Kerfdr: a semi-parametric kernel-based approach to local false discovery rate estimation
Directory of Open Access Journals (Sweden)
Robin Stephane
2009-03-01
Full Text Available Abstract Background The use of current high-throughput genetic, genomic and post-genomic data leads to the simultaneous evaluation of a large number of statistical hypothesis and, at the same time, to the multiple-testing problem. As an alternative to the too conservative Family-Wise Error-Rate (FWER, the False Discovery Rate (FDR has appeared for the last ten years as more appropriate to handle this problem. However one drawback of FDR is related to a given rejection region for the considered statistics, attributing the same value to those that are close to the boundary and those that are not. As a result, the local FDR has been recently proposed to quantify the specific probability for a given null hypothesis to be true. Results In this context we present a semi-parametric approach based on kernel estimators which is applied to different high-throughput biological data such as patterns in DNA sequences, genes expression and genome-wide association studies. Conclusion The proposed method has the practical advantages, over existing approaches, to consider complex heterogeneities in the alternative hypothesis, to take into account prior information (from an expert judgment or previous studies by allowing a semi-supervised mode, and to deal with truncated distributions such as those obtained in Monte-Carlo simulations. This method has been implemented and is available through the R package kerfdr via the CRAN or at http://stat.genopole.cnrs.fr/software/kerfdr.
SEMI PARAMETRIC ESTIMATION OF RISK-RETURN RELATIONSHIPS
Juan Carlos Escanciano; Juan Carlos Pardo-Fernández; Ingrid Van Keilegom
2013-01-01
This article proposes semi-parametric least squares estimation of parametric risk-return relationships, i.e. parametric restrictions between the conditional mean and the conditional variance of excess returns given a set of unobservable parametric factors. A distinctive feature of our estimator is that it does not require a parametric model for the conditional mean and variance. We establish consistency and asymptotic normality of the estimates. The theory is non-standard due to the presence ...
Non-Parametric Estimation of Correlation Functions
DEFF Research Database (Denmark)
Brincker, Rune; Rytter, Anders; Krenk, Steen
In this paper three methods of non-parametric correlation function estimation are reviewed and evaluated: the direct method, estimation by the Fast Fourier Transform and finally estimation by the Random Decrement technique. The basic ideas of the techniques are reviewed, sources of bias are pointed...... out, and methods to prevent bias are presented. The techniques are evaluated by comparing their speed and accuracy on the simple case of estimating auto-correlation functions for the response of a single degree-of-freedom system loaded with white noise....
SYNTHESIZED EXPECTED BAYESIAN METHOD OF PARAMETRIC ESTIMATE
Institute of Scientific and Technical Information of China (English)
Ming HAN; Yuanyao DING
2004-01-01
This paper develops a new method of parametric estimate, which is named as "synthesized expected Bayesian method". When samples of products are tested and no failure events occur, thedefinition of expected Bayesian estimate is introduced and the estimates of failure probability and failure rate are provided. After some failure information is introduced by making an extra-test, a synthesized expected Bayesian method is defined and used to estimate failure probability, failure rateand some other parameters in exponential distribution and Weibull distribution of populations. Finally,calculations are performed according to practical problems, which show that the synthesized expected Bayesian method is feasible and easy to operate.
Parametric Return Density Estimation for Reinforcement Learning
Morimura, Tetsuro; Kashima, Hisashi; Hachiya, Hirotaka; Tanaka, Toshiyuki
2012-01-01
Most conventional Reinforcement Learning (RL) algorithms aim to optimize decision- making rules in terms of the expected re- turns. However, especially for risk man- agement purposes, other risk-sensitive crite- ria such as the value-at-risk or the expected shortfall are sometimes preferred in real ap- plications. Here, we describe a parametric method for estimating density of the returns, which allows us to handle various criteria in a unified manner. We first extend the Bellman equation for the conditional expected return to cover a conditional probability density of the returns. Then we derive an extension of the TD-learning algorithm for estimating the return densities in an unknown environment. As test instances, several parametric density estimation algorithms are presented for the Gaussian, Laplace, and skewed Laplace dis- tributions. We show that these algorithms lead to risk-sensitive as well as robust RL paradigms through numerical experiments.
Parametric estimation of ultra wideband radar targets
Institute of Scientific and Technical Information of China (English)
Fan Ping; Jing Zhanrong
2009-01-01
Based on the analysis of impulse response properties, a scattering model of ultra wideband (UWB) radar targets is developed to estimate the target parameters exactly. With this model, two algorithms of multiple signal classification (MUSIC), and matrix pencil (MP), are introduced to calculate the scattering center parame-ters of targets and their performances are compared. The simulation experiments show that there are no differ-ences in the estimation precision of MUSIC and MP methods when the signal-to-noise ratio (SNR) is larger than 13 dB. However, the MP method has a better performance than that of MUSIC method when the SNR is smaller than 13 dB. Besides, the time consuming of MP method is leas than that of MUSIC method. Therefore, the MP algorithm is preferred for the parametric estimation of UWB radar targets.
Group Parametrized Tunneling and Local Symmetry Conditions
Harter, William; Mitchell, Justin
2010-06-01
Recently, Hougen showed an ad hoc symmetry-based parameterization scheme for analyzing tunneling dynamics and high resolution spectra of fluxional molecular structure similar to S-parameter analysis of superfine structure in SF_6 or NH_3 maser inversion dynamics by Feynman et.al. The problem is that ad hoc parametrization, like path integration in general, can lead to logjams of parameters or ``paths'' with no way to pick out the relevant ones. We show a way to identify and use relevant parameters for a tunneling Hamiltonian H having global G-symmetry-defined bases by first expressing H as a linear combination bar γ ^i {bar g}_i of operators in dual symmetry group bar G. The coefficients bar γ ^i are parameters that define a complete set of allowed paths for any H with G-symmetry and are related thru spectral decomposition of G to eigensolutions of H. Quantum G vs.bar G duality generalizes lab -vs. -body and state -vs. -particle. The number of relevant bar γ ^i-parameters is reduced if a system tends to stick in states of a local symmetry subgroup LsubsetG so the H spectrum forms level clusters labeled by induced representations d(ℓ)(L)\\uparrowG. A cluster-(ℓ) has one E(epsilon)-level labeled by G species (epsilon) for each L species (ℓ) in Depsilon(G)downarrowL by Frobenius reciprocity. Then we apply local symmetry conditions to each irrep Depsilon(bar γ ^i {bar g}_i) that has already been reduced with respect to local symmetry L. This amounts to setting each off-diagonal component Dj,kepsilon(H) to zero. Local symmetry conditions may tell which bar γ ^i-parameters are redundant or zero and directly determine d(ℓ)\\uparrowG tunneling matrix eigenvalues that give E(epsilon)-levels as well as eigenvectors. Otherwise one may need to choose a particular localizing subgroup chain LsubsetL_1subsetL_2...G and further reduce the number of path parameters to facilitate spectral fitting. J.T. Hougen, 2009 MSS RJ01, {J Mol Spect 123, 197 (1987) W.G. Harter and
Parametric resonance of intrinsic localized modes in coupled cantilever arrays
Kimura, Masayuki; Matsushita, Yasuo; Hikihara, Takashi
2016-08-01
In this study, the parametric resonances of pinned intrinsic localized modes (ILMs) were investigated by computing the unstable regions in parameter space consisting of parametric excitation amplitude and frequency. In the unstable regions, the pinned ILMs were observed to lose stability and begin to fluctuate. A nonlinear Klein-Gordon, Fermi-Pasta-Ulam-like, and mixed lattices were investigated. The pinned ILMs, particularly in the mixed lattice, were destabilized by parametric resonances, which were determined by comparing the shapes of the unstable regions with those in the Mathieu differential equation. In addition, traveling ILMs could be generated by parametric excitation.
Distributed estimation of a parametric field with random sensor placements
Alkhweldi, Marwan; Cao, Zhicheng; Schmid, Natalia A.
2015-05-01
This paper considers a problem of distributed function estimation in the case when sensor locations are modeled as Gaussian random variables. We consider a scenario where sensors are deployed in clusters with cluster centers known a priori (or estimated by a high performance GPS) and the average quadratic spread of sensors around the cluster center also known. Distributed sensors make noisy observations about an unknown parametric field generated by a physical object of interest (for example, magnetic field generated by a ferrous object and sensed by a network of magnetometers). Each sensor then performs local signal processing of its noisy observation and sends it to a central processor (called fusion center) in the wireless sensor network over parallel channels corrupted by fading and additive noise. The central processor combines the set of received signals to form an estimate of the unknown parametric field. In our numerical analysis, we involve a field shaped as a Gaussian bell. We experiment with the size of sensor clusters and with their number. A mean square error between the estimated parameters of the field and the true parameters used in simulations is involved as a performance measure. It can be shown that a relatively good estimate of the field can be obtained with only a small number of clusters. As the number of clusters increases, the estimation performance steadily improves. The results also indicate that, on the average, the number of clusters has more impact on the performance than the number of sensors per cluster, given the same size of the total network.
Estimating Mutual Information by Local Gaussian Approximation
Gao, Shuyang; Galstyan, Aram
2015-01-01
Estimating mutual information (MI) from samples is a fundamental problem in statistics, machine learning, and data analysis. Recently it was shown that a popular class of non-parametric MI estimators perform very poorly for strongly dependent variables and have sample complexity that scales exponentially with the true MI. This undesired behavior was attributed to the reliance of those estimators on local uniformity of the underlying (and unknown) probability density function. Here we present a novel semi-parametric estimator of mutual information, where at each sample point, densities are {\\em locally} approximated by a Gaussians distribution. We demonstrate that the estimator is asymptotically unbiased. We also show that the proposed estimator has a superior performance compared to several baselines, and is able to accurately measure relationship strengths over many orders of magnitude.
Kernel bandwidth estimation for non-parametric density estimation: a comparative study
CSIR Research Space (South Africa)
Van der Walt, CM
2013-12-01
Full Text Available We investigate the performance of conventional bandwidth estimators for non-parametric kernel density estimation on a number of representative pattern-recognition tasks, to gain a better understanding of the behaviour of these estimators in high...
Parametric estimation of medical care costs under conditions of censoring
Raikou, Maria; McGuire, Alistair
2009-01-01
This paper is concerned with a set of parametric estimators that attempt to provide consistent estimates of average medical care costs under conditions of censoring. The main finding is that incorporation of the inverse of the probability of an individual not being censored in the estimating equations is instrumental in deriving unbiased cost estimates. The success of the approach is dependent on the amount of available information on the cost history process. The value of this information in...
Error-Bars in Semi-Parametric Estimation
Van Ormondt, D.; Van der Veen, J.W.C.; Sima, D.M.; Graveron-Demilly, D.
In in vivo metabolite-quantitation with a magnetic resonance spectroscopy (MRS) scanner, the model function of the attendant MRS signal is often only partly known. This unfavourable condition requires semi-parametric estimation. In the present study the unknown part is the form of the decay function
Estimation of Parametric Fault in Closed-loop Systems
DEFF Research Database (Denmark)
Niemann, Hans Henrik; Poulsen, Niels Kjølstad
2015-01-01
The aim of this paper is to present a method for estimation of parametric faults in closed-loop systems. The key technology applied in this paper is coprime factorization of both the dynamic system as well as the feedback controller. Using the Youla-Jabr-Bongiorno-Kucera (YJBK) parameterization...
Non-Parametric Bayesian State Space Estimator for Negative Information
Directory of Open Access Journals (Sweden)
Guillaume de Chambrier
2017-09-01
Full Text Available Simultaneous Localization and Mapping (SLAM is concerned with the development of filters to accurately and efficiently infer the state parameters (position, orientation, etc. of an agent and aspects of its environment, commonly referred to as the map. A mapping system is necessary for the agent to achieve situatedness, which is a precondition for planning and reasoning. In this work, we consider an agent who is given the task of finding a set of objects. The agent has limited perception and can only sense the presence of objects if a direct contact is made, as a result most of the sensing is negative information. In the absence of recurrent sightings or direct measurements of objects, there are no correlations from the measurement errors that can be exploited. This renders SLAM estimators, for which this fact is their backbone such as EKF-SLAM, ineffective. In addition for our setting, no assumptions are taken with respect to the marginals (beliefs of both the agent and objects (map. From the loose assumptions we stipulate regarding the marginals and measurements, we adopt a histogram parametrization. We introduce a Bayesian State Space Estimator (BSSE, which we name Measurement Likelihood Memory Filter (MLMF, in which the values of the joint distribution are not parametrized but instead we directly apply changes from the measurement integration step to the marginals. This is achieved by keeping track of the history of likelihood functions’ parameters. We demonstrate that the MLMF gives the same filtered marginals as a histogram filter and show two implementations: MLMF and scalable-MLMF that both have a linear space complexity. The original MLMF retains an exponential time complexity (although an order of magnitude smaller than the histogram filter while the scalable-MLMF introduced independence assumption such to have a linear time complexity. We further quantitatively demonstrate the scalability of our algorithm with 25 beliefs having up to
DEFF Research Database (Denmark)
Nielsen, Morten Ø.; Frederiksen, Per Houmann
2005-01-01
In this paper we compare through Monte Carlo simulations the finite sample properties of estimators of the fractional differencing parameter, d. This involves frequency domain, time domain, and wavelet based approaches, and we consider both parametric and semiparametric estimation methods....... The estimators are briefly introduced and compared, and the criteria adopted for measuring finite sample performance are bias and root mean squared error. Most importantly, the simulations reveal that (1) the frequency domain maximum likelihood procedure is superior to the time domain parametric methods, (2) all...... the estimators are fairly robust to conditionally heteroscedastic errors, (3) the local polynomial Whittle and bias-reduced log-periodogram regression estimators are shown to be more robust to short-run dynamics than other semiparametric (frequency domain and wavelet) estimators and in some cases even outperform...
Directory of Open Access Journals (Sweden)
Jingjing Wu
2015-01-01
Full Text Available A robust particle filter (PF and its application to fault/defect detection of nonlinear system are investigated in this paper. First, an adaptive parametric model is exploited as the observation model for a nonlinear system. Second, by incorporating the parametric model, particle filter is employed to estimate more accurate hidden states for the nonlinear stochastic system. Third, by formulating the problem of defect detection within the hypothesis testing framework, the statistical properties of the proposed testing are established. Finally, experimental results demonstrate the effectiveness and robustness of the proposed detector on real defect detection and localization in images.
Touil, Sami; Degre, Aurore; Nacer Chabaca, Mohamed
2016-12-01
Improving the accuracy of pedotransfer functions (PTFs) requires studying how prediction uncertainty can be apportioned to different sources of uncertainty in inputs. In this study, the question addressed was as follows: which variable input is the main or best complementary predictor of water retention, and at which water potential? Two approaches were adopted to generate PTFs: multiple linear regressions (MLRs) for point PTFs and multiple nonlinear regressions (MNLRs) for parametric PTFs. Reliability tests showed that point PTFs provided better estimates than parametric PTFs (root mean square error, RMSE: 0.0414 and 0.0444 cm3 cm-3, and 0.0613 and 0.0605 cm3 cm-3 at -33 and -1500 kPa, respectively). The local parametric PTFs provided better estimates than Rosetta PTFs at -33 kPa. No significant difference in accuracy, however, was found between the parametric PTFs and Rosetta H2 at -1500 kPa with RMSE values of 0.0605 cm3 cm-3 and 0.0636 cm3 cm-3, respectively. The results of global sensitivity analyses (GSAs) showed that the mathematical formalism of PTFs and their input variables reacted differently in terms of point pressure and texture. The point and parametric PTFs were sensitive mainly to the sand fraction in the fine- and medium-textural classes. The use of clay percentage (C %) and bulk density (BD) as inputs in the medium-textural class improved the estimation of PTFs at -33 kPa.
Entropy-based parametric estimation of spike train statistics
Vasquez, J C; Viéville, T
2010-01-01
We consider the evolution of a network of neurons, focusing on the asymptotic behavior of spikes dynamics instead of membrane potential dynamics. The spike response is not sought as a deterministic response in this context, but as a conditional probability : "Reading out the code" consists of inferring such a probability. This probability is computed from empirical raster plots, by using the framework of thermodynamic formalism in ergodic theory. This gives us a parametric statistical model where the probability has the form of a Gibbs distribution. In this respect, this approach generalizes the seminal and profound work of Schneidman and collaborators. A minimal presentation of the formalism is reviewed here, while a general algorithmic estimation method is proposed yielding fast convergent implementations. It is also made explicit how several spike observables (entropy, rate, synchronizations, correlations) are given in closed-form from the parametric estimation. This paradigm does not only allow us to esti...
Parametric Variations Sensitivity Analysis on IM Discrete Speed Estimation
Directory of Open Access Journals (Sweden)
Mohamed BEN MESSAOUD
2007-09-01
Full Text Available Motivation: This paper will discuss sensitivity issues in rotor speed estimation for induction machine (IM drives using only voltage and current measurements. A supervised estimation algorithm is proposed with the aim to achieve good performances in the large variations of the speed. After a brief presentation on discrete feedback structure of the estimator formulated from d-q axis equations, we will expose its performances for machine parameters variations.Method: Hyperstability concept was applied to the synthesis adaptation low. A heuristic term is added to the algorithm to maintain good speed estimation factor in high speeds.Results: In simulation, the estimation error is maintained relatively low in wide range of speeds, and the robustness of the estimation algorithm is shown for machine parametric variations.Conclusions: Sensitivity analysis to motor parameter changes of proposed sensorless IM is then performed.
A novel SURE-based criterion for parametric PSF estimation.
Xue, Feng; Blu, Thierry
2015-02-01
We propose an unbiased estimate of a filtered version of the mean squared error--the blur-SURE (Stein's unbiased risk estimate)--as a novel criterion for estimating an unknown point spread function (PSF) from the degraded image only. The PSF is obtained by minimizing this new objective functional over a family of Wiener processings. Based on this estimated blur kernel, we then perform nonblind deconvolution using our recently developed algorithm. The SURE-based framework is exemplified with a number of parametric PSF, involving a scaling factor that controls the blur size. A typical example of such parametrization is the Gaussian kernel. The experimental results demonstrate that minimizing the blur-SURE yields highly accurate estimates of the PSF parameters, which also result in a restoration quality that is very similar to the one obtained with the exact PSF, when plugged into our recent multi-Wiener SURE-LET deconvolution algorithm. The highly competitive results obtained outline the great potential of developing more powerful blind deconvolution algorithms based on SURE-like estimates.
Adaptive Parametric Spectral Estimation with Kalman Smoothing for Online Early Seizure Detection
Park, Yun S.; Hochberg, Leigh R.; Eskandar, Emad N.; Cash, Sydney S.; Truccolo, Wilson
2014-01-01
Tracking spectral changes in neural signals, such as local field potentials (LFPs) and scalp or intracranial electroencephalograms (EEG, iEEG), is an important problem in early detection and prediction of seizures. Most approaches have focused on either parametric or nonparametric spectral estimation methods based on moving time windows. Here, we explore an adaptive (time-varying) parametric ARMA approach for tracking spectral changes in neural signals based on the fixed-interval Kalman smoother. We apply the method to seizure detection based on spectral features of intracortical LFPs recorded from a person with pharmacologically intractable focal epilepsy. We also devise and test an approach for real-time tracking of spectra based on the adaptive parametric method with the fixed-interval Kalman smoother. The order of ARMA models is determined via the AIC computed in moving time windows. We quantitatively demonstrate the advantages of using the adaptive parametric estimation method in seizure detection over nonparametric alternatives based exclusively on moving time windows. Overall, the adaptive parametric approach significantly improves the statistical separability of interictal and ictal epochs. PMID:24663686
A non-parametric framework for estimating threshold limit values
Directory of Open Access Journals (Sweden)
Ulm Kurt
2005-11-01
Full Text Available Abstract Background To estimate a threshold limit value for a compound known to have harmful health effects, an 'elbow' threshold model is usually applied. We are interested on non-parametric flexible alternatives. Methods We describe how a step function model fitted by isotonic regression can be used to estimate threshold limit values. This method returns a set of candidate locations, and we discuss two algorithms to select the threshold among them: the reduced isotonic regression and an algorithm considering the closed family of hypotheses. We assess the performance of these two alternative approaches under different scenarios in a simulation study. We illustrate the framework by analysing the data from a study conducted by the German Research Foundation aiming to set a threshold limit value in the exposure to total dust at workplace, as a causal agent for developing chronic bronchitis. Results In the paper we demonstrate the use and the properties of the proposed methodology along with the results from an application. The method appears to detect the threshold with satisfactory success. However, its performance can be compromised by the low power to reject the constant risk assumption when the true dose-response relationship is weak. Conclusion The estimation of thresholds based on isotonic framework is conceptually simple and sufficiently powerful. Given that in threshold value estimation context there is not a gold standard method, the proposed model provides a useful non-parametric alternative to the standard approaches and can corroborate or challenge their findings.
Parametric localized modes in quadratic nonlinear photonic structures
DEFF Research Database (Denmark)
Sukhorukov, Andrey A.; Kivshar, Yuri S.; Bang, Ole;
2001-01-01
We analyze two-color spatially localized nonlinear modes formed by parametrically coupled fundamental and second-harmonic fields excited at quadratic (or chi2) nonlinear interfaces embedded in a linear layered structure-a quadratic nonlinear photonic crystal. For a periodic lattice of nonlinear...... interfaces, we derive an effective discrete model for the amplitudes of the fundamental and second-harmonic waves at the interfaces (the so-called discrete chi2 equations) and find, numerically and analytically, the spatially localized solutions-discrete gap solitons. For a single nonlinear interface...... in a linear superlattice, we study the properties of two-color localized modes, and describe both similarities to and differences from quadratic solitons in homogeneous media....
DEFF Research Database (Denmark)
Montazeri, Najmeh; Nielsen, Ulrik Dam
2014-01-01
the ship’s wave-induced responses based on different statistical inferences including parametric and non-parametric approaches. This paper considers a concept to improve the estimate obtained by the parametric method for sea state estimation. The idea is illustrated by an analysis made on full-scale...
Non-parametric estimation of Fisher information from real data
Shemesh, Omri Har; Miñano, Borja; Hoekstra, Alfons G; Sloot, Peter M A
2015-01-01
The Fisher Information matrix is a widely used measure for applications ranging from statistical inference, information geometry, experiment design, to the study of criticality in biological systems. Yet there is no commonly accepted non-parametric algorithm to estimate it from real data. In this rapid communication we show how to accurately estimate the Fisher information in a nonparametric way. We also develop a numerical procedure to minimize the errors by choosing the interval of the finite difference scheme necessary to compute the derivatives in the definition of the Fisher information. Our method uses the recently published "Density Estimation using Field Theory" algorithm to compute the probability density functions for continuous densities. We use the Fisher information of the normal distribution to validate our method and as an example we compute the temperature component of the Fisher Information Matrix in the two dimensional Ising model and show that it obeys the expected relation to the heat capa...
Parametric estimation of discretely sampled Gamma-OU processes
Institute of Scientific and Technical Information of China (English)
ZHANG Shibin; ZHANG Xinsheng; SUN Shuguang
2006-01-01
The stationary Gamma-OU processes are recommended to be the volatility of the financial assets. A parametric estimation for the Gamma-OU processes based on the discrete observations is considered in this paper. The estimator of an intensity parameter λ and its convergence result are given, and the simulations show that the estimation is quite accurate. Assuming that the parameter λ is estimated, the maximum likelihood estimation of shape parameter c and scale parameter α, whose likelihood function is not explicitly computable, is considered. By means of the Gaver-Stehfest algorithm, we construct an explicit sequence of approximations to the likelihood function and show that it converges the true (but unkown) one. Maximizing the sequence results in an estimator that converges to the true maximum likelihood estimator and the approximation shares the asymptotic properties of the true maximum likelihood estimator. Some simulation experiments reveal that this method is still quite accurate in most of rational situations for the background of volatility.
Quantiles, parametric-select density estimation, and bi-information parameter estimators
Parzen, E.
1982-01-01
A quantile-based approach to statistical analysis and probability modeling of data is presented which formulates statistical inference problems as functional inference problems in which the parameters to be estimated are density functions. Density estimators can be non-parametric (computed independently of model identified) or parametric-select (approximated by finite parametric models that can provide standard models whose fit can be tested). Exponential models and autoregressive models are approximating densities which can be justified as maximum entropy for respectively the entropy of a probability density and the entropy of a quantile density. Applications of these ideas are outlined to the problems of modeling: (1) univariate data; (2) bivariate data and tests for independence; and (3) two samples and likelihood ratios. It is proposed that bi-information estimation of a density function can be developed by analogy to the problem of identification of regression models.
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
considered. It is shown that adaptive estimation in conditional parametric models can be performed by combining the well known methods of local polynomial regression and recursive least squares with exponential forgetting. The approach used for estimation in conditional parametric models also highlights how....... For this purpose non-parametric methods together with additive models are suggested. Also, a new approach specifically designed to detect non-linearities is introduced. Confidence intervals are constructed by use of bootstrapping. As a link between non-parametric and parametric methods a paper dealing with neural...... the focus is on combinations of parametric and non-parametric methods of regression. This combination can be in terms of additive models where e.g. one or more non-parametric term is added to a linear regression model. It can also be in terms of conditional parametric models where the coefficients...
Blom, Philip S.; Marcillo, Omar E.
2017-03-01
A method is developed to apply acoustic tomography methods to a localized network of infrasound arrays with intention of monitoring the atmosphere state in the region around the network using non-local sources without requiring knowledge of the precise source location or non-local atmosphere state. Closely spaced arrays provide a means to estimate phase velocities of signals that can provide limiting bounds on certain characteristics of the atmosphere. Larger spacing between such clusters provide a means to estimate celerity from propagation times along multiple unique stratospherically or thermospherically ducted propagation paths and compute more precise estimates of the atmosphere state. In order to avoid the commonly encountered complex, multimodal distributions for parametric atmosphere descriptions and to maximize the computational efficiency of the method, an optimal parametrization framework is constructed. This framework identifies the ideal combination of parameters for tomography studies in specific regions of the atmosphere and statistical model selection analysis shows that high quality corrections to the middle atmosphere winds can be obtained using as few as three parameters. Comparison of the resulting estimates for synthetic data sets shows qualitative agreement between the middle atmosphere winds and those estimated from infrasonic traveltime observations.
A parametric estimation approach to instantaneous spectral imaging.
Oktem, Figen S; Kamalabadi, Farzad; Davila, Joseph M
2014-12-01
Spectral imaging, the simultaneous imaging and spectroscopy of a radiating scene, is a fundamental diagnostic technique in the physical sciences with widespread application. Due to the intrinsic limitation of two-dimensional (2D) detectors in capturing inherently three-dimensional (3D) data, spectral imaging techniques conventionally rely on a spatial or spectral scanning process, which renders them unsuitable for dynamic scenes. In this paper, we present a nonscanning (instantaneous) spectral imaging technique that estimates the physical parameters of interest by combining measurements with a parametric model and solving the resultant inverse problem computationally. The associated inverse problem, which can be viewed as a multiframe semiblind deblurring problem (with shift-variant blur), is formulated as a maximum a posteriori (MAP) estimation problem since in many such experiments prior statistical knowledge of the physical parameters can be well estimated. Subsequently, an efficient dynamic programming algorithm is developed to find the global optimum of the nonconvex MAP problem. Finally, the algorithm and the effectiveness of the spectral imaging technique are illustrated for an application in solar spectral imaging. Numerical simulation results indicate that the physical parameters can be estimated with the same order of accuracy as state-of-the-art slit spectroscopy but with the added benefit of an instantaneous, 2D field-of-view. This technique will be particularly useful for studying the spectra of dynamic scenes encountered in space remote sensing.
Linear minimax estimation for random vectors with parametric uncertainty
Bitar, E
2010-06-01
In this paper, we take a minimax approach to the problem of computing a worst-case linear mean squared error (MSE) estimate of X given Y , where X and Y are jointly distributed random vectors with parametric uncertainty in their distribution. We consider two uncertainty models, PA and PB. Model PA represents X and Y as jointly Gaussian whose covariance matrix Λ belongs to the convex hull of a set of m known covariance matrices. Model PB characterizes X and Y as jointly distributed according to a Gaussian mixture model with m known zero-mean components, but unknown component weights. We show: (a) the linear minimax estimator computed under model PA is identical to that computed under model PB when the vertices of the uncertain covariance set in PA are the same as the component covariances in model PB, and (b) the problem of computing the linear minimax estimator under either model reduces to a semidefinite program (SDP). We also consider the dynamic situation where x(t) and y(t) evolve according to a discrete-time LTI state space model driven by white noise, the statistics of which is modeled by PA and PB as before. We derive a recursive linear minimax filter for x(t) given y(t).
DEFF Research Database (Denmark)
Nielsen, Morten Ø.; Frederiksen, Per Houmann
2005-01-01
In this paper we compare through Monte Carlo simulations the finite sample properties of estimators of the fractional differencing parameter, d. This involves frequency domain, time domain, and wavelet based approaches, and we consider both parametric and semiparametric estimation methods. The es...... the time domain parametric methods, and (4) without sufficient trimming of scales the wavelet-based estimators are heavily biased.......In this paper we compare through Monte Carlo simulations the finite sample properties of estimators of the fractional differencing parameter, d. This involves frequency domain, time domain, and wavelet based approaches, and we consider both parametric and semiparametric estimation methods....... The estimators are briefly introduced and compared, and the criteria adopted for measuring finite sample performance are bias and root mean squared error. Most importantly, the simulations reveal that (1) the frequency domain maximum likelihood procedure is superior to the time domain parametric methods, (2) all...
A Study on Parametric Wave Estimation Based on Measured Ship Motions
DEFF Research Database (Denmark)
Nielsen, Ulrik Dam; Iseki, Toshio
2011-01-01
The paper studies parametric wave estimation based on the ‘wave buoy analogy’, and data and results obtained from the training ship Shioji-maru are compared with estimates of the sea states obtained from other measurements and observations. Furthermore, the estimating characteristics of the param......The paper studies parametric wave estimation based on the ‘wave buoy analogy’, and data and results obtained from the training ship Shioji-maru are compared with estimates of the sea states obtained from other measurements and observations. Furthermore, the estimating characteristics...... of the parametric model are discussed by considering the results of a similar estimation concept based on Bayesian modelling. The purpose of the latter comparison is not to favour the one estimation approach to the other but rather to highlight some of the advantages and disadvantages of the two approaches....
Non-parametric star formation histories for 5 dwarf spheroidal galaxies of the local group
Hernández, X; Valls-Gabaud, D; Gilmore, Gerard; Valls-Gabaud, David
2000-01-01
We use recent HST colour-magnitude diagrams of the resolved stellar populations of a sample of local dSph galaxies (Carina, LeoI, LeoII, Ursa Minor and Draco) to infer the star formation histories of these systems, $SFR(t)$. Applying a new variational calculus maximum likelihood method which includes a full Bayesian analysis and allows a non-parametric estimate of the function one is solving for, we infer the star formation histories of the systems studied. This method has the advantage of yielding an objective answer, as one need not assume {\\it a priori} the form of the function one is trying to recover. The results are checked independently using Saha's $W$ statistic. The total luminosities of the systems are used to normalize the results into physical units and derive SN type II rates. We derive the luminosity weighted mean star formation history of this sample of galaxies.
DEFF Research Database (Denmark)
Montazeri, Najmeh; Nielsen, Ulrik Dam; Jensen, Jørgen Juncher
2016-01-01
Shipboard wave estimation has been of interest in recent years for the purpose of decision support. In this paper, estimation of sea state is studied using ship responses and a parametric description of directional wave spectra. A set of parameters, characterising a given wave spectrum is estimated...
Parametric methods for estimating covariate-dependent reference limits.
Virtanen, Arja; Kairisto, Veli; Uusipaikka, Esa
2004-01-01
Age-specific reference limits are required for many clinical laboratory measurements. Statistical assessment of calculated intervals must be performed to obtain reliable reference limits. When parametric, covariate-dependent limits are derived, normal distribution theory usually is applied due to its mathematical simplicity and relative ease of fitting. However, it is not always possible to transform data and achieve a normal distribution. Therefore, models other than those based on normal distribution theory are needed. Generalized linear model theory offers one such alternative. Regardless of the statistical model used, the assumptions behind the model should always be examined.
Guo, Qing; Sun, Ping; Yin, Jing-Min; Yu, Tian; Jiang, Dan
2016-05-01
Some unknown parameter estimation of electro-hydraulic system (EHS) should be considered in hydraulic controller design due to many parameter uncertainties in practice. In this study, a parametric adaptive backstepping control method is proposed to improve the dynamic behavior of EHS under parametric uncertainties and unknown disturbance (i.e., hydraulic parameters and external load). The unknown parameters of EHS model are estimated by the parametric adaptive estimation law. Then the recursive backstepping controller is designed by Lyapunov technique to realize the displacement control of EHS. To avoid explosion of virtual control in traditional backstepping, a decayed memory filter is presented to re-estimate the virtual control and the dynamic external load. The effectiveness of the proposed controller has been demonstrated by comparison with the controller without adaptive and filter estimation. The comparative experimental results in critical working conditions indicate the proposed approach can achieve better dynamic performance on the motion control of Two-DOF robotic arm.
Local polynomial Whittle estimation of perturbed fractional processes
DEFF Research Database (Denmark)
Frederiksen, Per; Nielsen, Frank; Nielsen, Morten Ørregaard
We propose a semiparametric local polynomial Whittle with noise (LPWN) estimator of the memory parameter in long memory time series perturbed by a noise term which may be serially correlated. The estimator approximates the spectrum of the perturbation as well as that of the short-memory component...... for d ε (0, 3/4), and if the spectral density is infinitely smooth near frequency zero, the rate of convergence can become arbitrarily close to the parametric rate, pn. A Monte Carlo study reveals that the LPWN estimator performs well in the presence of a serially correlated perturbation term....... Furthermore, an empirical investigation of the 30 DJIA stocks shows that this estimator indicates stronger persistence in volatility than the standard local Whittle estimator....
Purely nonlinear disorder-induced localizations and their parametric amplification
Folli, Viola; Conti, Claudio
2013-01-01
We investigate spatial localization in a quadratic nonlinear medium in the presence of randomness. By means of numerical simulations and theoretical analyses we show that, in the down conversion regime, the transverse random modulation of the nonlinear susceptibility generates localizations of the fundamental wave that grow exponentially in propagation. The localization length is optically controlled by the pump intensity which determines the amplification rate. The results also apply to cubic nonlinearities.
Improved estimation in a non-Gaussian parametric regression
Pchelintsev, Evgeny
2011-01-01
The paper considers the problem of estimating the parameters in a continuous time regression model with a non-Gaussian noise of pulse type. The noise is specified by the Ornstein-Uhlenbeck process driven by the mixture of a Brownian motion and a compound Poisson process. Improved estimates for the unknown regression parameters, based on a special modification of the James-Stein procedure with smaller quadratic risk than the usual least squares estimates, are proposed. The developed estimation scheme is applied for the improved parameter estimation in the discrete time regression with the autoregressive noise depending on unknown nuisance parameters.
A Parametric Procedure for Ultrametric Tree Estimation from Conditional Rank Order Proximity Data.
Young, Martin R.; DeSarbo, Wayne S.
1995-01-01
A new parametric maximum likelihood procedure is proposed for estimating ultrametric trees for the analysis of conditional rank order proximity data. Technical aspects of the model and the estimation algorithm are discussed, and Monte Carlo results illustrate its application. A consumer psychology application is also examined. (SLD)
Parametric optimization of optical devices based on strong photonic localization
Gui, Minmin; Yang, Xiangbo
2017-07-01
Symmetric two-segment-connected triangular defect waveguide networks (STSCTDWNs) can produce strong photonic localization, which is useful for designing highly efficient energy storage devices, high power superluminescent light emitting diodes, all-optical switches, and more. Although STSCTDWNs have been studied in previous works, in this paper we systematically optimize the parameters of STSCTDWNs to further enhance photonic localization so that the function of optical devices based on strong photonic localization can be improved. When optimizing the parameters, we find a linear relationship between the logarithm of photonic localization and the broken degree of networks. Furthermore, the slope and intercept of the linear relationship are larger than previous results. This means that the increasing speed of photonic localization is improved. The largest intensity of photonic localizations can reach 1036, which is 16 orders of magnitude larger than previous reported results. These optimized networks provide practical solutions for all optical devices based on strong photonic localization in the low frequency range, such as nanostructured devices.
Markov chain order estimation with parametric significance tests of conditional mutual information
Papapetrou, Maria
2015-01-01
Besides the different approaches suggested in the literature, accurate estimation of the order of a Markov chain from a given symbol sequence is an open issue, especially when the order is moderately large. Here, parametric significance tests of conditional mutual information (CMI) of increasing order $m$, $I_c(m)$, on a symbol sequence are conducted for increasing orders $m$ in order to estimate the true order $L$ of the underlying Markov chain. CMI of order $m$ is the mutual information of two variables in the Markov chain being $m$ time steps apart, conditioning on the intermediate variables of the chain. The null distribution of CMI is approximated with a normal and gamma distribution deriving analytic expressions of their parameters, and a gamma distribution deriving its parameters from the mean and variance of the normal distribution. The accuracy of order estimation is assessed with the three parametric tests, and the parametric tests are compared to the randomization significance test and other known ...
Parametric excitation of multiple resonant radiations from localized wavepackets
Conforti, Matteo; Mussot, Arnaud; Kudlinski, Alexandre
2015-01-01
Fundamental physical phenomena such as laser-induced ionization, driven quantum tunneling, Faraday waves, Bogoliubov quasiparticle excitations, and the control of new states of matter rely on time-periodic driving of the system. A remarkable property of such driving is that it can induce the localized (bound) states to resonantly couple to the continuum. Therefore experiments that allow for enlightening and controlling the mechanisms underlying such coupling are of paramount importance. We implement such an experiment in a special fiber optics system characterized by a dispersion oscillating along the propagation coordinate, which mimics "time". The quasi-momentum associated with such periodic perturbation is responsible for the efficient coupling of energy from the localized wave-packets sustained by the fiber nonlinearity into free-running linear dispersive waves (continuum), at multiple resonant frequencies. Remarkably, the observed resonances can be explained by means of a unified approach, regardless of ...
The Role of Parametric Assumptions in Adaptive Bayesian Estimation
Alcala-Quintana, Rocio; Garcia-Perez, Miguel A.
2004-01-01
Variants of adaptive Bayesian procedures for estimating the 5% point on a psychometric function were studied by simulation. Bias and standard error were the criteria to evaluate performance. The results indicated a superiority of (a) uniform priors, (b) model likelihood functions that are odd symmetric about threshold and that have parameter…
Non-parametric PSF estimation from celestial transit solar images using blind deconvolution
González, Adriana; Delouille, Véronique; Jacques, Laurent
2016-01-01
Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. The measured image in a real optical instrument is usually represented by the convolution of an ideal image with a Point Spread Function (PSF). Additionally, the image acquisition process is also contaminated by other sources of noise (read-out, photon-counting). The problem of estimating both the PSF and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, our method does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis prior model on the image and weak assumptions on the PSF. We use observations from a celestial transit, where the occulting body can be assumed to be a black disk. These constraints allow us to retain meaningful solutions for the filter and the image, eliminating trivial, translated, and interchanged solutions. Under an additive Gaussian noise assumption, they also enforce noise canceling and avoid reconstruction artifacts by promoting the whiteness of the residual between the blurred observations and the cleaned data. Results: Our method is applied to synthetic and experimental data. The PSF is estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for SDO/AIA using the 2012 Venus transit. Results show that the proposed non-parametric blind deconvolution method is able to estimate the core of the PSF with a similar quality to parametric methods proposed in the literature. We also show that, if these parametric estimations are incorporated in the acquisition model, the resulting PSF outperforms both the parametric and non-parametric methods.
Non-parametric PSF estimation from celestial transit solar images using blind deconvolution
Directory of Open Access Journals (Sweden)
González Adriana
2016-01-01
Full Text Available Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. The measured image in a real optical instrument is usually represented by the convolution of an ideal image with a Point Spread Function (PSF. Additionally, the image acquisition process is also contaminated by other sources of noise (read-out, photon-counting. The problem of estimating both the PSF and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, our method does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis prior model on the image and weak assumptions on the PSF. We use observations from a celestial transit, where the occulting body can be assumed to be a black disk. These constraints allow us to retain meaningful solutions for the filter and the image, eliminating trivial, translated, and interchanged solutions. Under an additive Gaussian noise assumption, they also enforce noise canceling and avoid reconstruction artifacts by promoting the whiteness of the residual between the blurred observations and the cleaned data. Results: Our method is applied to synthetic and experimental data. The PSF is estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for SDO/AIA using the 2012 Venus transit. Results show that the proposed non-parametric blind deconvolution method is able to estimate the core of the PSF with a similar quality to parametric methods proposed in the literature. We also show that, if these parametric estimations are incorporated in the acquisition model, the resulting PSF outperforms both the parametric and non-parametric methods.
Parametric algorithms for frequency estimation in PWM converter systems
Energy Technology Data Exchange (ETDEWEB)
Lobos, T.; Hejke, I.; Rezmer, J.; Sikorski, T.; Kostyla, P. [Dept. of Electrical Engineering, Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw (Poland)
2009-11-15
Proper estimation of voltage parameters has a significant meaning for power converters control systems. One of the measured parameter, directly associated with control process, is frequency of the basic voltage component at the converter output. Unfortunately, power converters generate a wide spectrum of harmonics as well as interharmonics, that makes difficulties in estimation this frequency. Measurement carried out in industrial applications exhibits complex nature of investigated signals often corrupted by resonances or overvoltages phenomena. It entails searching a new digital algorithms for frequency measurement. This work presents algorithm using modification of high-resolution Prony method connected with digital filtering. The investigations was based on simulations and real measured signals recorded in two different commercial PWM converters. Obtained results indicate accuracy of the method as well as its fast response. (author)
Non-parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean-reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Non-Parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
2003-01-01
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean--reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Non-Parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
2003-01-01
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean--reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Non-parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean-reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Parametrically guided estimation in nonparametric varying coefficient models with quasi-likelihood.
Davenport, Clemontina A; Maity, Arnab; Wu, Yichao
2015-04-01
Varying coefficient models allow us to generalize standard linear regression models to incorporate complex covariate effects by modeling the regression coefficients as functions of another covariate. For nonparametric varying coefficients, we can borrow the idea of parametrically guided estimation to improve asymptotic bias. In this paper, we develop a guided estimation procedure for the nonparametric varying coefficient models. Asymptotic properties are established for the guided estimators and a method of bandwidth selection via bias-variance tradeoff is proposed. We compare the performance of the guided estimator with that of the unguided estimator via both simulation and real data examples.
Estimating Financial Risk Measures for Futures Positions:A Non-Parametric Approach
Cotter, John; dowd, kevin
2011-01-01
This paper presents non-parametric estimates of spectral risk measures applied to long and short positions in 5 prominent equity futures contracts. It also compares these to estimates of two popular alternative measures, the Value-at-Risk (VaR) and Expected Shortfall (ES). The spectral risk measures are conditioned on the coefficient of absolute risk aversion, and the latter two are conditioned on the confidence level. Our findings indicate that all risk measures increase dramatically and the...
Principles of parametric estimation in modeling language competition.
Zhang, Menghan; Gong, Tao
2013-06-11
It is generally difficult to define reasonable parameters and interpret their values in mathematical models of social phenomena. Rather than directly fitting abstract parameters against empirical data, we should define some concrete parameters to denote the sociocultural factors relevant for particular phenomena, and compute the values of these parameters based upon the corresponding empirical data. Taking the example of modeling studies of language competition, we propose a language diffusion principle and two language inheritance principles to compute two critical parameters, namely the impacts and inheritance rates of competing languages, in our language competition model derived from the Lotka-Volterra competition model in evolutionary biology. These principles assign explicit sociolinguistic meanings to those parameters and calculate their values from the relevant data of population censuses and language surveys. Using four examples of language competition, we illustrate that our language competition model with thus-estimated parameter values can reliably replicate and predict the dynamics of language competition, and it is especially useful in cases lacking direct competition data.
Local Polynomial Estimation of Distribution Functions
Institute of Scientific and Technical Information of China (English)
LI Yong-hong; ZENG Xia
2007-01-01
Under the condition that the total distribution function is continuous and bounded on (-∞,∞), we constructed estimations for distribution and hazard functions with local polynomial method, and obtained the rate of strong convergence of the estimations.
Direct Parametric Reconstruction With Joint Motion Estimation/Correction for Dynamic Brain PET Data.
Jiao, Jieqing; Bousse, Alexandre; Thielemans, Kris; Burgos, Ninon; Weston, Philip S J; Schott, Jonathan M; Atkinson, David; Arridge, Simon R; Hutton, Brian F; Markiewicz, Pawel; Ourselin, Sebastien
2017-01-01
Direct reconstruction of parametric images from raw photon counts has been shown to improve the quantitative analysis of dynamic positron emission tomography (PET) data. However it suffers from subject motion which is inevitable during the typical acquisition time of 1-2 hours. In this work we propose a framework to jointly estimate subject head motion and reconstruct the motion-corrected parametric images directly from raw PET data, so that the effects of distorted tissue-to-voxel mapping due to subject motion can be reduced in reconstructing the parametric images with motion-compensated attenuation correction and spatially aligned temporal PET data. The proposed approach is formulated within the maximum likelihood framework, and efficient solutions are derived for estimating subject motion and kinetic parameters from raw PET photon count data. Results from evaluations on simulated [(11)C]raclopride data using the Zubal brain phantom and real clinical [(18)F]florbetapir data of a patient with Alzheimer's disease show that the proposed joint direct parametric reconstruction motion correction approach can improve the accuracy of quantifying dynamic PET data with large subject motion.
truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models
Directory of Open Access Journals (Sweden)
Maria Karlsson
2014-05-01
Full Text Available Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and ?nite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.
Local parametric instability near elliptic points in vortex flows under shear deformation
Energy Technology Data Exchange (ETDEWEB)
Koshel, Konstantin V., E-mail: kvkoshel@poi.dvo.ru [Pacific Oceanological Institute, FEB RAS, 43, Baltiyskaya Street, Vladivostok 690041 (Russian Federation); Institute of Applied Mathematics, FEB RAS, 7, Radio Street, Vladivostok 690022 (Russian Federation); Far Eastern Federal University, 8, Sukhanova Street, Vladivostok 690950 (Russian Federation); Ryzhov, Eugene A., E-mail: ryzhovea@gmail.com [Pacific Oceanological Institute, FEB RAS, 43, Baltiyskaya Street, Vladivostok 690041 (Russian Federation)
2016-08-15
The dynamics of two point vortices embedded in an oscillatory external flow consisted of shear and rotational components is addressed. The region associated with steady-state elliptic points of the vortex motion is established to experience local parametric instability. The instability forces the point vortices with initial positions corresponding to the steady-state elliptic points to move in spiral-like divergent trajectories. This divergent motion continues until the nonlinear effects suppress their motion near the region associated with the steady-state separatrices. The local parametric instability is then demonstrated not to contribute considerably to enhancing the size of the chaotic motion regions. Instead, the size of the chaotic motion region mostly depends on overlaps of the nonlinear resonances emerging in the perturbed system.
An estimating equation for parametric shared frailty models with marginal additive hazards
DEFF Research Database (Denmark)
Pipper, Christian Bressen; Martinussen, Torben
2004-01-01
Multivariate failure time data arise when data consist of clusters in which the failure times may be dependent. A popular approach to such data is the marginal proportional hazards model with estimation under the working independence assumption. In some contexts, however, it may be more reasonable...... to use the marginal additive hazards model. We derive asymptotic properties of the Lin and Ying estimators for the marginal additive hazards model for multivariate failure time data. Furthermore we suggest estimating equations for the regression parameters and association parameters in parametric shared...
Chacón, R.; García-Hoz, A. Martínez; Martínez, J. A.
2017-05-01
We study the effectiveness of locally controlling the impulse transmitted by parametric periodic excitations at inducing and suppressing chaos in starlike networks of driven damped pendula, leading to asynchronous chaotic states and equilibria, respectively. We found that the inducing (suppressor) effect of increasing (decreasing) the impulse transmitted by the parametric excitations acting on particular nodes depends strongly on their number and degree of connectivity as well as the coupling strength. Additionally, we provide a theoretical analysis explaining the basic physical mechanisms of the emergence and suppression of chaos as well as the main features of the chaos-control scenario. Our findings constitute proof of the impulse-induced control of chaos in a simple model of complex networks, thus opening the way to its application to real-world networks.
Parametric estimation of the Duffing system by using a modified gradient algorithm
Energy Technology Data Exchange (ETDEWEB)
Aguilar-Ibanez, Carlos [CIC-IPN, Av. Juan de Dios Batiz s/n Esq. Manuel Othon de M. Unidad Profesional Adolfo Lopez Mateos, Col. San Pedro Zacatenco, A.P. 75476, Mexico, D.F. 07700 (Mexico)], E-mail: caguilar@cic.ipn.mx; Sanchez Herrera, Jorge [CIC-IPN, Av. Juan de Dios Batiz s/n Esq. Manuel Othon de M. Unidad Profesional Adolfo Lopez Mateos, Col. San Pedro Zacatenco, A.P. 75476, Mexico, D.F. 07700 (Mexico)], E-mail: rguerra@ctrl.cinvestav.mx; Garrido-Moctezuma, Ruben [CINVESTAV-IPN, Departamento de Control Automatico, Av. IPN 2508, A.P. 14740, Mexico, D.F. 07360 (Mexico)
2008-01-14
The Letter presents a strategy for recovering the unknown parameters of the Duffing oscillator using a measurable output signal. The suggested approach employs the construction of an integral parametrization of one auxiliary output. It is calculated by measuring the difference between the output and its respective delay output. First we estimate the auxiliary output, followed by the application of a modified gradient algorithm, then we adjust the gains of the proposed linear estimator, until this error converges to zero. The convergence of the proposed scheme is shown using Lyapunov method.
Estimation in semi-parametric regression with non-stationary regressors
Chen, Jia; Li, Degui; 10.3150/10-BEJ344
2012-01-01
In this paper, we consider a partially linear model of the form $Y_t=X_t^{\\tau}\\theta_0+g(V_t)+\\epsilon_t$, $t=1,...,n$, where $\\{V_t\\}$ is a $\\beta$ null recurrent Markov chain, $\\{X_t\\}$ is a sequence of either strictly stationary or non-stationary regressors and $\\{\\epsilon_t\\}$ is a stationary sequence. We propose to estimate both $\\theta_0$ and $g(\\cdot)$ by a semi-parametric least-squares (SLS) estimation method. Under certain conditions, we then show that the proposed SLS estimator of $\\theta_0$ is still asymptotically normal with the same rate as for the case of stationary time series. In addition, we also establish an asymptotic distribution for the nonparametric estimator of the function $g(\\cdot)$. Some numerical examples are provided to show that our theory and estimation method work well in practice.
Flexible parametric modelling of cause-specific hazards to estimate cumulative incidence functions
2013-01-01
Background Competing risks are a common occurrence in survival analysis. They arise when a patient is at risk of more than one mutually exclusive event, such as death from different causes, and the occurrence of one of these may prevent any other event from ever happening. Methods There are two main approaches to modelling competing risks: the first is to model the cause-specific hazards and transform these to the cumulative incidence function; the second is to model directly on a transformation of the cumulative incidence function. We focus on the first approach in this paper. This paper advocates the use of the flexible parametric survival model in this competing risk framework. Results An illustrative example on the survival of breast cancer patients has shown that the flexible parametric proportional hazards model has almost perfect agreement with the Cox proportional hazards model. However, the large epidemiological data set used here shows clear evidence of non-proportional hazards. The flexible parametric model is able to adequately account for these through the incorporation of time-dependent effects. Conclusion A key advantage of using this approach is that smooth estimates of both the cause-specific hazard rates and the cumulative incidence functions can be obtained. It is also relatively easy to incorporate time-dependent effects which are commonly seen in epidemiological studies. PMID:23384310
Parametric fault estimation based on H∞ optimization in a satellite launch vehicle
DEFF Research Database (Denmark)
Soltani, Mohsen; Izadi-Zamanabadi, Roozbeh; Stoustrup, Jakob
2008-01-01
Correct diagnosis under harsh environmental conditions is crucial for space vehiclespsila health management systems to avoid possible hazardous situations. Consequently, the diagnosis methods are required to be robust toward these conditions. Design of a parametric fault detector, where the fault...... estimation is formulated in the so-called standard set-up for Hinfin control design problem, is addressed in this paper. In particular, we investigate the tunability of the design through the dedicated choice of the fault model. The method is applied to the model of turbopump as a subsystem of the jet engine...... for the satellite launch vehicle and the results are discussed....
Directory of Open Access Journals (Sweden)
S. Touil
2016-12-01
0.0636 cm3 cm−3, respectively. The results of global sensitivity analyses (GSAs showed that the mathematical formalism of PTFs and their input variables reacted differently in terms of point pressure and texture. The point and parametric PTFs were sensitive mainly to the sand fraction in the fine- and medium-textural classes. The use of clay percentage (C % and bulk density (BD as inputs in the medium-textural class improved the estimation of PTFs at −33 kPa.
Wang, Ying; Wu, Fengchang; Giesy, John P; Feng, Chenglian; Liu, Yuedan; Qin, Ning; Zhao, Yujie
2015-09-01
Due to use of different parametric models for establishing species sensitivity distributions (SSDs), comparison of water quality criteria (WQC) for metals of the same group or period in the periodic table is uncertain and results can be biased. To address this inadequacy, a new probabilistic model, based on non-parametric kernel density estimation was developed and optimal bandwidths and testing methods are proposed. Zinc (Zn), cadmium (Cd), and mercury (Hg) of group IIB of the periodic table are widespread in aquatic environments, mostly at small concentrations, but can exert detrimental effects on aquatic life and human health. With these metals as target compounds, the non-parametric kernel density estimation method and several conventional parametric density estimation methods were used to derive acute WQC of metals for protection of aquatic species in China that were compared and contrasted with WQC for other jurisdictions. HC5 values for protection of different types of species were derived for three metals by use of non-parametric kernel density estimation. The newly developed probabilistic model was superior to conventional parametric density estimations for constructing SSDs and for deriving WQC for these metals. HC5 values for the three metals were inversely proportional to atomic number, which means that the heavier atoms were more potent toxicants. The proposed method provides a novel alternative approach for developing SSDs that could have wide application prospects in deriving WQC and use in assessment of risks to ecosystems.
Estimating the loss in expectation of life due to cancer using flexible parametric survival models.
Andersson, Therese M-L; Dickman, Paul W; Eloranta, Sandra; Lambe, Mats; Lambert, Paul C
2013-12-30
A useful summary measure for survival data is the expectation of life, which is calculated by obtaining the area under a survival curve. The loss in expectation of life due to a certain type of cancer is the difference between the expectation of life in the general population and the expectation of life among the cancer patients. This measure is used little in practice as its estimation generally requires extrapolation of both the expected and observed survival. A parametric distribution can be used for extrapolation of the observed survival, but it is difficult to find a distribution that captures the underlying shape of the survival function after the end of follow-up. In this paper, we base our extrapolation on relative survival, because it is more stable and reliable. Relative survival is defined as the observed survival divided by the expected survival, and the mortality analogue is excess mortality. Approaches have been suggested for extrapolation of relative survival within life-table data, by assuming that the excess mortality has reached zero (statistical cure) or has stabilized to a constant. We propose the use of flexible parametric survival models for relative survival, which enables estimating the loss in expectation of life on individual level data by making these assumptions or by extrapolating the estimated linear trend at the end of follow-up. We have evaluated the extrapolation from this model using data on four types of cancer, and the results agree well with observed data.
Saarela, Olli; Liu, Zhihui Amy
2016-10-15
Marginal structural Cox models are used for quantifying marginal treatment effects on outcome event hazard function. Such models are estimated using inverse probability of treatment and censoring (IPTC) weighting, which properly accounts for the impact of time-dependent confounders, avoiding conditioning on factors on the causal pathway. To estimate the IPTC weights, the treatment assignment mechanism is conventionally modeled in discrete time. While this is natural in situations where treatment information is recorded at scheduled follow-up visits, in other contexts, the events specifying the treatment history can be modeled in continuous time using the tools of event history analysis. This is particularly the case for treatment procedures, such as surgeries. In this paper, we propose a novel approach for flexible parametric estimation of continuous-time IPTC weights and illustrate it in assessing the relationship between metastasectomy and mortality in metastatic renal cell carcinoma patients. Copyright © 2016 John Wiley & Sons, Ltd.
