Accurate parameter estimation for unbalanced three-phase system.
Chen, Yuan; So, Hing Cheung
2014-01-01
Smart grid is an intelligent power generation and control console in modern electricity networks, where the unbalanced three-phase power system is the commonly used model. Here, parameter estimation for this system is addressed. After converting the three-phase waveforms into a pair of orthogonal signals via the α β-transformation, the nonlinear least squares (NLS) estimator is developed for accurately finding the frequency, phase, and voltage parameters. The estimator is realized by the Newton-Raphson scheme, whose global convergence is studied in this paper. Computer simulations show that the mean square error performance of NLS method can attain the Cramér-Rao lower bound. Moreover, our proposal provides more accurate frequency estimation when compared with the complex least mean square (CLMS) and augmented CLMS. PMID:25162056
Accurate and robust estimation of camera parameters using RANSAC
NASA Astrophysics Data System (ADS)
Zhou, Fuqiang; Cui, Yi; Wang, Yexin; Liu, Liu; Gao, He
2013-03-01
Camera calibration plays an important role in the field of machine vision applications. The popularly used calibration approach based on 2D planar target sometimes fails to give reliable and accurate results due to the inaccurate or incorrect localization of feature points. To solve this problem, an accurate and robust estimation method for camera parameters based on RANSAC algorithm is proposed to detect the unreliability and provide the corresponding solutions. Through this method, most of the outliers are removed and the calibration errors that are the main factors influencing measurement accuracy are reduced. Both simulative and real experiments have been carried out to evaluate the performance of the proposed method and the results show that the proposed method is robust under large noise condition and quite efficient to improve the calibration accuracy compared with the original state.
Loewe, Axel; Wilhelms, Mathias; Schmid, Jochen; Krause, Mathias J.; Fischer, Fathima; Thomas, Dierk; Scholz, Eberhard P.; Dössel, Olaf; Seemann, Gunnar
2016-01-01
Computational models of cardiac electrophysiology provided insights into arrhythmogenesis and paved the way toward tailored therapies in the last years. To fully leverage in silico models in future research, these models need to be adapted to reflect pathologies, genetic alterations, or pharmacological effects, however. A common approach is to leave the structure of established models unaltered and estimate the values of a set of parameters. Today’s high-throughput patch clamp data acquisition methods require robust, unsupervised algorithms that estimate parameters both accurately and reliably. In this work, two classes of optimization approaches are evaluated: gradient-based trust-region-reflective and derivative-free particle swarm algorithms. Using synthetic input data and different ion current formulations from the Courtemanche et al. electrophysiological model of human atrial myocytes, we show that neither of the two schemes alone succeeds to meet all requirements. Sequential combination of the two algorithms did improve the performance to some extent but not satisfactorily. Thus, we propose a novel hybrid approach coupling the two algorithms in each iteration. This hybrid approach yielded very accurate estimates with minimal dependency on the initial guess using synthetic input data for which a ground truth parameter set exists. When applied to measured data, the hybrid approach yielded the best fit, again with minimal variation. Using the proposed algorithm, a single run is sufficient to estimate the parameters. The degree of superiority over the other investigated algorithms in terms of accuracy and robustness depended on the type of current. In contrast to the non-hybrid approaches, the proposed method proved to be optimal for data of arbitrary signal to noise ratio. The hybrid algorithm proposed in this work provides an important tool to integrate experimental data into computational models both accurately and robustly allowing to assess the often non
Accurate estimation of motion blur parameters in noisy remote sensing image
NASA Astrophysics Data System (ADS)
Shi, Xueyan; Wang, Lin; Shao, Xiaopeng; Wang, Huilin; Tao, Zhong
2015-05-01
The relative motion between remote sensing satellite sensor and objects is one of the most common reasons for remote sensing image degradation. It seriously weakens image data interpretation and information extraction. In practice, point spread function (PSF) should be estimated firstly for image restoration. Identifying motion blur direction and length accurately is very crucial for PSF and restoring image with precision. In general, the regular light-and-dark stripes in the spectrum can be employed to obtain the parameters by using Radon transform. However, serious noise existing in actual remote sensing images often causes the stripes unobvious. The parameters would be difficult to calculate and the error of the result relatively big. In this paper, an improved motion blur parameter identification method to noisy remote sensing image is proposed to solve this problem. The spectrum characteristic of noisy remote sensing image is analyzed firstly. An interactive image segmentation method based on graph theory called GrabCut is adopted to effectively extract the edge of the light center in the spectrum. Motion blur direction is estimated by applying Radon transform on the segmentation result. In order to reduce random error, a method based on whole column statistics is used during calculating blur length. Finally, Lucy-Richardson algorithm is applied to restore the remote sensing images of the moon after estimating blur parameters. The experimental results verify the effectiveness and robustness of our algorithm.
Damon, Bruce M.; Heemskerk, Anneriet M.; Ding, Zhaohua
2012-01-01
Fiber curvature is a functionally significant muscle structural property, but its estimation from diffusion-tensor MRI fiber tracking data may be confounded by noise. The purpose of this study was to investigate the use of polynomial fitting of fiber tracts for improving the accuracy and precision of fiber curvature (κ) measurements. Simulated image datasets were created in order to provide data with known values for κ and pennation angle (θ). Simulations were designed to test the effects of increasing inherent fiber curvature (3.8, 7.9, 11.8, and 15.3 m−1), signal-to-noise ratio (50, 75, 100, and 150), and voxel geometry (13.8 and 27.0 mm3 voxel volume with isotropic resolution; 13.5 mm3 volume with an aspect ratio of 4.0) on κ and θ measurements. In the originally reconstructed tracts, θ was estimated accurately under most curvature and all imaging conditions studied; however, the estimates of κ were imprecise and inaccurate. Fitting the tracts to 2nd order polynomial functions provided accurate and precise estimates of κ for all conditions except very high curvature (κ=15.3 m−1), while preserving the accuracy of the θ estimates. Similarly, polynomial fitting of in vivo fiber tracking data reduced the κ values of fitted tracts from those of unfitted tracts and did not change the θ values. Polynomial fitting of fiber tracts allows accurate estimation of physiologically reasonable values of κ, while preserving the accuracy of θ estimation. PMID:22503094
Lower bound on reliability for Weibull distribution when shape parameter is not estimated accurately
NASA Technical Reports Server (NTRS)
Huang, Zhaofeng; Porter, Albert A.
1991-01-01
The mathematical relationships between the shape parameter Beta and estimates of reliability and a life limit lower bound for the two parameter Weibull distribution are investigated. It is shown that under rather general conditions, both the reliability lower bound and the allowable life limit lower bound (often called a tolerance limit) have unique global minimums over a range of Beta. Hence lower bound solutions can be obtained without assuming or estimating Beta. The existence and uniqueness of these lower bounds are proven. Some real data examples are given to show how these lower bounds can be easily established and to demonstrate their practicality. The method developed here has proven to be extremely useful when using the Weibull distribution in analysis of no-failure or few-failures data. The results are applicable not only in the aerospace industry but anywhere that system reliabilities are high.
Lower bound on reliability for Weibull distribution when shape parameter is not estimated accurately
NASA Technical Reports Server (NTRS)
Huang, Zhaofeng; Porter, Albert A.
1990-01-01
The mathematical relationships between the shape parameter Beta and estimates of reliability and a life limit lower bound for the two parameter Weibull distribution are investigated. It is shown that under rather general conditions, both the reliability lower bound and the allowable life limit lower bound (often called a tolerance limit) have unique global minimums over a range of Beta. Hence lower bound solutions can be obtained without assuming or estimating Beta. The existence and uniqueness of these lower bounds are proven. Some real data examples are given to show how these lower bounds can be easily established and to demonstrate their practicality. The method developed here has proven to be extremely useful when using the Weibull distribution in analysis of no-failure or few-failures data. The results are applicable not only in the aerospace industry but anywhere that system reliabilities are high.
Accurate motion parameter estimation for colonoscopy tracking using a regression method
NASA Astrophysics Data System (ADS)
Liu, Jianfei; Subramanian, Kalpathi R.; Yoo, Terry S.
2010-03-01
Co-located optical and virtual colonoscopy images have the potential to provide important clinical information during routine colonoscopy procedures. In our earlier work, we presented an optical flow based algorithm to compute egomotion from live colonoscopy video, permitting navigation and visualization of the corresponding patient anatomy. In the original algorithm, motion parameters were estimated using the traditional Least Sum of squares(LS) procedure which can be unstable in the context of optical flow vectors with large errors. In the improved algorithm, we use the Least Median of Squares (LMS) method, a robust regression method for motion parameter estimation. Using the LMS method, we iteratively analyze and converge toward the main distribution of the flow vectors, while disregarding outliers. We show through three experiments the improvement in tracking results obtained using the LMS method, in comparison to the LS estimator. The first experiment demonstrates better spatial accuracy in positioning the virtual camera in the sigmoid colon. The second and third experiments demonstrate the robustness of this estimator, resulting in longer tracked sequences: from 300 to 1310 in the ascending colon, and 410 to 1316 in the transverse colon.
FAST TRACK COMMUNICATION Accurate estimate of α variation and isotope shift parameters in Na and Mg+
NASA Astrophysics Data System (ADS)
Sahoo, B. K.
2010-12-01
We present accurate calculations of fine-structure constant variation coefficients and isotope shifts in Na and Mg+ using the relativistic coupled-cluster method. In our approach, we are able to discover the roles of various correlation effects explicitly to all orders in these calculations. Most of the results, especially for the excited states, are reported for the first time. It is possible to ascertain suitable anchor and probe lines for the studies of possible variation in the fine-structure constant by using the above results in the considered systems.
Chon, K H; Cohen, R J; Holstein-Rathlou, N H
1997-01-01
A linear and nonlinear autoregressive moving average (ARMA) identification algorithm is developed for modeling time series data. The algorithm uses Laguerre expansion of kernals (LEK) to estimate Volterra-Wiener kernals. However, instead of estimating linear and nonlinear system dynamics via moving average models, as is the case for the Volterra-Wiener analysis, we propose an ARMA model-based approach. The proposed algorithm is essentially the same as LEK, but this algorithm is extended to include past values of the output as well. Thus, all of the advantages associated with using the Laguerre function remain with our algorithm; but, by extending the algorithm to the linear and nonlinear ARMA model, a significant reduction in the number of Laguerre functions can be made, compared with the Volterra-Wiener approach. This translates into a more compact system representation and makes the physiological interpretation of higher order kernels easier. Furthermore, simulation results show better performance of the proposed approach in estimating the system dynamics than LEK in certain cases, and it remains effective in the presence of significant additive measurement noise. PMID:9236985
Subramanian, Swetha; Mast, T Douglas
2015-10-01
Computational finite element models are commonly used for the simulation of radiofrequency ablation (RFA) treatments. However, the accuracy of these simulations is limited by the lack of precise knowledge of tissue parameters. In this technical note, an inverse solver based on the unscented Kalman filter (UKF) is proposed to optimize values for specific heat, thermal conductivity, and electrical conductivity resulting in accurately simulated temperature elevations. A total of 15 RFA treatments were performed on ex vivo bovine liver tissue. For each RFA treatment, 15 finite-element simulations were performed using a set of deterministically chosen tissue parameters to estimate the mean and variance of the resulting tissue ablation. The UKF was implemented as an inverse solver to recover the specific heat, thermal conductivity, and electrical conductivity corresponding to the measured area of the ablated tissue region, as determined from gross tissue histology. These tissue parameters were then employed in the finite element model to simulate the position- and time-dependent tissue temperature. Results show good agreement between simulated and measured temperature. PMID:26352462
NASA Astrophysics Data System (ADS)
Subramanian, Swetha; Mast, T. Douglas
2015-09-01
Computational finite element models are commonly used for the simulation of radiofrequency ablation (RFA) treatments. However, the accuracy of these simulations is limited by the lack of precise knowledge of tissue parameters. In this technical note, an inverse solver based on the unscented Kalman filter (UKF) is proposed to optimize values for specific heat, thermal conductivity, and electrical conductivity resulting in accurately simulated temperature elevations. A total of 15 RFA treatments were performed on ex vivo bovine liver tissue. For each RFA treatment, 15 finite-element simulations were performed using a set of deterministically chosen tissue parameters to estimate the mean and variance of the resulting tissue ablation. The UKF was implemented as an inverse solver to recover the specific heat, thermal conductivity, and electrical conductivity corresponding to the measured area of the ablated tissue region, as determined from gross tissue histology. These tissue parameters were then employed in the finite element model to simulate the position- and time-dependent tissue temperature. Results show good agreement between simulated and measured temperature.
NASA Astrophysics Data System (ADS)
Bloßfeld, Mathis; Panzetta, Francesca; Müller, Horst; Gerstl, Michael
2016-04-01
The GGOS vision is to integrate geometric and gravimetric observation techniques to estimate consistent geodetic-geophysical parameters. In order to reach this goal, the common estimation of station coordinates, Stokes coefficients and Earth Orientation Parameters (EOP) is necessary. Satellite Laser Ranging (SLR) provides the ability to study correlations between the different parameter groups since the observed satellite orbit dynamics are sensitive to the above mentioned geodetic parameters. To decrease the correlations, SLR observations to multiple satellites have to be combined. In this paper, we compare the estimated EOP of (i) single satellite SLR solutions and (ii) multi-satellite SLR solutions. Therefore, we jointly estimate station coordinates, EOP, Stokes coefficients and orbit parameters using different satellite constellations. A special focus in this investigation is put on the de-correlation of different geodetic parameter groups due to the combination of SLR observations. Besides SLR observations to spherical satellites (commonly used), we discuss the impact of SLR observations to non-spherical satellites such as, e.g., the JASON-2 satellite. The goal of this study is to discuss the existing parameter interactions and to present a strategy how to obtain reliable estimates of station coordinates, EOP, orbit parameter and Stokes coefficients in one common adjustment. Thereby, the benefits of a multi-satellite SLR solution are evaluated.
NASA Technical Reports Server (NTRS)
Iliff, Kenneth W.
1987-01-01
The aircraft parameter estimation problem is used to illustrate the utility of parameter estimation, which applies to many engineering and scientific fields. Maximum likelihood estimation has been used to extract stability and control derivatives from flight data for many years. This paper presents some of the basic concepts of aircraft parameter estimation and briefly surveys the literature in the field. The maximum likelihood estimator is discussed, and the basic concepts of minimization and estimation are examined for a simple simulated aircraft example. The cost functions that are to be minimized during estimation are defined and discussed. Graphic representations of the cost functions are given to illustrate the minimization process. Finally, the basic concepts are generalized, and estimation from flight data is discussed. Some of the major conclusions for the simulated example are also developed for the analysis of flight data from the F-14, highly maneuverable aircraft technology (HiMAT), and space shuttle vehicles.
Parameter estimating state reconstruction
NASA Technical Reports Server (NTRS)
George, E. B.
1976-01-01
Parameter estimation is considered for systems whose entire state cannot be measured. Linear observers are designed to recover the unmeasured states to a sufficient accuracy to permit the estimation process. There are three distinct dynamics that must be accommodated in the system design: the dynamics of the plant, the dynamics of the observer, and the system updating of the parameter estimation. The latter two are designed to minimize interaction of the involved systems. These techniques are extended to weakly nonlinear systems. The application to a simulation of a space shuttle POGO system test is of particular interest. A nonlinear simulation of the system is developed, observers designed, and the parameters estimated.
Parameter estimation through ignorance.
Du, Hailiang; Smith, Leonard A
2012-07-01
Dynamical modeling lies at the heart of our understanding of physical systems. Its role in science is deeper than mere operational forecasting, in that it allows us to evaluate the adequacy of the mathematical structure of our models. Despite the importance of model parameters, there is no general method of parameter estimation outside linear systems. A relatively simple method of parameter estimation for nonlinear systems is introduced, based on variations in the accuracy of probability forecasts. It is illustrated on the logistic map, the Henon map, and the 12-dimensional Lorenz96 flow, and its ability to outperform linear least squares in these systems is explored at various noise levels and sampling rates. As expected, it is more effective when the forecast error distributions are non-Gaussian. The method selects parameter values by minimizing a proper, local skill score for continuous probability forecasts as a function of the parameter values. This approach is easier to implement in practice than alternative nonlinear methods based on the geometry of attractors or the ability of the model to shadow the observations. Direct measures of inadequacy in the model, the "implied ignorance," and the information deficit are introduced. PMID:23005513
Phenological Parameters Estimation Tool
NASA Technical Reports Server (NTRS)
McKellip, Rodney D.; Ross, Kenton W.; Spruce, Joseph P.; Smoot, James C.; Ryan, Robert E.; Gasser, Gerald E.; Prados, Donald L.; Vaughan, Ronald D.
2010-01-01
The Phenological Parameters Estimation Tool (PPET) is a set of algorithms implemented in MATLAB that estimates key vegetative phenological parameters. For a given year, the PPET software package takes in temporally processed vegetation index data (3D spatio-temporal arrays) generated by the time series product tool (TSPT) and outputs spatial grids (2D arrays) of vegetation phenological parameters. As a precursor to PPET, the TSPT uses quality information for each pixel of each date to remove bad or suspect data, and then interpolates and digitally fills data voids in the time series to produce a continuous, smoothed vegetation index product. During processing, the TSPT displays NDVI (Normalized Difference Vegetation Index) time series plots and images from the temporally processed pixels. Both the TSPT and PPET currently use moderate resolution imaging spectroradiometer (MODIS) satellite multispectral data as a default, but each software package is modifiable and could be used with any high-temporal-rate remote sensing data collection system that is capable of producing vegetation indices. Raw MODIS data from the Aqua and Terra satellites is processed using the TSPT to generate a filtered time series data product. The PPET then uses the TSPT output to generate phenological parameters for desired locations. PPET output data tiles are mosaicked into a Conterminous United States (CONUS) data layer using ERDAS IMAGINE, or equivalent software package. Mosaics of the vegetation phenology data products are then reprojected to the desired map projection using ERDAS IMAGINE
Accurate pose estimation for forensic identification
NASA Astrophysics Data System (ADS)
Merckx, Gert; Hermans, Jeroen; Vandermeulen, Dirk
2010-04-01
In forensic authentication, one aims to identify the perpetrator among a series of suspects or distractors. A fundamental problem in any recognition system that aims for identification of subjects in a natural scene is the lack of constrains on viewing and imaging conditions. In forensic applications, identification proves even more challenging, since most surveillance footage is of abysmal quality. In this context, robust methods for pose estimation are paramount. In this paper we will therefore present a new pose estimation strategy for very low quality footage. Our approach uses 3D-2D registration of a textured 3D face model with the surveillance image to obtain accurate far field pose alignment. Starting from an inaccurate initial estimate, the technique uses novel similarity measures based on the monogenic signal to guide a pose optimization process. We will illustrate the descriptive strength of the introduced similarity measures by using them directly as a recognition metric. Through validation, using both real and synthetic surveillance footage, our pose estimation method is shown to be accurate, and robust to lighting changes and image degradation.
Accurate Biomass Estimation via Bayesian Adaptive Sampling
NASA Technical Reports Server (NTRS)
Wheeler, Kevin R.; Knuth, Kevin H.; Castle, Joseph P.; Lvov, Nikolay
2005-01-01
The following concepts were introduced: a) Bayesian adaptive sampling for solving biomass estimation; b) Characterization of MISR Rahman model parameters conditioned upon MODIS landcover. c) Rigorous non-parametric Bayesian approach to analytic mixture model determination. d) Unique U.S. asset for science product validation and verification.
Bibliography for aircraft parameter estimation
NASA Technical Reports Server (NTRS)
Iliff, Kenneth W.; Maine, Richard E.
1986-01-01
An extensive bibliography in the field of aircraft parameter estimation has been compiled. This list contains definitive works related to most aircraft parameter estimation approaches. Theoretical studies as well as practical applications are included. Many of these publications are pertinent to subjects peripherally related to parameter estimation, such as aircraft maneuver design or instrumentation considerations.
Accurate 3D quantification of the bronchial parameters in MDCT
NASA Astrophysics Data System (ADS)
Saragaglia, A.; Fetita, C.; Preteux, F.; Brillet, P. Y.; Grenier, P. A.
2005-08-01
The assessment of bronchial reactivity and wall remodeling in asthma plays a crucial role in better understanding such a disease and evaluating therapeutic responses. Today, multi-detector computed tomography (MDCT) makes it possible to perform an accurate estimation of bronchial parameters (lumen and wall areas) by allowing a quantitative analysis in a cross-section plane orthogonal to the bronchus axis. This paper provides the tools for such an analysis by developing a 3D investigation method which relies on 3D reconstruction of bronchial lumen and central axis computation. Cross-section images at bronchial locations interactively selected along the central axis are generated at appropriate spatial resolution. An automated approach is then developed for accurately segmenting the inner and outer bronchi contours on the cross-section images. It combines mathematical morphology operators, such as "connection cost", and energy-controlled propagation in order to overcome the difficulties raised by vessel adjacencies and wall irregularities. The segmentation accuracy was validated with respect to a 3D mathematically-modeled phantom of a pair bronchus-vessel which mimics the characteristics of real data in terms of gray-level distribution, caliber and orientation. When applying the developed quantification approach to such a model with calibers ranging from 3 to 10 mm diameter, the lumen area relative errors varied from 3.7% to 0.15%, while the bronchus area was estimated with a relative error less than 5.1%.
Fast and accurate estimation for astrophysical problems in large databases
NASA Astrophysics Data System (ADS)
Richards, Joseph W.
2010-10-01
A recent flood of astronomical data has created much demand for sophisticated statistical and machine learning tools that can rapidly draw accurate inferences from large databases of high-dimensional data. In this Ph.D. thesis, methods for statistical inference in such databases will be proposed, studied, and applied to real data. I use methods for low-dimensional parametrization of complex, high-dimensional data that are based on the notion of preserving the connectivity of data points in the context of a Markov random walk over the data set. I show how this simple parameterization of data can be exploited to: define appropriate prototypes for use in complex mixture models, determine data-driven eigenfunctions for accurate nonparametric regression, and find a set of suitable features to use in a statistical classifier. In this thesis, methods for each of these tasks are built up from simple principles, compared to existing methods in the literature, and applied to data from astronomical all-sky surveys. I examine several important problems in astrophysics, such as estimation of star formation history parameters for galaxies, prediction of redshifts of galaxies using photometric data, and classification of different types of supernovae based on their photometric light curves. Fast methods for high-dimensional data analysis are crucial in each of these problems because they all involve the analysis of complicated high-dimensional data in large, all-sky surveys. Specifically, I estimate the star formation history parameters for the nearly 800,000 galaxies in the Sloan Digital Sky Survey (SDSS) Data Release 7 spectroscopic catalog, determine redshifts for over 300,000 galaxies in the SDSS photometric catalog, and estimate the types of 20,000 supernovae as part of the Supernova Photometric Classification Challenge. Accurate predictions and classifications are imperative in each of these examples because these estimates are utilized in broader inference problems
NASA Technical Reports Server (NTRS)
Hocking, W. K.
1989-01-01
The objective of any radar experiment is to determine as much as possible about the entities which scatter the radiation. This review discusses many of the various parameters which can be deduced in a radar experiment, and also critically examines the procedures used to deduce them. Methods for determining the mean wind velocity, the RMS fluctuating velocities, turbulence parameters, and the shapes of the scatterers are considered. Complications with these determinations are discussed. It is seen throughout that a detailed understanding of the shape and cause of the scatterers is important in order to make better determinations of these various quantities. Finally, some other parameters, which are less easily acquired, are considered. For example, it is noted that momentum fluxes due to buoyancy waves and turbulence can be determined, and on occasions radars can be used to determine stratospheric diffusion coefficients and even temperature profiles in the atmosphere.
Estimation for large non-centrality parameters
NASA Astrophysics Data System (ADS)
Inácio, Sónia; Mexia, João; Fonseca, Miguel; Carvalho, Francisco
2016-06-01
We introduce the concept of estimability for models for which accurate estimators can be obtained for the respective parameters. The study was conducted for model with almost scalar matrix using the study of estimability after validation of these models. In the validation of these models we use F statistics with non centrality parameter τ =‖λ/‖2 σ2 when this parameter is sufficiently large we obtain good estimators for λ and α so there is estimability. Thus, we are interested in obtaining a lower bound for the non-centrality parameter. In this context we use for the statistical inference inducing pivot variables, see Ferreira et al. 2013, and asymptotic linearity, introduced by Mexia & Oliveira 2011, to derive confidence intervals for large non-centrality parameters (see Inácio et al. 2015). These results enable us to measure relevance of effects and interactions in multifactors models when we get highly statistically significant the values of F tests statistics.
Robust ODF smoothing for accurate estimation of fiber orientation.
Beladi, Somaieh; Pathirana, Pubudu N; Brotchie, Peter
2010-01-01
Q-ball imaging was presented as a model free, linear and multimodal diffusion sensitive approach to reconstruct diffusion orientation distribution function (ODF) using diffusion weighted MRI data. The ODFs are widely used to estimate the fiber orientations. However, the smoothness constraint was proposed to achieve a balance between the angular resolution and noise stability for ODF constructs. Different regularization methods were proposed for this purpose. However, these methods are not robust and quite sensitive to the global regularization parameter. Although, numerical methods such as L-curve test are used to define a globally appropriate regularization parameter, it cannot serve as a universal value suitable for all regions of interest. This may result in over smoothing and potentially end up in neglecting an existing fiber population. In this paper, we propose to include an interpolation step prior to the spherical harmonic decomposition. This interpolation based approach is based on Delaunay triangulation provides a reliable, robust and accurate smoothing approach. This method is easy to implement and does not require other numerical methods to define the required parameters. Also, the fiber orientations estimated using this approach are more accurate compared to other common approaches. PMID:21096202
Parameter estimation in food science.
Dolan, Kirk D; Mishra, Dharmendra K
2013-01-01
Modeling includes two distinct parts, the forward problem and the inverse problem. The forward problem-computing y(t) given known parameters-has received much attention, especially with the explosion of commercial simulation software. What is rarely made clear is that the forward results can be no better than the accuracy of the parameters. Therefore, the inverse problem-estimation of parameters given measured y(t)-is at least as important as the forward problem. However, in the food science literature there has been little attention paid to the accuracy of parameters. The purpose of this article is to summarize the state of the art of parameter estimation in food science, to review some of the common food science models used for parameter estimation (for microbial inactivation and growth, thermal properties, and kinetics), and to suggest a generic method to standardize parameter estimation, thereby making research results more useful. Scaled sensitivity coefficients are introduced and shown to be important in parameter identifiability. Sequential estimation and optimal experimental design are also reviewed as powerful parameter estimation methods that are beginning to be used in the food science literature. PMID:23297775
31 CFR 205.24 - How are accurate estimates maintained?
Code of Federal Regulations, 2010 CFR
2010-07-01
... 31 Money and Finance: Treasury 2 2010-07-01 2010-07-01 false How are accurate estimates maintained... Treasury-State Agreement § 205.24 How are accurate estimates maintained? (a) If a State has knowledge that an estimate does not reasonably correspond to the State's cash needs for a Federal assistance...
Accurate estimation of sigma(exp 0) using AIRSAR data
NASA Technical Reports Server (NTRS)
Holecz, Francesco; Rignot, Eric
1995-01-01
During recent years signature analysis, classification, and modeling of Synthetic Aperture Radar (SAR) data as well as estimation of geophysical parameters from SAR data have received a great deal of interest. An important requirement for the quantitative use of SAR data is the accurate estimation of the backscattering coefficient sigma(exp 0). In terrain with relief variations radar signals are distorted due to the projection of the scene topography into the slant range-Doppler plane. The effect of these variations is to change the physical size of the scattering area, leading to errors in the radar backscatter values and incidence angle. For this reason the local incidence angle, derived from sensor position and Digital Elevation Model (DEM) data must always be considered. Especially in the airborne case, the antenna gain pattern can be an additional source of radiometric error, because the radar look angle is not known precisely as a result of the the aircraft motions and the local surface topography. Consequently, radiometric distortions due to the antenna gain pattern must also be corrected for each resolution cell, by taking into account aircraft displacements (position and attitude) and position of the backscatter element, defined by the DEM data. In this paper, a method to derive an accurate estimation of the backscattering coefficient using NASA/JPL AIRSAR data is presented. The results are evaluated in terms of geometric accuracy, radiometric variations of sigma(exp 0), and precision of the estimated forest biomass.
Micromagnetometer calibration for accurate orientation estimation.
Zhang, Zhi-Qiang; Yang, Guang-Zhong
2015-02-01
Micromagnetometers, together with inertial sensors, are widely used for attitude estimation for a wide variety of applications. However, appropriate sensor calibration, which is essential to the accuracy of attitude reconstruction, must be performed in advance. Thus far, many different magnetometer calibration methods have been proposed to compensate for errors such as scale, offset, and nonorthogonality. They have also been used for obviate magnetic errors due to soft and hard iron. However, in order to combine the magnetometer with inertial sensor for attitude reconstruction, alignment difference between the magnetometer and the axes of the inertial sensor must be determined as well. This paper proposes a practical means of sensor error correction by simultaneous consideration of sensor errors, magnetic errors, and alignment difference. We take the summation of the offset and hard iron error as the combined bias and then amalgamate the alignment difference and all the other errors as a transformation matrix. A two-step approach is presented to determine the combined bias and transformation matrix separately. In the first step, the combined bias is determined by finding an optimal ellipsoid that can best fit the sensor readings. In the second step, the intrinsic relationships of the raw sensor readings are explored to estimate the transformation matrix as a homogeneous linear least-squares problem. Singular value decomposition is then applied to estimate both the transformation matrix and magnetic vector. The proposed method is then applied to calibrate our sensor node. Although there is no ground truth for the combined bias and transformation matrix for our node, the consistency of calibration results among different trials and less than 3(°) root mean square error for orientation estimation have been achieved, which illustrates the effectiveness of the proposed sensor calibration method for practical applications. PMID:25265625
Estimation of Damage Preference From Strike Parameters
Canavan, G.H.
1998-09-11
Estimation of an opponent's damage preference is illustrated by discussing the sensitivity of stability indices and strike parameters to it and inverting the results to study the sensitivity of estimates to uncertainties in strikes. Costs and stability indices do not generally have the monotonicity and sensitivity needed to support accurate estimation. First and second strikes do. Second strikes also have proportionality, although they are not unambiguously interpretable. First strikes are observable and have the greatest overall power for estimation, whether linear or numerical solutions are used.
Reionization history and CMB parameter estimation
Dizgah, Azadeh Moradinezhad; Kinney, William H.; Gnedin, Nickolay Y. E-mail: gnedin@fnal.edu
2013-05-01
We study how uncertainty in the reionization history of the universe affects estimates of other cosmological parameters from the Cosmic Microwave Background. We analyze WMAP7 data and synthetic Planck-quality data generated using a realistic scenario for the reionization history of the universe obtained from high-resolution numerical simulation. We perform parameter estimation using a simple sudden reionization approximation, and using the Principal Component Analysis (PCA) technique proposed by Mortonson and Hu. We reach two main conclusions: (1) Adopting a simple sudden reionization model does not introduce measurable bias into values for other parameters, indicating that detailed modeling of reionization is not necessary for the purpose of parameter estimation from future CMB data sets such as Planck. (2) PCA analysis does not allow accurate reconstruction of the actual reionization history of the universe in a realistic case.
Parameter Estimation Using VLA Data
NASA Astrophysics Data System (ADS)
Venter, Willem C.
The main objective of this dissertation is to extract parameters from multiple wavelength images, on a pixel-to-pixel basis, when the images are corrupted with noise and a point spread function. The data used are from the field of radio astronomy. The very large array (VLA) at Socorro in New Mexico was used to observe planetary nebula NGC 7027 at three different wavelengths, 2 cm, 6 cm and 20 cm. A temperature model, describing the temperature variation in the nebula as a function of optical depth, is postulated. Mathematical expressions for the brightness distribution (flux density) of the nebula, at the three observed wavelengths, are obtained. Using these three equations and the three data values available, one from the observed flux density map at each wavelength, it is possible to solve for two temperature parameters and one optical depth parameter at each pixel location. Due to the fact that the number of unknowns equal the number of equations available, estimation theory cannot be used to smooth any noise present in the data values. It was found that a direct solution of the three highly nonlinear flux density equations is very sensitive to noise in the data. Results obtained from solving for the three unknown parameters directly, as discussed above, were not physical realizable. This was partly due to the effect of incomplete sampling at the time when the data were gathered and to noise in the system. The application of rigorous digital parameter estimation techniques result in estimated parameters that are also not physically realizable. The estimated values for the temperature parameters are for example either too high or negative, which is not physically possible. Simulation studies have shown that a "double smoothing" technique improves the results by a large margin. This technique consists of two parts: in the first part the original observed data are smoothed using a running window and in the second part a similar smoothing of the estimated parameters
Estimation of pharmacokinetic model parameters.
Timcenko, A; Reich, D L; Trunfio, G
1995-01-01
This paper addresses the problem of estimating the depth of anesthesia in clinical practice where many drugs are used in combination. The aim of the project is to use pharmacokinetically-derived data to predict episodes of light anesthesia. The weighted linear combination of anesthetic drug concentrations was computed using a stochastic pharmacokinetic model. The clinical definition of light anesthesia was based on the hemodynamic consequences of autonomic nervous system responses to surgical stimuli. A rule-based expert system was used to review anesthesia records to determine instances of light anesthesia using hemodynamic criteria. It was assumed that light anesthesia was a direct consequence of the weighted linear combination of drug concentrations in the patient's body that decreased below a certain threshold. We augmented traditional two-compartment models with a stochastic component of anesthetics' concentrations to compensate for interpatient pharmacokinetic and pharmacodynamic variability. A cohort of 532 clinical anesthesia cases was examined and parameters of two compartment pharmacokinetic models for 6 intravenously administered anesthetic drugs (fentanyl, thiopenthal, morphine, propofol, midazolam, ketamine) were estimated, as well as the parameters for 2 inhalational anesthetics (N2O and isoflurane). These parameters were then prospectively applied to 22 cases that were not used for parameter estimation, and the predictive ability of the pharmacokinetic model was determined. The goal of the study is the development of a pharmacokinetic model that will be useful in predicting light anesthesia in the clinically relevant circumstance where many drugs are used concurrently. PMID:8563327
Quantifying Accurate Calorie Estimation Using the "Think Aloud" Method
ERIC Educational Resources Information Center
Holmstrup, Michael E.; Stearns-Bruening, Kay; Rozelle, Jeffrey
2013-01-01
Objective: Clients often have limited time in a nutrition education setting. An improved understanding of the strategies used to accurately estimate calories may help to identify areas of focused instruction to improve nutrition knowledge. Methods: A "Think Aloud" exercise was recorded during the estimation of calories in a standard dinner meal…
Machine learning of parameters for accurate semiempirical quantum chemical calculations
Dral, Pavlo O.; von Lilienfeld, O. Anatole; Thiel, Walter
2015-04-14
We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempiricalmore » OM2 method using a set of 6095 constitutional isomers C7H10O2, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.« less
Machine learning of parameters for accurate semiempirical quantum chemical calculations
Dral, Pavlo O.; von Lilienfeld, O. Anatole; Thiel, Walter
2015-04-14
We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempirical OM2 method using a set of 6095 constitutional isomers C_{7}H_{10}O_{2}, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.
Parameter estimation for transformer modeling
NASA Astrophysics Data System (ADS)
Cho, Sung Don
Large Power transformers, an aging and vulnerable part of our energy infrastructure, are at choke points in the grid and are key to reliability and security. Damage or destruction due to vandalism, misoperation, or other unexpected events is of great concern, given replacement costs upward of $2M and lead time of 12 months. Transient overvoltages can cause great damage and there is much interest in improving computer simulation models to correctly predict and avoid the consequences. EMTP (the Electromagnetic Transients Program) has been developed for computer simulation of power system transients. Component models for most equipment have been developed and benchmarked. Power transformers would appear to be simple. However, due to their nonlinear and frequency-dependent behaviors, they can be one of the most complex system components to model. It is imperative that the applied models be appropriate for the range of frequencies and excitation levels that the system experiences. Thus, transformer modeling is not a mature field and newer improved models must be made available. In this work, improved topologically-correct duality-based models are developed for three-phase autotransformers having five-legged, three-legged, and shell-form cores. The main problem in the implementation of detailed models is the lack of complete and reliable data, as no international standard suggests how to measure and calculate parameters. Therefore, parameter estimation methods are developed here to determine the parameters of a given model in cases where available information is incomplete. The transformer nameplate data is required and relative physical dimensions of the core are estimated. The models include a separate representation of each segment of the core, including hysteresis of the core, lambda-i saturation characteristic, capacitive effects, and frequency dependency of winding resistance and core loss. Steady-state excitation, and de-energization and re-energization transients
Accurate pose estimation using single marker single camera calibration system
NASA Astrophysics Data System (ADS)
Pati, Sarthak; Erat, Okan; Wang, Lejing; Weidert, Simon; Euler, Ekkehard; Navab, Nassir; Fallavollita, Pascal
2013-03-01
Visual marker based tracking is one of the most widely used tracking techniques in Augmented Reality (AR) applications. Generally, multiple square markers are needed to perform robust and accurate tracking. Various marker based methods for calibrating relative marker poses have already been proposed. However, the calibration accuracy of these methods relies on the order of the image sequence and pre-evaluation of pose-estimation errors, making the method offline. Several studies have shown that the accuracy of pose estimation for an individual square marker depends on camera distance and viewing angle. We propose a method to accurately model the error in the estimated pose and translation of a camera using a single marker via an online method based on the Scaled Unscented Transform (SUT). Thus, the pose estimation for each marker can be estimated with highly accurate calibration results independent of the order of image sequences compared to cases when this knowledge is not used. This removes the need for having multiple markers and an offline estimation system to calculate camera pose in an AR application.
Direct computation of parameters for accurate polarizable force fields
Verstraelen, Toon Vandenbrande, Steven; Ayers, Paul W.
2014-11-21
We present an improved electronic linear response model to incorporate polarization and charge-transfer effects in polarizable force fields. This model is a generalization of the Atom-Condensed Kohn-Sham Density Functional Theory (DFT), approximated to second order (ACKS2): it can now be defined with any underlying variational theory (next to KS-DFT) and it can include atomic multipoles and off-center basis functions. Parameters in this model are computed efficiently as expectation values of an electronic wavefunction, obviating the need for their calibration, regularization, and manual tuning. In the limit of a complete density and potential basis set in the ACKS2 model, the linear response properties of the underlying theory for a given molecular geometry are reproduced exactly. A numerical validation with a test set of 110 molecules shows that very accurate models can already be obtained with fluctuating charges and dipoles. These features greatly facilitate the development of polarizable force fields.
Blind estimation of compartmental model parameters.
Di Bella, E V; Clackdoyle, R; Gullberg, G T
1999-03-01
Computation of physiologically relevant kinetic parameters from dynamic PET or SPECT imaging requires knowledge of the blood input function. This work is concerned with developing methods to accurately estimate these kinetic parameters blindly; that is, without use of a directly measured blood input function. Instead, only measurements of the output functions--the tissue time-activity curves--are used. The blind estimation method employed here minimizes a set of cross-relation equations, from which the blood term has been factored out, to determine compartmental model parameters. The method was tested with simulated data appropriate for dynamic SPECT cardiac perfusion imaging with 99mTc-teboroxime and for dynamic PET cerebral blood flow imaging with 15O water. The simulations did not model the tomographic process. Noise levels typical of the respective modalities were employed. From three to eight different regions were simulated, each with different time-activity curves. The time-activity curve (24 or 70 time points) for each region was simulated with a compartment model. The simulation used a biexponential blood input function and washin rates between 0.2 and 1.3 min(-1) and washout rates between 0.2 and 1.0 min(-1). The system of equations was solved numerically and included constraints to bound the range of possible solutions. From the cardiac simulations, washin was determined to within a scale factor of the true washin parameters with less than 6% bias and 12% variability. 99mTc-teboroxime washout results had less than 5% bias, but variability ranged from 14% to 43%. The cerebral blood flow washin parameters were determined with less than 5% bias and 4% variability. The washout parameters were determined with less than 4% bias, but had 15-30% variability. Since washin is often the parameter of most use in clinical studies, the blind estimation approach may eliminate the current necessity of measuring the input function when performing certain dynamic studies
SURFACE VOLUME ESTIMATES FOR INFILTRATION PARAMETER ESTIMATION
Technology Transfer Automated Retrieval System (TEKTRAN)
Volume balance calculations used in surface irrigation engineering analysis require estimates of surface storage. These calculations are often performed by estimating upstream depth with a normal depth formula. That assumption can result in significant volume estimation errors when upstream flow d...
Measuring accurate body parameters of dressed humans with large-scale motion using a Kinect sensor.
Xu, Huanghao; Yu, Yao; Zhou, Yu; Li, Yang; Du, Sidan
2013-01-01
Non-contact human body measurement plays an important role in surveillance, physical healthcare, on-line business and virtual fitting. Current methods for measuring the human body without physical contact usually cannot handle humans wearing clothes, which limits their applicability in public environments. In this paper, we propose an effective solution that can measure accurate parameters of the human body with large-scale motion from a Kinect sensor, assuming that the people are wearing clothes. Because motion can drive clothes attached to the human body loosely or tightly, we adopt a space-time analysis to mine the information across the posture variations. Using this information, we recover the human body, regardless of the effect of clothes, and measure the human body parameters accurately. Experimental results show that our system can perform more accurate parameter estimation on the human body than state-of-the-art methods. PMID:24064597
Thermal Property Parameter Estimation of TPS Materials
NASA Technical Reports Server (NTRS)
Maddren, Jesse
1998-01-01
Accurate knowledge of the thermophysical properties of TPS (thermal protection system) materials is necessary for pre-flight design and post-flight data analysis. Thermal properties, such as thermal conductivity and the volumetric specific heat, can be estimated from transient temperature measurements using non-linear parameter estimation methods. Property values are derived by minimizing a functional of the differences between measured and calculated temperatures. High temperature thermal response testing of TPS materials is usually done in arc-jet or radiant heating facilities which provide a quasi one-dimensional heating environment. Last year, under the NASA-ASEE-Stanford Fellowship Program, my work focused on developing a radiant heating apparatus. This year, I have worked on increasing the fidelity of the experimental measurements, optimizing the experimental procedures and interpreting the data.
Parameter Estimation for Viscoplastic Material Modeling
NASA Technical Reports Server (NTRS)
Saleeb, Atef F.; Gendy, Atef S.; Wilt, Thomas E.
1997-01-01
A key ingredient in the design of engineering components and structures under general thermomechanical loading is the use of mathematical constitutive models (e.g. in finite element analysis) capable of accurate representation of short and long term stress/deformation responses. In addition to the ever-increasing complexity of recent viscoplastic models of this type, they often also require a large number of material constants to describe a host of (anticipated) physical phenomena and complicated deformation mechanisms. In turn, the experimental characterization of these material parameters constitutes the major factor in the successful and effective utilization of any given constitutive model; i.e., the problem of constitutive parameter estimation from experimental measurements.
Parameter Estimation of Spacecraft Fuel Slosh Model
NASA Technical Reports Server (NTRS)
Gangadharan, Sathya; Sudermann, James; Marlowe, Andrea; Njengam Charles
2004-01-01
Fuel slosh in the upper stages of a spinning spacecraft during launch has been a long standing concern for the success of a space mission. Energy loss through the movement of the liquid fuel in the fuel tank affects the gyroscopic stability of the spacecraft and leads to nutation (wobble) which can cause devastating control issues. The rate at which nutation develops (defined by Nutation Time Constant (NTC can be tedious to calculate and largely inaccurate if done during the early stages of spacecraft design. Pure analytical means of predicting the influence of onboard liquids have generally failed. A strong need exists to identify and model the conditions of resonance between nutation motion and liquid modes and to understand the general characteristics of the liquid motion that causes the problem in spinning spacecraft. A 3-D computerized model of the fuel slosh that accounts for any resonant modes found in the experimental testing will allow for increased accuracy in the overall modeling process. Development of a more accurate model of the fuel slosh currently lies in a more generalized 3-D computerized model incorporating masses, springs and dampers. Parameters describing the model include the inertia tensor of the fuel, spring constants, and damper coefficients. Refinement and understanding the effects of these parameters allow for a more accurate simulation of fuel slosh. The current research will focus on developing models of different complexity and estimating the model parameters that will ultimately provide a more realistic prediction of Nutation Time Constant obtained through simulation.
An Accurate Link Correlation Estimator for Improving Wireless Protocol Performance
Zhao, Zhiwei; Xu, Xianghua; Dong, Wei; Bu, Jiajun
2015-01-01
Wireless link correlation has shown significant impact on the performance of various sensor network protocols. Many works have been devoted to exploiting link correlation for protocol improvements. However, the effectiveness of these designs heavily relies on the accuracy of link correlation measurement. In this paper, we investigate state-of-the-art link correlation measurement and analyze the limitations of existing works. We then propose a novel lightweight and accurate link correlation estimation (LACE) approach based on the reasoning of link correlation formation. LACE combines both long-term and short-term link behaviors for link correlation estimation. We implement LACE as a stand-alone interface in TinyOS and incorporate it into both routing and flooding protocols. Simulation and testbed results show that LACE: (1) achieves more accurate and lightweight link correlation measurements than the state-of-the-art work; and (2) greatly improves the performance of protocols exploiting link correlation. PMID:25686314
An accurate link correlation estimator for improving wireless protocol performance.
Zhao, Zhiwei; Xu, Xianghua; Dong, Wei; Bu, Jiajun
2015-01-01
Wireless link correlation has shown significant impact on the performance of various sensor network protocols. Many works have been devoted to exploiting link correlation for protocol improvements. However, the effectiveness of these designs heavily relies on the accuracy of link correlation measurement. In this paper, we investigate state-of-the-art link correlation measurement and analyze the limitations of existing works. We then propose a novel lightweight and accurate link correlation estimation (LACE) approach based on the reasoning of link correlation formation. LACE combines both long-term and short-term link behaviors for link correlation estimation. We implement LACE as a stand-alone interface in TinyOS and incorporate it into both routing and flooding protocols. Simulation and testbed results show that LACE: (1) achieves more accurate and lightweight link correlation measurements than the state-of-the-art work; and (2) greatly improves the performance of protocols exploiting link correlation. PMID:25686314
Clinically accurate fetal ECG parameters acquired from maternal abdominal sensors
CLIFFORD, Gari; SAMENI, Reza; WARD, Mr. Jay; ROBINSON, Julian; WOLFBERG, Adam J.
2011-01-01
OBJECTIVE To evaluate the accuracy of a novel system for measuring fetal heart rate and ST-segment changes using non-invasive electrodes on the maternal abdomen. STUDY DESIGN Fetal ECGs were recorded using abdominal sensors from 32 term laboring women who had a fetal scalp electrode (FSE) placed for a clinical indication. RESULTS Good quality data for FHR estimation was available in 91.2% of the FSE segments, and 89.9% of the abdominal electrode segments. The root mean square (RMS) error between the FHR data calculated by both methods over all processed segments was 0.36 beats per minute. ST deviation from the isoelectric point ranged from 0 to 14.2% of R-wave amplitude. The RMS error between the ST change calculated by both methods averaged over all processed segments was 3.2%. CONCLUSION FHR and ST change acquired from the maternal abdomen is highly accurate and on average is clinically indistinguishable from FHR and ST change calculated using FSE data. PMID:21514560
Parameter estimation for lithium ion batteries
NASA Astrophysics Data System (ADS)
Santhanagopalan, Shriram
With an increase in the demand for lithium based batteries at the rate of about 7% per year, the amount of effort put into improving the performance of these batteries from both experimental and theoretical perspectives is increasing. There exist a number of mathematical models ranging from simple empirical models to complicated physics-based models to describe the processes leading to failure of these cells. The literature is also rife with experimental studies that characterize the various properties of the system in an attempt to improve the performance of lithium ion cells. However, very little has been done to quantify the experimental observations and relate these results to the existing mathematical models. In fact, the best of the physics based models in the literature show as much as 20% discrepancy when compared to experimental data. The reasons for such a big difference include, but are not limited to, numerical complexities involved in extracting parameters from experimental data and inconsistencies in interpreting directly measured values for the parameters. In this work, an attempt has been made to implement simplified models to extract parameter values that accurately characterize the performance of lithium ion cells. The validity of these models under a variety of experimental conditions is verified using a model discrimination procedure. Transport and kinetic properties are estimated using a non-linear estimation procedure. The initial state of charge inside each electrode is also maintained as an unknown parameter, since this value plays a significant role in accurately matching experimental charge/discharge curves with model predictions and is not readily known from experimental data. The second part of the dissertation focuses on parameters that change rapidly with time. For example, in the case of lithium ion batteries used in Hybrid Electric Vehicle (HEV) applications, the prediction of the State of Charge (SOC) of the cell under a variety of
Accurate Satellite-Derived Estimates of Tropospheric Ozone Radiative Forcing
NASA Technical Reports Server (NTRS)
Joiner, Joanna; Schoeberl, Mark R.; Vasilkov, Alexander P.; Oreopoulos, Lazaros; Platnick, Steven; Livesey, Nathaniel J.; Levelt, Pieternel F.
2008-01-01
Estimates of the radiative forcing due to anthropogenically-produced tropospheric O3 are derived primarily from models. Here, we use tropospheric ozone and cloud data from several instruments in the A-train constellation of satellites as well as information from the GEOS-5 Data Assimilation System to accurately estimate the instantaneous radiative forcing from tropospheric O3 for January and July 2005. We improve upon previous estimates of tropospheric ozone mixing ratios from a residual approach using the NASA Earth Observing System (EOS) Aura Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) by incorporating cloud pressure information from OMI. Since we cannot distinguish between natural and anthropogenic sources with the satellite data, our estimates reflect the total forcing due to tropospheric O3. We focus specifically on the magnitude and spatial structure of the cloud effect on both the shortand long-wave radiative forcing. The estimates presented here can be used to validate present day O3 radiative forcing produced by models.
Method for estimating solubility parameter
NASA Technical Reports Server (NTRS)
Lawson, D. D.; Ingham, J. D.
1973-01-01
Semiempirical correlations have been developed between solubility parameters and refractive indices for series of model hydrocarbon compounds and organic polymers. Measurement of intermolecular forces is useful for assessment of material compatibility, glass-transition temperature, and transport properties.
Predicting accurate line shape parameters for CO2 transitions
NASA Astrophysics Data System (ADS)
Gamache, Robert R.; Lamouroux, Julien
2013-11-01
The vibrational dependence of CO2 half-widths and line shifts are given by a modification of the model proposed by Gamache and Hartmann [Gamache R, Hartmann J-M. J Quant Spectrosc Radiat Transfer 2004;83:119]. This model allows the half-widths and line shifts for a ro-vibrational transition to be expressed in terms of the number of vibrational quanta exchanged in the transition raised to a power and a reference ro-vibrational transition. Calculations were made for 24 bands for lower rotational quantum numbers from 0 to 160 for N2-, O2-, air-, and self-collisions with CO2. These data were extrapolated to J″=200 to accommodate several databases. Comparison of the CRB calculations with measurement gives very high confidence in the data. In the model a Quantum Coordinate is defined by (c1 |Δν1|+c2 |Δν2|+c3|Δν3|)p. The power p is adjusted and a linear least-squares fit to the data by the model expression is made. The procedure is iterated on the correlation coefficient, R, until [|R|-1] is less than a threshold. The results demonstrate the appropriateness of the model. The model allows the determination of the slope and intercept as a function of rotational transition, broadening gas, and temperature. From the data of the fits, the half-width, line shift, and the temperature dependence of the half-width can be estimated for any ro-vibrational transition, allowing spectroscopic CO2 databases to have complete information for the line shape parameters.
Kalman filter data assimilation: Targeting observations and parameter estimation
Bellsky, Thomas Kostelich, Eric J.; Mahalov, Alex
2014-06-15
This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.
Accurate estimators of correlation functions in Fourier space
NASA Astrophysics Data System (ADS)
Sefusatti, E.; Crocce, M.; Scoccimarro, R.; Couchman, H. M. P.
2016-08-01
Efficient estimators of Fourier-space statistics for large number of objects rely on fast Fourier transforms (FFTs), which are affected by aliasing from unresolved small-scale modes due to the finite FFT grid. Aliasing takes the form of a sum over images, each of them corresponding to the Fourier content displaced by increasing multiples of the sampling frequency of the grid. These spurious contributions limit the accuracy in the estimation of Fourier-space statistics, and are typically ameliorated by simultaneously increasing grid size and discarding high-frequency modes. This results in inefficient estimates for e.g. the power spectrum when desired systematic biases are well under per cent level. We show that using interlaced grids removes odd images, which include the dominant contribution to aliasing. In addition, we discuss the choice of interpolation kernel used to define density perturbations on the FFT grid and demonstrate that using higher order interpolation kernels than the standard Cloud-In-Cell algorithm results in significant reduction of the remaining images. We show that combining fourth-order interpolation with interlacing gives very accurate Fourier amplitudes and phases of density perturbations. This results in power spectrum and bispectrum estimates that have systematic biases below 0.01 per cent all the way to the Nyquist frequency of the grid, thus maximizing the use of unbiased Fourier coefficients for a given grid size and greatly reducing systematics for applications to large cosmological data sets.
Parameter estimation by genetic algorithms
Reese, G.M.
1993-11-01
Test/Analysis correlation, or structural identification, is a process of reconciling differences in the structural dynamic models constructed analytically (using the finite element (FE) method) and experimentally (from modal test). This is a methodology for assessing the reliability of the computational model, and is very important in building models of high integrity, which may be used as predictive tools in design. Both the analytic and experimental models evaluate the same quantities: the natural frequencies (or eigenvalues, ({omega}{sub i}), and the mode shapes (or eigenvectors, {var_phi}). In this paper, selected frequencies are reconciled in the two models by modifying physical parameters in the FE model. A variety of parameters may be modified such as the stiffness of a joint member or the thickness of a plate. Engineering judgement is required to identify important frequencies, and to characterize the uncertainty of the model design parameters.
A parameter estimation subroutine package
NASA Technical Reports Server (NTRS)
Bierman, G. J.; Nead, M. W.
1978-01-01
Linear least squares estimation and regression analyses continue to play a major role in orbit determination and related areas. A library of FORTRAN subroutines were developed to facilitate analyses of a variety of estimation problems. An easy to use, multi-purpose set of algorithms that are reasonably efficient and which use a minimal amount of computer storage are presented. Subroutine inputs, outputs, usage and listings are given, along with examples of how these routines can be used. The routines are compact and efficient and are far superior to the normal equation and Kalman filter data processing algorithms that are often used for least squares analyses.
Identification of accurate nonlinear rainfall-runoff models with unique parameters
NASA Astrophysics Data System (ADS)
Schoups, G.; Vrugt, J. A.; Fenicia, F.; van de Giesen, N.
2009-04-01
We propose a strategy to identify models with unique parameters that yield accurate streamflow predictions, given a time-series of rainfall inputs. The procedure consists of five general steps. First, an a priori range of model structures is specified based on prior general and site-specific hydrologic knowledge. To this end, we rely on a flexible model code that allows a specification of a wide range of model structures, from simple to complex. Second, using global optimization each model structure is calibrated to a record of rainfall-runoff data, yielding optimal parameter values for each model structure. Third, accuracy of each model structure is determined by estimating model prediction errors using independent validation and statistical theory. Fourth, parameter identifiability of each calibrated model structure is estimated by means of Monte Carlo Markov Chain simulation. Finally, an assessment is made about each model structure in terms of its accuracy of mimicking rainfall-runoff processes (step 3), and the uniqueness of its parameters (step 4). The procedure results in the identification of the most complex and accurate model supported by the data, without causing parameter equifinality. As such, it provides insight into the information content of the data for identifying nonlinear rainfall-runoff models. We illustrate the method using rainfall-runoff data records from several MOPEX basins in the US.
Real-Time Parameter Estimation in the Frequency Domain
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.
2000-01-01
A method for real-time estimation of parameters in a linear dynamic state-space model was developed and studied. The application is aircraft dynamic model parameter estimation from measured data in flight. Equation error in the frequency domain was used with a recursive Fourier transform for the real-time data analysis. Linear and nonlinear simulation examples and flight test data from the F-18 High Alpha Research Vehicle were used to demonstrate that the technique produces accurate model parameter estimates with appropriate error bounds. Parameter estimates converged in less than one cycle of the dominant dynamic mode, using no a priori information, with control surface inputs measured in flight during ordinary piloted maneuvers. The real-time parameter estimation method has low computational requirements and could be implemented
Real-Time Parameter Estimation in the Frequency Domain
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.
1999-01-01
A method for real-time estimation of parameters in a linear dynamic state space model was developed and studied. The application is aircraft dynamic model parameter estimation from measured data in flight for indirect adaptive or reconfigurable control. Equation error in the frequency domain was used with a recursive Fourier transform for the real-time data analysis. Linear and nonlinear simulation examples and flight test data from the F-18 High Alpha Research Vehicle HARV) were used to demonstrate that the technique produces accurate model parameter estimates with appropriate error bounds. Parameter estimates converged in less than 1 cycle of the dominant dynamic mode natural frequencies, using control surface inputs measured in flight during ordinary piloted maneuvers. The real-time parameter estimation method has low computational requirements, and could be implemented aboard an aircraft in real time.
A parameter estimation subroutine package
NASA Technical Reports Server (NTRS)
Bierman, G. J.; Nead, M. W.
1978-01-01
Linear least squares estimation and regression analyses continue to play a major role in orbit determination and related areas. In this report we document a library of FORTRAN subroutines that have been developed to facilitate analyses of a variety of estimation problems. Our purpose is to present an easy to use, multi-purpose set of algorithms that are reasonably efficient and which use a minimal amount of computer storage. Subroutine inputs, outputs, usage and listings are given along with examples of how these routines can be used. The following outline indicates the scope of this report: Section (1) introduction with reference to background material; Section (2) examples and applications; Section (3) subroutine directory summary; Section (4) the subroutine directory user description with input, output, and usage explained; and Section (5) subroutine FORTRAN listings. The routines are compact and efficient and are far superior to the normal equation and Kalman filter data processing algorithms that are often used for least squares analyses.
Estimators for overdetermined linear Stokes parameters
NASA Astrophysics Data System (ADS)
Furey, John
2016-05-01
The mathematics of estimating overdetermined polarization parameters is worked out within the context of the inverse modeling of linearly polarized light, and as the primary new result the general solution is presented for estimators of the linear Stokes parameters from any number of measurements. The utility of the general solution is explored in several illustrative examples including the canonical case of two orthogonal pairs. In addition to the actual utility of these estimators in Stokes analysis, the pedagogical discussion illustrates many of the considerations involved in solving the ill-posed problem of overdetermined parameter estimation. Finally, suggestions are made for using a rapidly rotating polarizer for continuously updating polarization estimates.
Estimation of ground motion parameters
Boore, David M.; Joyner, W.B.; Oliver, A.A.; Page, R.A.
1978-01-01
Strong motion data from western North America for earthquakes of magnitude greater than 5 are examined to provide the basis for estimating peak acceleration, velocity, displacement, and duration as a function of distance for three magnitude classes. A subset of the data (from the San Fernando earthquake) is used to assess the effects of structural size and of geologic site conditions on peak motions recorded at the base of structures. Small but statistically significant differences are observed in peak values of horizontal acceleration, velocity and displacement recorded on soil at the base of small structures compared with values recorded at the base of large structures. The peak acceleration tends to b3e less and the peak velocity and displacement tend to be greater on the average at the base of large structures than at the base of small structures. In the distance range used in the regression analysis (15-100 km) the values of peak horizontal acceleration recorded at soil sites in the San Fernando earthquake are not significantly different from the values recorded at rock sites, but values of peak horizontal velocity and displacement are significantly greater at soil sites than at rock sites. Some consideration is given to the prediction of ground motions at close distances where there are insufficient recorded data points. As might be expected from the lack of data, published relations for predicting peak horizontal acceleration give widely divergent estimates at close distances (three well known relations predict accelerations between 0.33 g to slightly over 1 g at a distance of 5 km from a magnitude 6.5 earthquake). After considering the physics of the faulting process, the few available data close to faults, and the modifying effects of surface topography, at the present time it would be difficult to accept estimates less than about 0.8 g, 110 cm/s, and 40 cm, respectively, for the mean values of peak acceleration, velocity, and displacement at rock sites
Accuracy of Aerodynamic Model Parameters Estimated from Flight Test Data
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.; Klein, Vladislav
1997-01-01
An important put of building mathematical models based on measured date is calculating the accuracy associated with statistical estimates of the model parameters. Indeed, without some idea of this accuracy, the parameter estimates themselves have limited value. An expression is developed for computing quantitatively correct parameter accuracy measures for maximum likelihood parameter estimates when the output residuals are colored. This result is important because experience in analyzing flight test data reveals that the output residuals from maximum likelihood estimation are almost always colored. The calculations involved can be appended to conventional maximum likelihood estimation algorithms. Monte Carlo simulation runs were used to show that parameter accuracy measures from the new technique accurately reflect the quality of the parameter estimates from maximum likelihood estimation without the need for correction factors or frequency domain analysis of the output residuals. The technique was applied to flight test data from repeated maneuvers flown on the F-18 High Alpha Research Vehicle. As in the simulated cases, parameter accuracy measures from the new technique were in agreement with the scatter in the parameter estimates from repeated maneuvers, whereas conventional parameter accuracy measures were optimistic.
ESTIM: A parameter estimation computer program: Final report
Hills, R.G.
1987-08-01
The computer code, ESTIM, enables subroutine versions of existing simulation codes to be used to estimate model parameters. Nonlinear least squares techniques are used to find the parameter values that result in a best fit between measurements made in the simulation domain and the simulation code's prediction of these measurements. ESTIM utilizes the non-linear least square code DQED (Hanson and Krogh (1982)) to handle the optimization aspects of the estimation problem. In addition to providing weighted least squares estimates, ESTIM provides a propagation of variance analysis. A subroutine version of COYOTE (Gartling (1982)) is provided. The use of ESTIM with COYOTE allows one to estimate the thermal property model parameters that result in the best agreement (in a least squares sense) between internal temperature measurements and COYOTE's predictions of these internal temperature measurements. We demonstrate the use of ESTIM through several example problems which utilize the subroutine version of COYOTE.
Accurate lattice parameter measurements of stoichiometric uranium dioxide
NASA Astrophysics Data System (ADS)
Leinders, Gregory; Cardinaels, Thomas; Binnemans, Koen; Verwerft, Marc
2015-04-01
The paper presents and discusses lattice parameter analyses of pure, stoichiometric UO2. Attention was paid to prepare stoichiometric samples and to maintain stoichiometry throughout the analyses. The lattice parameter of UO2.000±0.001 was evaluated as being 547.127 ± 0.008 pm at 20 °C, which is substantially higher than many published values for the UO2 lattice constant and has an improved precision by about one order of magnitude. The higher value of the lattice constant is mainly attributed to the avoidance of hyperstoichiometry in the present study and to a minor extent to the use of the currently accepted Cu Kα1 X-ray wavelength value. Many of the early studies used Cu Kα1 wavelength values that differ from the currently accepted value, which also contributed to an underestimation of the true lattice parameter.
RECURSIVE PARAMETER ESTIMATION OF HYDROLOGIC MODELS
Proposed is a nonlinear filtering approach to recursive parameter estimation of conceptual watershed response models in state-space form. he conceptual model state is augmented by the vector of free parameters which are to be estimated from input-output data, and the extended Kal...
Estimation of ground motion parameters
Boore, David M.; Oliver, Adolph A., III; Page, Robert A.; Joyner, William B.
1978-01-01
Strong motion data from western North America for earthquakes of magnitude greater than 5 are examined to provide the basis for estimating peak acceleration, velocity, displacement, and duration as a function of distance for three magnitude classes. Data from the San Fernando earthquake are examined to assess the effects of associated structures and of geologic site conditions on peak recorded motions. Small but statistically significant differences are observed in peak values of horizontal acceleration, velocity, and displacement recorded on soil at the base of small structures compared with values recorded at the base of large structures. Values of peak horizontal acceleration recorded at soil sites in the San Fernando earthquake are not significantly different from the values recorded at rock sites, but values of peak horizontal velocity and displacement are significantly greater at soil sites than at rock sites. Three recently published relationships for predicting peak horizontal acceleration are compared and discussed. Considerations are reviewed relevant to ground motion predictions at close distances where there are insufficient recorded data points.
