Pointwise estimates of pseudo-differential operators
DEFF Research Database (Denmark)
Johnsen, Jon
As a new technique it is shown how general pseudo-differential operators can be estimated at arbitrary points in Euclidean space when acting on functions u with compact spectra.The estimate is a factorisation inequality, in which one factor is the Peetre–Fefferman–Stein maximal function of u......, whilst the other is a symbol factor carrying the whole information on the symbol. The symbol factor is estimated in terms of the spectral radius of u, so that the framework is well suited for Littlewood–Paley analysis. It is also shown how it gives easy access to results on polynomial bounds...... and estimates in Lp , including a new result for type 1,1-operators that they are always bounded on Lp -functions with compact spectra....
Pointwise estimates of pseudo-differential operators
DEFF Research Database (Denmark)
Johnsen, Jon
2011-01-01
As a new technique it is shown how general pseudo-differential operators can be estimated at arbitrary points in Euclidean space when acting on functions u with compact spectra. The estimate is a factorisation inequality, in which one factor is the Peetre–Fefferman–Stein maximal function of u......, whilst the other is a symbol factor carrying the whole information on the symbol. The symbol factor is estimated in terms of the spectral radius of u, so that the framework is well suited for Littlewood–Paley analysis. It is also shown how it gives easy access to results on polynomial bounds...... and estimates in Lp, including a new result for type 1, 1-operators that they are always bounded on Lp-functions with compact spectra....
Quantitative Pointwise Estimate of the Solution of the Linearized Boltzmann Equation
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
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.
Adaptive Spectral Doppler Estimation
DEFF Research Database (Denmark)
Gran, Fredrik; Jakobsson, Andreas; Jensen, Jørgen Arendt
2009-01-01
. The methods can also provide better quality of the estimated power spectral density (PSD) of the blood signal. Adaptive spectral estimation techniques are known to pro- vide good spectral resolution and contrast even when the ob- servation window is very short. The 2 adaptive techniques are tested......In this paper, 2 adaptive spectral estimation techniques are analyzed for spectral Doppler ultrasound. The purpose is to minimize the observation window needed to estimate the spectrogram to provide a better temporal resolution and gain more flexibility when designing the data acquisition sequence...... and compared with the averaged periodogram (Welch’s method). The blood power spectral capon (BPC) method is based on a standard minimum variance technique adapted to account for both averaging over slow-time and depth. The blood amplitude and phase estimation technique (BAPES) is based on finding a set...
Directory of Open Access Journals (Sweden)
W. Łenski
2015-01-01
Full Text Available The results generalizing some theorems on N, pnE, γ summability are shown. The same degrees of pointwise approximation as in earlier papers by weaker assumptions on considered functions and examined summability methods are obtained. From presented pointwise results, the estimation on norm approximation is derived. Some special cases as corollaries are also formulated.
Pointwise probability reinforcements for robust statistical inference.
Frénay, Benoît; Verleysen, Michel
2014-02-01
Statistical inference using machine learning techniques may be difficult with small datasets because of abnormally frequent data (AFDs). AFDs are observations that are much more frequent in the training sample that they should be, with respect to their theoretical probability, and include e.g. outliers. Estimates of parameters tend to be biased towards models which support such data. This paper proposes to introduce pointwise probability reinforcements (PPRs): the probability of each observation is reinforced by a PPR and a regularisation allows controlling the amount of reinforcement which compensates for AFDs. The proposed solution is very generic, since it can be used to robustify any statistical inference method which can be formulated as a likelihood maximisation. Experiments show that PPRs can be easily used to tackle regression, classification and projection: models are freed from the influence of outliers. Moreover, outliers can be filtered manually since an abnormality degree is obtained for each observation. Copyright © 2013 Elsevier Ltd. All rights reserved.
Pointwise convergence of Fourier series
Arias de Reyna, Juan
2002-01-01
This book contains a detailed exposition of Carleson-Hunt theorem following the proof of Carleson: to this day this is the only one giving better bounds. It points out the motivation of every step in the proof. Thus the Carleson-Hunt theorem becomes accessible to any analyst.The book also contains the first detailed exposition of the fine results of Hunt, Sjölin, Soria, etc on the convergence of Fourier Series. Its final chapters present original material. With both Fefferman's proof and the recent one of Lacey and Thiele in print, it becomes more important than ever to understand and compare these two related proofs with that of Carleson and Hunt. These alternative proofs do not yield all the results of the Carleson-Hunt proof. The intention of this monograph is to make Carleson's proof accessible to a wider audience, and to explain its consequences for the pointwise convergence of Fourier series for functions in spaces near $äcal Lü^1$, filling a well-known gap in the literature.
Adaptive vehicle motion estimation and prediction
Zhao, Liang; Thorpe, Chuck E.
1999-01-01
Accurate motion estimation and reliable maneuver prediction enable an automated car to react quickly and correctly to the rapid maneuvers of the other vehicles, and so allow safe and efficient navigation. In this paper, we present a car tracking system which provides motion estimation, maneuver prediction and detection of the tracked car. The three strategies employed - adaptive motion modeling, adaptive data sampling, and adaptive model switching probabilities - result in an adaptive interacting multiple model algorithm (AIMM). The experimental results on simulated and real data demonstrate that our tracking system is reliable, flexible, and robust. The adaptive tracking makes the system intelligent and useful in various autonomous driving tasks.
Rajaona, Harizo; Septier, François; Armand, Patrick; Delignon, Yves; Olry, Christophe; Albergel, Armand; Moussafir, Jacques
2015-12-01
In the eventuality of an accidental or intentional atmospheric release, the reconstruction of the source term using measurements from a set of sensors is an important and challenging inverse problem. A rapid and accurate estimation of the source allows faster and more efficient action for first-response teams, in addition to providing better damage assessment. This paper presents a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source. The release rate is evaluated analytically by using a Gaussian assumption on its prior distribution, and is enhanced with a positivity constraint to improve the estimation. The source location is obtained by the means of an advanced iterative Monte-Carlo technique called Adaptive Multiple Importance Sampling (AMIS), which uses a recycling process at each iteration to accelerate its convergence. The proposed methodology is tested using synthetic and real concentration data in the framework of the Fusion Field Trials 2007 (FFT-07) experiment. The quality of the obtained results is comparable to those coming from the Markov Chain Monte Carlo (MCMC) algorithm, a popular Bayesian method used for source estimation. Moreover, the adaptive processing of the AMIS provides a better sampling efficiency by reusing all the generated samples.
Energy-pointwise discrete ordinates transport methods
International Nuclear Information System (INIS)
Williams, M.L.; Asgari, M.; Tashakorri, R.
1997-01-01
A very brief description is given of a one-dimensional code, CENTRM, which computes a detailed, space-dependent flux spectrum in a pointwise-energy representation within the resolved resonance range. The code will become a component in the SCALE system to improve computation of self-shielded cross sections, thereby enhancing the accuracy of codes such as KENO. CENTRM uses discrete-ordinates transport theory with an arbitrary angular quadrature order and a Legendre expansion of scattering anisotropy for moderator materials and heavy nuclides. The CENTRM program provides capability to deterministically compute full energy range, space-dependent angular flux spectra, rigorously accounting for resonance fine-structure and scattering anisotropy effects
Adaptive Response Surface Techniques in Reliability Estimation
DEFF Research Database (Denmark)
Enevoldsen, I.; Faber, M. H.; Sørensen, John Dalsgaard
1993-01-01
Problems in connection with estimation of the reliability of a component modelled by a limit state function including noise or first order discontinuitics are considered. A gradient free adaptive response surface algorithm is developed. The algorithm applies second order polynomial surfaces...
Adaptive measurement selection for progressive damage estimation
Zhou, Wenfan; Kovvali, Narayan; Papandreou-Suppappola, Antonia; Chattopadhyay, Aditi; Peralta, Pedro
2011-04-01
Noise and interference in sensor measurements degrade the quality of data and have a negative impact on the performance of structural damage diagnosis systems. In this paper, a novel adaptive measurement screening approach is presented to automatically select the most informative measurements and use them intelligently for structural damage estimation. The method is implemented efficiently in a sequential Monte Carlo (SMC) setting using particle filtering. The noise suppression and improved damage estimation capability of the proposed method is demonstrated by an application to the problem of estimating progressive fatigue damage in an aluminum compact-tension (CT) sample using noisy PZT sensor measurements.
Error estimation and adaptivity for incompressible hyperelasticity
Whiteley, J.P.
2014-04-30
SUMMARY: A Galerkin FEM is developed for nonlinear, incompressible (hyper) elasticity that takes account of nonlinearities in both the strain tensor and the relationship between the strain tensor and the stress tensor. By using suitably defined linearised dual problems with appropriate boundary conditions, a posteriori error estimates are then derived for both linear functionals of the solution and linear functionals of the stress on a boundary, where Dirichlet boundary conditions are applied. A second, higher order method for calculating a linear functional of the stress on a Dirichlet boundary is also presented together with an a posteriori error estimator for this approach. An implementation for a 2D model problem with known solution, where the entries of the strain tensor exhibit large, rapid variations, demonstrates the accuracy and sharpness of the error estimators. Finally, using a selection of model problems, the a posteriori error estimate is shown to provide a basis for effective mesh adaptivity. © 2014 John Wiley & Sons, Ltd.
Raiche, Gilles; Blais, Jean-Guy
2009-01-01
In a computerized adaptive test, we would like to obtain an acceptable precision of the proficiency level estimate using an optimal number of items. Unfortunately, decreasing the number of items is accompanied by a certain degree of bias when the true proficiency level differs significantly from the a priori estimate. The authors suggest that it is possible to reduced the bias, and even the standard error of the estimate, by applying to each provisional estimation one or a combination of the following strategies: adaptive correction for bias proposed by Bock and Mislevy (1982), adaptive a priori estimate, and adaptive integration interval.
Rapid pointwise stabilization of vibrating strings and beams
Directory of Open Access Journals (Sweden)
Alia BARHOUMI
2009-11-01
Full Text Available Applying a general construction and using former results on the observability we prove, under rather general assumptions, a rapid pointwise stabilization of vibrating strings and beams.
The undefined function differs from the pointwise undefined function
Dosch, Walter (Prof.)
1993-01-01
The undefined function differs from the pointwise undefined function. - In: Joint Conference on Declarative Programming : Proceedings / Maria I. Sessa ... (eds.). - Salerno : Univ. degli Studi, 1995. - S. 257-268
Adaptive phase estimation with squeezed thermal light
DEFF Research Database (Denmark)
Berni, A. A.; Madsen, Lars Skovgaard; Lassen, Mikael Østergaard
2013-01-01
Summary form only given. The use of quantum states of light in optical interferometry improves the precision in the estimation of a phase shift, paving the way for applications in quantum metrology, computation and cryptography. Sub-shot noise phase sensing can for example be achieved by injecting...... investigate the performances of such protocol under the realistic assumption of thermalization of the probe state. Indeed, adaptive phase estimation schemes with squeezed states and Bayesian processing of homodyne data have been shown to be asymptotically optimal in the pure case, thus approaching the quantum...... Cramér-Rao bound. In our protocol we take advantage of the enhanced sensitivity of homodyne detection in proximity of the optimal phase which maximizes the homodyne Fisher information. A squeezed thermal probe state (signal) undergoes an unknown phase shift. The first estimation step involves...
Morales, Esteban; de Leon, John Mark S; Abdollahi, Niloufar; Yu, Fei; Nouri-Mahdavi, Kouros; Caprioli, Joseph
2016-03-01
The study was conducted to evaluate threshold smoothing algorithms to enhance prediction of the rates of visual field (VF) worsening in glaucoma. We studied 798 patients with primary open-angle glaucoma and 6 or more years of follow-up who underwent 8 or more VF examinations. Thresholds at each VF location for the first 4 years or first half of the follow-up time (whichever was greater) were smoothed with clusters defined by the nearest neighbor (NN), Garway-Heath, Glaucoma Hemifield Test (GHT), and weighting by the correlation of rates at all other VF locations. Thresholds were regressed with a pointwise exponential regression (PER) model and a pointwise linear regression (PLR) model. Smaller root mean square error (RMSE) values of the differences between the observed and the predicted thresholds at last two follow-ups indicated better model predictions. The mean (SD) follow-up times for the smoothing and prediction phase were 5.3 (1.5) and 10.5 (3.9) years. The mean RMSE values for the PER and PLR models were unsmoothed data, 6.09 and 6.55; NN, 3.40 and 3.42; Garway-Heath, 3.47 and 3.48; GHT, 3.57 and 3.74; and correlation of rates, 3.59 and 3.64. Smoothed VF data predicted better than unsmoothed data. Nearest neighbor provided the best predictions; PER also predicted consistently more accurately than PLR. Smoothing algorithms should be used when forecasting VF results with PER or PLR. The application of smoothing algorithms on VF data can improve forecasting in VF points to assist in treatment decisions.
Estimation of adaptive of bread spring wheat varieties
Directory of Open Access Journals (Sweden)
В. А. Власенко
2006-12-01
Full Text Available For estimation of adaptive of varieties it is offered to use the aggregate of estimations of stability and plasticity in the integrated index - rating of adaptive of varieties. The high rating of adaptive on the parameters of productivity have the varieties Elegia myronivska, Kolektyvna 3, Etud and Suita.
Pointwise Stabilization of a Hybrid System and Optimal Location of Actuator
International Nuclear Information System (INIS)
Ammari, Kais; Saidi, Abdelkader
2007-01-01
We consider a pointwise stabilization problem for a model arising in the control of noise. We prove that we have exponential stability for the low frequencies but not for the high frequencies. Thus, we give an explicit polynomial decay estimation at high frequencies that is valid for regular initial data while clarifying that the behavior of the constant which intervenes in this estimation there, functions as the frequency of cut. We propose a numerical approximation of the model and study numerically the best location of the actuator at low frequencies
Adaptive Estimation of Heteroscedastic Money Demand Model of Pakistan
Directory of Open Access Journals (Sweden)
Muhammad Aslam
2007-07-01
Full Text Available For the problem of estimation of Money demand model of Pakistan, money supply (M1 shows heteroscedasticity of the unknown form. For estimation of such model we compare two adaptive estimators with ordinary least squares estimator and show the attractive performance of the adaptive estimators, namely, nonparametric kernel estimator and nearest neighbour regression estimator. These comparisons are made on the basis standard errors of the estimated coefficients, standard error of regression, Akaike Information Criteria (AIC value, and the Durban-Watson statistic for autocorrelation. We further show that nearest neighbour regression estimator performs better when comparing with the other nonparametric kernel estimator.
A computer program for the pointwise functions generation
International Nuclear Information System (INIS)
Caldeira, Alexandre D.
1995-01-01
A computer program that was developed with the objective of generating pointwise functions, by a combination of tabulated values and/or mathematical expressions, to be used as weighting functions for nuclear data is presented. This simple program can be an important tool for researchers involved in group constants generation. (author). 5 refs, 2 figs
Learning With Mixed Hard/Soft Pointwise Constraints.
Gnecco, Giorgio; Gori, Marco; Melacci, Stefano; Sanguineti, Marcello
2015-09-01
A learning paradigm is proposed and investigated, in which the classical framework of learning from examples is enhanced by the introduction of hard pointwise constraints, i.e., constraints imposed on a finite set of examples that cannot be violated. Such constraints arise, e.g., when requiring coherent decisions of classifiers acting on different views of the same pattern. The classical examples of supervised learning, which can be violated at the cost of some penalization (quantified by the choice of a suitable loss function) play the role of soft pointwise constraints. Constrained variational calculus is exploited to derive a representer theorem that provides a description of the functional structure of the optimal solution to the proposed learning paradigm. It is shown that such an optimal solution can be represented in terms of a set of support constraints, which generalize the concept of support vectors and open the doors to a novel learning paradigm, called support constraint machines. The general theory is applied to derive the representation of the optimal solution to the problem of learning from hard linear pointwise constraints combined with soft pointwise constraints induced by supervised examples. In some cases, closed-form optimal solutions are obtained.
A Point-Wise Quantification of Asymmetry Using Deformation Fields
DEFF Research Database (Denmark)
Ólafsdóttir, Hildur; Lanche, Stephanie; Darvann, Tron Andre
2007-01-01
of the resulting displacement vectors on the left and right side of the symmetry plane, gives a point-wise measure of asymmetry. The asymmetry measure was applied to the study of Crouzon syndrome using Micro CT scans of genetically modified mice. Crouzon syndrome is characterised by the premature fusion of cranial...
Adaptive Nonparametric Variance Estimation for a Ratio Estimator ...
African Journals Online (AJOL)
Kernel estimators for smooth curves require modifications when estimating near end points of the support, both for practical and asymptotic reasons. The construction of such boundary kernels as solutions of variational problem is a difficult exercise. For estimating the error variance of a ratio estimator, we suggest an ...
Pointwise Partial Information Decomposition Using the Specificity and Ambiguity Lattices
Finn, Conor; Lizier, Joseph
2018-04-01
What are the distinct ways in which a set of predictor variables can provide information about a target variable? When does a variable provide unique information, when do variables share redundant information, and when do variables combine synergistically to provide complementary information? The redundancy lattice from the partial information decomposition of Williams and Beer provided a promising glimpse at the answer to these questions. However, this structure was constructed using a much criticised measure of redundant information, and despite sustained research, no completely satisfactory replacement measure has been proposed. In this paper, we take a different approach, applying the axiomatic derivation of the redundancy lattice to a single realisation from a set of discrete variables. To overcome the difficulty associated with signed pointwise mutual information, we apply this decomposition separately to the unsigned entropic components of pointwise mutual information which we refer to as the specificity and ambiguity. This yields a separate redundancy lattice for each component. Then based upon an operational interpretation of redundancy, we define measures of redundant specificity and ambiguity enabling us to evaluate the partial information atoms in each lattice. These atoms can be recombined to yield the sought-after multivariate information decomposition. We apply this framework to canonical examples from the literature and discuss the results and the various properties of the decomposition. In particular, the pointwise decomposition using specificity and ambiguity satisfies a chain rule over target variables, which provides new insights into the so-called two-bit-copy example.
Efficiently adapting graphical models for selectivity estimation
DEFF Research Database (Denmark)
Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.
2013-01-01
cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss...... in estimation accuracy. We show how to efficiently construct such a graphical model from the database using only two-way join queries, and we show how to perform selectivity estimation in a highly efficient manner. We integrate our algorithms into the PostgreSQL DBMS. Experimental results indicate...
Optimal Smoothing in Adaptive Location Estimation
Mammen, Enno; Park, Byeong U.
1997-01-01
In this paper higher order performance of kernel basedadaptive location estimators are considered. Optimalchoice of smoothing parameters is discussed and it isshown how much is lossed in efficiency by not knowingthe underlying translation density.
Adaptive estimation of binomial probabilities under misclassification
Albers, Willem/Wim; Veldman, H.J.
1984-01-01
If misclassification occurs the standard binomial estimator is usually seriously biased. It is known that an improvement can be achieved by using more than one observer in classifying the sample elements. Here it will be investigated which number of observers is optimal given the total number of
Adaptive Unscented Kalman Filter using Maximum Likelihood Estimation
DEFF Research Database (Denmark)
Mahmoudi, Zeinab; Poulsen, Niels Kjølstad; Madsen, Henrik
2017-01-01
The purpose of this study is to develop an adaptive unscented Kalman filter (UKF) by tuning the measurement noise covariance. We use the maximum likelihood estimation (MLE) and the covariance matching (CM) method to estimate the noise covariance. The multi-step prediction errors generated...
Adaptive Window Zero-Crossing-Based Instantaneous Frequency Estimation
Directory of Open Access Journals (Sweden)
Sekhar S Chandra
2004-01-01
Full Text Available We address the problem of estimating instantaneous frequency (IF of a real-valued constant amplitude time-varying sinusoid. Estimation of polynomial IF is formulated using the zero-crossings of the signal. We propose an algorithm to estimate nonpolynomial IF by local approximation using a low-order polynomial, over a short segment of the signal. This involves the choice of window length to minimize the mean square error (MSE. The optimal window length found by directly minimizing the MSE is a function of the higher-order derivatives of the IF which are not available a priori. However, an optimum solution is formulated using an adaptive window technique based on the concept of intersection of confidence intervals. The adaptive algorithm enables minimum MSE-IF (MMSE-IF estimation without requiring a priori information about the IF. Simulation results show that the adaptive window zero-crossing-based IF estimation method is superior to fixed window methods and is also better than adaptive spectrogram and adaptive Wigner-Ville distribution (WVD-based IF estimators for different signal-to-noise ratio (SNR.
Content Adaptive True Motion Estimator for H.264 Video Compression
Directory of Open Access Journals (Sweden)
P. Kulla
2007-12-01
Full Text Available Content adaptive true motion estimator for H.264 video coding is a fast block-based matching estimator with implemented multi-stage approach to estimate motion fields between two image frames. It considers the theory of 3D scene objects projection into 2D image plane for selection of motion vector candidates from the higher stages. The stages of the algorithm and its hierarchy are defined upon motion estimation reliability measurement (image blocks including two different directions of spatial gradient, blocks with one dominant spatial gradient and blocks including minimal spatial gradient. Parameters of the image classification into stages are set adaptively upon image structure. Due to search strategy are the estimated motion fields more corresponding to a true motion in an image sequence as in the case of conventional motion estimation algorithms that use fixed sets of motion vector candidates from tight neighborhood.
Multiple Estimation Architecture in Discrete-Time Adaptive Mixing Control
Directory of Open Access Journals (Sweden)
Simone Baldi
2013-05-01
Full Text Available Adaptive mixing control (AMC is a recently developed control scheme for uncertain plants, where the control action coming from a bank of precomputed controller is mixed based on the parameter estimates generated by an on-line parameter estimator. Even if the stability of the control scheme, also in the presence of modeling errors and disturbances, has been shown analytically, its transient performance might be sensitive to the initial conditions of the parameter estimator. In particular, for some initial conditions, transient oscillations may not be acceptable in practical applications. In order to account for such a possible phenomenon and to improve the learning capability of the adaptive scheme, in this paper a new mixing architecture is developed, involving the use of parallel parameter estimators, or multi-estimators, each one working on a small subset of the uncertainty set. A supervisory logic, using performance signals based on the past and present estimation error, selects the parameter estimate to determine the mixing of the controllers. The stability and robustness properties of the resulting approach, referred to as multi-estimator adaptive mixing control (Multi-AMC, are analytically established. Besides, extensive simulations demonstrate that the scheme improves the transient performance of the original AMC with a single estimator. The control scheme and the analysis are carried out in a discrete-time framework, for easier implementation of the method in digital control.
Demonstrating Heisenberg-limited unambiguous phase estimation without adaptive measurements
International Nuclear Information System (INIS)
Higgins, B L; Wiseman, H M; Pryde, G J; Berry, D W; Bartlett, S D; Mitchell, M W
2009-01-01
We derive, and experimentally demonstrate, an interferometric scheme for unambiguous phase estimation with precision scaling at the Heisenberg limit that does not require adaptive measurements. That is, with no prior knowledge of the phase, we can obtain an estimate of the phase with a standard deviation that is only a small constant factor larger than the minimum physically allowed value. Our scheme resolves the phase ambiguity that exists when multiple passes through a phase shift, or NOON states, are used to obtain improved phase resolution. Like a recently introduced adaptive technique (Higgins et al 2007 Nature 450 393), our experiment uses multiple applications of the phase shift on single photons. By not requiring adaptive measurements, but rather using a predetermined measurement sequence, the present scheme is both conceptually simpler and significantly easier to implement. Additionally, we demonstrate a simplified adaptive scheme that also surpasses the standard quantum limit for single passes.
Adaptive Methods for Permeability Estimation and Smart Well Management
Energy Technology Data Exchange (ETDEWEB)
Lien, Martha Oekland
2005-04-01
The main focus of this thesis is on adaptive regularization methods. We consider two different applications, the inverse problem of absolute permeability estimation and the optimal control problem of estimating smart well management. Reliable estimates of absolute permeability are crucial in order to develop a mathematical description of an oil reservoir. Due to the nature of most oil reservoirs, mainly indirect measurements are available. In this work, dynamic production data from wells are considered. More specifically, we have investigated into the resolution power of pressure data for permeability estimation. The inversion of production data into permeability estimates constitutes a severely ill-posed problem. Hence, regularization techniques are required. In this work, deterministic regularization based on adaptive zonation is considered, i.e. a solution approach with adaptive multiscale estimation in conjunction with level set estimation is developed for coarse scale permeability estimation. A good mathematical reservoir model is a valuable tool for future production planning. Recent developments within well technology have given us smart wells, which yield increased flexibility in the reservoir management. In this work, we investigate into the problem of finding the optimal smart well management by means of hierarchical regularization techniques based on multiscale parameterization and refinement indicators. The thesis is divided into two main parts, where Part I gives a theoretical background for a collection of research papers that has been written by the candidate in collaboration with others. These constitutes the most important part of the thesis, and are presented in Part II. A brief outline of the thesis follows below. Numerical aspects concerning calculations of derivatives will also be discussed. Based on the introduction to regularization given in Chapter 2, methods for multiscale zonation, i.e. adaptive multiscale estimation and refinement
Adaptive distributed parameter and input estimation in linear parabolic PDEs
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.
Pointwise convergence and Ascoli theorems for nearness spaces
Directory of Open Access Journals (Sweden)
Zhanbo Yang
2009-04-01
Full Text Available We first study subspaces and product spaces in the context of nearness spaces and prove that U-N spaces, C-N spaces, PN spaces and totally bounded nearness spaces are nearness hereditary; T-N spaces and compact nearness spaces are N-closed hereditary. We prove that N2 plus compact implies N-closed subsets. We prove that totally bounded, compact and N2 are productive. We generalize the concepts of neighborhood systems into the nearness spaces and prove that the nearness neighborhood systems are consistent with existing concepts of neighborhood systems in topological spaces, uniform spaces and proximity spaces respectively when considered in the respective sub-categories. We prove that a net of functions is convergent under the pointwise convergent nearness structure if and only if its cross-section at each point is convergent. We have also proved two Ascoli-Arzelà type of theorems.
On Using Exponential Parameter Estimators with an Adaptive Controller
Patre, Parag; Joshi, Suresh M.
2011-01-01
Typical adaptive controllers are restricted to using a specific update law to generate parameter estimates. This paper investigates the possibility of using any exponential parameter estimator with an adaptive controller such that the system tracks a desired trajectory. The goal is to provide flexibility in choosing any update law suitable for a given application. The development relies on a previously developed concept of controller/update law modularity in the adaptive control literature, and the use of a converse Lyapunov-like theorem. Stability analysis is presented to derive gain conditions under which this is possible, and inferences are made about the tracking error performance. The development is based on a class of Euler-Lagrange systems that are used to model various engineering systems including space robots and manipulators.
About an adaptively weighted Kaplan-Meier estimate.
Plante, Jean-François
2009-09-01
The minimum averaged mean squared error nonparametric adaptive weights use data from m possibly different populations to infer about one population of interest. The definition of these weights is based on the properties of the empirical distribution function. We use the Kaplan-Meier estimate to let the weights accommodate right-censored data and use them to define the weighted Kaplan-Meier estimate. The proposed estimate is smoother than the usual Kaplan-Meier estimate and converges uniformly in probability to the target distribution. Simulations show that the performances of the weighted Kaplan-Meier estimate on finite samples exceed that of the usual Kaplan-Meier estimate. A case study is also presented.
Blood velocity estimation using ultrasound and spectral iterative adaptive approaches
DEFF Research Database (Denmark)
Gudmundson, Erik; Jakobsson, Andreas; Jensen, Jørgen Arendt
2011-01-01
-mode images are interleaved with the Doppler emissions. Furthermore, the techniques are shown, using both simplified and more realistic Field II simulations as well as in vivo data, to outperform current state-of-the-art techniques, allowing for accurate estimation of the blood velocity spectrum using only 30......This paper proposes two novel iterative data-adaptive spectral estimation techniques for blood velocity estimation using medical ultrasound scanners. The techniques make no assumption on the sampling pattern of the emissions or the depth samples, allowing for duplex mode transmissions where B...
Adaptive distributed parameter and input estimation in linear parabolic PDEs
Mechhoud, Sarra
2016-01-01
In this paper, we discuss the on-line estimation of distributed source term, diffusion, and reaction coefficients of a linear parabolic partial differential equation using both distributed and interior-point measurements. First, new sufficient identifiability conditions of the input and the parameter simultaneous estimation are stated. Then, by means of Lyapunov-based design, an adaptive estimator is derived in the infinite-dimensional framework. It consists of a state observer and gradient-based parameter and input adaptation laws. The parameter convergence depends on the plant signal richness assumption, whereas the state convergence is established using a Lyapunov approach. The results of the paper are illustrated by simulation on tokamak plasma heat transport model using simulated data.
Adaptive Motion Estimation Processor for Autonomous Video Devices
Directory of Open Access Journals (Sweden)
Dias T
2007-01-01
Full Text Available Motion estimation is the most demanding operation of a video encoder, corresponding to at least 80% of the overall computational cost. As a consequence, with the proliferation of autonomous and portable handheld devices that support digital video coding, data-adaptive motion estimation algorithms have been required to dynamically configure the search pattern not only to avoid unnecessary computations and memory accesses but also to save energy. This paper proposes an application-specific instruction set processor (ASIP to implement data-adaptive motion estimation algorithms that is characterized by a specialized datapath and a minimum and optimized instruction set. Due to its low-power nature, this architecture is highly suitable to develop motion estimators for portable, mobile, and battery-supplied devices. Based on the proposed architecture and the considered adaptive algorithms, several motion estimators were synthesized both for a Virtex-II Pro XC2VP30 FPGA from Xilinx, integrated within an ML310 development platform, and using a StdCell library based on a 0.18 μm CMOS process. Experimental results show that the proposed architecture is able to estimate motion vectors in real time for QCIF and CIF video sequences with a very low-power consumption. Moreover, it is also able to adapt the operation to the available energy level in runtime. By adjusting the search pattern and setting up a more convenient operating frequency, it can change the power consumption in the interval between 1.6 mW and 15 mW.
Parameter Estimation Analysis for Hybrid Adaptive Fault Tolerant Control
Eshak, Peter B.
Research efforts have increased in recent years toward the development of intelligent fault tolerant control laws, which are capable of helping the pilot to safely maintain aircraft control at post failure conditions. Researchers at West Virginia University (WVU) have been actively involved in the development of fault tolerant adaptive control laws in all three major categories: direct, indirect, and hybrid. The first implemented design to provide adaptation was a direct adaptive controller, which used artificial neural networks to generate augmentation commands in order to reduce the modeling error. Indirect adaptive laws were implemented in another controller, which utilized online PID to estimate and update the controller parameter. Finally, a new controller design was introduced, which integrated both direct and indirect control laws. This controller is known as hybrid adaptive controller. This last control design outperformed the two earlier designs in terms of less NNs effort and better tracking quality. The performance of online PID has an important role in the quality of the hybrid controller; therefore, the quality of the estimation will be of a great importance. Unfortunately, PID is not perfect and the online estimation process has some inherited issues; the online PID estimates are primarily affected by delays and biases. In order to ensure updating reliable estimates to the controller, the estimator consumes some time to converge. Moreover, the estimator will often converge to a biased value. This thesis conducts a sensitivity analysis for the estimation issues, delay and bias, and their effect on the tracking quality. In addition, the performance of the hybrid controller as compared to direct adaptive controller is explored. In order to serve this purpose, a simulation environment in MATLAB/SIMULINK has been created. The simulation environment is customized to provide the user with the flexibility to add different combinations of biases and delays to
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems.
Smith, J E
2012-01-01
Among the most promising and active research areas in heuristic optimisation is the field of adaptive memetic algorithms (AMAs). These gain much of their reported robustness by adapting the probability with which each of a set of local improvement operators is applied, according to an estimate of their current value to the search process. This paper addresses the issue of how the current value should be estimated. Assuming the estimate occurs over several applications of a meme, we consider whether the extreme or mean improvements should be used, and whether this aggregation should be global, or local to some part of the solution space. To investigate these issues, we use the well-established COMA framework that coevolves the specification of a population of memes (representing different local search algorithms) alongside a population of candidate solutions to the problem at hand. Two very different memetic algorithms are considered: the first using adaptive operator pursuit to adjust the probabilities of applying a fixed set of memes, and a second which applies genetic operators to dynamically adapt and create memes and their functional definitions. For the latter, especially on combinatorial problems, credit assignment mechanisms based on historical records, or on notions of landscape locality, will have limited application, and it is necessary to estimate the value of a meme via some form of sampling. The results on a set of binary encoded combinatorial problems show that both methods are very effective, and that for some problems it is necessary to use thousands of variables in order to tease apart the differences between different reward schemes. However, for both memetic algorithms, a significant pattern emerges that reward based on mean improvement is better than that based on extreme improvement. This contradicts recent findings from adapting the parameters of operators involved in global evolutionary search. The results also show that local reward schemes
A Pointwise Dimension Analysis of the Las Campanas Redshift Survey
Best, J. S.
1999-12-01
The modern motivation for fractal geometry may best be summed up by this quote of Benoit Mandelbrot: ``Mountains are not cones, clouds are not spheres, coastlines are not circles, and bark is not smooth, nor does lightning travel in a straight line.'' Fractals are, in simplest terms, ``objects which are (approximately) self-similar on all scales.'' The renewed modern interest in fractals has found as one of its applications the study of large-scale structure, giving a quantitative descriptive scheme to ideas that had been expressed qualitatively as early as the 1920s. This paper presents the preliminary results of an analysis of the structure of the Las Campanas Redshift Survey, or LCRS. LCRS is an approximately 26000 galaxy survey (surveyed as six declination slices) that has been studied extensively over the past few years, with an eye towards understanding large-scale structure. For this analysis, I have used the pointwise dimension, an easy-to-apply fractal statistic which has been previously used to study cluster interiors, galactic distributions, and cluster distributions. The present analysis has been performed to serve as a guide for the study of future large redshift surveys. This research has been funded by National Science Foundation grant AST-9808608.
An Adaptive Motion Estimation Scheme for Video Coding
Directory of Open Access Journals (Sweden)
Pengyu Liu
2014-01-01
Full Text Available The unsymmetrical-cross multihexagon-grid search (UMHexagonS is one of the best fast Motion Estimation (ME algorithms in video encoding software. It achieves an excellent coding performance by using hybrid block matching search pattern and multiple initial search point predictors at the cost of the computational complexity of ME increased. Reducing time consuming of ME is one of the key factors to improve video coding efficiency. In this paper, we propose an adaptive motion estimation scheme to further reduce the calculation redundancy of UMHexagonS. Firstly, new motion estimation search patterns have been designed according to the statistical results of motion vector (MV distribution information. Then, design a MV distribution prediction method, including prediction of the size of MV and the direction of MV. At last, according to the MV distribution prediction results, achieve self-adaptive subregional searching by the new estimation search patterns. Experimental results show that more than 50% of total search points are dramatically reduced compared to the UMHexagonS algorithm in JM 18.4 of H.264/AVC. As a result, the proposed algorithm scheme can save the ME time up to 20.86% while the rate-distortion performance is not compromised.
Czech Academy of Sciences Publication Activity Database
Světlák, M.; Bob, P.; Roman, R.; Ježek, S.; Damborská, A.; Chládek, Jan; Shaw, D. J.; Kukleta, M.
2013-01-01
Roč. 62, č. 6 (2013), s. 711-719 ISSN 0862-8408 Institutional support: RVO:68081731 Keywords : electrodermal activity * pointwise trasinformation * autonomic nervous system * asymmetry * stress Subject RIV: CE - Biochemistry Impact factor: 1.487, year: 2013
An Iterative Adaptive Approach for Blood Velocity Estimation Using Ultrasound
DEFF Research Database (Denmark)
Gudmundson, Erik; Jakobsson, Andreas; Jensen, Jørgen Arendt
2010-01-01
This paper proposes a novel iterative data-adaptive spectral estimation technique for blood velocity estimation using medical ultrasound scanners. The technique makes no assumption on the sampling pattern of the slow-time or the fast-time samples, allowing for duplex mode transmissions where B......-mode images are interleaved with the Doppler emissions. Furthermore, the technique is shown, using both simplified and more realistic Field II simulations, to outperform current state-of-the-art techniques, allowing for accurate estimation of the blood velocity spectrum using only 30% of the transmissions......, thereby allowing for the examination of two separate vessel regions while retaining an adequate updating rate of the B-mode images. In addition, the proposed method also allows for more flexible transmission patterns, as well as exhibits fewer spectral artifacts as compared to earlier techniques....
Robust estimation of adaptive tensors of curvature by tensor voting.
Tong, Wai-Shun; Tang, Chi-Keung
2005-03-01
Although curvature estimation from a given mesh or regularly sampled point set is a well-studied problem, it is still challenging when the input consists of a cloud of unstructured points corrupted by misalignment error and outlier noise. Such input is ubiquitous in computer vision. In this paper, we propose a three-pass tensor voting algorithm to robustly estimate curvature tensors, from which accurate principal curvatures and directions can be calculated. Our quantitative estimation is an improvement over the previous two-pass algorithm, where only qualitative curvature estimation (sign of Gaussian curvature) is performed. To overcome misalignment errors, our improved method automatically corrects input point locations at subvoxel precision, which also rejects outliers that are uncorrectable. To adapt to different scales locally, we define the RadiusHit of a curvature tensor to quantify estimation accuracy and applicability. Our curvature estimation algorithm has been proven with detailed quantitative experiments, performing better in a variety of standard error metrics (percentage error in curvature magnitudes, absolute angle difference in curvature direction) in the presence of a large amount of misalignment noise.
Adaptive Distributed Video Coding with Correlation Estimation using Expectation Propagation.
Cui, Lijuan; Wang, Shuang; Jiang, Xiaoqian; Cheng, Samuel
2012-10-15
Distributed video coding (DVC) is rapidly increasing in popularity by the way of shifting the complexity from encoder to decoder, whereas no compression performance degrades, at least in theory. In contrast with conventional video codecs, the inter-frame correlation in DVC is explored at decoder based on the received syndromes of Wyner-Ziv (WZ) frame and side information (SI) frame generated from other frames available only at decoder. However, the ultimate decoding performances of DVC are based on the assumption that the perfect knowledge of correlation statistic between WZ and SI frames should be available at decoder. Therefore, the ability of obtaining a good statistical correlation estimate is becoming increasingly important in practical DVC implementations. Generally, the existing correlation estimation methods in DVC can be classified into two main types: pre-estimation where estimation starts before decoding and on-the-fly (OTF) estimation where estimation can be refined iteratively during decoding. As potential changes between frames might be unpredictable or dynamical, OTF estimation methods usually outperforms pre-estimation techniques with the cost of increased decoding complexity (e.g., sampling methods). In this paper, we propose a low complexity adaptive DVC scheme using expectation propagation (EP), where correlation estimation is performed OTF as it is carried out jointly with decoding of the factor graph-based DVC code. Among different approximate inference methods, EP generally offers better tradeoff between accuracy and complexity. Experimental results show that our proposed scheme outperforms the benchmark state-of-the-art DISCOVER codec and other cases without correlation tracking, and achieves comparable decoding performance but with significantly low complexity comparing with sampling method.
Adaptive distributed video coding with correlation estimation using expectation propagation
Cui, Lijuan; Wang, Shuang; Jiang, Xiaoqian; Cheng, Samuel
2012-10-01
Distributed video coding (DVC) is rapidly increasing in popularity by the way of shifting the complexity from encoder to decoder, whereas no compression performance degrades, at least in theory. In contrast with conventional video codecs, the inter-frame correlation in DVC is explored at decoder based on the received syndromes of Wyner-Ziv (WZ) frame and side information (SI) frame generated from other frames available only at decoder. However, the ultimate decoding performances of DVC are based on the assumption that the perfect knowledge of correlation statistic between WZ and SI frames should be available at decoder. Therefore, the ability of obtaining a good statistical correlation estimate is becoming increasingly important in practical DVC implementations. Generally, the existing correlation estimation methods in DVC can be classified into two main types: pre-estimation where estimation starts before decoding and on-the-fly (OTF) estimation where estimation can be refined iteratively during decoding. As potential changes between frames might be unpredictable or dynamical, OTF estimation methods usually outperforms pre-estimation techniques with the cost of increased decoding complexity (e.g., sampling methods). In this paper, we propose a low complexity adaptive DVC scheme using expectation propagation (EP), where correlation estimation is performed OTF as it is carried out jointly with decoding of the factor graph-based DVC code. Among different approximate inference methods, EP generally offers better tradeoff between accuracy and complexity. Experimental results show that our proposed scheme outperforms the benchmark state-of-the-art DISCOVER codec and other cases without correlation tracking, and achieves comparable decoding performance but with significantly low complexity comparing with sampling method.
Adaptive estimation and discrimination of Holevo-Werner channels
Directory of Open Access Journals (Sweden)
Cope Thomas P. W.
2017-12-01
Full Text Available The class of quantum states known as Werner states have several interesting properties, which often serve to illuminate unusual properties of quantum information. Closely related to these states are the Holevo- Werner channels whose Choi matrices are Werner states. Exploiting the fact that these channels are teleportation covariant, and therefore simulable by teleportation, we compute the ultimate precision in the adaptive estimation of their channel-defining parameter. Similarly, we bound the minimum error probability affecting the adaptive discrimination of any two of these channels. In this case, we prove an analytical formula for the quantum Chernoff bound which also has a direct counterpart for the class of depolarizing channels. Our work exploits previous methods established in [Pirandola and Lupo, PRL 118, 100502 (2017] to set the metrological limits associated with this interesting class of quantum channels at any finite dimension.
ALTERNATE PURSUIT WITH THREE PARTICIPANTS (THE CASE OF POINTWISE MEETING
Directory of Open Access Journals (Sweden)
Viktor Shiryayev
2016-03-01
Full Text Available The issues connected with alternate pursuit of escapees group are considered in a number of papers. So in papers [1–3] the solution of the problem has been found in the assumption that the next meeting is selected at the initial time (by the program and the players are moving straight. In paper [4] the solution of the task using the approach of R. Isaacs is given. In paper [5] the choice opportunities of the next meeting ( both software and positional are considered. The article deals with a simple differential game on the pursuer plane P and the coalition of two escapees E={E1,E2}.The movement of all the players are assumed as inertialess. The pursuer speed P exceeds the speed of each of the escapees. The targets, physical abilities and the exact location of each other in any moment of the game are known to all players. The price of the coalition (the pursuer P is (minus the total time spent by the pursuer P on the pointwise meeting with E1 and E2. A coincidence of pursuer and escapee location is meant under the meeting. The choice at the initial time of the persecution is supposed as given (software selectable regular meeting. The limit of the security zone of the second escapee has been found. A geometric approach is used in the problem solving. The resulting system of equations is solved numerically by means of computer algebra, in particular through the Wolfram Mathematics. After defining the boundary of the second escapee security zone one can study the game between the pursuer Р and three escapees acting in concord (the first escapee is eliminated from the game.
Adaptive optimisation-offline cyber attack on remote state estimator
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.
Data adaptive estimation of transversal blood flow velocities
DEFF Research Database (Denmark)
Pirnia, E.; Jakobsson, A.; Gudmundson, E.
2014-01-01
the transversal blood flow. In this paper, we propose a novel data-adaptive blood flow estimator exploiting this modulation scheme. Using realistic Field II simulations, the proposed estimator is shown to achieve a notable performance improvement as compared to current state-of-the-art techniques.......The examination of blood flow inside the body may yield important information about vascular anomalies, such as possible indications of, for example, stenosis. Current Medical ultrasound systems suffer from only allowing for measuring the blood flow velocity along the direction of irradiation......, posing natural difficulties due to the complex behaviour of blood flow, and due to the natural orientation of most blood vessels. Recently, a transversal modulation scheme was introduced to induce also an oscillation along the transversal direction, thereby allowing for the measurement of also...
Raiche, Gilles; Blais, Jean-Guy
In a computerized adaptive test (CAT), it would be desirable to obtain an acceptable precision of the proficiency level estimate using an optimal number of items. Decreasing the number of items is accompanied, however, by a certain degree of bias when the true proficiency level differs significantly from the a priori estimate. G. Raiche (2000) has…
A Balanced Approach to Adaptive Probability Density Estimation
Directory of Open Access Journals (Sweden)
Julio A. Kovacs
2017-04-01
Full Text Available Our development of a Fast (Mutual Information Matching (FIM of molecular dynamics time series data led us to the general problem of how to accurately estimate the probability density function of a random variable, especially in cases of very uneven samples. Here, we propose a novel Balanced Adaptive Density Estimation (BADE method that effectively optimizes the amount of smoothing at each point. To do this, BADE relies on an efficient nearest-neighbor search which results in good scaling for large data sizes. Our tests on simulated data show that BADE exhibits equal or better accuracy than existing methods, and visual tests on univariate and bivariate experimental data show that the results are also aesthetically pleasing. This is due in part to the use of a visual criterion for setting the smoothing level of the density estimate. Our results suggest that BADE offers an attractive new take on the fundamental density estimation problem in statistics. We have applied it on molecular dynamics simulations of membrane pore formation. We also expect BADE to be generally useful for low-dimensional applications in other statistical application domains such as bioinformatics, signal processing and econometrics.
System health monitoring using multiple-model adaptive estimation techniques
Sifford, Stanley Ryan
Monitoring system health for fault detection and diagnosis by tracking system parameters concurrently with state estimates is approached using a new multiple-model adaptive estimation (MMAE) method. This novel method is called GRid-based Adaptive Parameter Estimation (GRAPE). GRAPE expands existing MMAE methods by using new techniques to sample the parameter space. GRAPE expands on MMAE with the hypothesis that sample models can be applied and resampled without relying on a predefined set of models. GRAPE is initially implemented in a linear framework using Kalman filter models. A more generalized GRAPE formulation is presented using extended Kalman filter (EKF) models to represent nonlinear systems. GRAPE can handle both time invariant and time varying systems as it is designed to track parameter changes. Two techniques are presented to generate parameter samples for the parallel filter models. The first approach is called selected grid-based stratification (SGBS). SGBS divides the parameter space into equally spaced strata. The second approach uses Latin Hypercube Sampling (LHS) to determine the parameter locations and minimize the total number of required models. LHS is particularly useful when the parameter dimensions grow. Adding more parameters does not require the model count to increase for LHS. Each resample is independent of the prior sample set other than the location of the parameter estimate. SGBS and LHS can be used for both the initial sample and subsequent resamples. Furthermore, resamples are not required to use the same technique. Both techniques are demonstrated for both linear and nonlinear frameworks. The GRAPE framework further formalizes the parameter tracking process through a general approach for nonlinear systems. These additional methods allow GRAPE to either narrow the focus to converged values within a parameter range or expand the range in the appropriate direction to track the parameters outside the current parameter range boundary
Fused Adaptive Lasso for Spatial and Temporal Quantile Function Estimation
Sun, Ying
2015-09-01
Quantile functions are important in characterizing the entire probability distribution of a random variable, especially when the tail of a skewed distribution is of interest. This article introduces new quantile function estimators for spatial and temporal data with a fused adaptive Lasso penalty to accommodate the dependence in space and time. This method penalizes the difference among neighboring quantiles, hence it is desirable for applications with features ordered in time or space without replicated observations. The theoretical properties are investigated and the performances of the proposed methods are evaluated by simulations. The proposed method is applied to particulate matter (PM) data from the Community Multiscale Air Quality (CMAQ) model to characterize the upper quantiles, which are crucial for studying spatial association between PM concentrations and adverse human health effects. © 2016 American Statistical Association and the American Society for Quality.
In-vivo studies of new vector velocity and adaptive spectral estimators in medical ultrasound
DEFF Research Database (Denmark)
Hansen, Kristoffer Lindskov
2010-01-01
New ultrasound techniques for blood flow estimation have been investigated in-vivo. These are vector velocity estimators (Transverse Oscillation, Synthetic Transmit Aperture, Directional Beamforming and Plane Wave Excitation) and adaptive spectral estimators (Blood spectral Power Capon and Blood...
DEFF Research Database (Denmark)
Kock, Anders Bredahl; Callot, Laurent
We show that the adaptive Lasso (aLasso) and the adaptive group Lasso (agLasso) are oracle efficient in stationary vector autoregressions where the number of parameters per equation is smaller than the number of observations. In particular, this means that the parameters are estimated consistently...
Pointwise Multipliers on Spaces of Homogeneous Type in the Sense of Coifman and Weiss
Directory of Open Access Journals (Sweden)
Yanchang Han
2014-01-01
homogeneous type in the sense of Coifman and Weiss, pointwise multipliers of inhomogeneous Besov and Triebel-Lizorkin spaces are obtained. We make no additional assumptions on the quasi-metric or the doubling measure. Hence, the results of this paper extend earlier related results to a more general setting.
Energy Technology Data Exchange (ETDEWEB)
Ben Youssef, C; Dahhou, B; Roux, G [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Rols, J L [Institut National des Sciences Appliquees (INSA), 31 - Toulouse (France)
1996-12-31
Controlling the process of a fixed bed bioreactor imply solving filtering and adaptative control problems. Estimation processes have been developed for unmeasurable parameters. An adaptative non linear control has been built, instead of conventional approaches trying to linearize the system and apply a linear control system. (D.L.) 10 refs.
Fault Adaptive Control of Overactuated Systems Using Prognostic Estimation
National Aeronautics and Space Administration — Most fault adaptive control research addresses the preservation of system stability or functionality in the presence of a specific failure (fault). This paper...
Fused Adaptive Lasso for Spatial and Temporal Quantile Function Estimation
Sun, Ying; Wang, Huixia J.; Fuentes, Montserrat
2015-01-01
and temporal data with a fused adaptive Lasso penalty to accommodate the dependence in space and time. This method penalizes the difference among neighboring quantiles, hence it is desirable for applications with features ordered in time or space without
Energy Technology Data Exchange (ETDEWEB)
Eldred, Michael Scott; Subia, Samuel Ramirez; Neckels, David; Hopkins, Matthew Morgan; Notz, Patrick K.; Adams, Brian M.; Carnes, Brian; Wittwer, Jonathan W.; Bichon, Barron J.; Copps, Kevin D.
