The Adapted Ordering Method for Lie algebras and superalgebras and their generalizations
Gato-Rivera, Beatriz [Instituto de Matematicas y Fisica Fundamental, CSIC, Serrano 123, Madrid 28006 (Spain); NIKHEF-H, Kruislaan 409, NL-1098 SJ Amsterdam (Netherlands)
2008-02-01
In 1998 the Adapted Ordering Method was developed for the representation theory of the superconformal algebras in two dimensions. It allows us to determine maximal dimensions for a given type of space of singular vectors, to identify all singular vectors by only a few coefficients, to spot subsingular vectors and to set the basis for constructing embedding diagrams. In this paper we present the Adapted Ordering Method for general Lie algebras and superalgebras and their generalizations, provided they can be triangulated. We also review briefly the results obtained for the Virasoro algebra and for the N = 2 and Ramond N = 1 superconformal algebras.
Gato-Rivera, Beatriz
2008-01-01
In 1998 the Adapted Ordering Method was developed for the study of the representation theory of the superconformal algebras in two dimensions. It allows: to determine the maximal dimension for a given type of space of singular vectors, to identify all singular vectors by only a few coefficients, to spot subsingular vectors and to set the basis for constructing embedding diagrams. In this talk I introduce the present version of the Adapted Ordering Method, published in J. Phys. A: Math. Theor. 41 (2008) 045201, which can be applied to general Lie algebras and superalgebras and their generalizations, provided they can be triangulated.
Usefulness of noise adaptive non-linear Gaussian filter in FDG-PET study
In positron emission tomography (PET) studies, shortening transmission (TR) scan time can improve patient comfort and increase scanner throughput. However, PET images from short TR scans may be degraded due to the statistical noise included in the TR image. The purpose of this study was to apply non-linear Gaussian (NLG) and noise adaptive NLG (ANLG) filters to TR images, and to evaluate the extent of noise reduction by the ANLG filter in comparison with that by the NLG filter using phantom and clinical studies. In phantom studies, pool phantoms of various diameters and injected doses of 2-deoxy-2-[18F]fluoro-D-glucose (FDG) were used and the coefficients of variation (CVs) of the counts in the TR images processed with the NLG and ANLG filters were compared. In clinical studies, two normal volunteers and 13 patients with tumors were studied. In volunteer studies, the CV values in the liver were compared. In patient studies, the standardized uptake values (SUVs) of tumors in the emission images were obtained after processing the TR images using the NLG and ANLG filters. In phantom studies, the CV values in the TR images processed with the ANLG filter were smaller than those in the images processed with the NLG filter. When using the ANLG filter, their dependency on the phantom size, injected dose of FDG and TR scan time was smaller than when using the NLG filter. In volunteer studies, the CV values in the images processed with the ANLG filter were smaller than those in the images processed with the NLG filter, and were almost constant regardless of the TR scan time. In patient studies, there was an excellent correlation between the SUVs obtained from the images with a TR scan time of 7 min processed with the NLG filter (x) and those obtained from the images with a TR scan time of 4 min processed with the ANLG filter (y) (r=0.995, y=1.034x-0.075). Our results suggest that the ANLG filter is effective and useful for noise reduction in TR images and shortening TR scan
Prospects of measuring general Higgs couplings at e+e- linear colliders
We examine how accurately the general HZV couplings, with V=Z, γ, may be determined by studying e+e-→Hff processes at future e+e- linear colliders. By using the optimal-observable method, which makes use of all available experimental information, we find out which combinations of the various HZV coupling terms may be constrained most efficiently with high luminosity. We also assess the benefits of measuring the tau-lepton helicities, identifying the bottom-hadron charges, polarizing the electron beam and running at two different collider energies. The HZZ couplings are generally found to be well constrained, even without these options, while the HZγ couplings are not. The constraints on the latter may be significantly improved by beam polarization. (orig.)
Prospects of measuring general Higgs couplings at e+e- linear colliders
We examine how accurately the general HZV couplings, with V=Zγ, may be determined by studying e+e-→Hf anti f processes at future e+e- linear colliders. By using the optimal-observable method, which makes use of all available experimental information, we find out which combinations of the various HZV coupling terms may be constrained most efficiently with high luminosity. We also assess the benefits of measuring the tau-lepton helicities, identifying the bottom-hadron charges, polarizing the electron beam and running at two different collider energies. The HZZ couplings are generally found to be well constrained, even without these options, while the HZγ couplings are not. The constraints on the latter may be significantly improved by beam polarization. (orig.)
d'Eon, Eugene
2013-01-01
We derive new diffusion solutions to the monoenergetic generalized linear Boltzmann transport equation (GLBE) for the stationary collision density and scalar flux about an isotropic point source in an infinite $d$-dimensional absorbing medium with isotropic scattering. We consider both classical transport theory with exponentially-distributed free paths in arbitrary dimensions as well as a number of non-classical transport theories (non-exponential random flights) that describe a broader class of transport processes within partially-correlated random media. New rigorous asymptotic diffusion approximations are derived where possible. We also generalize Grosjean's moment-preserving approach of separating the first (or uncollided) distribution from the collided portion and approximating only the latter using diffusion. We find that for any spatial dimension and for many free-path distributions Grosjean's approach produces compact, analytic approximations that are, overall, more accurate for high absorption and f...
Vector generalized linear and additive models with an implementation in R
Yee, Thomas W
2015-01-01
This book presents a statistical framework that expands generalized linear models (GLMs) for regression modelling. The framework shared in this book allows analyses based on many semi-traditional applied statistics models to be performed as a coherent whole. This is possible through the approximately half-a-dozen major classes of statistical models included in the book and the software infrastructure component, which makes the models easily operable. The book’s methodology and accompanying software (the extensive VGAM R package) are directed at these limitations, and this is the first time the methodology and software are covered comprehensively in one volume. Since their advent in 1972, GLMs have unified important distributions under a single umbrella with enormous implications. The demands of practical data analysis, however, require a flexibility that GLMs do not have. Data-driven GLMs, in the form of generalized additive models (GAMs), are also largely confined to the exponential family. This book ...
Balas, Mark; Frost, Susan
2012-01-01
Flexible structures containing a large number of modes can benefit from adaptive control techniques which are well suited to applications that have unknown modeling parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend our adaptive control theory to accommodate troublesome modal subsystems of a plant that might inhibit the adaptive controller. In some cases the plant does not satisfy the requirements of Almost Strict Positive Realness. Instead, there maybe be a modal subsystem that inhibits this property. This section will present new results for our adaptive control theory. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for the troublesome modal subsystem, or the Q modes. Here we present the theory for adaptive controllers modified by RMFs, with attention to the issue of disturbances propagating through the Q modes. We apply the theoretical results to a flexible structure example to illustrate the behavior with and without the residual mode filter.
Ultra Linear Low-loss Varactors & Circuits for Adaptive RF Systems
Huang, C.
2010-01-01
With the evolution of wireless communication, varactors can play an important role in enabling adaptive transceivers as well as phase-diversity systems. This thesis presents various varactor diode-based circuit topologies that facilitate RF adaptivity. The proposed varactor configurations can act as
Steson eLo
2015-08-01
Full Text Available Linear mixed-effect models (LMMs are being increasingly widely used in psychology to analyse multi-level research designs. This feature allows LMMs to address some of the problems identified by Speelman and McGann (2013 about the use of mean data, because they do not average across individual responses. However, recent guidelines for using LMM to analyse skewed reaction time (RT data collected in many cognitive psychological studies recommend the application of non-linear transformations to satisfy assumptions of normality. Uncritical adoption of this recommendation has important theoretical implications which can yield misleading conclusions. For example, Balota, Aschenbrenner and Yap (2013 showed that analyses of raw RT produced additive effects of word frequency and stimulus quality on word identification, which conflicted with the interactive effects observed in analyses of transformed RT. Generalized linear mixed-effect models (GLMM provide a solution to this problem by satisfying normality assumptions without the need for transformation. This allows differences between individuals to be properly assessed, using the metric most appropriate to the researcher’s theoretical context. We outline the major theoretical decisions involved in specifying a GLMM, and illustrate them by reanalysing Balota et al.’s datasets. We then consider the broader benefits of using GLMM to investigate individual differences.
Blended General Linear Methods based on Boundary Value Methods in the GBDF family
Brugnano, Luigi
2010-01-01
Among the methods for solving ODE-IVPs, the class of General Linear Methods (GLMs) is able to encompass most of them, ranging from Linear Multistep Formulae (LMF) to RK formulae. Moreover, it is possible to obtain methods able to overcome typical drawbacks of the previous classes of methods. For example, order barriers for stable LMF and the problem of order reduction for RK methods. Nevertheless, these goals are usually achieved at the price of a higher computational cost. Consequently, many efforts have been made in order to derive GLMs with particular features, to be exploited for their efficient implementation. In recent years, the derivation of GLMs from particular Boundary Value Methods (BVMs), namely the family of Generalized BDF (GBDF), has been proposed for the numerical solution of stiff ODE-IVPs. In particular, this approach has been recently developed, resulting in a new family of L-stable GLMs of arbitrarily high order, whose theory is here completed and fully worked-out. Moreover, for each one o...
David T Redden
2006-08-01
Full Text Available Individual genetic admixture estimates, determined both across the genome and at specific genomic regions, have been proposed for use in identifying specific genomic regions harboring loci influencing phenotypes in regional admixture mapping (RAM. Estimates of individual ancestry can be used in structured association tests (SAT to reduce confounding induced by various forms of population substructure. Although presented as two distinct approaches, we provide a conceptual framework in which both RAM and SAT are special cases of a more general linear model. We clarify which variables are sufficient to condition upon in order to prevent spurious associations and also provide a simple closed form "semiparametric" method of evaluating the reliability of individual admixture estimates. An estimate of the reliability of individual admixture estimates is required to make an inherent errors-in-variables problem tractable. Casting RAM and SAT methods as a general linear model offers enormous flexibility enabling application to a rich set of phenotypes, populations, covariates, and situations, including interaction terms and multilocus models. This approach should allow far wider use of RAM and SAT, often using standard software, in addressing admixture as either a confounder of association studies or a tool for finding loci influencing complex phenotypes in species as diverse as plants, humans, and nonhuman animals.
Ma, Yaping; Xiao, Yegui; Wei, Guo; Sun, Jinwei
2016-01-01
In this paper, a multichannel nonlinear adaptive noise canceller (ANC) based on the generalized functional link artificial neural network (FLANN, GFLANN) is proposed for fetal electrocardiogram (FECG) extraction. A FIR filter and a GFLANN are equipped in parallel in each reference channel to respectively approximate the linearity and nonlinearity between the maternal ECG (MECG) and the composite abdominal ECG (AECG). A fast scheme is also introduced to reduce the computational cost of the FLANN and the GFLANN. Two (2) sets of ECG time sequences, one synthetic and one real, are utilized to demonstrate the improved effectiveness of the proposed nonlinear ANC. The real dataset is derived from the Physionet non-invasive FECG database (PNIFECGDB) including 55 multichannel recordings taken from a pregnant woman. It contains two subdatasets that consist of 14 and 8 recordings, respectively, with each recording being 90 s long. Simulation results based on these two datasets reveal, on the whole, that the proposed ANC does enjoy higher capability to deal with nonlinearity between MECG and AECG as compared with previous ANCs in terms of fetal QRS (FQRS)-related statistics and morphology of the extracted FECG waveforms. In particular, for the second real subdataset, the F1-measure results produced by the PCA-based template subtraction (TSpca) technique and six (6) single-reference channel ANCs using LMS- and RLS-based FIR filters, Volterra filter, FLANN, GFLANN, and adaptive echo state neural network (ESN a ) are 92.47%, 93.70%, 94.07%, 94.22%, 94.90%, 94.90%, and 95.46%, respectively. The same F1-measure statistical results from five (5) multi-reference channel ANCs (LMS- and RLS-based FIR filters, Volterra filter, FLANN, and GFLANN) for the second real subdataset turn out to be 94.08%, 94.29%, 94.68%, 94.91%, and 94.96%, respectively. These results indicate that the ESN a and GFLANN perform best, with the ESN a being slightly better than the GFLANN but about four times more
DENG Jiang-ming; CHEN Te-fang; CHEN Chun-yang
2015-01-01
An adaptive current compensation control for a single-sided linear induction motor (SLIM) with nonlinear disturbance observer was developed. First, to maintaint-axis secondary component flux constant with consideration of the specially dynamic eddy-effect (DEE) of the SLIM, a instantaneously tracing compensation ofm-axis current component was analyzed. Second, adaptive current compensation based on Taylor-discretization algorithm was proposed. Third, an effective kind of nonlinear disturbance observer (NDOB) was employed to estimate and compensate the undesired load vibrations, then the robustness of the control system could be guaranteed. Experimental verification of the feasibility of the proposed method for an SLIM control system was performed, and it showed that the proposed adaptive compensation scheme with NDOB could significantly promote speed dynamical response and minimize speed ripple under the conditions of external load coupled vibrations and unavoidable feedback control variables measured errors, i.e., current and speed.
A generalization of the MDS method by mixed integer linear and nonlinear mathematical models
Sadegh Niroomand
2014-09-01
Full Text Available The Multi-Dimensional Scaling (MDS method is used in statistics to detect hidden interrelations among multi-dimensional data and it has a wide range of applications. The method’s input is a matrix that describes the similarity/dissimilarity among objects of unknown dimension. The objects are generally reconstructed as points of a lower dimensional space to reveal the geometric configuration of the objects. The original MDS method uses Euclidean distance, for measuring both the distance of the reconstructed points and the bias of the reconstructed distances from the original similarity values. In this paper, these distances are distinguished, and distances other than Euclidean are also used, generalizing the MDS method. Two different distances may be used for the two different purposes. Therefore the instances of the generalized MDS model are denoted as model, where the first distance is the type of distance of the reconstructed points and the second one measures the bias of the reconstructed distances and the similarity values. In the case of and distances mixed-integer programming models are provided. The computational experiences show that the generalized model can catch the key properties of the original configuration, if any exist. Keywords: Multivariate Analysis; Multi-Dimensional Scaling; Optimization; Mixed Integer Linear Programming; Statistics.
HSUChaohsing; CHENTsongyi; PanJengshyang
2005-01-01
In this paper, a phase-amplitude perturbation method of an adaptive array factor based on the genetic algorithm is proposed. The design for an optimal beam pattern of an adaptive antenna is able to not only suppress interference by placing nulls at the directions of the interfering sources but also provide a maximized main lobe in the direction of the desired signal, i.e., to maximizethe Signal interference ratio (SIR). In order to achieve this goal, a kind of new convergent skill called the two-way convergent method for genetic algorithms is proposed. The phase-amplitude perturbation method is applied to realize the optimal beam pattern of an adaptively linear array antenna. The Genetic algorithms are applied to find the optimal phase-amplitude weighting vector of adaptive array factor. An optimal beam pattern of linear array is derived by phase-amplitude perturbations using a genetic algorithm. Computer simulation result is given to demonstrate the effectiveness of the proposed method.
Wang, Shi-bing; Wang, Xing-yuan; Wang, Xiu-you; Zhou, Yu-fei
2016-04-01
With comprehensive consideration of generalized synchronization, combination synchronization and adaptive control, this paper investigates a novel adaptive generalized combination complex synchronization (AGCCS) scheme for different real and complex nonlinear systems with unknown parameters. On the basis of Lyapunov stability theory and adaptive control, an AGCCS controller and parameter update laws are derived to achieve synchronization and parameter identification of two real drive systems and a complex response system, as well as two complex drive systems and a real response system. Two simulation examples, namely, ACGCS for chaotic real Lorenz and Chen systems driving a hyperchaotic complex Lü system, and hyperchaotic complex Lorenz and Chen systems driving a real chaotic Lü system, are presented to verify the feasibility and effectiveness of the proposed scheme.
Robust adaptive synchronization of general dynamical networks with multiple delays and uncertainties
LU YIMING; HE PING; MA SHU-HUA; LI GUO-ZHI; MOBAYBEN SALEH
2016-06-01
In this article, a general complex dynamical network which contains multiple delays and uncertainties is introduced, which contains time-varying coupling delays, time-varying node delay, and uncertainties of both the inner- and outer-coupling matrices. A robust adaptive synchronization scheme for these general complex networks with multiple delays and uncertainties is established and raised by employing the robust adaptive control principle and the Lyapunov stability theory. We choose some suitable adaptive synchronization controllers to ensure the robust synchronization of this dynamical network. The numerical simulations of the time-delay Lorenz chaotic system as local dynamical node are provided to observe and verify the viability and productivity of the theoretical research in this paper. Compared to the achievement of previous research, theresearch in this paper seems quite comprehensive and universal.
This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.
Ziemann, Amanda K.; Messinger, David W.
2014-06-01
Hyperspectral images comprise, by design, high dimensional image data. However, research has shown that for a d-dimensional hyperspectral image, it is typical for the data to inherently occupy an m-dimensional space, with m lie. Classic hyperspectral data models include statistical, linear subspace, and linear mixture models, but these can place restrictive assumptions on the distribution of the data. With graph theory and manifold learning based models, the only assumption is that the data reside on an underlying manifold. In previous publications, we have shown that manifold coordinate approximation using locally linear embedding (LLE) is a viable pre-processing step for target detection with the Adaptive Cosine/Coherence Estimator (ACE) algorithm. Here, we improve upon that methodology using a more rigorous, data-driven implementation of LLE that incorporates the injection of a cloud" of target pixels and the Spectral Angle Mapper (SAM) detector. The LLE algorithm, which holds that the data is locally linear, is typically governed by a user defined parameter k, indicating the number of nearest neighbors to use in the initial graph model. We use an adaptive approach to building the graph that is governed by the data itself and does not rely upon user input. This implementation of LLE can yield greater separation between the target pixels and the background pixels in the manifold space. We present an analysis of target detection performance in the manifold coordinates using scene-derived target spectra and laboratory-measured target spectra across two different data sets.
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
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).
We investigate whether the linearised equations and the fully non-linear Einstein equations with sources are equivalent in the cases where gravity is coupled to a massless scalar field, and a perfect fluid. The metrics are subject to generalised Kerr-Schild-type transformations. Simple algebraic conditions are determined on the matter fields such that the linear superposition of the (exact) background solution and the (approximate) linearised solution is, surprisingly, a new solution. For the case of a massless scalar field two subcases are obtained, for which the superposition holds. For the perfect fluids the conditions required are quite restrictive, leading to a superposition theorem for Einstein spaces. For both cases the analysis is exhaustive and inverse propositions showing the equivalence between the linearised and the exact equations are obtained. (author)
The quantum general linear supergroup,canonical bases and Kazhdan-Lusztig polynomials
ZHANG HeChun
2009-01-01
Canonical bases of the tensor powers of the natural Uq(glm|n)-module V are constructed by adapting the work of Frenkel,Khovanov and Kirrilov to the quantum supergroup setting.This result is generalized in several directions.We first construct the canonical bases of the Z2-graded symmetric algebra of V and tensor powers of this superalgebra;then construct canonical bases for the superalgebra Oq(Mm|n) of a quantum (m,n) x (m,n)-supermatrix;and finally deduce from the latter result the canonical basis of every irreducible tensor module for Uq(glm|n) by applying a quantum analogue of the Borel-Weil construction.
Galaxy bias and non-linear structure formation in general relativity
Length scales probed by the large scale structure surveys are becoming closer and closer to the horizon scale. Further, it has been recently understood that non-Gaussianity in the initial conditions could show up in a scale dependence of the bias of galaxies at the largest possible distances. It is therefore important to take General Relativistic effects into account. Here we provide a General Relativistic generalization of the bias that is valid both for Gaussian and for non-Gaussian initial conditions. The collapse of objects happens on very small scales, while long-wavelength modes are always in the quasi linear regime. Around every small collapsing region, it is therefore possible to find a reference frame that is valid for arbitrary times and where the space time is almost flat: the Fermi frame. Here the Newtonian approximation is applicable and the equations of motion are the ones of the standard N-body codes. The effects of long-wavelength modes are encoded in the mapping from the cosmological frame to the local Fermi frame. At the level of the linear bias, the effect of the long-wavelength modes on the dynamics of the short scales is all encoded in the local curvature of the Universe, which allows us to define a General Relativistic generalization of the bias in the standard Newtonian setting. We show that the bias due to this effect goes to zero as the square of the ratio between the physical wavenumber and the Hubble scale for modes longer than the horizon, confirming the intuitive picture that modes longer than the horizon do not have any dynamical effect. On the other hand, the bias due to non-Gaussianities does not need to vanish for modes longer than the Hubble scale, and for non-Gaussianities of the local kind it goes to a constant. As a further application of our setup, we show that it is not necessary to perform large N-body simulations to extract information about long-wavelength modes: N-body simulations can be done on small scales and long
Adaptive non-linear predictive control of a simulated flotation cell
This work illustrates the non-linear modelling and control of a flotation cell using a predictive strategy. A non-linear predictive model, based on local linear models, is used to represent the predictions of the system output and to calculate the control signal. This model is directly identified, using the available input-output data, and then used to calculate the control signal. Simulated results show not only the advantages of using simple models to approximate complex phenomenological models, but also the performance of the proposed predictive control approach. (author)
Cheng, Guang
2014-02-01
We consider efficient estimation of the Euclidean parameters in a generalized partially linear additive models for longitudinal/clustered data when multiple covariates need to be modeled nonparametrically, and propose an estimation procedure based on a spline approximation of the nonparametric part of the model and the generalized estimating equations (GEE). Although the model in consideration is natural and useful in many practical applications, the literature on this model is very limited because of challenges in dealing with dependent data for nonparametric additive models. We show that the proposed estimators are consistent and asymptotically normal even if the covariance structure is misspecified. An explicit consistent estimate of the asymptotic variance is also provided. Moreover, we derive the semiparametric efficiency score and information bound under general moment conditions. By showing that our estimators achieve the semiparametric information bound, we effectively establish their efficiency in a stronger sense than what is typically considered for GEE. The derivation of our asymptotic results relies heavily on the empirical processes tools that we develop for the longitudinal/clustered data. Numerical results are used to illustrate the finite sample performance of the proposed estimators. © 2014 ISI/BS.