Accurate Orientation Estimation Using AHRS under Conditions of Magnetic Distortion
Yadav, Nagesh; Bleakley, Chris
2014-01-01
Low cost, compact attitude heading reference systems (AHRS) are now being used to track human body movements in indoor environments by estimation of the 3D orientation of body segments. In many of these systems, heading estimation is achieved by monitoring the strength of the Earth's magnetic field. However, the Earth's magnetic field can be locally distorted due to the proximity of ferrous and/or magnetic objects. Herein, we propose a novel method for accurate 3D orientation estimation using an AHRS, comprised of an accelerometer, gyroscope and magnetometer, under conditions of magnetic field distortion. The system performs online detection and compensation for magnetic disturbances, due to, for example, the presence of ferrous objects. The magnetic distortions are detected by exploiting variations in magnetic dip angle, relative to the gravity vector, and in magnetic strength. We investigate and show the advantages of using both magnetic strength and magnetic dip angle for detecting the presence of magnetic distortions. The correction method is based on a particle filter, which performs the correction using an adaptive cost function and by adapting the variance during particle resampling, so as to place more emphasis on the results of dead reckoning of the gyroscope measurements and less on the magnetometer readings. The proposed method was tested in an indoor environment in the presence of various magnetic distortions and under various accelerations (up to 3 g). In the experiments, the proposed algorithm achieves <2° static peak-to-peak error and <5° dynamic peak-to-peak error, significantly outperforming previous methods. PMID:25347584
Parameter estimation of qubit states with unknown phase parameter
NASA Astrophysics Data System (ADS)
Suzuki, Jun
2015-02-01
We discuss a problem of parameter estimation for quantum two-level system, qubit system, in presence of unknown phase parameter. We analyze trade-off relations for mean square errors (MSEs) when estimating relevant parameters with separable measurements based on known precision bounds; the symmetric logarithmic derivative (SLD) Cramér-Rao (CR) bound and Hayashi-Gill-Massar (HGM) bound. We investigate the optimal measurement which attains the HGM bound and discuss its properties. We show that the HGM bound for relevant parameters can be attained asymptotically by using some fraction of given n quantum states to estimate the phase parameter. We also discuss the Holevo bound which can be attained asymptotically by a collective measurement.
Robust parameter estimation method for bilinear model
NASA Astrophysics Data System (ADS)
Ismail, Mohd Isfahani; Ali, Hazlina; Yahaya, Sharipah Soaad S.
2015-12-01
This paper proposed the method of parameter estimation for bilinear model, especially on BL(1,0,1,1) model without and with the presence of additive outlier (AO). In this study, the estimated parameters for BL(1,0,1,1) model are using nonlinear least squares (LS) method and also through robust approaches. The LS method employs the Newton-Raphson (NR) iterative procedure in estimating the parameters of bilinear model, but, using LS in estimating the parameters can be affected with the occurrence of outliers. As a solution, this study proposed robust approaches in dealing with the problem of outliers specifically on AO in BL(1,0,1,1) model. In robust estimation method, for improvement, we proposed to modify the NR procedure with robust scale estimators. We introduced two robust scale estimators namely median absolute deviation (MADn) and Tn in linear autoregressive model, AR(1) that be adequate and suitable for bilinear BL(1,0,1,1) model. We used the estimated parameter value in AR(1) model as an initial value in estimating the parameter values of BL(1,0,1,1) model. The investigation of the performance of LS and robust estimation methods in estimating the coefficients of BL(1,0,1,1) model is carried out through simulation study. The achievement of performance for both methods will be assessed in terms of bias values. Numerical results present that, the robust estimation method performs better than LS method in estimating the parameters without and with the presence of AO.
Parameter Estimation of Partial Differential Equation Models
Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J.; Maity, Arnab
2013-01-01
Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown, and need to be estimated from the measurements of the dynamic system in the present of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE, and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from LIDAR data. PMID:24363476
Parameter Estimation of Partial Differential Equation Models.
Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J; Maity, Arnab
2013-01-01
Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown, and need to be estimated from the measurements of the dynamic system in the present of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE, and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from LIDAR data. PMID:24363476
Practical Aspects of the Equation-Error Method for Aircraft Parameter Estimation
NASA Technical Reports Server (NTRS)
Morelli, Eugene a.
2006-01-01
Various practical aspects of the equation-error approach to aircraft parameter estimation were examined. The analysis was based on simulated flight data from an F-16 nonlinear simulation, with realistic noise sequences added to the computed aircraft responses. This approach exposes issues related to the parameter estimation techniques and results, because the true parameter values are known for simulation data. The issues studied include differentiating noisy time series, maximum likelihood parameter estimation, biases in equation-error parameter estimates, accurate computation of estimated parameter error bounds, comparisons of equation-error parameter estimates with output-error parameter estimates, analyzing data from multiple maneuvers, data collinearity, and frequency-domain methods.
How Accurately Do Spectral Methods Estimate Effective Elastic Thickness?
NASA Astrophysics Data System (ADS)
Perez-Gussinye, M.; Lowry, A. R.; Watts, A. B.; Velicogna, I.
2002-12-01
The effective elastic thickness, Te, is an important parameter that has the potential to provide information on the long-term thermal and mechanical properties of the the lithosphere. Previous studies have estimated Te using both forward and inverse (spectral) methods. While there is generally good agreement between the results obtained using these methods, spectral methods are limited because they depend on the spectral estimator and the window size chosen for analysis. In order to address this problem, we have used a multitaper technique which yields optimal estimates of the bias and variance of the Bouguer coherence function relating topography and gravity anomaly data. The technique has been tested using realistic synthetic topography and gravity. Synthetic data were generated assuming surface and sub-surface (buried) loading of an elastic plate with fractal statistics consistent with real data sets. The cases of uniform and spatially varying Te are examined. The topography and gravity anomaly data consist of 2000x2000 km grids sampled at 8 km interval. The bias in the Te estimate is assessed from the difference between the true Te value and the mean from analyzing 100 overlapping windows within the 2000x2000 km data grids. For the case in which Te is uniform, the bias and variance decrease with window size and increase with increasing true Te value. In the case of a spatially varying Te, however, there is a trade-off between spatial resolution and variance. With increasing window size the variance of the Te estimate decreases, but the spatial changes in Te are smeared out. We find that for a Te distribution consisting of a strong central circular region of Te=50 km (radius 600 km) and progressively smaller Te towards its edges, the 800x800 and 1000x1000 km window gave the best compromise between spatial resolution and variance. Our studies demonstrate that assumed stationarity of the relationship between gravity and topography data yields good results even in
Parameter Estimation in Atmospheric Data Sets
NASA Technical Reports Server (NTRS)
Wenig, Mark; Colarco, Peter
2004-01-01
In this study the structure tensor technique is used to estimate dynamical parameters in atmospheric data sets. The structure tensor is a common tool for estimating motion in image sequences. This technique can be extended to estimate other dynamical parameters such as diffusion constants or exponential decay rates. A general mathematical framework was developed for the direct estimation of the physical parameters that govern the underlying processes from image sequences. This estimation technique can be adapted to the specific physical problem under investigation, so it can be used in a variety of applications in trace gas, aerosol, and cloud remote sensing. As a test scenario this technique will be applied to modeled dust data. In this case vertically integrated dust concentrations were used to derive wind information. Those results can be compared to the wind vector fields which served as input to the model. Based on this analysis, a method to compute atmospheric data parameter fields will be presented. .
Parameter estimation for distributed parameter models of complex, flexible structures
NASA Technical Reports Server (NTRS)
Taylor, Lawrence W., Jr.
1991-01-01
Distributed parameter modeling of structural dynamics has been limited to simple spacecraft configurations because of the difficulty of handling several distributed parameter systems linked at their boundaries. Although there is other computer software able to generate such models or complex, flexible spacecraft, unfortunately, neither is suitable for parameter estimation. Because of this limitation the computer software PDEMOD is being developed for the express purposes of modeling, control system analysis, parameter estimation and structure optimization. PDEMOD is capable of modeling complex, flexible spacecraft which consist of a three-dimensional network of flexible beams and rigid bodies. Each beam has bending (Bernoulli-Euler or Timoshenko) in two directions, torsion, and elongation degrees of freedom. The rigid bodies can be attached to the beam ends at any angle or body location. PDEMOD is also capable of performing parameter estimation based on matching experimental modal frequencies and static deflection test data. The underlying formulation and the results of using this approach for test data of the Mini-MAST truss will be discussed. The resulting accuracy of the parameter estimates when using such limited data can impact significantly the instrumentation requirements for on-orbit tests.
DEB parameters estimation for Mytilus edulis
NASA Astrophysics Data System (ADS)
Saraiva, S.; van der Meer, J.; Kooijman, S. A. L. M.; Sousa, T.
2011-11-01
The potential of DEB theory to simulate an organism life-cycle has been demonstrated at numerous occasions. However, its applicability requires parameter estimates that are not easily obtained by direct observations. During the last years various attempts were made to estimate the main DEB parameters for bivalve species. The estimation procedure was by then, however, rather ad-hoc and based on additional assumptions that were not always consistent with the DEB theory principles. A new approach has now been developed - the covariation method - based on simultaneous minimization of the weighted sum of squared deviations between data sets and model predictions in one single procedure. This paper presents the implementation of this method to estimate the DEB parameters for the blue mussel Mytilus edulis, using several data sets from the literature. After comparison with previous trials we conclude that the parameter set obtained by the covariation method leads to a better fit between model and observations, with potentially more consistency and robustness.
Effects of Structural Errors on Parameter Estimates
NASA Technical Reports Server (NTRS)
Hadaegh, F. Y.; Bekey, G. A.
1987-01-01
Paper introduces concept of near equivalence in probability between different parameters or mathematical models of physical system. One in series of papers, each establishes different part of rigorous theory of mathematical modeling based on concepts of structural error, identifiability, and equivalence. This installment focuses upon effects of additive structural errors on degree of bias in estimates parameters.
MODFLOW-Style parameters in underdetermined parameter estimation.
D'Oria, Marco; Fienen, Michael N
2012-01-01
In this article, we discuss the use of MODFLOW-Style parameters in the numerical codes MODFLOW_2005 and MODFLOW_2005-Adjoint for the definition of variables in the Layer Property Flow package. Parameters are a useful tool to represent aquifer properties in both codes and are the only option available in the adjoint version. Moreover, for overdetermined parameter estimation problems, the parameter approach for model input can make data input easier. We found that if each estimable parameter is defined by one parameter, the codes require a large computational effort and substantial gains in efficiency are achieved by removing logical comparison of character strings that represent the names and types of the parameters. An alternative formulation already available in the current implementation of the code can also alleviate the efficiency degradation due to character comparisons in the special case of distributed parameters defined through multiplication matrices. The authors also hope that lessons learned in analyzing the performance of the MODFLOW family codes will be enlightening to developers of other Fortran implementations of numerical codes. PMID:21352210
MODFLOW-style parameters in underdetermined parameter estimation
D'Oria, Marco D.; Fienen, Michael J.
2012-01-01
In this article, we discuss the use of MODFLOW-Style parameters in the numerical codes MODFLOW_2005 and MODFLOW_2005-Adjoint for the definition of variables in the Layer Property Flow package. Parameters are a useful tool to represent aquifer properties in both codes and are the only option available in the adjoint version. Moreover, for overdetermined parameter estimation problems, the parameter approach for model input can make data input easier. We found that if each estimable parameter is defined by one parameter, the codes require a large computational effort and substantial gains in efficiency are achieved by removing logical comparison of character strings that represent the names and types of the parameters. An alternative formulation already available in the current implementation of the code can also alleviate the efficiency degradation due to character comparisons in the special case of distributed parameters defined through multiplication matrices. The authors also hope that lessons learned in analyzing the performance of the MODFLOW family codes will be enlightening to developers of other Fortran implementations of numerical codes.
MODFLOW-style parameters in underdetermined parameter estimation
D'Oria, M.; Fienen, M.N.
2012-01-01
In this article, we discuss the use of MODFLOW-Style parameters in the numerical codes MODFLOW-2005 and MODFLOW-2005-Adjoint for the definition of variables in the Layer Property Flow package. Parameters are a useful tool to represent aquifer properties in both codes and are the only option available in the adjoint version. Moreover, for overdetermined parameter estimation problems, the parameter approach for model input can make data input easier. We found that if each estimable parameter is defined by one parameter, the codes require a large computational effort and substantial gains in efficiency are achieved by removing logical comparison of character strings that represent the names and types of the parameters. An alternative formulation already available in the current implementation of the code can also alleviate the efficiency degradation due to character comparisons in the special case of distributed parameters defined through multiplication matrices. The authors also hope that lessons learned in analyzing the performance of the MODFLOW family codes will be enlightening to developers of other Fortran implementations of numerical codes. ?? 2011, National Ground Water Association.
GEODYN- ORBITAL AND GEODETIC PARAMETER ESTIMATION
NASA Technical Reports Server (NTRS)
Putney, B.
1994-01-01
The Orbital and Geodetic Parameter Estimation program, GEODYN, possesses the capability to estimate that set of orbital elements, station positions, measurement biases, and a set of force model parameters such that the orbital tracking data from multiple arcs of multiple satellites best fits the entire set of estimation parameters. The estimation problem can be divided into two parts: the orbit prediction problem, and the parameter estimation problem. GEODYN solves these two problems by employing Cowell's method for integrating the orbit and a Bayesian least squares statistical estimation procedure for parameter estimation. GEODYN has found a wide range of applications including determination of definitive orbits, tracking instrumentation calibration, satellite operational predictions, and geodetic parameter estimation, such as the estimations for global networks of tracking stations. The orbit prediction problem may be briefly described as calculating for some later epoch the new conditions of state for the satellite, given a set of initial conditions of state for some epoch, and the disturbing forces affecting the motion of the satellite. The user is required to supply only the initial conditions of state and GEODYN will provide the forcing function and integrate the equations of motion of the satellite. Additionally, GEODYN performs time and coordinate transformations to insure the continuity of operations. Cowell's method of numerical integration is used to solve the satellite equations of motion and the variational partials for force model parameters which are to be adjusted. This method uses predictor-corrector formulas for the equations of motion and corrector formulas only for the variational partials. The parameter estimation problem is divided into three separate parts: 1) instrument measurement modeling and partial derivative computation, 2) data error correction, and 3) statistical estimation of the parameters. Since all of the measurements modeled by
Bayesian auxiliary particle filters for estimating neural tuning parameters.
Mountney, John; Sobel, Marc; Obeid, Iyad
2009-01-01
A common challenge in neural engineering is to track the dynamic parameters of neural tuning functions. This work introduces the application of Bayesian auxiliary particle filters for this purpose. Based on Monte-Carlo filtering, Bayesian auxiliary particle filters use adaptive methods to model the prior densities of the state parameters being tracked. The observations used are the neural firing times, modeled here as a Poisson process, and the biological driving signal. The Bayesian auxiliary particle filter was evaluated by simultaneously tracking the three parameters of a hippocampal place cell and compared to a stochastic state point process filter. It is shown that Bayesian auxiliary particle filters are substantially more accurate and robust than alternative methods of state parameter estimation. The effects of time-averaging on parameter estimation are also evaluated. PMID:19963911
An investigation of new methods for estimating parameter sensitivities
NASA Technical Reports Server (NTRS)
Beltracchi, Todd J.; Gabriele, Gary A.
1989-01-01
The method proposed for estimating sensitivity derivatives is based on the Recursive Quadratic Programming (RQP) method and in conjunction a differencing formula to produce estimates of the sensitivities. This method is compared to existing methods and is shown to be very competitive in terms of the number of function evaluations required. In terms of accuracy, the method is shown to be equivalent to a modified version of the Kuhn-Tucker method, where the Hessian of the Lagrangian is estimated using the BFS method employed by the RQP algorithm. Initial testing on a test set with known sensitivities demonstrates that the method can accurately calculate the parameter sensitivity.
Fast and Accurate Learning When Making Discrete Numerical Estimates.
Sanborn, Adam N; Beierholm, Ulrik R
2016-04-01
Many everyday estimation tasks have an inherently discrete nature, whether the task is counting objects (e.g., a number of paint buckets) or estimating discretized continuous variables (e.g., the number of paint buckets needed to paint a room). While Bayesian inference is often used for modeling estimates made along continuous scales, discrete numerical estimates have not received as much attention, despite their common everyday occurrence. Using two tasks, a numerosity task and an area estimation task, we invoke Bayesian decision theory to characterize how people learn discrete numerical distributions and make numerical estimates. Across three experiments with novel stimulus distributions we found that participants fell between two common decision functions for converting their uncertain representation into a response: drawing a sample from their posterior distribution and taking the maximum of their posterior distribution. While this was consistent with the decision function found in previous work using continuous estimation tasks, surprisingly the prior distributions learned by participants in our experiments were much more adaptive: When making continuous estimates, participants have required thousands of trials to learn bimodal priors, but in our tasks participants learned discrete bimodal and even discrete quadrimodal priors within a few hundred trials. This makes discrete numerical estimation tasks good testbeds for investigating how people learn and make estimates. PMID:27070155
Fast and Accurate Learning When Making Discrete Numerical Estimates
Sanborn, Adam N.; Beierholm, Ulrik R.
2016-01-01
Many everyday estimation tasks have an inherently discrete nature, whether the task is counting objects (e.g., a number of paint buckets) or estimating discretized continuous variables (e.g., the number of paint buckets needed to paint a room). While Bayesian inference is often used for modeling estimates made along continuous scales, discrete numerical estimates have not received as much attention, despite their common everyday occurrence. Using two tasks, a numerosity task and an area estimation task, we invoke Bayesian decision theory to characterize how people learn discrete numerical distributions and make numerical estimates. Across three experiments with novel stimulus distributions we found that participants fell between two common decision functions for converting their uncertain representation into a response: drawing a sample from their posterior distribution and taking the maximum of their posterior distribution. While this was consistent with the decision function found in previous work using continuous estimation tasks, surprisingly the prior distributions learned by participants in our experiments were much more adaptive: When making continuous estimates, participants have required thousands of trials to learn bimodal priors, but in our tasks participants learned discrete bimodal and even discrete quadrimodal priors within a few hundred trials. This makes discrete numerical estimation tasks good testbeds for investigating how people learn and make estimates. PMID:27070155
Estimation of saxophone reed parameters during playing.
Muñoz Arancón, Alberto; Gazengel, Bruno; Dalmont, Jean-Pierre; Conan, Ewen
2016-05-01
An approach for the estimation of single reed parameters during playing, using an instrumented mouthpiece and an iterative method, is presented. Different physical models describing the reed tip movement are tested in the estimation method. The uncertainties of the sensors installed on the mouthpiece and the limits of the estimation method are studied. A tenor saxophone reed is mounted on this mouthpiece connected to a cylinder, played by a musician, and characterized at different dynamic levels. Results show that the method can be used to estimate the reed parameters with a small error for low and medium sound levels (piano and mezzoforte dynamic levels). The analysis reveals that the complexity of the physical model describing the reed behavior must increase with dynamic levels. For medium level dynamics, the most relevant physical model assumes that the reed is an oscillator with non-linear stiffness and damping, the effect of mass (inertia) being very small. PMID:27250168
Parameter inference with estimated covariance matrices
NASA Astrophysics Data System (ADS)
Sellentin, Elena; Heavens, Alan F.
2016-02-01
When inferring parameters from a Gaussian-distributed data set by computing a likelihood, a covariance matrix is needed that describes the data errors and their correlations. If the covariance matrix is not known a priori, it may be estimated and thereby becomes a random object with some intrinsic uncertainty itself. We show how to infer parameters in the presence of such an estimated covariance matrix, by marginalizing over the true covariance matrix, conditioned on its estimated value. This leads to a likelihood function that is no longer Gaussian, but rather an adapted version of a multivariate t-distribution, which has the same numerical complexity as the multivariate Gaussian. As expected, marginalization over the true covariance matrix improves inference when compared with Hartlap et al.'s method, which uses an unbiased estimate of the inverse covariance matrix but still assumes that the likelihood is Gaussian.
LISA Parameter Estimation using Numerical Merger Waveforms
NASA Technical Reports Server (NTRS)
Thorpe, J. I.; McWilliams, S.; Baker, J.
2008-01-01
Coalescing supermassive black holes are expected to provide the strongest sources for gravitational radiation detected by LISA. Recent advances in numerical relativity provide a detailed description of the waveforms of such signals. We present a preliminary study of LISA's sensitivity to waveform parameters using a hybrid numerical/analytic waveform describing the coalescence of two equal-mass, nonspinning black holes. The Synthetic LISA software package is used to simulate the instrument response and the Fisher information matrix method is used to estimate errors in the waveform parameters. Initial results indicate that inclusion of the merger signal can significantly improve the precision of some parameter estimates. For example, the median parameter errors for an ensemble of systems with total redshifted mass of 10(exp 6) deg M solar mass at a redshift of z is approximately 1 were found to decrease by a factor of slightly more than two when the merger was included.
Bioaccessibility tests accurately estimate bioavailability of lead to quail
Technology Transfer Automated Retrieval System (TEKTRAN)
Hazards of soil-borne Pb to wild birds may be more accurately quantified if the bioavailability of that Pb is known. To better understand the bioavailability of Pb, we incorporated Pb-contaminated soils or Pb acetate into diets for Japanese quail (Coturnix japonica), fed the quail for 15 days, and ...
BIOACCESSIBILITY TESTS ACCURATELY ESTIMATE BIOAVAILABILITY OF LEAD TO QUAIL
Hazards of soil-borne Pb to wild birds may be more accurately quantified if the bioavailability of that Pb is known. To better understand the bioavailability of Pb to birds, we measured blood Pb concentrations in Japanese quail (Coturnix japonica) fed diets containing Pb-contami...
Bayesian parameter estimation for effective field theories
NASA Astrophysics Data System (ADS)
Wesolowski, Sarah; Klco, Natalie; Furnstahl, Richard; Phillips, Daniel; Thapilaya, Arbin
2015-10-01
We present a procedure based on Bayesian statistics for effective field theory (EFT) parameter estimation from experimental or lattice data. The extraction of low-energy constants (LECs) is guided by physical principles such as naturalness in a quantifiable way and various sources of uncertainty are included by the specification of Bayesian priors. Special issues for EFT parameter estimation are demonstrated using representative model problems, and a set of diagnostics is developed to isolate and resolve these issues. We apply the framework to the extraction of the LECs of the nucleon mass expansion in SU(2) chiral perturbation theory from synthetic lattice data.
A novel multistage estimation of signal parameters
NASA Technical Reports Server (NTRS)
Kumar, Rajendra
1990-01-01
A multistage estimation scheme is presented for estimating the parameters of a received carrier signal possibly phase-modulated by unknown data and experiencing very high Doppler, Doppler rate, etc. Such a situation arises, for example, in the case of the Global Positioning Systems (GPS). In the proposed scheme, the first-stage estimator operates as a coarse estimator of the frequency and its derivatives, resulting in higher rms estimation errors but with a relatively small probability of the frequency estimation error exceeding one-half of the sampling frequency (an event termed cycle slip). The second stage of the estimator operates on the error signal available from the first stage, refining the overall estimates, and in the process also reduces the number of cycle slips. The first-stage algorithm is a modified least-squares algorithm operating on the differential signal model and referred to as differential least squares (DLS). The second-stage algorithm is an extended Kalman filter, which yields the estimate of the phase as well as refining the frequency estimate. A major advantage of the is a reduction in the threshold for the received carrier power-to-noise power spectral density ratio (CNR) as compared with the threshold achievable by either of the algorithms alone.
Estimation of Time-Varying Pilot Model Parameters
NASA Technical Reports Server (NTRS)
Zaal, Peter M. T.; Sweet, Barbara T.
2011-01-01
Human control behavior is rarely completely stationary over time due to fatigue or loss of attention. In addition, there are many control tasks for which human operators need to adapt their control strategy to vehicle dynamics that vary in time. In previous studies on the identification of time-varying pilot control behavior wavelets were used to estimate the time-varying frequency response functions. However, the estimation of time-varying pilot model parameters was not considered. Estimating these parameters can be a valuable tool for the quantification of different aspects of human time-varying manual control. This paper presents two methods for the estimation of time-varying pilot model parameters, a two-step method using wavelets and a windowed maximum likelihood estimation method. The methods are evaluated using simulations of a closed-loop control task with time-varying pilot equalization and vehicle dynamics. Simulations are performed with and without remnant. Both methods give accurate results when no pilot remnant is present. The wavelet transform is very sensitive to measurement noise, resulting in inaccurate parameter estimates when considerable pilot remnant is present. Maximum likelihood estimation is less sensitive to pilot remnant, but cannot detect fast changes in pilot control behavior.
ZASPE: Zonal Atmospheric Stellar Parameters Estimator
NASA Astrophysics Data System (ADS)
Brahm, Rafael; Jordan, Andres; Hartman, Joel; Bakos, Gaspar
2016-07-01
ZASPE (Zonal Atmospheric Stellar Parameters Estimator) computes the atmospheric stellar parameters (Teff, log(g), [Fe/H] and vsin(i)) from echelle spectra via least squares minimization with a pre-computed library of synthetic spectra. The minimization is performed only in the most sensitive spectral zones to changes in the atmospheric parameters. The uncertainities and covariances computed by ZASPE assume that the principal source of error is the systematic missmatch between the observed spectrum and the sythetic one that produces the best fit. ZASPE requires a grid of synthetic spectra and can use any pre-computed library minor modifications.
Tube-Load Model Parameter Estimation for Monitoring Arterial Hemodynamics
Zhang, Guanqun; Hahn, Jin-Oh; Mukkamala, Ramakrishna
2011-01-01
A useful model of the arterial system is the uniform, lossless tube with parametric load. This tube-load model is able to account for wave propagation and reflection (unlike lumped-parameter models such as the Windkessel) while being defined by only a few parameters (unlike comprehensive distributed-parameter models). As a result, the parameters may be readily estimated by accurate fitting of the model to available arterial pressure and flow waveforms so as to permit improved monitoring of arterial hemodynamics. In this paper, we review tube-load model parameter estimation techniques that have appeared in the literature for monitoring wave reflection, large artery compliance, pulse transit time, and central aortic pressure. We begin by motivating the use of the tube-load model for parameter estimation. We then describe the tube-load model, its assumptions and validity, and approaches for estimating its parameters. We next summarize the various techniques and their experimental results while highlighting their advantages over conventional techniques. We conclude the review by suggesting future research directions and describing potential applications. PMID:22053157
New approaches to estimation of magnetotelluric parameters
Egbert, G.D.
1991-01-01
Fully efficient robust data processing procedures were developed and tested for single station and remote reference magnetotelluric (Mr) data. Substantial progress was made on development, testing and comparison of optimal procedures for single station data. A principal finding of this phase of the research was that the simplest robust procedures can be more heavily biased by noise in the (input) magnetic fields, than standard least squares estimates. To deal with this difficulty we developed a robust processing scheme which combined the regression M-estimate with coherence presorting. This hybrid approach greatly improves impedance estimates, particularly in the low signal-to-noise conditions often encountered in the dead band'' (0.1--0.0 hz). The methods, and the results of comparisons of various single station estimators are described in detail. Progress was made on developing methods for estimating static distortion parameters, and for testing hypotheses about the underlying dimensionality of the geological section.
Hwang, Beomsoo; Jeon, Doyoung
2015-01-01
In exoskeletal robots, the quantification of the user's muscular effort is important to recognize the user's motion intentions and evaluate motor abilities. In this paper, we attempt to estimate users' muscular efforts accurately using joint torque sensor which contains the measurements of dynamic effect of human body such as the inertial, Coriolis, and gravitational torques as well as torque by active muscular effort. It is important to extract the dynamic effects of the user's limb accurately from the measured torque. The user's limb dynamics are formulated and a convenient method of identifying user-specific parameters is suggested for estimating the user's muscular torque in robotic exoskeletons. Experiments were carried out on a wheelchair-integrated lower limb exoskeleton, EXOwheel, which was equipped with torque sensors in the hip and knee joints. The proposed methods were evaluated by 10 healthy participants during body weight-supported gait training. The experimental results show that the torque sensors are to estimate the muscular torque accurately in cases of relaxed and activated muscle conditions. PMID:25860074
Effective Echo Detection and Accurate Orbit Estimation Algorithms for Space Debris Radar
NASA Astrophysics Data System (ADS)
Isoda, Kentaro; Sakamoto, Takuya; Sato, Toru
Orbit estimation of space debris, objects of no inherent value orbiting the earth, is a task that is important for avoiding collisions with spacecraft. The Kamisaibara Spaceguard Center radar system was built in 2004 as the first radar facility in Japan devoted to the observation of space debris. In order to detect the smaller debris, coherent integration is effective in improving SNR (Signal-to-Noise Ratio). However, it is difficult to apply coherent integration to real data because the motions of the targets are unknown. An effective algorithm is proposed for echo detection and orbit estimation of the faint echoes from space debris. The characteristics of the evaluation function are utilized by the algorithm. Experiments show the proposed algorithm improves SNR by 8.32dB and enables estimation of orbital parameters accurately to allow for re-tracking with a single radar.
Estimating physiological skin parameters from hyperspectral signatures.
Vyas, Saurabh; Banerjee, Amit; Burlina, Philippe
2013-05-01
We describe an approach for estimating human skin parameters, such as melanosome concentration, collagen concentration, oxygen saturation, and blood volume, using hyperspectral radiometric measurements (signatures) obtained from in vivo skin. We use a computational model based on Kubelka-Munk theory and the Fresnel equations. This model forward maps the skin parameters to a corresponding multiband reflectance spectra. Machine-learning-based regression is used to generate the inverse map, and hence estimate skin parameters from hyperspectral signatures. We test our methods using synthetic and in vivo skin signatures obtained in the visible through the short wave infrared domains from 24 patients of both genders and Caucasian, Asian, and African American ethnicities. Performance validation shows promising results: good agreement with the ground truth and well-established physiological precepts. These methods have potential use in the characterization of skin abnormalities and in minimally-invasive prescreening of malignant skin cancers. PMID:23722495
Estimating physiological skin parameters from hyperspectral signatures
NASA Astrophysics Data System (ADS)
Vyas, Saurabh; Banerjee, Amit; Burlina, Philippe
2013-05-01
We describe an approach for estimating human skin parameters, such as melanosome concentration, collagen concentration, oxygen saturation, and blood volume, using hyperspectral radiometric measurements (signatures) obtained from in vivo skin. We use a computational model based on Kubelka-Munk theory and the Fresnel equations. This model forward maps the skin parameters to a corresponding multiband reflectance spectra. Machine-learning-based regression is used to generate the inverse map, and hence estimate skin parameters from hyperspectral signatures. We test our methods using synthetic and in vivo skin signatures obtained in the visible through the short wave infrared domains from 24 patients of both genders and Caucasian, Asian, and African American ethnicities. Performance validation shows promising results: good agreement with the ground truth and well-established physiological precepts. These methods have potential use in the characterization of skin abnormalities and in minimally-invasive prescreening of malignant skin cancers.
Aquifer parameter estimation from surface resistivity data.
Niwas, Sri; de Lima, Olivar A L
2003-01-01
This paper is devoted to the additional use, other than ground water exploration, of surface geoelectrical sounding data for aquifer hydraulic parameter estimation. In a mesoscopic framework, approximated analytical equations are developed separately for saline and for fresh water saturations. A few existing useful aquifer models, both for clean and shaley sandstones, are discussed in terms of their electrical and hydraulic effects, along with the linkage between the two. These equations are derived for insight and physical understanding of the phenomenon. In a macroscopic scale, a general aquifer model is proposed and analytical relations are derived for meaningful estimation, with a higher level of confidence, of hydraulic parameter from electrical parameters. The physical reasons for two different equations at the macroscopic level are explicitly explained to avoid confusion. Numerical examples from existing literature are reproduced to buttress our viewpoint. PMID:12533080
Space shuttle propulsion parameter estimation using optimal estimation techniques
NASA Technical Reports Server (NTRS)
1983-01-01
The first twelve system state variables are presented with the necessary mathematical developments for incorporating them into the filter/smoother algorithm. Other state variables, i.e., aerodynamic coefficients can be easily incorporated into the estimation algorithm, representing uncertain parameters, but for initial checkout purposes are treated as known quantities. An approach for incorporating the NASA propulsion predictive model results into the optimal estimation algorithm was identified. This approach utilizes numerical derivatives and nominal predictions within the algorithm with global iterations of the algorithm. The iterative process is terminated when the quality of the estimates provided no longer significantly improves.
Global parameter estimation methods for stochastic biochemical systems
2010-01-01
described in this work have provided an effective and practical approach in the estimation of kinetic parameters of stochastic systems from either sparse or dense cell population data. Nevertheless, similar to kinetic parameter estimation in other modelling frameworks, not all parameters can be estimated accurately, which is a common problem arising from the lack of complete parameter identifiability from the available data. PMID:20691037
Cosmological parameter estimation: impact of CMB aberration
Catena, Riccardo; Notari, Alessio E-mail: notari@ffn.ub.es
2013-04-01
The peculiar motion of an observer with respect to the CMB rest frame induces an apparent deflection of the observed CMB photons, i.e. aberration, and a shift in their frequency, i.e. Doppler effect. Both effects distort the temperature multipoles a{sub lm}'s via a mixing matrix at any l. The common lore when performing a CMB based cosmological parameter estimation is to consider that Doppler affects only the l = 1 multipole, and neglect any other corrections. In this paper we reconsider the validity of this assumption, showing that it is actually not robust when sky cuts are included to model CMB foreground contaminations. Assuming a simple fiducial cosmological model with five parameters, we simulated CMB temperature maps of the sky in a WMAP-like and in a Planck-like experiment and added aberration and Doppler effects to the maps. We then analyzed with a MCMC in a Bayesian framework the maps with and without aberration and Doppler effects in order to assess the ability of reconstructing the parameters of the fiducial model. We find that, depending on the specific realization of the simulated data, the parameters can be biased up to one standard deviation for WMAP and almost two standard deviations for Planck. Therefore we conclude that in general it is not a solid assumption to neglect aberration in a CMB based cosmological parameter estimation.
Cosmological parameter estimation: impact of CMB aberration
NASA Astrophysics Data System (ADS)
Catena, Riccardo; Notari, Alessio
2013-04-01
The peculiar motion of an observer with respect to the CMB rest frame induces an apparent deflection of the observed CMB photons, i.e. aberration, and a shift in their frequency, i.e. Doppler effect. Both effects distort the temperature multipoles alm's via a mixing matrix at any l. The common lore when performing a CMB based cosmological parameter estimation is to consider that Doppler affects only the l = 1 multipole, and neglect any other corrections. In this paper we reconsider the validity of this assumption, showing that it is actually not robust when sky cuts are included to model CMB foreground contaminations. Assuming a simple fiducial cosmological model with five parameters, we simulated CMB temperature maps of the sky in a WMAP-like and in a Planck-like experiment and added aberration and Doppler effects to the maps. We then analyzed with a MCMC in a Bayesian framework the maps with and without aberration and Doppler effects in order to assess the ability of reconstructing the parameters of the fiducial model. We find that, depending on the specific realization of the simulated data, the parameters can be biased up to one standard deviation for WMAP and almost two standard deviations for Planck. Therefore we conclude that in general it is not a solid assumption to neglect aberration in a CMB based cosmological parameter estimation.
Accurate feature detection and estimation using nonlinear and multiresolution analysis
NASA Astrophysics Data System (ADS)
Rudin, Leonid; Osher, Stanley
1994-11-01
A program for feature detection and estimation using nonlinear and multiscale analysis was completed. The state-of-the-art edge detection was combined with multiscale restoration (as suggested by the first author) and robust results in the presence of noise were obtained. Successful applications to numerous images of interest to DOD were made. Also, a new market in the criminal justice field was developed, based in part, on this work.
Estimation of Seismicity Parameters Using a Computer
NASA Astrophysics Data System (ADS)
Veneziano, Daniele
The book is a translation from an original in Russian, published in 1972. After 15 years, the book appears dated, its emphasis being the use of computers as an innovative technology for seismicity parameter estimation.The book is divided into two parts. Part I (29 pages) reviews the literature for quantitative measures of seismicity and for earthquake recurrence models, and describes previous uses of the computer to determine seismicity parameters. The literature reviewed is mainly that of the 1960s, with prevalence of Russian and European titles. This part of the book may retain some interest for the historical perspective it gives on the subject.
Muscle parameters estimation based on biplanar radiography.
Dubois, G; Rouch, P; Bonneau, D; Gennisson, J L; Skalli, W
2016-11-01
The evaluation of muscle and joint forces in vivo is still a challenge. Musculo-Skeletal (musculo-skeletal) models are used to compute forces based on movement analysis. Most of them are built from a scaled-generic model based on cadaver measurements, which provides a low level of personalization, or from Magnetic Resonance Images, which provide a personalized model in lying position. This study proposed an original two steps method to access a subject-specific musculo-skeletal model in 30 min, which is based solely on biplanar X-Rays. First, the subject-specific 3D geometry of bones and skin envelopes were reconstructed from biplanar X-Rays radiography. Then, 2200 corresponding control points were identified between a reference model and the subject-specific X-Rays model. Finally, the shape of 21 lower limb muscles was estimated using a non-linear transformation between the control points in order to fit the muscle shape of the reference model to the X-Rays model. Twelfth musculo-skeletal models were reconstructed and compared to their reference. The muscle volume was not accurately estimated with a standard deviation (SD) ranging from 10 to 68%. However, this method provided an accurate estimation the muscle line of action with a SD of the length difference lower than 2% and a positioning error lower than 20 mm. The moment arm was also well estimated with SD lower than 15% for most muscle, which was significantly better than scaled-generic model for most muscle. This method open the way to a quick modeling method for gait analysis based on biplanar radiography. PMID:27082150
Estimation of Soft Tissue Mechanical Parameters from Robotic Manipulation Data.
Boonvisut, Pasu; Cavuşoğlu, M Cenk
2013-10-01
Robotic motion planning algorithms used for task automation in robotic surgical systems rely on availability of accurate models of target soft tissue's deformation. Relying on generic tissue parameters in constructing the tissue deformation models is problematic because, biological tissues are known to have very large (inter- and intra-subject) variability. A priori mechanical characterization (e.g., uniaxial bench test) of the target tissues before a surgical procedure is also not usually practical. In this paper, a method for estimating mechanical parameters of soft tissue from sensory data collected during robotic surgical manipulation is presented. The method uses force data collected from a multiaxial force sensor mounted on the robotic manipulator, and tissue deformation data collected from a stereo camera system. The tissue parameters are then estimated using an inverse finite element method. The effects of measurement and modeling uncertainties on the proposed method are analyzed in simulation. The results of experimental evaluation of the method are also presented. PMID:24031160
Parameter estimation uncertainty: Comparing apples and apples?
NASA Astrophysics Data System (ADS)
Hart, D.; Yoon, H.; McKenna, S. A.
2012-12-01
Given a highly parameterized ground water model in which the conceptual model of the heterogeneity is stochastic, an ensemble of inverse calibrations from multiple starting points (MSP) provides an ensemble of calibrated parameters and follow-on transport predictions. However, the multiple calibrations are computationally expensive. Parameter estimation uncertainty can also be modeled by decomposing the parameterization into a solution space and a null space. From a single calibration (single starting point) a single set of parameters defining the solution space can be extracted. The solution space is held constant while Monte Carlo sampling of the parameter set covering the null space creates an ensemble of the null space parameter set. A recently developed null-space Monte Carlo (NSMC) method combines the calibration solution space parameters with the ensemble of null space parameters, creating sets of calibration-constrained parameters for input to the follow-on transport predictions. Here, we examine the consistency between probabilistic ensembles of parameter estimates and predictions using the MSP calibration and the NSMC approaches. A highly parameterized model of the Culebra dolomite previously developed for the WIPP project in New Mexico is used as the test case. A total of 100 estimated fields are retained from the MSP approach and the ensemble of results defining the model fit to the data, the reproduction of the variogram model and prediction of an advective travel time are compared to the same results obtained using NSMC. We demonstrate that the NSMC fields based on a single calibration model can be significantly constrained by the calibrated solution space and the resulting distribution of advective travel times is biased toward the travel time from the single calibrated field. To overcome this, newly proposed strategies to employ a multiple calibration-constrained NSMC approach (M-NSMC) are evaluated. Comparison of the M-NSMC and MSP methods suggests
Bayesian parameter estimation for effective field theories
NASA Astrophysics Data System (ADS)
Wesolowski, S.; Klco, N.; Furnstahl, R. J.; Phillips, D. R.; Thapaliya, A.
2016-07-01
We present procedures based on Bayesian statistics for estimating, from data, the parameters of effective field theories (EFTs). The extraction of low-energy constants (LECs) is guided by theoretical expectations in a quantifiable way through the specification of Bayesian priors. A prior for natural-sized LECs reduces the possibility of overfitting, and leads to a consistent accounting of different sources of uncertainty. A set of diagnostic tools is developed that analyzes the fit and ensures that the priors do not bias the EFT parameter estimation. The procedures are illustrated using representative model problems, including the extraction of LECs for the nucleon-mass expansion in SU(2) chiral perturbation theory from synthetic lattice data.
Renal parameter estimates in unrestrained dogs
NASA Technical Reports Server (NTRS)
Rader, R. D.; Stevens, C. M.
1974-01-01
A mathematical formulation has been developed to describe the hemodynamic parameters of a conceptualized kidney model. The model was developed by considering regional pressure drops and regional storage capacities within the renal vasculature. Estimation of renal artery compliance, pre- and postglomerular resistance, and glomerular filtration pressure is feasible by considering mean levels and time derivatives of abdominal aortic pressure and renal artery flow. Changes in the smooth muscle tone of the renal vessels induced by exogenous angiotensin amide, acetylcholine, and by the anaesthetic agent halothane were estimated by use of the model. By employing totally implanted telemetry, the technique was applied on unrestrained dogs to measure renal resistive and compliant parameters while the dogs were being subjected to obedience training, to avoidance reaction, and to unrestrained caging.
CosmoSIS: Modular cosmological parameter estimation
Zuntz, J.; Paterno, M.; Jennings, E.; Rudd, D.; Manzotti, A.; Dodelson, S.; Bridle, S.; Sehrish, S.; Kowalkowski, J.
2015-06-09
Cosmological parameter estimation is entering a new era. Large collaborations need to coordinate high-stakes analyses using multiple methods; furthermore such analyses have grown in complexity due to sophisticated models of cosmology and systematic uncertainties. In this paper we argue that modularity is the key to addressing these challenges: calculations should be broken up into interchangeable modular units with inputs and outputs clearly defined. Here we present a new framework for cosmological parameter estimation, CosmoSIS, designed to connect together, share, and advance development of inference tools across the community. We describe the modules already available in CosmoSIS, including CAMB, Planck, cosmic shear calculations, and a suite of samplers. Lastly, we illustrate it using demonstration code that you can run out-of-the-box with the installer available at http://bitbucket.org/joezuntz/cosmosis
CosmoSIS: Modular cosmological parameter estimation
Zuntz, J.; Paterno, M.; Jennings, E.; Rudd, D.; Manzotti, A.; Dodelson, S.; Bridle, S.; Sehrish, S.; Kowalkowski, J.
2015-06-09
Cosmological parameter estimation is entering a new era. Large collaborations need to coordinate high-stakes analyses using multiple methods; furthermore such analyses have grown in complexity due to sophisticated models of cosmology and systematic uncertainties. In this paper we argue that modularity is the key to addressing these challenges: calculations should be broken up into interchangeable modular units with inputs and outputs clearly defined. Here we present a new framework for cosmological parameter estimation, CosmoSIS, designed to connect together, share, and advance development of inference tools across the community. We describe the modules already available in CosmoSIS, including CAMB, Planck, cosmicmore » shear calculations, and a suite of samplers. Lastly, we illustrate it using demonstration code that you can run out-of-the-box with the installer available at http://bitbucket.org/joezuntz/cosmosis« less
Generalized REGression Package for Nonlinear Parameter Estimation
1995-05-15
GREG computes modal (maximum-posterior-density) and interval estimates of the parameters in a user-provided Fortran subroutine MODEL, using a user-provided vector OBS of single-response observations or matrix OBS of multiresponse observations. GREG can also select the optimal next experiment from a menu of simulated candidates, so as to minimize the volume of the parametric inference region based on the resulting augmented data set.
Accurate tempo estimation based on harmonic + noise decomposition
NASA Astrophysics Data System (ADS)
Alonso, Miguel; Richard, Gael; David, Bertrand
2006-12-01
We present an innovative tempo estimation system that processes acoustic audio signals and does not use any high-level musical knowledge. Our proposal relies on a harmonic + noise decomposition of the audio signal by means of a subspace analysis method. Then, a technique to measure the degree of musical accentuation as a function of time is developed and separately applied to the harmonic and noise parts of the input signal. This is followed by a periodicity estimation block that calculates the salience of musical accents for a large number of potential periods. Next, a multipath dynamic programming searches among all the potential periodicities for the most consistent prospects through time, and finally the most energetic candidate is selected as tempo. Our proposal is validated using a manually annotated test-base containing 961 music signals from various musical genres. In addition, the performance of the algorithm under different configurations is compared. The robustness of the algorithm when processing signals of degraded quality is also measured.
Parameter estimation and optimal experimental design.
Banga, Julio R; Balsa-Canto, Eva
2008-01-01
Mathematical models are central in systems biology and provide new ways to understand the function of biological systems, helping in the generation of novel and testable hypotheses, and supporting a rational framework for possible ways of intervention, like in e.g. genetic engineering, drug development or treatment of diseases. Since the amount and quality of experimental 'omics' data continue to increase rapidly, there is great need for methods for proper model building which can handle this complexity. In the present chapter we review two key steps of the model building process, namely parameter estimation (model calibration) and optimal experimental design. Parameter estimation aims to find the unknown parameters of the model which give the best fit to a set of experimental data. Optimal experimental design aims to devise the dynamic experiments which provide the maximum information content for subsequent non-linear model identification, estimation and/or discrimination. We place emphasis on the need for robust global optimization methods for proper solution of these problems, and we present a motivating example considering a cell signalling model. PMID:18793133
Linear parameter estimation of rational biokinetic functions.
Doeswijk, T G; Keesman, K J
2009-01-01
For rational biokinetic functions such as the Michaelis-Menten equation, in general, a nonlinear least-squares method is a good estimator. However, a major drawback of a nonlinear least-squares estimator is that it can end up in a local minimum. Rearranging and linearizing rational biokinetic functions for parameter estimation is common practice (e.g. Lineweaver-Burk linearization). By rearranging, however, the error is distorted. In addition, the rearranged model frequently leads to a so-called 'errors-in-variables' estimation problem. Applying the ordinary least squares (OLS) method to the linearly reparameterized function ensures a global minimum, but its estimates become biased if the regression variables contain errors and thus bias compensation is needed. Therefore, in this paper, a bias compensated total least squares (CTLS) method, which as OLS is a direct method, is proposed to solve the estimation problem. The applicability of a general linear reparameterization procedure and the advances of CTLS over ordinary least squares and nonlinear least squares approaches are shown by two simulation examples. The examples contain Michaelis-Menten kinetics and enzyme kinetics with substrate inhibition. Furthermore, CTLS is demonstrated with real data of an activated sludge experiment. It is concluded that for rational biokinetic models CTLS is a powerful alternative to the existing least-squares methods. PMID:19004464
Reinbolt, Jeffrey A.; Haftka, Raphael T.; Chmielewski, Terese L.; Fregly, Benjamin J.
2013-01-01
Variations in joint parameter values (axis positions and orientations in body segments) and inertial parameter values (segment masses, mass centers, and moments of inertia) as well as kinematic noise alter the results of inverse dynamics analyses of gait. Three-dimensional linkage models with joint constraints have been proposed as one way to minimize the effects of noisy kinematic data. Such models can also be used to perform gait optimizations to predict post-treatment function given pre-treatment gait data. This study evaluates whether accurate patient-specific joint and inertial parameter values are needed in three-dimensional linkage models to produce accurate inverse dynamics results for gait. The study was performed in two stages. First, we used optimization analyses to evaluate whether patient-specific joint and inertial parameter values can be calibrated accurately from noisy kinematic data, and second, we used Monte Carlo analyses to evaluate how errors in joint and inertial parameter values affect inverse dynamics calculations. Both stages were performed using a dynamic, 27 degree-of-freedom, full-body linkage model and synthetic (i.e., computer generated) gait data corresponding to a nominal experimental gait motion. In general, joint but not inertial parameter values could be found accurately from noisy kinematic data. Root-mean-square (RMS) errors were 3° and 4 mm for joint parameter values and 1 kg, 22 mm, and 74,500 kg*mm2 for inertial parameter values. Furthermore, errors in joint but not inertial parameter values had a significant effect on calculated lower-extremity inverse dynamics joint torques. The worst RMS torque error averaged 4% bodyweight*height (BW*H) due to joint parameter variations but less than 0.25% BW*H due to inertial parameter variations. These results suggest that inverse dynamics analyses of gait utilizing linkage models with joint constraints should calibrate the model’s joint parameter values to obtain accurate joint
Bioaccessibility tests accurately estimate bioavailability of lead to quail
Beyer, W. Nelson; Basta, Nicholas T; Chaney, Rufus L.; Henry, Paula F.; Mosby, David; Rattner, Barnett A.; Scheckel, Kirk G.; Sprague, Dan; Weber, John
2016-01-01
Hazards of soil-borne Pb to wild birds may be more accurately quantified if the bioavailability of that Pb is known. To better understand the bioavailability of Pb to birds, we measured blood Pb concentrations in Japanese quail (Coturnix japonica) fed diets containing Pb-contaminated soils. Relative bioavailabilities were expressed by comparison with blood Pb concentrations in quail fed a Pb acetate reference diet. Diets containing soil from five Pb-contaminated Superfund sites had relative bioavailabilities from 33%-63%, with a mean of about 50%. Treatment of two of the soils with phosphorus significantly reduced the bioavailability of Pb. Bioaccessibility of Pb in the test soils was then measured in six in vitro tests and regressed on bioavailability. They were: the “Relative Bioavailability Leaching Procedure” (RBALP) at pH 1.5, the same test conducted at pH 2.5, the “Ohio State University In vitro Gastrointestinal” method (OSU IVG), the “Urban Soil Bioaccessible Lead Test”, the modified “Physiologically Based Extraction Test” and the “Waterfowl Physiologically Based Extraction Test.” All regressions had positive slopes. Based on criteria of slope and coefficient of determination, the RBALP pH 2.5 and OSU IVG tests performed very well. Speciation by X-ray absorption spectroscopy demonstrated that, on average, most of the Pb in the sampled soils was sorbed to minerals (30%), bound to organic matter (24%), or present as Pb sulfate (18%). Additional Pb was associated with P (chloropyromorphite, hydroxypyromorphite and tertiary Pb phosphate), and with Pb carbonates, leadhillite (a lead sulfate carbonate hydroxide), and Pb sulfide. The formation of chloropyromorphite reduced the bioavailability of Pb and the amendment of Pb-contaminated soils with P may be a thermodynamically favored means to sequester Pb.
Parameter estimate of signal transduction pathways
Arisi, Ivan; Cattaneo, Antonino; Rosato, Vittorio
2006-01-01
Background The "inverse" problem is related to the determination of unknown causes on the bases of the observation of their effects. This is the opposite of the corresponding "direct" problem, which relates to the prediction of the effects generated by a complete description of some agencies. The solution of an inverse problem entails the construction of a mathematical model and takes the moves from a number of experimental data. In this respect, inverse problems are often ill-conditioned as the amount of experimental conditions available are often insufficient to unambiguously solve the mathematical model. Several approaches to solving inverse problems are possible, both computational and experimental, some of which are mentioned in this article. In this work, we will describe in details the attempt to solve an inverse problem which arose in the study of an intracellular signaling pathway. Results Using the Genetic Algorithm to find the sub-optimal solution to the optimization problem, we have estimated a set of unknown parameters describing a kinetic model of a signaling pathway in the neuronal cell. The model is composed of mass action ordinary differential equations, where the kinetic parameters describe protein-protein interactions, protein synthesis and degradation. The algorithm has been implemented on a parallel platform. Several potential solutions of the problem have been computed, each solution being a set of model parameters. A sub-set of parameters has been selected on the basis on their small coefficient of variation across the ensemble of solutions. Conclusion Despite the lack of sufficiently reliable and homogeneous experimental data, the genetic algorithm approach has allowed to estimate the approximate value of a number of model parameters in a kinetic model of a signaling pathway: these parameters have been assessed to be relevant for the reproduction of the available experimental data. PMID:17118160
NASA Astrophysics Data System (ADS)
Yang, Que; Wang, Shanshan; Wang, Kai; Zhang, Chunyu; Zhang, Lu; Meng, Qingyu; Zhu, Qiudong
2015-08-01
For normal eyes without history of any ocular surgery, traditional equations for calculating intraocular lens (IOL) power, such as SRK-T, Holladay, Higis, SRK-II, et al., all were relativley accurate. However, for eyes underwent refractive surgeries, such as LASIK, or eyes diagnosed as keratoconus, these equations may cause significant postoperative refractive error, which may cause poor satisfaction after cataract surgery. Although some methods have been carried out to solve this problem, such as Hagis-L equation[1], or using preoperative data (data before LASIK) to estimate K value[2], no precise equations were available for these eyes. Here, we introduced a novel intraocular lens power estimation method by accurate ray tracing with optical design software ZEMAX. Instead of using traditional regression formula, we adopted the exact measured corneal elevation distribution, central corneal thickness, anterior chamber depth, axial length, and estimated effective lens plane as the input parameters. The calculation of intraocular lens power for a patient with keratoconus and another LASIK postoperative patient met very well with their visual capacity after cataract surgery.
Bioaccessibility tests accurately estimate bioavailability of lead to quail.
Beyer, W Nelson; Basta, Nicholas T; Chaney, Rufus L; Henry, Paula F P; Mosby, David E; Rattner, Barnett A; Scheckel, Kirk G; Sprague, Daniel T; Weber, John S
2016-09-01
Hazards of soil-borne lead (Pb) to wild birds may be more accurately quantified if the bioavailability of that Pb is known. To better understand the bioavailability of Pb to birds, the authors measured blood Pb concentrations in Japanese quail (Coturnix japonica) fed diets containing Pb-contaminated soils. Relative bioavailabilities were expressed by comparison with blood Pb concentrations in quail fed a Pb acetate reference diet. Diets containing soil from 5 Pb-contaminated Superfund sites had relative bioavailabilities from 33% to 63%, with a mean of approximately 50%. Treatment of 2 of the soils with phosphorus (P) significantly reduced the bioavailability of Pb. Bioaccessibility of Pb in the test soils was then measured in 6 in vitro tests and regressed on bioavailability: the relative bioavailability leaching procedure at pH 1.5, the same test conducted at pH 2.5, the Ohio State University in vitro gastrointestinal method, the urban soil bioaccessible lead test, the modified physiologically based extraction test, and the waterfowl physiologically based extraction test. All regressions had positive slopes. Based on criteria of slope and coefficient of determination, the relative bioavailability leaching procedure at pH 2.5 and Ohio State University in vitro gastrointestinal tests performed very well. Speciation by X-ray absorption spectroscopy demonstrated that, on average, most of the Pb in the sampled soils was sorbed to minerals (30%), bound to organic matter (24%), or present as Pb sulfate (18%). Additional Pb was associated with P (chloropyromorphite, hydroxypyromorphite, and tertiary Pb phosphate) and with Pb carbonates, leadhillite (a lead sulfate carbonate hydroxide), and Pb sulfide. The formation of chloropyromorphite reduced the bioavailability of Pb, and the amendment of Pb-contaminated soils with P may be a thermodynamically favored means to sequester Pb. Environ Toxicol Chem 2016;35:2311-2319. Published 2016 Wiley Periodicals Inc. on behalf of
Advanced Method to Estimate Fuel Slosh Simulation Parameters
NASA Technical Reports Server (NTRS)
Schlee, Keith; Gangadharan, Sathya; Ristow, James; Sudermann, James; Walker, Charles; Hubert, Carl
2005-01-01
The nutation (wobble) of a spinning spacecraft in the presence of energy dissipation is a well-known problem in dynamics and is of particular concern for space missions. The nutation of a spacecraft spinning about its minor axis typically grows exponentially and the rate of growth is characterized by the Nutation Time Constant (NTC). For launch vehicles using spin-stabilized upper stages, fuel slosh in the spacecraft propellant tanks is usually the primary source of energy dissipation. For analytical prediction of the NTC this fuel slosh is commonly modeled using simple mechanical analogies such as pendulums or rigid rotors coupled to the spacecraft. Identifying model parameter values which adequately represent the sloshing dynamics is the most important step in obtaining an accurate NTC estimate. Analytic determination of the slosh model parameters has met with mixed success and is made even more difficult by the introduction of propellant management devices and elastomeric diaphragms. By subjecting full-sized fuel tanks with actual flight fuel loads to motion similar to that experienced in flight and measuring the forces experienced by the tanks these parameters can be determined experimentally. Currently, the identification of the model parameters is a laborious trial-and-error process in which the equations of motion for the mechanical analog are hand-derived, evaluated, and their results are compared with the experimental results. The proposed research is an effort to automate the process of identifying the parameters of the slosh model using a MATLAB/SimMechanics-based computer simulation of the experimental setup. Different parameter estimation and optimization approaches are evaluated and compared in order to arrive at a reliable and effective parameter identification process. To evaluate each parameter identification approach, a simple one-degree-of-freedom pendulum experiment is constructed and motion is induced using an electric motor. By applying the
An investigation of new methods for estimating parameter sensitivities
NASA Technical Reports Server (NTRS)
Beltracchi, Todd J.; Gabriele, Gary A.
1988-01-01
Parameter sensitivity is defined as the estimation of changes in the modeling functions and the design variables due to small changes in the fixed parameters of the formulation. There are currently several methods for estimating parameter sensitivities requiring either difficult to obtain second order information, or do not return reliable estimates for the derivatives. Additionally, all the methods assume that the set of active constraints does not change in a neighborhood of the estimation point. If the active set does in fact change, than any extrapolations based on these derivatives may be in error. The objective here is to investigate more efficient new methods for estimating parameter sensitivities when the active set changes. The new method is based on the recursive quadratic programming (RQP) method and in conjunction a differencing formula to produce estimates of the sensitivities. This is compared to existing methods and is shown to be very competitive in terms of the number of function evaluations required. In terms of accuracy, the method is shown to be equivalent to a modified version of the Kuhn-Tucker method, where the Hessian of the Lagrangian is estimated using the BFS method employed by the RPQ algorithm. Inital testing on a test set with known sensitivities demonstrates that the method can accurately calculate the parameter sensitivity. To handle changes in the active set, a deflection algorithm is proposed for those cases where the new set of active constraints remains linearly independent. For those cases where dependencies occur, a directional derivative is proposed. A few simple examples are included for the algorithm, but extensive testing has not yet been performed.
Parameter Estimation and Model Selection in Computational Biology
Lillacci, Gabriele; Khammash, Mustafa
2010-01-01
A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants) are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection. PMID:20221262
High speed parameter estimation for a homogenized energy model
NASA Astrophysics Data System (ADS)
Ernstberger, Jon M.
Industrial, commercial, military, biomedical, and civilian uses of smart materials are increasingly investigated for high performance applications. These compounds couple applied field or thermal energy to mechanical forces that are generated within the material. The devices utilizing these compounds are often much smaller than their traditional counterparts and provide greater design capabilities and energy efficiency. The relations that couple field and mechanical energies are often hysteretic and nonlinear. To accurately control devices employing these compounds, models must quantify these effects. Further, since these compounds exhibit environment-dependent behavior, the models must be robust for accurate actuator quantification. In this dissertation, we investigate the construction of models that characterize these internal mechanisms and that manifest themselves in material deformation in a hysteretic fashion. Results of previously-presented model formulations are given. New techniques for generating model components are presented which reduce the computational load for parameter estimations. The use of various deterministic and stochastic search algorithms for parameter estimation are discussed with strengths and weaknesses of each examined. New end-user graphical tools for properly initiating the parameter estimation are also presented. Finally, results from model fits to data from ferroelectric---e.g., Lead Zirconate Titanate (PZT)---and ferromagnetic---e.g., Terfenol-D---materials are presented.
NASA Astrophysics Data System (ADS)
Lachaume, Regis; Rabus, Markus; Jordan, Andres
2015-08-01
In stellar interferometry, the assumption that the observables can be seen as Gaussian, independent variables is the norm. In particular, neither the optical interferometry FITS (OIFITS) format nor the most popular fitting software in the field, LITpro, offer means to specify a covariance matrix or non-Gaussian uncertainties. Interferometric observables are correlated by construct, though. Also, the calibration by an instrumental transfer function ensures that the resulting observables are not Gaussian, even if uncalibrated ones happened to be so.While analytic frameworks have been published in the past, they are cumbersome and there is no generic implementation available. We propose here a relatively simple way of dealing with correlated errors without the need to extend the OIFITS specification or making some Gaussian assumptions. By repeatedly picking at random which interferograms, which calibrator stars, and which are the errors on their diameters, and performing the data processing on the bootstrapped data, we derive a sampling of p(O), the multivariate probability density function (PDF) of the observables O. The results can be stored in a normal OIFITS file. Then, given a model m with parameters P predicting observables O = m(P), we can estimate the PDF of the model parameters f(P) = p(m(P)) by using a density estimation of the observables' PDF p.With observations repeated over different baselines, on nights several days apart, and with a significant set of calibrators systematic errors are de facto taken into account. We apply the technique to a precise and accurate assessment of stellar diameters obtained at the Very Large Telescope Interferometer with PIONIER.
Regularization Based Iterative Point Match Weighting for Accurate Rigid Transformation Estimation.
Liu, Yonghuai; De Dominicis, Luigi; Wei, Baogang; Chen, Liang; Martin, Ralph R
2015-09-01
Feature extraction and matching (FEM) for 3D shapes finds numerous applications in computer graphics and vision for object modeling, retrieval, morphing, and recognition. However, unavoidable incorrect matches lead to inaccurate estimation of the transformation relating different datasets. Inspired by AdaBoost, this paper proposes a novel iterative re-weighting method to tackle the challenging problem of evaluating point matches established by typical FEM methods. Weights are used to indicate the degree of belief that each point match is correct. Our method has three key steps: (i) estimation of the underlying transformation using weighted least squares, (ii) penalty parameter estimation via minimization of the weighted variance of the matching errors, and (iii) weight re-estimation taking into account both matching errors and information learnt in previous iterations. A comparative study, based on real shapes captured by two laser scanners, shows that the proposed method outperforms four other state-of-the-art methods in terms of evaluating point matches between overlapping shapes established by two typical FEM methods, resulting in more accurate estimates of the underlying transformation. This improved transformation can be used to better initialize the iterative closest point algorithm and its variants, making 3D shape registration more likely to succeed. PMID:26357287
A simple and accurate resist parameter extraction method for sub-80-nm DRAM patterns
NASA Astrophysics Data System (ADS)
Lee, Sook; Hwang, Chan; Park, Dong-Woon; Kim, In-Sung; Kim, Ho-Chul; Woo, Sang-Gyun; Cho, Han-Ku; Moon, Joo-Tae
2004-05-01
Due to the polarization effect of high NA lithography, the consideration of resist effect in lithography simulation becomes increasingly important. In spite of the importance of resist simulation, many process engineers are reluctant to consider resist effect in lithography simulation due to time-consuming procedure to extract required resist parameters and the uncertainty of measurement of some parameters. Weiss suggested simplified development model, and this model does not require the complex kinetic parameters. For the device fabrication engineers, there is a simple and accurate parameter extraction and optimizing method using Weiss model. This method needs refractive index, Dill"s parameters and development rate monitoring (DRM) data in parameter extraction. The parameters extracted using referred sequence is not accurate, so that we have to optimize the parameters to fit the critical dimension scanning electron microscopy (CD SEM) data of line and space patterns. Hence, the FiRM of Sigma-C is utilized as a resist parameter-optimizing program. According to our study, the illumination shape, the aberration and the pupil mesh point have a large effect on the accuracy of resist parameter in optimization. To obtain the optimum parameters, we need to find the saturated mesh points in terms of normalized intensity log slope (NILS) prior to an optimization. The simulation results using the optimized parameters by this method shows good agreement with experiments for iso-dense bias, Focus-Exposure Matrix data and sub 80nm device pattern simulation.
Parameter estimation, nonlinearity, and Occam's razor.