2006-10-01
This report documents the results for an FY06 ASC Algorithms Level 2 milestone combining error estimation and adaptivity, uncertainty quantification, and probabilistic design capabilities applied to the analysis and design of bistable MEMS. Through the use of error estimation and adaptive mesh refinement, solution verification can be performed in an automated and parameter-adaptive manner. The resulting uncertainty analysis and probabilistic design studies are shown to be more accurate, efficient, reliable, and convenient.
A qualitative assessment of climate adaptation options and some estimates of adaptation costs
International Nuclear Information System (INIS)
Van Ierland, E.C.; De Bruin, K.; Dellink, R.B.; Ruijs, A.
2006-12-01
The Routeplanner project aims to provide a 'systematic assessment' of potential adaptation options to respond to climate change in the Netherlands in connection to spatial planning. The study is the result of a policy oriented project that took place between May and September 2006. The aim of the current study is to provide a 'qualitative assessment' of the direct and indirect effects of adaptation options and to provide an assessment of some of the costs and benefits of adaptation options. The present report presents and summarizes the results of all phases of the study: an inventory of adaptation options, a qualitative assessment of the effects of the adaptation options for the Netherlands in the long run, a database which allows to rank the various options according to a set of criteria and a relative ranking on the basis of these criteria. Finally, the report also contains the best available information on costs and benefits of various adaptation options.
Adaptive Flight Envelope Estimation and Protection, Phase I
National Aeronautics and Space Administration — Impact Technologies, in collaboration with the Georgia Institute of Technology, proposes to develop and demonstrate an innovative flight envelope estimation and...
Forecasting the international diffusion of innovations: An adaptive estimation approach
Y.M. van Everdingen (Yvonne); W.B. Aghina (Wouter)
2003-01-01
textabstractWe introduce an international, adaptive diffusion model that can be used to forecast the cross-national diffusion of an innovation at early stages of the diffusion curve. We model the mutual influence between the diffusion processes in the different social systems (countries) by mixing
A qualitative assessment of climate adaptation options and some estimates of adaptation costs
International Nuclear Information System (INIS)
Van Ierland, E.C.; De Bruin, K.; Dellink, R.B.; Ruijs, A.
2007-02-01
The Routeplanner project aims to provide a 'systematic assessment' of potential adaptation options to respond to climate change in the Netherlands in connection to spatial planning. The study is the result of a policy oriented project that took place between May and September 2006. The aim of the current study is to provide a 'qualitative assessment' of the direct and indirect effects of adaptation options and to provide an assessment of some of the costs and benefits of adaptation options. The present report presents and summarizes the results of all phases of the study: an inventory of adaptation options, a qualitative assessment of the effects of the adaptation options for the Netherlands in the long run, a database which allows to rank the various options according to a set of criteria and a relative ranking on the basis of these criteria. Finally, the report also contains the best available information on costs and benefits of various adaptation options. However, while conducting the study the project team observed that reliable information in this respect is in many cases still lacking and an urgent need exists for more detailed studies on costs and benefits of adaptation options and the design of the best options to cope with climate change
Adaptive Disturbance Estimation for Offset-Free SISO Model Predictive Control
DEFF Research Database (Denmark)
Huusom, Jakob Kjøbsted; Poulsen, Niels Kjølstad; Jørgensen, Sten Bay
2011-01-01
Offset free tracking in Model Predictive Control requires estimation of unmeasured disturbances or the inclusion of an integrator. An algorithm for estimation of an unknown disturbance based on adaptive estimation with time varying forgetting is introduced and benchmarked against the classical...
Adaptive Estimation and Heuristic Optimization of Nonlinear Spacecraft Attitude Dynamics
2016-09-15
Biology, Control and Artificial Intelligence , MIT Press, Cambridge, MA, USA, 1992. 177 [89] Thompson, R. E., Colombi, J. M., Black, J. T., and Ayres...utilized for parameter and state estimates. MMAE algorithms involve constructing a bank of recursive estimators, each assuming a different hypothesis for...this research, MMAE routines employing parallel banks of unscented attitude filters are applied to analytical models of spacecraft with time- varying
Institute of Scientific and Technical Information of China (English)
Xiaogu ZHENG
2009-01-01
An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assimilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing -2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics are not known. A simple nonlinear model (Burgers' equation model) is used to demonstrate the efficacy of the proposed approach.
Adaptive nonparametric estimation for L\\'evy processes observed at low frequency
Kappus, Johanna
2013-01-01
This article deals with adaptive nonparametric estimation for L\\'evy processes observed at low frequency. For general linear functionals of the L\\'evy measure, we construct kernel estimators, provide upper risk bounds and derive rates of convergence under regularity assumptions. Our focus lies on the adaptive choice of the bandwidth, using model selection techniques. We face here a non-standard problem of model selection with unknown variance. A new approach towards this problem is proposed, ...
Data adaptive control parameter estimation for scaling laws
Energy Technology Data Exchange (ETDEWEB)
Dinklage, Andreas [Max-Planck-Institut fuer Plasmaphysik, Teilinstitut Greifswald, Wendelsteinstrasse 1, D-17491 Greifswald (Germany); Dose, Volker [Max-Planck- Institut fuer Plasmaphysik, Boltzmannstrasse 2, D-85748 Garching (Germany)
2007-07-01
Bayesian experimental design quantifies the utility of data expressed by the information gain. Data adaptive exploration determines the expected utility of a single new measurement using existing data and a data descriptive model. In other words, the method can be used for experimental planning. As an example for a multivariate linear case, we apply this method for constituting scaling laws of fusion devices. In detail, the scaling of the stellarator W7-AS is examined for a subset of {iota}=1/3 data. The impact of the existing data on the scaling exponents is presented. Furthermore, in control parameter space regions of high utility are identified which improve the accuracy of the scaling law. This approach is not restricted to the presented example only, but can also be extended to non-linear models.
Adaptive algorithm for mobile user positioning based on environment estimation
Directory of Open Access Journals (Sweden)
Grujović Darko
2014-01-01
Full Text Available This paper analyzes the challenges to realize an infrastructure independent and a low-cost positioning method in cellular networks based on RSS (Received Signal Strength parameter, auxiliary timing parameter and environment estimation. The proposed algorithm has been evaluated using field measurements collected from GSM (Global System for Mobile Communications network, but it is technology independent and can be applied in UMTS (Universal Mobile Telecommunication Systems and LTE (Long-Term Evolution networks, also.
Nonlinear adaptive control system design with asymptotically stable parameter estimation error
Mishkov, Rumen; Darmonski, Stanislav
2018-01-01
The paper presents a new general method for nonlinear adaptive system design with asymptotic stability of the parameter estimation error. The advantages of the approach include asymptotic unknown parameter estimation without persistent excitation and capability to directly control the estimates transient response time. The method proposed modifies the basic parameter estimation dynamics designed via a known nonlinear adaptive control approach. The modification is based on the generalised prediction error, a priori constraints with a hierarchical parameter projection algorithm, and the stable data accumulation concepts. The data accumulation principle is the main tool for achieving asymptotic unknown parameter estimation. It relies on the parametric identifiability system property introduced. Necessary and sufficient conditions for exponential stability of the data accumulation dynamics are derived. The approach is applied in a nonlinear adaptive speed tracking vector control of a three-phase induction motor.
Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.
Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu
2015-08-01
This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm.
Key Parameters Estimation and Adaptive Warning Strategy for Rear-End Collision of Vehicle
Directory of Open Access Journals (Sweden)
Xiang Song
2015-01-01
Full Text Available The rear-end collision warning system requires reliable warning decision mechanism to adapt the actual driving situation. To overcome the shortcomings of existing warning methods, an adaptive strategy is proposed to address the practical aspects of the collision warning problem. The proposed strategy is based on the parameter-adaptive and variable-threshold approaches. First, several key parameter estimation algorithms are developed to provide more accurate and reliable information for subsequent warning method. They include a two-stage algorithm which contains a Kalman filter and a Luenberger observer for relative acceleration estimation, a Bayesian theory-based algorithm of estimating the road friction coefficient, and an artificial neural network for estimating the driver’s reaction time. Further, the variable-threshold warning method is designed to achieve the global warning decision. In the method, the safety distance is employed to judge the dangerous state. The calculation method of the safety distance in this paper can be adaptively adjusted according to the different driving conditions of the leading vehicle. Due to the real-time estimation of the key parameters and the adaptive calculation of the warning threshold, the strategy can adapt to various road and driving conditions. Finally, the proposed strategy is evaluated through simulation and field tests. The experimental results validate the feasibility and effectiveness of the proposed strategy.
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.
Zanarini, Alessandro
2018-01-01
The progress of optical systems gives nowadays at disposal on lightweight structures complex dynamic measurements and modal tests, each with its own advantages, drawbacks and preferred usage domains. It is thus more easy than before to obtain highly spatially defined vibration patterns for many applications in vibration engineering, testing and general product development. The potential of three completely different technologies is here benchmarked on a common test rig and advanced applications. SLDV, dynamic ESPI and hi-speed DIC are here first deployed in a complex and unique test on the estimation of FRFs with high spatial accuracy from a thin vibrating plate. The latter exhibits a broad band dynamics and high modal density in the common frequency domain where the techniques can find an operative intersection. A peculiar point-wise comparison is here addressed by means of discrete geometry transforms to put all the three technologies on trial at each physical point of the surface. Full field measurement technologies cannot estimate only displacement fields on a refined grid, but can exploit the spatial consistency of the results through neighbouring locations by means of numerical differentiation operators in the spatial domain to obtain rotational degrees of freedom and superficial dynamic strain distributions, with enhanced quality, compared to other technologies in literature. Approaching the task with the aid of superior quality receptance maps from the three different full field gears, this work calculates and compares rotational and dynamic strain FRFs. Dynamic stress FRFs can be modelled directly from the latter, by means of a constitutive model, avoiding the costly and time-consuming steps of building and tuning a numerical dynamic model of a flexible component or a structure in real life conditions. Once dynamic stress FRFs are obtained, spectral fatigue approaches can try to predict the life of a component in many excitation conditions. Different
Theory and application of deterministic multidimensional pointwise energy lattice physics methods
International Nuclear Information System (INIS)
Zerkle, M.L.
1999-01-01
The theory and application of deterministic, multidimensional, pointwise energy lattice physics methods are discussed. These methods may be used to solve the neutron transport equation in multidimensional geometries using near-continuous energy detail to calculate equivalent few-group diffusion theory constants that rigorously account for spatial and spectral self-shielding effects. A dual energy resolution slowing down algorithm is described which reduces the computer memory and disk storage requirements for the slowing down calculation. Results are presented for a 2D BWR pin cell depletion benchmark problem
Fractional-order adaptive fault estimation for a class of nonlinear fractional-order systems
N'Doye, Ibrahima; Laleg-Kirati, Taous-Meriem
2015-01-01
This paper studies the problem of fractional-order adaptive fault estimation for a class of fractional-order Lipschitz nonlinear systems using fractional-order adaptive fault observer. Sufficient conditions for the asymptotical convergence of the fractional-order state estimation error, the conventional integer-order and the fractional-order faults estimation error are derived in terms of linear matrix inequalities (LMIs) formulation by introducing a continuous frequency distributed equivalent model and using an indirect Lyapunov approach where the fractional-order α belongs to 0 < α < 1. A numerical example is given to demonstrate the validity of the proposed approach.
Fractional-order adaptive fault estimation for a class of nonlinear fractional-order systems
N'Doye, Ibrahima
2015-07-01
This paper studies the problem of fractional-order adaptive fault estimation for a class of fractional-order Lipschitz nonlinear systems using fractional-order adaptive fault observer. Sufficient conditions for the asymptotical convergence of the fractional-order state estimation error, the conventional integer-order and the fractional-order faults estimation error are derived in terms of linear matrix inequalities (LMIs) formulation by introducing a continuous frequency distributed equivalent model and using an indirect Lyapunov approach where the fractional-order α belongs to 0 < α < 1. A numerical example is given to demonstrate the validity of the proposed approach.
Nonlinear Adaptive Descriptor Observer for the Joint States and Parameters Estimation
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
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.
Adaptive Kalman filter based state of charge estimation algorithm for lithium-ion battery
International Nuclear Information System (INIS)
Zheng Hong; Liu Xu; Wei Min
2015-01-01
In order to improve the accuracy of the battery state of charge (SOC) estimation, in this paper we take a lithium-ion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate. Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded. (paper)
An adaptive observer for on-line tool wear estimation in turning, Part I: Theory
Danai, Kourosh; Ulsoy, A. Galip
1987-04-01
On-line sensing of tool wear has been a long-standing goal of the manufacturing engineering community. In the absence of any reliable on-line tool wear sensors, a new model-based approach for tool wear estimation has been proposed. This approach is an adaptive observer, based on force measurement, which uses both parameter and state estimation techniques. The design of the adaptive observer is based upon a dynamic state model of tool wear in turning. This paper (Part I) presents the model, and explains its use as the basis for the adaptive observer design. This model uses flank wear and crater wear as state variables, feed as the input, and the cutting force as the output. The suitability of the model as the basis for adaptive observation is also verified. The implementation of the adaptive observer requires the design of a state observer and a parameter estimator. To obtain the model parameters for tuning the adaptive observer procedures for linearisation of the non-linear model are specified. The implementation of the adaptive observer in turning and experimental results are presented in a companion paper (Part II).
Common Fixed Points for Asymptotic Pointwise Nonexpansive Mappings in Metric and Banach Spaces
Directory of Open Access Journals (Sweden)
P. Pasom
2012-01-01
Full Text Available Let C be a nonempty bounded closed convex subset of a complete CAT(0 space X. We prove that the common fixed point set of any commuting family of asymptotic pointwise nonexpansive mappings on C is nonempty closed and convex. We also show that, under some suitable conditions, the sequence {xk}k=1∞ defined by xk+1=(1-tmkxk⊕tmkTmnky(m-1k, y(m-1k=(1-t(m-1kxk⊕t(m-1kTm-1nky(m-2k,y(m-2k=(1-t(m-2kxk⊕t(m-2kTm-2nky(m-3k,…,y2k=(1-t2kxk⊕t2kT2nky1k,y1k=(1-t1kxk⊕t1kT1nky0k,y0k=xk, k∈N, converges to a common fixed point of T1,T2,…,Tm where they are asymptotic pointwise nonexpansive mappings on C, {tik}k=1∞ are sequences in [0,1] for all i=1,2,…,m, and {nk} is an increasing sequence of natural numbers. The related results for uniformly convex Banach spaces are also included.
Converting point-wise nuclear cross sections to pole representation using regularized vector fitting
Peng, Xingjie; Ducru, Pablo; Liu, Shichang; Forget, Benoit; Liang, Jingang; Smith, Kord
2018-03-01
Direct Doppler broadening of nuclear cross sections in Monte Carlo codes has been widely sought for coupled reactor simulations. One recent approach proposed analytical broadening using a pole representation of the commonly used resonance models and the introduction of a local windowing scheme to improve performance (Hwang, 1987; Forget et al., 2014; Josey et al., 2015, 2016). This pole representation has been achieved in the past by converting resonance parameters in the evaluation nuclear data library into poles and residues. However, cross sections of some isotopes are only provided as point-wise data in ENDF/B-VII.1 library. To convert these isotopes to pole representation, a recent approach has been proposed using the relaxed vector fitting (RVF) algorithm (Gustavsen and Semlyen, 1999; Gustavsen, 2006; Liu et al., 2018). This approach however needs to specify ahead of time the number of poles. This article addresses this issue by adding a poles and residues filtering step to the RVF procedure. This regularized VF (ReV-Fit) algorithm is shown to efficiently converge the poles close to the physical ones, eliminating most of the superfluous poles, and thus enabling the conversion of point-wise nuclear cross sections.
Criticality benchmarks for COG: A new point-wise Monte Carlo code
International Nuclear Information System (INIS)
Alesso, H.P.; Pearson, J.; Choi, J.S.
1989-01-01
COG is a new point-wise Monte Carlo code being developed and tested at LLNL for the Cray computer. It solves the Boltzmann equation for the transport of neutrons, photons, and (in future versions) charged particles. Techniques included in the code for modifying the random walk of particles make COG most suitable for solving deep-penetration (shielding) problems. However, its point-wise cross-sections also make it effective for a wide variety of criticality problems. COG has some similarities to a number of other computer codes used in the shielding and criticality community. These include the Lawrence Livermore National Laboratory (LLNL) codes TART and ALICE, the Los Alamos National Laboratory code MCNP, the Oak Ridge National Laboratory codes 05R, 06R, KENO, and MORSE, the SACLAY code TRIPOLI, and the MAGI code SAM. Each code is a little different in its geometry input and its random-walk modification options. Validating COG consists in part of running benchmark calculations against critical experiments as well as other codes. The objective of this paper is to present calculational results of a variety of critical benchmark experiments using COG, and to present the resulting code bias. Numerous benchmark calculations have been completed for a wide variety of critical experiments which generally involve both simple and complex physical problems. The COG results, which they report in this paper, have been excellent
Fast Spectral Velocity Estimation Using Adaptive Techniques: In-Vivo Results
DEFF Research Database (Denmark)
Gran, Fredrik; Jakobsson, Andreas; Udesen, Jesper
2007-01-01
Adaptive spectral estimation techniques are known to provide good spectral resolution and contrast even when the observation window(OW) is very sbort. In this paper two adaptive techniques are tested and compared to the averaged perlodogram (Welch) for blood velocity estimation. The Blood Power...... the blood process over slow-time and averaging over depth to find the power spectral density estimate. In this paper, the two adaptive methods are explained, and performance Is assessed in controlled steady How experiments and in-vivo measurements. The three methods were tested on a circulating How rig...... with a blood mimicking fluid flowing in the tube. The scanning section is submerged in water to allow ultrasound data acquisition. Data was recorded using a BK8804 linear array transducer and the RASMUS ultrasound scanner. The controlled experiments showed that the OW could be significantly reduced when...
A new anisotropic mesh adaptation method based upon hierarchical a posteriori error estimates
Huang, Weizhang; Kamenski, Lennard; Lang, Jens
2010-03-01
A new anisotropic mesh adaptation strategy for finite element solution of elliptic differential equations is presented. It generates anisotropic adaptive meshes as quasi-uniform ones in some metric space, with the metric tensor being computed based on hierarchical a posteriori error estimates. A global hierarchical error estimate is employed in this study to obtain reliable directional information of the solution. Instead of solving the global error problem exactly, which is costly in general, we solve it iteratively using the symmetric Gauß-Seidel method. Numerical results show that a few GS iterations are sufficient for obtaining a reasonably good approximation to the error for use in anisotropic mesh adaptation. The new method is compared with several strategies using local error estimators or recovered Hessians. Numerical results are presented for a selection of test examples and a mathematical model for heat conduction in a thermal battery with large orthotropic jumps in the material coefficients.
Cenci, Simone; Montero-Castaño, Ana; Saavedra, Serguei
2018-01-21
A major challenge in community ecology is to understand how species respond to environmental changes. Previous studies have shown that the reorganization of interactions among co-occurring species can modulate their chances to adapt to novel environmental conditions. Moreover, empirical evidence has shown that these ecological dynamics typically facilitate the persistence of groups of species rather than entire communities. However, so far, we have no systematic methodology to identify those groups of species with the highest or lowest chances to adapt to new environments through a reorganization of their interactions. Yet, this could prove extremely valuable for developing new conservation strategies. Here, we introduce a theoretical framework to estimate the effect of the reorganization of interactions on the adaptability of a group of species, within a community, to novel environmental conditions. We introduce the concept of the adaptation space of a group of species based on a feasibility analysis of a population dynamics model. We define the adaptation space of a group as the set of environmental conditions that can be made compatible with its persistence thorough the reorganization of interactions among species within the group. The larger the adaptation space of a group, the larger its likelihood to adapt to a novel environment. We show that the interactions in the community outside a group can act as structural constraints and be used to quantitatively compare the size of the adaptation space among different groups of species within a community. To test our theoretical framework, we perform a data analysis on several pairs of natural and artificially perturbed ecological communities. Overall, we find that the groups of species present in both control and perturbed communities are among the ones with the largest adaptation space. We believe that the results derived from our framework point out towards new directions to understand and estimate the
Delay Estimator and Improved Proportionate Multi-Delay Adaptive Filtering Algorithm
Directory of Open Access Journals (Sweden)
E. Verteletskaya
2012-04-01
Full Text Available This paper pertains to speech and acoustic signal processing, and particularly to a determination of echo path delay and operation of echo cancellers. To cancel long echoes, the number of weights in a conventional adaptive filter must be large. The length of the adaptive filter will directly affect both the degree of accuracy and the convergence speed of the adaptation process. We present a new adaptive structure which is capable to deal with multiple dispersive echo paths. An adaptive filter according to the present invention includes means for storing an impulse response in a memory, the impulse response being indicative of the characteristics of a transmission line. It also includes a delay estimator for detecting ranges of samples within the impulse response having relatively large distribution of echo energy. These ranges of samples are being indicative of echoes on the transmission line. An adaptive filter has a plurality of weighted taps, each of the weighted taps having an associated tap weight value. A tap allocation/control circuit establishes the tap weight values in response to said detecting means so that only taps within the regions of relatively large distributions of echo energy are turned on. Thus, the convergence speed and the degree of estimation in the adaptation process can be improved.
A Design-Adaptive Local Polynomial Estimator for the Errors-in-Variables Problem
Delaigle, Aurore
2009-03-01
Local polynomial estimators are popular techniques for nonparametric regression estimation and have received great attention in the literature. Their simplest version, the local constant estimator, can be easily extended to the errors-in-variables context by exploiting its similarity with the deconvolution kernel density estimator. The generalization of the higher order versions of the estimator, however, is not straightforward and has remained an open problem for the last 15 years. We propose an innovative local polynomial estimator of any order in the errors-in-variables context, derive its design-adaptive asymptotic properties and study its finite sample performance on simulated examples. We provide not only a solution to a long-standing open problem, but also provide methodological contributions to error-invariable regression, including local polynomial estimation of derivative functions.
RLS Channel Estimation with Adaptive Forgetting Factor for DS-CDMA Frequency-Domain Equalization
Kojima, Yohei; Tomeba, Hiromichi; Takeda, Kazuaki; Adachi, Fumiyuki
Frequency-domain equalization (FDE) based on the minimum mean square error (MMSE) criterion can increase the downlink bit error rate (BER) performance of DS-CDMA beyond that possible with conventional rake combining in a frequency-selective fading channel. FDE requires accurate channel estimation. Recently, we proposed a pilot-assisted channel estimation (CE) based on the MMSE criterion. Using MMSE-CE, the channel estimation accuracy is almost insensitive to the pilot chip sequence, and a good BER performance is achieved. In this paper, we propose a channel estimation scheme using one-tap recursive least square (RLS) algorithm, where the forgetting factor is adapted to the changing channel condition by the least mean square (LMS)algorithm, for DS-CDMA with FDE. We evaluate the BER performance using RLS-CE with adaptive forgetting factor in a frequency-selective fast Rayleigh fading channel by computer simulation.
Dominant wave frequency and amplitude estimation for adaptive control of wave energy converters
Nguyen , Hoai-Nam; Tona , Paolino; Sabiron , Guillaume
2017-01-01
International audience; Adaptive control is of great interest for wave energy converters (WEC) due to the inherent time-varying nature of sea conditions. Robust and accurate estimation algorithms are required to improve the knowledge of the current sea state on a wave-to-wave basis in order to ensure power harvesting as close as possible to optimal behavior. In this paper, we present a simple but innovative approach for estimating the wave force dominant frequency and wave force dominant ampl...
Nonparametric adaptive estimation of linear functionals for low frequency observed Lévy processes
Kappus, Johanna
2012-01-01
For a Lévy process X having finite variation on compact sets and finite first moments, Âµ( dx) = xv( dx) is a finite signed measure which completely describes the jump dynamics. We construct kernel estimators for linear functionals of Âµ 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.
ADAPTIVE PARAMETER ESTIMATION OF PERSON RECOGNITION MODEL IN A STOCHASTIC HUMAN TRACKING PROCESS
W. Nakanishi; T. Fuse; T. Ishikawa
2015-01-01
This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation ...
Using statistical sensitivities for adaptation of a best-estimate thermo-hydraulic simulation model
International Nuclear Information System (INIS)
Liu, X.J.; Kerner, A.; Schaefer, A.
2010-01-01
On-line adaptation of best-estimate simulations of NPP behaviour to time-dependent measurement data can be used to insure that simulations performed in parallel to plant operation develop synchronously with the real plant behaviour even over extended periods of time. This opens a range of applications including operator support in non-standard-situations, improving diagnostics and validation of measurements in real plants or experimental facilities. A number of adaptation methods have been proposed and successfully applied to control problems. However, these methods are difficult to be applied to best-estimate thermal-hydraulic codes, such as TRACE and ATHLET, with their large nonlinear differential equation systems and sophisticated time integration techniques. This paper presents techniques to use statistical sensitivity measures to overcome those problems by reducing the number of parameters subject to adaptation. It describes how to identify the most significant parameters for adaptation and how this information can be used by combining: -decomposition techniques splitting the system into a small set of component parts with clearly defined interfaces where boundary conditions can be derived from the measurement data, -filtering techniques to insure that the time frame for adaptation is meaningful, -numerical sensitivities to find minimal error conditions. The suitability of combining those techniques is shown by application to an adaptive simulation of the PKL experiment.
Uncertainty quantification in a chemical system using error estimate-based mesh adaption
International Nuclear Information System (INIS)
Mathelin, Lionel; Le Maitre, Olivier P.
2012-01-01
This paper describes a rigorous a posteriori error analysis for the stochastic solution of non-linear uncertain chemical models. The dual-based a posteriori stochastic error analysis extends the methodology developed in the deterministic finite elements context to stochastic discretization frameworks. It requires the resolution of two additional (dual) problems to yield the local error estimate. The stochastic error estimate can then be used to adapt the stochastic discretization. Different anisotropic refinement strategies are proposed, leading to a cost-efficient tool suitable for multi-dimensional problems of moderate stochastic dimension. The adaptive strategies allow both for refinement and coarsening of the stochastic discretization, as needed to satisfy a prescribed error tolerance. The adaptive strategies were successfully tested on a model for the hydrogen oxidation in supercritical conditions having 8 random parameters. The proposed methodologies are however general enough to be also applicable for a wide class of models such as uncertain fluid flows. (authors)
Harutyunyan, D.; Izsak, F.; van der Vegt, Jacobus J.W.; Bochev, Mikhail A.
For the adaptive solution of the Maxwell equations on three-dimensional domains with N´ed´elec edge finite element methods, we consider an implicit a posteriori error estimation technique. On each element of the tessellation an equation for the error is formulated and solved with a properly chosen
Rahimi, Afshin; Kumar, Krishna Dev; Alighanbari, Hekmat
2017-05-01
Reaction wheels, as one of the most commonly used actuators in satellite attitude control systems, are prone to malfunction which could lead to catastrophic failures. Such malfunctions can be detected and addressed in time if proper analytical redundancy algorithms such as parameter estimation and control reconfiguration are employed. Major challenges in parameter estimation include speed and accuracy of the employed algorithm. This paper presents a new approach for improving parameter estimation with adaptive unscented Kalman filter. The enhancement in tracking speed of unscented Kalman filter is achieved by systematically adapting the covariance matrix to the faulty estimates using innovation and residual sequences combined with an adaptive fault annunciation scheme. The proposed approach provides the filter with the advantage of tracking sudden changes in the system non-measurable parameters accurately. Results showed successful detection of reaction wheel malfunctions without requiring a priori knowledge about system performance in the presence of abrupt, transient, intermittent, and incipient faults. Furthermore, the proposed approach resulted in superior filter performance with less mean squared errors for residuals compared to generic and adaptive unscented Kalman filters, and thus, it can be a promising method for the development of fail-safe satellites.
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.
DEFF Research Database (Denmark)
Wang, Yunlong; Soltani, Mohsen; Hussain, Dil muhammed Akbar
2016-01-01
, an adaptive Multiplicative Extended Kalman Filter (MEKF) for attitude estimation of Marine Satellite Tracking Antenna (MSTA) is presented with the measurement noise covariance matrix adjusted according to the norm of accelerometer measurements, which can significantly reduce the slamming influence from waves...
Zeng, X.
2015-12-01
A large number of model executions are required to obtain alternative conceptual models' predictions and their posterior probabilities in Bayesian model averaging (BMA). The posterior model probability is estimated through models' marginal likelihood and prior probability. The heavy computation burden hinders the implementation of BMA prediction, especially for the elaborated marginal likelihood estimator. For overcoming the computation burden of BMA, an adaptive sparse grid (SG) stochastic collocation method is used to build surrogates for alternative conceptual models through the numerical experiment of a synthetical groundwater model. BMA predictions depend on model posterior weights (or marginal likelihoods), and this study also evaluated four marginal likelihood estimators, including arithmetic mean estimator (AME), harmonic mean estimator (HME), stabilized harmonic mean estimator (SHME), and thermodynamic integration estimator (TIE). The results demonstrate that TIE is accurate in estimating conceptual models' marginal likelihoods. The BMA-TIE has better predictive performance than other BMA predictions. TIE has high stability for estimating conceptual model's marginal likelihood. The repeated estimated conceptual model's marginal likelihoods by TIE have significant less variability than that estimated by other estimators. In addition, the SG surrogates are efficient to facilitate BMA predictions, especially for BMA-TIE. The number of model executions needed for building surrogates is 4.13%, 6.89%, 3.44%, and 0.43% of the required model executions of BMA-AME, BMA-HME, BMA-SHME, and BMA-TIE, respectively.
Optimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators
Kammoun, Abla; Couillet, Romain; Pascal, Frederic; Alouini, Mohamed-Slim
2017-01-01
This article addresses improvements on the design of the adaptive normalized matched filter (ANMF) for radar detection. It is well-acknowledged that the estimation of the noise-clutter covariance matrix is a fundamental step in adaptive radar detection. In this paper, we consider regularized estimation methods which force by construction the eigenvalues of the covariance estimates to be greater than a positive regularization parameter ρ. This makes them more suitable for high dimensional problems with a limited number of secondary data samples than traditional sample covariance estimates. The motivation behind this work is to understand the effect and properly set the value of ρthat improves estimate conditioning while maintaining a low estimation bias. More specifically, we consider the design of the ANMF detector for two kinds of regularized estimators, namely the regularized sample covariance matrix (RSCM), the regularized Tyler estimator (RTE). The rationale behind this choice is that the RTE is efficient in mitigating the degradation caused by the presence of impulsive noises while inducing little loss when the noise is Gaussian. Based on asymptotic results brought by recent tools from random matrix theory, we propose a design for the regularization parameter that maximizes the asymptotic detection probability under constant asymptotic false alarm rates. Provided Simulations support the efficiency of the proposed method, illustrating its gain over conventional settings of the regularization parameter.
Optimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators
Kammoun, Abla
2017-10-25
This article addresses improvements on the design of the adaptive normalized matched filter (ANMF) for radar detection. It is well-acknowledged that the estimation of the noise-clutter covariance matrix is a fundamental step in adaptive radar detection. In this paper, we consider regularized estimation methods which force by construction the eigenvalues of the covariance estimates to be greater than a positive regularization parameter ρ. This makes them more suitable for high dimensional problems with a limited number of secondary data samples than traditional sample covariance estimates. The motivation behind this work is to understand the effect and properly set the value of ρthat improves estimate conditioning while maintaining a low estimation bias. More specifically, we consider the design of the ANMF detector for two kinds of regularized estimators, namely the regularized sample covariance matrix (RSCM), the regularized Tyler estimator (RTE). The rationale behind this choice is that the RTE is efficient in mitigating the degradation caused by the presence of impulsive noises while inducing little loss when the noise is Gaussian. Based on asymptotic results brought by recent tools from random matrix theory, we propose a design for the regularization parameter that maximizes the asymptotic detection probability under constant asymptotic false alarm rates. Provided Simulations support the efficiency of the proposed method, illustrating its gain over conventional settings of the regularization parameter.
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 fast pointwise strategy for anisotropic wave-mode separation in TI media
Liu, Qiancheng
2017-08-17
The multi-component wavefield contains both compressional and shear waves. Separating wave-modes has many applications in seismic workflows. Conventionally, anisotropic wave-mode separation is implemented by either directly filtering in the wavenumber domain or nonstationary filtering in the space domain, which are computationally expensive. These methods could be categorized into the pseudo-derivative family and only work well within Finite Difference (FD) methods. In this paper, we establish a relationship between group-velocity direction and polarity direction and propose a method, which could go beyond modeling by FD. In particular, we are interested in performing wave-mode separation in a Spectral Element Method (SEM), which is widely used for seismic wave propagation on various scales. The separation is implemented pointwise, independent of its neighbor points, suitable for running in parallel. Moreover, no correction for amplitude and phase changes caused by the derivative operator is required. We have verified our scheme using numerical examples.
Chernyshov, A. D.; Goryainov, V. V.; Danshin, A. A.
2018-03-01
The stress problem for the elastic wedge-shaped cutter of finite dimensions with mixed boundary conditions is considered. The differential problem is reduced to the system of linear algebraic equations by applying twice the fast expansions with respect to the angular and radial coordinate. In order to determine the unknown coefficients of fast expansions, the pointwise method is utilized. The problem solution derived has explicit analytical form and it’s valid for the entire domain including its boundary. The computed profiles of the displacements and stresses in a cross-section of the cutter are provided. The stress field is investigated for various values of opening angle and cusp’s radius.
A fast pointwise strategy for anisotropic wave-mode separation in TI media
Liu, Qiancheng; Peter, Daniel; Lu, Yongming
2017-01-01
The multi-component wavefield contains both compressional and shear waves. Separating wave-modes has many applications in seismic workflows. Conventionally, anisotropic wave-mode separation is implemented by either directly filtering in the wavenumber domain or nonstationary filtering in the space domain, which are computationally expensive. These methods could be categorized into the pseudo-derivative family and only work well within Finite Difference (FD) methods. In this paper, we establish a relationship between group-velocity direction and polarity direction and propose a method, which could go beyond modeling by FD. In particular, we are interested in performing wave-mode separation in a Spectral Element Method (SEM), which is widely used for seismic wave propagation on various scales. The separation is implemented pointwise, independent of its neighbor points, suitable for running in parallel. Moreover, no correction for amplitude and phase changes caused by the derivative operator is required. We have verified our scheme using numerical examples.
Frankowska, Hélène; Hoehener, Daniel
2017-06-01
This paper is devoted to pointwise second-order necessary optimality conditions for the Mayer problem arising in optimal control theory. We first show that with every optimal trajectory it is possible to associate a solution p (ṡ) of the adjoint system (as in the Pontryagin maximum principle) and a matrix solution W (ṡ) of an adjoint matrix differential equation that satisfy a second-order transversality condition and a second-order maximality condition. These conditions seem to be a natural second-order extension of the maximum principle. We then prove a Jacobson like necessary optimality condition for general control systems and measurable optimal controls that may be only ;partially singular; and may take values on the boundary of control constraints. Finally we investigate the second-order sensitivity relations along optimal trajectories involving both p (ṡ) and W (ṡ).
Directory of Open Access Journals (Sweden)
Irwin Yousept
2010-07-01
Full Text Available An optimal control problem arising in the context of 3D electromagnetic induction heating is investigated. The state equation is given by a quasilinear stationary heat equation coupled with a semilinear time harmonic eddy current equation. The temperature-dependent electrical conductivity and the presence of pointwise inequality state-constraints represent the main challenge of the paper. In the first part of the paper, the existence and regularity of the state are addressed. The second part of the paper deals with the analysis of the corresponding linearized equation. Some suffcient conditions are presented which guarantee thesolvability of the linearized system. The final part of the paper is concerned with the optimal control. The aim of the optimization is to find the optimal voltage such that a desired temperature can be achieved optimally. The corresponding first-order necessary optimality condition is presented.
Summary - COG: A new point-wise Monte Carlo code for burnup credit analysis
International Nuclear Information System (INIS)
Alesso, H.P.
1989-01-01
COG, a new point-wise Monte Carlo code being developed and tested at Lawrence Livermore National Laboratory (LLNL) for the Cray-1, solves the Boltzmann equation for the transport of neutrons, photons, and (in future versions) other particles. Techniques included in the code for modifying the random walk of particles make COG most suitable for solving deep-penetration (shielding) problems and a wide variety of criticality problems. COG is similar to a number of other computer codes used in the shielding community. Each code is a little different in its geometry input and its random-walk modification options. COG is a Monte Carlo code specifically designed for the CRAY (in 1986) to be as precise as the current state of physics knowledge. It has been extensively benchmarked and used as a shielding code at LLNL since 1986, and has recently been extended to accomplish criticality calculations. It will make an excellent tool for future shipping cask studies
Variable Kernel Density Estimation
Terrell, George R.; Scott, David W.
1992-01-01
We investigate some of the possibilities for improvement of univariate and multivariate kernel density estimates by varying the window over the domain of estimation, pointwise and globally. Two general approaches are to vary the window width by the point of estimation and by point of the sample observation. The first possibility is shown to be of little efficacy in one variable. In particular, nearest-neighbor estimators in all versions perform poorly in one and two dimensions, but begin to b...
Measuring global oil trade dependencies: An application of the point-wise mutual information method
International Nuclear Information System (INIS)
Kharrazi, Ali; Fath, Brian D.
2016-01-01
Oil trade is one of the most vital networks in the global economy. In this paper, we analyze the 1998–2012 oil trade networks using the point-wise mutual information (PMI) method and determine the pairwise trade preferences and dependencies. Using examples of the USA's trade partners, this research demonstrates the usefulness of the PMI method as an additional methodological tool to evaluate the outcomes from countries' decisions to engage in preferred trading partners. A positive PMI value indicates trade preference where trade is larger than would be expected. For example, in 2012 the USA imported 2,548.7 kbpd despite an expected 358.5 kbpd of oil from Canada. Conversely, a negative PMI value indicates trade dis-preference where the amount of trade is smaller than what would be expected. For example, the 15-year average of annual PMI between Saudi Arabia and the U.S.A. is −0.130 and between Russia and the USA −1.596. We reflect the three primary reasons of discrepancies between actual and neutral model trade can be related to position, price, and politics. The PMI can quantify the political success or failure of trade preferences and can more accurately account temporal variation of interdependencies. - Highlights: • We analyzed global oil trade networks using the point-wise mutual information method. • We identified position, price, & politics as drivers of oil trade preference. • The PMI method is useful in research on complex trade networks and dependency theory. • A time-series analysis of PMI can track dependencies & evaluate policy decisions.
Error estimation for goal-oriented spatial adaptivity for the SN equations on triangular meshes
International Nuclear Information System (INIS)
Lathouwers, D.
2011-01-01
In this paper we investigate different error estimation procedures for use within a goal oriented adaptive algorithm for the S N equations on unstructured meshes. The method is based on a dual-weighted residual approach where an appropriate adjoint problem is formulated and solved in order to obtain the importance of residual errors in the forward problem on the specific goal of interest. The forward residuals and the adjoint function are combined to obtain both economical finite element meshes tailored to the solution of the target functional as well as providing error estimates. Various approximations made to make the calculation of the adjoint angular flux more economically attractive are evaluated by comparing the performance of the resulting adaptive algorithm and the quality of the error estimators when applied to two shielding-type test problems. (author)
An adaptive state of charge estimation approach for lithium-ion series-connected battery system
Peng, Simin; Zhu, Xuelai; Xing, Yinjiao; Shi, Hongbing; Cai, Xu; Pecht, Michael
2018-07-01
Due to the incorrect or unknown noise statistics of a battery system and its cell-to-cell variations, state of charge (SOC) estimation of a lithium-ion series-connected battery system is usually inaccurate or even divergent using model-based methods, such as extended Kalman filter (EKF) and unscented Kalman filter (UKF). To resolve this problem, an adaptive unscented Kalman filter (AUKF) based on a noise statistics estimator and a model parameter regulator is developed to accurately estimate the SOC of a series-connected battery system. An equivalent circuit model is first built based on the model parameter regulator that illustrates the influence of cell-to-cell variation on the battery system. A noise statistics estimator is then used to attain adaptively the estimated noise statistics for the AUKF when its prior noise statistics are not accurate or exactly Gaussian. The accuracy and effectiveness of the SOC estimation method is validated by comparing the developed AUKF and UKF when model and measurement statistics noises are inaccurate, respectively. Compared with the UKF and EKF, the developed method shows the highest SOC estimation accuracy.
A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance.
Zheng, Binqi; Fu, Pengcheng; Li, Baoqing; Yuan, Xiaobing
2018-03-07
The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results.
Errors in the estimation method for the rejection of vibrations in adaptive optics systems
Kania, Dariusz
2017-06-01
In recent years the problem of the mechanical vibrations impact in adaptive optics (AO) systems has been renewed. These signals are damped sinusoidal signals and have deleterious effect on the system. One of software solutions to reject the vibrations is an adaptive method called AVC (Adaptive Vibration Cancellation) where the procedure has three steps: estimation of perturbation parameters, estimation of the frequency response of the plant, update the reference signal to reject/minimalize the vibration. In the first step a very important problem is the estimation method. A very accurate and fast (below 10 ms) estimation method of these three parameters has been presented in several publications in recent years. The method is based on using the spectrum interpolation and MSD time windows and it can be used to estimate multifrequency signals. In this paper the estimation method is used in the AVC method to increase the system performance. There are several parameters that affect the accuracy of obtained results, e.g. CiR - number of signal periods in a measurement window, N - number of samples in the FFT procedure, H - time window order, SNR, b - number of ADC bits, γ - damping ratio of the tested signal. Systematic errors increase when N, CiR, H decrease and when γ increases. The value for systematic error is approximately 10^-10 Hz/Hz for N = 2048 and CiR = 0.1. This paper presents equations that can used to estimate maximum systematic errors for given values of H, CiR and N before the start of the estimation process.
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-07-16
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.
Adaptive estimation for control of uncertain nonlinear systems with applications to target tracking
Madyastha, Venkatesh Kattigari
2005-08-01
Design of nonlinear observers has received considerable attention since the early development of methods for linear state estimation. The most popular approach is the extended Kalman filter (EKF), that goes through significant degradation in the presence of nonlinearities, particularly if unmodeled dynamics are coupled to the process and the measurement. For uncertain nonlinear systems, adaptive observers have been introduced to estimate the unknown state variables where no priori information about the unknown parameters is available. While establishing global results, these approaches are applicable only to systems transformable to output feedback form. Over the recent years, neural network (NN) based identification and estimation schemes have been proposed that relax the assumptions on the system at the price of sacrificing on the global nature of the results. However, most of the NN based adaptive observer approaches in the literature require knowledge of the full dimension of the system, therefore may not be suitable for systems with unmodeled dynamics. We first propose a novel approach to nonlinear state estimation from the perspective of augmenting a linear time invariant observer with an adaptive element. The class of nonlinear systems treated here are finite but of otherwise unknown dimension. The objective is to improve the performance of the linear observer when applied to a nonlinear system. The approach relies on the ability of the NNs to approximate the unknown dynamics from finite time histories of available measurements. Next we investigate nonlinear state estimation from the perspective of adaptively augmenting an existing time varying observer, such as an EKF. EKFs find their applications mostly in target tracking problems. The proposed approaches are robust to unmodeled dynamics, including unmodeled disturbances. Lastly, we consider the problem of adaptive estimation in the presence of feedback control for a class of uncertain nonlinear systems
Adaptive Hybrid Control of Vehicle Semiactive Suspension Based on Road Profile Estimation
Directory of Open Access Journals (Sweden)
Yechen Qin
2015-01-01
Full Text Available A new road estimation based suspension hybrid control strategy is proposed. Its aim is to adaptively change control gains to improve both ride comfort and road handling with the constraint of rattle space. To achieve this, analytical expressions for ride comfort, road handling, and rattle space with respect to road input are derived based on the hybrid control, and the problem is transformed into a MOOP (Multiobjective Optimization Problem and has been solved by NSGA-II (Nondominated Sorting Genetic Algorithm-II. A new road estimation and classification method, which is based on ANFIS (Adaptive Neurofuzzy Inference System and wavelet transforms, is then presented as a means of detecting the road profile level, and a Kalman filter is designed for observing unknown states. The results of simulations conducted with random road excitation show that the efficiency of the proposed control strategy compares favourably to that of a passive system.
International Nuclear Information System (INIS)
Wang Baosheng; Wang Dongqing; Zhang Jianmin; Jiang Jing
2012-01-01
In order to estimate the functional failure probability of passive systems, an innovative adaptive importance sampling methodology is presented. In the proposed methodology, information of variables is extracted with some pre-sampling of points in the failure region. An important sampling density is then constructed from the sample distribution in the failure region. Taking the AP1000 passive residual heat removal system as an example, the uncertainties related to the model of a passive system and the numerical values of its input parameters are considered in this paper. And then the probability of functional failure is estimated with the combination of the response surface method and adaptive importance sampling method. The numerical results demonstrate the high computed efficiency and excellent computed accuracy of the methodology compared with traditional probability analysis methods. (authors)
Language Adaptation for Extending Post-Editing Estimates for Closely Related Languages
Directory of Open Access Journals (Sweden)
Rios Miguel
2016-10-01
Full Text Available This paper presents an open-source toolkit for predicting human post-editing efforts for closely related languages. At the moment, training resources for the Quality Estimation task are available for very few language directions and domains. Available resources can be expanded on the assumption that MT errors and the amount of post-editing required to correct them are comparable across related languages, even if the feature frequencies differ. In this paper we report a toolkit for achieving language adaptation, which is based on learning new feature representation using transfer learning methods. In particular, we report performance of a method based on Self-Taught Learning which adapts the English-Spanish pair to produce Quality Estimation models for translation from English into Portuguese, Italian and other Romance languages using the publicly available Autodesk dataset.
A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops
Energy Technology Data Exchange (ETDEWEB)
Lu, Hai-liang; Wen, Xi-shan; Lan, Lei; An, Yun-zhu; Li, Xiao-ping
2015-01-15
A self-adaptive genetic algorithm for estimating Jiles–Atherton (JA) magnetic hysteresis model parameters is presented. The fitness function is established based on the distances between equidistant key points of normalized hysteresis loops. Linearity function and logarithm function are both adopted to code the five parameters of JA model. Roulette wheel selection is used and the selection pressure is adjusted adaptively by deducting a proportional which depends on current generation common value. The Crossover operator is established by combining arithmetic crossover and multipoint crossover. Nonuniform mutation is improved by adjusting the mutation ratio adaptively. The algorithm is used to estimate the parameters of one kind of silicon-steel sheet’s hysteresis loops, and the results are in good agreement with published data. - Highlights: • We present a method to find JA parameters for both major and minor loops. • Fitness function is based on distances between key points of normalized loops. • The selection pressure is adjusted adaptively based on generations.
A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops
International Nuclear Information System (INIS)
Lu, Hai-liang; Wen, Xi-shan; Lan, Lei; An, Yun-zhu; Li, Xiao-ping
2015-01-01
A self-adaptive genetic algorithm for estimating Jiles–Atherton (JA) magnetic hysteresis model parameters is presented. The fitness function is established based on the distances between equidistant key points of normalized hysteresis loops. Linearity function and logarithm function are both adopted to code the five parameters of JA model. Roulette wheel selection is used and the selection pressure is adjusted adaptively by deducting a proportional which depends on current generation common value. The Crossover operator is established by combining arithmetic crossover and multipoint crossover. Nonuniform mutation is improved by adjusting the mutation ratio adaptively. The algorithm is used to estimate the parameters of one kind of silicon-steel sheet’s hysteresis loops, and the results are in good agreement with published data. - Highlights: • We present a method to find JA parameters for both major and minor loops. • Fitness function is based on distances between key points of normalized loops. • The selection pressure is adjusted adaptively based on generations
Speed Estimation of Induction Motor Using Model Reference Adaptive System with Kalman Filter
Directory of Open Access Journals (Sweden)
Pavel Brandstetter
2013-01-01
Full Text Available The paper deals with a speed estimation of the induction motor using observer with Model Reference Adaptive System and Kalman Filter. For simulation, Hardware in Loop Simulation method is used. The first part of the paper includes the mathematical description of the observer for the speed estimation of the induction motor. The second part describes Kalman filter. The third part describes Hardware in Loop Simulation method and its realization using multifunction card MF 624. In the last section of the paper, simulation results are shown for different changes of the induction motor speed which confirm high dynamic properties of the induction motor drive with sensorless control.
Adaptive feedforward of estimated ripple improves the closed loop system performance significantly
International Nuclear Information System (INIS)
Kwon, S.; Regan, A.; Wang, Y.M.; Rohlev, A.S.
1998-01-01
The Low Energy Demonstration Accelerator (LEDA) being constructed at Los Alamos National Laboratory will serve as the prototype for the low energy section of Acceleration Production of Tritium (APT) accelerator. This paper addresses the problem of LLRF control system for LEDA. The authors propose an estimator of the ripple and its time derivative and a control law which is based on PID control and adaptive feedforward of estimated ripple. The control law reduces the effect of the deterministic cathode ripple that is due to high voltage power supply and achieves tracking of desired set points
Pose Estimation and Adaptive Robot Behaviour for Human-Robot Interaction
DEFF Research Database (Denmark)
Svenstrup, Mikael; Hansen, Søren Tranberg; Andersen, Hans Jørgen
2009-01-01
Abstract—This paper introduces a new method to determine a person’s pose based on laser range measurements. Such estimates are typically a prerequisite for any human-aware robot navigation, which is the basis for effective and timeextended interaction between a mobile robot and a human. The robot......’s pose. The resulting pose estimates are used to identify humans who wish to be approached and interacted with. The interaction motion of the robot is based on adaptive potential functions centered around the person that respect the persons social spaces. The method is tested in experiments...
