Improved linear least squares estimation using bounded data uncertainty

KAUST Repository

Ballal, Tarig; Al-Naffouri, Tareq Y.

2015-01-01

This paper addresses the problemof linear least squares (LS) estimation of a vector x from linearly related observations. In spite of being unbiased, the original LS estimator suffers from high mean squared error, especially at low signal-to-noise ratios. The mean squared error (MSE) of the LS estimator can be improved by introducing some form of regularization based on certain constraints. We propose an improved LS (ILS) estimator that approximately minimizes the MSE, without imposing any constraints. To achieve this, we allow for perturbation in the measurement matrix. Then we utilize a bounded data uncertainty (BDU) framework to derive a simple iterative procedure to estimate the regularization parameter. Numerical results demonstrate that the proposed BDU-ILS estimator is superior to the original LS estimator, and it converges to the best linear estimator, the linear-minimum-mean-squared error estimator (LMMSE), when the elements of x are statistically white.

Optimal difference-based estimation for partially linear models

KAUST Repository

Zhou, Yuejin; Cheng, Yebin; Dai, Wenlin; Tong, Tiejun

2017-01-01

Difference-based methods have attracted increasing attention for analyzing partially linear models in the recent literature. In this paper, we first propose to solve the optimal sequence selection problem in difference-based estimation for the linear component. To achieve the goal, a family of new sequences and a cross-validation method for selecting the adaptive sequence are proposed. We demonstrate that the existing sequences are only extreme cases in the proposed family. Secondly, we propose a new estimator for the residual variance by fitting a linear regression method to some difference-based estimators. Our proposed estimator achieves the asymptotic optimal rate of mean squared error. Simulation studies also demonstrate that our proposed estimator performs better than the existing estimator, especially when the sample size is small and the nonparametric function is rough.

Optimal difference-based estimation for partially linear models

KAUST Repository

Zhou, Yuejin

2017-12-16

Difference-based methods have attracted increasing attention for analyzing partially linear models in the recent literature. In this paper, we first propose to solve the optimal sequence selection problem in difference-based estimation for the linear component. To achieve the goal, a family of new sequences and a cross-validation method for selecting the adaptive sequence are proposed. We demonstrate that the existing sequences are only extreme cases in the proposed family. Secondly, we propose a new estimator for the residual variance by fitting a linear regression method to some difference-based estimators. Our proposed estimator achieves the asymptotic optimal rate of mean squared error. Simulation studies also demonstrate that our proposed estimator performs better than the existing estimator, especially when the sample size is small and the nonparametric function is rough.

Robust estimation for partially linear models with large-dimensional covariates.

Science.gov (United States)

Zhu, LiPing; Li, RunZe; Cui, HengJian

2013-10-01

We are concerned with robust estimation procedures to estimate the parameters in partially linear models with large-dimensional covariates. To enhance the interpretability, we suggest implementing a noncon-cave regularization method in the robust estimation procedure to select important covariates from the linear component. We establish the consistency for both the linear and the nonlinear components when the covariate dimension diverges at the rate of [Formula: see text], where n is the sample size. We show that the robust estimate of linear component performs asymptotically as well as its oracle counterpart which assumes the baseline function and the unimportant covariates were known a priori. With a consistent estimator of the linear component, we estimate the nonparametric component by a robust local linear regression. It is proved that the robust estimate of nonlinear component performs asymptotically as well as if the linear component were known in advance. Comprehensive simulation studies are carried out and an application is presented to examine the finite-sample performance of the proposed procedures.

Solutions to estimation problems for scalar hamilton-jacobi equations using linear programming

KAUST Repository

Claudel, Christian G.; Chamoin, Timothee; Bayen, Alexandre M.

2014-01-01

This brief presents new convex formulations for solving estimation problems in systems modeled by scalar Hamilton-Jacobi (HJ) equations. Using a semi-analytic formula, we show that the constraints resulting from a HJ equation are convex, and can be written as a set of linear inequalities. We use this fact to pose various (and seemingly unrelated) estimation problems related to traffic flow-engineering as a set of linear programs. In particular, we solve data assimilation and data reconciliation problems for estimating the state of a system when the model and measurement constraints are incompatible. We also solve traffic estimation problems, such as travel time estimation or density estimation. For all these problems, a numerical implementation is performed using experimental data from the Mobile Century experiment. In the context of reproducible research, the code and data used to compute the results presented in this brief have been posted online and are accessible to regenerate the results. © 2013 IEEE.

Linearized motion estimation for articulated planes.

Science.gov (United States)

Datta, Ankur; Sheikh, Yaser; Kanade, Takeo

2011-04-01

In this paper, we describe the explicit application of articulation constraints for estimating the motion of a system of articulated planes. We relate articulations to the relative homography between planes and show that these articulations translate into linearized equality constraints on a linear least-squares system, which can be solved efficiently using a Karush-Kuhn-Tucker system. The articulation constraints can be applied for both gradient-based and feature-based motion estimation algorithms and to illustrate this, we describe a gradient-based motion estimation algorithm for an affine camera and a feature-based motion estimation algorithm for a projective camera that explicitly enforces articulation constraints. We show that explicit application of articulation constraints leads to numerically stable estimates of motion. The simultaneous computation of motion estimates for all of the articulated planes in a scene allows us to handle scene areas where there is limited texture information and areas that leave the field of view. Our results demonstrate the wide applicability of the algorithm in a variety of challenging real-world cases such as human body tracking, motion estimation of rigid, piecewise planar scenes, and motion estimation of triangulated meshes.

Estimating linear effects in ANOVA designs: the easy way.

Science.gov (United States)

Pinhas, Michal; Tzelgov, Joseph; Ganor-Stern, Dana

2012-09-01

Research in cognitive science has documented numerous phenomena that are approximated by linear relationships. In the domain of numerical cognition, the use of linear regression for estimating linear effects (e.g., distance and SNARC effects) became common following Fias, Brysbaert, Geypens, and d'Ydewalle's (1996) study on the SNARC effect. While their work has become the model for analyzing linear effects in the field, it requires statistical analysis of individual participants and does not provide measures of the proportions of variability accounted for (cf. Lorch & Myers, 1990). In the present methodological note, using both the distance and SNARC effects as examples, we demonstrate how linear effects can be estimated in a simple way within the framework of repeated measures analysis of variance. This method allows for estimating effect sizes in terms of both slope and proportions of variability accounted for. Finally, we show that our method can easily be extended to estimate linear interaction effects, not just linear effects calculated as main effects.

Optimal state estimation theory applied to safeguards accounting

International Nuclear Information System (INIS)

Pike, D.H.; Morrison, G.W.

1977-01-01

This paper presents a unified theory for the application of modern state estimation techniques to nuclear material accountability. First a summary of the current MUF/LEMUF approach is detailed. It is shown that when inventory measurement error is large in comparison to transfer measurement error, improved estimates of the losses can be achieved using the cumulative summation technique. However, the optimal estimator is shown to be the Kalman filter. An enhancement of the retrospective estimation of losses can be achieved using linear smoothing. State space models are developed for a mixed oxide fuel fabrication facility and examples are presented

Exponentially convergent state estimation for delayed switched recurrent neural networks.

Science.gov (United States)

Ahn, Choon Ki

2011-11-01

This paper deals with the delay-dependent exponentially convergent state estimation problem for delayed switched neural networks. A set of delay-dependent criteria is derived under which the resulting estimation error system is exponentially stable. It is shown that the gain matrix of the proposed state estimator is characterised in terms of the solution to a set of linear matrix inequalities (LMIs), which can be checked readily by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed state estimator.

Bounded Perturbation Regularization for Linear Least Squares Estimation

KAUST Repository

Ballal, Tarig

2017-10-18

This paper addresses the problem of selecting the regularization parameter for linear least-squares estimation. We propose a new technique called bounded perturbation regularization (BPR). In the proposed BPR method, a perturbation with a bounded norm is allowed into the linear transformation matrix to improve the singular-value structure. Following this, the problem is formulated as a min-max optimization problem. Next, the min-max problem is converted to an equivalent minimization problem to estimate the unknown vector quantity. The solution of the minimization problem is shown to converge to that of the ℓ2 -regularized least squares problem, with the unknown regularizer related to the norm bound of the introduced perturbation through a nonlinear constraint. A procedure is proposed that combines the constraint equation with the mean squared error (MSE) criterion to develop an approximately optimal regularization parameter selection algorithm. Both direct and indirect applications of the proposed method are considered. Comparisons with different Tikhonov regularization parameter selection methods, as well as with other relevant methods, are carried out. Numerical results demonstrate that the proposed method provides significant improvement over state-of-the-art methods.

Approximation to estimation of critical state

International Nuclear Information System (INIS)

Orso, Jose A.; Rosario, Universidad Nacional

2011-01-01

The position of the control rod for the critical state of the nuclear reactor depends on several factors; including, but not limited to the temperature and configuration of the fuel elements inside the core. Therefore, the position can not be known in advance. In this paper theoretical estimations are developed to obtain an equation that allows calculating the position of the control rod for the critical state (approximation to critical) of the nuclear reactor RA-4; and will be used to create a software performing the estimation by entering the count rate of the reactor pulse channel and the length obtained from the control rod (in cm). For the final estimation of the approximation to critical state, a function obtained experimentally indicating control rods reactivity according to the function of their position is used, work is done mathematically to obtain a linear function, which gets the length of the control rod, which has to be removed to get the reactor in critical position. (author) [es

Surface tensor estimation from linear sections

DEFF Research Database (Denmark)

Kousholt, Astrid; Kiderlen, Markus; Hug, Daniel

From Crofton's formula for Minkowski tensors we derive stereological estimators of translation invariant surface tensors of convex bodies in the n-dimensional Euclidean space. The estimators are based on one-dimensional linear sections. In a design based setting we suggest three types of estimators....... These are based on isotropic uniform random lines, vertical sections, and non-isotropic random lines, respectively. Further, we derive estimators of the specific surface tensors associated with a stationary process of convex particles in the model based setting....

Surface tensor estimation from linear sections

DEFF Research Database (Denmark)

Kousholt, Astrid; Kiderlen, Markus; Hug, Daniel

2015-01-01

From Crofton’s formula for Minkowski tensors we derive stereological estimators of translation invariant surface tensors of convex bodies in the n-dimensional Euclidean space. The estimators are based on one-dimensional linear sections. In a design based setting we suggest three types of estimators....... These are based on isotropic uniform random lines, vertical sections, and non-isotropic random lines, respectively. Further, we derive estimators of the specific surface tensors associated with a stationary process of convex particles in the model based setting....

Efficient Estimation of Extreme Non-linear Roll Motions using the First-order Reliability Method (FORM)

DEFF Research Database (Denmark)

Jensen, Jørgen Juncher

2007-01-01

In on-board decision support systems efficient procedures are needed for real-time estimation of the maximum ship responses to be expected within the next few hours, given on-line information on the sea state and user defined ranges of possible headings and speeds. For linear responses standard...... frequency domain methods can be applied. To non-linear responses like the roll motion, standard methods like direct time domain simulations are not feasible due to the required computational time. However, the statistical distribution of non-linear ship responses can be estimated very accurately using...... the first-order reliability method (FORM), well-known from structural reliability problems. To illustrate the proposed procedure, the roll motion is modelled by a simplified non-linear procedure taking into account non-linear hydrodynamic damping, time-varying restoring and wave excitation moments...

Linear independence of localized magnon states

International Nuclear Information System (INIS)

Schmidt, Heinz-Juergen; Richter, Johannes; Moessner, Roderich

2006-01-01

At the magnetic saturation field, certain frustrated lattices have a class of states known as 'localized multi-magnon states' as exact ground states. The number of these states scales exponentially with the number N of spins and hence they have a finite entropy also in the thermodynamic limit N → ∞ provided they are sufficiently linearly independent. In this paper, we present rigorous results concerning the linear dependence or independence of localized magnon states and investigate special examples. For large classes of spin lattices, including what we call the orthogonal type and the isolated type, as well as the kagome, the checkerboard and the star lattice, we have proven linear independence of all localized multi-magnon states. On the other hand, the pyrochlore lattice provides an example of a spin lattice having localized multi-magnon states with considerable linear dependence

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

Science.gov (United States)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Estimating monotonic rates from biological data using local linear regression.

Science.gov (United States)

Olito, Colin; White, Craig R; Marshall, Dustin J; Barneche, Diego R

2017-03-01

Accessing many fundamental questions in biology begins with empirical estimation of simple monotonic rates of underlying biological processes. Across a variety of disciplines, ranging from physiology to biogeochemistry, these rates are routinely estimated from non-linear and noisy time series data using linear regression and ad hoc manual truncation of non-linearities. Here, we introduce the R package LoLinR, a flexible toolkit to implement local linear regression techniques to objectively and reproducibly estimate monotonic biological rates from non-linear time series data, and demonstrate possible applications using metabolic rate data. LoLinR provides methods to easily and reliably estimate monotonic rates from time series data in a way that is statistically robust, facilitates reproducible research and is applicable to a wide variety of research disciplines in the biological sciences. © 2017. Published by The Company of Biologists Ltd.

A concise account of techniques available for shipboard sea state estimation

DEFF Research Database (Denmark)

Nielsen, Ulrik Dam

2017-01-01

This article gives a review of techniques applied to make sea state estimation on the basis of measured responses on a ship. The general concept of the procedures is similar to that of a classical wave buoy, which exploits a linear assumption between waves and the associated motions. In the frequ......This article gives a review of techniques applied to make sea state estimation on the basis of measured responses on a ship. The general concept of the procedures is similar to that of a classical wave buoy, which exploits a linear assumption between waves and the associated motions...

Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

Science.gov (United States)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

Robust stability and ℋ ∞ -estimation for uncertain discrete systems with state-delay

Directory of Open Access Journals (Sweden)

Mahmoud Magdi S.

2001-01-01

Full Text Available In this paper, we investigate the problems of robust stability and ℋ ∞ -estimation for a class of linear discrete-time systems with time-varying norm-bounded parameter uncertainty and unknown state-delay. We provide complete results for robust stability with prescribed performance measure and establish a version of the discrete Bounded Real Lemma. Then, we design a linear estimator such that the estimation error dynamics is robustly stable with a guaranteed ℋ ∞ -performance irrespective of the parameteric uncertainties and unknown state delays. A numerical example is worked out to illustrate the developed theory.

Leaf area estimation of cassava from linear dimensions

Directory of Open Access Journals (Sweden)

SAMARA ZANETTI

2017-08-01

Full Text Available ABSTRACT The objective of this study was to determine predictor models of leaf area of cassava from linear leaf measurements. The experiment was carried out in greenhouse in the municipality of Botucatu, São Paulo state, Brazil. The stem cuttings with 5-7 nodes of the cultivar IAC 576-70 were planted in boxes filled with about 320 liters of soil, keeping soil moisture at field capacity, monitored by puncturing tensiometers. At 80 days after planting, 140 leaves were randomly collected from the top, middle third and base of cassava plants. We evaluated the length and width of the central lobe of leaves, number of lobes and leaf area. The measurements of leaf areas were correlated with the length and width of the central lobe and the number of lobes of the leaves, and adjusted to polynomial and multiple regression models. The linear function that used the length of the central lobe LA = -69.91114 + 15.06462L and linear multiple functions LA = -69.9188 + 15.5102L + 0.0197726K - 0.0768998J or LA = -69.9346 + 15.0106L + 0.188931K - 0.0264323H are suitable models to estimate leaf area of cassava cultivar IAC 576-70.

Vehicle State Information Estimation with the Unscented Kalman Filter

Directory of Open Access Journals (Sweden)

Hongbin Ren

2014-01-01

Full Text Available The vehicle state information plays an important role in the vehicle active safety systems; this paper proposed a new concept to estimate the instantaneous vehicle speed, yaw rate, tire forces, and tire kinemics information in real time. The estimator is based on the 3DoF vehicle model combined with the piecewise linear tire model. The estimator is realized using the unscented Kalman filter (UKF, since it is based on the unscented transfer technique and considers high order terms during the measurement and update stage. The numerical simulations are carried out to further investigate the performance of the estimator under high friction and low friction road conditions in the MATLAB/Simulink combined with the Carsim environment. The simulation results are compared with the numerical results from Carsim software, which indicate that UKF can estimate the vehicle state information accurately and in real time; the proposed estimation will provide the necessary and reliable state information to the vehicle controller in the future.

SNR Estimation in Linear Systems with Gaussian Matrices

KAUST Repository

Suliman, Mohamed Abdalla Elhag; Alrashdi, Ayed; Ballal, Tarig; Al-Naffouri, Tareq Y.

2017-01-01

This letter proposes a highly accurate algorithm to estimate the signal-to-noise ratio (SNR) for a linear system from a single realization of the received signal. We assume that the linear system has a Gaussian matrix with one sided left correlation. The unknown entries of the signal and the noise are assumed to be independent and identically distributed with zero mean and can be drawn from any distribution. We use the ridge regression function of this linear model in company with tools and techniques adapted from random matrix theory to achieve, in closed form, accurate estimation of the SNR without prior statistical knowledge on the signal or the noise. Simulation results show that the proposed method is very accurate.

SNR Estimation in Linear Systems with Gaussian Matrices

KAUST Repository

Suliman, Mohamed Abdalla Elhag

2017-09-27

This letter proposes a highly accurate algorithm to estimate the signal-to-noise ratio (SNR) for a linear system from a single realization of the received signal. We assume that the linear system has a Gaussian matrix with one sided left correlation. The unknown entries of the signal and the noise are assumed to be independent and identically distributed with zero mean and can be drawn from any distribution. We use the ridge regression function of this linear model in company with tools and techniques adapted from random matrix theory to achieve, in closed form, accurate estimation of the SNR without prior statistical knowledge on the signal or the noise. Simulation results show that the proposed method is very accurate.

Performances Of Estimators Of Linear Models With Autocorrelated ...

African Journals Online (AJOL)

The performances of five estimators of linear models with Autocorrelated error terms are compared when the independent variable is autoregressive. The results reveal that the properties of the estimators when the sample size is finite is quite similar to the properties of the estimators when the sample size is infinite although ...

Learning to Estimate Dynamical State with Probabilistic Population Codes.

Directory of Open Access Journals (Sweden)

Joseph G Makin

2015-11-01

Full Text Available Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF, the parameters of which can be learned via latent-variable density estimation (the EM algorithm. The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, "probabilistic population codes." We show that a recurrent neural network-a modified form of an exponential family harmonium (EFH-that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.

Unstable volatility functions: the break preserving local linear estimator

DEFF Research Database (Denmark)

Casas, Isabel; Gijbels, Irene

The objective of this paper is to introduce the break preserving local linear (BPLL) estimator for the estimation of unstable volatility functions. Breaks in the structure of the conditional mean and/or the volatility functions are common in Finance. Markov switching models (Hamilton, 1989......) and threshold models (Lin and Terasvirta, 1994) are amongst the most popular models to describe the behaviour of data with structural breaks. The local linear (LL) estimator is not consistent at points where the volatility function has a break and it may even report negative values for finite samples...

Estimation and variable selection for generalized additive partial linear models

KAUST Repository

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.

Error Estimation for the Linearized Auto-Localization Algorithm

Directory of Open Access Journals (Sweden)

Fernando Seco

2012-02-01

Full Text Available The Linearized Auto-Localization (LAL algorithm estimates the position of beacon nodes in Local Positioning Systems (LPSs, using only the distance measurements to a mobile node whose position is also unknown. The LAL algorithm calculates the inter-beacon distances, used for the estimation of the beacons’ positions, from the linearized trilateration equations. In this paper we propose a method to estimate the propagation of the errors of the inter-beacon distances obtained with the LAL algorithm, based on a first order Taylor approximation of the equations. Since the method depends on such approximation, a confidence parameter τ is defined to measure the reliability of the estimated error. Field evaluations showed that by applying this information to an improved weighted-based auto-localization algorithm (WLAL, the standard deviation of the inter-beacon distances can be improved by more than 30% on average with respect to the original LAL method.

Triangular and Trapezoidal Fuzzy State Estimation with Uncertainty on Measurements

Directory of Open Access Journals (Sweden)

Mohammad Sadeghi Sarcheshmah

2012-01-01

Full Text Available In this paper, a new method for uncertainty analysis in fuzzy state estimation is proposed. The uncertainty is expressed in measurements. Uncertainties in measurements are modelled with different fuzzy membership functions (triangular and trapezoidal. To find the fuzzy distribution of any state variable, the problem is formulated as a constrained linear programming (LP optimization. The viability of the proposed method would be verified with the ones obtained from the weighted least squares (WLS and the fuzzy state estimation (FSE in the 6-bus system and in the IEEE-14 and 30 bus system.

Fuzzy filter for state estimation of a glucoregulatory system.

Science.gov (United States)

Trajanoski, Z; Wach, P

1996-08-01

A filter based on fuzzy logic for state estimation of a glucoregulatory system is presented. A published non-linear model for the dynamics of glucose and its hormonal control including a single glucose compartment, five insulin compartments and a glucagon compartment was used for simulation. The simulated data were corrupted by an additive white noise with zero mean and a coefficient of variation (CV) of between 2 and 20% and then submitted to the state estimation procedure using a fuzzy filter (FF). The performance of the FF was compared with an extended Kalman filter (EKF) for state estimation. Both the FF and the EKF were evaluated in the following cases: (a) five state variables are measurable; three plasma variables are measurable; only plasma glucose is measurable; (b) for different measurement noise levels (CV of 2-20%); and (c) a mismatch between the glucoregulatory system and the given mathematical model (uncertain or approximate model). In contrast to the FF, in the case of approximate model of the glucose system, the EKF failed to achieve useful state estimation. Moreover, the performance of the FF was independent of the noise level. In conclusion, the FF approach is a viable alternative for state estimation in a noisy environment and with an uncertain mathematical model of the glucoregulatory system.

Estimation of group means when adjusting for covariates in generalized linear models.

Science.gov (United States)

Qu, Yongming; Luo, Junxiang

2015-01-01

Generalized linear models are commonly used to analyze categorical data such as binary, count, and ordinal outcomes. Adjusting for important prognostic factors or baseline covariates in generalized linear models may improve the estimation efficiency. The model-based mean for a treatment group produced by most software packages estimates the response at the mean covariate, not the mean response for this treatment group for the studied population. Although this is not an issue for linear models, the model-based group mean estimates in generalized linear models could be seriously biased for the true group means. We propose a new method to estimate the group mean consistently with the corresponding variance estimation. Simulation showed the proposed method produces an unbiased estimator for the group means and provided the correct coverage probability. The proposed method was applied to analyze hypoglycemia data from clinical trials in diabetes. Copyright © 2014 John Wiley & Sons, Ltd.

Thresholding projection estimators in functional linear models

OpenAIRE

Cardot, Hervé; Johannes, Jan

2010-01-01

We consider the problem of estimating the regression function in functional linear regression models by proposing a new type of projection estimators which combine dimension reduction and thresholding. The introduction of a threshold rule allows to get consistency under broad assumptions as well as minimax rates of convergence under additional regularity hypotheses. We also consider the particular case of Sobolev spaces generated by the trigonometric basis which permits to get easily mean squ...

Nonlinear Adaptive Descriptor Observer for the Joint States and Parameters Estimation

KAUST Repository

2016-08-29

In this note, the joint state and parameters estimation problem for nonlinear multi-input multi-output descriptor systems is considered. Asymptotic convergence of the adaptive descriptor observer is established by a sufficient set of linear matrix inequalities for the noise-free systems. The noise corrupted systems are also considered and it is shown that the state and parameters estimation errors are bounded for bounded noises. In addition, if the noises are bounded and have zero mean, then the estimation errors asymptotically converge to zero in the mean. The performance of the proposed adaptive observer is illustrated by a numerical example.

Nonlinear Adaptive Descriptor Observer for the Joint States and Parameters Estimation

KAUST Repository

Unknown author

2016-01-01

In this note, the joint state and parameters estimation problem for nonlinear multi-input multi-output descriptor systems is considered. Asymptotic convergence of the adaptive descriptor observer is established by a sufficient set of linear matrix inequalities for the noise-free systems. The noise corrupted systems are also considered and it is shown that the state and parameters estimation errors are bounded for bounded noises. In addition, if the noises are bounded and have zero mean, then the estimation errors asymptotically converge to zero in the mean. The performance of the proposed adaptive observer is illustrated by a numerical example.

State Estimation for Landing Maneuver on High Performance Aircraft

Science.gov (United States)

Suresh, P. S.; Sura, Niranjan K.; Shankar, K.

2018-01-01

State estimation methods are popular means for validating aerodynamic database on aircraft flight maneuver performance characteristics. In this work, the state estimation method during landing maneuver is explored for the first of its kind, using upper diagonal adaptive extended Kalman filter (UD-AEKF) with fuzzy based adaptive tunning of process noise matrix. The mathematical model for symmetrical landing maneuver consists of non-linear flight mechanics equation representing Aircraft longitudinal dynamics. The UD-AEKF algorithm is implemented in MATLAB environment and the states with bias is considered to be the initial conditions just prior to the flare. The measurement data is obtained from a non-linear 6 DOF pilot in loop simulation using FORTRAN. These simulated measurement data is additively mixed with process and measurement noises, which are used as an input for UD-AEKF. Then, the governing states that dictate the landing loads at the instant of touch down are compared. The method is verified using flight data wherein, the vertical acceleration at the aircraft center of gravity (CG) is compared. Two possible outcome of purely relying on the aircraft measured data is highlighted. It is observed that, with the implementation of adaptive fuzzy logic based extended Kalman filter tuned to adapt for aircraft landing dynamics, the methodology improves the data quality of the states that are sourced from noisy measurements.

Slope Estimation in Noisy Piecewise Linear Functions.

Science.gov (United States)

Ingle, Atul; Bucklew, James; Sethares, William; Varghese, Tomy

2015-03-01

This paper discusses the development of a slope estimation algorithm called MAPSlope for piecewise linear data that is corrupted by Gaussian noise. The number and locations of slope change points (also known as breakpoints) are assumed to be unknown a priori though it is assumed that the possible range of slope values lies within known bounds. A stochastic hidden Markov model that is general enough to encompass real world sources of piecewise linear data is used to model the transitions between slope values and the problem of slope estimation is addressed using a Bayesian maximum a posteriori approach. The set of possible slope values is discretized, enabling the design of a dynamic programming algorithm for posterior density maximization. Numerical simulations are used to justify choice of a reasonable number of quantization levels and also to analyze mean squared error performance of the proposed algorithm. An alternating maximization algorithm is proposed for estimation of unknown model parameters and a convergence result for the method is provided. Finally, results using data from political science, finance and medical imaging applications are presented to demonstrate the practical utility of this procedure.

Particle-filtering-based estimation of maximum available power state in Lithium-Ion batteries

International Nuclear Information System (INIS)

Burgos-Mellado, Claudio; Orchard, Marcos E.; Kazerani, Mehrdad; Cárdenas, Roberto; Sáez, Doris

2016-01-01

Highlights: • Approach to estimate the state of maximum power available in Lithium-Ion battery. • Optimisation problem is formulated on the basis of a non-linear dynamic model. • Solutions of the optimisation problem are functions of state of charge estimates. • State of charge estimates computed using particle filter algorithms. - Abstract: Battery Energy Storage Systems (BESS) are important for applications related to both microgrids and electric vehicles. If BESS are used as the main energy source, then it is required to include adequate procedures for the estimation of critical variables such as the State of Charge (SoC) and the State of Health (SoH) in the design of Battery Management Systems (BMS). Furthermore, in applications where batteries are exposed to high charge and discharge rates it is also desirable to estimate the State of Maximum Power Available (SoMPA). In this regard, this paper presents a novel approach to the estimation of SoMPA in Lithium-Ion batteries. This method formulates an optimisation problem for the battery power based on a non-linear dynamic model, where the resulting solutions are functions of the SoC. In the battery model, the polarisation resistance is modelled using fuzzy rules that are function of both SoC and the discharge (charge) current. Particle filtering algorithms are used as an online estimation technique, mainly because these algorithms allow approximating the probability density functions of the SoC and SoMPA even in the case of non-Gaussian sources of uncertainty. The proposed method for SoMPA estimation is validated using the experimental data obtained from an experimental setup designed for charging and discharging the Lithium-Ion batteries.

Optimal State Estimation for Discrete-Time Markov Jump Systems with Missing Observations

Directory of Open Access Journals (Sweden)

Qing Sun

2014-01-01

Full Text Available This paper is concerned with the optimal linear estimation for a class of direct-time Markov jump systems with missing observations. An observer-based approach of fault detection and isolation (FDI is investigated as a detection mechanic of fault case. For systems with known information, a conditional prediction of observations is applied and fault observations are replaced and isolated; then, an FDI linear minimum mean square error estimation (LMMSE can be developed by comprehensive utilizing of the correct information offered by systems. A recursive equation of filtering based on the geometric arguments can be obtained. Meanwhile, a stability of the state estimator will be guaranteed under appropriate assumption.

Constrained State Estimation for Individual Localization in Wireless Body Sensor Networks

Directory of Open Access Journals (Sweden)

Xiaoxue Feng

2014-11-01

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

Constrained State Estimation for Individual Localization in Wireless Body Sensor Networks

Science.gov (United States)

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

2014-01-01

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

Constrained state estimation for individual localization in wireless body sensor networks.

Science.gov (United States)

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

2014-11-10

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

Robust linear discriminant analysis with distance based estimators

Science.gov (United States)

Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Ali, Hazlina

2017-11-01

Linear discriminant analysis (LDA) is one of the supervised classification techniques concerning relationship between a categorical variable and a set of continuous variables. The main objective of LDA is to create a function to distinguish between populations and allocating future observations to previously defined populations. Under the assumptions of normality and homoscedasticity, the LDA yields optimal linear discriminant rule (LDR) between two or more groups. However, the optimality of LDA highly relies on the sample mean and pooled sample covariance matrix which are known to be sensitive to outliers. To alleviate these conflicts, a new robust LDA using distance based estimators known as minimum variance vector (MVV) has been proposed in this study. The MVV estimators were used to substitute the classical sample mean and classical sample covariance to form a robust linear discriminant rule (RLDR). Simulation and real data study were conducted to examine on the performance of the proposed RLDR measured in terms of misclassification error rates. The computational result showed that the proposed RLDR is better than the classical LDR and was comparable with the existing robust LDR.

Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation

Directory of Open Access Journals (Sweden)

Xi Liu

2016-09-01

Full Text Available A new algorithm called maximum correntropy unscented Kalman filter (MCUKF is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC, the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm.

Performances of estimators of linear auto-correlated error model ...

African Journals Online (AJOL)

The performances of five estimators of linear models with autocorrelated disturbance terms are compared when the independent variable is exponential. The results reveal that for both small and large samples, the Ordinary Least Squares (OLS) compares favourably with the Generalized least Squares (GLS) estimators in ...

Bounded Perturbation Regularization for Linear Least Squares Estimation

KAUST Repository

Ballal, Tarig; Suliman, Mohamed Abdalla Elhag; Al-Naffouri, Tareq Y.

2017-01-01

This paper addresses the problem of selecting the regularization parameter for linear least-squares estimation. We propose a new technique called bounded perturbation regularization (BPR). In the proposed BPR method, a perturbation with a bounded

Common Nearly Best Linear Estimates of Location and Scale ...

African Journals Online (AJOL)

Common nearly best linear estimates of location and scale parameters of normal and logistic distributions, which are based on complete samples, are considered. Here, the population from which the samples are drawn is either normal or logistic population or a fusion of both distributions and the estimates are computed ...

Probabilistic quantum cloning of a subset of linearly dependent states

Science.gov (United States)

Rui, Pinshu; Zhang, Wen; Liao, Yanlin; Zhang, Ziyun

2018-02-01

It is well known that a quantum state, secretly chosen from a certain set, can be probabilistically cloned with positive cloning efficiencies if and only if all the states in the set are linearly independent. In this paper, we focus on probabilistic quantum cloning of a subset of linearly dependent states. We show that a linearly-independent subset of linearly-dependent quantum states {| Ψ 1⟩,| Ψ 2⟩,…,| Ψ n ⟩} can be probabilistically cloned if and only if any state in the subset cannot be expressed as a linear superposition of the other states in the set {| Ψ 1⟩,| Ψ 2⟩,…,| Ψ n ⟩}. The optimal cloning efficiencies are also investigated.

Non-destructive linear model for leaf area estimation in Vernonia ferruginea Less

Directory of Open Access Journals (Sweden)

MC. Souza

Full Text Available Leaf area estimation is an important biometrical trait for evaluating leaf development and plant growth in field and pot experiments. We developed a non-destructive model to estimate the leaf area (LA of Vernonia ferruginea using the length (L and width (W leaf dimensions. Different combinations of linear equations were obtained from L, L2, W, W2, LW and L2W2. The linear regressions using the product of LW dimensions were more efficient to estimate the LA of V. ferruginea than models based on a single dimension (L, W, L2 or W2. Therefore, the linear regression “LA=0.463+0.676WL” provided the most accurate estimate of V. ferruginea leaf area. Validation of the selected model showed that the correlation between real measured leaf area and estimated leaf area was very high.

Linear system theory

Science.gov (United States)

Callier, Frank M.; Desoer, Charles A.

1991-01-01

The aim of this book is to provide a systematic and rigorous access to the main topics of linear state-space system theory in both the continuous-time case and the discrete-time case; and the I/O description of linear systems. The main thrusts of the work are the analysis of system descriptions and derivations of their properties, LQ-optimal control, state feedback and state estimation, and MIMO unity-feedback systems.

Variance estimation for complex indicators of poverty and inequality using linearization techniques

Directory of Open Access Journals (Sweden)

Guillaume Osier

2009-12-01

Full Text Available The paper presents the Eurostat experience in calculating measures of precision, including standard errors, confidence intervals and design effect coefficients - the ratio of the variance of a statistic with the actual sample design to the variance of that statistic with a simple random sample of same size - for the "Laeken" indicators, that is, a set of complex indicators of poverty and inequality which had been set out in the framework of the EU-SILC project (European Statistics on Income and Living Conditions. The Taylor linearization method (Tepping, 1968; Woodruff, 1971; Wolter, 1985; Tille, 2000 is actually a well-established method to obtain variance estimators for nonlinear statistics such as ratios, correlation or regression coefficients. It consists of approximating a nonlinear statistic with a linear function of the observations by using first-order Taylor Series expansions. Then, an easily found variance estimator of the linear approximation is used as an estimator of the variance of the nonlinear statistic. Although the Taylor linearization method handles all the nonlinear statistics which can be expressed as a smooth function of estimated totals, the approach fails to encompass the "Laeken" indicators since the latter are having more complex mathematical expressions. Consequently, a generalized linearization method (Deville, 1999, which relies on the concept of influence function (Hampel, Ronchetti, Rousseeuw and Stahel, 1986, has been implemented. After presenting the EU-SILC instrument and the main target indicators for which variance estimates are needed, the paper elaborates on the main features of the linearization approach based on influence functions. Ultimately, estimated standard errors, confidence intervals and design effect coefficients obtained from this approach are presented and discussed.

A Hierarchical Linear Model for Estimating Gender-Based Earnings Differentials.

Science.gov (United States)

Haberfield, Yitchak; Semyonov, Moshe; Addi, Audrey

1998-01-01

Estimates of gender earnings inequality in data from 116,431 Jewish workers were compared using a hierarchical linear model (HLM) and ordinary least squares model. The HLM allows estimation of the extent to which earnings inequality depends on occupational characteristics. (SK)

Non-Linear State Estimation Using Pre-Trained Neural Networks

DEFF Research Database (Denmark)

Bayramoglu, Enis; Andersen, Nils Axel; Ravn, Ole

2010-01-01

effecting the transformation. This function is approximated by a neural network using offline training. The training is based on monte carlo sampling. A way to obtain parametric distributions of flexible shape to be used easily with these networks is also presented. The method can also be used to improve...... other parametric methods around regions with strong non-linearities by including them inside the network....

State estimation for Markov-type genetic regulatory networks with delays and uncertain mode transition rates

International Nuclear Information System (INIS)

Liang Jinling; Lam, James; Wang Zidong

2009-01-01

This Letter is concerned with the robust state estimation problem for uncertain time-delay Markovian jumping genetic regulatory networks (GRNs) with SUM logic, where the uncertainties enter into both the network parameters and the mode transition rate. The nonlinear functions describing the feedback regulation are assumed to satisfy the sector-like conditions. The main purpose of the problem addressed is to design a linear estimator to approximate the true concentrations of the mRNA and protein through available measurement outputs. By resorting to the Lyapunov functional method and some stochastic analysis tools, it is shown that if a set of linear matrix inequalities (LMIs) is feasible, the desired state estimator, that can ensure the estimation error dynamics to be globally robustly asymptotically stable in the mean square, exists. The obtained LMI conditions are dependent on both the lower and the upper bounds of the delays. An illustrative example is presented to demonstrate the feasibility of the proposed estimation schemes.

H∞ state estimation of generalised neural networks with interval time-varying delays

Science.gov (United States)

Saravanakumar, R.; Syed Ali, M.; Cao, Jinde; Huang, He

2016-12-01

This paper focuses on studying the H∞ state estimation of generalised neural networks with interval time-varying delays. The integral terms in the time derivative of the Lyapunov-Krasovskii functional are handled by the Jensen's inequality, reciprocally convex combination approach and a new Wirtinger-based double integral inequality. A delay-dependent criterion is derived under which the estimation error system is globally asymptotically stable with H∞ performance. The proposed conditions are represented by linear matrix inequalities. Optimal H∞ norm bounds are obtained easily by solving convex problems in terms of linear matrix inequalities. The advantage of employing the proposed inequalities is illustrated by numerical examples.

Time-Dependent-Asymmetric-Linear-Parsimonious Ancestral State Reconstruction.

Science.gov (United States)

Didier, Gilles

2017-10-01

The time-dependent-asymmetric-linear parsimony is an ancestral state reconstruction method which extends the standard linear parsimony (a.k.a. Wagner parsimony) approach by taking into account both branch lengths and asymmetric evolutionary costs for reconstructing quantitative characters (asymmetric costs amount to assuming an evolutionary trend toward the direction with the lowest cost). A formal study of the influence of the asymmetry parameter shows that the time-dependent-asymmetric-linear parsimony infers states which are all taken among the known states, except for some degenerate cases corresponding to special values of the asymmetry parameter. This remarkable property holds in particular for the Wagner parsimony. This study leads to a polynomial algorithm which determines, and provides a compact representation of, the parametric reconstruction of a phylogenetic tree, that is for all the unknown nodes, the set of all the possible reconstructed states associated with the asymmetry parameters leading to them. The time-dependent-asymmetric-linear parsimony is finally illustrated with the parametric reconstruction of the body size of cetaceans.

Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints

Directory of Open Access Journals (Sweden)

Shun Xiang

2018-01-01

Full Text Available The paper aims to realize a rapid online estimation of the state-of-power (SOP with multiple constraints of a lithium-ion battery. Firstly, based on the improved first-order resistance-capacitance (RC model with one-state hysteresis, a linear state-space battery model is built; then, using the dual extended Kalman filtering (DEKF method, the battery parameters and states, including open-circuit voltage (OCV, are estimated. Secondly, by employing the estimated OCV as the observed value to build the second dual Kalman filters, the battery SOC is estimated. Thirdly, a novel rapid-calculating peak power/SOP method with multiple constraints is proposed in which, according to the bisection judgment method, the battery’s peak state is determined; then, one or two instantaneous peak powers are used to determine the peak power during T seconds. In addition, in the battery operating process, the actual constraint that the battery is under is analyzed specifically. Finally, three simplified versions of the Federal Urban Driving Schedule (SFUDS with inserted pulse experiments are conducted to verify the effectiveness and accuracy of the proposed online SOP estimation method.

Non-linear wave packet dynamics of coherent states

Indian Academy of Sciences (India)

In recent years, the non-linear quantum dynamics of these states have revealed some striking features. It was found that under the action of a Hamil- tonian which is a non-linear function of the photon operator(s) only, an initial coherent state loses its coherent structure quickly due to quantum dephasing induced by the non-.

Estimation of time- and state-dependent delays and other parameters in functional differential equations

Science.gov (United States)

Murphy, K. A.

1990-01-01

A parameter estimation algorithm is developed which can be used to estimate unknown time- or state-dependent delays and other parameters (e.g., initial condition) appearing within a nonlinear nonautonomous functional differential equation. The original infinite dimensional differential equation is approximated using linear splines, which are allowed to move with the variable delay. The variable delays are approximated using linear splines as well. The approximation scheme produces a system of ordinary differential equations with nice computational properties. The unknown parameters are estimated within the approximating systems by minimizing a least-squares fit-to-data criterion. Convergence theorems are proved for time-dependent delays and state-dependent delays within two classes, which say essentially that fitting the data by using approximations will, in the limit, provide a fit to the data using the original system. Numerical test examples are presented which illustrate the method for all types of delay.

Admissible Estimators in the General Multivariate Linear Model with Respect to Inequality Restricted Parameter Set

Directory of Open Access Journals (Sweden)

Shangli Zhang

2009-01-01

Full Text Available By using the methods of linear algebra and matrix inequality theory, we obtain the characterization of admissible estimators in the general multivariate linear model with respect to inequality restricted parameter set. In the classes of homogeneous and general linear estimators, the necessary and suffcient conditions that the estimators of regression coeffcient function are admissible are established.

State estimation in networked systems

NARCIS (Netherlands)

Sijs, J.

2012-01-01

This thesis considers state estimation strategies for networked systems. State estimation refers to a method for computing the unknown state of a dynamic process by combining sensor measurements with predictions from a process model. The most well known method for state estimation is the Kalman

Robust-BD Estimation and Inference for General Partially Linear Models

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Chunming Zhang

2017-11-01

Full Text Available The classical quadratic loss for the partially linear model (PLM and the likelihood function for the generalized PLM are not resistant to outliers. This inspires us to propose a class of “robust-Bregman divergence (BD” estimators of both the parametric and nonparametric components in the general partially linear model (GPLM, which allows the distribution of the response variable to be partially specified, without being fully known. Using the local-polynomial function estimation method, we propose a computationally-efficient procedure for obtaining “robust-BD” estimators and establish the consistency and asymptotic normality of the “robust-BD” estimator of the parametric component β o . For inference procedures of β o in the GPLM, we show that the Wald-type test statistic W n constructed from the “robust-BD” estimators is asymptotically distribution free under the null, whereas the likelihood ratio-type test statistic Λ n is not. This provides an insight into the distinction from the asymptotic equivalence (Fan and Huang 2005 between W n and Λ n in the PLM constructed from profile least-squares estimators using the non-robust quadratic loss. Numerical examples illustrate the computational effectiveness of the proposed “robust-BD” estimators and robust Wald-type test in the appearance of outlying observations.

Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts.

Science.gov (United States)

Lin, Huiming; Fu, Bo; Qin, Guoyou; Zhu, Zhongyi

2017-12-01

We develop a doubly robust estimation of generalized partial linear models for longitudinal data with dropouts. Our method extends the highly efficient aggregate unbiased estimating function approach proposed in Qu et al. (2010) to a doubly robust one in the sense that under missing at random (MAR), our estimator is consistent when either the linear conditional mean condition is satisfied or a model for the dropout process is correctly specified. We begin with a generalized linear model for the marginal mean, and then move forward to a generalized partial linear model, allowing for nonparametric covariate effect by using the regression spline smoothing approximation. We establish the asymptotic theory for the proposed method and use simulation studies to compare its finite sample performance with that of Qu's method, the complete-case generalized estimating equation (GEE) and the inverse-probability weighted GEE. The proposed method is finally illustrated using data from a longitudinal cohort study. © 2017, The International Biometric Society.

On the evaluation of uncertainties for state estimation with the Kalman filter

International Nuclear Information System (INIS)

Eichstädt, S; Makarava, N; Elster, C

2016-01-01

The Kalman filter is an established tool for the analysis of dynamic systems with normally distributed noise, and it has been successfully applied in numerous areas. It provides sequentially calculated estimates of the system states along with a corresponding covariance matrix. For nonlinear systems, the extended Kalman filter is often used. This is derived from the Kalman filter by linearization around the current estimate. A key issue in metrology is the evaluation of the uncertainty associated with the Kalman filter state estimates. The ‘Guide to the Expression of Uncertainty in Measurement’ (GUM) and its supplements serve as the de facto standard for uncertainty evaluation in metrology. We explore the relationship between the covariance matrix produced by the Kalman filter and a GUM-compliant uncertainty analysis. In addition, the results of a Bayesian analysis are considered. For the case of linear systems with known system matrices, we show that all three approaches are compatible. When the system matrices are not precisely known, however, or when the system is nonlinear, this equivalence breaks down and different results can then be reached. For precisely known nonlinear systems, though, the result of the extended Kalman filter still corresponds to the linearized uncertainty propagation of the GUM. The extended Kalman filter can suffer from linearization and convergence errors. These disadvantages can be avoided to some extent by applying Monte Carlo procedures, and we propose such a method which is GUM-compliant and can also be applied online during the estimation. We illustrate all procedures in terms of a 2D dynamic system and compare the results with those obtained by particle filtering, which has been proposed for the approximate calculation of a Bayesian solution. Finally, we give some recommendations based on our findings. (paper)

Linear Estimation of Standard Deviation of Logistic Distribution ...

African Journals Online (AJOL)

The paper presents a theoretical method based on order statistics and a FORTRAN program for computing the variance and relative efficiencies of the standard deviation of the logistic population with respect to the Cramer-Rao lower variance bound and the best linear unbiased estimators (BLUE\\'s) when the mean is ...

State Estimation for Sensor Monitoring System with Uncertainty and Disturbance

Directory of Open Access Journals (Sweden)

Jianhong Sun

2014-10-01

Full Text Available This paper considers the state estimation problem for the sensor monitoring system which contains system uncertainty and nonlinear disturbance. In the sensor monitoring system, states of each inner sensor node usually contains system uncertainty, and external noise often works as nonlinear item. Besides, information transmission in the system is also time consuming. All mentioned above may arouse in unstable of the monitoring system. In this case, states of sensors could be wrongly sampled. Under this circumstance, a proper mathematical model is proposed and by the use of Lipschitz condition, the nonlinear item is transformed to linear one. In addition, we suppose that all sensor nodes are distributed arranged, no interface occurs with each other. By establishing proper Lyapunov– Krasovskii functional, sufficient conditions are acquired by solving linear matrix inequality to make the error augmented system stable, and the gains of observers are also derived. Finally, an illustrated example is given to show that system observed value tracks system states well, which fully demonstrate the effectiveness of our result.

Anisotropic compacts stars on paraboloidal spacetime with linear equation of state

Energy Technology Data Exchange (ETDEWEB)

Thomas, V.O. [The Maharaja Sayajirao University of Baroda, Department of Mathematics, Faculty of Science, Vadodara, Gujarat (India); Pandya, D.M. [Pandit Deendayal Petroleum University, Department of Mathematics and Computer Science, Gandhinagar, Gujarat (India)

2017-06-15

New exact solutions of Einstein's field equations (EFEs) by assuming a linear equation of state, p{sub r} = α(ρ-ρ{sub R}), where p{sub r} is the radial pressure and ρ{sub R} is the surface density, are obtained on the background of a paraboloidal spacetime. By assuming estimated mass and radius of strange star candidate 4U 1820-30, various physical and energy conditions are used for estimating the range of parameter α. The suitability of the model for describing pulsars like PSR J1903+327, Vela X-1, Her X-1 and SAX J1808.4-3658 has been explored and respective ranges of α, for which all physical and energy conditions are satisfied throughout the distribution, are obtained. (orig.)

An Entropic Estimator for Linear Inverse Problems

Directory of Open Access Journals (Sweden)

Amos Golan

2012-05-01

Full Text Available In this paper we examine an Information-Theoretic method for solving noisy linear inverse estimation problems which encompasses under a single framework a whole class of estimation methods. Under this framework, the prior information about the unknown parameters (when such information exists, and constraints on the parameters can be incorporated in the statement of the problem. The method builds on the basics of the maximum entropy principle and consists of transforming the original problem into an estimation of a probability density on an appropriate space naturally associated with the statement of the problem. This estimation method is generic in the sense that it provides a framework for analyzing non-normal models, it is easy to implement and is suitable for all types of inverse problems such as small and or ill-conditioned, noisy data. First order approximation, large sample properties and convergence in distribution are developed as well. Analytical examples, statistics for model comparisons and evaluations, that are inherent to this method, are discussed and complemented with explicit examples.

State estimation for networked control systems using fixed data rates

Science.gov (United States)

Liu, Qing-Quan; Jin, Fang

2017-07-01

This paper investigates state estimation for linear time-invariant systems where sensors and controllers are geographically separated and connected via a bandwidth-limited and errorless communication channel with the fixed data rate. All plant states are quantised, coded and converted together into a codeword in our quantisation and coding scheme. We present necessary and sufficient conditions on the fixed data rate for observability of such systems, and further develop the data-rate theorem. It is shown in our results that there exists a quantisation and coding scheme to ensure observability of the system if the fixed data rate is larger than the lower bound given, which is less conservative than the one in the literature. Furthermore, we also examine the role that the disturbances have on the state estimation problem in the case with data-rate limitations. Illustrative examples are given to demonstrate the effectiveness of the proposed method.

A Comparison of Alternative Estimators of Linearly Aggregated Macro Models

Directory of Open Access Journals (Sweden)

Fikri Akdeniz

2012-07-01

Full Text Available Normal 0 false false false TR X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Times New Roman","serif"; mso-ansi-language:TR; mso-fareast-language:TR;} This paper deals with the linear aggregation problem. For the true underlying micro relations, which explain the micro behavior of the individuals, no restrictive rank conditions are assumed. Thus the analysis is presented in a framework utilizing generalized inverses of singular matrices. We investigate several estimators for certain linear transformations of the systematic part of the corresponding macro relations. Homogeneity of micro parameters is discussed. Best linear unbiased estimation for micro parameters is described.

Joint state and parameter estimation for a class of cascade systems: Application to a hemodynamic model

KAUST Repository

Zayane, Chadia

2014-06-01

In this paper, we address a special case of state and parameter estimation, where the system can be put on a cascade form allowing to estimate the state components and the set of unknown parameters separately. Inspired by the nonlinear Balloon hemodynamic model for functional Magnetic Resonance Imaging problem, we propose a hierarchical approach. The system is divided into two subsystems in cascade. The state and input are first estimated from a noisy measured signal using an adaptive observer. The obtained input is then used to estimate the parameters of a linear system using the modulating functions method. Some numerical results are presented to illustrate the efficiency of the proposed method.

Power system state estimation using an iteratively reweighted least squares method for sequential L{sub 1}-regression

Energy Technology Data Exchange (ETDEWEB)

Jabr, R.A. [Electrical, Computer and Communication Engineering Department, Notre Dame University, P.O. Box 72, Zouk Mikhael, Zouk Mosbeh (Lebanon)

2006-02-15

This paper presents an implementation of the least absolute value (LAV) power system state estimator based on obtaining a sequence of solutions to the L{sub 1}-regression problem using an iteratively reweighted least squares (IRLS{sub L1}) method. The proposed implementation avoids reformulating the regression problem into standard linear programming (LP) form and consequently does not require the use of common methods of LP, such as those based on the simplex method or interior-point methods. It is shown that the IRLS{sub L1} method is equivalent to solving a sequence of linear weighted least squares (LS) problems. Thus, its implementation presents little additional effort since the sparse LS solver is common to existing LS state estimators. Studies on the termination criteria of the IRLS{sub L1} method have been carried out to determine a procedure for which the proposed estimator is more computationally efficient than a previously proposed non-linear iteratively reweighted least squares (IRLS) estimator. Indeed, it is revealed that the proposed method is a generalization of the previously reported IRLS estimator, but is based on more rigorous theory. (author)

H∞ state estimation of stochastic memristor-based neural networks with time-varying delays.

Science.gov (United States)

Bao, Haibo; Cao, Jinde; Kurths, Jürgen; Alsaedi, Ahmed; Ahmad, Bashir

2018-03-01

This paper addresses the problem of H ∞ state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the H ∞ state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficient conditions are derived to ensure that the estimation error system is asymptotically stable in the mean square with a prescribed H ∞ performance. An explicit expression of the state estimator gain is given in terms of linear matrix inequalities (LMIs). Compared with other results, our results reduce control gain and control cost effectively. Finally, numerical simulations are provided to demonstrate the efficiency of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

On estimation of the noise variance in high-dimensional linear models

OpenAIRE

Golubev, Yuri; Krymova, Ekaterina

2017-01-01

We consider the problem of recovering the unknown noise variance in the linear regression model. To estimate the nuisance (a vector of regression coefficients) we use a family of spectral regularisers of the maximum likelihood estimator. The noise estimation is based on the adaptive normalisation of the squared error. We derive the upper bound for the concentration of the proposed method around the ideal estimator (the case of zero nuisance).

Linear estimation of coherent structures in wall-bounded turbulence at Re τ = 2000

Science.gov (United States)

Oehler, S.; Garcia–Gutiérrez, A.; Illingworth, S.

2018-04-01

The estimation problem for a fully-developed turbulent channel flow at Re τ = 2000 is considered. Specifically, a Kalman filter is designed using a Navier–Stokes-based linear model. The estimator uses time-resolved velocity measurements at a single wall-normal location (provided by DNS) to estimate the time-resolved velocity field at other wall-normal locations. The estimator is able to reproduce the largest scales with reasonable accuracy for a range of wavenumber pairs, measurement locations and estimation locations. Importantly, the linear model is also able to predict with reasonable accuracy the performance that will be achieved by the estimator when applied to the DNS. A more practical estimation scheme using the shear stress at the wall as measurement is also considered. The estimator is still able to estimate the largest scales with reasonable accuracy, although the estimator’s performance is reduced.

Resting State Network Estimation in Individual Subjects

Science.gov (United States)

Hacker, Carl D.; Laumann, Timothy O.; Szrama, Nicholas P.; Baldassarre, Antonello; Snyder, Abraham Z.

2014-01-01

Resting-state functional magnetic resonance imaging (fMRI) has been used to study brain networks associated with both normal and pathological cognitive function. The objective of this work is to reliably compute resting state network (RSN) topography in single participants. We trained a supervised classifier (multi-layer perceptron; MLP) to associate blood oxygen level dependent (BOLD) correlation maps corresponding to pre-defined seeds with specific RSN identities. Hard classification of maps obtained from a priori seeds was highly reliable across new participants. Interestingly, continuous estimates of RSN membership retained substantial residual error. This result is consistent with the view that RSNs are hierarchically organized, and therefore not fully separable into spatially independent components. After training on a priori seed-based maps, we propagated voxel-wise correlation maps through the MLP to produce estimates of RSN membership throughout the brain. The MLP generated RSN topography estimates in individuals consistent with previous studies, even in brain regions not represented in the training data. This method could be used in future studies to relate RSN topography to other measures of functional brain organization (e.g., task-evoked responses, stimulation mapping, and deficits associated with lesions) in individuals. The multi-layer perceptron was directly compared to two alternative voxel classification procedures, specifically, dual regression and linear discriminant analysis; the perceptron generated more spatially specific RSN maps than either alternative. PMID:23735260

A speed estimation unit for induction motors based on adaptive linear combiner

International Nuclear Information System (INIS)

Marei, Mostafa I.; Shaaban, Mostafa F.; El-Sattar, Ahmed A.

2009-01-01

This paper presents a new induction motor speed estimation technique, which can estimate the rotor resistance as well, from the measured voltage and current signals. Moreover, the paper utilizes a novel adaptive linear combiner (ADALINE) structure for speed and rotor resistance estimations. This structure can deal with the multi-output systems and it is called MO-ADALINE. The model of the induction motor is arranged in a linear form, in the stationary reference frame, to cope with the proposed speed estimator. There are many advantages of the proposed unit such as wide speed range capability, immunity against harmonics of measured waveforms, and precise estimation of the speed and the rotor resistance at different dynamic changes. Different types of induction motor drive systems are used to evaluate the dynamic performance and to examine the accuracy of the proposed unit for speed and rotor resistance estimation.

A Performance Comparison Between Extended Kalman Filter and Unscented Kalman Filter in Power System Dynamic State Estimation

DEFF Research Database (Denmark)

Khazraj, Hesam; Silva, Filipe Miguel Faria da; Bak, Claus Leth

2016-01-01

Dynamic State Estimation (DSE) is a critical tool for analysis, monitoring and planning of a power system. The concept of DSE involves designing state estimation with Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) methods, which can be used by wide area monitoring to improve......-linear state estimator is developed in MatLab to solve states by applying the unscented Kalman filter (UKF) and Extended Kalman Filter (EKF) algorithm. Finally, a DSE model is built for a 14 bus power system network to evaluate the proposed algorithm for the networks.This article will focus on comparing...

H∞ state estimation for discrete-time memristive recurrent neural networks with stochastic time-delays

Science.gov (United States)

Liu, Hongjian; Wang, Zidong; Shen, Bo; Alsaadi, Fuad E.

2016-07-01

This paper deals with the robust H∞ state estimation problem for a class of memristive recurrent neural networks with stochastic time-delays. The stochastic time-delays under consideration are governed by a Bernoulli-distributed stochastic sequence. The purpose of the addressed problem is to design the robust state estimator such that the dynamics of the estimation error is exponentially stable in the mean square, and the prescribed ? performance constraint is met. By utilizing the difference inclusion theory and choosing a proper Lyapunov-Krasovskii functional, the existence condition of the desired estimator is derived. Based on it, the explicit expression of the estimator gain is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is employed to demonstrate the effectiveness and applicability of the proposed estimation approach.

Parameter and State Estimator for State Space Models

Directory of Open Access Journals (Sweden)

Ruifeng Ding

2014-01-01

Full Text Available This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.

Tightness of M-estimators for multiple linear regression in time series

DEFF Research Database (Denmark)

Johansen, Søren; Nielsen, Bent

We show tightness of a general M-estimator for multiple linear regression in time series. The positive criterion function for the M-estimator is assumed lower semi-continuous and sufficiently large for large argument: Particular cases are the Huber-skip and quantile regression. Tightness requires...

Linearization Method and Linear Complexity

Science.gov (United States)

Tanaka, Hidema

We focus on the relationship between the linearization method and linear complexity and show that the linearization method is another effective technique for calculating linear complexity. We analyze its effectiveness by comparing with the logic circuit method. We compare the relevant conditions and necessary computational cost with those of the Berlekamp-Massey algorithm and the Games-Chan algorithm. The significant property of a linearization method is that it needs no output sequence from a pseudo-random number generator (PRNG) because it calculates linear complexity using the algebraic expression of its algorithm. When a PRNG has n [bit] stages (registers or internal states), the necessary computational cost is smaller than O(2n). On the other hand, the Berlekamp-Massey algorithm needs O(N2) where N(≅2n) denotes period. Since existing methods calculate using the output sequence, an initial value of PRNG influences a resultant value of linear complexity. Therefore, a linear complexity is generally given as an estimate value. On the other hand, a linearization method calculates from an algorithm of PRNG, it can determine the lower bound of linear complexity.

Adaptive optimisation-offline cyber attack on remote state estimator

Science.gov (United States)

Huang, Xin; Dong, Jiuxiang

2017-10-01

Security issues of cyber-physical systems have received increasing attentions in recent years. In this paper, deception attacks on the remote state estimator equipped with the chi-squared failure detector are considered, and it is assumed that the attacker can monitor and modify all the sensor data. A novel adaptive optimisation-offline cyber attack strategy is proposed, where using the current and previous sensor data, the attack can yield the largest estimation error covariance while ensuring to be undetected by the chi-squared monitor. From the attacker's perspective, the attack is better than the existing linear deception attacks to degrade the system performance. Finally, some numerical examples are provided to demonstrate theoretical results.

Wheeled vehicle deceleration as estimation parameter of adaptive brake control system state

Directory of Open Access Journals (Sweden)

Turenko A.

2012-06-01

Full Text Available The method of stability estimation of adaptive control system with signal adjustment based on Lyapunov’s direct method that allows to take into account the nonstationarity of the basic system and non-linearity in the form of limitation on control action restriction as well as error control is stated.

Inverse estimation of multiple muscle activations based on linear logistic regression.

Science.gov (United States)

Sekiya, Masashi; Tsuji, Toshiaki

2017-07-01

This study deals with a technology to estimate the muscle activity from the movement data using a statistical model. A linear regression (LR) model and artificial neural networks (ANN) have been known as statistical models for such use. Although ANN has a high estimation capability, it is often in the clinical application that the lack of data amount leads to performance deterioration. On the other hand, the LR model has a limitation in generalization performance. We therefore propose a muscle activity estimation method to improve the generalization performance through the use of linear logistic regression model. The proposed method was compared with the LR model and ANN in the verification experiment with 7 participants. As a result, the proposed method showed better generalization performance than the conventional methods in various tasks.

A model predictive control approach combined unscented Kalman filter vehicle state estimation in intelligent vehicle trajectory tracking

Directory of Open Access Journals (Sweden)

Hongxiao Yu

2015-05-01

Full Text Available Trajectory tracking and state estimation are significant in the motion planning and intelligent vehicle control. This article focuses on the model predictive control approach for the trajectory tracking of the intelligent vehicles and state estimation of the nonlinear vehicle system. The constraints of the system states are considered when applying the model predictive control method to the practical problem, while 4-degree-of-freedom vehicle model and unscented Kalman filter are proposed to estimate the vehicle states. The estimated states of the vehicle are used to provide model predictive control with real-time control and judge vehicle stability. Furthermore, in order to decrease the cost of solving the nonlinear optimization, the linear time-varying model predictive control is used at each time step. The effectiveness of the proposed vehicle state estimation and model predictive control method is tested by driving simulator. The results of simulations and experiments show that great and robust performance is achieved for trajectory tracking and state estimation in different scenarios.

Optimal allocation of sensors for state estimation of distributed parameter systems

International Nuclear Information System (INIS)

Sunahara, Yoshifumi; Ohsumi, Akira; Mogami, Yoshio.

1978-01-01

The purpose of this paper is to present a method for finding the optimal allocation of sensors for state estimation of linear distributed parameter systems. This method is based on the criterion that the error covariance associated with the state estimate becomes minimal with respect to the allocation of the sensors. A theorem is established, giving the sufficient condition for optimizing the allocation of sensors to make minimal the error covariance approximated by a modal expansion. The remainder of this paper is devoted to illustrate important phases of the general theory of the optimal measurement allocation problem. To do this, several examples are demonstrated, including extensive discussions on the mutual relation between the optimal allocation and the dynamics of sensors. (author)

Smoothing-based compressed state Kalman filter for joint state-parameter estimation: Applications in reservoir characterization and CO2 storage monitoring

Science.gov (United States)

Li, Y. J.; Kokkinaki, Amalia; Darve, Eric F.; Kitanidis, Peter K.

2017-08-01

The operation of most engineered hydrogeological systems relies on simulating physical processes using numerical models with uncertain parameters and initial conditions. Predictions by such uncertain models can be greatly improved by Kalman-filter techniques that sequentially assimilate monitoring data. Each assimilation constitutes a nonlinear optimization, which is solved by linearizing an objective function about the model prediction and applying a linear correction to this prediction. However, if model parameters and initial conditions are uncertain, the optimization problem becomes strongly nonlinear and a linear correction may yield unphysical results. In this paper, we investigate the utility of one-step ahead smoothing, a variant of the traditional filtering process, to eliminate nonphysical results and reduce estimation artifacts caused by nonlinearities. We present the smoothing-based compressed state Kalman filter (sCSKF), an algorithm that combines one step ahead smoothing, in which current observations are used to correct the state and parameters one step back in time, with a nonensemble covariance compression scheme, that reduces the computational cost by efficiently exploring the high-dimensional state and parameter space. Numerical experiments show that when model parameters are uncertain and the states exhibit hyperbolic behavior with sharp fronts, as in CO2 storage applications, one-step ahead smoothing reduces overshooting errors and, by design, gives physically consistent state and parameter estimates. We compared sCSKF with commonly used data assimilation methods and showed that for the same computational cost, combining one step ahead smoothing and nonensemble compression is advantageous for real-time characterization and monitoring of large-scale hydrogeological systems with sharp moving fronts.

Linear and nonlinear ARMA model parameter estimation using an artificial neural network

Science.gov (United States)

Chon, K. H.; Cohen, R. J.

1997-01-01

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

A Bayes linear Bayes method for estimation of correlated event rates.

Science.gov (United States)

Quigley, John; Wilson, Kevin J; Walls, Lesley; Bedford, Tim

2013-12-01

Typically, full Bayesian estimation of correlated event rates can be computationally challenging since estimators are intractable. When estimation of event rates represents one activity within a larger modeling process, there is an incentive to develop more efficient inference than provided by a full Bayesian model. We develop a new subjective inference method for correlated event rates based on a Bayes linear Bayes model under the assumption that events are generated from a homogeneous Poisson process. To reduce the elicitation burden we introduce homogenization factors to the model and, as an alternative to a subjective prior, an empirical method using the method of moments is developed. Inference under the new method is compared against estimates obtained under a full Bayesian model, which takes a multivariate gamma prior, where the predictive and posterior distributions are derived in terms of well-known functions. The mathematical properties of both models are presented. A simulation study shows that the Bayes linear Bayes inference method and the full Bayesian model provide equally reliable estimates. An illustrative example, motivated by a problem of estimating correlated event rates across different users in a simple supply chain, shows how ignoring the correlation leads to biased estimation of event rates. © 2013 Society for Risk Analysis.

Resource-efficient generation of linear cluster states by linear optics with postselection

International Nuclear Information System (INIS)

Uskov, D B; Alsing, P M; Fanto, M L; Szep, A; Smith, A M; Kaplan, L; Kim, R

2015-01-01

We report on theoretical research in photonic cluster-state computing. Finding optimal schemes of generating non-classical photonic states is of critical importance for this field as physically implementable photon–photon entangling operations are currently limited to measurement-assisted stochastic transformations. A critical parameter for assessing the efficiency of such transformations is the success probability of a desired measurement outcome. At present there are several experimental groups that are capable of generating multi-photon cluster states carrying more than eight qubits. Separate photonic qubits or small clusters can be fused into a single cluster state by a probabilistic optical CZ gate conditioned on simultaneous detection of all photons with 1/9 success probability for each gate. This design mechanically follows the original theoretical scheme of cluster state generation proposed more than a decade ago by Raussendorf, Browne and Briegel. The optimality of the destructive CZ gate in application to linear optical cluster state generation has not been analyzed previously. Our results reveal that this method is far from the optimal one. Employing numerical optimization we have identified that the maximal success probability of fusing n unentangled dual-rail optical qubits into a linear cluster state is equal to (1/2) n−1 ; an m-tuple of photonic Bell pair states, commonly generated via spontaneous parametric down-conversion, can be fused into a single cluster with the maximal success probability of (1/4) m−1 . (paper)

State and actuator fault estimation observer design integrated in a riderless bicycle stabilization system.

Science.gov (United States)

Brizuela Mendoza, Jorge Aurelio; Astorga Zaragoza, Carlos Manuel; Zavala Río, Arturo; Pattalochi, Leo; Canales Abarca, Francisco

2016-03-01

This paper deals with an observer design for Linear Parameter Varying (LPV) systems with high-order time-varying parameter dependency. The proposed design, considered as the main contribution of this paper, corresponds to an observer for the estimation of the actuator fault and the system state, considering measurement noise at the system outputs. The observer gains are computed by considering the extension of linear systems theory to polynomial LPV systems, in such a way that the observer reaches the characteristics of LPV systems. As a result, the actuator fault estimation is ready to be used in a Fault Tolerant Control scheme, where the estimated state with reduced noise should be used to generate the control law. The effectiveness of the proposed methodology has been tested using a riderless bicycle model with dependency on the translational velocity v, where the control objective corresponds to the system stabilization towards the upright position despite the variation of v along the closed-loop system trajectories. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Estimation of failure probabilities of linear dynamic systems by ...

Indian Academy of Sciences (India)

An iterative method for estimating the failure probability for certain time-variant reliability problems has been developed. In the paper, the focus is on the displacement response of a linear oscillator driven by white noise. Failure is then assumed to occur when the displacement response exceeds a critical threshold.

Towards Real-Time Maneuver Detection: Automatic State and Dynamics Estimation with the Adaptive Optimal Control Based Estimator

Science.gov (United States)

Lubey, D.; Scheeres, D.

Tracking objects in Earth orbit is fraught with complications. This is due to the large population of orbiting spacecraft and debris that continues to grow, passive (i.e. no direct communication) and data-sparse observations, and the presence of maneuvers and dynamics mismodeling. Accurate orbit determination in this environment requires an algorithm to capture both a system's state and its state dynamics in order to account for mismodelings. Previous studies by the authors yielded an algorithm called the Optimal Control Based Estimator (OCBE) - an algorithm that simultaneously estimates a system's state and optimal control policies that represent dynamic mismodeling in the system for an arbitrary orbit-observer setup. The stochastic properties of these estimated controls are then used to determine the presence of mismodelings (maneuver detection), as well as characterize and reconstruct the mismodelings. The purpose of this paper is to develop the OCBE into an accurate real-time orbit tracking and maneuver detection algorithm by automating the algorithm and removing its linear assumptions. This results in a nonlinear adaptive estimator. In its original form the OCBE had a parameter called the assumed dynamic uncertainty, which is selected by the user with each new measurement to reflect the level of dynamic mismodeling in the system. This human-in-the-loop approach precludes real-time application to orbit tracking problems due to their complexity. This paper focuses on the Adaptive OCBE, a version of the estimator where the assumed dynamic uncertainty is chosen automatically with each new measurement using maneuver detection results to ensure that state uncertainties are properly adjusted to account for all dynamic mismodelings. The paper also focuses on a nonlinear implementation of the estimator. Originally, the OCBE was derived from a nonlinear cost function then linearized about a nominal trajectory, which is assumed to be ballistic (i.e. the nominal optimal

Real-time muscle state estimation from EMG signals during isometric contractions using Kalman filters.

Science.gov (United States)

Menegaldo, Luciano L

2017-12-01

State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman filters (eKF), derived directly from Hill-type muscle mechanics equations, can be used as real-time muscle state estimators for isometric contractions using raw electromyography signals (EMG) as the only available measurement. The estimators' amplitude error, computational cost, filtering lags and smoothness are compared with usual EMG-driven analysis, performed offline, by integrating the nonlinear Hill-type muscle model differential equations (offline simulations-OS). EMG activity of the three triceps surae components (soleus, gastrocnemius medialis and gastrocnemius lateralis), in three torque levels, was collected for ten subjects. The actualization interval (AI) between two updates of the KF and eKF was also varied. The results show that computational costs are significantly reduced (70x for KF and 17[Formula: see text] for eKF). The filtering lags presented sharp linear relationships with the AI (0-300 ms), depending on the state and activation level. Under maximum excitation, amplitude errors varied in the range 10-24% for activation, 5-8% for tendon force and 1.4-1.8% for muscle length, reducing linearly with the excitation level. Smoothness, measured by the ratio between the average standard variations of KF/eKF and OS estimations, was greatly reduced for activation but converged exponentially to 1 for the other states by increasing AI. Compared to regular KF, extended KF does not seem to improve estimation accuracy significantly. Depending on the particular application requirements, the most appropriate KF actualization interval can be selected.

A Low-Complexity ESPRIT-Based DOA Estimation Method for Co-Prime Linear Arrays.

Science.gov (United States)

Sun, Fenggang; Gao, Bin; Chen, Lizhen; Lan, Peng

2016-08-25

The problem of direction-of-arrival (DOA) estimation is investigated for co-prime array, where the co-prime array consists of two uniform sparse linear subarrays with extended inter-element spacing. For each sparse subarray, true DOAs are mapped into several equivalent angles impinging on the traditional uniform linear array with half-wavelength spacing. Then, by applying the estimation of signal parameters via rotational invariance technique (ESPRIT), the equivalent DOAs are estimated, and the candidate DOAs are recovered according to the relationship among equivalent and true DOAs. Finally, the true DOAs are estimated by combining the results of the two subarrays. The proposed method achieves a better complexity-performance tradeoff as compared to other existing methods.

truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models

Directory of Open Access Journals (Sweden)

Maria Karlsson

2014-05-01

Full Text Available Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and ?nite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.

Remote optimal state estimation over communication channels with random delays

KAUST Repository

Mahmoud, Magdi S.

2014-01-22

This paper considers the optimal estimation of linear systems over unreliable communication channels with random delays. In this work, it is assumed that the system to be estimated is far away from the filter. The observations of the system are capsulized without time stamp and then transmitted to the network node at which the filter is located. The probabilities of time delays are assumed to be known. The event-driven estimation scheme is applied in this paper and the estimate of the states is updated only at each time instant when any measurement arrives. To capture the feature of communication, the system considered is augmented, and the arrived measurements are regarded as the uncertain observations of the augmented system. The corresponding optimal estimation algorithm is proposed and additionally, a numerical simulation represents the performance of this work. © 2014 The authors. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

Explicit estimating equations for semiparametric generalized linear latent variable models

KAUST Repository

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.

Fault tolerance in parity-state linear optical quantum computing

International Nuclear Information System (INIS)

Hayes, A. J. F.; Ralph, T. C.; Haselgrove, H. L.; Gilchrist, Alexei

2010-01-01

We use a combination of analytical and numerical techniques to calculate the noise threshold and resource requirements for a linear optical quantum computing scheme based on parity-state encoding. Parity-state encoding is used at the lowest level of code concatenation in order to efficiently correct errors arising from the inherent nondeterminism of two-qubit linear-optical gates. When combined with teleported error-correction (using either a Steane or Golay code) at higher levels of concatenation, the parity-state scheme is found to achieve a saving of approximately three orders of magnitude in resources when compared to the cluster state scheme, at a cost of a somewhat reduced noise threshold.

Robust best linear estimation for regression analysis using surrogate and instrumental variables.

Science.gov (United States)

Wang, C Y

2012-04-01

We investigate methods for regression analysis when covariates are measured with errors. In a subset of the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies the classical measurement error model, but it may not have repeated measurements. In addition to the surrogate variables that are available among the subjects in the calibration sample, we assume that there is an instrumental variable (IV) that is available for all study subjects. An IV is correlated with the unobserved true exposure variable and hence can be useful in the estimation of the regression coefficients. We propose a robust best linear estimator that uses all the available data, which is the most efficient among a class of consistent estimators. The proposed estimator is shown to be consistent and asymptotically normal under very weak distributional assumptions. For Poisson or linear regression, the proposed estimator is consistent even if the measurement error from the surrogate or IV is heteroscedastic. Finite-sample performance of the proposed estimator is examined and compared with other estimators via intensive simulation studies. The proposed method and other methods are applied to a bladder cancer case-control study.

General linear-optical quantum state generation scheme: Applications to maximally path-entangled states

International Nuclear Information System (INIS)

VanMeter, N. M.; Lougovski, P.; Dowling, Jonathan P.; Uskov, D. B.; Kieling, K.; Eisert, J.