Areej IsamBarham
2011-11-01
Full Text Available The study investigates students' strategies involved in the generalization of "linear patterns". The study followed thequalitative research approach by conducting task-based interviews with twenty-nine primary second grade students fromdifferent high, intermediate and low ability levels. Results of the study presented several strategies involved in thegeneralization of the patterns including visual, auditory, mental, finger counting, verbal counting, and traditional (paper andpencil strategies. The findings revealed that the type of the assigned pattern (simple or complex and the type of the structureof the pattern itself (increasing or decreasing play a big role for students' strategies involved to either discover the rule of thepattern or to extend it. However, students in early ages could master several skills and choose appropriate procedures to dealwith patterns, which indicate that they could develop their algebraic thinking from early stages. Findings of the study alsorevealed that using different senses, using the idea of coins, using the numbers line, recognizing musical sounds, using concretematerials like fingers, applying different visual and mental strategies, and even applying traditional calculations could helpstudents to work with “linear patterns". It is recommended that teachers introduce different strategies and procedures inteaching patterns to meet the needs of students as different learners, give them the opportunities to develop their thinkingstrategies and explore their thoughts. More research is recommended to explore students' strategies involved in thegeneralization of different kinds of patters at different stages.
The Potential in General Linear Electrodynamics: Causal Structure, Propagators and Quantization
Pfeifer, Christian
2016-01-01
An axiomatic approach to electrodynamics reveals that Maxwell electrodynamics is just one instance of a variety of theories for which the name electrodynamics is justified. They all have in common that their fundamental input are Maxwell's equations $\\textrm{d} F = 0$ (or $F = \\textrm{d} A$) and $\\textrm{d} H = J$ and a constitutive law $H = \\# F$ which relates the field strength two-form $F$ and the excitation two-form $H$. A local and linear constitutive law defines what is called general linear electrodynamics whose best known application are the effective description of electrodynamics inside media including, e.g., birefringence. We will analyze the classical theory of the electromagnetic potential $A$ before we use methods familiar from mathematical quantum field theory in curved spacetimes to quantize it in a locally covariant way. Our analysis of the classical theory contains the derivation of retarded and advanced propagators, the analysis of the causal structure on the basis of the constitutive law (...
Sachin Bhaskar
2015-07-01
Full Text Available Data mining techniques are the result of a long process of research and product development. Large amount of data are searched by the practice of Data Mining to find out the trends and patterns that go beyond simple analysis. For segmentation of data and also to evaluate the possibility of future events, complex mathematical algorithms are used here. Specific algorithm produces each Data Mining model. More than one algorithms are used to solve in best way by some Data Mining problems. Data Mining technologies can be used through Oracle. Generalized Linear Models (GLM Algorithm is used in Regression and Classification Oracle Data Mining functions. For linear modelling, GLM is one the popular statistical techniques. For regression and binary classification, GLM is implemented by Oracle Data Mining. Row diagnostics as well as model statistics and extensive co-efficient statistics are provided by GLM. It also supports confidence bounds.. This paper outlines and produces analysis of GLM algorithm, which will guide to understand the tuning, diagnostics & data preparation process and the importance of Regression & Classification supervised Oracle Data Mining functions and it is utilized in marketing, time series prediction, financial forecasting, overall business planning, trend analysis, environmental modelling, biomedical and drug response modelling, etc.
Variational Bayesian Adaptation of Noise Covariances in Non-Linear Kalman Filtering
Hartikainen, Simo Särkkä Jouni
2013-01-01
This paper is considered with joint estimation of state and time-varying noise covariance matrices in non-linear stochastic state space models. We present a variational Bayes and Gaussian filtering based algorithm for efficient computation of the approximate filtering posterior distributions. The Gaussian filtering based formulation of the non-linear state space model computation allows usage of efficient Gaussian integration methods such as unscented transform, cubature integration and Gauss...
Solving the Linear Balance Equation on the Globe as a Generalized Inverse Problem
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.
Wen-Min Zhou
2013-01-01
Full Text Available This paper is concerned with the consensus problem of general linear discrete-time multiagent systems (MASs with random packet dropout that happens during information exchange between agents. The packet dropout phenomenon is characterized as being a Bernoulli random process. A distributed consensus protocol with weighted graph is proposed to address the packet dropout phenomenon. Through introducing a new disagreement vector, a new framework is established to solve the consensus problem. Based on the control theory, the perturbation argument, and the matrix theory, the necessary and sufficient condition for MASs to reach mean-square consensus is derived in terms of stability of an array of low-dimensional matrices. Moreover, mean-square consensusable conditions with regard to network topology and agent dynamic structure are also provided. Finally, the effectiveness of the theoretical results is demonstrated through an illustrative example.
de Souza, R S; Killedar, M; Hilbe, J; Vilalta, R; Maio, U; Biffi, V; Ciardi, B; Riggs, J D
2014-01-01
Revealing hidden patterns in astronomical data is often the path to fundamental scientific breakthroughs; meanwhile the complexity of scientific inquiry 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 ...
Nurdan Cetin
2014-01-01
Full Text Available We consider a multiobjective linear fractional transportation problem (MLFTP with several fractional criteria, such as, the maximization of the transport profitability like profit/cost or profit/time, and its two properties are source and destination. Our aim is to introduce MLFTP which has not been studied in literature before and to provide a fuzzy approach which obtain a compromise Pareto-optimal solution for this problem. To do this, first, we present a theorem which shows that MLFTP is always solvable. And then, reducing MLFTP to the Zimmermann’s “min” operator model which is the max-min problem, we construct Generalized Dinkelbach’s Algorithm for solving the obtained problem. Furthermore, we provide an illustrative numerical example to explain this fuzzy approach.
Maximal freedom at minimum cost: linear large-scale structure in general modifications of gravity
Bellini, Emilio
2014-01-01
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 ...
Master equation solutions in the linear regime of characteristic formulation of general relativity
M., C E Cedeño
2015-01-01
From the field equations in the linear regime of the characteristic formulation of general relativity, Bishop, for a Schwarzschild's background, and M\\"adler, 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 $\\beta$, 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, M\\"adler 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 sol...
An Adaptive Total Generalized Variation Model with Augmented Lagrangian Method for Image Denoising
Chuan He
2014-01-01
Full Text Available We propose an adaptive total generalized variation (TGV based model, aiming at achieving a balance between edge preservation and region smoothness for image denoising. The variable splitting (VS and the classical augmented Lagrangian method (ALM are used to solve the proposed model. With the proposed adaptive model and ALM, the regularization parameter, which balances the data fidelity and the regularizer, is refreshed with a closed form in each iterate, and the image denoising can be accomplished without manual interference. Numerical results indicate that our method is effective in staircasing effect suppression and holds superiority over some other state-of-the-art methods both in quantitative and in qualitative assessment.
Noh, Maengseok; Lee, Youngjo; Oh, Seungyoung; Chu, Chaeshin; Gwack, Jin; Youn, Seung-Ki; Cho, Shin Hyeong; Lee, Won Ja; Huh, Sun
2012-01-01
Objectives The spatial and temporal correlations were estimated to determine Plasmodium vivax malarial transmission pattern in Korea from 2001–2011 with the hierarchical generalized linear model. Methods Malaria cases reported to the Korea Centers for Disease Control and Prevention from 2001 to 2011 were analyzed with descriptive statistics and the incidence was estimated according to age, sex, and year by the hierarchical generalized linear model. Spatial and temporal correlation was estimat...
A Blind Equalizer Based on Unsupervised Gaussian Cluster Formation with an Adaptive Non—Linearity
LiuHanyu; TongWen; 等
1997-01-01
In this paper we present a blind equalizer algorithm based on an unsupervised Gaussian cluster formation technique with an optimized gradient adaptive step-size to update the equalizer coefficients.The novelty of this work lies in the optimization of the nonlinearity of the cluster formation used to achieve an optimal soft decision.The proposed iterative procedure combined with the variable step-size gradient-based adaptation,significantly accelerates the convergence speed of the blind equalization.The advantages of the proposed equalization techniques are illustrated by simulation.Simulation results obtained are compared with the Sato and Godard blind equalizers.
Dynamic analysis on generalized linear elastic body subjected to large scale rigid rotations
刘占芳; 颜世军; 符志
2013-01-01
The dynamic analysis of a generalized linear elastic body undergoing large rigid rotations is investigated. The generalized linear elastic body is described in kine-matics through translational and rotational deformations, and a modified constitutive relation for the rotational deformation is proposed between the couple stress and the curvature tensor. Thus, the balance equations of momentum and moment are used for the motion equations of the body. The floating frame of reference formulation is applied to the elastic body that conducts rotations about a fixed axis. The motion-deformation coupled model is developed in which three types of inertia forces along with their incre-ments are elucidated. The finite element governing equations for the dynamic analysis of the elastic body under large rotations are subsequently formulated with the aid of the constrained variational principle. A penalty parameter is introduced, and the rotational angles at element nodes are treated as independent variables to meet the requirement of C1 continuity. The elastic body is discretized through the isoparametric element with 8 nodes and 48 degrees-of-freedom. As an example with an application of the motion-deformation coupled model, the dynamic analysis on a rotating cantilever with two spatial layouts relative to the rotational axis is numerically implemented. Dynamic frequencies of the rotating cantilever are presented at prescribed constant spin velocities. The maximal rigid rotational velocity is extended for ensuring the applicability of the linear model. A complete set of dynamical response of the rotating cantilever in the case of spin-up maneuver is examined, it is shown that, under the ultimate rigid rotational velocities less than the maximal rigid rotational velocity, the stress strength may exceed the material strength tolerance even though the displacement and rotational angle responses are both convergent. The influence of the cantilever layouts on their responses and
Assembling a computerized adaptive testing item pool as a set of linear tests
Linden, van der Wim J.; Ariel, Adelaide; Veldkamp, Bernard P.
2006-01-01
Test-item writing efforts typically results in item pools with an undesirable correlational structure between the content attributes of the items and their statistical information. If such pools are used in computerized adaptive testing (CAT), the algorithm may be forced to select items with less th
Motor learning and general adaptation syndrome Aprendizaje motor y síndrome general de adaptación
E. M. Ordoño
2010-09-01
Full Text Available
This work examines the General Adaptation Syndrome like a suitable framework to explain motor learning processes. Human motor behaviour is viewed like a complex system continuously interacting in the environment. Motor learning is proposed as an adaptation process to the tasks constraints. Training loads and practice load are also considered analogous. Practice is the vehicle of learning, but it must be applied with the enough amount of load to produce an adaptation to a new level of performance. The principles of sport training are presented related to motor learning topics. Common principles are proposed to explain the learning of motor skills, regardless of the level of complexity, and level of the performer, and providing basic criteria that should help to design learning tasks.
Key Words: Motor learning, adaptation, complex systems, training, motor skills.
Este trabajo examina las posibilidades del Síndrome General de Adaptación como un marco de referencia para explicar y predecir los cambios producidos por el Aprendizaje Motor. Se parte de la consideración del ser humano como un sistema complejo en continua interacción con su entorno y el aprendizaje como un proceso de adaptación a las condiciones impuestas por la tarea. Se propone el concepto de carga de práctica análogo al de carga de entrenamiento, considerando que la práctica, vehículo del aprendizaje, debe aplicarse como una estimulación suficiente como para desencadenar en el aprendiz una adaptación a un nuevo nivel de rendimiento. En base a esta propuesta, se relacionan los principios del entrenamiento deportivo con el aprendizaje de habilidades motrices. Se formula una perspectiva teórica que trata de explicar de forma común los procesos de modificación de los patrones motores independientemente del nivel de complejidad, conllevando los mismos
A General Framework for Sequential and Adaptive Methods in Survival Studies
Luo, Xiaolong; Ying, Zhiliang
2011-01-01
Adaptive treatment allocation schemes based on interim responses have generated a great deal of recent interest in clinical trials and other follow-up studies. An important application of such schemes is in survival studies, where the response variable of interest is time to the occurrence of a certain event. Due to possible dependency structures inherited from the enrollment and allocation schemes, existing approaches to survival models, including those that handle staggered entry, cannot be applied directly. This paper develops a new general framework with its theoretical foundation for handling such adaptive designs. The new approach is based on marked point processes and differs from existing approaches in that it considers entry and calender times rather than survival and calender times. Large sample properties, which are essential for statistical inference, are established. Special attention is given to the Cox model and related score processes. Applications to adaptive and sequential designs are discus...
Zemin Ding, Lingen Chen, Yanlin Ge, Fengrui Sun
2015-01-01
Full Text Available On the basis of a generalized model of irreversible thermal Brownian refrigerator, the Onsager coefficients and the analytical expressions for maximum coefficient of performance (COP and the COP at maximum cooling load are derived by using the theory of linear irreversible thermodynamics (LIT. The influences of heat leakage and the heat flow via the kinetic energy change of the particles on the LIT performance of the refrigerator are analyzed. It is shown that when the two kinds of irreversible heat flows are ignored, the Brownian refrigerator is built with the condition of tight coupling between fluxes and forces and it will operate in a reversible regime with zero entropy generation. Moreover, the results obtained by using the LIT theory are compared with those obtained by using the theory of finite time thermodynamics (FTT. It is found that connection between the LIT and FTT performances of the refrigerator can be interpreted by the coupling strength, and the theory of LIT and FTT can be used in a complementary way to analyze in detail the performance of the irreversible thermal Brownian refrigerators. Due to the consideration of several irreversibilities in the model, the results obtained about the Brownian refrigerator are of general significance and can be used to analyze the performance of several different kinds of Brownian refrigerators.
Non-linear general instability of ring-stiffened conical shells under external hydrostatic pressure
The paper presents the experimental results for 15 ring-stiffened circular steel conical shells, which failed by non-linear general instability. The results of these investigations were compared with various theoretical analyses, including an ANSYS eigen buckling analysis and another ANSYS analysis; which involved a step-by-step method until collapse; where both material and geometrical nonlinearity were considered. The investigation also involved an analysis using BS5500 (PD 5500), together with the method of Ross of the University of Portsmouth. The ANSYS eigen buckling analysis tended to overestimate the predicted buckling pressures; whereas the ANSYS nonlinear results compared favourably with the experimental results. The PD5500 analysis was very time consuming and tended to grossly underestimate the experimental buckling pressures and in some cases, overestimate them. In contrast to PD5500 and ANSYS, the design charts of Ross of the University of Portsmouth were the easiest of all these methods to use and generally only slightly underestimated the experimental collapse pressures. The ANSYS analyses gave some excellent graphical displays.
Elliott, J; Krone-Martins, A; Cameron, E; Ishida, E E O; Hilbe, J
2014-01-01
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...
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
Extracting H I cosmological signal with generalized needlet internal linear combination
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.
Impact of co-channel interference on the performance of adaptive generalized transmit beamforming
Radaydeh, Redha Mahmoud Mesleh
2011-08-01
The impact of co-channel interference on the performance of adaptive generalized transmit beamforming for low-complexity multiple-input single-output (MISO) configuration is investigated. The transmit channels are assumed to be sufficiently separated and undergo Rayleigh fading conditions. Due to the limited space, a single receive antenna is employed to capture desired user transmission. The number of active transmit channels is adjusted adaptively based on statistically unordered and/or ordered instantaneous signal-to-noise ratios (SNRs), where the transmitter has no information about the statistics of undesired signals. The adaptation thresholds are identified to guarantee a target performance level, and the adaptation schemes with enhanced spectral efficiency or power efficiency are studied and their performance are compared under various channels conditions. To facilitate comparison studies, results for the statistics of instantaneous combined signal-to-interference-plus-noise ratio (SINR) are derived, which can be applied for different fading conditions of interfering signals. The statistics for combined SNR and combined SINR are then used to quantify various performance measures, considering the impact of non-ideal estimation of the desired user channel state information (CSI) and the randomness in the number of active interferers. Numerical and simulation comparisons for the achieved performance of the adaptation schemes are presented. © 2006 IEEE.
Bergami, Leonardo; Poulsen, Niels Kjølstad
2015-01-01
The paper proposes a smart rotor configuration where adaptive trailing edge flaps (ATEFs) are employed for active alleviation of the aerodynamic loads on the blades of the NREL 5 MW reference turbine. The flaps extend for 20% of the blade length and are controlled by a linear quadratic (LQ...... effects of active flap control are assessed with aeroelastic simulations of the turbine in normal operation conditions, as prescribed by the International Electrotechnical Commission standard. The turbine lifetime fatigue damage equivalent loads provide a convenient summary of the results achieved with...
Ana Calabrese
Full Text Available In the auditory system, the stimulus-response properties of single neurons are often described in terms of the spectrotemporal receptive field (STRF, a linear kernel relating the spectrogram of the sound stimulus to the instantaneous firing rate of the neuron. Several algorithms have been used to estimate STRFs from responses to natural stimuli; these algorithms differ in their functional models, cost functions, and regularization methods. Here, we characterize the stimulus-response function of auditory neurons using a generalized linear model (GLM. In this model, each cell's input is described by: 1 a stimulus filter (STRF; and 2 a post-spike filter, which captures dependencies on the neuron's spiking history. The output of the model is given by a series of spike trains rather than instantaneous firing rate, allowing the prediction of spike train responses to novel stimuli. We fit the model by maximum penalized likelihood to the spiking activity of zebra finch auditory midbrain neurons in response to conspecific vocalizations (songs and modulation limited (ml noise. We compare this model to normalized reverse correlation (NRC, the traditional method for STRF estimation, in terms of predictive power and the basic tuning properties of the estimated STRFs. We find that a GLM with a sparse prior predicts novel responses to both stimulus classes significantly better than NRC. Importantly, we find that STRFs from the two models derived from the same responses can differ substantially and that GLM STRFs are more consistent between stimulus classes than NRC STRFs. These results suggest that a GLM with a sparse prior provides a more accurate characterization of spectrotemporal tuning than does the NRC method when responses to complex sounds are studied in these neurons.
Shibing Wang
2016-02-01
Full Text Available This paper introduces a new memristor-based hyperchaotic complex Lü system (MHCLS and investigates its adaptive complex generalized synchronization (ACGS. Firstly, the complex system is constructed based on a memristor-based hyperchaotic real Lü system, and its properties are analyzed theoretically. Secondly, its dynamical behaviors, including hyperchaos, chaos, transient phenomena, as well as periodic behaviors, are explored numerically by means of bifurcation diagrams, Lyapunov exponents, phase portraits, and time history diagrams. Thirdly, an adaptive controller and a parameter estimator are proposed to realize complex generalized synchronization and parameter identification of two identical MHCLSs with unknown parameters based on Lyapunov stability theory. Finally, the numerical simulation results of ACGS and its applications to secure communication are presented to verify the feasibility and effectiveness of the proposed method.
[Non-linear real-time adaptive filtration of ultrasound TI628A echotomoscope images].
Barannik, E A; Volokhov, Iu V; Marusenko, A I
1997-01-01
The statistical uncertainty caused by speckle noise artifacts is the reason for the great importance of the problem which is the optimum choice between the medical diagnostic systems resolution and the statistical accuracy of histological tissue identification. The way of speckle noise suppression, which is closely associated with the well-known idea of adaptive filtration and based on the physical analysis of the origin of true and false signals, is very promising. The testing results of the nonlinear real-time adaptive filter which has been designed for a TI628A echotomoscope are presented. The filter has been shown to have a rather high contrast and space resolution and reduces the speckle noise and other artifacts of the images. PMID:9445983
Purpose: It has been conventionally assumed that the repair rate for sublethal damage (SLD) remains constant during the entire radiation course. However, increasing evidence from animal studies suggest that this may not the case. Rather, it appears that the repair rate for radiation-induced SLD slows down with increasing time. Such a slowdown in repair would suggest that the exponential repair pattern would not necessarily accurately predict repair process. As a result, the purpose of this study was to investigate a new generalized linear-quadratic (LQ) model incorporating a repair pattern with reciprocal time. The new formulas were tested with published experimental data. Methods: The LQ model has been widely used in radiation therapy, and the parameter G in the surviving fraction represents the repair process of sublethal damage with Tr as the repair half-time. When a reciprocal pattern of repair process was adopted, a closed form of G was derived analytically for arbitrary radiation schemes. The published animal data adopted to test the reciprocal formulas. Results: A generalized LQ model to describe the repair process in a reciprocal pattern was obtained. Subsequently, formulas for special cases were derived from this general form. The reciprocal model showed a better fit to the animal data than the exponential model, particularly for the ED50 data (reduced χ2min of 2.0 vs 4.3, p = 0.11 vs 0.006), with the following gLQ parameters: α/β = 2.6-4.8 Gy, Tr = 3.2-3.9 h for rat feet skin, and α/β = 0.9 Gy, Tr = 1.1 h for rat spinal cord. Conclusions: These results of repair process following a reciprocal time suggest that the generalized LQ model incorporating the reciprocal time of sublethal damage repair shows a better fit than the exponential repair model. These formulas can be used to analyze the experimental and clinical data, where a slowing-down repair process appears during the course of radiation therapy.