A review of some a posteriori error estimates for adaptive finite element methods
Czech Academy of Sciences Publication Activity Database
Segeth, Karel
2010-01-01
Roč. 80, č. 8 (2010), s. 1589-1600 ISSN 0378-4754. [European Seminar on Coupled Problems. Jetřichovice, 08.06.2008-13.06.2008] R&D Projects: GA AV ČR(CZ) IAA100190803 Institutional research plan: CEZ:AV0Z10190503 Keywords : hp-adaptive finite element method * a posteriori error estimators * computational error estimates Subject RIV: BA - General Mathematics Impact factor: 0.812, year: 2010 http://www.sciencedirect.com/science/article/pii/S0378475408004230
A novel adaptive joint power control algorithm with channel estimation in a CDMA cellular system
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
Joint power control has advantages of multi-user detection and power control; and it can combat the multi-access interference and the near-far problem. A novel adaptive joint power control algorithm with channel estimation in a CDMA cellular system was designed. Simulation results show that the algorithm can control the power not only quickly but also precisely with a time change. The method is useful for increasing system capacity.
Bazile , Alban; Hachem , Elie; Larroya-Huguet , Juan-Carlos; Mesri , Youssef
2018-01-01
International audience; In this work, we present a new a posteriori error estimator based on the Variational Multiscale method for anisotropic adaptive fluid mechanics problems. The general idea is to combine the large scale error based on the solved part of the solution with the sub-mesh scale error based on the unresolved part of the solution. We compute the latter with two different methods: one using the stabilizing parameters and the other using bubble functions. We propose two different...
State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter
Directory of Open Access Journals (Sweden)
Bizhong Xia
2015-06-01
Full Text Available Accurate state of charge (SOC estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST and New European Driving Cycle (NEDC are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF and cubature Kalman filter (CKF algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms.
Polynomial Phase Estimation Based on Adaptive Short-Time Fourier Transform.
Jing, Fulong; Zhang, Chunjie; Si, Weijian; Wang, Yu; Jiao, Shuhong
2018-02-13
Polynomial phase signals (PPSs) have numerous applications in many fields including radar, sonar, geophysics, and radio communication systems. Therefore, estimation of PPS coefficients is very important. In this paper, a novel approach for PPS parameters estimation based on adaptive short-time Fourier transform (ASTFT), called the PPS-ASTFT estimator, is proposed. Using the PPS-ASTFT estimator, both one-dimensional and multi-dimensional searches and error propagation problems, which widely exist in PPSs field, are avoided. In the proposed algorithm, the instantaneous frequency (IF) is estimated by S-transform (ST), which can preserve information on signal phase and provide a variable resolution similar to the wavelet transform (WT). The width of the ASTFT analysis window is equal to the local stationary length, which is measured by the instantaneous frequency gradient (IFG). The IFG is calculated by the principal component analysis (PCA), which is robust to the noise. Moreover, to improve estimation accuracy, a refinement strategy is presented to estimate signal parameters. Since the PPS-ASTFT avoids parameter search, the proposed algorithm can be computed in a reasonable amount of time. The estimation performance, computational cost, and implementation of the PPS-ASTFT are also analyzed. The conducted numerical simulations support our theoretical results and demonstrate an excellent statistical performance of the proposed algorithm.
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
International Nuclear Information System (INIS)
Ye, Min; Guo, Hui; Cao, Binggang
2017-01-01
Highlights: • Propose an improved adaptive particle swarm filter method. • The SoC estimation method for the battery based on the adaptive particle swarm filter is presented. • The algorithm is validated by the case study of different aged extent batteries. • The effectiveness and applicability of the algorithm are validated by the LiPB batteries. - Abstract: Obtaining accurate parameters, state of charge (SoC) and capacity of a lithium-ion battery is crucial for a battery management system, and establishing a battery model online is complex. In addition, the errors and perturbations of the battery model dramatically increase throughout the battery lifetime, making it more challenging to model the battery online. To overcome these difficulties, this paper provides three contributions: (1) To improve the robustness of the adaptive particle filter algorithm, an error analysis method is added to the traditional adaptive particle swarm algorithm. (2) An online adaptive SoC estimator based on the improved adaptive particle filter is presented; this estimator can eliminate the estimation error due to battery degradation and initial SoC errors. (3) The effectiveness of the proposed method is verified using various initial states of lithium nickel manganese cobalt oxide (NMC) cells and lithium-ion polymer (LiPB) batteries. The experimental analysis shows that the maximum errors are less than 1% for both the voltage and SoC estimations and that the convergence time of the SoC estimation decreased to 120 s.
Adaptive OFDM Radar Waveform Design for Improved Micro-Doppler Estimation
Energy Technology Data Exchange (ETDEWEB)
Sen, Satyabrata [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Engineering Science Advanced Research, Computer Science and Mathematics Division
2014-07-01
Here we analyze the performance of a wideband orthogonal frequency division multiplexing (OFDM) signal in estimating the micro-Doppler frequency of a rotating target having multiple scattering centers. The use of a frequency-diverse OFDM signal enables us to independently analyze the micro-Doppler characteristics with respect to a set of orthogonal subcarrier frequencies. We characterize the accuracy of micro-Doppler frequency estimation by computing the Cramer-Rao bound (CRB) on the angular-velocity estimate of the target. Additionally, to improve the accuracy of the estimation procedure, we formulate and solve an optimization problem by minimizing the CRB on the angular-velocity estimate with respect to the OFDM spectral coefficients. We present several numerical examples to demonstrate the CRB variations with respect to the signal-to-noise ratios, number of temporal samples, and number of OFDM subcarriers. We also analysed numerically the improvement in estimation accuracy due to the adaptive waveform design. A grid-based maximum likelihood estimation technique is applied to evaluate the corresponding mean-squared error performance.
Adaptive order search and tangent-weighted trade-off for motion estimation in H.264
Directory of Open Access Journals (Sweden)
Srinivas Bachu
2018-04-01
Full Text Available Motion estimation and compensation play a major role in video compression to reduce the temporal redundancies of the input videos. A variety of block search patterns have been developed for matching the blocks with reduced computational complexity, without affecting the visual quality. In this paper, block motion estimation is achieved through integrating the square as well as the hexagonal search patterns with adaptive order. The proposed algorithm is called, AOSH (Adaptive Order Square Hexagonal Search algorithm, and it finds the best matching block with a reduced number of search points. The searching function is formulated as a trade-off criterion here. Hence, the tangent-weighted function is newly developed to evaluate the matching point. The proposed AOSH search algorithm and the tangent-weighted trade-off criterion are effectively applied to the block estimation process to enhance the visual quality and the compression performance. The proposed method is validated using three videos namely, football, garden and tennis. The quantitative performance of the proposed method and the existing methods is analysed using the Structural SImilarity Index (SSIM and the Peak Signal to Noise Ratio (PSNR. The results prove that the proposed method offers good visual quality than the existing methods. Keywords: Block motion estimation, Square search, Hexagon search, H.264, Video coding
Yi, J.; Choi, C.
2014-12-01
Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.
International Nuclear Information System (INIS)
Yashiki, Taturou; Yagawa, Genki; Okuda, Hiroshi
1995-01-01
The adaptive finite element method based on an 'a posteriori error estimation' is known to be a powerful technique for analyzing the engineering practical problems, since it excludes the instinctive aspect of the mesh subdivision and gives high accuracy with relatively low computational cost. In the adaptive procedure, both the error estimation and the mesh generation according to the error estimator are essential. In this paper, the adaptive procedure is realized by the automatic mesh generation based on the control of node density distribution, which is decided according to the error estimator. The global percentage error, CPU time, the degrees of freedom and the accuracy of the solution of the adaptive procedure are compared with those of the conventional method using regular meshes. Such numerical examples as the driven cavity flows of various Reynolds numbers and the flows around a cylinder have shown the very high performance of the proposed adaptive procedure. (author)
Estimation of Stator winding faults in induction motors using an adaptive observer scheme
DEFF Research Database (Denmark)
Kallesøe, C. S.; Vadstrup, P.; Rasmussen, Henrik
2004-01-01
This paper addresses the subject of inter-turn short circuit estimation in the stator of an induction motor. In the paper an adaptive observer scheme is proposed. The proposed observer is capable of simultaneously estimating the speed of the motor, the amount turns involved in the short circuit...... and an expression of the current in the short circuit. Moreover the states of the motor are estimated, meaning that the magnetizing currents are made available even though a fault has happened in the motor. To be able to develop this observer, a model particular suitable for the chosen observer design, is also...... derived. The efficiency of the proposed observer is demonstrated by tests performed on a test setup with a customized designed induction motor. With this motor it is possible to simulate inter-turn short circuit faults....
Estimation of Stator Winding Faults in Induction Motors using an Adaptive Observer Scheme
DEFF Research Database (Denmark)
Kallesøe, C. S.; Vadstrup, P.; Rasmussen, Henrik
2004-01-01
This paper addresses the subject of inter-turn short circuit estimation in the stator of an induction motor. In the paper an adaptive observer scheme is proposed. The proposed observer is capable of simultaneously estimating the speed of the motor, the amount turns involved in the short circuit...... and an expression of the current in the short circuit. Moreover the states of the motor are estimated, meaning that the magnetizing currents are made available even though a fault has happened in the motor. To be able to develop this observer, a model particular suitable for the chosen observer design, is also...... derived. The efficiency of the proposed observer is demonstrated by tests performed on a test setup with a customized designed induction motor. With this motor it is possible to simulate inter-turn short circuit faults....
Sparse Adaptive Channel Estimation Based on lp-Norm-Penalized Affine Projection Algorithm
Directory of Open Access Journals (Sweden)
Yingsong Li
2014-01-01
Full Text Available We propose an lp-norm-penalized affine projection algorithm (LP-APA for broadband multipath adaptive channel estimations. The proposed LP-APA is realized by incorporating an lp-norm into the cost function of the conventional affine projection algorithm (APA to exploit the sparsity property of the broadband wireless multipath channel, by which the convergence speed and steady-state performance of the APA are significantly improved. The implementation of the LP-APA is equivalent to adding a zero attractor to its iterations. The simulation results, which are obtained from a sparse channel estimation, demonstrate that the proposed LP-APA can efficiently improve channel estimation performance in terms of both the convergence speed and steady-state performance when the channel is exactly sparse.
Světlák, M; Bob, P; Roman, R; Ježek, S; Damborská, A; Chládek, J; Shaw, D J; Kukleta, M
2013-01-01
In this study, we tested the hypothesis that experimental stress induces a specific change of left-right electrodermal activity (EDA) coupling pattern, as indexed by pointwise transinformation (PTI). Further, we hypothesized that this change is associated with scores on psychometric measures of the chronic stress-related psychopathology. Ninety-nine university students underwent bilateral measurement of EDA during rest and stress-inducing Stroop test and completed a battery of self-report measures of chronic stress-related psychopathology. A significant decrease in the mean PTI value was the prevalent response to the stress conditions. No association between chronic stress and PTI was found. Raw scores of psychometric measures of stress-related psychopathology had no effect on either the resting levels of PTI or the amount of stress-induced PTI change. In summary, acute stress alters the level of coupling pattern of cortico-autonomic influences on the left and right sympathetic pathways to the palmar sweat glands. Different results obtained using the PTI, EDA laterality coefficient, and skin conductance level also show that the PTI algorithm represents a new analytical approach to EDA asymmetry description.
International Nuclear Information System (INIS)
Buchhardt, F.; Brandl, P.
1981-01-01
In the application of reinforced or prestressed concrete reactor containments, the safety enclosure will be obtained through a steel liner membrane, which is attached pointwise to the interior concrete surface. It is the objective and aim of this study to analyse the overall structural behaviour of the bonded system consisting of concrete containment, studs, and steel liner - especially under the aspect of extreme load and deformation conditions. The parametric analysis is carried out on the basis of the geometric length/depth ratio l/t = 12 of a single liner field. In order to reduce the considerable computational effort to a minimum, it is necessary to decouple the overall system in its structural components, i.e., at first an imperfect predeflected 'buckling' field and the residual 'plane' liner field are considered separately. A further reduction enables the use of stud anchor characteristics which are based on experiments. Three-dimensional analyses are performed for the single 'buckling' field to obtain specific load-displacement functions; the residual plane system is considered with two- as well as one-dimensional models. For the comprehensive parametric evalution of the overall system behaviour, a linear model is assumed to which these load-displacement functions are applied. Constraint temperatures are introduced as a unit scale - up to failure of the overall system; hereby partial structural failure might lead to temporary relief. (orig.)
An Adaptive Estimation Scheme for Open-Circuit Voltage of Power Lithium-Ion Battery
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Yun Zhang
2013-01-01
Full Text Available Open-circuit voltage (OCV is one of the most important parameters in determining state of charge (SoC of power battery. The direct measurement of it is costly and time consuming. This paper describes an adaptive scheme that can be used to derive OCV of the power battery. The scheme only uses the measurable input (terminal current and the measurable output (terminal voltage signals of the battery system and is simple enough to enable online implement. Firstly an equivalent circuit model is employed to describe the polarization characteristic and the dynamic behavior of the lithium-ion battery; the state-space representation of the electrical performance for the battery is obtained based on the equivalent circuit model. Then the implementation procedure of the adaptive scheme is given; also the asymptotic convergence of the observer error and the boundedness of all the parameter estimates are proven. Finally, experiments are carried out, and the effectiveness of the adaptive estimation scheme is validated by the experimental results.
Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors.
Zhu, Dongchen; Li, Jiamao; Wang, Xianshun; Peng, Jingquan; Shi, Wenjun; Zhang, Xiaolin
2018-04-03
Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison.
Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology
Petković, Dalibor; Shamshirband, Shahaboddin; Pavlović, Nenad T.; Anuar, Nor Badrul; Kiah, Miss Laiha Mat
2014-07-01
The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.
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Hongjian Wang
2014-01-01
Full Text Available We present a support vector regression-based adaptive divided difference filter (SVRADDF algorithm for improving the low state estimation accuracy of nonlinear systems, which are typically affected by large initial estimation errors and imprecise prior knowledge of process and measurement noises. The derivative-free SVRADDF algorithm is significantly simpler to compute than other methods and is implemented using only functional evaluations. The SVRADDF algorithm involves the use of the theoretical and actual covariance of the innovation sequence. Support vector regression (SVR is employed to generate the adaptive factor to tune the noise covariance at each sampling instant when the measurement update step executes, which improves the algorithm’s robustness. The performance of the proposed algorithm is evaluated by estimating states for (i an underwater nonmaneuvering target bearing-only tracking system and (ii maneuvering target bearing-only tracking in an air-traffic control system. The simulation results show that the proposed SVRADDF algorithm exhibits better performance when compared with a traditional DDF algorithm.
Wei, Zhongbao; Meng, Shujuan; Tseng, King Jet; Lim, Tuti Mariana; Soong, Boon Hee; Skyllas-Kazacos, Maria
2017-03-01
An accurate battery model is the prerequisite for reliable state estimate of vanadium redox battery (VRB). As the battery model parameters are time varying with operating condition variation and battery aging, the common methods where model parameters are empirical or prescribed offline lacks accuracy and robustness. To address this issue, this paper proposes to use an online adaptive battery model to reproduce the VRB dynamics accurately. The model parameters are online identified with both the recursive least squares (RLS) and the extended Kalman filter (EKF). Performance comparison shows that the RLS is superior with respect to the modeling accuracy, convergence property, and computational complexity. Based on the online identified battery model, an adaptive peak power estimator which incorporates the constraints of voltage limit, SOC limit and design limit of current is proposed to fully exploit the potential of the VRB. Experiments are conducted on a lab-scale VRB system and the proposed peak power estimator is verified with a specifically designed "two-step verification" method. It is shown that different constraints dominate the allowable peak power at different stages of cycling. The influence of prediction time horizon selection on the peak power is also analyzed.
Shen, Yi
2013-05-01
A subject's sensitivity to a stimulus variation can be studied by estimating the psychometric function. Generally speaking, three parameters of the psychometric function are of interest: the performance threshold, the slope of the function, and the rate at which attention lapses occur. In the present study, three psychophysical procedures were used to estimate the three-parameter psychometric function for an auditory gap detection task. These were an up-down staircase (up-down) procedure, an entropy-based Bayesian (entropy) procedure, and an updated maximum-likelihood (UML) procedure. Data collected from four young, normal-hearing listeners showed that while all three procedures provided similar estimates of the threshold parameter, the up-down procedure performed slightly better in estimating the slope and lapse rate for 200 trials of data collection. When the lapse rate was increased by mixing in random responses for the three adaptive procedures, the larger lapse rate was especially detrimental to the efficiency of the up-down procedure, and the UML procedure provided better estimates of the threshold and slope than did the other two procedures.
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V. Jayaraj
2010-08-01
Full Text Available A Non-linear adaptive decision based algorithm with robust motion estimation technique is proposed for removal of impulse noise, Gaussian noise and mixed noise (impulse and Gaussian with edge and fine detail preservation in images and videos. The algorithm includes detection of corrupted pixels and the estimation of values for replacing the corrupted pixels. The main advantage of the proposed algorithm is that an appropriate filter is used for replacing the corrupted pixel based on the estimation of the noise variance present in the filtering window. This leads to reduced blurring and better fine detail preservation even at the high mixed noise density. It performs both spatial and temporal filtering for removal of the noises in the filter window of the videos. The Improved Cross Diamond Search Motion Estimation technique uses Least Median Square as a cost function, which shows improved performance than other motion estimation techniques with existing cost functions. The results show that the proposed algorithm outperforms the other algorithms in the visual point of view and in Peak Signal to Noise Ratio, Mean Square Error and Image Enhancement Factor.
An adaptive ARX model to estimate the RUL of aluminum plates based on its crack growth
Barraza-Barraza, Diana; Tercero-Gómez, Víctor G.; Beruvides, Mario G.; Limón-Robles, Jorge
2017-01-01
A wide variety of Condition-Based Maintenance (CBM) techniques deal with the problem of predicting the time for an asset fault. Most statistical approaches rely on historical failure data that might not be available in several practical situations. To address this issue, practitioners might require the use of self-starting approaches that consider only the available knowledge about the current degradation process and the asset operating context to update the prognostic model. Some authors use Autoregressive (AR) models for this purpose that are adequate when the asset operating context is constant, however, if it is variable, the accuracy of the models can be affected. In this paper, three autoregressive models with exogenous variables (ARX) were constructed, and their capability to estimate the remaining useful life (RUL) of a process was evaluated following the case of the aluminum crack growth problem. An existing stochastic model of aluminum crack growth was implemented and used to assess RUL estimation performance of the proposed ARX models through extensive Monte Carlo simulations. Point and interval estimations were made based only on individual history, behavior, operating conditions and failure thresholds. Both analytic and bootstrapping techniques were used in the estimation process. Finally, by including recursive parameter estimation and a forgetting factor, the ARX methodology adapts to changing operating conditions and maintain the focus on the current degradation level of an asset.
International Nuclear Information System (INIS)
Daldaban, Ferhat; Ustkoyuncu, Nurettin; Guney, Kerim
2006-01-01
A new method based on an adaptive neuro-fuzzy inference system (ANFIS) for estimating the phase inductance of switched reluctance motors (SRMs) is presented. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of the ANFIS. The rotor position and the phase current of the 6/4 pole SRM are used to predict the phase inductance. The phase inductance results predicted by the ANFIS are in excellent agreement with the results of the finite element method
Angular-contact ball-bearing internal load estimation algorithm using runtime adaptive relaxation
Medina, H.; Mutu, R.
2017-07-01
An algorithm to estimate internal loads for single-row angular contact ball bearings due to externally applied thrust loads and high-operating speeds is presented. A new runtime adaptive relaxation procedure and blending function is proposed which ensures algorithm stability whilst also reducing the number of iterations needed to reach convergence, leading to an average reduction in computation time in excess of approximately 80%. The model is validated based on a 218 angular contact bearing and shows excellent agreement compared to published results.
An Adaptive Nonlinear Aircraft Maneuvering Envelope Estimation Approach for Online Applications
Schuet, Stefan R.; Lombaerts, Thomas Jan; Acosta, Diana; Wheeler, Kevin; Kaneshige, John
2014-01-01
A nonlinear aircraft model is presented and used to develop an overall unified robust and adaptive approach to passive trim and maneuverability envelope estimation with uncertainty quantification. The concept of time scale separation makes this method suitable for the online characterization of altered safe maneuvering limitations after impairment. The results can be used to provide pilot feedback and/or be combined with flight planning, trajectory generation, and guidance algorithms to help maintain safe aircraft operations in both nominal and off-nominal scenarios.
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Bahita Mohamed
2011-01-01
Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.
International Nuclear Information System (INIS)
Morio, Jerome
2011-01-01
Importance sampling (IS) is a useful simulation technique to estimate critical probability with a better accuracy than Monte Carlo methods. It consists in generating random weighted samples from an auxiliary distribution rather than the distribution of interest. The crucial part of this algorithm is the choice of an efficient auxiliary PDF that has to be able to simulate more rare random events. The optimisation of this auxiliary distribution is often in practice very difficult. In this article, we propose to approach the IS optimal auxiliary density with non-parametric adaptive importance sampling (NAIS). We apply this technique for the probability estimation of spatial launcher impact position since it has currently become a more and more important issue in the field of aeronautics.
Performance bounds on micro-Doppler estimation and adaptive waveform design using OFDM signals
Sen, Satyabrata; Barhen, Jacob; Glover, Charles W.
2014-05-01
We analyze the performance of a wideband orthogonal frequency division multiplexing (OFDM) signal in estimating the micro-Doppler frequency of a target having multiple rotating scatterers (e.g., rotor blades of a helicopter, propellers of a submarine). The presence of rotating scatterers introduces Doppler frequency modulation in the received signal by generating sidebands about the transmitted frequencies. This is called the micro-Doppler effects. The use of a frequency-diverse OFDM signal in this context enables us to independently analyze the micro-Doppler characteristics with respect to a set of orthogonal subcarrier frequencies. Therefore, to characterize the accuracy of micro-Doppler frequency estimation, we compute the Craḿer-Rao Bound (CRB) on the angular-velocity estimate of the target while considering the scatterer responses as deterministic but unknown nuisance parameters. Additionally, to improve the accuracy of the estimation procedure, we formulate and solve an optimization problem by minimizing the CRB on the angular-velocity estimate with respect to the transmitting OFDM spectral coefficients. We present several numerical examples to demonstrate the CRB variations at different values of the signal-to-noise ratio (SNR) and the number of OFDM subcarriers. The CRB values not only decrease with the increase in the SNR values, but also reduce as we increase the number of subcarriers implying the significance of frequency-diverse OFDM waveforms. The improvement in estimation accuracy due to the adaptive waveform design is also numerically analyzed. Interestingly, we find that the relative decrease in the CRBs on the angular-velocity estimate is more pronounced for larger number of OFDM subcarriers.
Performance Bounds on Micro-Doppler Estimation and Adaptive Waveform Design Using OFDM Signals
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Sen, Satyabrata [ORNL; Barhen, Jacob [ORNL; Glover, Charles Wayne [ORNL
2014-01-01
We analyze the performance of a wideband orthogonal frequency division multiplexing (OFDM) signal in estimating the micro-Doppler frequency of a target having multiple rotating scatterers (e.g., rotor blades of a helicopter, propellers of a submarine). The presence of rotating scatterers introduces Doppler frequency modulation in the received signal by generating sidebands about the transmitted frequencies. This is called the micro-Doppler effects. The use of a frequency-diverse OFDM signal in this context enables us to independently analyze the micro-Doppler characteristics with respect to a set of orthogonal subcarrier frequencies. Therefore, to characterize the accuracy of micro-Doppler frequency estimation, we compute the Cram er-Rao Bound (CRB) on the angular-velocity estimate of the target while considering the scatterer responses as deterministic but unknown nuisance parameters. Additionally, to improve the accuracy of the estimation procedure, we formulate and solve an optimization problem by minimizing the CRB on the angular-velocity estimate with respect to the transmitting OFDM spectral coefficients. We present several numerical examples to demonstrate the CRB variations at different values of the signal-to-noise ratio (SNR) and the number of OFDM subcarriers. The CRB values not only decrease with the increase in the SNR values, but also reduce as we increase the number of subcarriers implying the significance of frequency-diverse OFDM waveforms. The improvement in estimation accuracy due to the adaptive waveform design is also numerically analyzed. Interestingly, we find that the relative decrease in the CRBs on the angular-velocity estimate is more pronounced for larger number of OFDM subcarriers.
Generation of realistic scene using illuminant estimation and mixed chromatic adaptation
Kim, Jae-Chul; Hong, Sang-Gi; Kim, Dong-Ho; Park, Jong-Hyun
2003-12-01
The algorithm of combining a real image with a virtual model was proposed to increase the reality of synthesized images. Currently, synthesizing a real image with a virtual model facilitated the surface reflection model and various geometric techniques. In the current methods, the characteristics of various illuminants in the real image are not sufficiently considered. In addition, despite the chromatic adaptation plays a vital role for accommodating different illuminants in the two media viewing conditions, it is not taken into account in the existing methods. Thus, it is hardly to get high-quality synthesized images. In this paper, we proposed the two-phase image synthesis algorithm. First, the surface reflectance of the maximum high-light region (MHR) was estimated using the three eigenvectors obtained from the principal component analysis (PCA) applied to the surface reflectances of 1269 Munsell samples. The combined spectral value, i.e., the product of surface reflectance and the spectral power distributions (SPDs) of an illuminant, of MHR was then estimated using the three eigenvectors obtained from PCA applied to the products of surface reflectances of Munsell 1269 samples and the SPDs of four CIE Standard Illuminants (A, C, D50, D65). By dividing the average combined spectral values of MHR by the average surface reflectances of MHR, we could estimate the illuminant of a real image. Second, the mixed chromatic adaptation (S-LMS) using an estimated and an external illuminants was applied to the virtual-model image. For evaluating the proposed algorithm, experiments with synthetic and real scenes were performed. It was shown that the proposed method was effective in synthesizing the real and the virtual scenes under various illuminants.
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Daniel M Spagnolo
2016-01-01
Full Text Available Background: Measures of spatial intratumor heterogeneity are potentially important diagnostic biomarkers for cancer progression, proliferation, and response to therapy. Spatial relationships among cells including cancer and stromal cells in the tumor microenvironment (TME are key contributors to heterogeneity. Methods: We demonstrate how to quantify spatial heterogeneity from immunofluorescence pathology samples, using a set of 3 basic breast cancer biomarkers as a test case. We learn a set of dominant biomarker intensity patterns and map the spatial distribution of the biomarker patterns with a network. We then describe the pairwise association statistics for each pattern within the network using pointwise mutual information (PMI and visually represent heterogeneity with a two-dimensional map. Results: We found a salient set of 8 biomarker patterns to describe cellular phenotypes from a tissue microarray cohort containing 4 different breast cancer subtypes. After computing PMI for each pair of biomarker patterns in each patient and tumor replicate, we visualize the interactions that contribute to the resulting association statistics. Then, we demonstrate the potential for using PMI as a diagnostic biomarker, by comparing PMI maps and heterogeneity scores from patients across the 4 different cancer subtypes. Estrogen receptor positive invasive lobular carcinoma patient, AL13-6, exhibited the highest heterogeneity score among those tested, while estrogen receptor negative invasive ductal carcinoma patient, AL13-14, exhibited the lowest heterogeneity score. Conclusions: This paper presents an approach for describing intratumor heterogeneity, in a quantitative fashion (via PMI, which departs from the purely qualitative approaches currently used in the clinic. PMI is generalizable to highly multiplexed/hyperplexed immunofluorescence images, as well as spatial data from complementary in situ methods including FISSEQ and CyTOF, sampling many different
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Sheng, Zheng, E-mail: 19994035@sina.com [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Wang, Jun; Zhou, Bihua [National Defense Key Laboratory on Lightning Protection and Electromagnetic Camouflage, PLA University of Science and Technology, Nanjing 210007 (China); Zhou, Shudao [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044 (China)
2014-03-15
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
A new adaptive control scheme based on the interacting multiple model (IMM) estimation
International Nuclear Information System (INIS)
Afshari, Hamed H.; Al-Ani, Dhafar; Habibi, Saeid
2016-01-01
In this paper, an Interacting multiple model (IMM) adaptive estimation approach is incorporated to design an optimal adaptive control law for stabilizing an Unmanned vehicle. Due to variations of the forward velocity of the Unmanned vehicle, its aerodynamic derivatives are constantly changing. In order to stabilize the unmanned vehicle and achieve the control objectives for in-flight conditions, one seeks for an adaptive control strategy that can adjust itself to varying flight conditions. In this context, a bank of linear models is used to describe the vehicle dynamics in different operating modes. Each operating mode represents a particular dynamic with a different forward velocity. These models are then used within an IMM filter containing a bank of Kalman filters (KF) in a parallel operating mechanism. To regulate and stabilize the vehicle, a Linear quadratic regulator (LQR) law is designed and implemented for each mode. The IMM structure determines the particular mode based on the stored models and in-flight input-output measurements. The LQR controller also provides a set of controllers; each corresponds to a particular flight mode and minimizes the tracking error. Finally, the ultimate control law is obtained as a weighted summation of all individual controllers whereas weights are obtained using mode probabilities of each operating mode.
International Nuclear Information System (INIS)
Sheng, Zheng; Wang, Jun; Zhou, Bihua; Zhou, Shudao
2014-01-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm
Directory of Open Access Journals (Sweden)
Dalei Song
2012-10-01
Full Text Available The adaptive extended set-membership filter (AESMF for nonlinear ellipsoidal estimation suffers a mismatch between real process noise and its set boundaries, which may result in unstable estimation. In this paper, a MIT method-based adaptive set-membership filter, for the optimization of the set boundaries of process noise, is developed and applied to the nonlinear joint estimation of both time-varying states and parameters. As a result of using the proposed MIT-AESMF, the estimation effectiveness and boundary accuracy of traditional AESMF are substantially improved. Simulation results have shown the efficiency and robustness of the proposed method.
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Dongming Li
2017-04-01
Full Text Available An adaptive optics (AO system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image restoration algorithm via maximum likelihood estimation. Our proposed algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed algorithm. Experimental results show that our algorithm produces accurate AO image restoration results and outperforms the current state-of-the-art blind deconvolution methods.
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E. A. Feilat
2010-12-01
Full Text Available This paper demonstrates the assessment of the small-signal stability of a single-machine infinite- bus power system under widely varying loading conditions using the concept of synchronizing and damping torques coefficients. The coefficients are calculated from the time responses of the rotor angle, speed, and torque of the synchronous generator. Three adaptive computation algorithms including Kalman filtering, Adaline, and recursive least squares have been compared to estimate the synchronizing and damping torque coefficients. The steady-state performance of the three adaptive techniques is compared with the conventional static least squares technique by conducting computer simulations at different loading conditions. The algorithms are compared to each other in terms of speed of convergence and accuracy. The recursive least squares estimation offers several advantages including significant reduction in computing time and computational complexity. The tendency of an unsupplemented static exciter to degrade the system damping for medium and heavy loading is verified. Consequently, a power system stabilizer whose parameters are adjusted to compensate for variations in the system loading is designed using phase compensation method. The effectiveness of the stabilizer in enhancing the dynamic stability over wide range of operating conditions is verified through the calculation of the synchronizing and damping torque coefficients using recursive least square technique.
HIERARCHICAL ADAPTIVE ROOD PATTERN SEARCH FOR MOTION ESTIMATION AT VIDEO SEQUENCE ANALYSIS
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V. T. Nguyen
2016-05-01
Full Text Available Subject of Research.The paper deals with the motion estimation algorithms for the analysis of video sequences in compression standards MPEG-4 Visual and H.264. Anew algorithm has been offered based on the analysis of the advantages and disadvantages of existing algorithms. Method. Thealgorithm is called hierarchical adaptive rood pattern search (Hierarchical ARPS, HARPS. This new algorithm includes the classic adaptive rood pattern search ARPS and hierarchical search MP (Hierarchical search or Mean pyramid. All motion estimation algorithms have been implemented using MATLAB package and tested with several video sequences. Main Results. The criteria for evaluating the algorithms were: speed, peak signal to noise ratio, mean square error and mean absolute deviation. The proposed method showed a much better performance at a comparable error and deviation. The peak signal to noise ratio in different video sequences shows better and worse results than characteristics of known algorithms so it requires further investigation. Practical Relevance. Application of this algorithm in MPEG-4 and H.264 codecs instead of the standard can significantly reduce compression time. This feature enables to recommend it in telecommunication systems for multimedia data storing, transmission and processing.
Adaptive control based on an on-line parameter estimation of an upper limb exoskeleton.
Riani, Akram; Madani, Tarek; Hadri, Abdelhafid El; Benallegue, Abdelaziz
2017-07-01
This paper presents an adaptive control strategy for an upper-limb exoskeleton based on an on-line dynamic parameter estimator. The objective is to improve the control performance of this system that plays a critical role in assisting patients for shoulder, elbow and wrist joint movements. In general, the dynamic parameters of the human limb are unknown and differ from a person to another, which degrade the performances of the exoskeleton-human control system. For this reason, the proposed control scheme contains a supplementary loop based on a new efficient on-line estimator of the dynamic parameters. Indeed, the latter is acting upon the parameter adaptation of the controller to ensure the performances of the system in the presence of parameter uncertainties and perturbations. The exoskeleton used in this work is presented and a physical model of the exoskeleton interacting with a 7 Degree of Freedom (DoF) upper limb model is generated using the SimMechanics library of MatLab/Simulink. To illustrate the effectiveness of the proposed approach, an example of passive rehabilitation movements is performed using multi-body dynamic simulation. The aims is to maneuver the exoskeleton that drive the upper limb to track desired trajectories in the case of the passive arm movements.
An adaptive neuro fuzzy model for estimating the reliability of component-based software systems
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Kirti Tyagi
2014-01-01
Full Text Available Although many algorithms and techniques have been developed for estimating the reliability of component-based software systems (CBSSs, much more research is needed. Accurate estimation of the reliability of a CBSS is difficult because it depends on two factors: component reliability and glue code reliability. Moreover, reliability is a real-world phenomenon with many associated real-time problems. Soft computing techniques can help to solve problems whose solutions are uncertain or unpredictable. A number of soft computing approaches for estimating CBSS reliability have been proposed. These techniques learn from the past and capture existing patterns in data. The two basic elements of soft computing are neural networks and fuzzy logic. In this paper, we propose a model for estimating CBSS reliability, known as an adaptive neuro fuzzy inference system (ANFIS, that is based on these two basic elements of soft computing, and we compare its performance with that of a plain FIS (fuzzy inference system for different data sets.
Adaptive Parameter Estimation of Person Recognition Model in a Stochastic Human Tracking Process
Nakanishi, W.; Fuse, T.; Ishikawa, T.
2015-05-01
This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model, which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions.
Owens, A. R.; Kópházi, J.; Welch, J. A.; Eaton, M. D.
2017-04-01
In this paper a hanging-node, discontinuous Galerkin, isogeometric discretisation of the multigroup, discrete ordinates (SN) equations is presented in which each energy group has its own mesh. The equations are discretised using Non-Uniform Rational B-Splines (NURBS), which allows the coarsest mesh to exactly represent the geometry for a wide range of engineering problems of interest; this would not be the case using straight-sided finite elements. Information is transferred between meshes via the construction of a supermesh. This is a non-trivial task for two arbitrary meshes, but is significantly simplified here by deriving every mesh from a common coarsest initial mesh. In order to take full advantage of this flexible discretisation, goal-based error estimators are derived for the multigroup, discrete ordinates equations with both fixed (extraneous) and fission sources, and these estimators are used to drive an adaptive mesh refinement (AMR) procedure. The method is applied to a variety of test cases for both fixed and fission source problems. The error estimators are found to be extremely accurate for linear NURBS discretisations, with degraded performance for quadratic discretisations owing to a reduction in relative accuracy of the "exact" adjoint solution required to calculate the estimators. Nevertheless, the method seems to produce optimal meshes in the AMR process for both linear and quadratic discretisations, and is ≈×100 more accurate than uniform refinement for the same amount of computational effort for a 67 group deep penetration shielding problem.
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Linhui Zhao
2017-12-01
Full Text Available State of charge (SOC is an important evaluation index for lithium-ion batteries (LIBs in electric vehicles (EVs. This paper proposes a nonlinear observer with a new adaptive gain structure for SOC estimation based on a second-order RC model. It is able to dynamically adjust the gains and obtain a better balance between convergence speed and estimation accuracy with less computational time. A sufficient condition is derived to guarantee the uniform asymptotic stability of the observer, and its robustness with respect to disturbances and uncertainties is analyzed with the help of input-to-state stability (ISS theory. A selection guide of the observer gains in practical application is presented. The estimation accuracy and convergence rate of the observer are evaluated and compared with those of extended Kalman filter (EKF based on multi-temperature datasets from two different types of LIB cells. The robustness against different disturbances and uncertainties that may appear in a real vehicle is validated and discussed in detail. The experimental results show that the proposed observer is capable of achieving better performance with less computational time in comparison to EKF for different types of LIB cells under various working conditions. The observer is also capable of estimating SOC accurately for real life conditions according to the validation results of datasets from a battery management system (BMS in an EV battery pack. Furthermore, the observer is simple enough, and is suitable for implementation on embedded hardware for LIB cells of EVs.
Removing damped sinusoidal vibrations in adaptive optics systems using a DFT-based estimation method
Kania, Dariusz
2017-06-01
The problem of a vibrations rejection in adaptive optics systems is still present in publications. These undesirable signals emerge because of shaking the system structure, the tracking process, etc., and they usually are damped sinusoidal signals. There are some mechanical solutions to reduce the signals but they are not very effective. One of software solutions are very popular adaptive methods. An AVC (Adaptive Vibration Cancellation) method has been presented and developed in recent years. The method is based on the estimation of three vibrations parameters and values of frequency, amplitude and phase are essential to produce and adjust a proper signal to reduce or eliminate vibrations signals. This paper presents a fast (below 10 ms) and accurate estimation method of frequency, amplitude and phase of a multifrequency signal that can be used in the AVC method to increase the AO system performance. The method accuracy depends on several parameters: CiR - number of signal periods in a measurement window, N - number of samples in the FFT procedure, H - time window order, SNR, THD, b - number of A/D converter bits in a real time system, γ - the damping ratio of the tested signal, φ - the phase of the tested signal. Systematic errors increase when N, CiR, H decrease and when γ increases. The value of systematic error for γ = 0.1%, CiR = 1.1 and N = 32 is approximately 10^-4 Hz/Hz. This paper focuses on systematic errors of and effect of the signal phase and values of γ on the results.
On the Schauder estimates of solutions to parabolic equations
International Nuclear Information System (INIS)
Han Qing
1998-01-01
This paper gives a priori estimates on asymptotic polynomials of solutions to parabolic differential equations at any points. This leads to a pointwise version of Schauder estimates. The result improves the classical Schauder estimates in a way that the estimates of solutions and their derivatives at one point depend on the coefficient and nonhomogeneous terms at this particular point
Fast simulation of transport and adaptive permeability estimation in porous media
Energy Technology Data Exchange (ETDEWEB)
Berre, Inga
2005-07-01
The focus of the thesis is twofold: Both fast simulation of transport in porous media and adaptive estimation of permeability are considered. A short introduction that motivates the work on these topics is given in Chapter 1. In Chapter 2, the governing equations for one- and two-phase flow in porous media are presented. Overall numerical solution strategies for the two-phase flow model are also discussed briefly. The concepts of streamlines and time-of-flight are introduced in Chapter 3. Methods for computing streamlines and time-of-flight are also presented in this chapter. Subsequently, in Chapters 4 and 5, the focus is on simulation of transport in a time-of-flight perspective. In Chapter 4, transport of fluids along streamlines is considered. Chapter 5 introduces a different viewpoint based on the evolution of isocontours of the fluid saturation. While the first chapters focus on the forward problem, which consists in solving a mathematical model given the reservoir parameters, Chapters 6, 7 and 8 are devoted to the inverse problem of permeability estimation. An introduction to the problem of identifying spatial variability in reservoir permeability by inversion of dynamic production data is given in Chapter 6. In Chapter 7, adaptive multiscale strategies for permeability estimation are discussed. Subsequently, Chapter 8 presents a level-set approach for improving piecewise constant permeability representations. Finally, Chapter 9 summarizes the results obtained in the thesis; in addition, the chapter gives some recommendations and suggests directions for future work. Part II In Part II, the following papers are included in the order they were completed: Paper A: A Streamline Front Tracking Method for Two- and Three-Phase Flow Including Capillary Forces. I. Berre, H. K. Dahle, K. H. Karlsen, and H. F. Nordhaug. In Fluid flow and transport in porous media: mathematical and numerical treatment (South Hadley, MA, 2001), volume 295 of Contemp. Math., pages 49
International Nuclear Information System (INIS)
Sun, Fengchun; Hu, Xiaosong; Zou, Yuan; Li, Siguang
2011-01-01
An accurate battery State of Charge estimation is of great significance for battery electric vehicles and hybrid electric vehicles. This paper presents an adaptive unscented Kalman filtering method to estimate State of Charge of a lithium-ion battery for battery electric vehicles. The adaptive adjustment of the noise covariances in the State of Charge estimation process is implemented by an idea of covariance matching in the unscented Kalman filter context. Experimental results indicate that the adaptive unscented Kalman filter-based algorithm has a good performance in estimating the battery State of Charge. A comparison with the adaptive extended Kalman filter, extended Kalman filter, and unscented Kalman filter-based algorithms shows that the proposed State of Charge estimation method has a better accuracy. -- Highlights: → Adaptive unscented Kalman filtering is proposed to estimate State of Charge of a lithium-ion battery for electric vehicles. → The proposed method has a good performance in estimating the battery State of Charge. → A comparison with three other Kalman filtering algorithms shows that the proposed method has a better accuracy.
SU-G-BRA-09: Estimation of Motion Tracking Uncertainty for Real-Time Adaptive Imaging
Energy Technology Data Exchange (ETDEWEB)
Yan, H [Capital Medical University, Beijing, Beijing (China); Chen, Z [Yale New Haven Hospital, New Haven, CT (United States); Nath, R; Liu, W [Yale University School of Medicine, New Haven, CT (United States)
2016-06-15
Purpose: kV fluoroscopic imaging combined with MV treatment beam imaging has been investigated for intrafractional motion monitoring and correction. It is, however, subject to additional kV imaging dose to normal tissue. To balance tracking accuracy and imaging dose, we previously proposed an adaptive imaging strategy to dynamically decide future imaging type and moments based on motion tracking uncertainty. kV imaging may be used continuously for maximal accuracy or only when the position uncertainty (probability of out of threshold) is high if a preset imaging dose limit is considered. In this work, we propose more accurate methods to estimate tracking uncertainty through analyzing acquired data in real-time. Methods: We simulated motion tracking process based on a previously developed imaging framework (MV + initial seconds of kV imaging) using real-time breathing data from 42 patients. Motion tracking errors for each time point were collected together with the time point’s corresponding features, such as tumor motion speed and 2D tracking error of previous time points, etc. We tested three methods for error uncertainty estimation based on the features: conditional probability distribution, logistic regression modeling, and support vector machine (SVM) classification to detect errors exceeding a threshold. Results: For conditional probability distribution, polynomial regressions on three features (previous tracking error, prediction quality, and cosine of the angle between the trajectory and the treatment beam) showed strong correlation with the variation (uncertainty) of the mean 3D tracking error and its standard deviation: R-square = 0.94 and 0.90, respectively. The logistic regression and SVM classification successfully identified about 95% of tracking errors exceeding 2.5mm threshold. Conclusion: The proposed methods can reliably estimate the motion tracking uncertainty in real-time, which can be used to guide adaptive additional imaging to confirm the
SU-G-BRA-09: Estimation of Motion Tracking Uncertainty for Real-Time Adaptive Imaging
International Nuclear Information System (INIS)
Yan, H; Chen, Z; Nath, R; Liu, W
2016-01-01
Purpose: kV fluoroscopic imaging combined with MV treatment beam imaging has been investigated for intrafractional motion monitoring and correction. It is, however, subject to additional kV imaging dose to normal tissue. To balance tracking accuracy and imaging dose, we previously proposed an adaptive imaging strategy to dynamically decide future imaging type and moments based on motion tracking uncertainty. kV imaging may be used continuously for maximal accuracy or only when the position uncertainty (probability of out of threshold) is high if a preset imaging dose limit is considered. In this work, we propose more accurate methods to estimate tracking uncertainty through analyzing acquired data in real-time. Methods: We simulated motion tracking process based on a previously developed imaging framework (MV + initial seconds of kV imaging) using real-time breathing data from 42 patients. Motion tracking errors for each time point were collected together with the time point’s corresponding features, such as tumor motion speed and 2D tracking error of previous time points, etc. We tested three methods for error uncertainty estimation based on the features: conditional probability distribution, logistic regression modeling, and support vector machine (SVM) classification to detect errors exceeding a threshold. Results: For conditional probability distribution, polynomial regressions on three features (previous tracking error, prediction quality, and cosine of the angle between the trajectory and the treatment beam) showed strong correlation with the variation (uncertainty) of the mean 3D tracking error and its standard deviation: R-square = 0.94 and 0.90, respectively. The logistic regression and SVM classification successfully identified about 95% of tracking errors exceeding 2.5mm threshold. Conclusion: The proposed methods can reliably estimate the motion tracking uncertainty in real-time, which can be used to guide adaptive additional imaging to confirm the
Broom, Donald M
2006-01-01
The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and
Enzymatic Synthesis of Ampicillin: Nonlinear Modeling, Kinetics Estimation, and Adaptive Control
Directory of Open Access Journals (Sweden)
Monica Roman
2012-01-01
Full Text Available Nowadays, the use of advanced control strategies in biotechnology is quite low. A main reason is the lack of quality of the data, and the fact that more sophisticated control strategies must be based on a model of the dynamics of bioprocesses. The nonlinearity of the bioprocesses and the absence of cheap and reliable instrumentation require an enhanced modeling effort and identification strategies for the kinetics. The present work approaches modeling and control strategies for the enzymatic synthesis of ampicillin that is carried out inside a fed-batch bioreactor. First, a nonlinear dynamical model of this bioprocess is obtained by using a novel modeling procedure for biotechnology: the bond graph methodology. Second, a high gain observer is designed for the estimation of the imprecisely known kinetics of the synthesis process. Third, by combining an exact linearizing control law with the on-line estimation kinetics algorithm, a nonlinear adaptive control law is designed. The case study discussed shows that a nonlinear feedback control strategy applied to the ampicillin synthesis bioprocess can cope with disturbances, noisy measurements, and parametric uncertainties. Numerical simulations performed with MATLAB environment are included in order to test the behavior and the performances of the proposed estimation and control strategies.
Adaptive Model Predictive Vibration Control of a Cantilever Beam with Real-Time Parameter Estimation
Directory of Open Access Journals (Sweden)
Gergely Takács
2014-01-01
Full Text Available This paper presents an adaptive-predictive vibration control system using extended Kalman filtering for the joint estimation of system states and model parameters. A fixed-free cantilever beam equipped with piezoceramic actuators serves as a test platform to validate the proposed control strategy. Deflection readings taken at the end of the beam have been used to reconstruct the position and velocity information for a second-order state-space model. In addition to the states, the dynamic system has been augmented by the unknown model parameters: stiffness, damping constant, and a voltage/force conversion constant, characterizing the actuating effect of the piezoceramic transducers. The states and parameters of this augmented system have been estimated in real time, using the hybrid extended Kalman filter. The estimated model parameters have been applied to define the continuous state-space model of the vibrating system, which in turn is discretized for the predictive controller. The model predictive control algorithm generates state predictions and dual-mode quadratic cost prediction matrices based on the updated discrete state-space models. The resulting cost function is then minimized using quadratic programming to find the sequence of optimal but constrained control inputs. The proposed active vibration control system is implemented and evaluated experimentally to investigate the viability of the control method.
Improved remote gaze estimation using corneal reflection-adaptive geometric transforms
Ma, Chunfei; Baek, Seung-Jin; Choi, Kang-A.; Ko, Sung-Jea
2014-05-01
Recently, the remote gaze estimation (RGE) technique has been widely applied to consumer devices as a more natural interface. In general, the conventional RGE method estimates a user's point of gaze using a geometric transform, which represents the relationship between several infrared (IR) light sources and their corresponding corneal reflections (CRs) in the eye image. Among various methods, the homography normalization (HN) method achieves state-of-the-art performance. However, the geometric transform of the HN method requiring four CRs is infeasible for the case when fewer than four CRs are available. To solve this problem, this paper proposes a new RGE method based on three alternative geometric transforms, which are adaptive to the number of CRs. Unlike the HN method, the proposed method not only can operate with two or three CRs, but can also provide superior accuracy. To further enhance the performance, an effective error correction method is also proposed. By combining the introduced transforms with the error-correction method, the proposed method not only provides high accuracy and robustness for gaze estimation, but also allows for a more flexible system setup with a different number of IR light sources. Experimental results demonstrate the effectiveness of the proposed method.
Directory of Open Access Journals (Sweden)
Bingfei Fan
2017-05-01
Full Text Available Magnetic and inertial sensors have been widely used to estimate the orientation of human segments due to their low cost, compact size and light weight. However, the accuracy of the estimated orientation is easily affected by external factors, especially when the sensor is used in an environment with magnetic disturbances. In this paper, we propose an adaptive method to improve the accuracy of orientation estimations in the presence of magnetic disturbances. The method is based on existing gradient descent algorithms, and it is performed prior to sensor fusion algorithms. The proposed method includes stationary state detection and magnetic disturbance severity determination. The stationary state detection makes this method immune to magnetic disturbances in stationary state, while the magnetic disturbance severity determination helps to determine the credibility of magnetometer data under dynamic conditions, so as to mitigate the negative effect of the magnetic disturbances. The proposed method was validated through experiments performed on a customized three-axis instrumented gimbal with known orientations. The error of the proposed method and the original gradient descent algorithms were calculated and compared. Experimental results demonstrate that in stationary state, the proposed method is completely immune to magnetic disturbances, and in dynamic conditions, the error caused by magnetic disturbance is reduced by 51.2% compared with original MIMU gradient descent algorithm.