2007-01-01

We introduce schemes for linear-optical quantum state generation. A quantum state generator is a device that prepares a desired quantum state using product inputs from photon sources, linear-optical networks, and postselection using photon counters. We show that this device can be concisely described in terms of polynomial equations and unitary constraints. We illustrate the power of this language by applying the Groebner-basis technique along with the notion of vacuum extensions to solve the problem of how to construct a quantum state generator analytically for any desired state, and use methods of convex optimization to identify bounds to success probabilities. In particular, we disprove a conjecture concerning the preparation of the maximally path-entangled |n,0>+|0,n> (NOON) state by providing a counterexample using these methods, and we derive a new upper bound on the resources required for NOON-state generation

A Posteriori Error Estimation for Finite Element Methods and Iterative Linear Solvers

Energy Technology Data Exchange (ETDEWEB)

Melboe, Hallgeir

2001-10-01

This thesis addresses a posteriori error estimation for finite element methods and iterative linear solvers. Adaptive finite element methods have gained a lot of popularity over the last decades due to their ability to produce accurate results with limited computer power. In these methods a posteriori error estimates play an essential role. Not only do they give information about how large the total error is, they also indicate which parts of the computational domain should be given a more sophisticated treatment in order to reduce the error. A posteriori error estimates are traditionally aimed at estimating the global error, but more recently so called goal oriented error estimators have been shown a lot of interest. The name reflects the fact that they estimate the error in user-defined local quantities. In this thesis the main focus is on global error estimators for highly stretched grids and goal oriented error estimators for flow problems on regular grids. Numerical methods for partial differential equations, such as finite element methods and other similar techniques, typically result in a linear system of equations that needs to be solved. Usually such systems are solved using some iterative procedure which due to a finite number of iterations introduces an additional error. Most such algorithms apply the residual in the stopping criterion, whereas the control of the actual error may be rather poor. A secondary focus in this thesis is on estimating the errors that are introduced during this last part of the solution procedure. The thesis contains new theoretical results regarding the behaviour of some well known, and a few new, a posteriori error estimators for finite element methods on anisotropic grids. Further, a goal oriented strategy for the computation of forces in flow problems is devised and investigated. Finally, an approach for estimating the actual errors associated with the iterative solution of linear systems of equations is suggested. (author)

From neurons to circuits: linear estimation of local field potentials

Science.gov (United States)

Rasch, Malte; Logthetis, Nikos K.; Kreiman, Gabriel

2010-01-01

Extracellular physiological recordings are typically separated into two frequency bands: local field potentials (LFPs, a circuit property) and spiking multi-unit activity (MUA). There has been increased interest in LFPs due to their correlation with fMRI measurements and the possibility of studying local processing and neuronal synchrony. To further understand the biophysical origin of LFPs, we asked whether it is possible to estimate their time course based on the spiking activity from the same or nearby electrodes. We used Signal Estimation Theory to show that a linear filter operation on the activity of one/few neurons can explain a significant fraction of the LFP time course in the macaque primary visual cortex. The linear filter used to estimate the LFPs had a stereotypical shape characterized by a sharp downstroke at negative time lags and a slower positive upstroke for positve time lags. The filter was similar across neocortical regions and behavioral conditions including spontaneous activity and visual stimulation. The estimations had a spatial resolution of ~1 mm and a temporal resolution of ~200 ms. By considering a causal filter, we observed a temporal asymmetry such that the positive time lags in the filter contributed more to the LFP estimation than negative time lags. Additionally, we showed that spikes occurring within ~10 ms of spikes from nearby neurons yielded better estimation accuracies than nonsynchronous spikes. In sum, our results suggest that at least some circuit-level local properties of the field potentials can be predicted from the activity of one or a few neurons. PMID:19889990

Sensitive Constrained Optimal PMU Allocation with Complete Observability for State Estimation Solution

Directory of Open Access Journals (Sweden)

R. Manam

2017-12-01

Full Text Available In this paper, a sensitive constrained integer linear programming approach is formulated for the optimal allocation of Phasor Measurement Units (PMUs in a power system network to obtain state estimation. In this approach, sensitive buses along with zero injection buses (ZIB are considered for optimal allocation of PMUs in the network to generate state estimation solutions. Sensitive buses are evolved from the mean of bus voltages subjected to increase of load consistently up to 50%. Sensitive buses are ranked in order to place PMUs. Sensitive constrained optimal PMU allocation in case of single line and no line contingency are considered in observability analysis to ensure protection and control of power system from abnormal conditions. Modeling of ZIB constraints is included to minimize the number of PMU network allocations. This paper presents optimal allocation of PMU at sensitive buses with zero injection modeling, considering cost criteria and redundancy to increase the accuracy of state estimation solution without losing observability of the whole system. Simulations are carried out on IEEE 14, 30 and 57 bus systems and results obtained are compared with traditional and other state estimation methods available in the literature, to demonstrate the effectiveness of the proposed method.

Event-triggered sensor data transmission policy for receding horizon recursive state estimation

Directory of Open Access Journals (Sweden)

Yunji Li

2017-06-01

Full Text Available We consider a sensor data transmission policy for receding horizon recursive state estimation in a networked linear system. A good tradeoff between estimation error and communication rate could be achieved according to a transmission strategy, which decides the transfer time of the data packet. Here we give this transmission policy through proving the upper bound of system performance. Moreover, the lower bound of system performance is further analyzed in detail. A numerical example is given to verify the potential and effectiveness of the theoretical results.

Robust state estimation for uncertain fuzzy bidirectional associative memory networks with time-varying delays

Science.gov (United States)

Vadivel, P.; Sakthivel, R.; Mathiyalagan, K.; Arunkumar, A.

2013-09-01

This paper addresses the issue of robust state estimation for a class of fuzzy bidirectional associative memory (BAM) neural networks with time-varying delays and parameter uncertainties. By constructing the Lyapunov-Krasovskii functional, which contains the triple-integral term and using the free-weighting matrix technique, a set of sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to estimate the neuron states through available output measurements such that the dynamics of the estimation error system is robustly asymptotically stable. In particular, we consider a generalized activation function in which the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. More precisely, the design of the state estimator for such BAM neural networks can be obtained by solving some LMIs, which are dependent on the size of the time derivative of the time-varying delays. Finally, a numerical example with simulation result is given to illustrate the obtained theoretical results.

Robust state estimation for uncertain fuzzy bidirectional associative memory networks with time-varying delays

International Nuclear Information System (INIS)

Vadivel, P; Sakthivel, R; Mathiyalagan, K; Arunkumar, A

2013-01-01

This paper addresses the issue of robust state estimation for a class of fuzzy bidirectional associative memory (BAM) neural networks with time-varying delays and parameter uncertainties. By constructing the Lyapunov–Krasovskii functional, which contains the triple-integral term and using the free-weighting matrix technique, a set of sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to estimate the neuron states through available output measurements such that the dynamics of the estimation error system is robustly asymptotically stable. In particular, we consider a generalized activation function in which the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. More precisely, the design of the state estimator for such BAM neural networks can be obtained by solving some LMIs, which are dependent on the size of the time derivative of the time-varying delays. Finally, a numerical example with simulation result is given to illustrate the obtained theoretical results. (paper)

Estimation of the limit of detection in semiconductor gas sensors through linearized calibration models.

Science.gov (United States)

Burgués, Javier; Jiménez-Soto, Juan Manuel; Marco, Santiago

2018-07-12

The limit of detection (LOD) is a key figure of merit in chemical sensing. However, the estimation of this figure of merit is hindered by the non-linear calibration curve characteristic of semiconductor gas sensor technologies such as, metal oxide (MOX), gasFETs or thermoelectric sensors. Additionally, chemical sensors suffer from cross-sensitivities and temporal stability problems. The application of the International Union of Pure and Applied Chemistry (IUPAC) recommendations for univariate LOD estimation in non-linear semiconductor gas sensors is not straightforward due to the strong statistical requirements of the IUPAC methodology (linearity, homoscedasticity, normality). Here, we propose a methodological approach to LOD estimation through linearized calibration models. As an example, the methodology is applied to the detection of low concentrations of carbon monoxide using MOX gas sensors in a scenario where the main source of error is the presence of uncontrolled levels of humidity. Copyright © 2018 Elsevier B.V. All rights reserved.

Gain-scheduled Linear Quadratic Control of Wind Turbines Operating at High Wind Speed

DEFF Research Database (Denmark)

Østergaard, Kasper Zinck; Stoustrup, Jakob; Brath, Per

2007-01-01

This paper addresses state estimation and linear quadratic (LQ) control of variable speed variable pitch wind turbines. On the basis of a nonlinear model of a wind turbine, a set of operating conditions is identified and a LQ controller is designed for each operating point. The controller gains...... are then interpolated linearly to get a control law for the entire operating envelope. A nonlinear state estimator is designed as a combination of two unscented Kalman filters and a linear disturbance estimator. The gain-scheduling variable (wind speed) is then calculated from the output of these state estimators...

Efficient semiparametric estimation in generalized partially linear additive models for longitudinal/clustered data

KAUST Repository

Cheng, Guang; Zhou, Lan; Huang, Jianhua Z.

2014-01-01

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

STATE ESTIMATION IN ALCOHOLIC CONTINUOUS FERMENTATION OF ZYMOMONAS MOBILIS USING RECURSIVE BAYESIAN FILTERING: A SIMULATION APPROACH

Directory of Open Access Journals (Sweden)

Olga Lucia Quintero

2008-05-01

Full Text Available This work presents a state estimator for a continuous bioprocess. To this aim, the Non Linear Filtering theory based on the recursive application of Bayes rule and Monte Carlo techniques is used. Recursive Bayesian Filters Sampling Importance Resampling (SIR is employed, including different kinds of resampling. Generally, bio-processes have strong non-linear and non-Gaussian characteristics, and this tool becomes attractive. The estimator behavior and performance are illustrated with the continuous process of alcoholic fermentation of Zymomonas mobilis. Not too many applications with this tool have been reported in the biotechnological area.

Bounds and estimates for the linearly perturbed eigenvalue problem

International Nuclear Information System (INIS)

Raddatz, W.D.

1983-01-01

This thesis considers the problem of bounding and estimating the discrete portion of the spectrum of a linearly perturbed self-adjoint operator, M(x). It is supposed that one knows an incomplete set of data consisting in the first few coefficients of the Taylor series expansions of one or more of the eigenvalues of M(x) about x = 0. The foundations of the variational study of eigen-values are first presented. These are then used to construct the best possible upper bounds and estimates using various sets of given information. Lower bounds are obtained by estimating the error in the upper bounds. The extension of these bounds and estimates to the eigenvalues of the doubly-perturbed operator M(x,y) is discussed. The results presented have numerous practical application in the physical sciences, including problems in atomic physics and the theory of vibrations of acoustical and mechanical systems

The response of a linear monostable system and its application in parameters estimation for PSK signals

International Nuclear Information System (INIS)

Duan, Chaowei; Zhan, Yafeng

2016-01-01

The output characteristics of a linear monostable system driven with a periodic signal and an additive white Gaussian noise are studied in this paper. Theoretical analysis shows that the output signal-to-noise ratio (SNR) decreases monotonously with the increasing noise intensity but the output SNR-gain is stable. Inspired by this high SNR-gain phenomenon, this paper applies the linear monostable system in the parameters estimation algorithm for phase shift keying (PSK) signals and improves the estimation performance. - Highlights: • The response of a linear monostable system driven with a periodic signal and an additive white Gaussian noise is analyzed. • The optimal parameter of this linear monostable system to maximum the output SNR-gain is obtained. • Application of this linear monostable system in parameters estimation algorithm for PSK signals obtains performance improvement.

Estimation of Multiple Point Sources for Linear Fractional Order Systems Using Modulating Functions

KAUST Repository

Belkhatir, Zehor; Laleg-Kirati, Taous-Meriem

2017-01-01

This paper proposes an estimation algorithm for the characterization of multiple point inputs for linear fractional order systems. First, using polynomial modulating functions method and a suitable change of variables the problem of estimating

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

Science.gov (United States)

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2016-04-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. Copyright © 2016 Elsevier Inc. All rights reserved.

A non-linear state space approach to model groundwater fluctuations

NARCIS (Netherlands)

Berendrecht, W.L.; Heemink, A.W.; Geer, F.C. van; Gehrels, J.C.

2006-01-01

A non-linear state space model is developed for describing groundwater fluctuations. Non-linearity is introduced by modeling the (unobserved) degree of water saturation of the root zone. The non-linear relations are based on physical concepts describing the dependence of both the actual

Reexamination of optimal quantum state estimation of pure states

International Nuclear Information System (INIS)

Hayashi, A.; Hashimoto, T.; Horibe, M.

2005-01-01

A direct derivation is given for the optimal mean fidelity of quantum state estimation of a d-dimensional unknown pure state with its N copies given as input, which was first obtained by Hayashi in terms of an infinite set of covariant positive operator valued measures (POVM's) and by Bruss and Macchiavello establishing a connection to optimal quantum cloning. An explicit condition for POVM measurement operators for optimal estimators is obtained, by which we construct optimal estimators with finite POVMs using exact quadratures on a hypersphere. These finite optimal estimators are not generally universal, where universality means the fidelity is independent of input states. However, any optimal estimator with finite POVM for M(>N) copies is universal if it is used for N copies as input

State estimation for neural neutral-type networks with mixed time-varying delays and Markovian jumping parameters

International Nuclear Information System (INIS)

Lakshmanan, S.; Park, Ju H.; Jung, H. Y.; Balasubramaniam, P.

2012-01-01

This paper is concerned with a delay-dependent state estimator for neutral-type neural networks with mixed time-varying delays and Markovian jumping parameters. The addressed neural networks have a finite number of modes, and the modes may jump from one to another according to a Markov process. By construction of a suitable Lyapunov—Krasovskii functional, a delay-dependent condition is developed to estimate the neuron states through available output measurements such that the estimation error system is globally asymptotically stable in a mean square. The criterion is formulated in terms of a set of linear matrix inequalities (LMIs), which can be checked efficiently by use of some standard numerical packages

Introduction to quantum-state estimation

CERN Document Server

Teo, Yong Siah

2016-01-01

Quantum-state estimation is an important field in quantum information theory that deals with the characterization of states of affairs for quantum sources. This book begins with background formalism in estimation theory to establish the necessary prerequisites. This basic understanding allows us to explore popular likelihood- and entropy-related estimation schemes that are suitable for an introductory survey on the subject. Discussions on practical aspects of quantum-state estimation ensue, with emphasis on the evaluation of tomographic performances for estimation schemes, experimental realizations of quantum measurements and detection of single-mode multi-photon sources. Finally, the concepts of phase-space distribution functions, which compatibly describe these multi-photon sources, are introduced to bridge the gap between discrete and continuous quantum degrees of freedom. This book is intended to serve as an instructive and self-contained medium for advanced undergraduate and postgraduate students to gra...

State Estimation for Tensegrity Robots

Science.gov (United States)

Caluwaerts, Ken; Bruce, Jonathan; Friesen, Jeffrey M.; Sunspiral, Vytas

2016-01-01

Tensegrity robots are a class of compliant robots that have many desirable traits when designing mass efficient systems that must interact with uncertain environments. Various promising control approaches have been proposed for tensegrity systems in simulation. Unfortunately, state estimation methods for tensegrity robots have not yet been thoroughly studied. In this paper, we present the design and evaluation of a state estimator for tensegrity robots. This state estimator will enable existing and future control algorithms to transfer from simulation to hardware. Our approach is based on the unscented Kalman filter (UKF) and combines inertial measurements, ultra wideband time-of-flight ranging measurements, and actuator state information. We evaluate the effectiveness of our method on the SUPERball, a tensegrity based planetary exploration robotic prototype. In particular, we conduct tests for evaluating both the robot's success in estimating global position in relation to fixed ranging base stations during rolling maneuvers as well as local behavior due to small-amplitude deformations induced by cable actuation.

A state-space model for estimating detailed movements and home range from acoustic receiver data

DEFF Research Database (Denmark)

Pedersen, Martin Wæver; Weng, Kevin

2013-01-01

We present a state-space model for acoustic receiver data to estimate detailed movement and home range of individual fish while accounting for spatial bias. An integral part of the approach is the detection function, which models the probability of logging tag transmissions as a function of dista......We present a state-space model for acoustic receiver data to estimate detailed movement and home range of individual fish while accounting for spatial bias. An integral part of the approach is the detection function, which models the probability of logging tag transmissions as a function...... that the location error scales log-linearly with detection range and movement speed. This result can be used as guideline for designing network layout when species movement capacity and acoustic environment are known or can be estimated prior to network deployment. Finally, as an example, the state-space model...... is used to estimate home range and movement of a reef fish in the Pacific Ocean....

Estimation of Physical Parameters in Linear and Nonlinear Dynamic Systems

DEFF Research Database (Denmark)

Knudsen, Morten

variance and confidence ellipsoid is demonstrated. The relation is based on a new theorem on maxima of an ellipsoid. The procedure for input signal design and physical parameter estimation is tested on a number of examples, linear as well as nonlinear and simulated as well as real processes, and it appears...

State-space models’ dirty little secrets: even simple linear Gaussian models can have estimation problems

DEFF Research Database (Denmark)

Auger-Méthé, Marie; Field, Chris; Albertsen, Christoffer Moesgaard

2016-01-01

problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter......State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible...

A Robust WLS Power System State Estimation Method Integrating a Wide-Area Measurement System and SCADA Technology

Directory of Open Access Journals (Sweden)

Tao Jin

2015-04-01

Full Text Available With the development of modern society, the scale of the power system is rapidly increased accordingly, and the framework and mode of running of power systems are trending towards more complexity. It is nowadays much more important for the dispatchers to know exactly the state parameters of the power network through state estimation. This paper proposes a robust power system WLS state estimation method integrating a wide-area measurement system (WAMS and SCADA technology, incorporating phasor measurements and the results of the traditional state estimator in a post-processing estimator, which greatly reduces the scale of the non-linear estimation problem as well as the number of iterations and the processing time per iteration. This paper firstly analyzes the wide-area state estimation model in detail, then according to the issue that least squares does not account for bad data and outliers, the paper proposes a robust weighted least squares (WLS method that combines a robust estimation principle with least squares by equivalent weight. The performance assessment is discussed through setting up mathematical models of the distribution network. The effectiveness of the proposed method was proved to be accurate and reliable by simulations and experiments.

Bearing Estimation Using Double Frequency Reassignment for a Linear Passive Array

Directory of Open Access Journals (Sweden)

Czarnecki Krzysztof

2017-09-01

Full Text Available The paper demonstrates the use of frequency reassignment for bearing estimation. For this task, signals derived from a linear equispaced passive array are used. The presented method makes use of Fourier transformation based spatial spectrum estimation. It is further developed through the application of two-dimensional reassignment, which leads to obtaining highly concentrated energy distributions in the joint frequency-angle domain and sharp graphical imaging. The introduced method can be used for analysing, a priori, unknown signals of broadband, nonstationary, and/or multicomponent type. For such signals, the direction of arrival is obtained based upon the marginal energy distribution in the angle domain, through searching for arguments of its maxima. In the paper, bearing estimation of three popular types of sonar pulses, including linear and hyperbolic frequency modulated pulses, as well as no frequency modulation at all, is considered. The results of numerical experiments performed in the presence of additive white Gaussian noise are presented and compared to conventional digital sum-delay beamforming performed in the time domain. The root-mean-square error and the peak-to-average power ratio, also known as the crest factor, are introduced in order to estimate, respectively, the accuracy of the methods and the sharpness of the obtained energy distributions in the angle domain.

Integrated State Estimation and Contingency Analysis Software Implementation using High Performance Computing Techniques

Energy Technology Data Exchange (ETDEWEB)

Chen, Yousu; Glaesemann, Kurt R.; Rice, Mark J.; Huang, Zhenyu

2015-12-31

Power system simulation tools are traditionally developed in sequential mode and codes are optimized for single core computing only. However, the increasing complexity in the power grid models requires more intensive computation. The traditional simulation tools will soon not be able to meet the grid operation requirements. Therefore, power system simulation tools need to evolve accordingly to provide faster and better results for grid operations. This paper presents an integrated state estimation and contingency analysis software implementation using high performance computing techniques. The software is able to solve large size state estimation problems within one second and achieve a near-linear speedup of 9,800 with 10,000 cores for contingency analysis application. The performance evaluation is presented to show its effectiveness.

Estimating state-contingent production functions

DEFF Research Database (Denmark)

Rasmussen, Svend; Karantininis, Kostas

The paper reviews the empirical problem of estimating state-contingent production functions. The major problem is that states of nature may not be registered and/or that the number of observation per state is low. Monte Carlo simulation is used to generate an artificial, uncertain production...... environment based on Cobb Douglas production functions with state-contingent parameters. The pa-rameters are subsequently estimated based on different sizes of samples using Generalized Least Squares and Generalized Maximum Entropy and the results are compared. It is concluded that Maximum Entropy may...

Estimating integrated variance in the presence of microstructure noise using linear regression

Science.gov (United States)

Holý, Vladimír

2017-07-01

Using financial high-frequency data for estimation of integrated variance of asset prices is beneficial but with increasing number of observations so-called microstructure noise occurs. This noise can significantly bias the realized variance estimator. We propose a method for estimation of the integrated variance robust to microstructure noise as well as for testing the presence of the noise. Our method utilizes linear regression in which realized variances estimated from different data subsamples act as dependent variable while the number of observations act as explanatory variable. We compare proposed estimator with other methods on simulated data for several microstructure noise structures.

INTERVAL STATE ESTIMATION FOR SINGULAR DIFFERENTIAL EQUATION SYSTEMS WITH DELAYS

Directory of Open Access Journals (Sweden)

T. A. Kharkovskaia

2016-07-01

Full Text Available The paper deals with linear differential equation systems with algebraic restrictions (singular systems and a method of interval observer design for this kind of systems. The systems contain constant time delay, measurement noise and disturbances. Interval observer synthesis is based on monotone and cooperative systems technique, linear matrix inequations, Lyapunov function theory and interval arithmetic. The set of conditions that gives the possibility for interval observer synthesis is proposed. Results of synthesized observer operation are shown on the example of dynamical interindustry balance model. The advantages of proposed method are that it is adapted to observer design for uncertain systems, if the intervals of admissible values for uncertain parameters are given. The designed observer is capable to provide asymptotically definite limits on the estimation accuracy, since the interval of admissible values for the object state is defined at every instant. The obtained result provides an opportunity to develop the interval estimation theory for complex systems that contain parametric uncertainty, varying delay and nonlinear elements. Interval observers increasingly find applications in economics, electrical engineering, mechanical systems with constraints and optimal flow control.

Arbitrarily complete Bell-state measurement using only linear optical elements

Energy Technology Data Exchange (ETDEWEB)

Grice, W. P. [Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Tennessee (United States)

2011-10-15

A complete Bell-state measurement is not possible using only linear-optic elements, and most schemes achieve a success rate of no more than 50%, distinguishing, for example, two of the four Bell states but returning degenerate results for the other two. It is shown here that the introduction of a pair of ancillary entangled photons improves the success rate to 75%. More generally, the addition of 2{sup N}-2 ancillary photons yields a linear-optic Bell-state measurement with a success rate of 1-1/2{sup N}.

Development of a Probabilistic Technique for On-line Parameter and State Estimation in Non-linear Dynamic Systems

International Nuclear Information System (INIS)

Tunc Aldemir; Miller, Don W.; Hajek, Brian K.; Peng Wang

2002-01-01

The DSD (Dynamic System Doctor) is a system-independent, interactive software under development for on-line state/parameter estimation in dynamic systems (1), partially supported through a Nuclear Engineering Education (NEER) grant during 1998-2001. This paper summarizes the recent accomplishments in improving the user-friendliness and computational capability of DSD

Estimation of non-linear effective permeability of magnetic materials with fine structure

International Nuclear Information System (INIS)

Waki, H.; Igarashi, H.; Honma, T.

2006-01-01

This paper describes a homogenization method for magnetic materials with fine structure. In this method, the structures of the magnetic materials are assumed to be periodic, and the unit cell is defined. The effective permeability is determined on the basis of magnetic energy balance in the unit cell. This method can be applied not only for linear problems but also for non-linear ones. In this paper, estimation of the effective permeability of non-linear magnetic materials by using the homogenization method is described in detail, and then the validity for the non-liner problems is tested for two-dimensional problems. It is shown that this homogenization method gives accurate non-linear effective permeability

Estimating epidemic arrival times using linear spreading theory

Science.gov (United States)

Chen, Lawrence M.; Holzer, Matt; Shapiro, Anne

2018-01-01

We study the dynamics of a spatially structured model of worldwide epidemics and formulate predictions for arrival times of the disease at any city in the network. The model is composed of a system of ordinary differential equations describing a meta-population susceptible-infected-recovered compartmental model defined on a network where each node represents a city and the edges represent the flight paths connecting cities. Making use of the linear determinacy of the system, we consider spreading speeds and arrival times in the system linearized about the unstable disease free state and compare these to arrival times in the nonlinear system. Two predictions are presented. The first is based upon expansion of the heat kernel for the linearized system. The second assumes that the dominant transmission pathway between any two cities can be approximated by a one dimensional lattice or a homogeneous tree and gives a uniform prediction for arrival times independent of the specific network features. We test these predictions on a real network describing worldwide airline traffic.

Enhancing interferometer phase estimation, sensing sensitivity, and resolution using robust entangled states

Science.gov (United States)

Smith, James F.

2017-11-01

With the goal of designing interferometers and interferometer sensors, e.g., LADARs with enhanced sensitivity, resolution, and phase estimation, states using quantum entanglement are discussed. These states include N00N states, plain M and M states (PMMSs), and linear combinations of M and M states (LCMMS). Closed form expressions for the optimal detection operators; visibility, a measure of the state's robustness to loss and noise; a resolution measure; and phase estimate error, are provided in closed form. The optimal resolution for the maximum visibility and minimum phase error are found. For the visibility, comparisons between PMMSs, LCMMS, and N00N states are provided. For the minimum phase error, comparisons between LCMMS, PMMSs, N00N states, separate photon states (SPSs), the shot noise limit (SNL), and the Heisenberg limit (HL) are provided. A representative collection of computational results illustrating the superiority of LCMMS when compared to PMMSs and N00N states is given. It is found that for a resolution 12 times the classical result LCMMS has visibility 11 times that of N00N states and 4 times that of PMMSs. For the same case, the minimum phase error for LCMMS is 10.7 times smaller than that of PMMS and 29.7 times smaller than that of N00N states.

Using linear time-invariant system theory to estimate kinetic parameters directly from projection measurements

International Nuclear Information System (INIS)

Zeng, G.L.; Gullberg, G.T.

1995-01-01

It is common practice to estimate kinetic parameters from dynamically acquired tomographic data by first reconstructing a dynamic sequence of three-dimensional reconstructions and then fitting the parameters to time activity curves generated from the time-varying reconstructed images. However, in SPECT, the pharmaceutical distribution can change during the acquisition of a complete tomographic data set, which can bias the estimated kinetic parameters. It is hypothesized that more accurate estimates of the kinetic parameters can be obtained by fitting to the projection measurements instead of the reconstructed time sequence. Estimation from projections requires the knowledge of their relationship between the tissue regions of interest or voxels with particular kinetic parameters and the project measurements, which results in a complicated nonlinear estimation problem with a series of exponential factors with multiplicative coefficients. A technique is presented in this paper where the exponential decay parameters are estimated separately using linear time-invariant system theory. Once the exponential factors are known, the coefficients of the exponentials can be estimated using linear estimation techniques. Computer simulations demonstrate that estimation of the kinetic parameters directly from the projections is more accurate than the estimation from the reconstructed images

H infinity Integrated Fault Estimation and Fault Tolerant Control of Discrete-time Piecewise Linear Systems

DEFF Research Database (Denmark)

Tabatabaeipour, Seyed Mojtaba; Bak, Thomas

2012-01-01

In this paper we consider the problem of fault estimation and accommodation for discrete time piecewise linear systems. A robust fault estimator is designed to estimate the fault such that the estimation error converges to zero and H∞ performance of the fault estimation is minimized. Then, the es...

Estimating health state utility values for comorbid health conditions using SF-6D data.

Science.gov (United States)

Ara, Roberta; Brazier, John

2011-01-01

When health state utility values for comorbid health conditions are not available, data from cohorts with single conditions are used to estimate scores. The methods used can produce very different results and there is currently no consensus on which is the most appropriate approach. The objective of the current study was to compare the accuracy of five different methods within the same dataset. Data collected during five Welsh Health Surveys were subgrouped by health status. Mean short-form 6 dimension (SF-6D) scores for cohorts with a specific health condition were used to estimate mean SF-6D scores for cohorts with comorbid conditions using the additive, multiplicative, and minimum methods, the adjusted decrement estimator (ADE), and a linear regression model. The mean SF-6D for subgroups with comorbid health conditions ranged from 0.4648 to 0.6068. The linear model produced the most accurate scores for the comorbid health conditions with 88% of values accurate to within the minimum important difference for the SF-6D. The additive and minimum methods underestimated or overestimated the actual SF-6D scores respectively. The multiplicative and ADE methods both underestimated the majority of scores. However, both methods performed better when estimating scores smaller than 0.50. Although the range in actual health state utility values (HSUVs) was relatively small, our data covered the lower end of the index and the majority of previous research has involved actual HSUVs at the upper end of possible ranges. Although the linear model gave the most accurate results in our data, additional research is required to validate our findings. Copyright © 2011 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Adaptive distributed parameter and input estimation in linear parabolic PDEs

KAUST Repository

Mechhoud, Sarra

2016-01-01

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.

An Empirical Method to Fuse Partially Overlapping State Vectors for Distributed State Estimation

NARCIS (Netherlands)

Sijs, J.; Hanebeck, U.; Noack, B.

2013-01-01

State fusion is a method for merging multiple estimates of the same state into a single fused estimate. Dealing with multiple estimates is one of the main concerns in distributed state estimation, where an estimated value of the desired state vector is computed in each node of a networked system.

State Alcohol-Impaired-Driving Estimates

Science.gov (United States)

... 2012 Data DOT HS 812 017 May 2014 State Alcohol-Impaired-Driving Estimates This fact sheet contains ... alcohol involvement in fatal crashes for the United States and individually for the 50 States, the District ...

Stability and Linear Quadratic Differential Games of Discrete-Time Markovian Jump Linear Systems with State-Dependent Noise

Directory of Open Access Journals (Sweden)

Huiying Sun

2014-01-01

Full Text Available We mainly consider the stability of discrete-time Markovian jump linear systems with state-dependent noise as well as its linear quadratic (LQ differential games. A necessary and sufficient condition involved with the connection between stochastic Tn-stability of Markovian jump linear systems with state-dependent noise and Lyapunov equation is proposed. And using the theory of stochastic Tn-stability, we give the optimal strategies and the optimal cost values for infinite horizon LQ stochastic differential games. It is demonstrated that the solutions of infinite horizon LQ stochastic differential games are concerned with four coupled generalized algebraic Riccati equations (GAREs. Finally, an iterative algorithm is presented to solve the four coupled GAREs and a simulation example is given to illustrate the effectiveness of it.

The Dangers of Estimating V˙O2max Using Linear, Nonexercise Prediction Models.

Science.gov (United States)

Nevill, Alan M; Cooke, Carlton B

2017-05-01

This study aimed to compare the accuracy and goodness of fit of two competing models (linear vs allometric) when estimating V˙O2max (mL·kg·min) using nonexercise prediction models. The two competing models were fitted to the V˙O2max (mL·kg·min) data taken from two previously published studies. Study 1 (the Allied Dunbar National Fitness Survey) recruited 1732 randomly selected healthy participants, 16 yr and older, from 30 English parliamentary constituencies. Estimates of V˙O2max were obtained using a progressive incremental test on a motorized treadmill. In study 2, maximal oxygen uptake was measured directly during a fatigue limited treadmill test in older men (n = 152) and women (n = 146) 55 to 86 yr old. In both studies, the quality of fit associated with estimating V˙O2max (mL·kg·min) was superior using allometric rather than linear (additive) models based on all criteria (R, maximum log-likelihood, and Akaike information criteria). Results suggest that linear models will systematically overestimate V˙O2max for participants in their 20s and underestimate V˙O2max for participants in their 60s and older. The residuals saved from the linear models were neither normally distributed nor independent of the predicted values nor age. This will probably explain the absence of a key quadratic age term in the linear models, crucially identified using allometric models. Not only does the curvilinear age decline within an exponential function follow a more realistic age decline (the right-hand side of a bell-shaped curve), but the allometric models identified either a stature-to-body mass ratio (study 1) or a fat-free mass-to-body mass ratio (study 2), both associated with leanness when estimating V˙O2max. Adopting allometric models will provide more accurate predictions of V˙O2max (mL·kg·min) using plausible, biologically sound, and interpretable models.

State estimation of spatio-temporal phenomena

Science.gov (United States)

Yu, Dan

This dissertation addresses the state estimation problem of spatio-temporal phenomena which can be modeled by partial differential equations (PDEs), such as pollutant dispersion in the atmosphere. After discretizing the PDE, the dynamical system has a large number of degrees of freedom (DOF). State estimation using Kalman Filter (KF) is computationally intractable, and hence, a reduced order model (ROM) needs to be constructed first. Moreover, the nonlinear terms, external disturbances or unknown boundary conditions can be modeled as unknown inputs, which leads to an unknown input filtering problem. Furthermore, the performance of KF could be improved by placing sensors at feasible locations. Therefore, the sensor scheduling problem to place multiple mobile sensors is of interest. The first part of the dissertation focuses on model reduction for large scale systems with a large number of inputs/outputs. A commonly used model reduction algorithm, the balanced proper orthogonal decomposition (BPOD) algorithm, is not computationally tractable for large systems with a large number of inputs/outputs. Inspired by the BPOD and randomized algorithms, we propose a randomized proper orthogonal decomposition (RPOD) algorithm and a computationally optimal RPOD (RPOD*) algorithm, which construct an ROM to capture the input-output behaviour of the full order model, while reducing the computational cost of BPOD by orders of magnitude. It is demonstrated that the proposed RPOD* algorithm could construct the ROM in real-time, and the performance of the proposed algorithms on different advection-diffusion equations. Next, we consider the state estimation problem of linear discrete-time systems with unknown inputs which can be treated as a wide-sense stationary process with rational power spectral density, while no other prior information needs to be known. We propose an autoregressive (AR) model based unknown input realization technique which allows us to recover the input

Bad Data Detection and Identification for State Estimation

DEFF Research Database (Denmark)

Khazraj, Hesam; Silva, Filipe Miguel Faria da; Bak, Claus Leth

2017-01-01

state estimations. To achieve this object largest normalized residual test (rNmax) is applied to detect and analysis bad data in phasor measurements, power flow and power injections of buses used for the novel PMU-based state estimation. The main advantage of new PMU-based static state estimation......Bad data analysis is an important part of both dynamic and static state estimations. This paper present novel algorithm of phase measurement unit (PMU)-based static state estimation to detect and identify multiple bad data in critical measurements, which is not possible with traditional static...... is that phasor measurements can be added separately into the proposed state estimation. This paper proposes an ideal method to combine the phasor measurements into the conventional state estimator in a systematic way, so that no significant modification is necessary to the existing algorithm. The main advantage...

Linear polarization-discriminatory state inverter fabricated by oblique angle deposition.

Science.gov (United States)

Park, Yong Jun; Sobahan, K M A; Kim, Jin Joo; Hwangbo, Chang Kwon

2009-06-22

In this paper, we report a linear polarization-discriminatory state inverter made of three-layer sculpture thin film fabricated by oblique angle deposition technique. The first and third layers are quarter-wave plates of zigzag structure and the middle of them is a circular Bragg reflector of left-handed helical structure. It is found that the normal incidence of P-polarized light on this polarization-discriminatory state inverter becomes the S-polarized light at output, while the incident S-polarized light of wavelength lying in the Bragg regime is reflected. The microstructure of the linear polarization-discriminatory state inverter is also investigated by using a scanning electron microscope.

State estimation for a hexapod robot

CSIR Research Space (South Africa)

Lubbe, Estelle

2015-09-01

Full Text Available This paper introduces a state estimation methodology for a hexapod robot that makes use of proprioceptive sensors and a kinematic model of the robot. The methodology focuses on providing reliable full pose state estimation for a commercially...

State energy data report 1994: Consumption estimates

Energy Technology Data Exchange (ETDEWEB)

NONE

1996-10-01

This document provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), operated by EIA. SEDS provides State energy consumption estimates to members of Congress, Federal and State agencies, and the general public, and provides the historical series needed for EIA`s energy models. Division is made for each energy type and end use sector. Nuclear electric power is included.

State energy data report 1994: Consumption estimates

International Nuclear Information System (INIS)

1996-10-01

This document provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), operated by EIA. SEDS provides State energy consumption estimates to members of Congress, Federal and State agencies, and the general public, and provides the historical series needed for EIA's energy models. Division is made for each energy type and end use sector. Nuclear electric power is included

State energy data report 1995 - consumption estimates

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-12-01

The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sectors. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public, and (2) to provide the historical series necessary for EIA`s energy models.

Parameter estimation and hypothesis testing in linear models

CERN Document Server

Koch, Karl-Rudolf

1999-01-01

The necessity to publish the second edition of this book arose when its third German edition had just been published. This second English edition is there­ fore a translation of the third German edition of Parameter Estimation and Hypothesis Testing in Linear Models, published in 1997. It differs from the first English edition by the addition of a new chapter on robust estimation of parameters and the deletion of the section on discriminant analysis, which has been more completely dealt with by the author in the book Bayesian In­ ference with Geodetic Applications, Springer-Verlag, Berlin Heidelberg New York, 1990. Smaller additions and deletions have been incorporated, to im­ prove the text, to point out new developments or to eliminate errors which became apparent. A few examples have been also added. I thank Springer-Verlag for publishing this second edition and for the assistance in checking the translation, although the responsibility of errors remains with the author. I also want to express my thanks...

Development of realtime cognitive state estimator

International Nuclear Information System (INIS)

Takahashi, Makoto; Kitamura, Masashi; Yoshikaea, Hidekazu

2004-01-01

The realtime cognitive state estimator based on the set of physiological measures has been developed in order to provide valuable information on the human behavior during the interaction through the Man-Machine Interface. The artificial neural network has been adopted to categorize the cognitive states by using the qualitative physiological data pattern as the inputs. The laboratory experiments, in which the subjects' cognitive states were intentionally controlled by the task presented, were performed to obtain training data sets for the neural network. The developed system has been shown to be capable of estimating cognitive state with higher accuracy and realtime estimation capability has also been confirmed through the data processing experiments. (author)

The fastclime Package for Linear Programming and Large-Scale Precision Matrix Estimation in R.

Science.gov (United States)

Pang, Haotian; Liu, Han; Vanderbei, Robert

2014-02-01

We develop an R package fastclime for solving a family of regularized linear programming (LP) problems. Our package efficiently implements the parametric simplex algorithm, which provides a scalable and sophisticated tool for solving large-scale linear programs. As an illustrative example, one use of our LP solver is to implement an important sparse precision matrix estimation method called CLIME (Constrained L 1 Minimization Estimator). Compared with existing packages for this problem such as clime and flare, our package has three advantages: (1) it efficiently calculates the full piecewise-linear regularization path; (2) it provides an accurate dual certificate as stopping criterion; (3) it is completely coded in C and is highly portable. This package is designed to be useful to statisticians and machine learning researchers for solving a wide range of problems.

UAV State Estimation Modeling Techniques in AHRS

Science.gov (United States)

Razali, Shikin; Zhahir, Amzari

2017-11-01

Autonomous unmanned aerial vehicle (UAV) system is depending on state estimation feedback to control flight operation. Estimation on the correct state improves navigation accuracy and achieves flight mission safely. One of the sensors configuration used in UAV state is Attitude Heading and Reference System (AHRS) with application of Extended Kalman Filter (EKF) or feedback controller. The results of these two different techniques in estimating UAV states in AHRS configuration are displayed through position and attitude graphs.