Alonso, Leandro M
2015-03-01
Nonlinear systems are capable of displaying complex behavior even if this is the result of a small number of interacting time scales. A widely studied case is when complex dynamics emerges out of a nonlinear system being forced by a simple harmonic function. In order to identify if a recorded time series is the result of a nonlinear system responding to a simpler forcing, we develop a discrete nonlinear transformation for time series based on synchronization techniques. This allows a parameter estimation procedure which simultaneously searches for a good fit of the recorded data, and small complexity of a fluctuating driving parameter. We illustrate this procedure using data from respiratory patterns during birdsong production. PMID:25833426
Parameter estimation, nonlinearity, and Occam's razor
NASA Astrophysics Data System (ADS)
Alonso, Leandro M.
2015-03-01
Nonlinear systems are capable of displaying complex behavior even if this is the result of a small number of interacting time scales. A widely studied case is when complex dynamics emerges out of a nonlinear system being forced by a simple harmonic function. In order to identify if a recorded time series is the result of a nonlinear system responding to a simpler forcing, we develop a discrete nonlinear transformation for time series based on synchronization techniques. This allows a parameter estimation procedure which simultaneously searches for a good fit of the recorded data, and small complexity of a fluctuating driving parameter. We illustrate this procedure using data from respiratory patterns during birdsong production.
Parameter estimation techniques for LTP system identification
NASA Astrophysics Data System (ADS)
Nofrarias Serra, Miquel
LISA Pathfinder (LPF) is the precursor mission of LISA (Laser Interferometer Space Antenna) and the first step towards gravitational waves detection in space. The main instrument onboard the mission is the LTP (LISA Technology Package) whose scientific goal is to test LISA's drag-free control loop by reaching a differential acceleration noise level between two masses in √ geodesic motion of 3 × 10-14 ms-2 / Hz in the milliHertz band. The mission is not only challenging in terms of technology readiness but also in terms of data analysis. As with any gravitational wave detector, attaining the instrument performance goals will require an extensive noise hunting campaign to measure all contributions with high accuracy. But, opposite to on-ground experiments, LTP characterisation will be only possible by setting parameters via telecommands and getting a selected amount of information through the available telemetry downlink. These two conditions, high accuracy and high reliability, are the main restrictions that the LTP data analysis must overcome. A dedicated object oriented Matlab Toolbox (LTPDA) has been set up by the LTP analysis team for this purpose. Among the different toolbox methods, an essential part for the mission are the parameter estimation tools that will be used for system identification during operations: Linear Least Squares, Non-linear Least Squares and Monte Carlo Markov Chain methods have been implemented as LTPDA methods. The data analysis team has been testing those methods with a series of mock data exercises with the following objectives: to cross-check parameter estimation methods and compare the achievable accuracy for each of them, and to develop the best strategies to describe the physics underlying a complex controlled experiment as the LTP. In this contribution we describe how these methods were tested with simulated LTP-like data to recover the parameters of the model and we report on the latest results of these mock data exercises.
Real-Time Parameter Estimation Using Output Error
NASA Technical Reports Server (NTRS)
Grauer, Jared A.
2014-01-01
Output-error parameter estimation, normally a post- ight batch technique, was applied to real-time dynamic modeling problems. Variations on the traditional algorithm were investigated with the goal of making the method suitable for operation in real time. Im- plementation recommendations are given that are dependent on the modeling problem of interest. Application to ight test data showed that accurate parameter estimates and un- certainties for the short-period dynamics model were available every 2 s using time domain data, or every 3 s using frequency domain data. The data compatibility problem was also solved in real time, providing corrected sensor measurements every 4 s. If uncertainty corrections for colored residuals are omitted, this rate can be increased to every 0.5 s.
[Automatic Measurement of the Stellar Atmospheric Parameters Based Mass Estimation].
Tu, Liang-ping; Wei, Hui-ming; Luo, A-li; Zhao, Yong-heng
2015-11-01
We have collected massive stellar spectral data in recent years, which leads to the research on the automatic measurement of stellar atmospheric physical parameters (effective temperature Teff, surface gravity log g and metallic abundance [Fe/ H]) become an important issue. To study the automatic measurement of these three parameters has important significance for some scientific problems, such as the evolution of the universe and so on. But the research of this problem is not very widely, some of the current methods are not able to estimate the values of the stellar atmospheric physical parameters completely and accurately. So in this paper, an automatic method to predict stellar atmospheric parameters based on mass estimation was presented, which can achieve the prediction of stellar effective temperature Teff, surface gravity log g and metallic abundance [Fe/H]. This method has small amount of computation and fast training speed. The main idea of this method is that firstly it need us to build some mass distributions, secondly the original spectral data was mapped into the mass space and then to predict the stellar parameter with the support vector regression (SVR) in the mass space. we choose the stellar spectral data from the United States SDSS-DR8 for the training and testing. We also compared the predicted results of this method with the SSPP and achieve higher accuracy. The predicted results are more stable and the experimental results show that the method is feasible and can predict the stellar atmospheric physical parameters effectively. PMID:26978937
Jeong, Hyunjo; Zhang, Shuzeng; Li, Xiongbing; Barnard, Dan
2015-09-15
The accurate measurement of acoustic nonlinearity parameter β for fluids or solids generally requires making corrections for diffraction effects due to finite size geometry of transmitter and receiver. These effects are well known in linear acoustics, while those for second harmonic waves have not been well addressed and therefore not properly considered in previous studies. In this work, we explicitly define the attenuation and diffraction corrections using the multi-Gaussian beam (MGB) equations which were developed from the quasilinear solutions of the KZK equation. The effects of making these corrections are examined through the simulation of β determination in water. Diffraction corrections are found to have more significant effects than attenuation corrections, and the β values of water can be estimated experimentally with less than 5% errors when the exact second harmonic diffraction corrections are used together with the negligible attenuation correction effects on the basis of linear frequency dependence between attenuation coefficients, α{sub 2} ≃ 2α{sub 1}.
A new geometric-based model to accurately estimate arm and leg inertial estimates.
Wicke, Jason; Dumas, Geneviève A
2014-06-01
Segment estimates of mass, center of mass and moment of inertia are required input parameters to analyze the forces and moments acting across the joints. The objectives of this study were to propose a new geometric model for limb segments, to evaluate it against criterion values obtained from DXA, and to compare its performance to five other popular models. Twenty five female and 24 male college students participated in the study. For the criterion measures, the participants underwent a whole body DXA scan, and estimates for segment mass, center of mass location, and moment of inertia (frontal plane) were directly computed from the DXA mass units. For the new model, the volume was determined from two standing frontal and sagittal photographs. Each segment was modeled as a stack of slices, the sections of which were ellipses if they are not adjoining another segment and sectioned ellipses if they were adjoining another segment (e.g. upper arm and trunk). Length of axes of the ellipses was obtained from the photographs. In addition, a sex-specific, non-uniform density function was developed for each segment. A series of anthropometric measurements were also taken by directly following the definitions provided of the different body segment models tested, and the same parameters determined for each model. Comparison of models showed that estimates from the new model were consistently closer to the DXA criterion than those from the other models, with an error of less than 5% for mass and moment of inertia and less than about 6% for center of mass location. PMID:24735506
Fast cosmological parameter estimation using neural networks
NASA Astrophysics Data System (ADS)
Auld, T.; Bridges, M.; Hobson, M. P.; Gull, S. F.
2007-03-01
We present a method for accelerating the calculation of cosmic microwave background (CMB) power spectra, matter power spectra and likelihood functions for use in cosmological parameter estimation. The algorithm, called COSMONET, is based on training a multilayer perceptron neural network and shares all the advantages of the recently released PICO algorithm of Fendt & Wandelt, but has several additional benefits in terms of simplicity, computational speed, memory requirements and ease of training. We demonstrate the capabilities of COSMONET by computing CMB power spectra over a box in the parameter space of flat Λ cold dark matter (ΛCDM) models containing the 3σ WMAP1-year confidence region. We also use COSMONET to compute the WMAP3-year (WMAP3) likelihood for flat ΛCDM models and show that marginalized posteriors on parameters derived are very similar to those obtained using CAMB and the WMAP3 code. We find that the average error in the power spectra is typically 2-3 per cent of cosmic variance, and that COSMONET is ~7 × 104 faster than CAMB (for flat models) and ~6 × 106 times faster than the official WMAP3 likelihood code. COSMONET and an interface to COSMOMC are publically available at http://www.mrao.cam.ac.uk/software/cosmonet.
Maximum-likelihood estimation of circle parameters via convolution.
Zelniker, Emanuel E; Clarkson, I Vaughan L
2006-04-01
The accurate fitting of a circle to noisy measurements of circumferential points is a much studied problem in the literature. In this paper, we present an interpretation of the maximum-likelihood estimator (MLE) and the Delogne-Kåsa estimator (DKE) for circle-center and radius estimation in terms of convolution on an image which is ideal in a certain sense. We use our convolution-based MLE approach to find good estimates for the parameters of a circle in digital images. In digital images, it is then possible to treat these estimates as preliminary estimates into various other numerical techniques which further refine them to achieve subpixel accuracy. We also investigate the relationship between the convolution of an ideal image with a "phase-coded kernel" (PCK) and the MLE. This is related to the "phase-coded annulus" which was introduced by Atherton and Kerbyson who proposed it as one of a number of new convolution kernels for estimating circle center and radius. We show that the PCK is an approximate MLE (AMLE). We compare our AMLE method to the MLE and the DKE as well as the Cramér-Rao Lower Bound in ideal images and in both real and synthetic digital images. PMID:16579374
Estimation of stellar atmospheric parameters from SDSS/SEGUE spectra
NASA Astrophysics Data System (ADS)
Re Fiorentin, P.; Bailer-Jones, C. A. L.; Lee, Y. S.; Beers, T. C.; Sivarani, T.; Wilhelm, R.; Allende Prieto, C.; Norris, J. E.
2007-06-01
We present techniques for the estimation of stellar atmospheric parameters (T_eff, log~g, [Fe/H]) for stars from the SDSS/SEGUE survey. The atmospheric parameters are derived from the observed medium-resolution (R = 2000) stellar spectra using non-linear regression models trained either on (1) pre-classified observed data or (2) synthetic stellar spectra. In the first case we use our models to automate and generalize parametrization produced by a preliminary version of the SDSS/SEGUE Spectroscopic Parameter Pipeline (SSPP). In the second case we directly model the mapping between synthetic spectra (derived from Kurucz model atmospheres) and the atmospheric parameters, independently of any intermediate estimates. After training, we apply our models to various samples of SDSS spectra to derive atmospheric parameters, and compare our results with those obtained previously by the SSPP for the same samples. We obtain consistency between the two approaches, with RMS deviations on the order of 150 K in T_eff, 0.35 dex in log~g, and 0.22 dex in [Fe/H]. The models are applied to pre-processed spectra, either via Principal Component Analysis (PCA) or a Wavelength Range Selection (WRS) method, which employs a subset of the full 3850-9000Å spectral range. This is both for computational reasons (robustness and speed), and because it delivers higher accuracy (better generalization of what the models have learned). Broadly speaking, the PCA is demonstrated to deliver more accurate atmospheric parameters when the training data are the actual SDSS spectra with previously estimated parameters, whereas WRS appears superior for the estimation of log~g via synthetic templates, especially for lower signal-to-noise spectra. From a subsample of some 19 000 stars with previous determinations of the atmospheric parameters, the accuracies of our predictions (mean absolute errors) for each parameter are T_eff to 170/170 K, log~g to 0.36/0.45 dex, and [Fe/H] to 0.19/0.26 dex, for methods (1
Accurate and transferable extended Hückel-type tight-binding parameters
NASA Astrophysics Data System (ADS)
Cerdá, J.; Soria, F.
2000-03-01
We show how the simple extended Hückel theory can be easily parametrized in order to yield accurate band structures for bulk materials, while the resulting optimized atomic orbital basis sets present good transferability properties. The number of parameters involved is exceedingly small, typically ten or eleven per structural phase. We apply the method to almost fifty elemental and compound bulk phases.
Statistical cautions when estimating DEBtox parameters.
Billoir, Elise; Delignette-Muller, Marie Laure; Péry, Alexandre R R; Geffard, Olivier; Charles, Sandrine
2008-09-01
DEBtox (Dynamic Energy Budget in toxicology) models have been designed to analyse various results from classic tests in ecotoxicology. They consist of a set of mechanistic models describing how organisms manage their energy, when they are exposed to a contaminant. Until now, such a biology-based modeling approach has not been used within the regulatory context. However, these methods have been promoted and discussed in recent guidance documents on the statistical analysis of ecotoxicity data. Indeed, they help us to understand the underlying mechanisms. In this paper, we focused on the 21 day Daphnia magna reproduction test. We first aimed to clarify and detail the model building process leading to DEBtox models. Equations were rederived step by step, and for some of them we obtained results different from the published ones. Then, we statistically evaluated the estimation process quality when using a least squares approach. Using both experimental and simulated data, our analyses highlighted several statistical issues related to the fitting of DEBtox models on OECD-type reproduction data. In this case, particular attention had to be paid to parameter estimates and the interpretation of their confidence intervals. PMID:18571678
Schütt, Heiko H; Harmeling, Stefan; Macke, Jakob H; Wichmann, Felix A
2016-05-01
The psychometric function describes how an experimental variable, such as stimulus strength, influences the behaviour of an observer. Estimation of psychometric functions from experimental data plays a central role in fields such as psychophysics, experimental psychology and in the behavioural neurosciences. Experimental data may exhibit substantial overdispersion, which may result from non-stationarity in the behaviour of observers. Here we extend the standard binomial model which is typically used for psychometric function estimation to a beta-binomial model. We show that the use of the beta-binomial model makes it possible to determine accurate credible intervals even in data which exhibit substantial overdispersion. This goes beyond classical measures for overdispersion-goodness-of-fit-which can detect overdispersion but provide no method to do correct inference for overdispersed data. We use Bayesian inference methods for estimating the posterior distribution of the parameters of the psychometric function. Unlike previous Bayesian psychometric inference methods our software implementation-psignifit 4-performs numerical integration of the posterior within automatically determined bounds. This avoids the use of Markov chain Monte Carlo (MCMC) methods typically requiring expert knowledge. Extensive numerical tests show the validity of the approach and we discuss implications of overdispersion for experimental design. A comprehensive MATLAB toolbox implementing the method is freely available; a python implementation providing the basic capabilities is also available. PMID:27013261
Accelerated gravitational wave parameter estimation with reduced order modeling.
Canizares, Priscilla; Field, Scott E; Gair, Jonathan; Raymond, Vivien; Smith, Rory; Tiglio, Manuel
2015-02-20
Inferring the astrophysical parameters of coalescing compact binaries is a key science goal of the upcoming advanced LIGO-Virgo gravitational-wave detector network and, more generally, gravitational-wave astronomy. However, current approaches to parameter estimation for these detectors require computationally expensive algorithms. Therefore, there is a pressing need for new, fast, and accurate Bayesian inference techniques. In this Letter, we demonstrate that a reduced order modeling approach enables rapid parameter estimation to be performed. By implementing a reduced order quadrature scheme within the LIGO Algorithm Library, we show that Bayesian inference on the 9-dimensional parameter space of nonspinning binary neutron star inspirals can be sped up by a factor of ∼30 for the early advanced detectors' configurations (with sensitivities down to around 40 Hz) and ∼70 for sensitivities down to around 20 Hz. This speedup will increase to about 150 as the detectors improve their low-frequency limit to 10 Hz, reducing to hours analyses which could otherwise take months to complete. Although these results focus on interferometric gravitational wave detectors, the techniques are broadly applicable to any experiment where fast Bayesian analysis is desirable. PMID:25763948
Accelerated Gravitational Wave Parameter Estimation with Reduced Order Modeling
NASA Astrophysics Data System (ADS)
Canizares, Priscilla; Field, Scott E.; Gair, Jonathan; Raymond, Vivien; Smith, Rory; Tiglio, Manuel
2015-02-01
Inferring the astrophysical parameters of coalescing compact binaries is a key science goal of the upcoming advanced LIGO-Virgo gravitational-wave detector network and, more generally, gravitational-wave astronomy. However, current approaches to parameter estimation for these detectors require computationally expensive algorithms. Therefore, there is a pressing need for new, fast, and accurate Bayesian inference techniques. In this Letter, we demonstrate that a reduced order modeling approach enables rapid parameter estimation to be performed. By implementing a reduced order quadrature scheme within the LIGO Algorithm Library, we show that Bayesian inference on the 9-dimensional parameter space of nonspinning binary neutron star inspirals can be sped up by a factor of ˜30 for the early advanced detectors' configurations (with sensitivities down to around 40 Hz) and ˜70 for sensitivities down to around 20 Hz. This speedup will increase to about 150 as the detectors improve their low-frequency limit to 10 Hz, reducing to hours analyses which could otherwise take months to complete. Although these results focus on interferometric gravitational wave detectors, the techniques are broadly applicable to any experiment where fast Bayesian analysis is desirable.
CTER-rapid estimation of CTF parameters with error assessment.
Penczek, Pawel A; Fang, Jia; Li, Xueming; Cheng, Yifan; Loerke, Justus; Spahn, Christian M T
2014-05-01
In structural electron microscopy, the accurate estimation of the Contrast Transfer Function (CTF) parameters, particularly defocus and astigmatism, is of utmost importance for both initial evaluation of micrograph quality and for subsequent structure determination. Due to increases in the rate of data collection on modern microscopes equipped with new generation cameras, it is also important that the CTF estimation can be done rapidly and with minimal user intervention. Finally, in order to minimize the necessity for manual screening of the micrographs by a user it is necessary to provide an assessment of the errors of fitted parameters values. In this work we introduce CTER, a CTF parameters estimation method distinguished by its computational efficiency. The efficiency of the method makes it suitable for high-throughput EM data collection, and enables the use of a statistical resampling technique, bootstrap, that yields standard deviations of estimated defocus and astigmatism amplitude and angle, thus facilitating the automation of the process of screening out inferior micrograph data. Furthermore, CTER also outputs the spatial frequency limit imposed by reciprocal space aliasing of the discrete form of the CTF and the finite window size. We demonstrate the efficiency and accuracy of CTER using a data set collected on a 300kV Tecnai Polara (FEI) using the K2 Summit DED camera in super-resolution counting mode. Using CTER we obtained a structure of the 80S ribosome whose large subunit had a resolution of 4.03Å without, and 3.85Å with, inclusion of astigmatism parameters. PMID:24562077
CTER—Rapid estimation of CTF parameters with error assessment
Penczek, Pawel A.; Fang, Jia; Li, Xueming; Cheng, Yifan; Loerke, Justus; Spahn, Christian M.T.
2014-01-01
In structural electron microscopy, the accurate estimation of the Contrast Transfer Function (CTF) parameters, particularly defocus and astigmatism, is of utmost importance for both initial evaluation of micrograph quality and for subsequent structure determination. Due to increases in the rate of data collection on modern microscopes equipped with new generation cameras, it is also important that the CTF estimation can be done rapidly and with minimal user intervention. Finally, in order to minimize the necessity for manual screening of the micrographs by a user it is necessary to provide an assessment of the errors of fitted parameters values. In this work we introduce CTER, a CTF parameters estimation method distinguished by its computational efficiency. The efficiency of the method makes it suitable for high-throughput EM data collection, and enables the use of a statistical resampling technique, bootstrap, that yields standard deviations of estimated defocus and astigmatism amplitude and angle, thus facilitating the automation of the process of screening out inferior micrograph data. Furthermore, CTER also outputs the spatial frequency limit imposed by reciprocal space aliasing of the discrete form of the CTF and the finite window size. We demonstrate the efficiency and accuracy of CTER using a data set collected on a 300 kV Tecnai Polara (FEI) using the K2 Summit DED camera in super-resolution counting mode. Using CTER we obtained a structure of the 80S ribosome whose large subunit had a resolution of 4.03 Å without, and 3.85 Å with, inclusion of astigmatism parameters. PMID:24562077
Accurate Estimation of the Fine Layering Effect on the Wave Propagation in the Carbonate Rocks
NASA Astrophysics Data System (ADS)
Bouchaala, F.; Ali, M. Y.
2014-12-01
The attenuation caused to the seismic wave during its propagation can be mainly divided into two parts, the scattering and the intrinsic attenuation. The scattering is an elastic redistribution of the energy due to the medium heterogeneities. However the intrinsic attenuation is an inelastic phenomenon, mainly due to the fluid-grain friction during the wave passage. The intrinsic attenuation is directly related to the physical characteristics of the medium, so this parameter is very can be used for media characterization and fluid detection, which is beneficial for the oil and gas industry. The intrinsic attenuation is estimated by subtracting the scattering from the total attenuation, therefore the accuracy of the intrinsic attenuation is directly dependent on the accuracy of the total attenuation and the scattering. The total attenuation can be estimated from the recorded waves, by using in-situ methods as the spectral ratio and frequency shift methods. The scattering is estimated by assuming the heterogeneities as a succession of stacked layers, each layer is characterized by a single density and velocity. The accuracy of the scattering is strongly dependent on the layer thicknesses, especially in the case of the media composed of carbonate rocks, such media are known for their strong heterogeneity. Previous studies gave some assumptions for the choice of the layer thickness, but they showed some limitations especially in the case of carbonate rocks. In this study we established a relationship between the layer thicknesses and the frequency of the propagation, after certain mathematical development of the Generalized O'Doherty-Anstey formula. We validated this relationship through some synthetic tests and real data provided from a VSP carried out over an onshore oilfield in the emirate of Abu Dhabi in the United Arab Emirates, primarily composed of carbonate rocks. The results showed the utility of our relationship for an accurate estimation of the scattering
NASA Astrophysics Data System (ADS)
Catena, Riccardo; Notari, Alessio
2013-07-01
The peculiar motion of an observer with respect to the CMB rest frame induces an apparent deflection of the observed CMB photons, i.e. aberration, and a shift in their frequency, i.e. Doppler effect. Both effects distort the temperature multipoles alm's via a mixing matrix at any l. The common lore when performing a CMB based cosmological parameter estimation is to consider that Doppler affects only the l = 1 multipole, and neglect any other corrections. In ref. [1] we checked the validity of this assumption in parameter estimation for a Planck-like angular resolution, both for a full-sky ideal experiment and also when sky cuts are included to model CMB foreground contaminations with a sky fraction similar to the Planck satellite. The result to this analysis was that aberration and Doppler have a sizable impact on a CMB based parameter estimation. In this erratum we correct an error made in ref. [1] when comparing pseudo angular power spectra computed in the CMB rest frame with the ones measured by a moving observer. Properly comparing the two spectra we find now that although the corrections to the Cl due to aberration and Doppler are larger than the cosmic variance at l > 1000 and potentially important, the resulting bias on the parameters is negligible for Planck.
Parameter Estimation and Data Management System of Sea Clutter
NASA Astrophysics Data System (ADS)
Cong, Bo; Duan, Qingguang; Qu, Yuanxin
2016-02-01
In this paper, a parameter estimation and data management system of sea clutter is described, which can acquire the data of sea clutter, implement parameter estimation and realize real-time communications.
Cook, Andrea J.; Elmore, Joann G.; Zhu, Weiwei; Jackson, Sara L.; Carney, Patricia A.; Flowers, Chris; Onega, Tracy; Geller, Berta; Rosenberg, Robert D.; Miglioretti, Diana L.
2013-01-01
Objective To determine if U.S. radiologists accurately estimate their own interpretive performance of screening mammography and how they compare their performance to their peers’. Materials and Methods 174 radiologists from six Breast Cancer Surveillance Consortium (BCSC) registries completed a mailed survey between 2005 and 2006. Radiologists’ estimated and actual recall, false positive, and cancer detection rates and positive predictive value of biopsy recommendation (PPV2) for screening mammography were compared. Radiologists’ ratings of their performance as lower, similar, or higher than their peers were compared to their actual performance. Associations with radiologist characteristics were estimated using weighted generalized linear models. The study was approved by the institutional review boards of the participating sites, informed consent was obtained from radiologists, and procedures were HIPAA compliant. Results While most radiologists accurately estimated their cancer detection and recall rates (74% and 78% of radiologists), fewer accurately estimated their false positive rate and PPV2 (19% and 26%). Radiologists reported having similar (43%) or lower (31%) recall rates and similar (52%) or lower (33%) false positive rates compared to their peers, and similar (72%) or higher (23%) cancer detection rates and similar (72%) or higher (38%) PPV2. Estimation accuracy did not differ by radiologists’ characteristics except radiologists who interpret ≤1,000 mammograms annually were less accurate at estimating their recall rates. Conclusion Radiologists perceive their performance to be better than it actually is and at least as good as their peers. Radiologists have particular difficulty estimating their false positive rates and PPV2. PMID:22915414
System and method for motor parameter estimation
Luhrs, Bin; Yan, Ting
2014-03-18
A system and method for determining unknown values of certain motor parameters includes a motor input device connectable to an electric motor having associated therewith values for known motor parameters and an unknown value of at least one motor parameter. The motor input device includes a processing unit that receives a first input from the electric motor comprising values for the known motor parameters for the electric motor and receive a second input comprising motor data on a plurality of reference motors, including values for motor parameters corresponding to the known motor parameters of the electric motor and values for motor parameters corresponding to the at least one unknown motor parameter value of the electric motor. The processor determines the unknown value of the at least one motor parameter from the first input and the second input and determines a motor management strategy for the electric motor based thereon.
Efficient material parameters estimation with terahertz time-domain spectroscopy
NASA Astrophysics Data System (ADS)
Ahmed, Osman S.; Swillam, Mohamed A.; Bakr, Mohamed H.; Li, Xun
2011-02-01
Existing parameter extraction techniques in the terahertz range utilize the magnitude and phase of the transmission function at different frequencies. The number of unknowns is larger than the number of available information creating a nonuniqueness problem. The estimation of the material thickness thus suffers from inaccuracies. We propose a novel optimization technique for the estimation of material refractive index in the terahertz frequency range. The algorithm is applied for materials with arbitrary frequency dependence. Dispersive dielectric models are embedded for accurate parameter extraction of a sample with unknown thickness. Instead of solving N expensive nonlinear optimization problems with different possible material thickness, our technique obtains the optimal material thickness by solving only one optimization problem. The solution of the utilized optimization problem is accelerated by estimating both the first order derivatives (gradient) and second order derivatives (Hessian) of the objective function and supplying them to the optimizer. Our approach has been successfully illustrated through a number of examples with different dispersive models. The examples include the characterization of carbon nanotubes. The technique has also been successfully applied to materials characterized by the Cole-Cole, Debye, and Lorentz models.
Shen, Yan; Lou, Shuqin; Wang, Xin
2014-03-20
The evaluation accuracy of real optical properties of photonic crystal fibers (PCFs) is determined by the accurate extraction of air hole edges from microscope images of cross sections of practical PCFs. A novel estimation method of point spread function (PSF) based on Kalman filter is presented to rebuild the micrograph image of the PCF cross-section and thus evaluate real optical properties for practical PCFs. Through tests on both artificially degraded images and microscope images of cross sections of practical PCFs, we prove that the proposed method can achieve more accurate PSF estimation and lower PSF variance than the traditional Bayesian estimation method, and thus also reduce the defocus effect. With this method, we rebuild the microscope images of two kinds of commercial PCFs produced by Crystal Fiber and analyze the real optical properties of these PCFs. Numerical results are in accord with the product parameters. PMID:24663461
Parameter estimation with Sandage-Loeb test
Geng, Jia-Jia; Zhang, Jing-Fei; Zhang, Xin E-mail: jfzhang@mail.neu.edu.cn
2014-12-01
The Sandage-Loeb (SL) test directly measures the expansion rate of the universe in the redshift range of 2 ∼< z ∼< 5 by detecting redshift drift in the spectra of Lyman-α forest of distant quasars. We discuss the impact of the future SL test data on parameter estimation for the ΛCDM, the wCDM, and the w{sub 0}w{sub a}CDM models. To avoid the potential inconsistency with other observational data, we take the best-fitting dark energy model constrained by the current observations as the fiducial model to produce 30 mock SL test data. The SL test data provide an important supplement to the other dark energy probes, since they are extremely helpful in breaking the existing parameter degeneracies. We show that the strong degeneracy between Ω{sub m} and H{sub 0} in all the three dark energy models is well broken by the SL test. Compared to the current combined data of type Ia supernovae, baryon acoustic oscillation, cosmic microwave background, and Hubble constant, the 30-yr observation of SL test could improve the constraints on Ω{sub m} and H{sub 0} by more than 60% for all the three models. But the SL test can only moderately improve the constraint on the equation of state of dark energy. We show that a 30-yr observation of SL test could help improve the constraint on constant w by about 25%, and improve the constraints on w{sub 0} and w{sub a} by about 20% and 15%, respectively. We also quantify the constraining power of the SL test in the future high-precision joint geometric constraints on dark energy. The mock future supernova and baryon acoustic oscillation data are simulated based on the space-based project JDEM. We find that the 30-yr observation of SL test would help improve the measurement precision of Ω{sub m}, H{sub 0}, and w{sub a} by more than 70%, 20%, and 60%, respectively, for the w{sub 0}w{sub a}CDM model.
Estimation of high altitude Martian dust parameters
NASA Astrophysics Data System (ADS)
Pabari, Jayesh; Bhalodi, Pinali
2016-07-01
Dust devils are known to occur near the Martian surface mostly during the mid of Southern hemisphere summer and they play vital role in deciding background dust opacity in the atmosphere. The second source of high altitude Martian dust could be due to the secondary ejecta caused by impacts on Martian Moons, Phobos and Deimos. Also, the surfaces of the Moons are charged positively due to ultraviolet rays from the Sun and negatively due to space plasma currents. Such surface charging may cause fine grains to be levitated, which can easily escape the Moons. It is expected that the escaping dust form dust rings within the orbits of the Moons and therefore also around the Mars. One more possible source of high altitude Martian dust is interplanetary in nature. Due to continuous supply of the dust from various sources and also due to a kind of feedback mechanism existing between the ring or tori and the sources, the dust rings or tori can sustain over a period of time. Recently, very high altitude dust at about 1000 km has been found by MAVEN mission and it is expected that the dust may be concentrated at about 150 to 500 km. However, it is mystery how dust has reached to such high altitudes. Estimation of dust parameters before-hand is necessary to design an instrument for the detection of high altitude Martian dust from a future orbiter. In this work, we have studied the dust supply rate responsible primarily for the formation of dust ring or tori, the life time of dust particles around the Mars, the dust number density as well as the effect of solar radiation pressure and Martian oblateness on dust dynamics. The results presented in this paper may be useful to space scientists for understanding the scenario and designing an orbiter based instrument to measure the dust surrounding the Mars for solving the mystery. The further work is underway.
Discrete state model and accurate estimation of loop entropy of RNA secondary structures.
Zhang, Jian; Lin, Ming; Chen, Rong; Wang, Wei; Liang, Jie
2008-03-28
Conformational entropy makes important contribution to the stability and folding of RNA molecule, but it is challenging to either measure or compute conformational entropy associated with long loops. We develop optimized discrete k-state models of RNA backbone based on known RNA structures for computing entropy of loops, which are modeled as self-avoiding walks. To estimate entropy of hairpin, bulge, internal loop, and multibranch loop of long length (up to 50), we develop an efficient sampling method based on the sequential Monte Carlo principle. Our method considers excluded volume effect. It is general and can be applied to calculating entropy of loops with longer length and arbitrary complexity. For loops of short length, our results are in good agreement with a recent theoretical model and experimental measurement. For long loops, our estimated entropy of hairpin loops is in excellent agreement with the Jacobson-Stockmayer extrapolation model. However, for bulge loops and more complex secondary structures such as internal and multibranch loops, we find that the Jacobson-Stockmayer extrapolation model has large errors. Based on estimated entropy, we have developed empirical formulae for accurate calculation of entropy of long loops in different secondary structures. Our study on the effect of asymmetric size of loops suggest that loop entropy of internal loops is largely determined by the total loop length, and is only marginally affected by the asymmetric size of the two loops. Our finding suggests that the significant asymmetric effects of loop length in internal loops measured by experiments are likely to be partially enthalpic. Our method can be applied to develop improved energy parameters important for studying RNA stability and folding, and for predicting RNA secondary and tertiary structures. The discrete model and the program used to calculate loop entropy can be downloaded at http://gila.bioengr.uic.edu/resources/RNA.html. PMID:18376982
Estimation of Wheat Agronomic Parameters using New Spectral Indices
Jin, Xiu-liang; Diao, Wan-ying; Xiao, Chun-hua; Wang, Fang-yong; Chen, Bing; Wang, Ke-ru; Li, Shao-kun
2013-01-01
Crop agronomic parameters (leaf area index (LAI), nitrogen (N) uptake, total chlorophyll (Chl) content ) are very important for the prediction of crop growth. The objective of this experiment was to investigate whether the wheat LAI, N uptake, and total Chl content could be accurately predicted using spectral indices collected at different stages of wheat growth. Firstly, the product of the optimized soil-adjusted vegetation index and wheat biomass dry weight (OSAVI×BDW) were used to estimate LAI, N uptake, and total Chl content; secondly, BDW was replaced by spectral indices to establish new spectral indices (OSAVI×OSAVI, OSAVI×SIPI, OSAVI×CIred edge, OSAVI×CIgreen mode and OSAVI×EVI2); finally, we used the new spectral indices for estimating LAI, N uptake, and total Chl content. The results showed that the new spectral indices could be used to accurately estimate LAI, N uptake, and total Chl content. The highest R2 and the lowest RMSEs were 0.711 and 0.78 (OSAVI×EVI2), 0.785 and 3.98 g/m2 (OSAVI×CIred edge) and 0.846 and 0.65 g/m2 (OSAVI×CIred edge) for LAI, nitrogen uptake and total Chl content, respectively. The new spectral indices performed better than the OSAVI alone, and the problems of a lack of sensitivity at earlier growth stages and saturation at later growth stages, which are typically associated with the OSAVI, were improved. The overall results indicated that this new spectral indices provided the best approximation for the estimation of agronomic indices for all growth stages of wheat. PMID:24023639
Browning, Sharon R.; Browning, Brian L.
2015-01-01
Existing methods for estimating historical effective population size from genetic data have been unable to accurately estimate effective population size during the most recent past. We present a non-parametric method for accurately estimating recent effective population size by using inferred long segments of identity by descent (IBD). We found that inferred segments of IBD contain information about effective population size from around 4 generations to around 50 generations ago for SNP array data and to over 200 generations ago for sequence data. In human populations that we examined, the estimates of effective size were approximately one-third of the census size. We estimate the effective population size of European-ancestry individuals in the UK four generations ago to be eight million and the effective population size of Finland four generations ago to be 0.7 million. Our method is implemented in the open-source IBDNe software package. PMID:26299365
NASA Astrophysics Data System (ADS)
Peng, Liang-You; Gong, Qihuang
2010-12-01
The accurate computations of hydrogenic continuum wave functions are very important in many branches of physics such as electron-atom collisions, cold atom physics, and atomic ionization in strong laser fields, etc. Although there already exist various algorithms and codes, most of them are only reliable in a certain ranges of parameters. In some practical applications, accurate continuum wave functions need to be calculated at extremely low energies, large radial distances and/or large angular momentum number. Here we provide such a code, which can generate accurate hydrogenic continuum wave functions and corresponding Coulomb phase shifts at a wide range of parameters. Without any essential restrict to angular momentum number, the present code is able to give reliable results at the electron energy range [10,10] eV for radial distances of [10,10] a.u. We also find the present code is very efficient, which should find numerous applications in many fields such as strong field physics. Program summaryProgram title: HContinuumGautchi Catalogue identifier: AEHD_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHD_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 1233 No. of bytes in distributed program, including test data, etc.: 7405 Distribution format: tar.gz Programming language: Fortran90 in fixed format Computer: AMD Processors Operating system: Linux RAM: 20 MBytes Classification: 2.7, 4.5 Nature of problem: The accurate computation of atomic continuum wave functions is very important in many research fields such as strong field physics and cold atom physics. Although there have already existed various algorithms and codes, most of them can only be applicable and reliable in a certain range of parameters. We present here an accurate FORTRAN program for
Estimation Methods for One-Parameter Testlet Models
ERIC Educational Resources Information Center
Jiao, Hong; Wang, Shudong; He, Wei
2013-01-01
This study demonstrated the equivalence between the Rasch testlet model and the three-level one-parameter testlet model and explored the Markov Chain Monte Carlo (MCMC) method for model parameter estimation in WINBUGS. The estimation accuracy from the MCMC method was compared with those from the marginalized maximum likelihood estimation (MMLE)…
Updated Item Parameter Estimates Using Sparse CAT Data.
ERIC Educational Resources Information Center
Smith, Robert L.; Rizavi, Saba; Paez, Roxanna; Rotou, Ourania
A study was conducted to investigate whether augmenting the calibration of items using computerized adaptive test (CAT) data matrices produced estimates that were unbiased and improved the stability of existing item parameter estimates. Item parameter estimates from four pools of items constructed for operational use were used in the study to…
NASA Astrophysics Data System (ADS)
Vizireanu, D. N.; Halunga, S. V.
2012-04-01
A simple, fast and accurate amplitude estimation algorithm of sinusoidal signals for DSP based instrumentation is proposed. It is shown that eight samples, used in two steps, are sufficient. A practical analytical formula for amplitude estimation is obtained. Numerical results are presented. Simulations have been performed when the sampled signal is affected by white Gaussian noise and when the samples are quantized on a given number of bits.
Reduced order parameter estimation using quasilinearization and quadratic programming
NASA Astrophysics Data System (ADS)
Siade, Adam J.; Putti, Mario; Yeh, William W.-G.
2012-06-01
The ability of a particular model to accurately predict how a system responds to forcing is predicated on various model parameters that must be appropriately identified. There are many algorithms whose purpose is to solve this inverse problem, which is often computationally intensive. In this study, we propose a new algorithm that significantly reduces the computational burden associated with parameter identification. The algorithm is an extension of the quasilinearization approach where the governing system of differential equations is linearized with respect to the parameters. The resulting inverse problem therefore becomes a linear regression or quadratic programming problem (QP) for minimizing the sum of squared residuals; the solution becomes an update on the parameter set. This process of linearization and regression is repeated until convergence takes place. This algorithm has not received much attention, as the QPs can become quite large, often infeasible for real-world systems. To alleviate this drawback, proper orthogonal decomposition is applied to reduce the size of the linearized model, thereby reducing the computational burden of solving each QP. In fact, this study shows that the snapshots need only be calculated once at the very beginning of the algorithm, after which no further calculations of the reduced-model subspace are required. The proposed algorithm therefore only requires one linearized full-model run per parameter at the first iteration followed by a series of reduced-order QPs. The method is applied to a groundwater model with about 30,000 computation nodes where as many as 15 zones of hydraulic conductivity are estimated.
Attitude determination and parameter estimation using vector observations - Theory
NASA Technical Reports Server (NTRS)
Markley, F. Landis
1989-01-01
Procedures for attitude determination based on Wahba's loss function are generalized to include the estimation of parameters other than the attitude, such as sensor biases. Optimization with respect to the attitude is carried out using the q-method, which does not require an a priori estimate of the attitude. Optimization with respect to the other parameters employs an iterative approach, which does require an a priori estimate of these parameters. Conventional state estimation methods require a priori estimates of both the parameters and the attitude, while the algorithm presented in this paper always computes the exact optimal attitude for given values of the parameters. Expressions for the covariance of the attitude and parameter estimates are derived.
Najafizadeh, Laleh; Gandjbakhche, Amir H.; Pourrezaei, Kambiz; Daryoush, Afshin
2013-01-01
Abstract. Modeling behavior of broadband (30 to 1000 MHz) frequency modulated near-infrared (NIR) photons through a phantom is the basis for accurate extraction of optical absorption and scattering parameters of biological turbid media. Photon dynamics in a phantom are predicted using both analytical and numerical simulation and are related to the measured insertion loss (IL) and insertion phase (IP) for a given geometry based on phantom optical parameters. Accuracy of the extracted optical parameters using finite element method (FEM) simulation is compared to baseline analytical calculations from the diffusion equation (DE) for homogenous brain phantoms. NIR spectroscopy is performed using custom-designed, broadband, free-space optical transmitter (Tx) and receiver (Rx) modules that are developed for photon migration at wavelengths of 680, 780, and 820 nm. Differential detection between two optical Rx locations separated by 0.3 cm is employed to eliminate systemic artifacts associated with interfaces of the optical Tx and Rx with the phantoms. Optical parameter extraction is achieved for four solid phantom samples using the least-square-error method in MATLAB (for DE) and COMSOL (for FEM) simulation by fitting data to measured results over broadband and narrowband frequency modulation. Confidence in numerical modeling of the photonic behavior using FEM has been established here by comparing the transmission mode’s experimental results with the predictions made by DE and FEM for known commercial solid brain phantoms. PMID:23322361
Parameter and uncertainty estimation for mechanistic, spatially explicit epidemiological models
NASA Astrophysics Data System (ADS)
Finger, Flavio; Schaefli, Bettina; Bertuzzo, Enrico; Mari, Lorenzo; Rinaldo, Andrea
2014-05-01
Epidemiological models can be a crucially important tool for decision-making during disease outbreaks. The range of possible applications spans from real-time forecasting and allocation of health-care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. Our spatially explicit, mechanistic models for cholera epidemics have been successfully applied to several epidemics including, the one that struck Haiti in late 2010 and is still ongoing. Calibration and parameter estimation of such models represents a major challenge because of properties unusual in traditional geoscientific domains such as hydrology. Firstly, the epidemiological data available might be subject to high uncertainties due to error-prone diagnosis as well as manual (and possibly incomplete) data collection. Secondly, long-term time-series of epidemiological data are often unavailable. Finally, the spatially explicit character of the models requires the comparison of several time-series of model outputs with their real-world counterparts, which calls for an appropriate weighting scheme. It follows that the usual assumption of a homoscedastic Gaussian error distribution, used in combination with classical calibration techniques based on Markov chain Monte Carlo algorithms, is likely to be violated, whereas the construction of an appropriate formal likelihood function seems close to impossible. Alternative calibration methods, which allow for accurate estimation of total model uncertainty, particularly regarding the envisaged use of the models for decision-making, are thus needed. Here we present the most recent developments regarding methods for parameter and uncertainty estimation to be used with our mechanistic, spatially explicit models for cholera epidemics, based on informal measures of goodness of fit.
Parameter estimation and error analysis in environmental modeling and computation
NASA Technical Reports Server (NTRS)
Kalmaz, E. E.
1986-01-01
A method for the estimation of parameters and error analysis in the development of nonlinear modeling for environmental impact assessment studies is presented. The modular computer program can interactively fit different nonlinear models to the same set of data, dynamically changing the error structure associated with observed values. Parameter estimation techniques and sequential estimation algorithms employed in parameter identification and model selection are first discussed. Then, least-square parameter estimation procedures are formulated, utilizing differential or integrated equations, and are used to define a model for association of error with experimentally observed data.
Transient analysis of intercalation electrodes for parameter estimation
NASA Astrophysics Data System (ADS)
Devan, Sheba
An essential part of integrating batteries as power sources in any application, be it a large scale automotive application or a small scale portable application, is an efficient Battery Management System (BMS). The combination of a battery with the microprocessor based BMS (called "smart battery") helps prolong the life of the battery by operating in the optimal regime and provides accurate information regarding the battery to the end user. The main purposes of BMS are cell protection, monitoring and control, and communication between different components. These purposes are fulfilled by tracking the change in the parameters of the intercalation electrodes in the batteries. Consequently, the functions of the BMS should be prompt, which requires the methodology of extracting the parameters to be efficient in time. The traditional transient techniques applied so far may not be suitable due to reasons such as the inability to apply these techniques when the battery is under operation, long experimental time, etc. The primary aim of this research work is to design a fast, accurate and reliable technique that can be used to extract parameter values of the intercalation electrodes. A methodology based on analysis of the short time response to a sinusoidal input perturbation, in the time domain is demonstrated using a porous electrode model for an intercalation electrode. It is shown that the parameters associated with the interfacial processes occurring in the electrode can be determined rapidly, within a few milliseconds, by measuring the response in the transient region. The short time analysis in the time domain is then extended to a single particle model that involves bulk diffusion in the solid phase in addition to interfacial processes. A systematic procedure for sequential parameter estimation using sensitivity analysis is described. Further, the short time response and the input perturbation are transformed into the frequency domain using Fast Fourier Transform
New approaches to estimation of magnetotelluric parameters
Egbert, G.D. . Coll. of Oceanography); Booker, J.R. )
1990-01-01
This document proposed the development and application of some new statistical techniques for improving the collection and analysis of wide-band magnetotelluric (MT) data. The principle goal of our work is to develop and implement fully automatic single station and remote reference impedance estimation schemes which are robust, unbiased and statistically efficient. The initial proposal suggested several extensions to the regression M-estimates to better allow for non-stationary and non-Gaussian noise in both electric and magnetic field channels (measured at one or more simultaneous stations). A second goal of the proposal was to develop formal, reliable procedures for estimating undistorted 2-d strike directions and to develop statistics for assessing the validity of the 2-d assumption that are unaffected by near surface static distortion effects. To test and validate the methods, working with data selected from a series of over 200 wide-band MT sites was proposed. For the current budget period, setting up a data base, and completing development and initial testing of the single station and remote reference methods outlined in the proposal is suggested. 8 refs., 13 figs.
Applications of parameter estimation in the study of spinning airplanes
NASA Technical Reports Server (NTRS)
W Taylor, L., Jr.
1982-01-01
Spinning airplanes offer challenges to estimating dynamic parameters because of the nonlinear nature of the dynamics. In this paper, parameter estimation techniques are applied to spin flight test data for estimating the error in measuring post-stall angles of attack, deriving Euler angles from angular velocity data, and estimating nonlinear aerodynamic characteristics. The value of the scale factor for post-stall angles of attack agrees closely with that obtained from special wind-tunnel tests. The independently derived Euler angles are seen to be valid in spite of steep pitch angles. Estimates of flight derived nonlinear aerodynamic parameters are evaluated in terms of the expected fit error.
Advances in parameter estimation techniques applied to flexible structures
NASA Technical Reports Server (NTRS)
Maben, Egbert; Zimmerman, David C.
1994-01-01
In this work, various parameter estimation techniques are investigated in the context of structural system identification utilizing distributed parameter models and 'measured' time-domain data. Distributed parameter models are formulated using the PDEMOD software developed by Taylor. Enhancements made to PDEMOD for this work include the following: (1) a Wittrick-Williams based root solving algorithm; (2) a time simulation capability; and (3) various parameter estimation algorithms. The parameter estimations schemes will be contrasted using the NASA Mini-Mast as the focus structure.
On the accurate estimation of gap fraction during daytime with digital cover photography
NASA Astrophysics Data System (ADS)
Hwang, Y. R.; Ryu, Y.; Kimm, H.; Macfarlane, C.; Lang, M.; Sonnentag, O.
2015-12-01
Digital cover photography (DCP) has emerged as an indirect method to obtain gap fraction accurately. Thus far, however, the intervention of subjectivity, such as determining the camera relative exposure value (REV) and threshold in the histogram, hindered computing accurate gap fraction. Here we propose a novel method that enables us to measure gap fraction accurately during daytime under various sky conditions by DCP. The novel method computes gap fraction using a single DCP unsaturated raw image which is corrected for scattering effects by canopies and a reconstructed sky image from the raw format image. To test the sensitivity of the novel method derived gap fraction to diverse REVs, solar zenith angles and canopy structures, we took photos in one hour interval between sunrise to midday under dense and sparse canopies with REV 0 to -5. The novel method showed little variation of gap fraction across different REVs in both dense and spares canopies across diverse range of solar zenith angles. The perforated panel experiment, which was used to test the accuracy of the estimated gap fraction, confirmed that the novel method resulted in the accurate and consistent gap fractions across different hole sizes, gap fractions and solar zenith angles. These findings highlight that the novel method opens new opportunities to estimate gap fraction accurately during daytime from sparse to dense canopies, which will be useful in monitoring LAI precisely and validating satellite remote sensing LAI products efficiently.
Heidari, M.; Ranjithan, S.R.
1998-01-01
In using non-linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated-Newton search technique to estimate groundwater parameters for a confined steady-state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near-correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is experimentally demonstrated that only one piece of prior information of the least sensitive parameter is sufficient to arrive at the global or near-global optimum solution. For hydraulic head data with measurement errors, the error in the estimation of parameters increases as the standard deviation of the errors increases. Results from our experiments show that, in general, the accuracy of the estimated parameters depends on the level of noise in the hydraulic head data and the initial values used in the truncated-Newton search technique.In using non-linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated-Newton search technique to estimate groundwater parameters for a confined steady-state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near-correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is
Accurate Estimation of the Entropy of Rotation-Translation Probability Distributions.
Fogolari, Federico; Dongmo Foumthuim, Cedrix Jurgal; Fortuna, Sara; Soler, Miguel Angel; Corazza, Alessandra; Esposito, Gennaro
2016-01-12
The estimation of rotational and translational entropies in the context of ligand binding has been the subject of long-time investigations. The high dimensionality (six) of the problem and the limited amount of sampling often prevent the required resolution to provide accurate estimates by the histogram method. Recently, the nearest-neighbor distance method has been applied to the problem, but the solutions provided either address rotation and translation separately, therefore lacking correlations, or use a heuristic approach. Here we address rotational-translational entropy estimation in the context of nearest-neighbor-based entropy estimation, solve the problem numerically, and provide an exact and an approximate method to estimate the full rotational-translational entropy. PMID:26605696
Determining the Accuracy of Aerodynamic Model Parameters Estimated from Flight Test Data
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.; Klein, Vladislav
1995-01-01
An important part of building mathematical models based on measured data is calculating the accuracy associated with statistical estimates of the model parameters. Indeed, without some idea of this accuracy, the parameter estimates themselves have limited value. In this work, an expression for computing quantitatively correct parameter accuracy measures for maximum likelihood parameter estimates with colored residuals is developed and validated. This result is important because experience in analyzing flight test data reveals that the output residuals from maximum likelihood estimation are almost always colored. The calculations involved can be appended to conventional maximum likelihood estimation algorithms. Monte Carlo simulation runs were used to show that parameter accuracy measures from the new technique accurately reflect the quality of the parameter estimates from maximum likelihood estimation without the need for correction factors or frequency domain analysis of the output residuals. The technique was applied to flight test data from repeated maneuvers flown on the F-18 High Alpha Research Vehicle (HARV). As in the simulated cases, parameter accuracy measures from the new technique were in agreement with the scatter in the parameter estimates from repeated maneuvers, while conventional parameter accuracy measures were optimistic.
Information Gains in Cosmological Parameter Estimation
NASA Astrophysics Data System (ADS)
Seehars, Sebastian; Amara, Adam; Refregier, Alexandre; Paranjape, Aseem; Akeret, Joël
2014-05-01
Combining datasets from different experiments and probes to constrain cosmological models is an important challenge in observational cosmology. We summarize a framework for measuring the constraining power and the consistency of separately or jointly analyzed data within a given model that we proposed in earlier work (Seehars et al. 2014). Applying the Kullback-Leibler divergence to posterior distributions, we can quantify the difference between constraints and distinguish contributions from gains in precision and shifts in parameter space. We show results from applying this technique to a combination of datasets and probes such as the cosmic microwave background or baryon acoustic oscillations.
A new Bayesian recursive technique for parameter estimation
NASA Astrophysics Data System (ADS)
Kaheil, Yasir H.; Gill, M. Kashif; McKee, Mac; Bastidas, Luis
2006-08-01
The performance of any model depends on how well its associated parameters are estimated. In the current application, a localized Bayesian recursive estimation (LOBARE) approach is devised for parameter estimation. The LOBARE methodology is an extension of the Bayesian recursive estimation (BARE) method. It is applied in this paper on two different types of models: an artificial intelligence (AI) model in the form of a support vector machine (SVM) application for forecasting soil moisture and a conceptual rainfall-runoff (CRR) model represented by the Sacramento soil moisture accounting (SAC-SMA) model. Support vector machines, based on statistical learning theory (SLT), represent the modeling task as a quadratic optimization problem and have already been used in various applications in hydrology. They require estimation of three parameters. SAC-SMA is a very well known model that estimates runoff. It has a 13-dimensional parameter space. In the LOBARE approach presented here, Bayesian inference is used in an iterative fashion to estimate the parameter space that will most likely enclose a best parameter set. This is done by narrowing the sampling space through updating the "parent" bounds based on their fitness. These bounds are actually the parameter sets that were selected by BARE runs on subspaces of the initial parameter space. The new approach results in faster convergence toward the optimal parameter set using minimum training/calibration data and fewer sets of parameter values. The efficacy of the localized methodology is also compared with the previously used BARE algorithm.
Space Shuttle propulsion parameter estimation using optimal estimation techniques
NASA Technical Reports Server (NTRS)
1983-01-01
This fourth monthly progress report again contains corrections and additions to the previously submitted reports. The additions include a simplified SRB model that is directly incorporated into the estimation algorithm and provides the required partial derivatives. The resulting partial derivatives are analytical rather than numerical as would be the case using the SOBER routines. The filter and smoother routine developments have continued. These routines are being checked out.
Periodic orbits of hybrid systems and parameter estimation via AD.
Guckenheimer, John.; Phipps, Eric Todd; Casey, Richard
2004-07-01
Rhythmic, periodic processes are ubiquitous in biological systems; for example, the heart beat, walking, circadian rhythms and the menstrual cycle. Modeling these processes with high fidelity as periodic orbits of dynamical systems is challenging because: (1) (most) nonlinear differential equations can only be solved numerically; (2) accurate computation requires solving boundary value problems; (3) many problems and solutions are only piecewise smooth; (4) many problems require solving differential-algebraic equations; (5) sensitivity information for parameter dependence of solutions requires solving variational equations; and (6) truncation errors in numerical integration degrade performance of optimization methods for parameter estimation. In addition, mathematical models of biological processes frequently contain many poorly-known parameters, and the problems associated with this impedes the construction of detailed, high-fidelity models. Modelers are often faced with the difficult problem of using simulations of a nonlinear model, with complex dynamics and many parameters, to match experimental data. Improved computational tools for exploring parameter space and fitting models to data are clearly needed. This paper describes techniques for computing periodic orbits in systems of hybrid differential-algebraic equations and parameter estimation methods for fitting these orbits to data. These techniques make extensive use of automatic differentiation to accurately and efficiently evaluate derivatives for time integration, parameter sensitivities, root finding and optimization. The boundary value problem representing a periodic orbit in a hybrid system of differential algebraic equations is discretized via multiple-shooting using a high-degree Taylor series integration method [GM00, Phi03]. Numerical solutions to the shooting equations are then estimated by a Newton process yielding an approximate periodic orbit. A metric is defined for computing the distance
Gravity Field Parameter Estimation Using QR Factorization
NASA Astrophysics Data System (ADS)
Klokocnik, J.; Wagner, C. A.; McAdoo, D.; Kostelecky, J.; Bezdek, A.; Novak, P.; Gruber, C.; Marty, J.; Bruinsma, S. L.; Gratton, S.; Balmino, G.; Baboulin, M.
2007-12-01
This study compares the accuracy of the estimated geopotential coefficients when QR factorization is used instead of the classical method applied at our institute, namely the generation of normal equations that are solved by means of Cholesky decomposition. The objective is to evaluate the gain in numerical precision, which is obtained at considerable extra cost in terms of computer resources. Therefore, a significant increase in precision must be realized in order to justify the additional cost. Numerical simulations were done in order to examine the performance of both solution methods. Reference gravity gradients were simulated, using the EIGEN-GL04C gravity field model to degree and order 300, every 3 seconds along a near-circular, polar orbit at 250 km altitude. The simulation spanned a total of 60 days. A polar orbit was selected in this simulation in order to avoid the 'polar gap' problem, which causes inaccurate estimation of the low-order spherical harmonic coefficients. Regularization is required in that case (e.g., the GOCE mission), which is not the subject of the present study. The simulated gravity gradients, to which white noise was added, were then processed with the GINS software package, applying EIGEN-CG03 as the background gravity field model, followed either by the usual normal equation computation or using the QR approach for incremental linear least squares. The accuracy assessment of the gravity field recovery consists in computing the median error degree-variance spectra, accumulated geoid errors, geoid errors due to individual coefficients, and geoid errors calculated on a global grid. The performance, in terms of memory usage, required disk space, and CPU time, of the QR versus the normal equation approach is also evaluated.
Fuzzy Supernova Templates. II. Parameter Estimation
NASA Astrophysics Data System (ADS)
Rodney, Steven A.; Tonry, John L.
2010-05-01
Wide-field surveys will soon be discovering Type Ia supernovae (SNe) at rates of several thousand per year. Spectroscopic follow-up can only scratch the surface for such enormous samples, so these extensive data sets will only be useful to the extent that they can be characterized by the survey photometry alone. In a companion paper we introduced the Supernova Ontology with Fuzzy Templates (SOFT) method for analyzing SNe using direct comparison to template light curves, and demonstrated its application for photometric SN classification. In this work we extend the SOFT method to derive estimates of redshift and luminosity distance for Type Ia SNe, using light curves from the Sloan Digital Sky Survey (SDSS) and Supernova Legacy Survey (SNLS) as a validation set. Redshifts determined by SOFT using light curves alone are consistent with spectroscopic redshifts, showing an rms scatter in the residuals of rms z = 0.051. SOFT can also derive simultaneous redshift and distance estimates, yielding results that are consistent with the currently favored ΛCDM cosmological model. When SOFT is given spectroscopic information for SN classification and redshift priors, the rms scatter in Hubble diagram residuals is 0.18 mag for the SDSS data and 0.28 mag for the SNLS objects. Without access to any spectroscopic information, and even without any redshift priors from host galaxy photometry, SOFT can still measure reliable redshifts and distances, with an increase in the Hubble residuals to 0.37 mag for the combined SDSS and SNLS data set. Using Monte Carlo simulations, we predict that SOFT will be able to improve constraints on time-variable dark energy models by a factor of 2-3 with each new generation of large-scale SN surveys.
FUZZY SUPERNOVA TEMPLATES. II. PARAMETER ESTIMATION
Rodney, Steven A.; Tonry, John L. E-mail: jt@ifa.hawaii.ed
2010-05-20
Wide-field surveys will soon be discovering Type Ia supernovae (SNe) at rates of several thousand per year. Spectroscopic follow-up can only scratch the surface for such enormous samples, so these extensive data sets will only be useful to the extent that they can be characterized by the survey photometry alone. In a companion paper we introduced the Supernova Ontology with Fuzzy Templates (SOFT) method for analyzing SNe using direct comparison to template light curves, and demonstrated its application for photometric SN classification. In this work we extend the SOFT method to derive estimates of redshift and luminosity distance for Type Ia SNe, using light curves from the Sloan Digital Sky Survey (SDSS) and Supernova Legacy Survey (SNLS) as a validation set. Redshifts determined by SOFT using light curves alone are consistent with spectroscopic redshifts, showing an rms scatter in the residuals of rms{sub z} = 0.051. SOFT can also derive simultaneous redshift and distance estimates, yielding results that are consistent with the currently favored {Lambda}CDM cosmological model. When SOFT is given spectroscopic information for SN classification and redshift priors, the rms scatter in Hubble diagram residuals is 0.18 mag for the SDSS data and 0.28 mag for the SNLS objects. Without access to any spectroscopic information, and even without any redshift priors from host galaxy photometry, SOFT can still measure reliable redshifts and distances, with an increase in the Hubble residuals to 0.37 mag for the combined SDSS and SNLS data set. Using Monte Carlo simulations, we predict that SOFT will be able to improve constraints on time-variable dark energy models by a factor of 2-3 with each new generation of large-scale SN surveys.
Reddy, Chinthala P; Rathi, Yogesh
2016-01-01
Tracing white matter fiber bundles is an integral part of analyzing brain connectivity. An accurate estimate of the underlying tissue parameters is also paramount in several neuroscience applications. In this work, we propose to use a joint fiber model estimation and tractography algorithm that uses the NODDI (neurite orientation dispersion diffusion imaging) model to estimate fiber orientation dispersion consistently and smoothly along the fiber tracts along with estimating the intracellular and extracellular volume fractions from the diffusion signal. While the NODDI model has been used in earlier works to estimate the microstructural parameters at each voxel independently, for the first time, we propose to integrate it into a tractography framework. We extend this framework to estimate the NODDI parameters for two crossing fibers, which is imperative to trace fiber bundles through crossings as well as to estimate the microstructural parameters for each fiber bundle separately. We propose to use the unscented information filter (UIF) to accurately estimate the model parameters and perform tractography. The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF). Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters), which can be quite useful to measure model complexity. Results from in-vivo human brain data sets demonstrate the ability of our algorithm to trace through crossing fiber regions, while estimating orientation dispersion and other biophysical model parameters in a consistent manner along the tracts. PMID:27147956
Joint Multi-Fiber NODDI Parameter Estimation and Tractography Using the Unscented Information Filter
Reddy, Chinthala P.; Rathi, Yogesh
2016-01-01
Tracing white matter fiber bundles is an integral part of analyzing brain connectivity. An accurate estimate of the underlying tissue parameters is also paramount in several neuroscience applications. In this work, we propose to use a joint fiber model estimation and tractography algorithm that uses the NODDI (neurite orientation dispersion diffusion imaging) model to estimate fiber orientation dispersion consistently and smoothly along the fiber tracts along with estimating the intracellular and extracellular volume fractions from the diffusion signal. While the NODDI model has been used in earlier works to estimate the microstructural parameters at each voxel independently, for the first time, we propose to integrate it into a tractography framework. We extend this framework to estimate the NODDI parameters for two crossing fibers, which is imperative to trace fiber bundles through crossings as well as to estimate the microstructural parameters for each fiber bundle separately. We propose to use the unscented information filter (UIF) to accurately estimate the model parameters and perform tractography. The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF). Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters), which can be quite useful to measure model complexity. Results from in-vivo human brain data sets demonstrate the ability of our algorithm to trace through crossing fiber regions, while estimating orientation dispersion and other biophysical model parameters in a consistent manner along the tracts. PMID:27147956
ERIC Educational Resources Information Center
Kolen, Michael J.; Whitney, Douglas R.
The application of latent trait theory to classroom tests necessitates the use of small sample sizes for parameter estimation. Computer generated data were used to assess the accuracy of estimation of the slope and location parameters in the two parameter logistic model with fixed abilities and varying small sample sizes. The maximum likelihood…
Sample Size and Item Parameter Estimation Precision When Utilizing the One-Parameter "Rasch" Model
ERIC Educational Resources Information Center
Custer, Michael
2015-01-01
This study examines the relationship between sample size and item parameter estimation precision when utilizing the one-parameter model. Item parameter estimates are examined relative to "true" values by evaluating the decline in root mean squared deviation (RMSD) and the number of outliers as sample size increases. This occurs across…
NASA Astrophysics Data System (ADS)
Zhang, Chuan-Xin; Yuan, Yuan; Zhang, Hao-Wei; Shuai, Yong; Tan, He-Ping
2016-09-01
Considering features of stellar spectral radiation and sky surveys, we established a computational model for stellar effective temperatures, detected angular parameters and gray rates. Using known stellar flux data in some bands, we estimated stellar effective temperatures and detected angular parameters using stochastic particle swarm optimization (SPSO). We first verified the reliability of SPSO, and then determined reasonable parameters that produced highly accurate estimates under certain gray deviation levels. Finally, we calculated 177 860 stellar effective temperatures and detected angular parameters using data from the Midcourse Space Experiment (MSX) catalog. These derived stellar effective temperatures were accurate when we compared them to known values from literatures. This research makes full use of catalog data and presents an original technique for studying stellar characteristics. It proposes a novel method for calculating stellar effective temperatures and detecting angular parameters, and provides theoretical and practical data for finding information about radiation in any band.
Equating Parameter Estimates from the Generalized Graded Unfolding Model.
ERIC Educational Resources Information Center
Roberts, James S.
Three common methods for equating parameter estimates from binary item response theory models are extended to the generalized grading unfolding model (GGUM). The GGUM is an item response model in which single-peaked, nonmonotonic expected value functions are implemented for polytomous responses. GGUM parameter estimates are equated using extended…
Attitudinal Data: Dimensionality and Start Values for Estimating Item Parameters.
ERIC Educational Resources Information Center
Nandakumar, Ratna; Hotchkiss, Larry; Roberts, James S.
The purpose of this study was to assess the dimensionality of attitudinal data arising from unfolding models for discrete data and to compute rough estimates of item and individual parameters for use as starting values in other estimation parameters. One- and two-dimensional simulated test data were analyzed in this study. Results of limited…
Estimation of Graded Response Model Parameters Using MULTILOG.
ERIC Educational Resources Information Center
Baker, Frank B.