Kim, Sooyeon; Moses, Tim; Yoo, Hanwook Henry
2015-01-01
The purpose of this inquiry was to investigate the effectiveness of item response theory (IRT) proficiency estimators in terms of estimation bias and error under multistage testing (MST). We chose a 2-stage MST design in which 1 adaptation to the examinees' ability levels takes place. It includes 4 modules (1 at Stage 1, 3 at Stage 2) and 3 paths…
Brovelli, M. A.; Oxoli, D.; Zurbarán, M. A.
2016-06-01
During the past years Web 2.0 technologies have caused the emergence of platforms where users can share data related to their activities which in some cases are then publicly released with open licenses. Popular categories for this include community platforms where users can upload GPS tracks collected during slow travel activities (e.g. hiking, biking and horse riding) and platforms where users share their geolocated photos. However, due to the high heterogeneity of the information available on the Web, the sole use of these user-generated contents makes it an ambitious challenge to understand slow mobility flows as well as to detect the most visited locations in a region. Exploiting the available data on community sharing websites allows to collect near real-time open data streams and enables rigorous spatial-temporal analysis. This work presents an approach for collecting, unifying and analysing pointwise geolocated open data available from different sources with the aim of identifying the main locations and destinations of slow mobility activities. For this purpose, we collected pointwise open data from the Wikiloc platform, Twitter, Flickr and Foursquare. The analysis was confined to the data uploaded in Lombardy Region (Northern Italy) - corresponding to millions of pointwise data. Collected data was processed through the use of Free and Open Source Software (FOSS) in order to organize them into a suitable database. This allowed to run statistical analyses on data distribution in both time and space by enabling the detection of users' slow mobility preferences as well as places of interest at a regional scale.
Directory of Open Access Journals (Sweden)
M. A. Brovelli
2016-06-01
Full Text Available During the past years Web 2.0 technologies have caused the emergence of platforms where users can share data related to their activities which in some cases are then publicly released with open licenses. Popular categories for this include community platforms where users can upload GPS tracks collected during slow travel activities (e.g. hiking, biking and horse riding and platforms where users share their geolocated photos. However, due to the high heterogeneity of the information available on the Web, the sole use of these user-generated contents makes it an ambitious challenge to understand slow mobility flows as well as to detect the most visited locations in a region. Exploiting the available data on community sharing websites allows to collect near real-time open data streams and enables rigorous spatial-temporal analysis. This work presents an approach for collecting, unifying and analysing pointwise geolocated open data available from different sources with the aim of identifying the main locations and destinations of slow mobility activities. For this purpose, we collected pointwise open data from the Wikiloc platform, Twitter, Flickr and Foursquare. The analysis was confined to the data uploaded in Lombardy Region (Northern Italy – corresponding to millions of pointwise data. Collected data was processed through the use of Free and Open Source Software (FOSS in order to organize them into a suitable database. This allowed to run statistical analyses on data distribution in both time and space by enabling the detection of users’ slow mobility preferences as well as places of interest at a regional scale.
International Development Research Centre (IDRC) Digital Library (Canada)
building skills, knowledge or networks on adaptation, ... the African partners leading the AfricaAdapt network, together with the UK-based Institute of Development Studies; and ... UNCCD Secretariat, Regional Coordination Unit for Africa, Tunis, Tunisia .... 26 Rural–urban Cooperation on Water Management in the Context of.
Achieving Optimal Quantum Acceleration of Frequency Estimation Using Adaptive Coherent Control.
Naghiloo, M; Jordan, A N; Murch, K W
2017-11-03
Precision measurements of frequency are critical to accurate time keeping and are fundamentally limited by quantum measurement uncertainties. While for time-independent quantum Hamiltonians the uncertainty of any parameter scales at best as 1/T, where T is the duration of the experiment, recent theoretical works have predicted that explicitly time-dependent Hamiltonians can yield a 1/T^{2} scaling of the uncertainty for an oscillation frequency. This quantum acceleration in precision requires coherent control, which is generally adaptive. We experimentally realize this quantum improvement in frequency sensitivity with superconducting circuits, using a single transmon qubit. With optimal control pulses, the theoretically ideal frequency precision scaling is reached for times shorter than the decoherence time. This result demonstrates a fundamental quantum advantage for frequency estimation.
Directory of Open Access Journals (Sweden)
Marco Lombardo
Full Text Available PURPOSE: To investigate the influence of various technical factors on the variation of cone packing density estimates in adaptive optics flood illuminated retinal images. METHODS: Adaptive optics images of the photoreceptor mosaic were obtained in fifteen healthy subjects. The cone density and Voronoi diagrams were assessed in sampling windows of 320×320 µm, 160×160 µm and 64×64 µm at 1.5 degree temporal and superior eccentricity from the preferred locus of fixation (PRL. The technical factors that have been analyzed included the sampling window size, the corrected retinal magnification factor (RMFcorr, the conversion from radial to linear distance from the PRL, the displacement between the PRL and foveal center and the manual checking of cone identification algorithm. Bland-Altman analysis was used to assess the agreement between cone density estimated within the different sampling window conditions. RESULTS: The cone density declined with decreasing sampling area and data between areas of different size showed low agreement. A high agreement was found between sampling areas of the same size when comparing density calculated with or without using individual RMFcorr. The agreement between cone density measured at radial and linear distances from the PRL and between data referred to the PRL or the foveal center was moderate. The percentage of Voronoi tiles with hexagonal packing arrangement was comparable between sampling areas of different size. The boundary effect, presence of any retinal vessels, and the manual selection of cones missed by the automated identification algorithm were identified as the factors influencing variation of cone packing arrangements in Voronoi diagrams. CONCLUSIONS: The sampling window size is the main technical factor that influences variation of cone density. Clear identification of each cone in the image and the use of a large buffer zone are necessary to minimize factors influencing variation of Voronoi
Directory of Open Access Journals (Sweden)
Max Berniker
2011-10-01
Full Text Available Recent studies suggest that motor adaptation is the result of multiple, perhaps linear processes each with distinct time scales. While these models are consistent with some motor phenomena, they can neither explain the relatively fast re-adaptation after a long washout period, nor savings on a subsequent day. Here we examined if these effects can be explained if we assume that the CNS stores and retrieves movement parameters based on their possible relevance. We formalize this idea with a model that infers not only the sources of potential motor errors, but also their relevance to the current motor circumstances. In our model adaptation is the process of re-estimating parameters that represent the body and the world. The likelihood of a world parameter being relevant is then based on the mismatch between an observed movement and that predicted when not compensating for the estimated world disturbance. As such, adapting to large motor errors in a laboratory setting should alert subjects that disturbances are being imposed on them, even after motor performance has returned to baseline. Estimates of this external disturbance should be relevant both now and in future laboratory settings. Estimated properties of our bodies on the other hand should always be relevant. Our model demonstrates savings, interference, spontaneous rebound and differences between adaptation to sudden and gradual disturbances. We suggest that many issues concerning savings and interference can be understood when adaptation is conditioned on the relevance of parameters.
Directory of Open Access Journals (Sweden)
Hossein Riahi Modvar
2008-09-01
Full Text Available Longitudinal dispersion coefficient in rivers and natural streams is usually estimated by simple inaccurate empirical relations because of the complexity of the phenomenon. In this study, the adaptive neuro-fuzzy inference system (ANFIS is used to develop a new flexible tool for predicting the longitudinal dispersion coefficient. The system has the ability to understand and realize the phenomenon without the need for mathematical governing equations.. The training and testing of this new model are accomplished using a set of available published filed data. Several statistical and graphical criteria are used to check the accuracy of the model. The dispersion coefficient values predicted by the ANFIS model compares satisfactorily with the measured data. The predicted values are also compared with those predicted by existing empirical equations reported in the literature to find that the ANFIS model with R2=0.99 and RMSE=15.18 in training stage and R2=0.91 and RMSE=187.8 in testing stage is superior in predicting the dispersion coefficient to the most accurate empirical equation with R2=0.48 and RMSE=295.7. The proposed methodology is a new approach to estimating dispersion coefficient in streams and can be combined with mathematical models of pollutant transfer or real-time updating of these models.
Yang, Yanfu; Xiang, Qian; Zhang, Qun; Zhou, Zhongqing; Jiang, Wen; He, Qianwen; Yao, Yong
2017-09-01
We propose a joint estimation scheme for fast, accurate, and robust frequency offset (FO) estimation along with phase estimation based on modified adaptive Kalman filter (MAKF). The scheme consists of three key modules: extend Kalman filter (EKF), lock detector, and FO cycle slip recovery. The EKF module estimates time-varying phase induced by both FO and laser phase noise. The lock detector module makes decision between acquisition mode and tracking mode and consequently sets the EKF tuning parameter in an adaptive manner. The third module can detect possible cycle slip in the case of large FO and make proper correction. Based on the simulation and experimental results, the proposed MAKF has shown excellent estimation performance featuring high accuracy, fast convergence, as well as the capability of cycle slip recovery.
Directory of Open Access Journals (Sweden)
Z. Lari
2012-07-01
Full Text Available Over the past few years, LiDAR systems have been established as a leading technology for the acquisition of high density point clouds over physical surfaces. These point clouds will be processed for the extraction of geo-spatial information. Local point density is one of the most important properties of the point cloud that highly affects the performance of data processing techniques and the quality of extracted information from these data. Therefore, it is necessary to define a standard methodology for the estimation of local point density indices to be considered for the precise processing of LiDAR data. Current definitions of local point density indices, which only consider the 2D neighbourhood of individual points, are not appropriate for 3D LiDAR data and cannot be applied for laser scans from different platforms. In order to resolve the drawbacks of these methods, this paper proposes several approaches for the estimation of the local point density index which take the 3D relationship among the points and the physical properties of the surfaces they belong to into account. In the simplest approach, an approximate value of the local point density for each point is defined while considering the 3D relationship among the points. In the other approaches, the local point density is estimated by considering the 3D neighbourhood of the point in question and the physical properties of the surface which encloses this point. The physical properties of the surfaces enclosing the LiDAR points are assessed through eigen-value analysis of the 3D neighbourhood of individual points and adaptive cylinder methods. This paper will discuss these approaches and highlight their impact on various LiDAR data processing activities (i.e., neighbourhood definition, region growing, segmentation, boundary detection, and classification. Experimental results from airborne and terrestrial LiDAR data verify the efficacy of considering local point density variation for
Directory of Open Access Journals (Sweden)
H.Z. Igamberdiyev
2014-07-01
Full Text Available Dynamic systems condition estimation regularization algorithms in the conditions of signals and hindrances statistical characteristics aprioristic uncertainty are offered. Regular iterative algorithms of strengthening matrix factor elements of the Kalman filter, allowing to adapt the filter to changing hindrance-alarm conditions are developed. Steady adaptive estimation algorithms of a condition vector in the aprioristic uncertainty conditions of covariance matrixes of object noise and the measurements hindrances providing a certain roughness of filtration process in relation to changing statistical characteristics of signals information parameters are offered. Offered practical realization results of the dynamic systems condition estimation algorithms are given at the adaptive management systems synthesis problems solution by technological processes of granulation drying of an ammophos pulp and receiving ammonia.
Directory of Open Access Journals (Sweden)
S. Mohan Krishna
2016-09-01
Full Text Available This paper presents a real-time simulation study of Model Reference Adaptive System based rotor speed estimator with parallel stator resistance adaptation mechanism for speed sensorless induction motor drive. Both, the traditional Proportional Integral and Fuzzy logic based control mechanisms are utilised for stator resistance adaptation, while, the rotor speed is estimated parallely by means of Proportional Integral based mechanism. The estimator's response to dynamic changes in Load perturbation and doubling of the nominal value of the actual stator resistance of the motor is observed. The superiority of the fuzzy based stator resistance adaptation in the Model Reference Adaptive System estimator is proved through results validated in real-time. The purpose of employing a fairly new real-time platform is to reduce the test and prototype time. The model is initially built using Matlab/Simulink blocksets and the results are validated in real time using RT-Lab. The RT-lab blocksets are integrated into the Simulink model and then executed in real-time using the OP-4500 target developed by Opal-RT. The real-time simulation results are observed in the workstation.
Bargatze, L. F.
2015-12-01
Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted
Balas, Mark J.; Thapa Magar, Kaman S.; Frost, Susan A.
2013-01-01
A theory called Adaptive Disturbance Tracking Control (ADTC) is introduced and used to track the Tip Speed Ratio (TSR) of 5 MW Horizontal Axis Wind Turbine (HAWT). Since ADTC theory requires wind speed information, a wind disturbance generator model is combined with lower order plant model to estimate the wind speed as well as partial states of the wind turbine. In this paper, we present a proof of stability and convergence of ADTC theory with lower order estimator and show that the state feedback can be adaptive.
Global error estimation based on the tolerance proportionality for some adaptive Runge-Kutta codes
Calvo, M.; González-Pinto, S.; Montijano, J. I.
2008-09-01
Modern codes for the numerical solution of Initial Value Problems (IVPs) in ODEs are based in adaptive methods that, for a user supplied tolerance [delta], attempt to advance the integration selecting the size of each step so that some measure of the local error is [similar, equals][delta]. Although this policy does not ensure that the global errors are under the prescribed tolerance, after the early studies of Stetter [Considerations concerning a theory for ODE-solvers, in: R. Burlisch, R.D. Grigorieff, J. Schröder (Eds.), Numerical Treatment of Differential Equations, Proceedings of Oberwolfach, 1976, Lecture Notes in Mathematics, vol. 631, Springer, Berlin, 1978, pp. 188-200; Tolerance proportionality in ODE codes, in: R. März (Ed.), Proceedings of the Second Conference on Numerical Treatment of Ordinary Differential Equations, Humbold University, Berlin, 1980, pp. 109-123] and the extensions of Higham [Global error versus tolerance for explicit Runge-Kutta methods, IMA J. Numer. Anal. 11 (1991) 457-480; The tolerance proportionality of adaptive ODE solvers, J. Comput. Appl. Math. 45 (1993) 227-236; The reliability of standard local error control algorithms for initial value ordinary differential equations, in: Proceedings: The Quality of Numerical Software: Assessment and Enhancement, IFIP Series, Springer, Berlin, 1997], it has been proved that in many existing explicit Runge-Kutta codes the global errors behave asymptotically as some rational power of [delta]. This step-size policy, for a given IVP, determines at each grid point tn a new step-size hn+1=h(tn;[delta]) so that h(t;[delta]) is a continuous function of t. In this paper a study of the tolerance proportionality property under a discontinuous step-size policy that does not allow to change the size of the step if the step-size ratio between two consecutive steps is close to unity is carried out. This theory is applied to obtain global error estimations in a few problems that have been solved with
Reservoir characterisation by a binary level set method and adaptive multiscale estimation
Energy Technology Data Exchange (ETDEWEB)
Nielsen, Lars Kristian
2006-01-15
The main focus of this work is on estimation of the absolute permeability as a solution of an inverse problem. We have both considered a single-phase and a two-phase flow model. Two novel approaches have been introduced and tested numerical for solving the inverse problems. The first approach is a multi scale zonation technique which is treated in Paper A. The purpose of the work in this paper is to find a coarse scale solution based on production data from wells. In the suggested approach, the robustness of an already developed method, the adaptive multi scale estimation (AME), has been improved by utilising information from several candidate solutions generated by a stochastic optimizer. The new approach also suggests a way of combining a stochastic and a gradient search method, which in general is a problematic issue. The second approach is a piecewise constant level set approach and is applied in Paper B, C, D and E. Paper B considers the stationary single-phase problem, while Paper C, D and E use a two-phase flow model. In the two-phase flow problem we have utilised information from both production data in wells and spatially distributed data gathered from seismic surveys. Due to the higher content of information provided by the spatially distributed data, we search solutions on a slightly finer scale than one typically does with only production data included. The applied level set method is suitable for reconstruction of fields with a supposed known facies-type of solution. That is, the solution should be close to piecewise constant. This information is utilised through a strong restriction of the number of constant levels in the estimate. On the other hand, the flexibility in the geometries of the zones is much larger for this method than in a typical zonation approach, for example the multi scale approach applied in Paper A. In all these papers, the numerical studies are done on synthetic data sets. An advantage of synthetic data studies is that the true
Adaptive multiscale MCMC algorithm for uncertainty quantification in seismic parameter estimation
Tan, Xiaosi
2014-08-05
Formulating an inverse problem in a Bayesian framework has several major advantages (Sen and Stoffa, 1996). It allows finding multiple solutions subject to flexible a priori information and performing uncertainty quantification in the inverse problem. In this paper, we consider Bayesian inversion for the parameter estimation in seismic wave propagation. The Bayes\\' theorem allows writing the posterior distribution via the likelihood function and the prior distribution where the latter represents our prior knowledge about physical properties. One of the popular algorithms for sampling this posterior distribution is Markov chain Monte Carlo (MCMC), which involves making proposals and calculating their acceptance probabilities. However, for large-scale problems, MCMC is prohibitevely expensive as it requires many forward runs. In this paper, we propose a multilevel MCMC algorithm that employs multilevel forward simulations. Multilevel forward simulations are derived using Generalized Multiscale Finite Element Methods that we have proposed earlier (Efendiev et al., 2013a; Chung et al., 2013). Our overall Bayesian inversion approach provides a substantial speed-up both in the process of the sampling via preconditioning using approximate posteriors and the computation of the forward problems for different proposals by using the adaptive nature of multiscale methods. These aspects of the method are discussed n the paper. This paper is motivated by earlier work of M. Sen and his collaborators (Hong and Sen, 2007; Hong, 2008) who proposed the development of efficient MCMC techniques for seismic applications. In the paper, we present some preliminary numerical results.
Im, Subin; Min, Soonhong
2013-04-01
Exploratory factor analyses of the Kirton Adaption-Innovation Inventory (KAI), which serves to measure individual cognitive styles, generally indicate three factors: sufficiency of originality, efficiency, and rule/group conformity. In contrast, a 2005 study by Im and Hu using confirmatory factor analysis supported a four-factor structure, dividing the sufficiency of originality dimension into two subdimensions, idea generation and preference for change. This study extends Im and Hu's (2005) study of a derived version of the KAI by providing additional evidence of the four-factor structure. Specifically, the authors test the robustness of the parameter estimates to the violation of normality assumptions in the sample using bootstrap methods. A bias-corrected confidence interval bootstrapping procedure conducted among a sample of 356 participants--members of the Arkansas Household Research Panel, with middle SES and average age of 55.6 yr. (SD = 13.9)--showed that the four-factor model with two subdimensions of sufficiency of originality fits the data significantly better than the three-factor model in non-normality conditions.
Li, Zheng; Zhang, Hai; Zhou, Qifan; Che, Huan
2017-09-05
The main objective of the introduced study is to design an adaptive Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) tightly-coupled integration system that can provide more reliable navigation solutions by making full use of an adaptive Kalman filter (AKF) and satellite selection algorithm. To achieve this goal, we develop a novel redundant measurement noise covariance estimation (RMNCE) theorem, which adaptively estimates measurement noise properties by analyzing the difference sequences of system measurements. The proposed RMNCE approach is then applied to design both a modified weighted satellite selection algorithm and a type of adaptive unscented Kalman filter (UKF) to improve the performance of the tightly-coupled integration system. In addition, an adaptive measurement noise covariance expanding algorithm is developed to mitigate outliers when facing heavy multipath and other harsh situations. Both semi-physical simulation and field experiments were conducted to evaluate the performance of the proposed architecture and were compared with state-of-the-art algorithms. The results validate that the RMNCE provides a significant improvement in the measurement noise covariance estimation and the proposed architecture can improve the accuracy and reliability of the INS/GNSS tightly-coupled systems. The proposed architecture can effectively limit positioning errors under conditions of poor GNSS measurement quality and outperforms all the compared schemes.
Hofmann, K.M.; Gavrilla, D.M.
2009-01-01
We present a system for the estimation of unconstrained 3D human upper body movement from multiple cameras. Its main novelty lies in the integration of three components: single frame pose recovery, temporal integration and model adaptation. Single frame pose recovery consists of a hypothesis
Fuady, Ahmad; Houweling, Tanja A; Mansyur, Muchtaruddin; Richardus, Jan H
2018-01-01
Indonesia is the second-highest country for tuberculosis (TB) incidence worldwide. Hence, it urgently requires improvements and innovations beyond the strategies that are currently being implemented throughout the country. One fundamental step in monitoring its progress is by preparing a validated tool to measure total patient costs and catastrophic total costs. The World Health Organization (WHO) recommends using a version of the generic questionnaire that has been adapted to the local cultural context in order to interpret findings correctly. This study is aimed to adapt the Tool to Estimate Patient Costs questionnaire into the Indonesian context, which measures total costs and catastrophic total costs for tuberculosis-affected households. the tool was adapted using best-practice guidelines. On the basis of a pre-test performed in a previous study (referred to as Phase 1 Study), we refined the adaptation process by comparing it with the generic tool introduced by the WHO. We also held an expert committee review and performed pre-testing by interviewing 30 TB patients. After pre-testing, the tool was provided with complete explanation sheets for finalization. seventy-two major changes were made during the adaptation process including changing the answer choices to match the Indonesian context, refining the flow of questions, deleting questions, changing some words and restoring original questions that had been changed in Phase 1 Study. Participants indicated that most questions were clear and easy to understand. To address recall difficulties by the participants, we made some adaptations to obtain data that might be missing, such as tracking data to medical records, developing a proxy of costs and guiding interviewers to ask for a specific value when participants were uncertain about the estimated market value of property they had sold. the adapted Tool to Estimate Patient Costs in Bahasa Indonesia is comprehensive and ready for use in future studies on TB
Directory of Open Access Journals (Sweden)
Yong Tian
2014-12-01
Full Text Available State of charge (SOC estimation is essential to battery management systems in electric vehicles (EVs to ensure the safe operations of batteries and providing drivers with the remaining range of the EVs. A number of estimation algorithms have been developed to get an accurate SOC value because the SOC cannot be directly measured with sensors and is closely related to various factors, such as ambient temperature, current rate and battery aging. In this paper, two model-based adaptive algorithms, including the adaptive unscented Kalman filter (AUKF and adaptive slide mode observer (ASMO are applied and compared in terms of convergence behavior, tracking accuracy, computational cost and estimation robustness against parameter uncertainties of the battery model in SOC estimation. Two typical driving cycles, including the Dynamic Stress Test (DST and New European Driving Cycle (NEDC are applied to evaluate the performance of the two algorithms. Comparison results show that the AUKF has merits in convergence ability and tracking accuracy with an accurate battery model, while the ASMO has lower computational cost and better estimation robustness against parameter uncertainties of the battery model.
Directory of Open Access Journals (Sweden)
Yuan Zou
2010-09-01
Full Text Available In order to safely and efficiently use the power as well as to extend the lifetime of the traction battery pack, accurate estimation of State of Charge (SoC is very important and necessary. This paper presents an adaptive observer-based technique for estimating SoC of a lithium-ion battery pack used in an electric vehicle (EV. The RC equivalent circuit model in ADVISOR is applied to simulate the lithium-ion battery pack. The parameters of the battery model as a function of SoC, are identified and optimized using the numerically nonlinear least squares algorithm, based on an experimental data set. By means of the optimized model, an adaptive Luenberger observer is built to estimate online the SoC of the lithium-ion battery pack. The observer gain is adaptively adjusted using a stochastic gradient approach so as to reduce the error between the estimated battery output voltage and the filtered battery terminal voltage measurement. Validation results show that the proposed technique can accurately estimate SoC of the lithium-ion battery pack without a heavy computational load.
Jakeman, J. D.; Wildey, T.
2015-01-01
In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the physical discretization error and the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity of the sparse grid. Utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this paper we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation.
International Nuclear Information System (INIS)
Jakeman, J.D.; Wildey, T.
2015-01-01
In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the physical discretization error and the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity of the sparse grid. Utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this paper we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation
Directory of Open Access Journals (Sweden)
Eyad K Almaita
2017-03-01
Keywords: Energy efficiency, Power quality, Radial basis function, neural networks, adaptive, harmonic. Article History: Received Dec 15, 2016; Received in revised form Feb 2nd 2017; Accepted 13rd 2017; Available online How to Cite This Article: Almaita, E.K and Shawawreh J.Al (2017 Improving Stability and Convergence for Adaptive Radial Basis Function Neural Networks Algorithm (On-Line Harmonics Estimation Application. International Journal of Renewable Energy Develeopment, 6(1, 9-17. http://dx.doi.org/10.14710/ijred.6.1.9-17
Directory of Open Access Journals (Sweden)
Chidentree Treesatayapun
2015-06-01
Full Text Available An adaptive control scheme based on data-driven controller (DDC is proposed in this article. Unlike several DDC techniques, the proposed controller is constructed by an adaptive fuzzy rule emulated network (FREN which is able to include human knowledge based on controlled plant's input–output signals within the format of IF-THEN rules. Regarding to this advantage, an on-line estimation of pseudo partial derivative (PPD and resetting algorithms, which are commonly used by DDC, can be omitted here. Furthermore, a novel adaptive algorithm is introduced to minimize for both tracking error and control effort with stability analysis for the closed-loop system. The experimental system with brushed DC-motor current control is constructed to validate the performance of the proposed control scheme. Comparative results with conventional DDC and radial basis function (RBF controllers demonstrate that the proposed controller can provide the less tracking error and minimize the control effort.
Climate Change and Risk Management: Challenges for Insurance, Adaptation, and Loss Estimation
Kousky, Carolyn; Cooke, Roger
2009-01-01
Adapting to climate change will not only require responding to the physical effects of global warming, but will also require adapting the way we conceptualize, measure, and manage risks. Climate change is creating new risks, altering the risks we already face, and also, importantly, impacting the interdependencies between these risks. In this paper we focus on three particular phenomena of climate related risks that will require a change in our thinking about risk management: global micro-cor...
Belkhatir, Zehor
2016-08-05
This paper deals with joint parameters and input estimation for coupled PDE-ODE system. The system consists of a damped wave equation and an infinite dimensional ODE. This model describes the spatiotemporal hemodynamic response in the brain and the objective is to characterize brain regions using functional Magnetic Resonance Imaging (fMRI) data. For this reason, we propose an adaptive estimator and prove the asymptotic convergence of the state, the unknown input and the unknown parameters. The proof is based on a Lyapunov approach combined with a priori identifiability assumptions. The performance of the proposed observer is illustrated through some simulation results.
Directory of Open Access Journals (Sweden)
Ahmad Fuady
2018-04-01
Full Text Available Background: Indonesia is the second-highest country for tuberculosis (TB incidence worldwide. Hence, it urgently requires improvements and innovations beyond the strategies that are currently being implemented throughout the country. One fundamental step in monitoring its progress is by preparing a validated tool to measure total patient costs and catastrophic total costs. The World Health Organization (WHO recommends using a version of the generic questionnaire that has been adapted to the local cultural context in order to interpret findings correctly. This study is aimed to adapt the Tool to Estimate Patient Costs questionnaire into the Indonesian context, which measures total costs and catastrophic total costs for tuberculosis-affected households. Methods: the tool was adapted using best-practice guidelines. On the basis of a pre-test performed in a previous study (referred to as Phase 1 Study, we refined the adaptation process by comparing it with the generic tool introduced by the WHO. We also held an expert committee review and performed pre-testing by interviewing 30 TB patients. After pre-testing, the tool was provided with complete explanation sheets for finalization. Results: seventy-two major changes were made during the adaptation process including changing the answer choices to match the Indonesian context, refining the flow of questions, deleting questions, changing some words and restoring original questions that had been changed in Phase 1 Study. Participants indicated that most questions were clear and easy to understand. To address recall difficulties by the participants, we made some adaptations to obtain data that might be missing, such as tracking data to medical records, developing a proxy of costs and guiding interviewers to ask for a specific value when participants were uncertain about the estimated market value of property they had sold. Conclusion: the adapted Tool to Estimate Patient Costs in Bahasa Indonesia is
International Nuclear Information System (INIS)
Xu, Meng; Droguett, Enrique López; Lins, Isis Didier; Chagas Moura, Márcio das
2017-01-01
The q-Weibull model is based on the Tsallis non-extensive entropy and is able to model various behaviors of the hazard rate function, including bathtub curves, by using a single set of parameters. Despite its flexibility, the q-Weibull has not been widely used in reliability applications partly because of the complicated parameters estimation. In this work, the parameters of the q-Weibull are estimated by the maximum likelihood (ML) method. Due to the intricate system of nonlinear equations, derivative-based optimization methods may fail to converge. Thus, the heuristic optimization method of artificial bee colony (ABC) is used instead. To deal with the slow convergence of ABC, it is proposed an adaptive hybrid ABC (AHABC) algorithm that dynamically combines Nelder-Mead simplex search method with ABC for the ML estimation of the q-Weibull parameters. Interval estimates for the q-Weibull parameters, including confidence intervals based on the ML asymptotic theory and on bootstrap methods, are also developed. The AHABC is validated via numerical experiments involving the q-Weibull ML for reliability applications and results show that it produces faster and more accurate convergence when compared to ABC and similar approaches. The estimation procedure is applied to real reliability failure data characterized by a bathtub-shaped hazard rate. - Highlights: • Development of an Adaptive Hybrid ABC (AHABC) algorithm for q-Weibull distribution. • AHABC combines local Nelder-Mead simplex method with ABC to enhance local search. • AHABC efficiently finds the optimal solution for the q-Weibull ML problem. • AHABC outperforms ABC and self-adaptive hybrid ABC in accuracy and convergence speed. • Useful model for reliability data with non-monotonic hazard rate.
Wei, Zhongbao; Tseng, King Jet; Wai, Nyunt; Lim, Tuti Mariana; Skyllas-Kazacos, Maria
2016-11-01
Reliable state estimate depends largely on an accurate battery model. However, the parameters of battery model are time varying with operating condition variation and battery aging. The existing co-estimation methods address the model uncertainty by integrating the online model identification with state estimate and have shown improved accuracy. However, the cross interference may arise from the integrated framework to compromise numerical stability and accuracy. Thus this paper proposes the decoupling of model identification and state estimate to eliminate the possibility of cross interference. The model parameters are online adapted with the recursive least squares (RLS) method, based on which a novel joint estimator based on extended Kalman Filter (EKF) is formulated to estimate the state of charge (SOC) and capacity concurrently. The proposed joint estimator effectively compresses the filter order which leads to substantial improvement in the computational efficiency and numerical stability. Lab scale experiment on vanadium redox flow battery shows that the proposed method is highly authentic with good robustness to varying operating conditions and battery aging. The proposed method is further compared with some existing methods and shown to be superior in terms of accuracy, convergence speed, and computational cost.
Directory of Open Access Journals (Sweden)
Zhou Hao
2015-06-01
Full Text Available The traditional MUltiple SIgnal Classification (MUSIC algorithm requires significant computational effort and can not be employed for the Direction Of Arrival (DOA estimation of targets in a low-altitude multipath environment. As such, a novel MUSIC approach is proposed on the basis of the algorithm of Adaptive Step Glowworm Swarm Optimization (ASGSO. The virtual spatial smoothing of the matrix formed by each snapshot is used to realize the decorrelation of the multipath signal and the establishment of a fullorder correlation matrix. ASGSO optimizes the function and estimates the elevation of the target. The simulation results suggest that the proposed method can overcome the low altitude multipath effect and estimate the DOA of target readily and precisely without radar effective aperture loss.
Estimation of an Examinee's Ability in the Web-Based Computerized Adaptive Testing Program IRT-CAT
Directory of Open Access Journals (Sweden)
Yoon-Hwan Lee
2006-11-01
Full Text Available We developed a program to estimate an examinee's ability in order to provide freely available access to a web-based computerized adaptive testing (CAT program. We used PHP and Java Script as the program languages, PostgresSQL as the database management system on an Apache web server and Linux as the operating system. A system which allows for user input and searching within inputted items and creates tests was constructed. We performed an ability estimation on each test based on a Rasch model and 2- or 3-parametric logistic models. Our system provides an algorithm for a web-based CAT, replacing previous personal computer-based ones, and makes it possible to estimate an examinee?占퐏 ability immediately at the end of test.
DEFF Research Database (Denmark)
Skovgård Olsen, Anders; Zhou, Qianqian; Linde, Jens Jørgen
Estimating the expected annual damage (EAD) due to flooding in an urban area is of great interest for urban water managers and other stakeholders. It is a strong indicator for a given area showing how it will be affected by climate change and how much can be gained by implementing adaptation...... measures. This study investigates three different methods for estimating the EAD based on a loglinear relation between the damage costs and the return periods, one of which has been used in previous studies. The results show with the increased amount of data points there appears to be a shift in the log......-linear relation which could be contributed by the Danish design standards for drainage systems. Three different methods for estimating the EAD were tested and the choice of method is less important than accounting for the log-linear shift. This then also means that the statistical approximation of the EAD used...
Adaptive Variance Scaling in Continuous Multi-Objective Estimation-of-Distribution Algorithms
P.A.N. Bosman (Peter); D. Thierens (Dirk); D. Thierens (Dirk)
2007-01-01
htmlabstractRecent research into single-objective continuous Estimation-of-Distribution Algorithms (EDAs) has shown that when maximum-likelihood estimations are used for parametric distributions such as the normal distribution, the EDA can easily suffer from premature convergence. In this paper we
Time-Series Adaptive Estimation of Vaccination Uptake Using Web Search Queries
DEFF Research Database (Denmark)
Dalum Hansen, Niels; Mølbak, Kåre; Cox, Ingemar J.
2017-01-01
Estimating vaccination uptake is an integral part of ensuring public health. It was recently shown that vaccination uptake can be estimated automatically from web data, instead of slowly collected clinical records or population surveys [2]. All prior work in this area assumes that features of vac...
Miles, Jeffrey Hilton
2015-01-01
A cross-power spectrum phase based adaptive technique is discussed which iteratively determines the time delay between two digitized signals that are coherent. The adaptive delay algorithm belongs to a class of algorithms that identifies a minimum of a pattern matching function. The algorithm uses a gradient technique to find the value of the adaptive delay that minimizes a cost function based in part on the slope of a linear function that fits the measured cross power spectrum phase and in part on the standard error of the curve fit. This procedure is applied to data from a Honeywell TECH977 static-engine test. Data was obtained using a combustor probe, two turbine exit probes, and far-field microphones. Signals from this instrumentation are used estimate the post-combustion residence time in the combustor. Comparison with previous studies of the post-combustion residence time validates this approach. In addition, the procedure removes the bias due to misalignment of signals in the calculation of coherence which is a first step in applying array processing methods to the magnitude squared coherence data. The procedure also provides an estimate of the cross-spectrum phase-offset.
Moderate deviations principles for the kernel estimator of ...
African Journals Online (AJOL)
Abstract. The aim of this paper is to provide pointwise and uniform moderate deviations principles for the kernel estimator of a nonrandom regression function. Moreover, we give an application of these moderate deviations principles to the construction of condence regions for the regression function. Resume. L'objectif de ...
Karakawa, Ayako; Murata, Hiroshi; Hirasawa, Hiroyo; Mayama, Chihiro; Asaoka, Ryo
2013-01-01
To compare the performance of newly proposed point-wise linear regression (PLR) with the binomial test (binomial PLR) against mean deviation (MD) trend analysis and permutation analyses of PLR (PoPLR), in detecting global visual field (VF) progression in glaucoma. 15 VFs (Humphrey Field Analyzer, SITA standard, 24-2) were collected from 96 eyes of 59 open angle glaucoma patients (6.0 ± 1.5 [mean ± standard deviation] years). Using the total deviation of each point on the 2(nd) to 16(th) VFs (VF2-16), linear regression analysis was carried out. The numbers of VF test points with a significant trend at various probability levels (pbinomial test (one-side). A VF series was defined as "significant" if the median p-value from the binomial test was binomial PLR method (0.14 to 0.86) was significantly higher than MD trend analysis (0.04 to 0.89) and PoPLR (0.09 to 0.93). The PIS of the proposed method (0.0 to 0.17) was significantly lower than the MD approach (0.0 to 0.67) and PoPLR (0.07 to 0.33). The PBNS of the three approaches were not significantly different. The binomial BLR method gives more consistent results than MD trend analysis and PoPLR, hence it will be helpful as a tool to 'flag' possible VF deterioration.
Adaptive multiscale MCMC algorithm for uncertainty quantification in seismic parameter estimation
Tan, Xiaosi; Gibson, Richard L.; Leung, Wing Tat; Efendiev, Yalchin R.
2014-01-01
problem. In this paper, we consider Bayesian inversion for the parameter estimation in seismic wave propagation. The Bayes' theorem allows writing the posterior distribution via the likelihood function and the prior distribution where the latter represents
Energy Technology Data Exchange (ETDEWEB)
Jassar, S.; Zhao, L. [Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON (Canada); Liao, Z. [Department of Architectural Science, Ryerson University (Canada)
2009-08-15
The heating systems are conventionally controlled by open-loop control systems because of the absence of practical methods for estimating average air temperature in the built environment. An inferential sensor model, based on adaptive neuro-fuzzy inference system modeling, for estimating the average air temperature in multi-zone space heating systems is developed. This modeling technique has the advantage of expert knowledge of fuzzy inference systems (FISs) and learning capability of artificial neural networks (ANNs). A hybrid learning algorithm, which combines the least-square method and the back-propagation algorithm, is used to identify the parameters of the network. This paper describes an adaptive network based inferential sensor that can be used to design closed-loop control for space heating systems. The research aims to improve the overall performance of heating systems, in terms of energy efficiency and thermal comfort. The average air temperature results estimated by using the developed model are strongly in agreement with the experimental results. (author)
Jones, Reese E.; Mandadapu, Kranthi K.
2012-04-01
We present a rigorous Green-Kubo methodology for calculating transport coefficients based on on-the-fly estimates of: (a) statistical stationarity of the relevant process, and (b) error in the resulting coefficient. The methodology uses time samples efficiently across an ensemble of parallel replicas to yield accurate estimates, which is particularly useful for estimating the thermal conductivity of semi-conductors near their Debye temperatures where the characteristic decay times of the heat flux correlation functions are large. Employing and extending the error analysis of Zwanzig and Ailawadi [Phys. Rev. 182, 280 (1969)], 10.1103/PhysRev.182.280 and Frenkel [in Proceedings of the International School of Physics "Enrico Fermi", Course LXXV (North-Holland Publishing Company, Amsterdam, 1980)] to the integral of correlation, we are able to provide tight theoretical bounds for the error in the estimate of the transport coefficient. To demonstrate the performance of the method, four test cases of increasing computational cost and complexity are presented: the viscosity of Ar and water, and the thermal conductivity of Si and GaN. In addition to producing accurate estimates of the transport coefficients for these materials, this work demonstrates precise agreement of the computed variances in the estimates of the correlation and the transport coefficient with the extended theory based on the assumption that fluctuations follow a Gaussian process. The proposed algorithm in conjunction with the extended theory enables the calculation of transport coefficients with the Green-Kubo method accurately and efficiently.
Kunz, Cornelia U; Stallard, Nigel; Parsons, Nicholas; Todd, Susan; Friede, Tim
2017-03-01
Regulatory authorities require that the sample size of a confirmatory trial is calculated prior to the start of the trial. However, the sample size quite often depends on parameters that might not be known in advance of the study. Misspecification of these parameters can lead to under- or overestimation of the sample size. Both situations are unfavourable as the first one decreases the power and the latter one leads to a waste of resources. Hence, designs have been suggested that allow a re-assessment of the sample size in an ongoing trial. These methods usually focus on estimating the variance. However, for some methods the performance depends not only on the variance but also on the correlation between measurements. We develop and compare different methods for blinded estimation of the correlation coefficient that are less likely to introduce operational bias when the blinding is maintained. Their performance with respect to bias and standard error is compared to the unblinded estimator. We simulated two different settings: one assuming that all group means are the same and one assuming that different groups have different means. Simulation results show that the naïve (one-sample) estimator is only slightly biased and has a standard error comparable to that of the unblinded estimator. However, if the group means differ, other estimators have better performance depending on the sample size per group and the number of groups. © 2016 The Authors. Biometrical Journal Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
An adaptive segment method for smoothing lidar signal based on noise estimation
Wang, Yuzhao; Luo, Pingping
2014-10-01
An adaptive segmentation smoothing method (ASSM) is introduced in the paper to smooth the signal and suppress the noise. In the ASSM, the noise is defined as the 3σ of the background signal. An integer number N is defined for finding the changing positions in the signal curve. If the difference of adjacent two points is greater than 3Nσ, the position is recorded as an end point of the smoothing segment. All the end points detected as above are recorded and the curves between them will be smoothed separately. In the traditional method, the end points of the smoothing windows in the signals are fixed. The ASSM creates changing end points in different signals and the smoothing windows could be set adaptively. The windows are always set as the half of the segmentations and then the average smoothing method will be applied in the segmentations. The Iterative process is required for reducing the end-point aberration effect in the average smoothing method and two or three times are enough. In ASSM, the signals are smoothed in the spacial area nor frequent area, that means the frequent disturbance will be avoided. A lidar echo was simulated in the experimental work. The echo was supposed to be created by a space-born lidar (e.g. CALIOP). And white Gaussian noise was added to the echo to act as the random noise resulted from environment and the detector. The novel method, ASSM, was applied to the noisy echo to filter the noise. In the test, N was set to 3 and the Iteration time is two. The results show that, the signal could be smoothed adaptively by the ASSM, but the N and the Iteration time might be optimized when the ASSM is applied in a different lidar.
Revell, James; Mirmehdi, Majid; McNally, Donal
2005-06-01
We present the development and validation of an image based speckle tracking methodology, for determining temporal two-dimensional (2-D) axial and lateral displacement and strain fields from ultrasound video streams. We refine a multiple scale region matching approach incorporating novel solutions to known speckle tracking problems. Key contributions include automatic similarity measure selection to adapt to varying speckle density, quantifying trajectory fields, and spatiotemporal elastograms. Results are validated using tissue mimicking phantoms and in vitro data, before applying them to in vivo musculoskeletal ultrasound sequences. The method presented has the potential to improve clinical knowledge of tendon pathology from carpel tunnel syndrome, inflammation from implants, sport injuries, and many others.
CSIR Research Space (South Africa)
Herselman, PL
2008-09-01
Full Text Available and that is necessary to set the threshold χt as a function of the steering vector Doppler fd. Improvements to the estimation technique are suggested and evaluated where a more localised M is estimated using either frequency agility or the immediate time history... of frequency, calculated as NIM2(fd) = E{z(fd)2}/E2{z(fd)} , (3) where z(fd) is the power spectral density at fd. This is often used to quantify the Rayleigh-likeness of the envelope 0 5 10 15 −500 −250 0 250 500 Doppler frequency [Hz ] NIM2Time [s...
Mechhoud, Sarra; Laleg-Kirati, Taous-Meriem
2016-01-01
In this paper, boundary adaptive estimation of solar radiation in a solar collector plant is investigated. The solar collector is described by a 1D first-order hyperbolic partial differential equation where the solar radiation models the source term
Measurement-Based Transmission Line Parameter Estimation with Adaptive Data Selection Scheme
DEFF Research Database (Denmark)
Li, Changgang; Zhang, Yaping; Zhang, Hengxu
2017-01-01
Accurate parameters of transmission lines are critical for power system operation and control decision making. Transmission line parameter estimation based on measured data is an effective way to enhance the validity of the parameters. This paper proposes a multi-point transmission line parameter...
Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors
Garcia de Marina Peinado, Hector; Espinosa, Felipe; Santos, Carlos
2012-01-01
Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way,
Oscillation estimates relative to p-homogeneous forms and Kato measures data
Directory of Open Access Journals (Sweden)
Marco Biroli
2006-11-01
Full Text Available We state pointwise estimate for the positive subsolutions associated to a p-homogeneous form and nonnegative Radon measures data. As a by-product we establish an oscillation’s estimate for the solutions relative to Kato measures data.
International Nuclear Information System (INIS)
Sreenivasan, P.R.
2014-01-01
Many researchers had suggested a sort of scaling procedure for predicting the quasi-static J–R curves from dynamic J–R curves obtained from instrumented Charpy V-notch (CVN) impact tests using key-curve, compliance or other procedures. Chaouadi, based on extensive tests and literature data, had quantitatively formalized the method and suggested general applicability of his method for a class of steels. In this paper, first, the Chauoadi-procedure is tried on some selected data from the literature (including the data used by Chaouadi and other workers) and an adaptation of the method is suggested using Wallin's as well as Landes's lower bound methods for upper-shelf J–R curve estimation from CVN energy. Using Chaouadi and other data as the benchmark, suitable scaling factors have been determined that enable estimation of quasi-static J–R curves from CVN energy alone, without the need for dynamic CVN J–R curves. The final formulae are given. This new method can be called modified Wallin–Landes procedure. Then this method is applied to fracture toughness and reference temperature (T 0 – ASTM E-1921) estimation from the full Charpy-transition data. The results are compared with those from the author's IGC-procedure, and modifications, if any, are suggested. Based on the new results, it is suggested that the IGC-procedure may be modified as: final T Q-est = T Q-IGC for T Q-Sch dy ≤ 20 °C (in the IGC-procedure the dividing temperature was 60 °C); and for T Q-Sch dy > 20 °C, T Q-IGC = T Q-WLm (different from the IGC-procedre and subscript WLm indicating modified Wallin–Landes procedure). For the 59 or more steels examined (including highly irradiated steels), the T Q-WL estimates at higher temperatures are consistent and conservative; a few non-conservative values are acceptably less than 20 °C, whereas other predictions show non-conservatism of up to 40–50 °C. At lower temperatures, T Q-IGC is consistently conservative and not over
From cutting-edge pointwise cross-section to groupwise reaction rate: A primer
Sublet, Jean-Christophe; Fleming, Michael; Gilbert, Mark R.
2017-09-01
The nuclear research and development community has a history of using both integral and differential experiments to support accurate lattice-reactor, nuclear reactor criticality and shielding simulations, as well as verification and validation efforts of cross sections and emitted particle spectra. An important aspect to this type of analysis is the proper consideration of the contribution of the neutron spectrum in its entirety, with correct propagation of uncertainties and standard deviations derived from Monte Carlo simulations, to the local and total uncertainty in the simulated reactions rates (RRs), which usually only apply to one application at a time. This paper identifies deficiencies in the traditional treatment, and discusses correct handling of the RR uncertainty quantification and propagation, including details of the cross section components in the RR uncertainty estimates, which are verified for relevant applications. The methodology that rigorously captures the spectral shift and cross section contributions to the uncertainty in the RR are discussed with quantified examples that demonstrate the importance of the proper treatment of the spectrum profile and cross section contributions to the uncertainty in the RR and subsequent response functions. The recently developed inventory code FISPACT-II, when connected to the processed nuclear data libraries TENDL-2015, ENDF/B-VII.1, JENDL-4.0u or JEFF-3.2, forms an enhanced multi-physics platform providing a wide variety of advanced simulation methods for modelling activation, transmutation, burnup protocols and simulating radiation damage sources terms. The system has extended cutting-edge nuclear data forms, uncertainty quantification and propagation methods, which have been the subject of recent integral and differential, fission, fusion and accelerators validation efforts. The simulation system is used to accurately and predictively probe, understand and underpin a modern and sustainable understanding
Directory of Open Access Journals (Sweden)
Jonathon Taylor
2018-05-01
Full Text Available Mortality rates rise during hot weather in England, and projected future increases in heatwave frequency and intensity require the development of heat protection measures such as the adaptation of housing to reduce indoor overheating. We apply a combined building physics and health model to dwellings in the West Midlands, UK, using an English Housing Survey (EHS-derived stock model. Regional temperature exposures, heat-related mortality risk, and space heating energy consumption were estimated for 2030s, 2050s, and 2080s medium emissions climates prior to and following heat mitigating, energy-efficiency, and occupant behaviour adaptations. Risk variation across adaptations, dwellings, and occupant types were assessed. Indoor temperatures were greatest in converted flats, while heat mortality rates were highest in bungalows due to the occupant age profiles. Full energy efficiency retrofit reduced regional domestic space heating energy use by 26% but increased summertime heat mortality 3–4%, while reduced façade absorptance decreased heat mortality 12–15% but increased energy consumption by 4%. External shutters provided the largest reduction in heat mortality (37–43%, while closed windows caused a large increase in risk (29–64%. Ensuring adequate post-retrofit ventilation, targeted installation of shutters, and ensuring operable windows in dwellings with heat-vulnerable occupants may save energy and significantly reduce heat-related mortality.
Performance of the JPEG Estimated Spectrum Adaptive Postfilter (JPEG-ESAP) for Low Bit Rates
Linares, Irving (Inventor)
2016-01-01
Frequency-based, pixel-adaptive filtering using the JPEG-ESAP algorithm for low bit rate JPEG formatted color images may allow for more compressed images while maintaining equivalent quality at a smaller file size or bitrate. For RGB, an image is decomposed into three color bands--red, green, and blue. The JPEG-ESAP algorithm is then applied to each band (e.g., once for red, once for green, and once for blue) and the output of each application of the algorithm is rebuilt as a single color image. The ESAP algorithm may be repeatedly applied to MPEG-2 video frames to reduce their bit rate by a factor of 2 or 3, while maintaining equivalent video quality, both perceptually, and objectively, as recorded in the computed PSNR values.