Fast Kalman-like filtering for large-dimensional linear and Gaussian state-space models

KAUST Repository

Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim

2015-01-01

This paper considers the filtering problem for linear and Gaussian state-space models with large dimensions, a setup in which the optimal Kalman Filter (KF) might not be applicable owing to the excessive cost of manipulating huge covariance matrices. Among the most popular alternatives that enable cheaper and reasonable computation is the Ensemble KF (EnKF), a Monte Carlo-based approximation. In this paper, we consider a class of a posteriori distributions with diagonal covariance matrices and propose fast approximate deterministic-based algorithms based on the Variational Bayesian (VB) approach. More specifically, we derive two iterative KF-like algorithms that differ in the way they operate between two successive filtering estimates; one involves a smoothing estimate and the other involves a prediction estimate. Despite its iterative nature, the prediction-based algorithm provides a computational cost that is, on the one hand, independent of the number of iterations in the limit of very large state dimensions, and on the other hand, always much smaller than the cost of the EnKF. The cost of the smoothing-based algorithm depends on the number of iterations that may, in some situations, make this algorithm slower than the EnKF. The performances of the proposed filters are studied and compared to those of the KF and EnKF through a numerical example.

Fast Kalman-like filtering for large-dimensional linear and Gaussian state-space models

KAUST Repository

Ait-El-Fquih, Boujemaa

2015-08-13

This paper considers the filtering problem for linear and Gaussian state-space models with large dimensions, a setup in which the optimal Kalman Filter (KF) might not be applicable owing to the excessive cost of manipulating huge covariance matrices. Among the most popular alternatives that enable cheaper and reasonable computation is the Ensemble KF (EnKF), a Monte Carlo-based approximation. In this paper, we consider a class of a posteriori distributions with diagonal covariance matrices and propose fast approximate deterministic-based algorithms based on the Variational Bayesian (VB) approach. More specifically, we derive two iterative KF-like algorithms that differ in the way they operate between two successive filtering estimates; one involves a smoothing estimate and the other involves a prediction estimate. Despite its iterative nature, the prediction-based algorithm provides a computational cost that is, on the one hand, independent of the number of iterations in the limit of very large state dimensions, and on the other hand, always much smaller than the cost of the EnKF. The cost of the smoothing-based algorithm depends on the number of iterations that may, in some situations, make this algorithm slower than the EnKF. The performances of the proposed filters are studied and compared to those of the KF and EnKF through a numerical example.

Estimation of Multiple Point Sources for Linear Fractional Order Systems Using Modulating Functions

KAUST Repository

Belkhatir, Zehor

2017-06-28

This paper proposes an estimation algorithm for the characterization of multiple point inputs for linear fractional order systems. First, using polynomial modulating functions method and a suitable change of variables the problem of estimating the locations and the amplitudes of a multi-pointwise input is decoupled into two algebraic systems of equations. The first system is nonlinear and solves for the time locations iteratively, whereas the second system is linear and solves for the input’s amplitudes. Second, closed form formulas for both the time location and the amplitude are provided in the particular case of single point input. Finally, numerical examples are given to illustrate the performance of the proposed technique in both noise-free and noisy cases. The joint estimation of pointwise input and fractional differentiation orders is also presented. Furthermore, a discussion on the performance of the proposed algorithm is provided.

State Estimation of Induction Motor Drives Using the Unscented Kalman Filter

DEFF Research Database (Denmark)

Lascu, Cristian; Jafarzadeh, Saeed; Fadali, M.Sami

2012-01-01

This paper investigates the application, design, and implementation of unscented Kalman filters (KFs) (UKFs) for induction motor (IM) sensorless drives. UKFs use nonlinear unscented transforms (UTs) in the prediction step in order to preserve the stochastic characteristics of a nonlinear system....... The advantage of using UTs is their ability to capture the nonlinear behavior of the system, unlike extended KFs (EKFs) that use linearized models. Four original variants of the UKF for IM state estimation, based on different UTs, are described, analyzed, and compared. The four transforms are basic, general...

Minimum variance linear unbiased estimators of loss and inventory

International Nuclear Information System (INIS)

Stewart, K.B.

1977-01-01

The article illustrates a number of approaches for estimating the material balance inventory and a constant loss amount from the accountability data from a sequence of accountability periods. The approaches all lead to linear estimates that have minimum variance. Techniques are shown whereby ordinary least squares, weighted least squares and generalized least squares computer programs can be used. Two approaches are recursive in nature and lend themselves to small specialized computer programs. Another approach is developed that is easy to program; could be used with a desk calculator and can be used in a recursive way from accountability period to accountability period. Some previous results are also reviewed that are very similar in approach to the present ones and vary only in the way net throughput measurements are statistically modeled. 5 refs

Nonlinear bayesian state filtering with missing measurements and bounded noise and its application to vehicle position estimation

Czech Academy of Sciences Publication Activity Database

Pavelková, Lenka

2011-01-01

Roč. 47, č. 3 (2011), s. 370-384 ISSN 0023-5954 R&D Projects: GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : non-linear state space model * bounded uncertainty * missing measurements * state filtering * vehicle position estimation Subject RIV: BC - Control Systems Theory Impact factor: 0.454, year: 2011 http://library.utia.cas.cz/separaty/2011/AS/pavelkova-0360239.pdf

Avoiding Boundary Estimates in Hierarchical Linear Models through Weakly Informative Priors

Science.gov (United States)

Chung, Yeojin; Rabe-Hesketh, Sophia; Gelman, Andrew; Dorie, Vincent; Liu, Jinchen

2012-01-01

Hierarchical or multilevel linear models are widely used for longitudinal or cross-sectional data on students nested in classes and schools, and are particularly important for estimating treatment effects in cluster-randomized trials, multi-site trials, and meta-analyses. The models can allow for variation in treatment effects, as well as…

Quadratic Plus Linear Operators which Preserve Pure States of Quantum Systems: Small Dimensions

International Nuclear Information System (INIS)

Saburov, Mansoor

2014-01-01

A mathematical formalism of quantum mechanics says that a pure state of a quantum system corresponds to a vector of norm 1 and an observable is a self-adjoint operator on the space of states. It is of interest to describe all linear or nonlinear operators which preserve the pure states of the system. In the linear case, it is nothing more than isometries of Hilbert spaces. In the nonlinear case, this problem was open. In this paper, in the small dimensional spaces, we shall describe all quadratic plus linear operators which preserve pure states of the quantum system

Optimal state estimation over communication channels with random delays

KAUST Repository

Mahmoud, Magdi S.; Liu, Bo

2013-01-01

This paper is concerned with the optimal estimation of linear systems over unreliable communication channels with random delays. The measurements are delivered without time stamp, and the probabilities of time delays are assumed to be known. Since the estimation is time-driven, the actual time delays are converted into virtual time delays among the formulation. The receiver of estimation node stores the sum of arrived measurements between two adjacent processing time instants and also counts the number of arrived measurements. The original linear system is modeled as an extended system with uncertain observation to capture the feature of communication, then the optimal estimation algorithm of systems with uncertain observations is proposed. Additionally, a numerical simulation is presented to show the performance of this work. © 2013 The Franklin Institute.

Optimal state estimation over communication channels with random delays

KAUST Repository

Mahmoud, Magdi S.

2013-04-01

This paper is concerned with the optimal estimation of linear systems over unreliable communication channels with random delays. The measurements are delivered without time stamp, and the probabilities of time delays are assumed to be known. Since the estimation is time-driven, the actual time delays are converted into virtual time delays among the formulation. The receiver of estimation node stores the sum of arrived measurements between two adjacent processing time instants and also counts the number of arrived measurements. The original linear system is modeled as an extended system with uncertain observation to capture the feature of communication, then the optimal estimation algorithm of systems with uncertain observations is proposed. Additionally, a numerical simulation is presented to show the performance of this work. © 2013 The Franklin Institute.

State energy data report 1993: Consumption estimates

Energy Technology Data Exchange (ETDEWEB)

NONE

1995-07-01

The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public; and (2) to provide the historical series necessary for EIA`s energy models.

Estimates of emittance dilution and stability in high-energy linear accelerators

Directory of Open Access Journals (Sweden)

T. O. Raubenheimer

2000-12-01

Full Text Available In this paper, we present a series of analytic expressions to predict the beam dynamics in a long linear accelerator. These expressions can be used to model the linac optics, calculate the magnitude of the wakefields, estimate the emittance dilution due to misaligned accelerator components, and estimate the stability and jitter limitations. The analytic expressions are based on the results of simple physics models and are useful to understand the parameter sensitivities. They are also useful when using simple codes or spreadsheets to optimize a linac system.

Self-learning estimation of quantum states

International Nuclear Information System (INIS)

Hannemann, Th.; Reiss, D.; Balzer, Ch.; Neuhauser, W.; Toschek, P.E.; Wunderlich, Ch.

2002-01-01

We report the experimental estimation of arbitrary qubit states using a succession of N measurements on individual qubits, where the measurement basis is changed during the estimation procedure conditioned on the outcome of previous measurements (self-learning estimation). Two hyperfine states of a single trapped 171 Yb + ion serve as a qubit. It is demonstrated that the difference in fidelity between this adaptive strategy and passive strategies increases in the presence of decoherence

State Energy Data Report, 1991: Consumption estimates

International Nuclear Information System (INIS)

1993-05-01

The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to the Government, policy makers, and the public; and (2) to provide the historical series necessary for EIA's energy models

Spatial Signature Estimation with an Uncalibrated Uniform Linear Array

Directory of Open Access Journals (Sweden)

Xiang Cao

2015-06-01

Full Text Available In this paper, the problem of spatial signature estimation using a uniform linear array (ULA with unknown sensor gain and phase errors is considered. As is well known, the directions-of-arrival (DOAs can only be determined within an unknown rotational angle in this array model. However, the phase ambiguity has no impact on the identification of the spatial signature. Two auto-calibration methods are presented for spatial signature estimation. In our methods, the rotational DOAs and model error parameters are firstly obtained, and the spatial signature is subsequently calculated. The first method extracts two subarrays from the ULA to construct an estimator, and the elements of the array can be used several times in one subarray. The other fully exploits multiple invariances in the interior of the sensor array, and a multidimensional nonlinear problem is formulated. A Gauss–Newton iterative algorithm is applied for solving it. The first method can provide excellent initial inputs for the second one. The effectiveness of the proposed algorithms is demonstrated by several simulation results.

Estimating Lithium-Ion Battery State of Charge and Parameters Using a Continuous-Discrete Extended Kalman Filter

Directory of Open Access Journals (Sweden)

Yasser Diab

2017-07-01

Full Text Available A real-time determination of battery parameters is challenging because batteries are non-linear, time-varying systems. The transient behaviour of lithium-ion batteries is modelled by a Thevenin-equivalent circuit with two time constants characterising activation and concentration polarization. An experimental approach is proposed for directly determining battery parameters as a function of physical quantities. The model’s parameters are a function of the state of charge and of the discharge rate. These can be expressed by regression equations in the model to derive a continuous-discrete extended Kalman estimator of the state of charge and of other parameters. This technique is based on numerical integration of the ordinary differential equations to predict the state of the stochastic dynamic system and the corresponding error covariance matrix. Then a standard correction step of the extended Kalman filter (EKF is applied to increase the accuracy of estimated parameters. Simulations resulting from this proposed estimator model were compared with experimental results under a variety of operating scenarios—analysis of the results demonstrate the accuracy of the estimator for correctly identifying battery parameters.

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Directory of Open Access Journals (Sweden)

Chandra Nagasuma R

2009-02-01

Full Text Available Abstract Background A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN from transcript profiling data. Results The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting problem and solved finally by formulating a Linear Program (LP. A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known

Practical feasibility of Kalman filters for the state estimation of lithium-ion batteries

OpenAIRE

Campestrini, Christian

2018-01-01

This work investigates the feasibility of the Kalman filter for the state estimation of lithium-ion cells and modules under real conditions. Therefore, the dependencies of the cells during ageing are shown and various Kalman filter types are compared. The strongly varying model parameters, as well as the temperature and ageing dependent open circuit voltage, require an empirical adaptation of the inconstant and non-linear filter tuning parameters. The performance of the Kalman filter in a rea...

Compact and accurate linear and nonlinear autoregressive moving average model parameter estimation using laguerre functions

DEFF Research Database (Denmark)

Chon, K H; Cohen, R J; Holstein-Rathlou, N H

1997-01-01

A linear and nonlinear autoregressive moving average (ARMA) identification algorithm is developed for modeling time series data. The algorithm uses Laguerre expansion of kernals (LEK) to estimate Volterra-Wiener kernals. However, instead of estimating linear and nonlinear system dynamics via moving...... average models, as is the case for the Volterra-Wiener analysis, we propose an ARMA model-based approach. The proposed algorithm is essentially the same as LEK, but this algorithm is extended to include past values of the output as well. Thus, all of the advantages associated with using the Laguerre...

Estimating trajectories of energy intake through childhood and adolescence using linear-spline multilevel models.

Science.gov (United States)

Anderson, Emma L; Tilling, Kate; Fraser, Abigail; Macdonald-Wallis, Corrie; Emmett, Pauline; Cribb, Victoria; Northstone, Kate; Lawlor, Debbie A; Howe, Laura D

2013-07-01

Methods for the assessment of changes in dietary intake across the life course are underdeveloped. We demonstrate the use of linear-spline multilevel models to summarize energy-intake trajectories through childhood and adolescence and their application as exposures, outcomes, or mediators. The Avon Longitudinal Study of Parents and Children assessed children's dietary intake several times between ages 3 and 13 years, using both food frequency questionnaires (FFQs) and 3-day food diaries. We estimated energy-intake trajectories for 12,032 children using linear-spline multilevel models. We then assessed the associations of these trajectories with maternal body mass index (BMI), and later offspring BMI, and also their role in mediating the relation between maternal and offspring BMIs. Models estimated average and individual energy intake at 3 years, and linear changes in energy intake from age 3 to 7 years and from age 7 to 13 years. By including the exposure (in this example, maternal BMI) in the multilevel model, we were able to estimate the average energy-intake trajectories across levels of the exposure. When energy-intake trajectories are the exposure for a later outcome (in this case offspring BMI) or a mediator (between maternal and offspring BMI), results were similar, whether using a two-step process (exporting individual-level intercepts and slopes from multilevel models and using these in linear regression/path analysis), or a single-step process (multivariate multilevel models). Trajectories were similar when FFQs and food diaries were assessed either separately, or when combined into one model. Linear-spline multilevel models provide useful summaries of trajectories of dietary intake that can be used as an exposure, outcome, or mediator.

Dark energy cosmology with generalized linear equation of state

International Nuclear Information System (INIS)

Babichev, E; Dokuchaev, V; Eroshenko, Yu

2005-01-01

Dark energy with the usually used equation of state p = wρ, where w const 0 ), where the constants α and ρ 0 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

Estimativa da área foliar de Sida cordifolia e Sida rhombifolia usando dimensões lineares do limbo foliar Estimate of Sida cordifolia and Sida rhombifolia leaf area using leaf blade linear dimensions

Directory of Open Access Journals (Sweden)

S. Bianco

2008-01-01

Full Text Available A estimativa da área foliar pode auxiliar na compreensão de relações de interferência entre plantas daninhas e cultivadas. Com o objetivo de obter uma equação que, por meio de parâmetros lineares dimensionais das folhas, permita a estimativa da área foliar de Sida cordifolia e Sida rhombifolia, estudaram-se as correlações entre área foliar real (Af e parâmetros dimensionais do limbo foliar, como o comprimento (C ao longo da nervura principal e a largura máxima (L perpendicular à nervura principal. Foram analisados 200 limbos foliares de cada espécie, coletados em diferentes agroecossistemas na Universidade Estadual Paulista, campus de Jaboticabal. Os modelos estatísticos utilizados foram linear: Y = a + bx; linear simples: Y = bx; geométrico: Y = ax b; e exponencial: Y = ab x. Todos os modelos analisados podem ser empregados para estimação da área foliar de S. cordifolia e S. rhombifolia. Sugere-se optar pela equação linear simples, envolvendo o produto C*L, considerando-se o coeficiente linear igual a zero, em função da praticidade desta. Desse modo, a estimativa da área foliar de S. cordifolia pode ser obtida pela fórmula Af = 0,7878*(C*L, com coeficiente de determinação de 0,9307, enquanto para S. rhombifolia a estimativa da área foliar pode ser obtida pela fórmula Af = 0,6423*(C*L, com coeficiente de determinação de 0,9711.Leaf area estimate may contribute to understand the relationship of interference between weeds and crops. The objective of this research was to obtain a mathematical equation to estimate Sida cordifolia and Sida rhombifolia leaf area based on linear measures of leaf blade. Correlation studies were conducted between real leaf area (Af and dimensional leaf blade parameters such as leaf length (C and maximum leaf width (L. Around 200 leaf blades of each species were analyzed, collected from several agro-ecosystems at São Paulo State University, in Jaboticabal, SP, Brazil. The statistical

Estimation of the Dynamic States of Synchronous Machines Using an Extended Particle Filter

Energy Technology Data Exchange (ETDEWEB)

Zhou, Ning; Meng, Da; Lu, Shuai

2013-11-11

In this paper, an extended particle filter (PF) is proposed to estimate the dynamic states of a synchronous machine using phasor measurement unit (PMU) data. A PF propagates the mean and covariance of states via Monte Carlo simulation, is easy to implement, and can be directly applied to a non-linear system with non-Gaussian noise. The extended PF modifies a basic PF to improve robustness. Using Monte Carlo simulations with practical noise and model uncertainty considerations, the extended PF’s performance is evaluated and compared with the basic PF and an extended Kalman filter (EKF). The extended PF results showed high accuracy and robustness against measurement and model noise.

An Empirical State Error Covariance Matrix for Batch State Estimation

Science.gov (United States)

Frisbee, Joseph H., Jr.

2011-01-01

State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. Consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. It then follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully account for the error in the state estimate. By way of a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm, it is possible to arrive at an appropriate, and formally correct, empirical state error covariance matrix. The first specific step of the method is to use the average form of the weighted measurement residual variance performance index rather than its usual total weighted residual form. Next it is helpful to interpret the solution to the normal equations as the average of a collection of sample vectors drawn from a hypothetical parent population. From here, using a standard statistical analysis approach, it directly follows as to how to determine the standard empirical state error covariance matrix. This matrix will contain the total uncertainty in the

The response of a linear monostable system and its application in parameters estimation for PSK signals

Science.gov (United States)

Duan, Chaowei; Zhan, Yafeng

2016-03-01

The output characteristics of a linear monostable system driven with a periodic signal and an additive white Gaussian noise are studied in this paper. Theoretical analysis shows that the output signal-to-noise ratio (SNR) decreases monotonously with the increasing noise intensity but the output SNR-gain is stable. Inspired by this high SNR-gain phenomenon, this paper applies the linear monostable system in the parameters estimation algorithm for phase shift keying (PSK) signals and improves the estimation performance.

Quasi-Newton methods for parameter estimation in functional differential equations

Science.gov (United States)

Brewer, Dennis W.

1988-01-01

A state-space approach to parameter estimation in linear functional differential equations is developed using the theory of linear evolution equations. A locally convergent quasi-Newton type algorithm is applied to distributed systems with particular emphasis on parameters that induce unbounded perturbations of the state. The algorithm is computationally implemented on several functional differential equations, including coefficient and delay estimation in linear delay-differential equations.

Non-linear feedback control of the p53 protein-mdm2 inhibitor system using the derivative-free non-linear Kalman filter.

Science.gov (United States)

Rigatos, Gerasimos G

2016-06-01

It is proven that the model of the p53-mdm2 protein synthesis loop is a differentially flat one and using a diffeomorphism (change of state variables) that is proposed by differential flatness theory it is shown that the protein synthesis model can be transformed into the canonical (Brunovsky) form. This enables the design of a feedback control law that maintains the concentration of the p53 protein at the desirable levels. To estimate the non-measurable elements of the state vector describing the p53-mdm2 system dynamics, the derivative-free non-linear Kalman filter is used. Moreover, to compensate for modelling uncertainties and external disturbances that affect the p53-mdm2 system, the derivative-free non-linear Kalman filter is re-designed as a disturbance observer. The derivative-free non-linear Kalman filter consists of the Kalman filter recursion applied on the linearised equivalent of the protein synthesis model together with an inverse transformation based on differential flatness theory that enables to retrieve estimates for the state variables of the initial non-linear model. The proposed non-linear feedback control and perturbations compensation method for the p53-mdm2 system can result in more efficient chemotherapy schemes where the infusion of medication will be better administered.

Estimation of time-varying reactivity by the H∞ optimal linear filter

International Nuclear Information System (INIS)

Suzuki, Katsuo; Shimazaki, Junya; Watanabe, Koiti

1995-01-01

The problem of estimating the time-varying net reactivity from flux measurements is solved for a point reactor kinetics model using a linear filtering technique in an H ∞ settings. In order to sue this technique, an appropriate dynamical model of the reactivity is constructed that can be embedded into the reactor model as one of its variables. A filter, which minimizes the H ∞ norm of the estimation error power spectrum, operates on neutron density measurements corrupted by noise and provides an estimate of the dynamic net reactivity. Computer simulations are performed to reveal the basic characteristics of the H ∞ optimal filter. The results of the simulation indicate that the filter can be used to determine the time-varying reactivity from neutron density measurements that have been corrupted by noise

A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm

International Nuclear Information System (INIS)

Lin, Cheng; Mu, Hao; Xiong, Rui; Shen, Weixiang

2016-01-01

Highlights: • A novel multi-model probability battery SOC fusion estimation approach was proposed. • The linear matrix inequality-based H∞ technique is employed to estimate the SOC. • The Bayes theorem has been employed to realize the optimal weight for the fusion. • The robustness of the proposed approach is verified by different batteries. • The results show that the proposed method can promote global estimation accuracy. - Abstract: Due to the strong nonlinearity and complex time-variant property of batteries, the existing state of charge (SOC) estimation approaches based on a single equivalent circuit model (ECM) cannot provide the accurate SOC for the entire discharging period. This paper aims to present a novel SOC estimation approach based on a multiple ECMs fusion method for improving the practical application performance. In the proposed approach, three battery ECMs, namely the Thevenin model, the double polarization model and the 3rd order RC model, are selected to describe the dynamic voltage of lithium-ion batteries and the genetic algorithm is then used to determine the model parameters. The linear matrix inequality-based H-infinity technique is employed to estimate the SOC from the three models and the Bayes theorem-based probability method is employed to determine the optimal weights for synthesizing the SOCs estimated from the three models. Two types of lithium-ion batteries are used to verify the feasibility and robustness of the proposed approach. The results indicate that the proposed approach can improve the accuracy and reliability of the SOC estimation against uncertain battery materials and inaccurate initial states.

State estimation for large-scale wastewater treatment plants.

Science.gov (United States)

Busch, Jan; Elixmann, David; Kühl, Peter; Gerkens, Carine; Schlöder, Johannes P; Bock, Hans G; Marquardt, Wolfgang

2013-09-01

Many relevant process states in wastewater treatment are not measurable, or their measurements are subject to considerable uncertainty. This poses a serious problem for process monitoring and control. Model-based state estimation can provide estimates of the unknown states and increase the reliability of measurements. In this paper, an integrated approach is presented for the optimization-based sensor network design and the estimation problem. Using the ASM1 model in the reference scenario BSM1, a cost-optimal sensor network is designed and the prominent estimators EKF and MHE are evaluated. Very good estimation results for the system comprising 78 states are found requiring sensor networks of only moderate complexity. Copyright © 2013 Elsevier Ltd. All rights reserved.

Estimation and Inference for Very Large Linear Mixed Effects Models

OpenAIRE

Gao, K.; Owen, A. B.

2016-01-01

Linear mixed models with large imbalanced crossed random effects structures pose severe computational problems for maximum likelihood estimation and for Bayesian analysis. The costs can grow as fast as $N^{3/2}$ when there are N observations. Such problems arise in any setting where the underlying factors satisfy a many to many relationship (instead of a nested one) and in electronic commerce applications, the N can be quite large. Methods that do not account for the correlation structure can...

Estimation of Branch Topology Errors in Power Networks by WLAN State Estimation

Energy Technology Data Exchange (ETDEWEB)

Kim, Hong Rae [Soonchunhyang University(Korea); Song, Kyung Bin [Kei Myoung University(Korea)

2000-06-01

The purpose of this paper is to detect and identify topological errors in order to maintain a reliable database for the state estimator. In this paper, a two stage estimation procedure is used to identify the topology errors. At the first stage, the WLAV state estimator which has characteristics to remove bad data during the estimation procedure is run for finding out the suspected branches at which topology errors take place. The resulting residuals are normalized and the measurements with significant normalized residuals are selected. A set of suspected branches is formed based on these selected measurements; if the selected measurement if a line flow, the corresponding branch is suspected; if it is an injection, then all the branches connecting the injection bus to its immediate neighbors are suspected. A new WLAV state estimator adding the branch flow errors in the state vector is developed to identify the branch topology errors. Sample cases of single topology error and topology error with a measurement error are applied to IEEE 14 bus test system. (author). 24 refs., 1 fig., 9 tabs.

On the Use of Rank Tests and Estimates in the Linear Model.

Science.gov (United States)

1982-06-01

models," Journal of the Royal Statistical Society , Series B, 42, 366-371. Neter, J. and Wasserman, W. (1974), Applied Linear Statistical Models...University Park, PA. Schuster, E. (1974), "On the rate of convergence of an estimate of a functional of a probability density," Scandinavian Acturial

Augmented switching linear dynamical system model for gas concentration estimation with MOX sensors in an open sampling system.

Science.gov (United States)

Di Lello, Enrico; Trincavelli, Marco; Bruyninckx, Herman; De Laet, Tinne

2014-07-11

In this paper, we introduce a Bayesian time series model approach for gas concentration estimation using Metal Oxide (MOX) sensors in Open Sampling System (OSS). Our approach focuses on the compensation of the slow response of MOX sensors, while concurrently solving the problem of estimating the gas concentration in OSS. The proposed Augmented Switching Linear System model allows to include all the sources of uncertainty arising at each step of the problem in a single coherent probabilistic formulation. In particular, the problem of detecting on-line the current sensor dynamical regime and estimating the underlying gas concentration under environmental disturbances and noisy measurements is formulated and solved as a statistical inference problem. Our model improves, with respect to the state of the art, where system modeling approaches have been already introduced, but only provided an indirect relative measures proportional to the gas concentration and the problem of modeling uncertainty was ignored. Our approach is validated experimentally and the performances in terms of speed of and quality of the gas concentration estimation are compared with the ones obtained using a photo-ionization detector.

Ventricular enlargement in multiple sclerosis: a comparison of three-dimensional and linear MRI estimates

International Nuclear Information System (INIS)

Turner, B.; Blumhardt, L.D.; Ramli, N.; Jaspan, T.

2001-01-01

Atrophy of central white matter is related to irreversible clinical disability in multiple sclerosis (MS) and ventricular enlargement may be a sensitive marker of this tissue loss. Therapeutic trials in MS have provided MRI data for investigation of cerebral atrophy in MS. These studies use almost exclusively two-dimensional (2-D) images, which may be limited in the assessment of three-dimensional (3-D) structures. We used 3-D MRI data to estimate ventricular volumes in 40 patients with MS and 10 healthy controls, to look at associations with clinical disability and the stage of the disease. We then compared simple linear measures of ventricular size from conventional 2-D images, with 3-D volume estimates to establish the best available linear indices of ventricular volume. Mean ventricular volumes were increased in the patients and significantly larger in the more disabled patients. The estimated volume of the third ventricle obtained from 3-D MRI showed the strongest association with the clinical stage of the disease, duration of symptoms and levels of disability. Finally, we confirmed that in patients with MS accurate data on ventricular size can be obtained from 2-D images by two simple and convenient linear measures, the width of the third ventricle and of the anterior horn of the lateral ventricle. (orig.)

Markov Jump Linear Systems-Based Position Estimation for Lower Limb Exoskeletons

Directory of Open Access Journals (Sweden)

Samuel L. Nogueira

2014-01-01

Full Text Available In this paper, we deal with Markov Jump Linear Systems-based filtering applied to robotic rehabilitation. The angular positions of an impedance-controlled exoskeleton, designed to help stroke and spinal cord injured patients during walking rehabilitation, are estimated. Standard position estimate approaches adopt Kalman filters (KF to improve the performance of inertial measurement units (IMUs based on individual link configurations. Consequently, for a multi-body system, like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank are not taken into account in other link position estimation (e.g., the foot. In this paper, we propose a collective modeling of all inertial sensors attached to the exoskeleton, combining them in a Markovian estimation model in order to get the best information from each sensor. In order to demonstrate the effectiveness of our approach, simulation results regarding a set of human footsteps, with four IMUs and three encoders attached to the lower limb exoskeleton, are presented. A comparative study between the Markovian estimation system and the standard one is performed considering a wide range of parametric uncertainties.

Model-based stochastic-deterministic State and Force Estimation using Kalman filtering with Application to Hanko-1 Channel Marker

OpenAIRE

Petersen, Øyvind Wiig

2014-01-01

Force identification in structural dynamics is an inverse problem concerned with finding loads from measured structural response. The main objective of this thesis is to perform and study state (displacement and velocity) and force estimation by Kalman filtering. Theory on optimal control and state-space models are presented, adapted to linear structural dynamics. Accommodation for measurement noise and model inaccuracies are attained by stochastic-deterministic coupling. Explicit requirem...

Linear-scaling quantum mechanical methods for excited states.

Science.gov (United States)

Yam, ChiYung; Zhang, Qing; Wang, Fan; Chen, GuanHua

2012-05-21

The poor scaling of many existing quantum mechanical methods with respect to the system size hinders their applications to large systems. In this tutorial review, we focus on latest research on linear-scaling or O(N) quantum mechanical methods for excited states. Based on the locality of quantum mechanical systems, O(N) quantum mechanical methods for excited states are comprised of two categories, the time-domain and frequency-domain methods. The former solves the dynamics of the electronic systems in real time while the latter involves direct evaluation of electronic response in the frequency-domain. The localized density matrix (LDM) method is the first and most mature linear-scaling quantum mechanical method for excited states. It has been implemented in time- and frequency-domains. The O(N) time-domain methods also include the approach that solves the time-dependent Kohn-Sham (TDKS) equation using the non-orthogonal localized molecular orbitals (NOLMOs). Besides the frequency-domain LDM method, other O(N) frequency-domain methods have been proposed and implemented at the first-principles level. Except one-dimensional or quasi-one-dimensional systems, the O(N) frequency-domain methods are often not applicable to resonant responses because of the convergence problem. For linear response, the most efficient O(N) first-principles method is found to be the LDM method with Chebyshev expansion for time integration. For off-resonant response (including nonlinear properties) at a specific frequency, the frequency-domain methods with iterative solvers are quite efficient and thus practical. For nonlinear response, both on-resonance and off-resonance, the time-domain methods can be used, however, as the time-domain first-principles methods are quite expensive, time-domain O(N) semi-empirical methods are often the practical choice. Compared to the O(N) frequency-domain methods, the O(N) time-domain methods for excited states are much more mature and numerically stable, and

Dominant root locus in state estimator design for material flow processes: A case study of hot strip rolling.

Science.gov (United States)

Fišer, Jaromír; Zítek, Pavel; Skopec, Pavel; Knobloch, Jan; Vyhlídal, Tomáš

2017-05-01

The purpose of the paper is to achieve a constrained estimation of process state variables using the anisochronic state observer tuned by the dominant root locus technique. The anisochronic state observer is based on the state-space time delay model of the process. Moreover the process model is identified not only as delayed but also as non-linear. This model is developed to describe a material flow process. The root locus technique combined with the magnitude optimum method is utilized to investigate the estimation process. Resulting dominant roots location serves as a measure of estimation process performance. The higher the dominant (natural) frequency in the leftmost position of the complex plane the more enhanced performance with good robustness is achieved. Also the model based observer control methodology for material flow processes is provided by means of the separation principle. For demonstration purposes, the computer-based anisochronic state observer is applied to the strip temperatures estimation in the hot strip finishing mill composed of seven stands. This application was the original motivation to the presented research. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Nonparametric adaptive estimation of linear functionals for low frequency observed Lévy processes

OpenAIRE

Kappus, Johanna

2012-01-01

For a Lévy process X having finite variation on compact sets and finite first moments, µ( dx) = xv( dx) is a finite signed measure which completely describes the jump dynamics. We construct kernel estimators for linear functionals of µ and provide rates of convergence under regularity assumptions. Moreover, we consider adaptive estimation via model selection and propose a new strategy for the data driven choice of the smoothing parameter.

Measurement Error in Income and Schooling and the Bias of Linear Estimators

DEFF Research Database (Denmark)

Bingley, Paul; Martinello, Alessandro

2017-01-01

and Retirement in Europe data with Danish administrative registers. Contrary to most validation studies, we find that measurement error in income is classical once we account for imperfect validation data. We find nonclassical measurement error in schooling, causing a 38% amplification bias in IV estimators......We propose a general framework for determining the extent of measurement error bias in ordinary least squares and instrumental variable (IV) estimators of linear models while allowing for measurement error in the validation source. We apply this method by validating Survey of Health, Ageing...

Measurement error in income and schooling, and the bias of linear estimators

DEFF Research Database (Denmark)

Bingley, Paul; Martinello, Alessandro

The characteristics of measurement error determine the bias of linear estimators. We propose a method for validating economic survey data allowing for measurement error in the validation source, and we apply this method by validating Survey of Health, Ageing and Retirement in Europe (SHARE) data...... with Danish administrative registers. We find that measurement error in surveys is classical for annual gross income but non-classical for years of schooling, causing a 21% amplification bias in IV estimators of returns to schooling. Using a 1958 Danish schooling reform, we contextualize our result...

Mathematical model of transmission network static state estimation

Directory of Open Access Journals (Sweden)

Ivanov Aleksandar

2012-01-01

Full Text Available In this paper the characteristics and capabilities of the power transmission network static state estimator are presented. The solving process of the mathematical model containing the measurement errors and their processing is developed. To evaluate difference between the general model of state estimation and the fast decoupled state estimation model, the both models are applied to an example, and so derived results are compared.

Estimating traffic volume on Wyoming low volume roads using linear and logistic regression methods

Directory of Open Access Journals (Sweden)

Dick Apronti

2016-12-01

Full Text Available Traffic volume is an important parameter in most transportation planning applications. Low volume roads make up about 69% of road miles in the United States. Estimating traffic on the low volume roads is a cost-effective alternative to taking traffic counts. This is because traditional traffic counts are expensive and impractical for low priority roads. The purpose of this paper is to present the development of two alternative means of cost-effectively estimating traffic volumes for low volume roads in Wyoming and to make recommendations for their implementation. The study methodology involves reviewing existing studies, identifying data sources, and carrying out the model development. The utility of the models developed were then verified by comparing actual traffic volumes to those predicted by the model. The study resulted in two regression models that are inexpensive and easy to implement. The first regression model was a linear regression model that utilized pavement type, access to highways, predominant land use types, and population to estimate traffic volume. In verifying the model, an R2 value of 0.64 and a root mean square error of 73.4% were obtained. The second model was a logistic regression model that identified the level of traffic on roads using five thresholds or levels. The logistic regression model was verified by estimating traffic volume thresholds and determining the percentage of roads that were accurately classified as belonging to the given thresholds. For the five thresholds, the percentage of roads classified correctly ranged from 79% to 88%. In conclusion, the verification of the models indicated both model types to be useful for accurate and cost-effective estimation of traffic volumes for low volume Wyoming roads. The models developed were recommended for use in traffic volume estimations for low volume roads in pavement management and environmental impact assessment studies.

State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays

International Nuclear Information System (INIS)

Liu Yurong; Wang Zidong; Liu Xiaohui

2008-01-01

In this Letter, we investigate the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters as well as mode-dependent mixed time-delays. The parameters of the discrete-time neural networks are subject to the switching from one mode to another at different times according to a Markov chain, and the mixed time-delays consist of both discrete and distributed delays that are dependent on the Markovian jumping mode. New techniques are developed to deal with the mixed time-delays in the discrete-time setting, and a novel Lyapunov-Krasovskii functional is put forward to reflect the mode-dependent time-delays. Sufficient conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A numerical example is exploited to show the usefulness of the derived LMI-based conditions

On-Board State-of-Health Estimation at a Wide Ambient Temperature Range in Lithium-Ion Batteries

Directory of Open Access Journals (Sweden)

Tiansi Wang

2015-08-01

Full Text Available A state-of-health (SOH estimation method for electric vehicles (EVs is presented with three main advantages: (1 it provides joint estimation of cell’s aging states in terms of power and energy (i.e., SOHP and SOHE—because the determination of SOHP and SOHE can be reduced to the estimation of the ohmic resistance increase and capacity loss, respectively, the ohmic resistance at nominal temperature will be taken as a health indicator, and the capacity loss is estimated based on a mechanistic model that is developed to describe the correlation between resistance increase and capacity loss; (2 it has wide applicability to various ambient temperatures—to eliminate the effects of temperature on the resistance, another mechanistic model about the resistance against temperature is presented, which can normalize the resistance at various temperatures to its standard value at the nominal temperature; and (3 it needs low computational efforts for on-board application—based on a linear equation of cell’s dynamic behaviors, the recursive least-squares (RLS algorithm is used for the resistance estimation. Based on the designed performance and validation experiments, respectively, the coefficients of the models are determined and the accuracy of the proposed method is verified. The results at different aging states and temperatures show good accuracy and reliability.

State energy data report 1996: Consumption estimates

Energy Technology Data Exchange (ETDEWEB)

NONE

1999-02-01

The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sectors. The estimates are developed in the Combined State Energy Data System (CSEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining CSEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. CSEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public and (2) to provide the historical series necessary for EIA`s energy models. To the degree possible, energy consumption has been assigned to five sectors: residential, commercial, industrial, transportation, and electric utility sectors. Fuels covered are coal, natural gas, petroleum, nuclear electric power, hydroelectric power, biomass, and other, defined as electric power generated from geothermal, wind, photovoltaic, and solar thermal energy. 322 tabs.