Sun, W Y [Lawrence Berkeley Lab., CA (United States)
1993-04-01
This thesis solves the problem of finding the optimal linear noise-reduction filter for linear tomographic image reconstruction. The optimization is data dependent and results in minimizing the mean-square error of the reconstructed image. The error is defined as the difference between the result and the best possible reconstruction. Applications for the optimal filter include reconstructions of positron emission tomographic (PET), X-ray computed tomographic, single-photon emission tomographic, and nuclear magnetic resonance imaging. Using high resolution PET as an example, the optimal filter is derived and presented for the convolution backprojection, Moore-Penrose pseudoinverse, and the natural-pixel basis set reconstruction methods. Simulations and experimental results are presented for the convolution backprojection method.
Minerals detection for hyperspectral images using adapted linear unmixing: LinMin
Frederic, Schmidt; Stephane, Le Mouelic
2014-01-01
Minerals detection over large volume of spectra is the challenge addressed by current hyperspectral imaging spectrometer in Planetary Science. Instruments such OMEGA (Mars Express), CRISM (Mars Reconnaissance Orbiter), M^{3} (Chandrayaan-1), VIRTIS (Rosetta) and many more, have been producing very large datasets since one decade. We propose here a fast supervised detection algorithm called LinMin, in the framework of linear unmixing, with innovative arrangement in order to treat non-linear cases due to radiative transfer in both atmosphere and surface. We use reference laboratory and synthetic spectral library. Additional spectra are used in order to mimic the effect of Martian aerosols, grain size, and observation geometry discrepancies between reference and observed spectra. The proposed algorithm estimates the uncertainty on mixing coefficient from the uncertainty of observed spectra. Both numerical and observational tests validate the approach. Fast parallel implementation of the best algorithm (IPLS) on ...
Adaptions of ArcGIS' Linear Referencing System to the Coastal Environment
Balstrøm, Thomas
2008-01-01
For many years it has been problematic to store information for the coastal environment in a GIS. However, a system named "Linear referencing System" based upon a dynamic segmentation principle implemented in ESRIs ArcGIS 9 software has now made it possible to store and analyze information refere...... referenced to the coastline. This presentation demonstrates how to initialize this system, how to incorporate data into it and how to perform analysis queries hereon....
Generalized Jeans' Escape of Pick-Up Ions in Quasi-Linear Relaxation
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].
MGMRES: A generalization of GMRES for solving large sparse nonsymmetric linear systems
Young, D.M.; Chen, J.Y. [Univ. of Texas, Austin, TX (United States)
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.
The overlooked potential of Generalized Linear Models in astronomy, I: Binomial regression
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.
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
Spatial generalized linear mixed models of electric power outages due to hurricanes and ice storms
This paper presents new statistical models that predict the number of hurricane- and ice storm-related electric power outages likely to occur in each 3 kmx3 km grid cell in a region. The models are based on a large database of recent outages experienced by three major East Coast power companies in six hurricanes and eight ice storms. A spatial generalized linear mixed modeling (GLMM) approach was used in which spatial correlation is incorporated through random effects. Models were fitted using a composite likelihood approach and the covariance matrix was estimated empirically. A simulation study was conducted to test the model estimation procedure, and model training, validation, and testing were done to select the best models and assess their predictive power. The final hurricane model includes number of protective devices, maximum gust wind speed, hurricane indicator, and company indicator covariates. The final ice storm model includes number of protective devices, ice thickness, and ice storm indicator covariates. The models should be useful for power companies as they plan for future storms. The statistical modeling approach offers a new way to assess the reliability of electric power and other infrastructure systems in extreme events
Jian-Ying Feng
Full Text Available Many important phenotypic traits in plants are ordinal. However, relatively little is known about the methodologies for ordinal trait association studies. In this study, we proposed a hierarchical generalized linear mixed model for mapping quantitative trait locus (QTL of ordinal traits in crop cultivars. In this model, all the main-effect QTL and QTL-by-environment interaction were treated as random, while population mean, environmental effect and population structure were fixed. In the estimation of parameters, the pseudo data normal approximation of likelihood function and empirical Bayes approach were adopted. A series of Monte Carlo simulation experiments were performed to confirm the reliability of new method. The result showed that new method works well with satisfactory statistical power and precision. The new method was also adopted to dissect the genetic basis of soybean alkaline-salt tolerance in 257 soybean cultivars obtained, by stratified random sampling, from 6 geographic ecotypes in China. As a result, 6 main-effect QTL and 3 QTL-by-environment interactions were identified.
Maximal freedom at minimum cost: linear large-scale structure in general modifications of gravity
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
LU Yi-yi; GUO Yong; HE Chen
2009-01-01
Hybrid wavelength-division-multiplexing (WDM)/time-division-multiplexing (TDM) ethernet passive optical networks (EPONs) can achieve low per-subscriber cost and scalability to increase the number of subscribers. This paper discusses dynamic wavelength and bandwidth allocation (DWBA) algorithm in hybrid WDM/TDM EPONs. Based on the correlation structure of the variable bit rate (VBR) video traffic, we propose a quality-of-service (QoS) supported DWBA using adaptive linear traffic prediction. Wavelength and timeslot are allocated dynamically by optical line terminal (OLT) to all optical network units (ONUs) based on the bandwidth requests and the guaranteed service level agreements (SLA) of all ONUs. Mean square error of the predicted average arriv-ing rate of compound video traffic during waiting period is minimized through Wiener-Hopf equation. Simulation results show that the DWBA-adaptive-linear-prediction (DWBA-ALP) algorithm can significantly improve the QoS performances in terms of low delay and high bandwidth utilization.
Bahita Mohamed
2011-01-01
Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.
Hundebøll, Martin; Pedersen, Morten Videbæk; Roetter, Daniel Enrique Lucani;
2014-01-01
on the TCP protocol for reliability in data delivery. TCP is known to drop its throughput performance by several fold in the presence of even 1% or 2% packet losses, which are common in wireless systems. This will force DASH to settle at a much lower video resolution, thus reducing the user's quality...... of experience. We show that the use of FRANC, an adaptive network coding protocol that provides both low delay and high throughput to upper layers, as a reliability mechanism for TCP can significantly increase video quality. As part of our analysis, we benchmark the performance of various TCP versions......, including CUBIC, Reno, Veno, Vegas, and Westwood+, under different packet loss rates in wireless systems using a real testbed with Raspberry Pi devices. Our goal was to choose the most promising TCP version in terms of delay performance, in this case TCP Reno, and make a fair comparison between TCP running...
In this Letter, we propose an adaptive fuzzy bilinear feedback control (FBFC) design for synchronization of Takagi-Sugeno (TS) fuzzy bilinear generalized Lorenz system (FBGLS) with uncertain parameters. The generalized Lorenz system (GLS) can be described to TS FBGLS. We design an adaptive synchronization scheme of the response system based on TS FBGLS, feedback control scheme and Lyapunov theory. Lyapunov theory is employed to guarantee the stability of error dynamic system and to derive the adaptive laws to estimate unknown parameters. Numerical example is given to demonstrate the validity of our proposed adaptive FBFC approach with comparative results for synchronization.
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
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.
A new adaptation of linear reservoir models in parallel sets to assess actual hydrological events
Mateo Lázaro, Jesús; Sánchez Navarro, José Ángel; García Gil, Alejandro; Edo Romero, Vanesa
2015-05-01
A methodology based on Parallel Linear Reservoir (PLR) models is presented. To carry it out has been implemented within the software SHEE (Simulation of Hydrological Extreme Events), which is a tool for the analysis of hydrological processes in catchments with the management and display of DEM and datasets. The algorithms of the models pass throughout the cells and drainage network, by means of the Watershed Traversal Algorithm (WTA) that runs the entire drainage network of a basin in both directions, upwards and downwards, which is ideal for incorporating the models of the hydrological processes of the basins into its structure. The WTA methodology is combined with another one based on models of Parallel Linear Reservoirs (PLR) whose main qualities include: (1) the models are defined by observing the recession curves of actual hydrographs, i.e., the watershed actual responses; (2) the models serve as a way to simulate the routing through the watershed and its different reservoirs; and (3) the models allow calculating the water balance, which is essential to the study of actual events in the watershed. A complete hydrometeorological event needs the combination of several models, each one of which represents a hydrological process. The PLR model is a routing model, but it also contributes to the adjustment of other models (e.g., the rainfall-runoff model) and allows establishing a distributed model of effective rainfall for an actual event occurred in a basin. On the other hand, the proposed formulation solves the rainfall distribution problem for each deposit in the reservoir combination models.
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.
Adaptive change of basis in entropy-based moment closures for linear kinetic equations
Alldredge, Graham W; O'Leary, Dianne P; Tits, André L
2013-01-01
Entropy-based (M_N) moment closures for kinetic equations are defined by a constrained optimization problem that must be solved at every point in a space-time mesh, making it important to solve these optimization problems accurately and efficiently. We present a complete and practical numerical algorithm for solving the dual problem in one-dimensional, slab geometries. The closure is only well-defined on the set of moments that are realizable from a positive underlying distribution, and as the boundary of the realizable set is approached, the dual problem becomes increasingly difficult to solve due to ill-conditioning of the Hessian matrix. To improve the condition number of the Hessian, we advocate the use of a change of polynomial basis, defined using a Cholesky factorization of the Hessian, that permits solution of problems nearer to the boundary of the realizable set. We also advocate a fixed quadrature scheme, rather than adaptive quadrature, since the latter introduces unnecessary expense and changes th...
A Generalized Markov-Chain Modelling Approach to (1,lambda)-ES Linear Optimization
Chotard, A.; Holeňa, Martin
Cham: Springer, 2014 - (Bartz-Beielstein, T.; Branke, J.; Filipič, B.; Smith, J.), s. 902-911. (Lecture Notes in Computer Science . 8672). ISBN 978-3-319-10761-5. ISSN 0302-9743. [PPSN 2014. International Conference on Parallel Problem Solving from Nature /13./. Ljubljana (SI), 13.09.2014-17.09.2014] R&D Projects: GA ČR GA13-17187S Institutional support: RVO:67985807 Keywords : evolution strategies * continuous optimization * linear optimization * linear constraint * linear function * Markov chain models * Archimedean copulas Subject RIV: IN - Informatics, Computer Science
Dauda GuliburYAKUBU
2012-12-01
Full Text Available Accurate solutions to initial value systems of ordinary differential equations may be approximated efficiently by Runge-Kutta methods or linear multistep methods. Each of these has limitations of one sort or another. In this paper we consider, as a middle ground, the derivation of continuous general linear methods for solution of stiff systems of initial value problems in ordinary differential equations. These methods are designed to combine the advantages of both Runge-Kutta and linear multistep methods. Particularly, methods possessing the property of A-stability are identified as promising methods within this large class of general linear methods. We show that the continuous general linear methods are self-starting and have more ability to solve the stiff systems of ordinary differential equations, than the discrete ones. The initial value systems of ordinary differential equations are solved, for instance, without looking for any other method to start the integration process. This desirable feature of the proposed approach leads to obtaining very high accuracy of the solution of the given problem. Illustrative examples are given to demonstrate the novelty and reliability of the methods.
Ana Milstein
1979-01-01
Full Text Available The vertical distribution of each developmental stage of Paracalanus crassirostris was studied in a shallow water station at Ubatuba, SP, Brazil (23º30'S-45º07'W. Samples were collected monthly at the surface, 2m and near bottom levels . Salinity, temperature, dissolved oxygen, tide height, light penetration arid solar radiation were also recorded. Data were analysed by the general linear model. It showed that the amount of individuals at any developmental stage is affected diversely by hour, depth, hour-depth interaction and environmental factors throughout the year and that these effects are stronger in summer. All developmental stages were spread in the water column showing no regular vertical migrations. On the other hand, the number of organisms caught in a particular hour seemed to dependmore on the tide than on the animals behaviour. The results of the present paper showed, as observed by some other authors, the lack of vertical migration of a coastal copepod which is a grazer of fine particles throughout its life.A distribuição vertical dos diferentes estádios de desenvolvimento de P. crassirostris foi estudada durante um ano (junho 1976 - maio 1977, numa estação pouco profunda (5 m em Ubatuba. As amostras foram coletadas mensalmente, em tres profundidades, cada quatro horas, com garrafa van Dorn de 9 l registrando-se dados ambientais. Os dados foram processados com a técnica dos Mínimos Quadrados, na forma de uma Aralise de Regressão de um Modelo Linear que inclui covariáveis. O modelo foi construído a priori, considerando densidade de organismos por amostra, fatores ambientais, diferenças entre amostras procedentes de diferentes profundidades e horas, também como interações entre hora e profundidade. Para cada estádio de P. crassirostris, o modelo foi repetido 9 vezes, com os dados de dois meses cada vez, a fim de obter a variação das respostas no ano. Os resultados do modelo indicaram que a quantidade de indiv
Ren, Xiang
2012-01-01
Transform Invariant Low-rank Textures (TILT) is a novel and powerful tool that can effectively rectify a rich class of low-rank textures in 3D scenes from 2D images despite significant deformation and corruption. The existing algorithm for solving TILT is based on the alternating direction method (ADM). It suffers from high computational cost and is not theoretically guaranteed to converge to a correct solution. In this paper, we propose a novel algorithm to speed up solving TILT, with guaranteed convergence. Our method is based on the recently proposed linearized alternating direction method with adaptive penalty (LADMAP). To further reduce computation, warm starts are also introduced to initialize the variables better and cut the cost on singular value decomposition. Extensive experimental results on both synthetic and real data demonstrate that this new algorithm works much more efficiently and robustly than the existing algorithm. It could be at least five times faster than the previous method.
Yan, Bo; Li, Yuguo; Liu, Ying
2016-07-01
In this paper, we present an adaptive finite element (FE) algorithm for direct current (DC) resistivity modeling in 2-D generally anisotropic conductivity structures. Our algorithm is implemented on an unstructured triangular mesh that readily accommodates complex structures such as topography and dipping layers and so on. We implement a self-adaptive, goal-oriented grid refinement algorithm in which the finite element analysis is performed on a sequence of refined grids. The grid refinement process is guided by an a posteriori error estimator. The problem is formulated in terms of total potentials where mixed boundary conditions are incorporated. This type of boundary condition is superior to the Dirichlet type of conditions and improves numerical accuracy considerably according to model calculations. We have verified the adaptive finite element algorithm using a two-layered earth with azimuthal anisotropy. The FE algorithm with incorporation of mixed boundary conditions achieves high accuracy. The relative error between the numerical and analytical solutions is less than 1% except in the vicinity of the current source location, where the relative error is up to 2.4%. A 2-D anisotropic model is used to demonstrate the effects of anisotropy upon the apparent resistivity in DC soundings.
In flood forecasting modelling, large basins are often considered as hydrological systems with multiple inputs and one output. Inputs are hydrological variables such rainfall, runoff and physical characteristics of basin; output is runoff. Relating inputs to output can be achieved using deterministic, conceptual, or stochastic models. Rainfall runoff models generally lack of accuracy. Physical hydrological processes based models, either deterministic or conceptual are highly data requirement demanding and by the way very complex. Stochastic multiple input-output models, using only historical chronicles of hydrological variables particularly runoff are by the way very popular among the hydrologists for large river basin flood forecasting. Application is made on the Senegal River upstream of Bakel, where the River is formed by the main branch, Bafing, and two tributaries, Bakoye and Faleme; Bafing being regulated by Manantaly Dam. A three inputs and one output model has been used for flood forecasting on Bakel. Influence of the lead forecasting, and of the three inputs taken separately, then associated two by two, and altogether has been verified using a dimensionless variance as criterion of quality. Inadequacies occur generally between model output and observations; to put model in better compliance with current observations, we have compared four parameter updating procedure, recursive least squares, Kalman filtering, stochastic gradient method, iterative method, and an AR errors forecasting model. A combination of these model updating have been used in real time flood forecasting.(Author)
General methods for determining the linear stability of coronal magnetic fields
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.
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.
Meleshko, S. V.; S. Moyo
2014-01-01
In this paper we study the general group classification of systems of linear second-order ordinary differential equations inspired from earlier works and recent results on the group classification of such systems. Some interesting results and subsequent Theorem arising from this particular study are discussed here. This paper considers the study of irreducible systems of second-order ordinary differential equations.
Guillermo Bosque
2014-01-01
Full Text Available In a great diversity of knowledge areas, the variables that are involved in the behavior of a complex system, perform normally, a non-linear system. The search of a function that express those behavior, requires techniques as mathematics optimization techniques or others. The new paradigms introduced in the soft computing, as fuzzy logic, neuronal networks, genetics algorithms and the fusion of them like the neuro-fuzzy systems, and so on, represent a new point of view to deal this kind of problems due to the approximation properties of those systems (universal approximators.This work shows a methodology to develop a tool based on a neuro-fuzzy system of ANFIS (Adaptive Neuro-Fuzzy Inference System type with piecewise multilinear (PWM behaviour (introducing some restrictions on the membership functions -triangular- chosen in the ANFIS system. The obtained tool is named PWM-ANFIS Tool, that allows modelize a n-dimensional system with one output and, also, permits a comparison between the neuro-fuzzy system modelized, a purely PWM-ANFIS model, with a generic ANFIS (Gaussian membership functions modelized with the same tool. The proposed tool is an efficient tool to deal non-linearly complicated systems.Keywords: ANFIS model, Function approximation, Matlab environment, Neuro-Fuzzy CAD tool, Neuro-Fuzzy modelling.
Elayaraja Aruchunan
2010-01-01
Full Text Available Problem statement: This research purposely brought up to solve complicated equations such as partial differential equations, integral equations, Integro-Differential Equations (IDE, stochastic equations and others. Many physical phenomena contain mathematical formulations such integro-differential equations which are arise in fluid dynamics, biological models and chemical kinetics. In fact, several formulations and numerical solutions of the linear Fredholm integro-differential equation of second order currently have been proposed. This study presented the numerical solution of the linear Fredholm integro-differential equation of second order discretized by using finite difference and trapezoidal methods. Approach: The linear Fredholm integro-differential equation of second order will be discretized by using finite difference and trapezoidal methods in order to derive an approximation equation. Later this approximation equation will be used to generate a dense linear system and solved by using the Generalized Minimal Residual (GMRES method. Results: Several numerical experiments were conducted to examine the efficiency of GMRES method for solving linear system generated from the discretization of linear Fredholm integro-differential equation. For the comparison purpose, there are three parameters such as number of iterations, computational time and absolute error will be considered. Based on observation of numerical results, it can be seen that the number of iterations and computational time of GMRES have declined much faster than Gauss-Seidel (GS method. Conclusion: The efficiency of GMRES based on the proposed discretization is superior as compared to GS iterative method.
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.
Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models
Lanne, Markku; Nyberg, Henri
We propose a new generalized forecast error variance decomposition with the property that the proportions of the impact accounted for by innovations in each variable sum to unity. Our decomposition is based on the well-established concept of the generalized impulse response function. The use of the...
A general hybrid radiation transport scheme for star formation simulations on an adaptive grid
Klassen, Mikhail; Pudritz, Ralph E; Peters, Thomas; Banerjee, Robi; Buntemeyer, Lars
2014-01-01
Radiation feedback plays a crucial role in the process of star formation. In order to simulate the thermodynamic evolution of disks, filaments, and the molecular gas surrounding clusters of young stars, we require an efficient and accurate method for solving the radiation transfer problem. We describe the implementation of a hybrid radiation transport scheme in the adaptive grid-based FLASH general magnetohydrodynamics code. The hybrid scheme splits the radiative transport problem into a raytracing step and a diffusion step. The raytracer captures the first absorption event, as stars irradiate their environments, while the evolution of the diffuse component of the radiation field is handled by a flux-limited diffusion (FLD) solver. We demonstrate the accuracy of our method through a variety of benchmark tests including the irradiation of a static disk, subcritical and supercritical radiative shocks, and thermal energy equilibration. We also demonstrate the capability of our method for casting shadows and calc...
Generalization of the Wide-Sense Markov Concept to a Widely Linear Processing
In this paper we show that the classical definition and the associated characterizations of wide-sense Markov (WSM) signals are not valid for improper complex signals. For that, we propose an extension of the concept of WSM to a widely linear (WL) setting and the study of new characterizations. Specifically, we introduce a new class of signals, called widely linear Markov (WLM) signals, and we analyze some of their properties based either on second-order properties or on state-space models from a WL processing standpoint. The study is performed in both the forwards and backwards directions of time. Thus, we provide two forwards and backwards Markovian representations for WLM signals. Finally, different estimation recursive algorithms are obtained for these models
Comparison of MINQUE and Simple Estimate of the Error Variance in the General Linear Models
Song-gui Wang; Mi-xia Wu; Wei-qing Ma
2003-01-01
Comparison is made between the MINQUE and simple estimate of the error variance in the normal linear model under the mean square errors criterion, where the model matrix need not have full rank and the dispersion matrix can be singular. Our results show that any one of both estimates cannot be always superior to the other. Some sufficient criteria for any one of them to be better than the other are established. Some interesting relations between these two estimates are also given.