Non-parametric Estimation approach in statistical investigation of nuclear spectra
Jafarizadeh, M A; Sabri, H; Maleki, B Rashidian
2011-01-01
In this paper, Kernel Density Estimation (KDE) as a non-parametric estimation method is used to investigate statistical properties of nuclear spectra. The deviation to regular or chaotic dynamics, is exhibited by closer distances to Poisson or Wigner limits respectively which evaluated by Kullback-Leibler Divergence (KLD) measure. Spectral statistics of different sequences prepared by nuclei corresponds to three dynamical symmetry limits of Interaction Boson Model(IBM), oblate and prolate nuclei and also the pairing effect on nuclear level statistics are analyzed (with pure experimental data). KD-based estimated density function, confirm previous predictions with minimum uncertainty (evaluated with Integrate Absolute Error (IAE)) in compare to Maximum Likelihood (ML)-based method. Also, the increasing of regularity degrees of spectra due to pairing effect is reveal.
Similarity-based semi-local estimation of EMOS models
Lerch, Sebastian
2015-01-01
Weather forecasts are typically given in the form of forecast ensembles obtained from multiple runs of numerical weather prediction models with varying initial conditions and physics parameterizations. Such ensemble predictions tend to be biased and underdispersive and thus require statistical postprocessing. In the ensemble model output statistics (EMOS) approach, a probabilistic forecast is given by a single parametric distribution with parameters depending on the ensemble members. This article proposes two semi-local methods for estimating the EMOS coefficients where the training data for a specific observation station are augmented with corresponding forecast cases from stations with similar characteristics. Similarities between stations are determined using either distance functions or clustering based on various features of the climatology, forecast errors, ensemble predictions and locations of the observation stations. In a case study on wind speed over Europe with forecasts from the Grand Limited Area...
Edwards, Jessie K.; McGrath, Leah J.; Buckley, Jessie P.; Schubauer-Berigan, Mary K.; Cole, Stephen R.; Richardson, David B.
2015-01-01
Background Traditional regression analysis techniques used to estimate associations between occupational radon exposure and lung cancer focus on estimating the effect of cumulative radon exposure on lung cancer, while public health interventions are typically based on regulating radon concentration rather than workers’ cumulative exposure. Moreover, estimating the direct effect of cumulative occupational exposure on lung cancer may be difficult in situations vulnerable to the healthy worker survivor bias. Methods Workers in the Colorado Plateau Uranium Miners cohort (N=4,134) entered the study between 1950 and 1964 and were followed for lung cancer mortality through 2005. We use the parametric g-formula to compare the observed lung cancer mortality to the potential lung cancer mortality had each of 3 policies to limit monthly radon exposure been in place throughout follow-up. Results There were 617 lung cancer deaths over 135,275 person-years of follow-up. With no intervention on radon exposure, estimated lung cancer mortality by age 90 was 16%. Lung cancer mortality was reduced for all interventions considered, and larger reductions in lung cancer mortality were seen for interventions with lower monthly radon exposure limits. The most stringent guideline, the Mine Safety and Health Administration standard of 0.33 working level months, reduced lung cancer mortality from 16% to 10% (risk ratio 0.67; 95% confidence interval 0.61, 0.73). Conclusions This work illustrates the utility of the parametric g-formula for estimating the effects of policies regarding occupational exposures, particularly in situations vulnerable to the healthy worker survivor bias. PMID:25192403
Application of the LSQR algorithm in non-parametric estimation of aerosol size distribution
He, Zhenzong; Qi, Hong; Lew, Zhongyuan; Ruan, Liming; Tan, Heping; Luo, Kun
2016-05-01
Based on the Least Squares QR decomposition (LSQR) algorithm, the aerosol size distribution (ASD) is retrieved in non-parametric approach. The direct problem is solved by the Anomalous Diffraction Approximation (ADA) and the Lambert-Beer Law. An optimal wavelength selection method is developed to improve the retrieval accuracy of the ASD. The proposed optimal wavelength set is selected by the method which can make the measurement signals sensitive to wavelength and decrease the degree of the ill-condition of coefficient matrix of linear systems effectively to enhance the anti-interference ability of retrieval results. Two common kinds of monomodal and bimodal ASDs, log-normal (L-N) and Gamma distributions, are estimated, respectively. Numerical tests show that the LSQR algorithm can be successfully applied to retrieve the ASD with high stability in the presence of random noise and low susceptibility to the shape of distributions. Finally, the experimental measurement ASD over Harbin in China is recovered reasonably. All the results confirm that the LSQR algorithm combined with the optimal wavelength selection method is an effective and reliable technique in non-parametric estimation of ASD.
A non-parametric approach to estimate the total deviation index for non-normal data.
Perez-Jaume, Sara; Carrasco, Josep L
2015-11-10
Concordance indices are used to assess the degree of agreement between different methods that measure the same characteristic. In this context, the total deviation index (TDI) is an unscaled concordance measure that quantifies to which extent the readings from the same subject obtained by different methods may differ with a certain probability. Common approaches to estimate the TDI assume data are normally distributed and linearity between response and effects (subjects, methods and random error). Here, we introduce a new non-parametric methodology for estimation and inference of the TDI that can deal with any kind of quantitative data. The present study introduces this non-parametric approach and compares it with the already established methods in two real case examples that represent situations of non-normal data (more specifically, skewed data and count data). The performance of the already established methodologies and our approach in these contexts is assessed by means of a simulation study. Copyright © 2015 John Wiley & Sons, Ltd.
Institute of Scientific and Technical Information of China (English)
Juan J. Cuadrado Gallego; Daniel Rodríguez; Miguel (A)ngel Sicilia; Miguel Garre Rubio; Angel García Crespo
2007-01-01
Parametric software effort estimation models usually consists of only a single mathematical relationship. Withthe advent of software repositories containing data from heterogeneous projects, these types of models suffer from pooradjustment and predictive accuracy. One possible way to alleviate this problem is the use of a set of mathematical equationsobtained through dividing of the historical project datasets according to different parameters into subdatasets called parti-tions. In turn, partitions are divided into clusters that serve as a tool for more accurate models. In this paper, we describethe process, tool and results of such approach through a case study using a publicly available repository, ISBSG. Resultssuggest the adequacy of the technique as an extension of existing single-expression models without making the estimationprocess much more complex that uses a single estimation model. A tool to support the process is also presented.
A Robust Subpixel Motion Estimation Algorithm Using HOS in the Parametric Domain
Directory of Open Access Journals (Sweden)
E. M. Ismaili Aalaoui
2009-02-01
Full Text Available Motion estimation techniques are widely used in todays video processing systems. The most frequently used techniques are the optical flow method and phase correlation method. The vast majority of these algorithms consider noise-free data. Thus, in the case of the image sequences are severely corrupted by additive Gaussian (perhaps non-Gaussian noises of unknown covariance, the classical techniques will fail to work because they will also estimate the noise spatial correlation. In this paper, we have studied this topic from a viewpoint different from the above to explore the fundamental limits in image motion estimation. Our scheme is based on subpixel motion estimation algorithm using bispectrum in the parametric domain. The motion vector of a moving object is estimated by solving linear equations involving third-order hologram and the matrix containing Dirac delta function. Simulation results are presented and compared to the optical flow and phase correlation algorithms; this approach provides more reliable displacement estimates particularly for complex noisy image sequences. In our simulation, we used the database freely available on the web.
A Robust Subpixel Motion Estimation Algorithm Using HOS in the Parametric Domain
Directory of Open Access Journals (Sweden)
Ibn-Elhaj E
2009-01-01
Full Text Available Motion estimation techniques are widely used in todays video processing systems. The most frequently used techniques are the optical flow method and phase correlation method. The vast majority of these algorithms consider noise-free data. Thus, in the case of the image sequences are severely corrupted by additive Gaussian (perhaps non-Gaussian noises of unknown covariance, the classical techniques will fail to work because they will also estimate the noise spatial correlation. In this paper, we have studied this topic from a viewpoint different from the above to explore the fundamental limits in image motion estimation. Our scheme is based on subpixel motion estimation algorithm using bispectrum in the parametric domain. The motion vector of a moving object is estimated by solving linear equations involving third-order hologram and the matrix containing Dirac delta function. Simulation results are presented and compared to the optical flow and phase correlation algorithms; this approach provides more reliable displacement estimates particularly for complex noisy image sequences. In our simulation, we used the database freely available on the web.
Dexter, Troy A; Kowalewski, Michał
2013-12-01
Quantitative estimates of growth rates can augment ecological and paleontological applications of body-size data. However, in contrast to body-size estimates, assessing growth rates is often time-consuming, expensive, or unattainable. Here we use an indirect approach, a jackknife-corrected parametric bootstrap, for efficient approximation of growth rates using nearest living relatives with known age-size relationships. The estimate is developed by (1) collecting a sample of published growth rates of closely related species, (2) calculating the average growth curve using those published age-size relationships, (3) resampling iteratively these empirically known growth curves to estimate the standard errors and confidence bands around the average growth curve, and (4) applying the resulting estimate of uncertainty to bracket age-size relationships of the species of interest. This approach was applied to three monophyletic families (Donacidae, Mactridae, and Semelidae) of mollusk bivalves, a group characterized by indeterministic shell growth, but widely used in ecological, paleontological, and geochemical research. The resulting indirect estimates were tested against two previously published geochemical studies and, in both cases, yielded highly congruent age estimates. In addition, a case study in applied fisheries was used to illustrate the potential of the proposed approach for augmenting aquaculture management practices. The resulting estimates of growth rates place body size data in a constrained temporal context and confidence intervals associated with resampling estimates allow for assessing the statistical uncertainty around derived temporal ranges. The indirect approach should allow for improved evaluation of diverse research questions, from sustainability of industrial shellfish harvesting to climatic interpretations of stable isotope proxies extracted from fossil skeletons.
Preliminary investigation to estimate soil NAPL retention using parametric pedotransfer functions
Hernádi, Hilda; Makó, András
2014-10-01
Organic liquid retention of soils is a primary input variable for modelling the nonaqueous phase liquid transport and behaviour in the subsurface. In environmental and soil physical practice, it is mainly determined by scaling based on the water retention of soils or with charts of average empirical values of organic liquid retention or the fitting parameters of hydraulic functions. Predicting the fitting parameters of organic liquid retention curves with pedotransfer functions might be a promising alternative method, but this topic has only been researched to a limited extent. Thus we investigated the applicability of different hydraulic functions (3- and 4- parameter form of the van Genuchten equation and Brutsaert equation) for fitting organic liquid retention characteristics. Multivariate linear regression was used to build and develop pedotransfer functions, modelling relations between original and transformed values of basic soil properties and organic liquid retention. We attempted to generate parametric pedotransfer functions. According to our results, the applicability of hydraulic functions for fitting nonaqueous phase liquid retention curves to the experimental data was proven. The investigations gave promising results for the possibility to estimate soil nonaqueous phase liquid retention with parametric pedotransfer functions.
A Comparison of Parametric and Sample-Based Message Representation in Cooperative Localization
Directory of Open Access Journals (Sweden)
Jaime Lien
2012-01-01
Full Text Available Location awareness is a key enabling feature and fundamental challenge in present and future wireless networks. Most existing localization methods rely on existing infrastructure and thus lack the flexibility and robustness necessary for large ad hoc networks. In this paper, we build upon SPAWN (sum-product algorithm over a wireless network, which determines node locations through iterative message passing, but does so at a high computational cost. We compare different message representations for SPAWN in terms of performance and complexity and investigate several types of cooperation based on censoring. Our results, based on experimental data with ultra-wideband (UWB nodes, indicate that parametric message representation combined with simple censoring can give excellent performance at relatively low complexity.
Energy Technology Data Exchange (ETDEWEB)
Boubal, O.; Oksman, J. [Ecole Superieure d' Electricite, 91 - Gif-sur-Yvette (France)
1999-07-01
Knock on spark ignition engines goes against car manufacturers efforts to reduce fuel consumption and exhaust gas emissions. This article develops a signal processing method to quantify knock. After discussing some classical techniques of knock energy estimation, an acoustical measurement technique is presented. An original signal processing method based on a parametric behavioral model for both knock and apparatus and a special inversion technique are used to get actual knock parameters. The knock related parameters are computed in a two step process. A deconvolution algorithm is used to obtain a signal made of unitary pulses, followed by an efficient inversion method. The whole process is applied to real data from a one-cylinder engine. Moreover, the results are compared to those obtained from an existing technique to suit a common industrial application. (authors)
Applications of parametric spectral estimation methods on detection of power system harmonics
Energy Technology Data Exchange (ETDEWEB)
Yilmaz, Ahmet S. [Kahramanmaras Sutcu Imam University, Department of Electrical and Electronics Engineering, Kahramanmaras (Turkey); Alkan, Ahmet; Asyali, Musa H. [Yasar University, Department of Computer Engineering, Izmir (Turkey)
2008-04-15
Harmonics are the major power quality problems in industrial and commercial power systems. Several methods for detection of power system harmonics have been investigated by engineers due to increasing harmonic pollution. Since the non-integer multiple harmonics (inter and sub-harmonics) become wide spread, the importance of harmonic detection has increased for sensitive filtration. This paper suggests parametric spectral estimation methods for the detection of harmonics, inter-harmonics and sub-harmonics. Yule Walker, Burg, Covariance and Modified Covariance methods are applied to generate cases. Not only integer multiple harmonics but also non-integer multiple harmonics are successfully determined in the computer simulations. Further, performances of proposed methods are compared with each other in terms of frequency resolution. (author)
Directory of Open Access Journals (Sweden)
Meyer Karin
2001-11-01
Full Text Available Abstract A random regression model for the analysis of "repeated" records in animal breeding is described which combines a random regression approach for additive genetic and other random effects with the assumption of a parametric correlation structure for within animal covariances. Both stationary and non-stationary correlation models involving a small number of parameters are considered. Heterogeneity in within animal variances is modelled through polynomial variance functions. Estimation of parameters describing the dispersion structure of such model by restricted maximum likelihood via an "average information" algorithm is outlined. An application to mature weight records of beef cow is given, and results are contrasted to those from analyses fitting sets of random regression coefficients for permanent environmental effects.
Energy Technology Data Exchange (ETDEWEB)
Ray, Jaideep; Lee, Jina; Lefantzi, Sophia; Yadav, Vineet [Carnegie Institution for Science, Stanford, CA; Michalak, Anna M. [Carnegie Institution for Science, Stanford, CA; van Bloemen Waanders, Bart Gustaaf [Sandia National Laboratories, Albuquerque, NM; McKenna, Sean Andrew [IBM Research, Mulhuddart, Dublin 15, Ireland
2013-04-01
The estimation of fossil-fuel CO2 emissions (ffCO2) from limited ground-based and satellite measurements of CO2 concentrations will form a key component of the monitoring of treaties aimed at the abatement of greenhouse gas emissions. To that end, we construct a multiresolution spatial parametrization for fossil-fuel CO2 emissions (ffCO2), to be used in atmospheric inversions. Such a parametrization does not currently exist. The parametrization uses wavelets to accurately capture the multiscale, nonstationary nature of ffCO2 emissions and employs proxies of human habitation, e.g., images of lights at night and maps of built-up areas to reduce the dimensionality of the multiresolution parametrization. The parametrization is used in a synthetic data inversion to test its suitability for use in atmospheric inverse problem. This linear inverse problem is predicated on observations of ffCO2 concentrations collected at measurement towers. We adapt a convex optimization technique, commonly used in the reconstruction of compressively sensed images, to perform sparse reconstruction of the time-variant ffCO2 emission field. We also borrow concepts from compressive sensing to impose boundary conditions i.e., to limit ffCO2 emissions within an irregularly shaped region (the United States, in our case). We find that the optimization algorithm performs a data-driven sparsification of the spatial parametrization and retains only of those wavelets whose weights could be estimated from the observations. Further, our method for the imposition of boundary conditions leads to a 10computational saving over conventional means of doing so. We conclude with a discussion of the accuracy of the estimated emissions and the suitability of the spatial parametrization for use in inverse problems with a significant degree of regularization.
Directory of Open Access Journals (Sweden)
Eloranta Sandra
2011-06-01
Full Text Available Abstract Background When the mortality among a cancer patient group returns to the same level as in the general population, that is, the patients no longer experience excess mortality, the patients still alive are considered "statistically cured". Cure models can be used to estimate the cure proportion as well as the survival function of the "uncured". One limitation of parametric cure models is that the functional form of the survival of the "uncured" has to be specified. It can sometimes be hard to find a survival function flexible enough to fit the observed data, for example, when there is high excess hazard within a few months from diagnosis, which is common among older age groups. This has led to the exclusion of older age groups in population-based cancer studies using cure models. Methods Here we have extended the flexible parametric survival model to incorporate cure as a special case to estimate the cure proportion and the survival of the "uncured". Flexible parametric survival models use splines to model the underlying hazard function, and therefore no parametric distribution has to be specified. Results We have compared the fit from standard cure models to our flexible cure model, using data on colon cancer patients in Finland. This new method gives similar results to a standard cure model, when it is reliable, and better fit when the standard cure model gives biased estimates. Conclusions Cure models within the framework of flexible parametric models enables cure modelling when standard models give biased estimates. These flexible cure models enable inclusion of older age groups and can give stage-specific estimates, which is not always possible from parametric cure models.
Non-Parametric Evolutionary Algorithm for Estimating Root Zone Soil Moisture
Mohanty, B.; Shin, Y.; Ines, A. M.
2013-12-01
Prediction of root zone soil moisture is critical for water resources management. In this study, we explored a non-parametric evolutionary algorithm for estimating root zone soil moisture from a time series of spatially-distributed rainfall across multiple weather locations under two different hydro-climatic regions. A new genetic algorithm-based hidden Markov model (HMMGA) was developed to estimate long-term root zone soil moisture dynamics at different soil depths. Also, we analyzed rainfall occurrence probabilities and dry/wet spell lengths reproduced by this approach. The HMMGA was used to estimate the optimal state sequences (weather states) based on the precipitation history. Historical root zone soil moisture statistics were then determined based on the weather state conditions. To test the new approach, we selected two different soil moisture fields, Oklahoma (130 km x 130 km) and Illinois (300 km x 500 km), during 1995 to 2009 and 1994 to 2010, respectively. We found that the newly developed framework performed well in predicting root zone soil moisture dynamics at both the spatial scales. Also, the reproduced rainfall occurrence probabilities and dry/wet spell lengths matched well with the observations at the spatio-temporal scales. Since the proposed algorithm requires only precipitation and historical soil moisture data from existing, established weather stations, it can serve an attractive alternative for predicting root zone soil moisture in the future using climate change scenarios and root zone soil moisture history.
Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan
2012-01-01
Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.
The binned bispectrum estimator: template-based and non-parametric CMB non-Gaussianity searches
Bucher, Martin; van Tent, Bartjan
2015-01-01
We describe the details of the binned bispectrum estimator as used for the official 2013 and 2015 analyses of the temperature and polarization CMB maps from the ESA Planck satellite. The defining aspect of this estimator is the determination of a map bispectrum (3-point correlator) that has been binned in harmonic space. For a parametric determination of the non-Gaussianity in the map (the so-called fNL parameters), one takes the inner product of this binned bispectrum with theoretically motivated templates. However, as a complementary approach one can also smooth the binned bispectrum using a variable smoothing scale in order to suppress noise and make coherent features stand out above the noise. This allows one to look in a model-independent way for any statistically significant bispectral signal. This approach is useful for characterizing the bispectral shape of the galactic foreground emission, for which a theoretical prediction of the bispectral anisotropy is lacking, and for detecting a serendipitous pr...
Gugushvili, S.; Spreij, P.
2016-01-01
We consider the problem of non-parametric estimation of the deterministic dispersion coefficient of a linear stochastic differential equation based on discrete time observations on its solution. We take a Bayesian approach to the problem and under suitable regularity assumptions derive the posteror
Local polynomial Whittle estimation covering non-stationary fractional processes
DEFF Research Database (Denmark)
Nielsen, Frank
This paper extends the local polynomial Whittle estimator of Andrews & Sun (2004) to fractionally integrated processes covering stationary and non-stationary regions. We utilize the notion of the extended discrete Fourier transform and periodogram to extend the local polynomial Whittle estimator ...... study illustrates the performance of the proposed estimator compared to the classical local Whittle estimator and the local polynomial Whittle estimator. The empirical justi.cation of the proposed estimator is shown through an analysis of credit spreads....
Angelis, Georgios I; Matthews, Julian C; Kotasidis, Fotis A; Markiewicz, Pawel J; Lionheart, William R; Reader, Andrew J
2014-11-01
Estimation of nonlinear micro-parameters is a computationally demanding and fairly challenging process, since it involves the use of rather slow iterative nonlinear fitting algorithms and it often results in very noisy voxel-wise parametric maps. Direct reconstruction algorithms can provide parametric maps with reduced variance, but usually the overall reconstruction is impractically time consuming with common nonlinear fitting algorithms. In this work we employed a recently proposed direct parametric image reconstruction algorithm to estimate the parametric maps of all micro-parameters of a two-tissue compartment model, used to describe the kinetics of [[Formula: see text]F]FDG. The algorithm decouples the tomographic and the kinetic modelling problems, allowing the use of previously developed post-reconstruction methods, such as the generalised linear least squares (GLLS) algorithm. Results on both clinical and simulated data showed that the proposed direct reconstruction method provides considerable quantitative and qualitative improvements for all micro-parameters compared to the conventional post-reconstruction fitting method. Additionally, region-wise comparison of all parametric maps against the well-established filtered back projection followed by post-reconstruction non-linear fitting, as well as the direct Patlak method, showed substantial quantitative agreement in all regions. The proposed direct parametric reconstruction algorithm is a promising approach towards the estimation of all individual microparameters of any compartment model. In addition, due to the linearised nature of the GLLS algorithm, the fitting step can be very efficiently implemented and, therefore, it does not considerably affect the overall reconstruction time.
Low default credit scoring using two-class non-parametric kernel density estimation
CSIR Research Space (South Africa)
Rademeyer, E
2016-12-01
Full Text Available This paper investigates the performance of two-class classification credit scoring data sets with low default ratios. The standard two-class parametric Gaussian and non-parametric Parzen classifiers are extended, using Bayes’ rule, to include either...
Fouré, Alexandre; Le Troter, Arnaud; Guye, Maxime; Mattei, Jean-Pierre; Bendahan, David; Gondin, Julien
2015-12-22
In the present study, we proposed an original and robust methodology which combines the spatial normalization of skeletal muscle images, the statistical parametric mapping (SPM) analysis and the use of a specific parcellation in order to accurately localize and quantify the extent of skeletal muscle damage within the four heads of the quadriceps femoris. T2 maps of thigh muscles were characterized before, two (D2) and four (D4) days after 40 maximal isometric electrically-evoked contractions in 25 healthy young males. On the basis of SPM analysis of coregistrated T2 maps, the alterations were similarly detected at D2 and D4 in the superficial and distal regions of the vastus medialis (VM) whereas the proportion of altered muscle was higher in deep muscle regions of the vastus lateralis at D4 (deep: 35 ± 25%, superficial: 23 ± 15%) as compared to D2 (deep: 18 ± 13%, superficial: 17 ± 13%). The present methodology used for the first time on skeletal muscle would be of utmost interest to detect subtle intramuscular alterations not only for the diagnosis of muscular diseases but also for assessing the efficacy of potential therapeutic interventions and clinical treatment strategies.
Eskelson, Bianca N.I.; Hagar, Joan; Temesgen, Hailemariam
2012-01-01
Snags (standing dead trees) are an essential structural component of forests. Because wildlife use of snags depends on size and decay stage, snag density estimation without any information about snag quality attributes is of little value for wildlife management decision makers. Little work has been done to develop models that allow multivariate estimation of snag density by snag quality class. Using climate, topography, Landsat TM data, stand age and forest type collected for 2356 forested Forest Inventory and Analysis plots in western Washington and western Oregon, we evaluated two multivariate techniques for their abilities to estimate density of snags by three decay classes. The density of live trees and snags in three decay classes (D1: recently dead, little decay; D2: decay, without top, some branches and bark missing; D3: extensive decay, missing bark and most branches) with diameter at breast height (DBH) ≥ 12.7 cm was estimated using a nonparametric random forest nearest neighbor imputation technique (RF) and a parametric two-stage model (QPORD), for which the number of trees per hectare was estimated with a Quasipoisson model in the first stage and the probability of belonging to a tree status class (live, D1, D2, D3) was estimated with an ordinal regression model in the second stage. The presence of large snags with DBH ≥ 50 cm was predicted using a logistic regression and RF imputation. Because of the more homogenous conditions on private forest lands, snag density by decay class was predicted with higher accuracies on private forest lands than on public lands, while presence of large snags was more accurately predicted on public lands, owing to the higher prevalence of large snags on public lands. RF outperformed the QPORD model in terms of percent accurate predictions, while QPORD provided smaller root mean square errors in predicting snag density by decay class. The logistic regression model achieved more accurate presence/absence classification
DPRESS: Localizing estimates of predictive uncertainty
Directory of Open Access Journals (Sweden)
Clark Robert D
2009-07-01
conservative even when the training set was biased, but not excessively so. Conclusion DPRESS is a straightforward and powerful way to reliably estimate individual predictive uncertainties for compounds outside the training set based on their distance to the training set and the internal predictive uncertainty associated with its nearest neighbor in that set. It represents a sample-based, a posteriori approach to defining applicability domains in terms of localized uncertainty.
Non-parametric PSF estimation from celestial transit solar images using blind deconvolution
Gonzalez, Adriana; Jacques, Laurent
2016-01-01
Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. Optics are never perfect and the non-ideal path through the telescope is usually represented by the convolution of an ideal image with a Point Spread Function (PSF). Other sources of noise (read-out, Photon) also contaminate the image acquisition process. The problem of estimating both the PSF filter and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, it does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis image prior model and weak assumptions on the PSF filter's response. We use the observations from a celestial body transit where such object can be assumed to be a black disk. Such constraints limits the interchangeabil...
Local magnitude estimate at Mt. Etna
Directory of Open Access Journals (Sweden)
V. Maiolino
2005-06-01
Full Text Available In order to verify the duration magnitude MD we calculated local magnitude ML values of 288 earthquakes occurring from October 2002 to April 2003 at Mt. Etna. The analysis was computed at three digital stations of the permanent seismic network of Istituto Nazionale di Geofisica e Vulcanologia of Catania, using the relationship ML = logA+alog?-b, where A is maximum half-amplitude of the horizontal component of the seismic recording measured in mm and the term «+alog?-b» takes the place of the term «-logA0» of Richter relationship. In particular, a = 0.15 for ?<200 km, b=0.16 for ?<200 km. Duration magnitude MD values, moment magnitude MW values and other local magnitude values were compared. Differences between ML and MD were obtained for the strong seismic swarms occurring on October 27, during the onset of 2002-2003 Mt. Etna eruption, characterized by a high earthquake rate, with very strong events (seismogram results clipped in amplitude on drum recorder trace and high level of volcanic tremor, which not permit us to estimate the duration of the earthquakes correctly. ML and MD relationships were related and therefore a new relationship for MD is proposed. Cumulative strain release calculated after the eruption using ML values is about 1.75E+06 J1/2 higher than the one calculated using MD values.
污染线性模型的非参数估计%NON-PARAMETRIC ESTIMATION IN CONTAMINATED LINEAR MODEL
Institute of Scientific and Technical Information of China (English)
柴根象; 孙燕; 杨筱菡
2001-01-01
In this paper, the following contaminated linear model is considered: yi=(1-ε)xτiβ+zi, 1≤i≤n, where r.v.'s ｛yi｝ are contaminated with errors ｛zi｝. To assume that the errors have the finite moment of order 2 only. The non-parametric estimation of contaminated coefficient ε and regression parameter β are established, and the strong consistency and convergence rate almost surely of the estimators are obtained. A simulated example is also given to show the visual performance of the estimations.
Two-stage local M-estimation of additive models
Institute of Scientific and Technical Information of China (English)
JIANG JianCheng; LI JianTao
2008-01-01
This paper studies local M-estimation of the nonparametric components of additive models. A two-stage local M-estimation procedure is proposed for estimating the additive components and their derivatives. Under very mild conditions, the proposed estimators of each additive component and its derivative are jointly asymptotically normal and share the same asymptotic distributions as they would be if the other components were known. The established asymptotic results also hold for two particular local M-estimations: the local least squares and least absolute deviation estimations. However,for general two-stage local M-estimation with continuous and nonlinear ψ-functions, its implementation is time-consuming. To reduce the computational burden, one-step approximations to the two-stage local M-estimators are developed. The one-step estimators are shown to achieve the same efficiency as the fully iterative two-stage local M-estimators, which makes the two-stage local M-estimation more feasible in practice. The proposed estimators inherit the advantages and at the same time overcome the disadvantages of the local least-squares based smoothers. In addition, the practical implementation of the proposed estimation is considered in details. Simulations demonstrate the merits of the two-stage local M-estimation, and a real example illustrates the performance of the methodology.
Two-stage local M-estimation of additive models
Institute of Scientific and Technical Information of China (English)
2008-01-01
This paper studies local M-estimation of the nonparametric components of additive models.A two-stage local M-estimation procedure is proposed for estimating the additive components and their derivatives.Under very mild conditions,the proposed estimators of each additive component and its derivative are jointly asymptotically normal and share the same asymptotic distributions as they would be if the other components were known.The established asymptotic results also hold for two particular local M-estimations:the local least squares and least absolute deviation estimations.However,for general two-stage local M-estimation with continuous and nonlinear ψ-functions,its implementation is time-consuming.To reduce the computational burden,one-step approximations to the two-stage local M-estimators are developed.The one-step estimators are shown to achieve the same effciency as the fully iterative two-stage local M-estimators,which makes the two-stage local M-estimation more feasible in practice.The proposed estimators inherit the advantages and at the same time overcome the disadvantages of the local least-squares based smoothers.In addition,the practical implementation of the proposed estimation is considered in details.Simulations demonstrate the merits of the two-stage local M-estimation,and a real example illustrates the performance of the methodology.
Variable bandwidth and one-step local M-estimator
Institute of Scientific and Technical Information of China (English)
范剑青; 蒋建成
2000-01-01
A robust version of local linear regression smoothers augmented with variable bandwidth is studied. The proposed method inherits the advantages of local polynomial regression and overcomes the shortcoming of lack of robustness of least-squares techniques. The use of variable bandwidth enhances the flexibility of the resulting local M- estimators and makes them possible to cope well with spatially inho-mogeneous curves, heteroscedastic errors and nonuniform design densities. Under appropriate regularity conditions, it is shown that the proposed estimators exist and are asymptotically normal. Based on the robust estimation equation, one-step local M-estimators are introduced to reduce computational burden. It is demonstrated that the one-step local M-estimators share the same asymptotic distributions as the fully iterative M-estimators, as long as the initial estimators are good enough. In other words, the one-step local M-estimators reduce significantly the computation cost of the fully iterative M-estim
Local polynomial Whittle estimation of perturbed fractional processes
DEFF Research Database (Denmark)
Frederiksen, Per; Nielsen, Frank; Nielsen, Morten Ørregaard
for d ε (0, 3/4), and if the spectral density is infinitely smooth near frequency zero, the rate of convergence can become arbitrarily close to the parametric rate, pn. A Monte Carlo study reveals that the LPWN estimator performs well in the presence of a serially correlated perturbation term...... of the signal by two separate polynomials. Including these polynomials we obtain a reduction in the order of magnitude of the bias, but also in‡ate the asymptotic variance of the long memory estimate by a multiplicative constant. We show that the estimator is consistent for d 2 (0; 1), asymptotically normal...
Dustmann, C.; van Soest, A.H.O.
1999-01-01
We consider both a parametric and a semiparametric method to account for classification errors on the dependent variable in an ordered response model. The methods are applied to the analysis of self-reported speaking fluency of male immigrants in Germany. We find some substantial differences in para
Dustmann, C.; van Soest, A.H.O.
1999-01-01
We consider both a parametric and a semiparametric method to account for classification errors on the dependent variable in an ordered response model. The methods are applied to the analysis of self-reported speaking fluency of male immigrants in Germany. We find some substantial differences in
Are local wind power resources well estimated?
Lundtang Petersen, Erik; Troen, Ib; Jørgensen, Hans E.; Mann, Jakob
2013-03-01
Planning and financing of wind power installations require very importantly accurate resource estimation in addition to a number of other considerations relating to environment and economy. Furthermore, individual wind energy installations cannot in general be seen in isolation. It is well known that the spacing of turbines in wind farms is critical for maximum power production. It is also well established that the collective effect of wind turbines in large wind farms or of several wind farms can limit the wind power extraction downwind. This has been documented by many years of production statistics. For the very large, regional sized wind farms, a number of numerical studies have pointed to additional adverse changes to the regional wind climate, most recently by the detailed studies of Adams and Keith [1]. They show that the geophysical limit to wind power production is likely to be lower than previously estimated. Although this problem is of far future concern, it has to be considered seriously. In their paper they estimate that a wind farm larger than 100 km2 is limited to about 1 W m-2. However, a 20 km2 off shore farm, Horns Rev 1, has in the last five years produced 3.98 W m-2 [5]. In that light it is highly unlikely that the effects pointed out by [1] will pose any immediate threat to wind energy in coming decades. Today a number of well-established mesoscale and microscale models exist for estimating wind resources and design parameters and in many cases they work well. This is especially true if good local data are available for calibrating the models or for their validation. The wind energy industry is still troubled by many projects showing considerable negative discrepancies between calculated and actually experienced production numbers and operating conditions. Therefore it has been decided on a European Union level to launch a project, 'The New European Wind Atlas', aiming at reducing overall uncertainties in determining wind conditions. The
Recursive estimation of 3D motion and surface structure from local affine flow parameters.
Calway, Andrew
2005-04-01
A recursive structure from motion algorithm based on optical flow measurements taken from an image sequence is described. It provides estimates of surface normals in addition to 3D motion and depth. The measurements are affine motion parameters which approximate the local flow fields associated with near-planar surface patches in the scene. These are integrated over time to give estimates of the 3D parameters using an extended Kalman filter. This also estimates the camera focal length and, so, the 3D estimates are metric. The use of parametric measurements means that the algorithm is computationally less demanding than previous optical flow approaches and the recursive filter builds in a degree of noise robustness. Results of experiments on synthetic and real image sequences demonstrate that the algorithm performs well.
Dry Deposition Velocity Estimation for the Savannah River Site: Part 1 – Parametric Analysis
Energy Technology Data Exchange (ETDEWEB)
Napier, Bruce A.
2012-01-16
Values for the dry deposition velocity of airborne particles were estimated with the GENII Version 2.10 computer code for the Savannah River site using assumptions about surface roughness parameters and particle size and density. Use of the GENII code is recommended by the U.S. Department of Energy for this purpose. Meteorological conditions evaluated include atmospheric stability classes D, E, and F and wind speeds of 0.5, 1.0, 1.5, and 3.0 m/s. Local surface roughness values ranging from 0.03 to 2 meters were evaluated. Particles with mass mean diameters of 1, 5, and 10 microns and densities of 1, 3, and 5 g/cm3 were evaluated.
Moore, Julia L; Remais, Justin V
2014-03-01
Developmental models that account for the metabolic effect of temperature variability on poikilotherms, such as degree-day models, have been widely used to study organism emergence, range and development, particularly in agricultural and vector-borne disease contexts. Though simple and easy to use, structural and parametric issues can influence the outputs of such models, often substantially. Because the underlying assumptions and limitations of these models have rarely been considered, this paper reviews the structural, parametric, and experimental issues that arise when using degree-day models, including the implications of particular structural or parametric choices, as well as assumptions that underlie commonly used models. Linear and non-linear developmental functions are compared, as are common methods used to incorporate temperature thresholds and calculate daily degree-days. Substantial differences in predicted emergence time arose when using linear versus non-linear developmental functions to model the emergence time in a model organism. The optimal method for calculating degree-days depends upon where key temperature threshold parameters fall relative to the daily minimum and maximum temperatures, as well as the shape of the daily temperature curve. No method is shown to be universally superior, though one commonly used method, the daily average method, consistently provides accurate results. The sensitivity of model projections to these methodological issues highlights the need to make structural and parametric selections based on a careful consideration of the specific biological response of the organism under study, and the specific temperature conditions of the geographic regions of interest. When degree-day model limitations are considered and model assumptions met, the models can be a powerful tool for studying temperature-dependent development.
Localization estimation and global vs. local information measures
Energy Technology Data Exchange (ETDEWEB)
Pennini, F. [Departamento de Fisica, Universidad Catolica del Norte, Casilla 1280, Antofagasta (Chile) and Instituto de Fisica, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Argentina' s National Research Council (CONICET) C.C. 727, 1900 La Plata (Argentina)]. E-mail: fpennini@ucn.cl; Plastino, A. [Instituto de Fisica, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Argentina' s National Research Council (CONICET) C.C. 727, 1900 La Plata (Argentina)]. E-mail: plastino@fisica.unlp.edu.ar
2007-06-04
The maximum entropy principle is one of the great ideas of the last 50 years, with a multitude of applications in many areas of science. Its main ingredient is an information measure. We show that global and local information measures provide different types of physical information, which requires handling them with some care. The concomitant differences are illustrated with reference to the problem of localization in phase space, placing emphasis on some details of the smoothing of Wigner functions, as described in [G. Manfredi, M.R. Feix, Phys. Rev. E 62 (2000) 4665]. Our discussion is made in terms of a special version of Fisher's information measure, called the shift-invariant one.
Indoor Localization Accuracy Estimation from Fingerprint Data
DEFF Research Database (Denmark)
Nikitin, Artyom; Laoudias, Christos; Chatzimilioudis, Georgios
2017-01-01
The demand for indoor localization services has led to the development of techniques that create a Fingerprint Map (FM) of sensor signals (e.g., magnetic, Wi-Fi, bluetooth) at designated positions in an indoor space and then use FM as a reference for subsequent localization tasks...... on arbitrary FMs coined ACCES. Our framework comprises a generic interpolation method using Gaussian Processes (GP), upon which a navigability score at any location is derived using the Cramer-Rao Lower Bound (CRLB). Our approach does not rely on the underlying physical model of the fingerprint data. Our...
Nonparametric Estimates of Gene × Environment Interaction Using Local Structural Equation Modeling
Briley, Daniel A.; Harden, K. Paige; Bates, Timothy C.; Tucker-Drob, Elliot M.
2017-01-01
Gene × Environment (G×E) interaction studies test the hypothesis that the strength of genetic influence varies across environmental contexts. Existing latent variable methods for estimating G×E interactions in twin and family data specify parametric (typically linear) functions for the interaction effect. An improper functional form may obscure the underlying shape of the interaction effect and may lead to failures to detect a significant interaction. In this article, we introduce a novel approach to the behavior genetic toolkit, local structural equation modeling (LOSEM). LOSEM is a highly flexible nonparametric approach for estimating latent interaction effects across the range of a measured moderator. This approach opens up the ability to detect and visualize new forms of G×E interaction. We illustrate the approach by using LOSEM to estimate gene × socioeconomic status (SES) interactions for six cognitive phenotypes. Rather than continuously and monotonically varying effects as has been assumed in conventional parametric approaches, LOSEM indicated substantial nonlinear shifts in genetic variance for several phenotypes. The operating characteristics of LOSEM were interrogated through simulation studies where the functional form of the interaction effect was known. LOSEM provides a conservative estimate of G×E interaction with sufficient power to detect statistically significant G×E signal with moderate sample size. We offer recommendations for the application of LOSEM and provide scripts for implementing these biometric models in Mplus and in OpenMx under R. PMID:26318287
Chiao, Raymond Y
2012-01-01
An experiment is proposed to observe the dynamical Casimir effect by means of two tandem, high Q, superconducting microwave cavities, which are separated from each other by only a very thin wall consisting of a flexible superconducting membrane that can be driven into motion by means of resonant "pump" microwaves injected into the left cavity. Degenerate "signal" and "idler" microwave signals can then be generated by the exponential amplification of vacuum fluctuations in the initially empty right cavity, above a certain threshold. The purpose of this paper is calculate the threshold for this novel kind of opto-mechanical parametric oscillation, using energy considerations.
Estimating Mutual Information by Local Gaussian Approximation
2015-07-13
proposed a variety of methods to overcome the bias, such as the reflection method (Schuster, 1985), ( Silverman , 1986); the boundary kernel method...communication. The Bell System Technical Journal, 27:379423, 1948. Bernard W Silverman . Density estimation for statistics and data analysis, volume 26. CRC press
Wang, Ying; Feng, Chenglian; Liu, Yuedan; Zhao, Yujie; Li, Huixian; Zhao, Tianhui; Guo, Wenjing
2017-02-01
Transition metals in the fourth period of the periodic table of the elements are widely widespread in aquatic environments. They could often occur at certain concentrations to cause adverse effects on aquatic life and human health. Generally, parametric models are mostly used to construct species sensitivity distributions (SSDs), which result in comparison for water quality criteria (WQC) of elements in the same period or group of the periodic table might be inaccurate and the results could be biased. To address this inadequacy, the non-parametric kernel density estimation (NPKDE) with its optimal bandwidths and testing methods were developed for establishing SSDs. The NPKDE was better fit, more robustness and better predicted than conventional normal and logistic parametric density estimations for constructing SSDs and deriving acute HC5 and WQC for transition metals in the fourth period of the periodic table. The decreasing sequence of HC5 values for the transition metals in the fourth period was Ti > Mn > V > Ni > Zn > Cu > Fe > Co > Cr(VI), which were not proportional to atomic number in the periodic table, and for different metals the relatively sensitive species were also different. The results indicated that except for physical and chemical properties there are other factors affecting toxicity mechanisms of transition metals. The proposed method enriched the methodological foundation for WQC. Meanwhile, it also provided a relatively innovative, accurate approach for the WQC derivation and risk assessment of the same group and period metals in aquatic environments to support protection of aquatic organisms.
Chavanis, Pierre-Henri
2014-01-01
In the context of two-dimensional (2D) turbulence, we apply the maximum entropy production principle (MEPP) by enforcing a local conservation of energy. This leads to an equation for the vorticity distribution that conserves all the Casimirs, the energy, and that increases monotonically the mixing entropy ($H$-theorem). Furthermore, the equation for the coarse-grained vorticity dissipates monotonically all the generalized enstrophies. These equations may provide a parametrization of 2D turbulence. They do not generally relax towards the maximum entropy state. The vorticity current vanishes for any steady state of the 2D Euler equation. Interestingly, the equation for the coarse-grained vorticity obtained from the MEPP turns out to coincide, after some algebraic manipulations, with the one obtained with the anticipated vorticity method. This shows a connection between these two approaches when the conservation of energy is treated locally. Furthermore, the newly derived equation, which incorporates a diffusion...
An Non-parametrical Approach to Estimate Location Parameters under Simple Order
Institute of Scientific and Technical Information of China (English)
孙旭
2005-01-01
This paper deals with estimating parameters under simple order when samples come from location models. Based on the idea of Hodges and Lehmann estimator (H-L estimator), a new approach to estimate parameters is proposed, which is difference with the classical L1 isotoaic regression and L2 isotonic regression. An algorithm to corupute estimators is given. Simulations by the Monte-Carlo method is applied to compare the likelihood functions with respect to L1 estimators and weighted isotonic H-L estimators.
Variable bandwidth and one-step local M-estimator
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A robust version of local linear regression smoothers augmented with variable bandwidth is studied. The proposed method inherits the advantages of local polynomial regression and overcomes the shortcoming of lack of robustness of least-squares techniques. The use of variable bandwidth enhances the flexibility of the resulting local M-estimators and makes them possible to cope well with spatially inhomogeneous curves, heteroscedastic errors and nonuniform design densities. Under appropriate regularity conditions, it is shown that the proposed estimators exist and are asymptotically normal. Based on the robust estimation equation, one-step local M-estimators are introduced to reduce computational burden. It is demonstrated that the one-step local M-estimators share the same asymptotic distributions as the fully iterative M-estimators, as long as the initial estimators are good enough. In other words, the one-step local M-estimators reduce significantly the computation cost of the fully iterative M-estimators without deteriorating their performance. This fact is also illustrated via simulations.
Jaspers, Stijn; Verbeke, Geert; Böhning, Dankmar; Aerts, Marc
2016-01-01
In the last decades, considerable attention has been paid to the collection of antimicrobial resistance data, with the aim of monitoring non-wild-type isolates. This monitoring is performed based on minimum inhibition concentration (MIC) values, which are collected through dilution experiments. We present a semi-parametric mixture model to estimate the entire MIC density on the continuous scale. The parametric first component is extended with a non-parametric second component and a new back-fitting algorithm, based on the Vertex Exchange Method, is proposed. Our data example shows how to estimate the MIC density for Escherichia coli tested for ampicillin and how to use this estimate for model-based classification. A simulation study was performed, showing the promising behavior of the new method, both in terms of density estimation as well as classification.
Parameter Estimation with Entangled Photons Produced by Parametric Down-Conversion
Cable, Hugo; Durkin, Gabriel A.
2010-01-01
We explore the advantages offered by twin light beams produced in parametric down-conversion for precision measurement. The symmetry of these bipartite quantum states, even under losses, suggests that monitoring correlations between the divergent beams permits a high-precision inference of any symmetry-breaking effect, e.g., fiber birefringence. We show that the quantity of entanglement is not the key feature for such an instrument. In a lossless setting, scaling of precision at the ultimate "Heisenberg" limit is possible with photon counting alone. Even as photon losses approach 100% the precision is shot-noise limited, and we identify the crossover point between quantum and classical precision as a function of detected flux. The predicted hypersensitivity is demonstrated with a Bayesian simulation.
Parameter estimation with entangled photons produced by parametric down-conversion.
Cable, Hugo; Durkin, Gabriel A
2010-07-01
We explore the advantages offered by twin light beams produced in parametric down-conversion for precision measurement. The symmetry of these bipartite quantum states, even under losses, suggests that monitoring correlations between the divergent beams permits a high-precision inference of any symmetry-breaking effect, e.g., fiber birefringence. We show that the quantity of entanglement is not the key feature for such an instrument. In a lossless setting, scaling of precision at the ultimate "Heisenberg" limit is possible with photon counting alone. Even as photon losses approach 100% the precision is shot-noise limited, and we identify the crossover point between quantum and classical precision as a function of detected flux. The predicted hypersensitivity is demonstrated with a Bayesian simulation.
Energy Technology Data Exchange (ETDEWEB)
Logan, R W; Nitta, C K; Chidester, S K
2006-02-28
One of the final steps in building a numerical model of a physical, mechanical, thermal, or chemical process, is to assess its accuracy as well as its sensitivity to input parameters and modeling technique. In this work, we demonstrate one simple process to take a top-down or integral view of the model, one which can implicitly reflect any couplings between parameters, to assess the importance of each aspect of modeling technique. We illustrate with an example of a comparison of a finite element model with data for violent reaction of explosives in accident scenarios. We show the relative importance of each of the main parametric inputs, and the contributions of model form and grid convergence. These can be directly related to the importance factors for the system being analyzed as a whole, and help determine which factors need more attention in future analyses and tests.
Energy Technology Data Exchange (ETDEWEB)
Logan, R W; Nitta, C K; Chidester, S K
2006-02-28
One of the final steps in building a numerical model of a physical, mechanical, thermal, or chemical process, is to assess its accuracy as well as its sensitivity to input parameters and modeling technique. In this work, we demonstrate one simple process to take a top-down or integral view of the model, one which can implicitly reflect any couplings between parameters, to assess the importance of each aspect of modeling technique. We illustrate with an example of a comparison of a finite element model with data for violent reaction of explosives in accident scenarios. We show the relative importance of each of the main parametric inputs, and the contributions of model form and grid convergence. These can be directly related to the importance factors for the system being analyzed as a whole, and help determine which factors need more attention in future analyses and tests.
Parameter Estimation with Entangled Photons Produced by Parametric Down-Conversion
Cable, Hugo; Durkin, Gabriel A.
2010-01-01
We explore the advantages offered by twin light beams produced in parametric down-conversion for precision measurement. The symmetry of these bipartite quantum states, even under losses, suggests that monitoring correlations between the divergent beams permits a high-precision inference of any symmetry-breaking effect, e.g., fiber birefringence. We show that the quantity of entanglement is not the key feature for such an instrument. In a lossless setting, scaling of precision at the ultimate "Heisenberg" limit is possible with photon counting alone. Even as photon losses approach 100% the precision is shot-noise limited, and we identify the crossover point between quantum and classical precision as a function of detected flux. The predicted hypersensitivity is demonstrated with a Bayesian simulation.
Joint Parametric Fault Diagnosis and State Estimation Using KF-ML Method
DEFF Research Database (Denmark)
Sun, Zhen; Yang, Zhenyu
2014-01-01
) technique to identify the fault parameter and employs the result to make fault decision based on the predefined threshold. Then this estimated fault parameter value is substituted into parameterized state estimation of KF to obtain the state estimation. Finally, a robot case study with two different fault...... scenarios shows this method can lead to a good performance in terms of fast and accurate fault detection and state estimation....
分布函数的非参数最小二乘估计%NON-PARAMETRIC LEAST SQUARE ESTIMATION OF DISTRIBUTION FUNCTION
Institute of Scientific and Technical Information of China (English)
柴根象; 花虹; 尚汉冀
2002-01-01
By using the non-parametric least square method, the strong consistent estimations of distribution function and failure function are established,where the distribution function F(x) after logist transformation is assumed to be approximated by a polynomial.The performance of simulation shows that the estimations are highly satisfactory.
Estimation of leakage power and delay in CMOS circuits using parametric variation
Directory of Open Access Journals (Sweden)
Preeti Verma
2016-09-01
Full Text Available With the advent of deep-submicron technologies, leakage power dissipation is a major concern for scaling down portable devices that have burst-mode type integrated circuits. In this paper leakage reduction technique HTLCT (High Threshold Leakage Control Transistor is discussed. Using high threshold transistors at the place of low threshold leakage control transistors, result in more leakage power reduction as compared to LCT (leakage control transistor technique but at the scarifies of area and delay. Further, analysis of effect of parametric variation on leakage current and propagation delay in CMOS circuits is performed. It is found that the leakage power dissipation increases with increasing temperature, supply voltage and aspect ratio. However, opposite pattern is noticed for the propagation delay. Leakage power dissipation for LCT NAND gate increases up to 14.32%, 6.43% and 36.21% and delay decreases by 22.5%, 42% and 9% for variation of temperature, supply voltage and aspect ratio. Maximum peak of equivalent output noise is obtained as 127.531 nV/Sqrt(Hz at 400 mHz.
A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series.
Keshmiri, Soheil; Sumioka, Hidenobu; Yamazaki, Ryuji; Ishiguro, Hiroshi
2017-01-01
We present a non-parametric approach to prediction of the n-back n ∈ {1, 2} task as a proxy measure of mental workload using Near Infrared Spectroscopy (NIRS) data. In particular, we focus on measuring the mental workload through hemodynamic responses in the brain induced by these tasks, thereby realizing the potential that they can offer for their detection in real world scenarios (e.g., difficulty of a conversation). Our approach takes advantage of intrinsic linearity that is inherent in the components of the NIRS time series to adopt a one-step regression strategy. We demonstrate the correctness of our approach through its mathematical analysis. Furthermore, we study the performance of our model in an inter-subject setting in contrast with state-of-the-art techniques in the literature to show a significant improvement on prediction of these tasks (82.50 and 86.40% for female and male participants, respectively). Moreover, our empirical analysis suggest a gender difference effect on the performance of the classifiers (with male data exhibiting a higher non-linearity) along with the left-lateralized activation in both genders with higher specificity in females.
Korsten, Maarten J.; Houkes, Z.
1989-01-01
Presents a method combining shape, shading and motion models in order to obtain estimations of 3D shape and motion parameters directly from image grey values. The problem is considered as an application of optimal parameter estimation theory, according to Liebelt (1967)
Lin, Chun-Cheng
2008-09-01
This work analyzes and attempts to enhance the accuracy and reproducibility of parametric modeling in the discrete cosine transform (DCT) domain for the estimation of abnormal intra-QRS potentials (AIQP) in signal-averaged electrocardiograms. One hundred sets of white noise with a flat frequency response were introduced to simulate the unpredictable, broadband AIQP when quantitatively analyzing estimation error. Further, a high-frequency AIQP parameter was defined to minimize estimation error caused by the overlap between normal QRS and AIQP in low-frequency DCT coefficients. Seventy-two patients from Taiwan were recruited for the study, comprising 30 patients with ventricular tachycardia (VT) and 42 without VT. Analytical results showed that VT patients had a significant decrease in the estimated AIQP. The global diagnostic performance (area under the receiver operating characteristic curve) of AIQP rose from 73.0% to 84.2% in lead Y, and from 58.3% to 79.1% in lead Z, when the high-frequency range fell from 100% to 80%. The combination of AIQP and ventricular late potentials further enhanced performance to 92.9% (specificity=90.5%, sensitivity=90%). Therefore, the significantly reduced AIQP in VT patients, possibly also including dominant unpredictable potentials within the normal QRS complex, may be new promising evidence of ventricular arrhythmias.
Non-parametric estimation of the availability in a general repairable system
Energy Technology Data Exchange (ETDEWEB)
Gamiz, M.L. [Departamento de Estadistica e I.O., Facultad de Ciencias, Universidad de Granada, Granada 18071 (Spain)], E-mail: mgamiz@ugr.es; Roman, Y. [Departamento de Estadistica e I.O., Facultad de Ciencias, Universidad de Granada, Granada 18071 (Spain)
2008-08-15
This work deals with repairable systems with unknown failure and repair time distributions. We focus on the estimation of the instantaneous availability, that is, the probability that the system is functioning at a given time, which we consider as the most significant measure for evaluating the effectiveness of a repairable system. The estimation of the availability function is not, in general, an easy task, i.e., analytical techniques are difficult to apply. We propose a smooth estimation of the availability based on kernel estimator of the cumulative distribution functions (CDF) of the failure and repair times, for which the bandwidth parameters are obtained by bootstrap procedures. The consistency properties of the availability estimator are established by using techniques based on the Laplace transform.
DEFF Research Database (Denmark)
Petersen, Jørgen Holm
2016-01-01
. For each term in the composite likelihood, a conditional likelihood is used that eliminates the influence of the random effects, which results in a composite conditional likelihood consisting of only one-dimensional integrals that may be solved numerically. Good properties of the resulting estimator......This paper describes a new approach to the estimation in a logistic regression model with two crossed random effects where special interest is in estimating the variance of one of the effects while not making distributional assumptions about the other effect. A composite likelihood is studied...
Type I Error Rates and Power Estimates of Selected Parametric and Nonparametric Tests of Scale.
Olejnik, Stephen F.; Algina, James
1987-01-01
Estimated Type I Error rates and power are reported for the Brown-Forsythe, O'Brien, Klotz, and Siegal-Tukey procedures. The effect of aligning the data using deviations from group means or group medians is investigated. (RB)
Type I Error Rates and Power Estimates of Selected Parametric and Nonparametric Tests of Scale.
Olejnik, Stephen F.; Algina, James
1987-01-01
Estimated Type I Error rates and power are reported for the Brown-Forsythe, O'Brien, Klotz, and Siegal-Tukey procedures. The effect of aligning the data using deviations from group means or group medians is investigated. (RB)
Dewberry, B.
2000-01-01
Electrical impedance spectrometry involves measurement of the complex resistance of a load at multiple frequencies. With this information in the form of impedance magnitude and phase, or resistance and reactance, basic structure or function of the load can be estimated. The "load" targeted for measurement and estimation in this study consisted of the water-bearing tissues of the human calf. It was proposed and verified that by measuring the electrical impedance of the human calf and fitting this data to a model of fluid compartments, the lumped-model volume of intracellular and extracellular spaces could be estimated, By performing this estimation over time, the volume dynamics during application of stimuli which affect the direction of gravity can be viewed. The resulting data can form a basis for further modeling and verification of cardiovascular and compartmental modeling of fluid reactions to microgravity as well as countermeasures to the headward shift of fluid during head-down tilt or spaceflight.