State energy data report 1996: Consumption estimates

International Nuclear Information System (INIS)

1999-02-01

The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sectors. The estimates are developed in the Combined State Energy Data System (CSEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining CSEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. CSEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public and (2) to provide the historical series necessary for EIA's energy models. To the degree possible, energy consumption has been assigned to five sectors: residential, commercial, industrial, transportation, and electric utility sectors. Fuels covered are coal, natural gas, petroleum, nuclear electric power, hydroelectric power, biomass, and other, defined as electric power generated from geothermal, wind, photovoltaic, and solar thermal energy. 322 tabs

Typical Werner states satisfying all linear Bell inequalities with dichotomic measurements

Science.gov (United States)

Luo, Ming-Xing

2018-04-01

Quantum entanglement as a special resource inspires various distinct applications in quantum information processing. Unfortunately, it is NP-hard to detect general quantum entanglement using Bell testing. Our goal is to investigate quantum entanglement with white noises that appear frequently in experiment and quantum simulations. Surprisingly, for almost all multipartite generalized Greenberger-Horne-Zeilinger states there are entangled noisy states that satisfy all linear Bell inequalities consisting of full correlations with dichotomic inputs and outputs of each local observer. This result shows generic undetectability of mixed entangled states in contrast to Gisin's theorem of pure bipartite entangled states in terms of Bell nonlocality. We further provide an accessible method to show a nontrivial set of noisy entanglement with small number of parties satisfying all general linear Bell inequalities. These results imply typical incompleteness of special Bell theory in explaining entanglement.

New developments in state estimation for Nonlinear Systems

DEFF Research Database (Denmark)

Nørgård, Peter Magnus; Poulsen, Niels Kjølstad; Ravn, Ole

2000-01-01

Based on an interpolation formula, accurate state estimators for nonlinear systems can be derived. The estimators do not require derivative information which makes them simple to implement.; State estimators for nonlinear systems are derived based on polynomial approximations obtained with a mult......-known estimators, such as the extended Kalman filter (EKF) and its higher-order relatives, in most practical applications....

Linear estimation discriminates midline sources and motor cortex contribution to the readiness potential

NARCIS (Netherlands)

Knosche, Thomas; Knosche, T.R.; Praamstra, Peter; Peters, M.J.; Stegeman, Dick; Stegeman, D.

1996-01-01

Spatiotemporal dipole modelling of the generators of the readiness potential (RP) prior to voluntary movements has yielded diverging results concerning the contributions of supplementary motor area (SMA) and primary motor cortex. We applied an alternative approach (i.e. linear estimation theory) to

Simple, efficient estimators of treatment effects in randomized trials using generalized linear models to leverage baseline variables.

Science.gov (United States)

Rosenblum, Michael; van der Laan, Mark J

2010-04-01

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

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

Science.gov (United States)

Rosenblum, Michael; van der Laan, Mark J.

2010-01-01

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

Remote optimal state estimation over communication channels with random delays

KAUST Repository

Mahmoud, Magdi S.; Al-Sunni, Fouad; Liu, Bo

2014-01-01

This paper considers the optimal estimation of linear systems over unreliable communication channels with random delays. In this work, it is assumed that the system to be estimated is far away from the filter. The observations of the system

Multi-objective optimization of linear multi-state multiple sliding window system

International Nuclear Information System (INIS)

Konak, Abdullah; Kulturel-Konak, Sadan; Levitin, Gregory

2012-01-01

This paper considers the optimal element sequencing in a linear multi-state multiple sliding window system that consists of n linearly ordered multi-state elements. Each multi-state element can have different states: from complete failure up to perfect functioning. A performance rate is associated with each state. The failure of type i in the system occurs if for any i (1≤i≤I) the cumulative performance of any r i consecutive elements is lower than w i . The element sequence strongly affects the probability of any type of system failure. The sequence that minimizes the probability of certain type of failure can provide high probability of other types of failures. Therefore the optimization problem for the multiple sliding window system is essentially multi-objective. The paper formulates and solves the multi-objective optimization problem for the multiple sliding window systems. A multi-objective Genetic Algorithm is used as the optimization engine. Illustrative examples are presented.

Efficient Estimation of Non-Linear Dynamic Panel Data Models with Application to Smooth Transition Models

DEFF Research Database (Denmark)

Gørgens, Tue; Skeels, Christopher L.; Wurtz, Allan

This paper explores estimation of a class of non-linear dynamic panel data models with additive unobserved individual-specific effects. The models are specified by moment restrictions. The class includes the panel data AR(p) model and panel smooth transition models. We derive an efficient set...... of moment restrictions for estimation and apply the results to estimation of panel smooth transition models with fixed effects, where the transition may be determined endogenously. The performance of the GMM estimator, both in terms of estimation precision and forecasting performance, is examined in a Monte...

Probabilistic deletion of copies of linearly independent quantum states

International Nuclear Information System (INIS)

Feng Jian; Gao Yunfeng; Wang Jisuo; Zhan Mingsheng

2002-01-01

We show that each of two copies of the nonorthogonal states randomly selected from a certain set S can be probabilistically deleted by a general unitary-reduction operation if and only if the states are linearly independent. We derive a tight bound on the best possible deleting efficiencies. These results for 2→1 probabilistic deleting are also generalized into the case of N→M deleting (N,M positive integers and N>M)

Linear estimates of structure functions from deep inelastic lepton-nucleon scattering data. Part 1

International Nuclear Information System (INIS)

Anikeev, V.B.; Zhigunov, V.P.

1991-01-01

This paper concerns the linear estimation of structure functions from muon(electron)-nucleon scattering. The expressions obtained for the structure functions estimate provide correct analysis of the random error and the bias The bias arises because of the finite number of experimental data and the finite resolution of experiment. The approach suggested may become useful for data handling from experiments at HERA. 9 refs

On state estimation in electric drives

International Nuclear Information System (INIS)

Leon, A.E.; Solsona, J.A.

2010-01-01

This paper deals with state estimation in electric drives. On one hand a nonlinear observer is designed, whereas on the other hand the speed state is estimated by using the dirty derivative from the position measured. The dirty derivative is an approximate version of the perfect derivative which introduces an estimation error few times analyzed in drive applications. For this reason, our proposal in this work consists in illustrating several aspects on the performance of the dirty derivator in presence of both model uncertainties and noisy measurements. To this end, a case study is introduced. The case study considers rotor speed estimation in a permanent magnet stepper motor, by assuming that rotor position and electrical variables are measured. In addition, this paper presents comments about the connection between dirty derivators and observers, and advantages and disadvantages of both techniques are also remarked.

State-Level Estimates of Cancer-Related Absenteeism Costs

Science.gov (United States)

Tangka, Florence K.; Trogdon, Justin G.; Nwaise, Isaac; Ekwueme, Donatus U.; Guy, Gery P.; Orenstein, Diane

2016-01-01

Background Cancer is one of the top five most costly diseases in the United States and leads to substantial work loss. Nevertheless, limited state-level estimates of cancer absenteeism costs have been published. Methods In analyses of data from the 2004–2008 Medical Expenditure Panel Survey, the 2004 National Nursing Home Survey, the U.S. Census Bureau for 2008, and the 2009 Current Population Survey, we used regression modeling to estimate annual state-level absenteeism costs attributable to cancer from 2004 to 2008. Results We estimated that the state-level median number of days of absenteeism per year among employed cancer patients was 6.1 days and that annual state-level cancer absenteeism costs ranged from $14.9 million to $915.9 million (median = $115.9 million) across states in 2010 dollars. Absenteeism costs are approximately 6.5% of the costs of premature cancer mortality. Conclusions The results from this study suggest that lost productivity attributable to cancer is a substantial cost to employees and employers and contributes to estimates of the overall impact of cancer in a state population. PMID:23969498

Implementing nonprojective measurements via linear optics: An approach based on optimal quantum-state discrimination

International Nuclear Information System (INIS)

Loock, Peter van; Nemoto, Kae; Munro, William J.; Raynal, Philippe; Luetkenhaus, Norbert

2006-01-01

We discuss the problem of implementing generalized measurements [positive operator-valued measures (POVMs)] with linear optics, either based upon a static linear array or including conditional dynamics. In our approach, a given POVM shall be identified as a solution to an optimization problem for a chosen cost function. We formulate a general principle: the implementation is only possible if a linear-optics circuit exists for which the quantum mechanical optimum (minimum) is still attainable after dephasing the corresponding quantum states. The general principle enables us, for instance, to derive a set of necessary conditions for the linear-optics implementation of the POVM that realizes the quantum mechanically optimal unambiguous discrimination of two pure nonorthogonal states. This extends our previous results on projection measurements and the exact discrimination of orthogonal states

Identification of an Equivalent Linear Model for a Non-Linear Time-Variant RC-Structure

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune

are investigated and compared with ARMAX models used on a running window. The techniques are evaluated using simulated data generated by the non-linear finite element program SARCOF modeling a 10-storey 3-bay concrete structure subjected to amplitude modulated Gaussian white noise filtered through a Kanai......This paper considers estimation of the maximum softening for a RC-structure subjected to earthquake excitation. The so-called Maximum Softening damage indicator relates the global damage state of the RC-structure to the relative decrease of the fundamental eigenfrequency in an equivalent linear...

Algorithm of the managing systems state estimation

Directory of Open Access Journals (Sweden)

Skubilin M. D.

2010-02-01

Full Text Available The possibility of an electronic estimation of automatic and automated managing systems state is analyzed. An estimation of a current state (functional readiness of technical equipment and person-operator as integrated system allows to take operatively adequate measures on an exception and-or minimisation of consequences of system’s transition in a supernumerary state. The offered method is universal enough and can be recommended for normalisation of situations on transport, mainly in aircraft.

Asymptotic Stabilization of Continuous-Time Linear Systems with Input and State Quantizations

Directory of Open Access Journals (Sweden)

Sung Wook Yun

2014-01-01

Full Text Available This paper discusses the asymptotic stabilization problem of linear systems with input and state quantizations. In order to achieve asymptotic stabilization of such systems, we propose a state-feedback controller comprising two control parts: the main part is used to determine the fundamental characteristics of the system associated with the cost, and the additional part is employed to eliminate the effects of input and state quanizations. In particular, in order to implement the additional part, we introduce a quantizer with a region-decision making process (RDMP for a certain linear switching surface. The simulation results show the effectiveness of the proposed controller.

Linear Extended State Observer-Based Motion Synchronization Control for Hybrid Actuation System of More Electric Aircraft

Directory of Open Access Journals (Sweden)

Xingjian Wang

2017-10-01

Full Text Available Moving towards the more electric aircraft (MEA, a hybrid actuator configuration provides an opportunity to introduce electromechanical actuator (EMA into primary flight control. In the hybrid actuation system (HAS, an electro-hydraulic servo actuator (EHSA and an EMA operate on the same control surface. In order to solve force fighting problem in HAS, this paper proposes a novel linear extended state observer (LESO-based motion synchronization control method. To cope with the problem of unavailability of the state signals required by the motion synchronization controller, LESO is designed for EHSA and EMA to observe the state variables. Based on the observed states of LESO, motion synchronization controllers could enable EHSA and EMA to simultaneously track the desired motion trajectories. Additionally, nonlinearities, uncertainties and unknown disturbances as well as the coupling term between EHSA and EMA can be estimated and compensated by using the extended state of the proposed LESO. Finally, comparative simulation results indicate that the proposed LESO-based motion synchronization controller could reduce significant force fighting between EHSA and EMA.

Linear Extended State Observer-Based Motion Synchronization Control for Hybrid Actuation System of More Electric Aircraft

Science.gov (United States)

Liao, Rui; Shi, Cun; Wang, Shaoping

2017-01-01

Moving towards the more electric aircraft (MEA), a hybrid actuator configuration provides an opportunity to introduce electromechanical actuator (EMA) into primary flight control. In the hybrid actuation system (HAS), an electro-hydraulic servo actuator (EHSA) and an EMA operate on the same control surface. In order to solve force fighting problem in HAS, this paper proposes a novel linear extended state observer (LESO)-based motion synchronization control method. To cope with the problem of unavailability of the state signals required by the motion synchronization controller, LESO is designed for EHSA and EMA to observe the state variables. Based on the observed states of LESO, motion synchronization controllers could enable EHSA and EMA to simultaneously track the desired motion trajectories. Additionally, nonlinearities, uncertainties and unknown disturbances as well as the coupling term between EHSA and EMA can be estimated and compensated by using the extended state of the proposed LESO. Finally, comparative simulation results indicate that the proposed LESO-based motion synchronization controller could reduce significant force fighting between EHSA and EMA. PMID:29068392

A theoretical signal processing framework for linear diffusion MRI: Implications for parameter estimation and experiment design.

Science.gov (United States)

Varadarajan, Divya; Haldar, Justin P

2017-11-01

The data measured in diffusion MRI can be modeled as the Fourier transform of the Ensemble Average Propagator (EAP), a probability distribution that summarizes the molecular diffusion behavior of the spins within each voxel. This Fourier relationship is potentially advantageous because of the extensive theory that has been developed to characterize the sampling requirements, accuracy, and stability of linear Fourier reconstruction methods. However, existing diffusion MRI data sampling and signal estimation methods have largely been developed and tuned without the benefit of such theory, instead relying on approximations, intuition, and extensive empirical evaluation. This paper aims to address this discrepancy by introducing a novel theoretical signal processing framework for diffusion MRI. The new framework can be used to characterize arbitrary linear diffusion estimation methods with arbitrary q-space sampling, and can be used to theoretically evaluate and compare the accuracy, resolution, and noise-resilience of different data acquisition and parameter estimation techniques. The framework is based on the EAP, and makes very limited modeling assumptions. As a result, the approach can even provide new insight into the behavior of model-based linear diffusion estimation methods in contexts where the modeling assumptions are inaccurate. The practical usefulness of the proposed framework is illustrated using both simulated and real diffusion MRI data in applications such as choosing between different parameter estimation methods and choosing between different q-space sampling schemes. Copyright © 2017 Elsevier Inc. All rights reserved.

Information-geometric measures estimate neural interactions during oscillatory brain states

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Yimin eNie

2014-02-01

Full Text Available The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG, a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain.

Perturbation-Based Regularization for Signal Estimation in Linear Discrete Ill-posed Problems

KAUST Repository

Suliman, Mohamed Abdalla Elhag; Ballal, Tarig; Al-Naffouri, Tareq Y.

2016-01-01

Estimating the values of unknown parameters from corrupted measured data faces a lot of challenges in ill-posed problems. In such problems, many fundamental estimation methods fail to provide a meaningful stabilized solution. In this work, we propose a new regularization approach and a new regularization parameter selection approach for linear least-squares discrete ill-posed problems. The proposed approach is based on enhancing the singular-value structure of the ill-posed model matrix to acquire a better solution. Unlike many other regularization algorithms that seek to minimize the estimated data error, the proposed approach is developed to minimize the mean-squared error of the estimator which is the objective in many typical estimation scenarios. The performance of the proposed approach is demonstrated by applying it to a large set of real-world discrete ill-posed problems. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods in most cases. In addition, the approach also enjoys the lowest runtime and offers the highest level of robustness amongst all the tested benchmark regularization methods.

Perturbation-Based Regularization for Signal Estimation in Linear Discrete Ill-posed Problems

KAUST Repository

Suliman, Mohamed Abdalla Elhag

2016-11-29

Estimating the values of unknown parameters from corrupted measured data faces a lot of challenges in ill-posed problems. In such problems, many fundamental estimation methods fail to provide a meaningful stabilized solution. In this work, we propose a new regularization approach and a new regularization parameter selection approach for linear least-squares discrete ill-posed problems. The proposed approach is based on enhancing the singular-value structure of the ill-posed model matrix to acquire a better solution. Unlike many other regularization algorithms that seek to minimize the estimated data error, the proposed approach is developed to minimize the mean-squared error of the estimator which is the objective in many typical estimation scenarios. The performance of the proposed approach is demonstrated by applying it to a large set of real-world discrete ill-posed problems. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods in most cases. In addition, the approach also enjoys the lowest runtime and offers the highest level of robustness amongst all the tested benchmark regularization methods.

Distributed Dynamic State Estimation with Extended Kalman Filter

Energy Technology Data Exchange (ETDEWEB)

Du, Pengwei; Huang, Zhenyu; Sun, Yannan; Diao, Ruisheng; Kalsi, Karanjit; Anderson, Kevin K.; Li, Yulan; Lee, Barry

2011-08-04

Increasing complexity associated with large-scale renewable resources and novel smart-grid technologies necessitates real-time monitoring and control. Our previous work applied the extended Kalman filter (EKF) with the use of phasor measurement data (PMU) for dynamic state estimation. However, high computation complexity creates significant challenges for real-time applications. In this paper, the problem of distributed dynamic state estimation is investigated. One domain decomposition method is proposed to utilize decentralized computing resources. The performance of distributed dynamic state estimation is tested on a 16-machine, 68-bus test system.

Enhanced 2D-DOA Estimation for Large Spacing Three-Parallel Uniform Linear Arrays

Directory of Open Access Journals (Sweden)

Dong Zhang

2018-01-01

Full Text Available An enhanced two-dimensional direction of arrival (2D-DOA estimation algorithm for large spacing three-parallel uniform linear arrays (ULAs is proposed in this paper. Firstly, we use the propagator method (PM to get the highly accurate but ambiguous estimation of directional cosine. Then, we use the relationship between the directional cosine to eliminate the ambiguity. This algorithm not only can make use of the elements of the three-parallel ULAs but also can utilize the connection between directional cosine to improve the estimation accuracy. Besides, it has satisfied estimation performance when the elevation angle is between 70° and 90° and it can automatically pair the estimated azimuth and elevation angles. Furthermore, it has low complexity without using any eigen value decomposition (EVD or singular value decompostion (SVD to the covariance matrix. Simulation results demonstrate the effectiveness of our proposed algorithm.

Unconditional quantum cloning of coherent states with linear optics

International Nuclear Information System (INIS)

Leuchs, G.; Andersen, U.L.; Josse, V.

2005-01-01

Intense light pulses with non-classical properties are used to implement protocols for quantum communication. Most of the elements in the tool box needed to assemble the experimental set-ups for these protocols are readily described by Bogoliubov transformations corresponding to Gaussian transformations that map Gaussian states onto Gaussian states. One particularly interesting application is quantum cloning of a coherent state. A scheme for optimal Gaussian cloning of optical coherent states is proposed and experimentally demonstrated. Its optical realization is based entirely on simple linear optical elements and homodyne detection. The optimality of the presented scheme is only limited by detection inefficiencies. Experimentally we achieved a cloning fidelity of about 65%, which almost touches the optimal value of 2/3. (author)

Novel point estimation from a semiparametric ratio estimator (SPRE): long-term health outcomes from short-term linear data, with application to weight loss in obesity.

Science.gov (United States)

Weissman-Miller, Deborah

2013-11-02

Point estimation is particularly important in predicting weight loss in individuals or small groups. In this analysis, a new health response function is based on a model of human response over time to estimate long-term health outcomes from a change point in short-term linear regression. This important estimation capability is addressed for small groups and single-subject designs in pilot studies for clinical trials, medical and therapeutic clinical practice. These estimations are based on a change point given by parameters derived from short-term participant data in ordinary least squares (OLS) regression. The development of the change point in initial OLS data and the point estimations are given in a new semiparametric ratio estimator (SPRE) model. The new response function is taken as a ratio of two-parameter Weibull distributions times a prior outcome value that steps estimated outcomes forward in time, where the shape and scale parameters are estimated at the change point. The Weibull distributions used in this ratio are derived from a Kelvin model in mechanics taken here to represent human beings. A distinct feature of the SPRE model in this article is that initial treatment response for a small group or a single subject is reflected in long-term response to treatment. This model is applied to weight loss in obesity in a secondary analysis of data from a classic weight loss study, which has been selected due to the dramatic increase in obesity in the United States over the past 20 years. A very small relative error of estimated to test data is shown for obesity treatment with the weight loss medication phentermine or placebo for the test dataset. An application of SPRE in clinical medicine or occupational therapy is to estimate long-term weight loss for a single subject or a small group near the beginning of treatment.

Photoneutrons from medical linear accelerators--radiobiological measurements and risk estimates

International Nuclear Information System (INIS)

Hall, Eric J.; Martin, Stewart G.; Amols, Howard; Hei, Tom K.

1995-01-01

Purpose: To assess the oncogenic potential of the photoneutrons produced by high energy medical linear accelerators. Methods and Materials: An established line of cells of rodent origin (C 3 H 10T1/2) was used to assess the oncogenic potential of the radiation dose received in the breast of an anthropomorphic 'randoman' phanton, while the cervix received a dose of 70 Gy. Experiments were performed at 6 MV, below the threshold for the production of photoneutrons, and at 20 MV where the dose includes about 0.01 Gy of photoneutrons as well as scattered x-rays. Results: A significantly higher transformation incidence was observed for the 20-MV machine, consistent with the measured neutron dose of about 0.01 Gy and a quality factor of 20. Conclusion: An estimate can be made of the additional deaths from second malignancies that might result from the photoneutrons generated by higher energy linear accelerators (Linacs), which must be offset against the possible improvements in survival that might result from the higher tumor doses made possible by the increased percentage depth doses

Traffic State Estimation Using Connected Vehicles and Stationary Detectors

Directory of Open Access Journals (Sweden)

Ellen F. Grumert

2018-01-01

Full Text Available Real-time traffic state estimation is of importance for efficient traffic management. This is especially the case for traffic management systems that require fast detection of changes in the traffic conditions in order to apply an effective control measure. In this paper, we propose a method for estimating the traffic state and speed and density, by using connected vehicles combined with stationary detectors. The aim is to allow fast and accurate estimation of changes in the traffic conditions. The proposed method does only require information about the speed and the position of connected vehicles and can make use of sparsely located stationary detectors to limit the dependence on the infrastructure equipment. An evaluation of the proposed method is carried out by microscopic traffic simulation. The traffic state estimated using the proposed method is compared to the true simulated traffic state. Further, the density estimates are compared to density estimates from one detector-based method, one combined method, and one connected-vehicle-based method. The results of the study show that the proposed method is a promising alternative for estimating the traffic state in traffic management applications.

A primer on linear models

CERN Document Server

Monahan, John F

2008-01-01

Preface Examples of the General Linear Model Introduction One-Sample Problem Simple Linear Regression Multiple Regression One-Way ANOVA First Discussion The Two-Way Nested Model Two-Way Crossed Model Analysis of Covariance Autoregression Discussion The Linear Least Squares Problem The Normal Equations The Geometry of Least Squares Reparameterization Gram-Schmidt Orthonormalization Estimability and Least Squares Estimators Assumptions for the Linear Mean Model Confounding, Identifiability, and Estimability Estimability and Least Squares Estimators F

Lifetime estimation of zirconia ceramics by linear ageing kinetics

International Nuclear Information System (INIS)

Zhang, Fei; Inokoshi, Masanao; Vanmeensel, Kim; Van Meerbeek, Bart; Naert, Ignace; Vleugels, Jef

2015-01-01

Up to now, the ageing kinetics of zirconia ceramics were mainly derived from the sigmoidal evolution of the surface phase transformation as a function of time, as quantified by means of X-ray diffraction (XRD). However, the transformation propagation into the material should be better to monitor the ageing kinetics. In this work, μ-Raman spectroscopy was used to quantitatively measure the transformation profiles in depth as a function of ageing time at 160 °C, 140 °C, 134 °C and 110 °C. A linear relationship between the transformed depth and the ageing time was observed for all investigated yttria stabilized tetragonal zirconia polycrystals (3Y-TZP). Furthermore, the μ-Raman investigation of residual stresses in the subsurface of aged 3Y-TZPs showed that the highest tensile stress was located just ahead of the transformation front, indicating the key responsibility of stress accumulation for transformation front propagating into the material. Moreover, the linear kinetics of the transformation propagation were more accurate to calculate the apparent activation energy of the ageing process and allowed a more straightforward estimation of the lifetime of 3Y-TZP at body temperature, as compared to the conventional ageing kinetic parameters obtained from the surface transformation analysis by XRD

State Estimation-based Transmission line parameter identification

Directory of Open Access Journals (Sweden)

Fredy Andrés Olarte Dussán

2010-01-01

Full Text Available This article presents two state-estimation-based algorithms for identifying transmission line parameters. The identification technique used simultaneous state-parameter estimation on an artificial power system composed of several copies of the same transmission line, using measurements at different points in time. The first algorithm used active and reactive power measurements at both ends of the line. The second method used synchronised phasor voltage and current measurements at both ends. The algorithms were tested in simulated conditions on the 30-node IEEE test system. All line parameters for this system were estimated with errors below 1%.

Multistage optimal PMU placement for hybrid state estimation

DEFF Research Database (Denmark)

Hazra, J.; Das, Kaushik; Roy, B. K. S.

2017-01-01

placed by the proposed method are used in developing a hybrid state estimator (HSE). The HSE estimates the voltage phasor at all the buses of a power system with a limited numbers of PMUs in steady state as well as in the presence of disturbances even in that part of network which is unobservable through...... PMUs. Performance of the proposed phased installation scheme for HSE is evaluated on the number of standard test system and the simulation results shows an improvement in the accuracy of the estimated states as compared to the existing methods in the literature....

A Stationary North-Finding Scheme for an Azimuth Rotational IMU Utilizing a Linear State Equality Constraint

Directory of Open Access Journals (Sweden)

Huapeng Yu

2015-02-01

Full Text Available The Kalman filter (KF has always been used to improve north-finding performance under practical conditions. By analyzing the characteristics of the azimuth rotational inertial measurement unit (ARIMU on a stationary base, a linear state equality constraint for the conventional KF used in the fine north-finding filtering phase is derived. Then, a constrained KF using the state equality constraint is proposed and studied in depth. Estimation behaviors of the concerned navigation errors when implementing the conventional KF scheme and the constrained KF scheme during stationary north-finding are investigated analytically by the stochastic observability approach, which can provide explicit formulations of the navigation errors with influencing variables. Finally, multiple practical experimental tests at a fixed position are done on a postulate system to compare the stationary north-finding performance of the two filtering schemes. In conclusion, this study has successfully extended the utilization of the stochastic observability approach for analytic descriptions of estimation behaviors of the concerned navigation errors, and the constrained KF scheme has demonstrated its superiority over the conventional KF scheme for ARIMU stationary north-finding both theoretically and practically.

The SR Approach: a new Estimation Method for Non-Linear and Non-Gaussian Dynamic Term Structure Models

DEFF Research Database (Denmark)

Andreasen, Martin Møller; Christensen, Bent Jesper

This paper suggests a new and easy approach to estimate linear and non-linear dynamic term structure models with latent factors. We impose no distributional assumptions on the factors and they may therefore be non-Gaussian. The novelty of our approach is to use many observables (yields or bonds p...

Estimation of error components in a multi-error linear regression model, with an application to track fitting

International Nuclear Information System (INIS)

Fruehwirth, R.

1993-01-01

We present an estimation procedure of the error components in a linear regression model with multiple independent stochastic error contributions. After solving the general problem we apply the results to the estimation of the actual trajectory in track fitting with multiple scattering. (orig.)

Linear solvation energy relationships: "rule of thumb" for estimation of variable values

Science.gov (United States)

Hickey, James P.; Passino-Reader, Dora R.

1991-01-01

For the linear solvation energy relationship (LSER), values are listed for each of the variables (Vi/100, π*, &betam, αm) for fundamental organic structures and functional groups. We give the guidelines to estimate LSER variable values quickly for a vast array of possible organic compounds such as those found in the environment. The difficulty in generating these variables has greatly discouraged the application of this quantitative structure-activity relationship (QSAR) method. This paper present the first compilation of molecular functional group values together with a utilitarian set of the LSER variable estimation rules. The availability of these variable values and rules should facilitate widespread application of LSER for hazard evaluation of environmental contaminants.

Quantitative Pointwise Estimate of the Solution of the Linearized Boltzmann Equation

Science.gov (United States)

Lin, Yu-Chu; Wang, Haitao; Wu, Kung-Chien

2018-04-01

We study the quantitative pointwise behavior of the solutions of the linearized Boltzmann equation for hard potentials, Maxwellian molecules and soft potentials, with Grad's angular cutoff assumption. More precisely, for solutions inside the finite Mach number region (time like region), we obtain the pointwise fluid structure for hard potentials and Maxwellian molecules, and optimal time decay in the fluid part and sub-exponential time decay in the non-fluid part for soft potentials. For solutions outside the finite Mach number region (space like region), we obtain sub-exponential decay in the space variable. The singular wave estimate, regularization estimate and refined weighted energy estimate play important roles in this paper. Our results extend the classical results of Liu and Yu (Commun Pure Appl Math 57:1543-1608, 2004), (Bull Inst Math Acad Sin 1:1-78, 2006), (Bull Inst Math Acad Sin 6:151-243, 2011) and Lee et al. (Commun Math Phys 269:17-37, 2007) to hard and soft potentials by imposing suitable exponential velocity weight on the initial condition.

Quantitative Pointwise Estimate of the Solution of the Linearized Boltzmann Equation

Science.gov (United States)

Lin, Yu-Chu; Wang, Haitao; Wu, Kung-Chien

2018-06-01

We study the quantitative pointwise behavior of the solutions of the linearized Boltzmann equation for hard potentials, Maxwellian molecules and soft potentials, with Grad's angular cutoff assumption. More precisely, for solutions inside the finite Mach number region (time like region), we obtain the pointwise fluid structure for hard potentials and Maxwellian molecules, and optimal time decay in the fluid part and sub-exponential time decay in the non-fluid part for soft potentials. For solutions outside the finite Mach number region (space like region), we obtain sub-exponential decay in the space variable. The singular wave estimate, regularization estimate and refined weighted energy estimate play important roles in this paper. Our results extend the classical results of Liu and Yu (Commun Pure Appl Math 57:1543-1608, 2004), (Bull Inst Math Acad Sin 1:1-78, 2006), (Bull Inst Math Acad Sin 6:151-243, 2011) and Lee et al. (Commun Math Phys 269:17-37, 2007) to hard and soft potentials by imposing suitable exponential velocity weight on the initial condition.

FUNDAMENTAL MATRIX OF LINEAR CONTINUOUS SYSTEM IN THE PROBLEM OF ESTIMATING ITS TRANSPORT DELAY

Directory of Open Access Journals (Sweden)

N. A. Dudarenko

2014-09-01

Full Text Available The paper deals with the problem of quantitative estimation for transport delay of linear continuous systems. The main result is received by means of fundamental matrix of linear differential equations solutions specified in the normal Cauchy form for the cases of SISO and MIMO systems. Fundamental matrix has the dual property. It means that the weight function of the system can be formed as a free motion of systems. Last one is generated by the vector of initial system conditions, which coincides with the matrix input of the system being researched. Thus, using the properties of the system- solving for fundamental matrix has given the possibility to solve the problem of estimating transport linear continuous system delay without the use of derivation procedure in hardware environment and without formation of exogenous Dirac delta function. The paper is illustrated by examples. The obtained results make it possible to solve the problem of modeling the pure delay links using consecutive chain of aperiodic links of the first order with the equal time constants. Modeling results have proved the correctness of obtained computations. Knowledge of transport delay can be used when configuring multi- component technological complexes and in the diagnosis of their possible functional degeneration.

Effect of Smart Meter Measurements Data On Distribution State Estimation

DEFF Research Database (Denmark)

Pokhrel, Basanta Raj; Nainar, Karthikeyan; Bak-Jensen, Birgitte

2018-01-01

Smart distribution grids with renewable energy based generators and demand response resources (DRR) requires accurate state estimators for real time control. Distribution grid state estimators are normally based on accumulated smart meter measurements. However, increase of measurements in the phy......Smart distribution grids with renewable energy based generators and demand response resources (DRR) requires accurate state estimators for real time control. Distribution grid state estimators are normally based on accumulated smart meter measurements. However, increase of measurements...... in the physical grid can enforce significant stress not only on the communication infrastructure but also in the control algorithms. This paper aims to propose a methodology to analyze needed real time smart meter data from low voltage distribution grids and their applicability in distribution state estimation...

Simple estimating method of damages of concrete gravity dam based on linear dynamic analysis

Energy Technology Data Exchange (ETDEWEB)

Sasaki, T.; Kanenawa, K.; Yamaguchi, Y. [Public Works Research Institute, Tsukuba, Ibaraki (Japan). Hydraulic Engineering Research Group

2004-07-01

Due to the occurrence of large earthquakes like the Kobe Earthquake in 1995, there is a strong need to verify seismic resistance of dams against much larger earthquake motions than those considered in the present design standard in Japan. Problems exist in using nonlinear analysis to evaluate the safety of dams including: that the influence which the set material properties have on the results of nonlinear analysis is large, and that the results of nonlinear analysis differ greatly according to the damage estimation models or analysis programs. This paper reports the evaluation indices based on a linear dynamic analysis method and the characteristics of the progress of cracks in concrete gravity dams with different shapes using a nonlinear dynamic analysis method. The study concludes that if simple linear dynamic analysis is appropriately conducted to estimate tensile stress at potential locations of initiating cracks, the damage due to cracks would be predicted roughly. 4 refs., 1 tab., 13 figs.

Nonlinear Filtering Techniques Comparison for Battery State Estimation

Directory of Open Access Journals (Sweden)

Aspasia Papazoglou

2014-09-01

Full Text Available The performance of estimation algorithms is vital for the correct functioning of batteries in electric vehicles, as poor estimates will inevitably jeopardize the operations that rely on un-measurable quantities, such as State of Charge and State of Health. This paper compares the performance of three nonlinear estimation algorithms: the Extended Kalman Filter, the Unscented Kalman Filter and the Particle Filter, where a lithium-ion cell model is considered. The effectiveness of these algorithms is measured by their ability to produce accurate estimates against their computational complexity in terms of number of operations and execution time required. The trade-offs between estimators' performance and their computational complexity are analyzed.

Efficient semiparametric estimation in generalized partially linear additive models for longitudinal/clustered data

KAUST Repository

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.

On H∞ Fault Estimator Design for Linear Discrete Time-Varying Systems under Unreliable Communication Link

Directory of Open Access Journals (Sweden)

Yueyang Li

2014-01-01

Full Text Available This paper investigates the H∞ fixed-lag fault estimator design for linear discrete time-varying (LDTV systems with intermittent measurements, which is described by a Bernoulli distributed random variable. Through constructing a novel partially equivalent dynamic system, the fault estimator design is converted into a deterministic quadratic minimization problem. By applying the innovation reorganization technique and the projection formula in Krein space, a necessary and sufficient condition is obtained for the existence of the estimator. The parameter matrices of the estimator are derived by recursively solving two standard Riccati equations. An illustrative example is provided to show the effectiveness and applicability of the proposed algorithm.

Estimation of biological parameters of marine organisms using linear and nonlinear acoustic scattering model-based inversion methods.

Science.gov (United States)

Chu, Dezhang; Lawson, Gareth L; Wiebe, Peter H

2016-05-01

The linear inversion commonly used in fisheries and zooplankton acoustics assumes a constant inversion kernel and ignores the uncertainties associated with the shape and behavior of the scattering targets, as well as other relevant animal parameters. Here, errors of the linear inversion due to uncertainty associated with the inversion kernel are quantified. A scattering model-based nonlinear inversion method is presented that takes into account the nonlinearity of the inverse problem and is able to estimate simultaneously animal abundance and the parameters associated with the scattering model inherent to the kernel. It uses sophisticated scattering models to estimate first, the abundance, and second, the relevant shape and behavioral parameters of the target organisms. Numerical simulations demonstrate that the abundance, size, and behavior (tilt angle) parameters of marine animals (fish or zooplankton) can be accurately inferred from the inversion by using multi-frequency acoustic data. The influence of the singularity and uncertainty in the inversion kernel on the inversion results can be mitigated by examining the singular values for linear inverse problems and employing a non-linear inversion involving a scattering model-based kernel.

State Space Reduction of Linear Processes using Control Flow Reconstruction

NARCIS (Netherlands)

van de Pol, Jan Cornelis; Timmer, Mark

2009-01-01

We present a new method for fighting the state space explosion of process algebraic specifications, by performing static analysis on an intermediate format: linear process equations (LPEs). Our method consists of two steps: (1) we reconstruct the LPE's control flow, detecting control flow parameters

State Space Reduction of Linear Processes Using Control Flow Reconstruction

NARCIS (Netherlands)

van de Pol, Jan Cornelis; Timmer, Mark; Liu, Zhiming; Ravn, Anders P.

2009-01-01

We present a new method for fighting the state space explosion of process algebraic specifications, by performing static analysis on an intermediate format: linear process equations (LPEs). Our method consists of two steps: (1) we reconstruct the LPE's control flow, detecting control flow parameters

Dynamic state estimation assisted power system monitoring and protection

Science.gov (United States)

Cui, Yinan

The advent of phasor measurement units (PMUs) has unlocked several novel methods to monitor, control, and protect bulk electric power systems. This thesis introduces the concept of "Dynamic State Estimation" (DSE), aided by PMUs, for wide-area monitoring and protection of power systems. Unlike traditional State Estimation where algebraic variables are estimated from system measurements, DSE refers to a process to estimate the dynamic states associated with synchronous generators. This thesis first establishes the viability of using particle filtering as a technique to perform DSE in power systems. The utility of DSE for protection and wide-area monitoring are then shown as potential novel applications. The work is presented as a collection of several journal and conference papers. In the first paper, we present a particle filtering approach to dynamically estimate the states of a synchronous generator in a multi-machine setting considering the excitation and prime mover control systems. The second paper proposes an improved out-of-step detection method for generators by means of angular difference. The generator's rotor angle is estimated with a particle filter-based dynamic state estimator and the angular separation is then calculated by combining the raw local phasor measurements with this estimate. The third paper introduces a particle filter-based dual estimation method for tracking the dynamic states of a synchronous generator. It considers the situation where the field voltage measurements are not readily available. The particle filter is modified to treat the field voltage as an unknown input which is sequentially estimated along with the other dynamic states. The fourth paper proposes a novel framework for event detection based on energy functions. The key idea is that any event in the system will leave a signature in WAMS data-sets. It is shown that signatures for four broad classes of disturbance events are buried in the components that constitute the

Minimax estimation of qubit states with Bures risk

Science.gov (United States)

Acharya, Anirudh; Guţă, Mădălin

2018-04-01

The central problem of quantum statistics is to devise measurement schemes for the estimation of an unknown state, given an ensemble of n independent identically prepared systems. For locally quadratic loss functions, the risk of standard procedures has the usual scaling of 1/n. However, it has been noticed that for fidelity based metrics such as the Bures distance, the risk of conventional (non-adaptive) qubit tomography schemes scales as 1/\\sqrt{n} for states close to the boundary of the Bloch sphere. Several proposed estimators appear to improve this scaling, and our goal is to analyse the problem from the perspective of the maximum risk over all states. We propose qubit estimation strategies based on separate adaptive measurements, and collective measurements, that achieve 1/n scalings for the maximum Bures risk. The estimator involving local measurements uses a fixed fraction of the available resource n to estimate the Bloch vector direction; the length of the Bloch vector is then estimated from the remaining copies by measuring in the estimator eigenbasis. The estimator based on collective measurements uses local asymptotic normality techniques which allows us to derive upper and lower bounds to its maximum Bures risk. We also discuss how to construct a minimax optimal estimator in this setup. Finally, we consider quantum relative entropy and show that the risk of the estimator based on collective measurements achieves a rate O(n-1log n) under this loss function. Furthermore, we show that no estimator can achieve faster rates, in particular the ‘standard’ rate n ‑1.