Continuity and general perturbation of the Drazin inverse for closed linear operators
N. Castro González
2002-01-01
Full Text Available We study perturbations and continuity of the Drazin inverse of a closed linear operator A and obtain explicit error estimates in terms of the gap between closed operators and the gap between ranges and nullspaces of operators. The results are used to derive a theorem on the continuity of the Drazin inverse for closed operators and to describe the asymptotic behavior of operator semigroups.
Dias, Rolando; Costa, Mário Rui
2006-01-01
A method to predict the sequence length distribution (SLD) for homogeneous non-linear irreversible multicomponent polymerizations is described. With more than two monomers, it also predicts chain length distributions of the sub-domains containing a prescribed sub-set of the repeating units, but in all possible orderings. Its goal is the analysis of polymerization systems involving complex kinetic schemes in an automated way. The radical terpolymerization of two vinyl monomers with a divinyl m...
Hamiltonian and linear-space structure for damped oscillators: I. General theory
The phase space of N damped linear oscillators is endowed with a bilinear map under which the evolution operator is symmetric. This analogue of self-adjointness allows properties familiar from conservative systems to be recovered, e.g., eigenvectors are 'orthogonal' under the bilinear map and obey sum rules, initial-value problems are readily solved and perturbation theory applies to the complex eigenvalues. These concepts are conveniently represented in a biorthogonal basis
Manuel Alejandro Cardenete
2012-01-01
Full Text Available In this study we apply a multiplier decomposition methodology of a linear general equilibrium model based on the regional social accounting matrix to the Andalusian economy. The aim of this methodology is to separate the size of the different effects in terms of energy expenditure and total emissions generated by the whole productive system to satisfy the final demand of each branch of the Andalusian economy and the direct emissions generated to produce energy for each subsystem.
Jalden, Joakim; Elia, Petros
2010-01-01
The work identifies the first general, explicit, and non-random MIMO encoder-decoder structures that guarantee optimality with respect to the diversity-multiplexing tradeoff (DMT), without employing a computationally expensive maximum-likelihood (ML) receiver. Specifically, the work establishes the DMT optimality of a class of regularized lattice decoders, and more importantly the DMT optimality of their lattice-reduction (LR)-aided linear counterparts. The results hold for all channel statis...
Bandyopadhyay, Tanwi; Debnath, Ujjal
2011-01-01
In this work, we have considered the magnetic universe in non-linear electrodynamics. The Einstein's field equations for non-flat FRW model have been considered when the universe is filled with the matter and magnetic field only. We have discussed the validity of the generalized second law of thermodynamics of the magntic universe bounded by Hubble, apparent, particle and event horizons using Gibb's law and the first law of thermodynamics for interacting and non-interacting scenarios. It has ...
This paper addresses a General Linear Complementarity Problem (GLCP) that has found applications in global optimization. It is shown that a solution of the GLCP can be computed by finding a stationary point of a differentiable function over a set defined by simple bounds on the variables. The application of this result to the solution of bilinear programs and LCPs is discussed. Some computational evidence of its usefulness is included in the last part of the paper
Cross-Cultural adaptation of the General Functioning Scale of the Family
Pires, Thiago; de Assis, Simone Gonçalves; Avanci, Joviana Quintes; Pesce, Renata Pires
2016-01-01
ABSTRACT OBJECTIVE To describe the process of cross-cultural adaptation of the General Functioning Scale of the Family, a subscale of the McMaster Family Assessment Device, for the Brazilian population. METHODS The General Functioning Scale of the Family was translated into Portuguese and administered to 500 guardians of children in the second grade of elementary school in public schools of Sao Gonçalo, Rio de Janeiro, Southeastern Brazil. The types of equivalences investigated were: conceptual and of items, semantic, operational, and measurement. The study involved discussions with experts, translations and back-translations of the instrument, and psychometric assessment. Reliability and validity studies were carried out by internal consistency testing (Cronbach’s alpha), Guttman split-half correlation model, Pearson correlation coefficient, and confirmatory factor analysis. Associations between General Functioning of the Family and variables theoretically associated with the theme (father’s or mother’s drunkenness and violence between parents) were estimated by odds ratio. RESULTS Semantic equivalence was between 90.0% and 100%. Cronbach’s alpha ranged from 0.79 to 0.81, indicating good internal consistency of the instrument. Pearson correlation coefficient ranged between 0.303 and 0.549. Statistical association was found between the general functioning of the family score and the theoretically related variables, as well as good fit quality of the confirmatory analysis model. CONCLUSIONS The results indicate the feasibility of administering the instrument to the Brazilian population, as it is easy to understand and a good measurement of the construct of interest. PMID:27355464
Cross-Cultural adaptation of the General Functioning Scale of the Family
Thiago Pires
2016-01-01
Full Text Available ABSTRACT OBJECTIVE To describe the process of cross-cultural adaptation of the General Functioning Scale of the Family, a subscale of the McMaster Family Assessment Device, for the Brazilian population. METHODS The General Functioning Scale of the Family was translated into Portuguese and administered to 500 guardians of children in the second grade of elementary school in public schools of Sao Gonçalo, Rio de Janeiro, Southeastern Brazil. The types of equivalences investigated were: conceptual and of items, semantic, operational, and measurement. The study involved discussions with experts, translations and back-translations of the instrument, and psychometric assessment. Reliability and validity studies were carried out by internal consistency testing (Cronbach’s alpha, Guttman split-half correlation model, Pearson correlation coefficient, and confirmatory factor analysis. Associations between General Functioning of the Family and variables theoretically associated with the theme (father’s or mother’s drunkenness and violence between parents were estimated by odds ratio. RESULTS Semantic equivalence was between 90.0% and 100%. Cronbach’s alpha ranged from 0.79 to 0.81, indicating good internal consistency of the instrument. Pearson correlation coefficient ranged between 0.303 and 0.549. Statistical association was found between the general functioning of the family score and the theoretically related variables, as well as good fit quality of the confirmatory analysis model. CONCLUSIONS The results indicate the feasibility of administering the instrument to the Brazilian population, as it is easy to understand and a good measurement of the construct of interest.
GENERALIZED (COMPOSITE) SPACE VECTOR FOR A FLAT THREE-PHASE LINEARLY INDEPENDENT SYSTEM OF SIGNALS
Fedorenko, A.; Lazovskiy, Ed
2011-01-01
In the article the authors specify concept of composite vector for flat three-phase system of signals and give relators connecting instantaneous data of phase signals to coordinates of generalized vector in 3-D rectangular and cylindrical coordinate system.
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.
A Generalized Multiscale Finite Element Method for Poroelasticity Problems I: Linear Problems
Brown, Donald L.; Vasilyeva, Maria
2015-01-01
In this paper, we consider the numerical solution of poroelasticity problems that are of Biot type and develop a general algorithm for solving coupled systems. We discuss the challenges associated with mechanics and flow problems in heterogeneous media. The two primary issues being the multiscale nature of the media and the solutions of the fluid and mechanics variables traditionally developed with separate grids and methods. For the numerical solution we develop and implement a Generalized M...
LINEAR STIELTJES EQUATION WITH GENERALIZED RIEMANN INTEGRAL AND EXISTENCE OF REGULATED SOLUTIONS
L. BARBANTI
2001-01-01
In this work we establish an existence theorem of regulated solutions for a class of Stieltjes equations which involve generalized Riemann kind of integrals. The general method applied consists in considering the continuous-time Stieltjes equation as limit of discrete processes. This approach will prove fruitful in the study of the controllability of Stieltjes systems, because it will be possible to get properties on the continuous time equation by transferring properties of the discrete ones.
LINEARIZATION OF A GENERALIZED MATERIAL BALANCE EQUATION FOR COALBED METHANE RESERVOIRS
G PEÑUELA; A ORDOÑEZ R; Bejarano, A. (A.)
1998-01-01
A generalized material balance equation was presented at the Escuela de Petróleos de la Universidad Industrial de Santander for coal seam gas reservoirs based on Walsh 's method, who worked in an analogous approach for oil and gas conventional reservoirs (Walsh, 1 995). Our equation was based on twelve similar assumptions itemized by Walsh for his generalized expression for conventional reservoirs, it was started from the same volume balance consideration and was finally reorganized like Wals...
Bargatze, L. F.
2015-12-01
Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted
Lee, Dongyul; Lee, Chaewoo
2014-01-01
The advancement in wideband wireless network supports real time services such as IPTV and live video streaming. However, because of the sharing nature of the wireless medium, efficient resource allocation has been studied to achieve a high level of acceptability and proliferation of wireless multimedia. Scalable video coding (SVC) with adaptive modulation and coding (AMC) provides an excellent solution for wireless video streaming. By assigning different modulation and coding schemes (MCSs) to video layers, SVC can provide good video quality to users in good channel conditions and also basic video quality to users in bad channel conditions. For optimal resource allocation, a key issue in applying SVC in the wireless multicast service is how to assign MCSs and the time resources to each SVC layer in the heterogeneous channel condition. We formulate this problem with integer linear programming (ILP) and provide numerical results to show the performance under 802.16 m environment. The result shows that our methodology enhances the overall system throughput compared to an existing algorithm. PMID:25276862
Pei, J.-S.; Smyth, A. W.; Kosmatopoulos, E. B.
2004-08-01
This study attempts to demystify a powerful neural network approach for modelling non-linear hysteretic systems and in turn to streamline its architecture to achieve better computational efficiency. The recently developed neural network modelling approach, the Volterra/Wiener neural network (VWNN), demonstrated its usefulness in identifying the restoring forces for hysteretic systems in an off-line or even in an adaptive (on-line) mode, however, the mechanism of how and why it works has not been thoroughly explored especially in terms of a physical interpretation. Artificial neural network are often treated as "black box" modelling tools, in contrast, here the authors carry out a detailed analysis in terms of problem formulation and network architecture to explore the inner workings of this neural network. Based on the understanding of the dynamics of hysteretic systems, some simplifications and modifications are made to the original VWNN in predicting accelerations of hysteretic systems under arbitrary force excitations. Through further examination of the algorithm related to the VWNN applications, the efficiency of the previously published approach is improved by reducing the number of the hidden nodes without affecting the modelling accuracy of the network. One training example is presented to illustrate the application of the VWNN; and another is provided to demonstrate that the VWNN is able to yield a unique set of weights when the values of the controlling design parameters are fixed. The practical issue of how to choose the values of these important parameters is discussed to aid engineering applications.
GENERAL CAUCHY PROBLEM FOR THE LINEAR SHALLOW -WATER EQUATIONS ON AN EQUATORIAL BETA-PLANE
SHEN Chun; SHI Wei-hui
2006-01-01
Based on the theory of stratification, the well-posedness of the initial value problem for the linear shallow-water equations on an equatorial beta-plane was discussed. The sufficient and necessary conditions of the existence and uniqueness for the local solution of the equations were presented and the existence conditions for formal solutions of the equations were also given. For the Cauchy problem on the hyper-plane, the local analytic solution were worked out and a special case was discussed. Finally, an example was used to explain the variety of formal solutions for the ill-posed problem.
Examining secular trend and seasonality in count data using dynamic generalized linear modelling
Lundbye-Christensen, Søren; Dethlefsen, Claus; Gorst-Rasmussen, Anders;
series regression model for Poisson counts. It differs in allowing the regression coefficients to vary gradually over time in a random fashion. Data In the period January 1980 to 1999, 17,989 incidents of acute myocardial infarction were recorded in the county of Northern Jutland, Denmark. Records were...... updated daily. Results The model with a seasonal pattern and an approximately linear trend was fitted to the data, and diagnostic plots indicate a good model fit. The analysis with the dynamic model revealed peaks coinciding with influenza epidemics. On average the peak-to-trough ratio is estimated...
Use of a generalized linear model to evaluate range forage production estimates
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.
Generalized Coherent States of a Particle in a Time-Dependent Linear Potential
L.Krache; M.Maamache; Y.Saadi; A.Beniaiche
2009-01-01
We derive, with an invariant operator method and unitary transformation approach, that the Schr(o)dinger equation with a time-dependent linear potential possesses an infinite string of shape-preseving wave-packet states |ψα,λ(t)>having classical motion. The qualitative properties of the invariant eigenvalue spectrum (discrete or continuous)are described separately for the different values of the frequency ω of a harmonic oscillator. It is also shown that,for a discrete eigenvalue spectrum, the states |ψα,n(t)> could be obtained from the coherent state |ψα,0(t)>.
As a powerful machine-learning approach to pattern recognition problems, the support vector machine (SVM) is known to easily allow generalization. More importantly, it works very well in a high-dimensional feature space. This paper presents a nonlinear active suspension controller which achieves a high level performance by compensating for actuator dynamics. We use a linear quadratic regulator (LQR) to ensure optimal control of nonlinear systems. An LQR is used to solve the problem of state feedback and an SVM is used to address the question of the estimation and examination of the state. These two are then combined and designed in a way that outputs feedback control. The real-time simulation demonstrates that an active suspension using the combined SVM-LQR controller provides passengers with a much more comfortable ride and better road handling
Szadkowski, Zbigniew; Fraenkel, E. D.; van den Berg, Ad M.
2013-01-01
We present the FPGA/NIOS implementation of an adaptive finite impulse response (FIR) filter based on linear prediction to suppress radio frequency interference (RFI). This technique will be used for experiments that observe coherent radio emission from extensive air showers induced by ultra-high-ene
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 generalized material balance equation was presented at the Escuela de Petroleos de la Universidad Industrial de Santander for coal seam gas reservoirs based on Walsh's method, who worked in an analogous approach for oil and gas conventional reservoirs (Walsh, 1995). Our equation was based on twelve similar assumptions itemized by Walsh for his generalized expression for conventional reservoirs it was started from the same volume balance consideration and was finally reorganized like Walsh (1994) did. Because it is not expressed in terms of traditional (P/Z) plots, as proposed by King (1990), it allows to perform a lot of quantitative and qualitative analyses. It was also demonstrated that the existent equations are only particular cases of the generalized expression evaluated under certain restrictions. This equation is applicable to coal seam gas reservoirs in saturated, equilibrium and under saturated conditions, and to any type of coal beds without restriction on especial values of the constant diffusion
Xiuchun Li
2013-01-01
Full Text Available When the parameters of both drive and response systems are all unknown, an adaptive sliding mode controller, strongly robust to exotic perturbations, is designed for realizing generalized function projective synchronization. Sliding mode surface is given and the controlled system is asymptotically stable on this surface with the passage of time. Based on the adaptation laws and Lyapunov stability theory, an adaptive sliding controller is designed to ensure the occurrence of the sliding motion. Finally, numerical simulations are presented to verify the effectiveness and robustness of the proposed method even when both drive and response systems are perturbed with external disturbances.
Extending generalized linear models with random effects and components of dispersion.
Engel, B.
1997-01-01
This dissertation was born out of a need for general and numerically feasible procedures for inference in variance components models for non-normal data. The methodology should be widely applicable within the institutes of the Agricultural Research Department (DLO) of the Dutch Ministry of Agricultu
Exotic circles of a remarkable group of piecewise generalized (non linear) circle homeomorphisms
Let G be a subgroup of Homeo+(S1). An exotic circle of G is a subgroup of G which is conjugate to SO (2) in Homeo+(S1) but not conjugate to SO (2) in G . The existence of exotic circles shows that the subgroup G is far from being a Lie group. Let r ≥ 1 be an integer, r = +∞ or r ω. In this paper, we prove that the subgroup Pr( S1) of Homeo+(S1) consisting of piecewise class P Cr homeomorphisms of the circle has no exotic circles. However, we show that there exist exotic circles of a particular subgroup (denoted P1r (S1)) of Pr(S1) and we determine the conjugacy classes of all exotic circles in P1r (S1). In particular, for the group PL+(S1) consisting of piecewise linear homeomorphisms we give a simple proof of Minakawa's Theorems. (author)
Abrahamsen, Trine Julie; Hansen, Lars Kai
2011-01-01
We investigate sparse non-linear denoising of functional brain images by kernel Principal Component Analysis (kernel PCA). The main challenge is the mapping of denoised feature space points back into input space, also referred to as ”the pre-image problem”. Since the feature space mapping is...... typically not bijective, pre-image estimation is inherently illposed. In many applications, including functional magnetic resonance imaging (fMRI) data which is the application used for illustration in the present work, it is of interest to denoise a sparse signal. To meet this objective we investigate...... sparse pre-image reconstruction by Lasso regularization. We find that sparse estimation provides better brain state decoding accuracy and a more reproducible pre-image. These two important metrics are combined in an evaluation framework which allow us to optimize both the degree of sparsity and the non...
Iterative solution of general sparse linear systems on clusters of workstations
Lo, Gen-Ching; Saad, Y. [Univ. of Minnesota, Minneapolis, MN (United States)
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.
Explicit solutions of the generalized Ginzburg-Landau equation from linear systems
The singularity structure of the complex one-dimensional Ginzburg-Landau equation in the complex (x,t) plane shows the inadequacy of the representations (Re A,Im A), (A,A-bar) for A, even in the nonlinear Schroedinger (NLS) limit. The elementary representation consists of two fields (Z,Θ), respectively complex and real, uniquely defined by an explicit expression for Θ and A. The four famous solutions of Nozaki and Bekki are then represented by two linear partial differential equations with constant coefficients and a finite set of constants. It is also shown how the invariance by parity on A increases the class of expected solutions. (authors) 24 refs
Content-adaptive pentary steganography using the multivariate generalized Gaussian cover model
Sedighi, Vahid; Fridrich, Jessica; Cogranne, Rémi
2015-03-01
The vast majority of steganographic schemes for digital images stored in the raster format limit the amplitude of embedding changes to the smallest possible value. In this paper, we investigate the possibility to further improve the empirical security by allowing the embedding changes in highly textured areas to have a larger amplitude and thus embedding there a larger payload. Our approach is entirely model driven in the sense that the probabilities with which the cover pixels should be changed by a certain amount are derived from the cover model to minimize the power of an optimal statistical test. The embedding consists of two steps. First, the sender estimates the cover model parameters, the pixel variances, when modeling the pixels as a sequence of independent but not identically distributed generalized Gaussian random variables. Then, the embedding change probabilities for changing each pixel by 1 or 2, which can be transformed to costs for practical embedding using syndrome-trellis codes, are computed by solving a pair of non-linear algebraic equations. Using rich models and selection-channel-aware features, we compare the security of our scheme based on the generalized Gaussian model with pentary versions of two popular embedding algorithms: HILL and S-UNIWARD.
The adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions is investigated in this paper. Based on Lyapunov stability theory and Barbalat's lemma, generalized matrix projective lag synchronization criteria are derived by using the adaptive control method. Furthermore, each network can be undirected or directed, connected or disconnected, and nodes in either network may have identical or different dynamics. The proposed strategy is applicable to almost all kinds of complex networks. In addition, numerical simulation results are presented to illustrate the effectiveness of this method, showing that the synchronization speed is sensitively influenced by the adaptive law strength, the network size, and the network topological structure. (general)
A Generalized Multiscale Finite Element Method for Poroelasticity Problems I: Linear Problems
Brown, Donald L
2015-01-01
In this paper, we consider the numerical solution of poroelasticity problems that are of Biot type and develop a general algorithm for solving coupled systems. We discuss the challenges associated with mechanics and flow problems in heterogeneous media. The two primary issues being the multiscale nature of the media and the solutions of the fluid and mechanics variables traditionally developed with separate grids and methods. For the numerical solution we develop and implement a Generalized Multiscale Finite Element Method (GMsFEM) that solves problem on a coarse grid by constructing local multiscale basis functions. The procedure begins with construction of multiscale bases for both displacement and pressure in each coarse block. Using a snapshot space and local spectral problems, we construct a basis of reduced dimension. Finally, after multiplying by a multiscale partitions of unity, the multiscale basis is constructed in the offline phase and the coarse grid problem then can be solved for arbitrary forcin...
Generalized Fokker-Planck equation and its solution for linear non-Markovian Gaussian systems
O.Yu. Sliusarenko
2011-06-01
Full Text Available In this paper we suggest a consistent approach to derivation of generalized Fokker-Planck equation (GFPE for Gaussian non-Markovian processes with stationary increments. This approach allows us to construct the probability density function (PDF without a need to solve the GFPE. We employ our method to obtain the GFPE and PDFs for free generalized Brownian motion and the one in harmonic potential for the case of power-law correlation function of the noise. We prove the fact that the considered systems may be described with Einstein-Smoluchowski equation at high viscosity levels and long times. We also compare the results with those obtained by other authors. At last, we calculate PDF of thermodynamical work in the stochastic system which consists of a particle embedded in a harmonic potential moving with constant velocity, and check the work fluctuation theorem for such a system.