Unstable volatility functions: the break preserving local linear estimator
DEFF Research Database (Denmark)
Casas, Isabel; Gijbels, Irene
The objective of this paper is to introduce the break preserving local linear (BPLL) estimator for the estimation of unstable volatility functions. Breaks in the structure of the conditional mean and/or the volatility functions are common in Finance. Markov switching models (Hamilton, 1989) and t...
Improving the Parametric Method of Cost Estimating Relationships of Naval Ships
2014-06-01
Department of Defense sponsored software which works together with the Automated Cost Estimating Integrated Tools ( ACEIT ) suite. Depending on the software...basis used by the estimator, either the Microsoft 30 Excel add-on will be used or the ACEIT based. Crystal Ball shown in Figure 10, uses a...20 Hu and Smith, Proceedings of the 2004 Crystal Ball User Conference COMPARING CRYSTAL BALL ® WITH ACEIT . 21 Smart, “The Portfolio Effect
Localized Recursive Estimation in Energy Constrained Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Bang Wang
2006-06-01
Full Text Available This paper proposes a localized recursive estimation scheme for parameter estimation in wireless sensor networks. Given any parameter of a target occurring at some location and time, a number of sensors recursively estimate the parameter by using their local measurements of the parameter that is attenuated with the distance between a sensor and the target location and corrupted by noise. Compared with centralized estimation schemes that transmit all encoded measurements to a sink (or a fusion center, the recursive scheme needs only to transmit the final estimate to a sink. When the sink is faraway from the sensors and multihop communications have to be used, using localized recursive estimation can help to reduce energy consumption and reduce network traffic load. A sensor sequence with the fastest convergence rate is identified, by which the variance of estimation error reduces faster than all other sequences. In the case of adjustable transmission power, a heuristic has been proposed to find a sensor sequence with the minimum total transmission power when performing the recursive estimation. Numerical examples have been used to compare the performance of the proposed scheme with that of a centralized estimation scheme and have also shown the effectiveness of the proposed heuristic.
On the method of logarithmic cumulants for parametric probability density function estimation.
Krylov, Vladimir A; Moser, Gabriele; Serpico, Sebastiano B; Zerubia, Josiane
2013-10-01
Parameter estimation of probability density functions is one of the major steps in the area of statistical image and signal processing. In this paper we explore several properties and limitations of the recently proposed method of logarithmic cumulants (MoLC) parameter estimation approach which is an alternative to the classical maximum likelihood (ML) and method of moments (MoM) approaches. We derive the general sufficient condition for a strong consistency of the MoLC estimates which represents an important asymptotic property of any statistical estimator. This result enables the demonstration of the strong consistency of MoLC estimates for a selection of widely used distribution families originating from (but not restricted to) synthetic aperture radar image processing. We then derive the analytical conditions of applicability of MoLC to samples for the distribution families in our selection. Finally, we conduct various synthetic and real data experiments to assess the comparative properties, applicability and small sample performance of MoLC notably for the generalized gamma and K families of distributions. Supervised image classification experiments are considered for medical ultrasound and remote-sensing SAR imagery. The obtained results suggest that MoLC is a feasible and computationally fast yet not universally applicable alternative to MoM. MoLC becomes especially useful when the direct ML approach turns out to be unfeasible.
Error Estimation for the Linearized Auto-Localization Algorithm
Directory of Open Access Journals (Sweden)
Fernando Seco
2012-02-01
Full Text Available The Linearized Auto-Localization (LAL algorithm estimates the position of beacon nodes in Local Positioning Systems (LPSs, using only the distance measurements to a mobile node whose position is also unknown. The LAL algorithm calculates the inter-beacon distances, used for the estimation of the beacons’ positions, from the linearized trilateration equations. In this paper we propose a method to estimate the propagation of the errors of the inter-beacon distances obtained with the LAL algorithm, based on a first order Taylor approximation of the equations. Since the method depends on such approximation, a confidence parameter τ is defined to measure the reliability of the estimated error. Field evaluations showed that by applying this information to an improved weighted-based auto-localization algorithm (WLAL, the standard deviation of the inter-beacon distances can be improved by more than 30% on average with respect to the original LAL method.
Economic capacity estimation in fisheries: A non-parametric ray approach
Energy Technology Data Exchange (ETDEWEB)
Pascoe, Sean; Tingley, Diana [Centre for the Economics and Management of Aquatic Resources (CEMARE), University of Portsmouth, Boathouse No. 6, College Road, HM Naval Base, Portsmouth PO1 3LJ (United Kingdom)
2006-05-15
Data envelopment analysis (DEA) has generally been adopted as the most appropriate methodology for the estimation of fishing capacity, particularly in multi-species fisheries. More recently, economic DEA methods have been developed that incorporate the costs and benefits of increasing capacity utilisation. One such method was applied to estimate the capacity utilisation and output of the Scottish fleet. By comparing the results of the economic and traditional DEA approaches, it can be concluded that many fleet segments are operating at or close to full capacity, and that the vessels defining the frontier are operating consistent with profit maximising behaviour. (author)
Stationary solution and parametric estimation for Bilinear model driven by ARCH noises
Institute of Scientific and Technical Information of China (English)
潘家柱; 李国栋; 谢衷洁
2002-01-01
Bilinear model driven by ARCH (1) noises is proposed. Existence, uniqueness and form of sta-tionary solution to this new model are presented. Maximum likelihood estimation of the model is discussedand some simulation results are given to evaluate our algorithm.
Alternative methods of marginal abatement cost estimation: Non- parametric distance functions
Energy Technology Data Exchange (ETDEWEB)
Boyd, G.; Molburg, J. [Argonne National Lab., IL (United States). Decision and Information Sciences Div.; Prince, R. [USDOE Office of Environmental Analysis, Washington, DC (United States)
1996-12-31
This project implements a economic methodology to measure the marginal abatement costs of pollution by measuring the lost revenue implied by an incremental reduction in pollution. It utilizes observed performance, or `best practice`, of facilities to infer the marginal abatement cost. The initial stage of the project is to use data from an earlier published study on productivity trends and pollution in electric utilities to test this approach and to provide insights on its implementation to issues of cost-benefit analysis studies needed by the Department of Energy. The basis for this marginal abatement cost estimation is a relationship between the outputs and the inputs of a firm or plant. Given a fixed set of input resources, including quasi-fixed inputs like plant and equipment and variable inputs like labor and fuel, a firm is able to produce a mix of outputs. This paper uses this theoretical view of the joint production process to implement a methodology and obtain empirical estimates of marginal abatement costs. These estimates are compared to engineering estimates.
Energy Technology Data Exchange (ETDEWEB)
Chavanis, Pierre-Henri, E-mail: chavanis@irsamc.ups-tlse.fr [Laboratoire de Physique Théorique, Université Paul Sabatier, 118 route de Narbonne, F-31062 Toulouse (France)
2014-12-01
In the context of two-dimensional (2D) turbulence, we apply the maximum entropy production principle (MEPP) by enforcing a local conservation of energy. This leads to an equation for the vorticity distribution that conserves all the Casimirs, the energy, and that increases monotonically the mixing entropy (H-theorem). Furthermore, the equation for the coarse-grained vorticity dissipates monotonically all the generalized enstrophies. These equations may provide a parametrization of 2D turbulence. They do not generally relax towards the maximum entropy state. The vorticity current vanishes for any steady state of the 2D Euler equation. Interestingly, the equation for the coarse-grained vorticity obtained from the MEPP turns out to coincide, after some algebraic manipulations, with the one obtained with the anticipated vorticity method. This shows a connection between these two approaches when the conservation of energy is treated locally. Furthermore, the newly derived equation, which incorporates a diffusion term and a drift term, has a nice physical interpretation in terms of a selective decay principle. This sheds new light on both the MEPP and the anticipated vorticity method. (paper)
Essays on parametric and nonparametric modeling and estimation with applications to energy economics
Gao, Weiyu
My dissertation research is composed of two parts: a theoretical part on semiparametric efficient estimation and an applied part in energy economics under different dynamic settings. The essays are related in terms of their applications as well as the way in which models are constructed and estimated. In the first essay, efficient estimation of the partially linear model is studied. We work out the efficient score functions and efficiency bounds under four stochastic restrictions---independence, conditional symmetry, conditional zero mean, and partially conditional zero mean. A feasible efficient estimation method for the linear part of the model is developed based on the efficient score. A battery of specification test that allows for choosing between the alternative assumptions is provided. A Monte Carlo simulation is also conducted. The second essay presents a dynamic optimization model for a stylized oilfield resembling the largest developed light oil field in Saudi Arabia, Ghawar. We use data from different sources to estimate the oil production cost function and the revenue function. We pay particular attention to the dynamic aspect of the oil production by employing petroleum-engineering software to simulate the interaction between control variables and reservoir state variables. Optimal solutions are studied under different scenarios to account for the possible changes in the exogenous variables and the uncertainty about the forecasts. The third essay examines the effect of oil price volatility on the level of innovation displayed by the U.S. economy. A measure of innovation is calculated by decomposing an output-based Malmquist index. We also construct a nonparametric measure for oil price volatility. Technical change and oil price volatility are then placed in a VAR system with oil price and a variable indicative of monetary policy. The system is estimated and analyzed for significant relationships. We find that oil price volatility displays a significant
Som, Nicholas A.; Goodman, Damon H.; Perry, Russell W.; Hardy, Thomas B.
2016-01-01
Previous methods for constructing univariate habitat suitability criteria (HSC) curves have ranged from professional judgement to kernel-smoothed density functions or combinations thereof. We present a new method of generating HSC curves that applies probability density functions as the mathematical representation of the curves. Compared with previous approaches, benefits of our method include (1) estimation of probability density function parameters directly from raw data, (2) quantitative methods for selecting among several candidate probability density functions, and (3) concise methods for expressing estimation uncertainty in the HSC curves. We demonstrate our method with a thorough example using data collected on the depth of water used by juvenile Chinook salmon (Oncorhynchus tschawytscha) in the Klamath River of northern California and southern Oregon. All R code needed to implement our example is provided in the appendix. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
On Non-Parametric Field Estimation using Randomly Deployed, Noisy, Binary Sensors
Wang, Ye
2007-01-01
We consider the problem of reconstructing a deterministic data field from binary quantized noisy observations of sensors randomly deployed over the field domain. Our focus is on the extremes of lack of control in the sensor deployment, arbitrariness and lack of knowledge of the noise distribution, and low-precision and unreliability in the sensors. These adverse conditions are motivated by possible real-world scenarios where a large collection of low-cost, crudely manufactured sensors are mass-deployed in an environment where little can be assumed about the ambient noise. We propose a simple estimator that reconstructs the entire data field from these unreliable, binary quantized, noisy observations. Under the assumption of a bounded amplitude field, we prove almost sure and mean-square convergence of the estimator to the actual field as the number of sensors tends to infinity. For fields with bounded-variation, Sobolev differentiable, or finite-dimensionality properties, we derive specific mean squared error...
Parametric down-conversion with local operation and two-way classical communication
Institute of Scientific and Technical Information of China (English)
Liu Qiang; Tan Yong-Gang
2011-01-01
The decoy-state quantum key distribution protocol suggested by Adachi et al.(Phys. Rev. Lett. 99 180503(2007))is proven to be secure and feasible with current techniques. It owns two attractive merits, i.e,its longer secure transmission distance and more convenient operation design. In this paper, we first improve the protocol with the aid of local operation and two-way classical communication(2-LOCC). After our modifications, the secure transmission distance is increased by about 20 km, which will make the protocol more practicable.
Parametric study of the cost estimate for radio frequency system of compact linear collider
Nummela, Antti; Österberg, Kenneth
In this thesis the cost of so called RF units of CLIC particle collider was examined when RF units’ configuration was considered to be lengthened according to several alternative scenarios. According to current estimates these structures correspond to approximately 20 % of the total cost of CLIC collider and as such the savings achieved in their cost could be significant when total cost of CLIC project is looked into. The unit cost of longer RF units would be greater when compared to the baseline scenario but as smaller quantity would be required cost savings might be achieved. The aim was to find out if cost savings would accumulate and if so, how significant these savings might be. Research material used was mainly internal CERN resources such as earlier cost estimates and tenders received from the industry for production of different components. Based on these cost estimate models were created for three different configurations for lengthening the RF units. The research was limited to the cost of RF unit...
Liang, Rong; Zhou, Shu-dong; Li, Li-xia; Zhang, Jun-guo; Gao, Yan-hui
2013-09-01
This paper aims to achieve Bootstraping in hierarchical data and to provide a method for the estimation on confidence interval(CI) of intraclass correlation coefficient(ICC).First, we utilize the mixed-effects model to estimate data from ICC of repeated measurement and from the two-stage sampling. Then, we use Bootstrap method to estimate CI from related ICCs. Finally, the influences of different Bootstraping strategies to ICC's CIs are compared. The repeated measurement instance show that the CI of cluster Bootsraping containing the true ICC value. However, when ignoring the hierarchy characteristics of data, the random Bootsraping method shows that it has the invalid CI. Result from the two-stage instance shows that bias observed between cluster Bootstraping's ICC means while the ICC of the original sample is the smallest, but with wide CI. It is necessary to consider the structure of data as important, when hierarchical data is being resampled. Bootstrapping seems to be better on the higher than that on lower levels.
Ng, C M
2013-10-01
The development of a population PK/PD model, an essential component for model-based drug development, is both time- and labor-intensive. A graphical-processing unit (GPU) computing technology has been proposed and used to accelerate many scientific computations. The objective of this study was to develop a hybrid GPU-CPU implementation of parallelized Monte Carlo parametric expectation maximization (MCPEM) estimation algorithm for population PK data analysis. A hybrid GPU-CPU implementation of the MCPEM algorithm (MCPEMGPU) and identical algorithm that is designed for the single CPU (MCPEMCPU) were developed using MATLAB in a single computer equipped with dual Xeon 6-Core E5690 CPU and a NVIDIA Tesla C2070 GPU parallel computing card that contained 448 stream processors. Two different PK models with rich/sparse sampling design schemes were used to simulate population data in assessing the performance of MCPEMCPU and MCPEMGPU. Results were analyzed by comparing the parameter estimation and model computation times. Speedup factor was used to assess the relative benefit of parallelized MCPEMGPU over MCPEMCPU in shortening model computation time. The MCPEMGPU consistently achieved shorter computation time than the MCPEMCPU and can offer more than 48-fold speedup using a single GPU card. The novel hybrid GPU-CPU implementation of parallelized MCPEM algorithm developed in this study holds a great promise in serving as the core for the next-generation of modeling software for population PK/PD analysis.
Choi, Sae Il
2009-01-01
This study used simulation (a) to compare the kernel equating method to traditional equipercentile equating methods under the equivalent-groups (EG) design and the nonequivalent-groups with anchor test (NEAT) design and (b) to apply the parametric bootstrap method for estimating standard errors of equating. A two-parameter logistic item response…
Note: Localization based on estimated source energy homogeneity
Turkaya, Semih; Toussaint, Renaud; Eriksen, Fredrik Kvalheim; Lengliné, Olivier; Daniel, Guillaume; Flekkøy, Eirik G.; Mâløy, Knut Jørgen
2016-09-01
Acoustic signal localization is a complex problem with a wide range of industrial and academic applications. Herein, we propose a localization method based on energy attenuation and inverted source amplitude comparison (termed estimated source energy homogeneity, or ESEH). This inversion is tested on both synthetic (numerical) data using a Lamb wave propagation model and experimental 2D plate data (recorded with 4 accelerometers sensitive up to 26 kHz). We compare the performance of this technique with classic source localization algorithms: arrival time localization, time reversal localization, and localization based on energy amplitude. Our technique is highly versatile and out-performs the conventional techniques in terms of error minimization and cost (both computational and financial).
Parametric characterization and estimation of bi-azimuth dispersion of path components
DEFF Research Database (Denmark)
Yin, Xuefeng; Pedersen, Troels; Czink, Nicolai;
2006-01-01
-Mises-Fisher distributions. The elements in this family maximize the entropy under the constraint that the expectations and correlation matrix of the directions are known. The probability density function (pdf) of the proposed distribution is used to describe the bi-azimuth power spectrum of individual path components....... An estimator of the parameters of the pdf is derived and applied to characterize the spreads in both azimuth of departure and azimuth of arrival, as well as the correlation between both azimuths of individual path components. Preliminary results from an experimental investigation demonstrate the applicability...
spa: Semi-Supervised Semi-Parametric Graph-Based Estimation in R
Directory of Open Access Journals (Sweden)
Mark Culp
2011-04-01
Full Text Available In this paper, we present an R package that combines feature-based (X data and graph-based (G data for prediction of the response Y . In this particular case, Y is observed for a subset of the observations (labeled and missing for the remainder (unlabeled. We examine an approach for fitting Y = Xβ + f(G where β is a coefficient vector and f is a function over the vertices of the graph. The procedure is semi-supervised in nature (trained on the labeled and unlabeled sets, requiring iterative algorithms for fitting this estimate. The package provides several key functions for fitting and evaluating an estimator of this type. The package is illustrated on a text analysis data set, where the observations are text documents (papers, the response is the category of paper (either applied or theoretical statistics, the X information is the name of the journal in which the paper resides, and the graph is a co-citation network, with each vertex an observation and each edge the number of times that the two papers cite a common paper. An application involving classification of protein location using a protein interaction graph and an application involving classification on a manifold with part of the feature data converted to a graph are also presented.
Estimation of frequency wave spectrum from high frequency radar data using a parametric model
Toro, V. G.; Ocampo, F. J.; Flores-Vidal, X.; Durazo, R.; Flament, P. J.
2011-12-01
Models that obtain wave information from high frequency radars (HF) use information of the measured second order Doppler spectrum. The estimation is completed through an integral equation as in the case of the Barrick model, or linearly as in the Hasselmann model. For the latter, the linear form uses a parameter (α) obtained using an exclusive set of data (EuroROSE) which suggests a universal expression of such parameter. In this work we developed a methodology and better approach to extract second order information from the Doppler spectra, and a new parameterization for α was obtained by comparing with in situ measured information in the Gulf of Tehuantepec (GT), Mexico. We present frequency spectra and significant wave height obtained for a four-month data set in the GT, during the season of strong (> 10 ms-1) northerly gap winds. We found that signal strength of Doppler spectra showed a clear diurnal cycle. The time average of these spectra allowed us to select the spectra with high SNR value. The second-order information obtained was used in the mathematical model of Hasselmann, and found that α, which is a function of frequency, depends on wind speed (U10). The results suggest a good agreement between the data measured by the ASIS buoy and those obtained by the Hasselmann model. The results showed improvement in the estimation of wave frequency spectrum and pointed at the need to have a theoretical model for α to be used in any data set.
Noise removal in multichannel image data by a parametric maximum noise fraction estimator
DEFF Research Database (Denmark)
Conradsen, Knut; Ersbøll, Bjarne Kjær; Nielsen, Allan Aasbjerg
1991-01-01
Some approaches to noise removal in multispectral imagery are presented. The primary contribution of the present work is the establishment of several ways of estimating the noise covariance matrix from image data and a comparison of the noise separation performances. A case study with Landsat MSS...... data demonstrates that the principal components are not sorted correctly in terms of visual image quality, whereas the minimum/maximum autocorrelation factors and the maximum noise fractions (MAFs) are. A case study with Landsat TM data shows an ordering which is consistent with the spatial wavelength...... in the components. The case studies indicate that a better noise separation is attained when using more complex noise models than the simple model implied by MAF analysis. (L.M.)...
A parametric model and estimation techniques for the inharmonicity and tuning of the piano.
Rigaud, François; David, Bertrand; Daudet, Laurent
2013-05-01
Inharmonicity of piano tones is an essential property of their timbre that strongly influences the tuning, leading to the so-called octave stretching. It is proposed in this paper to jointly model the inharmonicity and tuning of pianos on the whole compass. While using a small number of parameters, these models are able to reflect both the specificities of instrument design and tuner's practice. An estimation algorithm is derived that can run either on a set of isolated note recordings, but also on chord recordings, assuming that the played notes are known. It is applied to extract parameters highlighting some tuner's choices on different piano types and to propose tuning curves for out-of-tune pianos or piano synthesizers.
Hubbard, Rebecca A.; Miglioretti, Diana L.
2013-01-01
False-positive test results are among the most common harms of screening tests and may lead to more invasive and expensive diagnostic testing procedures. Estimating the cumulative risk of a false-positive screening test result after repeat screening rounds is therefore important for evaluating potential screening regimens. Existing estimators of the cumulative false-positive risk are limited by strong assumptions about censoring mechanisms and parametric assumptions about variation in risk ac...
Cotini, Stefano; Caccianiga, Alessandro; Colpi, Monica; Della Ceca, Roberto; Mapelli, Michela; Severgnini, Paola; Segreto, Alberto; 10.1093/mnras/stt358
2013-01-01
We investigate the possible link between mergers and the enhanced activity of supermassive black holes (SMBHs) at the centre of galaxies, by comparing the merger fraction of a local sample (0.003 =< z < 0.03) of active galaxies - 59 active galactic nuclei (AGN) host galaxies selected from the all-sky Swift BAT (Burst Alert Telescope) survey - with an appropriate control sample (247 sources extracted from the Hyperleda catalogue) that has the same redshift distribution as the BAT sample. We detect the interacting systems in the two samples on the basis of non-parametric structural indexes of concentration (C), asymmetry (A), clumpiness (S), Gini coefficient (G) and second order momentum of light (M20). In particular, we propose a new morphological criterion, based on a combination of all these indexes, that improves the identification of interacting systems. We also present a new software - PyCASSo (Python CAS Software) - for the automatic computation of the structural indexes. After correcting for the c...
Lausch, Anthony; Chen, Jeff; Ward, Aaron D.; Gaede, Stewart; Lee, Ting-Yim; Wong, Eugene
2014-11-01
Parametric response map (PRM) analysis is a voxel-wise technique for predicting overall treatment outcome, which shows promise as a tool for guiding personalized locally adaptive radiotherapy (RT). However, image registration error (IRE) introduces uncertainty into this analysis which may limit its use for guiding RT. Here we extend the PRM method to include an IRE-related PRM analysis confidence interval and also incorporate multiple graded classification thresholds to facilitate visualization. A Gaussian IRE model was used to compute an expected value and confidence interval for PRM analysis. The augmented PRM (A-PRM) was evaluated using CT-perfusion functional image data from patients treated with RT for glioma and hepatocellular carcinoma. Known rigid IREs were simulated by applying one thousand different rigid transformations to each image set. PRM and A-PRM analyses of the transformed images were then compared to analyses of the original images (ground truth) in order to investigate the two methods in the presence of controlled IRE. The A-PRM was shown to help visualize and quantify IRE-related analysis uncertainty. The use of multiple graded classification thresholds also provided additional contextual information which could be useful for visually identifying adaptive RT targets (e.g. sub-volume boosts). The A-PRM should facilitate reliable PRM guided adaptive RT by allowing the user to identify if a patient’s unique IRE-related PRM analysis uncertainty has the potential to influence target delineation.
Directory of Open Access Journals (Sweden)
A. V. Artemyev
2013-04-01
Full Text Available The lifetimes of electrons trapped in Earth's radiation belts can be calculated from quasi-linear pitch-angle diffusion by whistler-mode waves, provided that their frequency spectrum is broad enough and/or their average amplitude is not too large. Extensive comparisons between improved analytical lifetime estimates and full numerical calculations have been performed in a broad parameter range representative of a large part of the magnetosphere from L ~ 2 to 6. The effects of observed very oblique whistler waves are taken into account in both numerical and analytical calculations. Analytical lifetimes (and pitch-angle diffusion coefficients are found to be in good agreement with full numerical calculations based on CRRES and Cluster hiss and lightning-generated wave measurements inside the plasmasphere and Cluster lower-band chorus waves measurements in the outer belt for electron energies ranging from 100 keV to 5 MeV. Comparisons with lifetimes recently obtained from electron flux measurements on SAMPEX, SCATHA, SAC-C and DEMETER also show reasonable agreement.
Productivity improvement in Korean rice farming: parametric and non-parametric analysis
Kwon, Oh Sang; Lee, Hyunok
2004-01-01
The published empirical literature on frontier production functions is dominated by two broadly defined estimation approaches – parametric and non‐parametric. Using panel data on Korean rice production, parametric and non‐parametric production frontiers are estimated and compared with estimated productivity. The non‐parametric approach employs two alternative measures based on the Malmquist index and the Luenberger indicator, while the parametric approach is closely related to the time‐varian...
Bistatic Sonar Localization Based on Best Linear Unbiased Estimation
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A best linear unbiased estimation (BLUE) algorithm for bistatic sonar localization is proposed. The Cramer-Rao bound for bistatic sonar and the geometrical dilution of precision (GDOP) in different conditions are given. The simulation results show that the location accuracy of BLUE algorithm is higher than the weighted least square method.
Sepka, Steven A.; Zarchi, Kerry; Maddock, Robert W.; Samareh, Jamshid A.
2013-01-01
Part of NASAs In-Space Propulsion Technology (ISPT) program is the development of the tradespace to support the design of a family of multi-mission Earth Entry Vehicles (MMEEV) to meet a wide range of mission requirements. An integrated tool called the Multi Mission System Analysis for Planetary Entry Descent and Landing or M-SAPE tool is being developed as part of Entry Vehicle Technology project under In-Space Technology program. The analysis and design of an Earth Entry Vehicle (EEV) is multidisciplinary in nature, requiring the application many disciplines. Part of M-SAPE's application required the development of parametric mass estimating relationships (MERs) to determine the vehicle's required Thermal Protection System (TPS) for safe Earth entry. For this analysis, the heat shield was assumed to be made of a constant thickness TPS. This resulting MERs will then e used to determine the pre-flight mass of the TPS. Two Mers have been developed for the vehicle forebaody. One MER was developed for PICA and the other consisting of Carbon Phenolic atop an Advanced Carbon-Carbon composition. For the the backshell, MERs have been developed for SIRCA, Acusil II, and LI-900. How these MERs were developed, the resulting equations, model limitations, and model accuracy are discussed in this poster.
Fjodorova, Natalja; Novič, Marjana
2015-09-03
Engineering optimization is an actual goal in manufacturing and service industries. In the tutorial we represented the concept of traditional parametric estimation models (Factorial Design (FD) and Central Composite Design (CCD)) for searching optimal setting parameters of technological processes. Then the 2D mapping method based on Auto Associative Neural Networks (ANN) (particularly, the Feed Forward Bottle Neck Neural Network (FFBN NN)) was described in comparison with traditional methods. The FFBN NN mapping technique enables visualization of all optimal solutions in considered processes due to the projection of input as well as output parameters in the same coordinates of 2D map. This phenomenon supports the more efficient way of improving the performance of existing systems. Comparison of two methods was performed on the bases of optimization of solder paste printing processes as well as optimization of properties of cheese. Application of both methods enables the double check. This increases the reliability of selected optima or specification limits. Copyright © 2015 Elsevier B.V. All rights reserved.
Chen, Hua Yun
2009-12-01
Theory on semiparametric efficient estimation in missing data problems has been systematically developed by Robins and his coauthors. Except in relatively simple problems, semiparametric efficient scores cannot be expressed in closed forms. Instead, the efficient scores are often expressed as solutions to integral equations. Neumann series was proposed in the form of successive approximation to the efficient scores in those situations. Statistical properties of the estimator based on the Neumann series approximation are difficult to obtain and as a result, have not been clearly studied. In this paper, we reformulate the successive approximation in a simple iterative form and study the statistical properties of the estimator based on the reformulation. We show that a doubly-robust locally-efficient estimator can be obtained following the algorithm in robustifying the likelihood score. The results can be applied to, among others, the parametric regression, the marginal regression, and the Cox regression when data are subject to missing values and the missing data are missing at random. A simulation study is conducted to evaluate the performance of the approach and a real data example is analyzed to demonstrate the use of the approach.
Spectral covolatility estimation from noisy observations using local weights
Bibinger, Markus
2011-01-01
We propose localized spectral estimators for the quadratic covariation and the spot covolatility of diffusion processes which are observed discretely with additive observation noise. The eligibility of this approach to lead to an appropriate estimation for time-varying volatilities stems from an asymptotic equivalence of the underlying statistical model to a white noise model with correlation and volatility processes being constant over small intervals. The asymptotic equivalence of the continuous-time and the discrete-time experiments are proved by a construction with linear interpolation in one direction and local means for the other. The new estimator outperforms earlier nonparametric approaches in the considered model. We investigate its finite sample size characteristics in simulations and draw a comparison between the various proposed methods.
Estimating monotonic rates from biological data using local linear regression.
Olito, Colin; White, Craig R; Marshall, Dustin J; Barneche, Diego R
2017-03-01
Accessing many fundamental questions in biology begins with empirical estimation of simple monotonic rates of underlying biological processes. Across a variety of disciplines, ranging from physiology to biogeochemistry, these rates are routinely estimated from non-linear and noisy time series data using linear regression and ad hoc manual truncation of non-linearities. Here, we introduce the R package LoLinR, a flexible toolkit to implement local linear regression techniques to objectively and reproducibly estimate monotonic biological rates from non-linear time series data, and demonstrate possible applications using metabolic rate data. LoLinR provides methods to easily and reliably estimate monotonic rates from time series data in a way that is statistically robust, facilitates reproducible research and is applicable to a wide variety of research disciplines in the biological sciences. © 2017. Published by The Company of Biologists Ltd.
Improving Empirical Approaches to Estimating Local Greenhouse Gas Emissions
Blackhurst, M.; Azevedo, I. L.; Lattanzi, A.
2016-12-01
Evidence increasingly indicates our changing climate will have significant global impacts on public health, economies, and ecosystems. As a result, local governments have become increasingly interested in climate change mitigation. In the U.S., cities and counties representing nearly 15% of the domestic population plan to reduce 300 million metric tons of greenhouse gases over the next 40 years (or approximately 1 ton per capita). Local governments estimate greenhouse gas emissions to establish greenhouse gas mitigation goals and select supporting mitigation measures. However, current practices produce greenhouse gas estimates - also known as a "greenhouse gas inventory " - of empirical quality often insufficient for robust mitigation decision making. Namely, current mitigation planning uses sporadic, annual, and deterministic estimates disaggregated by broad end use sector, obscuring sources of emissions uncertainty, variability, and exogeneity that influence mitigation opportunities. As part of AGU's Thriving Earth Exchange, Ari Lattanzi of City of Pittsburgh, PA recently partnered with Dr. Inez Lima Azevedo (Carnegie Mellon University) and Dr. Michael Blackhurst (University of Pittsburgh) to improve the empirical approach to characterizing Pittsburgh's greenhouse gas emissions. The project will produce first-order estimates of the underlying sources of uncertainty, variability, and exogeneity influencing Pittsburgh's greenhouse gases and discuss implications of mitigation decision making. The results of the project will enable local governments to collect more robust greenhouse gas inventories to better support their mitigation goals and improve measurement and verification efforts.
National Research Council Canada - National Science Library
L. MuhamadSafiih; A. A. Kamil; M. T. Abu Osman
2014-01-01
... this problem is through the use of semi-parametric method. However, the uncertainties and ambiguities exist in the models, particularly the relationship between the endogenous and exogenous variables...
Local Whittle estimation of multivariate fractionally integrated processes
DEFF Research Database (Denmark)
Nielsen, Frank
This paper derives a semiparametric estimator of multivariate fractionally integrated processes covering both stationary and non-stationary values of d. We utilize the notion of the extended discrete Fourier transform and periodogram to extend the multivariate local Whittle estimator of Shimotsu ...... analysis of log spot exchange rates. We find that the log spot exchange rates of Germany, United Kingdom, Japan, Canada, France, Italy, and Switzerland against the US Dollar for the period January 1974 until December 2001 are well decribed as I (1) processes....
Collective vs local measurements in qubit mixed state estimation
Bagán, E; Muñoz-Tàpia, R; Rodríguez, A
2004-01-01
We discuss the problem of estimating a general (mixed) qubit state. We give the optimal guess that can be inferred from any given set of measurements. For collective measurements and for a large number $N$ of copies, we show that the error in the estimation goes as 1/N. For local measurements we focus on the simpler case of states lying on the equatorial plane of the Bloch sphere. We show that standard tomographic techniques lead to an error proportional to $1/N^{1/4}$, while with our optimal data processing it is proportional to $1/N^{3/4}$.
DOA Estimation with Local-Peak-Weighted CSP
Directory of Open Access Journals (Sweden)
Ichikawa Osamu
2010-01-01
Full Text Available This paper proposes a novel weighting algorithm for Cross-power Spectrum Phase (CSP analysis to improve the accuracy of direction of arrival (DOA estimation for beamforming in a noisy environment. Our sound source is a human speaker and the noise is broadband noise in an automobile. The harmonic structures in the human speech spectrum can be used for weighting the CSP analysis, because harmonic bins must contain more speech power than the others and thus give us more reliable information. However, most conventional methods leveraging harmonic structures require pitch estimation with voiced-unvoiced classification, which is not sufficiently accurate in noisy environments. In our new approach, the observed power spectrum is directly converted into weights for the CSP analysis by retaining only the local peaks considered to be harmonic structures. Our experiment showed the proposed approach significantly reduced the errors in localization, and it showed further improvements when used with other weighting algorithms.
Häme, Yrjö; Pollari, Mika
2012-01-01
A novel liver tumor segmentation method for CT images is presented. The aim of this work was to reduce the manual labor and time required in the treatment planning of radiofrequency ablation (RFA), by providing accurate and automated tumor segmentations reliably. The developed method is semi-automatic, requiring only minimal user interaction. The segmentation is based on non-parametric intensity distribution estimation and a hidden Markov measure field model, with application of a spherical shape prior. A post-processing operation is also presented to remove the overflow to adjacent tissue. In addition to the conventional approach of using a single image as input data, an approach using images from multiple contrast phases was developed. The accuracy of the method was validated with two sets of patient data, and artificially generated samples. The patient data included preoperative RFA images and a public data set from "3D Liver Tumor Segmentation Challenge 2008". The method achieved very high accuracy with the RFA data, and outperformed other methods evaluated with the public data set, receiving an average overlap error of 30.3% which represents an improvement of 2.3% points to the previously best performing semi-automatic method. The average volume difference was 23.5%, and the average, the RMS, and the maximum surface distance errors were 1.87, 2.43, and 8.09 mm, respectively. The method produced good results even for tumors with very low contrast and ambiguous borders, and the performance remained high with noisy image data.
LOCAL ESTIMATES OF SINGULAR SOLUTION TO GAUSSIAN CURVATURE EQUATION
Institute of Scientific and Technical Information of China (English)
杨云雁
2003-01-01
In this paper, we derive the local estimates of a singular solution near its singular set Z of the Gaussian curvature equation △u(x) + K(x)eu(x) = 0 in Ω \\ Z,in the case that K(x) may be zero on Z, where Ω R2 is a bounded open domain, and Z is a set of finite points.
Energy Technology Data Exchange (ETDEWEB)
Constantinescu, C C; Yoder, K K; Normandin, M D; Morris, E D [Department of Radiology, Indiana University School of Medicine, Indianapolis, IN (United States); Kareken, D A [Department of Neurology, Indiana University School of Medicine, Indianapolis, IN (United States); Bouman, C A [Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN (United States); O' Connor, S J [Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN (United States)], E-mail: emorris@iupui.edu
2008-03-07
We previously developed a model-independent technique (non-parametric ntPET) for extracting the transient changes in neurotransmitter concentration from paired (rest and activation) PET studies with a receptor ligand. To provide support for our method, we introduced three hypotheses of validation based on work by Endres and Carson (1998 J. Cereb. Blood Flow Metab. 18 1196-210) and Yoder et al (2004 J. Nucl. Med. 45 903-11), and tested them on experimental data. All three hypotheses describe relationships between the estimated free (synaptic) dopamine curves (F{sup DA}(t)) and the change in binding potential ({delta}BP). The veracity of the F{sup DA}(t) curves recovered by nonparametric ntPET is supported when the data adhere to the following hypothesized behaviors: (1) {delta}BP should decline with increasing DA peak time, (2) {delta}BP should increase as the strength of the temporal correlation between F{sup DA}(t) and the free raclopride (F{sup RAC}(t)) curve increases, (3) {delta}BP should decline linearly with the effective weighted availability of the receptor sites. We analyzed regional brain data from 8 healthy subjects who received two [{sup 11}C]raclopride scans: one at rest, and one during which unanticipated IV alcohol was administered to stimulate dopamine release. For several striatal regions, nonparametric ntPET was applied to recover F{sup DA}(t), and binding potential values were determined. Kendall rank-correlation analysis confirmed that the F{sup DA}(t) data followed the expected trends for all three validation hypotheses. Our findings lend credence to our model-independent estimates of F{sup DA}(t). Application of nonparametric ntPET may yield important insights into how alterations in timing of dopaminergic neurotransmission are involved in the pathologies of addiction and other psychiatric disorders.
Directory of Open Access Journals (Sweden)
Hannah H Leslie
Full Text Available OBJECTIVE: To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation. BACKGROUND: Human immunodeficiency virus (HIV-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contraception are true causes is critical for informing public health strategies as longevity increases among HIV-positive women in developing countries. METHODS: We developed a causal model of the factors related to combined oral contraceptive (COC use and cervical intraepithelial neoplasia 2 or greater (CIN2+ and modified the model to fit the observed data, drawn from women in a cervical cancer screening program at HIV clinics in Kenya. Assumptions required for substantiation of a causal relationship were assessed. We estimated the population-level association using semi-parametric methods: g-computation, inverse probability of treatment weighting, and targeted maximum likelihood estimation. RESULTS: We identified 2 plausible causal paths from COC use to CIN2+: via HPV infection and via increased disease progression. Study data enabled estimation of the latter only with strong assumptions of no unmeasured confounding. Of 2,519 women under 50 screened per protocol, 219 (8.7% were diagnosed with CIN2+. Marginal modeling suggested a 2.9% (95% confidence interval 0.1%, 6.9% increase in prevalence of CIN2+ if all women under 50 were exposed to COC; the significance of this association was sensitive to method of estimation and exposure misclassification. CONCLUSION: Use of causal modeling enabled clear representation of the causal relationship of interest and the assumptions required to estimate that relationship from the observed data. Semi-parametric estimation methods provided flexibility and reduced reliance on correct model form. Although selected results suggest an
FEH Local: Improving flood estimates using historical data
Directory of Open Access Journals (Sweden)
Prosdocimi Ilaria
2016-01-01
Full Text Available The traditional approach to design flood estimation (for example, to derive the 100-year flood is to apply a statistical model to time series of peak river flow measured by gauging stations. Such records are typically not very long, for example in the UK only about 10% of the stations have records that are more than 50 years in length. Along-explored way to augment the data available from a gauging station is to derive information about historical flood events and paleo-floods, which can be obtained from careful exploration of archives, old newspapers, flood marks or other signs of past flooding that are still discernible in the catchment, and the history of settlements. The inclusion of historical data in flood frequency estimation has been shown to substantially reduce the uncertainty around the estimated design events and is likely to provide insight into the rarest events which might have pre-dated the relatively short systematic records. Among other things, the FEH Local project funded by the Environment Agency aims to develop methods to easily incorporate historical information into the standard method of statistical flood frequency estimation in the UK. Different statistical estimation procedures are explored, namely maximum likelihood and partial probability weighted moments, and the strengths and weaknesses of each method are investigated. The project assesses the usefulness of historical data and aims to provide practitioners with useful guidelines to indicate in what circumstances the inclusion of historical data is likely to be beneficial in terms of reducing both the bias and the variability of the estimated flood frequency curves. The guidelines are based on the results of a large Monte Carlo simulation study, in which different estimation procedures and different data availability scenarios are studied. The study provides some indication of the situations under which different estimation procedures might give a better performance.
GPS/DR Error Estimation for Autonomous Vehicle Localization.
Lee, Byung-Hyun; Song, Jong-Hwa; Im, Jun-Hyuck; Im, Sung-Hyuck; Heo, Moon-Beom; Jee, Gyu-In
2015-08-21
Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level.
GPS/DR Error Estimation for Autonomous Vehicle Localization
Directory of Open Access Journals (Sweden)
Byung-Hyun Lee
2015-08-01
Full Text Available Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level.
Estimation of local rainfall erosivity using artificial neural network
Directory of Open Access Journals (Sweden)
Paulo Tarso Sanches Oliveira
2011-08-01
Full Text Available The information retrieval of local values of rainfall erosivity is essential for soil loss estimation with the Universal Soil Loss Equation (USLE, and thus is very useful in soil and water conservation planning. In this manner, the objective of this study was to develop an Artificial Neural Network (ANN with the capacity of estimating, with satisfactory accuracy, the rainfall erosivity in any location of the Mato Grosso do Sul state. We used data from rain erosivity, latitude, longitude, altitude of pluviometric and pluviographic stations located in the state to train and test an ANN. After training with various network configurations, we selected the best performance and higher coefficient of determination calculated on the basis of data erosivity of the sample test and the values estimated by ANN. In evaluating the results, the confidence and the agreement indices were used in addition to the coefficient of determination. It was found that it is possible to estimate the rainfall erosivity for any location in the state of Mato Grosso do Sul, in a reliable way, using only data of geographical coordinates and altitude.
Apply a hydrological model to estimate local temperature trends
Igarashi, Masao; Shinozawa, Tatsuya
2014-03-01
Continuous times series {f(x)} such as a depth of water is written f(x) = T(x)+P(x)+S(x)+C(x) in hydrological science where T(x),P(x),S(x) and C(x) are called the trend, periodic, stochastic and catastrophic components respectively. We simplify this model and apply it to the local temperature data such as given E. Halley (1693), the UK (1853-2010), Germany (1880-2010), Japan (1876-2010). We also apply the model to CO2 data. The model coefficients are evaluated by a symbolic computation by using a standard personal computer. The accuracy of obtained nonlinear curve is evaluated by the arithmetic mean of relative errors between the data and estimations. E. Halley estimated the temperature of Gresham College from 11/1692 to 11/1693. The simplified model shows that the temperature at the time rather cold compared with the recent of London. The UK and Germany data sets show that the maximum and minimum temperatures increased slowly from the 1890s to 1940s, increased rapidly from the 1940s to 1980s and have been decreasing since the 1980s with the exception of a few local stations. The trend of Japan is similar to these results.
Consistency of EEG source localization and connectivity estimates.
Mahjoory, Keyvan; Nikulin, Vadim V; Botrel, Loïc; Linkenkaer-Hansen, Klaus; Fato, Marco M; Haufe, Stefan
2017-05-15
As the EEG inverse problem does not have a unique solution, the sources reconstructed from EEG and their connectivity properties depend on forward and inverse modeling parameters such as the choice of an anatomical template and electrical model, prior assumptions on the sources, and further implementational details. In order to use source connectivity analysis as a reliable research tool, there is a need for stability across a wider range of standard estimation routines. Using resting state EEG recordings of N=65 participants acquired within two studies, we present the first comprehensive assessment of the consistency of EEG source localization and functional/effective connectivity metrics across two anatomical templates (ICBM152 and Colin27), three electrical models (BEM, FEM and spherical harmonics expansions), three inverse methods (WMNE, eLORETA and LCMV), and three software implementations (Brainstorm, Fieldtrip and our own toolbox). Source localizations were found to be more stable across reconstruction pipelines than subsequent estimations of functional connectivity, while effective connectivity estimates where the least consistent. All results were relatively unaffected by the choice of the electrical head model, while the choice of the inverse method and source imaging package induced a considerable variability. In particular, a relatively strong difference was found between LCMV beamformer solutions on one hand and eLORETA/WMNE distributed inverse solutions on the other hand. We also observed a gradual decrease of consistency when results are compared between studies, within individual participants, and between individual participants. In order to provide reliable findings in the face of the observed variability, additional simulations involving interacting brain sources are required. Meanwhile, we encourage verification of the obtained results using more than one source imaging procedure. Copyright © 2017 Elsevier Inc. All rights reserved.
Local solutions of Maximum Likelihood Estimation in Quantum State Tomography
Gonçalves, Douglas S; Lavor, Carlile; Farías, Osvaldo Jiménez; Ribeiro, P H Souto
2011-01-01
Maximum likelihood estimation is one of the most used methods in quantum state tomography, where the aim is to find the best density matrix for the description of a physical system. Results of measurements on the system should match the expected values produced by the density matrix. In some cases however, if the matrix is parameterized to ensure positivity and unit trace, the negative log-likelihood function may have several local minima. In several papers in the field, authors associate a source of errors to the possibility that most of these local minima are not global, so that optimization methods can be trapped in the wrong minimum, leading to a wrong density matrix. Here we show that, for convex negative log-likelihood functions, all local minima are global. We also show that a practical source of errors is in fact the use of optimization methods that do not have global convergence property or present numerical instabilities. The clarification of this point has important repercussion on quantum informat...
Energy Technology Data Exchange (ETDEWEB)
Napier, Bruce A.; Rishel, Jeremy P.; Cook, Kary M.
2013-09-12
Values for the dry deposition velocity of airborne particles were estimated with the GENII Version 2.10.1 computer code for the Savannah River site using assumptions about surface roughness parameters and particle size and density. Use of the GENII code is recommended by the U.S. Department of Energy for this purpose. Meteorological conditions evaluated include atmospheric stability classes D, E, and F and wind speeds of 0.5, 1.0, 1.5, and 2.0 m/s. Local surface roughness values ranging from 0.03 to 2 meters were evaluated. Particles with mass mean diameters of 1, 5, and 10 microns and densities of 1, 3, 4, and 5 g/cm3 were evaluated. Site specific meteorology was used to predict deposition velocity for Savannah River conditions for a range of distances from 670 to 11,500 meters.
Dose-response curve estimation: a semiparametric mixture approach.
Yuan, Ying; Yin, Guosheng
2011-12-01
In the estimation of a dose-response curve, parametric models are straightforward and efficient but subject to model misspecifications; nonparametric methods are robust but less efficient. As a compromise, we propose a semiparametric approach that combines the advantages of parametric and nonparametric curve estimates. In a mixture form, our estimator takes a weighted average of the parametric and nonparametric curve estimates, in which a higher weight is assigned to the estimate with a better model fit. When the parametric model assumption holds, the semiparametric curve estimate converges to the parametric estimate and thus achieves high efficiency; when the parametric model is misspecified, the semiparametric estimate converges to the nonparametric estimate and remains consistent. We also consider an adaptive weighting scheme to allow the weight to vary according to the local fit of the models. We conduct extensive simulation studies to investigate the performance of the proposed methods and illustrate them with two real examples.
Energy Technology Data Exchange (ETDEWEB)
Laroche, E. [Centre National de la Recherche Scientifique (CNRS UMR), Lab. des Sciences de l' Image, de l' Informatique et de la Teledetection, 67 - Illkirch (France); Durieu, C.; Louis, J.P. [Centre National de la Recherche Scientifique (CNRS UPRESA 8029), Lab. d' Electricite, Signaux et Robotique, 94 - Cachan (France)
2002-07-01
Many parametric models of induction machines in sinusoidal mode, some of which account for saturation and iron losses, are available. These models must not only be identifiable, they must also provide for an accurate estimation of physical parameters. In this paper, parameter estimation errors due to measurement noise and model errors are analyzed. The most perturbed cases, such as those neglecting saturation or iron losses, are given special consideration herein. This study allows drawing conclusions as to the practical identifiability of the various models. Results are then used to design optimal experiments in which parameter estimation errors have been minimized. (authors)
Human Age Estimation Based on Locality and Ordinal Information.
Li, Changsheng; Liu, Qingshan; Dong, Weishan; Zhu, Xiaobin; Liu, Jing; Lu, Hanqing
2015-11-01
In this paper, we propose a novel feature selection-based method for facial age estimation. The face aging is a typical temporal process, and facial images should have certain ordinal patterns in the aging feature space. From the geometrical perspective, a facial image can be usually seen as sampled from a low-dimensional manifold embedded in the original high-dimensional feature space. Thus, we first measure the energy of each feature in preserving the underlying local structure information and the ordinal information of the facial images, respectively, and then we intend to learn a low-dimensional aging representation that can maximally preserve both kinds of information. To further improve the performance, we try to eliminate the redundant local information and ordinal information as much as possible by minimizing nonlinear correlation and rank correlation among features. Finally, we formulate all these issues into a unified optimization problem, which is similar to linear discriminant analysis in format. Since it is expensive to collect the labeled facial aging images in practice, we extend the proposed supervised method to a semi-supervised learning mode including the semi-supervised feature selection method and the semi-supervised age prediction algorithm. Extensive experiments are conducted on the FACES dataset, the Images of Groups dataset, and the FG-NET aging dataset to show the power of the proposed algorithms, compared to the state-of-the-arts.
Schutte, Willem D.; Swanepoel, Jan W. H.
2016-09-01
An automated tool to derive the off-pulse interval of a light curve originating from a pulsar is needed. First, we derive a powerful and accurate non-parametric sequential estimation technique to estimate the off-pulse interval of a pulsar light curve in an objective manner. This is in contrast to the subjective `eye-ball' (visual) technique, and complementary to the Bayesian Block method which is currently used in the literature. The second aim involves the development of a statistical package, necessary for the implementation of our new estimation technique. We develop a statistical procedure to estimate the off-pulse interval in the presence of noise. It is based on a sequential application of p-values obtained from goodness-of-fit tests for uniformity. The Kolmogorov-Smirnov, Cramér-von Mises, Anderson-Darling and Rayleigh test statistics are applied. The details of the newly developed statistical package SOPIE (Sequential Off-Pulse Interval Estimation) are discussed. The developed estimation procedure is applied to simulated and real pulsar data. Finally, the SOPIE estimated off-pulse intervals of two pulsars are compared to the estimates obtained with the Bayesian Block method and yield very satisfactory results. We provide the code to implement the SOPIE package, which is publicly available at http://CRAN.R-project.org/package=SOPIE (Schutte).
LOCAL A PRIORI AND A POSTERIORI ERROR ESTIMATE OF TQC9 ELEMENT FOR THE BIHARMONIC EQUATION
Institute of Scientific and Technical Information of China (English)
Ming Wang; Weimeng Zhang
2008-01-01
In this paper,local a priori,local a posteriori and global a posteriori error estimates are obtained for TQC9 element for the biharmonic equation.An adaptive algorithm is given based on the a posteriori error estimates.
Estimating Preferences for Treatments in Patients With Localized Prostate Cancer
Energy Technology Data Exchange (ETDEWEB)
Ávila, Mónica [Health Services Research Unit, IMIM (Hospital del Mar Medical Research Institute), Barcelona (Spain); CIBER en Epidemiología y Salud Pública (CIBERESP) (Spain); Universitat Pompeu Fabra, Barcelona (Spain); Becerra, Virginia [Health Services Research Unit, IMIM (Hospital del Mar Medical Research Institute), Barcelona (Spain); Guedea, Ferran [Servicio de Oncología Radioterápica, Institut Català d' Oncologia, L' Hospitalet de Llobregat (Spain); Suárez, José Francisco [Servicio de Urología, Hospital Universitari de Bellvitge, L' Hospitalet de Llobregat (Spain); Fernandez, Pablo [Servicio de Oncología Radioterápica, Instituto Oncológico de Guipúzcoa, San Sebastián (Spain); Macías, Víctor [Servicio de Oncología Radioterápica, Hospital Clínico Universitario de Salamanca, Salamanca (Spain); Servicio de Oncología Radioterápica, Institut Oncologic del Valles-Hospital General de Catalunya, Sant Cugat del Vallès (Spain); Mariño, Alfonso [Servicio de Oncología Radioterápica, Centro Oncológico de Galicia, A Coruña (Spain); and others
2015-02-01
Purpose: Studies of patients' preferences for localized prostate cancer treatments have assessed radical prostatectomy and external radiation therapy, but none of them has evaluated brachytherapy. The aim of our study was to assess the preferences and willingness to pay of patients with localized prostate cancer who had been treated with radical prostatectomy, external radiation therapy, or brachytherapy, and their related urinary, sexual, and bowel side effects. Methods and Materials: This was an observational, prospective cohort study with follow-up until 5 years after treatment. A total of 704 patients with low or intermediate risk localized prostate cancer were consecutively recruited from 2003 to 2005. The estimation of preferences was conducted using time trade-off, standard gamble, and willingness-to-pay methods. Side effects were measured with the Expanded Prostate Index Composite (EPIC), a prostate cancer-specific questionnaire. Tobit models were constructed to assess the impact of treatment and side effects on patients' preferences. Propensity score was applied to adjust for treatment selection bias. Results: Of the 580 patients reporting preferences, 165 were treated with radical prostatectomy, 152 with external radiation therapy, and 263 with brachytherapy. Both time trade-off and standard gamble results indicated that the preferences of patients treated with brachytherapy were 0.06 utilities higher than those treated with radical prostatectomy (P=.01). Similarly, willingness-to-pay responses showed a difference of €57/month (P=.004) between these 2 treatments. Severe urinary incontinence presented an independent impact on the preferences elicited (P<.05), whereas no significant differences were found by bowel and sexual side effects. Conclusions: Our findings indicate that urinary incontinence is the side effect with the highest impact on preferences and that brachytherapy and external radiation therapy are more valued than radical
Directory of Open Access Journals (Sweden)
Adalmir Marquetti
2007-04-01
Full Text Available This paper employs local regression to estimate the output elasticity with respect to labor, human capital, physical capital and the elasticity of scale for 90 countries in 1985 and 1995. The results support the hypotheses of constant returns to scale to factors and decreasing returns to accumulable factors. The low capital-labor ratio countries have important differences in factor elasticities in relation to other countries. The augmentation of the production function by human capital did not reduce the elasticity of physical capital as suggested by Mankiw, Romer and Weil (1992. Moreover, it is investigated if the factors shares are really equal to their output elasticity. The wage share raises with the capital labor ratio and the sum of the output elasticity of labor and human capital is below the wage share for high capital labor ratio countries, happening the inverse for low capital labor ratio countries. It indicates the presence of externalities, or imperfect competition or that the marginal theory of distribution is inaccurate.
Henri, Pierre; Briand, Carine; Mangeney, André; 10.1029/2009JA014969
2013-01-01
Recent observation of large amplitude Langmuir waveforms during a Type III event in the solar wind have been interpreted as the signature of the electrostatic decay of beam-driven Langmuir waves. This mechanism is thought to be a first step to explain the generation of solar Type III radio emission. The threshold for this parametric instability in typical solar wind condition is investigated here by means of 1D-1V Vlasov-Poisson simulations. We show that the amplitude of the observed Langmuir beat-like waveforms is of the order of the effective threshold computed from the full kinetic simulations. The expected level of associated ion acoustic density fluctuations have also been computed for comparison with observations.
Directory of Open Access Journals (Sweden)
Gądek Wiesław
2016-12-01
Full Text Available While determining theoretical flood hydrographs, different methods of their construction are used depending on the needs of the problem or the scope of the project. It should be remembered that these methods differ mainly with the principle of the waveform averaging, which may be done either according to the flow or time. The hydrographs may be divided into nonparametric (determining on the basis of registered floods and parametric (using mathematical description of the flood course. One of the analytical methods is Strupczewski method which has two parameters: responsible for the waveform and specifies the base flow, the flow above which values of hydrograph are calculated. The functional description uses the Pearson type III density distribution.
DEFF Research Database (Denmark)
Linnet, Kristian
2005-01-01
Bootstrap, HPLC, limit of blank, limit of detection, non-parametric statistics, type I and II errors......Bootstrap, HPLC, limit of blank, limit of detection, non-parametric statistics, type I and II errors...
Wallace, T; Avital, I; Stojadinovic, A; Brücher, B L D M; Cote, E; Yu, J
2013-01-01
Even with the technological advances of dose-escalated IMRT with the addition of the latest image guidance technologies, local failures still occur. The combination of MRI-based imaging techniques can yield quantitative information that reflects on the biological properties of prostatic tissues. These techniques provide unique information that can be used for tumor detection in the treated gland. With the advent of these improved imaging modalities, it has become possible to more effectively image local recurrences within the prostate gland. With better imaging, these focal recurrences can be differentially targeted with salvage brachytherapy minimizing rectal and bladder toxicity. Here we report a novel use of MRI-directed focal brachytherapy after local recurrence. This technique offers a unique opportunity to safely and successfully treat recurrent prostate cancer, previously treated with definitive radiation therapy. The use of multi-parametric MRI-directed focal salvage permanent interstitial brachytherapy for locally recurrent adenocarcinoma of the prostate is a promising strategy to avoid more aggressive and expensive treatments that are associated with increased morbidity, potentially improving survival at potentially lower costs.