1997-01-01
Describes an idiosyncracy of the MULTILOG (D. Thissen, 1991) parameter estimation process discovered during a simulation study involving the graded response model. A misordering reflected in boundary function location parameter estimates resulted in a large negative contribution to the true score followed by a large positive contribution. These…
Parameter Estimates in Differential Equation Models for Chemical Kinetics
ERIC Educational Resources Information Center
Winkel, Brian
2011-01-01
We discuss the need for devoting time in differential equations courses to modelling and the completion of the modelling process with efforts to estimate the parameters in the models using data. We estimate the parameters present in several differential equation models of chemical reactions of order n, where n = 0, 1, 2, and apply more general…
NASA Astrophysics Data System (ADS)
Aslan, Serdar; Taylan Cemgil, Ali; Akın, Ata
2016-08-01
Objective. In this paper, we aimed for the robust estimation of the parameters and states of the hemodynamic model by using blood oxygen level dependent signal. Approach. In the fMRI literature, there are only a few successful methods that are able to make a joint estimation of the states and parameters of the hemodynamic model. In this paper, we implemented a maximum likelihood based method called the particle smoother expectation maximization (PSEM) algorithm for the joint state and parameter estimation. Main results. Former sequential Monte Carlo methods were only reliable in the hemodynamic state estimates. They were claimed to outperform the local linearization (LL) filter and the extended Kalman filter (EKF). The PSEM algorithm is compared with the most successful method called square-root cubature Kalman smoother (SCKS) for both state and parameter estimation. SCKS was found to be better than the dynamic expectation maximization (DEM) algorithm, which was shown to be a better estimator than EKF, LL and particle filters. Significance. PSEM was more accurate than SCKS for both the state and the parameter estimation. Hence, PSEM seems to be the most accurate method for the system identification and state estimation for the hemodynamic model inversion literature. This paper do not compare its results with Tikhonov-regularized Newton—CKF (TNF-CKF), a recent robust method which works in filtering sense.
Accurate parameters of the oldest known rocky-exoplanet hosting system: Kepler-10 revisited
Fogtmann-Schulz, Alexandra; Hinrup, Brian; Van Eylen, Vincent; Christensen-Dalsgaard, Jørgen; Kjeldsen, Hans; Silva Aguirre, Víctor; Tingley, Brandon
2014-02-01
Since the discovery of Kepler-10, the system has received considerable interest because it contains a small, rocky planet which orbits the star in less than a day. The system's parameters, announced by the Kepler team and subsequently used in further research, were based on only five months of data. We have reanalyzed this system using the full span of 29 months of Kepler photometric data, and obtained improved information about its star and the planets. A detailed asteroseismic analysis of the extended time series provides a significant improvement on the stellar parameters: not only can we state that Kepler-10 is the oldest known rocky-planet-harboring system at 10.41 ± 1.36 Gyr, but these parameters combined with improved planetary parameters from new transit fits gives us the radius of Kepler-10b to within just 125 km. A new analysis of the full planetary phase curve leads to new estimates on the planetary temperature and albedo, which remain degenerate in the Kepler band. Our modeling suggests that the flux level during the occultation is slightly lower than at the transit wings, which would imply that the nightside of this planet has a non-negligible temperature.
NASA Astrophysics Data System (ADS)
Ghezzi, Luan; Dutra-Ferreira, Letícia; Lorenzo-Oliveira, Diego; Porto de Mello, Gustavo F.; Santiago, Basílio X.; De Lee, Nathan; Lee, Brian L.; da Costa, Luiz N.; Maia, Marcio A. G.; Ogando, Ricardo L. C.; Wisniewski, John P.; González Hernández, Jonay I.; Stassun, Keivan G.; Fleming, Scott W.; Schneider, Donald P.; Mahadevan, Suvrath; Cargile, Phillip; Ge, Jian; Pepper, Joshua; Wang, Ji; Paegert, Martin
2014-12-01
Studies of Galactic chemical, and dynamical evolution in the solar neighborhood depend on the availability of precise atmospheric parameters (effective temperature T eff, metallicity [Fe/H], and surface gravity log g) for solar-type stars. Many large-scale spectroscopic surveys operate at low to moderate spectral resolution for efficiency in observing large samples, which makes the stellar characterization difficult due to the high degree of blending of spectral features. Therefore, most surveys employ spectral synthesis, which is a powerful technique, but relies heavily on the completeness and accuracy of atomic line databases and can yield possibly correlated atmospheric parameters. In this work, we use an alternative method based on spectral indices to determine the atmospheric parameters of a sample of nearby FGK dwarfs and subgiants observed by the MARVELS survey at moderate resolving power (R ~ 12,000). To avoid a time-consuming manual analysis, we have developed three codes to automatically normalize the observed spectra, measure the equivalent widths of the indices, and, through a comparison of those with values calculated with predetermined calibrations, estimate the atmospheric parameters of the stars. The calibrations were derived using a sample of 309 stars with precise stellar parameters obtained from the analysis of high-resolution FEROS spectra, permitting the low-resolution equivalent widths to be directly related to the stellar parameters. A validation test of the method was conducted with a sample of 30 MARVELS targets that also have reliable atmospheric parameters derived from the high-resolution spectra and spectroscopic analysis based on the excitation and ionization equilibria method. Our approach was able to recover the parameters within 80 K for T eff, 0.05 dex for [Fe/H], and 0.15 dex for log g, values that are lower than or equal to the typical external uncertainties found between different high-resolution analyses. An additional test was
Ghezzi, Luan; Da Costa, Luiz N.; Maia, Marcio A. G.; Ogando, Ricardo L. C.; Dutra-Ferreira, Letícia; Lorenzo-Oliveira, Diego; Porto de Mello, Gustavo F.; Santiago, Basílio X.; De Lee, Nathan; Lee, Brian L.; Ge, Jian; Wisniewski, John P.; González Hernández, Jonay I.; Stassun, Keivan G.; Cargile, Phillip; Pepper, Joshua; Fleming, Scott W.; Schneider, Donald P.; Mahadevan, Suvrath; Wang, Ji; and others
2014-12-01
Studies of Galactic chemical, and dynamical evolution in the solar neighborhood depend on the availability of precise atmospheric parameters (effective temperature T {sub eff}, metallicity [Fe/H], and surface gravity log g) for solar-type stars. Many large-scale spectroscopic surveys operate at low to moderate spectral resolution for efficiency in observing large samples, which makes the stellar characterization difficult due to the high degree of blending of spectral features. Therefore, most surveys employ spectral synthesis, which is a powerful technique, but relies heavily on the completeness and accuracy of atomic line databases and can yield possibly correlated atmospheric parameters. In this work, we use an alternative method based on spectral indices to determine the atmospheric parameters of a sample of nearby FGK dwarfs and subgiants observed by the MARVELS survey at moderate resolving power (R ∼ 12,000). To avoid a time-consuming manual analysis, we have developed three codes to automatically normalize the observed spectra, measure the equivalent widths of the indices, and, through a comparison of those with values calculated with predetermined calibrations, estimate the atmospheric parameters of the stars. The calibrations were derived using a sample of 309 stars with precise stellar parameters obtained from the analysis of high-resolution FEROS spectra, permitting the low-resolution equivalent widths to be directly related to the stellar parameters. A validation test of the method was conducted with a sample of 30 MARVELS targets that also have reliable atmospheric parameters derived from the high-resolution spectra and spectroscopic analysis based on the excitation and ionization equilibria method. Our approach was able to recover the parameters within 80 K for T {sub eff}, 0.05 dex for [Fe/H], and 0.15 dex for log g, values that are lower than or equal to the typical external uncertainties found between different high-resolution analyses. An
NASA Technical Reports Server (NTRS)
Morris, A. Terry
1999-01-01
This paper examines various sources of error in MIT's improved top oil temperature rise over ambient temperature model and estimation process. The sources of error are the current parameter estimation technique, quantization noise, and post-processing of the transformer data. Results from this paper will show that an output error parameter estimation technique should be selected to replace the current least squares estimation technique. The output error technique obtained accurate predictions of transformer behavior, revealed the best error covariance, obtained consistent parameter estimates, and provided for valid and sensible parameters. This paper will also show that the output error technique should be used to minimize errors attributed to post-processing (decimation) of the transformer data. Models used in this paper are validated using data from a large transformer in service.
Eakin, T; Shouman, R; Qi, Y; Liu, G; Witten, M
1995-05-01
Studies of the biology of aging (both experimental and evolutionary) frequently involve the estimation of parameters arising in various multi-parameter survival models such as the Gompertz or Weibull distribution. Standard parameter estimation methodologies, such as maximum likelihood estimation (MLE) or nonlinear regression (NLR), require knowledge of the actual life spans or their explicit algebraic equivalents in order to provide reliable parameter estimates. Many fundamental biological discussions and conclusions are highly dependent upon accurate estimates of these survival parameters (this has historically been the case in the study of genetic and environmental effects on longevity and the evolutionary biology of aging). In this article, we examine some of the issues arising in the estimation of gerontologic survival model parameters. We not only address issues of accuracy when the original life-span data are unknown, we consider the accuracy of the estimates even when the exact life spans are known. We examine these issues as applied to known experimental data on diet restriction and we fit the frequently used, two-parameter Gompertzian survival distribution to these experimental data. Consequences of methodological misuse are demonstrated and subsequently related to the values of the final parameter estimates and their associated errors. These results generalize to other multiparametric distributions such as the Weibull, Makeham, and logistic survival distributions. PMID:7743396
Parameter estimation of hydrologic models using data assimilation
NASA Astrophysics Data System (ADS)
Kaheil, Y. H.
2005-12-01
The uncertainties associated with the modeling of hydrologic systems sometimes demand that data should be incorporated in an on-line fashion in order to understand the behavior of the system. This paper represents a Bayesian strategy to estimate parameters for hydrologic models in an iterative mode. The paper presents a modified technique called localized Bayesian recursive estimation (LoBaRE) that efficiently identifies the optimum parameter region, avoiding convergence to a single best parameter set. The LoBaRE methodology is tested for parameter estimation for two different types of models: a support vector machine (SVM) model for predicting soil moisture, and the Sacramento Soil Moisture Accounting (SAC-SMA) model for estimating streamflow. The SAC-SMA model has 13 parameters that must be determined. The SVM model has three parameters. Bayesian inference is used to estimate the best parameter set in an iterative fashion. This is done by narrowing the sampling space by imposing uncertainty bounds on the posterior best parameter set and/or updating the "parent" bounds based on their fitness. The new approach results in fast convergence towards the optimal parameter set using minimum training/calibration data and evaluation of fewer parameter sets. The efficacy of the localized methodology is also compared with the previously used Bayesian recursive estimation (BaRE) algorithm.
Accurate estimation of forest carbon stocks by 3-D remote sensing of individual trees.
Omasa, Kenji; Qiu, Guo Yu; Watanuki, Kenichi; Yoshimi, Kenji; Akiyama, Yukihide
2003-03-15
Forests are one of the most important carbon sinks on Earth. However, owing to the complex structure, variable geography, and large area of forests, accurate estimation of forest carbon stocks is still a challenge for both site surveying and remote sensing. For these reasons, the Kyoto Protocol requires the establishment of methodologies for estimating the carbon stocks of forests (Kyoto Protocol, Article 5). A possible solution to this challenge is to remotely measure the carbon stocks of every tree in an entire forest. Here, we present a methodology for estimating carbon stocks of a Japanese cedar forest by using a high-resolution, helicopter-borne 3-dimensional (3-D) scanning lidar system that measures the 3-D canopy structure of every tree in a forest. Results show that a digital image (10-cm mesh) of woody canopy can be acquired. The treetop can be detected automatically with a reasonable accuracy. The absolute error ranges for tree height measurements are within 42 cm. Allometric relationships of height to carbon stocks then permit estimation of total carbon storage by measurement of carbon stocks of every tree. Thus, we suggest that our methodology can be used to accurately estimate the carbon stocks of Japanese cedar forests at a stand scale. Periodic measurements will reveal changes in forest carbon stocks. PMID:12680675
Complexity analysis and parameter estimation of dynamic metabolic systems.
Tian, Li-Ping; Shi, Zhong-Ke; Wu, Fang-Xiang
2013-01-01
A metabolic system consists of a number of reactions transforming molecules of one kind into another to provide the energy that living cells need. Based on the biochemical reaction principles, dynamic metabolic systems can be modeled by a group of coupled differential equations which consists of parameters, states (concentration of molecules involved), and reaction rates. Reaction rates are typically either polynomials or rational functions in states and constant parameters. As a result, dynamic metabolic systems are a group of differential equations nonlinear and coupled in both parameters and states. Therefore, it is challenging to estimate parameters in complex dynamic metabolic systems. In this paper, we propose a method to analyze the complexity of dynamic metabolic systems for parameter estimation. As a result, the estimation of parameters in dynamic metabolic systems is reduced to the estimation of parameters in a group of decoupled rational functions plus polynomials (which we call improper rational functions) or in polynomials. Furthermore, by taking its special structure of improper rational functions, we develop an efficient algorithm to estimate parameters in improper rational functions. The proposed method is applied to the estimation of parameters in a dynamic metabolic system. The simulation results show the superior performance of the proposed method. PMID:24233242
Jiang, Shengyu; Wang, Chun; Weiss, David J
2016-01-01
Likert types of rating scales in which a respondent chooses a response from an ordered set of response options are used to measure a wide variety of psychological, educational, and medical outcome variables. The most appropriate item response theory model for analyzing and scoring these instruments when they provide scores on multiple scales is the multidimensional graded response model (MGRM) A simulation study was conducted to investigate the variables that might affect item parameter recovery for the MGRM. Data were generated based on different sample sizes, test lengths, and scale intercorrelations. Parameter estimates were obtained through the flexMIRT software. The quality of parameter recovery was assessed by the correlation between true and estimated parameters as well as bias and root-mean-square-error. Results indicated that for the vast majority of cases studied a sample size of N = 500 provided accurate parameter estimates, except for tests with 240 items when 1000 examinees were necessary to obtain accurate parameter estimates. Increasing sample size beyond N = 1000 did not increase the accuracy of MGRM parameter estimates. PMID:26903916
Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model
ERIC Educational Resources Information Center
de la Torre, Jimmy; Hong, Yuan; Deng, Weiling
2010-01-01
To better understand the statistical properties of the deterministic inputs, noisy "and" gate cognitive diagnosis (DINA) model, the impact of several factors on the quality of the item parameter estimates and classification accuracy was investigated. Results of the simulation study indicate that the fully Bayes approach is most accurate when the…
A more informative estimation procedure for the parameters of a diffusion process
NASA Astrophysics Data System (ADS)
Basso, A.; Pianca, P.
1999-07-01
The estimation procedures for the parameters of a diffusion process with constant coefficients have mainly focused on volatility. Nevertheless, even if the knowledge of the volatility alone suffices to compute the Black and Scholes option prices, other financial application models assume that the price dynamics follows a log-normal process and requires the knowledge of both parameters. On the other hand, while the usual ML estimator of volatility gives satisfactory results, the estimation of drift is much less accurate; moreover, the drift-estimated value highly depends on the phases of the business cycle included in the sample data. This contribution explicitly imposes a risk aversion or risk neutral assumption into the ML estimation procedure and makes a constrained maximization of the sample likelihood function. The aim is twofold: to obtain estimated values which are consistent with a widely accepted assumption and use the risk aversion constraint in order to improve the accuracy of the estimates.
Determining the accuracy of maximum likelihood parameter estimates with colored residuals
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.; Klein, Vladislav
1994-01-01
An important part of building high fidelity mathematical models based on measured data is calculating the accuracy associated with statistical estimates of the model parameters. Indeed, without some idea of the accuracy of parameter estimates, the estimates themselves have limited value. In this work, an expression based on theoretical analysis was developed to properly compute parameter accuracy measures for maximum likelihood estimates with colored residuals. This result is important because experience from the analysis of measured data reveals that the residuals from maximum likelihood estimation are almost always colored. The calculations involved can be appended to conventional maximum likelihood estimation algorithms. Simulated data runs were used to show that the parameter accuracy measures computed with this technique accurately reflect the quality of the parameter estimates from maximum likelihood estimation without the need for analysis of the output residuals in the frequency domain or heuristically determined multiplication factors. The result is general, although the application studied here is maximum likelihood estimation of aerodynamic model parameters from flight test data.
Estimating parameter of influenza transmission using regularized least square
NASA Astrophysics Data System (ADS)
Nuraini, N.; Syukriah, Y.; Indratno, S. W.
2014-02-01
Transmission process of influenza can be presented in a mathematical model as a non-linear differential equations system. In this model the transmission of influenza is determined by the parameter of contact rate of the infected host and susceptible host. This parameter will be estimated using a regularized least square method where the Finite Element Method and Euler Method are used for approximating the solution of the SIR differential equation. The new infected data of influenza from CDC is used to see the effectiveness of the method. The estimated parameter represents the contact rate proportion of transmission probability in a day which can influence the number of infected people by the influenza. Relation between the estimated parameter and the number of infected people by the influenza is measured by coefficient of correlation. The numerical results show positive correlation between the estimated parameters and the infected people.
Impacts of Different Types of Measurements on Estimating Unsaturatedflow Parameters
NASA Astrophysics Data System (ADS)
Shi, L.
2015-12-01
This study evaluates the value of different types of measurements for estimating soil hydraulic parameters. A numerical method based on ensemble Kalman filter (EnKF) is presented to solely or jointly assimilate point-scale soil water head data, point-scale soil water content data, surface soil water content data and groundwater level data. This study investigates the performance of EnKF under different types of data, the potential worth contained in these data, and the factors that may affect estimation accuracy. Results show that for all types of data, smaller measurements errors lead to faster convergence to the true values. Higher accuracy measurements are required to improve the parameter estimation if a large number of unknown parameters need to be identified simultaneously. The data worth implied by the surface soil water content data and groundwater level data is prone to corruption by a deviated initial guess. Surface soil moisture data are capable of identifying soil hydraulic parameters for the top layers, but exert less or no influence on deeper layers especially when estimating multiple parameters simultaneously. Groundwater level is one type of valuable information to infer the soil hydraulic parameters. However, based on the approach used in this study, the estimates from groundwater level data may suffer severe degradation if a large number of parameters must be identified. Combined use of two or more types of data is helpful to improve the parameter estimation.
Yu, Jihnhee; Yang, Luge; Vexler, Albert; Hutson, Alan D
2016-06-15
The receiver operating characteristic (ROC) curve is a popular technique with applications, for example, investigating an accuracy of a biomarker to delineate between disease and non-disease groups. A common measure of accuracy of a given diagnostic marker is the area under the ROC curve (AUC). In contrast with the AUC, the partial area under the ROC curve (pAUC) looks into the area with certain specificities (i.e., true negative rate) only, and it can be often clinically more relevant than examining the entire ROC curve. The pAUC is commonly estimated based on a U-statistic with the plug-in sample quantile, making the estimator a non-traditional U-statistic. In this article, we propose an accurate and easy method to obtain the variance of the nonparametric pAUC estimator. The proposed method is easy to implement for both one biomarker test and the comparison of two correlated biomarkers because it simply adapts the existing variance estimator of U-statistics. In this article, we show accuracy and other advantages of the proposed variance estimation method by broadly comparing it with previously existing methods. Further, we develop an empirical likelihood inference method based on the proposed variance estimator through a simple implementation. In an application, we demonstrate that, depending on the inferences by either the AUC or pAUC, we can make a different decision on a prognostic ability of a same set of biomarkers. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26790540
Estimating the Effective Permittivity for Reconstructing Accurate Microwave-Radar Images.
Lavoie, Benjamin R; Okoniewski, Michal; Fear, Elise C
2016-01-01
We present preliminary results from a method for estimating the optimal effective permittivity for reconstructing microwave-radar images. Using knowledge of how microwave-radar images are formed, we identify characteristics that are typical of good images, and define a fitness function to measure the relative image quality. We build a polynomial interpolant of the fitness function in order to identify the most likely permittivity values of the tissue. To make the estimation process more efficient, the polynomial interpolant is constructed using a locally and dimensionally adaptive sampling method that is a novel combination of stochastic collocation and polynomial chaos. Examples, using a series of simulated, experimental and patient data collected using the Tissue Sensing Adaptive Radar system, which is under development at the University of Calgary, are presented. These examples show how, using our method, accurate images can be reconstructed starting with only a broad estimate of the permittivity range. PMID:27611785
Accurate estimation of object location in an image sequence using helicopter flight data
NASA Technical Reports Server (NTRS)
Tang, Yuan-Liang; Kasturi, Rangachar
1994-01-01
In autonomous navigation, it is essential to obtain a three-dimensional (3D) description of the static environment in which the vehicle is traveling. For a rotorcraft conducting low-latitude flight, this description is particularly useful for obstacle detection and avoidance. In this paper, we address the problem of 3D position estimation for static objects from a monocular sequence of images captured from a low-latitude flying helicopter. Since the environment is static, it is well known that the optical flow in the image will produce a radiating pattern from the focus of expansion. We propose a motion analysis system which utilizes the epipolar constraint to accurately estimate 3D positions of scene objects in a real world image sequence taken from a low-altitude flying helicopter. Results show that this approach gives good estimates of object positions near the rotorcraft's intended flight-path.
How to fool cosmic microwave background parameter estimation
Kinney, William H.
2001-02-15
With the release of the data from the Boomerang and MAXIMA-1 balloon flights, estimates of cosmological parameters based on the cosmic microwave background (CMB) have reached unprecedented precision. In this paper I show that it is possible for these estimates to be substantially biased by features in the primordial density power spectrum. I construct primordial power spectra which mimic to within cosmic variance errors the effect of changing parameters such as the baryon density and neutrino mass, meaning that even an ideal measurement would be unable to resolve the degeneracy. Complementary measurements are necessary to resolve this ambiguity in parameter estimation efforts based on CMB temperature fluctuations alone.
Parameter Estimation in Epidemiology: from Simple to Complex Dynamics
NASA Astrophysics Data System (ADS)
Aguiar, Maíra; Ballesteros, Sebastién; Boto, João Pedro; Kooi, Bob W.; Mateus, Luís; Stollenwerk, Nico
2011-09-01
We revisit the parameter estimation framework for population biological dynamical systems, and apply it to calibrate various models in epidemiology with empirical time series, namely influenza and dengue fever. When it comes to more complex models like multi-strain dynamics to describe the virus-host interaction in dengue fever, even most recently developed parameter estimation techniques, like maximum likelihood iterated filtering, come to their computational limits. However, the first results of parameter estimation with data on dengue fever from Thailand indicate a subtle interplay between stochasticity and deterministic skeleton. The deterministic system on its own already displays complex dynamics up to deterministic chaos and coexistence of multiple attractors.
A simulation of water pollution model parameter estimation
NASA Technical Reports Server (NTRS)
Kibler, J. F.
1976-01-01
A parameter estimation procedure for a water pollution transport model is elaborated. A two-dimensional instantaneous-release shear-diffusion model serves as representative of a simple transport process. Pollution concentration levels are arrived at via modeling of a remote-sensing system. The remote-sensed data are simulated by adding Gaussian noise to the concentration level values generated via the transport model. Model parameters are estimated from the simulated data using a least-squares batch processor. Resolution, sensor array size, and number and location of sensor readings can be found from the accuracies of the parameter estimates.
Accurate reconstruction of viral quasispecies spectra through improved estimation of strain richness
2015-01-01
Background Estimating the number of different species (richness) in a mixed microbial population has been a main focus in metagenomic research. Existing methods of species richness estimation ride on the assumption that the reads in each assembled contig correspond to only one of the microbial genomes in the population. This assumption and the underlying probabilistic formulations of existing methods are not useful for quasispecies populations where the strains are highly genetically related. The lack of knowledge on the number of different strains in a quasispecies population is observed to hinder the precision of existing Viral Quasispecies Spectrum Reconstruction (QSR) methods due to the uncontrolled reconstruction of a large number of in silico false positives. In this work, we formulated a novel probabilistic method for strain richness estimation specifically targeting viral quasispecies. By using this approach we improved our recently proposed spectrum reconstruction pipeline ViQuaS to achieve higher levels of precision in reconstructed quasispecies spectra without compromising the recall rates. We also discuss how one other existing popular QSR method named ShoRAH can be improved using this new approach. Results On benchmark data sets, our estimation method provided accurate richness estimates (< 0.2 median estimation error) and improved the precision of ViQuaS by 2%-13% and F-score by 1%-9% without compromising the recall rates. We also demonstrate that our estimation method can be used to improve the precision and F-score of ShoRAH by 0%-7% and 0%-5% respectively. Conclusions The proposed probabilistic estimation method can be used to estimate the richness of viral populations with a quasispecies behavior and to improve the accuracy of the quasispecies spectra reconstructed by the existing methods ViQuaS and ShoRAH in the presence of a moderate level of technical sequencing errors. Availability http://sourceforge.net/projects/viquas/ PMID:26678073
Accurate parameters for HD 209458 and its planet from HST spectrophotometry
NASA Astrophysics Data System (ADS)
del Burgo, C.; Allende Prieto, C.
2016-08-01
We present updated parameters for the star HD 209458 and its transiting giant planet. The stellar angular diameter θ=0.2254±0.0017 mas is obtained from the average ratio between the absolute flux observed with the Hubble Space Telescope and that of the best-fitting Kurucz model atmosphere. This angular diameter represents an improvement in precision of more than four times compared to available interferometric determinations. The stellar radius R⋆=1.20±0.05 R⊙ is ascertained by combining the angular diameter with the Hipparcos trigonometric parallax, which is the main contributor to its uncertainty, and therefore the radius accuracy should be significantly improved with Gaia's measurements. The radius of the exoplanet Rp=1.41±0.06 RJ is derived from the corresponding transit depth in the light curve and our stellar radius. From the model fitting, we accurately determine the effective temperature, Teff=6071±20 K, which is in perfect agreement with the value of 6070±24 K calculated from the angular diameter and the integrated spectral energy distribution. We also find precise values from recent Padova Isochrones, such as R⋆=1.20±0.06 R⊙ and Teff=6099±41 K. We arrive at a consistent picture from these methods and compare the results with those from the literature.
Unrealistic parameter estimates in inverse modelling: A problem or a benefit for model calibration?
Poeter, E.P.; Hill, M.C.
1996-01-01
Estimation of unrealistic parameter values by inverse modelling is useful for constructed model discrimination. This utility is demonstrated using the three-dimensional, groundwater flow inverse model MODFLOWP to estimate parameters in a simple synthetic model where the true conditions and character of the errors are completely known. When a poorly constructed model is used, unreasonable parameter values are obtained even when using error free observations and true initial parameter values. This apparent problem is actually a benefit because it differentiates accurately and inaccurately constructed models. The problems seem obvious for a synthetic problem in which the truth is known, but are obscure when working with field data. Situations in which unrealistic parameter estimates indicate constructed model problems are illustrated in applications of inverse modelling to three field sites and to complex synthetic test cases in which it is shown that prediction accuracy also suffers when constructed models are inaccurate.
Exploratory Study for Continuous-time Parameter Estimation of Ankle Dynamics
NASA Technical Reports Server (NTRS)
Kukreja, Sunil L.; Boyle, Richard D.
2014-01-01
Recently, a parallel pathway model to describe ankle dynamics was proposed. This model provides a relationship between ankle angle and net ankle torque as the sum of a linear and nonlinear contribution. A technique to identify parameters of this model in discrete-time has been developed. However, these parameters are a nonlinear combination of the continuous-time physiology, making insight into the underlying physiology impossible. The stable and accurate estimation of continuous-time parameters is critical for accurate disease modeling, clinical diagnosis, robotic control strategies, development of optimal exercise protocols for longterm space exploration, sports medicine, etc. This paper explores the development of a system identification technique to estimate the continuous-time parameters of ankle dynamics. The effectiveness of this approach is assessed via simulation of a continuous-time model of ankle dynamics with typical parameters found in clinical studies. The results show that although this technique improves estimates, it does not provide robust estimates of continuous-time parameters of ankle dynamics. Due to this we conclude that alternative modeling strategies and more advanced estimation techniques be considered for future work.
NASA Astrophysics Data System (ADS)
Kasaragod, Deepa; Sugiyama, Satoshi; Ikuno, Yasushi; Alonso-Caneiro, David; Yamanari, Masahiro; Fukuda, Shinichi; Oshika, Tetsuro; Hong, Young-Joo; Li, En; Makita, Shuichi; Miura, Masahiro; Yasuno, Yoshiaki
2016-03-01
Polarization sensitive optical coherence tomography (PS-OCT) is a functional extension of OCT that contrasts the polarization properties of tissues. It has been applied to ophthalmology, cardiology, etc. Proper quantitative imaging is required for a widespread clinical utility. However, the conventional method of averaging to improve the signal to noise ratio (SNR) and the contrast of the phase retardation (or birefringence) images introduce a noise bias offset from the true value. This bias reduces the effectiveness of birefringence contrast for a quantitative study. Although coherent averaging of Jones matrix tomography has been widely utilized and has improved the image quality, the fundamental limitation of nonlinear dependency of phase retardation and birefringence to the SNR was not overcome. So the birefringence obtained by PS-OCT was still not accurate for a quantitative imaging. The nonlinear effect of SNR to phase retardation and birefringence measurement was previously formulated in detail for a Jones matrix OCT (JM-OCT) [1]. Based on this, we had developed a maximum a-posteriori (MAP) estimator and quantitative birefringence imaging was demonstrated [2]. However, this first version of estimator had a theoretical shortcoming. It did not take into account the stochastic nature of SNR of OCT signal. In this paper, we present an improved version of the MAP estimator which takes into account the stochastic property of SNR. This estimator uses a probability distribution function (PDF) of true local retardation, which is proportional to birefringence, under a specific set of measurements of the birefringence and SNR. The PDF was pre-computed by a Monte-Carlo (MC) simulation based on the mathematical model of JM-OCT before the measurement. A comparison between this new MAP estimator, our previous MAP estimator [2], and the standard mean estimator is presented. The comparisons are performed both by numerical simulation and in vivo measurements of anterior and
Estimation of real-time runway surface contamination using flight data recorder parameters
NASA Astrophysics Data System (ADS)
Curry, Donovan
Within this research effort, the development of an analytic process for friction coefficient estimation is presented. Under static equilibrium, the sum of forces and moments acting on the aircraft, in the aircraft body coordinate system, while on the ground at any instant is equal to zero. Under this premise the longitudinal, lateral and normal forces due to landing are calculated along with the individual deceleration components existent when an aircraft comes to a rest during ground roll. In order to validate this hypothesis a six degree of freedom aircraft model had to be created and landing tests had to be simulated on different surfaces. The simulated aircraft model includes a high fidelity aerodynamic model, thrust model, landing gear model, friction model and antiskid model. Three main surfaces were defined in the friction model; dry, wet and snow/ice. Only the parameters recorded by an FDR are used directly from the aircraft model all others are estimated or known a priori. The estimation of unknown parameters is also presented in the research effort. With all needed parameters a comparison and validation with simulated and estimated data, under different runway conditions, is performed. Finally, this report presents results of a sensitivity analysis in order to provide a measure of reliability of the analytic estimation process. Linear and non-linear sensitivity analysis has been performed in order to quantify the level of uncertainty implicit in modeling estimated parameters and how they can affect the calculation of the instantaneous coefficient of friction. Using the approach of force and moment equilibrium about the CG at landing to reconstruct the instantaneous coefficient of friction appears to be a reasonably accurate estimate when compared to the simulated friction coefficient. This is also true when the FDR and estimated parameters are introduced to white noise and when crosswind is introduced to the simulation. After the linear analysis the
On a variational approach to some parameter estimation problems
NASA Technical Reports Server (NTRS)
Banks, H. T.
1985-01-01
Examples (1-D seismic, large flexible structures, bioturbation, nonlinear population dispersal) in which a variation setting can provide a convenient framework for convergence and stability arguments in parameter estimation problems are considered. Some of these examples are 1-D seismic, large flexible structures, bioturbation, and nonlinear population dispersal. Arguments for convergence and stability via a variational approach of least squares formulations of parameter estimation problems for partial differential equations is one aspect of the problem considered.
Simultaneous optimal experimental design for in vitro binding parameter estimation.
Ernest, C Steven; Karlsson, Mats O; Hooker, Andrew C
2013-10-01
Simultaneous optimization of in vitro ligand binding studies using an optimal design software package that can incorporate multiple design variables through non-linear mixed effect models and provide a general optimized design regardless of the binding site capacity and relative binding rates for a two binding system. Experimental design optimization was employed with D- and ED-optimality using PopED 2.8 including commonly encountered factors during experimentation (residual error, between experiment variability and non-specific binding) for in vitro ligand binding experiments: association, dissociation, equilibrium and non-specific binding experiments. Moreover, a method for optimizing several design parameters (ligand concentrations, measurement times and total number of samples) was examined. With changes in relative binding site density and relative binding rates, different measurement times and ligand concentrations were needed to provide precise estimation of binding parameters. However, using optimized design variables, significant reductions in number of samples provided as good or better precision of the parameter estimates compared to the original extensive sampling design. Employing ED-optimality led to a general experimental design regardless of the relative binding site density and relative binding rates. Precision of the parameter estimates were as good as the extensive sampling design for most parameters and better for the poorly estimated parameters. Optimized designs for in vitro ligand binding studies provided robust parameter estimation while allowing more efficient and cost effective experimentation by reducing the measurement times and separate ligand concentrations required and in some cases, the total number of samples. PMID:23943088
A new method for parameter estimation in nonlinear dynamical equations
NASA Astrophysics Data System (ADS)
Wang, Liu; He, Wen-Ping; Liao, Le-Jian; Wan, Shi-Quan; He, Tao
2015-01-01
Parameter estimation is an important scientific problem in various fields such as chaos control, chaos synchronization and other mathematical models. In this paper, a new method for parameter estimation in nonlinear dynamical equations is proposed based on evolutionary modelling (EM). This will be achieved by utilizing the following characteristics of EM which includes self-organizing, adaptive and self-learning features which are inspired by biological natural selection, and mutation and genetic inheritance. The performance of the new method is demonstrated by using various numerical tests on the classic chaos model—Lorenz equation (Lorenz 1963). The results indicate that the new method can be used for fast and effective parameter estimation irrespective of whether partial parameters or all parameters are unknown in the Lorenz equation. Moreover, the new method has a good convergence rate. Noises are inevitable in observational data. The influence of observational noises on the performance of the presented method has been investigated. The results indicate that the strong noises, such as signal noise ratio (SNR) of 10 dB, have a larger influence on parameter estimation than the relatively weak noises. However, it is found that the precision of the parameter estimation remains acceptable for the relatively weak noises, e.g. SNR is 20 or 30 dB. It indicates that the presented method also has some anti-noise performance.
Simultaneous estimation of parameters in the bivariate Emax model.
Magnusdottir, Bergrun T; Nyquist, Hans
2015-12-10
In this paper, we explore inference in multi-response, nonlinear models. By multi-response, we mean models with m > 1 response variables and accordingly m relations. Each parameter/explanatory variable may appear in one or more of the relations. We study a system estimation approach for simultaneous computation and inference of the model and (co)variance parameters. For illustration, we fit a bivariate Emax model to diabetes dose-response data. Further, the bivariate Emax model is used in a simulation study that compares the system estimation approach to equation-by-equation estimation. We conclude that overall, the system estimation approach performs better for the bivariate Emax model when there are dependencies among relations. The stronger the dependencies, the more we gain in precision by using system estimation rather than equation-by-equation estimation. PMID:26190048
Generalized Limits for Single-Parameter Quantum Estimation
Boixo, Sergio; Flammia, Steven T.; Caves, Carlton M.; Geremia, JM
2007-03-02
We develop generalized bounds for quantum single-parameter estimation problems for which the coupling to the parameter is described by intrinsic multisystem interactions. For a Hamiltonian with k-system parameter-sensitive terms, the quantum limit scales as 1/N{sup k}, where N is the number of systems. These quantum limits remain valid when the Hamiltonian is augmented by any parameter-independent interaction among the systems and when adaptive measurements via parameter-independent coupling to ancillas are allowed.
A comparison of approximate interval estimators for the Bernoulli parameter
NASA Technical Reports Server (NTRS)
Leemis, Lawrence; Trivedi, Kishor S.
1993-01-01
The goal of this paper is to compare the accuracy of two approximate confidence interval estimators for the Bernoulli parameter p. The approximate confidence intervals are based on the normal and Poisson approximations to the binomial distribution. Charts are given to indicate which approximation is appropriate for certain sample sizes and point estimators.
On the Nature of SEM Estimates of ARMA Parameters.
ERIC Educational Resources Information Center
Hamaker, Ellen L.; Dolan, Conor V.; Molenaar, Peter C. M.
2002-01-01
Reexamined the nature of structural equation modeling (SEM) estimates of autoregressive moving average (ARMA) models, replicated the simulation experiments of P. Molenaar, and examined the behavior of the log-likelihood ratio test. Simulation studies indicate that estimates of ARMA parameters observed with SEM software are identical to those…
A Fully Conditional Estimation Procedure for Rasch Model Parameters.
ERIC Educational Resources Information Center
Choppin, Bruce
A strategy for overcoming problems with the Rasch model's inability to handle missing data involves a pairwise algorithm which manipulates the data matrix to separate out the information needed for the estimation of item difficulty parameters in a test. The method of estimation compares two or three items at a time, separating out the ability…
Cancilla, John C; Díaz-Rodríguez, Pablo; Matute, Gemma; Torrecilla, José S
2015-02-14
The estimation of the density and refractive index of ternary mixtures comprising the ionic liquid (IL) 1-butyl-3-methylimidazolium tetrafluoroborate, 2-propanol, and water at a fixed temperature of 298.15 K has been attempted through artificial neural networks. The obtained results indicate that the selection of this mathematical approach was a well-suited option. The mean prediction errors obtained, after simulating with a dataset never involved in the training process of the model, were 0.050% and 0.227% for refractive index and density estimation, respectively. These accurate results, which have been attained only using the composition of the dissolutions (mass fractions), imply that, most likely, ternary mixtures similar to the one analyzed, can be easily evaluated utilizing this algorithmic tool. In addition, different chemical processes involving ILs can be monitored precisely, and furthermore, the purity of the compounds in the studied mixtures can be indirectly assessed thanks to the high accuracy of the model. PMID:25583241
Sansone, Giuseppe; Maschio, Lorenzo; Usvyat, Denis; Schütz, Martin; Karttunen, Antti
2016-01-01
The black phosphorus (black-P) crystal is formed of covalently bound layers of phosphorene stacked together by weak van der Waals interactions. An experimental measurement of the exfoliation energy of black-P is not available presently, making theoretical studies the most important source of information for the optimization of phosphorene production. Here, we provide an accurate estimate of the exfoliation energy of black-P on the basis of multilevel quantum chemical calculations, which include the periodic local Møller-Plesset perturbation theory of second order, augmented by higher-order corrections, which are evaluated with finite clusters mimicking the crystal. Very similar results are also obtained by density functional theory with the D3-version of Grimme's empirical dispersion correction. Our estimate of the exfoliation energy for black-P of -151 meV/atom is substantially larger than that of graphite, suggesting the need for different strategies to generate isolated layers for these two systems. PMID:26651397
Synchronization-based parameter estimation from time series
NASA Astrophysics Data System (ADS)
Parlitz, U.; Junge, L.; Kocarev, L.
1996-12-01
The parameters of a given (chaotic) dynamical model are estimated from scalar time series by adapting a computer model until it synchronizes with the given data. This parameter identification method is applied to numerically generated and experimental data from Chua's circuit.
Computational methods for estimation of parameters in hyperbolic systems
NASA Technical Reports Server (NTRS)
Banks, H. T.; Ito, K.; Murphy, K. A.
1983-01-01
Approximation techniques for estimating spatially varying coefficients and unknown boundary parameters in second order hyperbolic systems are discussed. Methods for state approximation (cubic splines, tau-Legendre) and approximation of function space parameters (interpolatory splines) are outlined and numerical findings for use of the resulting schemes in model "one dimensional seismic inversion' problems are summarized.
Ralph, Duncan K.; Matsen, Frederick A.
2016-01-01
VDJ rearrangement and somatic hypermutation work together to produce antibody-coding B cell receptor (BCR) sequences for a remarkable diversity of antigens. It is now possible to sequence these BCRs in high throughput; analysis of these sequences is bringing new insight into how antibodies develop, in particular for broadly-neutralizing antibodies against HIV and influenza. A fundamental step in such sequence analysis is to annotate each base as coming from a specific one of the V, D, or J genes, or from an N-addition (a.k.a. non-templated insertion). Previous work has used simple parametric distributions to model transitions from state to state in a hidden Markov model (HMM) of VDJ recombination, and assumed that mutations occur via the same process across sites. However, codon frame and other effects have been observed to violate these parametric assumptions for such coding sequences, suggesting that a non-parametric approach to modeling the recombination process could be useful. In our paper, we find that indeed large modern data sets suggest a model using parameter-rich per-allele categorical distributions for HMM transition probabilities and per-allele-per-position mutation probabilities, and that using such a model for inference leads to significantly improved results. We present an accurate and efficient BCR sequence annotation software package using a novel HMM “factorization” strategy. This package, called partis (https://github.com/psathyrella/partis/), is built on a new general-purpose HMM compiler that can perform efficient inference given a simple text description of an HMM. PMID:26751373
Ralph, Duncan K; Matsen, Frederick A
2016-01-01
VDJ rearrangement and somatic hypermutation work together to produce antibody-coding B cell receptor (BCR) sequences for a remarkable diversity of antigens. It is now possible to sequence these BCRs in high throughput; analysis of these sequences is bringing new insight into how antibodies develop, in particular for broadly-neutralizing antibodies against HIV and influenza. A fundamental step in such sequence analysis is to annotate each base as coming from a specific one of the V, D, or J genes, or from an N-addition (a.k.a. non-templated insertion). Previous work has used simple parametric distributions to model transitions from state to state in a hidden Markov model (HMM) of VDJ recombination, and assumed that mutations occur via the same process across sites. However, codon frame and other effects have been observed to violate these parametric assumptions for such coding sequences, suggesting that a non-parametric approach to modeling the recombination process could be useful. In our paper, we find that indeed large modern data sets suggest a model using parameter-rich per-allele categorical distributions for HMM transition probabilities and per-allele-per-position mutation probabilities, and that using such a model for inference leads to significantly improved results. We present an accurate and efficient BCR sequence annotation software package using a novel HMM "factorization" strategy. This package, called partis (https://github.com/psathyrella/partis/), is built on a new general-purpose HMM compiler that can perform efficient inference given a simple text description of an HMM. PMID:26751373
The augmented Lagrangian method for parameter estimation in elliptic systems
NASA Technical Reports Server (NTRS)
Ito, Kazufumi; Kunisch, Karl
1990-01-01
In this paper a new technique for the estimation of parameters in elliptic partial differential equations is developed. It is a hybrid method combining the output-least-squares and the equation error method. The new method is realized by an augmented Lagrangian formulation, and convergence as well as rate of convergence proofs are provided. Technically the critical step is the verification of a coercivity estimate of an appropriately defined Lagrangian functional. To obtain this coercivity estimate a seminorm regularization technique is used.
Cosmological parameter estimation with free-form primordial power spectrum
NASA Astrophysics Data System (ADS)
Hazra, Dhiraj Kumar; Shafieloo, Arman; Souradeep, Tarun
2013-06-01
Constraints on the main cosmological parameters using cosmic microwave background (CMB) or large scale structure data are usually based on the power-law assumption of the primordial power spectrum (PPS). However, in the absence of a preferred model for the early Universe, this raises a concern that current cosmological parameter estimates are strongly prejudiced by the assumed power-law form of PPS. In this paper, for the first time, we perform cosmological parameter estimation allowing the free form of the primordial spectrum. This is in fact the most general approach to estimate cosmological parameters without assuming any particular form for the primordial spectrum. We use a direct reconstruction of the PPS for any point in the cosmological parameter space using the recently modified Richardson-Lucy algorithm; however, other alternative reconstruction methods could be used for this purpose as well. We use WMAP 9 year data in our analysis considering the CMB lensing effect, and we report, for the first time, that the flat spatial universe with no cosmological constant is ruled out by more than a 4σ confidence limit without assuming any particular form of the primordial spectrum. This would be probably the most robust indication for dark energy using CMB data alone. Our results on the estimated cosmological parameters show that higher values of the baryonic and matter density and a lower value of the Hubble parameter (in comparison to the estimated values by assuming power-law PPS) is preferred by the data. However, the estimated cosmological parameters by assuming a free form of the PPS have an overlap at 1σ confidence level with the estimated values assuming the power-law form of PPS.
NASA Astrophysics Data System (ADS)
Mandel, Ilya; Berry, Christopher P. L.; Ohme, Frank; Fairhurst, Stephen; Farr, Will M.
2014-08-01
Gravitational-wave (GW) astronomy seeks to extract information about astrophysical systems from the GW signals they emit. For coalescing compact-binary sources this requires accurate model templates for the inspiral and, potentially, the subsequent merger and ringdown. Models with frequency-domain waveforms that terminate abruptly in the sensitive band of the detector are often used for parameter-estimation studies. We show that the abrupt waveform termination contains significant information that affects parameter-estimation accuracy. If the sharp cutoff is not physically motivated, this extra information can lead to misleadingly good accuracy claims. We also show that using waveforms with a cutoff as templates to recover complete signals can lead to biases in parameter estimates. We evaluate when the information content in the cutoff is likely to be important in both cases. We also point out that the standard Fisher matrix formalism, frequently employed for approximately predicting parameter-estimation accuracy, cannot properly incorporate an abrupt cutoff that is present in both signals and templates; this observation explains some previously unexpected results found in the literature. These effects emphasize the importance of using complete waveforms with accurate merger and ringdown phases for parameter estimation.
NASA Astrophysics Data System (ADS)
Bao, Xingxian; Cao, Aixia; Zhang, Jing
2016-07-01
Modal parameters estimation plays an important role for structural health monitoring. Accurately estimating the modal parameters of structures is more challenging as the measured vibration response signals are contaminated with noise. This study develops a mathematical algorithm of solving the partially described inverse singular value problem (PDISVP) combined with the complex exponential (CE) method to estimate the modal parameters. The PDISVP solving method is to reconstruct an L2-norm optimized (filtered) data matrix from the measured (noisy) data matrix, when the prescribed data constraints are one or several sets of singular triplets of the matrix. The measured data matrix is Hankel structured, which is constructed based on the measured impulse response function (IRF). The reconstructed matrix must maintain the Hankel structure, and be lowered in rank as well. Once the filtered IRF is obtained, the CE method can be applied to extract the modal parameters. Two physical experiments, including a steel cantilever beam with 10 accelerometers mounted, and a steel plate with 30 accelerometers mounted, excited by an impulsive load, respectively, are investigated to test the applicability of the proposed scheme. In addition, the consistency diagram is proposed to exam the agreement among the modal parameters estimated from those different accelerometers. Results indicate that the PDISVP-CE method can significantly remove noise from measured signals and accurately estimate the modal frequencies and damping ratios.
EFFECT OF UNCERTAINTIES IN STELLAR MODEL PARAMETERS ON ESTIMATED MASSES AND RADII OF SINGLE STARS
Basu, Sarbani; Verner, Graham A.; Chaplin, William J.; Elsworth, Yvonne E-mail: gav@bison.ph.bham.ac.uk E-mail: y.p.elsworth@bham.ac.uk
2012-02-10
Accurate and precise values of radii and masses of stars are needed to correctly estimate properties of extrasolar planets. We examine the effect of uncertainties in stellar model parameters on estimates of the masses, radii, and average densities of solar-type stars. We find that in the absence of seismic data on solar-like oscillations, stellar masses can be determined to a greater accuracy than either stellar radii or densities; but to get reasonably accurate results the effective temperature, log g, and metallicity must be measured to high precision. When seismic data are available, stellar density is the most well-determined property, followed by radius, with mass the least well-determined property. Uncertainties in stellar convection, quantified in terms of uncertainties in the value of the mixing length parameter, cause the most significant errors in the estimates of stellar properties.
Linear Parameter Varying Control Synthesis for Actuator Failure, Based on Estimated Parameter
NASA Technical Reports Server (NTRS)
Shin, Jong-Yeob; Wu, N. Eva; Belcastro, Christine
2002-01-01
The design of a linear parameter varying (LPV) controller for an aircraft at actuator failure cases is presented. The controller synthesis for actuator failure cases is formulated into linear matrix inequality (LMI) optimizations based on an estimated failure parameter with pre-defined estimation error bounds. The inherent conservatism of an LPV control synthesis methodology is reduced using a scaling factor on the uncertainty block which represents estimated parameter uncertainties. The fault parameter is estimated using the two-stage Kalman filter. The simulation results of the designed LPV controller for a HiMXT (Highly Maneuverable Aircraft Technology) vehicle with the on-line estimator show that the desired performance and robustness objectives are achieved for actuator failure cases.
Analyzing and constraining signaling networks: parameter estimation for the user.
Geier, Florian; Fengos, Georgios; Felizzi, Federico; Iber, Dagmar
2012-01-01
The behavior of most dynamical models not only depends on the wiring but also on the kind and strength of interactions which are reflected in the parameter values of the model. The predictive value of mathematical models therefore critically hinges on the quality of the parameter estimates. Constraining a dynamical model by an appropriate parameterization follows a 3-step process. In an initial step, it is important to evaluate the sensitivity of the parameters of the model with respect to the model output of interest. This analysis points at the identifiability of model parameters and can guide the design of experiments. In the second step, the actual fitting needs to be carried out. This step requires special care as, on the one hand, noisy as well as partial observations can corrupt the identification of system parameters. On the other hand, the solution of the dynamical system usually depends in a highly nonlinear fashion on its parameters and, as a consequence, parameter estimation procedures get easily trapped in local optima. Therefore any useful parameter estimation procedure has to be robust and efficient with respect to both challenges. In the final step, it is important to access the validity of the optimized model. A number of reviews have been published on the subject. A good, nontechnical overview is provided by Jaqaman and Danuser (Nat Rev Mol Cell Biol 7(11):813-819, 2006) and a classical introduction, focussing on the algorithmic side, is given in Press (Numerical recipes: The art of scientific computing, Cambridge University Press, 3rd edn., 2007, Chapters 10 and 15). We will focus on the practical issues related to parameter estimation and use a model of the TGFβ-signaling pathway as an educative example. Corresponding parameter estimation software and models based on MATLAB code can be downloaded from the authors's web page ( http://www.bsse.ethz.ch/cobi ). PMID:23361979
Parameter estimation on gravitational waves from multiple coalescing binaries
Mandel, Ilya
2010-04-15
Future ground-based and space-borne interferometric gravitational-wave detectors may capture between tens and thousands of binary coalescence events per year. There is a significant and growing body of work on the estimation of astrophysically relevant parameters, such as masses and spins, from the gravitational-wave signature of a single event. This paper introduces a robust Bayesian framework for combining the parameter estimates for multiple events into a parameter distribution of the underlying event population. The framework can be readily deployed as a rapid post-processing tool.
Projection filters for modal parameter estimate for flexible structures
NASA Technical Reports Server (NTRS)
Huang, Jen-Kuang; Chen, Chung-Wen
1987-01-01
Single-mode projection filters are developed for eigensystem parameter estimates from both analytical results and test data. Explicit formulations of these projection filters are derived using the pseudoinverse matrices of the controllability and observability matrices in general use. A global minimum optimization algorithm is developed to update the filter parameters by using interval analysis method. Modal parameters can be attracted and updated in the global sense within a specific region by passing the experimental data through the projection filters. For illustration of this method, a numerical example is shown by using a one-dimensional global optimization algorithm to estimate model frequencies and dampings.
Estimation of nonlinear pilot model parameters including time delay.
NASA Technical Reports Server (NTRS)
Schiess, J. R.; Roland, V. R.; Wells, W. R.
1972-01-01
Investigation of the feasibility of using a Kalman filter estimator for the identification of unknown parameters in nonlinear dynamic systems with a time delay. The problem considered is the application of estimation theory to determine the parameters of a family of pilot models containing delayed states. In particular, the pilot-plant dynamics are described by differential-difference equations of the retarded type. The pilot delay, included as one of the unknown parameters to be determined, is kept in pure form as opposed to the Pade approximations generally used for these systems. Problem areas associated with processing real pilot response data are included in the discussion.
Lamb mode selection for accurate wall loss estimation via guided wave tomography
Huthwaite, P.; Ribichini, R.; Lowe, M. J. S.; Cawley, P.
2014-02-18
Guided wave tomography offers a method to accurately quantify wall thickness losses in pipes and vessels caused by corrosion. This is achieved using ultrasonic waves transmitted over distances of approximately 1–2m, which are measured by an array of transducers and then used to reconstruct a map of wall thickness throughout the inspected region. To achieve accurate estimations of remnant wall thickness, it is vital that a suitable Lamb mode is chosen. This paper presents a detailed evaluation of the fundamental modes, S{sub 0} and A{sub 0}, which are of primary interest in guided wave tomography thickness estimates since the higher order modes do not exist at all thicknesses, to compare their performance using both numerical and experimental data while considering a range of challenging phenomena. The sensitivity of A{sub 0} to thickness variations was shown to be superior to S{sub 0}, however, the attenuation from A{sub 0} when a liquid loading was present was much higher than S{sub 0}. A{sub 0} was less sensitive to the presence of coatings on the surface of than S{sub 0}.
Lamb mode selection for accurate wall loss estimation via guided wave tomography
NASA Astrophysics Data System (ADS)
Huthwaite, P.; Ribichini, R.; Lowe, M. J. S.; Cawley, P.
2014-02-01
Guided wave tomography offers a method to accurately quantify wall thickness losses in pipes and vessels caused by corrosion. This is achieved using ultrasonic waves transmitted over distances of approximately 1-2m, which are measured by an array of transducers and then used to reconstruct a map of wall thickness throughout the inspected region. To achieve accurate estimations of remnant wall thickness, it is vital that a suitable Lamb mode is chosen. This paper presents a detailed evaluation of the fundamental modes, S0 and A0, which are of primary interest in guided wave tomography thickness estimates since the higher order modes do not exist at all thicknesses, to compare their performance using both numerical and experimental data while considering a range of challenging phenomena. The sensitivity of A0 to thickness variations was shown to be superior to S0, however, the attenuation from A0 when a liquid loading was present was much higher than S0. A0 was less sensitive to the presence of coatings on the surface of than S0.
Removing the thermal component from heart rate provides an accurate VO2 estimation in forest work.
Dubé, Philippe-Antoine; Imbeau, Daniel; Dubeau, Denise; Lebel, Luc; Kolus, Ahmet
2016-05-01
Heart rate (HR) was monitored continuously in 41 forest workers performing brushcutting or tree planting work. 10-min seated rest periods were imposed during the workday to estimate the HR thermal component (ΔHRT) per Vogt et al. (1970, 1973). VO2 was measured using a portable gas analyzer during a morning submaximal step-test conducted at the work site, during a work bout over the course of the day (range: 9-74 min), and during an ensuing 10-min rest pause taken at the worksite. The VO2 estimated, from measured HR and from corrected HR (thermal component removed), were compared to VO2 measured during work and rest. Varied levels of HR thermal component (ΔHRTavg range: 0-38 bpm) originating from a wide range of ambient thermal conditions, thermal clothing insulation worn, and physical load exerted during work were observed. Using raw HR significantly overestimated measured work VO2 by 30% on average (range: 1%-64%). 74% of VO2 prediction error variance was explained by the HR thermal component. VO2 estimated from corrected HR, was not statistically different from measured VO2. Work VO2 can be estimated accurately in the presence of thermal stress using Vogt et al.'s method, which can be implemented easily by the practitioner with inexpensive instruments. PMID:26851474
Granata, Daniele; Carnevale, Vincenzo
2016-01-01
The collective behavior of a large number of degrees of freedom can be often described by a handful of variables. This observation justifies the use of dimensionality reduction approaches to model complex systems and motivates the search for a small set of relevant “collective” variables. Here, we analyze this issue by focusing on the optimal number of variable needed to capture the salient features of a generic dataset and develop a novel estimator for the intrinsic dimension (ID). By approximating geodesics with minimum distance paths on a graph, we analyze the distribution of pairwise distances around the maximum and exploit its dependency on the dimensionality to obtain an ID estimate. We show that the estimator does not depend on the shape of the intrinsic manifold and is highly accurate, even for exceedingly small sample sizes. We apply the method to several relevant datasets from image recognition databases and protein multiple sequence alignments and discuss possible interpretations for the estimated dimension in light of the correlations among input variables and of the information content of the dataset. PMID:27510265
Hybridization modeling of oligonucleotide SNP arrays for accurate DNA copy number estimation
Wan, Lin; Sun, Kelian; Ding, Qi; Cui, Yuehua; Li, Ming; Wen, Yalu; Elston, Robert C.; Qian, Minping; Fu, Wenjiang J
2009-01-01
Affymetrix SNP arrays have been widely used for single-nucleotide polymorphism (SNP) genotype calling and DNA copy number variation inference. Although numerous methods have achieved high accuracy in these fields, most studies have paid little attention to the modeling of hybridization of probes to off-target allele sequences, which can affect the accuracy greatly. In this study, we address this issue and demonstrate that hybridization with mismatch nucleotides (HWMMN) occurs in all SNP probe-sets and has a critical effect on the estimation of allelic concentrations (ACs). We study sequence binding through binding free energy and then binding affinity, and develop a probe intensity composite representation (PICR) model. The PICR model allows the estimation of ACs at a given SNP through statistical regression. Furthermore, we demonstrate with cell-line data of known true copy numbers that the PICR model can achieve reasonable accuracy in copy number estimation at a single SNP locus, by using the ratio of the estimated AC of each sample to that of the reference sample, and can reveal subtle genotype structure of SNPs at abnormal loci. We also demonstrate with HapMap data that the PICR model yields accurate SNP genotype calls consistently across samples, laboratories and even across array platforms. PMID:19586935
Granata, Daniele; Carnevale, Vincenzo
2016-01-01
The collective behavior of a large number of degrees of freedom can be often described by a handful of variables. This observation justifies the use of dimensionality reduction approaches to model complex systems and motivates the search for a small set of relevant "collective" variables. Here, we analyze this issue by focusing on the optimal number of variable needed to capture the salient features of a generic dataset and develop a novel estimator for the intrinsic dimension (ID). By approximating geodesics with minimum distance paths on a graph, we analyze the distribution of pairwise distances around the maximum and exploit its dependency on the dimensionality to obtain an ID estimate. We show that the estimator does not depend on the shape of the intrinsic manifold and is highly accurate, even for exceedingly small sample sizes. We apply the method to several relevant datasets from image recognition databases and protein multiple sequence alignments and discuss possible interpretations for the estimated dimension in light of the correlations among input variables and of the information content of the dataset. PMID:27510265
Parameter estimation of copula functions using an optimization-based method
NASA Astrophysics Data System (ADS)
Abdi, Amin; Hassanzadeh, Yousef; Talatahari, Siamak; Fakheri-Fard, Ahmad; Mirabbasi, Rasoul
2016-02-01
Application of the copulas can be useful for the accurate multivariate frequency analysis of hydrological phenomena. There are many copula functions and some methods were proposed for estimating the copula parameters. Since the copula functions are mathematically complicated, estimating of the copula parameter is an effortful work. In the present study, an optimization-based method (OBM) is proposed to obtain the parameters of copulas. The usefulness of the proposed method is illustrated on drought events. For this purpose, three commonly used copulas of Archimedean family, namely, Clayton, Frank, and Gumbel copulas are used to construct the joint probability distribution of drought characteristics of 60 gauging sites located in East-Azarbaijan province, Iran. The performance of OBM was compared with two conventional methods, namely, method of moments and inference function for margins. The results illustrate the supremacy of the OBM to estimate the copula parameters compared to the other considered methods.
Dynamic State Estimation and Parameter Calibration of DFIG based on Ensemble Kalman Filter
Fan, Rui; Huang, Zhenyu; Wang, Shaobu; Diao, Ruisheng; Meng, Da
2015-07-30
With the growing interest in the application of wind energy, doubly fed induction generator (DFIG) plays an essential role in the industry nowadays. To deal with the increasing stochastic variations introduced by intermittent wind resource and responsive loads, dynamic state estimation (DSE) are introduced in any power system associated with DFIGs. However, sometimes this dynamic analysis canould not work because the parameters of DFIGs are not accurate enough. To solve the problem, an ensemble Kalman filter (EnKF) method is proposed for the state estimation and parameter calibration tasks. In this paper, a DFIG is modeled and implemented with the EnKF method. Sensitivity analysis is demonstrated regarding the measurement noise, initial state errors and parameter errors. The results indicate this EnKF method has a robust performance on the state estimation and parameter calibration of DFIGs.
LIKELIHOOD OF THE POWER SPECTRUM IN COSMOLOGICAL PARAMETER ESTIMATION
Sun, Lei; Wang, Qiao; Zhan, Hu
2013-11-01
The likelihood function is a crucial element of parameter estimation. In analyses of galaxy overdensities and weak lensing shear, one often approximates the likelihood of the power spectrum with a Gaussian distribution. The posterior probability derived from such a likelihood deviates considerably from the exact posterior on the largest scales probed by any survey, where the central limit theorem does not apply. We show that various forms of Gaussian likelihoods can have a significant impact on the estimation of the primordial non-Gaussianity parameter f{sub NL} from the galaxy angular power spectrum. The Gaussian plus log-normal likelihood, which has been applied successfully in analyses of the cosmic microwave background, outperforms the Gaussian likelihoods. Nevertheless, even if the exact likelihood of the power spectrum is used, the estimated parameters may be still biased. As such, the likelihoods and estimators need to be thoroughly examined for potential systematic errors.
Iterative methods for distributed parameter estimation in parabolic PDE
Vogel, C.R.; Wade, J.G.
1994-12-31
The goal of the work presented is the development of effective iterative techniques for large-scale inverse or parameter estimation problems. In this extended abstract, a detailed description of the mathematical framework in which the authors view these problem is presented, followed by an outline of the ideas and algorithms developed. Distributed parameter estimation problems often arise in mathematical modeling with partial differential equations. They can be viewed as inverse problems; the `forward problem` is that of using the fully specified model to predict the behavior of the system. The inverse or parameter estimation problem is: given the form of the model and some observed data from the system being modeled, determine the unknown parameters of the model. These problems are of great practical and mathematical interest, and the development of efficient computational algorithms is an active area of study.
Methods for accurate estimation of net discharge in a tidal channel
Simpson, M.R.; Bland, R.
2000-01-01
Accurate estimates of net residual discharge in tidally affected rivers and estuaries are possible because of recently developed ultrasonic discharge measurement techniques. Previous discharge estimates using conventional mechanical current meters and methods based on stage/discharge relations or water slope measurements often yielded errors that were as great as or greater than the computed residual discharge. Ultrasonic measurement methods consist of: 1) the use of ultrasonic instruments for the measurement of a representative 'index' velocity used for in situ estimation of mean water velocity and 2) the use of the acoustic Doppler current discharge measurement system to calibrate the index velocity measurement data. Methods used to calibrate (rate) the index velocity to the channel velocity measured using the Acoustic Doppler Current Profiler are the most critical factors affecting the accuracy of net discharge estimation. The index velocity first must be related to mean channel velocity and then used to calculate instantaneous channel discharge. Finally, discharge is low-pass filtered to remove the effects of the tides. An ultrasonic velocity meter discharge-measurement site in a tidally affected region of the Sacramento-San Joaquin Rivers was used to study the accuracy of the index velocity calibration procedure. Calibration data consisting of ultrasonic velocity meter index velocity and concurrent acoustic Doppler discharge measurement data were collected during three time periods. Two sets of data were collected during a spring tide (monthly maximum tidal current) and one of data collected during a neap tide (monthly minimum tidal current). The relative magnitude of instrumental errors, acoustic Doppler discharge measurement errors, and calibration errors were evaluated. Calibration error was found to be the most significant source of error in estimating net discharge. Using a comprehensive calibration method, net discharge estimates developed from the three
MIDAS robust trend estimator for accurate GPS station velocities without step detection
NASA Astrophysics Data System (ADS)
Blewitt, Geoffrey; Kreemer, Corné; Hammond, William C.; Gazeaux, Julien
2016-03-01
Automatic estimation of velocities from GPS coordinate time series is becoming required to cope with the exponentially increasing flood of available data, but problems detectable to the human eye are often overlooked. This motivates us to find an automatic and accurate estimator of trend that is resistant to common problems such as step discontinuities, outliers, seasonality, skewness, and heteroscedasticity. Developed here, Median Interannual Difference Adjusted for Skewness (MIDAS) is a variant of the Theil-Sen median trend estimator, for which the ordinary version is the median of slopes vij = (xj-xi)/(tj-ti) computed between all data pairs i > j. For normally distributed data, Theil-Sen and least squares trend estimates are statistically identical, but unlike least squares, Theil-Sen is resistant to undetected data problems. To mitigate both seasonality and step discontinuities, MIDAS selects data pairs separated by 1 year. This condition is relaxed for time series with gaps so that all data are used. Slopes from data pairs spanning a step function produce one-sided outliers that can bias the median. To reduce bias, MIDAS removes outliers and recomputes the median. MIDAS also computes a robust and realistic estimate of trend uncertainty. Statistical tests using GPS data in the rigid North American plate interior show ±0.23 mm/yr root-mean-square (RMS) accuracy in horizontal velocity. In blind tests using synthetic data, MIDAS velocities have an RMS accuracy of ±0.33 mm/yr horizontal, ±1.1 mm/yr up, with a 5th percentile range smaller than all 20 automatic estimators tested. Considering its general nature, MIDAS has the potential for broader application in the geosciences.