Adaptive super twisting vibration control of a flexible spacecraft with state rate estimation
Malekzadeh, Maryam; Karimpour, Hossein
2018-05-01
The robust attitude and vibration control of a flexible spacecraft trying to perform accurate maneuvers in spite of various sources of uncertainty is addressed here. Difficulties for achieving precise and stable pointing arise from noisy onboard sensors, parameters indeterminacy, outer disturbances as well as un-modeled or hidden dynamics interactions. Based on high-order sliding-mode methods, the non-minimum phase nature of the problem is dealt with through output redefinition. An adaptive super-twisting algorithm (ASTA) is incorporated with its observer counterpart on the system under consideration to get reliable attitude and vibration control in the presence of sensor noise and momentum coupling. The closed-loop efficiency is verified through simulations under various indeterminate situations and got compared to other methods.
From cutting-edge pointwise cross-section to groupwise reaction rate: A primer
Directory of Open Access Journals (Sweden)
Sublet Jean-Christophe
2017-01-01
Full Text Available The nuclear research and development community has a history of using both integral and differential experiments to support accurate lattice-reactor, nuclear reactor criticality and shielding simulations, as well as verification and validation efforts of cross sections and emitted particle spectra. An important aspect to this type of analysis is the proper consideration of the contribution of the neutron spectrum in its entirety, with correct propagation of uncertainties and standard deviations derived from Monte Carlo simulations, to the local and total uncertainty in the simulated reactions rates (RRs, which usually only apply to one application at a time. This paper identifies deficiencies in the traditional treatment, and discusses correct handling of the RR uncertainty quantification and propagation, including details of the cross section components in the RR uncertainty estimates, which are verified for relevant applications. The methodology that rigorously captures the spectral shift and cross section contributions to the uncertainty in the RR are discussed with quantified examples that demonstrate the importance of the proper treatment of the spectrum profile and cross section contributions to the uncertainty in the RR and subsequent response functions. The recently developed inventory code FISPACT-II, when connected to the processed nuclear data libraries TENDL-2015, ENDF/B-VII.1, JENDL-4.0u or JEFF-3.2, forms an enhanced multi-physics platform providing a wide variety of advanced simulation methods for modelling activation, transmutation, burnup protocols and simulating radiation damage sources terms. The system has extended cutting-edge nuclear data forms, uncertainty quantification and propagation methods, which have been the subject of recent integral and differential, fission, fusion and accelerators validation efforts. The simulation system is used to accurately and predictively probe, understand and underpin a modern and
Simon, M.; Dolinar, S.
2005-08-01
A means is proposed for realizing the generalized split-symbol moments estimator (SSME) of signal-to-noise ratio (SNR), i.e., one whose implementation on the average allows for a number of subdivisions (observables), 2L, per symbol beyond the conventional value of two, with other than an integer value of L. In theory, the generalized SSME was previously shown to yield optimum performance for a given true SNR, R, when L=R/sqrt(2) and thus, in general, the resulting estimator was referred to as the fictitious SSME. Here we present a time-multiplexed version of the SSME that allows it to achieve its optimum value of L as above (to the extent that it can be computed as the average of a sum of integers) at each value of SNR and as such turns fiction into non-fiction. Also proposed is an adaptive algorithm that allows the SSME to rapidly converge to its optimum value of L when in fact one has no a priori information about the true value of SNR.
An adaptive spatio-temporal smoothing model for estimating trends and step changes in disease risk
Rushworth, Alastair; Lee, Duncan; Sarran, Christophe
2014-01-01
Statistical models used to estimate the spatio-temporal pattern in disease\\ud risk from areal unit data represent the risk surface for each time period with known\\ud covariates and a set of spatially smooth random effects. The latter act as a proxy\\ud for unmeasured spatial confounding, whose spatial structure is often characterised by\\ud a spatially smooth evolution between some pairs of adjacent areal units while other\\ud pairs exhibit large step changes. This spatial heterogeneity is not c...
Energy Technology Data Exchange (ETDEWEB)
Lipnikov, Konstantin [Los Alamos National Laboratory; Agouzal, Abdellatif [UNIV DE LYON; Vassilevski, Yuri [Los Alamos National Laboratory
2009-01-01
We present a new technology for generating meshes minimizing the interpolation and discretization errors or their gradients. The key element of this methodology is construction of a space metric from edge-based error estimates. For a mesh with N{sub h} triangles, the error is proportional to N{sub h}{sup -1} and the gradient of error is proportional to N{sub h}{sup -1/2} which are optimal asymptotics. The methodology is verified with numerical experiments.
koganova, Z I; Ingel', F I; Antipanova, N A; Legostoeva, T B; Poliakova, O V
2010-01-01
The paper provides the first fragment of a multiparameter study analyzing the influence of environmental pollution, the social and psychological features of a family, and some endogenous factors on genome stability and sensitivity in a developed ferrous metallurgy town. It also gives data on the urine and serum activity of the lysosomal enzyme N-acetyl-b-D-glucosaminidase (NAG) and the serum activity of catalase in an organized contingent of apparently healthy children (n = 178; 6 kindergartens) aged 5-7 years, who live permanently in Magnitogorsk at different distances from the metallurgical works. More than 70% of children selected for examination were found to have average normal levels of activity of the enzymes studied. According to the average levels of enzyme activity, there were only 2 kindergartens (both from the left-bank region). In the children from the left-bank area, enzyme activities varied more greatly, which suggests the higher prevalence of tense adaptation. Correlation analysis revealed association between the children's serum activity of enzymes and some components of snow pollution. It is anticipated that the found changes in serum activities of N-acetyl-beta-D-glucosaminidase and catalase may be determined by individual differences in a child's response to ambient air pollutants.
Kwon, Chung-Jin; Kim, Sung-Joong; Han, Woo-Young; Min, Won-Kyoung
2005-12-01
The rotor position and speed estimation of permanent-magnet synchronous motor(PMSM) was dealt with. By measuring the phase voltages and currents of the PMSM drive, two diagonally recurrent neural network(DRNN) based observers, a neural current observer and a neural velocity observer were developed. DRNN which has self-feedback of the hidden neurons ensures that the outputs of DRNN contain the whole past information of the system even if the inputs of DRNN are only the present states and inputs of the system. Thus the structure of DRNN may be simpler than that of feedforward and fully recurrent neural networks. If the backpropagation method was used for the training of the DRNN the problem of slow convergence arise. In order to reduce this problem, recursive prediction error(RPE) based learning method for the DRNN was presented. The simulation results show that the proposed approach gives a good estimation of rotor speed and position, and RPE based training has requires a shorter computation time compared to backpropagation based training.
Directory of Open Access Journals (Sweden)
P. Beinschob
2010-11-01
Full Text Available In this paper we present a novel approach in Multiple-Input Multiple Output (MIMO Orthogonal Frequency Division Multiplexing (OFDM channel estimation technique based on a Decision Directed Recursive Least Squares (RLS algorithm in which no pilot symbols need to be integrated in the data after a short initial preamble. The novelty and key concept of the proposed technique is the block-wise causal and anti-causal RLS processing that yields two independent processings of RLS along with the associated decisions. Due to the usage of low density parity check (LDPC channel code, the receiver operates with soft information, which enables us to introduce a new modification of the Turbo principle as well as a simple information combining approach based on approximated aposteriori log-likelihood ratios (LLRs. Although the computational complexity is increased by both of our approaches, the latter is relatively less complex than the former. Simulation results show that these implementations outperform the simple RLS-DDCE algorithm and yield lower bit error rates (BER and more accurate channel estimates.
Estimation Methods for Infinite-Dimensional Systems Applied to the Hemodynamic Response in the Brain
Belkhatir, Zehor
2018-05-01
Infinite-Dimensional Systems (IDSs) which have been made possible by recent advances in mathematical and computational tools can be used to model complex real phenomena. However, due to physical, economic, or stringent non-invasive constraints on real systems, the underlying characteristics for mathematical models in general (and IDSs in particular) are often missing or subject to uncertainty. Therefore, developing efficient estimation techniques to extract missing pieces of information from available measurements is essential. The human brain is an example of IDSs with severe constraints on information collection from controlled experiments and invasive sensors. Investigating the intriguing modeling potential of the brain is, in fact, the main motivation for this work. Here, we will characterize the hemodynamic behavior of the brain using functional magnetic resonance imaging data. In this regard, we propose efficient estimation methods for two classes of IDSs, namely Partial Differential Equations (PDEs) and Fractional Differential Equations (FDEs). This work is divided into two parts. The first part addresses the joint estimation problem of the state, parameters, and input for a coupled second-order hyperbolic PDE and an infinite-dimensional ordinary differential equation using sampled-in-space measurements. Two estimation techniques are proposed: a Kalman-based algorithm that relies on a reduced finite-dimensional model of the IDS, and an infinite-dimensional adaptive estimator whose convergence proof is based on the Lyapunov approach. We study and discuss the identifiability of the unknown variables for both cases. The second part contributes to the development of estimation methods for FDEs where major challenges arise in estimating fractional differentiation orders and non-smooth pointwise inputs. First, we propose a fractional high-order sliding mode observer to jointly estimate the pseudo-state and input of commensurate FDEs. Second, we propose a
Simon, Patrick; Schneider, Peter
2017-08-01
In weak gravitational lensing, weighted quadrupole moments of the brightness profile in galaxy images are a common way to estimate gravitational shear. We have employed general adaptive moments (GLAM ) to study causes of shear bias on a fundamental level and for a practical definition of an image ellipticity. The GLAM ellipticity has useful properties for any chosen weight profile: the weighted ellipticity is identical to that of isophotes of elliptical images, and in absence of noise and pixellation it is always an unbiased estimator of reduced shear. We show that moment-based techniques, adaptive or unweighted, are similar to a model-based approach in the sense that they can be seen as imperfect fit of an elliptical profile to the image. Due to residuals in the fit, moment-based estimates of ellipticities are prone to underfitting bias when inferred from observed images. The estimation is fundamentally limited mainly by pixellation which destroys information on the original, pre-seeing image. We give an optimised estimator for the pre-seeing GLAM ellipticity and quantify its bias for noise-free images. To deal with images where pixel noise is prominent, we consider a Bayesian approach to infer GLAM ellipticity where, similar to the noise-free case, the ellipticity posterior can be inconsistent with the true ellipticity if we do not properly account for our ignorance about fit residuals. This underfitting bias, quantified in the paper, does not vary with the overall noise level but changes with the pre-seeing brightness profile and the correlation or heterogeneity of pixel noise over the image. Furthermore, when inferring a constant ellipticity or, more relevantly, constant shear from a source sample with a distribution of intrinsic properties (sizes, centroid positions, intrinsic shapes), an additional, now noise-dependent bias arises towards low signal-to-noise if incorrect prior densities for the intrinsic properties are used. We discuss the origin of this
Temperature dependent estimator for load cells using an adaptive neuro-fuzzy inference system
Energy Technology Data Exchange (ETDEWEB)
Lee, K-C [Department of Automation Engineering, National Formosa University, Huwei, Yunlin 63208, Taiwan (China)
2005-01-01
Accurate weighting of pieces in various temperature environments for load cells is a key feature in many industrial applications. This paper proposes a method to achieve high-precision {+-}0.56/3000 grams for a load-cell-based weighting system by using ANFIS. ANFIS is used to model the relationship between the reading of load cells and the actual weight of samples considering temperature-varying effect and nonlinearity of the load cells. The model of the load-cell-based weighting system can accurately estimate the weight of test samples from the load cell reading. The proposed ANFIS-based method is convenient for use and can improve the precision of digital load cell measurement systems. Experiments demonstrate the validity and effectiveness of fuzzy neural networks for modeling of load cells and the results show that the proposed ANFIS-based method outperforms some existing methods in terms of modeling and prediction accuracy.
Temperature dependent estimator for load cells using an adaptive neuro-fuzzy inference system
International Nuclear Information System (INIS)
Lee, K-C
2005-01-01
Accurate weighting of pieces in various temperature environments for load cells is a key feature in many industrial applications. This paper proposes a method to achieve high-precision ±0.56/3000 grams for a load-cell-based weighting system by using ANFIS. ANFIS is used to model the relationship between the reading of load cells and the actual weight of samples considering temperature-varying effect and nonlinearity of the load cells. The model of the load-cell-based weighting system can accurately estimate the weight of test samples from the load cell reading. The proposed ANFIS-based method is convenient for use and can improve the precision of digital load cell measurement systems. Experiments demonstrate the validity and effectiveness of fuzzy neural networks for modeling of load cells and the results show that the proposed ANFIS-based method outperforms some existing methods in terms of modeling and prediction accuracy
DEFF Research Database (Denmark)
Blasone, Roberta-Serena; Vrugt, Jasper A.; Madsen, Henrik
2008-01-01
propose an alternative strategy to determine the value of the cutoff threshold based on the appropriate coverage of the resulting uncertainty bounds. We demonstrate the superiority of this revised GLUE method with three different conceptual watershed models of increasing complexity, using both synthetic......In the last few decades hydrologists have made tremendous progress in using dynamic simulation models for the analysis and understanding of hydrologic systems. However, predictions with these models are often deterministic and as such they focus on the most probable forecast, without an explicit...... of applications. However, the MC based sampling strategy of the prior parameter space typically utilized in GLUE is not particularly efficient in finding behavioral simulations. This becomes especially problematic for high-dimensional parameter estimation problems, and in the case of complex simulation models...
In-vivo studies of new vector velocity and adaptive spectral estimators in medical ultrasound
DEFF Research Database (Denmark)
Hansen, Kristoffer Lindskov
In this PhD project new ultrasound techniques for blood flow measurements have been investigated in-vivo. The focus has mainly been on vector velocity techniques and four different approaches have been examined: Transverse Oscillation, Synthetic Transmit Aperture, Directional Beamforming and Plane...... in conventional Doppler ultrasound. That is angle dependency, reduced temporal resolution and low frame rate. Transverse Oscillation, Synthetic Transmit Aperture and Directional Beamforming can estimate the blood velocity angle independently. The three methods were validated in-vivo against magnetic resonance...... phase contrast angiography when measuring stroke volumes in simple vessel geometry on 11 volunteers. Using linear regression and Bland-Altman analyses good agreements were found, indicating that vector velocity methods can be used for quantitative blood flow measurements. Plane Wave Excitation can...
Directory of Open Access Journals (Sweden)
Yong Tian
2014-09-01
Full Text Available The state of charge (SOC is important for the safety and reliability of battery operation since it indicates the remaining capacity of a battery. However, it is difficult to get an accurate value of SOC, because the SOC cannot be directly measured by a sensor. In this paper, an adaptive gain nonlinear observer (AGNO for SOC estimation of lithium-ion batteries (LIBs in electric vehicles (EVs is proposed. The second-order resistor–capacitor (2RC equivalent circuit model is used to simulate the dynamic behaviors of a LIB, based on which the state equations are derived to design the AGNO for SOC estimation. The model parameters are identified using the exponential-function fitting method. The sixth-order polynomial function is used to describe the highly nonlinear relationship between the open circuit voltage (OCV and the SOC. The convergence of the proposed AGNO is proved using the Lyapunov stability theory. Two typical driving cycles, including the New European Driving Cycle (NEDC and Federal Urban Driving Schedule (FUDS are adopted to evaluate the performance of the AGNO by comparing with the unscented Kalman filter (UKF algorithm. The experimental results show that the AGNO has better performance than the UKF algorithm in terms of reducing the computation cost, improving the estimation accuracy and enhancing the convergence ability.
Farmann, Alexander; Waag, Wladislaw; Sauer, Dirk Uwe
2015-12-01
Robust algorithms using reduced order equivalent circuit model (ECM) for an accurate and reliable estimation of battery states in various applications become more popular. In this study, a novel adaptive, self-learning heuristic algorithm for on-board impedance parameters and voltage estimation of lithium-ion batteries (LIBs) in electric vehicles is introduced. The presented approach is verified using LIBs with different composition of chemistries (NMC/C, NMC/LTO, LFP/C) at different aging states. An impedance-based reduced order ECM incorporating ohmic resistance and a combination of a constant phase element and a resistance (so-called ZARC-element) is employed. Existing algorithms in vehicles are much more limited in the complexity of the ECMs. The algorithm is validated using seven day real vehicle data with high temperature variation including very low temperatures (from -20 °C to +30 °C) at different Depth-of-Discharges (DoDs). Two possibilities to approximate both ZARC-elements with finite number of RC-elements on-board are shown and the results of the voltage estimation are compared. Moreover, the current dependence of the charge-transfer resistance is considered by employing Butler-Volmer equation. Achieved results indicate that both models yield almost the same grade of accuracy.
International Nuclear Information System (INIS)
Stenmark, Matthew H.; Cao, Yue; Wang, Hesheng; Jackson, Andrew; Ben-Josef, Edgar; Ten Haken, Randall K.; Lawrence, Theodore S.; Feng, Mary
2014-01-01
Purpose: To estimate the limit of functional liver reserve for safe application of hepatic irradiation using changes in indocyanine green, an established assay of liver function. Materials and methods: From 2005 to 2011, 60 patients undergoing hepatic irradiation were enrolled in a prospective study assessing the plasma retention fraction of indocyanine green at 15-min (ICG-R15) prior to, during (at 60% of planned dose), and after radiotherapy (RT). The limit of functional liver reserve was estimated from the damage fraction of functional liver (DFL) post-RT [1 − (ICG-R15 pre-RT /ICG-R15 post-RT )] where no toxicity was observed using a beta distribution function. Results: Of 48 evaluable patients, 3 (6%) developed RILD, all within 2.5 months of completing RT. The mean ICG-R15 for non-RILD patients pre-RT, during-RT and 1-month post-RT was 20.3%(SE 2.6), 22.0%(3.0), and 27.5%(2.8), and for RILD patients was 6.3%(4.3), 10.8%(2.7), and 47.6%(8.8). RILD was observed at post-RT damage fractions of ⩾78%. Both DFL assessed by during-RT ICG and MLD predicted for DFL post-RT (p < 0.0001). Limiting the post-RT DFL to 50%, predicted a 99% probability of a true complication rate <15%. Conclusion: The DFL as assessed by changes in ICG during treatment serves as an early indicator of a patient’s tolerance to hepatic irradiation
Mechhoud, Sarra
2016-08-04
In this paper, boundary adaptive estimation of solar radiation in a solar collector plant is investigated. The solar collector is described by a 1D first-order hyperbolic partial differential equation where the solar radiation models the source term and only boundary measurements are available. Using boundary injection, the estimator is developed in the Lyapunov approach and consists of a combination of a state observer and a parameter adaptation law which guarantee the asymptotic convergence of the state and parameter estimation errors. Simulation results are provided to illustrate the performance of the proposed identifier.
Directory of Open Access Journals (Sweden)
Baljuk J.A.
2014-12-01
Full Text Available In work the algorithm of adaptive strategy of optimum spatial sampling for studying of the spatial organisation of communities of soil animals in the conditions of an urbanization have been presented. As operating variables the principal components obtained as a result of the analysis of the field data on soil penetration resistance, soils electrical conductivity and density of a forest stand, collected on a quasiregular grid have been used. The locations of experimental polygons have been stated by means of program ESAP. The sampling has been made on a regular grid within experimental polygons. The biogeocoenological estimation of experimental polygons have been made on a basis of A.L.Belgard's ecomorphic analysis. The spatial configuration of biogeocoenosis types has been established on the basis of the data of earth remote sensing and the analysis of digital elevation model. The algorithm was suggested which allows to reveal the spatial organisation of soil animal communities at investigated point, biogeocoenosis, and landscape.
Directory of Open Access Journals (Sweden)
Shengxin Wang
2016-06-01
Full Text Available Pathological tremor is an approximately rhythmic movement and considerably affects patients’ daily living activities. Biomechanical loading and functional electrical stimulation are proposed as potential alternatives for canceling the pathological tremor. However, the performance of suppression methods is associated with the separation of tremor from the recorded signals. In this literature, an algorithm incorporating a fast Fourier transform augmented with a sliding convolution window, an interpolation procedure, and a damping module of the frequency is presented to isolate tremulous components from the measured signals and estimate the instantaneous tremor frequency. Meanwhile, a mechanism platform is designed to provide the simulation tremor signals with different degrees of voluntary movements. The performance of the proposed algorithm and existing procedures is compared with simulated signals and experimental signals collected from patients. The results demonstrate that the proposed solution could detect the unknown dominant frequency and distinguish the tremor components with higher accuracy. Therefore, this algorithm is useful for actively compensating tremor by functional electrical stimulation without affecting the voluntary movement.
Ågren, Jon; Oakley, Christopher G; Lundemo, Sverre; Schemske, Douglas W
2017-03-01
To identify the ecological and genetic mechanisms of local adaptation requires estimating selection on traits, identifying their genetic basis, and evaluating whether divergence in adaptive traits is due to conditional neutrality or genetic trade-offs. To this end, we conducted field experiments for three years using recombinant inbred lines (RILs) derived from two ecotypes of Arabidopsis thaliana (Italy, Sweden), and at each parental site examined selection on flowering time and mapped quantitative trait loci (QTL). There was strong selection for early flowering in Italy, but weak selection in Sweden. Eleven distinct flowering time QTL were detected, and for each the Italian genotype caused earlier flowering. Twenty-seven candidate genes were identified, two of which (FLC and VIN3) appear under major flowering time QTL in Italy. Seven of eight QTL in Italy with narrow credible intervals colocalized with previously reported fitness QTL, in comparison to three of four in Sweden. The results demonstrate that the magnitude of selection on flowering time differs strikingly between our study populations, that the genetic basis of flowering time variation is multigenic with some QTL of large effect, and suggest that divergence in flowering time between ecotypes is due mainly to conditional neutrality. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.
Garnier, Romain; Odunlami, Marc; Le Bris, Vincent; Bégué, Didier; Baraille, Isabelle; Coulaud, Olivier
2016-05-01
A new variational algorithm called adaptive vibrational configuration interaction (A-VCI) intended for the resolution of the vibrational Schrödinger equation was developed. The main advantage of this approach is to efficiently reduce the dimension of the active space generated into the configuration interaction (CI) process. Here, we assume that the Hamiltonian writes as a sum of products of operators. This adaptive algorithm was developed with the use of three correlated conditions, i.e., a suitable starting space, a criterion for convergence, and a procedure to expand the approximate space. The velocity of the algorithm was increased with the use of a posteriori error estimator (residue) to select the most relevant direction to increase the space. Two examples have been selected for benchmark. In the case of H2CO, we mainly study the performance of A-VCI algorithm: comparison with the variation-perturbation method, choice of the initial space, and residual contributions. For CH3CN, we compare the A-VCI results with a computed reference spectrum using the same potential energy surface and for an active space reduced by about 90%.
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.
Directory of Open Access Journals (Sweden)
Somayyeh Lotfi Noghabi
2012-07-01
Full Text Available Introduction: Epilepsy is a clinical syndrome in which seizures have a tendency to recur. Sodium valproate is the most effective drug in the treatment of all types of generalized seizures. Finding the optimal dosage (the lowest effective dose of sodium valproate is a real challenge to all neurologists. In this study, a new approach based on Adaptive Neuro-Fuzzy Inference System (ANFIS was presented for estimating the optimal dosage of sodium valproate in IGE (Idiopathic Generalized Epilepsy patients. Methods: 40 patients with Idiopathic Generalized Epilepsy, who were referred to the neurology department of Mashhad University of Medical Sciences between the years 2006-2011, were included in this study. The function Adaptive Neuro- Fuzzy Inference System (ANFIS constructs a Fuzzy Inference System (FIS whose membership function parameters are tuned (adjusted using either a back-propagation algorithm alone, or in combination with the least squares type of method (hybrid algorithm. In this study, we used hybrid method for adjusting the parameters. Methods: The R-square of the proposed system was %598 and the Pearson correlation coefficient was significant (P 0.05. Although the accuracy of the model was not high, it wasgood enough to be applied for treating the IGE patients with sodium valproate. Discussion: This paper presented a new application of ANFIS for estimating the optimal dosage of sodium valproate in IGE patients. Fuzzy set theory plays an important role in dealing with uncertainty when making decisions in medical applications. Collectively, it seems that ANFIS has a high capacity to be applied in medical sciences, especially neurology.
Energy Technology Data Exchange (ETDEWEB)
Sreenivasan, P.R., E-mail: sreeprs@yahoo.co.in
2014-04-01
Many researchers had suggested a sort of scaling procedure for predicting the quasi-static J–R curves from dynamic J–R curves obtained from instrumented Charpy V-notch (CVN) impact tests using key-curve, compliance or other procedures. Chaouadi, based on extensive tests and literature data, had quantitatively formalized the method and suggested general applicability of his method for a class of steels. In this paper, first, the Chauoadi-procedure is tried on some selected data from the literature (including the data used by Chaouadi and other workers) and an adaptation of the method is suggested using Wallin's as well as Landes's lower bound methods for upper-shelf J–R curve estimation from CVN energy. Using Chaouadi and other data as the benchmark, suitable scaling factors have been determined that enable estimation of quasi-static J–R curves from CVN energy alone, without the need for dynamic CVN J–R curves. The final formulae are given. This new method can be called modified Wallin–Landes procedure. Then this method is applied to fracture toughness and reference temperature (T{sub 0} – ASTM E-1921) estimation from the full Charpy-transition data. The results are compared with those from the author's IGC-procedure, and modifications, if any, are suggested. Based on the new results, it is suggested that the IGC-procedure may be modified as: final T{sub Q-est} = T{sub Q-IGC} for T{sub Q-Sch}{sup dy} ≤ 20 °C (in the IGC-procedure the dividing temperature was 60 °C); and for T{sub Q-Sch}{sup dy} > 20 °C, T{sub Q-IGC} = T{sub Q-WLm} (different from the IGC-procedre and subscript WLm indicating modified Wallin–Landes procedure). For the 59 or more steels examined (including highly irradiated steels), the T{sub Q-WL} estimates at higher temperatures are consistent and conservative; a few non-conservative values are acceptably less than 20 °C, whereas other predictions show non-conservatism of up to 40–50 °C. At lower temperatures
PDE-Foam - a probability-density estimation method using self-adapting phase-space binning
Dannheim, Dominik; Voigt, Alexander; Grahn, Karl-Johan; Speckmayer, Peter
2009-01-01
Probability-Density Estimation (PDE) is a multivariate discrimination technique based on sampling signal and background densities defined by event samples from data or Monte-Carlo (MC) simulations in a multi-dimensional phase space. To efficiently use large event samples to estimate the probability density, a binary search tree (range searching) is used in the PDE-RS implementation. It is a generalisation of standard likelihood methods and a powerful classification tool for problems with highly non-linearly correlated observables. In this paper, we present an innovative improvement of the PDE method that uses a self-adapting binning method to divide the multi-dimensional phase space in a finite number of hyper-rectangles (cells). The binning algorithm adjusts the size and position of a predefined number of cells inside the multidimensional phase space, minimizing the variance of the signal and background densities inside the cells. The binned density information is stored in binary trees, allowing for a very ...
Directory of Open Access Journals (Sweden)
Qingsong Ai
2017-12-01
Full Text Available A rehabilitation robot plays an important role in relieving the therapists’ burden and helping patients with ankle injuries to perform more accurate and effective rehabilitation training. However, a majority of current ankle rehabilitation robots are rigid and have drawbacks in terms of complex structure, poor flexibility and lack of safety. Taking advantages of pneumatic muscles’ good flexibility and light weight, we developed a novel two degrees of freedom (2-DOF parallel compliant ankle rehabilitation robot actuated by pneumatic muscles (PMs. To solve the PM’s nonlinear characteristics during operation and to tackle the human-robot uncertainties in rehabilitation, an adaptive backstepping sliding mode control (ABS-SMC method is proposed in this paper. The human-robot external disturbance can be estimated by an observer, who is then used to adjust the robot output to accommodate external changes. The system stability is guaranteed by the Lyapunov stability theorem. Experimental results on the compliant ankle rehabilitation robot show that the proposed ABS-SMC is able to estimate the external disturbance online and adjust the control output in real time during operation, resulting in a higher trajectory tracking accuracy and better response performance especially in dynamic conditions.
El Gharamti, Mohamad; Valstar, Johan R.; Hoteit, Ibrahim
2014-01-01
Reactive contaminant transport models are used by hydrologists to simulate and study the migration and fate of industrial waste in subsurface aquifers. Accurate transport modeling of such waste requires clear understanding of the system's parameters, such as sorption and biodegradation. In this study, we present an efficient sequential data assimilation scheme that computes accurate estimates of aquifer contamination and spatially variable sorption coefficients. This assimilation scheme is based on a hybrid formulation of the ensemble Kalman filter (EnKF) and optimal interpolation (OI) in which solute concentration measurements are assimilated via a recursive dual estimation of sorption coefficients and contaminant state variables. This hybrid EnKF-OI scheme is used to mitigate background covariance limitations due to ensemble under-sampling and neglected model errors. Numerical experiments are conducted with a two-dimensional synthetic aquifer in which cobalt-60, a radioactive contaminant, is leached in a saturated heterogeneous clayey sandstone zone. Assimilation experiments are investigated under different settings and sources of model and observational errors. Simulation results demonstrate that the proposed hybrid EnKF-OI scheme successfully recovers both the contaminant and the sorption rate and reduces their uncertainties. Sensitivity analyses also suggest that the adaptive hybrid scheme remains effective with small ensembles, allowing to reduce the ensemble size by up to 80% with respect to the standard EnKF scheme. © 2014 Elsevier Ltd.
Ai, Qingsong; Zhu, Chengxiang; Zuo, Jie; Meng, Wei; Liu, Quan; Xie, Sheng Q; Yang, Ming
2017-12-28
A rehabilitation robot plays an important role in relieving the therapists' burden and helping patients with ankle injuries to perform more accurate and effective rehabilitation training. However, a majority of current ankle rehabilitation robots are rigid and have drawbacks in terms of complex structure, poor flexibility and lack of safety. Taking advantages of pneumatic muscles' good flexibility and light weight, we developed a novel two degrees of freedom (2-DOF) parallel compliant ankle rehabilitation robot actuated by pneumatic muscles (PMs). To solve the PM's nonlinear characteristics during operation and to tackle the human-robot uncertainties in rehabilitation, an adaptive backstepping sliding mode control (ABS-SMC) method is proposed in this paper. The human-robot external disturbance can be estimated by an observer, who is then used to adjust the robot output to accommodate external changes. The system stability is guaranteed by the Lyapunov stability theorem. Experimental results on the compliant ankle rehabilitation robot show that the proposed ABS-SMC is able to estimate the external disturbance online and adjust the control output in real time during operation, resulting in a higher trajectory tracking accuracy and better response performance especially in dynamic conditions.
El Gharamti, Mohamad
2014-09-01
Reactive contaminant transport models are used by hydrologists to simulate and study the migration and fate of industrial waste in subsurface aquifers. Accurate transport modeling of such waste requires clear understanding of the system\\'s parameters, such as sorption and biodegradation. In this study, we present an efficient sequential data assimilation scheme that computes accurate estimates of aquifer contamination and spatially variable sorption coefficients. This assimilation scheme is based on a hybrid formulation of the ensemble Kalman filter (EnKF) and optimal interpolation (OI) in which solute concentration measurements are assimilated via a recursive dual estimation of sorption coefficients and contaminant state variables. This hybrid EnKF-OI scheme is used to mitigate background covariance limitations due to ensemble under-sampling and neglected model errors. Numerical experiments are conducted with a two-dimensional synthetic aquifer in which cobalt-60, a radioactive contaminant, is leached in a saturated heterogeneous clayey sandstone zone. Assimilation experiments are investigated under different settings and sources of model and observational errors. Simulation results demonstrate that the proposed hybrid EnKF-OI scheme successfully recovers both the contaminant and the sorption rate and reduces their uncertainties. Sensitivity analyses also suggest that the adaptive hybrid scheme remains effective with small ensembles, allowing to reduce the ensemble size by up to 80% with respect to the standard EnKF scheme. © 2014 Elsevier Ltd.
International Nuclear Information System (INIS)
Azadeh, A.; Asadzadeh, S.M.; Ghanbari, A.
2010-01-01
Accurate short-term natural gas (NG) demand estimation and forecasting is vital for policy and decision-making process in energy sector. Moreover, conventional methods may not provide accurate results. This paper presents an adaptive network-based fuzzy inference system (ANFIS) for estimation of NG demand. Standard input variables are used which are day of the week, demand of the same day in previous year, demand of a day before and demand of 2 days before. The proposed ANFIS approach is equipped with pre-processing and post-processing concepts. Moreover, input data are pre-processed (scaled) and finally output data are post-processed (returned to its original scale). The superiority and applicability of the ANFIS approach is shown for Iranian NG consumption from 22/12/2007 to 30/6/2008. Results show that ANFIS provides more accurate results than artificial neural network (ANN) and conventional time series approach. The results of this study provide policy makers with an appropriate tool to make more accurate predictions on future short-term NG demand. This is because the proposed approach is capable of handling non-linearity, complexity as well as uncertainty that may exist in actual data sets due to erratic responses and measurement errors.
Directory of Open Access Journals (Sweden)
Yuanyuan Liu
2013-08-01
Full Text Available Accurate estimation of the state of charge (SOC of batteries is one of the key problems in a battery management system. This paper proposes an adaptive SOC estimation method based on unscented Kalman filter algorithms for lithium (Li-ion batteries. First, an enhanced battery model is proposed to include the impacts due to different discharge rates and temperatures. An adaptive joint estimation of the battery SOC and battery internal resistance is then presented to enhance system robustness with battery aging. The SOC estimation algorithm has been developed and verified through experiments on different types of Li-ion batteries. The results indicate that the proposed method provides an accurate SOC estimation and is computationally efficient, making it suitable for embedded system implementation.
Penas, David R; González, Patricia; Egea, Jose A; Doallo, Ramón; Banga, Julio R
2017-01-21
The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times. Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies. The performance and robustness of saCeSS is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, bakerés yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The results consistently show that saCeSS is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used. The new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models.
Azarpour, Masoumeh; Enzner, Gerald
2017-12-01
Binaural noise reduction, with applications for instance in hearing aids, has been a very significant challenge. This task relates to the optimal utilization of the available microphone signals for the estimation of the ambient noise characteristics and for the optimal filtering algorithm to separate the desired speech from the noise. The additional requirements of low computational complexity and low latency further complicate the design. A particular challenge results from the desired reconstruction of binaural speech input with spatial cue preservation. The latter essentially diminishes the utility of multiple-input/single-output filter-and-sum techniques such as beamforming. In this paper, we propose a comprehensive and effective signal processing configuration with which most of the aforementioned criteria can be met suitably. This relates especially to the requirement of efficient online adaptive processing for noise estimation and optimal filtering while preserving the binaural cues. Regarding noise estimation, we consider three different architectures: interaural (ITF), cross-relation (CR), and principal-component (PCA) target blocking. An objective comparison with two other noise PSD estimation algorithms demonstrates the superiority of the blocking-based noise estimators, especially the CR-based and ITF-based blocking architectures. Moreover, we present a new noise reduction filter based on minimum mean-square error (MMSE), which belongs to the class of common gain filters, hence being rigorous in terms of spatial cue preservation but also efficient and competitive for the acoustic noise reduction task. A formal real-time subjective listening test procedure is also developed in this paper. The proposed listening test enables a real-time assessment of the proposed computationally efficient noise reduction algorithms in a realistic acoustic environment, e.g., considering time-varying room impulse responses and the Lombard effect. The listening test outcome
DEFF Research Database (Denmark)
Effraimidis, Georgios; Dahl, Christian Møller
In this paper, we develop a fully nonparametric approach for the estimation of the cumulative incidence function with Missing At Random right-censored competing risks data. We obtain results on the pointwise asymptotic normality as well as the uniform convergence rate of the proposed nonparametric...
International Nuclear Information System (INIS)
Ford, W.E. III; Diggs, B.R.; Petrie, L.M.; Webster, C.C.; Westfall, R.M.
1982-01-01
A P 3 227-neutron-group cross-section library has been processed for the subsequent generation of problem-dependent fine- or broad-group cross sections for a broad range of applications, including shipping cask calculations, general criticality safety analyses, and reactor core and shielding analyses. The energy group structure covers the range 10 -5 eV - 20 MeV, including 79 thermal groups below 3 eV. The 129-material library includes processed data for all materials in the ENDF/B-V General Purpose File, several data sets prepared from LENDL data, hydrogen with water- and polyethyelene-bound thermal kernels, deuterium with C 2 O-bound thermal kernels, carbon with a graphite thermal kernel, a special 1/V data set, and a dose factor data set. The library, which is in AMPX master format, is designated CSRL-V (Criticality Safety Reference Library based on ENDF/B-V data). Also included in CSRL-V is a pointwise total, fission, elastic scattering, and (n,γ) cross-section library containing data sets for all ENDF/B-V resonance materials. Data in the pointwise library were processed with the infinite dilute approximation at a temperature of 296 0 K
International Nuclear Information System (INIS)
Chuang, Kuo-Chih; Ma, Chien-Ching; Liao, Heng-Tseng
2012-01-01
In this work, active vibration suppression of a smart cantilever beam subjected to disturbances from multiple impact loadings is investigated with a point-wise fiber Bragg grating (FBG) displacement sensing system. An FBG demodulator is employed in the proposed fiber sensing system to dynamically demodulate the responses obtained by the FBG displacement sensor with high sensitivity. To investigate the ability of the proposed FBG displacement sensor as a feedback sensor, velocity feedback control and delay control are employed to suppress the vibrations of the first three bending modes of the smart cantilever beam. To improve the control performance for the first bending mode when the cantilever beam is subjected to an impact loading, we improve the conventional velocity feedback controller by tuning the control gain online with the aid of information from a higher vibration mode. Finally, active control of vibrations induced by multiple impact loadings due to a plastic ball is performed with the improved velocity feedback control. The experimental results show that active vibration control of smart structures subjected to disturbances such as impact loadings can be achieved by employing the proposed FBG sensing system to feed back out-of-plane point-wise displacement responses with high sensitivity. (paper)
A note on the conditional density estimate in single functional index model
2010-01-01
Abstract In this paper, we consider estimation of the conditional density of a scalar response variable Y given a Hilbertian random variable X when the observations are linked with a single-index structure. We establish the pointwise and the uniform almost complete convergence (with the rate) of the kernel estimate of this model. As an application, we show how our result can be applied in the prediction problem via the conditional mode estimate. Finally, the estimation of the funct...
Directory of Open Access Journals (Sweden)
Zoltán Endre Rákossy
2012-01-01
Full Text Available Due to the fast changing wireless communication standards coupled with strict performance constraints, the demand for flexible yet high-performance architectures is increasing. To tackle the flexibility requirement, software-defined radio (SDR is emerging as an obvious solution, where the underlying hardware implementation is tuned via software layers to the varied standards depending on power-performance and quality requirements leading to adaptable, cognitive radio. In this paper, we conduct a case study for representatives of two complexity classes of WCDMA channel estimation algorithms and explore the effect of flexibility on energy efficiency using different implementation options. Furthermore, we propose new design guidelines for both highly specialized architectures and highly flexible architectures using high-level synthesis, to enable the required performance and flexibility to support multiple applications. Our experiments with various design points show that the resulting architectures meet the performance constraints of WCDMA and a wide range of options are offered for tuning such architectures depending on power/performance/area constraints of SDR.
Benzy, V K; Jasmin, E A; Koshy, Rachel Cherian; Amal, Frank; Indiradevi, K P
2018-01-01
The advancement in medical research and intelligent modeling techniques has lead to the developments in anaesthesia management. The present study is targeted to estimate the depth of anaesthesia using cognitive signal processing and intelligent modeling techniques. The neurophysiological signal that reflects cognitive state of anaesthetic drugs is the electroencephalogram signal. The information available on electroencephalogram signals during anaesthesia are drawn by extracting relative wave energy features from the anaesthetic electroencephalogram signals. Discrete wavelet transform is used to decomposes the electroencephalogram signals into four levels and then relative wave energy is computed from approximate and detail coefficients of sub-band signals. Relative wave energy is extracted to find out the degree of importance of different electroencephalogram frequency bands associated with different anaesthetic phases awake, induction, maintenance and recovery. The Kruskal-Wallis statistical test is applied on the relative wave energy features to check the discriminating capability of relative wave energy features as awake, light anaesthesia, moderate anaesthesia and deep anaesthesia. A novel depth of anaesthesia index is generated by implementing a Adaptive neuro-fuzzy inference system based fuzzy c-means clustering algorithm which uses relative wave energy features as inputs. Finally, the generated depth of anaesthesia index is compared with a commercially available depth of anaesthesia monitor Bispectral index.
Zhao, Z.-G.; Zhou, L.-J.; Zhang, J.-T.; Zhu, Q.; Hedrick, J.-K.
2017-05-01
Considering the controllability and observability of the braking torques of the hub motor, Integrated Starter Generator (ISG), and hydraulic brake for four-wheel drive (4WD) hybrid electric cars, a distributed and self-adaptive vehicle speed estimation algorithm for different braking situations has been proposed by fully utilising the Electronic Stability Program (ESP) sensor signals and multiple powersource signals. Firstly, the simulation platform of a 4WD hybrid electric car was established, which integrates an electronic-hydraulic composited braking system model and its control strategy, a nonlinear seven degrees-of-freedom vehicle dynamics model, and the Burckhardt tyre model. Secondly, combining the braking torque signals with the ESP signals, self-adaptive unscented Kalman sub-filter and main-filter adaptable to the observation noise were, respectively, designed. Thirdly, the fusion rules for the sub-filters and master filter were proposed herein, and the estimation results were compared with the simulated value of a real vehicle speed. Finally, based on the hardware in-the-loop platform and by picking up the regenerative motor torque signals and wheel cylinder pressure signals, the proposed speed estimation algorithm was tested under the case of moderate braking on the highly adhesive road, and the case of Antilock Braking System (ABS) action on the slippery road, as well as the case of ABS action on the icy road. Test results show that the presented vehicle speed estimation algorithm has not only a high precision but also a strong adaptability in the composite braking case.
Laurence, Caroline O; Heywood, Troy; Bell, Janice; Atkinson, Kaye; Karnon, Jonathan
2018-03-27
Health workforce planning models have been developed to estimate the future health workforce requirements for a population whom they serve and have been used to inform policy decisions. To adapt and further develop a need-based GP workforce simulation model to incorporate current and estimated geographic distribution of patients and GPs. A need-based simulation model that estimates the supply of GPs and levels of services required in South Australia (SA) was adapted and applied to the Western Australian (WA) workforce. The main outcome measure was the differences in the number of full-time equivalent (FTE) GPs supplied and required from 2013 to 2033. The base scenario estimated a shortage of GPs in WA from 2019 onwards with a shortage of 493 FTE GPs in 2033, while for SA, estimates showed an oversupply over the projection period. The WA urban and rural models estimated an urban shortage of GPs over this period. A reduced international medical graduate recruitment scenario resulted in estimated shortfalls of GPs by 2033 for WA and SA. The WA-specific scenarios of lower population projections and registrar work value resulted in a reduced shortage of FTE GPs in 2033, while unfilled training places increased the shortfall of FTE GPs in 2033. The simulation model incorporates contextual differences to its structure that allows within and cross jurisdictional comparisons of workforce estimations. It also provides greater insights into the drivers of supply and demand and the impact of changes in workforce policy, promoting more informed decision-making.
Harudin, N.; Jamaludin, K. R.; Muhtazaruddin, M. Nabil; Ramlie, F.; Muhamad, Wan Zuki Azman Wan
2018-03-01
T-Method is one of the techniques governed under Mahalanobis Taguchi System that developed specifically for multivariate data predictions. Prediction using T-Method is always possible even with very limited sample size. The user of T-Method required to clearly understanding the population data trend since this method is not considering the effect of outliers within it. Outliers may cause apparent non-normality and the entire classical methods breakdown. There exist robust parameter estimate that provide satisfactory results when the data contain outliers, as well as when the data are free of them. The robust parameter estimates of location and scale measure called Shamos Bickel (SB) and Hodges Lehman (HL) which are used as a comparable method to calculate the mean and standard deviation of classical statistic is part of it. Embedding these into T-Method normalize stage feasibly help in enhancing the accuracy of the T-Method as well as analysing the robustness of T-method itself. However, the result of higher sample size case study shows that T-method is having lowest average error percentages (3.09%) on data with extreme outliers. HL and SB is having lowest error percentages (4.67%) for data without extreme outliers with minimum error differences compared to T-Method. The error percentages prediction trend is vice versa for lower sample size case study. The result shows that with minimum sample size, which outliers always be at low risk, T-Method is much better on that, while higher sample size with extreme outliers, T-Method as well show better prediction compared to others. For the case studies conducted in this research, it shows that normalization of T-Method is showing satisfactory results and it is not feasible to adapt HL and SB or normal mean and standard deviation into it since it’s only provide minimum effect of percentages errors. Normalization using T-method is still considered having lower risk towards outlier’s effect.
The output least-squares approach to estimating Lamé moduli
Gockenbach, Mark S.
2007-12-01
The Lamé moduli of a heterogeneous, isotropic, planar membrane can be estimated by observing the displacement of the membrane under a known edge traction, and choosing estimates of the moduli that best predict the observed displacement under a finite-element simulation. This algorithm converges to the exact moduli given pointwise measurements of the displacement on an increasingly fine mesh. The error estimates that prove this convergence also show the instability of the inverse problem.
International Nuclear Information System (INIS)
Shilina, Yu.V.; Gushcha, N.I.; Dyachenko, A.I.; Dmitriev, A.P.; Molozhava, O.S.; Romashko, V.M.
2008-01-01
Influence of anthropogenic factors on ecosystems causes their structure disturbance and reduction of species variety. Some resistance nonspecific forms of pathogenic microorganisms, which have high adaptation potential, become dominant. Thus their aggressiveness can increase. (authors)
Klinkenberg, S.; Straatemeier, M.; van der Maas, H.L.J.
2011-01-01
In this paper we present a model for computerized adaptive practice and monitoring. This model is used in the Maths Garden, a web-based monitoring system, which includes a challenging web environment for children to practice arithmetic. Using a new item response model based on the Elo (1978) rating
Smith, D.R.; Rogala, J.T.; Gray, B.R.; Zigler, S.J.; Newton, T.J.
2011-01-01
Reliable estimates of abundance are needed to assess consequences of proposed habitat restoration and enhancement projects on freshwater mussels in the Upper Mississippi River (UMR). Although there is general guidance on sampling techniques for population assessment of freshwater mussels, the actual performance of sampling designs can depend critically on the population density and spatial distribution at the project site. To evaluate various sampling designs, we simulated sampling of populations, which varied in density and degree of spatial clustering. Because of logistics and costs of large river sampling and spatial clustering of freshwater mussels, we focused on adaptive and non-adaptive versions of single and two-stage sampling. The candidate designs performed similarly in terms of precision (CV) and probability of species detection for fixed sample size. Both CV and species detection were determined largely by density, spatial distribution and sample size. However, designs did differ in the rate that occupied quadrats were encountered. Occupied units had a higher probability of selection using adaptive designs than conventional designs. We used two measures of cost: sample size (i.e. number of quadrats) and distance travelled between the quadrats. Adaptive and two-stage designs tended to reduce distance between sampling units, and thus performed better when distance travelled was considered. Based on the comparisons, we provide general recommendations on the sampling designs for the freshwater mussels in the UMR, and presumably other large rivers.
Issanchou, Clara; Bilbao, Stefan; Le Carrou, Jean-Loïc; Touzé, Cyril; Doaré, Olivier
2017-04-01
This article is concerned with the vibration of a stiff linear string in the presence of a rigid obstacle. A numerical method for unilateral and arbitrary-shaped obstacles is developed, based on a modal approach in order to take into account the frequency dependence of losses in strings. The contact force of the barrier interaction is treated using a penalty approach, while a conservative scheme is derived for time integration, in order to ensure long-term numerical stability. In this way, the linear behaviour of the string when not in contact with the barrier can be controlled via a mode by mode fitting, so that the model is particularly well suited for comparisons with experiments. An experimental configuration is used with a point obstacle either centered or near an extremity of the string. In this latter case, such a pointwise obstruction approximates the end condition found in the tanpura, an Indian stringed instrument. The second polarisation of the string is also analysed and included in the model. Numerical results are compared against experiments, showing good accuracy over a long time scale.
Estimation of Multiple Point Sources for Linear Fractional Order Systems Using Modulating Functions
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.
Belkhatir, Zehor; Mechhoud, Sarra; Laleg-Kirati, Taous-Meriem
2016-01-01
This paper deals with joint parameters and input estimation for coupled PDE-ODE system. The system consists of a damped wave equation and an infinite dimensional ODE. This model describes the spatiotemporal hemodynamic response in the brain
International Nuclear Information System (INIS)
Waag, Wladislaw; Sauer, Dirk Uwe
2013-01-01
Highlights: • New adaptive approach for the EMF estimation. • The EMF is estimated by observing the voltage change after the current interruption. • The approach enables an accurate SoC and capacity determination. • Real-time capable algorithm. - Abstract: The online estimation of battery states and parameters is one of the challenging tasks when battery is used as a part of the pure electric or hybrid energy system. For the determination of the available energy stored in the battery, the knowledge of the present state-of-charge (SOC) and capacity of the battery is required. For SOC and capacity determination often the estimation of the battery electromotive force (EMF) is employed. The electromotive force can be measured as an open circuit voltage (OCV) of the battery when a significant time has elapsed since the current interruption. This time may take up to some hours for lithium-ion batteries and is needed to eliminate the influence of the diffusion overvoltages. This paper proposes a new approach to estimate the EMF by considering the OCV relaxation process within only some first minutes after the current interruption. The approach is based on an online fitting of an OCV relaxation model to the measured OCV relaxation curve. This model is based on an equivalent circuit consisting of a voltage source (represents the EMF) in series with the parallel connection of the resistance and a constant phase element (CPE). Based on this fitting the model parameters are determined and the EMF is estimated. The application of this method is exemplarily demonstrated for the state-of-charge and capacity estimation of the lithium-ion battery in an electrical vehicle. In the presented example the battery capacity is determined with the maximal inaccuracy of 2% using the EMF estimated at two different levels of state-of-charge. The real-time capability of the proposed algorithm is proven by its implementation on a low-cost 16-bit microcontroller (Infineon XC2287)
Petkova, Elisaveta P.; Vink, Jan K.; Horton, Radley M.; Gasparrini, Antonio; Bader, Daniel A.; Francis, Joe D.; Kinney, Patrick L.