A Generalized Linear Transport Model for Spatially-Correlated Stochastic Media
Davis, Anthony B
2014-01-01
We formulate a new model for transport in stochastic media with long-range spatial correlations where exponential attenuation (controlling the propagation part of the transport) becomes power law. Direct transmission over optical distance $\\tau(s)$, for fixed physical distance $s$, thus becomes $(1+\\tau(s)/a)^{-a}$, with standard exponential decay recovered when $a\\to\\infty$. Atmospheric turbulence phenomenology for fluctuating optical properties rationalizes this switch. Foundational equations for this generalized transport model are stated in integral form for $d=1,2,3$ spatial dimensions. A deterministic numerical solution is developed in $d=1$ using Markov Chain formalism, verified with Monte Carlo, and used to investigate internal radiation fields. Standard two-stream theory, where diffusion is exact, is recovered when $a=\\infty$. Differential diffusion equations are not presently known when $a<\\infty$, nor is the integro-differential form of the generalized transport equation. Monte Carlo simulations...
Application of the generalized linear elastic fracture mechanics to NiTi catheter
Houfek, L.; Klusák, Jan; Profant, T.; Kotoul, M.; Knésl, Zdeněk
Rio de Janeiro: SBPMat, Brazil- MRS , 2009. s. 1-1. ISSN 1983-7542. [International Conference on Advanced Materials /11./. 20.09.2009-25.09.2009, Rio de Janeiro] R&D Projects: GA ČR GA101/08/0994 Institutional research plan: CEZ:AV0Z20410507 Keywords : generalized fracture mechanics * singular stress concentrator * crack initiation direction Subject RIV: JL - Materials Fatigue, Friction Mechanics
Extending generalized linear models with random effects and components of dispersion.
Engel, B
1997-01-01
This dissertation was born out of a need for general and numerically feasible procedures for inference in variance components models for non-normal data. The methodology should be widely applicable within the institutes of the Agricultural Research Department (DLO) of the Dutch Ministry of Agriculture, Nature Management and Fisheries. Available methodology employing maximum likelihood estimation, due to numerical limitations, was too restricted with respect to the choice of random structures....
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
Jain, Amit; Kuhls-Gilcrist, Andrew T.; Gupta, Sandesh K.; Bednarek, Daniel R.; Rudin, Stephen
2010-01-01
The MTF, NNPS, and DQE are standard linear system metrics used to characterize intrinsic detector performance. To evaluate total system performance for actual clinical conditions, generalized linear system metrics (GMTF, GNNPS and GDQE) that include the effect of the focal spot distribution, scattered radiation, and geometric unsharpness are more meaningful and appropriate. In this study, a two-dimensional (2D) generalized linear system analysis was carried out for a standard flat panel detec...
A general hybrid radiation transport scheme for star formation simulations on an adaptive grid
Radiation feedback plays a crucial role in the process of star formation. In order to simulate the thermodynamic evolution of disks, filaments, and the molecular gas surrounding clusters of young stars, we require an efficient and accurate method for solving the radiation transfer problem. We describe the implementation of a hybrid radiation transport scheme in the adaptive grid-based FLASH general magnetohydrodyanmics code. The hybrid scheme splits the radiative transport problem into a raytracing step and a diffusion step. The raytracer captures the first absorption event, as stars irradiate their environments, while the evolution of the diffuse component of the radiation field is handled by a flux-limited diffusion solver. We demonstrate the accuracy of our method through a variety of benchmark tests including the irradiation of a static disk, subcritical and supercritical radiative shocks, and thermal energy equilibration. We also demonstrate the capability of our method for casting shadows and calculating gas and dust temperatures in the presence of multiple stellar sources. Our method enables radiation-hydrodynamic studies of young stellar objects, protostellar disks, and clustered star formation in magnetized, filamentary environments.
A general hybrid radiation transport scheme for star formation simulations on an adaptive grid
Klassen, Mikhail; Pudritz, Ralph E. [Department of Physics and Astronomy, McMaster University 1280 Main Street W, Hamilton, ON L8S 4M1 (Canada); Kuiper, Rolf [Max Planck Institute for Astronomy Königstuhl 17, D-69117 Heidelberg (Germany); Peters, Thomas [Institut für Computergestützte Wissenschaften, Universität Zürich Winterthurerstrasse 190, CH-8057 Zürich (Switzerland); Banerjee, Robi; Buntemeyer, Lars, E-mail: klassm@mcmaster.ca [Hamburger Sternwarte, Universität Hamburg Gojenbergsweg 112, D-21029 Hamburg (Germany)
2014-12-10
Radiation feedback plays a crucial role in the process of star formation. In order to simulate the thermodynamic evolution of disks, filaments, and the molecular gas surrounding clusters of young stars, we require an efficient and accurate method for solving the radiation transfer problem. We describe the implementation of a hybrid radiation transport scheme in the adaptive grid-based FLASH general magnetohydrodyanmics code. The hybrid scheme splits the radiative transport problem into a raytracing step and a diffusion step. The raytracer captures the first absorption event, as stars irradiate their environments, while the evolution of the diffuse component of the radiation field is handled by a flux-limited diffusion solver. We demonstrate the accuracy of our method through a variety of benchmark tests including the irradiation of a static disk, subcritical and supercritical radiative shocks, and thermal energy equilibration. We also demonstrate the capability of our method for casting shadows and calculating gas and dust temperatures in the presence of multiple stellar sources. Our method enables radiation-hydrodynamic studies of young stellar objects, protostellar disks, and clustered star formation in magnetized, filamentary environments.
A General Hybrid Radiation Transport Scheme for Star Formation Simulations on an Adaptive Grid
Klassen, Mikhail; Kuiper, Rolf; Pudritz, Ralph E.; Peters, Thomas; Banerjee, Robi; Buntemeyer, Lars
2014-12-01
Radiation feedback plays a crucial role in the process of star formation. In order to simulate the thermodynamic evolution of disks, filaments, and the molecular gas surrounding clusters of young stars, we require an efficient and accurate method for solving the radiation transfer problem. We describe the implementation of a hybrid radiation transport scheme in the adaptive grid-based FLASH general magnetohydrodyanmics code. The hybrid scheme splits the radiative transport problem into a raytracing step and a diffusion step. The raytracer captures the first absorption event, as stars irradiate their environments, while the evolution of the diffuse component of the radiation field is handled by a flux-limited diffusion solver. We demonstrate the accuracy of our method through a variety of benchmark tests including the irradiation of a static disk, subcritical and supercritical radiative shocks, and thermal energy equilibration. We also demonstrate the capability of our method for casting shadows and calculating gas and dust temperatures in the presence of multiple stellar sources. Our method enables radiation-hydrodynamic studies of young stellar objects, protostellar disks, and clustered star formation in magnetized, filamentary environments.
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. (condensed matter: electronic structure, electrical, magnetic, and optical properties)
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.
In this work, we have considered the magnetic universe in non-linear electrodynamics. The Einstein field equations for non-flat FRW model have been considered when the universe is filled with the matter and magnetic field only. We have discussed the validity of the generalized second law of thermodynamics of the magnetic universe bounded by Hubble, apparent, particle and event horizons using Gibbs' law and the first law of thermodynamics for interacting and non-interacting scenarios. It has been shown that the GSL is always satisfied for Hubble, apparent and particle horizons but for event horizon, the GSL is violated initially and satisfied at late stage of the universe.
Radaydeh, Redha Mahmoud Mesleh
2010-09-01
The impact of co-channel interference and nonideal estimation of the desired user channel state information (CSI) on the performance of an adaptive threshold-based generalized transmit diversity for low-complexity multiple-input single-output configuration is investigated. The adaptation to channel conditions is assumed to be based on the desired user CSI, and the number of active transmit antennas is adjusted accordingly to guarantee predetermined target performance. To facilitate comparisons between different adaptation schemes, new analytical results for the statistics of combined signal-to-interference-plus-noise ratio (SINR) are derived, which can be applied for different fading conditions of interfering signals. Selected numerical results are presented to validate the analytical development and to compare the outage performance of the considered adaptation schemes. © 2010 IEEE.
Dai Hao; Jia Li-Xin; Zhang Yan-Bin
2012-01-01
The adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions is investigated in this paper.Based on Lyapunov stability theory and Barbalat's lemma,generalized matrix projective lag synchronization criteria are derived by using the adaptive control method.Furthermore,each network can be undirected or directed,connected or disconnected,and nodes in either network may have identical or different dynamics.The proposed strategy is applicable to almost all kinds of complex networks.In addition,numerical simulation results are presented to illustrate the effectiveness of this method,showing that the synchronization speed is sensitively influenced by the adaptive law strength,the network size,and the network topological structure.
Shishir B Sahay; T Meghasyam; Rahul K Roy; Gaurav Pooniwala; Sasank Chilamkurthy; Vikram Gadre
2015-06-01
This paper is targeted towards a general readership in signal processing. It intends to provide a brief tutorial exposure to the Fractional Fourier Transform, followed by a report on experiments performed by the authors on a Generalized Time Frequency Transform (GTFT) proposed by them in an earlier paper. The paper also discusses the extension of the uncertainty principle to the GTFT. This paper discusses some analytical results of the GTFT. We identify the eigenfunctions and eigenvalues of the GTFT. The time shift property of the GTFT is discussed. The paper describes methods for estimation of parameters of individual chirp signals on receipt of a noisy mixture of chirps. A priori knowledge of the nature of chirp signals in the mixture – linear or quadratic is required, as the two proposed methods fall in the category of model-dependent methods for chirp parameter estimation.
Generalized Uncertainty Quantification for Linear Inverse Problems in X-ray Imaging
Fowler, Michael James [Clarkson Univ., Potsdam, NY (United States)
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
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
Watanabe, Yurina; Yoshizaki, Kazuhito
2014-10-01
This study aimed to investigate the generality of conflict adaptation associated with block-wise conflict frequency between two types of stimulus scripts (Kanji and Hiragana). To this end, we examined whether the modulation of the compatibility effect with one type of script depending on block-wise conflict frequency (75% versus 25% generalized to the other type of script whose block-wise conflict frequency was kept constant (50%), using the Spatial Stroop task. In Experiment 1, 16 participants were required to identify the target orientation (up or down) presented in the upper or lower visual-field. The results showed that block-wise conflict adaptation with one type of stimulus script generalized to the other. The procedure in Experiment 2 was the same as that in Experiment 1, except that the presentation location differed between the two types of stimulus scripts. We did not find a generalization from one script to the other. These results suggest that presentation location is a critical factor contributing to the generality of block-wise conflict adaptation. PMID:25508979
Cai, Shanqing; Ghosh, Satrajit S; Guenther, Frank H; Perkell, Joseph S
2010-10-01
In order to test whether auditory feedback is involved in the planning of complex articulatory gestures in time-varying phonemes, the current study examined native Mandarin speakers' responses to auditory perturbations of their auditory feedback of the trajectory of the first formant frequency during their production of the triphthong /iau/. On average, subjects adaptively adjusted their productions to partially compensate for the perturbations in auditory feedback. This result indicates that auditory feedback control of speech movements is not restricted to quasi-static gestures in monophthongs as found in previous studies, but also extends to time-varying gestures. To probe the internal structure of the mechanisms of auditory-motor transformations, the pattern of generalization of the adaptation learned on the triphthong /iau/ to other vowels with different temporal and spatial characteristics (produced only under masking noise) was tested. A broad but weak pattern of generalization was observed; the strength of the generalization diminished with increasing dissimilarity from /iau/. The details and implications of the pattern of generalization are examined and discussed in light of previous sensorimotor adaptation studies of both speech and limb motor control and a neurocomputational model of speech motor control. PMID:20968374
Fiorenza, Alberto, E-mail: fiorenza@unina.i [Dipartimento di Costruzioni e Metodi Matematici in Architettura, Universita di Napoli, Via Monteoliveto, 3, I-80134 Napoli (Italy); Istituto per le Applicazioni del Calcolo ' Mauro Picone' , Sezione di Napoli, Consiglio Nazionale delle Ricerche, via Pietro Castellino, 111, I-80131 Napoli (Italy); Vincenzi, Giovanni, E-mail: vincenzi@unisa.i [Dipartimento di Matematica, Universita di Salerno, via Ponte Don Melillo, 4, I-84084 Fisciano, Salerno (Italy)
2011-01-15
Research highlights: We prove a result true for all linear homogeneous recurrences with constant coefficients. As a corollary of our results we immediately get the celebrated Poincare' theorem. The limit of the ratio of adjacent terms is characterized as the unique leading root of the characteristic polynomial. The Golden Ratio, Kepler limit of the classical Fibonacci sequence, is the unique leading root. The Kepler limit may differ from the unique root of maximum modulus and multiplicity. - Abstract: For complex linear homogeneous recursive sequences with constant coefficients we find a necessary and sufficient condition for the existence of the limit of the ratio of consecutive terms. The result can be applied even if the characteristic polynomial has not necessarily roots with modulus pairwise distinct, as in the celebrated Poincare's theorem. In case of existence, we characterize the limit as a particular root of the characteristic polynomial, which depends on the initial conditions and that is not necessarily the unique root with maximum modulus and multiplicity. The result extends to a quite general context the way used to find the Golden mean as limit of ratio of consecutive terms of the classical Fibonacci sequence.
Fang-Rong Yan
Full Text Available This article provides a fully bayesian approach for modeling of single-dose and complete pharmacokinetic data in a population pharmacokinetic (PK model. To overcome the impact of outliers and the difficulty of computation, a generalized linear model is chosen with the hypothesis that the errors follow a multivariate Student t distribution which is a heavy-tailed distribution. The aim of this study is to investigate and implement the performance of the multivariate t distribution to analyze population pharmacokinetic data. Bayesian predictive inferences and the Metropolis-Hastings algorithm schemes are used to process the intractable posterior integration. The precision and accuracy of the proposed model are illustrated by the simulating data and a real example of theophylline data.
Treuer, Harald; Hoevels, Moritz; Luyken, Klaus; Visser-Vandewalle, Veerle; Wirths, Jochen; Ruge, Maximilian [University Hospital Cologne, Department of Stereotaxy and Functional Neurosurgery, Cologne (Germany); Kocher, Martin [University Hospital Cologne, Department of Radiotherapy, Cologne (Germany)
2014-11-22
Stereotactic radiosurgery with an adapted linear accelerator (linac-SRS) is an established therapy option for brain metastases, benign brain tumors, and arteriovenous malformations. We intended to investigate whether the dosimetric quality of treatment plans achieved with a CyberKnife (CK) is at least equivalent to that for linac-SRS with circular or micromultileaf collimators (microMLC). A random sample of 16 patients with 23 target volumes, previously treated with linac-SRS, was replanned with CK. Planning constraints were identical dose prescription and clinical applicability. In all cases uniform optimization scripts and inverse planning objectives were used. Plans were compared with respect to coverage, minimal dose within target volume, conformity index, and volume of brain tissue irradiated with ≥ 10 Gy. Generating the CK plan was unproblematic with simple optimization scripts in all cases. With the CK plans, coverage, minimal target volume dosage, and conformity index were significantly better, while no significant improvement could be shown regarding the 10 Gy volume. Multiobjective comparison for the irradiated target volumes was superior in the CK plan in 20 out of 23 cases and equivalent in 3 out of 23 cases. Multiobjective comparison for the treated patients was superior in the CK plan in all 16 cases. The results clearly demonstrate the superiority of the irradiation plan for CK compared to classical linac-SRS with circular collimators and microMLC. In particular, the average minimal target volume dose per patient, increased by 1.9 Gy, and at the same time a 14 % better conformation index seems to be an improvement with clinical relevance. (orig.) [German] Stereotaktische Radiochirurgie mit einem adaptierten Linearbeschleuniger (Linac-SRS) ist eine erfolgreiche und etablierte Therapieoption fuer Hirnmetastasen, benigne Hirntumoren und arteriovenoese Malformationen. Ziel war es, zu untersuchen, ob die mit einem CyberKnife (CK) erreichbare
Lam, Wendy Wing Tak; Liao, Qiuyan; Wong, Jennifer Hiu Fai; Lai, Ching Lung; Yuen, Man Fung; Tsang, Janice Wing Hang; Fielding, Richard
2015-01-01
Background Illness perceptions are linked to individual help-seeking and preventive behaviors. Previous illness perception studies have identified five dimensions of illness-related experience and behaviour. The Revised Illness Perception Questionnaire (IPQ-R) for genetic predisposition (IPQ-R-GP) was developed to measure illness perceptions in those genetically-predisposed to blood disease. We adapted the IPQ-R-GP to measure perceptions of generalized cancer predisposition. This paper descri...
Yingjie WANG; Liu, Haiyuan; HAN, XUELONG; Wang, Kangan
2015-01-01
With some of the intermittent new energy and large nonlinear loads, grid voltage unbalance, harmonics, and frequency deviation are increasing year by year. The voltage source converter (VSC) is seriously affected by the various unexpected factors, and the presence of grid impedance makes the situation worse. In order to make the VSC track the nonideal grid quickly and accurately, this paper proposes a frequency-adaptive grid-virtual-flux synchronization by multiple second-order generalized in...
Ramos, Tomás; Obukhov, Yuri N
2013-01-01
We study the problem of the definition of the energy-momentum tensor of light in general moving media with linear constitutive law. Using the basic principles of classical field theory, we show that for the correct understanding of the problem, one needs to carefully distinguish situations when the material medium is modeled either as a background on which light propagates or as a dynamical part of the total system. In the former case, we prove that the (generalized) Belinfante-Rosenfeld (BR) tensor for the electromagnetic field coincides with the Minkowski tensor. We derive a complete set of balance equations for this open system and show that the symmetries of the background medium are directly related to the conservation of the Minkowski quantities. In particular, for isotropic media, the angular momentum of light is conserved despite of the fact that the Minkowski tensor is non-symmetric. For the closed system of light interacting with matter, we model the material medium as a relativistic non-dissipative...
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.
A Procedure for Controlling General Test Overlap in Computerized Adaptive Testing
Chen, Shu-Ying
2010-01-01
To date, exposure control procedures that are designed to control test overlap in computerized adaptive tests (CATs) are based on the assumption of item sharing between pairs of examinees. However, in practice, examinees may obtain test information from more than one previous test taker. This larger scope of information sharing needs to be…
Robust Adaptive Structural Control
Yang, Chi-Ming; Beck, James L.
1995-01-01
A new robust adaptive structural control design methodology is developed and presented which treats modeling uncertainties and limitations of control devices. Furthermore, no restriction is imposed on the structural models and the nature of the control devices so that the proposed method is very general. A simple linear single degree-of-freedom numerical example is presented to illustrate this approach.
Matéo, Tony; Mofid, Yassine; Grégoire, Jean-Marc; Ossant, Frédéric
In ophtalmic ultrasonography, axial B-scans are seriously deteriorated owing to the presence of the crystalline lens. This strongly aberrating medium affects both spatial and contrast resolution and causes important distortions. To deal with this issue, an adapted beamforming (BF) has been developed and experimented with a 20 MHz linear array working with a custom US research scanner. The adapted BF computes focusing delays that compensate for crystalline phase aberration, including refraction effects. This BF was tested in vitro by imaging a wire phantom through an eye phantom consisting of a synthetic gelatin lens, shaped according to the unaccommodated state of an adult human crystalline lens, anatomically set up in an appropriate liquid (turpentine) to approach the in vivo velocity ratio. Both image quality and fidelity from the adapted BF were assessed and compared with conventional delay-and-sum BF over the aberrating medium. Results showed 2-fold improvement of the lateral resolution, greater sensitivity and 90% reduction of the spatial error (from 758 μm to 76 μm) with adapted BF compared to conventional BF. Finally, promising first ex vivo axial B-scans of a human eye are presented.
Adaptive continuous time Markov chain approximation model to general jump-diffusions
Mario Cerrato; Chia Chun Lo; Konstantinos Skindilias
2011-01-01
We propose a non-equidistant Q rate matrix formula and an adaptive numerical algorithm for a continuous time Markov chain to approximate jump-diffusions with affine or non-affine functional specifications. Our approach also accommodates state-dependent jump intensity and jump distribution, a flexibility that is very hard to achieve with other numerical methods. The Kolmogorov-Smirnov test shows that the proposed Markov chain transition density converges to the one given by the likelihood expa...
This paper describes a nonlinear torque and stator flux controller for three phase induction motor (IM) drives on the basis of a space vector modulation (SVM) scheme. Using the IM fifth order model in a fixed 'ab' axis reference frame with stator currents (isa ,isb) and stator fluxes (ψsa,ψsb) as state variables, a sliding mode (SM) torque and flux controller is designed first in order to track a linear reference model, which has the desired dynamic behaviors for the IM drive system. The SM controller generates the reference voltages (vas*,vbs*) for a two level space vector modulation-pulse width modulation (SVM-PWM) inverter that feeds the motor. Then, the SM controller is combined with an adaptive input-output feedback linearization technique in order to preserve the system robustness with respect to stator and rotor resistances variations and uncertainties. Finally, simulations and comparative experimental results with the proposed control scheme are presented
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.
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.
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
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
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
Cyril R Pernet
2014-01-01
Full Text Available 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 (i model parameterization (modelling baseline or null events and scaling of the design matrix; (ii hemodynamic modelling using basis functions, and (iii computing percentage signal change. Using a simple controlled block design and an alternating block design, I first show why 'baseline' should not be modelled (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 haemodynamic model (haemodynamic 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 analysis and give some recommendations.