Accurate analytical method for the extraction of solar cell model parameters
NASA Astrophysics Data System (ADS)
Phang, J. C. H.; Chan, D. S. H.; Phillips, J. R.
1984-05-01
Single diode solar cell model parameters are rapidly extracted from experimental data by means of the presently derived analytical expressions. The parameter values obtained have a less than 5 percent error for most solar cells, in light of the extraction of model parameters for two cells of differing quality which were compared with parameters extracted by means of the iterative method.
High-Resolution Tsunami Inundation Simulations Based on Accurate Estimations of Coastal Waveforms
NASA Astrophysics Data System (ADS)
Oishi, Y.; Imamura, F.; Sugawara, D.; Furumura, T.
2015-12-01
We evaluate the accuracy of high-resolution tsunami inundation simulations in detail using the actual observational data of the 2011 Tohoku-Oki earthquake (Mw9.0) and investigate the methodologies to improve the simulation accuracy.Due to the recent development of parallel computing technologies, high-resolution tsunami inundation simulations are conducted more commonly than before. To evaluate how accurately these simulations can reproduce inundation processes, we test several types of simulation configurations on a parallel computer, where we can utilize the observational data (e.g., offshore and coastal waveforms and inundation properties) that are recorded during the Tohoku-Oki earthquake.Before discussing the accuracy of inundation processes on land, the incident waves at coastal sites must be accurately estimated. However, for megathrust earthquakes, it is difficult to find the tsunami source that can provide accurate estimations of tsunami waveforms at every coastal site because of the complex spatiotemporal distribution of the source and the limitation of observation. To overcome this issue, we employ a site-specific source inversion approach that increases the estimation accuracy within a specific coastal site by applying appropriate weighting to the observational data in the inversion process.We applied our source inversion technique to the Tohoku tsunami and conducted inundation simulations using 5-m resolution digital elevation model data (DEM) for the coastal area around Miyako Bay and Sendai Bay. The estimated waveforms at the coastal wave gauges of these bays successfully agree with the observed waveforms. However, the simulations overestimate the inundation extent indicating the necessity to improve the inundation model. We find that the value of Manning's roughness coefficient should be modified from the often-used value of n = 0.025 to n = 0.033 to obtain proper results at both cities.In this presentation, the simulation results with several
Estimation of Temperature Dependent Parameters of a Batch Alcoholic Fermentation Process
NASA Astrophysics Data System (ADS)
de Andrade, Rafael Ramos; Rivera, Elmer Ccopa; Costa, Aline C.; Atala, Daniel I. P.; Filho, Francisco Maugeri; Filho, Rubens Maciel
In this work, a procedure was established to develop a mathematical model considering the effect of temperature on reaction kinetics. Experiments were performed in batch mode in temperatures from 30 to 38°C. The microorganism used was Saccharomyces cerevisiae and the culture media, sugarcane molasses. The objective is to assess the difficulty in updating the kinetic parameters when there are changes in fermentation conditions. We conclude that, although the re-estimation is a time-consuming task, it is possible to accurately describe the process when there are changes in raw material composition if a re-estimation of parameters is performed.
Luo, Shezhou; Chen, Jing M; Wang, Cheng; Xi, Xiaohuan; Zeng, Hongcheng; Peng, Dailiang; Li, Dong
2016-05-30
Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R^{2} = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m^{2}), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data. PMID:27410085
Simultaneous parameter and state estimation of shear buildings
NASA Astrophysics Data System (ADS)
Concha, Antonio; Alvarez-Icaza, Luis; Garrido, Rubén
2016-03-01
This paper proposes an adaptive observer that simultaneously estimates the damping/mass and stiffness/mass ratios, and the state of a seismically excited building. The adaptive observer uses only acceleration measurements of the ground and floors for both parameter and state estimation; it identifies all the parameter ratios, velocities and displacements of the structure if all the floors are instrumented; and it also estimates the state and the damping/mass and stiffness/mass ratios of a reduced model of the building if only some floors are equipped with accelerometers. This observer does not resort to any particular canonical form and employs the Least Squares (LS) algorithm and a Luenberger state estimator. The LS method is combined with a smooth parameter projection technique that provides only positive estimates, which are employed by the state estimator. Boundedness of the estimate produced by the LS algorithm does not depend on the boundedness of the state estimates. Moreover, the LS method uses a parametrization based on Linear Integral Filters that eliminate offsets in the acceleration measurements in finite time and attenuate high-frequency measurement noise. Experimental results obtained using a reduced-scale five-story confirm the effectiveness of the proposed adaptive observer.
Estimation of Accumulation Parameters for Urban Runoff Quality Modeling
NASA Astrophysics Data System (ADS)
Alley, William M.; Smith, Peter E.
1981-12-01
Many recently developed watershed models utilize accumulation and washoff equations to simulate the quality of runofffrom urban impervious areas. These models often have been calibrated by trial and error and with little understanding of model sensitivity to the various parameters. Methodologies for estimating best fit values of the washoff parameters commonly used in these models have been presented previously. In this paper, parameter identification techniques for estimating the accumulation parameters from measured runoff quality data are presented along with a sensitivity analysis of the parameters. Results from application of the techniques and the sensitivity analysis suggest a need for data quantifying the magnitude and identifying the shape of constituent accumulation curves. An exponential accumulation curve is shown to be more general than the linear accumulation curves used in most urban runoff quality models. When determining accumulation rates, attention needs to be given to the effects of residual amounts of constituents remaining after the previous period of storm runoff or street sweeping.
Nonlinear estimation of Monod growth kinetic parameters from a single substrate depletion curve.
Robinson, J A; Tiedje, J M
1983-01-01
Monod growth kinetic parameters were estimated by fitting sigmoidal substrate depletion data to the integrated Monod equation, using nonlinear least-squares analysis. When the initial substrate concentration was in the mixed-order region, nonlinear estimation of simulated data sets containing known measurement errors provided accurate estimates of the mu max, Ks, and Y values used to create these data. Nonlinear regression analysis of sigmoidal substrate depletion data was also evaluated for H2-limited batch growth of Desulfovibrio sp. strain G11. The integrated Monod equation can be more convenient for the estimation of growth kinetic parameters, particularly for gaseous substrates, but it must be recognized that the estimates of mu max, Ks, and Y obtained may be influenced by the growth rate history of the inoculum. PMID:6870238
Stellar atmospheric parameter estimation using Gaussian process regression
NASA Astrophysics Data System (ADS)
Bu, Yude; Pan, Jingchang
2015-02-01
As is well known, it is necessary to derive stellar parameters from massive amounts of spectral data automatically and efficiently. However, in traditional automatic methods such as artificial neural networks (ANNs) and kernel regression (KR), it is often difficult to optimize the algorithm structure and determine the optimal algorithm parameters. Gaussian process regression (GPR) is a recently developed method that has been proven to be capable of overcoming these difficulties. Here we apply GPR to derive stellar atmospheric parameters from spectra. Through evaluating the performance of GPR on Sloan Digital Sky Survey (SDSS) spectra, Medium resolution Isaac Newton Telescope Library of Empirical Spectra (MILES) spectra, ELODIE spectra and the spectra of member stars of galactic globular clusters, we conclude that GPR can derive stellar parameters accurately and precisely, especially when we use data preprocessed with principal component analysis (PCA). We then compare the performance of GPR with that of several widely used regression methods (ANNs, support-vector regression and KR) and find that with GPR it is easier to optimize structures and parameters and more efficient and accurate to extract atmospheric parameters.
Single-tone parameter estimation from discrete-time observations
NASA Technical Reports Server (NTRS)
Rife, D. C.; Boorstyn, R. R.
1974-01-01
Estimation of the parameters of a single-frequency complex tone from a finite number of noisy discrete-time observations is discussed. The appropriate Cramer-Rao bounds and maximum-likelihood (ML) estimation algorithms are derived. Some properties of the ML estimators are proved. The relationship of ML estimation to the discrete Fourier transform is exploited to obtain practial algorithms. The threshold effect of one algorithm is analyzed and compared to simulation results. Other simulation results verify other aspects of the analysis.
Standard Errors of Estimated Latent Variable Scores with Estimated Structural Parameters
ERIC Educational Resources Information Center
Hoshino, Takahiro; Shigemasu, Kazuo
2008-01-01
The authors propose a concise formula to evaluate the standard error of the estimated latent variable score when the true values of the structural parameters are not known and must be estimated. The formula can be applied to factor scores in factor analysis or ability parameters in item response theory, without bootstrap or Markov chain Monte…
Parameter estimation and forecasting for multiplicative log-normal cascades.
Leövey, Andrés E; Lux, Thomas
2012-04-01
We study the well-known multiplicative log-normal cascade process in which the multiplication of Gaussian and log normally distributed random variables yields time series with intermittent bursts of activity. Due to the nonstationarity of this process and the combinatorial nature of such a formalism, its parameters have been estimated mostly by fitting the numerical approximation of the associated non-Gaussian probability density function to empirical data, cf. Castaing et al. [Physica D 46, 177 (1990)]. More recently, alternative estimators based upon various moments have been proposed by Beck [Physica D 193, 195 (2004)] and Kiyono et al. [Phys. Rev. E 76, 041113 (2007)]. In this paper, we pursue this moment-based approach further and develop a more rigorous generalized method of moments (GMM) estimation procedure to cope with the documented difficulties of previous methodologies. We show that even under uncertainty about the actual number of cascade steps, our methodology yields very reliable results for the estimated intermittency parameter. Employing the Levinson-Durbin algorithm for best linear forecasts, we also show that estimated parameters can be used for forecasting the evolution of the turbulent flow. We compare forecasting results from the GMM and Kiyono et al.'s procedure via Monte Carlo simulations. We finally test the applicability of our approach by estimating the intermittency parameter and forecasting of volatility for a sample of financial data from stock and foreign exchange markets. PMID:22680545
NASA Technical Reports Server (NTRS)
Orme, John S.; Gilyard, Glenn B.
1992-01-01
Integrated engine-airframe optimal control technology may significantly improve aircraft performance. This technology requires a reliable and accurate parameter estimator to predict unmeasured variables. To develop this technology base, NASA Dryden Flight Research Facility (Edwards, CA), McDonnell Aircraft Company (St. Louis, MO), and Pratt & Whitney (West Palm Beach, FL) have developed and flight-tested an adaptive performance seeking control system which optimizes the quasi-steady-state performance of the F-15 propulsion system. This paper presents flight and ground test evaluations of the propulsion system parameter estimation process used by the performance seeking control system. The estimator consists of a compact propulsion system model and an extended Kalman filter. The extended Laman filter estimates five engine component deviation parameters from measured inputs. The compact model uses measurements and Kalman-filter estimates as inputs to predict unmeasured propulsion parameters such as net propulsive force and fan stall margin. The ability to track trends and estimate absolute values of propulsion system parameters was demonstrated. For example, thrust stand results show a good correlation, especially in trends, between the performance seeking control estimated and measured thrust.
Accurate estimation of the RMS emittance from single current amplifier data
Stockli, Martin P.; Welton, R.F.; Keller, R.; Letchford, A.P.; Thomae, R.W.; Thomason, J.W.G.
2002-05-31
This paper presents the SCUBEEx rms emittance analysis, a self-consistent, unbiased elliptical exclusion method, which combines traditional data-reduction methods with statistical methods to obtain accurate estimates for the rms emittance. Rather than considering individual data, the method tracks the average current density outside a well-selected, variable boundary to separate the measured beam halo from the background. The average outside current density is assumed to be part of a uniform background and not part of the particle beam. Therefore the average outside current is subtracted from the data before evaluating the rms emittance within the boundary. As the boundary area is increased, the average outside current and the inside rms emittance form plateaus when all data containing part of the particle beam are inside the boundary. These plateaus mark the smallest acceptable exclusion boundary and provide unbiased estimates for the average background and the rms emittance. Small, trendless variations within the plateaus allow for determining the uncertainties of the estimates caused by variations of the measured background outside the smallest acceptable exclusion boundary. The robustness of the method is established with complementary variations of the exclusion boundary. This paper presents a detailed comparison between traditional data reduction methods and SCUBEEx by analyzing two complementary sets of emittance data obtained with a Lawrence Berkeley National Laboratory and an ISIS H{sup -} ion source.
Accurate estimation of human body orientation from RGB-D sensors.
Liu, Wu; Zhang, Yongdong; Tang, Sheng; Tang, Jinhui; Hong, Richang; Li, Jintao
2013-10-01
Accurate estimation of human body orientation can significantly enhance the analysis of human behavior, which is a fundamental task in the field of computer vision. However, existing orientation estimation methods cannot handle the various body poses and appearances. In this paper, we propose an innovative RGB-D-based orientation estimation method to address these challenges. By utilizing the RGB-D information, which can be real time acquired by RGB-D sensors, our method is robust to cluttered environment, illumination change and partial occlusions. Specifically, efficient static and motion cue extraction methods are proposed based on the RGB-D superpixels to reduce the noise of depth data. Since it is hard to discriminate all the 360 (°) orientation using static cues or motion cues independently, we propose to utilize a dynamic Bayesian network system (DBNS) to effectively employ the complementary nature of both static and motion cues. In order to verify our proposed method, we build a RGB-D-based human body orientation dataset that covers a wide diversity of poses and appearances. Our intensive experimental evaluations on this dataset demonstrate the effectiveness and efficiency of the proposed method. PMID:23893759
Non-stationary Bayesian estimation of parameters from a body cover model of the vocal folds.
Hadwin, Paul J; Galindo, Gabriel E; Daun, Kyle J; Zañartu, Matías; Erath, Byron D; Cataldo, Edson; Peterson, Sean D
2016-05-01
The evolution of reduced-order vocal fold models into clinically useful tools for subject-specific diagnosis and treatment hinges upon successfully and accurately representing an individual patient in the modeling framework. This, in turn, requires inference of model parameters from clinical measurements in order to tune a model to the given individual. Bayesian analysis is a powerful tool for estimating model parameter probabilities based upon a set of observed data. In this work, a Bayesian particle filter sampling technique capable of estimating time-varying model parameters, as occur in complex vocal gestures, is introduced. The technique is compared with time-invariant Bayesian estimation and least squares methods for determining both stationary and non-stationary parameters. The current technique accurately estimates the time-varying unknown model parameter and maintains tight credibility bounds. The credibility bounds are particularly relevant from a clinical perspective, as they provide insight into the confidence a clinician should have in the model predictions. PMID:27250162
Parameter estimation for chaotic systems with a Drift Particle Swarm Optimization method
NASA Astrophysics Data System (ADS)
Sun, Jun; Zhao, Ji; Wu, Xiaojun; Fang, Wei; Cai, Yujie; Xu, Wenbo
2010-06-01
Inspired by the motion of electrons in metal conductors in an electric field, we propose a variant of Particle Swarm Optimization (PSO), called Drift Particle Swarm Optimization (DPSO) algorithm, and apply it in estimating the unknown parameters of chaotic dynamic systems. The principle and procedure of DPSO are presented, and the algorithm is used to identify Lorenz system and Chen system. The experiment results show that for the given parameter configurations, DPSO can identify the parameters of the systems accurately and effectively, and it may be a promising tool for chaotic system identification as well as other numerical optimization problems in physics.
Assumptions of the primordial spectrum and cosmological parameter estimation
NASA Astrophysics Data System (ADS)
Shafieloo, Arman; Souradeep, Tarun
2011-10-01
The observables of the perturbed universe, cosmic microwave background (CMB) anisotropy and large structures depend on a set of cosmological parameters, as well as the assumed nature of primordial perturbations. In particular, the shape of the primordial power spectrum (PPS) is, at best, a well-motivated assumption. It is known that the assumed functional form of the PPS in cosmological parameter estimation can affect the best-fit-parameters and their relative confidence limits. In this paper, we demonstrate that a specific assumed form actually drives the best-fit parameters into distinct basins of likelihood in the space of cosmological parameters where the likelihood resists improvement via modifications to the PPS. The regions where considerably better likelihoods are obtained allowing free-form PPS lie outside these basins. In the absence of a preferred model of inflation, this raises a concern that current cosmological parameter estimates are strongly prejudiced by the assumed form of PPS. Our results strongly motivate approaches toward simultaneous estimation of the cosmological parameters and the shape of the primordial spectrum from upcoming cosmological data. It is equally important for theorists to keep an open mind towards early universe scenarios that produce features in the PPS.
Phase noise effects on turbulent weather radar spectrum parameter estimation
NASA Technical Reports Server (NTRS)
Lee, Jonggil; Baxa, Ernest G., Jr.
1990-01-01
Accurate weather spectrum moment estimation is important in the use of weather radar for hazardous windshear detection. The effect of the stable local oscillator (STALO) instability (jitter) on the spectrum moment estimation algorithm is investigated. Uncertainty in the stable local oscillator will affect both the transmitted signal and the received signal since the STALO provides transmitted and reference carriers. The proposed approach models STALO phase jitter as it affects the complex autocorrelation of the radar return. The results can therefore by interpreted in terms of any source of system phase jitter for which the model is appropriate and, in particular, may be considered as a cumulative effect of all radar system sources.
Compaction parameter estimation using surface movement data in Southern Flevoland
NASA Astrophysics Data System (ADS)
Fokker, P. A.; Gunnink, J.; de Lange, G.; Leeuwenburgh, O.; van der Veer, E. F.
2015-11-01
The Southern part of the Flevopolder has shown considerable subsidence since its reclamation in 1967. We have set up an integrated method to use subsidence data, water level data and forward models for compaction, oxidation and the resulting subsidence to estimate the driving parameters. Our procedure, an Ensemble Smoother with Multiple Data Assimilation, is very fast and gives insight into the variability of the estimated parameters and the correlations between them. We used two forward models: the Koppejan model and the Bjerrum model. In first instance, the Bjerrum model seems to perform better than the Koppejan model. This must, however, be corroborated with more elaborate parameter estimation exercises in which in particular the water level development is taken into account.
Estimation of Dynamical Parameters in Atmospheric Data Sets
NASA Technical Reports Server (NTRS)
Wenig, Mark O.
2004-01-01
In this study a new technique is used to derive dynamical parameters out of atmospheric data sets. This technique, called the structure tensor technique, can be used to estimate dynamical parameters such as motion, source strengths, diffusion constants or exponential decay rates. A general mathematical framework was developed for the direct estimation of the physical parameters that govern the underlying processes from image sequences. This estimation technique can be adapted to the specific physical problem under investigation, so it can be used in a variety of applications in trace gas, aerosol, and cloud remote sensing. The fundamental algorithm will be extended to the analysis of multi- channel (e.g. multi trace gas) image sequences and to provide solutions to the extended aperture problem. In this study sensitivity studies have been performed to determine the usability of this technique for data sets with different resolution in time and space and different dimensions.
Least-squares estimation of batch culture kinetic parameters.
Ong, S L
1983-10-01
This article concerns the development of a simple and effective least-squares procedure for estimating the kinetic parameters in Monod expressions from batch culture data. The basic approach employed in this work was to translate the problem of parameter estimation to a mathematical model containing a single decision variable. The resulting model was then solved by an efficient one-dimensional search algorithm which can be adapted to any microcomputer or advanced programmable calculator. The procedure was tested on synthetic data (substrate concentrations) with different types and levels of error. The effect of endogeneous respiration on the estimated values of the kinetic parameters was also assessed. From the results of these analyses the least-squares procedure developed was concluded to be very effective. PMID:18548565
Baker, Syed Murtuza; Poskar, C Hart; Junker, Björn H
2011-01-01
In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF), rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison. PMID:21989173
Quick and accurate estimation of the elastic constants using the minimum image method
NASA Astrophysics Data System (ADS)
Tretiakov, Konstantin V.; Wojciechowski, Krzysztof W.
2015-04-01
A method for determining the elastic properties using the minimum image method (MIM) is proposed and tested on a model system of particles interacting by the Lennard-Jones (LJ) potential. The elastic constants of the LJ system are determined in the thermodynamic limit, N → ∞, using the Monte Carlo (MC) method in the NVT and NPT ensembles. The simulation results show that when determining the elastic constants, the contribution of long-range interactions cannot be ignored, because that would lead to erroneous results. In addition, the simulations have revealed that the inclusion of further interactions of each particle with all its minimum image neighbors even in case of small systems leads to results which are very close to the values of elastic constants in the thermodynamic limit. This enables one for a quick and accurate estimation of the elastic constants using very small samples.
AMT-200S Motor Glider Parameter and Performance Estimation
NASA Technical Reports Server (NTRS)
Taylor, Brian R.
2011-01-01
Parameter and performance estimation of an instrumented motor glider was conducted at the National Aeronautics and Space Administration Dryden Flight Research Center in order to provide the necessary information to create a simulation of the aircraft. An output-error technique was employed to generate estimates from doublet maneuvers, and performance estimates were compared with results from a well-known flight-test evaluation of the aircraft in order to provide a complete set of data. Aircraft specifications are given along with information concerning instrumentation, flight-test maneuvers flown, and the output-error technique. Discussion of Cramer-Rao bounds based on both white noise and colored noise assumptions is given. Results include aerodynamic parameter and performance estimates for a range of angles of attack.
A Simple yet Accurate Method for the Estimation of the Biovolume of Planktonic Microorganisms.
Saccà, Alessandro
2016-01-01
Determining the biomass of microbial plankton is central to the study of fluxes of energy and materials in aquatic ecosystems. This is typically accomplished by applying proper volume-to-carbon conversion factors to group-specific abundances and biovolumes. A critical step in this approach is the accurate estimation of biovolume from two-dimensional (2D) data such as those available through conventional microscopy techniques or flow-through imaging systems. This paper describes a simple yet accurate method for the assessment of the biovolume of planktonic microorganisms, which works with any image analysis system allowing for the measurement of linear distances and the estimation of the cross sectional area of an object from a 2D digital image. The proposed method is based on Archimedes' principle about the relationship between the volume of a sphere and that of a cylinder in which the sphere is inscribed, plus a coefficient of 'unellipticity' introduced here. Validation and careful evaluation of the method are provided using a variety of approaches. The new method proved to be highly precise with all convex shapes characterised by approximate rotational symmetry, and combining it with an existing method specific for highly concave or branched shapes allows covering the great majority of cases with good reliability. Thanks to its accuracy, consistency, and low resources demand, the new method can conveniently be used in substitution of any extant method designed for convex shapes, and can readily be coupled with automated cell imaging technologies, including state-of-the-art flow-through imaging devices. PMID:27195667
A Simple yet Accurate Method for the Estimation of the Biovolume of Planktonic Microorganisms
2016-01-01
Determining the biomass of microbial plankton is central to the study of fluxes of energy and materials in aquatic ecosystems. This is typically accomplished by applying proper volume-to-carbon conversion factors to group-specific abundances and biovolumes. A critical step in this approach is the accurate estimation of biovolume from two-dimensional (2D) data such as those available through conventional microscopy techniques or flow-through imaging systems. This paper describes a simple yet accurate method for the assessment of the biovolume of planktonic microorganisms, which works with any image analysis system allowing for the measurement of linear distances and the estimation of the cross sectional area of an object from a 2D digital image. The proposed method is based on Archimedes’ principle about the relationship between the volume of a sphere and that of a cylinder in which the sphere is inscribed, plus a coefficient of ‘unellipticity’ introduced here. Validation and careful evaluation of the method are provided using a variety of approaches. The new method proved to be highly precise with all convex shapes characterised by approximate rotational symmetry, and combining it with an existing method specific for highly concave or branched shapes allows covering the great majority of cases with good reliability. Thanks to its accuracy, consistency, and low resources demand, the new method can conveniently be used in substitution of any extant method designed for convex shapes, and can readily be coupled with automated cell imaging technologies, including state-of-the-art flow-through imaging devices. PMID:27195667
Accurate biopsy-needle depth estimation in limited-angle tomography using multi-view geometry
NASA Astrophysics Data System (ADS)
van der Sommen, Fons; Zinger, Sveta; de With, Peter H. N.
2016-03-01
Recently, compressed-sensing based algorithms have enabled volume reconstruction from projection images acquired over a relatively small angle (θ < 20°). These methods enable accurate depth estimation of surgical tools with respect to anatomical structures. However, they are computationally expensive and time consuming, rendering them unattractive for image-guided interventions. We propose an alternative approach for depth estimation of biopsy needles during image-guided interventions, in which we split the problem into two parts and solve them independently: needle-depth estimation and volume reconstruction. The complete proposed system consists of the previous two steps, preceded by needle extraction. First, we detect the biopsy needle in the projection images and remove it by interpolation. Next, we exploit epipolar geometry to find point-to-point correspondences in the projection images to triangulate the 3D position of the needle in the volume. Finally, we use the interpolated projection images to reconstruct the local anatomical structures and indicate the position of the needle within this volume. For validation of the algorithm, we have recorded a full CT scan of a phantom with an inserted biopsy needle. The performance of our approach ranges from a median error of 2.94 mm for an distributed viewing angle of 1° down to an error of 0.30 mm for an angle larger than 10°. Based on the results of this initial phantom study, we conclude that multi-view geometry offers an attractive alternative to time-consuming iterative methods for the depth estimation of surgical tools during C-arm-based image-guided interventions.
ERIC Educational Resources Information Center
Zickar, Michael J.; Ury, Karen L.
2002-01-01
Attempted to relate content features of personality items to item parameter estimates from the partial credit model of E. Muraki (1990) by administering the Adjective Checklist (L. Goldberg, 1992) to 329 undergraduates. As predicted, the discrimination parameter was related to the item subtlety ratings of personality items but the level of word…
Accuracy of Parameter Estimation in Gibbs Sampling under the Two-Parameter Logistic Model.
ERIC Educational Resources Information Center
Kim, Seock-Ho; Cohen, Allan S.
The accuracy of Gibbs sampling, a Markov chain Monte Carlo procedure, was considered for estimation of item and ability parameters under the two-parameter logistic model. Memory test data were analyzed to illustrate the Gibbs sampling procedure. Simulated data sets were analyzed using Gibbs sampling and the marginal Bayesian method. The marginal…
Estimation of octanol/water partition coefficients using LSER parameters
Luehrs, Dean C.; Hickey, James P.; Godbole, Kalpana A.; Rogers, Tony N.
1998-01-01
The logarithms of octanol/water partition coefficients, logKow, were regressed against the linear solvation energy relationship (LSER) parameters for a training set of 981 diverse organic chemicals. The standard deviation for logKow was 0.49. The regression equation was then used to estimate logKow for a test of 146 chemicals which included pesticides and other diverse polyfunctional compounds. Thus the octanol/water partition coefficient may be estimated by LSER parameters without elaborate software but only moderate accuracy should be expected.
Inversion of canopy reflectance models for estimation of vegetation parameters
NASA Technical Reports Server (NTRS)
Goel, Narendra S.
1987-01-01
One of the keys to successful remote sensing of vegetation is to be able to estimate important agronomic parameters like leaf area index (LAI) and biomass (BM) from the bidirectional canopy reflectance (CR) data obtained by a space-shuttle or satellite borne sensor. One approach for such an estimation is through inversion of CR models which relate these parameters to CR. The feasibility of this approach was shown. The overall objective of the research carried out was to address heretofore uninvestigated but important fundamental issues, develop the inversion technique further, and delineate its strengths and limitations.
Maximum likelihood estimation for distributed parameter models of flexible spacecraft
NASA Technical Reports Server (NTRS)
Taylor, L. W., Jr.; Williams, J. L.
1989-01-01
A distributed-parameter model of the NASA Solar Array Flight Experiment spacecraft structure is constructed on the basis of measurement data and analyzed to generate a priori estimates of modal frequencies and mode shapes. A Newton-Raphson maximum-likelihood algorithm is applied to determine the unknown parameters, using a truncated model for the estimation and the full model for the computation of the higher modes. Numerical results are presented in a series of graphs and briefly discussed, and the significant improvement in computation speed obtained by parallel implementation of the method on a supercomputer is noted.
Chaos synchronization and parameter estimation from a scalar output signal.
Chen, Maoyin; Kurths, Jürgen
2007-08-01
We propose an observer-based approach for chaos synchronization and parameter estimation from a scalar output signal. To begin with, we use geometric control to transform the master system into a standard form with zero dynamics. Then we construct a slaver to synchronize with the master using a combination of slide mode control and linear feedback control. Within a finite time, partial synchronization is realized, which further results in complete synchronization as time tends to infinity. Even if there exists model uncertainty in the slaver, we can also estimate the unknown model parameter by a simple adaptive rule. PMID:17930180
Estimation of the elastic Earth parameters from the SLR technique
NASA Astrophysics Data System (ADS)
Rutkowska, Milena
ABSTRACT. The global elastic parameters (Love and Shida numbers) associated with the tide variations for satellite and stations are estimated from the Satellite Laser Ranging (SLR) data. The study is based on satellite observations taken by the global network of the ground stations during the period from January 1, 2005 until January 1, 2007 for monthly orbital arcs of Lageos 1 satellite. The observation equations contain unknown for orbital arcs, some constants and elastic Earth parameters which describe tide variations. The adjusted values are discussed and compared with geophysical estimations of Love numbers. All computations were performed employing the NASA software GEODYN II (eddy et al. 1990).
Estimation of effective hydrogeological parameters in heterogeneous and anisotropic aquifers
NASA Astrophysics Data System (ADS)
Lin, Hsien-Tsung; Tan, Yih-Chi; Chen, Chu-Hui; Yu, Hwa-Lung; Wu, Shih-Ching; Ke, Kai-Yuan
2010-07-01
SummaryObtaining reasonable hydrological input parameters is a key challenge in groundwater modeling. Analysis of temporal evolution during pump-induced drawdown is one common approach used to estimate the effective transmissivity and storage coefficients in a heterogeneous aquifer. In this study, we propose a Modified Tabu search Method (MTM), an improvement drawn from an alliance between the Tabu Search (TS) and the Adjoint State Method (ASM) developed by Tan et al. (2008). The latter is employed to estimate effective parameters for anisotropic, heterogeneous aquifers. MTM is validated by several numerical pumping tests. Comparisons are made to other well-known techniques, such as the type-curve method (TCM) and the straight-line method (SLM), to provide insight into the challenge of determining the most effective parameter for an anisotropic, heterogeneous aquifer. The results reveal that MTM can efficiently obtain the best representative and effective aquifer parameters in terms of the least mean square errors of the drawdown estimations. The use of MTM may involve less artificial errors than occur with TCM and SLM, and lead to better solutions. Therefore, effective transmissivity is more likely to be comprised of the geometric mean of all transmissivities within the cone of depression based on a precise estimation of MTM. Further investigation into the applicability of MTM shows that a higher level of heterogeneity in an aquifer can induce an uncertainty in estimations, while the changes in correlation length will affect the accuracy of MTM only once the degree of heterogeneity has also risen.
Adjustment of Sensor Locations During Thermal Property Parameter Estimation
NASA Technical Reports Server (NTRS)
Milos, Frank S.; Marschall, Jochen; Rasky, Daniel J. (Technical Monitor)
1996-01-01
The temperature dependent thermal properties of a material may be evaluated from transient temperature histories using nonlinear parameter estimation techniques. The usual approach is to minimize the sum of the squared errors between measured and calculated temperatures at specific locations in the body. Temperature measurements are usually made with thermocouples and it is customary to take thermocouple locations as known and fixed during parameter estimation computations. In fact, thermocouple locations are never known exactly. Location errors on the order of the thermocouple wire diameter are intrinsic to most common instrumentation procedures (e.g., inserting a thermocouple into a drilled hole) and additional errors can be expected for delicate materials, difficult installations, large thermocouple beads, etc.. Thermocouple location errors are especially significant when estimating thermal properties of low diffusively materials which can sustain large temperature gradients during testing. In the present work, a parameter estimation formulation is presented which allows for the direct inclusion of thermocouple positions into the primary parameter estimation procedure. It is straightforward to set bounds on thermocouple locations which exclude non-physical locations and are consistent with installation tolerances. Furthermore, bounds may be tightened to an extent consistent with any independent verification of thermocouple location, such as x-raying, and so the procedure is entirely consonant with experimental information. A mathematical outline of the procedure is given and its implementation is illustrated through numerical examples characteristic of light-weight, high-temperature ceramic insulation during transient heating. The efficacy and the errors associated with the procedure are discussed.
Parameter Estimation for Single Diode Models of Photovoltaic Modules
Hansen, Clifford
2015-03-01
Many popular models for photovoltaic system performance employ a single diode model to compute the I - V curve for a module or string of modules at given irradiance and temperature conditions. A single diode model requires a number of parameters to be estimated from measured I - V curves. Many available parameter estimation methods use only short circuit, o pen circuit and maximum power points for a single I - V curve at standard test conditions together with temperature coefficients determined separately for individual cells. In contrast, module testing frequently records I - V curves over a wide range of irradi ance and temperature conditions which, when available , should also be used to parameterize the performance model. We present a parameter estimation method that makes use of a fu ll range of available I - V curves. We verify the accuracy of the method by recov ering known parameter values from simulated I - V curves . We validate the method by estimating model parameters for a module using outdoor test data and predicting the outdoor performance of the module.
Parameter variability estimation using stochastic response surface model updating
NASA Astrophysics Data System (ADS)
Fang, Sheng-En; Zhang, Qiu-Hu; Ren, Wei-Xin
2014-12-01
From a practical point of view, uncertainties existing in structural parameters and measurements must be handled in order to provide reliable structural condition evaluations. At this moment, deterministic model updating loses its practicability and a stochastic updating procedure should be employed seeking for statistical properties of parameters and responses. Presently this topic has not been well investigated on account of its greater complexity in theoretical configuration and difficulty in inverse problem solutions after involving uncertainty analyses. Due to it, this paper attempts to develop a stochastic model updating method for parameter variability estimation. Uncertain parameters and responses are correlated through stochastic response surface models, which are actually explicit polynomial chaos expansions based on Hermite polynomials. Then by establishing a stochastic inverse problem, parameter means and standard deviations are updated in a separate and successive way. For the purposes of problem simplification and optimization efficiency, in each updating iteration stochastic response surface models are reconstructed to avoid the construction and analysis of sensitivity matrices. Meanwhile, in the interest of investigating the effects of parameter variability on responses, a parameter sensitivity analysis method has been developed based on the derivation of polynomial chaos expansions. Lastly the feasibility and reliability of the proposed methods have been validated using a numerical beam and then a set of nominally identical metal plates. After comparing with a perturbation method, it is found that the proposed method can estimate parameter variability with satisfactory accuracy and the complexity of the inverse problem can be highly reduced resulting in cost-efficient optimization.
NASA Technical Reports Server (NTRS)
Kukreja, Sunil L.; Wallin, Ragnar; Boyle, Richard D.
2013-01-01
The vestibulo-ocular reflex (VOR) is a well-known dual mode bifurcating system that consists of slow and fast modes associated with nystagmus and saccade, respectively. Estimation of continuous-time parameters of nystagmus and saccade models are known to be sensitive to estimation methodology, noise and sampling rate. The stable and accurate estimation of these parameters are critical for accurate disease modelling, clinical diagnosis, robotic control strategies, mission planning for space exploration and pilot safety, etc. This paper presents a novel indirect system identification method for the estimation of continuous-time parameters of VOR employing standardised least-squares with dual sampling rates in a sparse structure. This approach permits the stable and simultaneous estimation of both nystagmus and saccade data. The efficacy of this approach is demonstrated via simulation of a continuous-time model of VOR with typical parameters found in clinical studies and in the presence of output additive noise.
Parameter estimation for the Euler-Bernoulli-beam
NASA Technical Reports Server (NTRS)
Graif, E.; Kunisch, K.
1984-01-01
An approximation involving cubic spline functions for parameter estimation problems in the Euler-Bernoulli-beam equation (phrased as an optimization problem with respect to the parameters) is described and convergence is proved. The resulting algorithm was implemented and several of the test examples are documented. It is observed that the use of penalty terms in the cost functional can improve the rate of convergence.
Human ECG signal parameters estimation during controlled physical activity
NASA Astrophysics Data System (ADS)
Maciejewski, Marcin; Surtel, Wojciech; Dzida, Grzegorz
2015-09-01
ECG signal parameters are commonly used indicators of human health condition. In most cases the patient should remain stationary during the examination to decrease the influence of muscle artifacts. During physical activity, the noise level increases significantly. The ECG signals were acquired during controlled physical activity on a stationary bicycle and during rest. Afterwards, the signals were processed using a method based on Pan-Tompkins algorithms to estimate their parameters and to test the method.
Targeted estimation of nuisance parameters to obtain valid statistical inference.
van der Laan, Mark J
2014-01-01
In order to obtain concrete results, we focus on estimation of the treatment specific mean, controlling for all measured baseline covariates, based on observing independent and identically distributed copies of a random variable consisting of baseline covariates, a subsequently assigned binary treatment, and a final outcome. The statistical model only assumes possible restrictions on the conditional distribution of treatment, given the covariates, the so-called propensity score. Estimators of the treatment specific mean involve estimation of the propensity score and/or estimation of the conditional mean of the outcome, given the treatment and covariates. In order to make these estimators asymptotically unbiased at any data distribution in the statistical model, it is essential to use data-adaptive estimators of these nuisance parameters such as ensemble learning, and specifically super-learning. Because such estimators involve optimal trade-off of bias and variance w.r.t. the infinite dimensional nuisance parameter itself, they result in a sub-optimal bias/variance trade-off for the resulting real-valued estimator of the estimand. We demonstrate that additional targeting of the estimators of these nuisance parameters guarantees that this bias for the estimand is second order and thereby allows us to prove theorems that establish asymptotic linearity of the estimator of the treatment specific mean under regularity conditions. These insights result in novel targeted minimum loss-based estimators (TMLEs) that use ensemble learning with additional targeted bias reduction to construct estimators of the nuisance parameters. In particular, we construct collaborative TMLEs (C-TMLEs) with known influence curve allowing for statistical inference, even though these C-TMLEs involve variable selection for the propensity score based on a criterion that measures how effective the resulting fit of the propensity score is in removing bias for the estimand. As a particular special
Bayesian parameter estimation in spectral quantitative photoacoustic tomography
NASA Astrophysics Data System (ADS)
Pulkkinen, Aki; Cox, Ben T.; Arridge, Simon R.; Kaipio, Jari P.; Tarvainen, Tanja
2016-03-01
Photoacoustic tomography (PAT) is an imaging technique combining strong contrast of optical imaging to high spatial resolution of ultrasound imaging. These strengths are achieved via photoacoustic effect, where a spatial absorption of light pulse is converted into a measurable propagating ultrasound wave. The method is seen as a potential tool for small animal imaging, pre-clinical investigations, study of blood vessels and vasculature, as well as for cancer imaging. The goal in PAT is to form an image of the absorbed optical energy density field via acoustic inverse problem approaches from the measured ultrasound data. Quantitative PAT (QPAT) proceeds from these images and forms quantitative estimates of the optical properties of the target. This optical inverse problem of QPAT is illposed. To alleviate the issue, spectral QPAT (SQPAT) utilizes PAT data formed at multiple optical wavelengths simultaneously with optical parameter models of tissue to form quantitative estimates of the parameters of interest. In this work, the inverse problem of SQPAT is investigated. Light propagation is modelled using the diffusion equation. Optical absorption is described with chromophore concentration weighted sum of known chromophore absorption spectra. Scattering is described by Mie scattering theory with an exponential power law. In the inverse problem, the spatially varying unknown parameters of interest are the chromophore concentrations, the Mie scattering parameters (power law factor and the exponent), and Gruneisen parameter. The inverse problem is approached with a Bayesian method. It is numerically demonstrated, that estimation of all parameters of interest is possible with the approach.
Can student health professionals accurately estimate alcohol content in commonly occurring drinks?
Sinclair, Julia; Searle, Emma
2016-01-01
Objectives: Correct identification of alcohol as a contributor to, or comorbidity of, many psychiatric diseases requires health professionals to be competent and confident to take an accurate alcohol history. Being able to estimate (or calculate) the alcohol content in commonly consumed drinks is a prerequisite for quantifying levels of alcohol consumption. The aim of this study was to assess this ability in medical and nursing students. Methods: A cross-sectional survey of 891 medical and nursing students across different years of training was conducted. Students were asked the alcohol content of 10 different alcoholic drinks by seeing a slide of the drink (with picture, volume and percentage of alcohol by volume) for 30 s. Results: Overall, the mean number of correctly estimated drinks (out of the 10 tested) was 2.4, increasing to just over 3 if a 10% margin of error was used. Wine and premium strength beers were underestimated by over 50% of students. Those who drank alcohol themselves, or who were further on in their clinical training, did better on the task, but overall the levels remained low. Conclusions: Knowledge of, or the ability to work out, the alcohol content of commonly consumed drinks is poor, and further research is needed to understand the reasons for this and the impact this may have on the likelihood to undertake screening or initiate treatment. PMID:27536344
Ultrasound Fetal Weight Estimation: How Accurate Are We Now Under Emergency Conditions?
Dimassi, Kaouther; Douik, Fatma; Ajroudi, Mariem; Triki, Amel; Gara, Mohamed Faouzi
2015-10-01
The primary aim of this study was to evaluate the accuracy of sonographic estimation of fetal weight when performed at due date by first-line sonographers. This was a prospective study including 500 singleton pregnancies. Ultrasound examinations were performed by residents on delivery day. Estimated fetal weights (EFWs) were calculated and compared with the corresponding birth weights. The median absolute difference between EFW and birth weight was 200 g (100-330). This difference was within ±10% in 75.2% of the cases. The median absolute percentage error was 5.53% (2.70%-10.03%). Linear regression analysis revealed a good correlation between EFW and birth weight (r = 0.79, p < 0.0001). According to Bland-Altman analysis, bias was -85.06 g (95% limits of agreement: -663.33 to 494.21). In conclusion, EFWs calculated by residents were as accurate as those calculated by experienced sonographers. Nevertheless, predictive performance remains limited, with a low sensitivity in the diagnosis of macrosomia. PMID:26164286
NASA Astrophysics Data System (ADS)
Hu, Yongxiang; Behrenfeld, Mike; Hostetler, Chris; Pelon, Jacques; Trepte, Charles; Hair, John; Slade, Wayne; Cetinic, Ivona; Vaughan, Mark; Lu, Xiaomei; Zhai, Pengwang; Weimer, Carl; Winker, David; Verhappen, Carolus C.; Butler, Carolyn; Liu, Zhaoyan; Hunt, Bill; Omar, Ali; Rodier, Sharon; Lifermann, Anne; Josset, Damien; Hou, Weilin; MacDonnell, David; Rhew, Ray
2016-06-01
Beam attenuation coefficient, c, provides an important optical index of plankton standing stocks, such as phytoplankton biomass and total particulate carbon concentration. Unfortunately, c has proven difficult to quantify through remote sensing. Here, we introduce an innovative approach for estimating c using lidar depolarization measurements and diffuse attenuation coefficients from ocean color products or lidar measurements of Brillouin scattering. The new approach is based on a theoretical formula established from Monte Carlo simulations that links the depolarization ratio of sea water to the ratio of diffuse attenuation Kd and beam attenuation C (i.e., a multiple scattering factor). On July 17, 2014, the CALIPSO satellite was tilted 30° off-nadir for one nighttime orbit in order to minimize ocean surface backscatter and demonstrate the lidar ocean subsurface measurement concept from space. Depolarization ratios of ocean subsurface backscatter are measured accurately. Beam attenuation coefficients computed from the depolarization ratio measurements compare well with empirical estimates from ocean color measurements. We further verify the beam attenuation coefficient retrievals using aircraft-based high spectral resolution lidar (HSRL) data that are collocated with in-water optical measurements.
SCoPE: an efficient method of Cosmological Parameter Estimation
Das, Santanu; Souradeep, Tarun E-mail: tarun@iucaa.ernet.in
2014-07-01
Markov Chain Monte Carlo (MCMC) sampler is widely used for cosmological parameter estimation from CMB and other data. However, due to the intrinsic serial nature of the MCMC sampler, convergence is often very slow. Here we present a fast and independently written Monte Carlo method for cosmological parameter estimation named as Slick Cosmological Parameter Estimator (SCoPE), that employs delayed rejection to increase the acceptance rate of a chain, and pre-fetching that helps an individual chain to run on parallel CPUs. An inter-chain covariance update is also incorporated to prevent clustering of the chains allowing faster and better mixing of the chains. We use an adaptive method for covariance calculation to calculate and update the covariance automatically as the chains progress. Our analysis shows that the acceptance probability of each step in SCoPE is more than 95% and the convergence of the chains are faster. Using SCoPE, we carry out some cosmological parameter estimations with different cosmological models using WMAP-9 and Planck results. One of the current research interests in cosmology is quantifying the nature of dark energy. We analyze the cosmological parameters from two illustrative commonly used parameterisations of dark energy models. We also asses primordial helium fraction in the universe can be constrained by the present CMB data from WMAP-9 and Planck. The results from our MCMC analysis on the one hand helps us to understand the workability of the SCoPE better, on the other hand it provides a completely independent estimation of cosmological parameters from WMAP-9 and Planck data.
Simunek, J.; Nimmo, J.R.
2005-01-01
A modified version of the Hydrus software package that can directly or inversely simulate water flow in a transient centrifugal field is presented. The inverse solver for parameter estimation of the soil hydraulic parameters is then applied to multirotation transient flow experiments in a centrifuge. Using time-variable water contents measured at a sequence of several rotation speeds, soil hydraulic properties were successfully estimated by numerical inversion of transient experiments. The inverse method was then evaluated by comparing estimated soil hydraulic properties with those determined independently using an equilibrium analysis. The optimized soil hydraulic properties compared well with those determined using equilibrium analysis and steady state experiment. Multirotation experiments in a centrifuge not only offer significant time savings by accelerating time but also provide significantly more information for the parameter estimation procedure compared to multistep outflow experiments in a gravitational field. Copyright 2005 by the American Geophysical Union.
NASA Astrophysics Data System (ADS)
Li, Wenchao; Yang, Jianyu; Huang, Yulin; Kong, Lingjiang
For Doppler parameter estimation of forward-looking SAR, the third-order Doppler parameter can not be neglected. In this paper, the azimuth signal of the transmitter fixed bistatic forward-looking SAR is modeled as a cubic polynomial phase signal (CPPS) and multiple time-overlapped CPPSs, and the modified cubic phase function is presented to estimate the third-order Doppler parameter. By combining the cubic phase function (CPF) with Radon transform, the method can give an accurate estimation of the third-order Doppler parameter. Simulations validate the effectiveness of the algorithm.
ERIC Educational Resources Information Center
Jones, Douglas H.; And Others
How accurately ability is estimated when the test model does not fit the data is considered. To address this question, this study investigated the accuracy of the maximum likelihood estimator of ability for the one-, two- and three-parameter logistic (PL) models. The models were fitted into generated item characteristic curves derived from the…
An EM algorithm for estimating SPECT emission and transmission parameters from emissions data only.
Krol, A; Bowsher, J E; Manglos, S H; Feiglin, D H; Tornai, M P; Thomas, F D
2001-03-01
A maximum-likelihood (ML) expectation-maximization (EM) algorithm (called EM-IntraSPECT) is presented for simultaneously estimating single photon emission computed tomography (SPECT) emission and attenuation parameters from emission data alone. The algorithm uses the activity within the patient as transmission tomography sources, with which attenuation coefficients can be estimated. For this initial study, EM-IntraSPECT was tested on computer-simulated attenuation and emission maps representing a simplified human thorax as well as on SPECT data obtained from a physical phantom. Two evaluations were performed. First, to corroborate the idea of reconstructing attenuation parameters from emission data, attenuation parameters (mu) were estimated with the emission intensities (lambda) fixed at their true values. Accurate reconstructions of attenuation parameters were obtained. Second, emission parameters lambda and attenuation parameters mu were simultaneously estimated from the emission data alone. In this case there was crosstalk between estimates of lambda and mu and final estimates of lambda and mu depended on initial values. Estimates degraded significantly as the support extended out farther from the body, and an explanation for this is proposed. In the EM-IntraSPECT reconstructed attenuation images, the lungs, spine, and soft tissue were readily distinguished and had approximately correct shapes and sizes. As compared with standard EM reconstruction assuming a fix uniform attenuation map, EM-IntraSPECT provided more uniform estimates of cardiac activity in the physical phantom study and in the simulation study with tight support, but less uniform estimates with a broad support. The new EM algorithm derived here has additional applications, including reconstructing emission and transmission projection data under a unified statistical model. PMID:11341711
A parameter estimation framework for patient-specific hemodynamic computations
NASA Astrophysics Data System (ADS)
Itu, Lucian; Sharma, Puneet; Passerini, Tiziano; Kamen, Ali; Suciu, Constantin; Comaniciu, Dorin
2015-01-01
We propose a fully automated parameter estimation framework for performing patient-specific hemodynamic computations in arterial models. To determine the personalized values of the windkessel models, which are used as part of the geometrical multiscale circulation model, a parameter estimation problem is formulated. Clinical measurements of pressure and/or flow-rate are imposed as constraints to formulate a nonlinear system of equations, whose fixed point solution is sought. A key feature of the proposed method is a warm-start to the optimization procedure, with better initial solution for the nonlinear system of equations, to reduce the number of iterations needed for the calibration of the geometrical multiscale models. To achieve these goals, the initial solution, computed with a lumped parameter model, is adapted before solving the parameter estimation problem for the geometrical multiscale circulation model: the resistance and the compliance of the circulation model are estimated and compensated. The proposed framework is evaluated on a patient-specific aortic model, a full body arterial model, and multiple idealized anatomical models representing different arterial segments. For each case it leads to the best performance in terms of number of iterations required for the computational model to be in close agreement with the clinical measurements.
Parameter Estimates in Differential Equation Models for Population Growth
ERIC Educational Resources Information Center
Winkel, Brian J.
2011-01-01
We estimate the parameters present in several differential equation models of population growth, specifically logistic growth models and two-species competition models. We discuss student-evolved strategies and offer "Mathematica" code for a gradient search approach. We use historical (1930s) data from microbial studies of the Russian biologist,…
Online vegetation parameter estimation using passive microwave remote sensing observations
Technology Transfer Automated Retrieval System (TEKTRAN)
In adaptive system identification the Kalman filter can be used to identify the coefficient of the observation operator of a linear system. Here the ensemble Kalman filter is tested for adaptive online estimation of the vegetation opacity parameter of a radiative transfer model. A state augmentatio...
Loss of Information in Estimating Item Parameters in Incomplete Designs
ERIC Educational Resources Information Center
Eggen, Theo J. H. M.; Verelst, Norman D.
2006-01-01
In this paper, the efficiency of conditional maximum likelihood (CML) and marginal maximum likelihood (MML) estimation of the item parameters of the Rasch model in incomplete designs is investigated. The use of the concept of F-information (Eggen, 2000) is generalized to incomplete testing designs. The scaled determinant of the F-information…
Estimability of Parameters in the Generalized Graded Unfolding Model.
ERIC Educational Resources Information Center
Roberts, James S.; Donoghue, John R.; Laughlin, James E.
The generalized graded unfolding model (GGUM) (J. Roberts, J. Donoghue, and J. Laughlin, 1998) is an item response theory model designed to analyze binary or graded responses that are based on a proximity relation. The purpose of this study was to assess conditions under which item parameter estimation accuracy increases or decreases, with special…
Synchronous Generator Model Parameter Estimation Based on Noisy Dynamic Waveforms
NASA Astrophysics Data System (ADS)
Berhausen, Sebastian; Paszek, Stefan
2016-01-01
In recent years, there have occurred system failures in many power systems all over the world. They have resulted in a lack of power supply to a large number of recipients. To minimize the risk of occurrence of power failures, it is necessary to perform multivariate investigations, including simulations, of power system operating conditions. To conduct reliable simulations, the current base of parameters of the models of generating units, containing the models of synchronous generators, is necessary. In the paper, there is presented a method for parameter estimation of a synchronous generator nonlinear model based on the analysis of selected transient waveforms caused by introducing a disturbance (in the form of a pseudorandom signal) in the generator voltage regulation channel. The parameter estimation was performed by minimizing the objective function defined as a mean square error for deviations between the measurement waveforms and the waveforms calculated based on the generator mathematical model. A hybrid algorithm was used for the minimization of the objective function. In the paper, there is described a filter system used for filtering the noisy measurement waveforms. The calculation results of the model of a 44 kW synchronous generator installed on a laboratory stand of the Institute of Electrical Engineering and Computer Science of the Silesian University of Technology are also given. The presented estimation method can be successfully applied to parameter estimation of different models of high-power synchronous generators operating in a power system.
2011-01-01
Background Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor. Results We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise. Conclusions The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling. Reviewers This article was reviewed by Anthony Almudevar, Tomas Radivoyevitch, and Kristin Swanson (nominated by Georg Luebeck). PMID:22185645
Matched filtering and parameter estimation of ringdown waveforms
Berti, Emanuele; Cardoso, Jaime; Cardoso, Vitor; Cavaglia, Marco
2007-11-15
Using recent results from numerical relativity simulations of nonspinning binary black hole mergers, we revisit the problem of detecting ringdown waveforms and of estimating the source parameters, considering both LISA and Earth-based interferometers. We find that Advanced LIGO and EGO could detect intermediate-mass black holes of mass up to {approx}10{sup 3}M{sub {center_dot}} out to a luminosity distance of a few Gpc. For typical multipolar energy distributions, we show that the single-mode ringdown templates presently used for ringdown searches in the LIGO data stream can produce a significant event loss (>10% for all detectors in a large interval of black hole masses) and very large parameter estimation errors on the black hole's mass and spin. We estimate that more than {approx}10{sup 6} templates would be needed for a single-stage multimode search. Therefore, we recommend a ''two-stage'' search to save on computational costs: single-mode templates can be used for detection, but multimode templates or Prony methods should be used to estimate parameters once a detection has been made. We update estimates of the critical signal-to-noise ratio required to test the hypothesis that two or more modes are present in the signal and to resolve their frequencies, showing that second-generation Earth-based detectors and LISA have the potential to perform no-hair tests.
Inverse estimation of parameters for an estuarine eutrophication model
Shen, J.; Kuo, A.Y.
1996-11-01
An inverse model of an estuarine eutrophication model with eight state variables is developed. It provides a framework to estimate parameter values of the eutrophication model by assimilation of concentration data of these state variables. The inverse model using the variational technique in conjunction with a vertical two-dimensional eutrophication model is general enough to be applicable to aid model calibration. The formulation is illustrated by conducting a series of numerical experiments for the tidal Rappahannock River, a western shore tributary of the Chesapeake Bay. The numerical experiments of short-period model simulations with different hypothetical data sets and long-period model simulations with limited hypothetical data sets demonstrated that the inverse model can be satisfactorily used to estimate parameter values of the eutrophication model. The experiments also showed that the inverse model is useful to address some important questions, such as uniqueness of the parameter estimation and data requirements for model calibration. Because of the complexity of the eutrophication system, degrading of speed of convergence may occur. Two major factors which cause degradation of speed of convergence are cross effects among parameters and the multiple scales involved in the parameter system.
A parameter identifiability and estimation study in Yesilirmak River.
Berber, R; Yuceer, M; Karadurmus, E
2009-01-01
Water quality models have relatively large number of parameters, which need to be estimated against observed data through a non-trivial task that is associated with substantial difficulties. This work involves a systematic model calibration and validation study for river water quality. The model considered was composed of dynamic mass balances for eleven pollution constituents, stemming from QUAL2E water quality model by considering a river segment as a series of continuous stirred-tank reactors (CSTRs). Parameter identifiability was analyzed from the perspective of sensitivity measure and collinearity index, which indicated that 8 parameters would fall within the identifiability range. The model parameters were then estimated by an integration based optimization algorithm coupled with sequential quadratic programming. Dynamic field data consisting of major pollutant concentrations were collected from sampling stations along Yesilirmak River around the city of Amasya in Turkey, and compared with model predictions. The calibrated model responses were in good agreement with the observed river water quality data, and this indicated that the suggested procedure provided an effective means for reliable estimation of model parameters and dynamic simulation for river streams. PMID:19214006
Estimation of rice biophysical parameters using multitemporal RADARSAT-2 images
NASA Astrophysics Data System (ADS)
Li, S.; Ni, P.; Cui, G.; He, P.; Liu, H.; Li, L.; Liang, Z.
2016-04-01
Compared with optical sensors, synthetic aperture radar (SAR) has the capability of acquiring images in all-weather conditions. Thus, SAR images are suitable for using in rice growth regions that are characterized by frequent cloud cover and rain. The objective of this paper was to evaluate the probability of rice biophysical parameters estimation using multitemporal RADARSAT-2 images, and to develop the estimation models. Three RADARSTA-2 images were acquired during the rice critical growth stages in 2014 near Meishan, Sichuan province, Southwest China. Leaf area index (LAI), the fraction of photosynthetically active radiation (FPAR), height, biomass and canopy water content (WC) were observed at 30 experimental plots over 5 periods. The relationship between RADARSAT-2 backscattering coefficients (σ 0) or their ratios and rice biophysical parameters were analysed. These biophysical parameters were significantly and consistently correlated with the VV and VH σ 0 ratio (σ 0 VV/ σ 0 VH) throughout all growth stages. The regression model were developed between biophysical parameters and σ 0 VV/ σ 0 VH. The results suggest that the RADARSAT-2 data has great potential capability for the rice biophysical parameters estimation and the timely rice growth monitoring.
Parameter estimation of an air-bearing suspended test table
NASA Astrophysics Data System (ADS)
Fu, Zhenxian; Lin, Yurong; Liu, Yang; Chen, Xinglin; Chen, Fang
2015-02-01
A parameter estimation approach is proposed for parameter determination of a 3-axis air-bearing suspended test table. The table is to provide a balanced and frictionless environment for spacecraft ground test. To balance the suspension, the mechanical parameters of the table, including its angular inertias and centroid deviation from its rotating center, have to be determined first. Then sliding masses on the table can be adjusted by stepper motors to relocate the centroid of the table to its rotating center. Using the angular momentum theorem and the coriolis theorem, dynamic equations are derived describing the rotation of the table under the influence of gravity imbalance torque and activating torques. To generate the actuating torques, use of momentum wheels is proposed, whose virtue is that no active control is required to the momentum wheels, which merely have to spin at constant rates, thus avoiding the singularity problem and the difficulty of precisely adjusting the output torques, issues associated with control moment gyros. The gyroscopic torques generated by the momentum wheels, as they are forced by the table to precess, are sufficient to activate the table for parameter estimation. Then least-square estimation is be employed to calculate the desired parameters. The effectiveness of the method is validated by simulation.
Estimation of uncertain material parameters using modal test data
Veers, P.S.; Laird, D.L.; Carne, T.G.; Sagartz, M.J.
1997-11-01
Analytical models of wind turbine blades have many uncertainties, particularly with composite construction where material properties and cross-sectional dimension may not be known or precisely controllable. In this paper the authors demonstrate how modal testing can be used to estimate important material parameters and to update and improve a finite-element (FE) model of a prototype wind turbine blade. An example of prototype blade is used here to demonstrate how model parameters can be identified. The starting point is an FE model of the blade, using best estimates for the material constants. Frequencies of the lowest fourteen modes are used as the basis for comparisons between model predictions and test data. Natural frequencies and mode shapes calculated with the FE model are used in an optimal test design code to select instrumentation (accelerometer) and excitation locations that capture all the desired mode shapes. The FE model is also used to calculate sensitivities of the modal frequencies to each of the uncertain material parameters. These parameters are estimated, or updated, using a weighted least-squares technique to minimize the difference between test frequencies and predicted results. Updated material properties are determined for axial, transverse, and shear moduli in two separate regions of the blade cross section: in the central box, and in the leading and trailing panels. Static FE analyses are then conducted with the updated material parameters to determine changes in effective beam stiffness and buckling loads.
[Atmospheric parameter estimation for LAMOST/GUOSHOUJING spectra].
Lu, Yu; Li, Xiang-Ru; Yang, Tan
2014-11-01
It is a key task to estimate the atmospheric parameters from the observed stellar spectra in exploring the nature of stars and universe. With our Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) which begun its formal Sky Survey in September 2012, we are obtaining a mass of stellar spectra in an unprecedented speed. It has brought a new opportunity and a challenge for the research of galaxies. Due to the complexity of the observing system, the noise in the spectrum is relatively large. At the same time, the preprocessing procedures of spectrum are also not ideal, such as the wavelength calibration and the flow calibration. Therefore, there is a slight distortion of the spectrum. They result in the high difficulty of estimating the atmospheric parameters for the measured stellar spectra. It is one of the important issues to estimate the atmospheric parameters for the massive stellar spectra of LAMOST. The key of this study is how to eliminate noise and improve the accuracy and robustness of estimating the atmospheric parameters for the measured stellar spectra. We propose a regression model for estimating the atmospheric parameters of LAMOST stellar(SVM(lasso)). The basic idea of this model is: First, we use the Haar wavelet to filter spectrum, suppress the adverse effects of the spectral noise and retain the most discrimination information of spectrum. Secondly, We use the lasso algorithm for feature selection and extract the features of strongly correlating with the atmospheric parameters. Finally, the features are input to the support vector regression model for estimating the parameters. Because the model has better tolerance to the slight distortion and the noise of the spectrum, the accuracy of the measurement is improved. To evaluate the feasibility of the above scheme, we conduct experiments extensively on the 33 963 pilot surveys spectrums by LAMOST. The accuracy of three atmospheric parameters is log Teff: 0.006 8 dex, log g: 0.155 1 dex
ORAN- ORBITAL AND GEODETIC PARAMETER ESTIMATION ERROR ANALYSIS
NASA Technical Reports Server (NTRS)
Putney, B.
1994-01-01
The Orbital and Geodetic Parameter Estimation Error Analysis program, ORAN, was developed as a Bayesian least squares simulation program for orbital trajectories. ORAN does not process data, but is intended to compute the accuracy of the results of a data reduction, if measurements of a given accuracy are available and are processed by a minimum variance data reduction program. Actual data may be used to provide the time when a given measurement was available and the estimated noise on that measurement. ORAN is designed to consider a data reduction process in which a number of satellite data periods are reduced simultaneously. If there is more than one satellite in a data period, satellite-to-satellite tracking may be analyzed. The least squares estimator in most orbital determination programs assumes that measurements can be modeled by a nonlinear regression equation containing a function of parameters to be estimated and parameters which are assumed to be constant. The partitioning of parameters into those to be estimated (adjusted) and those assumed to be known (unadjusted) is somewhat arbitrary. For any particular problem, the data will be insufficient to adjust all parameters subject to uncertainty, and some reasonable subset of these parameters is selected for estimation. The final errors in the adjusted parameters may be decomposed into a component due to measurement noise and a component due to errors in the assumed values of the unadjusted parameters. Error statistics associated with the first component are generally evaluated in an orbital determination program. ORAN is used to simulate the orbital determination processing and to compute error statistics associated with the second component. Satellite observations may be simulated with desired noise levels given in many forms including range and range rate, altimeter height, right ascension and declination, direction cosines, X and Y angles, azimuth and elevation, and satellite-to-satellite range and
Estimation of the sea surface's two-scale backscatter parameters
NASA Technical Reports Server (NTRS)
Wentz, F. J.
1978-01-01
The relationship between the sea-surface normalized radar cross section and the friction velocity vector is determined using a parametric two-scale scattering model. The model parameters are found from a nonlinear maximum likelihood estimation. The estimation is based on aircraft scatterometer measurements and the sea-surface anemometer measurements collected during the JONSWAP '75 experiment. The estimates of the ten model parameters converge to realistic values that are in good agreement with the available oceanographic data. The rms discrepancy between the model and the cross section measurements is 0.7 db, which is the rms sum of a 0.3 db average measurement error and a 0.6 db modeling error.