2016-01-01
High temperatures have substantial impacts on mortality and, with growing concerns about climate change, numerous studies have developed projections of future heat-related deaths around the world. Projections of temperature-related mortality are often limited by insufficient information necessary to formulate hypotheses about population sensitivity to high temperatures and future demographics. This study has derived projections of temperature-related mortality in New York City by taking into account future patterns of adaptation or demographic change, both of which can have profound influences on future health burdens. We adopt a novel approach to modeling heat adaptation by incorporating an analysis of the observed population response to heat in New York City over the course of eight decades. This approach projects heat-related mortality until the end of the 21st century based on observed trends in adaptation over a substantial portion of the 20th century. In addition, we incorporate a range of new scenarios for population change until the end of the 21st century. We then estimate future heat-related deaths in New York City by combining the changing temperature-mortality relationship and population scenarios with downscaled temperature projections from the 33 global climate models (GCMs) and two Representative Concentration Pathways (RCPs).The median number of projected annual heat-related deaths across the 33 GCMs varied greatly by RCP and adaptation and population change scenario, ranging from 167 to 3331 in the 2080s compared to 638 heat-related deaths annually between 2000 and 2006.These findings provide a more complete picture of the range of potential future heat-related mortality risks across the 21st century in New York, and highlight the importance of both demographic change and adaptation responses in modifying future risks.
Directory of Open Access Journals (Sweden)
Linhui Zhao
2014-01-01
Full Text Available Vehicle velocity and roll angle are important information for active safety control systems of four-wheel independent drive electric vehicle. In order to obtain robustness estimation of vehicle velocity and roll angle, a novel method is proposed based on vehicle dynamics and the measurement information provided by the sensors equipped in modern cars. The method is robust with respect to different road and friction conditions. Firstly, the dynamic characteristics of four-wheel independent drive electric vehicle are analyzed, and a four-degree-of-freedom nonlinear dynamic model of vehicle and a tire longitudinal dynamic equation are established. The relationship between the longitudinal and lateral friction forces is derived based on Dugoff tire model. The unknown input reconstruction technique of sliding mode observer is used to achieve longitudinal tire friction force estimation. A simple observer is designed for the estimation of the roll angle of the vehicle. And then using the relationship, the estimated longitudinal friction forces and roll angle, a sliding mode observer for vehicle velocity estimation is provided, which does not need to know the tire-road friction coefficient and road angles. Finally, the proposed method is evaluated experimentally under a variety of maneuvers and road conditions.
DEFF Research Database (Denmark)
Ghzaiel, Walid; Jebali-Ben Ghorbal, Manel; Slama-Belkhodja, Ilhem
2013-01-01
and to take the decision of either keep the DG connected, or disconnect it from the utility grid. The proposed method is based on a fast and easy grid fault detection method. A virtual damping resistance is used to drive the system to the resonance in order to extract the grid impedance parameters, both...... the power quality and even damage some sensitive loads connected at the point of the common coupling (PCC). This paper presents detection-estimation method of the grid impedance variation. This estimation tehnique aims to improve the dynamic of the distributed generation (DG) interfacing inverter control...
Directory of Open Access Journals (Sweden)
Edgar Talavera
2018-01-01
Full Text Available In recent years, vehicular communications systems have evolved and allowed for the improvement of adaptive cruise control (ACC systems to make them cooperative (cooperative adaptive cruise control, CACC. Conventional ACC systems use sensors on the ego-vehicle, such as radar or computer vision, to generate their behavioral decisions. However, by having vehicle-to-X (V2X onboard communications, the need to incorporate perception in the vehicle is drastically reduced. Thus, in this paper a CACC solution is proposed that only uses communications to make its decisions with the help of previous road mapping. At the same time, a method to develop these maps is presented, combining the information of a computer vision system to correct the positions obtained from the navigation system. In addition, the cut-in and cut-out maneuvers for a CACC platoon are taken into account, showing the tests of these situations in real environments with instrumented vehicles. To show the potential of the system in a larger-scale implementation, simulations of the behavior are provided under dense traffic conditions where the positive impact on the reduction of traffic congestion and fuel consumption is appreciated.
Lim, Sungsoo; Lee, Seohyung; Kim, Jun-geon; Lee, Daeho
2018-01-01
The around-view monitoring (AVM) system is one of the major applications of advanced driver assistance systems and intelligent transportation systems. We propose an on-line calibration method, which can compensate misalignments for AVM systems. Most AVM systems use fisheye undistortion, inverse perspective transformation, and geometrical registration methods. To perform these procedures, the parameters for each process must be known; the procedure by which the parameters are estimated is referred to as the initial calibration. However, when only using the initial calibration data, we cannot compensate misalignments, caused by changing equilibria of cars. Moreover, even small changes such as tire pressure levels, passenger weight, or road conditions can affect a car's equilibrium. Therefore, to compensate for this misalignment, additional techniques are necessary, specifically an on-line calibration method. On-line calibration can recalculate homographies, which can correct any degree of misalignment using the unique features of ordinary parking lanes. To extract features from the parking lanes, this method uses corner detection and a pattern matching algorithm. From the extracted features, homographies are estimated using random sample consensus and parameter estimation. Finally, the misaligned epipolar geographies are compensated via the estimated homographies. Thus, the proposed method can render image planes parallel to the ground. This method does not require any designated patterns and can be used whenever cars are placed in a parking lot. The experimental results show the robustness and efficiency of the method.
Directory of Open Access Journals (Sweden)
Liu KJ Ray
2002-01-01
Full Text Available Orthogonal frequency division multiplexing (OFDM is an effective technique for the future 3G communications because of its great immunity to impulse noise and intersymbol interference. The channel estimation is a crucial aspect in the design of OFDM systems. In this work, we propose a channel estimation algorithm based on a time-frequency polynomial model of the fading multipath channels. The algorithm exploits the correlation of the channel responses in both time and frequency domains and hence reduce more noise than the methods using only time or frequency polynomial model. The estimator is also more robust compared to the existing methods based on Fourier transform. The simulation shows that it has more than improvement in terms of mean-squared estimation error under some practical channel conditions. The algorithm needs little prior knowledge about the delay and fading properties of the channel. The algorithm can be implemented recursively and can adjust itself to follow the variation of the channel statistics.
From point-wise stress data to a continuous description of the 3D crustal in situ stress state
Heidbach, O.; Ziegler, M.; Reiter, K.; Hergert, T.
2017-12-01
The in situ stress is a key parameter for the safe and sustainable management of geo-reservoirs or storage of waste and energy in deep geological repositories. It is also an essential initial condition for thermo-hydro-mechanical (THM) models that investigate man-made induced processes e.g. seismicity due to fluid injection/extraction, reservoir depletion or storage of heat producing high-level radioactive waste. Without a reasonable assumption on the initial stress condition it is not possible to assess if a man-made process is pushing the system into a critical state or not. However, modelling the initial 3D stress state on reservoir scale is challenging since data are hardly available before drilling in the area of interest. This is in particular the case for the stress magnitude data which are a prerequisite for a reliable model calibration. Here, we present a multi-stage 3D geomechanical-numerical model approach to estimate for a reservoir-scale volume the 3D in situ stress state. First, we set up a large-scale model which is calibrated by stress data and use the modelled stress field subsequently to calibrate a small-scale model located within the large-scale model. The local model contains a significantly higher resolution representation of the subsurface geometry around boreholes of a projected geothermal power plant. This approach incorporates two models and is an alternative to the required trade-off between resolution, computational cost and calibration data which is inevitable for a single model; an extension to a three-stage approach would be straight forward. We exemplify the two-stage approach for the area around Munich in the German Molasse Basin. The results of the reservoir-scale model are presented in terms of values for slip tendency as a measure for the criticality of fault reactivation. The model results show that variations due to uncertainties in the input data are mainly introduced by the uncertain material properties and missing
Fleischer, Christian; Waag, Wladislaw; Heyn, Hans-Martin; Sauer, Dirk Uwe
2014-09-01
Lithium-ion battery systems employed in high power demanding systems such as electric vehicles require a sophisticated monitoring system to ensure safe and reliable operation. Three major states of the battery are of special interest and need to be constantly monitored. These include: battery state of charge (SoC), battery state of health (capacity fade determination, SoH), and state of function (power fade determination, SoF). The second paper concludes the series by presenting a multi-stage online parameter identification technique based on a weighted recursive least quadratic squares parameter estimator to determine the parameters of the proposed battery model from the first paper during operation. A novel mutation based algorithm is developed to determine the nonlinear current dependency of the charge-transfer resistance. The influence of diffusion is determined by an on-line identification technique and verified on several batteries at different operation conditions. This method guarantees a short response time and, together with its fully recursive structure, assures a long-term stable monitoring of the battery parameters. The relative dynamic voltage prediction error of the algorithm is reduced to 2%. The changes of parameters are used to determine the states of the battery. The algorithm is real-time capable and can be implemented on embedded systems.
Energy Technology Data Exchange (ETDEWEB)
Romero-Lopez, Julia; Lopez-Rodas, Victoria [Genetica, Facultad de Veterinaria, Universidad Complutense de Madrid, Avda. Puerta de Hierro s/n, E-28040 Madrid (Spain); Costas, Eduardo, E-mail: ecostas@vet.ucm.es [Genetica, Facultad de Veterinaria, Universidad Complutense de Madrid, Avda. Puerta de Hierro s/n, E-28040 Madrid (Spain)
2012-11-15
Highlights: Black-Right-Pointing-Pointer Microalgae are able to physiological acclimatization low doses of petroleum and diesel oil. Black-Right-Pointing-Pointer When petroleum or oil concentration exceeds these limits, survival depend of rare mutations. Black-Right-Pointing-Pointer Petroleum-resistant and diesel oil mutants occur spontaneously prior to oil exposure. Black-Right-Pointing-Pointer After 300 generations of artificial selection resistant strains were obtained. Black-Right-Pointing-Pointer Cyanobacteria has more difficulties to achieve petroleum resistance than Chlorophyta. - Abstract: There is increasing scientific interest in how phytoplankton reacts to petroleum contamination, since crude oil and its derivatives are generating extensive contamination of aquatic environments. However, toxic effects of short-term petroleum exposure are more widely known than the adaptation of phytoplankton to long-term petroleum exposure. An analysis of short-term and long-term effects of petroleum exposure was done using experimental populations of freshwater (Scenedesmus intermedius and Microcystis aeruginosa) and marine (Dunaliella tertiolecta) microalgae isolated from pristine sites without crude oil product contamination. These strains were exposed to increased levels of petroleum and diesel oil. Short-term exposure to petroleum or diesel oil revealed a rapid inhibition of photosynthetic performance and cell proliferation in freshwater and marine phytoplankton species. A broad degree of inter-specific variation in lethal contamination level was observed. When different strains were exposed to petroleum or diesel oil over the long-term, the cultures showed massive destruction of the sensitive cells. Nonetheless, after further incubation, some cultures were able to grow again due to cells that were resistant to the toxins. By means of a fluctuation analysis, discrimination between cells that had become resistant due to physiological acclimatization and resistant
International Nuclear Information System (INIS)
Romero-Lopez, Julia; Lopez-Rodas, Victoria; Costas, Eduardo
2012-01-01
Highlights: ► Microalgae are able to physiological acclimatization low doses of petroleum and diesel oil. ► When petroleum or oil concentration exceeds these limits, survival depend of rare mutations. ► Petroleum-resistant and diesel oil mutants occur spontaneously prior to oil exposure. ► After 300 generations of artificial selection resistant strains were obtained. ► Cyanobacteria has more difficulties to achieve petroleum resistance than Chlorophyta. - Abstract: There is increasing scientific interest in how phytoplankton reacts to petroleum contamination, since crude oil and its derivatives are generating extensive contamination of aquatic environments. However, toxic effects of short-term petroleum exposure are more widely known than the adaptation of phytoplankton to long-term petroleum exposure. An analysis of short-term and long-term effects of petroleum exposure was done using experimental populations of freshwater (Scenedesmus intermedius and Microcystis aeruginosa) and marine (Dunaliella tertiolecta) microalgae isolated from pristine sites without crude oil product contamination. These strains were exposed to increased levels of petroleum and diesel oil. Short-term exposure to petroleum or diesel oil revealed a rapid inhibition of photosynthetic performance and cell proliferation in freshwater and marine phytoplankton species. A broad degree of inter-specific variation in lethal contamination level was observed. When different strains were exposed to petroleum or diesel oil over the long-term, the cultures showed massive destruction of the sensitive cells. Nonetheless, after further incubation, some cultures were able to grow again due to cells that were resistant to the toxins. By means of a fluctuation analysis, discrimination between cells that had become resistant due to physiological acclimatization and resistant cells arising from rare spontaneous mutations was accomplished. In addition, an analysis was done as to the maximum capacity of
International Nuclear Information System (INIS)
Hosseinpour, Soleiman; Aghbashlo, Mortaza; Tabatabaei, Meisam; Khalife, Esmail
2016-01-01
Highlights: • Estimating the biodiesel CN from its FAMEs profile using ANN-based PLS approach. • Comparing the capability of ANN-adapted PLS approach with the standard PLS model. • Exact prediction of biodiesel CN from it FAMEs profile using ANN-based PLS method. • Developing an easy-to-use software using ANN-PLS model for computing the biodiesel CN. - Abstract: Cetane number (CN) is among the most important properties of biodiesel because it quantifies combustion speed or in better words, ignition quality. Experimental measurement of biodiesel CN is rather laborious and expensive. However, the high proportionality of biodiesel fatty acid methyl esters (FAMEs) profile with its CN is very appealing to develop straightforward and inexpensive computerized tools for biodiesel CN estimation. Unfortunately, correlating the chemical structure of biodiesel to its CN using conventional statistical and mathematical approaches is very difficult. To solve this issue, partial least square (PLS) adapted by artificial neural network (ANN) was introduced and examined herein as an innovative approach for the exact estimation of biodiesel CN from its FAMEs profile. In the proposed approach, ANN paradigm was used for modeling the inner relation between the input and the output PLS score vectors. In addition, the capability of the developed method in predicting the biodiesel CN was compared with the basal PLS method. The accuracy of the developed approaches for computing the biodiesel CN was assessed using three statistical criteria, i.e., coefficient of determination (R"2), mean-squared error (MSE), and percentage error (PE). The ANN-adapted PLS method predicted the biodiesel CN with an R"2 value higher than 0.99 demonstrating the fidelity of the developed model over the classical PLS method with a markedly lower R"2 value of about 0.85. In order to facilitate the use of the proposed model, an easy-to-use computer program was also developed on the basis of ANN-adapted PLS
Adaptive estimation of a time-varying phase with a power-law spectrum via continuous squeezed states
Dinani, Hossein T.; Berry, Dominic W.
2017-06-01
When measuring a time-varying phase, the standard quantum limit and Heisenberg limit as usually defined, for a constant phase, do not apply. If the phase has Gaussian statistics and a power-law spectrum 1 /|ω| p with p >1 , then the generalized standard quantum limit and Heisenberg limit have recently been found to have scalings of 1 /N(p -1 )/p and 1 /N2 (p -1 )/(p +1 ) , respectively, where N is the mean photon flux. We show that this Heisenberg scaling can be achieved via adaptive measurements on squeezed states. We predict the experimental parameters analytically, and test them with numerical simulations. Previous work had considered the special case of p =2 .
Marzbanrad, Javad; Tahbaz-zadeh Moghaddam, Iman
2016-09-01
The main purpose of this paper is to design a self-tuning control algorithm for an adaptive cruise control (ACC) system that can adapt its behaviour to variations of vehicle dynamics and uncertain road grade. To this aim, short-time linear quadratic form (STLQF) estimation technique is developed so as to track simultaneously the trend of the time-varying parameters of vehicle longitudinal dynamics with a small delay. These parameters are vehicle mass, road grade and aerodynamic drag-area coefficient. Next, the values of estimated parameters are used to tune the throttle and brake control inputs and to regulate the throttle/brake switching logic that governs the throttle and brake switching. The performance of the designed STLQF-based self-tuning control (STLQF-STC) algorithm for ACC system is compared with the conventional method based on fixed control structure regarding the speed/distance tracking control modes. Simulation results show that the proposed control algorithm improves the performance of throttle and brake controllers, providing more comfort while travelling, enhancing driving safety and giving a satisfactory performance in the presence of different payloads and road grade variations.
E-CAI: a novel server to estimate an expected value of Codon Adaptation Index (eCAI
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Garcia-Vallvé Santiago
2008-01-01
Full Text Available Abstract Background The Codon Adaptation Index (CAI is a measure of the synonymous codon usage bias for a DNA or RNA sequence. It quantifies the similarity between the synonymous codon usage of a gene and the synonymous codon frequency of a reference set. Extreme values in the nucleotide or in the amino acid composition have a large impact on differential preference for synonymous codons. It is thence essential to define the limits for the expected value of CAI on the basis of sequence composition in order to properly interpret the CAI and provide statistical support to CAI analyses. Though several freely available programs calculate the CAI for a given DNA sequence, none of them corrects for compositional biases or provides confidence intervals for CAI values. Results The E-CAI server, available at http://genomes.urv.es/CAIcal/E-CAI, is a web-application that calculates an expected value of CAI for a set of query sequences by generating random sequences with G+C and amino acid content similar to those of the input. An executable file, a tutorial, a Frequently Asked Questions (FAQ section and several examples are also available. To exemplify the use of the E-CAI server, we have analysed the codon adaptation of human mitochondrial genes that codify a subunit of the mitochondrial respiratory chain (excluding those genes that lack a prokaryotic orthologue and are encoded in the nuclear genome. It is assumed that these genes were transferred from the proto-mitochondrial to the nuclear genome and that its codon usage was then ameliorated. Conclusion The E-CAI server provides a direct threshold value for discerning whether the differences in CAI are statistically significant or whether they are merely artifacts that arise from internal biases in the G+C composition and/or amino acid composition of the query sequences.
Wei, Jingwen; Dong, Guangzhong; Chen, Zonghai
2017-10-01
With the rapid development of battery-powered electric vehicles, the lithium-ion battery plays a critical role in the reliability of vehicle system. In order to provide timely management and protection for battery systems, it is necessary to develop a reliable battery model and accurate battery parameters estimation to describe battery dynamic behaviors. Therefore, this paper focuses on an on-board adaptive model for state-of-charge (SOC) estimation of lithium-ion batteries. Firstly, a first-order equivalent circuit battery model is employed to describe battery dynamic characteristics. Then, the recursive least square algorithm and the off-line identification method are used to provide good initial values of model parameters to ensure filter stability and reduce the convergence time. Thirdly, an extended-Kalman-filter (EKF) is applied to on-line estimate battery SOC and model parameters. Considering that the EKF is essentially a first-order Taylor approximation of battery model, which contains inevitable model errors, thus, a proportional integral-based error adjustment technique is employed to improve the performance of EKF method and correct model parameters. Finally, the experimental results on lithium-ion batteries indicate that the proposed EKF with proportional integral-based error adjustment method can provide robust and accurate battery model and on-line parameter estimation.
Directory of Open Access Journals (Sweden)
Yang Zhang
2015-11-01
Full Text Available Prognostics is necessary to ensure the reliability and safety of lithium-ion batteries for hybrid electric vehicles or satellites. This process can be achieved by capacity estimation, which is a direct fading indicator for assessing the state of health of a battery. However, the capacity of a lithium-ion battery onboard is difficult to monitor. This paper presents a data-driven approach for online capacity estimation. First, six novel features are extracted from cyclic charge/discharge cycles and used as indirect health indicators. An adaptive multi-kernel relevance machine (MKRVM based on accelerated particle swarm optimization algorithm is used to determine the optimal parameters of MKRVM and characterize the relationship between extracted features and battery capacity. The overall estimation process comprises offline and online stages. A supervised learning step in the offline stage is established for model verification to ensure the generalizability of MKRVM for online application. Cross-validation is further conducted to validate the performance of the proposed model. Experiment and comparison results show the effectiveness, accuracy, efficiency, and robustness of the proposed approach for online capacity estimation of lithium-ion batteries.
Oliveri, Alberto; Masi, Alessandro; Storace, Marco
2015-01-01
In this paper a piecewise affine virtual sensor is used for the estimation of the motor-side current of hybrid stepper motors, which actuate the LHC (Large Hadron Collider) collimators at CERN. The estimation is performed starting from measurements of the current in the driver, which is connected to the motor by a long cable (up to 720 m). The measured current is therefore affected by noise and ringing phenomena. The proposed method does not require a model of the cable, since it is only based on measured data and can be used with cables of different length. A circuit architecture suitable for FPGA implementation has been designed and the effects of fixed point representation of data are analyzed.
Smith, Paul L.; VonderHaar, Thomas H.
1996-01-01
The principal goal of this project is to establish relationships that would allow application of area-time integral (ATI) calculations based upon satellite data to estimate rainfall volumes. The research is being carried out as a collaborative effort between the two participating organizations, with the satellite data analysis to determine values for the ATIs being done primarily by the STC-METSAT scientists and the associated radar data analysis to determine the 'ground-truth' rainfall estimates being done primarily at the South Dakota School of Mines and Technology (SDSM&T). Synthesis of the two separate kinds of data and investigation of the resulting rainfall-versus-ATI relationships is then carried out jointly. The research has been pursued using two different approaches, which for convenience can be designated as the 'fixed-threshold approach' and the 'adaptive-threshold approach'. In the former, an attempt is made to determine a single temperature threshold in the satellite infrared data that would yield ATI values for identifiable cloud clusters which are closely related to the corresponding rainfall amounts as determined by radar. Work on the second, or 'adaptive-threshold', approach for determining the satellite ATI values has explored two avenues: (1) attempt involved choosing IR thresholds to match the satellite ATI values with ones separately calculated from the radar data on a case basis; and (2) an attempt involved a striaghtforward screening analysis to determine the (fixed) offset that would lead to the strongest correlation and lowest standard error of estimate in the relationship between the satellite ATI values and the corresponding rainfall volumes.
A New Entropy Formula and Gradient Estimates for the Linear Heat Equation on Static Manifold
Directory of Open Access Journals (Sweden)
Abimbola Abolarinwa
2014-08-01
Full Text Available In this paper we prove a new monotonicity formula for the heat equation via a generalized family of entropy functionals. This family of entropy formulas generalizes both Perelman’s entropy for evolving metric and Ni’s entropy on static manifold. We show that this entropy satisfies a pointwise differential inequality for heat kernel. The consequences of which are various gradient and Harnack estimates for all positive solutions to the heat equation on compact manifold.
Power spectral density of velocity fluctuations estimated from phase Doppler data
Jicha Miroslav; Lizal Frantisek; Jedelsky Jan
2012-01-01
Laser Doppler Anemometry (LDA) and its modifications such as PhaseDoppler Particle Anemometry (P/DPA) is point-wise method for optical nonintrusive measurement of particle velocity with high data rate. Conversion of the LDA velocity data from temporal to frequency domain – calculation of power spectral density (PSD) of velocity fluctuations, is a non trivial task due to nonequidistant data sampling in time. We briefly discuss possibilities for the PSD estimation and specify limitations caused...
Global a priori estimates for the inhomogeneous Landau equation with moderately soft potentials
Cameron, Stephen; Silvestre, Luis; Snelson, Stanley
2018-05-01
We establish a priori upper bounds for solutions to the spatially inhomogeneous Landau equation in the case of moderately soft potentials, with arbitrary initial data, under the assumption that mass, energy and entropy densities stay under control. Our pointwise estimates decay polynomially in the velocity variable. We also show that if the initial data satisfies a Gaussian upper bound, this bound is propagated for all positive times.
Directory of Open Access Journals (Sweden)
M. E. Ya’acob
2014-01-01
Full Text Available Mirror concentrating element and tracking mechanism has been seriously investigated and widely adapted in solar PV technology. In this study, a practical in-field method is conducted in Serdang, Selangor, Malaysia, for the two technologies in comparison to the common fixed flat PV arrays. The data sampling process is measured under stochastic weather characteristics with the main target of calculating the effectiveness of PV power output. The data are monitored, recorded, and analysed in real time via GPRS online monitoring system for 10 consecutive months. The analysis is based on a simple comparison of the actual daily power generation from each PV generator with statistical analysis of multiple linear regression (MLR and analysis of variance test (ANOVA. From the analysis, it is shown that tracking mechanism generates approximately 88 Watts (9.4% compared to the mirror concentrator which generates 144 Watts (23.4% of the cumulative dc power for different array configurations at standard testing condition (STC references. The significant increase in power generation shows feasibilities of implying both mechanisms for PV generators and thus contributes to additional reference in PV array design.
International Nuclear Information System (INIS)
Benitez R, J.S.; Perez C, J.H.; Rivero G, T.
2008-01-01
In this paper a novel procedure for power regulation in a TRIGA Mark III nuclear reactor is presented. The control scheme combines state variable feedback with a first order predictor, which is incorporated to speed up the power response of the reactor without exceeding the safety requirement imposed by the reactor period. The simulation results using the proposed control strategy attains different values of steady-state power from different values of initial power in short time, complying at all times with the safety restriction imposed on the reactor period. The predictor, derived from the theory of first order numerical integration, produces very good results during the ascent of power. These results include a fast response and independence of the wide variety of potential operating conditions something not easy and even impossible to obtain with other procedures. By using this control scheme, the reactor period is maintained within safety limits during the start up of the reactor, which is normally the operating condition where an occurrence of a period scram is common. However, the predictor can not be used when the power is reaching the desired power level because the instantaneous power increases far above the desired level. Thus, when the power increases above certain power level, the state feedback gain is set constant to a predefined value. This causes some oscillations that decrease in a few seconds. Afterwards, the power response smoothly approaches, with a small overshoot, the desired power. This constraint on the use of the predictor prevents the unbounded increase of the neutron power. The control law proposed requires all the system's state variables. Since only the neutron power is available, it is necessary the estimation of the non measurable states. The key issue of the existence of a solution to this problem has been previously considered. One of the conclusions is that the point kinetic equations are observable under certain restrictions on
Energy Technology Data Exchange (ETDEWEB)
Rybynok, V O; Kyriacou, P A [City University, London (United Kingdom)
2007-10-15
Diabetes is one of the biggest health challenges of the 21st century. The obesity epidemic, sedentary lifestyles and an ageing population mean prevalence of the condition is currently doubling every generation. Diabetes is associated with serious chronic ill health, disability and premature mortality. Long-term complications including heart disease, stroke, blindness, kidney disease and amputations, make the greatest contribution to the costs of diabetes care. Many of these long-term effects could be avoided with earlier, more effective monitoring and treatment. Currently, blood glucose can only be monitored through the use of invasive techniques. To date there is no widely accepted and readily available non-invasive monitoring technique to measure blood glucose despite the many attempts. This paper challenges one of the most difficult non-invasive monitoring techniques, that of blood glucose, and proposes a new novel approach that will enable the accurate, and calibration free estimation of glucose concentration in blood. This approach is based on spectroscopic techniques and a new adaptive modelling scheme. The theoretical implementation and the effectiveness of the adaptive modelling scheme for this application has been described and a detailed mathematical evaluation has been employed to prove that such a scheme has the capability of extracting accurately the concentration of glucose from a complex biological media.
Noori, Roohollah; Safavi, Salman; Nateghi Shahrokni, Seyyed Afshin
2013-07-01
The five-day biochemical oxygen demand (BOD5) is one of the key parameters in water quality management. In this study, a novel approach, i.e., reduced-order adaptive neuro-fuzzy inference system (ROANFIS) model was developed for rapid estimation of BOD5. In addition, an uncertainty analysis of adaptive neuro-fuzzy inference system (ANFIS) and ROANFIS models was carried out based on Monte-Carlo simulation. Accuracy analysis of ANFIS and ROANFIS models based on both developed discrepancy ratio and threshold statistics revealed that the selected ROANFIS model was superior. Pearson correlation coefficient (R) and root mean square error for the best fitted ROANFIS model were 0.96 and 7.12, respectively. Furthermore, uncertainty analysis of the developed models indicated that the selected ROANFIS had less uncertainty than the ANFIS model and accurately forecasted BOD5 in the Sefidrood River Basin. Besides, the uncertainty analysis also showed that bracketed predictions by 95% confidence bound and d-factor in the testing steps for the selected ROANFIS model were 94% and 0.83, respectively.
Rybynok, V. O.; Kyriacou, P. A.
2007-10-01
Diabetes is one of the biggest health challenges of the 21st century. The obesity epidemic, sedentary lifestyles and an ageing population mean prevalence of the condition is currently doubling every generation. Diabetes is associated with serious chronic ill health, disability and premature mortality. Long-term complications including heart disease, stroke, blindness, kidney disease and amputations, make the greatest contribution to the costs of diabetes care. Many of these long-term effects could be avoided with earlier, more effective monitoring and treatment. Currently, blood glucose can only be monitored through the use of invasive techniques. To date there is no widely accepted and readily available non-invasive monitoring technique to measure blood glucose despite the many attempts. This paper challenges one of the most difficult non-invasive monitoring techniques, that of blood glucose, and proposes a new novel approach that will enable the accurate, and calibration free estimation of glucose concentration in blood. This approach is based on spectroscopic techniques and a new adaptive modelling scheme. The theoretical implementation and the effectiveness of the adaptive modelling scheme for this application has been described and a detailed mathematical evaluation has been employed to prove that such a scheme has the capability of extracting accurately the concentration of glucose from a complex biological media.
International Nuclear Information System (INIS)
Rybynok, V O; Kyriacou, P A
2007-01-01
Diabetes is one of the biggest health challenges of the 21st century. The obesity epidemic, sedentary lifestyles and an ageing population mean prevalence of the condition is currently doubling every generation. Diabetes is associated with serious chronic ill health, disability and premature mortality. Long-term complications including heart disease, stroke, blindness, kidney disease and amputations, make the greatest contribution to the costs of diabetes care. Many of these long-term effects could be avoided with earlier, more effective monitoring and treatment. Currently, blood glucose can only be monitored through the use of invasive techniques. To date there is no widely accepted and readily available non-invasive monitoring technique to measure blood glucose despite the many attempts. This paper challenges one of the most difficult non-invasive monitoring techniques, that of blood glucose, and proposes a new novel approach that will enable the accurate, and calibration free estimation of glucose concentration in blood. This approach is based on spectroscopic techniques and a new adaptive modelling scheme. The theoretical implementation and the effectiveness of the adaptive modelling scheme for this application has been described and a detailed mathematical evaluation has been employed to prove that such a scheme has the capability of extracting accurately the concentration of glucose from a complex biological media
Energy Technology Data Exchange (ETDEWEB)
Keller, Brad M.; Nathan, Diane L.; Wang Yan; Zheng Yuanjie; Gee, James C.; Conant, Emily F.; Kontos, Despina [Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States)
2012-08-15
Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., 'FOR PROCESSING') and vendor postprocessed (i.e., 'FOR PRESENTATION'), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely
International Nuclear Information System (INIS)
Shagina, Natalia; Tolstykh, Evgenia; Degteva, Marina; Fell, Tim; Harrison, John
2008-01-01
Full text: Reliable estimation of tissue doses for exposed individuals is very important in epidemiological studies. Long-term cohort studies of the Techa River populations exposed in the early 1950s due to releases of liquid radioactive wastes from the Mayak plutonium production facility (Southern Urals, Russia) are unique in allowing the quantification of risks from low-level chronic exposure of the general population and providing information on risks for persons exposed in utero. Strontium isotopes were the most important contributors to haemopoietic tissue doses for people living in the riverside settlements. Large-scale monitoring of the exposed population has provided a comprehensive database, including post mortem and in vivo measurements of 90 Sr in bones and whole body, for use in the estimation of doses. The International Commission on Radiological Protection (ICRP) has published biokinetic and dosimetric models for the calculation of doses to members of the public, including doses from in utero exposures and from intakes with breast milk. However, the ICRP models as applied to Sr required modification to provide best estimates of doses to Techa River residents. Adaptations were made to the ICRP model for Sr in children and adults to take account of population-specific features relating to bone mineral turnover and to model age and gender differences in strontium retention. Refinements in the ICRP model for Sr uptake and retention in the fetus were made to improve the treatment of discrimination against Sr, relative to Ca, in transfer from maternal to foetal blood and to take account of population-specific data on the calcium content of the maternal and fetal skeleton. Modification of the ICRP model for Sr transfer in breast-milk included adaptations relating to changes in maternal mineral metabolism during lactation and consideration of population-specific features of breast feeding in the rural population. The improved models were successfully
International Nuclear Information System (INIS)
Zhang, Y; Yin, F; Ren, L; Zhang, Y
2016-01-01
Purpose: To develop an adaptive prior knowledge based image estimation method to reduce the scan angle needed in the LIVE system to reconstruct 4D-CBCT for intrafraction verification. Methods: The LIVE system has been previously proposed to reconstructs 4D volumetric images on-the-fly during arc treatment for intrafraction target verification and dose calculation. This system uses limited-angle beam’s eye view (BEV) MV cine images acquired from the treatment beam together with the orthogonally acquired limited-angle kV projections to reconstruct 4D-CBCT images for target verification during treatment. In this study, we developed an adaptive constrained free-form deformation reconstruction technique in LIVE to further reduce the scanning angle needed to reconstruct the CBCT images. This technique uses free form deformation with energy minimization to deform prior images to estimate 4D-CBCT based on projections acquired in limited angle (orthogonal 6°) during the treatment. Note that the prior images are adaptively updated using the latest CBCT images reconstructed by LIVE during treatment to utilize the continuity of patient motion.The 4D digital extended-cardiac-torso (XCAT) phantom was used to evaluate the efficacy of this technique with LIVE system. A lung patient was simulated with different scenario, including baseline drifts, amplitude change and phase shift. Limited-angle orthogonal kV and beam’s eye view (BEV) MV projections were generated for each scenario. The CBCT reconstructed by these projections were compared with the ground-truth generated in XCAT.Volume-percentage-difference (VPD) and center-of-mass-shift (COMS) were calculated between the reconstructed and the ground-truth tumors to evaluate the reconstruction accuracy. Results: Using orthogonal-view of 6° kV and BEV- MV projections, the VPD/COMS values were 12.7±4.0%/0.7±0.5 mm, 13.0±5.1%/0.8±0.5 mm, and 11.4±5.4%/0.5±0.3 mm for the three scenarios, respectively. Conclusion: The
Energy Technology Data Exchange (ETDEWEB)
Zhang, Y; Yin, F; Ren, L [Duke University Medical Center, Durham, NC (United States); Zhang, Y [UT Southwestern Medical Ctr at Dallas, Dallas, TX (United States)
2016-06-15
Purpose: To develop an adaptive prior knowledge based image estimation method to reduce the scan angle needed in the LIVE system to reconstruct 4D-CBCT for intrafraction verification. Methods: The LIVE system has been previously proposed to reconstructs 4D volumetric images on-the-fly during arc treatment for intrafraction target verification and dose calculation. This system uses limited-angle beam’s eye view (BEV) MV cine images acquired from the treatment beam together with the orthogonally acquired limited-angle kV projections to reconstruct 4D-CBCT images for target verification during treatment. In this study, we developed an adaptive constrained free-form deformation reconstruction technique in LIVE to further reduce the scanning angle needed to reconstruct the CBCT images. This technique uses free form deformation with energy minimization to deform prior images to estimate 4D-CBCT based on projections acquired in limited angle (orthogonal 6°) during the treatment. Note that the prior images are adaptively updated using the latest CBCT images reconstructed by LIVE during treatment to utilize the continuity of patient motion.The 4D digital extended-cardiac-torso (XCAT) phantom was used to evaluate the efficacy of this technique with LIVE system. A lung patient was simulated with different scenario, including baseline drifts, amplitude change and phase shift. Limited-angle orthogonal kV and beam’s eye view (BEV) MV projections were generated for each scenario. The CBCT reconstructed by these projections were compared with the ground-truth generated in XCAT.Volume-percentage-difference (VPD) and center-of-mass-shift (COMS) were calculated between the reconstructed and the ground-truth tumors to evaluate the reconstruction accuracy. Results: Using orthogonal-view of 6° kV and BEV- MV projections, the VPD/COMS values were 12.7±4.0%/0.7±0.5 mm, 13.0±5.1%/0.8±0.5 mm, and 11.4±5.4%/0.5±0.3 mm for the three scenarios, respectively. Conclusion: The
Krengel, Annette; Hauth, Jan; Taskinen, Marja-Riitta; Adiels, Martin; Jirstrand, Mats
2013-01-19
When mathematical modelling is applied to many different application areas, a common task is the estimation of states and parameters based on measurements. With this kind of inference making, uncertainties in the time when the measurements have been taken are often neglected, but especially in applications taken from the life sciences, this kind of errors can considerably influence the estimation results. As an example in the context of personalized medicine, the model-based assessment of the effectiveness of drugs is becoming to play an important role. Systems biology may help here by providing good pharmacokinetic and pharmacodynamic (PK/PD) models. Inference on these systems based on data gained from clinical studies with several patient groups becomes a major challenge. Particle filters are a promising approach to tackle these difficulties but are by itself not ready to handle uncertainties in measurement times. In this article, we describe a variant of the standard particle filter (PF) algorithm which allows state and parameter estimation with the inclusion of measurement time uncertainties (MTU). The modified particle filter, which we call MTU-PF, also allows the application of an adaptive stepsize choice in the time-continuous case to avoid degeneracy problems. The modification is based on the model assumption of uncertain measurement times. While the assumption of randomness in the measurements themselves is common, the corresponding measurement times are generally taken as deterministic and exactly known. Especially in cases where the data are gained from measurements on blood or tissue samples, a relatively high uncertainty in the true measurement time seems to be a natural assumption. Our method is appropriate in cases where relatively few data are used from a relatively large number of groups or individuals, which introduce mixed effects in the model. This is a typical setting of clinical studies. We demonstrate the method on a small artificial example
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Nauman Khalid Qureshi
2017-07-01
Full Text Available In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS signals utilizable in a two-class [motor imagery (MI and rest; mental rotation (MR and rest] brain–computer interface (BCI is presented. First, fNIRS signals corresponding to MI and MR are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for MI versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for MR versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine. These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher (p < 0.05. These results serve to demonstrate the feasibility of developing a high-classification-performance fNIRS-BCI.
International Nuclear Information System (INIS)
Keller, Brad M.; Nathan, Diane L.; Wang Yan; Zheng Yuanjie; Gee, James C.; Conant, Emily F.; Kontos, Despina
2012-01-01
Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., “FOR PROCESSING”) and vendor postprocessed (i.e., “FOR PRESENTATION”), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which
International Nuclear Information System (INIS)
Amor, H.; Bourgeois, M.
2012-01-01
using an adaptive mesh refinement strategy was introduced in MELODIE for the simulation of groundwater flow and solute transport in saturated porous media in 2 dimensions. The selected estimator, based on the explicit residual error, is expected to allow local refinements and thus minimization of the discretization error at an optimal computational cost. Test case: a realistic heterogeneous case with fracturing. In addition to theoretical test cases a more complex case was tested. The purpose of this test case was twofold: - to move from pure theoretical work to an illustrative case within a realistic generic context; however parameter values for hydrodynamic characteristics were chosen so as to highlight the investigated phenomena; - to account for large time and space scales, representative for those required for the simulation of radioactive waste repositories. The general shape of the geological media was designed to cover main features representative of sedimentary formations. Three distinct radionuclide source locations were chosen in order to obtain a set of flow and transport configurations. The entire layer sequence was structured into three hydrogeological units intersected by three sub-vertical faults. The vertical 2D cross-section dimensions are 5 km long by 500 m thick. Two source terms are located in a 100 m-thick layer in the right part of the domain and another one is located in a larger layer in the left part. These two 'host rock' layers consist of the same sedimentary unit with a low permeability, though an offset due to the middle fault. Faults are considered as conductive features. Radionuclides are assumed to be instantaneously released from the three source term locations at t = 0. The a posteriori error estimator and the adaptive mesh algorithm were applied to this heterogeneous problem. Preliminary calculations showed that the implemented a posteriori error estimator method is efficient to solve the equations of flow and advective
Ho, Tsung-Han
2010-01-01
Computerized adaptive testing (CAT) provides a highly efficient alternative to the paper-and-pencil test. By selecting items that match examinees' ability levels, CAT not only can shorten test length and administration time but it can also increase measurement precision and reduce measurement error. In CAT, maximum information (MI) is the most…
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Nils Ternès
2017-05-01
Full Text Available Abstract Background Thanks to the advances in genomics and targeted treatments, more and more prediction models based on biomarkers are being developed to predict potential benefit from treatments in a randomized clinical trial. Despite the methodological framework for the development and validation of prediction models in a high-dimensional setting is getting more and more established, no clear guidance exists yet on how to estimate expected survival probabilities in a penalized model with biomarker-by-treatment interactions. Methods Based on a parsimonious biomarker selection in a penalized high-dimensional Cox model (lasso or adaptive lasso, we propose a unified framework to: estimate internally the predictive accuracy metrics of the developed model (using double cross-validation; estimate the individual survival probabilities at a given timepoint; construct confidence intervals thereof (analytical or bootstrap; and visualize them graphically (pointwise or smoothed with spline. We compared these strategies through a simulation study covering scenarios with or without biomarker effects. We applied the strategies to a large randomized phase III clinical trial that evaluated the effect of adding trastuzumab to chemotherapy in 1574 early breast cancer patients, for which the expression of 462 genes was measured. Results In our simulations, penalized regression models using the adaptive lasso estimated the survival probability of new patients with low bias and standard error; bootstrapped confidence intervals had empirical coverage probability close to the nominal level across very different scenarios. The double cross-validation performed on the training data set closely mimicked the predictive accuracy of the selected models in external validation data. We also propose a useful visual representation of the expected survival probabilities using splines. In the breast cancer trial, the adaptive lasso penalty selected a prediction model with 4
International Nuclear Information System (INIS)
Akram, N.
1999-01-01
In this report we describe the concept of adaptive noise canceling, an alternative method of estimating signals corrupted by additive noise of interference. The method uses 'primary' input containing the corrupted signal and a 'reference' input containing noise correlated in some unknown way with the primary noise, the reference input is adaptively filtered and subtracted from the primary input to obtain the signal estimate. Adaptive filtering before subtraction allows the treatment of inputs that are deterministic or stochastic, stationary or time variable. When the reference input is free of signal and certain other conditions are met then noise in the primary input can be essentially eliminated without signal distortion. It is further shown that the adaptive filter also acts as notch filter. Simulated results illustrate the usefulness of the adaptive noise canceling technique. (author)
A gradient estimate for solutions to parabolic equations with discontinuous coefficients
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Jishan Fan
2013-04-01
Full Text Available Li-Vogelius and Li-Nirenberg gave a gradient estimate for solutions of strongly elliptic equations and systems of divergence forms with piecewise smooth coefficients, respectively. The discontinuities of the coefficients are assumed to be given by manifolds of codimension 1, which we called them emph{manifolds of discontinuities}. Their gradient estimate is independent of the distances between manifolds of discontinuities. In this paper, we gave a parabolic version of their results. That is, we gave a gradient estimate for parabolic equations of divergence forms with piecewise smooth coefficients. The coefficients are assumed to be independent of time and their discontinuities are likewise the previous elliptic equations. As an application of this estimate, we also gave a pointwise gradient estimate for the fundamental solution of a parabolic operator with piecewise smooth coefficients. Both gradient estimates are independent of the distances between manifolds of discontinuities.
Adaptive filtering prediction and control
Goodwin, Graham C
2009-01-01
Preface1. Introduction to Adaptive TechniquesPart 1. Deterministic Systems2. Models for Deterministic Dynamical Systems3. Parameter Estimation for Deterministic Systems4. Deterministic Adaptive Prediction5. Control of Linear Deterministic Systems6. Adaptive Control of Linear Deterministic SystemsPart 2. Stochastic Systems7. Optimal Filtering and Prediction8. Parameter Estimation for Stochastic Dynamic Systems9. Adaptive Filtering and Prediction10. Control of Stochastic Systems11. Adaptive Control of Stochastic SystemsAppendicesA. A Brief Review of Some Results from Systems TheoryB. A Summary o
Hybrid Adaptive Flight Control with Model Inversion Adaptation
Nguyen, Nhan
2011-01-01
This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.
Roslan, Nurul Fazlin; Suul, Jon Are; Luna Alloza, Álvaro; Candela García, José Ignacio; Rodríguez Cortés, Pedro
2015-01-01
This paper discusses the implementation of proportional resonant (PR) current controllers for a Voltage Source Converter (VSC) with LCL filter which is synchronized to the grid by virtual flux (VF) estimation with inherent sequence separation. Even though there is an extensive amount of literature and studies on the PR current controller for tracking the current reference of a VSC in the stationary reference frame, there is no discussion taking into account voltage sensor-less operation based...
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Zhongwei Deng
2016-06-01
Full Text Available In the field of state of charge (SOC estimation, the Kalman filter has been widely used for many years, although its performance strongly depends on the accuracy of the battery model as well as the noise covariance. The Kalman gain determines the confidence coefficient of the battery model by adjusting the weight of open circuit voltage (OCV correction, and has a strong correlation with the measurement noise covariance (R. In this paper, the online identification method is applied to acquire the real model parameters under different operation conditions. A criterion based on the OCV error is proposed to evaluate the reliability of online parameters. Besides, the equivalent circuit model produces an intrinsic model error which is dependent on the load current, and the property that a high battery current or a large current change induces a large model error can be observed. Based on the above prior knowledge, a fuzzy model is established to compensate the model error through updating R. Combining the positive strategy (i.e., online identification and negative strategy (i.e., fuzzy model, a more reliable and robust SOC estimation algorithm is proposed. The experiment results verify the proposed reliability criterion and SOC estimation method under various conditions for LiFePO4 batteries.
Jiang, Li; Peng, Jianxiong; Huang, Meisha; Liu, Jing; Wang, Ling; Ma, Quan; Zhao, Hui; Yang, Xin; Ji, Anquan; Li, Caixia
2018-02-10
Tibetans have adapted to the extreme environment of high altitude for hundreds of generations. A highly differentiated 5-SNP (Single Nucleotide Polymorphism) haplotype motif (AGGAA) on a hypoxic pathway gene, EPAS1, is observed in Tibetans and lowlanders. To evaluate the potential usage of the 5-SNP haplotype in ancestry inference for Tibetan or Tibetan-related populations, we analyzed this haplotype in 1053 individuals of 12 Chinese populations residing on the Tibetan Plateau, peripheral regions of Tibet, and plain regions. These data were integrated with the genotypes from the 1000 Genome populations and populations in a previously reported paper for population structure analyses. We found that populations representing highland and lowland groups have different dominant ancestry components. The core Denisovan haplotype (AGGAA) was observed at a frequency of 72.32% in the Tibetan Plateau, with a frequency range from 9.48 to 21.05% in the peripheral regions and Tibetan Plateau carried the archaic haplotype, while < 5% of the Chinese Han people carried the haplotype. Our findings indicate that the 5-SNP haplotype has a special distribution pattern in populations of Tibet and peripheral regions and could be integrated into AISNP (Ancestry Informative Single Nucleotide Polymorphism) panels to enhance ancestry resolution.
Anderson, Lorin W.
1979-01-01
Schools have devised several ways to adapt instruction to a wide variety of student abilities and needs. Judged by criteria for what adaptive education should be, most learning for mastery programs look good. (Author/JM)
Energy Technology Data Exchange (ETDEWEB)
Benitez R, J.S. [ININ, 52750 La Marquesa, Estado de Mexico (Mexico); Perez C, J.H. [CINVESTAV, IPN, A.P. 14740 07000 Mexico D.F. (Mexico); Rivero G, T. [ITT, 50140 Metepec, Estado de Mexico (Mexico)
2008-07-01
In this paper a novel procedure for power regulation in a TRIGA Mark III nuclear reactor is presented. The control scheme combines state variable feedback with a first order predictor, which is incorporated to speed up the power response of the reactor without exceeding the safety requirement imposed by the reactor period. The simulation results using the proposed control strategy attains different values of steady-state power from different values of initial power in short time, complying at all times with the safety restriction imposed on the reactor period. The predictor, derived from the theory of first order numerical integration, produces very good results during the ascent of power. These results include a fast response and independence of the wide variety of potential operating conditions something not easy and even impossible to obtain with other procedures. By using this control scheme, the reactor period is maintained within safety limits during the start up of the reactor, which is normally the operating condition where an occurrence of a period scram is common. However, the predictor can not be used when the power is reaching the desired power level because the instantaneous power increases far above the desired level. Thus, when the power increases above certain power level, the state feedback gain is set constant to a predefined value. This causes some oscillations that decrease in a few seconds. Afterwards, the power response smoothly approaches, with a small overshoot, the desired power. This constraint on the use of the predictor prevents the unbounded increase of the neutron power. The control law proposed requires all the system's state variables. Since only the neutron power is available, it is necessary the estimation of the non measurable states. The key issue of the existence of a solution to this problem has been previously considered. One of the conclusions is that the point kinetic equations are observable under certain restrictions
Algebraic and adaptive learning in neural control systems
Ferrari, Silvia
A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.
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Aitzol Astigarraga
2016-01-01
Full Text Available Brain-Computer Interfaces (BCIs have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG- based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.
DEFF Research Database (Denmark)
Petersen, Kjell Yngve; Søndergaard, Karin; Kongshaug, Jesper
2015-01-01
Adaptive Lighting Adaptive lighting is based on a partial automation of the possibilities to adjust the colour tone and brightness levels of light in order to adapt to people’s needs and desires. IT support is key to the technical developments that afford adaptive control systems. The possibilities...... offered by adaptive lighting control are created by the ways that the system components, the network and data flow can be coordinated through software so that the dynamic variations are controlled in ways that meaningfully adapt according to people’s situations and design intentions. This book discusses...... differently into an architectural body. We also examine what might occur when light is dynamic and able to change colour, intensity and direction, and when it is adaptive and can be brought into interaction with its surroundings. In short, what happens to an architectural space when artificial lighting ceases...