DEFF Research Database (Denmark)
Zimmermann, Ralf
2014-01-01
) in an offline stage. The claimed trajectory is obtained locally by interpolating the given local subspaces considered as sample points in the Grassmann manifold. It is shown that the manifold interpolation technique is subject to certain restrictions. Moreover, it turns out that the application of computing...... under a sinusoidal pitching motion....
Non-Parametric Inference in Astrophysics
Wasserman, L H; Nichol, R C; Genovese, C; Jang, W; Connolly, A J; Moore, A W; Schneider, J; Wasserman, Larry; Miller, Christopher J.; Nichol, Robert C.; Genovese, Chris; Jang, Woncheol; Connolly, Andrew J.; Moore, Andrew W.; Schneider, Jeff; group, the PICA
2001-01-01
We discuss non-parametric density estimation and regression for astrophysics problems. In particular, we show how to compute non-parametric confidence intervals for the location and size of peaks of a function. We illustrate these ideas with recent data on the Cosmic Microwave Background. We also briefly discuss non-parametric Bayesian inference.
Rawles, Christopher; Thurber, Clifford
2015-08-01
We present a simple, fast, and robust method for automatic detection of P- and S-wave arrivals using a nearest neighbours-based approach. The nearest neighbour algorithm is one of the most popular time-series classification methods in the data mining community and has been applied to time-series problems in many different domains. Specifically, our method is based on the non-parametric time-series classification method developed by Nikolov. Instead of building a model by estimating parameters from the data, the method uses the data itself to define the model. Potential phase arrivals are identified based on their similarity to a set of reference data consisting of positive and negative sets, where the positive set contains examples of analyst identified P- or S-wave onsets and the negative set contains examples that do not contain P waves or S waves. Similarity is defined as the square of the Euclidean distance between vectors representing the scaled absolute values of the amplitudes of the observed signal and a given reference example in time windows of the same length. For both P waves and S waves, a single pass is done through the bandpassed data, producing a score function defined as the ratio of the sum of similarity to positive examples over the sum of similarity to negative examples for each window. A phase arrival is chosen as the centre position of the window that maximizes the score function. The method is tested on two local earthquake data sets, consisting of 98 known events from the Parkfield region in central California and 32 known events from the Alpine Fault region on the South Island of New Zealand. For P-wave picks, using a reference set containing two picks from the Parkfield data set, 98 per cent of Parkfield and 94 per cent of Alpine Fault picks are determined within 0.1 s of the analyst pick. For S-wave picks, 94 per cent and 91 per cent of picks are determined within 0.2 s of the analyst picks for the Parkfield and Alpine Fault data set
高精度时变参数模型谱估计及应用%High Precision Time-Varying Parametric Model Spectrum Estimation and Its Application
Institute of Scientific and Technical Information of China (English)
邓卫强; 王跃钢; 杨颖涛
2011-01-01
To solve the problem of spectral deviation for Time-Varying Auto-Regressive(TVAR) model parametric spectrum estimation, this paper proposes a TVAR parameter estimation method based on Genetic Algorithm(GA) and combined objective function and the method is used to model the flight vibration time series and its spectrum estimation. An initial estimation of the parameters is acquired by the U-C algorithm; through modern spectrum estimation theory and the necessary condition of extreme value for continuous function, a constraint equation of model parameters is derived and a combined adaptability function is constructed; the GA is used to optimize the initial parameter estimation. Application results verifies the efficiency of the method.%现有时变自回归(TVAR)模型参数谱估计容易导致谱峰漂移.针对该问题,提出一种基于组合目标函数和遗传算法的TVAR参数估计方法,并将之应用于飞行器结构响应序列的建模及谱估计.通过U-C算法获得TVAR模型参数的初始估计;依据现代谱估计理论结合连续函数极值存在的必要条件,推导模型参数的频域约束条件并构造组合目标函数;采用遗传算法对模型参数初始估计值进行优化.应用结果证明了该方法的有效性.
Sparsity-based AOA Estimation for Emitter Localization
Directory of Open Access Journals (Sweden)
Lingwen Zhang
2012-08-01
Full Text Available Angle of arrival (AOA is able to achieve high accuracy when the antenna arrays are deployed much closer to the emitter. However, spatial resolution problem still exists. This paper presents a novel AOA estimation method called sparsity angle sensing (SAS to improve the resolution. It integrates compressive sensing theorem into the parameter estimation formula. Traditional approaches for AOA estimation such as beamforming (BF, minimum variance distortionless response (MVDR, multiple signal classification (MUSIC are compared with SAS, and simulation results are discussed. It is shown that SAS method outperforms the other three methods in spatial resolution and robustness.
OPTIMAL ERROR ESTIMATES OF THE PARTITION OF UNITY METHOD WITH LOCAL POLYNOMIAL APPROXIMATION SPACES
Institute of Scientific and Technical Information of China (English)
Yun-qing Huang; Wei Li; Fang Su
2006-01-01
In this paper, we provide a theoretical analysis of the partition of unity finite element method(PUFEM), which belongs to the family of meshfree methods. The usual error analysis only shows the order of error estimate to the same as the local approximations[12].Using standard linear finite element base functions as partition of unity and polynomials as local approximation space, in 1-d case, we derive optimal order error estimates for PUFEM interpolants. Our analysis show that the error estimate is of one order higher than the local approximations. The interpolation error estimates yield optimal error estimates for PUFEM solutions of elliptic boundary value problems.
Kratochvíla, Jiří; Jiřík, Radovan; Bartoš, Michal; Standara, Michal; Starčuk, Zenon; Taxt, Torfinn
2016-03-01
One of the main challenges in quantitative dynamic contrast-enhanced (DCE) MRI is estimation of the arterial input function (AIF). Usually, the signal from a single artery (ignoring contrast dispersion, partial volume effects and flow artifacts) or a population average of such signals (also ignoring variability between patients) is used. Multi-channel blind deconvolution is an alternative approach avoiding most of these problems. The AIF is estimated directly from the measured tracer concentration curves in several tissues. This contribution extends the published methods of multi-channel blind deconvolution by applying a more realistic model of the impulse residue function, the distributed capillary adiabatic tissue homogeneity model (DCATH). In addition, an alternative AIF model is used and several AIF-scaling methods are tested. The proposed method is evaluated on synthetic data with respect to the number of tissue regions and to the signal-to-noise ratio. Evaluation on clinical data (renal cell carcinoma patients before and after the beginning of the treatment) gave consistent results. An initial evaluation on clinical data indicates more reliable and less noise sensitive perfusion parameter estimates. Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup. © 2015 Wiley Periodicals, Inc.
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.
Energy Technology Data Exchange (ETDEWEB)
Gauthier, Y.
1997-10-20
Geostatistical tools are increasingly used to model permeability fields in subsurface reservoirs, which are considered as a particular random variable development depending of several geostatistical parameters such as variance and correlation length. The first part of the thesis is devoted to the study of relations existing between the transient well pressure (the well test) and the stochastic permeability field, using the apparent permeability concept.The well test performs a moving permeability average over larger and larger volume with increasing time. In the second part, the geostatistical parameters are evaluated using well test data; a Bayesian framework is used and parameters are estimated using the maximum likelihood principle by maximizing the well test data probability density function with respect to these parameters. This method, involving a well test fast evaluation, provides an estimation of the correlation length and the variance over different realizations of a two-dimensional permeability field
Alper, Kenneth; Raghavan, Manoj; Isenhart, Robert; Howard, Bryant; Doyle, Werner; John, Roy; Prichep, Leslie
2008-02-01
This preliminary study sought to localize epileptogenic regions in patients with partial epilepsy by analysis of interictal EEG activity utilizing variable resolution electromagnetic tomography (VARETA), a three-dimensional quantitative electroencephalographic (QEEG) frequency-domain distributed source modeling technique. The very narrow band (VNB) spectra spanned the frequency range 0.39 Hz to 19.1 Hz, in 0.39 Hz steps. These VNB spectra were compared to normative data and transformed to provide Z-scores for every scalp derivation, and the spatial distributions of the probable EEG generators of the most abnormal values were displayed on slices from a probabilistic MRI atlas. Each voxel was color-coded to represent the significance of the deviation relative to age appropriate normative values. We compared the resulting three-dimensional images to the localization of epileptogenic regions based on invasive intracranial EEG recordings of seizure onsets. The VARETA image indicated abnormal interictal spectral power values in regions of seizure onset identified by invasive monitoring, mainly in delta and theta range (1.5 to 8.0 Hz). The VARETA localization of the most abnormal voxel was congruent with the epileptogenic regions identified by intracranial recordings with regard to hemisphere in all 6 cases, and with regard to lobe in 5 cases. In contrast, abnormal findings with routine EEG agreed with invasive monitoring with regard to hemisphere in 3 cases and with regard to lobe in 2 cases. These results suggest that analysis of background interictal EEG utilizing distributed source models should be investigated further in clinical epilepsy.
Jongjoo, Kim; Davis, Scott K; Taylor, Jeremy F
2002-06-01
Empirical confidence intervals (CIs) for the estimated quantitative trait locus (QTL) location from selective and non-selective non-parametric bootstrap resampling methods were compared for a genome scan involving an Angus x Brahman reciprocal fullsib backcross population. Genetic maps, based on 357 microsatellite markers, were constructed for 29 chromosomes using CRI-MAP V2.4. Twelve growth, carcass composition and beef quality traits (n = 527-602) were analysed to detect QTLs utilizing (composite) interval mapping approaches. CIs were investigated for 28 likelihood ratio test statistic (LRT) profiles for the one QTL per chromosome model. The CIs from the non-selective bootstrap method were largest (87 7 cM average or 79-2% coverage of test chromosomes). The Selective II procedure produced the smallest CI size (42.3 cM average). However, CI sizes from the Selective II procedure were more variable than those produced by the two LOD drop method. CI ranges from the Selective II procedure were also asymmetrical (relative to the most likely QTL position) due to the bias caused by the tendency for the estimated QTL position to be at a marker position in the bootstrap samples and due to monotonicity and asymmetry of the LRT curve in the original sample.
Estimating Independent Locally Shifted Random Utility Models for Ranking Data
Lam, Kar Yin; Koning, Alex J.; Franses, Philip Hans
2011-01-01
We consider the estimation of probabilistic ranking models in the context of conjoint experiments. By using approximate rather than exact ranking probabilities, we avoided the computation of high-dimensional integrals. We extended the approximation technique proposed by Henery (1981) in the context of the Thurstone-Mosteller-Daniels model to any…
ACCES: Offline Accuracy Estimation for Fingerprint-Based Localization
DEFF Research Database (Denmark)
Nikitin, Artyom; Laoudias, Christos; Chatzimilioudis, Georgios
2017-01-01
will be able to use our service directly to collect signal measurements over the venue using an Android smartphone; and (ii) Reflection Mode, where attendees will be able to observe the collected measurements and the respective ACCES accuracy estimations in the form of an overlay heatmap....
Directory of Open Access Journals (Sweden)
J. Bohlin
2012-07-01
Full Text Available The recent development in software for automatic photogrammetric processing of multispectral aerial imagery, and the growing nation-wide availability of Digital Elevation Model (DEM data, are about to revolutionize data capture for forest management planning in Scandinavia. Using only already available aerial imagery and ALS-assessed DEM data, raster estimates of the forest variables mean tree height, basal area, total stem volume, and species-specific stem volumes were produced and evaluated. The study was conducted at a coniferous hemi-boreal test site in southern Sweden (lat. 58° N, long. 13° E. Digital aerial images from the Zeiss/Intergraph Digital Mapping Camera system were used to produce 3D point-cloud data with spectral information. Metrics were calculated for 696 field plots (10 m radius from point-cloud data and used in k-MSN to estimate forest variables. For these stands, the tree height ranged from 1.4 to 33.0 m (18.1 m mean, stem volume from 0 to 829 m3 ha-1 (249 m3 ha-1 mean and basal area from 0 to 62.2 m2 ha-1 (26.1 m2 ha-1 mean, with mean stand size of 2.8 ha. Estimates made using digital aerial images corresponding to the standard acquisition of the Swedish National Land Survey (Lantmäteriet showed RMSEs (in percent of the surveyed stand mean of 7.5% for tree height, 11.4% for basal area, 13.2% for total stem volume, 90.6% for pine stem volume, 26.4 for spruce stem volume, and 72.6% for deciduous stem volume. The results imply that photogrammetric matching of digital aerial images has significant potential for operational use in forestry.
Second order average estimates on local data of cusp forms
2005-01-01
We specify sufficient conditions for the square modulus of the local parameters of a family of GL(n) cusp forms to be bounded on average. These conditions are global in nature and are at present satisfied for n less than or equal to 4. As an application, we show that Rankin-Selberg L-functions on GL(m) x GL(n), when m and n are less than or equal to 4, satisfy the standard convexity bound.
Localization of periodic orbits of polynomial systems by ellipsoidal estimates
Energy Technology Data Exchange (ETDEWEB)
Starkov, Konstantin E. [CITEDI-IPN, Avenue del Parque 1310, Mesa de Otay, Tijuana, BC (Mexico)]. E-mail: konst@citedi.mx; Krishchenko, Alexander P. [Bauman Moscow State Technical University, 2nd Baumanskaya Street, 5, Moscow 105005 (Russian Federation)]. E-mail: apkri@999.ru
2005-02-01
In this paper we study the localization problem of periodic orbits of multidimensional continuous-time systems in the global setting. Our results are based on the solution of the conditional extremum problem and using sign-definite quadratic and quartic forms. As examples, the Rikitake system and the Lamb's equations for a three-mode operating cavity in a laser are considered.
Localization of acoustic sensors from passive Green's function estimation.
Nowakowski, Thibault; Daudet, Laurent; de Rosny, Julien
2015-11-01
A number of methods have recently been developed for passive localization of acoustic sensors, based on the assumption that the acoustic field is diffuse. This article presents the more general case of equipartition fields, which takes into account reflections off boundaries and/or scatterers. After a thorough discussion on the fundamental differences between the diffuse and equipartition models, it is shown that the method is more robust when dealing with wideband noise sources. Finally, experimental results show, for two types of boundary conditions, that this approach is especially relevant when acoustic sensors are close to boundaries.
Parallelized Local Volatility Estimation Using GP-GPU Hardware Acceleration
Douglas, Craig C.
2010-01-01
We introduce an inverse problem for the local volatility model in option pricing. We solve the problem using the Levenberg-Marquardt algorithm and use the notion of the Fréchet derivative when calculating the Jacobian matrix. We analyze the existence of the Fréchet derivative and its numerical computation. To reduce the computational time of the inverse problem, a GP-GPU environment is considered for parallel computation. Numerical results confirm the validity and efficiency of the proposed method. ©2010 IEEE.
Multi-person localization and orientation estimation in volumetric scene reconstructions
Liem, M.C.
2014-01-01
Accurate localization of persons and estimation of their pose are important topics in current-day computer vision research. As part of the pose estimation, estimating the body orientation of a person (i.e. rotation around torso major axis) conveys important information about the person's current act
Salim, Samir; Ly, Chun; Brinchmann, Jarle; Davé, Romeel; Dickinson, Mark; Salzer, John J; Charlot, Stéphane
2014-01-01
It has been proposed that the mass-metallicity relation of galaxies exhibits a secondary dependence on star formation rate (SFR), and that the resulting M-Z-SFR relation may be redshift-invariant, i.e., "fundamental." However, conflicting results on the character of the SFR dependence, and whether it exists, have been reported. To gain insight into the origins of the conflicting results, we (a) devise a non-parametric, astrophysically-motivated analysis framework based on the offset from the star-forming ("main") sequence at a given stellar mass (relative specific SFR), (b) apply this methodology and perform a comprehensive re-analysis of the local M-Z-SFR relation, based on SDSS, GALEX, and WISE data, and (c) study the impact of sample selection, and of using different metallicity and SFR indicators. We show that metallicity is anti-correlated with specific SFR regardless of the indicators used. We do not find that the relation is spurious due to correlations arising from biased metallicity measurements, or ...
Local Behavior of Sparse Analysis Regularization: Applications to Risk Estimation
Vaiter, Samuel; Peyré, Gabriel; Dossal, Charles; Fadili, Jalal
2012-01-01
This paper studies the recovery of an unknown signal $x_0$ from low dimensional noisy observations $y = \\Phi x_0 + w$, where $\\Phi$ is an ill-posed linear operator and $w$ accounts for some noise. We focus our attention to sparse analysis regularization. The recovery is performed by minimizing the sum of a quadratic data fidelity term and the $\\lun$-norm of the correlations between the sought after signal and atoms in a given (generally overcomplete) dictionary. The $\\lun$ prior is weighted by a regularization parameter $\\lambda > 0$ that accounts for the noise level. In this paper, we prove that minimizers of this problem are piecewise-affine functions of the observations $y$ and the regularization parameter $\\lambda$. As a byproduct, we exploit these properties to get an objectively guided choice of $\\lambda$. More precisely, we propose an extension of the Generalized Stein Unbiased Risk Estimator (GSURE) and show that it is an unbiased estimator of an appropriately defined risk. This encompasses special ca...
Estimation of health benefits from a local living wage ordinance.
Bhatia, R; Katz, M
2001-09-01
This study estimated the magnitude of health improvements resulting from a proposed living wage ordinance in San Francisco. Published observational models of the relationship of income to health were applied to predict improvements in health outcomes associated with proposed wage increases in San Francisco. With adoption of a living wage of $11.00 per hour, we predict decreases in premature death from all causes for adults aged 24 to 44 years working full-time in families whose current annual income is $20,000 (for men, relative hazard [RH] = 0.94, 95% confidence interval [CI] = 0.92, 0.97; for women, RH = 0.96, 95% CI = 0.95, 0.98). Improvements in subjectively rated health and reductions in the number of days sick in bed, in limitations of work and activities of daily living, and in depressive symptoms were also predicted, as were increases in daily alcohol consumption. For the offspring of full-time workers currently earning $20,000, a living wage predicts an increase of 0.25 years (95% CI = 0.20, 0.30) of completed education, increased odds of completing high school (odds ratio = 1.34, 95% CI = 1.20, 1.49), and a reduced risk of early childbirth (RH = 0.78, 95% CI = 0.69, 0.86). A living wage in San Francisco is associated with substantial health improvement.
Combined parametric-nonparametric identification of block-oriented systems
Mzyk, Grzegorz
2014-01-01
This book considers a problem of block-oriented nonlinear dynamic system identification in the presence of random disturbances. This class of systems includes various interconnections of linear dynamic blocks and static nonlinear elements, e.g., Hammerstein system, Wiener system, Wiener-Hammerstein ("sandwich") system and additive NARMAX systems with feedback. Interconnecting signals are not accessible for measurement. The combined parametric-nonparametric algorithms, proposed in the book, can be selected dependently on the prior knowledge of the system and signals. Most of them are based on the decomposition of the complex system identification task into simpler local sub-problems by using non-parametric (kernel or orthogonal) regression estimation. In the parametric stage, the generalized least squares or the instrumental variables technique is commonly applied to cope with correlated excitations. Limit properties of the algorithms have been shown analytically and illustrated in simple experiments.
Sanyal, Amit K.
2005-01-01
There are several attitude estimation algorithms in existence, all of which use local coordinate representations for the group of rigid body orientations. All local coordinate representations of the group of orientations have associated problems. While minimal coordinate representations exhibit kinematic singularities for large rotations, the quaternion representation requires satisfaction of an extra constraint. This paper treats the attitude estimation and filtering problem as an optimizati...
DOA Estimation for Local Scattered CDMA Signals by Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Jhih-Chung Chang
2012-03-01
Full Text Available This paper deals with the direction-of-arrival (DOA estimation of local scattered code-division multiple access (CDMA signals based on a particle swarm optimization (PSO search. For conventional spectral searching estimators with local scattering, the searching complexity and estimating accuracy strictly depend on the number of search grids used during the search. In order to obtain high-resolution and accurate DOA estimation, a smaller grid size is needed. This is time consuming and it is unclear how to determine the required number of search grids. In this paper, a modified PSO is presented to reduce the required search grids for the conventional spectral searching estimator with the effects of local scattering. Finally, several computer simulations are provided for illustration and comparison.
DOA estimation for local scattered CDMA signals by particle swarm optimization.
Chang, Jhih-Chung
2012-01-01
This paper deals with the direction-of-arrival (DOA) estimation of local scattered code-division multiple access (CDMA) signals based on a particle swarm optimization (PSO) search. For conventional spectral searching estimators with local scattering, the searching complexity and estimating accuracy strictly depend on the number of search grids used during the search. In order to obtain high-resolution and accurate DOA estimation, a smaller grid size is needed. This is time consuming and it is unclear how to determine the required number of search grids. In this paper, a modified PSO is presented to reduce the required search grids for the conventional spectral searching estimator with the effects of local scattering. Finally, several computer simulations are provided for illustration and comparison.
Energy Technology Data Exchange (ETDEWEB)
Rouzaud, C., E-mail: crouzaud@adm.estp.fr [LMT (ENS Cachan, CNRS, Université Paris Saclay) 61 avenue du Président Wilson, 94235 Cachan (France); Université Paris-Est, Institut de Recherche en Constructibilité, ESTP 28 avenue du Président Wilson, 94230 Cachan (France); AREVA, 10 rue Juliette Récamier, 69006 Lyon (France); Gatuingt, F. [LMT (ENS Cachan, CNRS, Université Paris Saclay) 61 avenue du Président Wilson, 94235 Cachan (France); Hervé, G. [Université Paris-Est, Institut de Recherche en Constructibilité, ESTP 28 avenue du Président Wilson, 94230 Cachan (France); Moussallam, N. [AREVA, 10 rue Juliette Récamier, 69006 Lyon (France); Dorival, O. [Icam, Site de Toulouse, 75 avenue de Grande-Bretagne, 31076 Toulouse Cedex 3 (France); Université de Toulouse, Institut Clément Ader (ICA), INSA, UPS, Mines Albi, ISAE 135 avenue de Rangueil, 31077 Toulouse Cedex (France)
2016-03-15
Highlights: • Structures could resist to the induced accelerations which they might undergo. • The characterization of non-linearities in the signal of an aircraft impact. • The non linear impact area are studied through a sensitivity analysis. • This analysis should allow to achieve a link between aircraft impact parameters. - Abstract: In the process of nuclear power plant design, the safety of structures is an important aspect. Civil engineering structures have to resist the accelerations induced by, for example, seismic loads or shaking loads resulting from the aircraft impact. This is even more important for the in-structures equipments that have also to be qualified against the vibrations generated by this kind of hazards. In the case of aircraft crash, as a large variety of scenarios has to be envisaged, it is necessary to use methods that are less CPU-time consuming and that consider appropriately the nonlinearities. The analysis presented in this paper deals with the problem of the characterization of nonlinearities (damaged area, transmitted force) in the response of a structure subjected to an aircraft impact. The purpose of our study is part of the development of a new decoupled nonlinear and elastic way for calculating the shaking of structures following an aircraft impact which could be very numerically costly if studied with classical finite element methods. The aim is to identify which parameters control the dimensions of the nonlinear zone and so will have a direct impact on the induced vibrations. In a design context, several load cases (and simulations) are analyzed in order to consider a wide range of impact (different loading surfaces, momentum) and data sets of the target (thickness, reinforcements). In this work, the nonlinear area generated by the impact is localized and studied through a parametric analysis associated with a sensitivity analysis to identify the boundaries between the elastic domain and this nonlinear area.
Estimation of the FRF Through the Improved Local Bandwidth Selection in the Local Polynomial Method
DEFF Research Database (Denmark)
Thummala, Prasanth; Schoukens, Johan
2012-01-01
This paper presents a nonparametric method to measure an improved frequency response function (FRF) of a linear dynamic system excited by a random input. Recently, the local polynomial method (LPM) has been proposed as a technique to reduce the leakage errors on FRF measurements. The noise...
Distributed parameter estimation in wireless sensor networks using fused local observations
Fanaei, Mohammad; Valenti, Matthew C.; Schmid, Natalia A.; Alkhweldi, Marwan M.
2012-05-01
The goal of this paper is to reliably estimate a vector of unknown deterministic parameters associated with an underlying function at a fusion center of a wireless sensor network based on its noisy samples made at distributed local sensors. A set of noisy samples of a deterministic function characterized by a nite set of unknown param- eters to be estimated is observed by distributed sensors. The parameters to be estimated can be some attributes associated with the underlying function, such as its height, its center, its variances in dierent directions, or even the weights of its specic components over a predened basis set. Each local sensor processes its observation and sends its processed sample to a fusion center through parallel impaired communication channels. Two local processing schemes, namely analog and digital, are considered. In the analog local processing scheme, each sensor transmits an amplied version of its local analog noisy observation to the fusion center, acting like a relay in a wireless network. In the digital local processing scheme, each sensor quantizes its noisy observation before trans- mitting it to the fusion center. A at-fading channel model is considered between the local sensors and fusion center. The fusion center combines all of the received locally-processed observations and estimates the vector of unknown parameters of the underlying function. Two dierent well-known estimation techniques, namely maximum-likelihood (ML), for both analog and digital local processing schemes, and expectation maximization (EM), for digital local processing scheme, are considered at the fusion center. The performance of the proposed distributed parameter estimation system is investigated through simulation of practical scenarios for a sample underlying function.
Estimating organic, local, and other price premiums in the Hawaii fluid milk market.
Loke, Matthew K; Xu, Xun; Leung, PingSun
2015-04-01
With retail scanner data, we applied hedonic price modeling to explore price premiums for organic, local, and other product attributes of fluid milk in Hawaii. Within the context of revealed preference, this analysis of organic and local attributes, under a single unified framework, is significant, as research in this area is deficient in the existing literature. This paper finds both organic and local attributes delivered price premiums over imported, conventional, whole fluid milk. However, the estimated price premium for organic milk (24.6%) is significantly lower than findings in the existing literature. Likewise, the price premium for the local attribute is estimated at 17.4%, again substantially lower compared with an earlier, stated preference study in Hawaii. Beyond that, we estimated a robust price premium of 19.7% for nutritional benefits claimed. The magnitude of this estimated coefficient reinforces the notion that nutrition information on food is deemed beneficial and valuable. Finally, package size measures the influence of product weight. With each larger package size, the estimate led to a corresponding larger price discount. This result is consistent with the practice of weight discounting that retailers usually offer with fresh packaged food. Additionally, we estimated a fairly high Armington elasticity of substitution, which suggests a relatively high degree of substitution between local and imported fluid milk when their relative price changes. Overall, this study establishes price premiums for organic, local, and nutrition benefits claimed for fluid milk in Hawaii.
A Simple Introduction to Moving Least Squares and Local Regression Estimation
Energy Technology Data Exchange (ETDEWEB)
Garimella, Rao Veerabhadra [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-06-22
In this brief note, a highly simpli ed introduction to esimating functions over a set of particles is presented. The note starts from Global Least Squares tting, going on to Moving Least Squares estimation (MLS) and nally, Local Regression Estimation (LRE).
Formisano, Antonio; Chieffo, Nicola; Milo, Bartolomeo; Fabbrocino, Francesco
2016-12-01
The current paper deals with the seismic vulnerability evaluation of masonry constructions grouped in aggregates through an "ad hoc" quick vulnerability form based on new assessment parameters considering local collapse mechanisms. First, a parametric kinematic analysis on masonry walls with different height (h) / thickness (t) ratios has been developed with the purpose of identifying the collapse load multiplier for activation of the main four first-order failure mechanisms. Subsequently, a form initially conceived for building aggregates suffering second-mode collapse mechanisms, has been expanded on the basis of the achieved results. Tre proposed quick vulnerability technique has been applied to one case study within the territory of Arsita (Teramo, Italy) and, finally, it has been also validated by the comparison of results with those deriving from application of the well-known FaMIVE procedure.
Institute of Scientific and Technical Information of China (English)
章仁江; 王国瑾
2004-01-01
Triangulation, subdivision and intersection of the surface are the basic and common operations in Computer Aided Design and computer graphics. A complicated surface can be approximated by some triangular patches. The key technique of this problem is the estimate of the approximate error, this work improves the previous result and obtains a new result. These results are valuable for improving the triangulation and subdivision algorithm of parametric surface in the design system.
DEFF Research Database (Denmark)
Steinø, Nicolai; Obeling, Esben
2013-01-01
. Therefore, the design of urban space happens in a space of power. And this is something which traditionally the average urban dweller does not have. Urban designers communicate about urban space design in a professional language and through graphics which are not always intelligible to laypersons...... the given time and resource limits. And again, the lay person, whether she is a resident, a local business person, or a NGO representative, is left with little influence, when it comes to the design of urban space. With the advent of parametric design tools, this need no longer be the case. Rather than...
Parametric Models of Periodogram
Indian Academy of Sciences (India)
P. Mohan; A. Mangalam; S. Chattopadhyay
2014-09-01
The maximum likelihood estimator is used to determine fit parameters for various parametric models of the Fourier periodogram followed by the selection of the best-fit model amongst competing models using the Akaike information criteria. This analysis, when applied to light curves of active galactic nuclei can be used to infer the presence of quasi-periodicity and break or knee frequencies. The extracted information can be used to place constraints on the mass, spin and other properties of the putative central black hole and the region surrounding it through theoretical models involving disk and jet physics.
H\\"older Estimates for Singular Non-local Parabolic Equations
Kim, Sunghoon
2011-01-01
In this paper, we establish local H\\"older estimate for non-negative solutions of the singular equation \\eqref{eq-nlocal-PME-1} below, for $m$ in the range of exponents $(\\frac{n-2\\sigma}{n+2\\sigma},1)$. Since we have trouble in finding the local energy inequality of $v$ directly. we use the fact that the operator $(-\\La)^{\\sigma}$ can be thought as the normal derivative of some extension $v^{\\ast}$ of $v$ to the upper half space, \\cite{CS}, i.e., $v$ is regarded as boundary value of $v^{\\ast}$ the solution of some local extension problem. Therefore, the local H\\"older estimate of $v$ can be obtained by the same regularity of $v^{\\ast}$. In addition, it enables us to describe the behaviour of solution of non-local fast diffusion equation near their extinction time.
Institute of Scientific and Technical Information of China (English)
林作铨; 李未
1995-01-01
Parametric logic is introduced. The language, semantics and axiom system of parametric logic are defined. Completeness theorem of parametric logic is provided. Parametric logic has formal ability powerful enough to capture a wide class of logic as its special cases, and therefore can be viewed as a uniform basis for modern logics.
Far-field DOA estimation and near-field localization for multipath signals
Elbir, Ahmet M.; Tuncer, T. Engin
2014-09-01
In direction finding and localization applications, multipath signals are important sources of error for parameter estimation. When the antenna array receives multipath reflections which are coherent with the far-field line-of-sight signal, estimating the far- and near-field components becomes an important problem. In this paper, a new method is proposed to estimate the direction-of-arrival (DOA) of the far-field source and to localize its near-field multipaths. Far-field source DOA is estimated using calibration of the antenna array. A near-to-far transformation is proposed for the estimation of the near-field source DOA angles. In order to estimate the near-field range parameters, a compressive sensing approach is presented where a dictionary with near-field sources with different ranges is employed. As a result, the proposed method estimates the far-field and near-field source DOAs as well as the range and the signal amplitudes of the near-field sources. This method is evaluated using close-to-real world data generated by a numerical electromagnetic tool, where the array and transmitter are placed in an irregular terrain and array data are generated using full 3-D propagation model. It is shown that unknown source parameters can be estimated effectively showing the potential of the proposed approach in applications involving high-frequency direction finding and indoor localization.
An Iterated Local Search Algorithm for Estimating the Parameters of the Gamma/Gompertz Distribution
Directory of Open Access Journals (Sweden)
Behrouz Afshar-Nadjafi
2014-01-01
Full Text Available Extensive research has been devoted to the estimation of the parameters of frequently used distributions. However, little attention has been paid to estimation of parameters of Gamma/Gompertz distribution, which is often encountered in customer lifetime and mortality risks distribution literature. This distribution has three parameters. In this paper, we proposed an algorithm for estimating the parameters of Gamma/Gompertz distribution based on maximum likelihood estimation method. Iterated local search (ILS is proposed to maximize likelihood function. Finally, the proposed approach is computationally tested using some numerical examples and results are analyzed.
Estimating local atmosphere-surface fluxes using eddy covariance and numerical Ogive optimization
DEFF Research Database (Denmark)
Sievers, Jakob; Papakyriakou, Tim; Larsen, Søren
2014-01-01
Estimating representative surface-fluxes using eddy covariance leads invariably to questions concerning inclusion or exclusion of low-frequency flux contributions. For studies where fluxes are linked to local physical parameters and up-scaled through numerical modeling efforts, low-frequency cont......Estimating representative surface-fluxes using eddy covariance leads invariably to questions concerning inclusion or exclusion of low-frequency flux contributions. For studies where fluxes are linked to local physical parameters and up-scaled through numerical modeling efforts, low......-frequency contributions interfere with our ability to isolate local biogeochemical processes of interest, as represented by turbulent fluxes. No method currently exists to disentangle low-frequency contributions on flux estimates. Here, we present a novel comprehensive numerical scheme to identify and separate out low...
Estimation of azimuth and slowness of teleseismic signals recorded by a local seismic network
Institute of Scientific and Technical Information of China (English)
靳平; 潘常周
2002-01-01
A new method that is applicable to local seismic networks to estimate the azimuth and slowness of teleseismic signals is introduced in the paper. The method is based on the correlation between the arrival times and station positions. The analyzed results indicate that the azimuth and slowness of teleseismic signals can be accurately estimated by the method. Average errors for azimuth and slowness measurements obtained by this method using data of Xi(an Digital Telemetry Seismic Network are 2.0o and 0.34 s/(o), respectively. The conclusions drawn from this study indicate that this method may be very useful to interpret teleseismic records of local seismic networks.
Majeed, Khaqan
2015-12-22
The Received Signal Strength (RSS) based fingerprinting approaches for indoor localization pose a need for updating the fingerprint databases due to dynamic nature of the indoor environment. This process is hectic and time-consuming when the size of the indoor area is large. The semi-supervised approaches reduce this workload and achieve good accuracy around 15% of the fingerprinting load but the performance is severely degraded if it is reduced below this level. We propose an indoor localization framework that uses unsupervised manifold alignment. It requires only 1% of the fingerprinting load, some crowd sourced readings and plan coordinates of the indoor area. The 1% fingerprinting load is used only in perturbing the local geometries of the plan coordinates. The proposed framework achieves less than 5m mean localization error, which is considerably better than semi-supervised approaches at very small amount of fingerprinting load. In addition, the few location estimations together with few fingerprints help to estimate the complete radio map of the indoor environment. The estimation of radio map does not demand extra workload rather it employs the already available information from the proposed indoor localization framework. The testing results for radio map estimation show almost 50% performance improvement by using this information as compared to using only fingerprints.
Application of Matrix Pencil Algorithm to Mobile Robot Localization Using Hybrid DOA/TOA Estimation
Directory of Open Access Journals (Sweden)
Lan Anh Trinh
2012-12-01
Full Text Available Localization plays an important role in robotics for the tasks of monitoring, tracking and controlling a robot. Much effort has been made to address robot localization problems in recent years. However, despite many proposed solutions and thorough consideration, in terms of developing a low‐cost and fast processing method for multiple‐source signals, the robot localization problem is still a challenge. In this paper, we propose a solution for robot localization with regards to these concerns. In order to locate the position of a robot, both the coordinate and the orientation of a robot are necessary. We develop a localization method using the Matrix Pencil (MP algorithm for hybrid detection of direction of arrival (DOA and time of arrival (TOA. TOA of the signal is estimated for computing the distance between the mobile robot and a base station (BS. Based on the distance and the estimated DOA, we can estimate the mobile robot’s position. The characteristics of the algorithm are examined through analysing simulated experiments and the results demonstrate the advantages of our method over previous works in dealing with the above challenges. The method is constructed based on the low‐cost infrastructure of radio frequency devices; the DOA/TOA estimation is performed with just single value decomposition for fast processing. Finally, the MP algorithm combined with tracking using a Kalman filter allows our proposed method to locate the positions of multiple source signals.
Estimation for Non-Gaussian Locally Stationary Processes with Empirical Likelihood Method
Directory of Open Access Journals (Sweden)
Hiroaki Ogata
2012-01-01
Full Text Available An application of the empirical likelihood method to non-Gaussian locally stationary processes is presented. Based on the central limit theorem for locally stationary processes, we give the asymptotic distributions of the maximum empirical likelihood estimator and the empirical likelihood ratio statistics, respectively. It is shown that the empirical likelihood method enables us to make inferences on various important indices in a time series analysis. Furthermore, we give a numerical study and investigate a finite sample property.
Parametric versus non-parametric simulation
Dupeux, Bérénice; Buysse, Jeroen
2014-01-01
Most of ex-ante impact assessment policy models have been based on a parametric approach. We develop a novel non-parametric approach, called Inverse DEA. We use non parametric efficiency analysis for determining the farm’s technology and behaviour. Then, we compare the parametric approach and the Inverse DEA models to a known data generating process. We use a bio-economic model as a data generating process reflecting a real world situation where often non-linear relationships exist. Results s...
Indoor Self-Localization and Orientation Estimation of Smartphones Using Acoustic Signals
Directory of Open Access Journals (Sweden)
Héctor A. Sánchez-Hevia
2017-01-01
Full Text Available We propose a new acoustic self-localization and orientation estimation algorithm for smartphones networks composed of commercial off-the-shelf devices equipped with two microphones and a speaker. Each smartphone acts as an acoustic transceiver, which emits and receives acoustic signals. Node locations are found by combining estimates of the range and direction of arrival (DoA between node pairs using a maximum likelihood (ML estimator. A tailored optimization algorithm is proposed to simultaneously solve the DoA uncertainty problem that arises from the use of only 2 microphones per node and obtain the azimuthal orientation of each node without requiring an electronic compass.
Energy Technology Data Exchange (ETDEWEB)
Jang, Hong; Lee, Jay H. [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of); Braatz, Richard D. [Massachusetts Institute of Technology (MIT), Cambridge (United States)
2016-01-15
This paper proposes a maximum likelihood estimation (MLE) method for estimating time varying local concentration of the target molecule proximate to the sensor from the time profile of monomolecular adsorption and desorption on the surface of the sensor at nanoscale. Recently, several carbon nanotube sensors have been developed that can selectively detect target molecules at a trace concentration level. These sensors use light intensity changes mediated by adsorption or desorption phenomena on their surfaces. The molecular events occurring at trace concentration levels are inherently stochastic, posing a challenge for optimal estimation. The stochastic behavior is modeled by the chemical master equation (CME), composed of a set of ordinary differential equations describing the time evolution of probabilities for the possible adsorption states. Given the significant stochastic nature of the underlying phenomena, rigorous stochastic estimation based on the CME should lead to an improved accuracy over than deterministic estimation formulated based on the continuum model. Motivated by this expectation, we formulate the MLE based on an analytical solution of the relevant CME, both for the constant and the time-varying local concentrations, with the objective of estimating the analyte concentration field in real time from the adsorption readings of the sensor array. The performances of the MLE and the deterministic least squares are compared using data generated by kinetic Monte Carlo (KMC) simulations of the stochastic process. Some future challenges are described for estimating and controlling the concentration field in a distributed domain using the sensor technology.
Craifaleanu, Iolanda-Gabriela
2013-01-01
The paper presents results of a comprehensive study of ground motions recorded during the strong earthquakes (moment magnitude Mw > 6) generated during last 34 years by the seismic source of Vrancea, Romania. By analyzing over 300 accelerograms, the capacity of different expressions in the literature to estimate the predominant period of a ground motion is compared. The correlation between the values obtained from different evaluations is assessed as well. The dependence of the predominant period of different factors of influence is analysed. Comparisons are made between the parameters determined for the same seismic event at different stations, as well as for ground motions recorded on the same site at successive earthquakes. The results are interpreted in correlation with the information provided by frequency bandwidth parameters. Considerations are made on the measure in which the influence on the frequency content of the source and of local geological conditions can be separated, for seismic motions recor...
Estimating local atmosphere-surface fluxes using eddy covariance and numerical Ogive optimization
DEFF Research Database (Denmark)
Sievers, Jakob; Papakyriakou, Tim; Larsen, Søren;
2014-01-01
-frequency contributions interfere with our ability to isolate local biogeochemical processes of interest, as represented by turbulent fluxes. No method currently exists to disentangle low-frequency contributions on flux estimates. Here, we present a novel comprehensive numerical scheme to identify and separate out low...
DEFF Research Database (Denmark)
Hounyo, Ulrich; Varneskov, Rasmus T.
We provide a new resampling procedure - the local stable bootstrap - that is able to mimic the dependence properties of realized power variations for pure-jump semimartingales observed at different frequencies. This allows us to propose a bootstrap estimator and inference procedure for the activi...
Hansen, Scott K.; Vesselinov, Velimir V.
2016-10-01
We develop empirically-grounded error envelopes for localization of a point contamination release event in the saturated zone of a previously uncharacterized heterogeneous aquifer into which a number of plume-intercepting wells have been drilled. We assume that flow direction in the aquifer is known exactly and velocity is known to within a factor of two of our best guess from well observations prior to source identification. Other aquifer and source parameters must be estimated by interpretation of well breakthrough data via the advection-dispersion equation. We employ high performance computing to generate numerous random realizations of aquifer parameters and well locations, simulate well breakthrough data, and then employ unsupervised machine optimization techniques to estimate the most likely spatial (or space-time) location of the source. Tabulating the accuracy of these estimates from the multiple realizations, we relate the size of 90% and 95% confidence envelopes to the data quantity (number of wells) and model quality (fidelity of ADE interpretation model to actual concentrations in a heterogeneous aquifer with channelized flow). We find that for purely spatial localization of the contaminant source, increased data quantities can make up for reduced model quality. For space-time localization, we find similar qualitative behavior, but significantly degraded spatial localization reliability and less improvement from extra data collection. Since the space-time source localization problem is much more challenging, we also tried a multiple-initial-guess optimization strategy. This greatly enhanced performance, but gains from additional data collection remained limited.
Parametric functional principal component analysis.
Sang, Peijun; Wang, Liangliang; Cao, Jiguo
2017-03-10
Functional principal component analysis (FPCA) is a popular approach in functional data analysis to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). Most existing FPCA approaches use a set of flexible basis functions such as B-spline basis to represent the FPCs, and control the smoothness of the FPCs by adding roughness penalties. However, the flexible representations pose difficulties for users to understand and interpret the FPCs. In this article, we consider a variety of applications of FPCA and find that, in many situations, the shapes of top FPCs are simple enough to be approximated using simple parametric functions. We propose a parametric approach to estimate the top FPCs to enhance their interpretability for users. Our parametric approach can also circumvent the smoothing parameter selecting process in conventional nonparametric FPCA methods. In addition, our simulation study shows that the proposed parametric FPCA is more robust when outlier curves exist. The parametric FPCA method is demonstrated by analyzing several datasets from a variety of applications. © 2017, The International Biometric Society.
DEFF Research Database (Denmark)
Göçmen Bozkurt, Tuhfe; Giebel, Gregor; Poulsen, Niels Kjølstad
2014-01-01
With increasing installed capacity, wind farms are requested to downregulate more frequently, especially in the offshore environment. Determination and verification of possible (or available) power of downregulated offshore wind farms are the aims of the PossPOW project (see PossPOW.dtu.dk). Two ...... period. The re-calibrated model has to be further parametrized to include dynamic effects such as wind direction variability and meandering also considering different averaging time scales before implemented in full scale wind farms....
Automotive FMCW Radar-enhanced Range Estimation via a Local Resampling Fourier Transform
Directory of Open Access Journals (Sweden)
Cailing Wang
2016-02-01
Full Text Available In complex traffic scenarios, more accurate measurement and discrimination for an automotive frequency-modulated continuous-wave (FMCW radar is required for intelligent robots, driverless cars and driver-assistant systems. A more accurate range estimation method based on a local resampling Fourier transform (LRFT for a FMCW radar is developed in this paper. Radar signal correlation in the phase space sees a higher signal-noise-ratio (SNR to achieve more accurate ranging, and the LRFT - which acts on a local neighbour as a refinement step - can achieve a more accurate target range. The rough range is estimated through conditional pulse compression (PC and then, around the initial rough estimation, a refined estimation through the LRFT in the local region achieves greater precision. Furthermore, the LRFT algorithm is tested in numerous simulations and physical system experiments, which show that the LRFT algorithm achieves a more precise range estimation than traditional FFT-based algorithms, especially for lower bandwidth signals.
On the estimate of earthquake magnitude at a local seismic network
Energy Technology Data Exchange (ETDEWEB)
Di Grazia, G.; Langer, H.; Ursino, A.; Scarfi, L. [Istituto Nazionale di Geofisica e Vulcanologia, Sez. di Catania, Priolo-Grgallo, Siracusa (Italy); Gresta, S. [Catania Univ., Catania (Italy). Dipt. di Scienze Geologiche
2001-06-01
It was investigated possible uncertainties and bases of magnitude estimate arising from instrument characteristics site conditions and routine data processing at a local seismic network running in Southeastern Sicily. Differences in instrument characteristics turned out to be of minor importance for small and moderate earthquakes. Magnitudes routinely calculated with the Hypoellipse program are obtained from the peak ground velocities applying a correction for the dominant period. This procedure yields slightly lower values than the standard procedure, where magnitudes are estimated from peak ground displacement. In order to provide the operators in the data center with a tool for an immediate estimate of earthquake size from drum records it was carried out a bivariate regression relating local magnitude (M{sub 1}) to the duration of the signal and the travel time difference of P- and S-waves.
Parametrizing Algebraic Curves
Lemmermeyer, Franz
2011-01-01
We present the technique of parametrization of plane algebraic curves from a number theorist's point of view and present Kapferer's simple and beautiful (but little known) proof that nonsingular curves of degree > 2 cannot be parametrized by rational functions.
Communicating Is Crowdsourcing：Wi-Fi Indoor Localization with CSI-Based Speed Estimation
Institute of Scientific and Technical Information of China (English)
韩劲松; 赵鲲; 王志; 肖波; 蒋志平; 惠维; 李向阳; 赵季中; 唐少杰
2014-01-01
Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location-based service (LBS). Among them, the most popular solutions are the Wi-Fi fingerprint-based approaches. The core challenge is to lower the cost of fingerprint site-survey. One of the trends is to collect the piecewise data from clients and establish the radio map in crowdsourcing manner. However the low participation rate blocks the practical use. In this work, we propose a passive crowdsourcing channel state information (CSI) based indoor localization scheme, C2IL. Despite a crowdsourcing based approach, our scheme is totally transparent to the client and the only requirement is to connect to our 802.11n access points (APs). C2IL is built upon an innovative method to accurately estimate the moving speed solely based on 802.11n CSI. Knowing the walking speed of a client and its surrounding APs, a graph matching algorithm is employed to extract the received signal strength (RSS) fingerprints and establish the fingerprint map. For localization phase, we design a trajectory clustering based localization algorithm to provide precise real-time indoor localization and tracking. We develop and deploy a practical working system of C2IL in a large office environment. Extensive evaluations indicate that the error of speed estimation is within 3%, and the localization error is within 2 m at 80%time in a very complex indoor environment.
Sakaeta, Kuniyuki; Nonaka, Kenichiro; Sekiguchi, Kazuma
2016-09-01
Localization is an important function for the robots to complete various tasks. For localization, both internal and external sensors are used generally. The odometry is widely used as the method based on the internal sensors, but it suffers from cumulative errors. In the method using the laser range sensor (LRS) which is a kind of external sensor, the estimation accuracy is affected by the number of available measurement data. In our previous study, we applied moving horizon estimation (MHE) to the vehicle localization for integrating the LRS measurement data and the odometry information where the weightings of them are balanced relatively adapting to the number of the available LRS measurement data. In this paper, the effectiveness of the proposed localization method is verified through both numerical simulations and experiments using a 1/10 scale vehicle. The verification is conducted in the situations where the vehicle position cannot be localized uniquely on a certain direction using the LRS measurement data only. We achieve accurate localization even in such a situation by integrating the odometry and LRS based on MHE. We also show the superiority of the method through comparisons with a method using extended Kalman filter (EKF).
Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks
Rahmani, Amirreza; Mesbahi, Mehran; Fathpour, Nanaz; Hadaegh, Fred Y.
2008-01-01
In this work, we develop an approach to formation estimation by explicitly characterizing formation's system-theoretic attributes in terms of the underlying inter-spacecraft information-exchange network. In particular, we approach the formation observer/estimator design by relaxing the accessibility to the global state information by a centralized observer/estimator- and in turn- providing an analysis and synthesis framework for formation observers/estimators that rely on local measurements. The noveltyof our approach hinges upon the explicit examination of the underlying distributed spacecraft network in the realm of guidance, navigation, and control algorithmic analysis and design. The overarching goal of our general research program, some of whose results are reported in this paper, is the development of distributed spacecraft estimation algorithms that are scalable, modular, and robust to variations inthe topology and link characteristics of the formation information exchange network. In this work, we consider the observability of a spacecraft formation from a single observation node and utilize the agreement protocol as a mechanism for observing formation states from local measurements. Specifically, we show how the symmetry structure of the network, characterized in terms of its automorphism group, directly relates to the observability of the corresponding multi-agent system The ramification of this notion of observability over networks is then explored in the context of distributed formation estimation.
Why preferring parametric forecasting to nonparametric methods?
Jabot, Franck
2015-05-07
A recent series of papers by Charles T. Perretti and collaborators have shown that nonparametric forecasting methods can outperform parametric methods in noisy nonlinear systems. Such a situation can arise because of two main reasons: the instability of parametric inference procedures in chaotic systems which can lead to biased parameter estimates, and the discrepancy between the real system dynamics and the modeled one, a problem that Perretti and collaborators call "the true model myth". Should ecologists go on using the demanding parametric machinery when trying to forecast the dynamics of complex ecosystems? Or should they rely on the elegant nonparametric approach that appears so promising? It will be here argued that ecological forecasting based on parametric models presents two key comparative advantages over nonparametric approaches. First, the likelihood of parametric forecasting failure can be diagnosed thanks to simple Bayesian model checking procedures. Second, when parametric forecasting is diagnosed to be reliable, forecasting uncertainty can be estimated on virtual data generated with the fitted to data parametric model. In contrast, nonparametric techniques provide forecasts with unknown reliability. This argumentation is illustrated with the simple theta-logistic model that was previously used by Perretti and collaborators to make their point. It should convince ecologists to stick to standard parametric approaches, until methods have been developed to assess the reliability of nonparametric forecasting. Copyright © 2015 Elsevier Ltd. All rights reserved.
Graf, S.; Craiem, D.; Barra, J. G.; Armentano, R. L.
2011-12-01
Increased arterial stiffness is associated with an increased risk of cardiovascular events. Estimation of arterial stiffness using local pulse wave velocity (PWV) promises to be very useful for noninvasive diagnosis of arteriosclerosis. In this work we estimated in an instrumented sheep, the local aortic pulse wave velocity using two sonomicrometry diameter sensors (separated 7.5 cm) according to the transit time method (PWVTT) with a sampling rate of 4 KHz. We simultaneously measured aortic pressure in order to determine from pressure-diameter loops (PWVPDLoop), the "true" local aortic pulse wave velocity. A pneumatic cuff occluder was implanted in the aorta in order to compare both methods under a wide range of pressure levels. Mean pressure values ranged from 47 to 101 mmHg and mean proximal diameter values from 12.5. to 15.2 mm. There were no significant differences between PWVTT and PWVPDLoop values (451±43 vs. 447±48 cm/s, p = ns, paired t-test). Both methods correlated significantly (R = 0.81, p<0.05). The mean difference between both methods was only -4±29 cm/s, whereas the range of the limits of agreement (mean ± 2 standard deviation) was -61 to +53 cm/s, showing no trend. In conclusion, the diameter waveforms transit time method was found to allow an accurate and precise estimation of the local aortic PWV.
Acomi, Nicoleta; Ancuţa, Cristian; Andrei, Cristian; Boştinǎ, Alina; Boştinǎ, Aurel
2016-12-01
Ships are mainly built to sail and transport cargo at sea. Environmental conditions and state of the sea are communicated to vessels through periodic weather forecasts. Despite officers being aware of the sea state, their sea time experience is a decisive factor when the vessel encounters severe environmental conditions. Another important factor is the loading condition of the vessel, which triggers different behaviour in similar marine environmental conditions. This paper aims to analyse the behaviour of a port container vessel in severe environmental conditions and to estimate the potential conditions of parametric roll resonance. Octopus software simulation is employed to simulate vessel motions under certain conditions of the sea, with possibility to analyse the behaviour of ships and the impact of high waves on ships due to specific wave encounter situations. The study should be regarded as a supporting tool during the decision making process.
Institute of Scientific and Technical Information of China (English)
YAN Hao; WANG Hu; WANG Yong-hui; ZHANG Yu-mei
2013-01-01
Background The classification of Alzheimer's disease (AD) from magnetic resonance imaging (MRI) has been challenged by lack of effective and reliable biomarkers due to inter-subject variability.This article presents a classification method for AD based on kernel density estimation (KDE) of local features.Methods First,a large number of local features were extracted from stable image blobs to represent various anatomical patterns for potential effective biomarkers.Based on distinctive descriptors and locations,the local features were robustly clustered to identify correspondences of the same underlying patterns.Then,the KDE was used to estimate distribution parameters of the correspondences by weighting contributions according to their distances.Thus,biomarkers could be reliably quantified by reducing the effects of further away correspondences which were more likely noises from inter-subject variability.Finally,the Bayes classifier was applied on the distribution parameters for the classification of AD.Results Experiments were performed on different divisions of a publicly available database to investigate the accuracy and the effects of age and AD severity.Our method achieved an equal error classification rate of 0.85 for subject aged 60-80 years exhibiting mild AD and outperformed a recent local feature-based work regardless of both effects.Conclusions We proposed a volumetric brain MRI classification method for neurodegenerative disease based on statistics of local features using KDE.The method may be potentially useful for the computer-aided diagnosis in clinical settings.
Pose Estimation using Local Structure-Specific Shape and Appearance Context
DEFF Research Database (Denmark)
Buch, Anders Glent; Kraft, Dirk; Kämäräinen, Joni-Kristian
2013-01-01
We address the problem of estimating the alignment pose between two models using structure-specific local descriptors. Our descriptors are generated using a combination of 2D image data and 3D contextual shape data, resulting in a set of semi-local descriptors containing rich appearance and shape...... information for both edge and texture structures. This is achieved by defining feature space relations which describe the neighborhood of a descriptor. By quantitative evaluations, we show that our descriptors provide high discriminative power compared to state of the art approaches. In addition, we show how...
Similarity Estimation Between DNA Sequences Based on Local Pattern Histograms of Binary Images
Institute of Scientific and Technical Information of China (English)
Yusei Kobori; Satoshi Mizuta
2016-01-01
Graphical representation of DNA sequences is one of the most popular techniques for alignment-free sequence comparison. Here, we propose a new method for the feature extraction of DNA sequences represented by binary images, by estimating the similarity between DNA sequences using the frequency histograms of local bitmap patterns of images. Our method shows linear time complexity for the length of DNA sequences, which is practical even when long sequences, such as whole genome sequences, are compared. We tested five distance measures for the estimation of sequence similarities, and found that the histogram intersection and Manhattan distance are the most appropriate ones for phylogenetic analyses.
Electric arc localization based on antenna arrays and MUSIC direction of arrival estimation
Paun, Mirel; Digulescu, Angela; Tamas, Razvan; Ioana, Cornel
2015-02-01
This paper presents an application of antenna arrays and MUSIC algorithm for estimating the location of an electric arc source. The proposed technique can be used to localize arc faults in photovoltaic arrays and their associated transformation stations. The technique was implemented and tested in the laboratory. For this purpose, an experimental setup consisting of 4 antennas, a digital storage oscilloscope with computer connectivity and a PC (Personal Computer) for data processing was built. The results proved that the proposed method is able to estimate the direction of the electric arc source with reasonable accuracy.
Estimating 3D tilt from local image cues in natural scenes
Burge, Johannes; McCann, Brian C.; Geisler, Wilson S.