Euclidean null controllability of linear systems with delays in state ...

African Journals Online (AJOL)

Sufficient conditions are developed for the Euclidean controllability of linear systems with delay in state and in control. Namely, if the uncontrolled system is uniformly asymptotically stable and the control equation proper, then the control system is Euclidean null controllable. Journal of the Nigerian Association of ...

Stochastic Parameter Estimation of Non-Linear Systems Using Only Higher Order Spectra of the Measured Response

Science.gov (United States)

Vasta, M.; Roberts, J. B.

1998-06-01

Methods for using fourth order spectral quantities to estimate the unknown parameters in non-linear, randomly excited dynamic systems are developed. Attention is focused on the case where only the response is measurable and the excitation is unmeasurable and known only in terms of a stochastic process model. The approach is illustrated through application to a non-linear oscillator with both non-linear damping and stiffness and with excitation modelled as a stationary Gaussian white noise process. The methods have applications in studies of the response of structures to random environmental loads, such as wind and ocean wave forces.

Enhanced Performance Controller Design for Stochastic Systems by Adding Extra State Estimation onto the Existing Closed Loop Control

Energy Technology Data Exchange (ETDEWEB)

Zhou, Yuyang; Zhang, Qichun; Wang, Hong

2016-08-30

To enhance the performance of the tracking property , this paper presents a novel control algorithm for a class of linear dynamic stochastic systems with unmeasurable states, where the performance enhancement loop is established based on Kalman filter. Without changing the existing closed loop with the PI controller, the compensative controller is designed to minimize the variances of the tracking errors using the estimated states and the propagation of state variances. Moreover, the stability of the closed-loop systems has been analyzed in the mean-square sense. A simulated example is included to show the effectiveness of the presented control algorithm, where encouraging results have been obtained.

On algebraic time-derivative estimation and deadbeat state reconstruction

DEFF Research Database (Denmark)

Reger, Johann; Jouffroy, Jerome

2009-01-01

This paper places into perspective the so-called algebraic time-derivative estimation method recently introduced by Fliess and co-authors with standard results from linear statespace theory for control systems. In particular, it is shown that the algebraic method can essentially be seen...

Event-based state estimation a stochastic perspective

CERN Document Server

Shi, Dawei; Chen, Tongwen

2016-01-01

This book explores event-based estimation problems. It shows how several stochastic approaches are developed to maintain estimation performance when sensors perform their updates at slower rates only when needed. The self-contained presentation makes this book suitable for readers with no more than a basic knowledge of probability analysis, matrix algebra and linear systems. The introduction and literature review provide information, while the main content deals with estimation problems from four distinct angles in a stochastic setting, using numerous illustrative examples and comparisons. The text elucidates both theoretical developments and their applications, and is rounded out by a review of open problems. This book is a valuable resource for researchers and students who wish to expand their knowledge and work in the area of event-triggered systems. At the same time, engineers and practitioners in industrial process control will benefit from the event-triggering technique that reduces communication costs ...

A High Power Linear Solid State Pulser

International Nuclear Information System (INIS)

Boris Yen; Brent Davis; Rex Booth

1999-01-01

Particle Accelerators require high voltage and often high power. Typically the high voltage/power generation utilizes a topology with an extra energy store and a switching means to extract that stored energy. The switches may be active or passive devices. Active switches are hard or soft vacuum tubes, or semiconductors. When required voltages exceed tens of kilovolts, numerous semiconductors are stacked to withstand that potential. Such topologies can use large numbers of critical parts that, when in series, compromise the system reliability and performance. This paper describes a modular, linear, solid state amplifier which uses a parallel array of semiconductors, coupled with transmission line transformers. Such a design can provide output signals with voltages exceeding 10kV (into 50-ohms), and with rise and fall times (10-90 % amplitude) that are less than 1--ns. This compact solid state amplifier is modular, and has both hot-swap and soft fail capabilities

Multivariate time series with linear state space structure

CERN Document Server

Gómez, Víctor

2016-01-01

This book presents a comprehensive study of multivariate time series with linear state space structure. The emphasis is put on both the clarity of the theoretical concepts and on efficient algorithms for implementing the theory. In particular, it investigates the relationship between VARMA and state space models, including canonical forms. It also highlights the relationship between Wiener-Kolmogorov and Kalman filtering both with an infinite and a finite sample. The strength of the book also lies in the numerous algorithms included for state space models that take advantage of the recursive nature of the models. Many of these algorithms can be made robust, fast, reliable and efficient. The book is accompanied by a MATLAB package called SSMMATLAB and a webpage presenting implemented algorithms with many examples and case studies. Though it lays a solid theoretical foundation, the book also focuses on practical application, and includes exercises in each chapter. It is intended for researchers and students wor...

Applications of Kalman Filtering to nuclear material control. [Kalman filtering and linear smoothing for detecting nuclear material losses

Energy Technology Data Exchange (ETDEWEB)

Pike, D.H.; Morrison, G.W.; Westley, G.W.

1977-10-01

The feasibility of using modern state estimation techniques (specifically Kalman Filtering and Linear Smoothing) to detect losses of material from material balance areas is evaluated. It is shown that state estimation techniques are not only feasible but in most situations are superior to existing methods of analysis. The various techniques compared include Kalman Filtering, linear smoothing, standard control charts, and average cumulative summation (CUSUM) charts. Analysis results indicated that the standard control chart is the least effective method for detecting regularly occurring losses. An improvement in the detection capability over the standard control chart can be realized by use of the CUSUM chart. Even more sensitivity in the ability to detect losses can be realized by use of the Kalman Filter and the linear smoother. It was found that the error-covariance matrix can be used to establish limits of error for state estimates. It is shown that state estimation techniques represent a feasible and desirable method of theft detection. The technique is usually more sensitive than the CUSUM chart in detecting losses. One kind of loss which is difficult to detect using state estimation techniques is a single isolated loss. State estimation procedures are predicated on dynamic models and are well-suited for detecting losses which occur regularly over several accounting periods. A single isolated loss does not conform to this basic assumption and is more difficult to detect.

A characterization of positive linear maps and criteria of entanglement for quantum states

International Nuclear Information System (INIS)

Hou Jinchuan

2010-01-01

Let H and K be (finite- or infinite-dimensional) complex Hilbert spaces. A characterization of positive completely bounded normal linear maps from B(H) into B(K) is given, which particularly gives a characterization of positive elementary operators including all positive linear maps between matrix algebras. This characterization is then applied to give a representation of quantum channels (operations) between infinite-dimensional systems. A necessary and sufficient criterion of separability is given which shows that a state ρ on HxK is separable if and only if (ΦxI)ρ ≥ 0 for all positive finite-rank elementary operators Φ. Examples of NCP and indecomposable positive linear maps are given and are used to recognize some entangled states that cannot be recognized by the PPT criterion and the realignment criterion.

A characterization of positive linear maps and criteria of entanglement for quantum states

Science.gov (United States)

Hou, Jinchuan

2010-09-01

Let H and K be (finite- or infinite-dimensional) complex Hilbert spaces. A characterization of positive completely bounded normal linear maps from {\\mathcal B}(H) into {\\mathcal B}(K) is given, which particularly gives a characterization of positive elementary operators including all positive linear maps between matrix algebras. This characterization is then applied to give a representation of quantum channels (operations) between infinite-dimensional systems. A necessary and sufficient criterion of separability is given which shows that a state ρ on HotimesK is separable if and only if (ΦotimesI)ρ >= 0 for all positive finite-rank elementary operators Φ. Examples of NCP and indecomposable positive linear maps are given and are used to recognize some entangled states that cannot be recognized by the PPT criterion and the realignment criterion.

Estimation methods for nonlinear state-space models in ecology

DEFF Research Database (Denmark)

Pedersen, Martin Wæver; Berg, Casper Willestofte; Thygesen, Uffe Høgsbro

2011-01-01

The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta...... logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden...... Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance...

Generation of Symmetric Dicke States of Remote Qubits with Linear Optics

International Nuclear Information System (INIS)

Thiel, C.; Zanthier, J. von; Bastin, T.; Solano, E.; Agarwal, G. S.

2007-01-01

We propose a method for generating all symmetric Dicke states, either in the long-lived internal levels of N massive particles or in the polarization degrees of freedom of photonic qubits, using linear optical tools only. By means of a suitable multiphoton detection technique, erasing Welcher-Weg information, our proposed scheme allows the generation and measurement of an important class of entangled multiqubit states

A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.

Directory of Open Access Journals (Sweden)

Daniel Durstewitz

2017-06-01

Full Text Available The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast maximum-likelihood estimation framework for PLRNNs that may enable to recover

Optimal Meter Placement for Distribution Network State Estimation: A Circuit Representation Based MILP Approach

DEFF Research Database (Denmark)

Chen, Xiaoshuang; Lin, Jin; Wan, Can

2016-01-01

State estimation (SE) in distribution networks is not as accurate as that in transmission networks. Traditionally, distribution networks (DNs) are lack of direct measurements due to the limitations of investments and the difficulties of maintenance. Therefore, it is critical to improve the accuracy...... of SE in distribution networks by placing additional physical meters. For state-of-the-art SE models, it is difficult to clearly quantify measurements' influences on SE errors, so the problems of optimal meter placement for reducing SE errors are mostly solved by heuristic or suboptimal algorithms....... Under this background, this paper proposes a circuit representation model to represent SE errors. Based on the matrix formulation of the circuit representation model, the problem of optimal meter placement can be transformed to a mixed integer linear programming problem (MILP) via the disjunctive model...

Convergence Rate Analysis of Distributed Gossip (Linear Parameter) Estimation: Fundamental Limits and Tradeoffs

Science.gov (United States)

Kar, Soummya; Moura, José M. F.

2011-08-01

The paper considers gossip distributed estimation of a (static) distributed random field (a.k.a., large scale unknown parameter vector) observed by sparsely interconnected sensors, each of which only observes a small fraction of the field. We consider linear distributed estimators whose structure combines the information \\emph{flow} among sensors (the \\emph{consensus} term resulting from the local gossiping exchange among sensors when they are able to communicate) and the information \\emph{gathering} measured by the sensors (the \\emph{sensing} or \\emph{innovations} term.) This leads to mixed time scale algorithms--one time scale associated with the consensus and the other with the innovations. The paper establishes a distributed observability condition (global observability plus mean connectedness) under which the distributed estimates are consistent and asymptotically normal. We introduce the distributed notion equivalent to the (centralized) Fisher information rate, which is a bound on the mean square error reduction rate of any distributed estimator; we show that under the appropriate modeling and structural network communication conditions (gossip protocol) the distributed gossip estimator attains this distributed Fisher information rate, asymptotically achieving the performance of the optimal centralized estimator. Finally, we study the behavior of the distributed gossip estimator when the measurements fade (noise variance grows) with time; in particular, we consider the maximum rate at which the noise variance can grow and still the distributed estimator being consistent, by showing that, as long as the centralized estimator is consistent, the distributed estimator remains consistent.

Information geometry of density matrices and state estimation

International Nuclear Information System (INIS)

Brody, Dorje C

2011-01-01

Given a pure state vector |x) and a density matrix ρ-hat, the function p(x|ρ-hat)= defines a probability density on the space of pure states parameterised by density matrices. The associated Fisher-Rao information measure is used to define a unitary invariant Riemannian metric on the space of density matrices. An alternative derivation of the metric, based on square-root density matrices and trace norms, is provided. This is applied to the problem of quantum-state estimation. In the simplest case of unitary parameter estimation, new higher-order corrections to the uncertainty relations, applicable to general mixed states, are derived. (fast track communication)

On Kolmogorov asymptotics of estimators of the misclassification error rate in linear discriminant analysis

KAUST Repository

Zollanvari, Amin

2013-05-24

We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.

On Kolmogorov asymptotics of estimators of the misclassification error rate in linear discriminant analysis

KAUST Repository

Zollanvari, Amin; Genton, Marc G.

2013-01-01

We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.

Efficient Semiparametric Marginal Estimation for the Partially Linear Additive Model for Longitudinal/Clustered Data

KAUST Repository

Carroll, Raymond; Maity, Arnab; Mammen, Enno; Yu, Kyusang

2009-01-01

We consider the efficient estimation of a regression parameter in a partially linear additive nonparametric regression model from repeated measures data when the covariates are multivariate. To date, while there is some literature in the scalar covariate case, the problem has not been addressed in the multivariate additive model case. Ours represents a first contribution in this direction. As part of this work, we first describe the behavior of nonparametric estimators for additive models with repeated measures when the underlying model is not additive. These results are critical when one considers variants of the basic additive model. We apply them to the partially linear additive repeated-measures model, deriving an explicit consistent estimator of the parametric component; if the errors are in addition Gaussian, the estimator is semiparametric efficient. We also apply our basic methods to a unique testing problem that arises in genetic epidemiology; in combination with a projection argument we develop an efficient and easily computed testing scheme. Simulations and an empirical example from nutritional epidemiology illustrate our methods.

Efficient Semiparametric Marginal Estimation for the Partially Linear Additive Model for Longitudinal/Clustered Data

KAUST Repository

Carroll, Raymond

2009-04-23

We consider the efficient estimation of a regression parameter in a partially linear additive nonparametric regression model from repeated measures data when the covariates are multivariate. To date, while there is some literature in the scalar covariate case, the problem has not been addressed in the multivariate additive model case. Ours represents a first contribution in this direction. As part of this work, we first describe the behavior of nonparametric estimators for additive models with repeated measures when the underlying model is not additive. These results are critical when one considers variants of the basic additive model. We apply them to the partially linear additive repeated-measures model, deriving an explicit consistent estimator of the parametric component; if the errors are in addition Gaussian, the estimator is semiparametric efficient. We also apply our basic methods to a unique testing problem that arises in genetic epidemiology; in combination with a projection argument we develop an efficient and easily computed testing scheme. Simulations and an empirical example from nutritional epidemiology illustrate our methods.

Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors

Directory of Open Access Journals (Sweden)

Dachuan Li

2015-04-01

Full Text Available This paper presents a non-linear state observer-based integrated navigation scheme for estimating the attitude, position and velocity of micro aerial vehicles (MAV operating in GPS-denied indoor environments, using the measurements from low-cost MEMS (micro electro-mechanical systems inertial sensors and an RGB-D camera. A robust RGB-D visual odometry (VO approach was developed to estimate the MAV’s relative motion by extracting and matching features captured by the RGB-D camera from the environment. The state observer of the RGB-D visual-aided inertial navigation was then designed based on the invariant observer theory for systems possessing symmetries. The motion estimates from the RGB-D VO were fused with inertial and magnetic measurements from the onboard MEMS sensors via the state observer, providing the MAV with accurate estimates of its full six degree-of-freedom states. Implementations on a quadrotor MAV and indoor flight test results demonstrate that the resulting state observer is effective in estimating the MAV’s states without relying on external navigation aids such as GPS. The properties of computational efficiency and simplicity in gain tuning make the proposed invariant observer-based navigation scheme appealing for actual MAV applications in indoor environments.

Chimera states in an ensemble of linearly locally coupled bistable oscillators

Science.gov (United States)

Shchapin, D. S.; Dmitrichev, A. S.; Nekorkin, V. I.

2017-11-01

Chimera states in a system with linear local connections have been studied. The system is a ring ensemble of analog bistable self-excited oscillators with a resistive coupling. It has been shown that the existence of chimera states is not due to the nonidentity of oscillators and noise, which is always present in real experiments, but is due to the nonlinear dynamics of the system on invariant tori with various dimensions.

Observer-based linear parameter varying H∞ tracking control for hypersonic vehicles

Directory of Open Access Journals (Sweden)

Yiqing Huang

2016-11-01

Full Text Available This article aims to develop observer-based linear parameter varying output feedback H∞ tracking controller for hypersonic vehicles. Due to the complexity of an original nonlinear model of the hypersonic vehicle dynamics, a slow–fast loop linear parameter varying polytopic model is introduced for system stability analysis and controller design. Then, a state observer is developed by linear parameter varying technique in order to estimate the unmeasured attitude angular for slow loop system. Also, based on the designed linear parameter varying state observer, a kind of attitude tracking controller is presented to reduce tracking errors for all bounded reference attitude angular inputs. The closed-loop linear parameter varying system is proved to be quadratically stable by Lypapunov function technique. Finally, simulation results show that the developed linear parameter varying H∞ controller has good tracking capability for reference commands.

Estimating annualized earthquake losses for the conterminous United States

Science.gov (United States)

Jaiswal, Kishor S.; Bausch, Douglas; Chen, Rui; Bouabid, Jawhar; Seligson, Hope

2015-01-01

We make use of the most recent National Seismic Hazard Maps (the years 2008 and 2014 cycles), updated census data on population, and economic exposure estimates of general building stock to quantify annualized earthquake loss (AEL) for the conterminous United States. The AEL analyses were performed using the Federal Emergency Management Agency's (FEMA) Hazus software, which facilitated a systematic comparison of the influence of the 2014 National Seismic Hazard Maps in terms of annualized loss estimates in different parts of the country. The losses from an individual earthquake could easily exceed many tens of billions of dollars, and the long-term averaged value of losses from all earthquakes within the conterminous U.S. has been estimated to be a few billion dollars per year. This study estimated nationwide losses to be approximately $4.5 billion per year (in 2012$), roughly 80% of which can be attributed to the States of California, Oregon and Washington. We document the change in estimated AELs arising solely from the change in the assumed hazard map. The change from the 2008 map to the 2014 map results in a 10 to 20% reduction in AELs for the highly seismic States of the Western United States, whereas the reduction is even more significant for Central and Eastern United States.

HERITABILITY AND BREEDING VALUE OF SHEEP FERTILITY ESTIMATED BY MEANS OF THE GIBBS SAMPLING METHOD USING THE LINEAR AND THRESHOLD MODELS

Directory of Open Access Journals (Sweden)

DARIUSZ Piwczynski

2013-03-01

Full Text Available The research was carried out on 4,030 Polish Merino ewes born in the years 1991- 2001, kept in 15 flocks from the Pomorze and Kujawy region. Fertility of ewes in subsequent reproduction seasons was analysed with the use of multiple logistic regression. The research showed that there is a statistical influence of the flock, year of birth, age of dam, flock year interaction of birth on the ewes fertility. In order to estimate the genetic parameters, the Gibbs sampling method was applied, using the univariate animal models, both linear as well as threshold. Estimates of fertility depending on the model equalled 0.067 to 0.104, whereas the estimates of repeatability equalled respectively: 0.076 and 0.139. The obtained genetic parameters were then used to estimate the breeding values of the animals in terms of controlled trait (Best Linear Unbiased Prediction method using linear and threshold models. The obtained animal breeding values rankings in respect of the same trait with the use of linear and threshold models were strongly correlated with each other (rs = 0.972. Negative genetic trends of fertility (0.01-0.08% per year were found.

Steady-state evoked potentials possibilities for mental-state estimation

Science.gov (United States)

Junker, Andrew M.; Schnurer, John H.; Ingle, David F.; Downey, Craig W.

1988-01-01

The use of the human steady-state evoked potential (SSEP) as a possible measure of mental-state estimation is explored. A method for evoking a visual response to a sum-of-ten sine waves is presented. This approach provides simultaneous multiple frequency measurements of the human EEG to the evoking stimulus in terms of describing functions (gain and phase) and remnant spectra. Ways in which these quantities vary with the addition of performance tasks (manual tracking, grammatical reasoning, and decision making) are presented. Models of the describing function measures can be formulated using systems engineering technology. Relationships between model parameters and performance scores during manual tracking are discussed. Problems of unresponsiveness and lack of repeatability of subject responses are addressed in terms of a need for loop closure of the SSEP. A technique to achieve loop closure using a lock-in amplifier approach is presented. Results of a study designed to test the effectiveness of using feedback to consciously connect humans to their evoked response are presented. Findings indicate that conscious control of EEG is possible. Implications of these results in terms of secondary tasks for mental-state estimation and brain actuated control are addressed.

Artificial Neural Network Based State Estimators Integrated into Kalmtool

DEFF Research Database (Denmark)

Bayramoglu, Enis; Ravn, Ole; Poulsen, Niels Kjølstad

2012-01-01

In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation as...

Stated Preference Survey Estimating the Willingness to Pay ...

Science.gov (United States)

A national stated preference survey designed to elicit household willingness to pay for reductions in impinged and entrained fish at cooling water intake structures. To improve estimation of environmental benefits estimation

A Bayesian approach for estimating under-reported dengue incidence with a focus on non-linear associations between climate and dengue in Dhaka, Bangladesh.

Science.gov (United States)

Sharmin, Sifat; Glass, Kathryn; Viennet, Elvina; Harley, David

2018-04-01

Determining the relation between climate and dengue incidence is challenging due to under-reporting of disease and consequent biased incidence estimates. Non-linear associations between climate and incidence compound this. Here, we introduce a modelling framework to estimate dengue incidence from passive surveillance data while incorporating non-linear climate effects. We estimated the true number of cases per month using a Bayesian generalised linear model, developed in stages to adjust for under-reporting. A semi-parametric thin-plate spline approach was used to quantify non-linear climate effects. The approach was applied to data collected from the national dengue surveillance system of Bangladesh. The model estimated that only 2.8% (95% credible interval 2.7-2.8) of all cases in the capital Dhaka were reported through passive case reporting. The optimal mean monthly temperature for dengue transmission is 29℃ and average monthly rainfall above 15 mm decreases transmission. Our approach provides an estimate of true incidence and an understanding of the effects of temperature and rainfall on dengue transmission in Dhaka, Bangladesh.

Power system dynamic state estimation using prediction based evolutionary technique

International Nuclear Information System (INIS)

Basetti, Vedik; Chandel, Ashwani K.; Chandel, Rajeevan

2016-01-01

In this paper, a new robust LWS (least winsorized square) estimator is proposed for dynamic state estimation of a power system. One of the main advantages of this estimator is that it has an inbuilt bad data rejection property and is less sensitive to bad data measurements. In the proposed approach, Brown's double exponential smoothing technique has been utilised for its reliable performance at the prediction step. The state estimation problem is solved as an optimisation problem using a new jDE-self adaptive differential evolution with prediction based population re-initialisation technique at the filtering step. This new stochastic search technique has been embedded with different state scenarios using the predicted state. The effectiveness of the proposed LWS technique is validated under different conditions, namely normal operation, bad data, sudden load change, and loss of transmission line conditions on three different IEEE test bus systems. The performance of the proposed approach is compared with the conventional extended Kalman filter. On the basis of various performance indices, the results thus obtained show that the proposed technique increases the accuracy and robustness of power system dynamic state estimation performance. - Highlights: • To estimate the states of the power system under dynamic environment. • The performance of the EKF method is degraded during anomaly conditions. • The proposed method remains robust towards anomalies. • The proposed method provides precise state estimates even in the presence of anomalies. • The results show that prediction accuracy is enhanced by using the proposed model.

State estimation for wave energy converters

Energy Technology Data Exchange (ETDEWEB)

Bacelli, Giorgio; Coe, Ryan Geoffrey

2017-04-01

This report gives a brief discussion and examples on the topic of state estimation for wave energy converters (WECs). These methods are intended for use to enable real-time closed loop control of WECs.

An optimally weighted estimator of the linear power spectrum disentangling the growth of density perturbations across galaxy surveys

International Nuclear Information System (INIS)

Sorini, D.

2017-01-01

Measuring the clustering of galaxies from surveys allows us to estimate the power spectrum of matter density fluctuations, thus constraining cosmological models. This requires careful modelling of observational effects to avoid misinterpretation of data. In particular, signals coming from different distances encode information from different epochs. This is known as ''light-cone effect'' and is going to have a higher impact as upcoming galaxy surveys probe larger redshift ranges. Generalising the method by Feldman, Kaiser and Peacock (1994) [1], I define a minimum-variance estimator of the linear power spectrum at a fixed time, properly taking into account the light-cone effect. An analytic expression for the estimator is provided, and that is consistent with the findings of previous works in the literature. I test the method within the context of the Halofit model, assuming Planck 2014 cosmological parameters [2]. I show that the estimator presented recovers the fiducial linear power spectrum at present time within 5% accuracy up to k ∼ 0.80 h Mpc −1 and within 10% up to k ∼ 0.94 h Mpc −1 , well into the non-linear regime of the growth of density perturbations. As such, the method could be useful in the analysis of the data from future large-scale surveys, like Euclid.

State space model extraction of thermohydraulic systems – Part I: A linear graph approach

International Nuclear Information System (INIS)

Uren, K.R.; Schoor, G. van

2013-01-01

Thermohydraulic simulation codes are increasingly making use of graphical design interfaces. The user can quickly and easily design a thermohydraulic system by placing symbols on the screen resembling system components. These components can then be connected to form a system representation. Such system models may then be used to obtain detailed simulations of the physical system. Usually this kind of simulation models are too complex and not ideal for control system design. Therefore, a need exists for automated techniques to extract lumped parameter models useful for control system design. The goal of this first paper, in a two part series, is to propose a method that utilises a graphical representation of a thermohydraulic system, and a lumped parameter modelling approach, to extract state space models. In this methodology each physical domain of the thermohydraulic system is represented by a linear graph. These linear graphs capture the interaction between all components within and across energy domains – hydraulic, thermal and mechanical. These linear graphs are analysed using a graph-theoretic approach to derive reduced order state space models. These models capture the dominant dynamics of the thermohydraulic system and are ideal for control system design purposes. The proposed state space model extraction method is demonstrated by considering a U-tube system. A non-linear state space model is extracted representing both the hydraulic and thermal domain dynamics of the system. The simulated state space model is compared with a Flownex ® model of the U-tube. Flownex ® is a validated systems thermal-fluid simulation software package. - Highlights: • A state space model extraction methodology based on graph-theoretic concepts. • An energy-based approach to consider multi-domain systems in a common framework. • Allow extraction of transparent (white-box) state space models automatically. • Reduced order models containing only independent state

Joint estimation over multiple individuals improves behavioural state inference from animal movement data.

Science.gov (United States)

Jonsen, Ian

2016-02-08

State-space models provide a powerful way to scale up inference of movement behaviours from individuals to populations when the inference is made across multiple individuals. Here, I show how a joint estimation approach that assumes individuals share identical movement parameters can lead to improved inference of behavioural states associated with different movement processes. I use simulated movement paths with known behavioural states to compare estimation error between nonhierarchical and joint estimation formulations of an otherwise identical state-space model. Behavioural state estimation error was strongly affected by the degree of similarity between movement patterns characterising the behavioural states, with less error when movements were strongly dissimilar between states. The joint estimation model improved behavioural state estimation relative to the nonhierarchical model for simulated data with heavy-tailed Argos location errors. When applied to Argos telemetry datasets from 10 Weddell seals, the nonhierarchical model estimated highly uncertain behavioural state switching probabilities for most individuals whereas the joint estimation model yielded substantially less uncertainty. The joint estimation model better resolved the behavioural state sequences across all seals. Hierarchical or joint estimation models should be the preferred choice for estimating behavioural states from animal movement data, especially when location data are error-prone.

Projective Synchronization of N-Dimensional Chaotic Fractional-Order Systems via Linear State Error Feedback Control

Directory of Open Access Journals (Sweden)

Baogui Xin

2012-01-01

Full Text Available Based on linear feedback control technique, a projective synchronization scheme of N-dimensional chaotic fractional-order systems is proposed, which consists of master and slave fractional-order financial systems coupled by linear state error variables. It is shown that the slave system can be projectively synchronized with the master system constructed by state transformation. Based on the stability theory of linear fractional order systems, a suitable controller for achieving synchronization is designed. The given scheme is applied to achieve projective synchronization of chaotic fractional-order financial systems. Numerical simulations are given to verify the effectiveness of the proposed projective synchronization scheme.

Stochastic linearization of turbulent dynamics of dispersive waves in equilibrium and non-equilibrium state

International Nuclear Information System (INIS)

Jiang, Shixiao W; Lu, Haihao; Zhou, Douglas; Cai, David

2016-01-01

Characterizing dispersive wave turbulence in the long time dynamics is central to understanding of many natural phenomena, e.g., in atmosphere ocean dynamics, nonlinear optics, and plasma physics. Using the β -Fermi–Pasta–Ulam nonlinear system as a prototypical example, we show that in thermal equilibrium and non-equilibrium steady state the turbulent state even in the strongly nonlinear regime possesses an effective linear stochastic structure in renormalized normal variables. In this framework, we can well characterize the spatiotemporal dynamics, which are dominated by long-wavelength renormalized waves. We further demonstrate that the energy flux is nearly saturated by the long-wavelength renormalized waves in non-equilibrium steady state. The scenario of such effective linear stochastic dynamics can be extended to study turbulent states in other nonlinear wave systems. (paper)

Parameters and Fractional Differentiation Orders Estimation for Linear Continuous-Time Non-Commensurate Fractional Order Systems

KAUST Repository

Belkhatir, Zehor; Laleg-Kirati, Taous-Meriem

2017-01-01

This paper proposes a two-stage estimation algorithm to solve the problem of joint estimation of the parameters and the fractional differentiation orders of a linear continuous-time fractional system with non-commensurate orders. The proposed algorithm combines the modulating functions and the first-order Newton methods. Sufficient conditions ensuring the convergence of the method are provided. An error analysis in the discrete case is performed. Moreover, the method is extended to the joint estimation of smooth unknown input and fractional differentiation orders. The performance of the proposed approach is illustrated with different numerical examples. Furthermore, a potential application of the algorithm is proposed which consists in the estimation of the differentiation orders of a fractional neurovascular model along with the neural activity considered as input for this model.

Parameters and Fractional Differentiation Orders Estimation for Linear Continuous-Time Non-Commensurate Fractional Order Systems

KAUST Repository

Belkhatir, Zehor

2017-05-31

This paper proposes a two-stage estimation algorithm to solve the problem of joint estimation of the parameters and the fractional differentiation orders of a linear continuous-time fractional system with non-commensurate orders. The proposed algorithm combines the modulating functions and the first-order Newton methods. Sufficient conditions ensuring the convergence of the method are provided. An error analysis in the discrete case is performed. Moreover, the method is extended to the joint estimation of smooth unknown input and fractional differentiation orders. The performance of the proposed approach is illustrated with different numerical examples. Furthermore, a potential application of the algorithm is proposed which consists in the estimation of the differentiation orders of a fractional neurovascular model along with the neural activity considered as input for this model.

Maximum profile likelihood estimation of differential equation parameters through model based smoothing state estimates.

Science.gov (United States)

Campbell, D A; Chkrebtii, O

2013-12-01

Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

Área foliar de duas trepadeiras infestantes de cana-de-açúcar utilizando dimensões lineares de folhas Foliar area estimate of two sugarcane-infesting weeds using leaf blade linear dimensions

Directory of Open Access Journals (Sweden)

N.P. Cardozo

2009-01-01

Full Text Available Esta pesquisa teve como objetivo obter uma equação, por meio de medidas lineares dimensionais das folhas, que permitisse a estimativa da área foliar de Momordica charantia e Pyrostegia venusta. Entre maio e dezembro de 2007, foram estudadas as correlações entre a área folia real (Sf e as medidas dimensionais do limbo foliar, como o comprimento ao longo da nervura principal (C e a largura máxima (L perpendicular à nervura principal. Todas as equações, exponenciais geométricas ou lineares simples, permitiram boas estimativas da área foliar. Do ponto de vista prático, sugere-se optar pela equação linear simples envolvendo o produto C x L, considerando-se o coeficiente linear igual a zero. Desse modo, a estimativa da área foliar de Momordica charantia pode ser feita pela fórmula Sf = 0,4963 x (C x L, e a de Pyrostegia venusta, por Sf = 0,6649 x (C x L.The aim of this study was to obtain a mathematical equation to estimate the leaf area of Momordica charantia and Pyrostegia venusta using linear leaf blade measurements. Correlation studies were conducted involving real leaf area (Sf and leaf length (C, maximum leaf width (L and C x L. The linear and geometric equations involving parameter C provided good leaf area estimates. From a practical viewpoint, the simple linear equation of the regression model is suggested using the C x L parameter, i.e., considering the linear coefficient equal to zero. Thus, leaf area estimate of Momordica charantia can be obtained by using the equation Sf = 0.4963 x (C x L, and that of Pyrostegia venusta by using equation Sf = 0.6649 x (C x L.

Comparison of Four Different Energy Balance Models for Estimating Evapotranspiration in the Midwestern United States

Directory of Open Access Journals (Sweden)

Ramesh K. Singh

2015-12-01

Full Text Available The development of different energy balance models has allowed users to choose a model based on its suitability in a region. We compared four commonly used models—Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC model, Surface Energy Balance Algorithm for Land (SEBAL model, Surface Energy Balance System (SEBS model, and the Operational Simplified Surface Energy Balance (SSEBop model—using Landsat images to estimate evapotranspiration (ET in the Midwestern United States. Our models validation using three AmeriFlux cropland sites at Mead, Nebraska, showed that all four models captured the spatial and temporal variation of ET reasonably well with an R2 of more than 0.81. Both the METRIC and SSEBop models showed a low root mean square error (<0.93 mm·day−1 and a high Nash–Sutcliffe coefficient of efficiency (>0.80, whereas the SEBAL and SEBS models resulted in relatively higher bias for estimating daily ET. The empirical equation of daily average net radiation used in the SEBAL and SEBS models for upscaling instantaneous ET to daily ET resulted in underestimation of daily ET, particularly when the daily average net radiation was more than 100 W·m−2. Estimated daily ET for both cropland and grassland had some degree of linearity with METRIC, SEBAL, and SEBS, but linearity was stronger for evaporative fraction. Thus, these ET models have strengths and limitations for applications in water resource management.

Event-Based $H_\\infty $ State Estimation for Time-Varying Stochastic Dynamical Networks With State- and Disturbance-Dependent Noises.

Science.gov (United States)

Sheng, Li; Wang, Zidong; Zou, Lei; Alsaadi, Fuad E

2017-10-01

In this paper, the event-based finite-horizon H ∞ state estimation problem is investigated for a class of discrete time-varying stochastic dynamical networks with state- and disturbance-dependent noises [also called (x,v) -dependent noises]. An event-triggered scheme is proposed to decrease the frequency of the data transmission between the sensors and the estimator, where the signal is transmitted only when certain conditions are satisfied. The purpose of the problem addressed is to design a time-varying state estimator in order to estimate the network states through available output measurements. By employing the completing-the-square technique and the stochastic analysis approach, sufficient conditions are established to ensure that the error dynamics of the state estimation satisfies a prescribed H ∞ performance constraint over a finite horizon. The desired estimator parameters can be designed via solving coupled backward recursive Riccati difference equations. Finally, a numerical example is exploited to demonstrate the effectiveness of the developed state estimation scheme.

Robust estimation of partially linear models for longitudinal data with dropouts and measurement error.

Science.gov (United States)

Qin, Guoyou; Zhang, Jiajia; Zhu, Zhongyi; Fung, Wing

2016-12-20

Outliers, measurement error, and missing data are commonly seen in longitudinal data because of its data collection process. However, no method can address all three of these issues simultaneously. This paper focuses on the robust estimation of partially linear models for longitudinal data with dropouts and measurement error. A new robust estimating equation, simultaneously tackling outliers, measurement error, and missingness, is proposed. The asymptotic properties of the proposed estimator are established under some regularity conditions. The proposed method is easy to implement in practice by utilizing the existing standard generalized estimating equations algorithms. The comprehensive simulation studies show the strength of the proposed method in dealing with longitudinal data with all three features. Finally, the proposed method is applied to data from the Lifestyle Education for Activity and Nutrition study and confirms the effectiveness of the intervention in producing weight loss at month 9. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Robust control of uncertain dynamic systems a linear state space approach

CERN Document Server

Yedavalli, Rama K

2014-01-01

This textbook aims to provide a clear understanding of the various tools of analysis and design for robust stability and performance of uncertain dynamic systems. In model-based control design and analysis, mathematical models can never completely represent the “real world” system that is being modeled, and thus it is imperative to incorporate and accommodate a level of uncertainty into the models. This book directly addresses these issues from a deterministic uncertainty viewpoint and focuses on the interval parameter characterization of uncertain systems. Various tools of analysis and design are presented in a consolidated manner. This volume fills a current gap in published works by explicitly addressing the subject of control of dynamic systems from linear state space framework, namely using a time-domain, matrix-theory based approach. This book also: Presents and formulates the robustness problem in a linear state space model framework Illustrates various systems level methodologies with examples and...

State estimation and optimal long period clinical treatment of HIV seropositive patients

Directory of Open Access Journals (Sweden)

Juliana M. Grégio

2009-03-01

Full Text Available Optimal control theory provides a very interesting quantitative method that can be used to assist the decision making process in several areas of application, such as engineering, biology, economics and sociology. The main idea is to determine the values of the manipulated variables, such as drug doses, so that some cost function is minimized, subject to physical constraints. In this work, the cost function reflects the number of CD4+T cells, viral particles and the drug doses. It is worth noticing that high drug doses are related to more intense side-effects, apart from the impact on the actual cost of the treatment. In a previous paper by the authors, the LQR - Linear Quadratic Regulator approach was proposed for the computation of long period maintenance doses for the drugs, which turns out to be of state feedback form. However, it is not practical to determine all the components of the state vector, due to the fact that infected and uninfected CD4+T cells are not microscopically distinguishable. In order to overcome this difficulty, this work proposes the use of Extended Kalman Filter to estimate the state, even though, because of the nonlinear nature of the involved state equations, the separation principle may not be valid. Extensive simulations were then carried out to investigate numerically if the control strategy consisting of the feedback of estimated states yielded satisfactory clinical results.A teoria de controle ótimo apresenta um método quantitativo muito interessante que pode ajudar no processo de tomada de decisão em algumas áreas de aplicação, tais como engenharia, biologia, economia e sociologia. A principal idéia é determinar os valores das variáveis controladas, tais como doses de medicamentos, onde alguma função-custo é minimizada, sujeito às restrições físicas. Neste trabalho, a função-custo reflete o número de células CD4+T, partículas virais e doses de medicamentos. É fato que altas dosagens de

A linear model for estimation of neurotransmitter response profiles from dynamic PET data

OpenAIRE

Normandin, M.D.; Schiffer, W.K.; Morris, E.D.