Characterization, parameter estimation, and aircraft response statistics of atmospheric turbulence
NASA Technical Reports Server (NTRS)
Mark, W. D.
1981-01-01
A nonGaussian three component model of atmospheric turbulence is postulated that accounts for readily observable features of turbulence velocity records, their autocorrelation functions, and their spectra. Methods for computing probability density functions and mean exceedance rates of a generic aircraft response variable are developed using nonGaussian turbulence characterizations readily extracted from velocity recordings. A maximum likelihood method is developed for optimal estimation of the integral scale and intensity of records possessing von Karman transverse of longitudinal spectra. Formulas for the variances of such parameter estimates are developed. The maximum likelihood and least-square approaches are combined to yield a method for estimating the autocorrelation function parameters of a two component model for turbulence.
Modal parameters estimation using ant colony optimisation algorithm
NASA Astrophysics Data System (ADS)
Sitarz, Piotr; Powałka, Bartosz
2016-08-01
The paper puts forward a new estimation method of modal parameters for dynamical systems. The problem of parameter estimation has been simplified to optimisation which is carried out using the ant colony system algorithm. The proposed method significantly constrains the solution space, determined on the basis of frequency plots of the receptance FRFs (frequency response functions) for objects presented in the frequency domain. The constantly growing computing power of readily accessible PCs makes this novel approach a viable solution. The combination of deterministic constraints of the solution space with modified ant colony system algorithms produced excellent results for systems in which mode shapes are defined by distinctly different natural frequencies and for those in which natural frequencies are similar. The proposed method is fully autonomous and the user does not need to select a model order. The last section of the paper gives estimation results for two sample frequency plots, conducted with the proposed method and the PolyMAX algorithm.
Ocean optics estimation for absorption, backscattering, and phase function parameters.
Hakim, Ammar H; McCormick, Norman J
2003-02-20
We propose and test an inverse ocean optics procedure with numerically simulated data for the determination of inherent optical properties using in-water radiance measurements. If data are available at only one depth within a deep homogeneous water layer, then the single-scattering albedo and the single parameter that characterizes the Henyey-Greenstein phase function can be estimated. If data are available at two depths, then these two parameters can be determined along with the optical thickness so that the absorption and scattering coefficients, and also the backscattering coefficient, can be estimated. With a knowledge of these parameters, the albedo and Lambertian fraction of reflected radiance of the bottom can be determined if measurements are made close to the bottom. A simplified method for determining the optical properties of the water also is developed for only three irradiance-type measurements if the radiance is approximately in the asymptotic regime. PMID:12617207
Aerodynamic parameter estimation via Fourier modulating function techniques
NASA Technical Reports Server (NTRS)
Pearson, A. E.
1995-01-01
Parameter estimation algorithms are developed in the frequency domain for systems modeled by input/output ordinary differential equations. The approach is based on Shinbrot's method of moment functionals utilizing Fourier based modulating functions. Assuming white measurement noises for linear multivariable system models, an adaptive weighted least squares algorithm is developed which approximates a maximum likelihood estimate and cannot be biased by unknown initial or boundary conditions in the data owing to a special property attending Shinbrot-type modulating functions. Application is made to perturbation equation modeling of the longitudinal and lateral dynamics of a high performance aircraft using flight-test data. Comparative studies are included which demonstrate potential advantages of the algorithm relative to some well established techniques for parameter identification. Deterministic least squares extensions of the approach are made to the frequency transfer function identification problem for linear systems and to the parameter identification problem for a class of nonlinear-time-varying differential system models.
Estimating Arrhenius parameters using temperature programmed molecular dynamics
NASA Astrophysics Data System (ADS)
Imandi, Venkataramana; Chatterjee, Abhijit
2016-07-01
Kinetic rates at different temperatures and the associated Arrhenius parameters, whenever Arrhenius law is obeyed, are efficiently estimated by applying maximum likelihood analysis to waiting times collected using the temperature programmed molecular dynamics method. When transitions involving many activated pathways are available in the dataset, their rates may be calculated using the same collection of waiting times. Arrhenius behaviour is ascertained by comparing rates at the sampled temperatures with ones from the Arrhenius expression. Three prototype systems with corrugated energy landscapes, namely, solvated alanine dipeptide, diffusion at the metal-solvent interphase, and lithium diffusion in silicon, are studied to highlight various aspects of the method. The method becomes particularly appealing when the Arrhenius parameters can be used to find rates at low temperatures where transitions are rare. Systematic coarse-graining of states can further extend the time scales accessible to the method. Good estimates for the rate parameters are obtained with 500-1000 waiting times.
Estimating Arrhenius parameters using temperature programmed molecular dynamics.
Imandi, Venkataramana; Chatterjee, Abhijit
2016-07-21
Kinetic rates at different temperatures and the associated Arrhenius parameters, whenever Arrhenius law is obeyed, are efficiently estimated by applying maximum likelihood analysis to waiting times collected using the temperature programmed molecular dynamics method. When transitions involving many activated pathways are available in the dataset, their rates may be calculated using the same collection of waiting times. Arrhenius behaviour is ascertained by comparing rates at the sampled temperatures with ones from the Arrhenius expression. Three prototype systems with corrugated energy landscapes, namely, solvated alanine dipeptide, diffusion at the metal-solvent interphase, and lithium diffusion in silicon, are studied to highlight various aspects of the method. The method becomes particularly appealing when the Arrhenius parameters can be used to find rates at low temperatures where transitions are rare. Systematic coarse-graining of states can further extend the time scales accessible to the method. Good estimates for the rate parameters are obtained with 500-1000 waiting times. PMID:27448871
Lupaşcu, Carmen Alina; Tegolo, Domenico; Trucco, Emanuele
2013-12-01
We present an algorithm estimating the width of retinal vessels in fundus camera images. The algorithm uses a novel parametric surface model of the cross-sectional intensities of vessels, and ensembles of bagged decision trees to estimate the local width from the parameters of the best-fit surface. We report comparative tests with REVIEW, currently the public database of reference for retinal width estimation, containing 16 images with 193 annotated vessel segments and 5066 profile points annotated manually by three independent experts. Comparative tests are reported also with our own set of 378 vessel widths selected sparsely in 38 images from the Tayside Scotland diabetic retinopathy screening programme and annotated manually by two clinicians. We obtain considerably better accuracies compared to leading methods in REVIEW tests and in Tayside tests. An important advantage of our method is its stability (success rate, i.e., meaningful measurement returned, of 100% on all REVIEW data sets and on the Tayside data set) compared to a variety of methods from the literature. We also find that results depend crucially on testing data and conditions, and discuss criteria for selecting a training set yielding optimal accuracy. PMID:24001930
Estimating Building Simulation Parameters via Bayesian Structure Learning
Edwards, Richard E; New, Joshua Ryan; Parker, Lynne Edwards
2013-01-01
Many key building design policies are made using sophisticated computer simulations such as EnergyPlus (E+), the DOE flagship whole-building energy simulation engine. E+ and other sophisticated computer simulations have several major problems. The two main issues are 1) gaps between the simulation model and the actual structure, and 2) limitations of the modeling engine's capabilities. Currently, these problems are addressed by having an engineer manually calibrate simulation parameters to real world data or using algorithmic optimization methods to adjust the building parameters. However, some simulations engines, like E+, are computationally expensive, which makes repeatedly evaluating the simulation engine costly. This work explores addressing this issue by automatically discovering the simulation's internal input and output dependencies from 20 Gigabytes of E+ simulation data, future extensions will use 200 Terabytes of E+ simulation data. The model is validated by inferring building parameters for E+ simulations with ground truth building parameters. Our results indicate that the model accurately represents parameter means with some deviation from the means, but does not support inferring parameter values that exist on the distribution's tail.
Global parameter estimation for thermodynamic models of transcriptional regulation.
Suleimenov, Yerzhan; Ay, Ahmet; Samee, Md Abul Hassan; Dresch, Jacqueline M; Sinha, Saurabh; Arnosti, David N
2013-07-15
Deciphering the mechanisms involved in gene regulation holds the key to understanding the control of central biological processes, including human disease, population variation, and the evolution of morphological innovations. New experimental techniques including whole genome sequencing and transcriptome analysis have enabled comprehensive modeling approaches to study gene regulation. In many cases, it is useful to be able to assign biological significance to the inferred model parameters, but such interpretation should take into account features that affect these parameters, including model construction and sensitivity, the type of fitness calculation, and the effectiveness of parameter estimation. This last point is often neglected, as estimation methods are often selected for historical reasons or for computational ease. Here, we compare the performance of two parameter estimation techniques broadly representative of local and global approaches, namely, a quasi-Newton/Nelder-Mead simplex (QN/NMS) method and a covariance matrix adaptation-evolutionary strategy (CMA-ES) method. The estimation methods were applied to a set of thermodynamic models of gene transcription applied to regulatory elements active in the Drosophila embryo. Measuring overall fit, the global CMA-ES method performed significantly better than the local QN/NMS method on high quality data sets, but this difference was negligible on lower quality data sets with increased noise or on data sets simplified by stringent thresholding. Our results suggest that the choice of parameter estimation technique for evaluation of gene expression models depends both on quality of data, the nature of the models [again, remains to be established] and the aims of the modeling effort. PMID:23726942
Parameter estimation and forecasting for multiplicative log-normal cascades
NASA Astrophysics Data System (ADS)
Leövey, Andrés E.; Lux, Thomas
2012-04-01
We study the well-known multiplicative log-normal cascade process in which the multiplication of Gaussian and log normally distributed random variables yields time series with intermittent bursts of activity. Due to the nonstationarity of this process and the combinatorial nature of such a formalism, its parameters have been estimated mostly by fitting the numerical approximation of the associated non-Gaussian probability density function to empirical data, cf. Castaing [Physica DPDNPDT0167-278910.1016/0167-2789(90)90035-N 46, 177 (1990)]. More recently, alternative estimators based upon various moments have been proposed by Beck [Physica DPDNPDT0167-278910.1016/j.physd.2004.01.020 193, 195 (2004)] and Kiyono [Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.76.041113 76, 041113 (2007)]. In this paper, we pursue this moment-based approach further and develop a more rigorous generalized method of moments (GMM) estimation procedure to cope with the documented difficulties of previous methodologies. We show that even under uncertainty about the actual number of cascade steps, our methodology yields very reliable results for the estimated intermittency parameter. Employing the Levinson-Durbin algorithm for best linear forecasts, we also show that estimated parameters can be used for forecasting the evolution of the turbulent flow. We compare forecasting results from the GMM and Kiyono 's procedure via Monte Carlo simulations. We finally test the applicability of our approach by estimating the intermittency parameter and forecasting of volatility for a sample of financial data from stock and foreign exchange markets.
English, Anthony E; Moy, Alan B; Kruse, Kara L; Ward, Richard C; Kirkpatrick, Stacy S; GoldmanM.D., Mitchell H
2009-04-01
A novel transcellular micro-impedance biosensor, referred to as the electric cell-substrate impedance sensor or ECIS, has become increasingly applied to the study and quantification of endothelial cell physiology. In principle, frequency dependent impedance measurements obtained from this sensor can be used to estimate the cell cell and cell matrix impedance components of endothelial cell barrier function based on simple geometric models. Few studies, however, have examined the numerical optimization of these barrier function parameters and established their error bounds. This study, therefore, illustrates the implementation of a multi-response Levenberg Marquardt algorithm that includes instrumental noise estimates and applies it to frequency dependent porcine pulmonary artery endothelial cell impedance measurements. The stability of cell cell, cell matrix and membrane impedance parameter estimates based on this approach is carefully examined, and several forms of parameter instability and refinement illustrated. Including frequency dependent noise variance estimates in the numerical optimization reduced the parameter value dependence on the frequency range of measured impedances. The increased stability provided by a multi-response non-linear fit over one-dimensional algorithms indicated that both real and imaginary data should be used in the parameter optimization. Error estimates based on single fits and Monte Carlo simulations showed that the model barrier parameters were often highly correlated with each other. Independently resolving the different parameters can, therefore, present a challenge to the experimentalist and demand the use of non-linear multivariate statistical methods when comparing different sets of parameters.
Karr, Jonathan R; Williams, Alex H; Zucker, Jeremy D; Raue, Andreas; Steiert, Bernhard; Timmer, Jens; Kreutz, Clemens; Wilkinson, Simon; Allgood, Brandon A; Bot, Brian M; Hoff, Bruce R; Kellen, Michael R; Covert, Markus W; Stolovitzky, Gustavo A; Meyer, Pablo
2015-05-01
Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation. PMID:26020786
Karr, Jonathan R.; Williams, Alex H.; Zucker, Jeremy D.; Raue, Andreas; Steiert, Bernhard; Timmer, Jens; Kreutz, Clemens; Wilkinson, Simon; Allgood, Brandon A.; Bot, Brian M.; Hoff, Bruce R.; Kellen, Michael R.; Covert, Markus W.; Stolovitzky, Gustavo A.; Meyer, Pablo
2015-01-01
Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation. PMID:26020786
Estimation of Cometary Rotation Parameters Based on Camera Images
NASA Technical Reports Server (NTRS)
Spindler, Karlheinz
2007-01-01
The purpose of the Rosetta mission is the in situ analysis of a cometary nucleus using both remote sensing equipment and scientific instruments delivered to the comet surface by a lander and transmitting measurement data to the comet-orbiting probe. Following a tour of planets including one Mars swing-by and three Earth swing-bys, the Rosetta probe is scheduled to rendezvous with comet 67P/Churyumov-Gerasimenko in May 2014. The mission poses various flight dynamics challenges, both in terms of parameter estimation and maneuver planning. Along with spacecraft parameters, the comet's position, velocity, attitude, angular velocity, inertia tensor and gravitatonal field need to be estimated. The measurements on which the estimation process is based are ground-based measurements (range and Doppler) yielding information on the heliocentric spacecraft state and images taken by an on-board camera yielding informaton on the comet state relative to the spacecraft. The image-based navigation depends on te identification of cometary landmarks (whose body coordinates also need to be estimated in the process). The paper will describe the estimation process involved, focusing on the phase when, after orbit insertion, the task arises to estimate the cometary rotational motion from camera images on which individual landmarks begin to become identifiable.
Tumor parameter estimation considering the body geometry by thermography.
Hossain, Shazzat; Mohammadi, Farah A
2016-09-01
Implementation of non-invasive, non-contact, radiation-free thermal diagnostic tools requires an accurate correlation between surface temperature and interior physiology derived from living bio-heat phenomena. Such associations in the chest, forearm, and natural and deformed breasts have been investigated using finite element analysis (FEA), where the geometry and heterogeneity of an organ are accounted for by creating anatomically-accurate FEA models. The quantitative links are involved in the proposed evolutionary methodology for forecasting unknown Physio-thermo-biological parameters, including the depth, size and metabolic rate of the underlying nodule. A Custom Genetic Algorithm (GA) is tailored to parameterize a tumor by minimizing a fitness function. The study has employed the finite element method to develop simulated data sets and gradient matrix. Furthermore, simulated thermograms are obtained by enveloping the data sets with ±10% random noise. PMID:27416548
NASA Astrophysics Data System (ADS)
Mizukami, Naoki; Clark, Martyn; Newman, Andrew; Wood, Andy
2016-04-01
Estimation of spatially distributed parameters is one of the biggest challenges in hydrologic modeling over a large spatial domain. This problem arises from methodological challenges such as the transfer of calibrated parameters to ungauged locations. Consequently, many current large scale hydrologic assessments rely on spatially inconsistent parameter fields showing patchwork patterns resulting from individual basin calibration or spatially constant parameters resulting from the adoption of default or a-priori estimates. In this study we apply the Multi-scale Parameter Regionalization (MPR) framework (Samaniego et al., 2010) to generate spatially continuous and optimized parameter fields for the Variable Infiltration Capacity (VIC) model over the contiguous United States(CONUS). The MPR method uses transfer functions that relate geophysical attributes (e.g., soil) to model parameters (e.g., parameters that describe the storage and transmission of water) at the native resolution of the geophysical attribute data and then scale to the model spatial resolution with several scaling functions, e.g., arithmetic mean, harmonic mean, and geometric mean. Model parameter adjustments are made by calibrating the parameters of the transfer function rather than the model parameters themselves. In this presentation, we first discuss conceptual challenges in a "model agnostic" continental-domain application of the MPR approach. We describe development of transfer functions for the soil parameters, and discuss challenges associated with extending MPR for VIC to multiple models. Next, we discuss the "computational shortcut" of headwater basin calibration where we estimate the parameters for only 500 headwater basins rather than conducting simulations for every grid box across the entire domain. We first performed individual basin calibration to obtain a benchmark of the maximum achievable performance in each basin, and examined their transferability to the other basins. We then
Selişteanu, Dan; Șendrescu, Dorin; Georgeanu, Vlad
2015-01-01
Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies. PMID:25685797
Selişteanu, Dan; Șendrescu, Dorin; Georgeanu, Vlad; Roman, Monica
2015-01-01
Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies. PMID:25685797
Plumb, John M.; Moffitt, Christine M.
2015-01-01
Researchers have cautioned against the borrowing of consumption and growth parameters from other species and life stages in bioenergetics growth models. In particular, the function that dictates temperature dependence in maximum consumption (Cmax) within the Wisconsin bioenergetics model for Chinook Salmon Oncorhynchus tshawytscha produces estimates that are lower than those measured in published laboratory feeding trials. We used published and unpublished data from laboratory feeding trials with subyearling Chinook Salmon from three stocks (Snake, Nechako, and Big Qualicum rivers) to estimate and adjust the model parameters for temperature dependence in Cmax. The data included growth measures in fish ranging from 1.5 to 7.2 g that were held at temperatures from 14°C to 26°C. Parameters for temperature dependence in Cmax were estimated based on relative differences in food consumption, and bootstrapping techniques were then used to estimate the error about the parameters. We found that at temperatures between 17°C and 25°C, the current parameter values did not match the observed data, indicating that Cmax should be shifted by about 4°C relative to the current implementation under the bioenergetics model. We conclude that the adjusted parameters for Cmax should produce more accurate predictions from the bioenergetics model for subyearling Chinook Salmon.
Hopfield neural networks for on-line parameter estimation.
Alonso, Hugo; Mendonça, Teresa; Rocha, Paula
2009-05-01
This paper addresses the problem of using Hopfield Neural Networks (HNNs) for on-line parameter estimation. As presented here, a HNN is a nonautonomous nonlinear dynamical system able to produce a time-evolving estimate of the actual parameterization. The stability analysis of the HNN is carried out under more general assumptions than those previously considered in the literature, yielding a weaker sufficient condition under which the estimation error asymptotically converges to zero. Furthermore, a robustness analysis is made, showing that, under the presence of perturbations, the estimation error converges to a bounded neighbourhood of zero, whose size decreases with the size of the perturbations. The results obtained are illustrated by means of two case studies, where the HNN is compared with two other methods. PMID:19386467
Seafloor elastic parameters estimation based on AVO inversion
NASA Astrophysics Data System (ADS)
Liu, Yangting; Liu, Xuewei
2015-12-01
Seafloor elastic parameters play an important role in many fields as diverse as marine construction, seabed resources exploration and seafloor acoustics. In order to estimate seafloor elastic parameters, we perform AVO inversion with seafloor reflected seismic data. As a particular reflection interface, the seafloor reflector does not support S-waves and the elastic parameters change dramatically across it. Conventional approximations to the Zoeppritz equations are not applicable for the seafloor situation. In this paper, we perform AVO inversion with the exact Zoeppritz equations through an unconstrained optimization method. Our synthetic study proves that the inversion method does not show strong dependence on the initial model for both unconsolidated and semi-consolidated seabed situations. The inversion uncertainty of the elastic parameters increases with the noise level, and decreases with the incidence angle range. Finally, we perform inversion of data from the South China Sea, and obtain satisfactory results, which are in good agreement with previous research.
Accurate Visual Heading Estimation at High Rotation Rate Without Oculomotor or Static-Depth Cues
NASA Technical Reports Server (NTRS)
Stone, Leland S.; Perrone, John A.; Null, Cynthia H. (Technical Monitor)
1995-01-01
It has been claimed that either oculomotor or static depth cues provide the signals about self-rotation necessary approx.-1 deg/s. We tested this hypothesis by simulating self-motion along a curved path with the eyes fixed in the head (plus or minus 16 deg/s of rotation). Curvilinear motion offers two advantages: 1) heading remains constant in retinotopic coordinates, and 2) there is no visual-oculomotor conflict (both actual and simulated eye position remain stationary). We simulated 400 ms of rotation combined with 16 m/s of translation at fixed angles with respect to gaze towards two vertical planes of random dots initially 12 and 24 m away, with a field of view of 45 degrees. Four subjects were asked to fixate a central cross and to respond whether they were translating to the left or right of straight-ahead gaze. From the psychometric curves, heading bias (mean) and precision (semi-interquartile) were derived. The mean bias over 2-5 runs was 3.0, 4.0, -2.0, -0.4 deg for the first author and three naive subjects, respectively (positive indicating towards the rotation direction). The mean precision was 2.0, 1.9, 3.1, 1.6 deg. respectively. The ability of observers to make relatively accurate and precise heading judgments, despite the large rotational flow component, refutes the view that extra-flow-field information is necessary for human visual heading estimation at high rotation rates. Our results support models that process combined translational/rotational flow to estimate heading, but should not be construed to suggest that other cues do not play an important role when they are available to the observer.
Bayesian hemodynamic parameter estimation by bolus tracking perfusion weighted imaging.
Boutelier, Timothé; Kudo, Koshuke; Pautot, Fabrice; Sasaki, Makoto
2012-07-01
A delay-insensitive probabilistic method for estimating hemodynamic parameters, delays, theoretical residue functions, and concentration time curves by computed tomography (CT) and magnetic resonance (MR) perfusion weighted imaging is presented. Only a mild stationarity hypothesis is made beyond the standard perfusion model. New microvascular parameters with simple hemodynamic interpretation are naturally introduced. Simulations on standard digital phantoms show that the method outperforms the oscillating singular value decomposition (oSVD) method in terms of goodness-of-fit, linearity, statistical and systematic errors on all parameters, especially at low signal-to-noise ratios (SNRs). Delay is always estimated sharply with user-supplied resolution and is purely arterial, by contrast to oSVD time-to-maximum TMAX that is very noisy and biased by mean transit time (MTT), blood volume, and SNR. Residue functions and signals estimates do not suffer overfitting anymore. One CT acute stroke case confirms simulation results and highlights the ability of the method to reliably estimate MTT when SNR is low. Delays look promising for delineating the arterial occlusion territory and collateral circulation. PMID:22410325
Anisotropic parameter estimation using velocity variation with offset analysis
Herawati, I.; Saladin, M.; Pranowo, W.; Winardhie, S.; Priyono, A.
2013-09-09
Seismic anisotropy is defined as velocity dependent upon angle or offset. Knowledge about anisotropy effect on seismic data is important in amplitude analysis, stacking process and time to depth conversion. Due to this anisotropic effect, reflector can not be flattened using single velocity based on hyperbolic moveout equation. Therefore, after normal moveout correction, there will still be residual moveout that relates to velocity information. This research aims to obtain anisotropic parameters, ε and δ, using two proposed methods. The first method is called velocity variation with offset (VVO) which is based on simplification of weak anisotropy equation. In VVO method, velocity at each offset is calculated and plotted to obtain vertical velocity and parameter δ. The second method is inversion method using linear approach where vertical velocity, δ, and ε is estimated simultaneously. Both methods are tested on synthetic models using ray-tracing forward modelling. Results show that δ value can be estimated appropriately using both methods. Meanwhile, inversion based method give better estimation for obtaining ε value. This study shows that estimation on anisotropic parameters rely on the accuracy of normal moveout velocity, residual moveout and offset to angle transformation.
Informed spectral analysis: audio signal parameter estimation using side information
NASA Astrophysics Data System (ADS)
Fourer, Dominique; Marchand, Sylvain
2013-12-01
Parametric models are of great interest for representing and manipulating sounds. However, the quality of the resulting signals depends on the precision of the parameters. When the signals are available, these parameters can be estimated, but the presence of noise decreases the resulting precision of the estimation. Furthermore, the Cramér-Rao bound shows the minimal error reachable with the best estimator, which can be insufficient for demanding applications. These limitations can be overcome by using the coding approach which consists in directly transmitting the parameters with the best precision using the minimal bitrate. However, this approach does not take advantage of the information provided by the estimation from the signal and may require a larger bitrate and a loss of compatibility with existing file formats. The purpose of this article is to propose a compromised approach, called the 'informed approach,' which combines analysis with (coded) side information in order to increase the precision of parameter estimation using a lower bitrate than pure coding approaches, the audio signal being known. Thus, the analysis problem is presented in a coder/decoder configuration where the side information is computed and inaudibly embedded into the mixture signal at the coder. At the decoder, the extra information is extracted and is used to assist the analysis process. This study proposes applying this approach to audio spectral analysis using sinusoidal modeling which is a well-known model with practical applications and where theoretical bounds have been calculated. This work aims at uncovering new approaches for audio quality-based applications. It provides a solution for challenging problems like active listening of music, source separation, and realistic sound transformations.
Improving the quality of parameter estimates obtained from slug tests
Butler, J.J., Jr.; McElwee, C.D.; Liu, W.
1996-01-01
The slug test is one of the most commonly used field methods for obtaining in situ estimates of hydraulic conductivity. Despite its prevalence, this method has received criticism from many quarters in the ground-water community. This criticism emphasizes the poor quality of the estimated parameters, a condition that is primarily a product of the somewhat casual approach that is often employed in slug tests. Recently, the Kansas Geological Survey (KGS) has pursued research directed it improving methods for the performance and analysis of slug tests. Based on extensive theoretical and field research, a series of guidelines have been proposed that should enable the quality of parameter estimates to be improved. The most significant of these guidelines are: (1) three or more slug tests should be performed at each well during a given test period; (2) two or more different initial displacements (Ho) should be used at each well during a test period; (3) the method used to initiate a test should enable the slug to be introduced in a near-instantaneous manner and should allow a good estimate of Ho to be obtained; (4) data-acquisition equipment that enables a large quantity of high quality data to be collected should be employed; (5) if an estimate of the storage parameter is needed, an observation well other than the test well should be employed; (6) the method chosen for analysis of the slug-test data should be appropriate for site conditions; (7) use of pre- and post-analysis plots should be an integral component of the analysis procedure, and (8) appropriate well construction parameters should be employed. Data from slug tests performed at a number of KGS field sites demonstrate the importance of these guidelines.
Estimating demographic parameters using a combination of known-fate and open N-mixture models.
Schmidt, Joshua H; Johnson, Devin S; Lindberg, Mark S; Adams, Layne G
2015-10-01
Accurate estimates of demographic parameters are required to infer appropriate ecological relationships and inform management actions. Known-fate data from marked individuals are commonly used to estimate survival rates, whereas N-mixture models use count data from unmarked individuals to estimate multiple demographic parameters. However, a joint approach combining the strengths of both analytical tools has not been developed. Here we develop an integrated model combining known-fate and open N-mixture models, allowing the estimation of detection probability, recruitment, and the joint estimation of survival. We demonstrate our approach through both simulations and an applied example using four years of known-fate and pack count data for wolves (Canis lupus). Simulation results indicated that the integrated model reliably recovered parameters with no evidence of bias, and survival estimates were more precise under the joint model. Results from the applied example indicated that the marked sample of wolves was biased toward individuals with higher apparent survival rates than the unmarked pack mates, suggesting that joint estimates may be more representative of the overall population. Our integrated model is a practical approach for reducing bias while increasing precision and the amount of information gained from mark-resight data sets. We provide implementations in both the BUGS language and an R package. PMID:26649379
Analytic Nakagami fading parameter estimation in dependent noise channel using copula
NASA Astrophysics Data System (ADS)
Gholizadeh, Mohammad Hossein; Amindavar, Hamidreza; Ritcey, James A.
2013-12-01
In this paper, the probability density function (PDF) estimation is introduced in the framework of estimating the Nakagami fading parameter. This approach provides an analytic procedure for finding the fading parameter. Using the copula theory, an accurate PDF estimate is obtained even when the desired signal is corrupted in a noisy environment. In the real world, the noise samples could be highly dependent on the main signal. Copula-based models are a general set of statistical models defined for any multivariate random variable. Thus, they depict the statistical behavior of a received signal including two dependent terms, representative of the desired signal and noise. Previous works in the Nakagami parameter determination have mainly examined estimation based on either a noiseless sample model or an independent trivial noisy one. In this paper, we consider a more comprehensive situation about the noise destruction and our investigation is done in low signal-to-noise ratios. The parametric bootstrap method approves the accuracy of the analytically estimated PDF, and simulation results show that the new estimator has superior performance over conventional estimators.
Estimating demographic parameters using a combination of known-fate and open N-mixture models
Schmidt, Joshua H.; Johnson, Devin S.; Lindberg, Mark S.; Adams, Layne G.
2015-01-01
Accurate estimates of demographic parameters are required to infer appropriate ecological relationships and inform management actions. Known-fate data from marked individuals are commonly used to estimate survival rates, whereas N-mixture models use count data from unmarked individuals to estimate multiple demographic parameters. However, a joint approach combining the strengths of both analytical tools has not been developed. Here we develop an integrated model combining known-fate and open N-mixture models, allowing the estimation of detection probability, recruitment, and the joint estimation of survival. We demonstrate our approach through both simulations and an applied example using four years of known-fate and pack count data for wolves (Canis lupus). Simulation results indicated that the integrated model reliably recovered parameters with no evidence of bias, and survival estimates were more precise under the joint model. Results from the applied example indicated that the marked sample of wolves was biased toward individuals with higher apparent survival rates than the unmarked pack mates, suggesting that joint estimates may be more representative of the overall population. Our integrated model is a practical approach for reducing bias while increasing precision and the amount of information gained from mark–resight data sets. We provide implementations in both the BUGS language and an R package.
Estimation of atmospheric parameters from time-lapse imagery
NASA Astrophysics Data System (ADS)
McCrae, Jack E.; Basu, Santasri; Fiorino, Steven T.
2016-05-01
A time-lapse imaging experiment was conducted to estimate various atmospheric parameters for the imaging path. Atmospheric turbulence caused frame-to-frame shifts of the entire image as well as parts of the image. The statistics of these shifts encode information about the turbulence strength (as characterized by Cn2, the refractive index structure function constant) along the optical path. The shift variance observed is simply proportional to the variance of the tilt of the optical field averaged over the area being tracked. By presuming this turbulence follows the Kolmogorov spectrum, weighting functions can be derived which relate the turbulence strength along the path to the shifts measured. These weighting functions peak at the camera and fall to zero at the object. The larger the area observed, the more quickly the weighting function decays. One parameter we would like to estimate is r0 (the Fried parameter, or atmospheric coherence diameter.) The weighting functions derived for pixel sized or larger parts of the image all fall faster than the weighting function appropriate for estimating the spherical wave r0. If we presume Cn2 is constant along the path, then an estimate for r0 can be obtained for each area tracked, but since the weighting function for r0 differs substantially from that for every realizable tracked area, it can be expected this approach would yield a poor estimator. Instead, the weighting functions for a number of different patch sizes can be combined through the Moore-Penrose pseudo-inverse to create a new weighting function which yields the least-squares optimal linear combination of measurements for estimation of r0. This approach is carried out, and it is observed that this approach is somewhat noisy because the pseudo-inverse assigns weights much greater than one to many of the observations.
Estimation of Nutation Time Constant Model Parameters for On-Axis Spinning Spacecraft
NASA Technical Reports Server (NTRS)
Schlee, Keith; Sudermann, James
2008-01-01
Calculating an accurate nutation time constant for a spinning spacecraft is an important step for ensuring mission success. Spacecraft nutation is caused by energy dissipation about the spin axis. Propellant slosh in the spacecraft fuel tanks is the primary source for this dissipation and can be simulated using a forced motion spin table. Mechanical analogs, such as pendulums and rotors, are typically used to simulate propellant slosh. A strong desire exists for an automated method to determine these analog parameters. The method presented accomplishes this task by using a MATLAB Simulink/SimMechanics based simulation that utilizes the Parameter Estimation Tool.
Estimation of economic parameters of U.S. hydropower resources
Hall, Douglas G.; Hunt, Richard T.; Reeves, Kelly S.; Carroll, Greg R.
2003-06-01
Tools for estimating the cost of developing and operating and maintaining hydropower resources in the form of regression curves were developed based on historical plant data. Development costs that were addressed included: licensing, construction, and five types of environmental mitigation. It was found that the data for each type of cost correlated well with plant capacity. A tool for estimating the annual and monthly electric generation of hydropower resources was also developed. Additional tools were developed to estimate the cost of upgrading a turbine or a generator. The development and operation and maintenance cost estimating tools, and the generation estimating tool were applied to 2,155 U.S. hydropower sites representing a total potential capacity of 43,036 MW. The sites included totally undeveloped sites, dams without a hydroelectric plant, and hydroelectric plants that could be expanded to achieve greater capacity. Site characteristics and estimated costs and generation for each site were assembled in a database in Excel format that is also included within the EERE Library under the title, “Estimation of Economic Parameters of U.S. Hydropower Resources - INL Hydropower Resource Economics Database.”
Beef quality parameters estimation using ultrasound and color images
2015-01-01
Background Beef quality measurement is a complex task with high economic impact. There is high interest in obtaining an automatic quality parameters estimation in live cattle or post mortem. In this paper we set out to obtain beef quality estimates from the analysis of ultrasound (in vivo) and color images (post mortem), with the measurement of various parameters related to tenderness and amount of meat: rib eye area, percentage of intramuscular fat and backfat thickness or subcutaneous fat. Proposal An algorithm based on curve evolution is implemented to calculate the rib eye area. The backfat thickness is estimated from the profile of distances between two curves that limit the steak and the rib eye, previously detected. A model base in Support Vector Regression (SVR) is trained to estimate the intramuscular fat percentage. A series of features extracted on a region of interest, previously detected in both ultrasound and color images, were proposed. In all cases, a complete evaluation was performed with different databases including: color and ultrasound images acquired by a beef industry expert, intramuscular fat estimation obtained by an expert using a commercial software, and chemical analysis. Conclusions The proposed algorithms show good results to calculate the rib eye area and the backfat thickness measure and profile. They are also promising in predicting the percentage of intramuscular fat. PMID:25734452
Parameter Estimation for Groundwater Models under Uncertain Irrigation Data.
Demissie, Yonas; Valocchi, Albert; Cai, Ximing; Brozovic, Nicholas; Senay, Gabriel; Gebremichael, Mekonnen
2015-01-01
The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p < 0.05) bias in estimated parameters and model predictions that persist despite calibrating the models to different calibration data and sample sizes. However, by directly accounting for the irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes. PMID:25040235
Iterative procedure for camera parameters estimation using extrinsic matrix decomposition
NASA Astrophysics Data System (ADS)
Goshin, Yegor V.; Fursov, Vladimir A.
2016-03-01
This paper addresses the problem of 3D scene reconstruction in cases when the extrinsic parameters (rotation and translation) of the camera are unknown. This problem is both important and urgent because the accuracy of the camera parameters significantly influences the resulting 3D model. A common approach is to determine the fundamental matrix from corresponding points on two views of a scene and then to use singular value decomposition for camera projection matrix estimation. However, this common approach is very sensitive to fundamental matrix errors. In this paper we propose a novel approach in which camera parameters are determined directly from the equations of the projective transformation by using corresponding points on the views. The proposed decomposition allows us to use an iterative procedure for determining the parameters of the camera. This procedure is implemented in two steps: the translation determination and the rotation determination. The experimental results of the camera parameters estimation and 3D scene reconstruction demonstrate the reliability of the proposed approach.
Accurate nuclear masses from a three parameter Kohn-Sham DFT approach (BCPM)
Baldo, M.; Robledo, L. M.; Schuck, P.; Vinas, X.
2012-10-20
Given the promising features of the recently proposed Barcelona-Catania-Paris (BCP) functional [1], it is the purpose of this work to still improve on it. It is, for instance, shown that the number of open parameters can be reduced from 4-5 to 2-3, i.e. by practically a factor of two without deteriorating the results.
An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.
Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N V
2013-01-01
The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test. PMID:23469172
Estimation of the parameters of ETAS models by Simulated Annealing
Lombardi, Anna Maria
2015-01-01
This paper proposes a new algorithm to estimate the maximum likelihood parameters of an Epidemic Type Aftershock Sequences (ETAS) model. It is based on Simulated Annealing, a versatile method that solves problems of global optimization and ensures convergence to a global optimum. The procedure is tested on both simulated and real catalogs. The main conclusion is that the method performs poorly as the size of the catalog decreases because the effect of the correlation of the ETAS parameters is more significant. These results give new insights into the ETAS model and the efficiency of the maximum-likelihood method within this context. PMID:25673036
Gatti, Christopher J.; Doro, Lisa Case; Langenderfer, Joseph E.; Mell, Amy G.; Maratt, Joseph D.; Carpenter, James E.; Hughes, Richard E.
2008-01-01
Background Accurate prediction of in vivo muscle forces is essential for relevant analyses of musculoskeletal biomechanics. The purpose of this study was to evaluate three methods for predicting muscle forces of the shoulder by comparing calculated muscle parameters, which relate electromyographic activity to muscle forces. Methods Thirteen subjects performed sub-maximal, isometric contractions consisting of six actions about the shoulder and two actions about the elbow. Electromyography from 12 shoulder muscles and internal shoulder moments were used to determine muscle parameters using traditional multiple linear regression, principal-components regression, and a sequential muscle parameter determination process using principal-components regression. Muscle parameters were evaluated based on their sign (positive or negative), standard deviations, and error between the measured and predicted internal shoulder moments. Findings It was found that no method was superior with respect to all evaluation criteria. The sequential principal-components regression method most frequently produced muscle parameters that could be used to estimate muscle forces, multiple regression best predicted the measured internal shoulder moments, and the results of principal-components regression fell between those of sequential principal-components regression and multiple regression. Interpretation The selection of a muscle parameter estimation method should be based on the importance of the evaluation criteria. Sequential principal-components regression should be used if a greater number of physiologically accurate muscle forces are desired, while multiple regression should be used for a more accurate prediction of measured internal shoulder moments. However, all methods produced muscle parameters which can be used to predict in vivo muscle forces of the shoulder. PMID:17945401
Mariño, Inés P; Míguez, Joaquín
2005-11-01
We introduce a numerical approximation method for estimating an unknown parameter of a (primary) chaotic system which is partially observed through a scalar time series. Specifically, we show that the recursive minimization of a suitably designed cost function that involves the dynamic state of a fully observed (secondary) system and the observed time series can lead to the identical synchronization of the two systems and the accurate estimation of the unknown parameter. The salient feature of the proposed technique is that the only external input to the secondary system is the unknown parameter which needs to be adjusted. We present numerical examples for the Lorenz system which show how our algorithm can be considerably faster than some previously proposed methods. PMID:16383795
ProViDE: A software tool for accurate estimation of viral diversity in metagenomic samples
Ghosh, Tarini Shankar; Mohammed, Monzoorul Haque; Komanduri, Dinakar; Mande, Sharmila Shekhar
2011-01-01
Given the absence of universal marker genes in the viral kingdom, researchers typically use BLAST (with stringent E-values) for taxonomic classification of viral metagenomic sequences. Since majority of metagenomic sequences originate from hitherto unknown viral groups, using stringent e-values results in most sequences remaining unclassified. Furthermore, using less stringent e-values results in a high number of incorrect taxonomic assignments. The SOrt-ITEMS algorithm provides an approach to address the above issues. Based on alignment parameters, SOrt-ITEMS follows an elaborate work-flow for assigning reads originating from hitherto unknown archaeal/bacterial genomes. In SOrt-ITEMS, alignment parameter thresholds were generated by observing patterns of sequence divergence within and across various taxonomic groups belonging to bacterial and archaeal kingdoms. However, many taxonomic groups within the viral kingdom lack a typical Linnean-like taxonomic hierarchy. In this paper, we present ProViDE (Program for Viral Diversity Estimation), an algorithm that uses a customized set of alignment parameter thresholds, specifically suited for viral metagenomic sequences. These thresholds capture the pattern of sequence divergence and the non-uniform taxonomic hierarchy observed within/across various taxonomic groups of the viral kingdom. Validation results indicate that the percentage of ‘correct’ assignments by ProViDE is around 1.7 to 3 times higher than that by the widely used similarity based method MEGAN. The misclassification rate of ProViDE is around 3 to 19% (as compared to 5 to 42% by MEGAN) indicating significantly better assignment accuracy. ProViDE software and a supplementary file (containing supplementary figures and tables referred to in this article) is available for download from http://metagenomics.atc.tcs.com/binning/ProViDE/ PMID:21544173
A new method based on the subpixel Gaussian model for accurate estimation of asteroid coordinates
NASA Astrophysics Data System (ADS)
Savanevych, V. E.; Briukhovetskyi, O. B.; Sokovikova, N. S.; Bezkrovny, M. M.; Vavilova, I. B.; Ivashchenko, Yu. M.; Elenin, L. V.; Khlamov, S. V.; Movsesian, Ia. S.; Dashkova, A. M.; Pogorelov, A. V.
2015-08-01
We describe a new iteration method to estimate asteroid coordinates, based on a subpixel Gaussian model of the discrete object image. The method operates by continuous parameters (asteroid coordinates) in a discrete observational space (the set of pixel potentials) of the CCD frame. In this model, the kind of coordinate distribution of the photons hitting a pixel of the CCD frame is known a priori, while the associated parameters are determined from a real digital object image. The method that is developed, which is flexible in adapting to any form of object image, has a high measurement accuracy along with a low calculating complexity, due to the maximum-likelihood procedure that is implemented to obtain the best fit instead of a least-squares method and Levenberg-Marquardt algorithm for minimization of the quadratic form. Since 2010, the method has been tested as the basis of our Collection Light Technology (COLITEC) software, which has been installed at several observatories across the world with the aim of the automatic discovery of asteroids and comets in sets of CCD frames. As a result, four comets (C/2010 X1 (Elenin), P/2011 NO1(Elenin), C/2012 S1 (ISON) and P/2013 V3 (Nevski)) as well as more than 1500 small Solar system bodies (including five near-Earth objects (NEOs), 21 Trojan asteroids of Jupiter and one Centaur object) have been discovered. We discuss these results, which allowed us to compare the accuracy parameters of the new method and confirm its efficiency. In 2014, the COLITEC software was recommended to all members of the Gaia-FUN-SSO network for analysing observations as a tool to detect faint moving objects in frames.
NASA Astrophysics Data System (ADS)
Hochlaf, M.; Puzzarini, C.; Senent, M. L.
2015-07-01
We present multi-component computations for rotational constants, vibrational and torsional levels of medium-sized molecules. Through the treatment of two organic sulphur molecules, ethyl mercaptan and dimethyl sulphide, which are relevant for atmospheric and astrophysical media, we point out the outstanding capabilities of explicitly correlated coupled clusters (CCSD(T)-F12) method in conjunction with the cc-pVTZ-F12 basis set for the accurate predictions of such quantities. Indeed, we show that the CCSD(T)-F12/cc-pVTZ-F12 equilibrium rotational constants are in good agreement with those obtained by means of a composite scheme based on CCSD(T) calculations that accounts for the extrapolation to the complete basis set (CBS) limit and core-correlation effects [CCSD(T)/CBS+CV], thus leading to values of ground-state rotational constants rather close to the corresponding experimental data. For vibrational and torsional levels, our analysis reveals that the anharmonic frequencies derived from CCSD(T)-F12/cc-pVTZ-F12 harmonic frequencies and anharmonic corrections (Δν = ω - ν) at the CCSD/cc-pVTZ level closely agree with experimental results. The pattern of the torsional transitions and the shape of the potential energy surfaces along the torsional modes are also well reproduced using the CCSD(T)-F12/cc-pVTZ-F12 energies. Interestingly, this good accuracy is accompanied with a strong reduction of the computational costs. This makes the procedures proposed here as schemes of choice for effective and accurate prediction of spectroscopic properties of organic compounds. Finally, popular density functional approaches are compared with the coupled cluster (CC) methodologies in torsional studies. The long-range CAM-B3LYP functional of Handy and co-workers is recommended for large systems.
Estimation of Geodetic and Geodynamical Parameters with VieVS
NASA Technical Reports Server (NTRS)
Spicakova, Hana; Bohm, Johannes; Bohm, Sigrid; Nilsson, tobias; Pany, Andrea; Plank, Lucia; Teke, Kamil; Schuh, Harald
2010-01-01
Since 2008 the VLBI group at the Institute of Geodesy and Geophysics at TU Vienna has focused on the development of a new VLBI data analysis software called VieVS (Vienna VLBI Software). One part of the program, currently under development, is a unit for parameter estimation in so-called global solutions, where the connection of the single sessions is done by stacking at the normal equation level. We can determine time independent geodynamical parameters such as Love and Shida numbers of the solid Earth tides. Apart from the estimation of the constant nominal values of Love and Shida numbers for the second degree of the tidal potential, it is possible to determine frequency dependent values in the diurnal band together with the resonance frequency of Free Core Nutation. In this paper we show first results obtained from the 24-hour IVS R1 and R4 sessions.
A Bayesian approach to parameter estimation in HIV dynamical models.
Putter, H; Heisterkamp, S H; Lange, J M A; de Wolf, F
2002-08-15
In the context of a mathematical model describing HIV infection, we discuss a Bayesian modelling approach to a non-linear random effects estimation problem. The model and the data exhibit a number of features that make the use of an ordinary non-linear mixed effects model intractable: (i) the data are from two compartments fitted simultaneously against the implicit numerical solution of a system of ordinary differential equations; (ii) data from one compartment are subject to censoring; (iii) random effects for one variable are assumed to be from a beta distribution. We show how the Bayesian framework can be exploited by incorporating prior knowledge on some of the parameters, and by combining the posterior distributions of the parameters to obtain estimates of quantities of interest that follow from the postulated model. PMID:12210633
On Using Exponential Parameter Estimators with an Adaptive Controller
NASA Technical Reports Server (NTRS)
Patre, Parag; Joshi, Suresh M.
2011-01-01
Typical adaptive controllers are restricted to using a specific update law to generate parameter estimates. This paper investigates the possibility of using any exponential parameter estimator with an adaptive controller such that the system tracks a desired trajectory. The goal is to provide flexibility in choosing any update law suitable for a given application. The development relies on a previously developed concept of controller/update law modularity in the adaptive control literature, and the use of a converse Lyapunov-like theorem. Stability analysis is presented to derive gain conditions under which this is possible, and inferences are made about the tracking error performance. The development is based on a class of Euler-Lagrange systems that are used to model various engineering systems including space robots and manipulators.
Identification of vehicle parameters and estimation of vertical forces
NASA Astrophysics Data System (ADS)
Imine, H.; Fridman, L.; Madani, T.
2015-12-01
The aim of the present work is to estimate the vertical forces and to identify the unknown dynamic parameters of a vehicle using the sliding mode observers approach. The estimation of vertical forces needs a good knowledge of dynamic parameters such as damping coefficient, spring stiffness and unsprung masses, etc. In this paper, suspension stiffness and unsprung masses have been identified by the Least Square Method. Real-time tests have been carried out on an instrumented static vehicle, excited vertically by hydraulic jacks. The vehicle is equipped with different sensors in order to measure its dynamics. The measurements coming from these sensors have been considered as unknown inputs of the system. However, only the roll angle and the suspension deflection measurements have been used in order to perform the observer. Experimental results are presented and discussed to show the quality of the proposed approach.
CosmoSIS: A System for MC Parameter Estimation
Zuntz, Joe; Paterno, Marc; Jennings, Elise; Rudd, Douglas; Manzotti, Alessandro; Dodelson, Scott; Bridle, Sarah; Sehrish, Saba; Kowalkowski, James
2015-01-01
Cosmological parameter estimation is entering a new era. Large collaborations need to coordinate high-stakes analyses using multiple methods; furthermore such analyses have grown in complexity due to sophisticated models of cosmology and systematic uncertainties. In this paper we argue that modularity is the key to addressing these challenges: calculations should be broken up into interchangeable modular units with inputs and outputs clearly defined. We present a new framework for cosmological parameter estimation, CosmoSIS, designed to connect together, share, and advance development of inference tools across the community. We describe the modules already available in Cosmo- SIS, including camb, Planck, cosmic shear calculations, and a suite of samplers. We illustrate it using demonstration code that you can run out-of-the-box with the installer available at http://bitbucket.org/joezuntz/cosmosis.
NASA Astrophysics Data System (ADS)
Ait-El-Fquih, Boujemaa; El Gharamti, Mohamad; Hoteit, Ibrahim
2016-08-01
Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface groundwater models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model's state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKFOSA. Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25 % more accurate state and parameter estimations than the joint and dual approaches.
Space Shuttle propulsion parameter estimation using optimal estimation techniques, volume 1
NASA Technical Reports Server (NTRS)
1983-01-01
The mathematical developments and their computer program implementation for the Space Shuttle propulsion parameter estimation project are summarized. The estimation approach chosen is the extended Kalman filtering with a modified Bryson-Frazier smoother. Its use here is motivated by the objective of obtaining better estimates than those available from filtering and to eliminate the lag associated with filtering. The estimation technique uses as the dynamical process the six degree equations-of-motion resulting in twelve state vector elements. In addition to these are mass and solid propellant burn depth as the ""system'' state elements. The ""parameter'' state elements can include aerodynamic coefficient, inertia, center-of-gravity, atmospheric wind, etc. deviations from referenced values. Propulsion parameter state elements have been included not as options just discussed but as the main parameter states to be estimated. The mathematical developments were completed for all these parameters. Since the systems dynamics and measurement processes are non-linear functions of the states, the mathematical developments are taken up almost entirely by the linearization of these equations as required by the estimation algorithms.
Estimation of Parameters from Discrete Random Nonstationary Time Series
NASA Astrophysics Data System (ADS)
Takayasu, H.; Nakamura, T.
For the analysis of nonstationary stochastic time series we introduce a formulation to estimate the underlying time-dependent parameters. This method is designed for random events with small numbers that are out of the applicability range of the normal distribution. The method is demonstrated for numerical data generated by a known system, and applied to time series of traffic accidents, batting average of a baseball player and sales volume of home electronics.
On optimal detection and estimation of the FCN parameters
NASA Astrophysics Data System (ADS)
Yatskiv, Y.
2009-09-01
Statistical approach for detection and estimation of parameters of short-term quasi- periodic processes was used in order to investigate the Free Core Nutation (FCN) signal in the Celestial Pole Offset (CPO). The results show that this signal is very unstable and that it disappeared in year 2000. The amplitude of oscillation with period of about 435 days is larger for dX as compared with that for dY .
Statistical methods of parameter estimation for deterministically chaotic time series.
Pisarenko, V F; Sornette, D
2004-03-01
We discuss the possibility of applying some standard statistical methods (the least-square method, the maximum likelihood method, and the method of statistical moments for estimation of parameters) to deterministically chaotic low-dimensional dynamic system (the logistic map) containing an observational noise. A "segmentation fitting" maximum likelihood (ML) method is suggested to estimate the structural parameter of the logistic map along with the initial value x(1) considered as an additional unknown parameter. The segmentation fitting method, called "piece-wise" ML, is similar in spirit but simpler and has smaller bias than the "multiple shooting" previously proposed. Comparisons with different previously proposed techniques on simulated numerical examples give favorable results (at least, for the investigated combinations of sample size N and noise level). Besides, unlike some suggested techniques, our method does not require the a priori knowledge of the noise variance. We also clarify the nature of the inherent difficulties in the statistical analysis of deterministically chaotic time series and the status of previously proposed Bayesian approaches. We note the trade off between the need of using a large number of data points in the ML analysis to decrease the bias (to guarantee consistency of the estimation) and the unstable nature of dynamical trajectories with exponentially fast loss of memory of the initial condition. The method of statistical moments for the estimation of the parameter of the logistic map is discussed. This method seems to be the unique method whose consistency for deterministically chaotic time series is proved so far theoretically (not only numerically). PMID:15089376
Estimating Sea Ice Parameters from Multi-Look SAR Images Using - and Second-Order Variograms
NASA Astrophysics Data System (ADS)
Wang, Xiaojian; Li, Yu; Zhao, Quanhua
2016-06-01
The spatial structures revealed in SAR intensity imagery provide essential information characterizing the natural variation processes of sea ice. This paper proposes a new method to extract the spatial structures of sea ice based on two spatial stochastic models. One is a multi-Gamma model, which characterizes continuous variations corresponding to ice-free area or the background. The other is a Poisson line mosaic model, which characterizes the regional variations of sea ice with different types. The linear combination of the two models builds the mixture model to represent spatial structures of sea ice within SAR intensity imagery. To estimate different sea ice parameters, such as its concentration, scale etc. We define two kinds of geostatistic metrics, theoretical first- and second-order variograms. Their experimental alternatives can be calculated from the SAR intensity imagery directly, then the parameters of the mixture model are estimated through fitting the theoretical variograms to the experimental ones, and by comparing the estimated parameters to the egg code, it is verified that the estimated parameters can indicate sea ice structure information showing in the egg code. The proposed method is applied to simulated images and Radarsat-1 images. The results of the experiments show that the proposed method can estimate the sea ice concentration and floe size accurately and stably within SAR testing images.
NASA Astrophysics Data System (ADS)
Ostrowski, J.; Shlomi, S.; Michalak, A.
2007-12-01
The process of estimating the release history of a contaminant in groundwater relies on coupling a limited number of concentration measurements with a groundwater flow and transport model in an inverse modeling framework. The information provided by available measurements is generally not sufficient to fully characterize the unknown release history; therefore, an accurate assessment of the estimation uncertainty is required. The modeler's level of confidence in the transport parameters, expressed as pdfs, can be incorporated into the inverse model to improve the accuracy of the release estimates. In this work, geostatistical inverse modeling is used in conjunction with Monte Carlo sampling of transport parameters to estimate groundwater contaminant release histories. Concentration non-negativity is enforced using a Gibbs sampling algorithm based on a truncated normal distribution. The method is applied to two one-dimensional test cases: a hypothetical dataset commonly used in validating contaminant source identification methods, and data collected from a tetrachloroethylene and trichloroethylene plume at the Dover Air Force Base in Delaware. The estimated release histories and associated uncertainties are compared to results from a geostatistical inverse model where uncertainty in transport parameters is ignored. Results show that the a posteriori uncertainty associated with the model that accounts for parameter uncertainty is higher, but that this model provides a more realistic representation of the release history based on available data. This modified inverse modeling technique has many applications, including assignment of liability in groundwater contamination cases, characterization of groundwater contamination, and model calibration.
Billinge S. J.; Tian, P.
2011-12-01
The advent of fast computing allows more computationally expensive, though possibly more accurate, approaches for estimating uncertainties on Rietveld refined parameters. Here we compare three such methods, using two different refinement programs, FullProf and GSAS. This is facilitated by the use of a new Rietveld refinement package, SrRietveld, that provides Python wrappers for FullProf and GSAS. The refined values on the parameters from two different refinement engines match each other very well. The uncertainty estimates determined using the different methods are also consistent, though FullProf and GSAS estimate the parameter uncertainties slightly differently from each other, which is discussed. More importantly, we find that the refined results are very sensitive to the statistical weights used in the least squares equation. Different weights, for example from uncertain or incorrectly propagated random errors on the data, lead to significantly different refined values. This means that uncertainty estimates on refined parameters should be increased when the random errors on the data are not well known, as for example in many cases where area detectors are used, and care should be taken to propagate errors correctly in any data preprocessing such as normalization by a smoothed spectrum. The computationally expensive bootstrap error estimation methods are facilitated by the use of SrRietveld, but are not required in most cases though may be useful in some cases, for example if the random errors on the data are not well known.
Serial lung model for simulation and parameter estimation in body plethysmography.
Verbraak, A F; Bogaard, J M; Beneken, J E; Hoorn, E; Versprille, A
1991-05-01
A serial lung model with a compressible segment has been implemented to simulate different types of lung and airway disorders such as asthma, emphysema, fibrosis and upper airway obstruction. The model described can be used during normal breathing, and moreover the compliant segment is structured according to more recent physiological data. A parameter estimation technique was applied and its reliability and uniqueness were tested by means of sine wave input signals. The characteristics of the alveolar pressure/flow patterns simulated with the model agree to a great extent with those found in the literature. In the case of absence of noise the parameter estimation routine produced unique solutions for different simulated pathologic classes. The sensitivity of the different parameters depended on the values belonging to each class of pathology. Some more simplified models are presented and their advantages over the complex model in special types of pathology are demonstrated. Noise added to the simulated flow appeared to have no influence on the estimated parameters, in contradiction to the effects with noise added to the pressure signal. In that case effective resistance was accurately estimated. Where parameters had no influence, as for instance upper airway resistance in emphysema or peripheral airway resistance in upper airway obstruction, the measurement accuracy was less. In all other cases, a satisfactory accuracy could be obtained. PMID:1943264
Chaudhuri, Shomesh E; Merfeld, Daniel M
2013-03-01
Psychophysics generally relies on estimating a subject's ability to perform a specific task as a function of an observed stimulus. For threshold studies, the fitted functions are called psychometric functions. While fitting psychometric functions to data acquired using adaptive sampling procedures (e.g., "staircase" procedures), investigators have encountered a bias in the spread ("slope" or "threshold") parameter that has been attributed to the serial dependency of the adaptive data. Using simulations, we confirm this bias for cumulative Gaussian parametric maximum likelihood fits on data collected via adaptive sampling procedures, and then present a bias-reduced maximum likelihood fit that substantially reduces the bias without reducing the precision of the spread parameter estimate and without reducing the accuracy or precision of the other fit parameters. As a separate topic, we explain how to implement this bias reduction technique using generalized linear model fits as well as other numeric maximum likelihood techniques such as the Nelder-Mead simplex. We then provide a comparison of the iterative bootstrap and observed information matrix techniques for estimating parameter fit variance from adaptive sampling procedure data sets. The iterative bootstrap technique is shown to be slightly more accurate; however, the observed information technique executes in a small fraction (0.005 %) of the time required by the iterative bootstrap technique, which is an advantage when a real-time estimate of parameter fit variance is required. PMID:23250442
Estimating hydraulic parameters when poroelastic effects are significant.
Berg, Steven J; Hsieh, Paul A; Illman, Walter A
2011-01-01
For almost 80 years, deformation-induced head changes caused by poroelastic effects have been observed during pumping tests in multilayered aquifer-aquitard systems. As water in the aquifer is released from compressive storage during pumping, the aquifer is deformed both in the horizontal and vertical directions. This deformation in the pumped aquifer causes deformation in the adjacent layers, resulting in changes in pore pressure that may produce drawdown curves that differ significantly from those predicted by traditional groundwater theory. Although these deformation-induced head changes have been analyzed in several studies by poroelasticity theory, there are at present no practical guidelines for the interpretation of pumping test data influenced by these effects. To investigate the impact that poroelastic effects during pumping tests have on the estimation of hydraulic parameters, we generate synthetic data for three different aquifer-aquitard settings using a poroelasticity model, and then analyze the synthetic data using type curves and parameter estimation techniques, both of which are based on traditional groundwater theory and do not account for poroelastic effects. Results show that even when poroelastic effects result in significant deformation-induced head changes, it is possible to obtain reasonable estimates of hydraulic parameters using methods based on traditional groundwater theory, as long as pumping is sufficiently long so that deformation-induced effects have largely dissipated. PMID:21204832
Estimating Hydraulic Parameters When Poroelastic Effects Are Significant
Berg, S.J.; Hsieh, P.A.; Illman, W.A.
2011-01-01
For almost 80 years, deformation-induced head changes caused by poroelastic effects have been observed during pumping tests in multilayered aquifer-aquitard systems. As water in the aquifer is released from compressive storage during pumping, the aquifer is deformed both in the horizontal and vertical directions. This deformation in the pumped aquifer causes deformation in the adjacent layers, resulting in changes in pore pressure that may produce drawdown curves that differ significantly from those predicted by traditional groundwater theory. Although these deformation-induced head changes have been analyzed in several studies by poroelasticity theory, there are at present no practical guidelines for the interpretation of pumping test data influenced by these effects. To investigate the impact that poroelastic effects during pumping tests have on the estimation of hydraulic parameters, we generate synthetic data for three different aquifer-aquitard settings using a poroelasticity model, and then analyze the synthetic data using type curves and parameter estimation techniques, both of which are based on traditional groundwater theory and do not account for poroelastic effects. Results show that even when poroelastic effects result in significant deformation-induced head changes, it is possible to obtain reasonable estimates of hydraulic parameters using methods based on traditional groundwater theory, as long as pumping is sufficiently long so that deformation-induced effects have largely dissipated. ?? 2011 The Author(s). Journal compilation ?? 2011 National Ground Water Association.
Estimating stellar atmospheric parameters based on Lasso features
NASA Astrophysics Data System (ADS)
Liu, Chuan-Xing; Zhang, Pei-Ai; Lu, Yu
2014-04-01
With the rapid development of large scale sky surveys like the Sloan Digital Sky Survey (SDSS), GAIA and LAMOST (Guoshoujing telescope), stellar spectra can be obtained on an ever-increasing scale. Therefore, it is necessary to estimate stellar atmospheric parameters such as Teff, log g and [Fe/H] automatically to achieve the scientific goals and make full use of the potential value of these observations. Feature selection plays a key role in the automatic measurement of atmospheric parameters. We propose to use the least absolute shrinkage selection operator (Lasso) algorithm to select features from stellar spectra. Feature selection can reduce redundancy in spectra, alleviate the influence of noise, improve calculation speed and enhance the robustness of the estimation system. Based on the extracted features, stellar atmospheric parameters are estimated by the support vector regression model. Three typical schemes are evaluated on spectral data from both the ELODIE library and SDSS. Experimental results show the potential performance to a certain degree. In addition, results show that our method is stable when applied to different spectra.
Hydraulic parameters estimation from well logging resistivity and geoelectrical measurements
NASA Astrophysics Data System (ADS)
Perdomo, S.; Ainchil, J. E.; Kruse, E.
2014-06-01
In this paper, a methodology is suggested for deriving hydraulic parameters, such as hydraulic conductivity or transmissivity combining classical hydrogeological data with geophysical measurements. Estimates values of transmissivity and conductivity, with this approach, can reduce uncertainties in numerical model calibration and improve data coverage, reducing time and cost of a hydrogeological investigation at a regional scale. The conventional estimation of hydrogeological parameters needs to be done by analyzing wells data or laboratory measurements. Furthermore, to make a regional survey many wells should be considered, and the location of each one plays an important role in the interpretation stage. For this reason, the use of geoelectrical methods arises as an effective complementary technique, especially in developing countries where it is necessary to optimize resources. By combining hydraulic parameters from pumping tests and electrical resistivity from well logging profiles, it was possible to adjust three empirical laws in a semi-confined alluvial aquifer in the northeast of the province of Buenos Aires (Argentina). These relations were also tested to be used with surficial geoelectrical data. The hydraulic conductivity and transmissivity estimated in porous material were according to expected values for the region (20 m/day; 457 m2/day), and are very consistent with previous results from other authors (25 m/day and 500 m2/day). The methodology described could be used with similar data sets and applied to other areas with similar hydrogeological conditions.
Parameter Estimation and Parameterization Uncertainty Using Bayesian Model Averaging
NASA Astrophysics Data System (ADS)
Tsai, F. T.; Li, X.
2007-12-01
This study proposes Bayesian model averaging (BMA) to address parameter estimation uncertainty arisen from non-uniqueness in parameterization methods. BMA provides a means of incorporating multiple parameterization methods for prediction through the law of total probability, with which an ensemble average of hydraulic conductivity distribution is obtained. Estimation uncertainty is described by the BMA variances, which contain variances within and between parameterization methods. BMA shows the facts that considering more parameterization methods tends to increase estimation uncertainty and estimation uncertainty is always underestimated using a single parameterization method. Two major problems in applying BMA to hydraulic conductivity estimation using a groundwater inverse method will be discussed in the study. The first problem is the use of posterior probabilities in BMA, which tends to single out one best method and discard other good methods. This problem arises from Occam's window that only accepts models in a very narrow range. We propose a variance window to replace Occam's window to cope with this problem. The second problem is the use of Kashyap information criterion (KIC), which makes BMA tend to prefer high uncertain parameterization methods due to considering the Fisher information matrix. We found that Bayesian information criterion (BIC) is a good approximation to KIC and is able to avoid controversial results. We applied BMA to hydraulic conductivity estimation in the 1,500-foot sand aquifer in East Baton Rouge Parish, Louisiana.