2016-01-01
Estimating three-dimensional (3D) surface orientation (slant and tilt) is an important first step toward estimating 3D shape. Here, we examine how three local image cues from the same location (disparity gradient, luminance gradient, and dominant texture orientation) should be combined to estimate 3D tilt in natural scenes. We collected a database of natural stereoscopic images with precisely co-registered range images that provide the ground-truth distance at each pixel location. We then analyzed the relationship between ground-truth tilt and image cue values. Our analysis is free of assumptions about the joint probability distributions and yields the Bayes optimal estimates of tilt, given the cue values. Rich results emerge: (a) typical tilt estimates are only moderately accurate and strongly influenced by the cardinal bias in the prior probability distribution; (b) when cue values are similar, or when slant is greater than 40°, estimates are substantially more accurate; (c) when luminance and texture cues agree, they often veto the disparity cue, and when they disagree, they have little effect; and (d) simplifying assumptions common in the cue combination literature is often justified for estimating tilt in natural scenes. The fact that tilt estimates are typically not very accurate is consistent with subjective impressions from viewing small patches of natural scene. The fact that estimates are substantially more accurate for a subset of image locations is also consistent with subjective impressions and with the hypothesis that perceived surface orientation, at more global scales, is achieved by interpolation or extrapolation from estimates at key locations. PMID:27738702
Parametric or nonparametric? A parametricness index for model selection
Liu, Wei; 10.1214/11-AOS899
2012-01-01
In model selection literature, two classes of criteria perform well asymptotically in different situations: Bayesian information criterion (BIC) (as a representative) is consistent in selection when the true model is finite dimensional (parametric scenario); Akaike's information criterion (AIC) performs well in an asymptotic efficiency when the true model is infinite dimensional (nonparametric scenario). But there is little work that addresses if it is possible and how to detect the situation that a specific model selection problem is in. In this work, we differentiate the two scenarios theoretically under some conditions. We develop a measure, parametricness index (PI), to assess whether a model selected by a potentially consistent procedure can be practically treated as the true model, which also hints on AIC or BIC is better suited for the data for the goal of estimating the regression function. A consequence is that by switching between AIC and BIC based on the PI, the resulting regression estimator is si...
Numerical experiments on the efficiency of local grid refinement based on truncation error estimates
Syrakos, Alexandros; Bartzis, John G; Goulas, Apostolos
2015-01-01
Local grid refinement aims to optimise the relationship between accuracy of the results and number of grid nodes. In the context of the finite volume method no single local refinement criterion has been globally established as optimum for the selection of the control volumes to subdivide, since it is not easy to associate the discretisation error with an easily computable quantity in each control volume. Often the grid refinement criterion is based on an estimate of the truncation error in each control volume, because the truncation error is a natural measure of the discrepancy between the algebraic finite-volume equations and the original differential equations. However, it is not a straightforward task to associate the truncation error with the optimum grid density because of the complexity of the relationship between truncation and discretisation errors. In the present work several criteria based on a truncation error estimate are tested and compared on a regularised lid-driven cavity case at various Reyno...
Institute of Scientific and Technical Information of China (English)
张海峰; 姜海燕; 王发丽
2014-01-01
在分析参数法成本估算的基础上，针对飞机研制项目分析成本影响因素，运用相关性分析确定影响成本的关键影响因素，构建飞机研制项目成本影响因素模型，并在此基础上运用多元非线性回归分析建立飞机研制项目的参数法成本估算模型，最后从航空飞机制造企业、研发机构和政府三个角度对我国航空飞机研制项目实施参数法成本估算提出建议。%The modern aircraft development work has significant characteristics,such as the more complex product struc-ture,the longer development cycle,the higher risks and poor reproducibility.And the cost estimating of development pro-ject is difficult.Based on the method of parametric cost estimating,the paper analyzes the influencing factors of aircraft de-velopment project costs,and uses the correlation analysis to determine the key influencing factors of project costs and estab-lish the cost impact factor model.And on this basis,the paper uses multiple nonlinear regression analysis method to estab-lish the cost estimation model of aircraft development project.Finally,the recommendations of using the method of paramet-ric cost estimating to estimate the cost of China's aviation aircraft development project are given from the three aspects of en-terprise,development institution and government.
On Parametric (and Non-Parametric Variation
Directory of Open Access Journals (Sweden)
Neil Smith
2009-11-01
Full Text Available This article raises the issue of the correct characterization of ‘Parametric Variation’ in syntax and phonology. After specifying their theoretical commitments, the authors outline the relevant parts of the Principles–and–Parameters framework, and draw a three-way distinction among Universal Principles, Parameters, and Accidents. The core of the contribution then consists of an attempt to provide identity criteria for parametric, as opposed to non-parametric, variation. Parametric choices must be antecedently known, and it is suggested that they must also satisfy seven individually necessary and jointly sufficient criteria. These are that they be cognitively represented, systematic, dependent on the input, deterministic, discrete, mutually exclusive, and irreversible.
Directory of Open Access Journals (Sweden)
Orlov A. I.
2015-05-01
Full Text Available According to the new paradigm of applied mathematical statistics one should prefer non-parametric methods and models. However, in applied statistics we currently use a variety of parametric models. The term "parametric" means that the probabilistic-statistical model is fully described by a finite-dimensional vector of fixed dimension, and this dimension does not depend on the size of the sample. In parametric statistics the estimation problem is to estimate the unknown value (for statistician of parameter by means of the best (in some sense method. In the statistical problems of standardization and quality control we use a three-parameter family of gamma distributions. In this article, it is considered as an example of the parametric distribution family. We compare the methods for estimating the parameters. The method of moments is universal. However, the estimates obtained with the help of method of moments have optimal properties only in rare cases. Maximum likelihood estimation (MLE belongs to the class of the best asymptotically normal estimates. In most cases, analytical solutions do not exist; therefore, to find MLE it is necessary to apply numerical methods. However, the use of numerical methods creates numerous problems. Convergence of iterative algorithms requires justification. In a number of examples of the analysis of real data, the likelihood function has many local maxima, and because of that natural iterative procedures do not converge. We suggest the use of one-step estimates (OS-estimates. They have equally good asymptotic properties as the maximum likelihood estimators, under the same conditions of regularity that MLE. One-step estimates are written in the form of explicit formulas. In this article it is proved that the one-step estimates are the best asymptotically normal estimates (under natural conditions. We have found OS-estimates for the gamma distribution and given the results of calculations using data on operating time
Local digital estimators of intrinsic volumes for Boolean models and in the design based setting
DEFF Research Database (Denmark)
Svane, Anne Marie
In order to estimate the specific intrinsic volumes of a planar Boolean model from a binary image, we consider local digital algorithms based on weigted sums of 2×2 configuration counts. For Boolean models with balls as grains, explicit formulas for the bias of such algorithms are derived...... for the bias obtained for Boolean models are applied to existing algorithms in order to compare their accuracy....
Automotive FMCW Radar-enhanced Range Estimation via a Local Resampling Fourier Transform
2016-01-01
In complex traffic scenarios, more accurate measurement and discrimination for an automotive frequency-modulated continuous-wave (FMCW) radar is required for intelligent robots, driverless cars and driver-assistant systems. A more accurate range estimation method based on a local resampling Fourier transform (LRFT) for a FMCW radar is developed in this paper. Radar signal correlation in the phase space sees a higher signal-noise-ratio (SNR) to achieve more accurate ranging, and the LRFT - whi...
Local L∞-estimates, weak Harnack inequality, and stochastic continuity of solutions of SPDEs
Dareiotis, Konstantinos; Gerencsér, Máté
2017-01-01
We consider stochastic partial differential equations under minimal assumptions: the coefficients are merely bounded and measurable and satisfy the stochastic parabolicity condition. In particular, the diffusion term is allowed to be scaling-critical. We derive local supremum estimates with a stochastic adaptation of De Giorgi's iteration and establish a weak Harnack inequality for the solutions. The latter is then used to obtain pointwise almost sure continuity.
Directory of Open Access Journals (Sweden)
Erin O Sills
Full Text Available Quasi-experimental methods increasingly are used to evaluate the impacts of conservation interventions by generating credible estimates of counterfactual baselines. These methods generally require large samples for statistical comparisons, presenting a challenge for evaluating innovative policies implemented within a few pioneering jurisdictions. Single jurisdictions often are studied using comparative methods, which rely on analysts' selection of best case comparisons. The synthetic control method (SCM offers one systematic and transparent way to select cases for comparison, from a sizeable pool, by focusing upon similarity in outcomes before the intervention. We explain SCM, then apply it to one local initiative to limit deforestation in the Brazilian Amazon. The municipality of Paragominas launched a multi-pronged local initiative in 2008 to maintain low deforestation while restoring economic production. This was a response to having been placed, due to high deforestation, on a federal "blacklist" that increased enforcement of forest regulations and restricted access to credit and output markets. The local initiative included mapping and monitoring of rural land plus promotion of economic alternatives compatible with low deforestation. The key motivation for the program may have been to reduce the costs of blacklisting. However its stated purpose was to limit deforestation, and thus we apply SCM to estimate what deforestation would have been in a (counterfactual scenario of no local initiative. We obtain a plausible estimate, in that deforestation patterns before the intervention were similar in Paragominas and the synthetic control, which suggests that after several years, the initiative did lower deforestation (significantly below the synthetic control in 2012. This demonstrates that SCM can yield helpful land-use counterfactuals for single units, with opportunities to integrate local and expert knowledge and to test innovations and
Sills, Erin O; Herrera, Diego; Kirkpatrick, A Justin; Brandão, Amintas; Dickson, Rebecca; Hall, Simon; Pattanayak, Subhrendu; Shoch, David; Vedoveto, Mariana; Young, Luisa; Pfaff, Alexander
2015-01-01
Quasi-experimental methods increasingly are used to evaluate the impacts of conservation interventions by generating credible estimates of counterfactual baselines. These methods generally require large samples for statistical comparisons, presenting a challenge for evaluating innovative policies implemented within a few pioneering jurisdictions. Single jurisdictions often are studied using comparative methods, which rely on analysts' selection of best case comparisons. The synthetic control method (SCM) offers one systematic and transparent way to select cases for comparison, from a sizeable pool, by focusing upon similarity in outcomes before the intervention. We explain SCM, then apply it to one local initiative to limit deforestation in the Brazilian Amazon. The municipality of Paragominas launched a multi-pronged local initiative in 2008 to maintain low deforestation while restoring economic production. This was a response to having been placed, due to high deforestation, on a federal "blacklist" that increased enforcement of forest regulations and restricted access to credit and output markets. The local initiative included mapping and monitoring of rural land plus promotion of economic alternatives compatible with low deforestation. The key motivation for the program may have been to reduce the costs of blacklisting. However its stated purpose was to limit deforestation, and thus we apply SCM to estimate what deforestation would have been in a (counterfactual) scenario of no local initiative. We obtain a plausible estimate, in that deforestation patterns before the intervention were similar in Paragominas and the synthetic control, which suggests that after several years, the initiative did lower deforestation (significantly below the synthetic control in 2012). This demonstrates that SCM can yield helpful land-use counterfactuals for single units, with opportunities to integrate local and expert knowledge and to test innovations and permutations on policies
Directory of Open Access Journals (Sweden)
Bronson W Griscom
Full Text Available Forest conservation efforts are increasingly being implemented at the scale of sub-national jurisdictions in order to mitigate global climate change and provide other ecosystem services. We see an urgent need for robust estimates of historic forest carbon emissions at this scale, as the basis for credible measures of climate and other benefits achieved. Despite the arrival of a new generation of global datasets on forest area change and biomass, confusion remains about how to produce credible jurisdictional estimates of forest emissions. We demonstrate a method for estimating the relevant historic forest carbon fluxes within the Regency of Berau in eastern Borneo, Indonesia. Our method integrates best available global and local datasets, and includes a comprehensive analysis of uncertainty at the regency scale.We find that Berau generated 8.91 ± 1.99 million tonnes of net CO2 emissions per year during 2000-2010. Berau is an early frontier landscape where gross emissions are 12 times higher than gross sequestration. Yet most (85% of Berau's original forests are still standing. The majority of net emissions were due to conversion of native forests to unspecified agriculture (43% of total, oil palm (28%, and fiber plantations (9%. Most of the remainder was due to legal commercial selective logging (17%. Our overall uncertainty estimate offers an independent basis for assessing three other estimates for Berau. Two other estimates were above the upper end of our uncertainty range. We emphasize the importance of including an uncertainty range for all parameters of the emissions equation to generate a comprehensive uncertainty estimate-which has not been done before. We believe comprehensive estimates of carbon flux uncertainty are increasingly important as national and international institutions are challenged with comparing alternative estimates and identifying a credible range of historic emissions values.
Griscom, Bronson W; Ellis, Peter W; Baccini, Alessandro; Marthinus, Delon; Evans, Jeffrey S; Ruslandi
2016-01-01
Forest conservation efforts are increasingly being implemented at the scale of sub-national jurisdictions in order to mitigate global climate change and provide other ecosystem services. We see an urgent need for robust estimates of historic forest carbon emissions at this scale, as the basis for credible measures of climate and other benefits achieved. Despite the arrival of a new generation of global datasets on forest area change and biomass, confusion remains about how to produce credible jurisdictional estimates of forest emissions. We demonstrate a method for estimating the relevant historic forest carbon fluxes within the Regency of Berau in eastern Borneo, Indonesia. Our method integrates best available global and local datasets, and includes a comprehensive analysis of uncertainty at the regency scale. We find that Berau generated 8.91 ± 1.99 million tonnes of net CO2 emissions per year during 2000-2010. Berau is an early frontier landscape where gross emissions are 12 times higher than gross sequestration. Yet most (85%) of Berau's original forests are still standing. The majority of net emissions were due to conversion of native forests to unspecified agriculture (43% of total), oil palm (28%), and fiber plantations (9%). Most of the remainder was due to legal commercial selective logging (17%). Our overall uncertainty estimate offers an independent basis for assessing three other estimates for Berau. Two other estimates were above the upper end of our uncertainty range. We emphasize the importance of including an uncertainty range for all parameters of the emissions equation to generate a comprehensive uncertainty estimate-which has not been done before. We believe comprehensive estimates of carbon flux uncertainty are increasingly important as national and international institutions are challenged with comparing alternative estimates and identifying a credible range of historic emissions values.
Williford, Anna; Comeron, Josep M
2010-01-01
Recent years have witnessed the integration of theoretical advances in population genetics with large-scale analyses of complete genomes, with a growing number of studies suggesting pervasive natural selection that includes frequent deleterious as well as adaptive mutations. In finite populations, however, mutations under selection alter the fate of genetically linked mutations (the so-called Hill-Robertson effect). Here we review the evolutionary consequences of selection at linked sites (linked selection) focusing on its effects on nearby nucleotides in genomic regions with nonreduced recombination. We argue that these local effects of linkage may account for differences in selection intensity among genes. We also show that even high levels of recombination are unlikely to remove all effects of linked selection, causing a reduction in the polymorphism to divergence ratio (r(pd)) at neutral sites. Because a number of methods employed to estimate the magnitude and frequency of adaptive mutations take reduced r(pd) as evidence of positive selection, ignoring local linkage effects may lead to misleading estimates of the proportion of adaptive substitutions and estimates of positive selection. These biases are caused by employing methods that do not account for local variation in the relative effective population size (N(e)) caused by linked selection.
Directory of Open Access Journals (Sweden)
Sandhi Imam Maulana
2016-10-01
Full Text Available Recently, pantropical allometric equations have been commonly used across the globe to estimate the aboveground biomass of the forests, including in Indonesia. However, in relation to regional differences in diameter, height and wood density, the lack of data measured, particularly from eastern part of Indonesia, may raise the question on accuracy of pantropical allometric in such area. Hence, this paper examines the differences of local allometric equations of Papua Island with equations developed by Chave and his research groups.. Measurements of biomass in this study were conducted directly based on weighing and destructive samplings. Results show that the most appropriate local equation to estimate total aboveground biomass in Papua tropical forest is Log(TAGB = -0.267 + 2.23 Log(DBH +0.649 Log(WD (CF=1.013; VIF=1.6; R2= 95%; R2-adj= 95.1%; RMSE= 0.149; P<0.001. This equation is also a better option in comparison to those of previously published pantropical equations with only 6.47% average deviation and 5.37 points of relative bias. This finding implies that the locally developed equation should be a better option to produce more accurate site specific total aboveground biomass estimation.
Constrained State Estimation for Individual Localization in Wireless Body Sensor Networks
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Xiaoxue Feng
2014-11-01
Full Text Available Wireless body sensor networks based on ultra-wideband radio have recently received much research attention due to its wide applications in health-care, security, sports and entertainment. Accurate localization is a fundamental problem to realize the development of effective location-aware applications above. In this paper the problem of constrained state estimation for individual localization in wireless body sensor networks is addressed. Priori knowledge about geometry among the on-body nodes as additional constraint is incorporated into the traditional filtering system. The analytical expression of state estimation with linear constraint to exploit the additional information is derived. Furthermore, for nonlinear constraint, first-order and second-order linearizations via Taylor series expansion are proposed to transform the nonlinear constraint to the linear case. Examples between the first-order and second-order nonlinear constrained filters based on interacting multiple model extended kalman filter (IMM-EKF show that the second-order solution for higher order nonlinearity as present in this paper outperforms the first-order solution, and constrained IMM-EKF obtains superior estimation than IMM-EKF without constraint. Another brownian motion individual localization example also illustrates the effectiveness of constrained nonlinear iterative least square (NILS, which gets better filtering performance than NILS without constraint.
Estimation of LISS(local input-to-state stability) properties for nonlinear systems
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
Compared with input-to-state stability(ISS) in global version,the concept of local input-to-state stability(LISS) is more relevant and meaningful in practice.The key of assessing LISS properties lies in investigating three main ingredients,the local region of initial states,the local region of external inputs and the asymptotic gain.It is the objective of this paper to propose a numerical algorithm for estimating LISS properties on the theoretical foundation of quadratic form LISS-Lyapunov function.Given developments of linear matrix inequality(LMI) methods,this algorithm is effective and powerful.A typical power electronics based system with common DC bus is served as a demonstration for quantitative results.
Directory of Open Access Journals (Sweden)
Kaifeng Yang
2014-01-01
Full Text Available A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics.
Estimating rates of local species extinction, colonization and turnover in animal communities
Nichols, James D.; Boulinier, T.; Hines, J.E.; Pollock, K.H.; Sauer, J.R.
1998-01-01
Species richness has been identified as a useful state variable for conservation and management purposes. Changes in richness over time provide a basis for predicting and evaluating community responses to management, to natural disturbance, and to changes in factors such as community composition (e.g., the removal of a keystone species). Probabilistic capture-recapture models have been used recently to estimate species richness from species count and presence-absence data. These models do not require the common assumption that all species are detected in sampling efforts. We extend this approach to the development of estimators useful for studying the vital rates responsible for changes in animal communities over time; rates of local species extinction, turnover, and colonization. Our approach to estimation is based on capture-recapture models for closed animal populations that permit heterogeneity in detection probabilities among the different species in the sampled community. We have developed a computer program, COMDYN, to compute many of these estimators and associated bootstrap variances. Analyses using data from the North American Breeding Bird Survey (BBS) suggested that the estimators performed reasonably well. We recommend estimators based on probabilistic modeling for future work on community responses to management efforts as well as on basic questions about community dynamics.
Parametric Dwarf Spheroidal Tidal Interaction
Fleck, J J; Fleck, Jean-Julien; Kuhn, Jeff R.
2003-01-01
The time dependent tidal interaction of the Local Group Dwarf Spheroidal (dS) Galaxies with the Milky Way (MW) can fundamentally affect their dynamical properties. The model developed here extends earlier numerical descriptions of dS-MW tidal interactions. We explore the dynamical evolution of dS systems in circular or elliptical MW orbits in the framework of a parametric oscillator. An analytic model is developed and compared with more general numerical solutions and N-body simulation experiments.
Rajabioun, Mehdi; Nasrabadi, Ali Motie; Shamsollahi, Mohammad Bagher
2017-08-29
Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions.
An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering
Uwe, Aickelin; Jingpeng, Li
2007-01-01
This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, i.e. we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, i.e. an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (i.e. the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is ...
Han, Xujun; Li, Xin; Rigon, Riccardo; Jin, Rui; Endrizzi, Stefano
2015-01-01
The observation could be used to reduce the model uncertainties with data assimilation. If the observation cannot cover the whole model area due to spatial availability or instrument ability, how to do data assimilation at locations not covered by observation? Two commonly used strategies were firstly described: One is covariance localization (CL); the other is observation localization (OL). Compared with CL, OL is easy to parallelize and more efficient for large-scale analysis. This paper evaluated OL in soil moisture profile characterizations, in which the geostatistical semivariogram was used to fit the spatial correlated characteristics of synthetic L-Band microwave brightness temperature measurement. The fitted semivariogram model and the local ensemble transform Kalman filter algorithm are combined together to weight and assimilate the observations within a local region surrounding the grid cell of land surface model to be analyzed. Six scenarios were compared: 1_Obs with one nearest observation assimilated, 5_Obs with no more than five nearest local observations assimilated, and 9_Obs with no more than nine nearest local observations assimilated. The scenarios with no more than 16, 25, and 36 local observations were also compared. From the results we can conclude that more local observations involved in assimilation will improve estimations with an upper bound of 9 observations in this case. This study demonstrates the potentials of geostatistical correlation representation in OL to improve data assimilation of catchment scale soil moisture using synthetic L-band microwave brightness temperature, which cannot cover the study area fully in space due to vegetation effects.
Berges, Jürgen; Reygers, Klaus; Tanji, Naoto; Venugopalan, Raju
2017-05-01
Recent classical-statistical numerical simulations have established the "bottom-up" thermalization scenario of Baier et al. [Phys. Lett. B 502, 51 (2001), 10.1016/S0370-2693(01)00191-5] as the correct weak coupling effective theory for thermalization in ultrarelativistic heavy-ion collisions. We perform a parametric study of photon production in the various stages of this bottom-up framework to ascertain the relative contribution of the off-equilibrium "glasma" relative to that of a thermalized quark-gluon plasma. Taking into account the constraints imposed by the measured charged hadron multiplicities at Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC), we find that glasma contributions are important especially for large values of the saturation scale at both energies. These nonequilibrium effects should therefore be taken into account in studies where weak coupling methods are employed to compute photon yields.
Image-based human age estimation by manifold learning and locally adjusted robust regression.
Guo, Guodong; Fu, Yun; Dyer, Charles R; Huang, Thomas S
2008-07-01
Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human ages. The aging process is determined by not only the person's gene, but also many external factors, such as health, living style, living location, and weather conditions. Males and females may also age differently. The current age estimation performance is still not good enough for practical use and more effort has to be put into this research direction. In this paper, we introduce the age manifold learning scheme for extracting face aging features and design a locally adjusted robust regressor for learning and prediction of human ages. The novel approach improves the age estimation accuracy significantly over all previous methods. The merit of the proposed approaches for image-based age estimation is shown by extensive experiments on a large internal age database and the public available FG-NET database.
Fatigue Strength Estimation Based on Local Mechanical Properties for Aluminum Alloy FSW Joints
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Kittima Sillapasa
2017-02-01
Full Text Available Overall fatigue strengths and hardness distributions of the aluminum alloy similar and dissimilar friction stir welding (FSW joints were determined. The local fatigue strengths as well as local tensile strengths were also obtained by using small round bar specimens extracted from specific locations, such as the stir zone, heat affected zone, and base metal. It was found from the results that fatigue fracture of the FSW joint plate specimen occurred at the location of the lowest local fatigue strength as well as the lowest hardness, regardless of microstructural evolution. To estimate the fatigue strengths of aluminum alloy FSW joints from the hardness measurements, the relationship between fatigue strength and hardness for aluminum alloys was investigated based on the present experimental results and the available wide range of data from the references. It was found as: σa (R = −1 = 1.68 HV (σa is in MPa and HV has no unit. It was also confirmed that the estimated fatigue strengths were in good agreement with the experimental results for aluminum alloy FSW joints.
Estimating the Mass of the Local Group using Machine Learning Applied to Numerical Simulations
McLeod, Michael; Lahav, Ofer; Hoffman, Yehuda
2016-01-01
We revisit the estimation of the combined mass of the Milky Way and Andromeda (M31), which dominate the mass of the Local Group. We make use of an ensemble of 30,190 halo pairs from the Small MultiDark simulation, assuming a $\\Lambda$CDM (Cosmological Constant with Cold Dark Matter) cosmology, to investigate the relationship between the bound mass and parameters characterising the orbit of the binary and their local environment with the aid of machine learning methods (artificial neural networks, ANN). Results from the ANN are most successful when information about the velocity shear is provided, which demonstrates the flexibility of machine learning to model physical phenomena and readily incorporate new information as it becomes available. The resulting estimate for the Local Group mass, when shear information is included, is $4.9 \\times 10^{12} M_\\odot$, with an error of $\\pm0.8 \\times 10^{12} M_\\odot$ from the 68% uncertainty in observables, and a 68% confidence interval of $^{+1.3}_{-1.4} \\times 10^{12}M...
Position Estimation and Local Mapping Using Omnidirectional Images and Global Appearance Descriptors
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Yerai Berenguer
2015-10-01
Full Text Available This work presents some methods to create local maps and to estimate the position of a mobile robot, using the global appearance of omnidirectional images. We use a robot that carries an omnidirectional vision system on it. Every omnidirectional image acquired by the robot is described only with one global appearance descriptor, based on the Radon transform. In the work presented in this paper, two different possibilities have been considered. In the first one, we assume the existence of a map previously built composed of omnidirectional images that have been captured from previously-known positions. The purpose in this case consists of estimating the nearest position of the map to the current position of the robot, making use of the visual information acquired by the robot from its current (unknown position. In the second one, we assume that we have a model of the environment composed of omnidirectional images, but with no information about the location of where the images were acquired. The purpose in this case consists of building a local map and estimating the position of the robot within this map. Both methods are tested with different databases (including virtual and real images taking into consideration the changes of the position of different objects in the environment, different lighting conditions and occlusions. The results show the effectiveness and the robustness of both methods.
Institute of Scientific and Technical Information of China (English)
陈彦龙; 张培林; 王怀光
2014-01-01
A novel denoising method for mechanical vibration signals was proposed based on quantum superposition inspired parametric estimation.Considering the relation between real coefficients and imaginary ones of the dual-tree complex wavelet transformation,a new two-dimensional probability density function model with an adaptive parameter was built.Through investigating the inter-scale dependency of coefficients and those of their parents,the proability for quantum superposition inspired signal and noise to occur was presented.Combined with Bayesian estimation theory,an adaptive shrinkage function was deuced based on quantum superposition inspired parametric estimation.At last,the simulated signals and rolling bearing fault vibration signals were analyzed.The results showed that using the proposed method can reduce noise effectively,can achieve much better performance than that of the traditional soft and hard thresholds denoising algorithms.%提出基于量子叠加态参数估计的机械振动信号降噪方法。考虑双树复小波系数虚、实部关系，建立带自适应参数的二维概率密度函数模型；研究父-子代小波系数相关性，提出量子叠加态信号与噪声出现概率，并结合贝叶斯估计理论推导出基于量子叠加态参数估计的自适应收缩函数；分析仿真信号与滚动轴承故障振动信号。结果表明该方法较传统软硬阈值算法适应性更好，降噪效果显著。
Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks
Fathpour, Nanaz; Hadaegh, Fred Y.; Mesbahi, Mehran; Rahmani, Amirreza
2011-01-01
Spacecraft formation flying involves the coordination of states among multiple spacecraft through relative sensing, inter-spacecraft communication, and control. Most existing formation-flying estimation algorithms can only be supported via highly centralized, all-to-all, static relative sensing. New algorithms are proposed that are scalable, modular, and robust to variations in the topology and link characteristics of the formation exchange network. These distributed algorithms rely on a local information exchange network, relaxing the assumptions on existing algorithms. Distributed space systems rely on a signal transmission network among multiple spacecraft for their operation. Control and coordination among multiple spacecraft in a formation is facilitated via a network of relative sensing and interspacecraft communications. Guidance, navigation, and control rely on the sensing network. This network becomes more complex the more spacecraft are added, or as mission requirements become more complex. The observability of a formation state was observed by a set of local observations from a particular node in the formation. Formation observability can be parameterized in terms of the matrices appearing in the formation dynamics and observation matrices. An agreement protocol was used as a mechanism for observing formation states from local measurements. An agreement protocol is essentially an unforced dynamic system whose trajectory is governed by the interconnection geometry and initial condition of each node, with a goal of reaching a common value of interest. The observability of the interconnected system depends on the geometry of the network, as well as the position of the observer relative to the topology. For the first time, critical GN&C (guidance, navigation, and control estimation) subsystems are synthesized by bringing the contribution of the spacecraft information-exchange network to the forefront of algorithmic analysis and design. The result is a
A new adaptive fast motion estimation algorithm based on local motion similarity degree (LMSD)
Institute of Scientific and Technical Information of China (English)
LIU Long; HAN Chongzhao; BAI Yan
2005-01-01
In the motion vector field adaptive search technique (MVFAST) and the predictive motion vector field adaptive search technique (PMVFAST), the size of the largest motion vector from the three adjacent blocks (left, top, top-right) is compared with the threshold to select different search scheme. But a suitable search center and search pattern will not be selected in the adaptive search technique when the adjacent motion vectors are not coherent in local region. This paper presents an efficient adaptive search algorithm. The motion vector variation degree (MVVD) is considered a reasonable factor for adaptive search selection. By the relationship between local motion similarity degree (LMSD) and the variation degree of motion vector (MVVD), the motion vectors are classified as three categories according to corresponding LMSD; then different proposed search schemes are adopted for motion estimation. The experimental results show that the proposed algorithm has a significant computational speedup compared with MVFAST and PMVFAST algorithms, and offers a similar, even better performance.
Estimating risks of importation and local transmission of Zika virus infection
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Kyeongah Nah
2016-04-01
Full Text Available Background. An international spread of Zika virus (ZIKV infection has attracted global attention. ZIKV is conveyed by a mosquito vector, Aedes species, which also acts as the vector species of dengue and chikungunya viruses. Methods. Arrival time of ZIKV importation (i.e., the time at which the first imported case was diagnosed in each imported country was collected from publicly available data sources. Employing a survival analysis model in which the hazard is an inverse function of the effective distance as informed by the airline transportation network data, and using dengue and chikungunya virus transmission data, risks of importation and local transmission were estimated. Results. A total of 78 countries with imported case(s have been identified, with the arrival time ranging from 1 to 44 weeks since the first ZIKV was identified in Brazil, 2015. Whereas the risk of importation was well explained by the airline transportation network data, the risk of local transmission appeared to be best captured by additionally accounting for the presence of dengue and chikungunya viruses. Discussion. The risk of importation may be high given continued global travel of mildly infected travelers but, considering that the public health concerns over ZIKV infection stems from microcephaly, it is more important to focus on the risk of local and widespread transmission that could involve pregnant women. The predicted risk of local transmission was frequently seen in tropical and subtropical countries with dengue or chikungunya epidemic experience.
Estimating risks of importation and local transmission of Zika virus infection.
Nah, Kyeongah; Mizumoto, Kenji; Miyamatsu, Yuichiro; Yasuda, Yohei; Kinoshita, Ryo; Nishiura, Hiroshi
2016-01-01
Background. An international spread of Zika virus (ZIKV) infection has attracted global attention. ZIKV is conveyed by a mosquito vector, Aedes species, which also acts as the vector species of dengue and chikungunya viruses. Methods. Arrival time of ZIKV importation (i.e., the time at which the first imported case was diagnosed) in each imported country was collected from publicly available data sources. Employing a survival analysis model in which the hazard is an inverse function of the effective distance as informed by the airline transportation network data, and using dengue and chikungunya virus transmission data, risks of importation and local transmission were estimated. Results. A total of 78 countries with imported case(s) have been identified, with the arrival time ranging from 1 to 44 weeks since the first ZIKV was identified in Brazil, 2015. Whereas the risk of importation was well explained by the airline transportation network data, the risk of local transmission appeared to be best captured by additionally accounting for the presence of dengue and chikungunya viruses. Discussion. The risk of importation may be high given continued global travel of mildly infected travelers but, considering that the public health concerns over ZIKV infection stems from microcephaly, it is more important to focus on the risk of local and widespread transmission that could involve pregnant women. The predicted risk of local transmission was frequently seen in tropical and subtropical countries with dengue or chikungunya epidemic experience.
Detecting and estimating continuous-variable entanglement by local orthogonal observables.
Zhang, Chengjie; Yu, Sixia; Chen, Qing; Oh, C H
2013-11-01
Entanglement detection and estimation are fundamental problems in quantum information science. Compared with discrete-variable states, for which lots of efficient entanglement detection criteria and lower bounds of entanglement measures have been proposed, the continuous-variable entanglement is much less understood. Here we shall present a family of entanglement witnesses based on continuous-variable local orthogonal observables (CVLOOs) to detect and estimate entanglement of Gaussian and non-Gaussian states, especially for bound entangled states. By choosing an optimal set of CVLOOs, our entanglement witness is equivalent to the realignment criterion and can be used to detect bound entanglement of a class of 2+2 mode Gaussian states. Via our entanglement witness, lower bounds of two typical entanglement measures for arbitrary two-mode continuous-variable states are provided.
Institute of Scientific and Technical Information of China (English)
WEN Zeng-ping; GAO Meng-tan; ZHAO Feng-xin; LI Xiao-jun; LU Hong-shan; HE Shao-lin
2006-01-01
A procedure is developed to incorporate seismic environment and site condition into the framework of seismic vulnerability estimation of building to consider the effects of the severity and/or frequency content of ground motion due to seismic environment and site condition. Localized damage distribution can be strongly influenced by seismic environment and surficial soil conditions and any attempt to quantify seismic vulnerability of building should consider the impact of these effects. The seismic environment, site and structure are coupled to estimate damage probability distribution among different damage states for the building. Response spectra at rock site are estimated by probabilistic seismic hazard assessment approach. Based upon engineering representations of soil and amplifying spectral coordinates, frequency content and severity of ground motion are considered. Furthermore the impacts of severity and/or frequency of ground motion effects are considered to estimate the seismic response of reinforced concrete building and damage probability distribution for the building. In addition, a new method for presenting the distribution of damage is developed to express damage probability distribution for the building for different seismic hazard levels.
Controlling Parametric Resonance
DEFF Research Database (Denmark)
Galeazzi, Roberto; Pettersen, Kristin Ytterstad
2012-01-01
Parametric resonance is a resonant phenomenon which takes place in systems characterized by periodic variations of some parameters. While seen as a threatening condition, whose onset can drive a system into instability, this chapter advocates that parametric resonance may become an advantage if t...
Making sense of the local Galactic escape speed estimates in direct dark matter searches
Lavalle, Julien
2014-01-01
Direct detection (DD) of dark matter (DM) candidates in the $\\lesssim$10 GeV mass range is very sensitive to the tail of their velocity distribution. The important quantity is the maximum WIMP speed in the observer's rest frame, i.e. in average the sum of the local Galactic escape speed $v_{\\rm esc}$ and of the circular velocity of the Sun $v_c$. While the latter has been receiving continuous attention, the former is more difficult to constrain. The RAVE Collaboration has just released a new estimate of $v_{\\rm esc}$ (Piffl {\\em et al.}, 2014 --- P14) that supersedes the previous one (Smith {\\em et al.}, 2007), which is of interest in the perspective of reducing the astrophysical uncertainties in DD. Nevertheless, these new estimates cannot be used blindly as they rely on assumptions in the dark halo modeling which induce tight correlations between the escape speed and other local astrophysical parameters. We make a self-consistent study of the implications of the RAVE results on DD assuming isotropic DM velo...
Lee, Duncan; Rushworth, Alastair; Sahu, Sujit K
2014-06-01
Estimation of the long-term health effects of air pollution is a challenging task, especially when modeling spatial small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial autocorrelation structure in these data, which is accounted for using random effects modeled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localized conditional autoregressive model is developed for the random effects. This localized model is flexible spatially, in the sense that it is not only able to model areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2011. The model shows substantial health effects of particulate matter air pollution and nitrogen dioxide, whose effects have been consistently attenuated by the currently available globally smooth models.
Static roll-tilt over 5 minutes locally distorts the internal estimate of direction of gravity.
Tarnutzer, A A; Bockisch, C J; Straumann, D; Marti, S; Bertolini, G
2014-12-01
The subjective visual vertical (SVV) indicates perceived direction of gravity. Even in healthy human subjects, roll angle-dependent misestimations, roll overcompensation (A-effect, head-roll > 60° and <135°) and undercompensation (E-effect, head-roll < 60°), occur. Previously, we demonstrated that, after prolonged roll-tilt, SVV estimates when upright are biased toward the preceding roll position, which indicates that perceived vertical (PV) is shifted by the prior tilt (Tarnutzer AA, Bertolini G, Bockisch CJ, Straumann D, Marti S. PLoS One 8: e78079, 2013). Hypothetically, PV in any roll position could be biased toward the previous roll position. We asked whether such a "global" bias occurs or whether the bias is "local". The SVV of healthy human subjects (N = 9) was measured in nine roll positions (-120° to +120°, steps = 30°) after 5 min of roll-tilt in one of two adaptation positions (±90°) and compared with control trials without adaptation. After adapting, adjustments were shifted significantly (P < 0.05) toward the previous adaptation position for nearby roll-tilted positions (±30°, ±60°) and upright only. We computationally simulated errors based on the sum of a monotonically increasing function (producing roll undercompensation) and a mixture of Gaussian functions (representing roll overcompensation centered around PV). In combination, the pattern of A- and E-effects could be generated. By shifting the function representing local overcompensation toward the adaptation position, the experimental postadaptation data could be fitted successfully. We conclude that prolonged roll-tilt locally distorts PV rather than globally shifting it. Short-term adaptation of roll overcompensation may explain these shifts and could reflect the brain's strategy to optimize SVV estimates around recent roll positions. Thus postural stability can be improved by visually-mediated compensatory responses at any sustained body-roll orientation.
Estimating individual exposure to malaria using local prevalence of malaria infection in the field.
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Ally Olotu
Full Text Available BACKGROUND: Heterogeneity in malaria exposure complicates survival analyses of vaccine efficacy trials and confounds the association between immune correlates of protection and malaria infection in longitudinal studies. Analysis may be facilitated by taking into account the variability in individual exposure levels, but it is unclear how exposure can be estimated at an individual level. METHOD AND FINDINGS: We studied three cohorts (Chonyi, Junju and Ngerenya in Kilifi District, Kenya to assess measures of malaria exposure. Prospective data were available on malaria episodes, geospatial coordinates, proximity to infected and uninfected individuals and residence in predefined malaria hotspots for 2,425 individuals. Antibody levels to the malaria antigens AMA1 and MSP1(142 were available for 291 children from Junju. We calculated distance-weighted local prevalence of malaria infection within 1 km radius as a marker of individual's malaria exposure. We used multivariable modified Poisson regression model to assess the discriminatory power of these markers for malaria infection (i.e. asymptomatic parasitaemia or clinical malaria. The area under the receiver operating characteristic (ROC curve was used to assess the discriminatory power of the models. Local malaria prevalence within 1 km radius and AMA1 and MSP1(142 antibodies levels were independently associated with malaria infection. Weighted local malaria prevalence had an area under ROC curve of 0.72 (95%CI: 0.66-0.73, 0.71 (95%CI: 0.69-0.73 and 0.82 (95%CI: 0.80-0.83 among cohorts in Chonyi, Junju and Ngerenya respectively. In a small subset of children from Junju, a model incorporating weighted local malaria prevalence with AMA1 and MSP1(142 antibody levels provided an AUC of 0.83 (95%CI: 0.79-0.88. CONCLUSION: We have proposed an approach to estimating the intensity of an individual's malaria exposure in the field. The weighted local malaria prevalence can be used as individual marker of
Materna, K.; Herring, T.
2013-12-01
Error in modeling atmospheric delay is one of the limiting factors in the accuracy of GPS position determination. In regions with uneven topography, atmospheric delay phenomena can be especially complicated. Current delay models used in analyzing daily GPS data from the Plate Boundary Observatory (PBO) are successful in achieving millimeter-level accuracy at most locations; however, at a subset of stations, the time-series for position estimates contain an unusually large number of outliers. In many cases these outliers are oriented in the same direction. The stations which exhibit asymmetric outliers occur in various places across the PBO network, but they are especially numerous in California's Mammoth Lakes region, which served as a case study for this presentation. The phenomenon was analyzed by removing secular trends and variations with periods longer than 75 days from the signal using a median filter. We subsequently calculated the skewness of the station position residuals in north, east and up directions. In the cases examined, typical position outliers are 5-15 mm. In extreme cases, skewed position residuals, not related to snow on antennas, can be as large as 20 mm. We examine the causes of the skewness through site-by-site comparisons with topographic data and numerical weather models. Analysis suggests that the direction of the skewness is generally parallel to the local topographic gradient at a scale of several kilometers, and that outlier data points occur when certain atmospheric conditions are met. The results suggest that a coupling between the atmosphere and local topography is responsible for the phenomenon of skewed residuals. In this presentation, we examine the characteristics of the sites that we have analyzed in detail. From these analyses, we postulate possible parameterizations of the atmospheric and topographic effects that could be incorporated into geodetic analysis programs, thus allowing the inhomogeneous atmospheric delays to be
Simultaneous Localization and Appearance Estimation with a Consumer RGB-D Camera.
Wu, Hongzhi; Wang, Zhaotian; Zhou, Kun
2016-08-01
Acquiring general material appearance with hand-held consumer RGB-D cameras is difficult for casual users, due to the inaccuracy in reconstructed camera poses and geometry, as well as the unknown lighting that is coupled with materials in measured color images. To tackle these challenges, we present a novel technique for estimating the spatially varying isotropic surface reflectance, solely from color and depth images captured with an RGB-D camera under unknown environment illumination. The core of our approach is a joint optimization, which alternates among solving for plausible camera poses, materials, the environment lighting and normals. To refine camera poses, we exploit the rich spatial and view-dependent variations of materials, treating the object as a localization-self-calibrating model. To recover the unknown lighting, measured color images along with the current estimate of materials are used in a global optimization, efficiently solved by exploiting the sparsity in the wavelet domain. We demonstrate the substantially improved quality of estimated appearance on a variety of daily objects.
Estimating the correlation between bursty spike trains and local field potentials.
Li, Zhaohui; Ouyang, Gaoxiang; Yao, Li; Li, Xiaoli
2014-09-01
To further understand rhythmic neuronal synchronization, an increasingly useful method is to determine the relationship between the spiking activity of individual neurons and the local field potentials (LFPs) of neural ensembles. Spike field coherence (SFC) is a widely used method for measuring the synchronization between spike trains and LFPs. However, due to the strong dependency of SFC on the burst index, it is not suitable for analyzing the relationship between bursty spike trains and LFPs, particularly in high frequency bands. To address this issue, we developed a method called weighted spike field correlation (WSFC), which uses the first spike in each burst multiple times to estimate the relationship. In the calculation, the number of times that the first spike is used is equal to the spike count per burst. The performance of this method was demonstrated using simulated bursty spike trains and LFPs, which comprised sinusoids with different frequencies, amplitudes, and phases. This method was also used to estimate the correlation between pyramidal cells in the hippocampus and gamma oscillations in rats performing behaviors. Analyses using simulated and real data demonstrated that the WSFC method is a promising measure for estimating the correlation between bursty spike trains and high frequency LFPs.
Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly
MacKenzie, D.I.; Nichols, J.D.; Hines, J.E.; Knutson, M.G.; Franklin, A.B.
2003-01-01
Few species are likely to be so evident that they will always be detected when present. Failing to allow for the possibility that a target species was present, but undetected, at a site will lead to biased estimates of site occupancy, colonization, and local extinction probabilities. These population vital rates are often of interest in long-term monitoring programs and metapopulation studies. We present a model that enables direct estimation of these parameters when the probability of detecting the species is less than 1. The model does not require any assumptions of process stationarity, as do some previous methods, but does require detection/nondetection data to be collected in a manner similar to Pollock's robust design as used in mark?recapture studies. Via simulation, we show that the model provides good estimates of parameters for most scenarios considered. We illustrate the method with data from monitoring programs of Northern Spotted Owls (Strix occidentalis caurina) in northern California and tiger salamanders (Ambystoma tigrinum) in Minnesota, USA.
Capell, Rene; Hankin, Barry; Strömqvist, Johan; Lamb, Rob; Arheimer, Berit
2017-04-01
Nutrient transport models are important tools for large scale assessments of macro-nutrient fluxes (nitrate, phosphate) and thus can serve as support tool for environmental assessment and management. Results from model applications over large areas, i.e. on major river basin to continental scales can fill a gap where monitoring data is not available. However, both phosphate and nitrate transport are highly complex processes, and nutrient models must balance data requirements and process simplification. Data typically become increasingly sparse and less detailed with increasing spatial scale. Here, we compare model estimates of riverine nitrate concentrations in the Weaver-Dane basin (UK) and to evaluate the role of available environmental data sources for model performance by using (a) open environmental data sources available at European scale and (b) closed data sources which are more localised and typically not openly available. In particular, we aim to evaluate, how model structure, spatial model resolution, climate forcing products, and land use and management information impact on model-estimated nitrate concentrations. We use the European rainfall-runoff and nutrient model E-HYPE (http://hypeweb.smhi.se/europehype/about/) as a baseline large-scale model built on open data sources, and compare with more detailed model set-ups in different configurations using local data. Nitrate estimates are compared using a GLUE uncertainty framework.
A hierarchical Bayesian GEV model for improving local and regional flood quantile estimates
Lima, Carlos H. R.; Lall, Upmanu; Troy, Tara; Devineni, Naresh
2016-10-01
We estimate local and regional Generalized Extreme Value (GEV) distribution parameters for flood frequency analysis in a multilevel, hierarchical Bayesian framework, to explicitly model and reduce uncertainties. As prior information for the model, we assume that the GEV location and scale parameters for each site come from independent log-normal distributions, whose mean parameter scales with the drainage area. From empirical and theoretical arguments, the shape parameter for each site is shrunk towards a common mean. Non-informative prior distributions are assumed for the hyperparameters and the MCMC method is used to sample from the joint posterior distribution. The model is tested using annual maximum series from 20 streamflow gauges located in an 83,000 km2 flood prone basin in Southeast Brazil. The results show a significant reduction of uncertainty estimates of flood quantile estimates over the traditional GEV model, particularly for sites with shorter records. For return periods within the range of the data (around 50 years), the Bayesian credible intervals for the flood quantiles tend to be narrower than the classical confidence limits based on the delta method. As the return period increases beyond the range of the data, the confidence limits from the delta method become unreliable and the Bayesian credible intervals provide a way to estimate satisfactory confidence bands for the flood quantiles considering parameter uncertainties and regional information. In order to evaluate the applicability of the proposed hierarchical Bayesian model for regional flood frequency analysis, we estimate flood quantiles for three randomly chosen out-of-sample sites and compare with classical estimates using the index flood method. The posterior distributions of the scaling law coefficients are used to define the predictive distributions of the GEV location and scale parameters for the out-of-sample sites given only their drainage areas and the posterior distribution of the
Estimation of potential scour at bridges on local government roads in South Dakota, 2009-12
Thompson, Ryan F.; Wattier, Chelsea M.; Liggett, Richard R.; Truax, Ryan A.
2014-01-01
In 2009, the U.S. Geological Survey and South Dakota Department of Transportation (SDDOT) began a study to estimate potential scour at selected bridges on local government (county, township, and municipal) roads in South Dakota. A rapid scour-estimation method (level-1.5) and a more detailed method (level-2) were used to develop estimates of contraction, abutment, and pier scour. Data from 41 level-2 analyses completed for this study were combined with data from level-2 analyses completed in previous studies to develop new South Dakota-specific regression equations: four regional equations for main-channel velocity at the bridge contraction to account for the widely varying stream conditions within South Dakota, and one equation for head change. Velocity data from streamgages also were used in the regression for average velocity through the bridge contraction. Using these new regression equations, scour analyses were completed using the level-1.5 method on 361 bridges on local government roads. Typically, level-1.5 analyses are completed at flows estimated to have annual exceedance probabilities of 1 percent (100-year flood) and 0.2 percent (500-year flood); however, at some sites the bridge would not pass these flows. A level-1.5 analysis was then completed at the flow expected to produce the maximum scour. Data presented for level-1.5 scour analyses at the 361 bridges include contraction, abutment, and pier scour. Estimates of potential contraction scour ranged from 0 to 32.5 feet for the various flows evaluated. Estimated potential abutment scour ranged from 0 to 40.9 feet for left abutments, and from 0 to 37.7 feet for right abutments. Pier scour values ranged from 2.7 to 31.6 feet. The scour depth estimates provided in this report can be used by the SDDOT to compare with foundation depths at each bridge to determine if abutments or piers are at risk of being undermined by scour at the flows evaluated. Replicate analyses were completed at 24 of the 361 bridges
Efficient Characterization of Parametric Uncertainty of Complex (Biochemical Networks.
Directory of Open Access Journals (Sweden)
Claudia Schillings
2015-08-01
Full Text Available Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is "non-intrusive" and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design.
Digital spectral analysis parametric, non-parametric and advanced methods
Castanié, Francis
2013-01-01
Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature.The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models.An entire chapter is devoted to the non-parametric methods most widely used in industry.High resolution methods a
de Graaf-Ruizendaal, Willemijn A; de Bakker, Dinny H
2013-10-27
This study addresses the growing academic and policy interest in the appropriate provision of local healthcare services to the healthcare needs of local populations to increase health status and decrease healthcare costs. However, for most local areas information on the demand for primary care and supply is missing. The research goal is to examine the construction of a decision tool which enables healthcare planners to analyse local supply and demand in order to arrive at a better match. National sample-based medical record data of general practitioners (GPs) were used to predict the local demand for GP care based on local populations using a synthetic estimation technique. Next, the surplus or deficit in local GP supply were calculated using the national GP registry. Subsequently, a dynamic internet tool was built to present demand, supply and the confrontation between supply and demand regarding GP care for local areas and their surroundings in the Netherlands. Regression analysis showed a significant relationship between sociodemographic predictors of postcode areas and GP consultation time (F [14, 269,467] = 2,852.24; P 1,000 inhabitants in the Netherlands covering 97% of the total population. Confronting these estimated demand figures with the actual GP supply resulted in the average GP workload and the number of full-time equivalent (FTE) GP too much/too few for local areas to cover the demand for GP care. An estimated shortage of one FTE GP or more was prevalent in about 19% of the postcode areas with >1,000 inhabitants if the surrounding postcode areas were taken into consideration. Underserved areas were mainly found in rural regions. The constructed decision tool is freely accessible on the Internet and can be used as a starting point in the discussion on primary care service provision in local communities and it can make a considerable contribution to a primary care system which provides care when and where people need it.
The Stellar parametrization using Artificial Neural Network
Giridhar, Sunetra; Kunder, Andrea; Muneer, S; Kumar, G Selva
2012-01-01
An update on recent methods for automated stellar parametrization is given. We present preliminary results of the ongoing program for rapid parametrization of field stars using medium resolution spectra obtained using Vainu Bappu Telescope at VBO, Kavalur, India. We have used Artificial Neural Network for estimating temperature, gravity, metallicity and absolute magnitude of the field stars. The network for each parameter is trained independently using a large number of calibrating stars. The trained network is used for estimating atmospheric parameters of unexplored field stars.
Prince, Frank A.
2017-01-01
Building a parametric cost model is hard work. The data is noisy and often does not behave like we want it to. We need statistics to give us an indication of the goodness of our models, but; statistics can be manipulated and mislead. On top of all of that, our own very human biases can lead us astray; causing us to see patterns in the noise and draw false conclusions from the data. Yet, it is the data itself that is the foundation for making better cost estimates and cost models. I believe the mistake we often make is we believe that our models are representative of the data; that our models summarize the experiences, the knowledge, and the stories contained in the data. However, it is the opposite that is true. Our models are but imitations of reality. They give us trends, but not truth. The experiences, the knowledge, and the stories that we need in order to make good cost estimates is bound up in the data. You cannot separate good cost estimating from a knowledge of the historical data. One final thought. It is our attempts to make sense out of the randomness that leads us astray. In order to make progress as cost modelers and cost estimators, we must accept that there are real limitations on our ability to model the past and predict the future. I do not believe we should throw up our hands and say this is the best we can do. Rather, to see real improvement we must first recognize these limitations, avoid the easy but misleading solutions, and seek to find ways to better model the world we live in. I don't have any simple solutions. Perhaps the answers lie in better data or in a totally different approach to simulating how the world works. All I know is that we must do our best to speak truth to ourselves and our customers. Misleading ourselves and our customers will, in the end, result in an inability to have a positive impact on those we serve.
Directory of Open Access Journals (Sweden)
Sabyasachi Guharay
2017-07-01
Full Text Available Value-at-Risk (VaR is a well-accepted risk metric in modern quantitative risk management (QRM. The classical Monte Carlo simulation (MCS approach, denoted henceforth as the classical approach, assumes the independence of loss severity and loss frequency. In practice, this assumption does not always hold true. Through mathematical analyses, we show that the classical approach is prone to significant biases when the independence assumption is violated. This is also corroborated by studying both simulated and real-world datasets. To overcome the limitations and to more accurately estimate VaR, we develop and implement the following two approaches for VaR estimation: the data-driven partitioning of frequency and severity (DPFS using clustering analysis, and copula-based parametric modeling of frequency and severity (CPFS. These two approaches are verified using simulation experiments on synthetic data and validated on five publicly available datasets from diverse domains; namely, the financial indices data of Standard & Poor’s 500 and the Dow Jones industrial average, chemical loss spills as tracked by the US Coast Guard, Australian automobile accidents, and US hurricane losses. The classical approach estimates VaR inaccurately for 80% of the simulated data sets and for 60% of the real-world data sets studied in this work. Both the DPFS and the CPFS methodologies attain VaR estimates within 99% bootstrap confidence interval bounds for both simulated and real-world data. We provide a process flowchart for risk practitioners describing the steps for using the DPFS versus the CPFS methodology for VaR estimation in real-world loss datasets.
Local surface sampling step estimation for extracting boundaries of planar point clouds
Brie, David; Bombardier, Vincent; Baeteman, Grégory; Bennis, Abdelhamid
2016-09-01
This paper presents a new approach to estimate the surface sampling step of planar point clouds acquired by Terrestrial Laser Scanner (TLS) which is varying with the distance to the surface and the angular positions. The local surface sampling step is obtained by doing a first order Taylor expansion of planar point coordinates. Then, it is shown how to use it in Delaunay-based boundary point extraction. The resulting approach, which is implemented in the ModiBuilding software, is applied to two facade point clouds of a building. The first is acquired with a single station and the second with two stations. In both cases, the proposed approach performs very accurately and appears to be robust to the variations of the point cloud density.
Energy Technology Data Exchange (ETDEWEB)
Ratib, O.; Phelps, M.E.; Huang, S.C.; Henze, E.; Selin, C.E.; Schelbert, H.R.
1981-01-01
The deoxyglucose method originally developed for measurements of the local cerebral metabolic rate for glucose has been investigated in terms of its application to studies of the heart with positron computed tomography (PCT) and FDG. Studies were performed in dogs to measure the tissue kinetics of FDG with PCT and by direct arterial-venous sampling. The operational equation developed in our laboratory as an extension of the Sokoloff model was used to analyze the data. The FDG method accurately predicted the true MMRGlc even when the glucose metabolic rate was normal but myocardial blood flow (MBF) was elevated 5 times the control value or when metabolism was reduced to 10% of normal and MBF increased 5 times normal. Improvements in PCT resolution are required to improve the accuracy of the estimates of the rate constants and the MMRGlc.
Estimations of local thermal impact on living organisms irradiated by non-thermal microwaves
Shatalov, Vladimir
2013-01-01
Pennes' differential equation for bioheat transfer and the heat transfer equation are solved for the temperature distribution in a living tissue with spherical inclusions, irradiated by microwave power. It is shown that relative temperature excess in a small inclusion in the tissue in some cases is inversely proportional to its radius and does not depend on the applied power. In pulsing RF fields the effect is amplified proportionally to the ratio of the pulse period to the pulse duration. The local temperature rise significantly outpaces the averaged one and therefore the Watt to Weight SAR limits may be insufficient to estimate the safety of RF radiation and the conventional division of the biological effects of electromagnetic fields on the thermal and non-thermal needs to be revised.