SafeBox: adaptable spatio-temporal generalization for location privacy protection
Sergio Mascetti
2014-08-01
Full Text Available Spatial and temporal generalization emerged in the literature as a common approach to preserve location privacy. However, existing solutions have two main shortcomings. First, spatiotemporal generalization can be used with different objectives: for example, to guarantee anonymity or to decrease the sensitivity of the location information. Hence, the strategy used to compute the generalization can follow different semantics often depending on the privacy threat, while most of the existing solutions are specifically designed for a single semantics. Second, existing techniques prevent the so-called inversion attack by adopting a top-down strategy that needs to acquire a large amount of information. This may not be feasible when this information is dynamic (e.g., position or properties of objects and needs to be acquired from external services (e.g., Google Maps. In this contribution we present a formal model of the problem that is compatible with most of the semantics proposed so far in the literature, and that supports new semantics as well. Our BottomUp algorithm for spatio-temporal generalization is compatible with the use of online services, it supports generalizations based on arbitrary semantics, and it is safe with respect to the inversion attack. By considering two datasets and two examples of semantics, we experimentally compare BottomUp with a more classical top-down algorithm, showing that BottomUp is efficient and guarantees better performance in terms of the average size (space and time of the generalized regions.
Minar Naomi Damanik-Ambarita
2016-07-01
Full Text Available The biotic integrity of the Guayas River basin in Ecuador is at environmental risk due to extensive anthropogenic activities. We investigated the potential impacts of hydromorphological and chemical variables on biotic integrity using macroinvertebrate-based bioassessments. The bioassessment methods utilized included the Biological Monitoring Working Party adapted for Colombia (BMWP-Col and the average score per taxon (ASPT, via an extensive sampling campaign that was completed throughout the river basin at 120 sampling sites. The BMWP-Col classification ranged from very bad to good, and from probable severe pollution to clean water based on the ASPT scores. Generalized linear models (GLMs and sensitivity analysis were used to relate the bioassessment index to hydromorphological and chemical variables. It was found that elevation, nitrate-N, sediment angularity, logs, presence of macrophytes, flow velocity, turbidity, bank shape, land use and chlorophyll were the key environmental variables affecting the BMWP-Col. From the analyses, it was observed that the rivers at the upstream higher elevations of the river basin were in better condition compared to lowland systems and that a higher flow velocity was linked to a better BMWP-Col score. The nitrate concentrations were very low in the entire river basin and did not relate to a negative impact on the macroinvertebrate communities. Although the results of the models provided insights into the ecosystem, cross fold model development and validation also showed that there was a level of uncertainty in the outcomes. However, the results of the models and sensitivity analysis can support water management actions to determine and focus on alterable variables, such as the land use at different elevations, monitoring of nitrate and chlorophyll concentrations, macrophyte presence, sediment transport and bank stability.
Guo, Meng; Elmedyb, Thomas Bo; Jensen, Søren Holdt; Jensen, Jesper
In this paper, we analyze a general multiple-microphone and single-loudspeaker system, where an adaptive algorithm is used to cancel acoustic feedback/echo and a beamformer processes the feedback/echo canceled signals. This system can be viewed as part of a typical hearing aid system and/or a...... traditional acoustic echo cancelation system. We introduce and derive an approximation of a useful frequency domain measure - the power transfer function - and show how to predict the system stability bound, convergence rate and the steady-state behavior across time and frequency. Furthermore, we show how the...
General strategy for adaptation of industrial processes to the use of geothermal steam
In summary, assuming that conventional criteria are fulfilled for a plant location at a geothermal field, then the following aspects should receive primary consideration: (a) Is the process energy intensive? (b) Is energy price a significant fraction of the product price? (c) Can it be adapted to geothermal steam? (d) What are the necessary process modifications? (e) What do they cost? (f)What is the cost of the steam when the steam supply system has been optimized in conjunction with the plant heat exchanger system? (g) Do the increased transport costs of raw materials to the site, and the product to market, do more than outweigh the advantages of low energy costs? The paper describes and illustrates ways to optimize these systems and illustrates what is the best potential system based on the temperature and amount of geothermal energy available
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).
D. Cariolle
2007-01-01
Full Text Available 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
陈皓文; 高爱国; 孙海青; 矫玉田
2004-01-01
The abundance of general aerobic heterotrophic bacteria(GAB) from the water and sediment in the Bering Sea and the Chukchi Sea was determined by using petri dish cultivation and counting method. The abundance of GAB among the different sea areas, sampling sites, layers of sediments surveyed and adaptability to differential temperatures was studied. The result obtained showed that: the occurrence percentage of GAB in the surface water was higher than that in sediment, but the abundance was only 0.17% of sediment. The occurrence percentage of GAB in surficial layer of sediment was higher than that in the other layers. The occurrence percentage of GAB in surficial layer of sediment was higher than that in the other layers. The occurrence percentage, abundance and its variation of GAB in the Bering Sea were higher than that in the Chukchi Sea respectively. The average value of the abundance of GAB in sediment showed a trend: roughly higher in the lower latitudinal area than higher latitude. The results from temperature test mean that: the majority of bacteria tested were cold -adapted ones, minority might be mesophilic bacteria. The results indicated that, Arctic ocean bacteria had a stronger adaptability to environmental temperature.
This report is concerned with multi-exponential fitting of a model function f(t) = Σsub(j=1)sup(n) asub(j) esup(-αsub(j)t) + eta(t) (asub(j), αsub(j) > o, 1 <= j <= n, eta(t) = a + bt) to given experimental data (tsub(k), ysub(k)), 1 <= k <= m, where the number n of exponential terms contained in (*) is not known in advance. An automatic version of the well-known manually performed peeling technique is realized and implemented in the subroutine SEARCH. This program yields the above mentioned number n and initial values for the parameters a, b, asub(j), αsub(j), 1 <= j <= n, in addition, which serve as input data for a final non-linear fitting of model (*) by a convenient non-linear fit program, e.g. VARPRO (from FORTLIB of KFA) or VA13AD (from Harwell Subroutine Library). Moreover, auxiliary programs for evaluation of f, the partial exponential terms in f, and the appertaining possibly weighted Least squares functional F, respectively, as well as subroutines for determination of the first and second partial derivatives of f and F with respect to the parameters are made accessible. Characteristic examples of multi-exponential fitting to simulated and experimental data demonstrate the efficiency of the presented method. (orig.)
ZHENG Yong-Ai
2012-01-01
Time-delay Takagi-Sugeno fuzzy drive-response dynamical networks (TD-TSFDRDNs) are defined by extending the drive-response dynamical networks. Based on the LaSalle invariant principle, a simple and systematic adaptive control scheme is proposed to synchronize the TD-TSFDRDNs with a desired scalar factor. A sufficient condition for the generalized projective synchronization in TD-TSFDRDNs is derived. Moreover, numerical simulations are provided to verify the correctness and effectiveness of the scheme.%Time-delay Takagi-Sugeno fuzzy drive-response dynamical networks (TD-TSFDRDNs) are defined by extending the drive-response dynamical networks.Based on the LaSalle invariant principle,a simple and systematic adaptive control scheme is proposed to synchronize the TD-TSFDRDNs with a desired scalar factor.A sufficient condition for the generalized projective synchronization in TD-TSFDRDNs is derived.Moreover,numerical simulations are provided to verify the correctness and effectiveness of the scheme.
Diffractive generalized phase contrast for adaptive phase imaging and optical security
Palima, Darwin; Glückstad, Jesper
2012-01-01
We analyze the properties of Generalized Phase Contrast (GPC) when the input phase modulation is implemented using diffractive gratings. In GPC applications for patterned illumination, the use of a dynamic diffractive optical element for encoding the GPC input phase allows for onthe- fly optimiza......We analyze the properties of Generalized Phase Contrast (GPC) when the input phase modulation is implemented using diffractive gratings. In GPC applications for patterned illumination, the use of a dynamic diffractive optical element for encoding the GPC input phase allows for onthe- fly...... optimization of the input aperture parameters according to desired output characteristics. For wavefront sensing, the achieved aperture control opens a new degree of freedom for improving the accuracy of quantitative phase imaging. Diffractive GPC input modulation also fits well with grating-based optical...
A Generalized Learning Curve Adapted for Purchasing and Cost Reduction Negotiations
Wong, Lam F.
2013-01-01
This paper presents the use and validation of a generalized learning curve in the economies of scale purchasing experience. The model, based on Wright’s curve, incorporates two extra degrees of freedom to accommodate initial purchases of multiple (instead of single) units and a finite asymptotic price at high volumes. The study shows that each time the part purchase quantity is doubled, the price is reduced either by a constant percentage (a learning rate) or by an approach to an asymptotic p...
Diffractive generalized phase contrast for adaptive phase imaging and optical security
Palima, Darwin; Glückstad, Jesper
2012-01-01
We analyze the properties of Generalized Phase Contrast (GPC) when the input phase modulation is implemented using diffractive gratings. In GPC applications for patterned illumination, the use of a dynamic diffractive optical element for encoding the GPC input phase allows for onthe- fly optimization of the input aperture parameters according to desired output characteristics. For wavefront sensing, the achieved aperture control opens a new degree of freedom for improving the accuracy of quan...
Monod before Monod: enzymatic adaptation, Lwoff, and the legacy of general biology.
Loison, Laurent
2013-01-01
For most of his scientific career, Jacques Monod appeared to be a man of a single problem: the formation of enzymes and the regulation of their properties. His ability to produce theoretical models led him to play a major role in both the discovery of the operon regulation and the model of allosteric transitions. The successes of Monod, from the 1950s to the Noble Prize (1965), are already well documented. In this paper, I will focus on the Monod before Monod, that is, the Monod who, during the 1940s, tried to explain the fundamental phenomenon of enzymatic adaptation. To begin with, however, I will survey how this phenomenon was discovered and explained by French Pasteurians at the very beginning of the twentieth century. This first explanation took place amidst an entrenched Lamarckian atmosphere in French thought, which was still alive during the 1920s and the 1930s, when Monod commenced the study of biology at the Sorbonne. Because of his will to construct a scientific biology free from teleology, Monod always tried to break from the legacy of this traditional background of Lamarckism, and he consequently developed ways of thinking that, in the main, were not part of the French biological tradition. Nevertheless, one point did link Monod to French history: his fruitful interactions with André Lwoff. As we shall see, these interactions were necessary for the development of Monod's science, both technically and intellectually speaking. PMID:24466631
Nikitin, A. G.
2004-01-01
Group classification of the generalized complex Ginzburg-Landau equations is presented. An approach to group classification of systems of reaction-diffusion equations with general diffusion matrix is developed.
Ramos, Tomás; Rubilar, Guillermo F.; Obukhov, Yuri N.
2013-01-01
We study the problem of the definition of the energy-momentum tensor of light in general moving non-dispersive media with linear constitutive law. Using the basic principles of classical field theory, we show that for the correct understanding of the problem, one needs to carefully distinguish situations when the material medium is modeled either as a background on which light propagates or as a dynamical part of the total system. In the former case, we prove that the (generalized) Belinfante...
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 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 water)) to the Irr-Cad model accounted for over 79% of the variation in mean Field Order bio-available (DTPA (diethylenetriaminepentaacetic acid)-extractable) soil Cd. Rice is the staple food of countries of the Greater Mekong Sub-region (includes Vietnam, Myanmar, Lao PDR, Thailand and Yunnan Province, China). These countries also have actively and historically mined Zn, Pb, and Cu deposits where Cd is likely to be a potential hazard if un-controlled discharge/runoff enters areas of rice cultivation. As such, it is envisaged that the Irr-Cad model could be applied for Cd hazard assessment and effectively form the basis of intervention options and policy decisions to protect public health, livelihoods, and export security. PMID:18311588
A Generalized Learning Curve Adapted for Purchasing and Cost Reduction Negotiations
Lam F. Wong
2013-01-01
Full Text Available This paper presents the use and validation of a generalized learning curve in the economies of scale purchasing experience. The model, based on Wright’s curve, incorporates two extra degrees of freedom to accommodate initial purchases of multiple (instead of single units and a finite asymptotic price at high volumes. The study shows that each time the part purchase quantity is doubled, the price is reduced either by a constant percentage (a learning rate or by an approach to an asymptotic plateau rate indicating a point of diminishing returns. Supplier price quotations at multiple purchase quantities were obtained for a pool of 17 critical parts. The data were fitted with the generalized learning curve by the method of least squares regression. The regressed learning rate, first unit price, and the asymptotic price can be used to infer supplier pricing strategies. Coupled with a “should-cost” analysis based on estimates of standard time and material, a system cost reduction task was carried out by the supply chain organization.
SHEN Shou-Feng; ZHANG Jun; PAN Zu-Liang
2005-01-01
By using a Backlund transformation and the multi-linear variable separation approach, we find a new generalsolution ofa (2+1)-dimensional generalization of the nonlinear Schrodinger system. The new "universal" formula is defined, and then, rich coherent structures can be found by selecting corresponding functions appropriately.
有限域Fnq上的广义线性正形置换计数%Numeration of generalized linear orthomorphisms over the galois field Fnq
韩海清; 张焕国
2013-01-01
The orthomorphisms have good cryptologic properties on the finite field and are widely used.This paper generalizes the concept of orthomorphisms,and obtains the counting formula on the generalized linear orthomorphisms over the Galois field with the arbitrary prime number pas the characteristic and the partial generation algorithm of generalized linear orthomorphisms.The counting formula of the linear orthomorphisms over the finite field with characteristic 2 is the special case in this paper.Furthermore,the generalized linear orthomorphisms that this paper has generated can gain the maximum branch number when they are designed as P-permutations.%有限域上的正形置换具有良好密码学性质,被广泛应用.本文推广了正形置换的概念,得到了一般有限域上广义线性正形置换的计数公式和部分广义线性正形置换的生成算法.以2为特征的有限域上线性正形置换计数公式是本文公式的特例.本文生成的广义线性正形置换用于设计P-置换时,分支数可以达到最优.
Lundahl, P. Johan
2011-01-01
This article presents a new design of flow-orientation device for the study of bio-macromolecules, including DNA and protein complexes, as well as aggregates such as amyloid fibrils and liposome membranes, using Linear Dichroism (LD) spectroscopy. The design provides a number of technical advantages that should make the device inexpensive to manufacture, easier to use and more reliable than existing techniques. The degree of orientation achieved is of the same order of magnitude as that of the commonly used concentric cylinders Couette flow cell, however, since the device exploits a set of flat strain-free quartz plates, a number of problems associated with refraction and birefringence of light are eliminated, increasing the sensitivity and accuracy of measurement. The device provides similar shear rates to those of the Couette cell but is superior in that the shear rate is constant across the gap. Other major advantages of the design is the possibility to change parts and vary sample volume and path length easily and at a low cost. © 2011 The Royal Society of Chemistry.
Zhang, Yan-jun; Liu, Wen-zhe; Fu, Xing-hu; Bi, Wei-hong
2015-10-01
According to the high precision extracting characteristics of scattering spectrum in Brillouin optical time domain reflection optical fiber sensing system, this paper proposes a new algorithm based on flies optimization algorithm with adaptive mutation and generalized regression neural network. The method takes advantages of the generalized regression neural network which has the ability of the approximation ability, learning speed and generalization of the model. Moreover, by using the strong search ability of flies optimization algorithm with adaptive mutation, it can enhance the learning ability of the neural network. Thus the fitting degree of Brillouin scattering spectrum and the extraction accuracy of frequency shift is improved. Model of actual Brillouin spectrum are constructed by Gaussian white noise on theoretical spectrum, whose center frequency is 11.213 GHz and the linewidths are 40-50, 30-60 and 20-70 MHz, respectively. Comparing the algorithm with the Levenberg-Marquardt fitting method based on finite element analysis, hybrid algorithm particle swarm optimization, Levenberg-Marquardt and the least square method, the maximum frequency shift error of the new algorithm is 0.4 MHz, the fitting degree is 0.991 2 and the root mean square error is 0.024 1. The simulation results show that the proposed algorithm has good fitting degree and minimum absolute error. Therefore, the algorithm can be used on distributed optical fiber sensing system based on Brillouin optical time domain reflection, which can improve the fitting of Brillouin scattering spectrum and the precision of frequency shift extraction effectively. PMID:26904844
In this paper, we propose an adaptive strategy based on the linear prediction of queue length to minimize congestion in Barabási—Albert (BA) scale-free networks. This strategy uses local knowledge of traffic conditions and allows nodes to be able to self-coordinate their accepting probability to the incoming packets. We show that the strategy can delay remarkably the onset of congestion and systems avoiding the congestion can benefit from hierarchical organization of accepting rates of nodes. Furthermore, with the increase of prediction orders, we achieve larger values for the critical load together with a smooth transition from free-flow to congestion. (interdisciplinary physics and related areas of science and technology)
Blasone, Roberta-Serena; Vrugt, Jasper A.; Madsen, Henrik;
2008-01-01
estimate of the associated uncertainty. This uncertainty arises from incomplete process representation, uncertainty in initial conditions, input, output and parameter error. The generalized likelihood uncertainty estimation (GLUE) framework was one of the first attempts to represent prediction uncertainty...... within the context of Monte Carlo (MC) analysis coupled with Bayesian estimation and propagation of uncertainty. Because of its flexibility, ease of implementation and its suitability for parallel implementation on distributed computer systems, the GLUE method has been used in a wide variety of...... applications. However, the MC based sampling strategy of the prior parameter space typically utilized in GLUE is not particularly efficient in finding behavioral simulations. This becomes especially problematic for high-dimensional parameter estimation problems, and in the case of complex simulation models...
Pérez-Páramo María
2010-01-01
Full Text Available Abstract Background Generalized anxiety disorder (GAD is a prevalent mental health condition which is underestimated worldwide. This study carried out the cultural adaptation into Spanish of the 7-item self-administered GAD-7 scale, which is used to identify probable patients with GAD. Methods The adaptation was performed by an expert panel using a conceptual equivalence process, including forward and backward translations in duplicate. Content validity was assessed by interrater agreement. Criteria validity was explored using ROC curve analysis, and sensitivity, specificity, predictive positive value and negative value for different cut-off values were determined. Concurrent validity was also explored using the HAM-A, HADS, and WHO-DAS-II scales. Results The study sample consisted of 212 subjects (106 patients with GAD with a mean age of 50.38 years (SD = 16.76. Average completion time was 2'30''. No items of the scale were left blank. Floor and ceiling effects were negligible. No patients with GAD had to be assisted to fill in the questionnaire. The scale was shown to be one-dimensional through factor analysis (explained variance = 72%. A cut-off point of 10 showed adequate values of sensitivity (86.8% and specificity (93.4%, with AUC being statistically significant [AUC = 0.957-0.985; p 0.001. Limitations Elderly people, particularly those very old, may need some help to complete the scale. Conclusion After the cultural adaptation process, a Spanish version of the GAD-7 scale was obtained. The validity of its content and the relevance and adequacy of items in the Spanish cultural context were confirmed.
Yazdanpanah, R.; Soltani, J. [Faculty of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan (Iran); Arab Markadeh, G.R. [Department of Electrical Engineering, Technical Faculty, Shahrekord University, Shahrekord (Iran)
2008-04-15
This paper describes a nonlinear torque and stator flux controller for three phase induction motor (IM) drives on the basis of a space vector modulation (SVM) scheme. Using the IM fifth order model in a fixed 'ab' axis reference frame with stator currents (i{sub sa},i{sub sb}) and stator fluxes ({psi}{sub sa},{psi}{sub sb}) as state variables, a sliding mode (SM) torque and flux controller is designed first in order to track a linear reference model, which has the desired dynamic behaviors for the IM drive system. The SM controller generates the reference voltages (v{sub as}{sup *},v{sub bs}{sup *}) for a two level space vector modulation-pulse width modulation (SVM-PWM) inverter that feeds the motor. Then, the SM controller is combined with an adaptive input-output feedback linearization technique in order to preserve the system robustness with respect to stator and rotor resistances variations and uncertainties. Finally, simulations and comparative experimental results with the proposed control scheme are presented. (author)
Futao Guo; Guangyu Wang; John L Innes; Xiangqing Ma; Long Sun; Haiqing Hu
2015-01-01
The purpose of this study was to determine a suitable model for investigating the effects of climate factors on the area burned by forest fire in the Tahe forest region, Daxing’an Mountains, in northeast China. The response variables were the area burned by lightning-caused fire, human-caused fire, and total burned area. The predictor variables were nine climate variables collected from the local weather station. Three regression models were utilized, including multiple linear regression, log-linear model (log-transformation on both response and predictor variables), and gamma-generalized linear model. The goodness-of-fit of the models were compared based on model fitting statistics such as R2, AIC, and RMSE. The results revealed that the gamma-generalized linear model was generally superior to both multiple linear regression model and log-linear model for fitting the fire data. Further, the best models were selected based on the criteria that the climate variables were statistically significant at a=0.05. The gamma best models indicated that maximum wind speed, precipitation, and days that rainfall greater than 0.1 mm had significant impacts on the area burned by the lightning-caused fire, while the mean temperature and minimum relative humidity were the main drivers of the burned area caused by human activities. Overall, the total burned area by forest fire was significantly influenced by days that rainfall greater than 0.1 mm and minimum rela-tive humidity, indicating that the moisture condition of forest stands determine the burned area by forest fire.