Rapid estimation of high-parameter auditory-filter shapes
Shen, Yi; Sivakumar, Rajeswari; Richards, Virginia M.
2014-01-01
A Bayesian adaptive procedure, the quick-auditory-filter (qAF) procedure, was used to estimate auditory-filter shapes that were asymmetric about their peaks. In three experiments, listeners who were naive to psychoacoustic experiments detected a fixed-level, pure-tone target presented with a spectrally notched noise masker. The qAF procedure adaptively manipulated the masker spectrum level and the position of the masker notch, which was optimized for the efficient estimation of the five parameters of an auditory-filter model. Experiment I demonstrated that the qAF procedure provided a convergent estimate of the auditory-filter shape at 2 kHz within 150 to 200 trials (approximately 15 min to complete) and, for a majority of listeners, excellent test-retest reliability. In experiment II, asymmetric auditory filters were estimated for target frequencies of 1 and 4 kHz and target levels of 30 and 50 dB sound pressure level. The estimated filter shapes were generally consistent with published norms, especially at the low target level. It is known that the auditory-filter estimates are narrower for forward masking than simultaneous masking due to peripheral suppression, a result replicated in experiment III using fewer than 200 qAF trials. PMID:25324086
Rapid estimation of high-parameter auditory-filter shapes.
Shen, Yi; Sivakumar, Rajeswari; Richards, Virginia M
2014-10-01
A Bayesian adaptive procedure, the quick-auditory-filter (qAF) procedure, was used to estimate auditory-filter shapes that were asymmetric about their peaks. In three experiments, listeners who were naive to psychoacoustic experiments detected a fixed-level, pure-tone target presented with a spectrally notched noise masker. The qAF procedure adaptively manipulated the masker spectrum level and the position of the masker notch, which was optimized for the efficient estimation of the five parameters of an auditory-filter model. Experiment I demonstrated that the qAF procedure provided a convergent estimate of the auditory-filter shape at 2 kHz within 150 to 200 trials (approximately 15 min to complete) and, for a majority of listeners, excellent test-retest reliability. In experiment II, asymmetric auditory filters were estimated for target frequencies of 1 and 4 kHz and target levels of 30 and 50 dB sound pressure level. The estimated filter shapes were generally consistent with published norms, especially at the low target level. It is known that the auditory-filter estimates are narrower for forward masking than simultaneous masking due to peripheral suppression, a result replicated in experiment III using fewer than 200 qAF trials. PMID:25324086
Kimura, Akatsuki; Celani, Antonio; Nagao, Hiromichi; Stasevich, Timothy; Nakamura, Kazuyuki
2015-01-01
Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE) in a prediction or to maximize likelihood. A (local) maximum of likelihood or (local) minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge of parameter optimization, cell and developmental biologists can develop realistic models that reproduce their observations and thus, obtain mechanistic insights into phenomena of interest. PMID:25784880
Kimura, Akatsuki; Celani, Antonio; Nagao, Hiromichi; Stasevich, Timothy; Nakamura, Kazuyuki
2015-01-01
Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE) in a prediction or to maximize likelihood. A (local) maximum of likelihood or (local) minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge of parameter optimization, cell and developmental biologists can develop realistic models that reproduce their observations and thus, obtain mechanistic insights into phenomena of interest. PMID:25784880
Spacecraft design impacts on the post-Newtonian parameter estimation
NASA Astrophysics Data System (ADS)
Schuster, Anja Katharina; et al.
2015-08-01
The ESA mission BepiColombo, reaching out to explore the elusive planet Mercury, features unprecedented tracking techniques. The highly precise orbit determination around Mercury is a compelling opportunity for a modern test of General Relativity (GR). Using the software tool GRETCHEN incorporating the Square Root Information Filter (SRIF), MPO's orbit is simulated and the post-Newtonian parameters (PNP) are estimated. In this work, the influence of a specific constraint of the Mercury Orbiter Radio science Experiment (MORE) on the achievable accuracy of the PNP estimates is investigated. The power system design of the spacecraft requires that ±35° around perihelion the Ka transponder needs to be switched off, thus radiometric data is only gathered via X band. This analysis shows the impact of this constraint on the achievable accuracy of PNP estimates. On a bigger scale, if GR shows some violation at a detectable level it inevitably leads to its invalidation.
Maximum-Likelihood Fits to Histograms for Improved Parameter Estimation
NASA Astrophysics Data System (ADS)
Fowler, J. W.
2014-08-01
Straightforward methods for adapting the familiar statistic to histograms of discrete events and other Poisson distributed data generally yield biased estimates of the parameters of a model. The bias can be important even when the total number of events is large. For the case of estimating a microcalorimeter's energy resolution at 6 keV from the observed shape of the Mn K fluorescence spectrum, a poor choice of can lead to biases of at least 10 % in the estimated resolution when up to thousands of photons are observed. The best remedy is a Poisson maximum-likelihood fit, through a simple modification of the standard Levenberg-Marquardt algorithm for minimization. Where the modification is not possible, another approach allows iterative approximation of the maximum-likelihood fit.
Parameter Estimation in a Delay Differential Model of ENSO
NASA Astrophysics Data System (ADS)
Roux, J.; Gerchinovitz, S.; Ghil, M.
2009-04-01
In this talk, we present very generic statistical methods to perform parameter estimation in a Delay Differential Equation. Our reference DDE is the toy model of El Nino/Southern Oscillation introduced by Ghil, Zaliapin and Thompson (2008). We first recall some properties of this model in comparison with other models, together with basic results in Functional Differential Equation theory. We then briefly describe two statistical estimation procedures (the very classic Ordinary Least Squares estimator computed via simulated annealing, and a new two stage method based on nonparametric regression using the Nadaraya-Watson kernel). We finally comment on the numerical tests we performed on simulated noised data. These results encourage further application of this kind of methods to more complex (and more realistic) models of ENSO, to other problems in the Geosciences or to other fields.
Estimating demographic parameters using hidden process dynamic models.
Gimenez, Olivier; Lebreton, Jean-Dominique; Gaillard, Jean-Michel; Choquet, Rémi; Pradel, Roger
2012-12-01
Structured population models are widely used in plant and animal demographic studies to assess population dynamics. In matrix population models, populations are described with discrete classes of individuals (age, life history stage or size). To calibrate these models, longitudinal data are collected at the individual level to estimate demographic parameters. However, several sources of uncertainty can complicate parameter estimation, such as imperfect detection of individuals inherent to monitoring in the wild and uncertainty in assigning a state to an individual. Here, we show how recent statistical models can help overcome these issues. We focus on hidden process models that run two time series in parallel, one capturing the dynamics of the true states and the other consisting of observations arising from these underlying possibly unknown states. In a first case study, we illustrate hidden Markov models with an example of how to accommodate state uncertainty using Frequentist theory and maximum likelihood estimation. In a second case study, we illustrate state-space models with an example of how to estimate lifetime reproductive success despite imperfect detection, using a Bayesian framework and Markov Chain Monte Carlo simulation. Hidden process models are a promising tool as they allow population biologists to cope with process variation while simultaneously accounting for observation error. PMID:22373775
CosmoSIS: A system for MC parameter estimation
Bridle, S.; Dodelson, S.; Jennings, E.; Kowalkowski, J.; Manzotti, A.; Paterno, M.; Rudd, D.; Sehrish, S.; Zuntz, J.
2015-01-01
CosmoSIS is a modular system for cosmological parameter estimation, based on Markov Chain Monte Carlo and related techniques. It provides a series of samplers, which drive the exploration of the parameter space, and a series of modules, which calculate the likelihood of the observed data for a given physical model, determined by the location of a sample in the parameter space. While CosmoSIS ships with a set of modules that calculate quantities of interest to cosmologists, there is nothing about the framework itself, nor in the Markov Chain Monte Carlo technique, that is specific to cosmology. Thus CosmoSIS could be used for parameter estimation problems in other fields, including HEP. This paper describes the features of CosmoSIS and show an example of its use outside of cosmology. Furthermore, it also discusses how collaborative development strategies differ between two different communities: that of HEP physicists, accustomed to working in large collaborations, and that of cosmologists, who have traditionally not worked in large groups.
CosmoSIS: A system for MC parameter estimation
Bridle, S.; Dodelson, S.; Jennings, E.; Kowalkowski, J.; Manzotti, A.; Paterno, M.; Rudd, D.; Sehrish, S.; Zuntz, J.
2015-01-01
CosmoSIS is a modular system for cosmological parameter estimation, based on Markov Chain Monte Carlo and related techniques. It provides a series of samplers, which drive the exploration of the parameter space, and a series of modules, which calculate the likelihood of the observed data for a given physical model, determined by the location of a sample in the parameter space. While CosmoSIS ships with a set of modules that calculate quantities of interest to cosmologists, there is nothing about the framework itself, nor in the Markov Chain Monte Carlo technique, that is specific to cosmology. Thus CosmoSIS could bemore » used for parameter estimation problems in other fields, including HEP. This paper describes the features of CosmoSIS and show an example of its use outside of cosmology. Furthermore, it also discusses how collaborative development strategies differ between two different communities: that of HEP physicists, accustomed to working in large collaborations, and that of cosmologists, who have traditionally not worked in large groups.« less
NASA Astrophysics Data System (ADS)
Luo, Ning; Illman, Walter A.
2016-04-01
Analyses are presented of long-term hydrographs perturbed by variable pumping/injection events in a confined aquifer at a municipal water-supply well field in the Region of Waterloo, Ontario (Canada). Such records are typically not considered for aquifer test analysis. Here, the water-level variations are fingerprinted to pumping/injection rate changes using the Theis model implemented in the WELLS code coupled with PEST. Analyses of these records yield a set of transmissivity (T) and storativity (S) estimates between each monitoring and production borehole. These individual estimates are found to poorly predict water-level variations at nearby monitoring boreholes not used in the calibration effort. On the other hand, the geometric means of the individual T and S estimates are similar to those obtained from previous pumping tests conducted at the same site and adequately predict water-level variations in other boreholes. The analyses reveal that long-term municipal water-level records are amenable to analyses using a simple analytical solution to estimate aquifer parameters. However, uniform parameters estimated with analytical solutions should be considered as first rough estimates. More accurate hydraulic parameters should be obtained by calibrating a three-dimensional numerical model that rigorously captures the complexities of the site with these data.
Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias
2015-01-01
Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions. PMID:25794106
Estimates of genetic parameters for fat yield in Murrah buffaloes
Kumar, Manoj; Vohra, Vikas; Ratwan, Poonam; Valsalan, Jamuna; Patil, C. S.; Chakravarty, A. K.
2016-01-01
Aim: The present study was performed to investigate the effect of genetic and non-genetic factors affecting milk fat yield and to estimate genetic parameters of monthly test day fat yields (MTDFY) and lactation 305-day fat yield (L305FY) in Murrah buffaloes. Materials and Methods: The data on total of 10381 MTDFY records comprising the first four lactations of 470 Murrah buffaloes calved from 1993 to 2014 were assessed. These buffaloes were sired by 75 bulls maintained in an organized farm at ICAR-National Dairy Research Institute, Karnal. Least squares maximum likelihood program was used to estimate genetic and non-genetic parameters. Heritability estimates were obtained using paternal half-sib correlation method. Genetic and phenotypic correlations among MTDFY, and 305-day fat yield were calculated from the analysis of variance and covariance matrix among sire groups. Results: The overall least squares mean of L305FY was found to be 175.74±4.12 kg. The least squares mean of overall MTDFY ranged from 3.33±0.14 kg (TD-11) to 7.06±0.17 kg (TD-3). The h2 estimate of L305FY was found to be 0.33±0.16 in this study. The estimates of phenotypic and genetic correlations between 305-day fat yield and different MTDFY ranged from 0.32 to 0.48 and 0.51 to 0.99, respectively. Conclusions: In this study, all the genetic and non-genetic factors except age at the first calving group, significantly affected the traits under study. The estimates of phenotypic and genetic correlations of MTDFY with 305-day fat yield was generally higher in the MTDFY-5 of lactation suggesting that this TD yields could be used as the selection criteria for early evaluation and selection of Murrah buffaloes. PMID:27057114
Imani, Farsad; Karimi Rouzbahani, Hamid Reza; Goudarzi, Mehrdad; Tarrahi, Mohammad Javad; Ebrahim Soltani, Alireza
2016-01-01
Background: During anesthesia, continuous body temperature monitoring is essential, especially in children. Anesthesia can increase the risk of loss of body temperature by three to four times. Hypothermia in children results in increased morbidity and mortality. Since the measurement points of the core body temperature are not easily accessible, near core sites, like rectum, are used. Objectives: The purpose of this study was to measure skin temperature over the carotid artery and compare it with the rectum temperature, in order to propose a model for accurate estimation of near core body temperature. Patients and Methods: Totally, 124 patients within the age range of 2 - 6 years, undergoing elective surgery, were selected. Temperature of rectum and skin over the carotid artery was measured. Then, the patients were randomly divided into two groups (each including 62 subjects), namely modeling (MG) and validation groups (VG). First, in the modeling group, the average temperature of the rectum and skin over the carotid artery were measured separately. The appropriate model was determined, according to the significance of the model’s coefficients. The obtained model was used to predict the rectum temperature in the second group (VG group). Correlation of the predicted values with the real values (the measured rectum temperature) in the second group was investigated. Also, the difference in the average values of these two groups was examined in terms of significance. Results: In the modeling group, the average rectum and carotid temperatures were 36.47 ± 0.54°C and 35.45 ± 0.62°C, respectively. The final model was obtained, as follows: Carotid temperature × 0.561 + 16.583 = Rectum temperature. The predicted value was calculated based on the regression model and then compared with the measured rectum value, which showed no significant difference (P = 0.361). Conclusions: The present study was the first research, in which rectum temperature was compared with that
Estimation of accuracy of earth-rotation parameters in different frequency bands
NASA Astrophysics Data System (ADS)
Vondrak, J.
1986-11-01
The accuracies of earth-rotation parameters as determined by five different observational techniques now available (i.e., optical astrometry /OA/, Doppler tracking of satellites /DTS/, satellite laser ranging /SLR/, very long-base interferometry /VLBI/ and lunar laser ranging /LLR/) are estimated. The differences between the individual techniques in all possible combinations, separated by appropriate filters into three frequency bands, were used to estimate the accuracies of the techniques for periods from 0 to 200 days, from 200 to 1000 days and longer than 1000 days. It is shown that for polar motion the most accurate results are obtained with VLBI anad SLR, especially in the short-period region; OA and DTS are less accurate, but with longer periods the differences in accuracy are less pronounced. The accuracies of UTI-UTC as determined by OA, VLBI and LLR are practically equivalent, the differences being less than 40 percent.
An Accurate and Generic Testing Approach to Vehicle Stability Parameters Based on GPS and INS
Miao, Zhibin; Zhang, Hongtian; Zhang, Jinzhu
2015-01-01
With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. As a common method, usually GPS sensors and INS sensors are applied to measure vehicle stability parameters by fusing data from the two system sensors. Although prior model parameters should be recognized in a Kalman filter, it is usually used to fuse data from multi-sensors. In this paper, a robust, intelligent and precise method to the measurement of vehicle stability is proposed. First, a fuzzy interpolation method is proposed, along with a four-wheel vehicle dynamic model. Second, a two-stage Kalman filter, which fuses the data from GPS and INS, is established. Next, this approach is applied to a case study vehicle to measure yaw rate and sideslip angle. The results show the advantages of the approach. Finally, a simulation and real experiment is made to verify the advantages of this approach. The experimental results showed the merits of this method for measuring vehicle stability, and the approach can meet the design requirements of a vehicle stability controller. PMID:26690154
An Accurate and Generic Testing Approach to Vehicle Stability Parameters Based on GPS and INS.
Miao, Zhibin; Zhang, Hongtian; Zhang, Jinzhu
2015-01-01
With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. As a common method, usually GPS sensors and INS sensors are applied to measure vehicle stability parameters by fusing data from the two system sensors. Although prior model parameters should be recognized in a Kalman filter, it is usually used to fuse data from multi-sensors. In this paper, a robust, intelligent and precise method to the measurement of vehicle stability is proposed. First, a fuzzy interpolation method is proposed, along with a four-wheel vehicle dynamic model. Second, a two-stage Kalman filter, which fuses the data from GPS and INS, is established. Next, this approach is applied to a case study vehicle to measure yaw rate and sideslip angle. The results show the advantages of the approach. Finally, a simulation and real experiment is made to verify the advantages of this approach. The experimental results showed the merits of this method for measuring vehicle stability, and the approach can meet the design requirements of a vehicle stability controller. PMID:26690154
Accurate solutions, parameter studies and comparisons for the Euler and potential flow equations
NASA Technical Reports Server (NTRS)
Anderson, W. Kyle; Batina, John T.
1988-01-01
Parameter studies are conducted using the Euler and potential flow equation models for steady and unsteady flows in both two and three dimensions. The Euler code is an implicit, upwind, finite volume code which uses the Van Leer method of flux vector splitting which has been recently extended for use on dynamic meshes and maintain all the properties of the original splitting. The potential flow code is an implicit, finite difference method for solving the transonic small disturbance equations and incorporates both entropy and vorticity corrections into the solution procedures thereby extending its applicability into regimes where shock strength normally precludes its use. Parameter studies resulting in benchmark type calculations include the effects of spatial and temporal refinement, spatial order of accuracy, far field boundary conditions for steady flow, frequency of oscillation, and the use of subiterations at each time step to reduce linearization and factorization errors. Comparisons between Euler and potential flow results are made, as well as with experimental data where available.
Accurate solutions, parameter studies and comparisons for the Euler and potential flow equations
NASA Technical Reports Server (NTRS)
Anderson, W. Kyle; Batina, John T.
1988-01-01
Parameter studies are conducted using the Euler and potential flow equation models for unsteady and steady flows in both two and three dimensions. The Euler code is an implicit, upwind, finite volume code which uses the Van Leer method of flux-vector-splitting which has been recently extended for use on dynamic meshes and maintain all the properties of the original splitting. The potential flow code is an implicit, finite difference method for solving the transonic small disturbance equations and incorporates both entropy and vorticity corrections into the solution procedures thereby extending its applicability into regimes where shock strength normally precludes its use. Parameter studies resulting in benchmark type calculations include the effects of spatial and temporal refinement, spatial order of accuracy, far field boundary conditions for steady flow, frequency of oscillation, and the use of subiterations at each time step to reduce linearization and factorization errors. Comparisons between Euler and potential flows results are made as well as with experimental data where available.
ERIC Educational Resources Information Center
Karkee, Thakur B.; Wright, Karen R.
2004-01-01
Different item response theory (IRT) models may be employed for item calibration. Change of testing vendors, for example, may result in the adoption of a different model than that previously used with a testing program. To provide scale continuity and preserve cut score integrity, item parameter estimates from the new model must be linked to the…
Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty
Meyer, Philip D.; Ye, Ming; Neuman, Shlomo P.; Cantrell, Kirk J.
2004-03-01
The objective of the research described in this report is the development and application of a methodology for comprehensively assessing the hydrogeologic uncertainties involved in dose assessment, including uncertainties associated with conceptual models, parameters, and scenarios. This report describes and applies a statistical method to quantitatively estimate the combined uncertainty in model predictions arising from conceptual model and parameter uncertainties. The method relies on model averaging to combine the predictions of a set of alternative models. Implementation is driven by the available data. When there is minimal site-specific data the method can be carried out with prior parameter estimates based on generic data and subjective prior model probabilities. For sites with observations of system behavior (and optionally data characterizing model parameters), the method uses model calibration to update the prior parameter estimates and model probabilities based on the correspondence between model predictions and site observations. The set of model alternatives can contain both simplified and complex models, with the requirement that all models be based on the same set of data. The method was applied to the geostatistical modeling of air permeability at a fractured rock site. Seven alternative variogram models of log air permeability were considered to represent data from single-hole pneumatic injection tests in six boreholes at the site. Unbiased maximum likelihood estimates of variogram and drift parameters were obtained for each model. Standard information criteria provided an ambiguous ranking of the models, which would not justify selecting one of them and discarding all others as is commonly done in practice. Instead, some of the models were eliminated based on their negligibly small updated probabilities and the rest were used to project the measured log permeabilities by kriging onto a rock volume containing the six boreholes. These four
Sediment load estimation using statistical distributions with streamflow dependent parameters
NASA Astrophysics Data System (ADS)
Mailhot, A.; Rousseau, A. N.; Talbot, G.; Quilbé, R.
2005-12-01
The classical approaches to estimate sediment and chemical loads are all deterministic: averaging methods, ratio estimators, regression methods (rating curves) and planning level load estimation methods. However, none of these methods is satisfactory since they are often inaccurate and do not take into account nor quantify uncertainty. To fill this gap, statistical methods have to be investigated. This presentation proposes a new statistical method in which sediment concentration is assimilated to a random variable and is described by distribution functions. Three types of distributions are considered: Log-Normal, Gamma and Weibull distributions. Correlation between sediment concentrations and streamflows is integrated to the model by assuming that distribution parameters (mean and coefficient of variation) are related to streamflow using several different functional forms: exponential, quadratic and power law forms for the mean, constant and linear for the coefficient of variation. Parameter estimation is realized through maximization of the likelihood function. This approach is applied on a data set (1989 to 2004) from the Beaurivage River (Quebec, Canada) with weekly to monthly sampling for sediment concentration. A comparison of different models (selection of a distribution function with functional forms relating the mean and the coefficient of variation to streamflow) shows that the Log-Normal distribution with power law mean and coefficient of variation independent of streamflow provides the best result. When comparing annual load results with those obtained using deterministic methods, we observe that ratio estimators values are rarely within the [0.1, 0.9] quantile interval. For the 1997-2004 period, ratio estimator values are almost systematically smaller than the 0.1 quantile. This could presumably be due to the small number of sediment concentration samples for these years. This study suggests that, if deterministic methods such as the ratio estimator
Cartwright, Michael S; Dupuis, Janae E; Bargoil, Jessica M; Foster, Dana C
2015-09-01
Mild traumatic brain injury, often referred to as concussion, is a common, potentially debilitating, and costly condition. One of the main challenges in diagnosing and managing concussion is that there is not currently an objective test to determine the presence of a concussion and to guide return-to-play decisions for athletes. Traditional neuroimaging tests, such as brain magnetic resonance imaging, are normal in concussion, and therefore diagnosis and management are guided by reported symptoms. Some athletes will under-report symptoms to accelerate their return-to-play and others will over-report symptoms out of fear of further injury or misinterpretation of underlying conditions, such as migraine headache. Therefore, an objective measure is needed to assist in several facets of concussion management. Limited data in animal and human testing indicates that intracranial pressure increases slightly and cerebrovascular reactivity (the ability of the cerebral arteries to auto-regulate in response to changes in carbon dioxide) decreases slightly following mild traumatic brain injury. We hypothesize that a combination of ultrasonographic measurements (optic nerve sheath diameter and transcranial Doppler assessment of cerebrovascular reactivity) into a single index will allow for an accurate and non-invasive measurement of intracranial pressure and cerebrovascular reactivity, and this index will be clinically relevant and useful for guiding concussion diagnosis and management. Ultrasound is an ideal modality for the evaluation of concussion because it is portable (allowing for evaluation in many settings, such as on the playing field or in a combat zone), radiation-free (making repeat scans safe), and relatively inexpensive (resulting in nearly universal availability). This paper reviews the literature supporting our hypothesis that an ultrasonographic index can assist in the diagnosis and management of concussion, and it also presents limited data regarding the
Joint Estimation of Hydraulic and Poroelastic Parameters from a Pumping Test.
Berg, Steven J; Illman, Walter A; Mok, Chin Man W
2015-01-01
The coupling of hydraulic and poroelastic processes is critical in predicting processes involving the deformation of the geologic medium in response to fluid extraction or injection. Numerical models that consider the coupling of hydraulic and poroelastic processes require the knowledge of relevant parameters for both aquifer and aquitard units. In this study, we jointly estimated hydraulic and poroelastic parameters from pumping test data exhibiting "reverse water level fluctuations," known as the Noordbergum effect, in aquitards adjacent to a pumped aquifer. The joint estimation was performed by coupling BIOT2, a finite element, two-dimensional, axisymmetric, groundwater model that considers poroelastic effects with the parameter estimation code PEST. We first tested our approach using a synthetic data set with known parameters. Results of the synthetic case showed that for a simple layered system, it was possible to reproduce accurately both the hydraulic and poroelastic properties for each layer. We next applied the approach to pumping test data collected at the North Campus Research Site (NCRS) on the University of Waterloo (UW) campus. Based on the detailed knowledge of stratigraphy, a five-layer system was modeled. Parameter estimation was performed by: (1) matching drawdown data individually from each observation port and (2) matching drawdown data from all ports at a single well simultaneously. The estimated hydraulic parameters were compared to those obtained by other means at the site yielding good agreement. However, the estimated shear modulus was higher than the static shear modulus, but was within the range of dynamic shear modulus reported in the literature, potentially suggesting a loading rate effect. PMID:25243589
NASA Astrophysics Data System (ADS)
Masterlark, Timothy; Donovan, Theodore; Feigl, Kurt L.; Haney, Matthew; Thurber, Clifford H.; Tung, Sui
2016-04-01
The eruption cycle of a volcano is controlled in part by the upward migration of magma. The characteristics of the magma flux produce a deformation signature at the Earth's surface. Inverse analyses use geodetic data to estimate strategic controlling parameters that describe the position and pressurization of a magma chamber at depth. The specific distribution of material properties controls how observed surface deformation translates to source parameter estimates. Seismic tomography models describe the spatial distributions of material properties that are necessary for accurate models of volcano deformation. This study investigates how uncertainties in seismic tomography models propagate into variations in the estimates of volcano deformation source parameters inverted from geodetic data. We conduct finite element model-based nonlinear inverse analyses of interferometric synthetic aperture radar (InSAR) data for Okmok volcano, Alaska, as an example. We then analyze the estimated parameters and their uncertainties to characterize the magma chamber. Analyses are performed separately for models simulating a pressurized chamber embedded in a homogeneous domain as well as for a domain having a heterogeneous distribution of material properties according to seismic tomography. The estimated depth of the source is sensitive to the distribution of material properties. The estimated depths for the homogeneous and heterogeneous domains are 2666 ± 42 and 3527 ± 56 m below mean sea level, respectively (99% confidence). A Monte Carlo analysis indicates that uncertainties of the seismic tomography cannot account for this discrepancy at the 99% confidence level. Accounting for the spatial distribution of elastic properties according to seismic tomography significantly improves the fit of the deformation model predictions and significantly influences estimates for parameters that describe the location of a pressurized magma chamber.
2012-01-01
A natural bond orbital (NBO) analysis of unpaired electron spin density in metalloproteins is presented, which allows a fast and robust calculation of paramagnetic NMR parameters. Approximately 90% of the unpaired electron spin density occupies metal–ligand NBOs, allowing the majority of the density to be modeled by only a few NBOs that reflect the chemical bonding environment. We show that the paramagnetic relaxation rate of protons can be calculated accurately using only the metal–ligand NBOs and that these rates are in good agreement with corresponding rates measured experimentally. This holds, in particular, for protons of ligand residues where the point-dipole approximation breaks down. To describe the paramagnetic relaxation of heavy nuclei, also the electron spin density in the local orbitals must be taken into account. Geometric distance restraints for 15N can be derived from the paramagnetic relaxation enhancement and the Fermi contact shift when local NBOs are included in the analysis. Thus, the NBO approach allows us to include experimental paramagnetic NMR parameters of 15N nuclei as restraints in a structure optimization protocol. We performed a molecular dynamics simulation and structure determination of oxidized rubredoxin using the experimentally obtained paramagnetic NMR parameters of 15N. The corresponding structures obtained are in good agreement with the crystal structure of rubredoxin. Thus, the NBO approach allows an accurate description of the geometric structure and the dynamics of metalloproteins, when NMR parameters are available of nuclei in the immediate vicinity of the metal-site. PMID:22329704
Crop area estimation based on remotely-sensed data with an accurate but costly subsample
NASA Technical Reports Server (NTRS)
Gunst, R. F.
1983-01-01
Alternatives to sampling-theory stratified and regression estimators of crop production and timber biomass were examined. An alternative estimator which is viewed as especially promising is the errors-in-variable regression estimator. Investigations established the need for caution with this estimator when the ratio of two error variances is not precisely known.
Estimation of Eruption Source Parameters from Plume Growth Rate
NASA Astrophysics Data System (ADS)
Pouget, Solene; Bursik, Marcus; Webley, Peter; Dehn, Jon; Pavalonis, Michael; Singh, Tarunraj; Singla, Puneet; Patra, Abani; Pitman, Bruce; Stefanescu, Ramona; Madankan, Reza; Morton, Donald; Jones, Matthew
2013-04-01
The eruption of Eyjafjallajokull, Iceland in April and May, 2010, brought to light the hazards of airborne volcanic ash and the importance of Volcanic Ash Transport and Dispersion models (VATD) to estimate the concentration of ash with time. These models require Eruption Source Parameters (ESP) as input, which typically include information about the plume height, the mass eruption rate, the duration of the eruption and the particle size distribution. However much of the time these ESP are unknown or poorly known a priori. We show that the mass eruption rate can be estimated from the downwind plume or umbrella cloud growth rate. A simple version of the continuity equation can be applied to the growth of either an umbrella cloud or the downwind plume. The continuity equation coupled with the momentum equation using only inertial and gravitational terms provides another model. Numerical modeling or scaling relationships can be used, as necessary, to provide values for unknown or unavailable parameters. Use of these models applied to data on plume geometry provided by satellite imagery allows for direct estimation of plume volumetric and mass growth with time. To test our methodology, we compared our results with five well-studied and well-characterized historical eruptions: Mount St. Helens, 1980; Pinatubo, 1991, Redoubt, 1990; Hekla, 2000 and Eyjafjallajokull, 2010. These tests show that the methodologies yield results comparable to or better than currently accepted methodologies of ESP estimation. We then applied the methodology to umbrella clouds produced by the eruptions of Okmok, 12 July 2008, and Sarychev Peak, 12 June 2009, and to the downwind plume produced by the eruptions of Hekla, 2000; Kliuchevsko'i, 1 October 1994; Kasatochi 7-8 August 2008 and Bezymianny, 1 September 2012. The new methods allow a fast, remote assessment of the mass eruption rate, even for remote volcanoes. They thus provide an additional path to estimation of the ESP and the forecasting
NEWBOX: A computer program for parameter estimation in diffusion problems
Nestor, C.W. Jr.; Godbee, H.W.; Joy, D.S. )
1989-01-01
In the analysis of experiments to determine amounts of material transferred form 1 medium to another (e.g., the escape of chemically hazardous and radioactive materials from solids), there are at least 3 important considerations. These are (1) is the transport amenable to treatment by established mass transport theory; (2) do methods exist to find estimates of the parameters which will give a best fit, in some sense, to the experimental data; and (3) what computational procedures are available for evaluating the theoretical expressions. The authors have made the assumption that established mass transport theory is an adequate model for the situations under study. Since the solutions of the diffusion equation are usually nonlinear in some parameters (diffusion coefficient, reaction rate constants, etc.), use of a method of parameter adjustment involving first partial derivatives can be complicated and prone to errors in the computation of the derivatives. In addition, the parameters must satisfy certain constraints; for example, the diffusion coefficient must remain positive. For these reasons, a variant of the constrained simplex method of M. J. Box has been used to estimate parameters. It is similar, but not identical, to the downhill simplex method of Nelder and Mead. In general, they calculate the fraction of material transferred as a function of time from expressions obtained by the inversion of the Laplace transform of the fraction transferred, rather than by taking derivatives of a calculated concentration profile. With the above approaches to the 3 considerations listed at the outset, they developed a computer program NEWBOX, usable on a personal computer, to calculate the fractional release of material from 4 different geometrical shapes (semi-infinite medium, finite slab, finite circular cylinder, and sphere), accounting for several different boundary conditions.
Uncertainty Analysis and Parameter Estimation For Nearshore Hydrodynamic Models
NASA Astrophysics Data System (ADS)
Ardani, S.; Kaihatu, J. M.
2012-12-01
Numerical models represent deterministic approaches used for the relevant physical processes in the nearshore. Complexity of the physics of the model and uncertainty involved in the model inputs compel us to apply a stochastic approach to analyze the robustness of the model. The Bayesian inverse problem is one powerful way to estimate the important input model parameters (determined by apriori sensitivity analysis) and can be used for uncertainty analysis of the outputs. Bayesian techniques can be used to find the range of most probable parameters based on the probability of the observed data and the residual errors. In this study, the effect of input data involving lateral (Neumann) boundary conditions, bathymetry and off-shore wave conditions on nearshore numerical models are considered. Monte Carlo simulation is applied to a deterministic numerical model (the Delft3D modeling suite for coupled waves and flow) for the resulting uncertainty analysis of the outputs (wave height, flow velocity, mean sea level and etc.). Uncertainty analysis of outputs is performed by random sampling from the input probability distribution functions and running the model as required until convergence to the consistent results is achieved. The case study used in this analysis is the Duck94 experiment, which was conducted at the U.S. Army Field Research Facility at Duck, North Carolina, USA in the fall of 1994. The joint probability of model parameters relevant for the Duck94 experiments will be found using the Bayesian approach. We will further show that, by using Bayesian techniques to estimate the optimized model parameters as inputs and applying them for uncertainty analysis, we can obtain more consistent results than using the prior information for input data which means that the variation of the uncertain parameter will be decreased and the probability of the observed data will improve as well. Keywords: Monte Carlo Simulation, Delft3D, uncertainty analysis, Bayesian techniques
The challenges of accurately estimating time of long bone injury in children.
Pickett, Tracy A
2015-07-01
The ability to determine the time an injury occurred can be of crucial significance in forensic medicine and holds special relevance to the investigation of child abuse. However, dating paediatric long bone injury, including fractures, is nuanced by complexities specific to the paediatric population. These challenges include the ability to identify bone injury in a growing or only partially-calcified skeleton, different injury patterns seen within the spectrum of the paediatric population, the effects of bone growth on healing as a separate entity from injury, differential healing rates seen at different ages, and the relative scarcity of information regarding healing rates in children, especially the very young. The challenges posed by these factors are compounded by a lack of consistency in defining and categorizing healing parameters. This paper sets out the primary limitations of existing knowledge regarding estimating timing of paediatric bone injury. Consideration and understanding of the multitude of factors affecting bone injury and healing in children will assist those providing opinion in the medical-legal forum. PMID:26048508
NASA Astrophysics Data System (ADS)
Vuye, Cedric; Vanlanduit, Steve; Guillaume, Patrick
2009-06-01
When using optical measurements of the sound fields inside a glass tube, near the material under test, to estimate the reflection and absorption coefficients, not only these acoustical parameters but also confidence intervals can be determined. The sound fields are visualized using a scanning laser Doppler vibrometer (SLDV). In this paper the influence of different test signals on the quality of the results, obtained with this technique, is examined. The amount of data gathered during one measurement scan makes a thorough statistical analysis possible leading to the knowledge of confidence intervals. The use of a multi-sine, constructed on the resonance frequencies of the test tube, shows to be a very good alternative for the traditional periodic chirp. This signal offers the ability to obtain data for multiple frequencies in one measurement, without the danger of a low signal-to-noise ratio. The variability analysis in this paper clearly shows the advantages of the proposed multi-sine compared to the periodic chirp. The measurement procedure and the statistical analysis are validated by measuring the reflection ratio at a closed end and comparing the results with the theoretical value. Results of the testing of two building materials (an acoustic ceiling tile and linoleum) are presented and compared to supplier data.
Campbell, D A; Chkrebtii, O
2013-12-01
Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories. PMID:23579098
Trapping phenomenon of the parameter estimation in asymptotic quantum states
NASA Astrophysics Data System (ADS)
Berrada, K.
2016-09-01
In this paper, we study in detail the behavior of the precision of the parameter estimation in open quantum systems using the quantum Fisher information (QFI). In particular, we study the sensitivity of the estimation on a two-qubit system evolving under Kossakowski-type quantum dynamical semigroups of completely positive maps. In such an environment, the precision of the estimation can even persist asymptotically for different effects of the initial parameters. We find that the QFI can be resistant to the action of the environment with respect to the initial asymptotic states, and it can persist even in the asymptotic long-time regime. In addition, our results provide further evidence that the initial pure and separable mixed states of the input state may enhance quantum metrology. These features make quantum states in this kind of environment a good candidate for the implementation of different schemes of quantum optics and information with high precision. Finally, we show that this quantity may be proposed to detect the amount of the total quantum information that the whole state contains with respect to projective measurements.
Temporal parameters estimation for wheelchair propulsion using wearable sensors.
Ojeda, Manoela; Ding, Dan
2014-01-01
Due to lower limb paralysis, individuals with spinal cord injury (SCI) rely on their upper limbs for mobility. The prevalence of upper extremity pain and injury is high among this population. We evaluated the performance of three triaxis accelerometers placed on the upper arm, wrist, and under the wheelchair, to estimate temporal parameters of wheelchair propulsion. Twenty-six participants with SCI were asked to push their wheelchair equipped with a SMART(Wheel). The estimated stroke number was compared with the criterion from video observations and the estimated push frequency was compared with the criterion from the SMART(Wheel). Mean absolute errors (MAE) and mean absolute percentage of error (MAPE) were calculated. Intraclass correlation coefficients and Bland-Altman plots were used to assess the agreement. Results showed reasonable accuracies especially using the accelerometer placed on the upper arm where the MAPE was 8.0% for stroke number and 12.9% for push frequency. The ICC was 0.994 for stroke number and 0.916 for push frequency. The wrist and seat accelerometer showed lower accuracy with a MAPE for the stroke number of 10.8% and 13.4% and ICC of 0.990 and 0.984, respectively. Results suggested that accelerometers could be an option for monitoring temporal parameters of wheelchair propulsion. PMID:25105133
Sela, Itamar; Ashkenazy, Haim; Katoh, Kazutaka; Pupko, Tal
2015-01-01
Inference of multiple sequence alignments (MSAs) is a critical part of phylogenetic and comparative genomics studies. However, from the same set of sequences different MSAs are often inferred, depending on the methodologies used and the assumed parameters. Much effort has recently been devoted to improving the ability to identify unreliable alignment regions. Detecting such unreliable regions was previously shown to be important for downstream analyses relying on MSAs, such as the detection of positive selection. Here we developed GUIDANCE2, a new integrative methodology that accounts for: (i) uncertainty in the process of indel formation, (ii) uncertainty in the assumed guide tree and (iii) co-optimal solutions in the pairwise alignments, used as building blocks in progressive alignment algorithms. We compared GUIDANCE2 with seven methodologies to detect unreliable MSA regions using extensive simulations and empirical benchmarks. We show that GUIDANCE2 outperforms all previously developed methodologies. Furthermore, GUIDANCE2 also provides a set of alternative MSAs which can be useful for downstream analyses. The novel algorithm is implemented as a web-server, available at: http://guidance.tau.ac.il. PMID:25883146
Estimating Mass of Inflatable Aerodynamic Decelerators Using Dimensionless Parameters
NASA Technical Reports Server (NTRS)
Samareh, Jamshid A.
2011-01-01
This paper describes a technique for estimating mass for inflatable aerodynamic decelerators. The technique uses dimensional analysis to identify a set of dimensionless parameters for inflation pressure, mass of inflation gas, and mass of flexible material. The dimensionless parameters enable scaling of an inflatable concept with geometry parameters (e.g., diameter), environmental conditions (e.g., dynamic pressure), inflation gas properties (e.g., molecular mass), and mass growth allowance. This technique is applicable for attached (e.g., tension cone, hypercone, and stacked toroid) and trailing inflatable aerodynamic decelerators. The technique uses simple engineering approximations that were developed by NASA in the 1960s and 1970s, as well as some recent important developments. The NASA Mars Entry and Descent Landing System Analysis (EDL-SA) project used this technique to estimate the masses of the inflatable concepts that were used in the analysis. The EDL-SA results compared well with two independent sets of high-fidelity finite element analyses.
Spatial dependence clusters in the estimation of forest structural parameters
NASA Astrophysics Data System (ADS)
Wulder, Michael Albert
1999-12-01
In this thesis we provide a summary of the methods by which remote sensing may be applied in forestry, while also acknowledging the various limitations which are faced. The application of spatial statistics to high spatial resolution imagery is explored as a means of increasing the information which may be extracted from digital images. A number of high spatial resolution optical remote sensing satellites that are soon to be launched will increase the availability of imagery for the monitoring of forest structure. This technological advancement is timely as current forest management practices have been altered to reflect the need for sustainable ecosystem level management. The low accuracy level at which forest structural parameters have been estimated in the past is partly due to low image spatial resolution. A large pixel is often composed of a number of surface features, resulting in a spectral value which is due to the reflectance characteristics of all surface features within that pixel. In the case of small pixels, a portion of a surface feature may be represented by a single pixel. When a single pixel represents a portion of a surface object, the potential to isolate distinct surface features exists. Spatial statistics, such as the Gets statistic, provide for an image processing method to isolate distinct surface features. In this thesis, high spatial resolution imagery sensed over a forested landscape is processed with spatial statistics to combine distinct image objects into clusters, representing individual or groups of trees. Tree clusters are a means to deal with the inevitable foliage overlap which occurs within complex mixed and deciduous forest stands. The generation of image objects, that is, clusters, is necessary to deal with the presence of spectrally mixed pixels. The ability to estimate forest inventory and biophysical parameters from image clusters generated from spatially dependent image features is tested in this thesis. The inventory
NASA Astrophysics Data System (ADS)
Cardiff, M. A.; Kitanidis, P. K.
2005-12-01
In this presentation we revisit the problem of semivariogram estimation and present a modular, reusable, and encapsulated set of MATLAB programs that use a hybrid Ant Colony Optimization (ACO) heuristic to solve the "optimal fit" problem. Though the ACO heuristic involves a stochastic component, advantages of the heuristic over traditional gradient-search methods, like the Gauss-Newton method, include the ability to estimate model semivariogram parameters accurately without initial guesses input by the user. The ACO heuristic is also superiorly suited for strongly nonlinear optimization over spaces that may contain several local minima. The presentation will focus on the application of ACO to existing weighted least squares and restricted maximum likelihood estimation methods with a comparison of results. The presentation will also discuss parameter uncertainty, particularly in the context of restricted maximum likelihood and Bayesian methods. We compare the local linearized parameter estimates (or Cramer-Rao lower bounds) with modern Monte Carlo methods, such as acceptance-rejection. Finally, we present ensemble kriging in which conditional realizations are generated in a way that uncertainty in semi-variogram parameters is fully accounted for. Results for a variety of sample problems will be presented along with a discussion of solution accuracy and computational efficiency.
NASA Astrophysics Data System (ADS)
Dai, Yu; Xue, Yuan; Zhang, Jianxun
2016-01-01
Impulsive noise caused by some random events has the main character of short rise-time and wide frequency spectrum range, so it has the potential to degrade the performance and reliability of the harmonic estimation. This paper focuses on the harmonic estimation procedure based on continuous wavelet transform (CWT) when the analyzed signal is corrupted by the impulsive noise. The digital CWT of both the time-varying sinusoidal signal and the impulsive noise are analyzed, and there are two cross ridges in the time-frequency plane of CWT, which are generated by the signal and the noise separately. In consideration of the amplitude of the noise and the number of the spike event, two inequalities are derived to provide limitations on the wavelet parameters. Based on the amplitude distribution of the noise, the optimal wavelet parameters determined by solving these inequalities are used to suppress the contamination of the noise, as well as increase the amplitude of the ridge corresponding to the signal, so the parameters of each harmonic component can be estimated accurately. The proposed procedure is applied to a numerical simulation and a bone vibration signal test giving satisfactory results of stationary and time-varying harmonic parameter estimation.
NASA Astrophysics Data System (ADS)
Martínez, M. J.; Marco, F. J.; López, J. A.
2009-02-01
The Hipparcos catalog provides a reference frame at optical wavelengths for the new International Celestial Reference System (ICRS). This new reference system was adopted following the resolution agreed at the 23rd IAU General Assembly held in Kyoto in 1997. Differences in the Hipparcos system of proper motions and the previous materialization of the reference frame, the FK5, are expected to be caused only by the combined effects of the motion of the equinox of the FK5 and the precession of the equator and the ecliptic. Several authors have pointed out an inconsistency between the differences in proper motion of the Hipparcos-FK5 and the correction of the precessional values derived from VLBI and lunar laser ranging (LLR) observations. Most of them have claimed that these discrepancies are due to slightly biased proper motions in the FK5 catalog. The different mathematical models that have been employed to explain these errors have not fully accounted for the discrepancies in the correction of the precessional parameters. Our goal here is to offer an explanation for this fact. We propose the use of independent parametric and nonparametric models. The introduction of a nonparametric model, combined with the inner product in the square integrable functions over the unitary sphere, would give us values which do not depend on the possible interdependencies existing in the data set. The evidence shows that zonal studies are needed. This would lead us to introduce a local nonparametric model. All these models will provide independent corrections to the precessional values, which could then be compared in order to study the reliability in each case. Finally, we obtain values for the precession corrections that are very consistent with those that are currently adopted.
Cuenca-Navalon, Elena; Laumen, Marco; Finocchiaro, Thomas; Steinseifer, Ulrich
2016-07-01
A physiological control algorithm is being developed to ensure an optimal physiological interaction between the ReinHeart total artificial heart (TAH) and the circulatory system. A key factor for that is the long-term, accurate determination of the hemodynamic state of the cardiovascular system. This study presents a method to determine estimation models for predicting hemodynamic parameters (pump chamber filling and afterload) from both left and right cardiovascular circulations. The estimation models are based on linear regression models that correlate filling and afterload values with pump intrinsic parameters derived from measured values of motor current and piston position. Predictions for filling lie in average within 5% from actual values, predictions for systemic afterload (AoPmean , AoPsys ) and mean pulmonary afterload (PAPmean ) lie in average within 9% from actual values. Predictions for systolic pulmonary afterload (PAPsys ) present an average deviation of 14%. The estimation models show satisfactory prediction and confidence intervals and are thus suitable to estimate hemodynamic parameters. This method and derived estimation models are a valuable alternative to implanted sensors and are an essential step for the development of a physiological control algorithm for a fully implantable TAH. PMID:26634983
NASA Astrophysics Data System (ADS)
Küng, Alain; Meli, Felix; Nicolet, Anaïs; Thalmann, Rudolf
2014-09-01
Tactile ultra-precise coordinate measuring machines (CMMs) are very attractive for accurately measuring optical components with high slopes, such as aspheres. The METAS µ-CMM, which exhibits a single point measurement repeatability of a few nanometres, is routinely used for measurement services of microparts, including optical lenses. However, estimating the measurement uncertainty is very demanding. Because of the many combined influencing factors, an analytic determination of the uncertainty of parameters that are obtained by numerical fitting of the measured surface points is almost impossible. The application of numerical simulation (Monte Carlo methods) using a parametric fitting algorithm coupled with a virtual CMM based on a realistic model of the machine errors offers an ideal solution to this complex problem: to each measurement data point, a simulated measurement variation calculated from the numerical model of the METAS µ-CMM is added. Repeated several hundred times, these virtual measurements deliver the statistical data for calculating the probability density function, and thus the measurement uncertainty for each parameter. Additionally, the eventual cross-correlation between parameters can be analyzed. This method can be applied for the calibration and uncertainty estimation of any parameter of the equation representing a geometric element. In this article, we present the numerical simulation model of the METAS µ-CMM and the application of a Monte Carlo method for the uncertainty estimation of measured asphere parameters.
[Research on maize multispectral image accurate segmentation and chlorophyll index estimation].
Wu, Qian; Sun, Hong; Li, Min-zan; Song, Yuan-yuan; Zhang, Yan-e
2015-01-01
In order to rapidly acquire maize growing information in the field, a non-destructive method of maize chlorophyll content index measurement was conducted based on multi-spectral imaging technique and imaging processing technology. The experiment was conducted at Yangling in Shaanxi province of China and the crop was Zheng-dan 958 planted in about 1 000 m X 600 m experiment field. Firstly, a 2-CCD multi-spectral image monitoring system was available to acquire the canopy images. The system was based on a dichroic prism, allowing precise separation of the visible (Blue (B), Green (G), Red (R): 400-700 nm) and near-infrared (NIR, 760-1 000 nm) band. The multispectral images were output as RGB and NIR images via the system vertically fixed to the ground with vertical distance of 2 m and angular field of 50°. SPAD index of each sample was'measured synchronously to show the chlorophyll content index. Secondly, after the image smoothing using adaptive smooth filtering algorithm, the NIR maize image was selected to segment the maize leaves from background, because there was a big difference showed in gray histogram between plant and soil background. The NIR image segmentation algorithm was conducted following steps of preliminary and accuracy segmentation: (1) The results of OTSU image segmentation method and the variable threshold algorithm were discussed. It was revealed that the latter was better one in corn plant and weed segmentation. As a result, the variable threshold algorithm based on local statistics was selected for the preliminary image segmentation. The expansion and corrosion were used to optimize the segmented image. (2) The region labeling algorithm was used to segment corn plants from soil and weed background with an accuracy of 95. 59 %. And then, the multi-spectral image of maize canopy was accurately segmented in R, G and B band separately. Thirdly, the image parameters were abstracted based on the segmented visible and NIR images. The average gray
Parameter Estimation for a Model of Space-Time Rainfall
NASA Astrophysics Data System (ADS)
Smith, James A.; Karr, Alan F.
1985-08-01
In this paper, parameter estimation procedures, based on data from a network of rainfall gages, are developed for a class of space-time rainfall models. The models, which are designed to represent the spatial distribution of daily rainfall, have three components, one that governs the temporal occurrence of storms, a second that distributes rain cells spatially for a given storm, and a third that determines the rainfall pattern within a rain cell. Maximum likelihood and method of moments procedures are developed. We illustrate that limitations on model structure are imposed by restricting data sources to rain gage networks. The estimation procedures are applied to a 240-mi2 (621 km2) catchment in the Potomac River basin.
Estimation of Aircraft Nonlinear Unsteady Parameters From Wind Tunnel Data
NASA Technical Reports Server (NTRS)
Klein, Vladislav; Murphy, Patrick C.
1998-01-01
Aerodynamic equations were formulated for an aircraft in one-degree-of-freedom large amplitude motion about each of its body axes. The model formulation based on indicial functions separated the resulting aerodynamic forces and moments into static terms, purely rotary terms and unsteady terms. Model identification from experimental data combined stepwise regression and maximum likelihood estimation in a two-stage optimization algorithm that can identify the unsteady term and rotary term if necessary. The identification scheme was applied to oscillatory data in two examples. The model identified from experimental data fit the data well, however, some parameters were estimated with limited accuracy. The resulting model was a good predictor for oscillatory and ramp input data.
Earth-moon system: Dynamics and parameter estimation
NASA Technical Reports Server (NTRS)
Breedlove, W. J., Jr.
1975-01-01
A theoretical development of the equations of motion governing the earth-moon system is presented. The earth and moon were treated as finite rigid bodies and a mutual potential was utilized. The sun and remaining planets were treated as particles. Relativistic, non-rigid, and dissipative effects were not included. The translational and rotational motion of the earth and moon were derived in a fully coupled set of equations. Euler parameters were used to model the rotational motions. The mathematical model is intended for use with data analysis software to estimate physical parameters of the earth-moon system using primarily LURE type data. Two program listings are included. Program ANEAMO computes the translational/rotational motion of the earth and moon from analytical solutions. Program RIGEM numerically integrates the fully coupled motions as described above.
On Spectral Classification and Astrophysical Parameter Estimation for Galactic Surveys
NASA Astrophysics Data System (ADS)
Re Fiorentin, Paola; Bailer-Jones, Coryn A. L.; Beers, Timothy C.; Zwitter, Tomaž
2008-12-01
We present several strategies that are being developed in order to classify and parameterize individual stars observed by Galactic surveys, and illustrate some results obtained from spectra obtained by the RAdial Velocity Experiment (RAVE) and the Sloan Digital Sky Survey (SDSS/SEGUE). We demonstrate the efficiency of our models for discrete source classification and stellar atmospheric parameter estimation (effective temperature, surface gravity, and metallicity), which use supervised machine learning algorithms along with a principal component analysis front-end compression phase that also enables knowledge discovery.
Error estimates and specification parameters for functional renormalization
Schnoerr, David; Boettcher, Igor; Pawlowski, Jan M.; Wetterich, Christof
2013-07-15
We present a strategy for estimating the error of truncated functional flow equations. While the basic functional renormalization group equation is exact, approximated solutions by means of truncations do not only depend on the choice of the retained information, but also on the precise definition of the truncation. Therefore, results depend on specification parameters that can be used to quantify the error of a given truncation. We demonstrate this for the BCS–BEC crossover in ultracold atoms. Within a simple truncation the precise definition of the frequency dependence of the truncated propagator affects the results, indicating a shortcoming of the choice of a frequency independent cutoff function.
Estimation of Modal Parameters Using a Wavelet-Based Approach
NASA Technical Reports Server (NTRS)
Lind, Rick; Brenner, Marty; Haley, Sidney M.
1997-01-01
Modal stability parameters are extracted directly from aeroservoelastic flight test data by decomposition of accelerometer response signals into time-frequency atoms. Logarithmic sweeps and sinusoidal pulses are used to generate DAST closed loop excitation data. Novel wavelets constructed to extract modal damping and frequency explicitly from the data are introduced. The so-called Haley and Laplace wavelets are used to track time-varying modal damping and frequency in a matching pursuit algorithm. Estimation of the trend to aeroservoelastic instability is demonstrated successfully from analysis of the DAST data.
Source parameter estimation in inhomogeneous volume conductors of arbitrary shape.
Oostendorp, T F; van Oosterom, A
1989-03-01
In this paper it is demonstrated that the use of a direct matrix inverse in the solution of the forward problem in volume conduction problems greatly facilitates the application of standard, nonlinear parameter estimation procedures for finding the strength as well as the location of current sources inside an inhomogeneous volume conductor of arbitrary shape from potential measurements at the outer surface (inverse procedure). This, in turn, facilitates the inclusion of a priori constraints. Where possible, the performance of the method is compared to that of the Gabor-Nelson method. Applications are in the fields of bioelectricity (e.g., electrocardiography and electroencephalography). PMID:2921073
Parameter estimation using NOON states over a relativistic quantum channel
NASA Astrophysics Data System (ADS)
Hosler, Dominic; Kok, Pieter
2013-11-01
We study the effect of the acceleration of the observer on a parameter estimation protocol using NOON states. An inertial observer, Alice, prepares a NOON state in Unruh modes of the quantum field, and sends it to an accelerated observer, Rob. We calculate the quantum Fisher information of the state received by Rob. We find the counterintuitive result that the single-rail encoding outperforms the dual rail. The NOON states have an optimal N for the maximum information extractable by Rob, given his acceleration. This optimal N decreases with increasing acceleration.
Estimation of modal parameters using bilinear joint time frequency distributions
NASA Astrophysics Data System (ADS)
Roshan-Ghias, A.; Shamsollahi, M. B.; Mobed, M.; Behzad, M.
2007-07-01
In this paper, a new method is proposed for modal parameter estimation using time-frequency representations. Smoothed Pseudo Wigner-Ville distribution which is a member of the Cohen's class distributions is used to decouple vibration modes completely in order to study each mode separately. This distribution reduces cross-terms which are troublesome in Wigner-Ville distribution and retains the resolution as well. The method was applied to highly damped systems, and results were superior to those obtained via other conventional methods.
Linearly Supporting Feature Extraction for Automated Estimation of Stellar Atmospheric Parameters
NASA Astrophysics Data System (ADS)
Li, Xiangru; Lu, Yu; Comte, Georges; Luo, Ali; Zhao, Yongheng; Wang, Yongjun
2015-05-01
We describe a scheme to extract linearly supporting (LSU) features from stellar spectra to automatically estimate the atmospheric parameters {{T}{\\tt{eff} }}, log g, and [Fe/H]. “Linearly supporting” means that the atmospheric parameters can be accurately estimated from the extracted features through a linear model. The successive steps of the process are as follow: first, decompose the spectrum using a wavelet packet (WP) and represent it by the derived decomposition coefficients; second, detect representative spectral features from the decomposition coefficients using the proposed method Least Absolute Shrinkage and Selection Operator (LARS)bs; third, estimate the atmospheric parameters {{T}{\\tt{eff} }}, log g, and [Fe/H] from the detected features using a linear regression method. One prominent characteristic of this scheme is its ability to evaluate quantitatively the contribution of each detected feature to the atmospheric parameter estimate and also to trace back the physical significance of that feature. This work also shows that the usefulness of a component depends on both the wavelength and frequency. The proposed scheme has been evaluated on both real spectra from the Sloan Digital Sky Survey (SDSS)/SEGUE and synthetic spectra calculated from Kurucz's NEWODF models. On real spectra, we extracted 23 features to estimate {{T}{\\tt{eff} }}, 62 features for log g, and 68 features for [Fe/H]. Test consistencies between our estimates and those provided by the Spectroscopic Parameter Pipeline of SDSS show that the mean absolute errors (MAEs) are 0.0062 dex for log {{T}{\\tt{eff} }} (83 K for {{T}{\\tt{eff} }}), 0.2345 dex for log g, and 0.1564 dex for [Fe/H]. For the synthetic spectra, the MAE test accuracies are 0.0022 dex for log {{T}{\\tt{eff} }} (32 K for {{T}{\\tt{eff} }}), 0.0337 dex for log g, and 0.0268 dex for [Fe/H].
Wind effect on PV module temperature: Analysis of different techniques for an accurate estimation.
NASA Astrophysics Data System (ADS)
Schwingshackl, Clemens; Petitta, Marcello; Ernst Wagner, Jochen; Belluardo, Giorgio; Moser, David; Castelli, Mariapina; Zebisch, Marc; Tetzlaff, Anke
2013-04-01
temperature estimation using meteorological parameters. References: [1] Skoplaki, E. et al., 2008: A simple correlation for the operating temperature of photovoltaic modules of arbitrary mounting, Solar Energy Materials & Solar Cells 92, 1393-1402 [2] Skoplaki, E. et al., 2008: Operating temperature of photovoltaic modules: A survey of pertinent correlations, Renewable Energy 34, 23-29 [3] Koehl, M. et al., 2011: Modeling of the nominal operating cell temperature based on outdoor weathering, Solar Energy Materials & Solar Cells 95, 1638-1646 [4] Mattei, M. et al., 2005: Calculation of the polycrystalline PV module temperature using a simple method of energy balance, Renewable Energy 31, 553-567 [5] Kurtz, S. et al.: Evaluation of high-temperature exposure of rack-mounted photovoltaic modules
Statistical parameter estimation in ultrasound backscattering from tissue mimicking media
Chen, J.F.
1994-12-31
Several tissue characterization parameters, including the effective scatterer number density and the backscatter coefficient, were derived from the statistical properties of ultrasonic echo signals. The effective scatterer number density is the actual scatterer number density in a medium multiplied by a frequency-dependent factor that depends on the differential scattering cross-sections of all scatterers. The method described in this thesis for determining the scatterer number density explicitly retains both the temporal nature of the data acquisition and the properties of the ultrasound field in the data reduction. Moreover, it accounts for the possibility that different sets of scatterers may dominate the echo signal at different frequencies. The random processes involved in forming ultrasound echo signals from random media give rise to an uncertainty in the estimated effective scatterer number density. This uncertainty is evaluated using error propagation. The statistical uncertainty depends on the effective number of scatterers contributing to the segmented echo signal, increasing when the effective number of scatterers increases. Tests of the scatterer number density data reduction method and the statistical uncertainty estimator were done using phantoms with known ultrasound scattering properties. Good agreement was found between measured values and those calculated from first-principles. The properties of the non-Gaussian and non-Rayleigh parameters of ultrasound echo signals are also studied. Both parameters depend on the measurement system, including the transducer field and pulse frequency content, as well as on the medium`s properties. The latter is expressed in terms of the scatterer number density and the second and fourth moments of the medium`s scattering function. A simple relationship between the non-Gaussian and non-Rayleigh parameters is derived and verified experimentally.
NASA Astrophysics Data System (ADS)
Yong, Kilyuk; Jo, Sujang; Bang, Hyochoong
This paper presents a modified Rodrigues parameter (MRP)-based nonlinear observer design to estimate bias, scale factor and misalignment of gyroscope measurements. A Lyapunov stability analysis is carried out for the nonlinear observer. Simulation is performed and results are presented illustrating the performance of the proposed nonlinear observer under the condition of persistent excitation maneuver. In addition, a comparison between the nonlinear observer and alignment Kalman filter (AKF) is made to highlight favorable features of the nonlinear observer.