Discrete Plane Segmentation and Estimation from a Point Cloud Using Local Geometric Patterns
Institute of Scientific and Technical Information of China (English)
Yukiko Kenmochi; Lilian Buzer; Akihiro Sugimoto; Ikuko Shimizu
2008-01-01
This paper presents a method for segmenting a 3D point cloud into planar surfaces using recently obtained discrete-geometry results. In discrete geometry, a discrete plane is defined as a set of grid points lying between two parallel planes with a small distance, called thickness. In contrast to the continuous case, there exist a finite number of local geometric patterns (LGPs) appearing on discrete planes. Moreover, such an LGP does not possess the unique normal vector but a set of normal vectors. By using those LGP properties, we first reject non-linear points from a point cloud, and then classify non-rejected points whose LGPs have common normal vectors into a planar-surface-point set. From each segmented point set, we also estimate the values of parameters of a discrete plane by minimizing its thickness.
Eckhard, Timo; Valero, Eva M; Hernández-Andrés, Javier; Heikkinen, Ville
2014-03-01
In this work, we evaluate the conditionally positive definite logarithmic kernel in kernel-based estimation of reflectance spectra. Reflectance spectra are estimated from responses of a 12-channel multispectral imaging system. We demonstrate the performance of the logarithmic kernel in comparison with the linear and Gaussian kernel using simulated and measured camera responses for the Pantone and HKS color charts. Especially, we focus on the estimation model evaluations in case the selection of model parameters is optimized using a cross-validation technique. In experiments, it was found that the Gaussian and logarithmic kernel outperformed the linear kernel in almost all evaluation cases (training set size, response channel number) for both sets. Furthermore, the spectral and color estimation accuracies of the Gaussian and logarithmic kernel were found to be similar in several evaluation cases for real and simulated responses. However, results suggest that for a relatively small training set size, the accuracy of the logarithmic kernel can be markedly lower when compared to the Gaussian kernel. Further it was found from our data that the parameter of the logarithmic kernel could be fixed, which simplified the use of this kernel when compared with the Gaussian kernel.
Outlier detection for particle image velocimetry data using a locally estimated noise variance
Lee, Yong; Yang, Hua; Yin, ZhouPing
2017-03-01
This work describes an adaptive spatial variable threshold outlier detection algorithm for raw gridded particle image velocimetry data using a locally estimated noise variance. This method is an iterative procedure, and each iteration is composed of a reference vector field reconstruction step and an outlier detection step. We construct the reference vector field using a weighted adaptive smoothing method (Garcia 2010 Comput. Stat. Data Anal. 54 1167-78), and the weights are determined in the outlier detection step using a modified outlier detector (Ma et al 2014 IEEE Trans. Image Process. 23 1706-21). A hard decision on the final weights of the iteration can produce outlier labels of the field. The technical contribution is that the spatial variable threshold motivation is embedded in the modified outlier detector with a locally estimated noise variance in an iterative framework for the first time. It turns out that a spatial variable threshold is preferable to a single spatial constant threshold in complicated flows such as vortex flows or turbulent flows. Synthetic cellular vortical flows with simulated scattered or clustered outliers are adopted to evaluate the performance of our proposed method in comparison with popular validation approaches. This method also turns out to be beneficial in a real PIV measurement of turbulent flow. The experimental results demonstrated that the proposed method yields the competitive performance in terms of outlier under-detection count and over-detection count. In addition, the outlier detection method is computational efficient and adaptive, requires no user-defined parameters, and corresponding implementations are also provided in supplementary materials.
Estimating local scaling properties for the classification of interstitial lung disease patterns
Huber, Markus B.; Nagarajan, Mahesh B.; Leinsinger, Gerda; Ray, Lawrence A.; Wismueller, Axel
2011-03-01
Local scaling properties of texture regions were compared in their ability to classify morphological patterns known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honeycombing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. 241 regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and the estimation of local scaling properties with Scaling Index Method (SIM). A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions including the Bonferroni correction. The best classification results were obtained by the set of SIM features, which performed significantly better than all the standard GLCM and MD features (p interstitial lung diseases when compared to standard texture analysis methods.
Ely, Gregory
2013-01-01
In this paper we present a novel technique for micro-seismic localization using a group sparse penalization that is robust to the focal mechanism of the source and requires only a velocity model of the stratigraphy rather than a full Green's function model of the earth's response. In this technique we construct a set of perfect delta detector responses, one for each detector in the array, to a seismic event at a given location and impose a group sparsity across the array. This scheme is independent of the moment tensor and exploits the time compactness of the incident seismic signal. Furthermore we present a method for improving the inversion of the moment tensor and Green's function when the geometry of seismic array is limited. In particular we demonstrate that both Tikhonov regularization and truncated SVD can improve the recovery of the moment tensor and be robust to noise. We evaluate our algorithm on synthetic data and present error bounds for both estimation of the moment tensor as well as localization...
The variance of the locally measured Hubble parameter explained with different estimators
DEFF Research Database (Denmark)
Odderskov, Io; Hannestad, Steen; Brandbyge, Jacob
2017-01-01
We study the expected variance of measurements of the Hubble constant, H0, as calculated in either linear perturbation theory or using non-linear velocity power spectra derived from N-body simulations. We compare the variance with that obtained by carrying out mock observations in the N-body simu......We study the expected variance of measurements of the Hubble constant, H0, as calculated in either linear perturbation theory or using non-linear velocity power spectra derived from N-body simulations. We compare the variance with that obtained by carrying out mock observations in the N......-body simulations, and show that the estimator typically used for the local Hubble constant in studies based on perturbation theory is different from the one used in studies based on N-body simulations. The latter gives larger weight to distant sources, which explains why studies based on N-body simulations tend...... of the percent determination of the Hubble constant in the local universe....
A novel cost-effective parallel narrowband ANC system with local secondary-path estimation
Delegà, Riccardo; Bernasconi, Giancarlo; Piroddi, Luigi
2017-08-01
Many noise reduction applications are targeted at multi-tonal disturbances. Active noise control (ANC) solutions for such problems are generally based on the combination of multiple adaptive notch filters. Both the performance and the computational cost are negatively affected by an increase in the number of controlled frequencies. In this work we study a different modeling approach for the secondary path, based on the estimation of various small local models in adjacent frequency subbands, that greatly reduces the impact of reference-filtering operations in the ANC algorithm. Furthermore, in combination with a frequency-specific step size tuning method it provides a balanced attenuation performance over the whole controlled frequency range (and particularly in the high end of the range). Finally, the use of small local models is greatly beneficial for the reactivity of the online secondary path modeling algorithm when the characteristics of the acoustic channels are time-varying. Several simulations are provided to illustrate the positive features of the proposed method compared to other well-known techniques.
A method for Bayesian estimation of the probability of local intensity for some cities in Japan
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G. C. Koravos
2002-06-01
Full Text Available Seismic hazard in terms of probability of exceedance of a given intensity in a given time span,was assessed for 12 sites in Japan.The method does not use any attenuation law.Instead,the dependence of local intensity on epicentral intensity I 0 is calculated directly from the data,using a Bayesian model.According to this model (Meroni et al., 1994,local intensity follows the binomial distribution with parameters (I 0 ,p .The parameter p is considered as a random variable following the Beta distribution.This manner of Bayesian estimates of p are assessed for various values of epicentral intensity and epicentral distance.In order to apply this model for the assessment of seismic hazard,the area under consideration is divided into seismic sources (zonesof known seismicity.The contribution of each source on the seismic hazard at every site is calculated according to the Bayesian model and the result is the combined effect of all the sources.High probabilities of exceedance were calculated for the sites that are in the central part of the country,with hazard decreasing slightly towards the north and the south parts.
Hellander, Andreas; Lawson, Michael J.; Drawert, Brian; Petzold, Linda
2014-06-01
The efficiency of exact simulation methods for the reaction-diffusion master equation (RDME) is severely limited by the large number of diffusion events if the mesh is fine or if diffusion constants are large. Furthermore, inherent properties of exact kinetic-Monte Carlo simulation methods limit the efficiency of parallel implementations. Several approximate and hybrid methods have appeared that enable more efficient simulation of the RDME. A common feature to most of them is that they rely on splitting the system into its reaction and diffusion parts and updating them sequentially over a discrete timestep. This use of operator splitting enables more efficient simulation but it comes at the price of a temporal discretization error that depends on the size of the timestep. So far, existing methods have not attempted to estimate or control this error in a systematic manner. This makes the solvers hard to use for practitioners since they must guess an appropriate timestep. It also makes the solvers potentially less efficient than if the timesteps were adapted to control the error. Here, we derive estimates of the local error and propose a strategy to adaptively select the timestep when the RDME is simulated via a first order operator splitting. While the strategy is general and applicable to a wide range of approximate and hybrid methods, we exemplify it here by extending a previously published approximate method, the diffusive finite-state projection (DFSP) method, to incorporate temporal adaptivity.
Hellander, Andreas; Lawson, Michael J; Drawert, Brian; Petzold, Linda
2015-01-01
The efficiency of exact simulation methods for the reaction-diffusion master equation (RDME) is severely limited by the large number of diffusion events if the mesh is fine or if diffusion constants are large. Furthermore, inherent properties of exact kinetic-Monte Carlo simulation methods limit the efficiency of parallel implementations. Several approximate and hybrid methods have appeared that enable more efficient simulation of the RDME. A common feature to most of them is that they rely on splitting the system into its reaction and diffusion parts and updating them sequentially over a discrete timestep. This use of operator splitting enables more efficient simulation but it comes at the price of a temporal discretization error that depends on the size of the timestep. So far, existing methods have not attempted to estimate or control this error in a systematic manner. This makes the solvers hard to use for practitioners since they must guess an appropriate timestep. It also makes the solvers potentially less efficient than if the timesteps are adapted to control the error. Here, we derive estimates of the local error and propose a strategy to adaptively select the timestep when the RDME is simulated via a first order operator splitting. While the strategy is general and applicable to a wide range of approximate and hybrid methods, we exemplify it here by extending a previously published approximate method, the Diffusive Finite-State Projection (DFSP) method, to incorporate temporal adaptivity. PMID:26865735
Local land-use change based risk estimation for future glacier lake outburst flood
Directory of Open Access Journals (Sweden)
S. Nussbaumer
2013-08-01
Full Text Available Effects of climate change are particularly strong in high-mountain regions. Most visibly, glaciers are shrinking at a rapid pace, and as a consequence, glacier lakes are forming or growing. At the same time the stability of mountain slopes is reduced by glacier retreat, permafrost thaw and other factors, resulting in an increasing risk of landslides which can potentially impact lakes and therewith trigger far reaching and devastating outburst floods. To manage risks from existing or future lakes, strategies need to be developed to plan in time for adequate risk reduction measures at a local level. However, methods to assess risks from future lake outbursts are not available. It is actually a challenge to develop methods to evaluate both, future hazard potential and future damage potential. Here we present an analysis of future risks related to glacier lake outbursts for a local site in southern Switzerland (Naters, Valais. To estimate two hazard scenarios, we used glacier shrinkage and lake formation modelling, simple flood modelling and field work. Further we developed a land-use model to quantify and allocate land-use changes based on local-to-regional storylines and three scenarios of land-use driving forces. Results are conceptualized in a matrix of three land-use and two hazard scenarios for a time period of 2045, and show the distribution of risk in the community of Naters, including high and very high risk areas. The study corroborates the importance of land-use planning to effectively reduce future risks related to lake outburst floods.
Local land-use change based risk estimation for future glacier lake outburst flood
Nussbaumer, S.; Huggel, C.; Schaub, Y.; Walz, A.
2013-08-01
Effects of climate change are particularly strong in high-mountain regions. Most visibly, glaciers are shrinking at a rapid pace, and as a consequence, glacier lakes are forming or growing. At the same time the stability of mountain slopes is reduced by glacier retreat, permafrost thaw and other factors, resulting in an increasing risk of landslides which can potentially impact lakes and therewith trigger far reaching and devastating outburst floods. To manage risks from existing or future lakes, strategies need to be developed to plan in time for adequate risk reduction measures at a local level. However, methods to assess risks from future lake outbursts are not available. It is actually a challenge to develop methods to evaluate both, future hazard potential and future damage potential. Here we present an analysis of future risks related to glacier lake outbursts for a local site in southern Switzerland (Naters, Valais). To estimate two hazard scenarios, we used glacier shrinkage and lake formation modelling, simple flood modelling and field work. Further we developed a land-use model to quantify and allocate land-use changes based on local-to-regional storylines and three scenarios of land-use driving forces. Results are conceptualized in a matrix of three land-use and two hazard scenarios for a time period of 2045, and show the distribution of risk in the community of Naters, including high and very high risk areas. The study corroborates the importance of land-use planning to effectively reduce future risks related to lake outburst floods.
Risk estimation for future glacier lake outburst floods based on local land-use changes
Nussbaumer, S.; Schaub, Y.; Huggel, C.; Walz, A.
2014-06-01
Effects of climate change are particularly strong in high-mountain regions. Most visibly, glaciers are shrinking at a rapid pace, and as a consequence, glacier lakes are forming or growing. At the same time the stability of mountain slopes is reduced by glacier retreat, permafrost thaw and other factors, resulting in an increasing landslide hazard which can potentially impact lakes and therewith trigger far-reaching and devastating outburst floods. To manage risks from existing or future lakes, strategies need to be developed to plan in time for adequate risk reduction measures at a local level. However, methods to assess risks from future lake outbursts are not available and need to be developed to evaluate both future hazard and future damage potential. Here a method is presented to estimate future risks related to glacier lake outbursts for a local site in southern Switzerland (Naters, Valais). To generate two hazard scenarios, glacier shrinkage and lake formation modelling was applied, combined with simple flood modelling and field work. Furthermore, a land-use model was developed to quantify and allocate land-use changes based on local-to-regional storylines and three scenarios of land-use driving forces. Results are conceptualized in a matrix of three land-use and two hazard scenarios for the year 2045, and show the distribution of risk in the community of Naters, including high and very high risk areas. The study underlines the importance of combined risk management strategies focusing on land-use planning, on vulnerability reduction, as well as on structural measures (where necessary) to effectively reduce future risks related to lake outburst floods.
Local gravity disturbance estimation from multiple-high-single-low satellite-to-satellite tracking
Jekeli, Christopher
1989-01-01
The idea of satellite-to-satellite tracking in the high-low mode has received renewed attention in light of the uncertain future of NASA's proposed low-low mission, Geopotential Research Mission (GRM). The principal disadvantage with a high-low system is the increased time interval required to obtain global coverage since the intersatellite visibility is often obscured by Earth. The U.S. Air Force has begun to investigate high-low satellite-to-satellite tracking between the Global Positioning System (GPS) of satellites (high component) and NASA's Space Transportation System (STS), the shuttle (low component). Because the GPS satellites form, or will form, a constellation enabling continuous three-dimensional tracking of a low-altitude orbiter, there will be no data gaps due to lack of intervisibility. Furthermore, all three components of the gravitation vector are estimable at altitude, a given grid of which gives a stronger estimate of gravity on Earth's surface than a similar grid of line-of-sight gravitation components. The proposed Air Force mission is STAGE (Shuttle-GPS Tracking for Anomalous Gravitation Estimation) and is designed for local gravity field determinations since the shuttle will likely not achieve polar orbits. The motivation for STAGE was the feasibility to obtain reasonable accuracies with absolutely minimal cost. Instead of simulating drag-free orbits, STAGE uses direct measurements of the nongravitational forces obtained by an inertial package onboard the shuttle. The sort of accuracies that would be achievable from STAGE vis-a-vis other satellite tracking missions such as GRM and European Space Agency's POPSAT-GRM are analyzed.
Knox, C. E.; Vicroy, D. D.; Scanlon, C.
1984-01-01
Simulation and flight tests were conducted to compare the accuracy of two algorithms designed to compute a position estimate with an airborne navigation computer. Both algorithms used ILS localizer and DME radio signals to compute a position difference vector to be used as an input to the navigation computer position estimate filter. The results of these tests show that the position estimate accuracy and response to artificially induced errors are improved when the position estimate is computed by an algorithm that geometrically combines DME and ILS localizer information to form a single component of error rather than by an algorithm that produces two independent components of error, one from a DMD input and the other from the ILS localizer input.
Kaye, Jason; Yang, Chao
2014-01-01
Kohn-Sham density functional theory is one of the most widely used electronic structure theories. The recently developed adaptive local basis functions form an accurate and systematically improvable basis set for solving Kohn-Sham density functional theory using discontinuous Galerkin methods, requiring a small number of basis functions per atom. In this paper we develop residual-based a posteriori error estimates for the adaptive local basis approach, which can be used to guide non-uniform basis refinement for highly inhomogeneous systems such as surfaces and large molecules. The adaptive local basis functions are non-polynomial basis functions, and standard a posteriori error estimates for $hp$-refinement using polynomial basis functions do not directly apply. We generalize the error estimates for $hp$-refinement to non-polynomial basis functions. We demonstrate the practical use of the a posteriori error estimator in performing three-dimensional Kohn-Sham density functional theory calculations for quasi-2D...
Goovaerts, P
2009-06-01
Indicator kriging provides a flexible interpolation approach that is well suited for datasets where: 1) many observations are below the detection limit, 2) the histogram is strongly skewed, or 3) specific classes of attribute values are better connected in space than others (e.g. low pollutant concentrations). To apply indicator kriging at its full potential requires, however, the tedious inference and modeling of multiple indicator semivariograms, as well as the post-processing of the results to retrieve attribute estimates and associated measures of uncertainty. This paper presents a computer code that performs automatically the following tasks: selection of thresholds for binary coding of continuous data, computation and modeling of indicator semivariograms, modeling of probability distributions at unmonitored locations (regular or irregular grids), and estimation of the mean and variance of these distributions. The program also offers tools for quantifying the goodness of the model of uncertainty within a cross-validation and jack-knife frameworks. The different functionalities are illustrated using heavy metal concentrations from the well-known soil Jura dataset. A sensitivity analysis demonstrates the benefit of using more thresholds when indicator kriging is implemented with a linear interpolation model, in particular for variables with positively skewed histograms.
Goovaerts, P.
2009-06-01
Indicator kriging (IK) provides a flexible interpolation approach that is well suited for datasets where: (1) many observations are below the detection limit, (2) the histogram is strongly skewed, or (3) specific classes of attribute values are better connected in space than others (e.g. low pollutant concentrations). To apply indicator kriging at its full potential requires, however, the tedious inference and modeling of multiple indicator semivariograms, as well as the post-processing of the results to retrieve attribute estimates and associated measures of uncertainty. This paper presents a computer code that performs automatically the following tasks: selection of thresholds for binary coding of continuous data, computation and modeling of indicator semivariograms, modeling of probability distributions at unmonitored locations (regular or irregular grids), and estimation of the mean and variance of these distributions. The program also offers tools for quantifying the goodness of the model of uncertainty within a cross-validation and jack-knife frameworks. The different functionalities are illustrated using heavy metal concentrations from the well-known soil Jura dataset. A sensitivity analysis demonstrates the benefit of using more thresholds when indicator kriging is implemented with a linear interpolation model, in particular for variables with positively skewed histograms.
Spatiotemporal structures in the internally pumped optical parametric oscillator
DEFF Research Database (Denmark)
Lodahl, Peter; Bache, Morten; Saffman, Mark
2001-01-01
We analyze pattern formation in doubly resonant second-harmonic generation in the presence of a competing parametric process, also named the internally pumped optical parametric oscillator. Different scenarios are established where either the up- or down-conversion processes dominate the spatiote...... patterns and gray solitons. Estimates of the thresholds for pattern formation under experimentally relevant conditions are given....
Energy Technology Data Exchange (ETDEWEB)
Takamiya, Masanori [Department of Nuclear Engineering, Graduate School of Engineering, Kyoto University, Kyoto 606-8501, Japan and Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto 606-8507 (Japan); Nakamura, Mitsuhiro, E-mail: m-nkmr@kuhp.kyoto-u.ac.jp; Akimoto, Mami; Ueki, Nami; Yamada, Masahiro; Matsuo, Yukinori; Mizowaki, Takashi; Hiraoka, Masahiro [Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto 606-8507 (Japan); Tanabe, Hiroaki [Division of Radiation Oncology, Institute of Biomedical Research and Innovation, Kobe 650-0047 (Japan); Kokubo, Masaki [Division of Radiation Oncology, Institute of Biomedical Research and Innovation, Kobe 650-0047, Japan and Department of Radiation Oncology, Kobe City Medical Center General Hospital, Kobe 650-0047 (Japan); Itoh, Akio [Department of Nuclear Engineering, Graduate School of Engineering, Kyoto University, Kyoto 606-8501 (Japan)
2016-04-15
Purpose: To assess the target localization error (TLE) in terms of the distance between the target and the localization point estimated from the surrogates (|TMD|), the average of respiratory motion for the surrogates and the target (|aRM|), and the number of fiducial markers used for estimating the target (n). Methods: This study enrolled 17 lung cancer patients who subsequently underwent four fractions of real-time tumor tracking irradiation. Four or five fiducial markers were implanted around the lung tumor. The three-dimensional (3D) distance between the tumor and markers was at maximum 58.7 mm. One of the markers was used as the target (P{sub t}), and those markers with a 3D |TMD{sub n}| ≤ 58.7 mm at end-exhalation were then selected. The estimated target position (P{sub e}) was calculated from a localization point consisting of one to three markers except P{sub t}. Respiratory motion for P{sub t} and P{sub e} was defined as the root mean square of each displacement, and |aRM| was calculated from the mean value. TLE was defined as the root mean square of each difference between P{sub t} and P{sub e} during the monitoring of each fraction. These procedures were performed repeatedly using the remaining markers. To provide the best guidance on the answer with n and |TMD|, fiducial markers with a 3D |aRM ≥ 10 mm were selected. Finally, a total of 205, 282, and 76 TLEs that fulfilled the 3D |TMD| and 3D |aRM| criteria were obtained for n = 1, 2, and 3, respectively. Multiple regression analysis (MRA) was used to evaluate TLE as a function of |TMD| and |aRM| in each n. Results: |TMD| for n = 1 was larger than that for n = 3. Moreover, |aRM| was almost constant for all n, indicating a similar scale for the marker’s motion near the lung tumor. MRA showed that |aRM| in the left–right direction was the major cause of TLE; however, the contribution made little difference to the 3D TLE because of the small amount of motion in the left–right direction. The TLE
A new stylolite classification scheme to estimate compaction and local permeability variations
Koehn, D.; Rood, M. P.; Beaudoin, N.; Chung, P.; Bons, P. D.; Gomez-Rivas, E.
2016-12-01
We modeled the geometrical roughening of bedding-parallel, mainly layer-dominated stylolites in order to understand their structural evolution, to present an advanced classification of stylolite shapes and to relate this classification to chemical compaction and permeability variations at stylolites. Stylolites are rough dissolution seams that develop in sedimentary basins during chemical compaction. In the Zechstein 2 carbonate units, an important lean gas reservoir in the southern Permian Zechstein basin in Germany, stylolites influence local fluid flow, mineral replacement reactions and hence the permeability of the reservoir. Our simulations demonstrate that layer-dominated stylolites can grow in three distinct stages: an initial slow nucleation phase, a fast layer-pinning phase and a final freezing phase if the layer is completely dissolved during growth. Dissolution of the pinning layer and thus destruction of the stylolite's compaction tracking capabilities is a function of the background noise in the rock and the dissolution rate of the layer itself. Low background noise needs a slower dissolving layer for pinning to be successful but produces flatter teeth than higher background noise. We present an advanced classification based on our simulations and separate stylolites into four classes: (1) rectangular layer type, (2) seismogram pinning type, (3) suture/sharp peak type and (4) simple wave-like type. Rectangular layer type stylolites are the most appropriate for chemical compaction estimates because they grow linearly and record most of the actual compaction (up to 40 mm in the Zechstein example). Seismogram pinning type stylolites also provide good tracking capabilities, with the largest teeth tracking most of the compaction. Suture/sharp peak type stylolites grow in a non-linear fashion and thus do not record most of the actual compaction. However, when a non-linear growth law is used, the compaction estimates are similar to those making use of the
Improved phase arrival estimate and location for local earthquakes in South Korea
Morton, E. A.; Rowe, C. A.; Begnaud, M. L.
2012-12-01
The Korean Institute of Geoscience and Mineral Resources (KIGAM) and the Korean Meteorological Agency (KMA) regularly report local (distance travel-time information for events within the KIGAM and KMA networks, and also recorded by some regional stations. Toward that end, we are using a combination of manual phase identification and arrival-time picking, with waveform cross-correlation, to cluster events that have occurred in close proximity to one another, which allows for improved phase identification by comparing the highly correlating waveforms. We cross-correlate the known events with one another on 5 seismic stations and cluster events that correlate above a correlation coefficient threshold of 0.7, which reveals few clusters containing few events each. The small number of repeating events suggests that the online catalogs have had mining and quarry blasts removed before publication, as these can contribute significantly to repeating seismic sources in relatively aseismic regions such as South Korea. The dispersed source locations in our catalog, however, are ideal for seismic velocity modeling by providing superior sampling through the dense seismic station arrangement, which produces favorable event-to-station ray path coverage. Following careful manual phase picking on 104 events chosen to provide adequate ray coverage, we re-locate the events to obtain improved source coordinates. The re-located events are used with Thurber's Simul2000 pseudo-bending local tomography code to estimate the crustal structure on the Korean Peninsula, which is an important contribution to ongoing calibration for events of interest in the region.
Yiannikopoulou, I.; Philippopoulos, K.; Deligiorgi, D.
2012-04-01
The vertical thermal structure of the atmosphere is defined by a combination of dynamic and radiation transfer processes and plays an important role in describing the meteorological conditions at local scales. The scope of this work is to develop and quantify the predictive ability of a hybrid dynamic-statistical downscaling procedure to estimate the vertical profile of ambient temperature at finer spatial scales. The study focuses on the warm period of the year (June - August) and the method is applied to an urban coastal site (Hellinikon), located in eastern Mediterranean. The two-step methodology initially involves the dynamic downscaling of coarse resolution climate data via the RegCM4.0 regional climate model and subsequently the statistical downscaling of the modeled outputs by developing and training site-specific artificial neural networks (ANN). The 2.5ox2.5o gridded NCEP-DOE Reanalysis 2 dataset is used as initial and boundary conditions for the dynamic downscaling element of the methodology, which enhances the regional representivity of the dataset to 20km and provides modeled fields in 18 vertical levels. The regional climate modeling results are compared versus the upper-air Hellinikon radiosonde observations and the mean absolute error (MAE) is calculated between the four grid point values nearest to the station and the ambient temperature at the standard and significant pressure levels. The statistical downscaling element of the methodology consists of an ensemble of ANN models, one for each pressure level, which are trained separately and employ the regional scale RegCM4.0 output. The ANN models are theoretically capable of estimating any measurable input-output function to any desired degree of accuracy. In this study they are used as non-linear function approximators for identifying the relationship between a number of predictor variables and the ambient temperature at the various vertical levels. An insight of the statistically derived input
Rapid object indexing using locality sensitive hashing and joint 3D-signature space estimation.
Matei, Bogdan; Shan, Ying; Sawhney, Harpreet S; Tan, Yi; Kumar, Rakesh; Huber, Daniel; Hebert, Martial
2006-07-01
We propose a new method for rapid 3D object indexing that combines feature-based methods with coarse alignment-based matching techniques. Our approach achieves a sublinear complexity on the number of models, maintaining at the same time a high degree of performance for real 3D sensed data that is acquired in largely uncontrolled settings. The key component of our method is to first index surface descriptors computed at salient locations from the scene into the whole model database using the Locality Sensitive Hashing (LSH), a probabilistic approximate nearest neighbor method. Progressively complex geometric constraints are subsequently enforced to further prune the initial candidates and eliminate false correspondences due to inaccuracies in the surface descriptors and the errors of the LSH algorithm. The indexed models are selected based on the MAP rule using posterior probability of the models estimated in the joint 3D-signature space. Experiments with real 3D data employing a large database of vehicles, most of them very similar in shape, containing 1,000,000 features from more than 365 models demonstrate a high degree of performance in the presence of occlusion and obscuration, unmodeled vehicle interiors and part articulations, with an average processing time between 50 and 100 seconds per query.
About Bifurcational Parametric Simplification
Gol'dshtein, V; Yablonsky, G
2015-01-01
A concept of "critical" simplification was proposed by Yablonsky and Lazman in 1996 for the oxidation of carbon monoxide over a platinum catalyst using a Langmuir-Hinshelwood mechanism. The main observation was a simplification of the mechanism at ignition and extinction points. The critical simplification is an example of a much more general phenomenon that we call \\emph{a bifurcational parametric simplification}. Ignition and extinction points are points of equilibrium multiplicity bifurcations, i.e., they are points of a corresponding bifurcation set for parameters. Any bifurcation produces a dependence between system parameters. This is a mathematical explanation and/or justification of the "parametric simplification". It leads us to a conjecture that "maximal bifurcational parametric simplification" corresponds to the "maximal bifurcation complexity." This conjecture can have practical applications for experimental study, because at points of "maximal bifurcation complexity" the number of independent sys...
Directory of Open Access Journals (Sweden)
Wang Cong
2016-01-01
Full Text Available Because of the poor radio frequency coil uniformity and gradient-driven eddy currents, there is much noise and intensity inhomogeneity (bias in brain magnetic resonance (MR image, and it severely affects the segmentation accuracy. Better segmentation results are difficult to achieve by traditional methods; therefore, in this paper, a modified brain MR image segmentation and bias field estimation model based on local and global information is proposed. We first construct local constraints including image neighborhood information in Gaussian kernel mapping space, and then the complete regularization is established by introducing nonlocal spatial information of MR image. The weighting between local and global information is automatically adjusted according to image local information. At the same time, bias field information is coupled with the model, and it makes the model reduce noise interference but also can effectively estimate the bias field information. Experimental results demonstrate that the proposed algorithm has strong robustness to noise and bias field is well corrected.
Parametric Methods for Order Tracking Analysis
DEFF Research Database (Denmark)
Jensen, Tobias Lindstrøm; Nielsen, Jesper Kjær
2017-01-01
Order tracking analysis is often used to find the critical speeds at which structural resonances are excited by a rotating machine. Typically, order tracking analysis is performed via non-parametric methods. In this report, however, we demonstrate some of the advantages of using a parametric method...... for order tracking analysis. Specifically, we show that we get a much better time and frequency resolution, obtain a much more robust and accurate estimate of the RPM profile, and are able to perform accurate order tracking analysis even without the tachometer signal....
Energy Technology Data Exchange (ETDEWEB)
Plodinec, M.J.
1998-11-20
After being filled with glass, DWPF canistered waste forms will be welded closed using an upset resistance welding process. This final closure weld must be leaktight, and must remain so during extended storage at SRS. As part of the DWPF Startup Test Program, a parametric study (DWPF-WP-24) has been performed to determine a range of welder operating parameters which will produce acceptable welds. The parametric window of acceptable welds defined by this study is 90,000 + 15,000 lb of force, 248,000 + 22,000 amps of current, and 95 + 15 cycles* for the time of application of the current.
Gilliland, Jason; Clark, Andrew F; Kobrzynski, Marta; Filler, Guido
2015-07-01
Childhood obesity is a critical public health matter associated with numerous pediatric comorbidities. Local-level data are required to monitor obesity and to help administer prevention efforts when and where they are most needed. We hypothesized that samples of children visiting hospital clinics could provide representative local population estimates of childhood obesity using data from 2007 to 2013. Such data might provide more accurate, timely, and cost-effective obesity estimates than national surveys. Results revealed that our hospital-based sample could not serve as a population surrogate. Further research is needed to confirm this finding.
Parametrizing arbitrary galaxy morphologies: potentials and pitfalls
Andrae, René; Jahnke, Knud; Melchior, Peter
2011-02-01
Given the enormous galaxy data bases of modern sky surveys, parametrizing galaxy morphologies is a very challenging task due to the huge number and variety of objects. We assess the different problems faced by existing parametrization schemes (CAS, Gini, M20, Sérsic profile, shapelets) in an attempt to understand why parametrization is so difficult and in order to suggest improvements for future parametrization schemes. We demonstrate that morphological observables (e.g. steepness of the radial light profile, ellipticity, asymmetry) are intertwined and cannot be measured independently of each other. We present strong arguments in favour of model-based parametrization schemes, namely reliability assessment, disentanglement of morphological observables and point spread function modelling. Furthermore, we demonstrate that estimates of the concentration and Sérsic index obtained from the Zurich Structure & Morphology catalogue are in excellent agreement with theoretical predictions. We also demonstrate that the incautious use of the concentration index for classification purposes can cause a severe loss of the discriminative information contained in a given data sample. Moreover, we show that, for poorly resolved galaxies, concentration index and M20 suffer from strong discontinuities, i.e. similar morphologies are not necessarily mapped to neighbouring points in the parameter space. This limits the reliability of these parameters for classification purposes. Two-dimensional Sérsic profiles accounting for centroid and ellipticity are identified as the currently most reliable parametrization scheme in the regime of intermediate signal-to-noise ratios and resolutions, where asymmetries and substructures do not play an important role. We argue that basis functions provide good parametrization schemes in the regimes of high signal-to-noise ratios and resolutions. Concerning Sérsic profiles, we show that scale radii cannot be compared directly for profiles of different
Bykov, D. L.; Konovalov, D. N.
2007-12-01
Material fracture experiments on specimens and structures testify that materials can resist greater stresses in local stress concentration regions than in regions with a nearly homogeneous stress state. Taking this fact into account in design stress analysis permits one to reveal additional structure loading and/or service life margins. One approach aimed at taking into account the increased strength in local stress concentration regions is to use averaged limit characteristics parametrically depending on the characteristic size L of the averaging region. One version of this approach is the concept of "elementary block" of a material [1, 2]. The averaged limit characteristics are determined by an experiment-calculation method involving the analysis of the stress-strain state of a material specimen with a stress concentrator at the time when the specimen attains the limit state preceding macrofracture. In [3], the dependence of the averaged limit separation stresses on the size of the averaging region was determined on the basis of numerical analysis of the singular stress state of the specimen used to determine the standard characteristics of the adhesion strength of a filled polymer material. In the present paper, we generalize the above approach to the case of a viscoelastic material. For the limit characteristics of the material in the local stress concentration region we take the volume-averaged components of the specific work of internal forces [4, 5] (the averaged specific absorbed energy and the averaged specific instantaneously reversible energy). The introduction of two limit energies originates from the fact that, to initiate the process of macrofracture, it is necessary to satisfy the following two conditions simultaneously: the material must be "damaged" sufficiently strongly by the preceding loading, and the "damaged" material must be loaded sufficiently strongly. As an example of determining the material averaged limit energy characteristics in a
Wang, Hong; Pardo-Igúzquiza, Eulogio; Dowd, Peter A.; Yang, Yongguo
2017-09-01
This paper provides a solution to the problem of estimating the mean value of near-land-surface temperature over a relatively large area (here, by way of example, applied to mainland Spain covering an area of around half a million square kilometres) from a limited number of weather stations covering a non-representative (biased) range of altitudes. As evidence mounts for altitude-dependent global warming, this bias is a significant problem when temperatures at high altitudes are under-represented. We correct this bias by using altitude as a secondary variable and using a novel clustering method for identifying geographical regions (clusters) that maximize the correlation between altitude and mean temperature. In addition, the paper provides an improved regression kriging estimator, which is optimally determined by the cluster analysis. The optimal areal values of near-land-surface temperature are used to generate time series of areal temperature averages in order to assess regional changes in temperature trends. The methodology is applied to records of annual mean temperatures over the period 1950-2011 across mainland Spain. The robust non-parametric Theil-Sen method is used to test for temperature trends in the regional temperature time series. Our analysis shows that, over the 62-year period of the study, 78% of mainland Spain has had a statistically significant increase in annual mean temperature.
Directory of Open Access Journals (Sweden)
Renbiao Wu
2013-08-01
Full Text Available DOA (Direction of Arrival estimation is a major problem in array signal processing applications. Recently, compressive sensing algorithms, including convex relaxation algorithms and greedy algorithms, have been recognized as a kind of novel DOA estimation algorithm. However, the success of these algorithms is limited by the RIP (Restricted Isometry Property condition or the mutual coherence of measurement matrix. In the DOA estimation problem, the columns of measurement matrix are steering vectors corresponding to different DOAs. Thus, it violates the mutual coherence condition. The situation gets worse when there are two sources from two adjacent DOAs. In this paper, an algorithm based on OMP (Orthogonal Matching Pursuit, called ILS-OMP (Iterative Local Searching-Orthogonal Matching Pursuit, is proposed to improve DOA resolution by Iterative Local Searching. Firstly, the conventional OMP algorithm is used to obtain initial estimated DOAs. Then, in each iteration, a local searching process for every estimated DOA is utilized to find a new DOA in a given DOA set to further decrease the residual. Additionally, the estimated DOAs are updated by substituting the initial DOA with the new one. The simulation results demonstrate the advantages of the proposed algorithm.
Wang, Wenyi; Wu, Renbiao
2013-08-22
DOA (Direction of Arrival) estimation is a major problem in array signal processing applications. Recently, compressive sensing algorithms, including convex relaxation algorithms and greedy algorithms, have been recognized as a kind of novel DOA estimation algorithm. However, the success of these algorithms is limited by the RIP (Restricted Isometry Property) condition or the mutual coherence of measurement matrix. In the DOA estimation problem, the columns of measurement matrix are steering vectors corresponding to different DOAs. Thus, it violates the mutual coherence condition. The situation gets worse when there are two sources from two adjacent DOAs. In this paper, an algorithm based on OMP (Orthogonal Matching Pursuit), called ILS-OMP (Iterative Local Searching-Orthogonal Matching Pursuit), is proposed to improve DOA resolution by Iterative Local Searching. Firstly, the conventional OMP algorithm is used to obtain initial estimated DOAs. Then, in each iteration, a local searching process for every estimated DOA is utilized to find a new DOA in a given DOA set to further decrease the residual. Additionally, the estimated DOAs are updated by substituting the initial DOA with the new one. The simulation results demonstrate the advantages of the proposed algorithm.
Non-parametric partitioning of SAR images
Delyon, G.; Galland, F.; Réfrégier, Ph.
2006-09-01
We describe and analyse a generalization of a parametric segmentation technique adapted to Gamma distributed SAR images to a simple non parametric noise model. The partition is obtained by minimizing the stochastic complexity of a quantized version on Q levels of the SAR image and lead to a criterion without parameters to be tuned by the user. We analyse the reliability of the proposed approach on synthetic images. The quality of the obtained partition will be studied for different possible strategies. In particular, one will discuss the reliability of the proposed optimization procedure. Finally, we will precisely study the performance of the proposed approach in comparison with the statistical parametric technique adapted to Gamma noise. These studies will be led by analyzing the number of misclassified pixels, the standard Hausdorff distance and the number of estimated regions.
Singh, S. K.; Kumar, P.; Turbelin, G.; Issartel, J. P.; Feiz, A. A.; Ngae, P.; Bekka, N.
2016-12-01
In accidental release scenarios, a reliable prediction of origin and strength of unknown releases is attentive for emergency response authorities in order to ensure safety and security towards human health and environment. The accidental scenarios might involve one or more simultaneous releases emitting the same contaminant. In this case, the field of plumes may overlap significantly and the sampled concentrations may become the mixture of the concentrations originating from all the releases. The study addresses an inverse modelling procedure for identifying the origin and strength of known number of simultaneous releases from the sampled mixture of concentrations. A two-step inversion algorithm is developed in conjunction with an adjoint representation of source-receptor relationship. The computational efficiency is increased by deriving the distributed source information observable from the given monitoring design and number of measurements. The technique leads to an exact retrieval of the true release parameters when measurements are noise free and exactly described by the dispersion model. The inversion algorithm is evaluated using the real data from Fusion Field Trials, involving multiple (two, three and four sources) release experiments emitting Propylene, in September 2007 at Dugway Proving Ground, Utah, USA. The release locations are retrieved, on average, within 45 m to the true sources. The analysis of posterior uncertainties shows that the variations in location error and retrieved strength are within 10 m and 0.07%, respectively. Further, the inverse modelling is tested using 4-16 measurements in retrieval of four releases and found to be working reasonably well (within 146±79 m). The sensitivity studies highlight that the covariance statistics, model representativeness errors, source-receptor distance, distance between localized sources, monitoring design and number of measurements plays an important role in multiple source estimation.
Directory of Open Access Journals (Sweden)
Andrea Furková
2007-06-01
Full Text Available This paper explores the aplication of parametric and non-parametric benchmarking methods in measuring cost efficiency of Slovak and Czech electricity distribution companies. We compare the relative cost efficiency of Slovak and Czech distribution companies using two benchmarking methods: the non-parametric Data Envelopment Analysis (DEA and the Stochastic Frontier Analysis (SFA as the parametric approach. The first part of analysis was based on DEA models. Traditional cross-section CCR and BCC model were modified to cost efficiency estimation. In further analysis we focus on two versions of stochastic frontier cost functioin using panel data: MLE model and GLS model. These models have been applied to an unbalanced panel of 11 (Slovakia 3 and Czech Republic 8 regional electricity distribution utilities over a period from 2000 to 2004. The differences in estimated scores, parameters and ranking of utilities were analyzed. We observed significant differences between parametric methods and DEA approach.
Parametric Differentiation and Integration
Chen, Hongwei
2009-01-01
Parametric differentiation and integration under the integral sign constitutes a powerful technique for calculating integrals. However, this topic is generally not included in the undergraduate mathematics curriculum. In this note, we give a comprehensive review of this approach, and show how it can be systematically used to evaluate most of the…
Parametric Differentiation and Integration
Chen, Hongwei
2009-01-01
Parametric differentiation and integration under the integral sign constitutes a powerful technique for calculating integrals. However, this topic is generally not included in the undergraduate mathematics curriculum. In this note, we give a comprehensive review of this approach, and show how it can be systematically used to evaluate most of the…
Multiple Frequency Parametric Sonar
2015-09-28
300003 1 MULTIPLE FREQUENCY PARAMETRIC SONAR STATEMENT OF GOVERNMENT INTEREST [0001] The invention described herein may be manufactured and...beams. However, the multiple nonlinear interactions are not taken advantage of in order to generate additional efficiencies, bandwidth, and SNR...array. [0050] It will be understood that many additional changes in details, materials , steps, and arrangements of parts which have been described
Parametric Room Acoustic Workflows
DEFF Research Database (Denmark)
Parigi, Dario; Svidt, Kjeld; Molin, Erik
2017-01-01
The paper investigates and assesses different room acoustics software and the opportunities they offer to engage in parametric acoustics workflow and to influence architectural designs. The first step consists in the testing and benchmarking of different tools on the basis of accuracy, speed and ...
Zhu, Ke; 10.1214/11-AOS895
2012-01-01
This paper investigates the asymptotic theory of the quasi-maximum exponential likelihood estimators (QMELE) for ARMA--GARCH models. Under only a fractional moment condition, the strong consistency and the asymptotic normality of the global self-weighted QMELE are obtained. Based on this self-weighted QMELE, the local QMELE is showed to be asymptotically normal for the ARMA model with GARCH (finite variance) and IGARCH errors. A formal comparison of two estimators is given for some cases. A simulation study is carried out to assess the performance of these estimators, and a real example on the world crude oil price is given.
Directory of Open Access Journals (Sweden)
Carola V. Basualdo
2011-06-01
Full Text Available Non-parametric estimators allow to compare the estimates of richness among data sets from heterogeneous sources. However, since the estimator performance depends on the species-abundance distribution of the sample, preference for one or another is a difficult issue. The present study recovers and revalues some criteria already present in the literature in order to choose the most suitable estimator for streams macroinvertebrates, and provides some tools to apply them. Two abundance and four incidence estimators were applied to a regional database at family and genus level. They were evaluated under four criteria: sub-sample size required to estimate the observed richness; constancy of the sub-sample size; lack of erratic behavior and similarity in curve shape through different data sets. Among incidence estimators, Jack1 had the best performance. Between abundance estimators, ACE was the best when the observed richness was small and Chao1 when the observed richness was high. The uniformity of curves shapes allowed to describe the general sequences of curves behavior that could act as references to compare estimations of small databases and to infer the possible behavior of the curve (i.e the expected richness if the sample were larger. These results can be very useful for environmental management, and update the state of knowledge of regional macroinvertebrates.Los estimadores no paramétricos permiten comparar la riqueza estimada de conjuntos de datos de origen diverso. Empero, como su comportamiento depende de la distribución de abundancia del conjunto de datos, la preferencia por alguno representa una decisión difícil. Este trabajo rescata algunos criterios presentes en la literatura para elegir el estimador más adecuado para macroinvertebrados bentónicos de ríos y ofrece algunas herramientas para su aplicación. Cuatro estimadores de incidencia y dos de abundancia se aplicaron a un inventario regional a nivel de familia y género. Para
Precise local blur estimation based on the first-order derivative
Bouma, H.; Dijk, J.; Eekeren, A.W.M. van
2012-01-01
Blur estimation is an important technique for super resolution, image restoration, turbulence mitigation, deblurring and autofocus. Low-cost methods have been proposed for blur estimation. However, they can have large stochastic errors when computed close to the edge location and biased estimates at
Semi-parametric regression: Efficiency gains from modeling the nonparametric part
Yu, Kyusang; Park, Byeong U; 10.3150/10-BEJ296
2011-01-01
It is widely admitted that structured nonparametric modeling that circumvents the curse of dimensionality is important in nonparametric estimation. In this paper we show that the same holds for semi-parametric estimation. We argue that estimation of the parametric component of a semi-parametric model can be improved essentially when more structure is put into the nonparametric part of the model. We illustrate this for the partially linear model, and investigate efficiency gains when the nonparametric part of the model has an additive structure. We present the semi-parametric Fisher information bound for estimating the parametric part of the partially linear additive model and provide semi-parametric efficient estimators for which we use a smooth backfitting technique to deal with the additive nonparametric part. We also present the finite sample performances of the proposed estimators and analyze Boston housing data as an illustration.
Local time estimation for the slotted correlation function of randomly sampled LDA data
Energy Technology Data Exchange (ETDEWEB)
Nobach, H. [Fachgebiet Stroemungslehre und Aerodynamik, Technische Universitaet Darmstadt (Germany)
2002-03-01
The task of autocorrelation and power spectral density estimation from velocity data sampled irregularly in time by a laser-Doppler anemometer (LDA) is addressed in this article. A new method based on the slotting technique was found to be a very reliable estimator. This article describes specific improvements of the slotting technique, the model-based variance estimation and the spectral transform leading to more accurate estimates of the autocorrelation function and the power spectral density. Furthermore, the new method yields more information especially at short time lags of the autocorrelation function, which can be used to derive improved estimates of the Taylor time scale. (orig.)
Fraternali, Fernando; Marcelli, Gianluca
2011-01-01
We present a meshfree method for the curvature estimation of membrane networks based on the Local Maximum Entropy approach recently presented in (Arroyo and Ortiz, 2006). A continuum regularization of the network is carried out by balancing the maximization of the information entropy corresponding to the nodal data, with the minimization of the total width of the shape functions. The accuracy and convergence properties of the given curvature prediction procedure are assessed through numerical applications to benchmark problems, which include coarse grained molecular dynamics simulations of the fluctuations of red blood cell membranes (Marcelli et al., 2005; Hale et al., 2009). We also provide an energetic discrete-to-continuum approach to the prediction of the zero-temperature bending rigidity of membrane networks, which is based on the integration of the local curvature estimates. The Local Maximum Entropy approach is easily applicable to the continuum regularization of fluctuating membranes, and the predict...
Heat Transfer Parametric System Identification
1993-06-01
Transfer Parametric System Identification 6. AUTHOR(S Parker, Gregory K. 7. PERFORMING ORGANIZATION NAME(S) AND AOORESS(ES) 8. PERFORMING ORGANIZATION...distribution is unlimited. Heat Transfer Parametric System Identification by Gregory K. Parker Lieutenant, United States Navy BS., DeVry Institute of...Modeling Concept ........ ........... 3 2. Lumped Parameter Approach ...... ......... 4 3. Parametric System Identification ....... 4 B. BASIC MODELING
Towards Stabilizing Parametric Active Contours
DEFF Research Database (Denmark)
Liu, Jinchao; Fan, Zhun; Olsen, Søren Ingvor;
2014-01-01
Numerical instability often occurs in evolving of parametric active contours. This is mainly due to the undesired change of parametrization during evolution. In this paper, we propose a new tangential diffusion term to compensate this undesired change. As a result, the parametrization will converge...
Polarization effect in parametric amplifier
Institute of Scientific and Technical Information of China (English)
Junhe Zhou; Jianping Chen; Xinwan Li; Guiling Wu; Yiping Wang
2005-01-01
@@ Polarization effect in parametric amplifiers is studied. Coupled equations are derived from the basic propagation equations and numerical solutions are given for both one-wavelength-pump and two-wavelengthpump systems. Several parametric amplifiers driven by pumps at one wavelength and two wavelengths are analyzed and the polarization independent parametric amplifier is proposed.
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
2012-01-01
Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify a functional form of the production function of which the Cobb-Douglas a......Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify a functional form of the production function of which the Cobb...... parameter estimates, but also in biased measures which are derived from the parameters, such as elasticities. Therefore, we propose to use non-parametric econometric methods. First, these can be applied to verify the functional form used in parametric production analysis. Second, they can be directly used...... to estimate production functions without the specification of a functional form. Therefore, they avoid possible misspecification errors due to the use of an unsuitable functional form. In this paper, we use parametric and non-parametric methods to identify the optimal size of Polish crop farms...
Kalicka, Renata; Pietrenko-Dabrowska, Anna
2007-03-01
In the paper MRI measurements are used for assessment of brain tissue perfusion and other features and functions of the brain (cerebral blood flow - CBF, cerebral blood volume - CBV, mean transit time - MTT). Perfusion is an important indicator of tissue viability and functioning as in pathological tissue blood flow, vascular and tissue structure are altered with respect to normal tissue. MRI enables diagnosing diseases at an early stage of their course. The parametric and non-parametric approaches to the identification of MRI models are presented and compared. The non-parametric modeling adopts gamma variate functions. The parametric three-compartmental catenary model, based on the general kinetic model, is also proposed. The parameters of the models are estimated on the basis of experimental data. The goodness of fit of the gamma variate and the three-compartmental models to the data and the accuracy of the parameter estimates are compared. Kalman filtering, smoothing the measurements, was adopted to improve the estimate accuracy of the parametric model. Parametric modeling gives a better fit and better parameter estimates than non-parametric and allows an insight into the functioning of the system. To improve the accuracy optimal experiment design related to the input signal was performed.
Parametric Equations for Estimating Aircraft Airframe Costs
1976-02-01
prototype program for the first few aircraft is substantially lower because many costs are deferred until a decision to produce for inventory is made...overhead rates. It is necessary to begin with labor hours and convert tbam into dollars. That conversion can result in a serious misstatement of...general and administrative expense (G&A), miscellaneous direct charges (overtime premium, travel, per diem, miscellaneous taxes , etc.), and, in the
Q Estimates using the Coda of Local Earthquakes in Western Turkey
Akyol, Nihal
2015-04-01
The regional extension in the central west Turkey has been associated to different deformation processes, such as: spreading and thinning of over-thickened crust following the latest collision across the Neotethys, Arabia-Eurasia convergence resulting in westward extrusion of the Anatolian Plate and Africa-Eurasia convergence forming regional tectonics in the back-arc extensional area. Utilizing single isotropic scattering model, the Coda quality factor (Qc) at five frequency bands (1.5, 3, 5, 7, 10 Hz) and for eight window lengths (25-60 s, in steps of 5 s) were estimated in the region. The data comes from 228 earthquakes with local magnitudes and depths range from 2.9 - 4.9 and 2.2 - 27.0 km, respectively. The source to receiver distance of the records changes between 11 and 72 km. Spatial differences of attenuation characteristics were examined by dividing the region into four subregions. The frequency dependence of Qc values between 1.5 and 10 Hz has been inferred utilizing Qc = Q0fn relationship. Q0 values change between 32.7 and 82.1, while n values changes between 0.91 and 0.79 for the main- and four sub-regions, respectively. Obtained frequency dependence of Qc values for a lapse time of 40 s in the main region is Qc(f) = 49.6±1.0f0.85±0.02. The obtained low Q0 values show that the central west Turkey region is characterized by a high seismic attenuation, in general. Strong frequency and lapse time dependencies of Qc values for the main- and four sub-region imply tectonic complexity in the region. The attenuation and its frequency dependency values versus the lapse time for the easternmost subregion, confirm the slab tear inferred from previous studies. The highest frequency dependency values, at all lapse times, in the westernmost subregion imply high degree of heterogeneity supported by severe anti-clockwise rotation in this area. Lapse time dependencies of attenuation and its frequency dependencies were examined for two different ranges of event depth
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
-Douglas function nor the Translog function are consistent with the “true” relationship between the inputs and the output in our data set. We solve this problem by using non-parametric regression. This approach delivers reasonable results, which are on average not too different from the results of the parametric......Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb...... results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used...
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
2012-01-01
by investigating the relationship between the elasticity of scale and the farm size. We use a balanced panel data set of 371~specialised crop farms for the years 2004-2007. A non-parametric specification test shows that neither the Cobb-Douglas function nor the Translog function are consistent with the "true......Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify a functional form of the production function of which the Cobb...... parameter estimates, but also in biased measures which are derived from the parameters, such as elasticities. Therefore, we propose to use non-parametric econometric methods. First, these can be applied to verify the functional form used in parametric production analysis. Second, they can be directly used...
Relational Parametricity for Computational Effects
Møgelberg, Rasmus Ejlers
2009-01-01
According to Strachey, a polymorphic program is parametric if it applies a uniform algorithm independently of the type instantiations at which it is applied. The notion of relational parametricity, introduced by Reynolds, is one possible mathematical formulation of this idea. Relational parametricity provides a powerful tool for establishing data abstraction properties, proving equivalences of datatypes, and establishing equalities of programs. Such properties have been well studied in a pure functional setting. Many programs, however, exhibit computational effects, and are not accounted for by the standard theory of relational parametricity. In this paper, we develop a foundational framework for extending the notion of relational parametricity to programming languages with effects.
Parametric Explosion Spectral Model
Energy Technology Data Exchange (ETDEWEB)
Ford, S R; Walter, W R
2012-01-19
Small underground nuclear explosions need to be confidently detected, identified, and characterized in regions of the world where they have never before occurred. We develop a parametric model of the nuclear explosion seismic source spectrum derived from regional phases that is compatible with earthquake-based geometrical spreading and attenuation. Earthquake spectra are fit with a generalized version of the Brune spectrum, which is a three-parameter model that describes the long-period level, corner-frequency, and spectral slope at high-frequencies. Explosion spectra can be fit with similar spectral models whose parameters are then correlated with near-source geology and containment conditions. We observe a correlation of high gas-porosity (low-strength) with increased spectral slope. The relationship between the parametric equations and the geologic and containment conditions will assist in our physical understanding of the nuclear explosion source.
MEMS digital parametric loudspeaker
Carreno, Armando Arpys Arevalo
2016-03-23
This paper reports on the design and fabrication of MEMS actuator arrays suitable for Digital Sound reconstruction and Parametric Directional Loudspeakers. Two distinct versions of the device were fabricated: one using the electrostatic principle actuation and the other one, the piezoelectric principle. Both versions used similar membrane dimensions, with a diameter of 500 μm. These devices are the smallest Micro-Machined Ultrasound Transducer (MUT) arrays that can be operated for both modes: Digital Sound Reconstruction and Parametric Loudspeaker. The chips consist of an array with 256 transducers, in a footprint of 12 mm by 12 mm. The total single chip size is: 2.3 cm by 2.3 cm, including the contact pads. © 2016 IEEE.
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
The estimation of the technical efficiency comprises a vast literature in the field of applied production economics. There are two predominant approaches: the non-parametric and non-stochastic Data Envelopment Analysis (DEA) and the parametric Stochastic Frontier Analysis (SFA). The DEA...... of specifying an unsuitable functional form and thus, model misspecification and biased parameter estimates. Given these problems of the DEA and the SFA, Fan, Li and Weersink (1996) proposed a semi-parametric stochastic frontier model that estimates the production function (frontier) by non-parametric......), Kumbhakar et al. (2007), and Henningsen and Kumbhakar (2009). The aim of this paper and its main contribution to the existing literature is the estimation semi-parametric stochastic frontier models using a different non-parametric estimation technique: spline regression (Ma et al. 2011). We apply...