2011-01-01

The parametric ntPET model (p-ntPET) estimates the kinetics of neurotransmitter release from dynamic PET data with receptor-ligand radiotracers. Here we introduce a linearization (lp-ntPET) that is computationally efficient and can be applied to single-scan data. lp-ntPET employs a non-invasive reference region input function and extends the LSRRM of Alpert et al. (2003) using basis functions to characterize the time course of neurotransmitter activation. In simulation studies, the temporal p...

Latent degradation indicators estimation and prediction: A Monte Carlo approach

Science.gov (United States)

Zhou, Yifan; Sun, Yong; Mathew, Joseph; Wolff, Rodney; Ma, Lin

2011-01-01

Asset health inspections can produce two types of indicators: (1) direct indicators (e.g. the thickness of a brake pad, and the crack depth on a gear) which directly relate to a failure mechanism; and (2) indirect indicators (e.g. the indicators extracted from vibration signals and oil analysis data) which can only partially reveal a failure mechanism. While direct indicators enable more precise references to asset health condition, they are often more difficult to obtain than indirect indicators. The state space model provides an efficient approach to estimating direct indicators by using indirect indicators. However, existing state space models to estimate direct indicators largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires fixed inspection intervals. The discrete state assumption entails discretising continuous degradation indicators, which often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This paper proposes a state space model without these assumptions. Monte Carlo-based algorithms are developed to estimate the model parameters and the remaining useful life. These algorithms are evaluated for performance using numerical simulations through MATLAB. The result shows that both the parameters and the remaining useful life are estimated accurately. Finally, the new state space model is used to process vibration and crack depth data from an accelerated test of a gearbox. During this application, the new state space model shows a better fitness result than the state space model with linear and Gaussian assumption.

Vision Aided State Estimation for Helicopter Slung Load System

DEFF Research Database (Denmark)

Bisgaard, Morten; Bendtsen, Jan Dimon; la Cour-Harbo, Anders

2007-01-01

This paper presents the design and verification of a state estimator for a helicopter based slung load system. The estimator is designed to augment the IMU driven estimator found in many helicopter UAV s and uses vision based updates only. The process model used for the estimator is a simple 4...

Multivariate generalized linear mixed models using R

CERN Document Server

Berridge, Damon Mark

2011-01-01

Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model...

Estimation of pump operational state with model-based methods

International Nuclear Information System (INIS)

Ahonen, Tero; Tamminen, Jussi; Ahola, Jero; Viholainen, Juha; Aranto, Niina; Kestilae, Juha

2010-01-01

Pumps are widely used in industry, and they account for 20% of the industrial electricity consumption. Since the speed variation is often the most energy-efficient method to control the head and flow rate of a centrifugal pump, frequency converters are used with induction motor-driven pumps. Although a frequency converter can estimate the operational state of an induction motor without external measurements, the state of a centrifugal pump or other load machine is not typically considered. The pump is, however, usually controlled on the basis of the required flow rate or output pressure. As the pump operational state can be estimated with a general model having adjustable parameters, external flow rate or pressure measurements are not necessary to determine the pump flow rate or output pressure. Hence, external measurements could be replaced with an adjustable model for the pump that uses estimates of the motor operational state. Besides control purposes, modelling the pump operation can provide useful information for energy auditing and optimization purposes. In this paper, two model-based methods for pump operation estimation are presented. Factors affecting the accuracy of the estimation methods are analyzed. The applicability of the methods is verified by laboratory measurements and tests in two pilot installations. Test results indicate that the estimation methods can be applied to the analysis and control of pump operation. The accuracy of the methods is sufficient for auditing purposes, and the methods can inform the user if the pump is driven inefficiently.

An overview of distributed microgrid state estimation and control for smart grids.

Science.gov (United States)

Rana, Md Masud; Li, Li

2015-02-12

Given the significant concerns regarding carbon emission from the fossil fuels, global warming and energy crisis, the renewable distributed energy resources (DERs) are going to be integrated in the smart grid. This grid can spread the intelligence of the energy distribution and control system from the central unit to the long-distance remote areas, thus enabling accurate state estimation (SE) and wide-area real-time monitoring of these intermittent energy sources. In contrast to the traditional methods of SE, this paper proposes a novel accuracy dependent Kalman filter (KF) based microgrid SE for the smart grid that uses typical communication systems. Then this article proposes a discrete-time linear quadratic regulation to control the state deviations of the microgrid incorporating multiple DERs. Therefore, integrating these two approaches with application to the smart grid forms a novel contributions in green energy and control research communities. Finally, the simulation results show that the proposed KF based microgrid SE and control algorithm provides an accurate SE and control compared with the existing method.

An Overview of Distributed Microgrid State Estimation and Control for Smart Grids

Science.gov (United States)

Rana, Md Masud; Li, Li

2015-01-01

Given the significant concerns regarding carbon emission from the fossil fuels, global warming and energy crisis, the renewable distributed energy resources (DERs) are going to be integrated in the smart grid. This grid can spread the intelligence of the energy distribution and control system from the central unit to the long-distance remote areas, thus enabling accurate state estimation (SE) and wide-area real-time monitoring of these intermittent energy sources. In contrast to the traditional methods of SE, this paper proposes a novel accuracy dependent Kalman filter (KF) based microgrid SE for the smart grid that uses typical communication systems. Then this article proposes a discrete-time linear quadratic regulation to control the state deviations of the microgrid incorporating multiple DERs. Therefore, integrating these two approaches with application to the smart grid forms a novel contributions in green energy and control research communities. Finally, the simulation results show that the proposed KF based microgrid SE and control algorithm provides an accurate SE and control compared with the existing method. PMID:25686316

State, Parameter, and Unknown Input Estimation Problems in Active Automotive Safety Applications

Science.gov (United States)

Phanomchoeng, Gridsada

A variety of driver assistance systems such as traction control, electronic stability control (ESC), rollover prevention and lane departure avoidance systems are being developed by automotive manufacturers to reduce driver burden, partially automate normal driving operations, and reduce accidents. The effectiveness of these driver assistance systems can be significant enhanced if the real-time values of several vehicle parameters and state variables, namely tire-road friction coefficient, slip angle, roll angle, and rollover index, can be known. Since there are no inexpensive sensors available to measure these variables, it is necessary to estimate them. However, due to the significant nonlinear dynamics in a vehicle, due to unknown and changing plant parameters, and due to the presence of unknown input disturbances, the design of estimation algorithms for this application is challenging. This dissertation develops a new approach to observer design for nonlinear systems in which the nonlinearity has a globally (or locally) bounded Jacobian. The developed approach utilizes a modified version of the mean value theorem to express the nonlinearity in the estimation error dynamics as a convex combination of known matrices with time varying coefficients. The observer gains are then obtained by solving linear matrix inequalities (LMIs). A number of illustrative examples are presented to show that the developed approach is less conservative and more useful than the standard Lipschitz assumption based nonlinear observer. The developed nonlinear observer is utilized for estimation of slip angle, longitudinal vehicle velocity, and vehicle roll angle. In order to predict and prevent vehicle rollovers in tripped situations, it is necessary to estimate the vertical tire forces in the presence of unknown road disturbance inputs. An approach to estimate unknown disturbance inputs in nonlinear systems using dynamic model inversion and a modified version of the mean value theorem is

State control of discrete-time linear systems to be bound in state variables by equality constraints

International Nuclear Information System (INIS)

Filasová, Anna; Krokavec, Dušan; Serbák, Vladimír

2014-01-01

The paper is concerned with the problem of designing the discrete-time equivalent PI controller to control the discrete-time linear systems in such a way that the closed-loop state variables satisfy the prescribed equality constraints. Since the problem is generally singular, using standard form of the Lyapunov function and a symmetric positive definite slack matrix, the design conditions are proposed in the form of the enhanced Lyapunov inequality. The results, offering the conditions of the control existence and the optimal performance with respect to the prescribed equality constraints for square discrete-time linear systems, are illustrated with the numerical example to note effectiveness and applicability of the considered approach

The classical Pierce diode: Using particle simulations on linear and nonlinear behavior and final states

International Nuclear Information System (INIS)

Crystal, T.L.; Kuhn, S.; Birdsall, C.K.

1984-01-01

The classical Pierce diode is a simple 1-d system of two shorted metal plates, a cold beam of electrons injected from one side and a neutralizing background of rigid ions. While the plasma medium is technically stable, the finiteness of the Pierce system allows stable and unstable operation. It is usefully studied as an archetypical bounded plasma system, related e.g., to Q-machines, particle accelerators, thermionic converters. New particle simulations of the Pierce diode have successfully recovered many novel linear phenomena including the dominant linear eigenmodes (seen in the internal electrostatic fields), and the dominant and subdominant eigenfrequencies, (seen both in the internal electrostatics and in the external circuit current, J/sub ext/(t)). These simulation results conform very well to detailed predictions of a new linear analysis. The final (nonlinear) state recovered can show critical dependence on initial (linear perturbation) conditions, and can be made steady-state (d.c.) or periodic-oscillatory by simply changing the initial conditions by a factor of 10/sup -4/ or less. A third class of final state is also possible which has oscillations which seem to be nonperiodic

Introduction to generalized linear models

CERN Document Server

Dobson, Annette J

2008-01-01

Introduction Background Scope Notation Distributions Related to the Normal Distribution Quadratic Forms Estimation Model Fitting Introduction Examples Some Principles of Statistical Modeling Notation and Coding for Explanatory Variables Exponential Family and Generalized Linear Models Introduction Exponential Family of Distributions Properties of Distributions in the Exponential Family Generalized Linear Models Examples Estimation Introduction Example: Failure Times for Pressure Vessels Maximum Likelihood Estimation Poisson Regression Example Inference Introduction Sampling Distribution for Score Statistics Taylor Series Approximations Sampling Distribution for MLEs Log-Likelihood Ratio Statistic Sampling Distribution for the Deviance Hypothesis Testing Normal Linear Models Introduction Basic Results Multiple Linear Regression Analysis of Variance Analysis of Covariance General Linear Models Binary Variables and Logistic Regression Probability Distributions ...

Distributed state estimation for multi-agent based active distribution networks

NARCIS (Netherlands)

Nguyen, H.P.; Kling, W.L.

2010-01-01

Along with the large-scale implementation of distributed generators, the current distribution networks have changed gradually from passive to active operation. State estimation plays a vital role to facilitate this transition. In this paper, a suitable state estimation method for the active network

Uniqueness of non-linear ground states for fractional Laplacians in R

DEFF Research Database (Denmark)

Frank, Rupert L.; Lenzmann, Enno

2013-01-01

We prove uniqueness of ground state solutions Q = Q(|x|) ≥ 0 of the non-linear equation (−Δ)sQ+Q−Qα+1=0inR,where 0 fractional Laplacian in one dimension. In particular, we answer affirmatively an open question...... recently raised by Kenig–Martel–Robbiano and we generalize (by completely different techniques) the specific uniqueness result obtained by Amick and Toland for s=12 and α = 1 in [5] for the Benjamin–Ono equation. As a technical key result in this paper, we show that the associated linearized operator L...... + = (−Δ) s +1−(α+1)Q α is non-degenerate; i.e., its kernel satisfies ker L + = span{Q′}. This result about L + proves a spectral assumption, which plays a central role for the stability of solitary waves and blowup analysis for non-linear dispersive PDEs with fractional Laplacians, such as the generalized...

Pipeline heating method based on optimal control and state estimation

Energy Technology Data Exchange (ETDEWEB)

Vianna, F.L.V. [Dept. of Subsea Technology. Petrobras Research and Development Center - CENPES, Rio de Janeiro, RJ (Brazil)], e-mail: fvianna@petrobras.com.br; Orlande, H.R.B. [Dept. of Mechanical Engineering. POLI/COPPE, Federal University of Rio de Janeiro - UFRJ, Rio de Janeiro, RJ (Brazil)], e-mail: helcio@mecanica.ufrj.br; Dulikravich, G.S. [Dept. of Mechanical and Materials Engineering. Florida International University - FIU, Miami, FL (United States)], e-mail: dulikrav@fiu.edu

2010-07-01

In production of oil and gas wells in deep waters the flowing of hydrocarbon through pipeline is a challenging problem. This environment presents high hydrostatic pressures and low sea bed temperatures, which can favor the formation of solid deposits that in critical operating conditions, as unplanned shutdown conditions, may result in a pipeline blockage and consequently incur in large financial losses. There are different methods to protect the system, but nowadays thermal insulation and chemical injection are the standard solutions normally used. An alternative method of flow assurance is to heat the pipeline. This concept, which is known as active heating system, aims at heating the produced fluid temperature above a safe reference level in order to avoid the formation of solid deposits. The objective of this paper is to introduce a Bayesian statistical approach for the state estimation problem, in which the state variables are considered as the transient temperatures within a pipeline cross-section, and to use the optimal control theory as a design tool for a typical heating system during a simulated shutdown condition. An application example is presented to illustrate how Bayesian filters can be used to reconstruct the temperature field from temperature measurements supposedly available on the external surface of the pipeline. The temperatures predicted with the Bayesian filter are then utilized in a control approach for a heating system used to maintain the temperature within the pipeline above the critical temperature of formation of solid deposits. The physical problem consists of a pipeline cross section represented by a circular domain with four points over the pipe wall representing heating cables. The fluid is considered stagnant, homogeneous, isotropic and with constant thermo-physical properties. The mathematical formulation governing the direct problem was solved with the finite volume method and for the solution of the state estimation problem

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

Science.gov (United States)

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

2018-03-01

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

Online wave estimation using vessel motion measurements

DEFF Research Database (Denmark)

H. Brodtkorb, Astrid; Nielsen, Ulrik D.; J. Sørensen, Asgeir

2018-01-01

parameters and motion transfer functions are required as input. Apart from this the method is signal-based, with no assumptions on the wave spectrum shape, and as a result it is computationally efficient. The algorithm is implemented in a dynamic positioning (DP)control system, and tested through simulations......In this paper, a computationally efficient online sea state estimation algorithm isproposed for estimation of the on site sea state. The algorithm finds the wave spectrum estimate from motion measurements in heave, roll and pitch by iteratively solving a set of linear equations. The main vessel...

Estimation of dynamic reactivity using an H∞ optimal filter with a nonlinear term

International Nuclear Information System (INIS)

Suzuki, Katsuo; Watanabe, Koiti

1996-01-01

A method of nonlinear filtering is applied to the problem of estimating the dynamic reactivity of a nonlinear reactor system. The nonlinear filtering algorithm developed is a simple modification of a linear H ∞ optimal filter with a nonlinear feedback loop added. The linear filter is designed on the basis of a linearized dynamical system model that consists of linearized point reactor kinetic equations and a reactivity state equation driven by a fictitious signal. The latter is artificially introduced to deal with the reactivity as a state variable. The results of the computer simulation show that the nonlinear filtering algorithm can be applied to estimate the dynamic reactivity of the nonlinear reactor system, even under relatively large reactivity disturbances

Online Synchrophasor-Based Dynamic State Estimation using Real-Time Digital Simulator

DEFF Research Database (Denmark)

Khazraj, Hesam; Adewole, Adeyemi Charles; Udaya, Annakkage

2018-01-01

Dynamic state estimation is a very important control center application used in the dynamic monitoring of state variables. This paper presents and validates a time-synchronized phasor measurement unit (PMU)-based for dynamic state estimation by unscented Kalman filter (UKF) method using the real-...... using the RTDS (real-time digital simulator). The dynamic state variables of multi-machine systems are monitored and measured for the study on the transient behavior of power systems.......Dynamic state estimation is a very important control center application used in the dynamic monitoring of state variables. This paper presents and validates a time-synchronized phasor measurement unit (PMU)-based for dynamic state estimation by unscented Kalman filter (UKF) method using the real......-time digital simulator (RTDS). The dynamic state variables of the system are the rotor angle and speed of the generators. The performance of the UKF method is tested with PMU measurements as inputs using the IEEE 14-bus test system. This test system was modeled in the RSCAD software and tested in real time...

Enhancing Groundwater Cost Estimation with the Interpolation of Water Tables across the United States

Science.gov (United States)

Rosli, A. U. M.; Lall, U.; Josset, L.; Rising, J. A.; Russo, T. A.; Eisenhart, T.

2017-12-01

Analyzing the trends in water use and supply across the United States is fundamental to efforts in ensuring water sustainability. As part of this, estimating the costs of producing or obtaining water (water extraction) and the correlation with water use is an important aspect in understanding the underlying trends. This study estimates groundwater costs by interpolating the depth to water level across the US in each county. We use Ordinary and Universal Kriging, accounting for the differences between aquifers. Kriging generates a best linear unbiased estimate at each location and has been widely used to map ground-water surfaces (Alley, 1993).The spatial covariates included in the universal Kriging were land-surface elevation as well as aquifer information. The average water table is computed for each county using block kriging to obtain a national map of groundwater cost, which we compare with survey estimates of depth to the water table performed by the USDA. Groundwater extraction costs were then assumed to be proportional to water table depth. Beyond estimating the water cost, the approach can provide an indication of groundwater-stress by exploring the historical evolution of depth to the water table using time series information between 1960 and 2015. Despite data limitations, we hope to enable a more compelling and meaningful national-level analysis through the quantification of cost and stress for more economically efficient water management.

Geometry of perturbed Gaussian states and quantum estimation

International Nuclear Information System (INIS)

Genoni, Marco G; Giorda, Paolo; Paris, Matteo G A

2011-01-01

We address the non-Gaussianity (nG) of states obtained by weakly perturbing a Gaussian state and investigate the relationships with quantum estimation. For classical perturbations, i.e. perturbations to eigenvalues, we found that the nG of the perturbed state may be written as the quantum Fisher information (QFI) distance minus a term depending on the infinitesimal energy change, i.e. it provides a lower bound to statistical distinguishability. Upon moving on isoenergetic surfaces in a neighbourhood of a Gaussian state, nG thus coincides with a proper distance in the Hilbert space and exactly quantifies the statistical distinguishability of the perturbations. On the other hand, for perturbations leaving the covariance matrix unperturbed, we show that nG provides an upper bound to the QFI. Our results show that the geometry of non-Gaussian states in the neighbourhood of a Gaussian state is definitely not trivial and cannot be subsumed by a differential structure. Nevertheless, the analysis of perturbations to a Gaussian state reveals that nG may be a resource for quantum estimation. The nG of specific families of perturbed Gaussian states is analysed in some detail with the aim of finding the maximally non-Gaussian state obtainable from a given Gaussian one. (fast track communication)

Fidelity estimation between two finite ensembles of unknown pure equatorial qubit states

Energy Technology Data Exchange (ETDEWEB)

Siomau, Michael, E-mail: siomau@physi.uni-heidelberg.de [Physikalisches Institut, Heidelberg Universitaet, D-69120 Heidelberg (Germany); Department of Theoretical Physics, Belarussian State University, 220030 Minsk (Belarus)

2011-09-05

Suppose, we are given two finite ensembles of pure qubit states, so that the qubits in each ensemble are prepared in identical (but unknown for us) states lying on the equator of the Bloch sphere. What is the best strategy to estimate fidelity between these two finite ensembles of qubit states? We discuss three possible strategies for the fidelity estimation. We show that the best strategy includes two stages: a specific unitary transformation on two ensembles and state estimation of the output states of this transformation. -- Highlights: → We search for the best strategy for the fidelity estimation. → A measurement-based, a cloning-based and a unified strategies are considered. → The last strategy includes a specific unitary transformation and state estimation. → The unified strategy is shown to be the best among the three.

Disformal invariance of continuous media with linear equation of state

Energy Technology Data Exchange (ETDEWEB)

Celoria, Marco [Gran Sasso Science Institute (INFN), Viale Francesco Crispi 7, L' Aquila, I-67100 Italy (Italy); Matarrese, Sabino [Dipartimento di Fisica e Astronomia ' G. Galilei' , Università degli Studi di Padova, via Marzolo 8, Padova, I-35131 Italy (Italy); Pilo, Luigi, E-mail: marco.celoria@gssi.infn.it, E-mail: sabino.matarrese@pd.infn.it, E-mail: luigi.pilo@aquila.infn.it [Dipartimento di Fisica, Università di L' Aquila, L' Aquila, I-67010 Italy (Italy)

2017-02-01

We show that the effective theory describing single component continuous media with a linear and constant equation of state of the form p = w ρ is invariant under a 1-parameter family of continuous disformal transformations. In the special case of w =1/3 (ultrarelativistic gas), such a family reduces to conformal transformations. As examples, perfect fluids, irrotational dust (mimetic matter) and homogeneous and isotropic solids are discussed.

Memory State Feedback RMPC for Multiple Time-Delayed Uncertain Linear Systems with Input Constraints

Directory of Open Access Journals (Sweden)

Wei-Wei Qin

2014-01-01

Full Text Available This paper focuses on the problem of asymptotic stabilization for a class of discrete-time multiple time-delayed uncertain linear systems with input constraints. Then, based on the predictive control principle of receding horizon optimization, a delayed state dependent quadratic function is considered for incorporating MPC problem formulation. By developing a memory state feedback controller, the information of the delayed plant states can be taken into full consideration. The MPC problem is formulated to minimize the upper bound of infinite horizon cost that satisfies the sufficient conditions. Then, based on the Lyapunov-Krasovskii function, a delay-dependent sufficient condition in terms of linear matrix inequality (LMI can be derived to design a robust MPC algorithm. Finally, the digital simulation results prove availability of the proposed method.

A sensor network architecture for urban traffic state estimation with mixed eulerian/lagrangian sensing based on distributed computing

KAUST Repository

Canepa, Edward S.; Odat, Enas M.; Dehwah, Ahmad H.; Mousa, Mustafa; Jiang, Jiming; Claudel, Christian G.

2014-01-01

This article describes a new approach to urban traffic flow sensing using decentralized traffic state estimation. Traffic sensor data is generated both by fixed traffic flow sensor nodes and by probe vehicles equipped with a short range transceiver. The data generated by these sensors is sent to a local coordinator node, that poses the problem of estimating the local state of traffic as a mixed integer linear program (MILP). The resulting optimization program is then solved by the nodes in a distributed manner, using branch-and-bound methods. An optimal amount of noise is then added to the maps before dissemination to a central database. Unlike existing probe-based traffic monitoring systems, this system does not transmit user generated location tracks nor any user presence information to a centralized server, effectively preventing privacy attacks. A simulation of the system performance on computer-generated traffic data shows that the system can be implemented with currently available technology. © 2014 Springer International Publishing Switzerland.

Guidelines for preparation of State water-use estimates for 2015

Science.gov (United States)

Bradley, Michael W.

2017-05-01

The U.S. Geological Survey (USGS) has estimated the use of water in the United States at 5-year intervals since 1950. This report describes the water-use categories and data elements used for the national water-use compilation conducted as part of the USGS National Water-Use Science Project. The report identifies sources of water-use information, provides standard methods and techniques for estimating water use at the county level, and outlines steps for preparing documentation for the United States, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands.As part of this USGS program to document water use on a national scale, estimates of water withdrawals for the categories of public supply, self-supplied domestic, industrial, irrigation, and thermoelectric power are prepared for each county in each State, District, or territory by using the guidelines in this report. County estimates of water withdrawals for aquaculture, livestock, and mining are prepared for each State by using a county-based national model, although water-use programs in each State or Water Science Center have the option of producing independent county estimates of water withdrawals for these categories. Estimates of water withdrawals and consumptive use for thermoelectric power will be aggregated to the county level for each State by the national project; additionally, irrigation consumptive use at the county level will also be provided, although study chiefs in each State have the option of producing independent county estimates of water withdrawals and consumptive use for these categories.Estimates of deliveries of water from public supplies for domestic use by county also will be prepared for each State. As a result, total domestic water use can be determined for each State by combining self-supplied domestic withdrawals and public-supplied domestic deliveries. Fresh groundwater and surface-water estimates will be prepared for all categories of use, and saline groundwater and

Steady state and linear stability analysis of a supercritical water natural circulation loop

International Nuclear Information System (INIS)

Sharma, Manish; Pilkhwal, D.S.; Vijayan, P.K.; Saha, D.; Sinha, R.K.

2010-01-01

Supercritical water (SCW) has excellent heat transfer characteristics as a coolant for nuclear reactors. Besides it results in high thermal efficiency of the plant. However, the flow can experience instabilities in supercritical water reactors, as the density change is very large for the supercritical fluids. A computer code SUCLIN using supercritical water properties has been developed to carry out the steady state and linear stability analysis of a SCW natural circulation loop. The conservation equations of mass, momentum and energy have been linearized by imposing small perturbation in flow rate, enthalpy, pressure and specific volume. The equations have been solved analytically to generate the characteristic equation. The roots of the equation determine the stability of the system. The code has been qualitatively assessed with published results and has been extensively used for studying the effect of diameter, height, heater inlet temperature, pressure and local loss coefficients on steady state and stability behavior of a Supercritical Water Natural Circulation Loop (SCWNCL). The present paper describes the linear stability analysis model and the results obtained in detail.

Estimating GSP and labor productivity by state

OpenAIRE

Paul W. Bauer; Yoonsoo Lee

2006-01-01

In gauging the health of state economies, arguably the two most important series to track are employment and output. While employment by state is available about three weeks after the end of a month, data on output, as measured by Gross State Product (GSP), are only available annually and with a significant lag. This Policy Discussion Paper details how more current estimates of GSP can be generated using U.S. Gross Domestic Product and personal income along with individual states’ personal in...

Kalman filter data assimilation: targeting observations and parameter estimation.

Science.gov (United States)

Bellsky, Thomas; Kostelich, Eric J; Mahalov, Alex

2014-06-01

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.

Kalman filter data assimilation: Targeting observations and parameter estimation

International Nuclear Information System (INIS)

Bellsky, Thomas; Kostelich, Eric J.; Mahalov, Alex

2014-01-01

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation

State and parameter estimation in biotechnical batch reactors

NARCIS (Netherlands)

Keesman, K.J.

2000-01-01

In this paper the problem of state and parameter estimation in biotechnical batch reactors is considered. Models describing the biotechnical process behaviour are usually nonlinear with time-varying parameters. Hence, the resulting large dimensions of the augmented state vector, roughly > 7, in

Validity of a Commercial Linear Encoder to Estimate Bench Press 1 RM from the Force-Velocity Relationship.

Science.gov (United States)

Bosquet, Laurent; Porta-Benache, Jeremy; Blais, Jérôme

2010-01-01

The aim of this study was to assess the validity and accuracy of a commercial linear encoder (Musclelab, Ergotest, Norway) to estimate Bench press 1 repetition maximum (1RM) from the force - velocity relationship. Twenty seven physical education students and teachers (5 women and 22 men) with a heterogeneous history of strength training participated in this study. They performed a 1 RM test and a force - velocity test using a Bench press lifting task in a random order. Mean 1 RM was 61.8 ± 15.3 kg (range: 34 to 100 kg), while 1 RM estimated by the Musclelab's software from the force-velocity relationship was 56.4 ± 14.0 kg (range: 33 to 91 kg). Actual and estimated 1 RM were very highly correlated (r = 0.93, pvelocity relationship was a good measure for monitoring training induced adaptations, but also that it was not accurate enough to prescribe training intensities. Additional studies are required to determine whether accuracy is affected by age, sex or initial level. Key pointsSome commercial devices allow to estimate 1 RM from the force-velocity relationship.These estimations are valid. However, their accuracy is not high enough to be of practical help for training intensity prescription.Day-to-day reliability of force and velocity measured by the linear encoder has been shown to be very high, but the specific reliability of 1 RM estimated from the force-velocity relationship has to be determined before concluding to the usefulness of this approach in the monitoring of training induced adaptations.

An open source framework for tracking and state estimation ('Stone Soup')

Science.gov (United States)

Thomas, Paul A.; Barr, Jordi; Balaji, Bhashyam; White, Kruger

2017-05-01

The ability to detect and unambiguously follow all moving entities in a state-space is important in multiple domains both in defence (e.g. air surveillance, maritime situational awareness, ground moving target indication) and the civil sphere (e.g. astronomy, biology, epidemiology, dispersion modelling). However, tracking and state estimation researchers and practitioners have difficulties recreating state-of-the-art algorithms in order to benchmark their own work. Furthermore, system developers need to assess which algorithms meet operational requirements objectively and exhaustively rather than intuitively or driven by personal favourites. We have therefore commenced the development of a collaborative initiative to create an open source framework for production, demonstration and evaluation of Tracking and State Estimation algorithms. The initiative will develop a (MIT-licensed) software platform for researchers and practitioners to test, verify and benchmark a variety of multi-sensor and multi-object state estimation algorithms. The initiative is supported by four defence laboratories, who will contribute to the development effort for the framework. The tracking and state estimation community will derive significant benefits from this work, including: access to repositories of verified and validated tracking and state estimation algorithms, a framework for the evaluation of multiple algorithms, standardisation of interfaces and access to challenging data sets. Keywords: Tracking,

Genetic parameters for racing records in trotters using linear and generalized linear models.

Science.gov (United States)

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.

Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems.

Science.gov (United States)

Chen, Tengpeng; Foo, Yi Shyh Eddy; Ling, K V; Chen, Xuebing

2017-10-11

In this paper, a distributed state estimation method based on moving horizon estimation (MHE) is proposed for the large-scale power system state estimation. The proposed method partitions the power systems into several local areas with non-overlapping states. Unlike the centralized approach where all measurements are sent to a processing center, the proposed method distributes the state estimation task to the local processing centers where local measurements are collected. Inspired by the partitioned moving horizon estimation (PMHE) algorithm, each local area solves a smaller optimization problem to estimate its own local states by using local measurements and estimated results from its neighboring areas. In contrast with PMHE, the error from the process model is ignored in our method. The proposed modified PMHE (mPMHE) approach can also take constraints on states into account during the optimization process such that the influence of the outliers can be further mitigated. Simulation results on the IEEE 14-bus and 118-bus systems verify that our method achieves comparable state estimation accuracy but with a significant reduction in the overall computation load.

Inline state of health estimation of lithium-ion batteries using state of charge calculation

Science.gov (United States)

Sepasi, Saeed; Ghorbani, Reza; Liaw, Bor Yann

2015-12-01

The determination of state-of-health (SOH) and state-of-charge (SOC) is challenging and remains as an active research area in academia and industry due to its importance for Li-ion battery applications. The estimation process poses more challenges after substantial battery aging. This paper presents an inline SOH and SOC estimation method for Li-ion battery packs, specifically for those based on LiFePO4 chemistry. This new hybridized SOC and SOH estimator can be used for battery packs. Inline estimated model parameters were used in a compounded SOC + SOH estimator consisting of the SOC calculation based on coulomb counting method as an expedient approach and an SOH observer using an extended Kalman filter (EKF) technique for calibrating the estimates from the coulomb counting method. The algorithm's low SOC and SOH estimation error, fast response time, and less-demanding computational requirement make it practical for on-board estimations. The simulation and experimental results, along with the test bed structure, are presented to validate the proposed methodology on a single cell and a 3S1P LiFePO4 battery pack.

Metric Indices for Performance Evaluation of a Mixed Measurement based State Estimator

Directory of Open Access Journals (Sweden)

Paula Sofia Vide

2013-01-01

Full Text Available With the development of synchronized phasor measurement technology in recent years, it gains great interest the use of PMU measurements to improve state estimation performances due to their synchronized characteristics and high data transmission speed. The ability of the Phasor Measurement Units (PMU to directly measure the system state is a key over SCADA measurement system. PMU measurements are superior to the conventional SCADA measurements in terms of resolution and accuracy. Since the majority of measurements in existing estimators are from conventional SCADA measurement system, it is hard to be fully replaced by PMUs in the near future so state estimators including both phasor and conventional SCADA measurements are being considered. In this paper, a mixed measurement (SCADA and PMU measurements state estimator is proposed. Several useful measures for evaluating various aspects of the performance of the mixed measurement state estimator are proposed and explained. State Estimator validity, performance and characteristics of the results on IEEE 14 bus test system and IEEE 30 bus test system are presented.

Multi-objective optimization in quantum parameter estimation

Science.gov (United States)

Gong, BeiLi; Cui, Wei

2018-04-01

We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.

Improvement of Storm Forecasts Using Gridded Bayesian Linear Regression for Northeast United States

Science.gov (United States)

Yang, J.; Astitha, M.; Schwartz, C. S.

2017-12-01

Bayesian linear regression (BLR) is a post-processing technique in which regression coefficients are derived and used to correct raw forecasts based on pairs of observation-model values. This study presents the development and application of a gridded Bayesian linear regression (GBLR) as a new post-processing technique to improve numerical weather prediction (NWP) of rain and wind storm forecasts over northeast United States. Ten controlled variables produced from ten ensemble members of the National Center for Atmospheric Research (NCAR) real-time prediction system are used for a GBLR model. In the GBLR framework, leave-one-storm-out cross-validation is utilized to study the performances of the post-processing technique in a database composed of 92 storms. To estimate the regression coefficients of the GBLR, optimization procedures that minimize the systematic and random error of predicted atmospheric variables (wind speed, precipitation, etc.) are implemented for the modeled-observed pairs of training storms. The regression coefficients calculated for meteorological stations of the National Weather Service are interpolated back to the model domain. An analysis of forecast improvements based on error reductions during the storms will demonstrate the value of GBLR approach. This presentation will also illustrate how the variances are optimized for the training partition in GBLR and discuss the verification strategy for grid points where no observations are available. The new post-processing technique is successful in improving wind speed and precipitation storm forecasts using past event-based data and has the potential to be implemented in real-time.

Application of radial basis neural network for state estimation of ...

African Journals Online (AJOL)

An original application of radial basis function (RBF) neural network for power system state estimation is proposed in this paper. The property of massive parallelism of neural networks is employed for this. The application of RBF neural network for state estimation is investigated by testing its applicability on a IEEE 14 bus ...

Generation of concatenated Greenberger-Horne-Zeilinger-type entangled coherent state based on linear optics

Science.gov (United States)

Guo, Rui; Zhou, Lan; Gu, Shi-Pu; Wang, Xing-Fu; Sheng, Yu-Bo

2017-03-01

The concatenated Greenberger-Horne-Zeilinger (C-GHZ) state is a new type of multipartite entangled state, which has potential application in future quantum information. In this paper, we propose a protocol of constructing arbitrary C-GHZ entangled state approximatively. Different from previous protocols, each logic qubit is encoded in the coherent state. This protocol is based on the linear optics, which is feasible in experimental technology. This protocol may be useful in quantum information based on the C-GHZ state.

Introduction to State Estimation of High-Rate System Dynamics.

Science.gov (United States)

Hong, Jonathan; Laflamme, Simon; Dodson, Jacob; Joyce, Bryan

2018-01-13

Engineering systems experiencing high-rate dynamic events, including airbags, debris detection, and active blast protection systems, could benefit from real-time observability for enhanced performance. However, the task of high-rate state estimation is challenging, in particular for real-time applications where the rate of the observer's convergence needs to be in the microsecond range. This paper identifies the challenges of state estimation of high-rate systems and discusses the fundamental characteristics of high-rate systems. A survey of applications and methods for estimators that have the potential to produce accurate estimations for a complex system experiencing highly dynamic events is presented. It is argued that adaptive observers are important to this research. In particular, adaptive data-driven observers are advantageous due to their adaptability and lack of dependence on the system model.

Estimates of Hadronic Backgrounds in a 5 TeV e+e- Linear Collider

International Nuclear Information System (INIS)

Murayama, H.; Ohgaki, Tomomi; Xie, M.

1998-01-01

We have estimated hadronic backgrounds by γγ collisions in an e + e - linear collider at a center-of-mass energy of 5 TeV. We introduce a simple ansatz, that is, a total γγ cross section of σ γγ = (σγ p ) 2 /σ pp shall be saturated by minijet productions, whose rate is controlled by p t,min (√s). We present that the background yields are small and the energy deposits are tinier than the collision energy of the initial electron and positron beams by a simulation

Linear combination of auditory steady-state responses evoked by co-modulated tones

DEFF Research Database (Denmark)

Guérit, François; Marozeau, Jeremy; Epp, Bastian

2017-01-01

Up to medium intensities and in the 80–100-Hz region, the auditory steady-state response (ASSR) to a multi-tone carrier is commonly considered to be a linear sum of the dipoles from each tone specific ASSR generator. Here, this hypothesis was investigated when a unique modulation frequency is used...... for all carrier components. Listeners were presented with a co-modulated dual-frequency carrier (1 and 4 kHz), from which the modulator starting phase Ui of the 1-kHz component was systematically varied. The results support the hypothesis of a linear superposition of the dipoles originating from different...

Estimation with applications to tracking and navigation theory, algorthims and software

CERN Document Server

Bar-Shalom, Yaakov; Kirubarajan, Thiagalingam

2001-01-01

Expert coverage of the design and implementation of state estimation algorithms for tracking and navigation Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations. It explains state estimator design using a balanced combination of linear systems, probability, and statistics. The authors provide a review of the necessary background mathematical techniques and offer an overview of the basic concepts in estimation. They then provide detailed treatments of all the major issues in estimatio

Power system static state estimation using Kalman filter algorithm

Directory of Open Access Journals (Sweden)

Saikia Anupam

2016-01-01

Full Text Available State estimation of power system is an important tool for operation, analysis and forecasting of electric power system. In this paper, a Kalman filter algorithm is presented for static estimation of power system state variables. IEEE 14 bus system is employed to check the accuracy of this method. Newton Raphson load flow study is first carried out on our test system and a set of data from the output of load flow program is taken as measurement input. Measurement inputs are simulated by adding Gaussian noise of zero mean. The results of Kalman estimation are compared with traditional Weight Least Square (WLS method and it is observed that Kalman filter algorithm is numerically more efficient than traditional WLS method. Estimation accuracy is also tested for presence of parametric error in the system. In addition, numerical stability of Kalman filter algorithm is tested by considering inclusion of zero mean errors in the initial estimates.

State energy data report 1992: Consumption estimates

Energy Technology Data Exchange (ETDEWEB)

1994-05-01

This is a report of energy consumption by state for the years 1960 to 1992. The report contains summaries of energy consumption for the US and by state, consumption by source, comparisons to other energy use reports, consumption by energy use sector, and describes the estimation methodologies used in the preparation of the report. Some years are not listed specifically although they are included in the summary of data.