Regularity of pointwise boundary control systems
DEFF Research Database (Denmark)
Pedersen, Michael
1992-01-01
We will in these notes address some problems arising in "real-life" control application, namely problems concerning distributional control inputs on the boundary of the spatial domain. We extend the classical variational approach and give easily checkable sufficient conditions for the solutions...
Pointwise extensions of GSOS-defined operations
Hansen, H.H.; Klin, B.
2011-01-01
Final coalgebras capture system behaviours such as streams, infinite trees and processes. Algebraic operations on a final coalgebra can be defined by distributive laws (of a syntax functor S over a behaviour functor F). Such distributive laws correspond to abstract specification formats. One such
Pointwise Extensions of GSOS-Defined Operations
H.H. Hansen (Helle); B. Klin
2011-01-01
textabstractFinal coalgebras capture system behaviours such as streams, infinite trees and processes. Algebraic operations on a final coalgebra can be defined by distributive laws (of a syntax functor $\\FSig$ over a behaviour functor $F$). Such distributive laws correspond to abstract specification
National Aeronautics and Space Administration — Advanced Diagnostics and Prognostics Testbed (ADAPT) Project Lead: Scott Poll Subject Fault diagnosis in electrical power systems Description The Advanced...
DEFF Research Database (Denmark)
Møller Larsen, Marcus; Lyngsie, Jacob
2017-01-01
We investigate the connection between contract duration, relational mechanisms, and premature relationship termination. Based on an analysis of a large sample of exchange relationships in the global service-provider industry, we argue that investments in either longer contract duration or more in...... ambiguous reference points for adaption and thus increase the likelihood of premature termination by restricting the parties' set of adaptive actions....
Kinzig, Ann P.
2015-03-01
This paper is intended as a brief introduction to climate adaptation in a conference devoted otherwise to the physics of sustainable energy. Whereas mitigation involves measures to reduce the probability of a potential event, such as climate change, adaptation refers to actions that lessen the impact of climate change. Mitigation and adaptation differ in other ways as well. Adaptation does not necessarily have to be implemented immediately to be effective; it only needs to be in place before the threat arrives. Also, adaptation does not necessarily require global, coordinated action; many effective adaptation actions can be local. Some urban communities, because of land-use change and the urban heat-island effect, currently face changes similar to some expected under climate change, such as changes in water availability, heat-related morbidity, or changes in disease patterns. Concern over those impacts might motivate the implementation of measures that would also help in climate adaptation, despite skepticism among some policy makers about anthropogenic global warming. Studies of ancient civilizations in the southwestern US lends some insight into factors that may or may not be important to successful adaptation.
Chandramouli, Rajarathnam; Li, Grace; Memon, Nasir D.
2002-04-01
Steganalysis techniques attempt to differentiate between stego-objects and cover-objects. In recent work we developed an explicit analytic upper bound for the steganographic capacity of LSB based steganographic techniques for a given false probability of detection. In this paper we look at adaptive steganographic techniques. Adaptive steganographic techniques take explicit steps to escape detection. We explore different techniques that can be used to adapt message embedding to the image content or to a known steganalysis technique. We investigate the advantages of adaptive steganography within an analytical framework. We also give experimental results with a state-of-the-art steganalysis technique demonstrating that adaptive embedding results in a significant number of bits embedded without detection.
DEFF Research Database (Denmark)
Petersen, Kjell Yngve; Søndergaard, Karin; Kongshaug, Jesper
2015-01-01
the investigations of lighting scenarios carried out in two test installations: White Cube and White Box. The test installations are discussed as large-scale experiential instruments. In these test installations we examine what could potentially occur when light using LED technology is integrated and distributed......Adaptive Lighting Adaptive lighting is based on a partial automation of the possibilities to adjust the colour tone and brightness levels of light in order to adapt to people’s needs and desires. IT support is key to the technical developments that afford adaptive control systems. The possibilities...... differently into an architectural body. We also examine what might occur when light is dynamic and able to change colour, intensity and direction, and when it is adaptive and can be brought into interaction with its surroundings. In short, what happens to an architectural space when artificial lighting ceases...
Estimating Instantaneous Energetic Cost During Gait Adaptation
2014-08-31
energetic cost. Its 327 accuracy benefits from a personalized model for each subject, but for some situations, it may suffice to 328 use the...Activity 380 Patterns During Robotic - and Therapist-Assisted Treadmill Walking in Individuals With 381 Incomplete Spinal Cord Injury. Phys Ther 86...of level walking with powered ankle 410 exoskeletons . Journal of Experimental Biology 211: 1402–1413, 2008. 411 25. Schmalz T, Blumentritt S
Error estimation and adaptivity for incompressible hyperelasticity
Whiteley, J.P.; Tavener, S.J.
2014-01-01
SUMMARY: A Galerkin FEM is developed for nonlinear, incompressible (hyper) elasticity that takes account of nonlinearities in both the strain tensor and the relationship between the strain tensor and the stress tensor. By using suitably defined
Item selection and ability estimation adaptive testing
Pashley, Peter J.; van der Linden, Wim J.; van der Linden, Willem J.; Glas, Cornelis A.W.; Glas, Cees A.W.
2010-01-01
The last century saw a tremendous progression in the refinement and use of standardized linear tests. The first administered College Board exam occurred in 1901 and the first Scholastic Assessment Test (SAT) was given in 1926. Since then, progressively more sophisticated standardized linear tests
Adaptive metric kernel regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
2000-01-01
Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Adaptive Metric Kernel Regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
1998-01-01
Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...
International Development Research Centre (IDRC) Digital Library (Canada)
Addressing Climate Change Adaptation in Africa through Participatory Action Research. A Regional Observatory ... while the average annual rainfall recorded between. 1968 and 1999 was .... the region of Thies. For sustainability reasons, the.
International Development Research Centre (IDRC) Digital Library (Canada)
By Reg'
adaptation to climate change from various regions of the Sahel. Their .... This simple system, whose cost and maintenance were financially sustainable, brought ... method that enables him to learn from experience and save time, which he ...
Is adaptation. Truly an adaptation? Is adaptation. Truly an adaptation?
Directory of Open Access Journals (Sweden)
Thais Flores Nogueira Diniz
2008-04-01
Full Text Available The article begins by historicizing film adaptation from the arrival of cinema, pointing out the many theoretical approaches under which the process has been seen: from the concept of “the same story told in a different medium” to a comprehensible definition such as “the process through which works can be transformed, forming an intersection of textual surfaces, quotations, conflations and inversions of other texts”. To illustrate this new concept, the article discusses Spike Jonze’s film Adaptation. according to James Naremore’s proposal which considers the study of adaptation as part of a general theory of repetition, joined with the study of recycling, remaking, and every form of retelling. The film deals with the attempt by the scriptwriter Charles Kaufman, cast by Nicholas Cage, to adapt/translate a non-fictional book to the cinema, but ends up with a kind of film which is by no means what it intended to be: a film of action in the model of Hollywood productions. During the process of creation, Charles and his twin brother, Donald, undergo a series of adventures involving some real persons from the world of film, the author and the protagonist of the book, all of them turning into fictional characters in the film. In the film, adaptation then signifies something different from itstraditional meaning. The article begins by historicizing film adaptation from the arrival of cinema, pointing out the many theoretical approaches under which the process has been seen: from the concept of “the same story told in a different medium” to a comprehensible definition such as “the process through which works can be transformed, forming an intersection of textual surfaces, quotations, conflations and inversions of other texts”. To illustrate this new concept, the article discusses Spike Jonze’s film Adaptation. according to James Naremore’s proposal which considers the study of adaptation as part of a general theory of repetition
Brault , Patrice
2005-01-01
The first part of this thesis presents a new vision of the motion estimation problem, and hence of the compression of video sequences. On onehand, we have chosen to investigate motion estimation from redundant wavelet families tuned to different kind of transformations and, in particular,to speed. These families, not well known, have already been studied in the framework of target tracking. On the other hand, today video standards,like MPEG4, are supposed to realize the compression in an obje...
DEFF Research Database (Denmark)
Andersen, Torben Juul
2015-01-01
This article provides an overview of theoretical contributions that have influenced the discourse around strategic adaptation including contingency perspectives, strategic fit reasoning, decision structure, information processing, corporate entrepreneurship, and strategy process. The related...... concepts of strategic renewal, dynamic managerial capabilities, dynamic capabilities, and strategic response capabilities are discussed and contextualized against strategic responsiveness. The insights derived from this article are used to outline the contours of a dynamic process of strategic adaptation....... This model incorporates elements of central strategizing, autonomous entrepreneurial behavior, interactive information processing, and open communication systems that enhance the organization's ability to observe exogenous changes and respond effectively to them....
DEFF Research Database (Denmark)
Petersen, Kjell Yngve; Kongshaug, Jesper; Søndergaard, Karin
2015-01-01
offered by adaptive lighting control are created by the ways that the system components, the network and data flow can be coordinated through software so that the dynamic variations are controlled in ways that meaningfully adapt according to people’s situations and design intentions. This book discusses...... to be static, and no longer acts as a kind of spatial constancy maintaining stability and order? Moreover, what new potentials open in lighting design? This book is one of four books that is published in connection with the research project entitled LED Lighting; Interdisciplinary LED Lighting Research...
DEFF Research Database (Denmark)
Kjeldsen, Lars Peter; Eriksen, Mette Rose
2010-01-01
Artikelen er en evaluering af de adaptive tests, som blev indført i folkeskolen. Artiklen sætter særligt fokus på evaluering i folkeskolen, herunder bidrager den med vejledning til evaluering, evalueringsværktøjer og fagspecifkt evalueringsmateriale.......Artikelen er en evaluering af de adaptive tests, som blev indført i folkeskolen. Artiklen sætter særligt fokus på evaluering i folkeskolen, herunder bidrager den med vejledning til evaluering, evalueringsværktøjer og fagspecifkt evalueringsmateriale....
Is adaptation. Truly an adaptation?
Directory of Open Access Journals (Sweden)
Thais Flores Nogueira Diniz
2006-04-01
Full Text Available The article begins by historicizing film adaptation from the arrival of cinema, pointing out the many theoretical approaches under which the process has been seen: from the concept of “the same story told in a different medium” to a comprehensible definition such as “the process through which works can be transformed, forming an intersection of textual surfaces, quotations, conflations and inversions of other texts”. To illustrate this new concept, the article discusses Spike Jonze’s film Adaptation. according to James Naremore’s proposal which considers the study of adaptation as part of a general theory of repetition, joined with the study of recycling, remaking, and every form of retelling. The film deals with the attempt by the scriptwriter Charles Kaufman, cast by Nicholas Cage, to adapt/translate a non-fictional book to the cinema, but ends up with a kind of film which is by no means what it intended to be: a film of action in the model of Hollywood productions. During the process of creation, Charles and his twin brother, Donald, undergo a series of adventures involving some real persons from the world of film, the author and the protagonist of the book, all of them turning into fictional characters in the film. In the film, adaptation then signifies something different from itstraditional meaning.
International Development Research Centre (IDRC) Digital Library (Canada)
IDRC
vital sector is under threat. While it is far from the only development challenge facing local farmers, extreme variations in the climate of West Africa in the past several decades have dealt the region a bad hand. Drought and flood now follow each other in succession. Adaptation is... “The floods spoiled our harvests and we.
DEFF Research Database (Denmark)
Møller Larsen, Marcus; Lyngsie, Jacob
and reciprocal adaptation of informal governance structure create ambiguity in situations of contingencies, which, subsequently, increases the likelihood of premature relationship termination. Using a large sample of exchange relationships in the global service provider industry, we find support for a hypothesis...
Directory of Open Access Journals (Sweden)
Paul Rozin
2008-02-01
Full Text Available People live in a world in which they are surrounded by potential disgust elicitors such as ``used'' chairs, air, silverware, and money as well as excretory activities. People function in this world by ignoring most of these, by active avoidance, reframing, or adaptation. The issue is particularly striking for professions, such as morticians, surgeons, or sanitation workers, in which there is frequent contact with major disgust elicitors. In this study, we study the ``adaptation'' process to dead bodies as disgust elicitors, by measuring specific types of disgust sensitivity in medical students before and after they have spent a few months dissecting a cadaver. Using the Disgust Scale, we find a significant reduction in disgust responses to death and body envelope violation elicitors, but no significant change in any other specific type of disgust. There is a clear reduction in discomfort at touching a cold dead body, but not in touching a human body which is still warm after death.
Energy Technology Data Exchange (ETDEWEB)
Huq, Saleemul
2011-11-15
Efforts to help the world's poor will face crises in coming decades as climate change radically alters conditions. Action Research for Community Adapation in Bangladesh (ARCAB) is an action-research programme on responding to climate change impacts through community-based adaptation. Set in Bangladesh at 20 sites that are vulnerable to floods, droughts, cyclones and sea level rise, ARCAB will follow impacts and adaptation as they evolve over half a century or more. National and international 'research partners', collaborating with ten NGO 'action partners' with global reach, seek knowledge and solutions applicable worldwide. After a year setting up ARCAB, we share lessons on the programme's design and move into our first research cycle.
International Nuclear Information System (INIS)
Labrador Pavon, I.
1993-01-01
This paper describes the circuits and programs in assembly language, developed to control the two DC motors that give mobility to a mechanical arm with two degrees of freedom. As a whole, the system is based in a adaptable regulator designed around a 8 bit microprocessor that, starting from a mode of regulation based in the successive approximation method, evolve to another mode through which, only one approximation is sufficient to get the right position of each motor. (Author) 22 fig. 6 ref
International Nuclear Information System (INIS)
Labrador Pavon, I.
1993-01-01
This paper describes the circuits and programs in assembly language, developed to control the two DC motors that give mobility to a mechanical arm with two degrees of freedom. As a whole, the system is based in a adaptable regulator designed around a 8 bit microprocessor that, starting from a mode of regulation based in the successive approximation method, evolve to another mode through which, only one approximation is sufficient to get the right position of each motor. (Author) 6 refs
DEFF Research Database (Denmark)
Berth, Mette
2005-01-01
This paper focuses on the use of an adaptive ethnography when studying such phenomena as young people's use of mobile media in a learning perspective. Mobile media such as PDAs and mobile phones have a number of affordances which make them potential tools for learning. However, before we begin to...... formal and informal learning contexts. The paper also proposes several adaptive methodological techniques for studying young people's interaction with mobiles.......This paper focuses on the use of an adaptive ethnography when studying such phenomena as young people's use of mobile media in a learning perspective. Mobile media such as PDAs and mobile phones have a number of affordances which make them potential tools for learning. However, before we begin...... to design and develop educational materials for mobile media platforms we must first understand everyday use and behaviour with a medium such as a mobile phone. The paper outlines the research design for a PhD project on mobile learning which focuses on mobile phones as a way to bridge the gap between...
An Overview of the Adaptive Robust DFT
Directory of Open Access Journals (Sweden)
Djurović Igor
2010-01-01
Full Text Available Abstract This paper overviews basic principles and applications of the robust DFT (RDFT approach, which is used for robust processing of frequency-modulated (FM signals embedded in non-Gaussian heavy-tailed noise. In particular, we concentrate on the spectral analysis and filtering of signals corrupted by impulsive distortions using adaptive and nonadaptive robust estimators. Several adaptive estimators of location parameter are considered, and it is shown that their application is preferable with respect to non-adaptive counterparts. This fact is demonstrated by efficiency comparison of adaptive and nonadaptive RDFT methods for different noise environments.
Understanding the adaptive approach to thermal comfort
Energy Technology Data Exchange (ETDEWEB)
Humphreys, M.A. [Oxford Univ. (United Kingdom). Centre for the Study of Christianity and Culture; Nicol, J.F. [Oxford Brookes Univ. (United Kingdom). School of Architecture
1998-10-01
This paper explains the adaptive approach to thermal comfort, and an adaptive model for thermal comfort is presented. The model is an example of a complex adaptive system (Casti 1996) whose equilibria are determined by the restrictions acting upon it. People`s adaptive actions are generally effective in securing comfort, which occurs at a wide variety of indoor temperatures. These comfort temperatures depend upon the circumstances in which people live, such as the climate and the heating or cooling regime. The temperatures may be estimated from the mean outdoor temperature and the availability of a heating or cooling plant. The evaluation of the parameters of the adaptive model requires cross-sectional surveys to establish current norms and sequential surveys (with and without intervention) to evaluate the rapidity of people`s adaptive actions. Standards for thermal comfort will need revision in the light of the adaptive approach. Implications of the adaptive model for the HVAC industry are noted.
Gatenby, Robert A; Silva, Ariosto S; Gillies, Robert J; Frieden, B Roy
2009-06-01
A number of successful systemic therapies are available for treatment of disseminated cancers. However, tumor response is often transient, and therapy frequently fails due to emergence of resistant populations. The latter reflects the temporal and spatial heterogeneity of the tumor microenvironment as well as the evolutionary capacity of cancer phenotypes to adapt to therapeutic perturbations. Although cancers are highly dynamic systems, cancer therapy is typically administered according to a fixed, linear protocol. Here we examine an adaptive therapeutic approach that evolves in response to the temporal and spatial variability of tumor microenvironment and cellular phenotype as well as therapy-induced perturbations. Initial mathematical models find that when resistant phenotypes arise in the untreated tumor, they are typically present in small numbers because they are less fit than the sensitive population. This reflects the "cost" of phenotypic resistance such as additional substrate and energy used to up-regulate xenobiotic metabolism, and therefore not available for proliferation, or the growth inhibitory nature of environments (i.e., ischemia or hypoxia) that confer resistance on phenotypically sensitive cells. Thus, in the Darwinian environment of a cancer, the fitter chemosensitive cells will ordinarily proliferate at the expense of the less fit chemoresistant cells. The models show that, if resistant populations are present before administration of therapy, treatments designed to kill maximum numbers of cancer cells remove this inhibitory effect and actually promote more rapid growth of the resistant populations. We present an alternative approach in which treatment is continuously modulated to achieve a fixed tumor population. The goal of adaptive therapy is to enforce a stable tumor burden by permitting a significant population of chemosensitive cells to survive so that they, in turn, suppress proliferation of the less fit but chemoresistant
DEFF Research Database (Denmark)
Hansen, Lars Kai; Rasmussen, Carl Edward; Svarer, C.
1994-01-01
Regularization, e.g., in the form of weight decay, is important for training and optimization of neural network architectures. In this work the authors provide a tool based on asymptotic sampling theory, for iterative estimation of weight decay parameters. The basic idea is to do a gradient desce...
DEFF Research Database (Denmark)
Rist, Lucy; Campbell, Bruce Morgan; Frost, Peter
2013-01-01
Adaptive management (AM) emerged in the literature in the mid-1970s in response both to a realization of the extent of uncertainty involved in management, and a frustration with attempts to use modelling to integrate knowledge and make predictions. The term has since become increasingly widely used...... in scientific articles, policy documents and management plans, but both understanding and application of the concept is mixed. This paper reviews recent literature from conservation and natural resource management journals to assess diversity in how the term is used, highlight ambiguities and consider how...... the concept might be further assessed. AM is currently being used to describe many different management contexts, scales and locations. Few authors define the term explicitly or describe how it offers a means to improve management outcomes in their specific management context. Many do not adhere to the idea...
DEFF Research Database (Denmark)
Arndt, Channing; Strzepek, Kenneth; Tarp, Finn
2011-01-01
Mozambique, like many African countries, is already highly susceptible to climate variability and extreme weather events. Climate change threatens to heighten this vulnerability. In order to evaluate potential impacts and adaptation options for Mozambique, we develop an integrated modeling...... framework that translates atmospheric changes from general circulation model projections into biophysical outcomes via detailed hydrologic, crop, hydropower and infrastructure models. These sector models simulate a historical baseline and four extreme climate change scenarios. Sector results are then passed...... down to a dynamic computable general equilibrium model, which is used to estimate economy-wide impacts on national welfare, as well as the total cost of damages caused by climate change. Potential damages without changes in policy are significant; our discounted estimates range from US2.3 to US2.3toUS7...
Building nonredundant adaptive wavelets by update lifting
H.J.A.M. Heijmans (Henk); B. Pesquet-Popescu; G. Piella (Gema)
2002-01-01
textabstractAdaptive wavelet decompositions appear useful in various applications in image and video processing, such as image analysis, compression, feature extraction, denoising and deconvolution, or optic flow estimation. For such tasks it may be important that the multiresolution representations
Adaptive techniques for diagnostics of vibrating structures
International Nuclear Information System (INIS)
Skormin, V.A.; Sankar, S.
1983-01-01
An adaptive diagnostic procedure for vibrating structures based on correspondence between current estimates of stiffness matrix and structure status is proposed. Procedure employs adaptive mathematical description of the vibrating structure in frequency domain, statistical techniques for detection and location of changes of structure properties, 'recognition' and prediction of defects. (orig.)
DEFF Research Database (Denmark)
Sales-Cruz, Mauricio; Heitzig, Martina; Cameron, Ian
2011-01-01
of optimisation techniques coupled with dynamic solution of the underlying model. Linear and nonlinear approaches to parameter estimation are investigated. There is also the application of maximum likelihood principles in the estimation of parameters, as well as the use of orthogonal collocation to generate a set......In this chapter the importance of parameter estimation in model development is illustrated through various applications related to reaction systems. In particular, rate constants in a reaction system are obtained through parameter estimation methods. These approaches often require the application...... of algebraic equations as the basis for parameter estimation.These approaches are illustrated using estimations of kinetic constants from reaction system models....
Adaptive image interrogation for PIV : Application to compressible flows and interfaces
Theunissen, R.
2010-01-01
As an experimental tool, Particle Image Velocimetry has quickly superseded traditional point-wise measurements. The inherent image processing has become standardized though the performances are strongly dependent on user experience. Moreover, the arduously selected image interrogation parameters are
Supporting Adaptive and Adaptable Hypermedia Presentation Semantics
D.C.A. Bulterman (Dick); L. Rutledge (Lloyd); L. Hardman (Lynda); J.R. van Ossenbruggen (Jacco)
1999-01-01
textabstractHaving the content of a presentation adapt to the needs, resources and prior activities of a user can be an important benefit of electronic documents. While part of this adaptation is related to the encodings of individual data streams, much of the adaptation can/should be guided by the
National Research Council Canada - National Science Library
Lukesh, Gordon
2004-01-01
.... Pointing estimates are available after 25 shots. As a prime example of the utility and feasibility, estimates of boresight will be available to adaptively control pointing with a goal of boresight reduction via feedback...
Adaptation illustrations: Chapter 4
Maria Janowiak; Patricia Butler; Chris Swanston; Matt St. Pierre; Linda. Parker
2012-01-01
In this chapter, we demonstrate how the Adaptation Workbook (Chapter 3) can be used with the Adaptation Strategies and Approaches (Chapter 2) to develop adaptation tactics for two real-world management issues. The two illustrations in this chapter are intended to provide helpful tips to managers completing the Adaptation Workbook, as well as to show how the anticipated...
DEFF Research Database (Denmark)
Lund, Henrik Hautop; Þorsteinsson, Arnar Tumi
2011-01-01
In this paper, we describe the concept of adaptive modular playware, where the playware adapts to the interaction of the individual user. We hypothesize that there are individual differences in user interaction capabilities and styles, and that adaptive playware may adapt to the individual user...
Broeke, ten Guus; Voorn, van George A.K.; Ligtenberg, Arend; Molenaar, Jaap
2017-01-01
Adaptation of agents through learning or evolution is an important component of the resilience of Complex Adaptive Systems (CAS). Without adaptation, the flexibility of such systems to cope with outside pressures would be much lower. To study the capabilities of CAS to adapt, social simulations
Adaptation in integrated assessment modeling: where do we stand?
Patt, A.; van Vuuren, D.P.; Berkhout, F.G.H.; Aaheim, A.; Hof, A.F.; Isaac, M.; Mechler, R.
2010-01-01
Adaptation is an important element on the climate change policy agenda. Integrated assessment models, which are key tools to assess climate change policies, have begun to address adaptation, either by including it implicitly in damage cost estimates, or by making it an explicit control variable. We analyze how modelers have chosen to describe adaptation within an integrated framework, and suggest many ways they could improve the treatment of adaptation by considering more of its bottom-up cha...
Fleischer, Christian; Waag, Wladislaw; Heyn, Hans-Martin; Sauer, Dirk Uwe
2014-08-01
Lithium-ion battery systems employed in high power demanding systems such as electric vehicles require a sophisticated monitoring system to ensure safe and reliable operation. Three major states of the battery are of special interest and need to be constantly monitored, these include: battery state of charge (SoC), battery state of health (capcity fade determination, SoH), and state of function (power fade determination, SoF). In a series of two papers, we propose a system of algorithms based on a weighted recursive least quadratic squares parameter estimator, that is able to determine the battery impedance and diffusion parameters for accurate state estimation. The functionality was proven on different battery chemistries with different aging conditions. The first paper investigates the general requirements on BMS for HEV/EV applications. In parallel, the commonly used methods for battery monitoring are reviewed to elaborate their strength and weaknesses in terms of the identified requirements for on-line applications. Special emphasis will be placed on real-time capability and memory optimized code for cost-sensitive industrial or automotive applications in which low-cost microcontrollers must be used. Therefore, a battery model is presented which includes the influence of the Butler-Volmer kinetics on the charge-transfer process. Lastly, the mass transport process inside the battery is modeled in a novel state-space representation.
Two-component mixture cure rate model with spline estimated nonparametric components.
Wang, Lu; Du, Pang; Liang, Hua
2012-09-01
In some survival analysis of medical studies, there are often long-term survivors who can be considered as permanently cured. The goals in these studies are to estimate the noncured probability of the whole population and the hazard rate of the susceptible subpopulation. When covariates are present as often happens in practice, to understand covariate effects on the noncured probability and hazard rate is of equal importance. The existing methods are limited to parametric and semiparametric models. We propose a two-component mixture cure rate model with nonparametric forms for both the cure probability and the hazard rate function. Identifiability of the model is guaranteed by an additive assumption that allows no time-covariate interactions in the logarithm of hazard rate. Estimation is carried out by an expectation-maximization algorithm on maximizing a penalized likelihood. For inferential purpose, we apply the Louis formula to obtain point-wise confidence intervals for noncured probability and hazard rate. Asymptotic convergence rates of our function estimates are established. We then evaluate the proposed method by extensive simulations. We analyze the survival data from a melanoma study and find interesting patterns for this study. © 2011, The International Biometric Society.
Adaptive finite element methods for differential equations
Bangerth, Wolfgang
2003-01-01
These Lecture Notes discuss concepts of `self-adaptivity' in the numerical solution of differential equations, with emphasis on Galerkin finite element methods. The key issues are a posteriori error estimation and it automatic mesh adaptation. Besides the traditional approach of energy-norm error control, a new duality-based technique, the Dual Weighted Residual method for goal-oriented error estimation, is discussed in detail. This method aims at economical computation of arbitrary quantities of physical interest by properly adapting the computational mesh. This is typically required in the design cycles of technical applications. For example, the drag coefficient of a body immersed in a viscous flow is computed, then it is minimized by varying certain control parameters, and finally the stability of the resulting flow is investigated by solving an eigenvalue problem. `Goal-oriented' adaptivity is designed to achieve these tasks with minimal cost. At the end of each chapter some exercises are posed in order ...
Expressing Adaptation Strategies Using Adaptation Patterns
Zemirline, N.; Bourda, Y.; Reynaud, C.
2012-01-01
Today, there is a real challenge to enable personalized access to information. Several systems have been proposed to address this challenge including Adaptive Hypermedia Systems (AHSs). However, the specification of adaptation strategies remains a difficult task for creators of such systems. In this paper, we consider the problem of the definition…
An adaptive Cartesian control scheme for manipulators
Seraji, H.
1987-01-01
A adaptive control scheme for direct control of manipulator end-effectors to achieve trajectory tracking in Cartesian space is developed. The control structure is obtained from linear multivariable theory and is composed of simple feedforward and feedback controllers and an auxiliary input. The direct adaptation laws are derived from model reference adaptive control theory and are not based on parameter estimation of the robot model. The utilization of feedforward control and the inclusion of auxiliary input are novel features of the present scheme and result in improved dynamic performance over existing adaptive control schemes. The adaptive controller does not require the complex mathematical model of the robot dynamics or any knowledge of the robot parameters or the payload, and is computationally fast for online implementation with high sampling rates.
Adaptive Rationality, Adaptive Behavior and Institutions
Directory of Open Access Journals (Sweden)
Volchik Vyacheslav, V.
2015-12-01
Full Text Available The economic literature focused on understanding decision-making and choice processes reveals a vast collection of approaches to human rationality. Theorists’ attention has moved from absolutely rational, utility-maximizing individuals to boundedly rational and adaptive ones. A number of economists have criticized the concepts of adaptive rationality and adaptive behavior. One of the recent trends in the economic literature is to consider humans irrational. This paper offers an approach which examines adaptive behavior in the context of existing institutions and constantly changing institutional environment. It is assumed that adaptive behavior is a process of evolutionary adjustment to fundamental uncertainty. We emphasize the importance of actors’ engagement in trial and error learning, since if they are involved in this process, they obtain experience and are able to adapt to existing and new institutions. The paper aims at identifying relevant institutions, adaptive mechanisms, informal working rules and practices that influence actors’ behavior in the field of Higher Education in Russia (Rostov Region education services market has been taken as an example. The paper emphasizes the application of qualitative interpretative methods (interviews and discourse analysis in examining actors’ behavior.
Uncertainty in adaptive capacity
International Nuclear Information System (INIS)
Neil Adger, W.; Vincent, K.
2005-01-01
The capacity to adapt is a critical element of the process of adaptation: it is the vector of resources that represent the asset base from which adaptation actions can be made. Adaptive capacity can in theory be identified and measured at various scales, from the individual to the nation. The assessment of uncertainty within such measures comes from the contested knowledge domain and theories surrounding the nature of the determinants of adaptive capacity and the human action of adaptation. While generic adaptive capacity at the national level, for example, is often postulated as being dependent on health, governance and political rights, and literacy, and economic well-being, the determinants of these variables at national levels are not widely understood. We outline the nature of this uncertainty for the major elements of adaptive capacity and illustrate these issues with the example of a social vulnerability index for countries in Africa. (authors)
Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation
DEFF Research Database (Denmark)
This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Conference on Adaptive Multimedia Retrieval, AMR 2012, held in Copenhagen, Denmark, in October 2012. The 17 revised full papers presented were carefully reviewed and selected from numerous submissi......This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Conference on Adaptive Multimedia Retrieval, AMR 2012, held in Copenhagen, Denmark, in October 2012. The 17 revised full papers presented were carefully reviewed and selected from numerous...... submissions. The papers cover topics of state of the art contributions, features and classification, location context, language and semantics, music retrieval, and adaption and HCI....
Carmin, J.; Tierney, K.; Chu, E.; Hunter, L.M.; Roberts, J.T.; Shi, L.; Dunlap, R.E.; Brulle, R.J.
2015-01-01
Climate change adaptation involves major global and societal challenges such as finding adequate and equitable adaptation funding and integrating adaptation and development programs. Current funding is insufficient. Debates between the Global North and South center on how best to allocate the
Adaptation and Cultural Diffusion.
Ormrod, Richard K.
1992-01-01
Explores the role of adaptation in cultural diffusion. Explains that adaptation theory recognizes the lack of independence between innovations and their environmental settings. Discusses testing and selection, modification, motivation, and cognition. Suggests that adaptation effects are pervasive in cultural diffusion but require a broader, more…
1990-01-01
This book describes techniques for designing and building adaptive user interfaces developed in the large AID project undertaken by the contributors.Key Features* Describes one of the few large-scale adaptive interface projects in the world* Outlines the principles of adaptivity in human-computer interaction
Power spectral density of velocity fluctuations estimated from phase Doppler data
Jedelsky, Jan; Lizal, Frantisek; Jicha, Miroslav
2012-04-01
Laser Doppler Anemometry (LDA) and its modifications such as PhaseDoppler Particle Anemometry (P/DPA) is point-wise method for optical nonintrusive measurement of particle velocity with high data rate. Conversion of the LDA velocity data from temporal to frequency domain - calculation of power spectral density (PSD) of velocity fluctuations, is a non trivial task due to nonequidistant data sampling in time. We briefly discuss possibilities for the PSD estimation and specify limitations caused by seeding density and other factors of the flow and LDA setup. Arbitrary results of LDA measurements are compared with corresponding Hot Wire Anemometry (HWA) data in the frequency domain. Slot correlation (SC) method implemented in software program Kern by Nobach (2006) is used for the PSD estimation. Influence of several input parameters on resulting PSDs is described. Optimum setup of the software for our data of particle-laden air flow in realistic human airway model is documented. Typical character of the flow is described using PSD plots of velocity fluctuations with comments on specific properties of the flow. Some recommendations for improvements of future experiments to acquire better PSD results are given.
Power spectral density of velocity fluctuations estimated from phase Doppler data
Directory of Open Access Journals (Sweden)
Jicha Miroslav
2012-04-01
Full Text Available Laser Doppler Anemometry (LDA and its modifications such as PhaseDoppler Particle Anemometry (P/DPA is point-wise method for optical nonintrusive measurement of particle velocity with high data rate. Conversion of the LDA velocity data from temporal to frequency domain – calculation of power spectral density (PSD of velocity fluctuations, is a non trivial task due to nonequidistant data sampling in time. We briefly discuss possibilities for the PSD estimation and specify limitations caused by seeding density and other factors of the flow and LDA setup. Arbitrary results of LDA measurements are compared with corresponding Hot Wire Anemometry (HWA data in the frequency domain. Slot correlation (SC method implemented in software program Kern by Nobach (2006 is used for the PSD estimation. Influence of several input parameters on resulting PSDs is described. Optimum setup of the software for our data of particle-laden air flow in realistic human airway model is documented. Typical character of the flow is described using PSD plots of velocity fluctuations with comments on specific properties of the flow. Some recommendations for improvements of future experiments to acquire better PSD results are given.
Resilience through adaptation.
Directory of Open Access Journals (Sweden)
Guus A Ten Broeke
Full Text Available Adaptation of agents through learning or evolution is an important component of the resilience of Complex Adaptive Systems (CAS. Without adaptation, the flexibility of such systems to cope with outside pressures would be much lower. To study the capabilities of CAS to adapt, social simulations with agent-based models (ABMs provide a helpful tool. However, the value of ABMs for studying adaptation depends on the availability of methodologies for sensitivity analysis that can quantify resilience and adaptation in ABMs. In this paper we propose a sensitivity analysis methodology that is based on comparing time-dependent probability density functions of output of ABMs with and without agent adaptation. The differences between the probability density functions are quantified by the so-called earth-mover's distance. We use this sensitivity analysis methodology to quantify the probability of occurrence of critical transitions and other long-term effects of agent adaptation. To test the potential of this new approach, it is used to analyse the resilience of an ABM of adaptive agents competing for a common-pool resource. Adaptation is shown to contribute positively to the resilience of this ABM. If adaptation proceeds sufficiently fast, it may delay or avert the collapse of this system.
Resilience through adaptation.
Ten Broeke, Guus A; van Voorn, George A K; Ligtenberg, Arend; Molenaar, Jaap
2017-01-01
Adaptation of agents through learning or evolution is an important component of the resilience of Complex Adaptive Systems (CAS). Without adaptation, the flexibility of such systems to cope with outside pressures would be much lower. To study the capabilities of CAS to adapt, social simulations with agent-based models (ABMs) provide a helpful tool. However, the value of ABMs for studying adaptation depends on the availability of methodologies for sensitivity analysis that can quantify resilience and adaptation in ABMs. In this paper we propose a sensitivity analysis methodology that is based on comparing time-dependent probability density functions of output of ABMs with and without agent adaptation. The differences between the probability density functions are quantified by the so-called earth-mover's distance. We use this sensitivity analysis methodology to quantify the probability of occurrence of critical transitions and other long-term effects of agent adaptation. To test the potential of this new approach, it is used to analyse the resilience of an ABM of adaptive agents competing for a common-pool resource. Adaptation is shown to contribute positively to the resilience of this ABM. If adaptation proceeds sufficiently fast, it may delay or avert the collapse of this system.
Behavioural strategy: Adaptability context
Directory of Open Access Journals (Sweden)
Piórkowska Katarzyna
2016-05-01
Full Text Available The paper is embedded in the following fields: strategic management in terms of behavioural strategy concept, adaptability construct, and micro-foundations realm as well as organizational theory and psychology. Moreover, the paper concerns to some extent a multi-level approach in strategic management involving individual, team, and organizational level. The aim of the paper is to contribute to extend, on one hand, the ascertainment set in the field of behavioural strategy as behavioural strategy encompasses a mindboggling diversity of topics and methods and its conceptual unity has been hard to achieve (Powell, Lovallo, Fox 2011, p. 1371, and on the other hand, to order mixed approaches to adaptability especially to gain insights on micro-level adapting processes (individual adaptability and adaptive performance in terms of the multi-level approach. The method that has been used is literature studies and the interference is mostly deductive. The structure of the manuscript is four-fold. The first part involves the considerations in the field of adaptability and adaptive performance at the individual level. The issues of adaptability and adaptive performance at the team level have been presented in the second part. The third part encompasses the organizational adaptability assertions. Finally, the conclusion, limitations of the considerations highlighted as well as the future research directions have been emphasized. The overarching key finding is that the behavioural strategy concept may constitute the boundary spanner in exploring and explaining adaptability phenomenon at different levels of analysis.
Migliorati, Giovanni
2015-08-28
We study the accuracy of the discrete least-squares approximation on a finite dimensional space of a real-valued target function from noisy pointwise evaluations at independent random points distributed according to a given sampling probability measure. The convergence estimates are given in mean-square sense with respect to the sampling measure. The noise may be correlated with the location of the evaluation and may have nonzero mean (offset). We consider both cases of bounded or square-integrable noise / offset. We prove conditions between the number of sampling points and the dimension of the underlying approximation space that ensure a stable and accurate approximation. Particular focus is on deriving estimates in probability within a given confidence level. We analyze how the best approximation error and the noise terms affect the convergence rate and the overall confidence level achieved by the convergence estimate. The proofs of our convergence estimates in probability use arguments from the theory of large deviations to bound the noise term. Finally we address the particular case of multivariate polynomial approximation spaces with any density in the beta family, including uniform and Chebyshev.
Pointwise asymptotic convergence of solutions for a phase separation model
Czech Academy of Sciences Publication Activity Database
Krejčí, Pavel; Zheng, S.
2006-01-01
Roč. 16, č. 1 (2006), s. 1-18 ISSN 1078-0947 Institutional research plan: CEZ:AV0Z10190503 Keywords : phase-field system * asymptotic phase separation * energy Subject RIV: BA - General Mathematics Impact factor: 1.087, year: 2006 http://aimsciences.org/journals/pdfs.jsp?paperID=1875&mode=full
Directory of Open Access Journals (Sweden)
R. Sitharthan
2016-09-01
Full Text Available This paper aims at modelling an electronically coupled distributed energy resource with an adaptive protection scheme. The electronically coupled distributed energy resource is a microgrid framework formed by coupling the renewable energy source electronically. Further, the proposed adaptive protection scheme provides a suitable protection to the microgrid for various fault conditions irrespective of the operating mode of the microgrid: namely, grid connected mode and islanded mode. The outstanding aspect of the developed adaptive protection scheme is that it monitors the microgrid and instantly updates relay fault current according to the variations that occur in the system. The proposed adaptive protection scheme also employs auto reclosures, through which the proposed adaptive protection scheme recovers faster from the fault and thereby increases the consistency of the microgrid. The effectiveness of the proposed adaptive protection is studied through the time domain simulations carried out in the PSCAD⧹EMTDC software environment.
Technology transfer for adaptation
Biagini, Bonizella; Kuhl, Laura; Gallagher, Kelly Sims; Ortiz, Claudia
2014-09-01
Technology alone will not be able to solve adaptation challenges, but it is likely to play an important role. As a result of the role of technology in adaptation and the importance of international collaboration for climate change, technology transfer for adaptation is a critical but understudied issue. Through an analysis of Global Environment Facility-managed adaptation projects, we find there is significantly more technology transfer occurring in adaptation projects than might be expected given the pessimistic rhetoric surrounding technology transfer for adaptation. Most projects focused on demonstration and early deployment/niche formation for existing technologies rather than earlier stages of innovation, which is understandable considering the pilot nature of the projects. Key challenges for the transfer process, including technology selection and appropriateness under climate change, markets and access to technology, and diffusion strategies are discussed in more detail.
Liongue, Clifford; John, Liza B; Ward, Alister
2011-01-01
Adaptive immunity, involving distinctive antibody- and cell-mediated responses to specific antigens based on "memory" of previous exposure, is a hallmark of higher vertebrates. It has been argued that adaptive immunity arose rapidly, as articulated in the "big bang theory" surrounding its origins, which stresses the importance of coincident whole-genome duplications. Through a close examination of the key molecules and molecular processes underpinning adaptive immunity, this review suggests a less-extreme model, in which adaptive immunity emerged as part of longer evolutionary journey. Clearly, whole-genome duplications provided additional raw genetic materials that were vital to the emergence of adaptive immunity, but a variety of other genetic events were also required to generate some of the key molecules, whereas others were preexisting and simply co-opted into adaptive immunity.
Two-dimensional hazard estimation for longevity analysis
DEFF Research Database (Denmark)
Fledelius, Peter; Guillen, M.; Nielsen, J.P.
2004-01-01
is smooth. Cross-validation is applied for optimal bandwidth selection to ensure the proper amount of smoothing to help distinguishing between random and systematic variation in data. A bootstrap technique is used for construction of pointwise confidence bounds. We study the mortality profiles by slicing up...
An automated dose tracking system for adaptive radiation therapy.
Liu, Chang; Kim, Jinkoo; Kumarasiri, Akila; Mayyas, Essa; Brown, Stephen L; Wen, Ning; Siddiqui, Farzan; Chetty, Indrin J
2018-02-01
The implementation of adaptive radiation therapy (ART) into routine clinical practice is technically challenging and requires significant resources to perform and validate each process step. The objective of this report is to identify the key components of ART, to illustrate how a specific automated procedure improves efficiency, and to facilitate the routine clinical application of ART. Data was used from patient images, exported from a clinical database and converted to an intermediate format for point-wise dose tracking and accumulation. The process was automated using in-house developed software containing three modularized components: an ART engine, user interactive tools, and integration tools. The ART engine conducts computing tasks using the following modules: data importing, image pre-processing, dose mapping, dose accumulation, and reporting. In addition, custom graphical user interfaces (GUIs) were developed to allow user interaction with select processes such as deformable image registration (DIR). A commercial scripting application programming interface was used to incorporate automated dose calculation for application in routine treatment planning. Each module was considered an independent program, written in C++or C#, running in a distributed Windows environment, scheduled and monitored by integration tools. The automated tracking system was retrospectively evaluated for 20 patients with prostate cancer and 96 patients with head and neck cancer, under institutional review board (IRB) approval. In addition, the system was evaluated prospectively using 4 patients with head and neck cancer. Altogether 780 prostate dose fractions and 2586 head and neck cancer dose fractions went processed, including DIR and dose mapping. On average, daily cumulative dose was computed in 3 h and the manual work was limited to 13 min per case with approximately 10% of cases requiring an additional 10 min for image registration refinement. An efficient and convenient
Adaptive friction compensation: a globally stable approach
Verbert, K.A.; Tóth, R.; Babuska, R.
2016-01-01
In this paper, an adaptive friction compensation scheme is proposed. The friction force is computed as a timevarying friction coefficient multiplied by the sign of the velocity and an on-line update law is designed to estimate this coefficient based on the actual position and velocity errors.
Inference in models with adaptive learning
Chevillon, G.; Massmann, M.; Mavroeidis, S.
2010-01-01
Identification of structural parameters in models with adaptive learning can be weak, causing standard inference procedures to become unreliable. Learning also induces persistent dynamics, and this makes the distribution of estimators and test statistics non-standard. Valid inference can be
A model for optimal constrained adaptive testing
van der Linden, Willem J.; Reese, Lynda M.
2001-01-01
A model for constrained computerized adaptive testing is proposed in which the information on the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum
A model for optimal constrained adaptive testing
van der Linden, Willem J.; Reese, Lynda M.
1997-01-01
A model for constrained computerized adaptive testing is proposed in which the information in the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum
METHOD OF ADAPTIVE MAGNETOTHERAPY
Rudyk, Valentine Yu.; Tereshchenko, Mykola F.; Rudyk, Tatiana A.
2016-01-01
Practical realization of adaptive control in magnetotherapy apparatus acquires an actual importance on the modern stage of development of magnetotherapy.The structural scheme of method of adaptive impulsive magnetotherapy and algorithm of adaptive control of feed-back signal during procedure of magnetotherapy is represented.A feed-back in magnetotherapy complex will be realized with control of magnetic induction and analysis of man's physiological indexes (temperature, pulse, blood prassure, ...
Adaptive transmit selection with interference suppression
Radaydeh, Redha Mahmoud Mesleh
2010-01-01
This paper studies the performance of adaptive transmit channel selection in multipath fading channels. The adaptive selection algorithms are configured for single-antenna bandwidth-efficient or power-efficient transmission with as low transmit channel estimations as possible. Due to the fact that the number of active co-channel interfering signals and their corresponding powers experience random behavior, the adaptation to channels conditions, assuming uniform buffer and traffic loading, is proposed to be jointly based on the transmit channels instantaneous signal-to-noise ratios (SNRs) and signal-to- interference-plus- noise ratios (SINRs). Two interference cancelation algorithms, which are the dominant cancelation and the less complex arbitrary cancelation, are considered, for which the receive antenna array is assumed to have small angular spread. Analytical formulation for some performance measures in addition to several processing complexity and numerical comparisons between various adaptation schemes are presented. ©2010 IEEE.
Quantifying the Adaptive Cycle.
Directory of Open Access Journals (Sweden)
David G Angeler
Full Text Available The adaptive cycle was proposed as a conceptual model to portray patterns of change in complex systems. Despite the model having potential for elucidating change across systems, it has been used mainly as a metaphor, describing system dynamics qualitatively. We use a quantitative approach for testing premises (reorganisation, conservatism, adaptation in the adaptive cycle, using Baltic Sea phytoplankton communities as an example of such complex system dynamics. Phytoplankton organizes in recurring spring and summer blooms, a well-established paradigm in planktology and succession theory, with characteristic temporal trajectories during blooms that may be consistent with adaptive cycle phases. We used long-term (1994-2011 data and multivariate analysis of community structure to assess key components of the adaptive cycle. Specifically, we tested predictions about: reorganisation: spring and summer blooms comprise distinct community states; conservatism: community trajectories during individual adaptive cycles are conservative; and adaptation: phytoplankton species during blooms change in the long term. All predictions were supported by our analyses. Results suggest that traditional ecological paradigms such as phytoplankton successional models have potential for moving the adaptive cycle from a metaphor to a framework that can improve our understanding how complex systems organize and reorganize following collapse. Quantifying reorganization, conservatism and adaptation provides opportunities to cope with the intricacies and uncertainties associated with fast ecological change, driven by shifting system controls. Ultimately, combining traditional ecological paradigms with heuristics of complex system dynamics using quantitative approaches may help refine ecological theory and improve our understanding of the resilience of ecosystems.
Tu, Yuhai; Rappel, Wouter-Jan
2018-03-01
Adaptation refers to the biological phenomenon where living systems change their internal states in response to changes in their environments in order to maintain certain key functions critical for their survival and fitness. Adaptation is one of the most ubiquitous and arguably one of the most fundamental properties of living systems. It occurs throughout all biological scales, from adaptation of populations of species over evolutionary time to adaptation of a single cell to different environmental stresses during its life span. In this article, we review some of the recent progress made in understanding molecular mechanisms of cellular-level adaptation. We take the minimalist (or the physicist) approach and study the simplest systems that exhibit generic adaptive behaviors, namely chemotaxis in bacterium cells (Escherichia coli) and eukaryotic cells (Dictyostelium). We focus on understanding the basic biochemical interaction networks that are responsible for adaptation dynamics. By combining theoretical modeling with quantitative experimentation, we demonstrate universal features in adaptation as well as important differences in different cellular systems. Future work in extending the modeling framework to study adaptation in more complex systems such as sensory neurons is also discussed.
Gardner, Andy
2017-10-06
A central feature of Darwin's theory of natural selection is that it explains the purpose of biological adaptation. Here, I: emphasize the scientific importance of understanding what adaptations are for, in terms of facilitating the derivation of empirically testable predictions; discuss the population genetical basis for Darwin's theory of the purpose of adaptation, with reference to Fisher's 'fundamental theorem of natural selection'; and show that a deeper understanding of the purpose of adaptation is achieved in the context of social evolution, with reference to inclusive fitness and superorganisms.
Directory of Open Access Journals (Sweden)
Franklin Nantui Mabe
2012-11-01
Full Text Available This study estimated the adaptive capacities of farmers to climate change adaptation strategies and their effects on rice production in the Northern Region of Ghana. The adaptive capacities of rice farmers were estimated quantitatively and categorized into high, moderate and low adaptive capacities. Double logarithmic regression model of Cobb-Douglas production function was used to quantity the effects of adaptive capacities of farmers on rice production. On the average, the farmers interviewed are moderately adaptive to climate change. Also, high adaptive farmers obtain nine more bags of 50 kg bag of paddy rice than farmers with low adaptive capacities. Therefore, the more a farmer has the ability to adjust to climate change, the more the number of bags of rice he or she obtains. Rice farmers should be empowered through better extension services in order to attain high adaptive capacity status so as to help them obtain more rice output.