NASA Astrophysics Data System (ADS)
Aasi, J.; Abadie, J.; Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M.; Accadia, T.; Acernese, F.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R.; Affeldt, C.; Agathos, M.; Agatsuma, K.; Ajith, P.; Allen, B.; Allocca, A.; Amador Ceron, E.; Amariutei, D.; Anderson, S. B.; Anderson, W. G.; Arai, K.; Araya, M. C.; Ast, S.; Aston, S. M.; Astone, P.; Atkinson, D.; Aufmuth, P.; Aulbert, C.; Aylott, B. E.; Babak, S.; Baker, P.; Ballardin, G.; Ballmer, S.; Bao, Y.; Barayoga, J. C. B.; Barker, D.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barton, M. A.; Bartos, I.; Bassiri, R.; Bastarrika, M.; Basti, A.; Batch, J.; Bauchrowitz, J.; Bauer, Th. S.; Bebronne, M.; Beck, D.; Behnke, B.; Bejger, M.; Beker, M. G.; Bell, A. S.; Bell, C.; Belopolski, I.; Benacquista, M.; Berliner, J. M.; Bertolini, A.; Betzwieser, J.; Beveridge, N.; Beyersdorf, P. T.; Bhadbade, T.; Bilenko, I. A.; Billingsley, G.; Birch, J.; Biswas, R.; Bitossi, M.; Bizouard, M. A.; Black, E.; Blackburn, J. K.; Blackburn, L.; Blair, D.; Bland, B.; Blom, M.; Bock, O.; Bodiya, T. P.; Bogan, C.; Bond, C.; Bondarescu, R.; Bondu, F.; Bonelli, L.; Bonnand, R.; Bork, R.; Born, M.; Boschi, V.; Bose, S.; Bosi, L.; Bouhou, B.; Braccini, S.; Bradaschia, C.; Brady, P. R.; Braginsky, V. B.; Branchesi, M.; Brau, J. E.; Breyer, J.; Briant, T.; Bridges, D. O.; Brillet, A.; Brinkmann, M.; Brisson, V.; Britzger, M.; Brooks, A. F.; Brown, D. A.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Burguet–Castell, J.; Buskulic, D.; Buy, C.; Byer, R. L.; Cadonati, L.; Cagnoli, G.; Calloni, E.; Camp, J. B.; Campsie, P.; Cannon, K.; Canuel, B.; Cao, J.; Capano, C. D.; Carbognani, F.; Carbone, L.; Caride, S.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C.; Cesarini, E.; Chalermsongsak, T.; Charlton, P.; Chassande-Mottin, E.; Chen, W.; Chen, X.; Chen, Y.; Chincarini, A.; Chiummo, A.; Cho, H. S.; Chow, J.; Christensen, N.; Chua, S. S. Y.; Chung, C. T. Y.; Chung, S.; Ciani, G.; Clara, F.; Clark, D. E.; Clark, J. A.; Clayton, J. H.; Cleva, F.; Coccia, E.; Cohadon, P.-F.; Colacino, C. N.; Colla, A.; Colombini, M.; Conte, A.; Conte, R.; Cook, D.; Corbitt, T. R.; Cordier, M.; Cornish, N.; Corsi, A.; Costa, C. A.; Coughlin, M.; Coulon, J.-P.; Couvares, P.; Coward, D. M.; Cowart, M.; Coyne, D. C.; Creighton, J. D. E.; Creighton, T. D.; Cruise, A. M.; Cumming, A.; Cunningham, L.; Cuoco, E.; Cutler, R. M.; Dahl, K.; Damjanic, M.; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Dattilo, V.; Daudert, B.; Daveloza, H.; Davier, M.; Daw, E. J.; Dayanga, T.; De Rosa, R.; DeBra, D.; Debreczeni, G.; Degallaix, J.; Del Pozzo, W.; Dent, T.; Dergachev, V.; DeRosa, R.; Dhurandhar, S.; Di Fiore, L.; Di Lieto, A.; Di Palma, I.; Di Paolo Emilio, M.; Di Virgilio, A.; Díaz, M.; Dietz, A.; Donovan, F.; Dooley, K. L.; Doravari, S.; Dorsher, S.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Du, Z.; Dumas, J.-C.; Dwyer, S.; Eberle, T.; Edgar, M.; Edwards, M.; Effler, A.; Ehrens, P.; Endrőczi, G.; Engel, R.; Etzel, T.; Evans, K.; Evans, M.; Evans, T.; Factourovich, M.; Fafone, V.; Fairhurst, S.; Farr, B. F.; Farr, W. M.; Favata, M.; Fazi, D.; Fehrmann, H.; Feldbaum, D.; Feroz, F.; Ferrante, I.; Ferrini, F.; Fidecaro, F.; Finn, L. S.; Fiori, I.; Fisher, R. P.; Flaminio, R.; Foley, S.; Forsi, E.; Forte, L. A.; Fotopoulos, N.; Fournier, J.-D.; Franc, J.; Franco, S.; Frasca, S.; Frasconi, F.; Frede, M.; Frei, M. A.; Frei, Z.; Freise, A.; Frey, R.; Fricke, T. T.; Friedrich, D.; Fritschel, P.; Frolov, V. V.; Fujimoto, M.-K.; Fulda, P. J.; Fyffe, M.; Gair, J.; Galimberti, M.; Gammaitoni, L.; Garcia, J.; Garufi, F.; Gáspár, M. E.; Gelencser, G.; Gemme, G.; Genin, E.; Gennai, A.; Gergely, L. Á.; Ghosh, S.; Giaime, J. A.; Giampanis, S.; Giardina, K. D.; Giazotto, A.; Gil-Casanova, S.; Gill, C.; Gleason, J.; Goetz, E.; González, G.; Gorodetsky, M. L.; Goßler, S.; Gouaty, R.; Graef, C.; Graff, P. B.; Granata, M.; Grant, A.; Gray, C.; Greenhalgh, R. J. S.; Gretarsson, A. M.; Griffo, C.; Grote, H.; Grover, K.; Grunewald, S.; Guidi, G. M.; Guido, C.; Gupta, R.; Gustafson, E. K.; Gustafson, R.; Hallam, J. M.; Hammer, D.; Hammond, G.; Hanks, J.; Hanna, C.; Hanson, J.; Harms, J.; Harry, G. M.; Harry, I. W.; Harstad, E. D.; Hartman, M. T.; Haster, C.-J.; Haughian, K.; Hayama, K.; Hayau, J.-F.; Heefner, J.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M. A.; Heng, I. S.; Heptonstall, A. W.; Herrera, V.; Heurs, M.; Hewitson, M.; Hild, S.; Hoak, D.; Hodge, K. A.; Holt, K.; Holtrop, M.; Hong, T.; Hooper, S.; Hough, J.; Howell, E. J.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Ingram, D. R.; Inta, R.; Isogai, T.; Ivanov, A.; Izumi, K.; Jacobson, M.; James, E.; Jang, Y. J.; Jaranowski, P.; Jesse, E.; Johnson, W. W.; Jones, D. I.; Jones, R.; Jonker, R. J. G.; Ju, L.; Kalmus, P.; Kalogera, V.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Kasprzack, M.; Kasturi, R.; Katsavounidis, E.; Katzman, W.; Kaufer, H.; Kaufman, K.; Kawabe, K.; Kawamura, S.; Kawazoe, F.; Keitel, D.; Kelley, D.; Kells, W.; Keppel, D. G.; Keresztes, Z.; Khalaidovski, A.; Khalili, F. Y.; Khazanov, E. A.; Kim, B. K.; Kim, C.; Kim, H.; Kim, K.; Kim, N.; Kim, Y. M.; King, P. J.; Kinzel, D. L.; Kissel, J. S.; Klimenko, S.; Kline, J.; Kokeyama, K.; Kondrashov, V.; Koranda, S.; Korth, W. Z.; Kowalska, I.; Kozak, D.; Kringel, V.; Krishnan, B.; Królak, A.; Kuehn, G.; Kumar, P.; Kumar, R.; Kurdyumov, R.; Kwee, P.; Lam, P. K.; Landry, M.; Langley, A.; Lantz, B.; Lastzka, N.; Lawrie, C.; Lazzarini, A.; Le Roux, A.; Leaci, P.; Lee, C. H.; Lee, H. K.; Lee, H. M.; Leong, J. R.; Leonor, I.; Leroy, N.; Letendre, N.; Lhuillier, V.; Li, J.; Li, T. G. F.; Lindquist, P. E.; Litvine, V.; Liu, Y.; Liu, Z.; Lockerbie, N. A.; Lodhia, D.; Logue, J.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J.; Lubinski, M.; Lück, H.; Lundgren, A. P.; Macarthur, J.; Macdonald, E.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Mageswaran, M.; Mailand, K.; Majorana, E.; Maksimovic, I.; Malvezzi, V.; Man, N.; Mandel, I.; Mandic, V.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A.; Maros, E.; Marque, J.; Martelli, F.; Martin, I. W.; Martin, R. M.; Marx, J. N.; Mason, K.; Masserot, A.; Matichard, F.; Matone, L.; Matzner, R. A.; Mavalvala, N.; Mazzolo, G.; McCarthy, R.; McClelland, D. E.; McGuire, S. C.; McIntyre, G.; McIver, J.; Meadors, G. D.; Mehmet, M.; Meier, T.; Melatos, A.; Melissinos, A. C.; Mendell, G.; Menéndez, D. F.; Mercer, R. A.; Meshkov, S.; Messenger, C.; Meyer, M. S.; Miao, H.; Michel, C.; Milano, L.; Miller, J.; Minenkov, Y.; Mingarelli, C. M. F.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moe, B.; Mohan, M.; Mohapatra, S. R. P.; Moraru, D.; Moreno, G.; Morgado, N.; Morgia, A.; Mori, T.; Morriss, S. R.; Mosca, S.; Mossavi, K.; Mours, B.; Mow–Lowry, C. M.; Mueller, C. L.; Mueller, G.; Mukherjee, S.; Mullavey, A.; Müller-Ebhardt, H.; Munch, J.; Murphy, D.; Murray, P. G.; Mytidis, A.; Nash, T.; Naticchioni, L.; Necula, V.; Nelson, J.; Neri, I.; Newton, G.; Nguyen, T.; Nishizawa, A.; Nitz, A.; Nocera, F.; Nolting, D.; Normandin, M. E.; Nuttall, L.; Ochsner, E.; O'Dell, J.; Oelker, E.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; Oldenberg, R. G.; O'Reilly, B.; O'Shaughnessy, R.; Osthelder, C.; Ott, C. D.; Ottaway, D. J.; Ottens, R. S.; Overmier, H.; Owen, B. J.; Page, A.; Palladino, L.; Palomba, C.; Pan, Y.; Pankow, C.; Paoletti, F.; Paoletti, R.; Papa, M. A.; Parisi, M.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Pedraza, M.; Penn, S.; Perreca, A.; Persichetti, G.; Phelps, M.; Pichot, M.; Pickenpack, M.; Piergiovanni, F.; Pierro, V.; Pihlaja, M.; Pinard, L.; Pinto, I. M.; Pitkin, M.; Pletsch, H. J.; Plissi, M. V.; Poggiani, R.; Pöld, J.; Postiglione, F.; Poux, C.; Prato, M.; Predoi, V.; Prestegard, T.; Price, L. R.; Prijatelj, M.; Principe, M.; Privitera, S.; Prodi, G. A.; Prokhorov, L. G.; Puncken, O.; Punturo, M.; Puppo, P.; Quetschke, V.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Rácz, I.; Radkins, H.; Raffai, P.; Rakhmanov, M.; Ramet, C.; Rankins, B.; Rapagnani, P.; Raymond, V.; Re, V.; Reed, C. M.; Reed, T.; Regimbau, T.; Reid, S.; Reitze, D. H.; Ricci, F.; Riesen, R.; Riles, K.; Roberts, M.; Robertson, N. A.; Robinet, F.; Robinson, C.; Robinson, E. L.; Rocchi, A.; Roddy, S.; Rodriguez, C.; Rodruck, M.; Rolland, L.; Rollins, J. G.; Romano, R.; Romie, J. H.; Rosińska, D.; Röver, C.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Salemi, F.; Sammut, L.; Sandberg, V.; Sankar, S.; Sannibale, V.; Santamaría, L.; Santiago-Prieto, I.; Santostasi, G.; Saracco, E.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Savage, R. L.; Schilling, R.; Schnabel, R.; Schofield, R. M. S.; Schulz, B.; Schutz, B. F.; Schwinberg, P.; Scott, J.; Scott, S. M.; Seifert, F.; Sellers, D.; Sentenac, D.; Sergeev, A.; Shaddock, D. A.; Shaltev, M.; Shapiro, B.; Shawhan, P.; Shoemaker, D. H.; Sidery, T. L.; Siemens, X.; Sigg, D.; Simakov, D.; Singer, A.; Singer, L.; Sintes, A. M.; Skelton, G. R.; Slagmolen, B. J. J.; Slutsky, J.; Smith, J. R.; Smith, M. R.; Smith, R. J. E.; Smith-Lefebvre, N. D.; Somiya, K.; Sorazu, B.; Speirits, F. C.; Sperandio, L.; Stefszky, M.; Steinert, E.; Steinlechner, J.; Steinlechner, S.; Steplewski, S.; Stochino, A.; Stone, R.; Strain, K. A.; Strigin, S. E.; Stroeer, A. S.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sung, M.; Susmithan, S.; Sutton, P. J.; Swinkels, B.; Szeifert, G.; Tacca, M.; Taffarello, L.; Talukder, D.; Tanner, D. B.; Tarabrin, S. P.; Taylor, R.; ter Braack, A. P. M.; Thomas, P.; Thorne, K. A.; Thorne, K. S.; Thrane, E.; Thüring, A.; Titsler, C.; Tokmakov, K. V.; Tomlinson, C.; Toncelli, A.; Tonelli, M.; Torre, O.; Torres, C. V.; Torrie, C. I.; Tournefier, E.; Travasso, F.; Traylor, G.; Tse, M.; Ugolini, D.; Vahlbruch, H.; Vajente, G.; van den Brand, J. F. J.; Van Den Broeck, C.; van der Putten, S.; van Veggel, A. A.; Vass, S.; Vasuth, M.; Vaulin, R.; Vavoulidis, M.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Verkindt, D.; Vetrano, F.; Viceré, A.; Villar, A. E.; Vinet, J.-Y.; Vitale, S.; Vocca, H.; Vorvick, C.; Vyatchanin, S. P.; Wade, A.; Wade, L.; Wade, M.; Waldman, S. J.; Wallace, L.; Wan, Y.; Wang, M.; Wang, X.; Wanner, A.; Ward, R. L.; Was, M.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Welborn, T.; Wen, L.; Wessels, P.; West, M.; Westphal, T.; Wette, K.; Whelan, J. T.; Whitcomb, S. E.; White, D. J.; Whiting, B. F.; Wiesner, K.; Wilkinson, C.; Willems, P. A.; Williams, L.; Williams, R.; Willke, B.; Wimmer, M.; Winkelmann, L.; Winkler, W.; Wipf, C. C.; Wiseman, A. G.; Wittel, H.; Woan, G.; Wooley, R.; Worden, J.; Yablon, J.; Yakushin, I.; Yamamoto, H.; Yamamoto, K.; Yancey, C. C.; Yang, H.; Yeaton-Massey, D.; Yoshida, S.; Yvert, M.; Zadrożny, A.; Zanolin, M.; Zendri, J.-P.; Zhang, F.; Zhang, L.; Zhao, C.; Zotov, N.; Zucker, M. E.; Zweizig, J.
2013-09-01
Compact binary systems with neutron stars or black holes are one of the most promising sources for ground-based gravitational-wave detectors. Gravitational radiation encodes rich information about source physics; thus parameter estimation and model selection are crucial analysis steps for any detection candidate events. Detailed models of the anticipated waveforms enable inference on several parameters, such as component masses, spins, sky location and distance, that are essential for new astrophysical studies of these sources. However, accurate measurements of these parameters and discrimination of models describing the underlying physics are complicated by artifacts in the data, uncertainties in the waveform models and in the calibration of the detectors. Here we report such measurements on a selection of simulated signals added either in hardware or software to the data collected by the two LIGO instruments and the Virgo detector during their most recent joint science run, including a “blind injection” where the signal was not initially revealed to the collaboration. We exemplify the ability to extract information about the source physics on signals that cover the neutron-star and black-hole binary parameter space over the component mass range 1M⊙-25M⊙ and the full range of spin parameters. The cases reported in this study provide a snapshot of the status of parameter estimation in preparation for the operation of advanced detectors.
Estimating canopy fuel parameters for Atlantic Coastal Plain forest types.
Parresol, Bernard, R.
2007-01-15
Abstract It is necessary to quantify forest canopy characteristics to assess crown fire hazard, prioritize treatment areas, and design treatments to reduce crown fire potential. A number of fire behavior models such as FARSITE, FIRETEC, and NEXUS require as input four particular canopy fuel parameters: 1) canopy cover, 2) stand height, 3) crown base height, and 4) canopy bulk density. These canopy characteristics must be mapped across the landscape at high spatial resolution to accurately simulate crown fire. Currently no models exist to forecast these four canopy parameters for forests of the Atlantic Coastal Plain, a region that supports millions of acres of loblolly, longleaf, and slash pine forests as well as pine-broadleaf forests and mixed species broadleaf forests. Many forest cover types are recognized, too many to efficiently model. For expediency, forests of the Savannah River Site are categorized as belonging to 1 of 7 broad forest type groups, based on composition: 1) loblolly pine, 2) longleaf pine, 3) slash pine, 4) pine-hardwood, 5) hardwood-pine, 6) hardwoods, and 7) cypress-tupelo. These 7 broad forest types typify forests of the Atlantic Coastal Plain region, from Maryland to Florida.
NASA Astrophysics Data System (ADS)
Uenaka, Yutaro; Sazawa, Masaki; Ohishi, Kiyoshi; Kenji, Takahashi
The servo system of a permanent magnet (PM) motor should always maintain fine torque and fine speed responses. Accurate motor parameter identification is necessary for the PM motor servo system because the current control system is designed by considering the electric parameters of the PM motor. However, the motor parameters vary with the age of the motor and temperature. Moreover, current sensors have offset values. When the current sensor has offset values, the PM motor servo system produces torque ripple. In order to overcome these problems, this paper proposes a new real-time estimation method for both current sensor offsets and electrical parameters (resistance Ra, inductance La, and magnetic flux φfa) of the surface permanent magnet (SPM) motor. The proposed method involves the use of a real-time algorithm and a current simulator, which is operated using a DSP software system. In order to accurately estimate the motor parametera, the proposed method is using estimate currents, DC terms of sensor currents, and nominal motor parameter value. The experimental results of this study confirm that the proposed method satisfactorily estimates the current sensor offset of the U phase and V phase, as well as the electrical motor parametersRa, La, and φfa accurately.
NASA Astrophysics Data System (ADS)
Behmanesh, Iman; Moaveni, Babak
2016-07-01
This paper presents a Hierarchical Bayesian model updating framework to account for the effects of ambient temperature and excitation amplitude. The proposed approach is applied for model calibration, response prediction and damage identification of a footbridge under changing environmental/ambient conditions. The concrete Young's modulus of the footbridge deck is the considered updating structural parameter with its mean and variance modeled as functions of temperature and excitation amplitude. The identified modal parameters over 27 months of continuous monitoring of the footbridge are used to calibrate the updating parameters. One of the objectives of this study is to show that by increasing the levels of information in the updating process, the posterior variation of the updating structural parameter (concrete Young's modulus) is reduced. To this end, the calibration is performed at three information levels using (1) the identified modal parameters, (2) modal parameters and ambient temperatures, and (3) modal parameters, ambient temperatures, and excitation amplitudes. The calibrated model is then validated by comparing the model-predicted natural frequencies and those identified from measured data after deliberate change to the structural mass. It is shown that accounting for modeling error uncertainties is crucial for reliable response prediction, and accounting only the estimated variability of the updating structural parameter is not sufficient for accurate response predictions. Finally, the calibrated model is used for damage identification of the footbridge.
NASA Astrophysics Data System (ADS)
Shi, Y.; Davis, K. J.; Zhang, F.; Duffy, C.
2012-12-01
A fully-coupled physically-based land surface hydrologic model, Flux-PIHM, is developed by incorporating a land-surface scheme into the Penn State Integrated Hydrologic Model (PIHM). The land-surface scheme is mainly adapted from the Noah LSM, which is widely used in mesoscale atmospheric models and has undergone extensive testing. Because PIHM is capable of simulating lateral water flow and deep groundwater, Flux-PIHM is able to represent both the link between groundwater and the surface energy balance, as well as some of the land surface heterogeneities caused by topography. Flux-PIHM has been implemented and manually calibrated at the Shale Hills watershed (0.08 km2) in central Pennsylvania. Model predictions of discharge, soil moisture, water table depth, sensible and latent heat fluxes, and soil temperature show good agreement with observations. The discharge prediction is significantly better than state-of-the-art conceptual models implemented at similar watersheds. The ensemble Kalman filter (EnKF) provides a promising approach for physically-based land surface hydrologic model calibration. A Flux-PHIM data assimilation system is developed by incorporating EnKF into Flux-PIHM for model parameter and state estimation. This is the first parameter estimation using EnKF for a physically-based hydrologic model. Both synthetic and real data experiments are performed at the Shale Hills watershed to test the capability of EnKF in parameter estimation. Six model parameters selected from a model parameter sensitivity test are estimated. In the synthetic experiments, synthetic observations of discharge, water table depth, soil moisture, land surface temperature, sensible and latent heat fluxes, and transpiration are assimilated into the system. Observations are assimilated every 72 hours in wet periods, and every 144 hours in dry periods. Results show that EnKF is capable of accurately estimating model parameter values for Flux-PIHM. In the first set of experiments
Technology Transfer Automated Retrieval System (TEKTRAN)
Vegetation affects the ability to estimate soil moisture from passive microwave observations by attenuating the surface soil moisture signal. To use radiobrightness observations in land data assimilation a vegetation opacity parameter is required as input to a radiative transfer model, which maps su...
NASA Astrophysics Data System (ADS)
Grasso, Robert J.; Russo, Leonard P.; Barrett, John L.; Odhner, Jefferson E.; Egbert, Paul I.
2007-09-01
BAE Systems presents the results of a program to model the performance of Raman LIDAR systems for the remote detection of atmospheric gases, air polluting hydrocarbons, chemical and biological weapons, and other molecular species of interest. Our model, which integrates remote Raman spectroscopy, 2D and 3D LADAR, and USAF atmospheric propagation codes permits accurate determination of the performance of a Raman LIDAR system. The very high predictive performance accuracy of our model is due to the very accurate calculation of the differential scattering cross section for the specie of interest at user selected wavelengths. We show excellent correlation of our calculated cross section data, used in our model, with experimental data obtained from both laboratory measurements and the published literature. In addition, the use of standard USAF atmospheric models provides very accurate determination of the atmospheric extinction at both the excitation and Raman shifted wavelengths.
Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data
Lynch, Eoin P.; Houghton, Conor J.
2015-01-01
Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolutionary algorithm which uses a spike train metric as a fitness function. We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model. PMID:25941485
Parameter estimation and uncertainty for gravitational waves from binary black holes
NASA Astrophysics Data System (ADS)
Berry, Christopher; LIGO Scientific Collaboration; Virgo Collaboration
2016-03-01
Binary black holes are one of the most promising sources of gravitational waves that could be observed by Advanced LIGO. To accurately infer the parameters of an astrophysical signal, it is necessary to have a reliable model of the gravitational waveform. Uncertainty in the waveform leads to uncertainty in the measured parameters. For loud signals, this theoretical uncertainty could dominate statistical uncertainty, to be the primary source of error in gravitational-wave astronomy. However, we expect the first candidate events will be closer to the detection threshold. We look at how parameter estimation would be influenced by the use of different waveform models for a binary black-hole signal near detection threshold, and how this can be folded in to a Bayesian analysis.
Beibei, Zhou; Quanjiu, Wang; Shuai, Tan
2014-01-01
A theory based on Manning roughness equation, Philip equation and water balance equation was developed which only employed the advance distance in the calculation of the infiltration parameters and irrigation coefficients in both the border irrigation and the surge irrigation. The improved procedure was validated with both the border irrigation and surge irrigation experiments. The main results are shown as follows. Infiltration parameters of the Philip equation could be calculated accurately only using water advance distance in the irrigation process comparing to the experimental data. With the calculated parameters and the water balance equation, the irrigation coefficients were also estimated. The water advance velocity should be measured at about 0.5 m to 1.0 m far from the water advance in the experimental corn fields. PMID:25061664
NASA Astrophysics Data System (ADS)
Kimura, H.; Asano, Y.; Matsumoto, T.
2012-12-01
The rapid determination of hypocentral parameters and their transmission to the public are valuable components of disaster mitigation. We have operated an automatic system for this purpose—termed the Accurate and QUick Analysis system for source parameters (AQUA)—since 2005 (Matsumura et al., 2006). In this system, the initial hypocenter, the moment tensor (MT), and the centroid moment tensor (CMT) solutions are automatically determined and posted on the NIED Hi-net Web site (www.hinet.bosai.go.jp). This paper describes improvements made to the AQUA to overcome limitations that became apparent after the 2011 Tohoku Earthquake (05:46:17, March 11, 2011 in UTC). The improvements included the processing of NIED F-net velocity-type strong motion records, because NIED F-net broadband seismographs are saturated for great earthquakes such as the 2011 Tohoku Earthquake. These velocity-type strong motion seismographs provide unsaturated records not only for the 2011 Tohoku Earthquake, but also for recording stations located close to the epicenters of M>7 earthquakes. We used 0.005-0.020 Hz records for M>7.5 earthquakes, in contrast to the 0.01-0.05 Hz records employed in the original system. The initial hypocenters determined based on arrival times picked by using seismograms recorded by NIED Hi-net stations can have large errors in terms of magnitude and hypocenter location, especially for great earthquakes or earthquakes located far from the onland Hi-net network. The size of the 2011 Tohoku Earthquake was initially underestimated in the AQUA to be around M5 at the initial stage of rupture. Numerous aftershocks occurred at the outer rise east of the Japan trench, where a great earthquake is anticipated to occur. Hence, we modified the system to repeat the MT analyses assuming a larger size, for all earthquakes for which the magnitude was initially underestimated. We also broadened the search range of centroid depth for earthquakes located far from the onland Hi
NASA Astrophysics Data System (ADS)
Blackman, Jonathan; Field, Scott; Galley, Chad; Hemberger, Daniel; Scheel, Mark; Schmidt, Patricia; Smith, Rory; SXS Collaboration Collaboration
2016-03-01
We are now in the advanced detector era of gravitational wave astronomy, and the merger of two black holes (BHs) is one of the most promising sources of gravitational waves that could be detected on earth. To infer the BH masses and spins, the observed signal must be compared to waveforms predicted by general relativity for millions of binary configurations. Numerical relativity (NR) simulations can produce accurate waveforms, but are prohibitively expensive to use for parameter estimation. Other waveform models are fast enough but may lack accuracy in portions of the parameter space. Numerical relativity surrogate models attempt to rapidly predict the results of a NR code with a small or negligible modeling error, after being trained on a set of input waveforms. Such surrogate models are ideal for parameter estimation, as they are both fast and accurate, and have already been built for the case of non-spinning BHs. Using 250 input waveforms, we build a surrogate model for waveforms from the Spectral Einstein Code (SpEC) for a subspace of precessing systems.
Estimation of genetic parameters for reproductive traits in alpacas.
Cruz, A; Cervantes, I; Burgos, A; Morante, R; Gutiérrez, J P
2015-12-01
One of the main deficiencies affecting animal breeding programs in Peruvian alpacas is the low reproductive performance leading to low number of animals available to select from, decreasing strongly the selection intensity. Some reproductive traits could be improved by artificial selection, but very few information about genetic parameters exists for these traits in this specie. The aim of this study was to estimate genetic parameters for six reproductive traits in alpacas both in Suri (SU) and Huacaya (HU) ecotypes, as well as their genetic relationship with fiber and morphological traits. Dataset belonging to Pacomarca experimental farm collected between 2000 and 2014 was used. Number of records for age at first service (AFS), age at first calving (AFC), copulation time (CT), pregnancy diagnosis (PD), gestation length (GL), and calving interval (CI) were, respectively, 1704, 854, 19,770, 5874, 4290 and 934. Pedigree consisted of 7742 animals. Regarding reproductive traits, model of analysis included additive and residual random effects for all traits, and also permanent environmental effect for CT, PD, GL and CI traits, with color and year of recording as fixed effects for all the reproductive traits and also age at mating and sex of calf for GL trait. Estimated heritabilities, respectively for HU and SU were 0.19 and 0.09 for AFS, 0.45 and 0.59 for AFC, 0.04 and 0.05 for CT, 0.07 and 0.05 for PD, 0.12 and 0.20 for GL, and 0.14 and 0.09 for CI. Genetic correlations between them ranged from -0.96 to 0.70. No important genetic correlations were found between reproductive traits and fiber or morphological traits in HU. However, some moderate favorable genetic correlations were found between reproductive and either fiber and morphological traits in SU. According to estimated genetic correlations, some reproductive traits might be included as additional selection criteria in HU. PMID:26490188
Estimating Parameters of Aquifer Heterogeneity from Transient Pumping Test
NASA Astrophysics Data System (ADS)
Zech, Alraune; Müller, Sebastian; Attinger, Sabine
2015-04-01
We present a new method for interpreting drawdowns of transient pumping tests in heterogeneous porous media. The vast majority of natural aquifers are characterized by heterogeneity, which can be statistically represented by parameters such as geometric mean bar K, variance σ^2, and correlation length ℓ of hydraulic conductivity. Our method can be understood as extension of the effective well flow method [Zech et al., 2012] from steady state to transient pumping tests. It allows a direct parameter estimation of bar K, σ^2, ℓ from head measurements under well flow conditions. The method is based on a representative description of hydraulic conductivity for radial flow regimes K_CG. It was derived previously using the upscaling procedure Radial Coarse Graining in combination with log-normal hydraulic conductivity. A semi-analytical solution for the mean drawdown of transient pumping tests was derived by combining the upscaled solution for the radially adapted hydraulic conductivity K_CG and the groundwater flow equation under well flow conditions. The dependency of the drawdown solution on the statistical quantities of the porous medium allows us to inversely estimate bar K, σ^2, ℓ from pumping test data. We used an ensemble of transient pumping test simulations to verify the drawdown solution. We generated pumping tests in heterogeneous media for various values of the statistical parameters bar K, σ^2, ℓ and evaluated their impact on the drawdown behavior as well as on the temporal evolution. We further examined the impact of several aspects like the location of an observation well or the local conductivity at the pumping well on the drawdown behavior. Zech, A., C. L. Schneider, and S. Attinger, 2012, The Extended Thiem's solution: Including the impact of heterogeneity, Water Resour. Res., 48, W10535, doi:10.1029/2012WR011852.
Learn-as-you-go acceleration of cosmological parameter estimates
NASA Astrophysics Data System (ADS)
Aslanyan, Grigor; Easther, Richard; Price, Layne C.
2015-09-01
Cosmological analyses can be accelerated by approximating slow calculations using a training set, which is either precomputed or generated dynamically. However, this approach is only safe if the approximations are well understood and controlled. This paper surveys issues associated with the use of machine-learning based emulation strategies for accelerating cosmological parameter estimation. We describe a learn-as-you-go algorithm that is implemented in the Cosmo++ code and (1) trains the emulator while simultaneously estimating posterior probabilities; (2) identifies unreliable estimates, computing the exact numerical likelihoods if necessary; and (3) progressively learns and updates the error model as the calculation progresses. We explicitly describe and model the emulation error and show how this can be propagated into the posterior probabilities. We apply these techniques to the Planck likelihood and the calculation of ΛCDM posterior probabilities. The computation is significantly accelerated without a pre-defined training set and uncertainties in the posterior probabilities are subdominant to statistical fluctuations. We have obtained a speedup factor of 6.5 for Metropolis-Hastings and 3.5 for nested sampling. Finally, we discuss the general requirements for a credible error model and show how to update them on-the-fly.
Extracting galactic structure parameters from multivariated density estimation
NASA Technical Reports Server (NTRS)
Chen, B.; Creze, M.; Robin, A.; Bienayme, O.
1992-01-01
Multivariate statistical analysis, including includes cluster analysis (unsupervised classification), discriminant analysis (supervised classification) and principle component analysis (dimensionlity reduction method), and nonparameter density estimation have been successfully used to search for meaningful associations in the 5-dimensional space of observables between observed points and the sets of simulated points generated from a synthetic approach of galaxy modelling. These methodologies can be applied as the new tools to obtain information about hidden structure otherwise unrecognizable, and place important constraints on the space distribution of various stellar populations in the Milky Way. In this paper, we concentrate on illustrating how to use nonparameter density estimation to substitute for the true densities in both of the simulating sample and real sample in the five-dimensional space. In order to fit model predicted densities to reality, we derive a set of equations which include n lines (where n is the total number of observed points) and m (where m: the numbers of predefined groups) unknown parameters. A least-square estimation will allow us to determine the density law of different groups and components in the Galaxy. The output from our software, which can be used in many research fields, will also give out the systematic error between the model and the observation by a Bayes rule.
Real-Time Aerodynamic Parameter Estimation without Air Flow Angle Measurements
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.
2010-01-01
A technique for estimating aerodynamic parameters in real time from flight data without air flow angle measurements is described and demonstrated. The method is applied to simulated F-16 data, and to flight data from a subscale jet transport aircraft. Modeling results obtained with the new approach using flight data without air flow angle measurements were compared to modeling results computed conventionally using flight data that included air flow angle measurements. Comparisons demonstrated that the new technique can provide accurate aerodynamic modeling results without air flow angle measurements, which are often difficult and expensive to obtain. Implications for efficient flight testing and flight safety are discussed.
Statistical Parameter Estimation in Ultrasound Backscattering from Tissue Mimicking Media.
NASA Astrophysics Data System (ADS)
Chen, Jian-Feng
Several tissue characterization parameters, including the effective scatterer number density and the backscatter coefficient, were derived from the statistical properties of ultrasonic echo signals. The effective scatterer number density is the actual scatterer number density in a medium multiplied by a frequency-dependent factor that depends on the differential scattering cross-sections of all scatterers. The method described in this thesis for determining the scatterer number density explicitly retains both the temporal nature of the data acquisition and the properties of the ultrasound field in the data reduction. Moreover, it accounts for the possibility that different sets of scatterers may dominate the echo signal at different frequencies. The random processes involved in forming ultrasound echo signals from random media give rise to an uncertainty in the estimated effective scatterer number density. This uncertainty is evaluated using error propagation. The statistical uncertainty depends on the effective number of scatterers contributing to the segmented echo signal, increasing when the effective number of scatterers increases. Tests of the scatterer number density data reduction method and the statistical uncertainty estimator were done using phantoms with known ultrasound scattering properties. Good agreement was found between measured values and those calculated from first-principles. The properties of the non-Gaussian and non-Rayleigh parameters of ultrasound echo signals are also studied. Both parameters depend on the measurement system, including the transducer field and pulse frequency content, as well as on the medium's properties. The latter is expressed in terms of the scatterer number density and the second and fourth moments of the medium's scattering function. A simple relationship between the non-Gaussian and non-Rayleigh parameters is derived and verified experimentally. Finally, a reference phantom method is proposed for measuring the
Automatic parameter estimation for atmospheric turbulence mitigation techniques
NASA Astrophysics Data System (ADS)
Kozacik, Stephen; Paolini, Aaron; Kelmelis, Eric
2015-05-01
Several image processing techniques for turbulence mitigation have been shown to be effective under a wide range of long-range capture conditions; however, complex, dynamic scenes have often required manual interaction with the algorithm's underlying parameters to achieve optimal results. While this level of interaction is sustainable in some workflows, in-field determination of ideal processing parameters greatly diminishes usefulness for many operators. Additionally, some use cases, such as those that rely on unmanned collection, lack human-in-the-loop usage. To address this shortcoming, we have extended a well-known turbulence mitigation algorithm based on bispectral averaging with a number of techniques to greatly reduce (and often eliminate) the need for operator interaction. Automations were made in the areas of turbulence strength estimation (Fried's parameter), as well as the determination of optimal local averaging windows to balance turbulence mitigation and the preservation of dynamic scene content (non-turbulent motions). These modifications deliver a level of enhancement quality that approaches that of manual interaction, without the need for operator interaction. As a consequence, the range of operational scenarios where this technology is of benefit has been significantly expanded.
Parameter Estimation And Sensitivity Analysis for Root Zone Soil Moisture in SVAT Models.
NASA Astrophysics Data System (ADS)
Judge, J.; Agrawal, D.; Graham, W. D.
2008-05-01
Accurate knowledge of root zone soil moisture is crucial in hydrology, micrometeorology and agriculture for estimating energy and moisture fluxes at the land surface. Soil Vegetation Atmosphere Transfer (SVAT) models are typically used to simulate energy and moisture transport in soil and vegetation, and estimate these fluxes at the land surface and in the vadose zone. Coupling an SVAT model with a vegetation model allows inclusion of canopy effects on the fluxes, without relying on observations or empirical functions. An SVAT model, viz. Land Surface Process (LSP) model, has been coupled with a widely used crop-growth model, Decision Support System for Agrotechnology Transfer (DSSAT). The LSP-DSSAT was calibrated for a growing season of sweet corn in North Central Florida, using extensive field observations from the second Microwave Water and Energy Balance Experiment (MicroWEX-2). In this research, we address uncertainty of parameters in the LSP-DSSAT model, due to uncertainty in forcings and initial conditions, and due to accumulated errors from numerical computation. We also conduct sensitivity analyses to identify key model parameters to which the root zone soil moisture estimates are most sensitive. We will present a stochastic approach to estimate correlations between the parameters and root zone soil moisture.
Massive black-hole binary inspirals: results from the LISA parameter estimation taskforce
NASA Astrophysics Data System (ADS)
Arun, K. G.; Babak, Stas; Berti, Emanuele; Cornish, Neil; Cutler, Curt; Gair, Jonathan; Hughes, Scott A.; Iyer, Bala R.; Lang, Ryan N.; Mandel, Ilya; Porter, Edward K.; Sathyaprakash, Bangalore S.; Sinha, Siddhartha; Sintes, Alicia M.; Trias, Miquel; Van Den Broeck, Chris; Volonteri, Marta
2009-05-01
The LISA Parameter Estimation Taskforce was formed in September 2007 to provide the LISA Project with vetted codes, source distribution models and results related to parameter estimation. The Taskforce's goal is to be able to quickly calculate the impact of any mission design changes on LISA's science capabilities, based on reasonable estimates of the distribution of astrophysical sources in the universe. This paper describes our Taskforce's work on massive black-hole binaries (MBHBs). Given present uncertainties in the formation history of MBHBs, we adopt four different population models, based on (i) whether the initial black-hole seeds are small or large and (ii) whether accretion is efficient or inefficient at spinning up the holes. We compare four largely independent codes for calculating LISA's parameter-estimation capabilities. All codes are based on the Fisher-matrix approximation, but in the past they used somewhat different signal models, source parametrizations and noise curves. We show that once these differences are removed, the four codes give results in extremely close agreement with each other. Using a code that includes both spin precession and higher harmonics in the gravitational-wave signal, we carry out Monte Carlo simulations and determine the number of events that can be detected and accurately localized in our four population models.
Kang, Mingon; Gao, Jean; Tang, Liping
2011-01-01
Developing vigorous mathematical equations and estimating accurate parameters within feasible computational time are two indispensable parts to build reliable system models for representing biological properties of the system and for producing reliable simulation. For a complex biological system with limited observations, one of the daunting tasks is the large number of unknown parameters in the mathematical modeling whose values directly determine the performance of computational modeling. To tackle this problem, we have developed a data-driven global optimization method, nonlinear RANSAC, based on RANdom SAmple Consensus (a.k.a. RANSAC) method for parameter estimation of nonlinear system models. Conventional RANSAC method is sound and simple, but it is oriented for linear system models. We not only adopt the strengths of RANSAC, but also extend the method to nonlinear systems with outstanding performance. As a specific application example, we have targeted understanding phagocyte transmigration which is involved in the fibrosis process for biomedical device implantation. With well-defined mathematical nonlinear equations of the system, nonlinear RANSAC is performed for the parameter estimation. In order to evaluate the general performance of the method, we also applied the method to signalling pathways with ordinary differential equations as a general format. PMID:23227455
ERIC Educational Resources Information Center
Shoemaker, David M.
Described and listed herein with concomitant sample input and output is the Fortran IV program which estimates parameters and standard errors of estimate per parameters for parameters estimated through multiple matrix sampling. The specific program is an improved and expanded version of an earlier version. (Author/BJG)
An improved swarm optimization for parameter estimation and biological model selection.
Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail
2013-01-01
One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This
An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection
Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail
2013-01-01
One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This
Bi-fluorescence imaging for estimating accurately the nuclear condition of Rhizoctonia spp.
Technology Transfer Automated Retrieval System (TEKTRAN)
Aims: To simplify the determination of the nuclear condition of the pathogenic Rhizoctonia, which currently needs to be performed either using two fluorescent dyes, thus is more costly and time-consuming, or using only one fluorescent dye, and thus less accurate. Methods and Results: A red primary ...
Excitations for Rapidly Estimating Flight-Control Parameters
NASA Technical Reports Server (NTRS)
Moes, Tim; Smith, Mark; Morelli, Gene
2006-01-01
A flight test on an F-15 airplane was performed to evaluate the utility of prescribed simultaneous independent surface excitations (PreSISE) for real-time estimation of flight-control parameters, including stability and control derivatives. The ability to extract these derivatives in nearly real time is needed to support flight demonstration of intelligent flight-control system (IFCS) concepts under development at NASA, in academia, and in industry. Traditionally, flight maneuvers have been designed and executed to obtain estimates of stability and control derivatives by use of a post-flight analysis technique. For an IFCS, it is required to be able to modify control laws in real time for an aircraft that has been damaged in flight (because of combat, weather, or a system failure). The flight test included PreSISE maneuvers, during which all desired control surfaces are excited simultaneously, but at different frequencies, resulting in aircraft motions about all coordinate axes. The objectives of the test were to obtain data for post-flight analysis and to perform the analysis to determine: 1) The accuracy of derivatives estimated by use of PreSISE, 2) The required durations of PreSISE inputs, and 3) The minimum required magnitudes of PreSISE inputs. The PreSISE inputs in the flight test consisted of stacked sine-wave excitations at various frequencies, including symmetric and differential excitations of canard and stabilator control surfaces and excitations of aileron and rudder control surfaces of a highly modified F-15 airplane. Small, medium, and large excitations were tested in 15-second maneuvers at subsonic, transonic, and supersonic speeds. Typical excitations are shown in Figure 1. Flight-test data were analyzed by use of pEst, which is an industry-standard output-error technique developed by Dryden Flight Research Center. Data were also analyzed by use of Fourier-transform regression (FTR), which was developed for onboard, real-time estimation of the
US-based Drug Cost Parameter Estimation for Economic Evaluations
Levy, Joseph F; Meek, Patrick D; Rosenberg, Marjorie A
2014-01-01
Introduction In the US, more than 10% of national health expenditures are for prescription drugs. Assessing drug costs in US economic evaluation studies is not consistent, as the true acquisition cost of a drug is not known by decision modelers. Current US practice focuses on identifying one reasonable drug cost and imposing some distributional assumption to assess uncertainty. Methods We propose a set of Rules based on current pharmacy practice that account for the heterogeneity of drug product costs. The set of products derived from our Rules, and their associated costs, form an empirical distribution that can be used for more realistic sensitivity analyses, and create transparency in drug cost parameter computation. The Rules specify an algorithmic process to select clinically equivalent drug products that reduce pill burden, use an appropriate package size, and assume uniform weighting of substitutable products. Three diverse examples show derived empirical distributions and are compared with previously reported cost estimates. Results The shapes of the empirical distributions among the three drugs differ dramatically, including multiple modes and different variation. Previously published estimates differed from the means of the empirical distributions. Published ranges for sensitivity analyses did not cover the ranges of the empirical distributions. In one example using lisinopril, the empirical mean cost of substitutable products was $444 (range $23–$953) as compared to a published estimate of $305 (range $51–$523). Conclusions Our Rules create a simple and transparent approach to create cost estimates of drug products and assess their variability. The approach is easily modified to include a subset of, or different weighting for, substitutable products. The derived empirical distribution is easily incorporated into one-way or probabilistic sensitivity analyses. PMID:25532826
Wang, Mingyu; Han, Lijuan; Liu, Shasha; Zhao, Xuebing; Yang, Jinghua; Loh, Soh Kheang; Sun, Xiaomin; Zhang, Chenxi; Fang, Xu
2015-09-01
Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed. PMID:26121186
Simultaneous estimation of thermal parameters for large-format laminated lithium-ion batteries
NASA Astrophysics Data System (ADS)
Zhang, Jianbo; Wu, Bin; Li, Zhe; Huang, Jun
2014-08-01
In-situ determination of the battery thermal parameters is important to provide accurate inputs for battery thermal models. This paper develops a method to estimate the multiple thermal parameters of large-format laminated lithium-ion batteries both simultaneously and in-situ. The central area of one battery surface is heated with a circular planar heater, while the temperature responses on the opposite surface at multiple strategically-chosen locations are recorded with the attached thermocouples. This thermal system is modeled in COMSOL v4.2 using a two-dimensional axially-symmetric thermal conduction equation containing thermal parameters such as the thermal capacity, anisotropic thermal conductivities, and thermal interfacial conductance between the Al-plastic film package and the electrode core. Using optimization techniques, these thermal parameters are adjusted step by step till the difference between the simulated and the experimental temperature responses at the corresponding locations reaches a minimum. As one validation of the developed method, the estimated specific heat capacity agreed with the value measured with an accelerating rate calorimeter within 10%. The proposed method can be applied to simultaneously determine the thermal parameters of generic objects consisting of anisotropic internal materials and an outer packaging made of different material.
Technical note: tree truthing: how accurate are substrate estimates in primate field studies?
Bezanson, Michelle; Watts, Sean M; Jobin, Matthew J
2012-04-01
Field studies of primate positional behavior typically rely on ground-level estimates of substrate size, angle, and canopy location. These estimates potentially influence the identification of positional modes by the observer recording behaviors. In this study we aim to test ground-level estimates against direct measurements of support angles, diameters, and canopy heights in trees at La Suerte Biological Research Station in Costa Rica. After reviewing methods that have been used by past researchers, we provide data collected within trees that are compared to estimates obtained from the ground. We climbed five trees and measured 20 supports. Four observers collected measurements of each support from different locations on the ground. Diameter estimates varied from the direct tree measures by 0-28 cm (Mean: 5.44 ± 4.55). Substrate angles varied by 1-55° (Mean: 14.76 ± 14.02). Height in the tree was best estimated using a clinometer as estimates with a two-meter reference placed by the tree varied by 3-11 meters (Mean: 5.31 ± 2.44). We determined that the best support size estimates were those generated relative to the size of the focal animal and divided into broader categories. Support angles were best estimated in 5° increments and then checked using a Haglöf clinometer in combination with a laser pointer. We conclude that three major factors should be addressed when estimating support features: observer error (e.g., experience and distance from the target), support deformity, and how support size and angle influence the positional mode selected by a primate individual. individual. PMID:22371099
Research on Parameter Estimation Methods for Alpha Stable Noise in a Laser Gyroscope’s Random Error
Wang, Xueyun; Li, Kui; Gao, Pengyu; Meng, Suxia
2015-01-01
Alpha stable noise, determined by four parameters, has been found in the random error of a laser gyroscope. Accurate estimation of the four parameters is the key process for analyzing the properties of alpha stable noise. Three widely used estimation methods—quantile, empirical characteristic function (ECF) and logarithmic moment method—are analyzed in contrast with Monte Carlo simulation in this paper. The estimation accuracy and the application conditions of all methods, as well as the causes of poor estimation accuracy, are illustrated. Finally, the highest precision method, ECF, is applied to 27 groups of experimental data to estimate the parameters of alpha stable noise in a laser gyroscope’s random error. The cumulative probability density curve of the experimental data fitted by an alpha stable distribution is better than that by a Gaussian distribution, which verifies the existence of alpha stable noise in a laser gyroscope’s random error. PMID:26230698
Multiphase flow parameter estimation based on laser scattering
NASA Astrophysics Data System (ADS)
Vendruscolo, Tiago P.; Fischer, Robert; Martelli, Cicero; Rodrigues, Rômulo L. P.; Morales, Rigoberto E. M.; da Silva, Marco J.
2015-07-01
The flow of multiple constituents inside a pipe or vessel, known as multiphase flow, is commonly found in many industry branches. The measurement of the individual flow rates in such flow is still a challenge, which usually requires a combination of several sensor types. However, in many applications, especially in industrial process control, it is not necessary to know the absolute flow rate of the respective phases, but rather to continuously monitor flow conditions in order to quickly detect deviations from the desired parameters. Here we show how a simple and low-cost sensor design can achieve this, by using machine-learning techniques to distinguishing the characteristic patterns of oblique laser light scattered at the phase interfaces. The sensor is capable of estimating individual phase fluxes (as well as their changes) in multiphase flows and may be applied to safety applications due to its quick response time.
Enhancing parameter precision of optimal quantum estimation by quantum screening
NASA Astrophysics Data System (ADS)
Jiang, Huang; You-Neng, Guo; Qin, Xie
2016-02-01
We propose a scheme of quantum screening to enhance the parameter-estimation precision in open quantum systems by means of the dynamics of quantum Fisher information. The principle of quantum screening is based on an auxiliary system to inhibit the decoherence processes and erase the excited state to the ground state. By comparing the case without quantum screening, the results show that the dynamics of quantum Fisher information with quantum screening has a larger value during the evolution processes. Project supported by the National Natural Science Foundation of China (Grant No. 11374096), the Natural Science Foundation of Guangdong Province, China (Grants No. 2015A030310354), and the Project of Enhancing School with Innovation of Guangdong Ocean University (Grants Nos. GDOU2014050251 and GDOU2014050252).
Estimating Phenomenological Parameters in Multi-Assets Markets
NASA Astrophysics Data System (ADS)
Raffaelli, Giacomo; Marsili, Matteo
Financial correlations exhibit a non-trivial dynamic behavior. This is reproduced by a simple phenomenological model of a multi-asset financial market, which takes into account the impact of portfolio investment on price dynamics. This captures the fact that correlations determine the optimal portfolio but are affected by investment based on it. Such a feedback on correlations gives rise to an instability when the volume of investment exceeds a critical value. Close to the critical point the model exhibits dynamical correlations very similar to those observed in real markets. We discuss how the model's parameter can be estimated in real market data with a maximum likelihood principle. This confirms the main conclusion that real markets operate close to a dynamically unstable point.
Cosmological parameter estimation with large scale structure observations
NASA Astrophysics Data System (ADS)
Di Dio, Enea; Montanari, Francesco; Durrer, Ruth; Lesgourgues, Julien
2014-01-01
We estimate the sensitivity of future galaxy surveys to cosmological parameters, using the redshift dependent angular power spectra of galaxy number counts, Cl(z1,z2), calculated with all relativistic corrections at first order in perturbation theory. We pay special attention to the redshift dependence of the non-linearity scale and present Fisher matrix forecasts for Euclid-like and DES-like galaxy surveys. We compare the standard P(k) analysis with the new Cl(z1,z2) method. We show that for surveys with photometric redshifts the new analysis performs significantly better than the P(k) analysis. For spectroscopic redshifts, however, the large number of redshift bins which would be needed to fully profit from the redshift information, is severely limited by shot noise. We also identify surveys which can measure the lensing contribution and we study the monopole, C0(z1,z2).
Simplified horn antenna parameter estimation using selective criteria
Ewing, P.D.
1991-01-01
An approximation can be used to avoid the complex mathematics and computation methods typically required for calculating the gain and radiation pattern of electromagnetic horn antenna. Because of the curvature of the antenna wave front, calculations using conventional techniques involve solving the Fresnel integrals and using computer-aided numerical integration. With this model, linear approximations give a reasonable estimate of the gain and radiation pattern using simple trigonometric functions, thereby allowing a hand calculator to replace the computer. Applying selected criteria, the case of the E-plane horn antenna was used to evaluate this technique. Results showed that the gain approximation holds for an antenna flare angle of less than 10{degree} for typical antenna dimensions, and the E field radiation pattern approximation holds until the antenna's phase error approaches 60{degree}, both within typical design parameters. This technique is a useful engineering tool. 4 refs., 11 figs.
Accurate state estimation for a hydraulic actuator via a SDRE nonlinear filter
NASA Astrophysics Data System (ADS)
Strano, Salvatore; Terzo, Mario
2016-06-01
The state estimation in hydraulic actuators is a fundamental tool for the detection of faults or a valid alternative to the installation of sensors. Due to the hard nonlinearities that characterize the hydraulic actuators, the performances of the linear/linearization based techniques for the state estimation are strongly limited. In order to overcome these limits, this paper focuses on an alternative nonlinear estimation method based on the State-Dependent-Riccati-Equation (SDRE). The technique is able to fully take into account the system nonlinearities and the measurement noise. A fifth order nonlinear model is derived and employed for the synthesis of the estimator. Simulations and experimental tests have been conducted and comparisons with the largely used Extended Kalman Filter (EKF) are illustrated. The results show the effectiveness of the SDRE based technique for applications characterized by not negligible nonlinearities such as dead zone and frictions.
Technology Transfer Automated Retrieval System (TEKTRAN)
The extractability of the phenolic compounds is influenced by their chemical structure, degree of polymerization, and interaction with other components in the matrix. This talk describes the influence of sample preparation parameters (extraction techniques, extraction solvents, temperature, pressu...
Bayesian parameter estimation for stochastic models of biological cell migration
NASA Astrophysics Data System (ADS)
Dieterich, Peter; Preuss, Roland
2013-08-01
Cell migration plays an essential role under many physiological and patho-physiological conditions. It is of major importance during embryonic development and wound healing. In contrast, it also generates negative effects during inflammation processes, the transmigration of tumors or the formation of metastases. Thus, a reliable quantification and characterization of cell paths could give insight into the dynamics of these processes. Typically stochastic models are applied where parameters are extracted by fitting models to the so-called mean square displacement of the observed cell group. We show that this approach has several disadvantages and problems. Therefore, we propose a simple procedure directly relying on the positions of the cell's trajectory and the covariance matrix of the positions. It is shown that the covariance is identical with the spatial aging correlation function for the supposed linear Gaussian models of Brownian motion with drift and fractional Brownian motion. The technique is applied and illustrated with simulated data showing a reliable parameter estimation from single cell paths.
ESTIMATION OF THE VISCOSITY PARAMETER IN ACCRETION DISKS OF BLAZARS
Xie, Z. H.; Ma, L.; Zhang, X.; Du, L. M.; Hao, J. M.; Yi, T. F.; Qiao, E. L.
2009-12-20
For an optical monitoring blazar sample set whose typical minimum variability timescale is about 1 hr, we estimate a mean value of the viscosity parameter in their accretion disk. We assume that optical variability on timescales of hours is caused by local instabilities in the inner accretion disk. Comparing the observed variability timescales to the thermal timescales of alpha-disk models, we could obtain constraints on the viscosity parameter (alpha) and the intrinsic Eddington ratio (L{sup in}/L{sub Edd}=m-dot), 0.104 <= alpha <= 0.337, and 0.0201 <= L {sup in}/L{sub Edd} <= 0.1646. These narrow ranges suggest that all these blazars are observed in a single state, and thus provide a new evidence for the unification of flat-spectrum radio quasars and BL Lacs into a single blazar population. The values of alpha we derive are consistent with the theoretical expectation alpha approx 0.1-0.3 of Narayan and Mcclintock for advection-dominated accretion flow and are also compatible with Pessah et al.'s predictions (alpha >= 0.1) by numerical simulations in which magnetohydrodynamic turbulence is driven by the saturated magnetorotational instability.
Forage quantity estimation from MERIS using band depth parameters
NASA Astrophysics Data System (ADS)
Ullah, Saleem; Yali, Si; Schlerf, Martin
Saleem Ullah1 , Si Yali1 , Martin Schlerf1 Forage quantity is an important factor influencing feeding pattern and distribution of wildlife. The main objective of this study was to evaluate the predictive performance of vegetation indices and band depth analysis parameters for estimation of green biomass using MERIS data. Green biomass was best predicted by NBDI (normalized band depth index) and yielded a calibration R2 of 0.73 and an accuracy (independent validation dataset, n=30) of 136.2 g/m2 (47 % of the measured mean) compared to a much lower accuracy obtained by soil adjusted vegetation index SAVI (444.6 g/m2, 154 % of the mean) and by other vegetation indices. This study will contribute to map and monitor foliar biomass over the year at regional scale which intern can aid the understanding of bird migration pattern. Keywords: Biomass, Nitrogen density, Nitrogen concentration, Vegetation indices, Band depth analysis parameters 1 Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, The Netherlands
Parameter estimation for models of ligninolytic and cellulolytic enzyme kinetics
Wang, Gangsheng; Post, Wilfred M; Mayes, Melanie; Frerichs, Joshua T; Jagadamma, Sindhu
2012-01-01
While soil enzymes have been explicitly included in the soil organic carbon (SOC) decomposition models, there is a serious lack of suitable data for model parameterization. This study provides well-documented enzymatic parameters for application in enzyme-driven SOC decomposition models from a compilation and analysis of published measurements. In particular, we developed appropriate kinetic parameters for five typical ligninolytic and cellulolytic enzymes ( -glucosidase, cellobiohydrolase, endo-glucanase, peroxidase, and phenol oxidase). The kinetic parameters included the maximum specific enzyme activity (Vmax) and half-saturation constant (Km) in the Michaelis-Menten equation. The activation energy (Ea) and the pH optimum and sensitivity (pHopt and pHsen) were also analyzed. pHsen was estimated by fitting an exponential-quadratic function. The Vmax values, often presented in different units under various measurement conditions, were converted into the same units at a reference temperature (20 C) and pHopt. Major conclusions are: (i) Both Vmax and Km were log-normal distributed, with no significant difference in Vmax exhibited between enzymes originating from bacteria or fungi. (ii) No significant difference in Vmax was found between cellulases and ligninases; however, there was significant difference in Km between them. (iii) Ligninases had higher Ea values and lower pHopt than cellulases; average ratio of pHsen to pHopt ranged 0.3 0.4 for the five enzymes, which means that an increase or decrease of 1.1 1.7 pH units from pHopt would reduce Vmax by 50%. (iv) Our analysis indicated that the Vmax values from lab measurements with purified enzymes were 1 2 orders of magnitude higher than those for use in SOC decomposition models under field conditions.
NASA Astrophysics Data System (ADS)
Kumar, R.; Mai, J.; Rakovec, O.; Cuntz, M.; Thober, S.; Zink, M.; Attinger, S.; Schaefer, D.; Schrön, M.; Samaniego, L. E.
2015-12-01
Accurate representation of water fluxes and states is crucial for hydrological assessments of societally relevant events such as floods and droughts. Hydrologic and/or land surface models are now commonly used for this purpose. The seamless prediction of continental scale water fluxes from these models requires among other things (i) a robust parameterization technique that allows the model to operate across a range of spatial resolutions and (ii) an efficient parameter estimation technique to derive a representative set of spatially consistent hydrologic parameters to avoid discontinuities of simulated hydrologic fields. In this study, we demonstrate the applicability of a mesoscale hydrologic modeling framework that incorporates a novel multiscale parameter regionalization technique (mHM-MPR) to derive the long-term gridded estimates of water fluxes and states over the Pan-EU domain. The MPR technique allows establishing linkages between hydrologic parameter fields and basin geophysical attributes (e.g., terrain, soil, vegetation properties) through a set of transfer functions and quasi-scale invariant global parameters. We devise a multi-basin parameter estimation strategy that utilizes observed streamflows from a reduced set of hydrologically diverse basins to infer a representative set of global parameters. The selection of diverse basins is guided through a stepwise clustering algorithm based on the basins geophysical and hydro-climatic attributes. Results of this strategy are contrasted against the single-basin calibration strategy across 400 European basins varying from approximately 100 km2 to 500000 km2. The single-basin parameter estimates although produced the site-specific best results, but their transferability to other basins resulted in poor performance. Initial results indicate that the multi-basin calibration strategy is at least as good as the best single-basin cross-validated results. Furthermore, the gridded fields of hydrologic parameters and
Improving a regional model using reduced complexity and parameter estimation
Kelson, Victor A.; Hunt, Randall J.; Haitjema, Henk M.
2002-01-01
The availability of powerful desktop computers and graphical user interfaces for ground water flow models makes possible the construction of ever more complex models. A proposed copper-zinc sulfide mine in northern Wisconsin offers a unique case in which the same hydrologic system has been modeled using a variety of techniques covering a wide range of sophistication and complexity. Early in the permitting process, simple numerical models were used to evaluate the necessary amount of water to be pumped from the mine, reductions in streamflow, and the drawdowns in the regional aquifer. More complex models have subsequently been used in an attempt to refine the predictions. Even after so much modeling effort, questions regarding the accuracy and reliability of the predictions remain. We have performed a new analysis of the proposed mine using the two-dimensional analytic element code GFLOW coupled with the nonlinear parameter estimation code UCODE. The new model is parsimonious, containing fewer than 10 parameters, and covers a region several times larger in areal extent than any of the previous models. The model demonstrates the suitability of analytic element codes for use with parameter estimation codes. The simplified model results are similar to the more complex models; predicted mine inflows and UCODE-derived 95% confidence intervals are consistent with the previous predictions. More important, the large areal extent of the model allowed us to examine hydrological features not included in the previous models, resulting in new insights about the effects that far-field boundary conditions can have on near-field model calibration and parameterization. In this case, the addition of surface water runoff into a lake in the headwaters of a stream while holding recharge constant moved a regional ground watershed divide and resulted in some of the added water being captured by the adjoining basin. Finally, a simple analytical solution was used to clarify the GFLOW model
Pharmacokinetic parameter estimations by minimum relative entropy method.
Amisaki, T; Eguchi, S
1995-10-01
For estimating pharmacokinetic parameters, we introduce the minimum relative entropy (MRE) method and compare its performance with least squares methods. There are several variants of least squares, such as ordinary least squares (OLS), weighted least squares, and iteratively reweighted least squares. In addition to these traditional methods, even extended least squares (ELS), a relatively new approach to nonlinear regression analysis, can be regarded as a variant of least squares. These methods are different from each other in their manner of handling weights. It has been recognized that least squares methods with an inadequate weighting scheme may cause misleading results (the "choice of weights" problem). Although least squares with uniform weights, i.e., OLS, is rarely used in pharmacokinetic analysis, it offers the principle of least squares. The objective function of OLS can be regarded as a distance between observed and theoretical pharmacokinetic values on the Euclidean space RN, where N is the number of observations. Thus OLS produces its estimates by minimizing the Euclidean distance. On the other hand, MRE works by minimizing the relative entropy which expresses discrepancy between two probability densities. Because pharmacokinetic functions are not density function in general, we use a particular form of the relative entropy whose domain is extended to the space of all positive functions. MRE never assumes any distribution of errors involved in observations. Thus, it can be a possible solution to the choice of weights problem. Moreover, since the mathematical form of the relative entropy, i.e., an expectation of the log-ratio of two probability density functions, is different from that of a usual Euclidean distance, the behavior of MRE may be different from those of least squares methods. To clarify the behavior of MRE, we have compared the performance of MRE with those of ELS and OLS by carrying out an intensive simulation study, where four pharmaco
Fu, Yunxin
2014-01-01
The mutation parameter θ is fundamental and ubiquitous in the analysis of population samples of DNA sequences. This paper presents a new highly efficient estimator of θ by utilizing the phylogenetic information among distinct alleles in a sample of DNA sequences. The new estimator, called Allelic BLUE, is derived from a generalized linear model about the mutations in the allelic genealogy. This estimator is not only highly accurate, but also computational efficient, which makes it particularly useful for estimating θ for large samples, as well as for a large number of cases, such as the situation of analyzing sequence data from a large genome project, such as the 1000 Genomes Project. Simulation shows that Allelic BLUE is nearly unbiased, with variance nearly as small as the minimum achievable variance, and in many situations, it can be hundreds- or thousands-fold more efficient than a previous method, which was already quite efficient compared to other approaches. One useful feature of the new estimator is its applicability to collections of distinct alleles without detailed frequencies. The utility of the new estimator is demonstrated by analyzing the pattern of θ in the data from the 1000 Genomes Project. PMID:25055200
Parameter estimation for boundary value problems by integral equations of the second kind
NASA Technical Reports Server (NTRS)
Kojima, Fumio
1988-01-01
This paper is concerned with the parameter estimation for boundary integral equations of the second kind. The parameter estimation technique through use of the spline collocation method is proposed. Based on the compactness assumption imposed on the parameter space, the convergence analysis for the numerical method of parameter estimation is discussed. The results obtained here are applied to a boundary parameter estimation for 2-D elliptic systems.
NASA Astrophysics Data System (ADS)
Sazawa, Masaki; Senko, Tomohiro; Uenaka, Yutaro; Ohishi, Kiyoshi
The servo system of a PM motor is required to have a fine torque response and fine speed response. The current controller of the PM motor should have a fine current response. However, when the current sensors of the U phase and V phase have offset values, the servo system of the PM motor shows a torque ripple and cannot accurately identify motor parameters such as the motor resistance Ra and motor inductance La. In order to overcome these problems, this paper proposes a new method for the fine estimation and automatic adjustment of current sensor offsets and the electrical parameters of the PM motor. The proposed method involves the use of a real-time algorithm and a current simulator, which is operated using a DSP software system. The experimental results of this study confirm that the proposed method satisfactorily estimates the offsets of the current of sensors of theU phase and V phase, Ra, and La accurately; the method also helps in the fine self-tuning of the current controller of the servo system.
Accurate State Estimation and Tracking of a Non-Cooperative Target Vehicle
NASA Technical Reports Server (NTRS)
Thienel, Julie K.; Sanner, Robert M.
2006-01-01
Autonomous space rendezvous scenarios require knowledge of the target vehicle state in order to safely dock with the chaser vehicle. Ideally, the target vehicle state information is derived from telemetered data, or with the use of known tracking points on the target vehicle. However, if the target vehicle is non-cooperative and does not have the ability to maintain attitude control, or transmit attitude knowledge, the docking becomes more challenging. This work presents a nonlinear approach for estimating the body rates of a non-cooperative target vehicle, and coupling this estimation to a tracking control scheme. The approach is tested with the robotic servicing mission concept for the Hubble Space Telescope (HST). Such a mission would not only require estimates of the HST attitude and rates, but also precision control to achieve the desired rate and maintain the orientation to successfully dock with HST.
Precision Pointing Control to and Accurate Target Estimation of a Non-Cooperative Vehicle
NASA Technical Reports Server (NTRS)
VanEepoel, John; Thienel, Julie; Sanner, Robert M.
2006-01-01
In 2004, NASA began investigating a robotic servicing mission for the Hubble Space Telescope (HST). Such a mission would not only require estimates of the HST attitude and rates in order to achieve capture by the proposed Hubble Robotic Vehicle (HRV), but also precision control to achieve the desired rate and maintain the orientation to successfully dock with HST. To generalize the situation, HST is the target vehicle and HRV is the chaser. This work presents a nonlinear approach for estimating the body rates of a non-cooperative target vehicle, and coupling this estimation to a control scheme. Non-cooperative in this context relates to the target vehicle no longer having the ability to maintain attitude control or transmit attitude knowledge.
A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system
Metcalf, Jessica L; Wegener Parfrey, Laura; Gonzalez, Antonio; Lauber, Christian L; Knights, Dan; Ackermann, Gail; Humphrey, Gregory C; Gebert, Matthew J; Van Treuren, Will; Berg-Lyons, Donna; Keepers, Kyle; Guo, Yan; Bullard, James; Fierer, Noah; Carter, David O; Knight, Rob
2013-01-01
Establishing the time since death is critical in every death investigation, yet existing techniques are susceptible to a range of errors and biases. For example, forensic entomology is widely used to assess the postmortem interval (PMI), but errors can range from days to months. Microbes may provide a novel method for estimating PMI that avoids many of these limitations. Here we show that postmortem microbial community changes are dramatic, measurable, and repeatable in a mouse model system, al