Wei, Zheng; Hongxing, Liu; Jianchun, Cheng
2011-12-01
This paper proposes an improved local principal component analysis (LPCA) in pseudo phase space for fetal heart rate estimation from a single lead abdominal ECG signal. The improved LPCA process can extract both the maternal ECG component and the fetal ECG component in an abdominal signal. The instantaneous fetal heart rate can then be estimated from the extracted fetal ECG waveform. Compared with the classical LPCA procedure and another single lead based fetal heart rate estimation method, our improved LPCA method has shown better robustness and efficiency in fetal heart estimation, testing with synthetic ECG signals and a real fetal ECG database from PhysioBank. For the real fetal ECG validating dataset of six long-duration recordings (obtained between the 22(nd) and 40(th) week of gestation), the average accuracy of the improved LPCA method is 84.1%.
Parametric Resonance in Dynamical Systems
Nijmeijer, Henk
2012-01-01
Parametric Resonance in Dynamical Systems discusses the phenomenon of parametric resonance and its occurrence in mechanical systems,vehicles, motorcycles, aircraft and marine craft, and micro-electro-mechanical systems. The contributors provide an introduction to the root causes of this phenomenon and its mathematical equivalent, the Mathieu-Hill equation. Also included is a discussion of how parametric resonance occurs on ships and offshore systems and its frequency in mechanical and electrical systems. This book also: Presents the theory and principles behind parametric resonance Provides a unique collection of the different fields where parametric resonance appears including ships and offshore structures, automotive vehicles and mechanical systems Discusses ways to combat, cope with and prevent parametric resonance including passive design measures and active control methods Parametric Resonance in Dynamical Systems is ideal for researchers and mechanical engineers working in application fields such as MEM...
Local dark matter and dark energy as estimated on a scale of ~1 Mpc in a self-consistent way
Chernin, A. D.; Teerikorpi, P.; Valtonen, M. J.; Dolgachev, V. P.; Domozhilova, L. M.; Byrd, G. G.
2009-12-01
Context: Dark energy was first detected from large distances on gigaparsec scales. If it is vacuum energy (or Einstein's Λ), it should also exist in very local space. Here we discuss its measurement on megaparsec scales of the Local Group. Aims: We combine the modified Kahn-Woltjer method for the Milky Way-M 31 binary and the HST observations of the expansion flow around the Local Group in order to study in a self-consistent way and simultaneously the local density of dark energy and the dark matter mass contained within the Local Group. Methods: A theoretical model is used that accounts for the dynamical effects of dark energy on a scale of ~1 Mpc. Results: The local dark energy density is put into the range 0.8-3.7ρv (ρv is the globally measured density), and the Local Group mass lies within 3.1-5.8×1012 M⊙. The lower limit of the local dark energy density, about 4/5× the global value, is determined by the natural binding condition for the group binary and the maximal zero-gravity radius. The near coincidence of two values measured with independent methods on scales differing by ~1000 times is remarkable. The mass ~4×1012 M⊙ and the local dark energy density ~ρv are also consistent with the expansion flow close to the Local Group, within the standard cosmological model. Conclusions: One should take into account the dark energy in dynamical mass estimation methods for galaxy groups, including the virial theorem. Our analysis gives new strong evidence in favor of Einstein's idea of the universal antigravity described by the cosmological constant.
Directory of Open Access Journals (Sweden)
Basile Pauthier
2016-01-01
Full Text Available A 24-hour heavy rainfall event occurred in northeastern France from November 3 to 4, 2014. The accuracy of the quantitative precipitation estimation (QPE by PANTHERE and ANTILOPE radar-based gridded products during this particular event, is examined at both mesoscale and local scale, in comparison with two reference rain-gauge networks. Mesoscale accuracy was assessed for the total rainfall accumulated during the 24-hour event, using the Météo France operational rain-gauge network. Local scale accuracy was assessed for both total event rainfall and hourly rainfall accumulations, using the recently developed HydraVitis high-resolution rain gauge network Evaluation shows that (1 PANTHERE radar-based QPE underestimates rainfall fields at mesoscale and local scale; (2 both PANTHERE and ANTILOPE successfully reproduced the spatial variability of rainfall at local scale; (3 PANTHERE underestimates can be significantly improved at local scale by merging these data with rain gauge data interpolation (i.e., ANTILOPE. This study provides a preliminary evaluation of radar-based QPE at local scale, suggesting that merged products are invaluable for applications at very high resolution. The results obtained underline the importance of using high-density rain-gauge networks to obtain information at high spatial and temporal resolution, for better understanding of local rainfall variation, to calibrate remotely sensed rainfall products.
Parametric inference for discretely sampled stochastic differential equations
DEFF Research Database (Denmark)
Sørensen, Michael
A review is given of parametric estimation methods for discretely sampled mul- tivariate diffusion processes. The main focus is on estimating functions and asymp- totic results. Maximum likelihood estimation is briefly considered, but the emphasis is on computationally less demanding martingale e...
Navas, Rafael; Delrieu, Guy
2017-04-01
The Cévennes-Vivarais is a Mediterranean medium-elevation mountainous region of about 32000 km2 located in the south-east of France, prone to heavy precipitation events and subsequent flash floods and floods occurring mainly during the autumn season. Due to this vulnerability, it is a well instrumented region in terms of rainfall (4 weather radars of the French ARAMIS radar network, 250 hourly raingauges) and river discharge (45 stations) observations. A high-resolution (1 km2, 1 hour) radar-raingauge rainfall re-analysis has been established for the period 2007-2014 by using the kriging with external drift (KED) technique (Delrieu et al. 2014; Boudevillain et al. 2016). In the present communication, we present first a geostatistical method aimed at generating radar-raingauge rainfall ensembles based on the KED error standard deviations and the space-time structure of the residuals to the drift. The method is implemented over the four main watersheds of the Cévennes-Vivarais region by considering a spatial segmentation in hydrological meshes of variable sizes from 10 to 300 km2. A distributed hydrological model based on the SCS curve number and unit hydrograph concepts is then implemented in continuous mode for these watersheds. A sensitivity analysis allows us to identify the most sensitive parameters and to generate ensembles of "acceptable" hydrological simulations by using 16 discharge time series. Several results of this simulation framework will be highlighted: (1) the overall quality of the hydrological simulations as a function of the gauged watershed characteristics, (2) the transferability of the acceptable parameter sets from one year to another, (3) the effect of the space and time resolution of rainfall estimations on the hydrological simulations for gauged watersheds, (4) the respective impact of rainfall and model parametric uncertainties over a range of spatial and temporal scales for ungauged watersheds. References: Delrieu, G., A. Wijbrans, B
Directory of Open Access Journals (Sweden)
Yasar Abbas Ur Rehman
Full Text Available Object localization plays a key role in many popular applications of Wireless Multimedia Sensor Networks (WMSN and as a result, it has acquired a significant status for the research community. A significant body of research performs this task without considering node orientation, object geometry and environmental variations. As a result, the localized object does not reflect the real world scenarios. In this paper, a novel object localization scheme for WMSN has been proposed that utilizes range free localization, computer vision, and principle component analysis based algorithms. The proposed approach provides the best possible approximation of distance between a wmsn sink and an object, and the orientation of the object using image based information. Simulation results report 99% efficiency and an error ratio of 0.01 (around 1 ft when compared to other popular techniques.
Ur Rehman, Yasar Abbas; Tariq, Muhammad; Khan, Omar Usman
2015-01-01
Object localization plays a key role in many popular applications of Wireless Multimedia Sensor Networks (WMSN) and as a result, it has acquired a significant status for the research community. A significant body of research performs this task without considering node orientation, object geometry and environmental variations. As a result, the localized object does not reflect the real world scenarios. In this paper, a novel object localization scheme for WMSN has been proposed that utilizes range free localization, computer vision, and principle component analysis based algorithms. The proposed approach provides the best possible approximation of distance between a wmsn sink and an object, and the orientation of the object using image based information. Simulation results report 99% efficiency and an error ratio of 0.01 (around 1 ft) when compared to other popular techniques.
Hopla, Emma-Jayne; Edwards, Mary; Langdon, Pete
2016-04-01
Vegetation is already responding to increasing global temperatures, with shrubs expanding northwards in the Arctic in a process called "greening". Lakes are important features within these changing landscapes, and lake ecosystems are affected by the vegetation in their catchments. Use of dated sediment archives can reveal how lake ecosystems responded to past changes over timescales relevant to vegetation dynamics (decades to centuries). Holocene vegetation changes have been reconstructed for small lake catchments in Alaska to help understand the long-term interactions between vegetation and within lake processes. A quantitative estimate of vegetation cover around these small lakes clarifies the catchment drivers of lake ecosystem processes. Pollen productivity is one of the major parameters used to make quantitative estimates of land cover from palaeodata. Based on extensive fieldwork, we obtained first Pollen Productivity Estimates (PPEs) for the main arboreal taxa in interior Alaska. We used the model REVEALS to estimate the regional vegetation abundance from existing pollen data from large lakes in the region based on Alaskan and European pollen productivity estimates (PPEs). Quantitative estimates of vegetation cover differ from those based on pollen percentages alone. The model LOVE will then be applied to smaller lake basins that are the subject of detailed palaeoliminological investigations in order to estimate the local composition at these sites.
Parametric study of modern airship productivity
Ardema, M. D.; Flaig, K.
1980-01-01
A method for estimating the specific productivity of both hybrid and fully buoyant airships is developed. Various methods of estimating structural weight of deltoid hybrids are discussed and a derived weight estimating relationship is presented. Specific productivity is used as a figure of merit in a parametric study of fully buoyant ellipsoidal and deltoid hybrid semi-buoyant vehicles. The sensitivity of results as a function of assumptions is also determined. No airship configurations were found to have superior specific productivity to transport airplanes.
Directory of Open Access Journals (Sweden)
Abílio Amiguinho
2005-01-01
Full Text Available The process of socio-educational territorialisation in rural contexts is the topic of this text. The theme corresponds to a challenge to address it having as main axis of discussion either the problem of social exclusion or that of local development. The reasons to locate the discussion in this last field of analysis are discussed in the first part of the text. Theoretical and political reasons are there articulated because the question is about projects whose intentions and practices call for the political both in the theoretical debate and in the choices that anticipate intervention. From research conducted for several years, I use contributions that aim at discuss and enlighten how school can be a potential locus of local development. Its identification and recognition as local institution (either because of those that work and live in it or because of those that act in the surrounding context are crucial steps to progressively constitute school as a partner for development. The promotion of the local values and roots, the reconstruction of socio-personal and local identities, the production of sociabilities and the equation and solution of shared problems were the dimensions of a socio-educative intervention, markedly globalising. This scenario, as it is argued, was also, intentionally, one of transformation and of deliberate change of school and of the administration of the educative territoires.
A Method for Direct Localized Sound Speed Estimates Using Registered Virtual Detectors
DEFF Research Database (Denmark)
Byram, Brett C.; Trahey, Gregg E.; Jensen, Jørgen Arendt
2012-01-01
-tonoise ratio and geometry. With two-layer geometries, the algorithm has a worst-case spatial registration bias of 0.02%. With three-layer geometries, the axial registration error gets worse with a bias magnitude up to 2.1% but is otherwise relatively stable over depth. The stability over depth of the bias...... in a given medium still allows for accurate sound speed estimates with a mean relative error less than 0.2%.......Accurate sound speed estimates are desirable in a number of fields. In an effort to increase the spatial resolution of sound speed estimates, a new method is proposed for direct measurement of sound speed between arbitrary spatial locations. The method uses the sound speed estimator developed...
Energy Technology Data Exchange (ETDEWEB)
Baaaath, Haerje; Gaellerspaang, Andreas; Hallsby, Goeran; Lundstroem, Anders; Loefgren, Per; Nilsson, Mats; Staahl, Goeran [Swedish Univ. of Agricultural Sciences, Umeaa (Sweden). Dept. of Forest Resource Management and Geomatics
2000-05-01
A new method for detailed estimation of local above-ground woody biomass is presented. The procedure has been developed with the aim to support large and small scale strategic planning, from various aspects of bio energy utilisation in Sweden. Important features of the method are options to deal with areas defined by the user, and to include local harvest restrictions. A planning tool suitable for local forest owners, municipalities, and bio energy enterprises has previously not been available since costume-made local estimates of woody biomass have lacked sufficient precision or been considered to costly. In the suggested method satellite image data and a sample of field plots from the Swedish National Forest Inventory (NFI) are combined using the 'k Nearest Neighbour' method (kNN). The results are transferred to the forestry planning system Hugin which provides estimates of the present and future potentials of forest bio-fuels. In the Hugin system there are great possibilities to take into account various local interests and restrictions that influence the potential of woody biomass utilisation. As an example, the priority and the intensity of forestry treatments such as establishment of new stands, pre-commercial thinning, thinning, and final cut can be considered. Other factors that can be incorporated are harvest restrictions for specific areas like nature preservation areas or riparian zones. In addition minimum requirements for harvest amounts per hectare can be set as general restrictions. In the initial steps geographical objects like roads, railroads, or forests at a specified distance from roads can be excluded from further calculations. As a demonstration example the above-ground woody biomass was calculated for the municipality AeIvsbyn in northern Sweden. The estimates were based on traditionally performed cutting operations and results can be presented separately for different species by tree fractions (bark, needles, branches and tops
DEFF Research Database (Denmark)
Rakotonarivo, Onjamirindra Sarobidy
Discrete choice experiments (DCEs) are increasingly used for ex-ante evaluations of environmental policies but their validity and reliability are largely untested in low-income settings. My thesis examines whether DCEs provide valid and reliable estimates of welfare impacts in these contexts and ...... techniques. It also has major implications for how forest conservation policy may be devised in low-income countries, including devolution of secure forestland tenure to local people and genuinely negotiating conservation with forest users....
Parametric Portfolio Policies with Common Volatility Dynamics
Ergemen, Yunus Emre; Taamouti, Abderrahim
2015-01-01
A parametric portfolio policy function is considered that incorporates common stock volatility dynamics to optimally determine portfolio weights. Reducing dimension of the traditional portfolio selection problem significantly, only a number of policy parameters corresponding to first- and second-order characteristics are estimated based on a standard method-of-moments technique. The method, allowing for the calculation of portfolio weight and return statistics, is illustrated with an empirica...
The fast parametric slantlet transform with applications
Agaian, Sos S.; Tourshan, Khaled; Noonan, Joseph P.
2004-05-01
Transform methods have played an important role in signal and image processing applications. Recently, Selesnick has constructed the new orthogonal discrete wavelet transform, called the slantlet wavelet, with two zero moments and with improved time localization. The discrete slantlet wavelet transform is carried out by an existing filterbank which lacks a tree structure and has a complexity problem. The slantlet wavelet has been successfully applied in compression and denoising. In this paper, we present a new class of orthogonal parametric fast Haar slantlet transform system where the slantlet wavelet and Haar transforms are special cases of it. We propose designing the slantlet wavelet transform using Haar slantlet transform matrix. A new class of parametric filterbanks is developed. The behavior of the parametric Haar slantlet transforms in signal and image denoising is presented. We show that the new technique performs better than the slantlet wavelet transform in denoising for piecewise constant signals. We also show that the parametric Haar slantlet transform performs better than the cosine and Fourier transforms for grey level images.
Robust 3D object localization and pose estimation for random bin picking with the 3DMaMa algorithm
Skotheim, Øystein; Thielemann, Jens T.; Berge, Asbjørn; Sommerfelt, Arne
2010-02-01
Enabling robots to automatically locate and pick up randomly placed and oriented objects from a bin is an important challenge in factory automation, replacing tedious and heavy manual labor. A system should be able to recognize and locate objects with a predefined shape and estimate the position with the precision necessary for a gripping robot to pick it up. We describe a system that consists of a structured light instrument for capturing 3D data and a robust approach for object location and pose estimation. The method does not depend on segmentation of range images, but instead searches through pairs of 2D manifolds to localize candidates for object match. This leads to an algorithm that is not very sensitive to scene complexity or the number of objects in the scene. Furthermore, the strategy for candidate search is easily reconfigurable to arbitrary objects. Experiments reported in this paper show the utility of the method on a general random bin picking problem, in this paper exemplified by localization of car parts with random position and orientation. Full pose estimation is done in less than 380 ms per image. We believe that the method is applicable for a wide range of industrial automation problems where precise localization of 3D objects in a scene is needed.
Eppenhof, Koen A. J.; Pluim, Josien P. W.
2017-02-01
Error estimation in medical image registration is valuable when validating, comparing, or combining registration methods. To validate a nonlinear image registration method, ideally the registration error should be known for the entire image domain. We propose a supervised method for the estimation of a registration error map for nonlinear image registration. The method is based on a convolutional neural network that estimates the norm of the residual deformation from patches around each pixel in two registered images. This norm is interpreted as the registration error, and is defined for every pixel in the image domain. The network is trained using a set of artificially deformed images. Each training example is a pair of images: the original image, and a random deformation of that image. No manually labeled ground truth error is required. At test time, only the two registered images are required as input. We train and validate the network on registrations in a set of 2D digital subtraction angiography sequences, such that errors up to eight pixels can be estimated. We show that for this range of errors the convolutional network is able to learn the registration error in pairs of 2D registered images at subpixel precision. Finally, we present a proof of principle for the extension to 3D registration problems in chest CTs, showing that the method has the potential to estimate errors in 3D registration problems.
A parametric reconstruction of the deceleration parameter
Mamon, Abdulla Al; Das, Sudipta
2017-07-01
The present work is based on a parametric reconstruction of the deceleration parameter q( z) in a model for the spatially flat FRW universe filled with dark energy and non-relativistic matter. In cosmology, the parametric reconstruction technique deals with an attempt to build up a model by choosing some specific evolution scenario for a cosmological parameter and then estimate the values of the parameters with the help of different observational datasets. In this paper, we have proposed a logarithmic parametrization of q( z) to probe the evolution history of the universe. Using the type Ia supernova, baryon acoustic oscillation and the cosmic microwave background datasets, the constraints on the arbitrary model parameters q0 and q1 are obtained (within 1σ and 2σ confidence limits) by χ 2-minimization technique. We have then reconstructed the deceleration parameter, the total EoS parameter ω _tot, the jerk parameter and have compared the reconstructed results of q( z) with other well-known parametrizations of q( z). We have also shown that two model selection criteria (namely, the Akaike information criterion and Bayesian information criterion) provide a clear indication that our reconstructed model is well consistent with other popular models.
A general framework for parametric survival analysis.
Crowther, Michael J; Lambert, Paul C
2014-12-30
Parametric survival models are being increasingly used as an alternative to the Cox model in biomedical research. Through direct modelling of the baseline hazard function, we can gain greater understanding of the risk profile of patients over time, obtaining absolute measures of risk. Commonly used parametric survival models, such as the Weibull, make restrictive assumptions of the baseline hazard function, such as monotonicity, which is often violated in clinical datasets. In this article, we extend the general framework of parametric survival models proposed by Crowther and Lambert (Journal of Statistical Software 53:12, 2013), to incorporate relative survival, and robust and cluster robust standard errors. We describe the general framework through three applications to clinical datasets, in particular, illustrating the use of restricted cubic splines, modelled on the log hazard scale, to provide a highly flexible survival modelling framework. Through the use of restricted cubic splines, we can derive the cumulative hazard function analytically beyond the boundary knots, resulting in a combined analytic/numerical approach, which substantially improves the estimation process compared with only using numerical integration. User-friendly Stata software is provided, which significantly extends parametric survival models available in standard software. Copyright © 2014 John Wiley & Sons, Ltd.
DEFF Research Database (Denmark)
Nisset, J.; Acin, A.; Andersen, Ulrik Lund
2007-01-01
It is shown that the ensemble {P(alpha),vertical bar alpha >vertical bar alpha(*)>}, where P(alpha) is a Gaussian distribution of finite variance and |alpha > is a coherent state, can be better discriminated with an entangled measurement than with any local strategy supplemented by classical...... communication. Although this ensemble consists of products of quasiclassical states without any squeezing, it thus exhibits a purely quantum feature. This remarkable effect is demonstrated experimentally by implementing the optimal local strategy on coherent states of light together with a global strategy...
Adaptive Ensemble Covariance Localization in Ensemble 4D-VAR State Estimation
2011-04-01
Tposterr (t)] 2 give the global average of the square of the posterior error of the state estimate in the tropo - sphere of zonal wind u, meridional...decreases with the cosine of latitude. To make sure that the errors pertained primarily to the tropo - sphere, only the lower 23 model levels
Local digital algorithms for estimating the mean integrated curvature of r-regular sets
DEFF Research Database (Denmark)
Svane, Anne Marie
Consider the design based situation where an r-regular set is sampled on a random lattice. A fast algorithm for estimating the integrated mean curvature based on this observation is to use a weighted sum of 2×⋯×2 configuration counts. We show that for a randomly translated lattice, no asymptotica......-or-miss transforms of r-regular sets....
LOCAL ASYMPTOTIC PROPERTIES OF HAZARD RATE ESTIMATORS FOR TRUNCATED AND CENSORED DATA
Institute of Scientific and Technical Information of China (English)
SUN Liuquan; WU Guofu; WEI Xianhua
2001-01-01
Functional laws of the iterated logarithm are obtained for cumulative hazard processes in the neighborhood of a fixed point when the data are subject to left truncation and right censorship. On the basis of these results the exact rates of pointwise almost sure convergence for various types of kernel hazard rate estimators are derived.
Localization of underwater moving sound source based on time delay estimation using hydrophone array
Rahman, S. A.; Arifianto, D.; Dhanardono, T.; Wirawan
2016-11-01
Signal and noise of an underwater moving sound source is used to track the azimuth of a target. Uniform linear array with four hydrophones is used to detect azimuth of target by obtain the time delay information to get azimuth information. Success rate of time delay estimation influenced by characteristics of sound propagation like reflection, reverberation, etc. Experiment in real environment was done to analyze performance of the cross correlation (CC) and generalized cross correlation with the phase transform (PHAT) weighting to estimate time delay between two signal. The simulation done by convolute two signal that has been given time delay and impulse response of the medium test. Then the time delay of two signal estimated by CC and PHAT algorithm in Matlab in the various SNR. Then the algorithm tested in a pool to detect stationary and moving position of sound source. Result of the simulation and experiment in real environment shown that PHAT better than CC. The best azimuth tracking achieved by using PHAT algorithm with error of 0 - 9.48 degree in stationary position. In moving sound experiments, tracking the bearing and azimuth of the mini vessel (sound source) can be done by time delay estimation using PHAT.
Yuan, Shenfang; Bao, Qiao; Qiu, Lei; Zhong, Yongteng
2015-10-01
The growing use of composite materials on aircraft structures has attracted much attention for impact monitoring as a kind of structural health monitoring (SHM) method. Multiple signal classification (MUSIC)-based monitoring technology is a promising method because of its directional scanning ability and easy arrangement of the sensor array. However, for applications on real complex structures, some challenges still exist. The impact-induced elastic waves usually exhibit a wide-band performance, giving rise to the difficulty in obtaining the phase velocity directly. In addition, composite structures usually have obvious anisotropy, and the complex structural style of real aircrafts further enhances this performance, which greatly reduces the localization precision of the MUSIC-based method. To improve the MUSIC-based impact monitoring method, this paper first analyzes and demonstrates the influence of measurement precision of the phase velocity on the localization results of the MUSIC impact localization method. In order to improve the accuracy of the phase velocity measurement, a single frequency component extraction method is presented. Additionally, a single frequency component-based re-estimated MUSIC (SFCBR-MUSIC) algorithm is proposed to reduce the localization error caused by the anisotropy of the complex composite structure. The proposed method is verified on a real composite aircraft wing box, which has T-stiffeners and screw holes. Three typical categories of 41 impacts are monitored. Experimental results show that the SFCBR-MUSIC algorithm can localize impact on complex composite structures with an obviously improved accuracy.
Institute of Scientific and Technical Information of China (English)
Yang Fengfan
2004-01-01
A new technique for turbo decoder is proposed by using a local subsidiary maximum likelihood decoding and a probability distributions family for the extrinsic information. The optimal distribution of the extrinsic information is dynamically specified for each component decoder.The simulation results show that the iterative decoder with the new technique outperforms that of the decoder with the traditional Gaussian approach for the extrinsic information under the same conditions.
Communicating Is Crowdsourcing: Wi-Fi Indoor Localization with CSI-based Speed Estimation
Jiang, Zhiping; Zhao, Jizhong; Li, Xiang-Yang; XI, WEI; Zhao, Kun; Tang, Shaojie; Han, Jinsong
2013-01-01
Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location based service, and Wi-Fi fingerprint-based approaches are the most popular and inexpensive solutions. Among them, one of the main trends is to incorporate the built-in sensors of smartphone and to exploit crowdsourcing potentials. However the noisy built-in sensors and multi-tasking limitation of underline OS often hinder the effectiveness of these schemes. In this work, we propose a ...
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
other aspects, the properties of a method for parameter estimation in stochastic differential equations is considered within the field of heat dynamics of buildings. In the second paper a lack-of-fit test for stochastic differential equations is presented. The test can be applied to both linear and non-linear...... networks is included. In this paper, neural networks are used for predicting the electricity production of a wind farm. The results are compared with results obtained using an adaptively estimated ARX-model. Finally, two papers on stochastic differential equations are included. In the first paper, among...... stochastic differential equations. Some applications are presented in the papers. In the summary report references are made to a number of other applications. Resumé på dansk: Nærværende afhandling består af ti artikler publiceret i perioden 1996-1999 samt et sammendrag og en perspektivering heraf. I...
Estimating the Cumulative Ecological Effect of Local Scale Landscape Changes in South Florida
Hogan, Dianna M.; Labiosa, William; Pearlstine, Leonard; Hallac, David; Strong, David; Hearn, Paul; Bernknopf, Richard
2012-01-01
Ecosystem restoration in south Florida is a state and national priority centered on the Everglades wetlands. However, urban development pressures affect the restoration potential and remaining habitat functions of the natural undeveloped areas. Land use (LU) planning often focuses at the local level, but a better understanding of the cumulative effects of small projects at the landscape level is needed to support ecosystem restoration and preservation. The South Florida Ecosystem Portfolio Model (SFL EPM) is a regional LU planning tool developed to help stakeholders visualize LU scenario evaluation and improve communication about regional effects of LU decisions. One component of the SFL EPM is ecological value (EV), which is evaluated through modeled ecological criteria related to ecosystem services using metrics for (1) biodiversity potential, (2) threatened and endangered species, (3) rare and unique habitats, (4) landscape pattern and fragmentation, (5) water quality buffer potential, and (6) ecological restoration potential. In this article, we demonstrate the calculation of EV using two case studies: (1) assessing altered EV in the Biscayne Gateway area by comparing 2004 LU to potential LU in 2025 and 2050, and (2) the cumulative impact of adding limestone mines south of Miami. Our analyses spatially convey changing regional EV resulting from conversion of local natural and agricultural areas to urban, industrial, or extractive use. Different simulated local LU scenarios may result in different alterations in calculated regional EV. These case studies demonstrate methods that may facilitate evaluation of potential future LU patterns and incorporate EV into decision making.
Parabolic inverse convection-diffusion-reaction problem solved using an adaptive parametrization
Deolmi, Giulia
2011-01-01
This paper investigates the solution of a parabolic inverse problem based upon the convection-diffusion-reaction equation, which can be used to estimate both water and air pollution. We will consider both known and unknown source location: while in the first case the problem is solved using a projected damped Gauss-Newton, in the second one it is ill-posed and an adaptive parametrization with time localization will be adopted to regularize it. To solve the optimization loop a model reduction technique (Proper Orthogonal Decomposition) is used.
Palace, M. W.; Sullivan, F. B.; Ducey, M.; Czarnecki, C.; Zanin Shimbo, J.; Mota e Silva, J.
2012-12-01
Forests are complex ecosystems with diverse species assemblages, crown structures, size class distributions, and historical disturbances. This complexity makes monitoring, understanding and forecasting carbon dynamics difficult. Still, this complexity is also central in carbon cycling of terrestrial vegetation. Lidar data often is used solely to associate plot level biomass measurements with canopy height models. There is much more that may be gleaned from examining the full profile from lidar data. Using discrete return airborne light detection and ranging (lidar) data collected in 2009 by the Tropical Ecology Assessment and Monitoring Network (TEAM), we compared synthetic vegetation profiles to lidar-derived relative vegetation profiles (RVPs) in La Selva, Costa Rica. To accomplish this, we developed RVPs to describe the vertical distribution of plant material on 20 plots at La Selva by transforming cumulative lidar observations to account for obscured plant material. Hundreds of synthetic profiles were developed for forests containing approximately 200,000 trees with random diameter at breast height (DBH), assuming a Weibull distribution with a shape of 1.0, and mean DBH ranging from 0cm to 500cm. For each tree in the synthetic forests, crown shape (width, depth) and total height were estimated using previously developed allometric equations for tropical forests. Profiles for each synthetic forest were generated and compared to TEAM lidar data to determine the best fitting synthetic profile to lidar profiles for each of 20 field plots at La Selva. After determining the best fit synthetic profile using the minimum sum of squared differences, we are able to estimate forest structure (diameter distribution, height, and biomass) and to compare our estimates to field data for each of the twenty field plots. Our preliminary results show promise for estimating forest structure and biomass using lidar data and computer modeling.
Pinsker estimators for local helioseismology: inversion of travel times for mass-conserving flows
Fournier, Damien; Gizon, Laurent; Holzke, Martin; Hohage, Thorsten
2016-10-01
A major goal of helioseismology is the three-dimensional reconstruction of the three velocity components of convective flows in the solar interior from sets of wave travel-time measurements. For small amplitude flows, the forward problem is described in good approximation by a large system of convolution equations. The input observations are highly noisy random vectors with a known dense covariance matrix. This leads to a large statistical linear inverse problem. Whereas for deterministic linear inverse problems several computationally efficient minimax optimal regularization methods exist, only one minimax-optimal linear estimator exists for statistical linear inverse problems: the Pinsker estimator. However, it is often computationally inefficient because it requires a singular value decomposition of the forward operator or it is not applicable because of an unknown noise covariance matrix, so it is rarely used for real-world problems. These limitations do not apply in helioseismology. We present a simplified proof of the optimality properties of the Pinsker estimator and show that it yields significantly better reconstructions than traditional inversion methods used in helioseismology, i.e. regularized least squares (Tikhonov regularization) and SOLA (approximate inverse) methods. Moreover, we discuss the incorporation of the mass conservation constraint in the Pinsker scheme using staggered grids. With this improvement we can reconstruct not only horizontal, but also vertical velocity components that are much smaller in amplitude.
Fernandes, Rigel P.; Ramos, António L. L.; Apolinário, José A.
2017-05-01
Shooter localization systems have been subject of a growing attention lately owing to its wide span of possible applications, e.g., civil protection, law enforcement, and support to soldiers in missions where snipers might pose a serious threat. These devices are based on the processing of electromagnetic or acoustic signatures associated with the firing of a gun. This work is concerned with the latter, where the shooter's position can be obtained based on the estimation of the direction-of-arrival (DoA) of the acoustic components of a gunshot signal (muzzle blast and shock wave). A major limitation of current commercially available acoustic sniper localization systems is the impossibility of finding the shooter's position when one of these acoustic signatures is not detected. This is very likely to occur in real-life situations, especially when the microphones are not in the field of view of the shockwave or when the presence of obstacles like buildings can prevent a direct-path to sensors. This work addresses the problem of DoA estimation of the muzzle blast using a planar array of sensors deployed in a drone. Results supported by actual gunshot data from a realistic setup are very promising and pave the way for the development of enhanced sniper localization systems featuring two main advantages over stationary ones: (1) wider surveillance area; and (2) increased likelihood of a direct-path detection of at least one of the gunshot signals, thereby adding robustness and reliability to the system.
Directory of Open Access Journals (Sweden)
Zina Mitraka
2015-04-01
Full Text Available The study of urban climate requires frequent and accurate monitoring of land surface temperature (LST, at the local scale. Since currently, no space-borne sensor provides frequent thermal infrared imagery at high spatial resolution, the scientific community has focused on synergistic methods for retrieving LST that can be suitable for urban studies. Synergistic methods that combine the spatial structure of visible and near-infrared observations with the more frequent, but low-resolution surface temperature patterns derived by thermal infrared imagery provide excellent means for obtaining frequent LST estimates at the local scale in cities. In this study, a new approach based on spatial-spectral unmixing techniques was developed for improving the spatial resolution of thermal infrared observations and the subsequent LST estimation. The method was applied to an urban area in Crete, Greece, for the time period of one year. The results were evaluated against independent high-resolution LST datasets and found to be very promising, with RMSE less than 2 K in all cases. The developed approach has therefore a high potential to be operationally used in the near future, exploiting the Copernicus Sentinel (2 and 3 observations, to provide high spatio-temporal resolution LST estimates in cities.
Institute of Scientific and Technical Information of China (English)
王强; 王刚; 张绿云; 邓培民
2012-01-01
inside convex hull,a pair of extra quadratic equations should be built with horizontal and vertical coordinates using the polynomial theory to solve the six parameters. The main advantage of the proposed algorithm is that only the correspondence of point sets instead of the one-to-one correspondence of feature points between the template image and observation are needed to be found. Experimental results show that the proposed algorithm is more accurate in parametric estimation, and its computational complexity is much lower than that of the region-based approach.
parfm : Parametric Frailty Models in R
Directory of Open Access Journals (Sweden)
Marco Munda
2012-11-01
Full Text Available Frailty models are getting more and more popular to account for overdispersion and/or clustering in survival data. When the form of the baseline hazard is somehow known in advance, the parametric estimation approach can be used advantageously. Nonetheless, there is no unified widely available software that deals with the parametric frailty model. The new parfm package remedies that lack by providing a wide range of parametric frailty models in R. The gamma, inverse Gaussian, and positive stable frailty distributions can be specified, together with five different baseline hazards. Parameter estimation is done by maximising the marginal log-likelihood, with right-censored and possibly left-truncated data. In the multivariate setting, the inverse Gaussian may encounter numerical difficulties with a huge number of events in at least one cluster. The positive stable model shows analogous difficulties but an ad-hoc solution is implemented, whereas the gamma model is very resistant due to the simplicity of its Laplace transform.
Localization of deformable tumors from short-arc projections using Bayesian estimation
Energy Technology Data Exchange (ETDEWEB)
Hoegele, W.; Zygmanski, P.; Dobler, B.; Kroiss, M.; Koelbl, O.; Loeschel, R. [Department of Radiation Oncology, Regensburg University Medical Center, 93053 Regensburg (Germany) and Department of Computer Science and Mathematics, University of Applied Sciences, 93053 Regensburg (Germany); Department of Radiation Oncology, Brigham and Women' s Hospital and Harvard Medical School, Boston, Massachusetts 02115 (United States); Department of Radiation Oncology, Regensburg University Medical Center, 93053 Regensburg (Germany); Department of Radiation Oncology, Hospital of the Sisters of Mercy, 4010 Linz (Austria); Department of Radiation Oncology, Regensburg University Medical Center, 93053 Regensburg (Germany); Department of Computer Science and Mathematics, University of Applied Sciences, 93053 Regensburg (Germany)
2012-12-15
Purpose: The authors present a stochastic framework for radiotherapy patient positioning directly utilizing radiographic projections. This framework is developed to be robust against anatomical nonrigid deformations and to cope with challenging imaging scenarios, involving only a few cone beam CT projections from short arcs. Methods: Specifically, a Bayesian estimator (BE) is explicitly derived for the given scanning geometry. This estimator is compared to reference methods such as chamfer matching (CM) and the minimization of the median absolute error adapted as tools of robust image processing and statistics. In order to show the performance of the stochastic short-arc patient positioning method, a CIRS IMRT thorax phantom study is presented with movable markers and the utilization of an Elekta Synergy{sup Registered-Sign} XVI system. Furthermore, a clinical prostate CBCT scan of a Varian{sup Registered-Sign} On-Board Imager{sup Registered-Sign} system is utilized to investigate the robustness of the method for large variations of image quality (anterior-posterior vs lateral views). Results: The results show that the BE shifts reduce the initial setup error of up to 3 cm down to 3 mm at maximum for an imaging arc as short as 10 Degree-Sign while CM achieves residual errors of 7 mm at maximum only for arcs longer than 40 Degree-Sign . Furthermore, the BE can compensate robustly for low image qualities using several low quality projections simultaneously. Conclusions: In conclusion, an estimation method for marker-based patient positioning for short imaging arcs is presented and shown to be robust and accurate for deformable anatomies.
A consistent local linear estimator of the covariate adjusted correlation coefficient.
Nguyen, Danh V; Sentürk, Damla
2009-08-01
Consider the correlation between two random variables (X, Y), both not directly observed. One only observes X̃ = φ(1)(U)X + φ(2)(U) and Ỹ = ψ(1)(U)Y + ψ(2)(U), where all four functions {φ(l)(·),ψ(l)(·), l = 1, 2} are unknown/unspecified smooth functions of an observable covariate U. We consider consistent estimation of the correlation between the unobserved variables X and Y, adjusted for the above general dual additive and multiplicative effects of U, based on the observed data (X̃, Ỹ, U).
Parametric Design Strategies for Collaborative Urban Design
DEFF Research Database (Denmark)
Steinø, Nicolai; Yıldırım, Miray Baş; Özkar, Mine
2013-01-01
to the collaboration between professionals, participation by different non-professional stakeholders, such as residents, local authorities, non-governmental organizations and investors, is another important component of collaborative urban design processes. The involvement of community in decision making process...... urban space, subject to urban renewal. A key aspect of the workshop therefore, was to develop different design scenarios and to use parametric design software to communicate the scenarios spatially, as well as to mediate between them. Parametric urban design is a potentially powerful tool...... is working alone with distributed design problem packages which means decomposition of design problems into tasks and working on them individually. On the other hand, collaborative design is based on communication. Participants work together on design problems in an integrated design process. In addition...
Directory of Open Access Journals (Sweden)
Viljanen Ari
2015-01-01
Full Text Available Previous studies have demonstrated a close relationship between the time derivative of the horizontal geomagnetic field vector (dH/dt and geomagnetically induced currents (GIC at a nearby location in a power grid. Similarly, a high correlation exists between GIC and the local horizontal geoelectric field (E, typically modelled from a measured magnetic field. Considering GIC forecasting, it is not feasible to assume that detailed prediction of time series will be possible. Instead, other measures summarising the activity level over a given period are preferable. In this paper, we consider the 30-min maximum of dH/dt or E as a local activity indicator (|dH/dt|30 or |E|30. Concerning GIC, we use the sum of currents through the neutral leads at substations and apply its 30-min maximum as a regional activity measure (GIC30. We show that |dH/dt|30 at a single point yields a proxy for GIC activity in a larger region. A practical consequence is that if |dH/dt|30 can be predicted at some point then it is also possible to assess the expected GIC level in the surrounding area. As is also demonstrated, |E|30 and GIC30 depend linearly on |dH/dt|30, so there is no saturation with increasing geomagnetic activity contrary to often used activity indices.
mBEEF: An accurate semi-local Bayesian error estimation density functional
Wellendorff, Jess; Lundgaard, Keld T.; Jacobsen, Karsten W.; Bligaard, Thomas
2014-04-01
We present a general-purpose meta-generalized gradient approximation (MGGA) exchange-correlation functional generated within the Bayesian error estimation functional framework [J. Wellendorff, K. T. Lundgaard, A. Møgelhøj, V. Petzold, D. D. Landis, J. K. Nørskov, T. Bligaard, and K. W. Jacobsen, Phys. Rev. B 85, 235149 (2012)]. The functional is designed to give reasonably accurate density functional theory (DFT) predictions of a broad range of properties in materials physics and chemistry, while exhibiting a high degree of transferability. Particularly, it improves upon solid cohesive energies and lattice constants over the BEEF-vdW functional without compromising high performance on adsorption and reaction energies. We thus expect it to be particularly well-suited for studies in surface science and catalysis. An ensemble of functionals for error estimation in DFT is an intrinsic feature of exchange-correlation models designed this way, and we show how the Bayesian ensemble may provide a systematic analysis of the reliability of DFT based simulations.
Parametric Room Acoustic Workflows
DEFF Research Database (Denmark)
Parigi, Dario; Svidt, Kjeld; Molin, Erik
2017-01-01
The paper investigates and assesses different room acoustics software and the opportunities they offer to engage in parametric acoustics workflow and to influence architectural designs. The first step consists in the testing and benchmarking of different tools on the basis of accuracy, speed...... and interoperability with Grasshopper 3d. The focus will be placed to the benchmarking of three different acoustic analysis tools based on raytracing. To compare the accuracy and speed of the acoustic evaluation across different tools, a homogeneous set of acoustic parameters is chosen. The room acoustics parameters...... included in the set are reverberation time (EDT, RT30), clarity (C50), loudness (G), and definition (D50). Scenarios are discussed for determining at different design stages the most suitable acoustic tool. Those scenarios are characterized, by the use of less accurate but fast evaluation tools to be used...
Parametric lattice Boltzmann method
Shim, Jae Wan
2017-06-01
The discretized equilibrium distributions of the lattice Boltzmann method are presented by using the coefficients of the Lagrange interpolating polynomials that pass through the points related to discrete velocities and using moments of the Maxwell-Boltzmann distribution. The ranges of flow velocity and temperature providing positive valued distributions vary with regulating discrete velocities as parameters. New isothermal and thermal compressible models are proposed for flows of the level of the isothermal and thermal compressible Navier-Stokes equations. Thermal compressible shock tube flows are simulated by only five on-lattice discrete velocities. Two-dimensional isothermal and thermal vortices provoked by the Kelvin-Helmholtz instability are simulated by the parametric models.
Parametric modal transition systems
DEFF Research Database (Denmark)
Beneš, Nikola; Křetínský, Jan; Larsen, Kim Guldstrand;
2011-01-01
Modal transition systems (MTS) is a well-studied specification formalism of reactive systems supporting a step-wise refinement methodology. Despite its many advantages, the formalism as well as its currently known extensions are incapable of expressing some practically needed aspects in the refin......Modal transition systems (MTS) is a well-studied specification formalism of reactive systems supporting a step-wise refinement methodology. Despite its many advantages, the formalism as well as its currently known extensions are incapable of expressing some practically needed aspects...... in the refinement process like exclusive, conditional and persistent choices. We introduce a new model called parametric modal transition systems (PMTS) together with a general modal refinement notion that overcome many of the limitations and we investigate the computational complexity of modal refinement checking....
Nanoscale electromechanical parametric amplifier
Energy Technology Data Exchange (ETDEWEB)
Aleman, Benjamin Jose; Zettl, Alexander
2016-09-20
This disclosure provides systems, methods, and apparatus related to a parametric amplifier. In one aspect, a device includes an electron source electrode, a counter electrode, and a pumping electrode. The electron source electrode may include a conductive base and a flexible conductor. The flexible conductor may have a first end and a second end, with the second end of the flexible conductor being coupled to the conductive base. A cross-sectional dimension of the flexible conductor may be less than about 100 nanometers. The counter electrode may be disposed proximate the first end of the flexible conductor and spaced a first distance from the first end of the flexible conductor. The pumping electrode may be disposed proximate a length of the flexible conductor and spaced a second distance from the flexible conductor.
Nanoscale electromechanical parametric amplifier
Aleman, Benjamin Jose; Zettl, Alexander
2016-09-20
This disclosure provides systems, methods, and apparatus related to a parametric amplifier. In one aspect, a device includes an electron source electrode, a counter electrode, and a pumping electrode. The electron source electrode may include a conductive base and a flexible conductor. The flexible conductor may have a first end and a second end, with the second end of the flexible conductor being coupled to the conductive base. A cross-sectional dimension of the flexible conductor may be less than about 100 nanometers. The counter electrode may be disposed proximate the first end of the flexible conductor and spaced a first distance from the first end of the flexible conductor. The pumping electrode may be disposed proximate a length of the flexible conductor and spaced a second distance from the flexible conductor.
Directory of Open Access Journals (Sweden)
Noelia Hernández
2017-01-01
Full Text Available Although much research has taken place in WiFi indoor localization systems, their accuracy can still be improved. When designing this kind of system, fingerprint-based methods are a common choice. The problem with fingerprint-based methods comes with the need of site surveying the environment, which is effort consuming. In this work, we propose an approach, based on support vector regression, to estimate the received signal strength at non-site-surveyed positions of the environment. Experiments, performed in a real environment, show that the proposed method could be used to improve the resolution of fingerprint-based indoor WiFi localization systems without increasing the site survey effort.
Hernández, Noelia; Ocaña, Manuel; Alonso, Jose M; Kim, Euntai
2017-01-13
Although much research has taken place in WiFi indoor localization systems, their accuracy can still be improved. When designing this kind of system, fingerprint-based methods are a common choice. The problem with fingerprint-based methods comes with the need of site surveying the environment, which is effort consuming. In this work, we propose an approach, based on support vector regression, to estimate the received signal strength at non-site-surveyed positions of the environment. Experiments, performed in a real environment, show that the proposed method could be used to improve the resolution of fingerprint-based indoor WiFi localization systems without increasing the site survey effort.
Hernández, Noelia; Ocaña, Manuel; Alonso, Jose M.; Kim, Euntai
2017-01-01
Although much research has taken place in WiFi indoor localization systems, their accuracy can still be improved. When designing this kind of system, fingerprint-based methods are a common choice. The problem with fingerprint-based methods comes with the need of site surveying the environment, which is effort consuming. In this work, we propose an approach, based on support vector regression, to estimate the received signal strength at non-site-surveyed positions of the environment. Experiments, performed in a real environment, show that the proposed method could be used to improve the resolution of fingerprint-based indoor WiFi localization systems without increasing the site survey effort. PMID:28098773
Optimal parametric sensitivity control for a fed-batch reactor
Stigter, J.D.; Keesman, K.J.
2001-01-01
The paper presents a method to derive an optimal parametric sensitivity controller for optimal estimation of a set of parameters in an experiment. The method is demonstrated for a fed batch bio-reactor case study for optimal estimation of the saturation constant Ks and, albeit intuitively, the param
Optimal parametric sensitivity control of a fed-batch reactor
Stigter, J.D.; Keesman, K.J.
2004-01-01
The paper presents an optimal parametric sensitivity controller for estimation of a set of parameters in an experiment. The method is demonstrated for a fed-batch bioreactor case study for optimal estimation of the half-saturation constant KS and the parameter combination µmaxX/Y in which µmax is th
SEMIPARAMETRIC VERSUS PARAMETRIC CLASSIFICATION MODELS - AN APPLICATION TO DIRECT MARKETING
BULT, [No Value
1993-01-01
In this paper we are concerned with estimation of a classification model using semiparametric and parametric methods. Benefits and limitations of semiparametric models in general, and of Manski's maximum score method in particular, are discussed. The maximum score method yields consistent estimates
DEFF Research Database (Denmark)
Rakotonarivo, Onjamirindra Sarobidy
the validity of DCE in estimating the costs of conservation restrictions ex-ante. I found that experience of forest protection matters; households who have been exposed to forest protection for a comparatively longer period had significantly higher welfare costs for restricting forest clearance than those who...... are less experienced. I conclude that although DCE can elicit current preferences in my study context, DCE is not a valid ex-ante tool for estimating compensations for such a long-term and complex intervention. I then used a within-subject design to evaluate whether giving respondents more time...... techniques. It also has major implications for how forest conservation policy may be devised in low-income countries, including devolution of secure forestland tenure to local people and genuinely negotiating conservation with forest users....
Energy Technology Data Exchange (ETDEWEB)
Lee, J.; Yun, G. S., E-mail: gunsu@postech.ac.kr; Lee, J. E.; Kim, M.; Choi, M. J.; Lee, W. [Pohang University of Science and Technology, Pohang 790-784 (Korea, Republic of); Park, H. K. [Ulsan National Institute of Science and Technology, Ulsan 689-798 (Korea, Republic of); Domier, C. W.; Luhmann, N. C. [University of California at Davis, Davis, California 95616 (United States); Sabbagh, S. A.; Park, Y. S. [Columbia University, New York, New York 10027 (United States); Lee, S. G.; Bak, J. G. [National Fusion Research Institute, Daejeon 305-333 (Korea, Republic of)
2014-06-15
A new and more accurate technique is presented for determining the toroidal mode number n of edge-localized modes (ELMs) using two independent electron cyclotron emission imaging (ECEI) systems in the Korea Superconducting Tokamak Advanced Research (KSTAR) device. The technique involves the measurement of the poloidal spacing between adjacent ELM filaments, and of the pitch angle α{sub *} of filaments at the plasma outboard midplane. Equilibrium reconstruction verifies that α{sub *} is nearly constant and thus well-defined at the midplane edge. Estimates of n obtained using two ECEI systems agree well with n measured by the conventional technique employing an array of Mirnov coils.
Maydeu-Olivares, Albert
2005-04-01
Chernyshenko, Stark, Chan, Drasgow, and Williams (2001) investigated the fit of Samejima's logistic graded model and Levine's non-parametric MFS model to the scales of two personality questionnaires and found that the graded model did not fit well. We attribute the poor fit of the graded model to small amounts of multidimensionality present in their data. To verify this conjecture, we compare the fit of these models to the Social Problem Solving Inventory-Revised, whose scales were designed to be unidimensional. A calibration and a cross-validation sample of new observations were used. We also included the following parametric models in the comparison: Bock's nominal model, Masters' partial credit model, and Thissen and Steinberg's extension of the latter. All models were estimated using full information maximum likelihood. We also included in the comparison a normal ogive model version of Samejima's model estimated using limited information estimation. We found that for all scales Samejima's model outperformed all other parametric IRT models in both samples, regardless of the estimation method employed. The non-parametric model outperformed all parametric models in the calibration sample. However, the graded model outperformed MFS in the cross-validation sample in some of the scales. We advocate employing the graded model estimated using limited information methods in modeling Likert-type data, as these methods are more versatile than full information methods to capture the multidimensionality that is generally present in personality data.
Allergic disease associations with regional and localized estimates of air pollution.
Schultz, Amy A; Schauer, Jamie J; Malecki, Kristen Mc
2017-05-01
Exposure to multiple types of air pollution may contribute to and exacerbate allergic diseases including asthma and wheezing. However, few studies have examined chronic air pollution exposure and allergic disease outcomes among an adult population. Associations between potential estimates of annual average fine particulate matter (PM2.5), traffic related air pollution, and industrial source air emissions and three allergic disease outcomes (asthma, allergies and wheezing) were examined in a state-wide general population of adults. The study includes a representative sample of 3381 adult Wisconsin residents who participated in the 2008-2013 Survey of the Health of Wisconsin (SHOW) program. Participant data were geographically linked to The United States Environmental Protection Agency (USEPA) Baysian space-time downscaler air pollution model for PM2.5, the United States Census roadway, and USEPA's Toxic Release Inventory data. Self-report and lung function (FEV1) estimates were used to define prevalence of asthma, allergies and wheezing symptoms. Annual mean exposure to fine particulate matter (PM2.5) was between 6.59 and 15.14μg/m(3). An increase of 5μg/m(3) in the annual mean PM2.5 resulted in a 3.58 (2.36, 5.43) increase in the adjusted odds (95% CI) of having asthma. Exposure to vehicle traffic increased the odds of both current allergies [OR (95% CI)=1.35 (1.07, 1.35)] and current asthma [OR (95% CI)=1.51 (1.14, 2.00)]. Living within 300m of an Interstate roadway was associated with a 3-fold increase in the odds of asthma. Those living within 800m of an industrial site were 47% more likely to have asthma. No significant associations were seen with wheezing. Within this population exposed to overall annual average levels of estimated low level chronic exposure to fine particulate matter (PM2.5) at or near 12μg/m(3), the USEPA standard for air quality, significant association between both modeled PM2.5 exposure and proximity to roadways with asthma and
Guerreiro, J Rafaela L; Teixeira, Natércia; De Freitas, Victor; Sales, M Goreti F; Sutherland, Duncan S
2017-10-15
Wine astringency was evaluated based on the interaction of two complex matrices (red wine and saliva) by combining localized surface plasmon resonance (LSPR) and molecular imprinted polymers (MIP) at gold nanodisks as an alternative to sensorial analysis. The main objective of the work was to simulate wine astringency inside the mouth by mimicking this biological system. The LSPR/MIP sensor provided a linear response for astringency expressed in pentagalloyl glucose (PGG) units in concentrations ranging from 1 to 140μmol/L. The sensor was also applied to wine samples correlating well with sensorial analysis obtained by a trained panel. The correlation of astringency and wine composition was also evaluated showing that anthocyanins may have an important role, not only for pigmentation but also in astringency. Copyright © 2017 Elsevier Ltd. All rights reserved.
Early Advanced LIGO binary neutron-star sky localization and parameter estimation
Berry, C P L; Farr, W M; Haster, C-J; Mandel, I; Middleton, H; Singer, L P; Urban, A L; Vecchio, A; Vitale, S; Cannon, K; Graff, P B; Hanna, C; Mohapatra, S; Pankow, C; Price, L R; Sidery, T; Veitch, J
2016-01-01
2015 will see the first observations of Advanced LIGO and the start of the gravitational-wave (GW) advanced-detector era. One of the most promising sources for ground-based GW detectors are binary neutron-star (BNS) coalescences. In order to use any detections for astrophysics, we must understand the capabilities of our parameter-estimation analysis. By simulating the GWs from an astrophysically motivated population of BNSs, we examine the accuracy of parameter inferences in the early advanced-detector era. We find that sky location, which is important for electromagnetic follow-up, can be determined rapidly (~5 s), but that sky areas may be hundreds of square degrees. The degeneracy between component mass and spin means there is significant uncertainty for measurements of the individual masses and spins; however, the chirp mass is well measured (typically better than 0.1%).
Bolève, A.; Vandemeulebrouck, J.; Grangeon, J.
2012-11-01
In the present study, we propose the combination of two geophysical techniques, which we have applied to a dyke located in southeastern France that has a visible downstream flood area: the self-potential (SP) and hydro-acoustic methods. These methods are sensitive to two different types of signals: electric signals and water-soil pressure disturbances, respectively. The advantages of the SP technique lie in the high rate of data acquisition, which allows assessment of long dykes, and direct diagnosis in terms of leakage area delimitation and quantification. Coupled with punctual hydro-acoustic cartography, a leakage position can be precisely located, therefore allowing specific remediation decisions with regard to the results of the geophysical investigation. Here, the precise localization of leakage from an earth dyke has been identified using SP and hydro-acoustic signals, with the permeability of the preferential fluid flow area estimated by forward SP modeling. Moreover, we propose a general 'abacus' diagram for the estimation of hydraulic permeability of dyke leakage according to the magnitude of over water SP anomalies and the associated uncertainty.
Enea Romano, Antonio; Andrés Vallejo, Sergio
2015-02-01
Recent measurements of the cosmic microwave background (CMB) radiation have shown an apparent tension with the present value of the Hubble parameter inferred from local observations of supernovae, which look closer, i.e. brighter, than what is expected in a homogeneous model with a value of H0 equal to the one estimated from CMB observations. We examine the possibility that such a discrepancy is the consequence of the presence of a local inhomogeneity seeded by primordial curvature perturbations, finding that a negative peak of the order of less than two standard deviations could allow to fit low-redshift supernovae observations without the need of using a value of the Hubble parameter different from H0CMB. The type of inhomogeneity we consider does not modify the distance to the last scattering, making it compatible with the constraints of the PLANCK mission data. The effect on the luminosity distance is in fact localized around the region in space where the transition between different values of the curvature perturbations occurs, producing a local decrease, while the distance outside the inhomogeneity is not affected. Our calculation is fully relativistic and nonperturbative, and for this reason shows important effects which were missed in the previous investigations using relativistic perturbations or Newtonian approximations, because the structures seeded by primordial curvature perturbations can be today highly nonlinear, and relativist Doppler terms cannot be neglected. Because of these effects the correction to the luminosity distance necessary to explain observations is associated to a compensated structure which involves both an underdense central region and an overdense outer shell, ensuring that the distance to the last scattering surface is unaffected. Comparison with studies of local structure based on galaxy surveys analysis reveals that the density profile we find could in fact be compatible with the one obtained for the same region of sky where
Signal-to-noise ratio in parametrically driven oscillators.