Classification algorithms using adaptive partitioning
Binev, Peter; Cohen, Albert; Dahmen, Wolfgang; DeVore, Ronald
2014-01-01
© 2014 Institute of Mathematical Statistics. Algorithms for binary classification based on adaptive tree partitioning are formulated and analyzed for both their risk performance and their friendliness to numerical implementation. The algorithms can be viewed as generating a set approximation to the Bayes set and thus fall into the general category of set estimators. In contrast with the most studied tree-based algorithms, which utilize piecewise constant approximation on the generated partition [IEEE Trans. Inform. Theory 52 (2006) 1335.1353; Mach. Learn. 66 (2007) 209.242], we consider decorated trees, which allow us to derive higher order methods. Convergence rates for these methods are derived in terms the parameter - of margin conditions and a rate s of best approximation of the Bayes set by decorated adaptive partitions. They can also be expressed in terms of the Besov smoothness β of the regression function that governs its approximability by piecewise polynomials on adaptive partition. The execution of the algorithms does not require knowledge of the smoothness or margin conditions. Besov smoothness conditions are weaker than the commonly used Holder conditions, which govern approximation by nonadaptive partitions, and therefore for a given regression function can result in a higher rate of convergence. This in turn mitigates the compatibility conflict between smoothness and margin parameters.
Classification algorithms using adaptive partitioning
Binev, Peter
2014-12-01
© 2014 Institute of Mathematical Statistics. Algorithms for binary classification based on adaptive tree partitioning are formulated and analyzed for both their risk performance and their friendliness to numerical implementation. The algorithms can be viewed as generating a set approximation to the Bayes set and thus fall into the general category of set estimators. In contrast with the most studied tree-based algorithms, which utilize piecewise constant approximation on the generated partition [IEEE Trans. Inform. Theory 52 (2006) 1335.1353; Mach. Learn. 66 (2007) 209.242], we consider decorated trees, which allow us to derive higher order methods. Convergence rates for these methods are derived in terms the parameter - of margin conditions and a rate s of best approximation of the Bayes set by decorated adaptive partitions. They can also be expressed in terms of the Besov smoothness β of the regression function that governs its approximability by piecewise polynomials on adaptive partition. The execution of the algorithms does not require knowledge of the smoothness or margin conditions. Besov smoothness conditions are weaker than the commonly used Holder conditions, which govern approximation by nonadaptive partitions, and therefore for a given regression function can result in a higher rate of convergence. This in turn mitigates the compatibility conflict between smoothness and margin parameters.
Strong tracking adaptive Kalman filters for underwater vehicle dead reckoning
Institute of Scientific and Technical Information of China (English)
XIAO Kun; FANG Shao-ji; PANG Yong-jie
2007-01-01
To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.
Diagnostic analysis of vibration signals using adaptive digital filtering techniques
Jewell, R. E.; Jones, J. H.; Paul, J. E.
1983-01-01
Signal enhancement techniques are described using recently developed digital adaptive filtering equipment. Adaptive filtering concepts are not new; however, as a result of recent advances in microprocessor-based electronics, hardware has been developed that has stable characteristics and of a size exceeding 1000th order. Selected data processing examples are presented illustrating spectral line enhancement, adaptive noise cancellation, and transfer function estimation in the presence of corrupting noise.
Migliorati, Giovanni; Nobile, Fabio; Tempone, Raul
2015-01-01
We study the accuracy of the discrete least-squares approximation on a finite dimensional space of a real-valued target function from noisy pointwise evaluations at independent random points distributed according to a given sampling probability
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
Adaptive single-antenna transmit selection with interference suppression
Radaydeh, Redha Mahmoud Mesleh; Alouini, Mohamed-Slim
2011-01-01
-efficient transmission with as low transmit channel estimations as possible. Due to the fact that the number of active co-channel interfering signals and their corresponding powers experience random behavior, the adaptation to channels conditions, assuming uniform buffer
Adaptive nonparametric Bayesian inference using location-scale mixture priors
Jonge, de R.; Zanten, van J.H.
2010-01-01
We study location-scale mixture priors for nonparametric statistical problems, including multivariate regression, density estimation and classification. We show that a rate-adaptive procedure can be obtained if the prior is properly constructed. In particular, we show that adaptation is achieved if
Multidimensional adaptive testing with a minimum error-variance criterion
van der Linden, Willem J.
1997-01-01
The case of adaptive testing under a multidimensional logistic response model is addressed. An adaptive algorithm is proposed that minimizes the (asymptotic) variance of the maximum-likelihood (ML) estimator of a linear combination of abilities of interest. The item selection criterion is a simple
Adaptation in integrated assessment modeling: where do we stand?
Patt, A.; van Vuuren, D.P.; Berkhout, F.G.H.; Aaheim, A.; Hof, A.F.; Isaac, M.; Mechler, R.
2010-01-01
Adaptation is an important element on the climate change policy agenda. Integrated assessment models, which are key tools to assess climate change policies, have begun to address adaptation, either by including it implicitly in damage cost estimates, or by making it an explicit control variable. We
Adaptive Kernel in Meshsize Boosting Algorithm in KDE ...
African Journals Online (AJOL)
This paper proposes the use of adaptive kernel in a meshsize boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...
Adaptive Kernel In The Bootstrap Boosting Algorithm In KDE ...
African Journals Online (AJOL)
This paper proposes the use of adaptive kernel in a bootstrap boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...
John Innes; Linda A. Joyce; Seppo Kellomaki; Bastiaan Louman; Aynslie Ogden; Ian Thompson; Matthew Ayres; Chin Ong; Heru Santoso; Brent Sohngen; Anita Wreford
2009-01-01
This chapter develops a framework to explore examples of adaptation options that could be used to ensure that the ecosystem services provided by forests are maintained under future climates. The services are divided into broad areas within which managers can identify specific management goals for individual forests or landscapes. Adaptation options exist for the major...
Joost Bruin; dr. Koos Zwaan
2012-01-01
Introduction book Adapting Idols Since the first series of Pop Idol aired in the UK just over a decade ago, Idols television shows have been broadcast in more than forty countries all over the world. In all those countries the global Idols format has been adapted to local cultures and production
DEFF Research Database (Denmark)
Andrade, Stefan Bastholm
2015-01-01
This article analyses how Danish farm families adapted to harsh and changing conditions in the period after the great western agricultural crisis in the early 1980s. Drawing on Bourdieu's concepts of habitus and adaptation, I analyse the creation and consolidation of different class fractions amo...
Energy Technology Data Exchange (ETDEWEB)
Szu, H.; Hsu, C. [Univ. of Southwestern Louisiana, Lafayette, LA (United States)
1996-12-31
Human sensors systems (HSS) may be approximately described as an adaptive or self-learning version of the Wavelet Transforms (WT) that are capable to learn from several input-output associative pairs of suitable transform mother wavelets. Such an Adaptive WT (AWT) is a redundant combination of mother wavelets to either represent or classify inputs.
Successfully Adapting to Change.
Baird, James R.
1989-01-01
Describes methods used to successfully adapt to reductions in budget allocations in the University of Utah's Instructional Media Services Department. Three main areas of concern are addressed: morale and staff development; adapting to change in the areas of funding, control, media priorities, and technology; and planning for the future. (LRW)
Behavioral Adaptation and Acceptance
Martens, M.H.; Jenssen, G.D.
2012-01-01
One purpose of Intelligent Vehicles is to improve road safety, throughput, and emissions. However, the predicted effects are not always as large as aimed for. Part of this is due to indirect behavioral changes of drivers, also called behavioral adaptation. Behavioral adaptation (BA) refers to
Human pathogen avoidance adaptations
Tybur, J.M.; Lieberman, D.
2016-01-01
Over the past few decades, researchers have become increasingly interested in the adaptations guiding the avoidance of disease-causing organisms. Here we discuss the latest developments in this area, including a recently developed information-processing model of the adaptations underlying pathogen
Financing climate change adaptation
Bouwer, L.M.; Aerts, J.C.J.H.
2006-01-01
This paper examines the topic of financing adaptation in future climate change policies. A major question is whether adaptation in developing countries should be financed under the 1992 United Nations Framework Convention on Climate Change (UNFCCC), or whether funding should come from other sources.
Adaptation investments and homeownership
DEFF Research Database (Denmark)
Hansen, Jørgen Drud; Skak, Morten
2008-01-01
the home through a supplementary investment. Ownership offers low costs of adaptation, but has high contract costs compared with renting. Consumers simultaneously decide housing demand and tenure, and because of the different cost structure only consumers with strong preferences for individual adaptation...
Adaptation investments and homeownership
DEFF Research Database (Denmark)
Hansen, Jørgen Drud; Skak, Morten
2008-01-01
by adapting the home through a supplementary investment. Ownership offers low costs of adaptation, but has high contract costs compared with renting. Consumers simultaneously choose housing demand and tenure, and because of the different cost structure only consumers with strong preferences for individual...
Estimation of morbidity effects
International Nuclear Information System (INIS)
Ostro, B.
1994-01-01
microns in diameter. For this report, dose-response functions have been identified and adapted from published epidemiologic and economics literature. These functions allow the estimation of the change in health effects that would be expected to occur with changes in ambient air pollution levels. For each health effect, a range is presented within which the estimated effect is likely to fall, based on the judgement of this author. The central estimate is typically selected from the middle of the range of results reported in a given study or is based on the best regression specification. The range of the estimate typically reflects the reported standard error of the estimating regression coefficient
Adaptive Detection and Parameter Estimation for Multidimensional Signal Models
1989-04-19
first of Equations (3-3), it follows that H = fH (3-12) p BpP Moreover, with the help of Equations (Al-8) of Appendix I and Equation (3-6). we find that...7-29) 127 Substituting these results, we find that II + ZBSBBZB +Y T- YJ =+ Zi~t ÷ B SBR ZBI By introducing the definitions -t +BS1 ZB V E Y Ct
Adapting Autonomous Behavior Using an Inverse Trust Estimation
2014-07-01
be undertrusting the agent so trust should be increased. It does not take into ac- count situations where overtrust may be occurring. To account for...Bisantz, A.M., Drury , C.G.: Foundations for an empirically determined scale of trust in automated systems. International Journal of Cognitive
Applying Hyperspectral Imaging to Heart Rate Estimation for Adaptive Automation
2013-03-01
Shoji, Takae , 18 Kuge, & Yamamura, 2009). In this study 15 participants performed three MATB trials in the same order and came back three days in a...Miyake, Yamada, Shoji, Takae , Kuge, & Yamamura, 2009). They found that LF/HF did not correlate with difficulty level; however, they did find that the LF...0.1 Hz) component did show high test/retest correlations (Miyake, Yamada, Shoji, Takae , Kuge, & Yamamura, 2009). Although this technique shows much
DEFF Research Database (Denmark)
Arndt, Channing; Simler, Kenneth R.
2010-01-01
A fundamental premise of absolute poverty lines is that they represent the same level of utility through time and space. Disturbingly, a series of recent studies in middle- and low-income economies show that even carefully derived poverty lines rarely satisfy this premise. This article proposes a......, with the current approach tending to systematically overestimate (underestimate) poverty in urban (rural) zones.......A fundamental premise of absolute poverty lines is that they represent the same level of utility through time and space. Disturbingly, a series of recent studies in middle- and low-income economies show that even carefully derived poverty lines rarely satisfy this premise. This article proposes...... an information-theoretic approach to estimating cost-of-basic-needs (CBN) poverty lines that are utility consistent. Applications to date illustrate that utility-consistent poverty measurements derived from the proposed approach and those derived from current CBN best practices often differ substantially...
Rotor Field Oriented Control with adaptive Iron Loss Compensation
DEFF Research Database (Denmark)
Rasmussen, Henrik; Vadstrup, P.; Børsting, H.
1999-01-01
It is well known from the literature that iron loses in an induction motor implies field angle estimation errors and hence detuning problems. In this paper a new method for estimating the iron loss resistor in an induction motor is presented. The method is based on a traditional dynamic model...... controlled in a Field Oriented Control scheme. This deviation is used to force a MIT-rule based adaptive estimator. An adaptive compensator containing the developed estimator is introduced and verified by simulations and tested by real time experiments....
Scalable Harmonization of Complex Networks With Local Adaptive Controllers
Czech Academy of Sciences Publication Activity Database
Kárný, Miroslav; Herzallah, R.
2017-01-01
Roč. 47, č. 3 (2017), s. 394-404 ISSN 2168-2216 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Adaptive control * Adaptive estimation * Bayes methods * Complex networks * Decentralized control * Fee dback * Fee dforward systems * Recursive estimation Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 2.350, year: 2016 http://library.utia.cas.cz/separaty/2016/AS/karny-0457337.pdf
Hogberg, Nicholas Alvin; Garcia-Crespo, Andres Jose
2017-05-30
A turbine system and adapter are disclosed. The adapter includes a turbine attachment portion having a first geometry arranged to receive a corresponding geometry of a wheelpost of a turbine rotor, and a bucket attachment portion having a second geometry arranged to receive a corresponding geometry of a root portion of a non-metallic turbine bucket. Another adapter includes a turbine attachment portion arranged to receive a plurality of wheelposts of a turbine rotor, and a bucket attachment portion arranged to receive a plurality of non-metallic turbine buckets having single dovetail configuration root portions. The turbine system includes a turbine rotor wheel configured to receive metal buckets, at least one adapter secured to at least one wheelpost on the turbine rotor wheel, and at least one non-metallic bucket secured to the at least one adapter.
Inhabiting Adaptive Architecture
Directory of Open Access Journals (Sweden)
Holger Schnädelbach
2017-12-01
Full Text Available Adaptive Architecture concerns buildings that are specifically designed to adapt to their inhabitants and to their environments. Work in this space has a very long history, with a number of adaptive buildings emerging during the modernist period, such as Rietveld’s Schröder house, Gaudi’s Casa Batlló and Chareau's Maison de Verre. Such early work included manual adaptivity, even if that was motor-assisted. Today, buildings have started to combine this with varying degrees of automation and designed-for adaptivity is commonplace in office buildings and eco homes, where lighting, air conditioning, access and energy generation respond to and influence the behaviour of people, and the internal and external climate.
Appraising Adaptive Management
Directory of Open Access Journals (Sweden)
Kai N. Lee
1999-12-01
Full Text Available Adaptive management is appraised as a policy implementation approach by examining its conceptual, technical, equity, and practical strengths and limitations. Three conclusions are drawn: (1 Adaptive management has been more influential, so far, as an idea than as a practical means of gaining insight into the behavior of ecosystems utilized and inhabited by humans. (2 Adaptive management should be used only after disputing parties have agreed to an agenda of questions to be answered using the adaptive approach; this is not how the approach has been used. (3 Efficient, effective social learning, of the kind facilitated by adaptive management, is likely to be of strategic importance in governing ecosystems as humanity searches for a sustainable economy.
Energy Technology Data Exchange (ETDEWEB)
Walz, H.V.
1980-07-01
An experimental, general purpose adaptive signal processor system has been developed, utilizing a quantized (clipped) version of the Widrow-Hoff least-mean-square adaptive algorithm developed by Moschner. The system accommodates 64 adaptive weight channels with 8-bit resolution for each weight. Internal weight update arithmetic is performed with 16-bit resolution, and the system error signal is measured with 12-bit resolution. An adapt cycle of adjusting all 64 weight channels is accomplished in 8 ..mu..sec. Hardware of the signal processor utilizes primarily Schottky-TTL type integrated circuits. A prototype system with 24 weight channels has been constructed and tested. This report presents details of the system design and describes basic experiments performed with the prototype signal processor. Finally some system configurations and applications for this adaptive signal processor are discussed.
International Nuclear Information System (INIS)
Walz, H.V.
1980-07-01
An experimental, general purpose adaptive signal processor system has been developed, utilizing a quantized (clipped) version of the Widrow-Hoff least-mean-square adaptive algorithm developed by Moschner. The system accommodates 64 adaptive weight channels with 8-bit resolution for each weight. Internal weight update arithmetic is performed with 16-bit resolution, and the system error signal is measured with 12-bit resolution. An adapt cycle of adjusting all 64 weight channels is accomplished in 8 μsec. Hardware of the signal processor utilizes primarily Schottky-TTL type integrated circuits. A prototype system with 24 weight channels has been constructed and tested. This report presents details of the system design and describes basic experiments performed with the prototype signal processor. Finally some system configurations and applications for this adaptive signal processor are discussed
Reference-shaping adaptive control by using gradient descent optimizers.
Directory of Open Access Journals (Sweden)
Baris Baykant Alagoz
Full Text Available This study presents a model reference adaptive control scheme based on reference-shaping approach. The proposed adaptive control structure includes two optimizer processes that perform gradient descent optimization. The first process is the control optimizer that generates appropriate control signal for tracking of the controlled system output to a reference model output. The second process is the adaptation optimizer that performs for estimation of a time-varying adaptation gain, and it contributes to improvement of control signal generation. Numerical update equations derived for adaptation gain and control signal perform gradient descent optimization in order to decrease the model mismatch errors. To reduce noise sensitivity of the system, a dead zone rule is applied to the adaptation process. Simulation examples show the performance of the proposed Reference-Shaping Adaptive Control (RSAC method for several test scenarios. An experimental study demonstrates application of method for rotor control.
van Vianen, Annelies E. M.; Klehe, Ute-Christine; Koen, Jessie; Dries, Nicky
2012-01-01
The Career Adapt-Abilities Scale (CAAS)--Netherlands Form consists of four scales, each with six items, which measure concern, control, curiosity, and confidence as psychosocial resources for managing occupational transitions, developmental tasks, and work traumas. Internal consistency estimates for the subscale and total scores ranged from…
75 FR 57859 - Specially Adapted Housing and Special Home Adaptation
2010-09-23
... Home Adaptation AGENCY: Department of Veterans Affairs. ACTION: Final rule. SUMMARY: The Department of... specially adapted housing and special home adaptation grants. This final rule incorporates certain... regulations pertaining to eligibility for specially adapted housing (SAH) grants and special home adaptation...
Dynamical adaptation in photoreceptors.
Directory of Open Access Journals (Sweden)
Damon A Clark
Full Text Available Adaptation is at the heart of sensation and nowhere is it more salient than in early visual processing. Light adaptation in photoreceptors is doubly dynamical: it depends upon the temporal structure of the input and it affects the temporal structure of the response. We introduce a non-linear dynamical adaptation model of photoreceptors. It is simple enough that it can be solved exactly and simulated with ease; analytical and numerical approaches combined provide both intuition on the behavior of dynamical adaptation and quantitative results to be compared with data. Yet the model is rich enough to capture intricate phenomenology. First, we show that it reproduces the known phenomenology of light response and short-term adaptation. Second, we present new recordings and demonstrate that the model reproduces cone response with great precision. Third, we derive a number of predictions on the response of photoreceptors to sophisticated stimuli such as periodic inputs, various forms of flickering inputs, and natural inputs. In particular, we demonstrate that photoreceptors undergo rapid adaptation of response gain and time scale, over ∼ 300[Formula: see text] ms-i. e., over the time scale of the response itself-and we confirm this prediction with data. For natural inputs, this fast adaptation can modulate the response gain more than tenfold and is hence physiologically relevant.
Introduction to adaptive arrays
Monzingo, Bob; Haupt, Randy
2011-01-01
This second edition is an extensive modernization of the bestselling introduction to the subject of adaptive array sensor systems. With the number of applications of adaptive array sensor systems growing each year, this look at the principles and fundamental techniques that are critical to these systems is more important than ever before. Introduction to Adaptive Arrays, 2nd Edition is organized as a tutorial, taking the reader by the hand and leading them through the maze of jargon that often surrounds this highly technical subject. It is easy to read and easy to follow as fundamental concept
[Adaptive optics for ophthalmology].
Saleh, M
2016-04-01
Adaptive optics is a technology enhancing the visual performance of an optical system by correcting its optical aberrations. Adaptive optics have already enabled several breakthroughs in the field of visual sciences, such as improvement of visual acuity in normal and diseased eyes beyond physiologic limits, and the correction of presbyopia. Adaptive optics technology also provides high-resolution, in vivo imaging of the retina that may eventually help to detect the onset of retinal conditions at an early stage and provide better assessment of treatment efficacy. Copyright © 2016 Elsevier Masson SAS. All rights reserved.
Adaptive algebraic reconstruction technique
International Nuclear Information System (INIS)
Lu Wenkai; Yin Fangfang
2004-01-01
Algebraic reconstruction techniques (ART) are iterative procedures for reconstructing objects from their projections. It is proven that ART can be computationally efficient by carefully arranging the order in which the collected data are accessed during the reconstruction procedure and adaptively adjusting the relaxation parameters. In this paper, an adaptive algebraic reconstruction technique (AART), which adopts the same projection access scheme in multilevel scheme algebraic reconstruction technique (MLS-ART), is proposed. By introducing adaptive adjustment of the relaxation parameters during the reconstruction procedure, one-iteration AART can produce reconstructions with better quality, in comparison with one-iteration MLS-ART. Furthermore, AART outperforms MLS-ART with improved computational efficiency
DEFF Research Database (Denmark)
Kaulakiene, Dalia; Thomsen, Christian; Pedersen, Torben Bach
2015-01-01
by Amazon Web Services (AWS). The users aiming for the spot market are presented with many instance types placed in multiple datacenters in the world, and thus it is difficult to choose the optimal deployment. In this paper, we propose the framework SpotADAPT (Spot-Aware (re-)Deployment of Analytical...... of typical analytical workloads and real spot price traces. SpotADAPT's suggested deployments are comparable to the theoretically optimal ones, and in particular, it shows good cost benefits for the budget optimization -- on average SpotADAPT is at most 0.3% more expensive than the theoretically optimal...
The process of organisational adaptation through innovations, and organisational adaptability
Tikka, Tommi
2010-01-01
This study is about the process of organisational adaptation and organisational adaptability. The study generates a theoretical framework about organisational adaptation behaviour and conditions that have influence on success of organisational adaptation. The research questions of the study are: How does an organisation adapt through innovations, and which conditions enhance or impede organisational adaptation through innovations? The data were gathered from five case organisations withi...
Adaptive training of feedforward neural networks by Kalman filtering
International Nuclear Information System (INIS)
Ciftcioglu, Oe.
1995-02-01
Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.)
Condition Number Regularized Covariance Estimation.
Won, Joong-Ho; Lim, Johan; Kim, Seung-Jean; Rajaratnam, Bala
2013-06-01
Estimation of high-dimensional covariance matrices is known to be a difficult problem, has many applications, and is of current interest to the larger statistics community. In many applications including so-called the "large p small n " setting, the estimate of the covariance matrix is required to be not only invertible, but also well-conditioned. Although many regularization schemes attempt to do this, none of them address the ill-conditioning problem directly. In this paper, we propose a maximum likelihood approach, with the direct goal of obtaining a well-conditioned estimator. No sparsity assumption on either the covariance matrix or its inverse are are imposed, thus making our procedure more widely applicable. We demonstrate that the proposed regularization scheme is computationally efficient, yields a type of Steinian shrinkage estimator, and has a natural Bayesian interpretation. We investigate the theoretical properties of the regularized covariance estimator comprehensively, including its regularization path, and proceed to develop an approach that adaptively determines the level of regularization that is required. Finally, we demonstrate the performance of the regularized estimator in decision-theoretic comparisons and in the financial portfolio optimization setting. The proposed approach has desirable properties, and can serve as a competitive procedure, especially when the sample size is small and when a well-conditioned estimator is required.
Condition Number Regularized Covariance Estimation*
Won, Joong-Ho; Lim, Johan; Kim, Seung-Jean; Rajaratnam, Bala
2012-01-01
Estimation of high-dimensional covariance matrices is known to be a difficult problem, has many applications, and is of current interest to the larger statistics community. In many applications including so-called the “large p small n” setting, the estimate of the covariance matrix is required to be not only invertible, but also well-conditioned. Although many regularization schemes attempt to do this, none of them address the ill-conditioning problem directly. In this paper, we propose a maximum likelihood approach, with the direct goal of obtaining a well-conditioned estimator. No sparsity assumption on either the covariance matrix or its inverse are are imposed, thus making our procedure more widely applicable. We demonstrate that the proposed regularization scheme is computationally efficient, yields a type of Steinian shrinkage estimator, and has a natural Bayesian interpretation. We investigate the theoretical properties of the regularized covariance estimator comprehensively, including its regularization path, and proceed to develop an approach that adaptively determines the level of regularization that is required. Finally, we demonstrate the performance of the regularized estimator in decision-theoretic comparisons and in the financial portfolio optimization setting. The proposed approach has desirable properties, and can serve as a competitive procedure, especially when the sample size is small and when a well-conditioned estimator is required. PMID:23730197
Estimations of actual availability
International Nuclear Information System (INIS)
Molan, M.; Molan, G.
2001-01-01
Adaptation of working environment (social, organizational, physical and physical) should assure higher level of workers' availability and consequently higher level of workers' performance. A special theoretical model for description of connections between environmental factors, human availability and performance was developed and validated. The central part of the model is evaluations of human actual availability in the real working situation or fitness for duties self-estimation. The model was tested in different working environments. On the numerous (2000) workers, standardized values and critical limits for an availability questionnaire were defined. Standardized method was used in identification of the most important impact of environmental factors. Identified problems were eliminated by investments in the organization in modification of selection and training procedures in humanization of working .environment. For workers with behavioural and health problems individual consultancy was offered. The described method is a tool for identification of impacts. In combination with behavioural analyses and mathematical analyses of connections, it offers possibilities to keep adequate level of human availability and fitness for duty in each real working situation. The model should be a tool for achieving adequate level of nuclear safety by keeping the adequate level of workers' availability and fitness for duty. For each individual worker possibility for estimation of level of actual fitness for duty is possible. Effects of prolonged work and additional tasks should be evaluated. Evaluations of health status effects and ageing are possible on the individual level. (author)
Hygienic significance of radiostability as measures of adaptive feasibilities
International Nuclear Information System (INIS)
Kudritskij, Yu.K.
1987-01-01
An attempt is made to substantiate hygienic significance of radiostability analysis as measures of adaptive feasibilities variation under the low dose ionizing radiation effect (IR). Examples of this substantiation are presented. Not only biological radiation effects but social adaptivity problems may be analysed. With more information adaptive feasibilities of human body to radiation factor are extended, its radiostability increases. Analysis of the state of adaptive feasibilities and their development estimation are vital problems of radiation hygiene, the basis for regulation and normalization of radiation factor
Superresolution restoration of an image sequence: adaptive filtering approach.
Elad, M; Feuer, A
1999-01-01
This paper presents a new method based on adaptive filtering theory for superresolution restoration of continuous image sequences. The proposed methodology suggests least squares (LS) estimators which adapt in time, based on adaptive filters, least mean squares (LMS) or recursive least squares (RLS). The adaptation enables the treatment of linear space and time-variant blurring and arbitrary motion, both of them assumed known. The proposed new approach is shown to be of relatively low computational requirements. Simulations demonstrating the superresolution restoration algorithms are presented.
Fairer flying: an international air travel levy for adaptation
Energy Technology Data Exchange (ETDEWEB)
Chambwera, Muyeye; Muller, Benito
2008-11-15
For the world's poorest countries and communities, adaptation to climate change is urgently needed, but costly: estimates run into tens of billions of dollars a year. Given the shortfall in current international adaptation funding, how can resources for the developing world be raised? An adaptation levy on international air travel could help fill the gap. A small per-trip payment by passengers could contribute US$8 billion to US$10 billion a year towards adaptation. Similar schemes in France and elsewhere show that this kind of ethical solidarity and 'polluter pays' approach would be simple to implement in practical and institutional terms.
Kovačević, Branko; Milosavljević, Milan
2013-01-01
“Adaptive Digital Filters” presents an important discipline applied to the domain of speech processing. The book first makes the reader acquainted with the basic terms of filtering and adaptive filtering, before introducing the field of advanced modern algorithms, some of which are contributed by the authors themselves. Working in the field of adaptive signal processing requires the use of complex mathematical tools. The book offers a detailed presentation of the mathematical models that is clear and consistent, an approach that allows everyone with a college level of mathematics knowledge to successfully follow the mathematical derivations and descriptions of algorithms. The algorithms are presented in flow charts, which facilitates their practical implementation. The book presents many experimental results and treats the aspects of practical application of adaptive filtering in real systems, making it a valuable resource for both undergraduate and graduate students, and for all others interested in m...
National Aeronautics and Space Administration — Adaptive Trajectory Design (ATD) is an original concept for quick and efficient end-to-end trajectory designs using proven piece-wise dynamical methods. With ongoing...
International Nuclear Information System (INIS)
Ikushima, Takaji
1991-01-01
An adaptive response to radiation stress was found in cultured Chinese hamster V79 cells, as a suppressed induction of micronuclei (MNs) and sister chromatid exchanges (SCEs) in the cells conditioned by very low doses. The important characteristics of the novel chromosomal response, called radio-adaptive response (RAR), that have newly emerged in this study are: 1) Low doses of beta-rays from tritiated water (HTO) as well as tritiated thymidine can cause the RAR. 2) Thermal neutrons, a high LET radiation, can not act as tritium beta-rays or gamma-rays. 3) The RAR expression is suppressed by an inhibition of protein synthesis. 4) Several proteins are newly synthesized concurrently with the RAR expression after adapting doses, viewed by two-dimensional electrophoresis of cellular proteins. These results suggest that the RAR is an adaptive chromosomal DNA repair induced by very low doses of low LET radiations under restricted conditions, accompanying the inducible specific gene expression. (author)
DEFF Research Database (Denmark)
Jørgensen, Ida Kathrine Hammeleff
2017-01-01
2010). Such adaptations make use of different strategies for capturing the various aspects of their source. Talisman: Digital Edition (Nomad Games 2014) largely adopts the same primary mechanics of the board game Talisman (Fantasy Flight Games 2008) while StarCraft: The Board Game (Fantasy Flight Games...... from television or cinema (Woods 2012). Furthermore, since the early days of digital games, tabletop games have served as a source of inspiration for many video-game designers, and more recently we have seen the occurrence of tabletop game adaptations of popular video-games such as StarCraft (Blizzard...... similarities and differences equally and thus challenges the ‘ideology of fidelity’ that has long permeated the field of adaption studies. This presentation explores adaptations between digital and non-digital games. This analysis is inspired by intermedial studies (e.g. Elleström 2010) that distinguishes...
DEFF Research Database (Denmark)
Santurette, Sébastien; Christensen-Dalsgaard, Jakob; Tranebjærg, Lisbeth
2018-01-01
, and is essential to achieve successful speech communication, correct orientation in our full environment, and eventually survival. These adaptive processes may differ in individuals with hearing loss, whose auditory system may cope via ‘‘readapting’’ itself over a longer time scale to the changes in sensory input...... induced by hearing impairment and the compensation provided by hearing devices. These devices themselves are now able to adapt to the listener’s individual environment, attentional state, and behavior. These topics related to auditory adaptation, in the broad sense of the term, were central to the 6th...... International Symposium on Auditory and Audiological Research held in Nyborg, Denmark, in August 2017. The symposium addressed adaptive processes in hearing from different angles, together with a wide variety of other auditory and audiological topics. The papers in this special issue result from some...
DEFF Research Database (Denmark)
Paster, Thomas
on influence. These two dimensions - adaptation and influence - result in four ideal types: business-dominated social compromise, imposed social compromise, business dominance, and political confrontation. Examples from German welfare state history illustrate these four types. The paper suggests...
DEFF Research Database (Denmark)
Brody, Joshua Eric; Larsen, Kasper Green
2015-01-01
In this paper, we study the role non-adaptivity plays in maintaining dynamic data structures. Roughly speaking, a data structure is non-adaptive if the memory locations it reads and/or writes when processing a query or update depend only on the query or update and not on the contents of previously...... read cells. We study such non-adaptive data structures in the cell probe model. This model is one of the least restrictive lower bound models and in particular, cell probe lower bounds apply to data structures developed in the popular word-RAM model. Unfortunately, this generality comes at a high cost......: the highest lower bound proved for any data structure problem is only polylogarithmic. Our main result is to demonstrate that one can in fact obtain polynomial cell probe lower bounds for non-adaptive data structures. To shed more light on the seemingly inherent polylogarithmic lower bound barrier, we study...
Adaptive Multilevel Monte Carlo Simulation
Hoel, H; von Schwerin, E; Szepessy, A; Tempone, Raul
2011-01-01
. An adaptive algorithm for ordinary, stochastic and partial differential equations. In Recent advances in adaptive computation, volume 383 of Contemp. Math., pages 325–343. Amer. Math. Soc., Providence, RI, 2005.). This form of the adaptive algorithm generates
Adaptive self-correcting control system
International Nuclear Information System (INIS)
Ellis, S.H.
1984-01-01
A control system for regulating a controlled device or process, such as a turbofan engine, produces independent multiple estimates of one or more controlled variables of the device or process by combining the signals from a plurality of feedback sensors, which provide information related to the controlled variables, in weighted nonordered pairs. The independent multiple estimates of each controlled variable are combined into a weighted average, and individual estimates which differ by more than a specified amount from the weighted average are edited and temporarily removed from consideration. A revised weighted average value of each controlled variable is then produced, and this value is used to limit or control operation of the device or process. Adaptive trim is provided to compensate for changes in the device or process being controlled, such as engine deterioration, by slowly trimming each individual estimate toward the mean, and includes error compensation which constrains the weighted sum of the adaptive trims to equal zero, thereby preventing the adaptive trim from changing the operating level of the device or process. A secondary editing circuit based on a majority rule principle identifies a failed feedback sensor and permanently excludes all individual estimates of the controlled variable based on the failed sensor. Editing boundaries are increased and adaptive trim rate is varied when a transient occurs in the operation of the device or process. Further transient compensation may be required for a system with more severe transient requirements, and this invention includes compensation to selected feedback parameters such as turbine temperature to account for differences between steady state and transient values
International Nuclear Information System (INIS)
Parry, M.; McGlade, J.; Verschoor, M.; Isoard, S.; Anema, K.; Boer, J.; Cowan, C.; Collins, R.; Smeets, M.
2009-01-01
At the Conference of Parties in Copenhagen, Denmark, December 7-18, 2009 Change Magazine will present a special issue on 'Climate Adaptation in Europe'. The magazine contains articles on climate policy strategies in European countries and cross-border studies on climate change, articles on climate adaptation in the Alps, on water quality as a bottleneck for the agricultural sector, and drought in the mediterranean countries. How will member countries in the European Union tackle the climate crisis?.
Chaudhuri, Arijit
2014-01-01
Combining the two statistical techniques of network sampling and adaptive sampling, this book illustrates the advantages of using them in tandem to effectively capture sparsely located elements in unknown pockets. It shows how network sampling is a reliable guide in capturing inaccessible entities through linked auxiliaries. The text also explores how adaptive sampling is strengthened in information content through subsidiary sampling with devices to mitigate unmanageable expanding sample sizes. Empirical data illustrates the applicability of both methods.
DEFF Research Database (Denmark)
Kock, Anders Bredahl
2016-01-01
We show that the adaptive Lasso is oracle efficient in stationary and nonstationary autoregressions. This means that it estimates parameters consistently, selects the correct sparsity pattern, and estimates the coefficients belonging to the relevant variables at the same asymptotic efficiency...
Adaptation and perceptual norms
Webster, Michael A.; Yasuda, Maiko; Haber, Sara; Leonard, Deanne; Ballardini, Nicole
2007-02-01
We used adaptation to examine the relationship between perceptual norms--the stimuli observers describe as psychologically neutral, and response norms--the stimulus levels that leave visual sensitivity in a neutral or balanced state. Adapting to stimuli on opposite sides of a neutral point (e.g. redder or greener than white) biases appearance in opposite ways. Thus the adapting stimulus can be titrated to find the unique adapting level that does not bias appearance. We compared these response norms to subjectively defined neutral points both within the same observer (at different retinal eccentricities) and between observers. These comparisons were made for visual judgments of color, image focus, and human faces, stimuli that are very different and may depend on very different levels of processing, yet which share the property that for each there is a well defined and perceptually salient norm. In each case the adaptation aftereffects were consistent with an underlying sensitivity basis for the perceptual norm. Specifically, response norms were similar to and thus covaried with the perceptual norm, and under common adaptation differences between subjectively defined norms were reduced. These results are consistent with models of norm-based codes and suggest that these codes underlie an important link between visual coding and visual experience.
The Climate Adaptation Frontier
Energy Technology Data Exchange (ETDEWEB)
Preston, Benjamin L [ORNL
2013-01-01
Climate adaptation has emerged as a mainstream risk management strategy for assisting in maintaining socio-ecological systems within the boundaries of a safe operating space. Yet, there are limits to the ability of systems to adapt. Here, we introduce the concept of an adaptation frontier , which is defined as a socio-ecological system s transitional adaptive operating space between safe and unsafe domains. A number of driving forces are responsible for determining the sustainability of systems on the frontier. These include path dependence, adaptation/development deficits, values conflicts and discounting of future loss and damage. The cumulative implications of these driving forces are highly uncertain. Nevertheless, the fact that a broad range of systems already persist at the edge of their frontiers suggests a high likelihood that some limits will eventually be exceeded. The resulting system transformation is likely to manifest as anticipatory modification of management objectives or loss and damage. These outcomes vary significantly with respect to their ethical implications. Successful navigation of the adaptation frontier will necessitate new paradigms of risk governance to elicit knowledge that encourages reflexive reevaluation of societal values that enable or constrain sustainability.
Learning to Adapt. Organisational Adaptation to Climate Change Impacts
International Nuclear Information System (INIS)
Berkhout, F.; Hertin, J.; Gann, D.M.
2006-01-01
Analysis of human adaptation to climate change should be based on realistic models of adaptive behaviour at the level of organisations and individuals. The paper sets out a framework for analysing adaptation to the direct and indirect impacts of climate change in business organisations with new evidence presented from empirical research into adaptation in nine case-study companies. It argues that adaptation to climate change has many similarities with processes of organisational learning. The paper suggests that business organisations face a number of obstacles in learning how to adapt to climate change impacts, especially in relation to the weakness and ambiguity of signals about climate change and the uncertainty about benefits flowing from adaptation measures. Organisations rarely adapt 'autonomously', since their adaptive behaviour is influenced by policy and market conditions, and draws on resources external to the organisation. The paper identifies four adaptation strategies that pattern organisational adaptive behaviour
Turbulent Output-Based Anisotropic Adaptation
Park, Michael A.; Carlson, Jan-Renee
2010-01-01
Controlling discretization error is a remaining challenge for computational fluid dynamics simulation. Grid adaptation is applied to reduce estimated discretization error in drag or pressure integral output functions. To enable application to high O(10(exp 7)) Reynolds number turbulent flows, a hybrid approach is utilized that freezes the near-wall boundary layer grids and adapts the grid away from the no slip boundaries. The hybrid approach is not applicable to problems with under resolved initial boundary layer grids, but is a powerful technique for problems with important off-body anisotropic features. Supersonic nozzle plume, turbulent flat plate, and shock-boundary layer interaction examples are presented with comparisons to experimental measurements of pressure and velocity. Adapted grids are produced that resolve off-body features in locations that are not known a priori.
Adaptive Extremum Control and Wind Turbine Control
DEFF Research Database (Denmark)
Ma, Xin
1997-01-01
This thesis is divided into two parts, i.e., adaptive extremum control and modelling and control of a wind turbine. The rst part of the thesis deals with the design of adaptive extremum controllers for some processes which have the behaviour that process should have as high e ciency as possible...... in parameters, and thus directly lends itself to parameter estimation and adaptive control. The extremum control law is derived based on static optimization of a performance function. For a process with nonlinearity at output the intermediate signal between the linear part and nonlinear part plays an important....... Firstly, it is assumed that the nonlinear processes can be divided into a dynamic linear part and static nonlinear part. Consequently the processes with input nonlinearity and output nonlinearity are treated separately. With the nonlinearity at the input it is easy to set up a model which is linear...
Reference Device-Assisted Adaptive Location Fingerprinting
Directory of Open Access Journals (Sweden)
Dongjin Wu
2016-06-01
Full Text Available Location fingerprinting suffers in dynamic environments and needs recalibration from time to time to maintain system performance. This paper proposes an adaptive approach for location fingerprinting. Based on real-time received signal strength indicator (RSSI samples measured by a group of reference devices, the approach applies a modified Universal Kriging (UK interpolant to estimate adaptive temporal and environmental radio maps. The modified UK can take the spatial distribution characteristics of RSSI into account. In addition, the issue of device heterogeneity caused by multiple reference devices is further addressed. To compensate the measuring differences of heterogeneous reference devices, differential RSSI metric is employed. Extensive experiments were conducted in an indoor field and the results demonstrate that the proposed approach not only adapts to dynamic environments and the situation of changing APs’ positions, but it is also robust toward measuring differences of heterogeneous reference devices.
Estimation of stochastic environment force for master–slave robotic ...
Indian Academy of Sciences (India)
Neelu Nagpal
Subsequently, convergence analysis of error in the estimates is performed. Also, an expression of ... nonlinear and composite adaptive controller [7, 9] and disturbance ... block processing method and acts as an efficient estimator since this estimation ...... 0949-2. [32] Smith L 2006 Sequential Monte Carlo particle filtering for.
On estimation of the intensity function of a point process
Lieshout, van M.N.M.
2010-01-01
Abstract. Estimation of the intensity function of spatial point processes is a fundamental problem. In this paper, we interpret the Delaunay tessellation field estimator recently introduced by Schaap and Van de Weygaert as an adaptive kernel estimator and give explicit expressions for the mean and
Direct adaptive control of manipulators in Cartesian space
Seraji, H.
1987-01-01
A new adaptive-control scheme for direct control of manipulator end effector to achieve trajectory tracking in Cartesian space is developed in this article. The control structure is obtained from linear multivariable theory and is composed of simple feedforward and feedback controllers and an auxiliary input. The direct adaptation laws are derived from model reference adaptive control theory and are not based on parameter estimation of the robot model. The utilization of adaptive feedforward control and the inclusion of auxiliary input are novel features of the present scheme and result in improved dynamic performance over existing adaptive control schemes. The adaptive controller does not require the complex mathematical model of the robot dynamics or any knowledge of the robot parameters or the payload, and is computationally fast for on-line implementation with high sampling rates. The control scheme is applied to a two-link manipulator for illustration.
Adaptation, plant evolution, and the fossil record
Knoll, A. H.; Niklas, K. J.
1987-01-01
The importance of adaptation in determining patterns of evolution has become an important focus of debate in evolutionary biology. As it pertains to paleobotany, the issue is whether or not adaptive evolution mediated by natural selection is sufficient to explain the stratigraphic distributions of taxa and character states observed in the plant fossil record. One means of addressing this question is the functional evaluation of stratigraphic series of plant organs set in the context of paleoenvironmental change and temporal patterns of floral composition within environments. For certain organ systems, quantitative estimates of biophysical performance can be made on the basis of structures preserved in the fossil record. Performance estimates for plants separated in time or space can be compared directly. Implicit in different hypotheses of the forces that shape the evolutionary record (e.g. adaptation, mass extinction, rapid environmental change, chance) are predictions about stratigraphic and paleoenvironmental trends in the efficacy of functional performance. Existing data suggest that following the evolution of a significant structural innovation, adaptation for improved functional performance can be a major determinant of evolutionary changes in plants; however, there are structural and development limits to functional improvement, and once these are reached, the structure in question may no longer figure strongly in selection until and unless a new innovation evolves. The Silurian-Devonian paleobotanical record is consistent with the hypothesis that the succession of lowland floodplain dominants preserved in the fossil record of this interval was determined principally by the repeated evolution of new taxa that rose to ecological importance because of competitive advantages conferred by improved biophysical performance. This does not seem to be equally true for Carboniferous-Jurassic dominants of swamp and lowland floodplain environments. In these cases
Adapting Activity and Participation (The ADAPT intervention program)
DEFF Research Database (Denmark)
von Bülow, Cecilie
Præsentation af et ergoterapeutisk gruppebaseret program, ADAPT programmet. ADAPT programmet er designet på baggrund af evidens samt understøttet af ergoterapeutiske teorier og modeller......Præsentation af et ergoterapeutisk gruppebaseret program, ADAPT programmet. ADAPT programmet er designet på baggrund af evidens samt understøttet af ergoterapeutiske teorier og modeller...
Adaptive synchronization of uncertain chaotic colpitts oscillators based on parameter identification
International Nuclear Information System (INIS)
Fotsin, H.B.; Daafouz, J.
2005-01-01
This Letter uses systematic tools from recent papers to design non-linear observers for synchronization of a chaotic colpitts oscillator both in the non adaptive and adaptive cases. It is shown that all parameters of a totally uncertain model of the oscillator can be estimated through adaptive synchronization. A strategy for practical implementation of a secure communication strategy is also discussed
Multiple model adaptive control with mixing
Kuipers, Matthew
Despite the remarkable theoretical accomplishments and successful applications of adaptive control, the field is not sufficiently mature to solve challenging control problems requiring strict performance and safety guarantees. Towards addressing these issues, a novel deterministic multiple-model adaptive control approach called adaptive mixing control is proposed. In this approach, adaptation comes from a high-level system called the supervisor that mixes into feedback a number of candidate controllers, each finely-tuned to a subset of the parameter space. The mixing signal, the supervisor's output, is generated by estimating the unknown parameters and, at every instant of time, calculating the contribution level of each candidate controller based on certainty equivalence. The proposed architecture provides two characteristics relevant to solving stringent, performance-driven applications. First, the full-suite of linear time invariant control tools is available. A disadvantage of conventional adaptive control is its restriction to utilizing only those control laws whose solutions can be feasibly computed in real-time, such as model reference and pole-placement type controllers. Because its candidate controllers are computed off line, the proposed approach suffers no such restriction. Second, the supervisor's output is smooth and does not necessarily depend on explicit a priori knowledge of the disturbance model. These characteristics can lead to improved performance by avoiding the unnecessary switching and chattering behaviors associated with some other multiple adaptive control approaches. The stability and robustness properties of the adaptive scheme are analyzed. It is shown that the mean-square regulation error is of the order of the modeling error. And when the parameter estimate converges to its true value, which is guaranteed if a persistence of excitation condition is satisfied, the adaptive closed-loop system converges exponentially fast to a closed
Directory of Open Access Journals (Sweden)
Thomas R. Rimmele
2011-06-01
Full Text Available Adaptive optics (AO has become an indispensable tool at ground-based solar telescopes. AO enables the ground-based observer to overcome the adverse effects of atmospheric seeing and obtain diffraction limited observations. Over the last decade adaptive optics systems have been deployed at major ground-based solar telescopes and revitalized ground-based solar astronomy. The relatively small aperture of solar telescopes and the bright source make solar AO possible for visible wavelengths where the majority of solar observations are still performed. Solar AO systems enable diffraction limited observations of the Sun for a significant fraction of the available observing time at ground-based solar telescopes, which often have a larger aperture than equivalent space based observatories, such as HINODE. New ground breaking scientific results have been achieved with solar adaptive optics and this trend continues. New large aperture telescopes are currently being deployed or are under construction. With the aid of solar AO these telescopes will obtain observations of the highly structured and dynamic solar atmosphere with unprecedented resolution. This paper reviews solar adaptive optics techniques and summarizes the recent progress in the field of solar adaptive optics. An outlook to future solar AO developments, including a discussion of Multi-Conjugate AO (MCAO and Ground-Layer AO (GLAO will be given.
Rimmele, Thomas R; Marino, Jose
Adaptive optics (AO) has become an indispensable tool at ground-based solar telescopes. AO enables the ground-based observer to overcome the adverse effects of atmospheric seeing and obtain diffraction limited observations. Over the last decade adaptive optics systems have been deployed at major ground-based solar telescopes and revitalized ground-based solar astronomy. The relatively small aperture of solar telescopes and the bright source make solar AO possible for visible wavelengths where the majority of solar observations are still performed. Solar AO systems enable diffraction limited observations of the Sun for a significant fraction of the available observing time at ground-based solar telescopes, which often have a larger aperture than equivalent space based observatories, such as HINODE. New ground breaking scientific results have been achieved with solar adaptive optics and this trend continues. New large aperture telescopes are currently being deployed or are under construction. With the aid of solar AO these telescopes will obtain observations of the highly structured and dynamic solar atmosphere with unprecedented resolution. This paper reviews solar adaptive optics techniques and summarizes the recent progress in the field of solar adaptive optics. An outlook to future solar AO developments, including a discussion of Multi-Conjugate AO (MCAO) and Ground-Layer AO (GLAO) will be given. Supplementary material is available for this article at 10.12942/lrsp-2011-2.
Solar tomography adaptive optics.
Ren, Deqing; Zhu, Yongtian; Zhang, Xi; Dou, Jiangpei; Zhao, Gang
2014-03-10
Conventional solar adaptive optics uses one deformable mirror (DM) and one guide star for wave-front sensing, which seriously limits high-resolution imaging over a large field of view (FOV). Recent progress toward multiconjugate adaptive optics indicates that atmosphere turbulence induced wave-front distortion at different altitudes can be reconstructed by using multiple guide stars. To maximize the performance over a large FOV, we propose a solar tomography adaptive optics (TAO) system that uses tomographic wave-front information and uses one DM. We show that by fully taking advantage of the knowledge of three-dimensional wave-front distribution, a classical solar adaptive optics with one DM can provide an extra performance gain for high-resolution imaging over a large FOV in the near infrared. The TAO will allow existing one-deformable-mirror solar adaptive optics to deliver better performance over a large FOV for high-resolution magnetic field investigation, where solar activities occur in a two-dimensional field up to 60'', and where the near infrared is superior to the visible in terms of magnetic field sensitivity.
Accuracies Of Optical Processors For Adaptive Optics
Downie, John D.; Goodman, Joseph W.
1992-01-01
Paper presents analysis of accuracies and requirements concerning accuracies of optical linear-algebra processors (OLAP's) in adaptive-optics imaging systems. Much faster than digital electronic processor and eliminate some residual distortion. Question whether errors introduced by analog processing of OLAP overcome advantage of greater speed. Paper addresses issue by presenting estimate of accuracy required in general OLAP that yields smaller average residual aberration of wave front than digital electronic processor computing at given speed.