Validity of a Commercial Linear Encoder to Estimate Bench Press 1 RM from the Force-Velocity Relationship

Science.gov (United States)

Bosquet, Laurent; Porta-Benache, Jeremy; Blais, Jérôme

2010-01-01

The aim of this study was to assess the validity and accuracy of a commercial linear encoder (Musclelab, Ergotest, Norway) to estimate Bench press 1 repetition maximum (1RM) from the force - velocity relationship. Twenty seven physical education students and teachers (5 women and 22 men) with a heterogeneous history of strength training participated in this study. They performed a 1 RM test and a force - velocity test using a Bench press lifting task in a random order. Mean 1 RM was 61.8 ± 15.3 kg (range: 34 to 100 kg), while 1 RM estimated by the Musclelab’s software from the force-velocity relationship was 56.4 ± 14.0 kg (range: 33 to 91 kg). Actual and estimated 1 RM were very highly correlated (r = 0.93, p<0.001) but largely different (Bias: 5.4 ± 5.7 kg, p < 0.001, ES = 1.37). The 95% limits of agreement were ±11.2 kg, which represented ±18% of actual 1 RM. It was concluded that 1 RM estimated from the force-velocity relationship was a good measure for monitoring training induced adaptations, but also that it was not accurate enough to prescribe training intensities. Additional studies are required to determine whether accuracy is affected by age, sex or initial level. Key points Some commercial devices allow to estimate 1 RM from the force-velocity relationship. These estimations are valid. However, their accuracy is not high enough to be of practical help for training intensity prescription. Day-to-day reliability of force and velocity measured by the linear encoder has been shown to be very high, but the specific reliability of 1 RM estimated from the force-velocity relationship has to be determined before concluding to the usefulness of this approach in the monitoring of training induced adaptations. PMID:24149641

Transferring Instantly the State of Higher-Order Linear Descriptor (Regular Differential Systems Using Impulsive Inputs

Directory of Open Access Journals (Sweden)

Athanasios D. Karageorgos

2009-01-01

Full Text Available In many applications, and generally speaking in many dynamical differential systems, the problem of transferring the initial state of the system to a desired state in (almost zero-time time is desirable but difficult to achieve. Theoretically, this can be achieved by using a linear combination of Dirac -function and its derivatives. Obviously, such an input is physically unrealizable. However, we can think of it approximately as a combination of small pulses of very high magnitude and infinitely small duration. In this paper, the approximation process of the distributional behaviour of higher-order linear descriptor (regular differential systems is presented. Thus, new analytical formulae based on linear algebra methods and generalized inverses theory are provided. Our approach is quite general and some significant conditions are derived. Finally, a numerical example is presented and discussed.

Joint estimation of the fractional differentiation orders and the unknown input for linear fractional non-commensurate system

KAUST Repository

Belkhatir, Zehor

2015-11-05

This paper deals with the joint estimation of the unknown input and the fractional differentiation orders of a linear fractional order system. A two-stage algorithm combining the modulating functions with a first-order Newton method is applied to solve this estimation problem. First, the modulating functions approach is used to estimate the unknown input for a given fractional differentiation orders. Then, the method is combined with a first-order Newton technique to identify the fractional orders jointly with the input. To show the efficiency of the proposed method, numerical examples illustrating the estimation of the neural activity, considered as input of a fractional model of the neurovascular coupling, along with the fractional differentiation orders are presented in both noise-free and noisy cases.

National intelligence estimates and the Failed State Index.

Science.gov (United States)

Voracek, Martin

2013-10-01

Across 177 countries around the world, the Failed State Index, a measure of state vulnerability, was reliably negatively associated with the estimates of national intelligence. Psychometric analysis of the Failed State Index, compounded of 12 social, economic, and political indicators, suggested factorial unidimensionality of this index. The observed correspondence of higher national intelligence figures to lower state vulnerability might arise through these two macro-level variables possibly being proxies of even more pervasive historical and societal background variables that affect both.

On Estimating Marginal Tax Rates for U.S. States

OpenAIRE

Reed, W. Robert; Rogers, Cynthia L; Skidmore, Mark

2011-01-01

This paper presents a procedure for generating state-specific time-varying estimates of marginal tax rates (MTRs). Most estimates of MTRs follow a procedure developed by Koester and Kormendi (1989) (K&K). Unfortunately, the time-invariant nature of the K&K estimates precludes their use as explanatory variables in panel data studies with fixed effects. Furthermore, the associated MTR estimates are not explicitly linked to statutory tax parameters. Our approach addresses both shortcomings. Usin...

Parameter and State Estimation of Large-Scale Complex Systems Using Python Tools

Directory of Open Access Journals (Sweden)

M. Anushka S. Perera

2015-07-01

Full Text Available This paper discusses the topics related to automating parameter, disturbance and state estimation analysis of large-scale complex nonlinear dynamic systems using free programming tools. For large-scale complex systems, before implementing any state estimator, the system should be analyzed for structural observability and the structural observability analysis can be automated using Modelica and Python. As a result of structural observability analysis, the system may be decomposed into subsystems where some of them may be observable --- with respect to parameter, disturbances, and states --- while some may not. The state estimation process is carried out for those observable subsystems and the optimum number of additional measurements are prescribed for unobservable subsystems to make them observable. In this paper, an industrial case study is considered: the copper production process at Glencore Nikkelverk, Kristiansand, Norway. The copper production process is a large-scale complex system. It is shown how to implement various state estimators, in Python, to estimate parameters and disturbances, in addition to states, based on available measurements.

Theory and Applications of Discontinuous State Feedback Generating Chaos for Linear Systems

International Nuclear Information System (INIS)

Xiao-Dan, Zhang; Zhen, Wang; Pin-Dong, Zhao

2008-01-01

We investigate a kind of chaos generating technique on a type of n-dimensional linear differential systems by adding feedback control items under a discontinuous state. This method is checked with some examples of numeric simulation. A constructive theorem is proposed for generalized synchronization related to the above chaotic system

Comparing Consider-Covariance Analysis with Sigma-Point Consider Filter and Linear-Theory Consider Filter Formulations

Science.gov (United States)

Lisano, Michael E.

2007-01-01

Recent literature in applied estimation theory reflects growing interest in the sigma-point (also called unscented ) formulation for optimal sequential state estimation, often describing performance comparisons with extended Kalman filters as applied to specific dynamical problems [c.f. 1, 2, 3]. Favorable attributes of sigma-point filters are described as including a lower expected error for nonlinear even non-differentiable dynamical systems, and a straightforward formulation not requiring derivation or implementation of any partial derivative Jacobian matrices. These attributes are particularly attractive, e.g. in terms of enabling simplified code architecture and streamlined testing, in the formulation of estimators for nonlinear spaceflight mechanics systems, such as filter software onboard deep-space robotic spacecraft. As presented in [4], the Sigma-Point Consider Filter (SPCF) algorithm extends the sigma-point filter algorithm to the problem of consider covariance analysis. Considering parameters in a dynamical system, while estimating its state, provides an upper bound on the estimated state covariance, which is viewed as a conservative approach to designing estimators for problems of general guidance, navigation and control. This is because, whether a parameter in the system model is observable or not, error in the knowledge of the value of a non-estimated parameter will increase the actual uncertainty of the estimated state of the system beyond the level formally indicated by the covariance of an estimator that neglects errors or uncertainty in that parameter. The equations for SPCF covariance evolution are obtained in a fashion similar to the derivation approach taken with standard (i.e. linearized or extended) consider parameterized Kalman filters (c.f. [5]). While in [4] the SPCF and linear-theory consider filter (LTCF) were applied to an illustrative linear dynamics/linear measurement problem, in the present work examines the SPCF as applied to

Estimation of the number of wild pigs found in the United States

Energy Technology Data Exchange (ETDEWEB)

Mayer, J. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

2014-08-01

Based on a compilation of three estimation approaches, the total nationwide population of wild pigs in the United States numbers approximately 6.3 million animals, with that total estimate ranging from 4.4 up to 11.3 million animals. The majority of these numbers (99 percent), which were encompassed by ten states (i.e., Alabama, Arkansas, California, Florida, Georgia, Louisiana, Mississippi, Oklahoma, South Carolina and Texas), were based on defined estimation methodologies (e.g., density estimates correlated to the total potential suitable wild pig habitat statewide, statewide harvest percentages, statewide agency surveys regarding wild pig distribution and numbers). In contrast to the pre-1990 estimates, none of these more recent efforts, collectively encompassing 99 percent of the total, were based solely on anecdotal information or speculation. To that end, one can defensibly state that the wild pigs found in the United States number in the millions of animals, with the nationwide population estimated to arguably vary from about four million up to about eleven million individuals.

High-fidelity state transfer over an unmodulated linear XY spin chain

International Nuclear Information System (INIS)

Bishop, C. Allen; Ou Yongcheng; Byrd, Mark S.; Wang Zhaoming

2010-01-01

We provide a class of initial encodings that can be sent with a high fidelity over an unmodulated, linear, XY spin chain. As an example, an average fidelity of 96% can be obtained using an 11-spin encoding to transmit a state over a chain containing 10 000 spins. An analysis of the magnetic-field dependence is given, and conditions for field optimization are provided.

Estimating the state of large spatio-temporally chaotic systems

International Nuclear Information System (INIS)

Ott, E.; Hunt, B.R.; Szunyogh, I.; Zimin, A.V.; Kostelich, E.J.; Corazza, M.; Kalnay, E.; Patil, D.J.; Yorke, J.A.

2004-01-01

We consider the estimation of the state of a large spatio-temporally chaotic system from noisy observations and knowledge of a system model. Standard state estimation techniques using the Kalman filter approach are not computationally feasible for systems with very many effective degrees of freedom. We present and test a new technique (called a Local Ensemble Kalman Filter), generally applicable to large spatio-temporally chaotic systems for which correlations between system variables evaluated at different points become small at large separation between the points

The regular indefinite linear-quadratic problem with linear endpoint constraints

NARCIS (Netherlands)

Soethoudt, J.M.; Trentelman, H.L.

1989-01-01

This paper deals with the infinite horizon linear-quadratic problem with indefinite cost. Given a linear system, a quadratic cost functional and a subspace of the state space, we consider the problem of minimizing the cost functional over all inputs for which the state trajectory converges to that

Estimating repetitive spatiotemporal patterns from resting-state brain activity data.

Science.gov (United States)

Takeda, Yusuke; Hiroe, Nobuo; Yamashita, Okito; Sato, Masa-Aki

2016-06-01

Repetitive spatiotemporal patterns in spontaneous brain activities have been widely examined in non-human studies. These studies have reported that such patterns reflect past experiences embedded in neural circuits. In human magnetoencephalography (MEG) and electroencephalography (EEG) studies, however, spatiotemporal patterns in resting-state brain activities have not been extensively examined. This is because estimating spatiotemporal patterns from resting-state MEG/EEG data is difficult due to their unknown onsets. Here, we propose a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data, including MEG/EEG. Without the information of onsets, the proposed method can estimate several spatiotemporal patterns, even if they are overlapping. We verified the performance of the method by detailed simulation tests. Furthermore, we examined whether the proposed method could estimate the visual evoked magnetic fields (VEFs) without using stimulus onset information. The proposed method successfully detected the stimulus onsets and estimated the VEFs, implying the applicability of this method to real MEG data. The proposed method was applied to resting-state functional magnetic resonance imaging (fMRI) data and MEG data. The results revealed informative spatiotemporal patterns representing consecutive brain activities that dynamically change with time. Using this method, it is possible to reveal discrete events spontaneously occurring in our brains, such as memory retrieval. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models

KAUST Repository

Ting, Chee-Ming

2017-12-06

We consider the challenges in estimating state-related changes in brain connectivity networks with a large number of nodes. Existing studies use sliding-window analysis or time-varying coefficient models which are unable to capture both smooth and abrupt changes simultaneously, and rely on ad-hoc approaches to the high-dimensional estimation. To overcome these limitations, we propose a Markov-switching dynamic factor model which allows the dynamic connectivity states in functional magnetic resonance imaging (fMRI) data to be driven by lower-dimensional latent factors. We specify a regime-switching vector autoregressive (SVAR) factor process to quantity the time-varying directed connectivity. The model enables a reliable, data-adaptive estimation of change-points of connectivity regimes and the massive dependencies associated with each regime. We develop a three-step estimation procedure: 1) extracting the factors using principal component analysis, 2) identifying connectivity regimes in a low-dimensional subspace based on the factor-based SVAR model, 3) constructing high-dimensional state connectivity metrics based on the subspace estimates. Simulation results show that our estimator outperforms K-means clustering of time-windowed coefficients, providing more accurate estimate of time-evolving connectivity. It achieves percentage of reduction in mean squared error by 60% when the network dimension is comparable to the sample size. When applied to resting-state fMRI data, our method successfully identifies modular organization in resting-state networks in consistency with other studies. It further reveals changes in brain states with variations across subjects and distinct large-scale directed connectivity patterns across states.

Battery state-of-charge estimation using approximate least squares

Science.gov (United States)

Unterrieder, C.; Zhang, C.; Lunglmayr, M.; Priewasser, R.; Marsili, S.; Huemer, M.

2015-03-01

In recent years, much effort has been spent to extend the runtime of battery-powered electronic applications. In order to improve the utilization of the available cell capacity, high precision estimation approaches for battery-specific parameters are needed. In this work, an approximate least squares estimation scheme is proposed for the estimation of the battery state-of-charge (SoC). The SoC is determined based on the prediction of the battery's electromotive force. The proposed approach allows for an improved re-initialization of the Coulomb counting (CC) based SoC estimation method. Experimental results for an implementation of the estimation scheme on a fuel gauge system on chip are illustrated. Implementation details and design guidelines are presented. The performance of the presented concept is evaluated for realistic operating conditions (temperature effects, aging, standby current, etc.). For the considered test case of a GSM/UMTS load current pattern of a mobile phone, the proposed method is able to re-initialize the CC-method with a high accuracy, while state-of-the-art methods fail to perform a re-initialization.

A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds.

Directory of Open Access Journals (Sweden)

Ana Calabrese

2011-01-01

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.

A Novel Methodology for Estimating State-Of-Charge of Li-Ion Batteries Using Advanced Parameters Estimation

Directory of Open Access Journals (Sweden)

Ibrahim M. Safwat

2017-11-01

Full Text Available State-of-charge (SOC estimations of Li-ion batteries have been the focus of many research studies in previous years. Many articles discussed the dynamic model’s parameters estimation of the Li-ion battery, where the fixed forgetting factor recursive least square estimation methodology is employed. However, the change rate of each parameter to reach the true value is not taken into consideration, which may tend to poor estimation. This article discusses this issue, and proposes two solutions to solve it. The first solution is the usage of a variable forgetting factor instead of a fixed one, while the second solution is defining a vector of forgetting factors, which means one factor for each parameter. After parameters estimation, a new idea is proposed to estimate state-of-charge (SOC of the Li-ion battery based on Newton’s method. Also, the error percentage and computational cost are discussed and compared with that of nonlinear Kalman filters. This methodology is applied on a 36 V 30 A Li-ion pack to validate this idea.

Methods for Estimating Water Withdrawals for Mining in the United States, 2005

Science.gov (United States)

Lovelace, John K.

2009-01-01

The mining water-use category includes groundwater and surface water that is withdrawn and used for nonfuels and fuels mining. Nonfuels mining includes the extraction of ores, stone, sand, and gravel. Fuels mining includes the extraction of coal, petroleum, and natural gas. Water is used for mineral extraction, quarrying, milling, and other operations directly associated with mining activities. For petroleum and natural gas extraction, water often is injected for secondary oil or gas recovery. Estimates of water withdrawals for mining are needed for water planning and management. This report documents methods used to estimate withdrawals of fresh and saline groundwater and surface water for mining during 2005 for each county and county equivalent in the United States, Puerto Rico, and the U.S. Virgin Islands. Fresh and saline groundwater and surface-water withdrawals during 2005 for nonfuels- and coal-mining operations in each county or county equivalent in the United States, Puerto Rico, and the U.S. Virgin Islands were estimated. Fresh and saline groundwater withdrawals for oil and gas operations in counties of six states also were estimated. Water withdrawals for nonfuels and coal mining were estimated by using mine-production data and water-use coefficients. Production data for nonfuels mining included the mine location and weight (in metric tons) of crude ore, rock, or mineral produced at each mine in the United States, Puerto Rico, and the U.S. Virgin Islands during 2004. Production data for coal mining included the weight, in metric tons, of coal produced in each county or county equivalent during 2004. Water-use coefficients for mined commodities were compiled from various sources including published reports and written communications from U.S. Geological Survey National Water-use Information Program (NWUIP) personnel in several states. Water withdrawals for oil and gas extraction were estimated for six States including California, Colorado, Louisiana, New

Spin State Estimation of Tumbling Small Bodies

Science.gov (United States)

Olson, Corwin; Russell, Ryan P.; Bhaskaran, Shyam

2016-06-01

It is expected that a non-trivial percentage of small bodies that future missions may visit are in non-principal axis rotation (i.e. "tumbling"). The primary contribution of this paper is the application of the Extended Kalman Filter (EKF) Simultaneous Localization and Mapping (SLAM) method to estimate the small body spin state, mass, and moments of inertia; the spacecraft position and velocity; and the surface landmark locations. The method uses optical landmark measurements, and an example scenario based on the Rosetta mission is used. The SLAM method proves effective, with order of magnitude decreases in the spacecraft and small body spin state errors after less than a quarter of the comet characterization phase. The SLAM method converges nicely for initial small body angular velocity errors several times larger than the true rates (effectively having no a priori knowledge of the angular velocity). Surface landmark generation and identification are not treated in this work, but significant errors in the initial body-fixed landmark positions are effectively estimated. The algorithm remains effective for a range of different truth spin states, masses, and center of mass offsets that correspond to expected tumbling small bodies throughout the solar system.

Distributed Dynamic State Estimator, Generator Parameter Estimation and Stability Monitoring Demonstration

Energy Technology Data Exchange (ETDEWEB)

Meliopoulos, Sakis [Georgia Inst. of Technology, Atlanta, GA (United States); Cokkinides, George [Georgia Inst. of Technology, Atlanta, GA (United States); Fardanesh, Bruce [New York Power Authority, NY (United States); Hedrington, Clinton [U.S. Virgin Islands Water and Power Authority (WAPA), St. Croix (U.S. Virgin Islands)

2013-12-31

This is the final report for this project that was performed in the period: October1, 2009 to June 30, 2013. In this project, a fully distributed high-fidelity dynamic state estimator (DSE) that continuously tracks the real time dynamic model of a wide area system with update rates better than 60 times per second is achieved. The proposed technology is based on GPS-synchronized measurements but also utilizes data from all available Intelligent Electronic Devices in the system (numerical relays, digital fault recorders, digital meters, etc.). The distributed state estimator provides the real time model of the system not only the voltage phasors. The proposed system provides the infrastructure for a variety of applications and two very important applications (a) a high fidelity generating unit parameters estimation and (b) an energy function based transient stability monitoring of a wide area electric power system with predictive capability. Also the dynamic distributed state estimation results are stored (the storage scheme includes data and coincidental model) enabling an automatic reconstruction and “play back” of a system wide disturbance. This approach enables complete play back capability with fidelity equal to that of real time with the advantage of “playing back” at a user selected speed. The proposed technologies were developed and tested in the lab during the first 18 months of the project and then demonstrated on two actual systems, the USVI Water and Power Administration system and the New York Power Authority’s Blenheim-Gilboa pumped hydro plant in the last 18 months of the project. The four main thrusts of this project, mentioned above, are extremely important to the industry. The DSE with the achieved update rates (more than 60 times per second) provides a superior solution to the “grid visibility” question. The generator parameter identification method fills an important and practical need of the industry. The “energy function” based

State space and input-output linear systems

CERN Document Server

Delchamps, David F

1988-01-01

It is difficult for me to forget the mild sense of betrayal I felt some ten years ago when I discovered, with considerable dismay, that my two favorite books on linear system theory - Desoer's Notes for a Second Course on Linear Systems and Brockett's Finite Dimensional Linear Systems - were both out of print. Since that time, of course, linear system theory has undergone a transformation of the sort which always attends the maturation of a theory whose range of applicability is expanding in a fashion governed by technological developments and by the rate at which such advances become a part of engineering practice. The growth of the field has inspired the publication of some excellent books; the encyclopedic treatises by Kailath and Chen, in particular, come immediately to mind. Nonetheless, I was inspired to write this book primarily by my practical needs as a teacher and researcher in the field. For the past five years, I have taught a one semester first year gradu­ ate level linear system theory course i...

Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries

International Nuclear Information System (INIS)

Ng, Kong Soon; Moo, Chin-Sien; Chen, Yi-Ping; Hsieh, Yao-Ching

2009-01-01

The coulomb counting method is expedient for state-of-charge (SOC) estimation of lithium-ion batteries with high charging and discharging efficiencies. The charging and discharging characteristics are investigated and reveal that the coulomb counting method is convenient and accurate for estimating the SOC of lithium-ion batteries. A smart estimation method based on coulomb counting is proposed to improve the estimation accuracy. The corrections are made by considering the charging and operating efficiencies. Furthermore, the state-of-health (SOH) is evaluated by the maximum releasable capacity. Through the experiments that emulate practical operations, the SOC estimation method is verified to demonstrate the effectiveness and accuracy.

Adaptive estimation of a time-varying phase with coherent states: Smoothing can give an unbounded improvement over filtering

Science.gov (United States)

Laverick, Kiarn T.; Wiseman, Howard M.; Dinani, Hossein T.; Berry, Dominic W.

2018-04-01

The problem of measuring a time-varying phase, even when the statistics of the variation is known, is considerably harder than that of measuring a constant phase. In particular, the usual bounds on accuracy, such as the 1 /(4 n ¯) standard quantum limit with coherent states, do not apply. Here, by restricting to coherent states, we are able to analytically obtain the achievable accuracy, the equivalent of the standard quantum limit, for a wide class of phase variation. In particular, we consider the case where the phase has Gaussian statistics and a power-law spectrum equal to κp -1/|ω| p for large ω , for some p >1 . For coherent states with mean photon flux N , we give the quantum Cramér-Rao bound on the mean-square phase error as [psin(π /p ) ] -1(4N /κ ) -(p -1 )/p . Next, we consider whether the bound can be achieved by an adaptive homodyne measurement in the limit N /κ ≫1 , which allows the photocurrent to be linearized. Applying the optimal filtering for the resultant linear Gaussian system, we find the same scaling with N , but with a prefactor larger by a factor of p . By contrast, if we employ optimal smoothing we can exactly obtain the quantum Cramér-Rao bound. That is, contrary to previously considered (p =2 ) cases of phase estimation, here the improvement offered by smoothing over filtering is not limited to a factor of 2 but rather can be unbounded by a factor of p . We also study numerically the performance of these estimators for an adaptive measurement in the limit where N /κ is not large and find a more complicated picture.

Anti-symmetrically fused model and non-linear integral equations in the three-state Uimin-Sutherland model

International Nuclear Information System (INIS)

Fujii, Akira; Kluemper, Andreas

1999-01-01

We derive the non-linear integral equations determining the free energy of the three-state pure bosonic Uimin-Sutherland model. In order to find a complete set of auxiliary functions, the anti-symmetric fusion procedure is utilized. We solve the non-linear integral equations numerically and see that the low-temperature behavior coincides with that predicted by conformal field theory. The magnetization and magnetic susceptibility are also calculated by means of the non-linear integral equation

A Novel Method of Robust Trajectory Linearization Control Based on Disturbance Rejection

Directory of Open Access Journals (Sweden)

Xingling Shao

2014-01-01

Full Text Available A novel method of robust trajectory linearization control for a class of nonlinear systems with uncertainties based on disturbance rejection is proposed. Firstly, on the basis of trajectory linearization control (TLC method, a feedback linearization based control law is designed to transform the original tracking error dynamics to the canonical integral-chain form. To address the issue of reducing the influence made by uncertainties, with tracking error as input, linear extended state observer (LESO is constructed to estimate the tracking error vector, as well as the uncertainties in an integrated manner. Meanwhile, the boundedness of the estimated error is investigated by theoretical analysis. In addition, decoupled controller (which has the characteristic of well-tuning and simple form based on LESO is synthesized to realize the output tracking for closed-loop system. The closed-loop stability of the system under the proposed LESO-based control structure is established. Also, simulation results are presented to illustrate the effectiveness of the control strategy.

Enhancement of loss detection capability using a combination of the Kalman Filter/Linear Smoother and controllable unit accounting approach

International Nuclear Information System (INIS)

Pike, D.H.; Morrison, G.W.

1979-01-01

An approach to loss detection is presented which combines the optimal loss detection capability of state estimation techniques with a controllable unit accounting approach. The state estimation theory makes use of a linear system model which is capable of modeling the interaction of various controllable unit areas within a given facility. An example is presented which illustrates the increase in loss detection probability which is realizable with state estimation techniques. Comparisons are made with a Shewhart Control Chart and the CUSUM statistic

Broadband implementation of coprime linear microphone arrays for direction of arrival estimation.

Science.gov (United States)

Bush, Dane; Xiang, Ning

2015-07-01

Coprime arrays represent a form of sparse sensing which can achieve narrow beams using relatively few elements, exceeding the spatial Nyquist sampling limit. The purpose of this paper is to expand on and experimentally validate coprime array theory in an acoustic implementation. Two nested sparse uniform linear subarrays with coprime number of elements ( M and N) each produce grating lobes that overlap with one another completely in just one direction. When the subarray outputs are combined it is possible to retain the shared beam while mostly canceling the other superfluous grating lobes. In this way a small number of microphones ( N+M-1) creates a narrow beam at higher frequencies, comparable to a densely populated uniform linear array of MN microphones. In this work beampatterns are simulated for a range of single frequencies, as well as bands of frequencies. Narrowband experimental beampatterns are shown to correspond with simulated results even at frequencies other than the arrays design frequency. Narrowband side lobe locations are shown to correspond to the theoretical values. Side lobes in the directional pattern are mitigated by increasing bandwidth of analyzed signals. Direction of arrival estimation is also implemented for two simultaneous noise sources in a free field condition.

State-Level Estimates of Obesity-Attributable Costs of Absenteeism

Science.gov (United States)

Andreyeva, Tatiana; Luedicke, Joerg; Wang, Y. Claire

2014-01-01

Objective To provide state-level estimates of obesity-attributable costs of absenteeism among working adults in the U.S. Methods Nationally-representative data from the National Health and Nutrition Examination Survey (NHANES) for 1998–2008 and from the Behavioral Risk Factor Surveillance System (BRFSS) for 2012 are examined. The outcome is obesity-attributable workdays missed in the previous year due to health, and their costs to states. Results Obesity, but not overweight, is associated with a significant increase in workdays absent, from 1.1 to 1.7 extra days missed annually compared to normal weight employees. Obesity-attributable absenteeism among American workers costs the nation an estimated $8.65 billion per year. Conclusion Obesity imposes a considerable financial burden on states, accounting for 6.5%–12.6% of total absenteeism costs in the workplace. State legislature and employers should seek effective ways to reduce these costs. PMID:25376405

Estimating net joint torques from kinesiological data using optimal linear system theory.

Science.gov (United States)

Runge, C F; Zajac, F E; Allum, J H; Risher, D W; Bryson, A E; Honegger, F

1995-12-01

Net joint torques (NJT) are frequently computed to provide insights into the motor control of dynamic biomechanical systems. An inverse dynamics approach is almost always used, whereby the NJT are computed from 1) kinematic measurements (e.g., position of the segments), 2) kinetic measurements (e.g., ground reaction forces) that are, in effect, constraints defining unmeasured kinematic quantities based on a dynamic segmental model, and 3) numerical differentiation of the measured kinematics to estimate velocities and accelerations that are, in effect, additional constraints. Due to errors in the measurements, the segmental model, and the differentiation process, estimated NJT rarely produce the observed movement in a forward simulation when the dynamics of the segmental system are inherently unstable (e.g., human walking). Forward dynamic simulations are, however, essential to studies of muscle coordination. We have developed an alternative approach, using the linear quadratic follower (LQF) algorithm, which computes the NJT such that a stable simulation of the observed movement is produced and the measurements are replicated as well as possible. The LQF algorithm does not employ constraints depending on explicit differentiation of the kinematic data, but rather employs those depending on specification of a cost function, based on quantitative assumptions about data confidence. We illustrate the usefulness of the LQF approach by using it to estimate NJT exerted by standing humans perturbed by support-surface movements. We show that unless the number of kinematic and force variables recorded is sufficiently high, the confidence that can be placed in the estimates of the NJT, obtained by any method (e.g., LQF, or the inverse dynamics approach), may be unsatisfactorily low.

Global control of reaction wheel pendulum through energy regulation and extended linearization of the state variables

Directory of Open Access Journals (Sweden)

Oscar D. Montoya-Giraldo

2014-01-01

Full Text Available This paper presents the design and simulation of a global controller for the Reaction Wheel Pendulum system using energy regulation and extended linearization methods for the state feedback. The proposed energy regulation is based on the gradual reduction of the energy of the system to reach the unstable equilibrium point. The signal input for this task is obtained from the Lyapunov stability theory. The extended state feedback controller design is used to get a smooth nonlinear function that extends the region of operation to a bigger range, in contrast with the static linear state feedback obtained through the method of approximate linearization around an operating point. The general designed controller operates with a switching between the two control signals depending upon the region of operation; perturbations are applied in the control signal and the (simulated measured variables to verify the robustness and efficiency of the controller. Finally, simulations and tests using the model of the reaction wheel pendulum system, allow to observe the versatility and functionality of the proposed controller in the entire operation region of the pendulum.

Uncertainty of feedback and state estimation determines the speed of motor adaptation

Directory of Open Access Journals (Sweden)

Kunlin Wei

2010-05-01

Full Text Available Humans can adapt their motor behaviors to deal with ongoing changes. To achieve this, the nervous system needs to estimate central variables for our movement based on past knowledge and new feedback, both of which are uncertain. In the Bayesian framework, rates of adaptation characterize how noisy feedback is in comparison to the uncertainty of the state estimate. The predictions of Bayesian models are intuitive: the nervous system should adapt slower when sensory feedback is more noisy and faster when its state estimate is more uncertain. Here we want to quantitatively understand how uncertainty in these two factors affects motor adaptation. In a hand reaching experiment we measured trial-by-trial adaptation to a randomly changing visual perturbation to characterize the way the nervous system handles uncertainty in state estimation and feedback. We found both qualitative predictions of Bayesian models confirmed. Our study provides evidence that the nervous system represents and uses uncertainty in state estimate and feedback during motor adaptation.

PySSM: A Python Module for Bayesian Inference of Linear Gaussian State Space Models

Directory of Open Access Journals (Sweden)

Christopher Strickland

2014-04-01

Full Text Available PySSM is a Python package that has been developed for the analysis of time series using linear Gaussian state space models. PySSM is easy to use; models can be set up quickly and efficiently and a variety of different settings are available to the user. It also takes advantage of scientific libraries NumPy and SciPy and other high level features of the Python language. PySSM is also used as a platform for interfacing between optimized and parallelized Fortran routines. These Fortran routines heavily utilize basic linear algebra and linear algebra Package functions for maximum performance. PySSM contains classes for filtering, classical smoothing as well as simulation smoothing.

Minimum error discrimination for an ensemble of linearly independent pure states

International Nuclear Information System (INIS)

Singal, Tanmay; Ghosh, Sibasish

2016-01-01

Inspired by the work done by Belavkin (1975 Stochastics 1 315) and independently by Mochon, (2006 Phys. Rev. A 73 032328), we formulate the problem of minimum error discrimination (MED) of any ensemble of n linearly independent pure states by stripping the problem of its rotational covariance and retaining only the rotationally invariant aspect of the problem. This is done by embedding the optimal conditions in a matrix equality as well as matrix inequality. Employing the implicit function theorem in these conditions we get a set of first-order coupled ordinary nonlinear differential equations which can be used to drag the solution from an initial point (where solution is known) to another point (whose solution is sought). This way of obtaining the solution can be done through a simple Taylor series expansion and analytic continuation when required. Thus, we complete the work done by Belavkin and Mochon by ultimately leading their theory to a solution for the MED problem of linearly independent pure state ensembles. We also compare the computational complexity of our technique with the barrier-type interior point method of SDP and show that our technique is computationally as efficient as (actually, a bit more than) the SDP algorithm, with the added advantage of being much simpler to implement. (paper)

State estimation for integrated vehicle dynamics control

NARCIS (Netherlands)

Zuurbier, J.; Bremmer, P.

2002-01-01

This paper discusses a vehicle controller and a state estimator that was implemented and tested in a vehicle equipped with a combined braking and chassis control system to improve handling. The vehicle dynamics controller consists of a feed forward body roll compensation and a feedback stability

Decentralized State-Observer-Based Traffic Density Estimation of Large-Scale Urban Freeway Network by Dynamic Model

Directory of Open Access Journals (Sweden)

Yuqi Guo

2017-08-01

Full Text Available In order to estimate traffic densities in a large-scale urban freeway network in an accurate and timely fashion when traffic sensors do not cover the freeway network completely and thus only local measurement data can be utilized, this paper proposes a decentralized state observer approach based on a macroscopic traffic flow model. Firstly, by using the well-known cell transmission model (CTM, the urban freeway network is modeled in the way of distributed systems. Secondly, based on the model, a decentralized observer is designed. With the help of the Lyapunov function and S-procedure theory, the observer gains are computed by using linear matrix inequality (LMI technique. So, the traffic densities of the whole road network can be estimated by the designed observer. Finally, this method is applied to the outer ring of the Beijing’s second ring road and experimental results demonstrate the effectiveness and applicability of the proposed approach.

Decentralized Observer with a Consensus Filter for Distributed Discrete-Time Linear Systems

Science.gov (United States)

Acikmese, Behcet; Mandic, Milan

2011-01-01

This paper presents a decentralized observer with a consensus filter for the state observation of a discrete-time linear distributed systems. In this setup, each agent in the distributed system has an observer with a model of the plant that utilizes the set of locally available measurements, which may not make the full plant state detectable. This lack of detectability is overcome by utilizing a consensus filter that blends the state estimate of each agent with its neighbors' estimates. We assume that the communication graph is connected for all times as well as the sensing graph. It is proven that the state estimates of the proposed observer asymptotically converge to the actual plant states under arbitrarily changing, but connected, communication and sensing topologies. As a byproduct of this research, we also obtained a result on the location of eigenvalues, the spectrum, of the Laplacian for a family of graphs with self-loops.

Projective Synchronization of Chaotic Discrete Dynamical Systems via Linear State Error Feedback Control

Directory of Open Access Journals (Sweden)

Baogui Xin

2015-04-01

Full Text Available A projective synchronization scheme for a kind of n-dimensional discrete dynamical system is proposed by means of a linear feedback control technique. The scheme consists of master and slave discrete dynamical systems coupled by linear state error variables. A kind of novel 3-D chaotic discrete system is constructed, to which the test for chaos is applied. By using the stability principles of an upper or lower triangular matrix, two controllers for achieving projective synchronization are designed and illustrated with the novel systems. Lastly some numerical simulations are employed to validate the effectiveness of the proposed projective synchronization scheme.

Bistable states of TM polarized non-linear waves guided by symmetric layered structures

International Nuclear Information System (INIS)

Mihalache, D.

1985-04-01

Dispersion relations for TM polarized non-linear waves propagating in a symmetric single film optical waveguide are derived. The system consists of a layer of thickness d with dielectric constant epsilon 1 bounded at two sides by a non-linear medium characterized by the diagonal dielectric tensor epsilon 11 =epsilon 22 =epsilon 0 , epsilon 33 =epsilon 0 +α|E 3 | 2 , where E 3 is the normal electric field component. For sufficiently large d/lambda (lambda is the wavelength) we predict bistable states of both symmetric and antisymmetric modes provided that the power flow is the control parameter. (author)

Application of Constrained Linear MPC to a Spray Dryer

DEFF Research Database (Denmark)

Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

2014-01-01

In this paper we develop a linear model predictive control (MPC) algorithm for control of a two stage spray dryer. The states are estimated by a stationary Kalman filter. A non-linear first-principle engineering model is developed to simulate the spray drying process. The model is validated against...... experimental data and able to precisely predict the temperatures, the air humidity and the residual moisture in the dryer. The MPC controls these variables to the target and reject disturbances. Spray drying is a cost-effective method to evaporate water from liquid foods and produces a free flowing powder...

Continuous-variable quantum cloning of coherent states with phase-conjugate input modes using linear optics

International Nuclear Information System (INIS)

Chen, Haixia; Zhang, Jing

2007-01-01

We propose a scheme for continuous-variable quantum cloning of coherent states with phase-conjugate input modes using linear optics. The quantum cloning machine yields M identical optimal clones from N replicas of a coherent state and N replicas of its phase conjugate. This scheme can be straightforwardly implemented with the setups accessible at present since its optical implementation only employs simple linear optical elements and homodyne detection. Compared with the original scheme for continuous-variable quantum cloning with phase-conjugate input modes proposed by Cerf and Iblisdir [Phys. Rev. Lett. 87, 247903 (2001)], which utilized a nondegenerate optical parametric amplifier, our scheme loses the output of phase-conjugate clones and is regarded as irreversible quantum cloning

Study of the Convergence in State Estimators for LTI Systems with Event Detection

Directory of Open Access Journals (Sweden)

Juan C. Posada

2016-01-01

Full Text Available The methods frequently used to estimate the state of an LTI system require that the precise value of the output variable is known at all times, or at equidistant sampling times. In LTI systems, in which the output signal is measured through binary sensors (detectors, the traditional way of state observers design is not applicable even though the system has a complete observability matrix. This type of state observers design is known as passive. It is necessary, then, to introduce a new state estimation technique, which allows reckoning the state from the information of the variable’s crossing through a detector’s action threshold (switch. This paper seeks, therefore, to study the convergence in this type of estimators in finite time, allowing establishing, theoretically, whether some family of the proposed models can be estimated in a convergent way through the use of the estimation technique based on events.

Quantum teleportation of an arbitrary two-mode coherent state using only linear optics elements

International Nuclear Information System (INIS)

Ho Ngoc Phien; Nguyen Ba An

2008-01-01

We propose a linear optics scheme to teleport an arbitrary two-mode coherent state. The devices used are beam-splitters, phase-shifters and ideal photo-detectors capable of distinguishing between even and odd photon numbers. The scheme achieves faithful teleportation with a probability of 1/4. However, with additional use of an appropriate displacement operator, the teleported state can always be made near-faithful