Batista, Adriano A; Moreira, Raoni S N
2011-12-01
We report a theoretical model based on Green's functions and averaging techniques that gives analytical estimates to the signal-to-noise ratio (SNR) near the first parametric instability zone in parametrically driven oscillators in the presence of added ac drive and added thermal noise. The signal term is given by the response of the parametrically driven oscillator to the added ac drive, while the noise term has two different measures: one is dc and the other is ac. The dc measure of noise is given by a time average of the statistically averaged fluctuations of the displacement from equilibrium in the parametric oscillator due to thermal noise. The ac measure of noise is given by the amplitude of the statistically averaged fluctuations at the frequency of the parametric pump. We observe a strong dependence of the SNR on the phase between the external drive and the parametric pump. For some range of the phase there is a high SNR, while for other values of phase the SNR remains flat or decreases with increasing pump amplitude. Very good agreement between analytical estimates and numerical results is achieved.
Thermal lensing in silver gallium selenide parametric oscillator crystals.
Marquardt, C L; Cooper, D G; Budni, P A; Knights, M G; Schepler, K L; Dedomenico, R; Catella, G C
1994-05-20
We performed an experimental investigation of thermal lensing in silver gallium selenide (AgGaSe(2)) optical parametric oscillator crystals pumped by a 2-µm laser at ambient temperature. We determined an empirical expression for the effective thermal focusing power in terms of the pump power, beam diameter, crystal length, and absorption coefficient. This relation may be used to estimate average power limitations in designing AgGaSe(2) optical parametric oscillators. We also demonstrated an 18% slope efficiency from a 2-µm pumped AgGaSe(2) optical parametric oscillator operated at 77 K, at which temperature thermal lensing is substantially reduced because of an increase in the thermal conductivity and a decrease in the thermal index gradient dn/dT. Cryogenic cooling may provide an additional option for scaling up the average power capability of a 2-µm pumped AgGaSe(2) optical parametric oscillator.
Directory of Open Access Journals (Sweden)
Supriyono
2013-07-01
Full Text Available Conventional velocity analysis is usually done in a relatively spare grid, for instance every half kilometers, during the processing of seismic data. It is very laborious work and very subjective. To deliver an accurate velocity picking, processing geophysicists must have a good understanding of geological background of area being analyzed and experiences. Velocity errors often occur during picking. Proper quality control and checking are a must. A good and reliable velocity field is very important in seismic processing for achieving high-quality seismic images as well as for delivering an accurate depth conversion. The new method presented here, was developed to correct velocity errors automatically by means of residual velocity correction, and to produce an offset-dependent RMS velocity field at the same time. The method is data driven, based on the normal move out equation (NMO and measuring the local even correlation between adjacent traces. The stacking velocity is derived simply by averaging the velocity field. The proposed method was tested on synthetic and real data examples with good result. The velocity field has certain characteristics related to hydrocarbon presence. Supriyono (2011 and 2012 developed a new DHI method using velocity gradient attributes by cross-plotting the velocity versus offset (VVO. The velocity gradient exhibits high anomalous values in the presence of gas.
Liang, Liang; Martin, Caitlin; Wang, Qian; Sun, Wei; Duncan, James
2016-03-01
Aortic valve (AV) disease is a significant cause of morbidity and mortality. The preferred treatment modality for severe AV disease is surgical resection and replacement of the native valve with either a mechanical or tissue prosthetic. In order to develop effective and long-lasting treatment methods, computational analyses, e.g., structural finite element (FE) and computational fluid dynamic simulations, are very effective for studying valve biomechanics. These computational analyses are based on mesh models of the aortic valve, which are usually constructed from 3D CT images though many hours of manual annotation, and therefore an automatic valve shape reconstruction method is desired. In this paper, we present a method for estimating the aortic valve shape from 3D cardiac CT images, which is represented by triangle meshes. We propose a pipeline for aortic valve shape estimation which includes novel algorithms for building local shape dictionaries and for building landmark detectors and curve detectors using local shape dictionaries. The method is evaluated on real patient image dataset using a leave-one-out approach and achieves an average accuracy of 0.69 mm. The work will facilitate automatic patient-specific computational modeling of the aortic valve.
Estimate of main local sources to ambient ultrafine particle number concentrations in an urban area
Rahman, Md Mahmudur; Mazaheri, Mandana; Clifford, Sam; Morawska, Lidia
2017-09-01
Quantifying and apportioning the contribution of a range of sources to ultrafine particles (UFPs, D statistical modelling and other exploratory tools. The Bayesian model was trained on the PNC data on days where NP formations were known to have not occurred, hourly traffic counts, solar radiation data, and smooth daily trend. The model was applied to apportion and quantify the contribution of NP formations and local traffic and non-traffic sources to UFPs. The data analysis incorporated long-term measured time-series of total PNC (D ≥ 6 nm), particle number size distributions (PSD, D = 8 to 400 nm), PM2.5, PM10, NOx, CO, meteorological parameters and traffic counts at a stationary monitoring site. The developed Bayesian model showed reliable predictive performances in quantifying the contribution of NP formation events to UFPs (up to 4 × 104 particles cm- 3), with a significant day to day variability. The model identified potential NP formation and no-formations days based on PNC data and quantified the sources contribution to UFPs. Exploratory statistical analyses show that total mean PNC during the middle of the day was up to 32% higher than during peak morning and evening traffic periods, which were associated with NP formation events. The majority of UFPs measured during the peak traffic and NP formation periods were between 30-100 nm and smaller than 30 nm, respectively. To date, this is the first application of Bayesian model to apportion different sources contribution to UFPs, and therefore the importance of this study is not only in its modelling outcomes but in demonstrating the applicability and advantages of this statistical approach to air pollution studies.
Energy Technology Data Exchange (ETDEWEB)
Vivancos, E; Healy, C; Mueller, F; Whalley, D
2001-05-09
Embedded systems often have real-time constraints. Traditional timing analysis statically determines the maximum execution time of a task or a program in a real-time system. These systems typically depend on the worst-case execution time of tasks in order to make static scheduling decisions so that tasks can meet their deadlines. Static determination of worst-case execution times imposes numerous restrictions on real-time programs, which include that the maximum number of iterations of each loop must be known statically. These restrictions can significantly limit the class of programs that would be suitable for a real-time embedded system. This paper describes work-in-progress that uses static timing analysis to aid in making dynamic scheduling decisions. For instance, different algorithms with varying levels of accuracy may be selected based on the algorithm's predicted worst-case execution time and the time allotted for the task. We represent the worst-case execution time of a function or a loop as a formula, where the unknown values affecting the execution time are parameterized. This parametric timing analysis produces formulas that can then be quickly evaluated at run-time so dynamic scheduling decisions can be made with little overhead. Benefits of this work include expanding the class of applications that can be used in a real-time system, improving the accuracy of dynamic scheduling decisions, and more effective utilization of system resources. This paper describes how static timing analysis can be used to aid in making dynamic scheduling decisions. The WCET of a function or a loop is represented as a formula, where the values affecting the execution time are parameterized. Such formulas can then be quickly evaluated at run-time so dynamic scheduling decisions can be made when scheduling a task or choosing algorithms within a task. Benefits of this parametric timing analysis include expanding the class of applications that can be used in a real
An Non-parametrical Approach to Estimate Location Parameters under Simple Order%简单半序约束下估计位置参数的一个非参方法
Institute of Scientific and Technical Information of China (English)
孙旭
2005-01-01
This paper deals with estimating parameters under simple order whensamples come from location models. Based on the idea of Hodges and Lehmann es-timator (H-L estimator), a new approach to estimate parameters is proposed, whichis difference with the classical L1 isotonic regression and L2 isotonic regression. Analgorithm to compute estimators is given. Simulations by the Monte-Carlo methodis applied to compare the likelihood functions with respect to L1 estimators andweighted isotonic H-L estimators.
Estimation of methane emissions from local and crossbreed beef cattle in Daklak province of Vietnam
Directory of Open Access Journals (Sweden)
Carlos Alberto Ramírez-Restrepo
2017-07-01
Full Text Available Objective This study was aimed at evaluating effects of cattle breed resources and alternative mixed-feeding practices on meat productivity and emission intensities from household farming systems (HFS in Daklak Province, Vietnam. Methods Records from Local Yellow×Red Sindhi (Bos indicus; Lai Sind and 1/2 Limousin, 1/2 Drought Master, and 1/2 Red Angus cattle during the growth (0 to 21 months and fattening (22 to 25 months periods were used to better understand variations on meat productivity and enteric methane emissions. Parameters were determined by the ruminant model. Four scenarios were developed: (HFS1 grazing from birth to slaughter on native grasses for approximately 10 h plus 1.5 kg dry matter/d (0.8% live weight [LW] of a mixture of guinea grass (19%, cassava (43% powder, cotton (23% seed, and rice (15% straw; (HFS2 growth period fed with elephant grass (1% of LW plus supplementation (1.5% of LW of rice bran (36%, maize (33%, and cassava (31% meals; and HFS3 and HFS4 computed elephant grass, but concentrate supplementation reaching 2% and 1% of LW, respectively. Results Results show that compared to HFS1, emissions (72.3±0.96 kg CH4/animal/life; least squares means± standard error of the mean were 15%, 6%, and 23% lower (p<0.01 for the HFS2, HFS3, and HFS4, respectively. The predicted methane efficiencies (CO2eq per kg of LW at slaughter (4.3±0.15, carcass weight (8.8±0.25 kg and kg of edible protein (44.1±1.29 were also lower (p<0.05 in the HFS4. In particular, irrespective of the HSF, feed supply and ratio changes had a more positive impact on emission intensities when crossbred 1/2 Red Angus cattle were fed than in their crossbred counterparts. Conclusion Modest improvements on feeding practices and integrated modelling frameworks may offer potential trade-offs to respond to climate change in Vietnam.
DEFF Research Database (Denmark)
Tscherning, Carl Christian
2015-01-01
The method of Least-Squares Collocation (LSC) may be used for the modeling of the anomalous gravity potential (T) and for the computation (prediction) of quantities related to T by a linear functional. Errors may also be estimated. However, when using an isotropic covariance function or equivalen...... on gravity anomalies (at 10 km altitude) predicted from GOCE Tzz. This has given an improved agreement between errors based on the differences between values derived from EGM2008 (to degree 512) and predicted gravity anomalies.......The method of Least-Squares Collocation (LSC) may be used for the modeling of the anomalous gravity potential (T) and for the computation (prediction) of quantities related to T by a linear functional. Errors may also be estimated. However, when using an isotropic covariance function or equivalent...... outside the data area. On the other hand, a comparison of predicted quantities with observed values show that the error also varies depending on the local data standard deviation. This quantity may be (and has been) estimated using the GOCE second order vertical derivative, Tzz, in the area covered...
De Marco, Stefano
2011-01-01
We study smoothness of densities for the solutions of SDEs whose coefficients are smooth and nondegenerate only on an open domain $D$. We prove that a smooth density exists on $D$ and give upper bounds for this density. Under some additional conditions (mainly dealing with the growth of the coefficients and their derivatives), we formulate upper bounds that are suitable to obtain asymptotic estimates of the density for large values of the state variable ("tail" estimates). These results specify and extend some results by Kusuoka and Stroock [J. Fac. Sci. Univ. Tokyo Sect. IA Math. 32 (1985) 1--76], but our approach is substantially different and based on a technique to estimate the Fourier transform inspired from Fournier [Electron. J. Probab. 13 (2008) 135--156] and Bally [Integration by parts formula for locally smooth laws and applications to equations with jumps I (2007) The Royal Swedish Academy of Sciences]. This study is motivated by existing models for financial securities which rely on SDEs with non-...
Roels, Joris; Aelterman, Jan; De Vylder, Jonas; Hiep Luong; Saeys, Yvan; Philips, Wilfried
2016-08-01
Microscopy is one of the most essential imaging techniques in life sciences. High-quality images are required in order to solve (potentially life-saving) biomedical research problems. Many microscopy techniques do not achieve sufficient resolution for these purposes, being limited by physical diffraction and hardware deficiencies. Electron microscopy addresses optical diffraction by measuring emitted or transmitted electrons instead of photons, yielding nanometer resolution. Despite pushing back the diffraction limit, blur should still be taken into account because of practical hardware imperfections and remaining electron diffraction. Deconvolution algorithms can remove some of the blur in post-processing but they depend on knowledge of the point-spread function (PSF) and should accurately regularize noise. Any errors in the estimated PSF or noise model will reduce their effectiveness. This paper proposes a new procedure to estimate the lateral component of the point spread function of a 3D scanning electron microscope more accurately. We also propose a Bayesian maximum a posteriori deconvolution algorithm with a non-local image prior which employs this PSF estimate and previously developed noise statistics. We demonstrate visual quality improvements and show that applying our method improves the quality of subsequent segmentation steps.
Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks.
Schillings, Claudia; Sunnåker, Mikael; Stelling, Jörg; Schwab, Christoph
2015-08-01
Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is "non-intrusive" and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design.
Non-parametric versus parametric methods in environmental sciences
Directory of Open Access Journals (Sweden)
Muhammad Riaz
2016-01-01
Full Text Available This current report intends to highlight the importance of considering background assumptions required for the analysis of real datasets in different disciplines. We will provide comparative discussion of parametric methods (that depends on distributional assumptions (like normality relative to non-parametric methods (that are free from many distributional assumptions. We have chosen a real dataset from environmental sciences (one of the application areas. The findings may be extended to the other disciplines following the same spirit.
Stellar parametrization from Gaia RVS spectra
Recio-Blanco, A; Prieto, C Allende; Fustes, D; Manteiga, M; Arcay, B; Bijaoui, A; Dafonte, C; Ordenovic, C; Blanco, D Ordoñez
2016-01-01
Among the myriad of data collected by the ESA Gaia satellite, about 150 million spectra will be delivered by the Radial Velocity Spectrometer (RVS) for stars as faint as G_RVS~16. A specific stellar parametrization will be performed for most of these RVS spectra. Some individual chemical abundances will also be estimated for the brightest targets. We describe the different parametrization codes that have been specifically developed or adapted for RVS spectra within the GSP-spec working group of the analysis consortium. The tested codes are based on optimization (FERRE and GAUGUIN), projection (MATISSE) or pattern recognition methods (Artificial Neural Networks). We present and discuss their expected performances in the recovered stellar atmospheric parameters (Teff, log(g), [M/H]) for B- to K- type stars. The performances for the determinations of [alpha/Fe] ratios are also presented for cool stars. For all the considered stellar types, stars brighter than G_RVS~12.5 will be very efficiently parametrized by t...
Choi, Jongseong
The performance of a hypersonic flight vehicle will depend on existing materials and fuels; this work presents the performance of the ideal scramjet engine for three different combustion chamber materials and three different candidate fuels. Engine performance is explored by parametric cycle analysis for the ideal scramjet as a function of material maximum service temperature and the lower heating value of jet engine fuels. The thermodynamic analysis is based on the Brayton cycle as similarly employed in describing the performance of the ramjet, turbojet, and fanjet ideal engines. The objective of this work is to explore material operating temperatures and fuel possibilities for the combustion chamber of a scramjet propulsion system to show how they relate to scramjet performance and the seven scramjet engine parameters: specific thrust, fuel-to-air ratio, thrust-specific fuel consumption, thermal efficiency, propulsive efficiency, overall efficiency, and thrust flux. The information presented in this work has not been done by others in the scientific literature. This work yields simple algebraic equations for scramjet performance which are similar to that of the ideal ramjet, ideal turbojet and ideal turbofan engines.
Parametric Mass Reliability Study
Holt, James P.
2014-01-01
The International Space Station (ISS) systems are designed based upon having redundant systems with replaceable orbital replacement units (ORUs). These ORUs are designed to be swapped out fairly quickly, but some are very large, and some are made up of many components. When an ORU fails, it is replaced on orbit with a spare; the failed unit is sometimes returned to Earth to be serviced and re-launched. Such a system is not feasible for a 500+ day long-duration mission beyond low Earth orbit. The components that make up these ORUs have mixed reliabilities. Components that make up the most mass-such as computer housings, pump casings, and the silicon board of PCBs-typically are the most reliable. Meanwhile components that tend to fail the earliest-such as seals or gaskets-typically have a small mass. To better understand the problem, my project is to create a parametric model that relates both the mass of ORUs to reliability, as well as the mass of ORU subcomponents to reliability.
Parametric motivation bases of floranimic nomination
Directory of Open Access Journals (Sweden)
Olga P. Ryabko
2016-09-01
Full Text Available The period of further development in the cognitive theory of nomination has been extensive in recent years. Our research has been concentrated on the formation of conceptual foundations in cognitive theory of flora nomination. The macrofield of flora namings embraces three microfields: parametric, pragmatic and locative-temporal ones. They determine motivation processes in cognitive theory of flora nomination, i.e., the presentation of systematic qualities in flora namings in the English language. The description and characterization of such qualities presupposes the existence of their taxonomic organization and methodology criteria, both general and practical ones. Flora namings on the phenomenological level are considered to be the products of naöve-cognitive consciousness of language speakers. They are determined, from the one hand, by the external perceptive adaptations (parametric nomination and, from the other hand, by practical needs (pure pragmatic nomination and local-temporal nomination. In this article we have concentrated on the complex parametric motivated basis of flora nomination. It is presented by a number of qualities, firstly, by dominative qualities («form», «appearance and manner of growth», «color», secondly, by peripheral qualities («odour», «taste», «size» and, finally, by minor qualities («sound», «weight», «genger». In the structure of complex parametric nomination the only one conerete qualitative element from the whole combination of qualities becomes the leading one. The cultural-archetypal dominant element determines. In each concrete situation, the choice of preferable prototypal motivated quality.
Jeong, Jeho; Setton, Jeremy S.; Lee, Nancy Y.; Oh, Jung Hun; Deasy, Joseph O.
2016-01-01
Background and purpose Although FDG-avid tumors are recognized as a potential target for dose escalation, there is no clear basis for selecting a boost dose to counter this apparent radioresistance. Using a novel analysis method, based on the new concept of an outcome-equivalent dose, we estimate the extra dose required to equalize local control between FDG-avid and non-avid head and neck tumors. Materials and methods Based on a literature review, five reports of head and neck cancer (423 patients in total), along with an internal validation dataset from our institution (135 oropharynx patients), were used in this analysis. To compensate for the heterogeneity among multi-institutional patient cohorts and corresponding treatment techniques, local control data of the cohorts were fit to a single dose–response curve with a clinically representative steepness (γ50 = 2), thereby defining an ‘outcome-equivalent dose’ (OED) for each institutional cohort. Separate dose–response curves were then determined for the FDG-avid and FDG-non-avid patient cohorts, and the ratio of TD50 (tumor dose required for 50% of control) values between the high- and low-FDG-uptake groups (TD50,high/TD50,low) was estimated, resulting in an estimated metabolic dose-modifying factor (mDMF) due to FDG-avidity. Results For individual datasets, the estimated mDMFs were found to be in the range of 1.07–1.62, decreasing if the assumed slope (γ50) increased. Weighted logistic regression for the six datasets resulted in a mDMF of 1.19 [95% CI: 1.04–1.34] for a γ50 value of 2, which translates to a needed dose increase of about 1.5 Gy per unit increase in the maximum standardized uptake value (SUVm) of FDG-PET [95% CI: 0.3–2.7]. Assumptions of lower or higher γ50 values (1.5 or 2.5) resulted in slightly different mDMFs: 1.26 or 1.15, respectively. A validation analysis with seven additional datasets, based on relaxed criteria, was consistent with the estimated mDMF. Conclusions We
Planar Parametrization in Isogeometric Analysis
DEFF Research Database (Denmark)
Gravesen, Jens; Evgrafov, Anton; Nguyen, Dang-Manh
2012-01-01
Before isogeometric analysis can be applied to solving a partial differential equation posed over some physical domain, one needs to construct a valid parametrization of the geometry. The accuracy of the analysis is affected by the quality of the parametrization. The challenge of computing...... and maintaining a valid geometry parametrization is particularly relevant in applications of isogemetric analysis to shape optimization, where the geometry varies from one optimization iteration to another. We propose a general framework for handling the geometry parametrization in isogeometric analysis and shape...... are suitable for our framework. The non-linear methods we consider are based on solving a constrained optimization problem numerically, and are divided into two classes, geometry-oriented methods and analysis-oriented methods. Their performance is illustrated through a few numerical examples....
Planar Parametrization in Isogeometric Analysis
DEFF Research Database (Denmark)
Gravesen, Jens; Evgrafov, Anton; Nguyen, Dang-Manh;
2012-01-01
Before isogeometric analysis can be applied to solving a partial differential equation posed over some physical domain, one needs to construct a valid parametrization of the geometry. The accuracy of the analysis is affected by the quality of the parametrization. The challenge of computing...... and maintaining a valid geometry parametrization is particularly relevant in applications of isogemetric analysis to shape optimization, where the geometry varies from one optimization iteration to another. We propose a general framework for handling the geometry parametrization in isogeometric analysis and shape...... are suitable for our framework. The non-linear methods we consider are based on solving a constrained optimization problem numerically, and are divided into two classes, geometry-oriented methods and analysis-oriented methods. Their performance is illustrated through a few numerical examples....
Directory of Open Access Journals (Sweden)
Luis C. J. Moreira
2010-12-01
Full Text Available Em face da importância em conhecer a evapotranspiração (ET para uso racional da água na irrigação no contexto atual de escassez desse recurso, algoritmos de estimativa da ET a nível regional foram desenvolvidos utilizando-se de ferramentas de sensoriamento remoto. Este estudo objetivou aplicar o algoritmo SEBAL (Surface Energy Balance Algorithms for Land em três imagens do satélite Landsat 5, do segundo semestre de 2006. As imagens correspondem a áreas irrigadas, floresta nativa densa e a Caatinga do Estado do Ceará (Baixo Acaraú, Chapada do Apodi e Chapada do Araripe. Este algoritmo calcula a evapotranspiração horária a partir do fluxo de calor latente, estimado como resíduo do balanço de energia na superfície. Os valores de ET obtidos nas três regiões foram superiores a 0,60 mm h-1 nas áreas irrigadas ou de vegetação nativa densa. As áreas de vegetação nativa menos densa apresentaram taxa da ET horária de 0,35 a 0,60 mm h-1, e valores quase nulos em áreas degradadas. A análise das médias de evapotranspiração horária pelo teste de Tukey a 5% de probabilidade permitiu evidenciar uma variabilidade significativa local, bem como regional no Estado do Ceará.In the context of water resources scarcity, the rational use of water for irrigation is necessary, implying precise estimations of the actual evapotranspiration (ET. With the recent progresses of remote-sensed technologies, regional algorithms estimating evapotranspiration from satellite observations were developed. This work aimed at applying the SEBAL algorithm (Surface Energy Balance Algorithms for Land at three Landsat-5 images during the second semester of 2006. These images cover irrigated areas, dense native forest areas and caatinga areas in three regions of the state of Ceará (Baixo Acaraú, Chapada do Apodi and Chapada do Araripe. The SEBAL algorithm calculates the hourly evapotranspiration from the latent heat flux, estimated from the surface energy
Parametric Coding of Stereo Audio
Directory of Open Access Journals (Sweden)
Erik Schuijers
2005-06-01
Full Text Available Parametric-stereo coding is a technique to efficiently code a stereo audio signal as a monaural signal plus a small amount of parametric overhead to describe the stereo image. The stereo properties are analyzed, encoded, and reinstated in a decoder according to spatial psychoacoustical principles. The monaural signal can be encoded using any (conventional audio coder. Experiments show that the parameterized description of spatial properties enables a highly efficient, high-quality stereo audio representation.
Parametric Portfolio Policies with Common Volatility Dynamics
DEFF Research Database (Denmark)
Ergemen, Yunus Emre; Taamouti, Abderrahim
A parametric portfolio policy function is considered that incorporates common stock volatility dynamics to optimally determine portfolio weights. Reducing dimension of the traditional portfolio selection problem significantly, only a number of policy parameters corresponding to first- and second......-order characteristics are estimated based on a standard method-of-moments technique. The method, allowing for the calculation of portfolio weight and return statistics, is illustrated with an empirical application to 30 U.S. industries to study the economic activity before and after the recent financial crisis....
Parametric Regression Models Using Reversed Hazard Rates
Directory of Open Access Journals (Sweden)
Asokan Mulayath Variyath
2014-01-01
Full Text Available Proportional hazard regression models are widely used in survival analysis to understand and exploit the relationship between survival time and covariates. For left censored survival times, reversed hazard rate functions are more appropriate. In this paper, we develop a parametric proportional hazard rates model using an inverted Weibull distribution. The estimation and construction of confidence intervals for the parameters are discussed. We assess the performance of the proposed procedure based on a large number of Monte Carlo simulations. We illustrate the proposed method using a real case example.
Applicability of Parametrized Form of Fully Dressed Quark Propagator
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
According to extensive study of the Dyson-Schwinger equations for a fully dressed quark propagator in the "rainbow" approximation with an effective gluon propagator, a parametrized fully dressed confining quark propagator is suggested in this paper. The parametrized quark propagator describes a confined quark propagation in hadron, and is analytic everywhere in complex p2-plane and has no Lehmann representation. The vector and scalar self-energy functions [1 - Af(p2)] and [Bf(p2) - mf], dynamically running effective mass of quark Mf(p2) and the structure of non-local quark vacuum condensates as well as local quark vacuum condensates are predicted by use of the parametrized quark propagator. The results are compatible with other theoretical calculations.
Boutsikas, Michael V; 10.3150/09-BEJ201
2010-01-01
Let $X_1,X_2,...,X_n$ be a sequence of independent or locally dependent random variables taking values in $\\mathbb{Z}_+$. In this paper, we derive sharp bounds, via a new probabilistic method, for the total variation distance between the distribution of the sum $\\sum_{i=1}^nX_i$ and an appropriate Poisson or compound Poisson distribution. These bounds include a factor which depends on the smoothness of the approximating Poisson or compound Poisson distribution. This "smoothness factor" is of order $\\mathrm{O}(\\sigma ^{-2})$, according to a heuristic argument, where $\\sigma ^2$ denotes the variance of the approximating distribution. In this way, we offer sharp error estimates for a large range of values of the parameters. Finally, specific examples concerning appearances of rare runs in sequences of Bernoulli trials are presented by way of illustration.
Directory of Open Access Journals (Sweden)
Mohamed Khalaf-Allah
2008-01-01
Full Text Available The mobile terminal positioning problem is categorized into three different types according to the availability of (1 initial accurate location information and (2 motion measurement data.Location estimation refers to the mobile positioning problem when both the initial location and motion measurement data are not available. If both are available, the positioning problem is referred to as position tracking. When only motion measurements are available, the problem is known as global localization. These positioning problems were solved within the Bayesian filtering framework. Filter derivation and implementation algorithms are provided with emphasis on the mapping approach. The radio maps of the experimental area have been created by a 3D deterministic radio propagation tool with a grid resolution of 5Ã¢Â€Â‰m. Real-world experimentation was conducted in a GSM network deployed in a semiurban environment in order to investigate the performance of the different positioning algorithms.
Examples in parametric inference with R
Dixit, Ulhas Jayram
2016-01-01
This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed in the book are fundamental and common to many fields of statistical inference and thus serve as a point of departure for in-depth study. The book is divided into eight chapters: Chapter 1 provides an overview of topics on sufficiency and completeness, while Chapter 2 briefly discusses unbiased estimation. Chapter 3 focuses on the study of moments and maximum likelihood estimators, and Chapter 4 presents bounds for the variance. In Chapter 5, topics on consistent estimator are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies some more powerful tests. Lastly, Chapter 8 examines unbiased and other tests. Senior undergraduate and graduate students in statistics and mathematics, and those who have taken an introductory cou...
Briggs, Martin A.; Day-Lewis, Frederick D.; Ong, John B.; Curtis, Gary P.; Lane, Jr., John W.
2013-01-01
Anomalous solute transport, modeled as rate-limited mass transfer, has an observable geoelectrical signature that can be exploited to infer the controlling parameters. Previous experiments indicate the combination of time-lapse geoelectrical and fluid conductivity measurements collected during ionic tracer experiments provides valuable insight into the exchange of solute between mobile and immobile porosity. Here, we use geoelectrical measurements to monitor tracer experiments at a former uranium mill tailings site in Naturita, Colorado. We use nonlinear regression to calibrate dual-domain mass transfer solute-transport models to field data. This method differs from previous approaches by calibrating the model simultaneously to observed fluid conductivity and geoelectrical tracer signals using two parameter scales: effective parameters for the flow path upgradient of the monitoring point and the parameters local to the monitoring point. We use regression statistics to rigorously evaluate the information content and sensitivity of fluid conductivity and geophysical data, demonstrating multiple scales of mass transfer parameters can simultaneously be estimated. Our results show, for the first time, field-scale spatial variability of mass transfer parameters (i.e., exchange-rate coefficient, porosity) between local and upgradient effective parameters; hence our approach provides insight into spatial variability and scaling behavior. Additional synthetic modeling is used to evaluate the scope of applicability of our approach, indicating greater range than earlier work using temporal moments and a Lagrangian-based Damköhler number. The introduced Eulerian-based Damköhler is useful for estimating tracer injection duration needed to evaluate mass transfer exchange rates that range over several orders of magnitude.
Capano, Manuela; Marzaioli, Fabio; Sirignano, Carmina; Altieri, Simona; Lubritto, Carmine; D'Onofrio, Antonio; Terrasi, Filippo
2010-04-01
Radiocarbon concentration in atmosphere changes overtime due to anthropogenic and natural factors. Species growth preserves the local atmospheric radiocarbon signature over their life span in the annual tree rings and make it possible to use tree rings for the monitoring of changes in fossil-fuel emissions due to an increase of traffic exhaust, during the last decades. In this paper, the CIRCE AMS system has been used to measure the 14C concentration in tree rings of plants grown near an industrial area and a very busy State Road, in a forest in north Italy. Preliminary results related to tree rings of several years of plants respectively near and far the emitting sources are displayed, in order to estimate the local pollution effect. It is possible to find a dilution in years 2000 and 2006 in both the trees analysed, but not enough data have been analysed yet in order to distinguish the fossil dilution derived from the street vehicular traffic or that from the industries.
Directory of Open Access Journals (Sweden)
A. T. Kazymov
2015-01-01
Full Text Available To estimate the extent of local tumor spread is a main goal in the diagnosis of prostate cancer (PC. The value of this criterion is that its clinical stage plays a key role in choosing a treatment policy. Overestimation of the clinical stage of cancer leads to the fact that specialists refuse radical and its underestimation gives rise to its recurrence. Our trial defined criteria for the diagnostic efficiency of magnetic resonance imaging (MRI in 150 PC patients who had undergone radical prostatectomy. The findings were as follows: the diagnostic sensitivity of the method in determining the spread of the cancer beyond the organ was 76.8 %; its diagnostic specificity and accuracy were 80.2 and 78.7 %, respectively. The positive predictive value in detecting the extra-organ spread of the tumor was equal to 76.8 %; the negative predictive value was 80.2 %. A prognostic classification of a risk for locally advanced PS has been developed using the independent clinical and MRI signs found.
Bias-reduced estimation of long memory stochastic volatility
DEFF Research Database (Denmark)
Frederiksen, Per; Nielsen, Morten Ørregaard
We propose to use a variant of the local polynomial Whittle estimator to estimate the memory parameter in volatility for long memory stochastic volatility models with potential nonstation- arity in the volatility process. We show that the estimator is asymptotically normal and capable of obtaining...... bias reduction as well as a rate of convergence arbitrarily close to the parametric rate, n1=2. A Monte Carlo study is conducted to support the theoretical results, and an analysis of daily exchange rates demonstrates the empirical usefulness of the estimators....
Directory of Open Access Journals (Sweden)
B. de Foy
2012-10-01
Full Text Available Gaseous elemental mercury is a global pollutant that can lead to serious health concerns via deposition to the biosphere and bio-accumulation in the food chain. Hourly measurements between June 2004 and May 2005 in an urban site (Milwaukee, WI show elevated levels of mercury in the atmosphere with numerous short-lived peaks as well as longer-lived episodes. The measurements are analyzed with an inverse model to obtain information about mercury emissions. The model is based on high resolution meteorological simulations (WRF, hourly back-trajectories (WRF-FLEXPART and a chemical transport model (CAMx. The hybrid formulation combining back-trajectories and Eulerian simulations is used to identify potential source regions as well as the impacts of forest fires and lake surface emissions. Uncertainty bounds are estimated using a bootstrap method on the inversions. Comparison with the US Environmental Protection Agency's National Emission Inventory (NEI and Toxic Release Inventory (TRI shows that emissions from coal-fired power plants are properly characterized, but emissions from local urban sources, waste incineration and metal processing could be significantly under-estimated. Emissions from the lake surface and from forest fires were found to have significant impacts on mercury levels in Milwaukee, and to be underestimated by a factor of two or more.
Directory of Open Access Journals (Sweden)
Zixi Jia
2015-11-01
Full Text Available Indoor localization is a significant research area in wireless sensor networks (WSNs. Generally, the nodes of WSNs are deployed in the same plane, i.e., the floor, as the target to be positioned, which causes the sensing signal to be influenced or even blocked by unpredictable obstacles, like furniture. However, a 3D system, like Cricket, can reduce the negative impact of obstacles to the maximum extent and guarantee the sensing signal transmission by using the line of sight (LOS. However, most of the traditional localization methods are not available for the new deployment mode. In this paper, we propose the self-localization of beacons method based on the Cayley–Menger determinant, which can determine the positions of beacons stuck in the ceiling; and differential sensitivity analysis (DSA is also applied to eliminate measurement errors in measurement data fusion. Then, the calibration of beacons scheme is proposed to further refine the locations of beacons by the mobile robot. According to the robot’s motion model based on dead reckoning, which is the process of determining one’s current position, we employ the H ∞ filter and the strong tracking filter (STF to calibrate the rough locations, respectively. Lastly, the optimal node selection scheme based on geometric dilution precision (GDOP is presented here, which is able to pick the group of beacons with the minimum GDOP from all of the beacons. Then, we propose the GDOP-based weighting estimation method (GWEM to associate redundant information with the position of the target. To verify the proposed methods in the paper, we design and conduct a simulation and an experiment in an indoor setting. Compared to EKF and the H ∞ filter, the adopted STF method can more effectively calibrate the locations of beacons; GWEM can provide centimeter-level precision in 3D environments by using the combination of beacons that minimizes GDOP.
Jia, Zixi; Wu, Chengdong; Li, Zhao; Zhang, Yunzhou; Guan, Bo
2015-11-24
Indoor localization is a significant research area in wireless sensor networks (WSNs). Generally, the nodes of WSNs are deployed in the same plane, i.e., the floor, as the target to be positioned, which causes the sensing signal to be influenced or even blocked by unpredictable obstacles, like furniture. However, a 3D system, like Cricket, can reduce the negative impact of obstacles to the maximum extent and guarantee the sensing signal transmission by using the line of sight (LOS). However, most of the traditional localization methods are not available for the new deployment mode. In this paper, we propose the self-localization of beacons method based on the Cayley-Menger determinant, which can determine the positions of beacons stuck in the ceiling; and differential sensitivity analysis (DSA) is also applied to eliminate measurement errors in measurement data fusion. Then, the calibration of beacons scheme is proposed to further refine the locations of beacons by the mobile robot. According to the robot's motion model based on dead reckoning, which is the process of determining one's current position, we employ the H∞ filter and the strong tracking filter (STF) to calibrate the rough locations, respectively. Lastly, the optimal node selection scheme based on geometric dilution precision (GDOP) is presented here, which is able to pick the group of beacons with the minimum GDOP from all of the beacons. Then, we propose the GDOP-based weighting estimation method (GWEM) to associate redundant information with the position of the target. To verify the proposed methods in the paper, we design and conduct a simulation and an experiment in an indoor setting. Compared to EKF and the H∞ filter, the adopted STF method can more effectively calibrate the locations of beacons; GWEM can provide centimeter-level precision in 3D environments by using the combination of beacons that minimizes GDOP.
Melis, Nikolaos S.; Konstantinou, Konstantinos; Kalogeras, Ioannis; Sokos, Efthimios; Tselentis, G.-Akis
2017-04-01
It is of a great importance to assess rapidly the intensity of a felt event in a highly populated environment. Rapid and reliable information plays a key role to decision making responses, by performing correctly the first steps after a felt ground shaking. Thus, it is important to accurately respond to urgent societal demand using reliable information. A strong motion array is under deployment and trial operation in the area of Patras, Greece. It combines: (a) standard accelerometric stations operated by the National Observatory of Athens, Institute of Geodynamics (NOA), (b) QCN-type USB MEMS acceleration sensors deployed in schools and (c) P-alert MEMS acceleration devices deployed in public sector buildings as well as in private dwellings. The array intends to cover the whole city of Patras and the populated suburbs. All instruments are operating in near real time and they are linked to a combined Earthworm - SeisComP3 server at NOA, Athens. Rapid intensity estimation can be also performed by the P-alert accelerometers locally, but the performance of a near real time intensity estimation system is under operation at NOA. The procedure is based on observing the maximum PGA value at each instrument and empirically estimate the corresponding intensity. The values are also fed to a SeisComP3 based ShakeMap procedure that is served at NOA and uses the scwfparam module of SeisComP3. Earthquake activity has been recorded so far from the western Corinth Gulf, the Ionian Islands and Achaia-Elia area, western Peloponnesus. The first phase involves correlation tests of collocated instruments and assessment of their performance to low intensity as well as to strongly felt events in the Patras city area. Steps of expanding the array are also under consideration, in order to cover the wider area of northwestern Peloponnesus and Ionian islands.
Graaf-Ruizendaal, W.A. de; Bakker, D.H. de
2013-01-01
Background: This study addresses the growing academic and policy interest in the appropriate provision of local healthcare services to the healthcare needs of local populations to increase health status and decrease healthcare costs. However, for most local areas information on the demand for primar
Loktev, D.; Spivak, A.
2013-05-01
A method for obtaining estimates of geodynamic state of the local crust and rock masses on the base of microseismic noise analysis is discussed. Microseismic noise is considered as a superposition of background microvibrations and a discrete component in the form of weak microseismic pulses generated by relaxational processes in the medium [1]. Currently active tectonic faults can be identified as zones with clustered sources of microseismic pulses and more intense amplitude variations of background microvibrations in tidal waves and baric variations in the atmosphere [2,3]. The presence of underground nonheterogeneities (i.e. contrasts in mechanic properties) and their scales are obtained from analysis of spectral characteristics of microseismic noise [4]. In the epicentral zone of an underground inhomogeneity we evidence characteristic quasi-chromatic pulses, stronger spectral density of local noise at high frequencies (more than 10 Hz) as well as maximum of spatial distribution of horizontal to vertical component spectral noise ratio (Nakamura parameter). The size of structural elements (blocks) of the Earth's crust is estimated by peak frequencies of momochromatic components of the spectrum on the base of the elaborated analytical model [1]. Parameters of weak pulses generated by relaxation (such as max velocity of oscillations, dominating (observed) period, etc.) yield estimates of differential movements of structural blocks in the medium as well as max stresses in the latter [5,6]. Examples are given to illustrate application of the proposed method to locating and mapping fault zones and underground nonheterogeneities in the Earth's crust, as well as to estimating scales of active structural blocks and their mobility potential when assessing places for nuclear atomic plants and underground nuclear waste storages. The method has also been successfully used for ranging hillsides of South Alps in terms of their liability to landslides. [1]. A.A. Spivak, S
Optimal Design of Experiments for Parametric Identification of Civil Engineering Structures
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning
Optimal Systems of experiments for parametric identification of civil engineering structures is investigated. Design of experiments for parametric identification of dynamic systems is usually done by minimizing a scalar measure, e.g the determinant, the trace ect., of an estimated parameter...
Grootes, M W; Popescu, C C; Robotham, A S G; Seibert, M; Kelvin, L S
2013-01-01
(Abridged) We present a non-parametric cell-based method of selecting highly pure and largely complete samples of spiral galaxies using photometric and structural parameters as provided by standard photometric pipelines and simple shape fitting algorithms, demonstrably superior to commonly used proxies. Furthermore, we find structural parameters derived using passbands longwards of the $g$ band and linked to older stellar populations, especially the stellar mass surface density $\\mu_*$ and the $r$ band effective radius $r_e$, to perform at least equally well as parameters more traditionally linked to the identification of spirals by means of their young stellar populations. In particular the distinct bimodality in the parameter $\\mu_*$, consistent with expectations of different evolutionary paths for spirals and ellipticals, represents an often overlooked yet powerful parameter in differentiating between spiral and non-spiral/elliptical galaxies. We investigate the intrinsic specific star-formation rate - ste...
Local asymptotic normality and asymptotical minimax efficiency of the MLE under random censorship
Institute of Scientific and Technical Information of China (English)
王启华; 荆炳义
2000-01-01
Here we study the problems of local asymptotic normality of the parametric family of distri-butions and asymptotic minimax efficient estimators when the observations are subject to right censor-ing. Local asymptotic normality will be established under some mild regularity conditions. A lower bound for local asymptotic minimax risk is given with respect to a bowl-shaped loss function, and fur-thermore a necessary and sufficient condition is given in order to achieve this lower bound. Finally, we show that this lower bound can be attained by the maximum likelihood estimator in the censored case and hence it is local asymptotic minimax efficient.
Local asymptotic normality and asymptotical minimax efficiency of the MLE under random censorship
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Here we study the problems of local asymptotic normality of the parametric family of distributions and asymptotic minimax efficient estimators when the observations are subject to right censoring. Local asymptotic normality will be established under some mild regularity conditions. A lower bound for local asymptotic minimax risk is given with respect to a bowl-shaped loss function, and furthermore a necessary and sufficient condition is given in order to achieve this lower bound. Finally, we show that this lower bound can be attained by the maximum likelihood estimator in the censored case and hence it is local asymptotic minimax efficient.
A non-parametric model for the cosmic velocity field
Branchini, E; Teodoro, L; Frenk, CS; Schmoldt, [No Value; Efstathiou, G; White, SDM; Saunders, W; Sutherland, W; Rowan-Robinson, M; Keeble, O; Tadros, H; Maddox, S; Oliver, S
1999-01-01
We present a self-consistent non-parametric model of the local cosmic velocity field derived from the distribution of IRAS galaxies in the PSCz redshift survey. The survey has been analysed using two independent methods, both based on the assumptions of gravitational instability and linear biasing.
Discharge estimation based on machine learning
Institute of Scientific and Technical Information of China (English)
Zhu JIANG; Hui-yan WANG; Wen-wu SONG
2013-01-01
To overcome the limitations of the traditional stage-discharge models in describing the dynamic characteristics of a river, a machine learning method of non-parametric regression, the locally weighted regression method was used to estimate discharge. With the purpose of improving the precision and efficiency of river discharge estimation, a novel machine learning method is proposed:the clustering-tree weighted regression method. First, the training instances are clustered. Second, the k-nearest neighbor method is used to cluster new stage samples into the best-fit cluster. Finally, the daily discharge is estimated. In the estimation process, the interference of irrelevant information can be avoided, so that the precision and efficiency of daily discharge estimation are improved. Observed data from the Luding Hydrological Station were used for testing. The simulation results demonstrate that the precision of this method is high. This provides a new effective method for discharge estimation.
Non-parametric analysis of rating transition and default data
DEFF Research Database (Denmark)
Fledelius, Peter; Lando, David; Perch Nielsen, Jens
2004-01-01
We demonstrate the use of non-parametric intensity estimation - including construction of pointwise confidence sets - for analyzing rating transition data. We find that transition intensities away from the class studied here for illustration strongly depend on the direction of the previous move b...... but that this dependence vanishes after 2-3 years....
Measurement of dynamic efficiency: a directional distance function parametric approach
Serra, T.; Oude Lansink, A.G.J.M.; Stefanou, S.E.
2011-01-01
This research proposes a parametric estimation of the structural dynamic efficiency measures proposed by Silva and Oude Lansink (2009). Overall, technical and allocative efficiency measurements are derived based on a directional distance function and the duality between this function and the optimal
Non-parametric analysis of rating transition and default data
DEFF Research Database (Denmark)
Fledelius, Peter; Lando, David; Perch Nielsen, Jens
2004-01-01
We demonstrate the use of non-parametric intensity estimation - including construction of pointwise confidence sets - for analyzing rating transition data. We find that transition intensities away from the class studied here for illustration strongly depend on the direction of the previous move...
Parametric Verification of Weighted Systems
DEFF Research Database (Denmark)
Christoffersen, Peter; Hansen, Mikkel; Mariegaard, Anders
2015-01-01
This paper addresses the problem of parametric model checking for weighted transition systems. We consider transition systems labelled with linear equations over a set of parameters and we use them to provide semantics for a parametric version of weighted CTL where the until and next operators ar...... finitely many iterations. To demonstrate the utility of our technique, we have implemented a prototype tool that computes the constraints on parameters for model checking problems.......This paper addresses the problem of parametric model checking for weighted transition systems. We consider transition systems labelled with linear equations over a set of parameters and we use them to provide semantics for a parametric version of weighted CTL where the until and next operators...... are themselves indexed with linear equations. The parameters change the model-checking problem into a problem of computing a linear system of inequalities that characterizes the parameters that guarantee the satisfiability. To address this problem, we use parametric dependency graphs (PDGs) and we propose...
The Local Luminosity Function at 25 Microns
Shupe, D L; Hacking, P B; Huchra, J P; Shupe, David L.; Fang, Fan; Hacking, Perry B.; Huchra, John P.
1998-01-01
The local luminosity function at 25 $\\mu$m provides the basis for interpreting the results of deep mid-infrared surveys planned or in progress with space astrophysics missions including ISO, WIRE and SIRTF. We have selected a sample of 1458 galaxies from the IRAS Faint Source Survey with a flux density limit of 250 mJy at 25 $\\mu$m. The local luminosity function is derived using both parametric and non-parametric maximum-likelihood techniques, and the classical $1/V_{max}$ estimator. Comparison of these results shows that the $1/V_{max}$ estimate of the luminosity function is significantly affected by the Local Supercluster. A maximum-likelihood fit to the radial density shows no systematic increase that would be caused by density evolution of the galaxy population. The density fit is used to correct the $1/V_{max}$ estimate. We also demonstrate the high quality and completeness of our sample by a variety of methods. The luminosity function derived from this sample is compared to previously published estimate...
Parametric Fires for Structural Design
DEFF Research Database (Denmark)
Hertz, Kristian
2012-01-01
The authorities, the construction association, and a number of companies in Denmark have supported the author writing a guide for design of building structures for parametric fires. The guide is published by the ministry as a supplement to the building regulations. However, consultants and contra......The authorities, the construction association, and a number of companies in Denmark have supported the author writing a guide for design of building structures for parametric fires. The guide is published by the ministry as a supplement to the building regulations. However, consultants...... and contractors have asked for a reference in English in order to make the guide-lines and the background for them available internationally. The paper therefore presents recommendations from the design guide especially concerning how to assess parametric design fires based on the opening factor method for large...
Validity of Parametrized Quark Propagator
Institute of Scientific and Technical Information of China (English)
ZHUJi-Zhen; ZHOULi-Juan; MAWei-Xing
2005-01-01
Based on an extensively study of the Dyson-Schwinger equations for a fully dressed quark propagator in the “rainbow”approximation, a parametrized fully dressed quark propagator is proposed in this paper. The parametrized propagator describes a confining quark propagator in hadron since it is analytic everywhere in complex p2-plane and has no Lemmann representation. The validity of the new propagator is discussed by comparing its predictions on selfenergy functions A/(p2), Bl(p2) and effective mass M$(p2) of quark with flavor f to their corresponding theoretical results produced by Dyson-Schwinger equations. Our comparison shows that the parametrized quark propagator is a good approximation to the fully dressed quark propagator given by the solutions of Dyson-Schwinger equations in the rainbow approximation and is convenient to use in any theoretical calculations.
Validity of Parametrized Quark Propagator
Institute of Scientific and Technical Information of China (English)
ZHU Ji-Zhen; ZHOU Li-Juan; MA Wei-Xing
2005-01-01
Based on an extensively study of the Dyson-Schwinger equations for a fully dressed quark propagator in the "rainbow" approximation, a parametrized fully dressed quark propagator is proposed in this paper. The parametrized propagator describes a confining quark propagator in hadron since it is analytic everywhere in complex p2-plane and has no Lemmann representation. The validity of the new propagator is discussed by comparing its predictions on selfenergy functions Af(p2), Bf(p2) and effective mass Mf(p2) of quark with flavor f to their corresponding theoretical results produced by Dyson-Schwinger equations. Our comparison shows that the parametrized quark propagator is a good approximation to the fully dressed quark propagator given by the solutions of Dyson-Schwinger equations in the rainbow approximation and is convenient to use in any theoretical calculations.
Parametric Thinking in Urban Design
DEFF Research Database (Denmark)
Steinø, Nicolai
2010-01-01
The paper states that most applications of parametric mod- elling to architecture and urban design fall into one of two strands of either form for form’s sake, or the negotiation of environmental con- cerns, while approaches which allow scenarios to be easily tested and modified without the appli...... of the paper. The pros and cons of this simple approach is discussed, and the paper con- cludes, that while it does not represent a suitable solution in all cases, it fills a gap among the existing approaches to parametric urban de- sign.......The paper states that most applications of parametric mod- elling to architecture and urban design fall into one of two strands of either form for form’s sake, or the negotiation of environmental con- cerns, while approaches which allow scenarios to be easily tested and modified without...
Zhang, Yu; Seo, Dong-Jun
2017-03-01
This paper presents novel formulations of Mean field bias (MFB) and local bias (LB) correction schemes that incorporate conditional bias (CB) penalty. These schemes are based on the operational MFB and LB algorithms in the National Weather Service (NWS) Multisensor Precipitation Estimator (MPE). By incorporating CB penalty in the cost function of exponential smoothers, we are able to derive augmented versions of recursive estimators of MFB and LB. Two extended versions of MFB algorithms are presented, one incorporating spatial variation of gauge locations only (MFB-L), and the second integrating both gauge locations and CB penalty (MFB-X). These two MFB schemes and the extended LB scheme (LB-X) are assessed relative to the original MFB and LB algorithms (referred to as MFB-O and LB-O, respectively) through a retrospective experiment over a radar domain in north-central Texas, and through a synthetic experiment over the Mid-Atlantic region. The outcome of the former experiment indicates that introducing the CB penalty to the MFB formulation leads to small, but consistent improvements in bias and CB, while its impacts on hourly correlation and Root Mean Square Error (RMSE) are mixed. Incorporating CB penalty in LB formulation tends to improve the RMSE at high rainfall thresholds, but its impacts on bias are also mixed. The synthetic experiment suggests that beneficial impacts are more conspicuous at low gauge density (9 per 58,000 km2), and tend to diminish at higher gauge density. The improvement at high rainfall intensity is partly an outcome of the conservativeness of the extended LB scheme. This conservativeness arises in part from the more frequent presence of negative eigenvalues in the extended covariance matrix which leads to no, or smaller incremental changes to the smoothed rainfall amounts.
Fuller, Christina H.; Brugge, Doug; Williams, Paige L.; Mittleman, Murray A.; Durant, John L.; Spengler, John D.
2012-09-01
Ultrafine particles (UFP; aerodynamic diameter predict hourly UFP concentration measured at residences in an urban community with a major interstate highway and; (2) determine if meteorology and proximity to traffic improve explanatory power. Short-term (1-3 weeks) residential monitoring of UFP concentration was conducted at 18 homes. Long-term monitoring was conducted at two near-highway monitoring sites and a central site. We created models of outdoor residential UFP concentration based on concentrations at the near-highway site, at the central site, at both sites together and without fixed sites. UFP concentration at residential sites was more highly correlated with those at a near-highway site than a central site. In regression models of each site alone, a 10% increase in UFP concentration at a near-highway site was associated with a 6% (95% CI: 6%, 7%) increase at residences while a 10% increase in UFP concentration at the central site was associated with a 3% (95% CI: 2%, 3%) increase at residences. A model including both sites showed minimal change in the magnitude of the association between the near-highway site and the residences, but the estimated association with UFP concentration at the central site was substantially attenuated. These associations remained after adjustment for other significant predictors of residential UFP concentration, including distance from highway, wind speed, wind direction, highway traffic volume and precipitation. The use of a central site as an estimate of personal exposure for populations near local emissions of traffic-related air pollutants may result in exposure misclassification.
Parametrization of contrails in a comprehensive climate model
Energy Technology Data Exchange (ETDEWEB)
Ponater, M.; Brinkop, S.; Sausen, R.; Schumann, U. [Deutsche Forschungs- und Versuchsanstalt fuer Luft- und Raumfahrt e.V., Oberpfaffenhofen (Germany). Inst. fuer Physik der Atmosphaere
1997-12-31
A contrail parametrization scheme for a general circulation model (GCM) is presented. Guidelines for its development were that it should be based on the thermodynamic theory of contrail formation and that it should be consistent with the cloud parametrization scheme of the GCM. Results of a six-year test integration indicate reasonable results concerning the spatial and temporal development of both contrail coverage and contrail optical properties. Hence, the scheme forms a promising basis for the quantitative estimation of the contrail climatic impact. (author) 9 refs.
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb......-Douglas or the Translog production function is used. However, the specification of a functional form for the production function involves the risk of specifying a functional form that is not similar to the “true” relationship between the inputs and the output. This misspecification might result in biased estimation...... results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used...
Biological parametric mapping with robust and non-parametric statistics.
Yang, Xue; Beason-Held, Lori; Resnick, Susan M; Landman, Bennett A
2011-07-15
Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, regions of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrices. Recently, biological parametric mapping has extended the widely popular statistical parametric mapping approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provide a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities. Copyright © 2011 Elsevier Inc. All rights reserved.
Parametric Studies for Scenario Earthquakes: Site Effects and Differential Motion
Panza, G. F.; Panza, G. F.; Romanelli, F.
2001-12-01
In presence of strong lateral heterogeneities, the generation of local surface waves and local resonance can give rise to a complicated pattern in the spatial groundshaking scenario. For any object of the built environment with dimensions greater than the characteristic length of the ground motion, different parts of its foundations can experience severe non-synchronous seismic input. In order to perform an accurate estimate of the site effects, and of differential motion, in realistic geometries, it is necessary to make a parametric study that takes into account the complex combination of the source and propagation parameters. The computation of a wide set of time histories and spectral information, corresponding to possible seismotectonic scenarios for different source and structural models, allows us the construction of damage scenarios that are out of reach of stochastic models. Synthetic signals, to be used as seismic input in a subsequent engineering analysis, e.g. for the design of earthquake-resistant structures or for the estimation of differential motion, can be produced at a very low cost/benefit ratio. We illustrate the work done in the framework of a large international cooperation following the guidelines of the UNESCO IUGS IGCP Project 414 "Realistic Modeling of Seismic Input for Megacities and Large Urban Areas" and show the very recent numerical experiments carried out within the EC project "Advanced methods for assessing the seismic vulnerability of existing motorway bridges" (VAB) to assess the importance of non-synchronous seismic excitation of long structures. >http://www.ictp.trieste.it/www_users/sand/projects.html
Tu, Xiaoguang; Gao, Jingjing; Zhu, Chongjing; Cheng, Jie-Zhi; Ma, Zheng; Dai, Xin; Xie, Mei
2016-12-01
Though numerous segmentation algorithms have been proposed to segment brain tissue from magnetic resonance (MR) images, few of them consider combining the tissue segmentation and bias field correction into a unified framework while simultaneously removing the noise. In this paper, we present a new unified MR image segmentation algorithm whereby tissue segmentation, bias correction and noise reduction are integrated within the same energy model. Our method is presented by a total variation term introduced to the coherent local intensity clustering criterion function. To solve the nonconvex problem with respect to membership functions, we add auxiliary variables in the energy function such as Chambolle's fast dual projection method can be used and the optimal segmentation and bias field estimation can be achieved simultaneously throughout the reciprocal iteration. Experimental results show that the proposed method has a salient advantage over the other three baseline methods on either tissue segmentation or bias correction, and the noise is significantly reduced via its applications on highly noise-corrupted images. Moreover, benefiting from the fast convergence of the proposed solution, our method is less time-consuming and robust to parameter setting.