彭艳; 刘梅; 罗均; 谢少荣
2013-01-01
研究无人旋翼机器人在干扰情况下的航向控制问题.无人旋翼机航向动力学包含输入非线性、时变参数和主-尾旋翼之间的强耦合,难以建立精确的数学模型,并且易受外部扰动影响,很难达到良好的控制性能.针对这一问题提出基于线性自抗扰控制(linear adaptive disturbance rejection control,LADRC)的航向控制方法,通过设计扩张线性状态观测器对未知模型和外界干扰进行实时估计并进行在线补偿.以自主研制的无人旋翼机为例,建立其航向动力学方程,把通道间的交叉耦合影响视为不确定扰动,将其与外部干扰作为扩张状态,利用观测器带宽确定观测器增益,设计线性扩张状态观测器来跟踪各阶扩张状态变量,为说明LADRC的有效性,选用PD控制为非线性状态误差反馈控制律实现航向控制.仿真以及试验结果表明在外部扰动或模型结构参数发生变化时控制器仍可获得理想的动态性能,具有很好的适应性和鲁棒性.%The yaw tracking problem of unmanned rotorcraft under disturbance condition is studied.The yaw dynamics of unmanned rotorcraft involves input nonlinearity,time-varying parameters and the strong coupling between main and tail rotors,which is difficult to establish an accurate mathematic model and vulnerable to external disturbance.All of these make it difficult to have a good tracking performance.Aiming at these problems,a yaw tracking control method based on linear adaptive disturbance rejection control (LADRC) is presented.The linear extended state observer (LESO) is designed to implement the real-time estimation and online compensation of the unknown model and external disturbance.The self-made unmanned rotorcraft is studied using this method;The yaw dynamics model is built up;then the cross coupling effect between channels is taken as the uncertain disturbance,which is combined with the external disturbance and both of them are taken as the
Ozol-Godfrey, Ayca
2004-01-01
Graphical summaries are becoming important tools for evaluating designs. The need to compare designs in term of their prediction variance properties advanced this development. A recent graphical tool, the Fraction of Design Space plot, is useful to calculate the fraction of the design space where the scaled prediction variance (SPV) is less than or equal to a given value. In this dissertation we adapt FDS plots, to study three specific design problems: robustness to model assumptions, robustn...
Monteiro, Giselle Antunes; Tvrdý, Milan
-, č. 71 (2014), s. 1-18. ISSN 1687-2770 R&D Projects: GA ČR(CZ) GA14-06958S Institutional support: RVO:67985840 Keywords : abstract generalized differential equation * continuous dependence * time scale dynamics Subject RIV: BA - General Mathematics Impact factor: 1.014, year: 2014 http://www.boundaryvalueproblems.com/content/2014/1/71
This paper aims at presenting the methodology and the non-linear modelling used for the seismic evaluation of existing nuclear facilities. The main problems to be solved during the seismic assessment of existing facilities regarding the non linear modeling such as the definition of damage index according safety requirements are discussed in this paper. Push over and time history analysis have been conducted with a non linear fibre type beam element and have shown the capability of such modeling to reproduce the complex physical phenomena which can occur during a seismic loading. These non linear models allow to estimate the local flexural ductility demand and check the shear demand in the main structural members. The good agreement between numerical and experimental results for the 2 different structures studied in the present paper must not make forbidden the difficulties in the application of such modeling to the seismic assessment of existing structure. One of the main difficulties is the definition of the damage index: what can be defined as failure?, for which level of damage are designed the structures such as the nuclear facilities? Furthermore the lack of common rules for the seismic assessment of nuclear facilities make problematic the choice of some modeling parameters such as the damping matrix, the strength of the brittle failure mechanisms, the influence of the construction details on the modeling parameters, etc Documents similar to the FEMA 273 Guidelines might be developed specifically for the nuclear facilities. Finally the approaches applied to the study of the 2 structures remains deterministic. A next step is to determine the fragility curve of the structure and compute a failure probability by convoluting fragility and seismic hazard. Although this approach is very attractive in term of safety analysis, one must not forget the problem carried out by the new parameters to introduce: enough experimental results are they available to determine the
Elayaraja Aruchunan; Jumat Sulaiman
2010-01-01
Problem statement: This research purposely brought up to solve complicated equations such as partial differential equations, integral equations, Integro-Differential Equations (IDE), stochastic equations and others. Many physical phenomena contain mathematical formulations such integro-differential equations which are arise in fluid dynamics, biological models and chemical kinetics. In fact, several formulations and numerical solutions of the linear Fredholm integro-differential equation of s...
Eusebio Eduardo Hernández Martinez
2013-01-01
Full Text Available In robotics, solving the direct kinematics problem (DKP for parallel robots is very often more difficult and time consuming than for their serial counterparts. The problem is stated as follows: given the joint variables, the Cartesian variables should be computed, namely the pose of the mobile platform. Most of the time, the DKP requires solving a non‐linear system of equations. In addition, given that the system could be non‐convex, Newton or Quasi‐Newton (Dogleg based solvers get trapped on local minima. The capacity of such kinds of solvers to find an adequate solution strongly depends on the starting point. A well‐known problem is the selection of such a starting point, which requires a priori information about the neighbouring region of the solution. In order to circumvent this issue, this article proposes an efficient method to select and to generate the starting point based on probabilistic learning. Experiments and discussion are presented to show the method performance. The method successfully avoids getting trapped on local minima without the need for human intervention, which increases its robustness when compared with a single Dogleg approach. This proposal can be extended to other structures, to any non‐linear system of equations, and of course, to non‐linear optimization problems.
广义线性热弹性力学模型的唯一性%Uniqueness for the Linear Theory of Generalized Thermo-Elasticity
郭兴明
2000-01-01
In this paper, the uniqueness for the linear theory of a new generalized thermo-elastic model of the continuum with the centre symmetry is shown under less assumptions, whose constitutive equations contain deformation, temperature and its rate as well as its gradient, electric field and its gradient. So the phase variation is pemitted when the deformation proceeds as long as the constitutive equations preserve their original forms.
This paper describes the generalized inverse method which is used for the inversion of resistivity sounding. The technique has been applied to a resistivity soundings survey whose stations are selected in the zones of resistivity anomalies over an archaeological site. The site covers an area of 0.75 square kilometer in the north-western part of Madagascar, bordering the east Mozambic Channel. The one-dimensional (1-D) model fits well the observed data and the generalized linear inverse method appears to be a powerful scheme with which to interpret resistivity sounding data. (author). 29 refs, 6 figs
Wallace Moreira Bessa
2010-04-01
Full Text Available This paper presents a detailed discussion about the convergence properties of a variable structure controller for uncertain single-input-single-output nonlinear systems (SISO. The adopted approach is based on the sliding mode control strategy and enhanced by an adaptive fuzzy algorithm to cope with modeling inaccuracies and external disturbances that can arise. The boundedness of all closed-loop signals and the convergence properties of the tracking error are analytically proven using Lyapunov's direct method and Barbalat's lemma. This result corrects flawed conclusions previously reached in the literature. An application of this adaptive fuzzy sliding mode controller to a second-order nonlinear system is also presented. The obtained numerical results demonstrate the improved control system performance.Este trabalho apresenta uma discussão detalhada acerca das propriedades de convergência de um controlador à estrutura variável para sistemas incertos com uma entrada e uma saída (SISO. A abordagem adotada baseia-se na estratégia de controle por modos deslizantes e incorpora um algoritmo difuso adaptativo para compensar imprecisões de modelagem e perturbações externas que possam ocorrer. A limitação de todos os sinais do sistema em malha-fechada e as propriedades de convergência do erro de rastreamento são demonstradas analiticamente através do método direto de Liapunov e do lema de Barbalat. Este resultado corrige conclusões errôneas apresentadas anteriormente na literatura. Uma aplicação do controlador por modos deslizantes difuso adaptativo em um sistema não-linear de segunda ordem também é discutida. Os resultados obtidos numericamente confirmam o desempenho do controlador.
The piecewise analytical perturbation series (PAPS) approach to the solution of linear ordinary differential equations is shown to provide both PAPS algorithms and equations for the accumulated discretization errors, each of which involving the same set of propagators, a property which is very important for the implementation of PAPS codes with global control over the discretization error. Finite, simple and flexible PAPS algorithms can be derived provided the coefficients of the given differential equation are approximated by trucated piecewise Legendre series. Theoretical realization of such algorithms are outlined and their orders of accuracy are established
William Alvarez Gaviria
2004-09-01
Full Text Available The origin of climacteric has been subject of debate. Most opinions agree in that it arises exclusively from natural selection. In this paper the author argues that, besides this reason there is another, even more important; for him, climacteric is the final response to fatigue or the third stage of the general adaptation syndrome, just as in elderly people there is a loss of the capacity of proliferation of fibroblasts and lack of response to insulin. From a genetic point of view, this corresponds to an antagonic pleiotropy: the genetic program that has made the human adrenergic and corticotropic systems hyperactive, has also caused that they do not reach senescence intact. High concentrations of stress hormones during youth and adulthood in humans, as compared to chimpanzees, gorillas and orangutans, and the hormonal cascade reactions elicited by them are meaningfully related to our most conspicuous illnesses, our genotype/phenotype and, in the long term, with climacteric. Se ha conjeturado a menudo sobre las razones del climaterio y la mayoría de los autores sostiene que es un fenómeno que surge exclusivamente de la selección natural. Aquí asumimos que, aunque esa sea parte de la explicación, no es la razón primordial. Así como con la edad se da la pérdida, por ejemplo, de la capacidad proliferativa de los fibroblastos y de la sensibilidad a la insulina, el climaterio podría corresponder no más que a la fatiga o tercera etapa del Síndrome de Adaptación General. En un enfoque genético correspondería, pues, a una pleiotropía antagónica: el programa genético que ha hecho hiperactivos a los sistemas adrenérgico y corticotrópico del ser humano, evitaría también que llegara incólume al punto final de senescencia. Las altas concentraciones de hormonas de estrés en la juventud y la edad adulta que distinguen a nuestra especie, comparada con el chimpancé, el gorila y el orangután, y las reacciones hormonales en cascada que
Darzi, Soodabeh; Kiong, Tiong Sieh; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Salem, Balasem
2014-01-01
Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program. PMID:25147859
Nikitin, A. G.
2005-01-01
Group classification of systems of two coupled nonlinear reaction-diffusion equation with a general diffusion matrix started in papers math-ph/0411027,, math-ph/0411028 is completed in present paper where all non-equivalent equations with triangular diffusion matrix are classified. In addition, symmetries of diffusion systems with nilpotent diffusion matrix and additional first order derivative terms are described.
Gabrielli, Alessandro [INFN and Physics Department, University of Bologna, Viale Berti Pichat 6/2, 40127 Bologna (Italy)], E-mail: alessandro.gabrielli@bo.infn.it; Loddo, Flavio; Ranieri, Antonio; De Robertis, Giuseppe [INFN and Physics Department, University of Bari, Via E. Orabona 4, 70126 Bari (Italy)
2008-10-21
This work is aimed at defining the architecture of a new digital ASIC, namely Slow-Control Adapter (SCA), which will be designed in a commercial 130-nm CMOS technology. This chip will be embedded within a high-speed data acquisition optical link (GBT) to control and monitor the front-end electronics in future high-energy physics experiments. The GBT link provides a transparent transport layer between the SCA and control electronics in the counting room. The proposed SCA supports a variety of common bus protocols to interface with end-user general-purpose electronics. Between the GBT and the SCA a standard 100 Mb/s IEEE-802.3 compatible protocol will be implemented. This standard protocol allows off-line tests of the prototypes using commercial components that support the same standard. The project is justified because embedded applications in modern large HEP experiments require particular care to assure the lowest possible power consumption, still offering the highest reliability demanded by very large particle detectors.
This work is aimed at defining the architecture of a new digital ASIC, namely Slow-Control Adapter (SCA), which will be designed in a commercial 130-nm CMOS technology. This chip will be embedded within a high-speed data acquisition optical link (GBT) to control and monitor the front-end electronics in future high-energy physics experiments. The GBT link provides a transparent transport layer between the SCA and control electronics in the counting room. The proposed SCA supports a variety of common bus protocols to interface with end-user general-purpose electronics. Between the GBT and the SCA a standard 100 Mb/s IEEE-802.3 compatible protocol will be implemented. This standard protocol allows off-line tests of the prototypes using commercial components that support the same standard. The project is justified because embedded applications in modern large HEP experiments require particular care to assure the lowest possible power consumption, still offering the highest reliability demanded by very large particle detectors.
In [1] (Z. Tsuboi, Nucl. Phys. B 826 (2010) 399, (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:1010.2720)) in relation to the AdS5/CFT4 spectral problem
Junfeng Yin; Quanyu Dou
2012-01-01
In this paper,a generalized preconditioned Hermitian and skew-Hermitian splitting (GPHSS) iteration method for a non-Hermitian positive-definite matrix is studied,which covers standard Hermitian and skew-Hermitian splitting (HSS) iteration and also many existing variants.Theoretical analysis gives an upper bound for the spectral radius of the iteration matrix.From practical point of view,we have analyzed and implemented inexact generalized preconditioned Hermitian and skew-Hermitian splitting (IGPHSS) iteration,which employs Krylov subspace methods as its inner processes.Numerical experiments from three-dimensional convection-diffusion equation show that the GPHSS and IGPHSS iterations are efficient and competitive with standard HSS iteration and AHSS iteration.
张银兵; 赵俊渭; 郭业才; 李金明
2011-01-01
The problem of blind adaptive equalization of underwater single-input multiple-output (SIMO)acoustic channels was analyzed by using the linear prediction method. Minimum mean square error (MMSE)blind equalizers with arbitrary delay were described on a basis of channel identification. Two methods for calculating linear MMSE equalizers were proposed. One was based on full channel identification and realized using RLS adaptive algorithms, and the other was based on the zero-delay MMSE equalizer and realized using LMS and RLS adaptive algorithms, respectively. Performance of the three proposed algorithms and comparison with two existing zero-forcing (ZF) equalization algorithms were investigated by simulations utilizing two underwater acoustic channels. The results show that the proposed algorithms are robust enough to channel order mismatch. They have almost the same performance as the corresponding ZF algorithms under a high signal-to-noise (SNR) ratio and better performance under a low SNR.
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.
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.
Amini, Nina H. [Stanford University, Edward L. Ginzton Laboratory, Stanford, CA (United States); CNRS, Laboratoire des Signaux et Systemes (L2S) CentraleSupelec, Gif-sur-Yvette (France); Miao, Zibo; Pan, Yu; James, Matthew R. [Australian National University, ARC Centre for Quantum Computation and Communication Technology, Research School of Engineering, Canberra, ACT (Australia); Mabuchi, Hideo [Stanford University, Edward L. Ginzton Laboratory, Stanford, CA (United States)
2015-12-15
The purpose of this paper is to study the problem of generalizing the Belavkin-Kalman filter to the case where the classical measurement signal is replaced by a fully quantum non-commutative output signal. We formulate a least mean squares estimation problem that involves a non-commutative system as the filter processing the non-commutative output signal. We solve this estimation problem within the framework of non-commutative probability. Also, we find the necessary and sufficient conditions which make these non-commutative estimators physically realizable. These conditions are restrictive in practice. (orig.)
Lieberman, Lauren; Lucas, Mark; Jones, Jeffery; Humphreys, Dan; Cody, Ann; Vaughn, Bev; Storms, Tommie
2013-01-01
"Helping General Physical Educators and Adapted Physical Educators Address the Office of Civil Rights Dear Colleague Guidance Letter: Part IV--Sport Groups" provides the the following articles: (1) "Sport Programming Offered by Camp Abilities and the United States Association for Blind Athletes" (Lauren Lieberman and Mark…
Wendy Wing Tak Lam
Full Text Available Illness perceptions are linked to individual help-seeking and preventive behaviors. Previous illness perception studies have identified five dimensions of illness-related experience and behaviour. The Revised Illness Perception Questionnaire (IPQ-R for genetic predisposition (IPQ-R-GP was developed to measure illness perceptions in those genetically-predisposed to blood disease. We adapted the IPQ-R-GP to measure perceptions of generalized cancer predisposition. This paper describes the development and validation of the Cancer Predisposition Perception Scale (CPPS.The draft CPPS scale was first administered to 167 well Hepatitis B carriers and 123 other healthy individuals and the factor structure was examined using Exploratory Factor Analysis. Then the factor structure was confirmed in a second sample comprising 148 healthy controls, 150 smokers and 152 passive smokers using Confirmatory Factor Analysis (CFA.Six-factors comprising 26 items provided optimal fit by eigen and scree-plot methods, accounting for 58.9% of the total variance. CFA indicated good fit of the six-factor model after further excluding three items. The six factors, Emotional representation (5 items, Illness coherence (4 items, Treatment control (3 items, Consequences (5 items, Internal locus of control (2 items and External locus of control (4 items demonstrated adequate-to-good subscale internal consistency (Cronbach's α = 0.63-0.90. Divergent validity was suggested by low correlations with optimism, self-efficacy, and scales for measuring physical and psychological health symptoms.The CPPS appears to be a valid measure of perceived predisposition to generic cancer risks and can be used to examine cancer-risk-related cognitions in individuals at higher and lower cancer risk.
Some Conditions for Matrices over an Incline To Be Invertible and General Linear Group on an Incline
Song Chol HAN; Hong Xing LI
2005-01-01
Inclines are the additively idempotent semirings in which products are less than or equal to either factor. In this paper, some necessary and sufficient conditions for a matrix over L to be invertible are given, where L is an incline with 0 and 1. Also it is proved that L is an integral incline if and only if GLn(L) ＝ PLn(L) for any n (n ≥ 2), in which GLn(L) is the group of all n × n invertible matrices over L and PLn (L) is the group of all n × n permutation matrices over L. These results should be regarded as the generalizations and developments of the previous results on the invertible matrices over a distributive lattice.
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, E-mail: flippari@uni-mainz.de [Sorbonne Universités, UPMC Univ. Paris 06, UMR 7598, Laboratoire Jacques-Louis Lions, F-75005 Paris (France); Sorbonne Universités, UPMC Univ. Paris 06, UMR 7616, Laboratoire de Chimie Théorique, F-75005 Paris (France); Sorbonne Universités, UPMC Univ. Paris 06, Institut du Calcul et de la Simulation, F-75005 Paris (France); Scalmani, Giovanni; Frisch, Michael J. [Gaussian, Inc., 340 Quinnipiac St. Bldg. 40, Wallingford, Connecticut 06492 (United States); Lagardère, Louis [Sorbonne Universités, UPMC Univ. Paris 06, Institut du Calcul et de la Simulation, F-75005 Paris (France); Stamm, Benjamin [Sorbonne Universités, UPMC Univ. Paris 06, UMR 7598, Laboratoire Jacques-Louis Lions, F-75005 Paris (France); CNRS, UMR 7598 and 7616, F-75005 Paris (France); Cancès, Eric [Université Paris-Est, CERMICS, Ecole des Ponts and INRIA, 6 and 8 avenue Blaise Pascal, 77455 Marne-la-Vallée Cedex 2 (France); Maday, Yvon [Sorbonne Universités, UPMC Univ. Paris 06, UMR 7598, Laboratoire Jacques-Louis Lions, F-75005 Paris (France); Institut Universitaire de France, Paris, France and Division of Applied Maths, Brown University, Providence, Rhode Island 02912 (United States); Piquemal, Jean-Philip [Sorbonne Universités, UPMC Univ. Paris 06, UMR 7616, Laboratoire de Chimie Théorique, F-75005 Paris (France); CNRS, UMR 7598 and 7616, F-75005 Paris (France); Mennucci, Benedetta [Dipartimento di Chimica e Chimica Industriale, Università di Pisa, Via Risorgimento 35, 56126 Pisa (Italy)
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.
Larson, Michael J; Clayson, Peter E; Keith, Cierra M; Hunt, Isaac J; Hedges, Dawson W; Nielsen, Brent L; Call, Vaughn R A
2016-03-01
Older adults display alterations in neural reflections of conflict-related processing. We examined response times (RTs), error rates, and event-related potential (ERP; N2 and P3 components) indices of conflict adaptation (i.e., congruency sequence effects) a cognitive control process wherein previous-trial congruency influences current-trial performance, along with post-error slowing, correct-related negativity (CRN), error-related negativity (ERN) and error positivity (Pe) amplitudes in 65 healthy older adults and 94 healthy younger adults. Older adults showed generalized slowing, had decreased post-error slowing, and committed more errors than younger adults. Both older and younger adults showed conflict adaptation effects; magnitude of conflict adaptation did not differ by age. N2 amplitudes were similar between groups; younger, but not older, adults showed conflict adaptation effects for P3 component amplitudes. CRN and Pe, but not ERN, amplitudes differed between groups. Data support generalized declines in cognitive control processes in older adults without specific deficits in conflict adaptation. PMID:26802364
Harko, T
2016-01-01
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 equa...
Ramos, Tomás; Rubilar, Guillermo F.; Obukhov, Yuri N.
2015-02-01
We study the problem of the definition of the energy-momentum tensor of light in general moving non-dispersive media with linear constitutive law. Using the basic principles of classical field theory, we show that for the correct understanding of the problem, one needs to carefully distinguish situations when the material medium is modeled either as a background on which light propagates or as a dynamical part of the total system. In the former case, we prove that the (generalized) Belinfante-Rosenfeld (BR) tensor for the electromagnetic field coincides with the Minkowski tensor. We derive a complete set of balance equations for this open system and show that the symmetries of the background medium are directly related to the conservation of the Minkowski quantities. In particular, for isotropic media, the angular momentum of light is conserved despite of the fact that the Minkowski tensor is non-symmetric. For the closed system of light interacting with matter, we model the material medium as a relativistic non-dissipative fluid and we prove that it is always possible to express the total BR tensor of the closed system either in the Abraham or in the Minkowski separation. However, in the case of dynamical media, the balance equations have a particularly convenient form in terms of the Abraham tensor. Our results generalize previous attempts and provide a first principles basis for a unified understanding of the long-standing Abraham-Minkowski controversy without ad hoc arguments.
Stochl, Jan; Böhnke, Jan R.; Pickett, Kate E.; Croudace, Tim J.
2016-01-01
BACKGROUND: Recent developments in psychometric modeling and technology allow pooling well-validated items from existing instruments into larger item banks and their deployment through methods of computerized adaptive testing (CAT). Use of item response theory-based bifactor methods and integrative data analysis overcomes barriers in cross-instrument comparison. This paper presents the joint calibration of an item bank for researchers keen to investigate population variations in general psych...
Stochl, Jan; Böhnke, Jan R.; Pickett, Kate E.; Croudace, Tim J.
2016-01-01
Background Recent developments in psychometric modeling and technology allow pooling well-validated items from existing instruments into larger item banks and their deployment through methods of computerized adaptive testing (CAT). Use of item response theory-based bifactor methods and integrative data analysis overcomes barriers in cross-instrument comparison. This paper presents the joint calibration of an item bank for researchers keen to investigate population variations in general psycho...
Soltanian-Zadeh, Hamid; Windham, Joe P.
1992-04-01
Maximizing the minimum absolute contrast-to-noise ratios (CNRs) between a desired feature and multiple interfering processes, by linear combination of images in a magnetic resonance imaging (MRI) scene sequence, is attractive for MRI analysis and interpretation. A general formulation of the problem is presented, along with a novel solution utilizing the simple and numerically stable method of Gram-Schmidt orthogonalization. We derive explicit solutions for the case of two interfering features first, then for three interfering features, and, finally, using a typical example, for an arbitrary number of interfering feature. For the case of two interfering features, we also provide simplified analytical expressions for the signal-to-noise ratios (SNRs) and CNRs of the filtered images. The technique is demonstrated through its applications to simulated and acquired MRI scene sequences of a human brain with a cerebral infarction. For these applications, a 50 to 100% improvement for the smallest absolute CNR is obtained.
Ana Milstein
1980-12-01
Full Text Available The material analysed here is part of a collection made with a 91 van Dorn bottle in a 5 m depth station at Ubatuba - São Pauto, between June 1976 and May 1977. Two sets of general linear models were constructed, one for the winter months (June, July and August and the other for the summer ones (December, January and February. In each set, copepodid stages and adults were studied separately, in relation to hour of sampling, depth, interaction hour-depth, and environmental factors (light penetration, dissolved oxygen, salinity and temperature, considered as a whole. All the stages studied were spread in the water column and show no vertical migration. The hour was not significant for any stage except for copepodid V female in winter. This results agree with a previous analysis of the same material performed with the data of each two months throughout the year (Milstein, 1978.
On Comparison of Adaptive Regularization Methods
Sigurdsson, Sigurdur; Larsen, Jan; Hansen, Lars Kai
2000-01-01
, a very flexible regularization may substitute the need for selection procedures. This paper investigates recently suggested adaptive regularization schemes. Some methods focus directly on minimizing an estimate of the generalization error (either algebraic or empirical), whereas others start from...... is presented. Next, different adaptive regularization schemes are reviewed and extended. Finally, the experimental section presents a comparative study concerning linear models for regression/time series problems....
In this paper we study the design and implementation of adaptive Generalized Projective Synchronization (GPS) problem between two chaotic circuits (master and slave) via a scalar transmitted signal and by a new method not requiring the same structure of master and slave circuits. Starting from a simple synchronization method, a renormalization technique enables us to design an adaptive observer (slave) of the known Chua’s circuit using simple and standard electronic components. This observer identifies some parameters proportional to values of components of the master with which it synchronizes through GPS when values of these parameters vary with time. Both numerical simulation and Pspice simulation of GPS for chaos communication by parametric modulation between the two circuits are performed
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.
Quaternion Linear Canonical Transform Application
Bahri, Mawardi
2015-01-01
Quaternion linear canonical transform (QLCT) is a generalization of the classical linear canonical transfom (LCT) using quaternion algebra. The focus of this paper is to introduce an application of the QLCT to study of generalized swept-frequency filter
周勇; 张玉峰; 张超; 张举中
2013-01-01
Aiming at the flaws of the standard Kalman Filter(KF) and Extended Kalman Filter (EKF) , and based on the square-root filtering algorithm, we modify traditional Sage-Husa adaptive filter and present a novel algorithm of Linear Adaptive Square-Root Kalman Filtering( LASRKF) in this paper. With this new filter, the square root of system state covariance matrix is calculated recursively and the estimation of the square root of the system noise co-variance matrix is obtained straightforwardly. Then the positive semi-definiteness of system state and noise covariance matrix are guaranteed; the stability and the adaptability of filter are also enhanced. Compared with the traditional Sage-Husa adaptive filtering algorithm, LASRKF algorithm improves the anti-divergence capability. Simulation results show preliminarily that the stability, accuracy and adaptability of the filter are improved greatly.%针对标准卡尔曼滤波和扩展卡尔曼滤波存在的局限性,结合平方根滤波的思想,对传统Sage-Husa估计器进行改进,提出了一种新的线性自适应平方根卡尔曼滤波(Linear Adaptive Square-Root Kalman Filtering,LASRKF)算法.该算法直接对系统状态方差阵和噪声方差阵的平方根进行递推与估算,确保了状态和噪声方差阵的对称性和非负定性；算法还增添了对系统噪声统计特性估计的计算,强化了滤波器的稳定性和自适应能力；与传统Sage-Husa自适应滤波算法相比LASRKF可提高滤波器抗发散的能力.仿真实验表明,LASRKF可有效提高滤波器的精确性、稳定性和自适应能力.
Emmanuel Kengne; Fathi Ben Hamouda; Ahmed Lakhssassi
2013-01-01
Analytical thermal traveling-wave distribution in biological tissues through a bio-heat transfer (BHT) model with linear/quadratic temperature-dependent blood perfusion is discussed in this paper. Using the extended generalized Riccati equation mapping method, we find analytical traveling wave solutions of the considered BHT equation. All the travelling wave solutions obtained have been used to explicitly investigate the effect of linear and quadratic coefficients of te...
Mohammad Akram Alzu'bi
2014-04-01
Full Text Available The study aimed at analyzing English questions of the Jordanian Secondary Certificate Examinations via Blooms' cognitive levels. An analysis sheet was prepared by the researcher for the purpose of the study, which was ensured to be valid and reliable. The whole questions of the general secondary examinations for English course in both levels (level three and level four during 2010-2013 composed the sample of the study. Frequencies and percentages were tabulated to facilitate the analysis of the results. The result of the study revealed that the total percentage of the first three levels (comprehension, knowledge, and analysis is (69.6 but the total percentage of the last three levels (application, synthesis, and evaluation is (30.4 so it indicated that the English questions included in general secondary examinations emphasize low order thinking levels. The researcher recommended that the questions designers should improve their questioning techniques in writing questions of exams.
Irina-Alina Preda; Sorinel Capusneanu
2008-01-01
In this article, we analyze the causes that have led to the improvement of the Romanian general accounting plan according to the Activity- Based Costing (ABC) method. We explain the advantages presented by the dissociated organization of management accounting, in contrast with the tabular- statistical form. The article also describes the methodological steps to be taken in the process of recording book entries, according to the Activity-Based Costing (ABC) method in Romania.
Irina-Alina Preda
2008-11-01
Full Text Available In this article, we analyze the causes that have led to the improvement of the Romanian general accounting plan according to the Activity- Based Costing (ABC method. We explain the advantages presented by the dissociated organization of management accounting, in contrast with the tabular- statistical form. The article also describes the methodological steps to be taken in the process of recording book entries, according to the Activity-Based Costing (ABC method in Romania.
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
Functional MRI (fMRI) of the spinal cord is able to provide maps of neuronal activity. Spinal fMRI data have been analyzed in previous studies by calculating the cross-correlation (CC) between the stimulus and the time course of every voxel and, more recently, by using the general linear model (GLM). The aim of this study was to compare three different approaches (CC analysis, GLM and independent component analysis (ICA)) for analyzing fMRI scans of the cervical spinal cord. We analyzed spinal fMRI data from healthy subjects during a proprioceptive and a tactile stimulation by using two model-based approaches, i.e., CC analysis between the stimulus shape and the time course of every voxel, and the GLM. Moreover, we applied independent component analysis, a model-free approach which decomposes the data in a set of source signals. All methods were able to detect cervical cord areas of activity corresponding to the expected regions of neuronal activations. Model-based approaches (CC and GLM) revealed similar patterns of activity. ICA could identify a component correlated to fMRI stimulation, although with a lower statistical threshold than model-based approaches, and many components, consistent across subjects, which are likely to be secondary to noise present in the data. Model-based approaches seem to be more robust for estimating task-related activity, whereas ICA seems to be useful for eliminating noise components from the data. Combined use of ICA and GLM might improve the reliability of spinal fMRI results. (orig.)
The electromagnetic part of an extensive air shower developing in the atmosphere provides significant information complementary to that obtained by water Cherenkov detectors which are predominantly sensitive to the muonic content of an air shower at ground. The emissions can be observed in the frequency band between 10 and 100 MHz. However, this frequency range is significantly contaminated by narrow-band RFI and other human-made distortions. The Auger Engineering Radio Array currently suppresses the RFI by multiple time-to-frequency domain conversions using an FFT procedure as well as by a set of manually chosen IIR notch filters in the time-domain. An alternative approach developed in this paper is an adaptive FIR filter based on linear prediction (LP). The coefficients for the linear predictor are dynamically refreshed and calculated in the virtual NIOS processor. The radio detector is an autonomous system installed on the Argentinean pampas and supplied from a solar panel. Powerful calculation capacity inside the FPGA is a factor. Power consumption versus the degree of effectiveness of the calculation inside the FPGA is a figure of merit to be minimized. Results show that the RFI contamination can be significantly suppressed by the LP FIR filter for 64 or less stages. -- Highlights: • We propose an adaptive method using linear prediction for periodic RFI suppression. • Requirements are the detection of short transient signals powered by solar panels. • The RFI is significantly suppressed by ∼70%, even in a very contaminated environment. • This method consumes less energy than the current method based on FFT used in AERA. • Distortion of the short transient signals is negligible
Szadkowski, Zbigniew, E-mail: zszadkow@kfd2.phys.uni.lodz.pl [University of Lodz, Department of Physics and Applied Informatics (Poland); Fraenkel, E.D. [Kernfysisch Versneller Instituut of the University of Groningen, Groningen (Netherlands); Glas, Dariusz; Legumina, Remigiusz [University of Lodz, Department of Physics and Applied Informatics (Poland)
2013-12-21
The electromagnetic part of an extensive air shower developing in the atmosphere provides significant information complementary to that obtained by water Cherenkov detectors which are predominantly sensitive to the muonic content of an air shower at ground. The emissions can be observed in the frequency band between 10 and 100 MHz. However, this frequency range is significantly contaminated by narrow-band RFI and other human-made distortions. The Auger Engineering Radio Array currently suppresses the RFI by multiple time-to-frequency domain conversions using an FFT procedure as well as by a set of manually chosen IIR notch filters in the time-domain. An alternative approach developed in this paper is an adaptive FIR filter based on linear prediction (LP). The coefficients for the linear predictor are dynamically refreshed and calculated in the virtual NIOS processor. The radio detector is an autonomous system installed on the Argentinean pampas and supplied from a solar panel. Powerful calculation capacity inside the FPGA is a factor. Power consumption versus the degree of effectiveness of the calculation inside the FPGA is a figure of merit to be minimized. Results show that the RFI contamination can be significantly suppressed by the LP FIR filter for 64 or less stages. -- Highlights: • We propose an adaptive method using linear prediction for periodic RFI suppression. • Requirements are the detection of short transient signals powered by solar panels. • The RFI is significantly suppressed by ∼70%, even in a very contaminated environment. • This method consumes less energy than the current method based on FFT used in AERA. • Distortion of the short transient signals is negligible.
STOCHASTIC ADAPTIVE SWITCHING CONTROL BASED ON MULTIPLE MODELS
ZHANG Yanxia; GUO Lei
2002-01-01
It is well known that the transient behaviors of the traditional adaptive control may be very poor in general, and that the adaptive control designed based on switching between multiple models is an intuitively appealing and practically feasible approach to improve the transient performances. In this paper, we shall prove that for a typical class of linear systems disturbed by random noises, the multiple model based least-squares (LS)adaptive switching control is stable and convergent, and has the same convergence rate as that established for the standard least-squares-based self-tunning regulators. Moreover,the mixed case combining adaptive models with fixed models is also considered.
Kong, Xiangxi; Zhang, Xueliang; Chen, Xiaozhe; Wen, Bangchun; Wang, Bo
2016-05-01
In this paper, phase and speed synchronization control of four eccentric rotors (ERs) driven by induction motors in a linear vibratory feeder with unknown time-varying load torques is studied. Firstly, the electromechanical coupling model of the linear vibratory feeder is established by associating induction motor's model with the dynamic model of the system, which is a typical under actuated model. According to the characteristics of the linear vibratory feeder, the complex control problem of the under actuated electromechanical coupling model converts to phase and speed synchronization control of four ERs. In order to keep the four ERs operating synchronously with zero phase differences, phase and speed synchronization controllers are designed by employing adaptive sliding mode control (ASMC) algorithm via a modified master-slave structure. The stability of the controllers is proved by Lyapunov stability theorem. The proposed controllers are verified by simulation via Matlab/Simulink program and compared with the conventional sliding mode control (SMC) algorithm. The results show the proposed controllers can reject the time-varying load torques effectively and four ERs can operate synchronously with zero phase differences. Moreover, the control performance is better than the conventional SMC algorithm and the chattering phenomenon is attenuated. Furthermore, the effects of reference speed and parametric perturbations are discussed to show the strong robustness of the proposed controllers. Finally, experiments on a simple vibratory test bench are operated by using the proposed controllers and without control, respectively, to validate the effectiveness of the proposed controllers further.
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
M. Kalyan Chakravarthi
2015-05-01
Full Text Available The aim of this study is to design and implement an Adaptive Model Based Gain Scheduled (AMBGS Controller using classical controller tuning techniques for a Single Spherical Nonlinear Tank System (SSTLLS. A varying range of development in the control mechanisms have been evidently seen in the last two decades. The control of level has always been a topic of discussion in the process control scenario. In this study a real time SSTLLS has been chosen for investigation. System identification of these different regions of nonlinear process is done using black box model, which is identified to be nonlinear and approximated to be a First Order plus Dead Time (FOPDT model. A proportional and integral controller is designed using LabVIEW and Skogestad’s and Ziegler Nichols (ZN tuning methods are implemented. The paper will provide details about the data acquisition unit, shows the implementation of the controller and compare the results of PI tuning methods used for an AMBGS Controller.
RLS Adaptive Filtering Algorithms Based on Parallel Computations
V. I. Djigan
2005-01-01
The paper presents a family of the sliding window RLS adaptive filtering algorithms with the regularization of adaptive filter correlation matrix. The algorithms are developed in forms, fitted to the implementation by means of parallel computations. The family includes RLS and fast RLS algorithms based on generalized matrix inversion lemma, fast RLS algorithms based on square root free inverse QR decomposition and linearly constrained RLS algorithms. The considered algorithms are mathematical...
Poulin, David; Martinez, David; Aenchbacher, Amy; Aiello, Rocco; Doyle, Mike; Hilgenbrinck, Linda; Busse, Sean; Cappuccio, Jim
2013-01-01
In Part III of the feature, physical educators and adapted physical educators offer current best practices as models of implementation for readers. Contributions included are: (1) Answer to the Dear Colleague Letter from the Anchorage School District's Adapted Sport Program (David Poulin); (2) Georgia's Adapted Physical Educators…
Is adaptation. Truly an adaptation? Is adaptation. Truly an adaptation?
Thais Flores Nogueira Diniz
2008-04-01
Full Text Available The article begins by historicizing film adaptation from the arrival of cinema, pointing out the many theoretical approaches under which the process has been seen: from the concept of “the same story told in a different medium” to a comprehensible definition such as “the process through which works can be transformed, forming an intersection of textual surfaces, quotations, conflations and inversions of other texts”. To illustrate this new concept, the article discusses Spike Jonze’s film Adaptation. according to James Naremore’s proposal which considers the study of adaptation as part of a general theory of repetition, joined with the study of recycling, remaking, and every form of retelling. The film deals with the attempt by the scriptwriter Charles Kaufman, cast by Nicholas Cage, to adapt/translate a non-fictional book to the cinema, but ends up with a kind of film which is by no means what it intended to be: a film of action in the model of Hollywood productions. During the process of creation, Charles and his twin brother, Donald, undergo a series of adventures involving some real persons from the world of film, the author and the protagonist of the book, all of them turning into fictional characters in the film. In the film, adaptation then signifies something different from itstraditional meaning. The article begins by historicizing film adaptation from the arrival of cinema, pointing out the many theoretical approaches under which the process has been seen: from the concept of “the same story told in a different medium” to a comprehensible definition such as “the process through which works can be transformed, forming an intersection of textual surfaces, quotations, conflations and inversions of other texts”. To illustrate this new concept, the article discusses Spike Jonze’s film Adaptation. according to James Naremore’s proposal which considers the study of adaptation as part of a general theory of repetition
General pripciples, instruments and adaptations
Experience of using roentgenoendovascular occlusion (REO) of vessels in clinical medicine have been analysed. Literary data are presented, experience in closure of vessel aneurysm, blocking of pathological arteriovenous anastomoses, functional exclusion of kidney at chronic renal insufficiency with hypertension and albuminuria, before kidney transplantation, functional splenectomy at hematologic diseases and hypersplenism and also of adducting arteries aimed at artificial ischemisation of neoplasms, is presented
Carr, Joseph
1996-01-01
The linear IC market is large and growing, as is the demand for well trained technicians and engineers who understand how these devices work and how to apply them. Linear Integrated Circuits provides in-depth coverage of the devices and their operation, but not at the expense of practical applications in which linear devices figure prominently. This book is written for a wide readership from FE and first degree students, to hobbyists and professionals.Chapter 1 offers a general introduction that will provide students with the foundations of linear IC technology. From chapter 2 onwa
Ishida, Mayumi; Toyota, Kazuo; Ehara, Masahiro; Frisch, Michael J; Nakatsuji, Hiroshi
2004-02-01
A method of calculating analytical energy gradients of the singlet and triplet excited states, ionized states, electron-attached states, and high-spin states from quartet to septet states by the symmetry-adapted-cluster configuration-interaction general-R method is developed and implemented. This method is a powerful tool in the studies of geometries, dynamics, and properties of the states of molecules in which not only one-electron processes but also two- and multielectron processes are involved. The performance of the present method was confirmed by calculating the geometries and the spectroscopic constants of the diatomic and polyatomic molecules in various electronic states involving the ground state and the one- to three-electron excited states. The accurate descriptions were obtained for the equilibrium geometries, vibrational frequencies, and adiabatic excitation energies, which show the potential usefulness of the present method. The particularly interesting applications were to the C' 1Ag state of acetylene, the A 2Deltau and B 2Sigmau+ states of CNC and the 4B1 and a 4Piu states of N3 radical. PMID:15268403
Fabrice G. Renaud
2012-04-01
Full Text Available Water is the primary medium through which climate change influences the Earth’s ecosystems and therefore people’s livelihoods and wellbeing. Besides climatic change, current demographic trends, economic development and related land use changes have direct impact on increasing demand for freshwater resources. Taken together, the net effect of these supply and demand changes is affecting the vulnerability of water resources. The concept of ‘vulnerability’ is not straightforward as there is no universally accepted approach for assessing vulnerability. In this study, we review the evolution of approaches to vulnerability assessment related to water resources. From the current practices, we identify research gaps, and approaches to overcome these gaps a generalized assessment framework is developed. A feasibility study is then presented in the context of the Lower Brahmaputra River Basin (LBRB. The results of the feasibility study identify the current main constraints (e.g., lack of institutional coordination and opportunities (e.g., adaptation of LBRB. The results of this study can be helpful for innovative research and management initiatives and the described framework can be widely used as a guideline for the vulnerability assessment of water resources systems, particularly in developing countries.
A linear gate providing a variable gate duration from 0,40μsec to 4μsec was developed. The electronic circuity consists of a linear circuit and an enable circuit. The input signal can be either unipolar or bipolar. If the input signal is bipolar, the negative portion will be filtered. The operation of the linear gate is controlled by the application of a positive enable pulse. (author)
Vretenar, M
2014-01-01
The main features of radio-frequency linear accelerators are introduced, reviewing the different types of accelerating structures and presenting the main characteristics aspects of linac beam dynamics.