Can nonstandard interactions jeopardize the hierarchy sensitivity of DUNE?

NASA Astrophysics Data System (ADS)

Deepthi, K. N.; Goswami, Srubabati; Nath, Newton

2017-10-01

We study the effect of nonstandard interactions (NSIs) on the propagation of neutrinos through the Earth's matter and how it affects the hierarchy sensitivity of the DUNE experiment. We emphasize the special case when the diagonal NSI parameter ɛe e=-1 , nullifying the standard matter effect. We show that if, in addition, C P violation is maximal then this gives rise to an exact intrinsic hierarchy degeneracy in the appearance channel, irrespective of the baseline and energy. Introduction of the off diagonal NSI parameter, ɛe τ, shifts the position of this degeneracy to a different ɛe e. Moreover the unknown magnitude and phases of the off diagonal NSI parameters can give rise to additional degeneracies. Overall, given the current model independent limits on NSI parameters, the hierarchy sensitivity of DUNE can get seriously impacted. However, a more precise knowledge of the NSI parameters, especially ɛe e, can give rise to an improved sensitivity. Alternatively, if a NSI exists in nature, and still DUNE shows hierarchy sensitivity, certain ranges of the NSI parameters can be excluded. Additionally, we briefly discuss the implications of ɛe e=-1 (in the Earth) on the Mikheyev-Smirnov-Wolfenstein effect in the Sun.

[Application of precursor ion scanning method in rapid screening of illegally added phosphodiesterase-5 inhibitors and their unknown derivatives in Chinese traditional patent medicines and health foods].

PubMed

Sun, Jing; Cao, Ling; Feng, Youlong; Tan, Li

2014-11-01

The compounds with similar structure often have similar pharmacological activities. So it is a trend for illegal addition that new derivatives of effective drugs are synthesized to avoid the statutory test. This bring challenges to crack down on illegal addition behavior, however, modified derivatives usually have similar product ions, which allow for precursor ion scanning. In this work, precursor ion scanning mode of a triple quadrupole mass spectrometer was first applied to screen illegally added drugs in complex matrix such as Chinese traditional patent medicines and healthy foods. Phosphodiesterase-5 inhibitors were used as experimental examples. Through the analysis of the structure and mass spectrum characteristics of the compounds, phosphodiesterase-5 inhibitors were classified, and their common product ions were screened by full scan of product ions of typical compounds. Then high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method with precursor ion scanning mode was established based on the optimization of MS parameters. The effect of mass parameters and the choice of fragment ions were also studied. The method was applied to determine actual samples and further refined. The results demonstrated that this method can meet the need of rapid screening of unknown derivatives of phosphodiesterase-5 inhibitors in complex matrix, and prevent unknown derivatives undetected. This method shows advantages in sensitivity, specificity and efficiency, and is worth to be further investigated.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stroeer, Alexander; Veitch, John

The Laser Interferometer Space Antenna (LISA) defines new demands on data analysis efforts in its all-sky gravitational wave survey, recording simultaneously thousands of galactic compact object binary foreground sources and tens to hundreds of background sources like binary black hole mergers and extreme-mass ratio inspirals. We approach this problem with an adaptive and fully automatic Reversible Jump Markov Chain Monte Carlo sampler, able to sample from the joint posterior density function (as established by Bayes theorem) for a given mixture of signals ''out of the box'', handling the total number of signals as an additional unknown parameter beside the unknownmore » parameters of each individual source and the noise floor. We show in examples from the LISA Mock Data Challenge implementing the full response of LISA in its TDI description that this sampler is able to extract monochromatic Double White Dwarf signals out of colored instrumental noise and additional foreground and background noise successfully in a global fitting approach. We introduce 2 examples with fixed number of signals (MCMC sampling), and 1 example with unknown number of signals (RJ-MCMC), the latter further promoting the idea behind an experimental adaptation of the model indicator proposal densities in the main sampling stage. We note that the experienced runtimes and degeneracies in parameter extraction limit the shown examples to the extraction of a low but realistic number of signals.« less

Stability of neutrino parameters and self-complementarity relation with varying SUSY breaking scale

NASA Astrophysics Data System (ADS)

Singh, K. Sashikanta; Roy, Subhankar; Singh, N. Nimai

2018-03-01

The scale at which supersymmetry (SUSY) breaks (ms) is still unknown. The present article, following a top-down approach, endeavors to study the effect of varying ms on the radiative stability of the observational parameters associated with the neutrino mixing. These parameters get additional contributions in the minimal supersymmetric model (MSSM). A variation in ms will influence the bounds for which the Standard Model (SM) and MSSM work and hence, will account for the different radiative contributions received from both sectors, respectively, while running the renormalization group equations (RGE). The present work establishes the invariance of the self complementarity relation among the three mixing angles, θ13+θ12≈θ23 against the radiative evolution. A similar result concerning the mass ratio, m2:m1 is also found to be valid. In addition to varying ms, the work incorporates a range of different seesaw (SS) scales and tries to see how the latter affects the parameters.

Exploring theory space with Monte Carlo reweighting

DOE PAGES

Gainer, James S.; Lykken, Joseph; Matchev, Konstantin T.; ...

2014-10-13

Theories of new physics often involve a large number of unknown parameters which need to be scanned. Additionally, a putative signal in a particular channel may be due to a variety of distinct models of new physics. This makes experimental attempts to constrain the parameter space of motivated new physics models with a high degree of generality quite challenging. We describe how the reweighting of events may allow this challenge to be met, as fully simulated Monte Carlo samples generated for arbitrary benchmark models can be effectively re-used. Specifically, we suggest procedures that allow more efficient collaboration between theorists andmore » experimentalists in exploring large theory parameter spaces in a rigorous way at the LHC.« less

The Simultaneous Additive and Relative SysRem Algorithm

NASA Astrophysics Data System (ADS)

Ofir, A.

2011-02-01

We present the SARS algorithm, which is a generalization of the popular SysRem detrending technique. This generalization allows including multiple external parameters in a simultaneous solution with the unknown effects. Using SARS allowed us to show that the magnitude-dependant systematic effect discovered by Mazeh et al. (2009) in the CoRoT data is probably caused by an additive -rather than relative- noise source. A post-processing scheme based on SARS performs well and indeed allows for the detection of new transit-like signals that were not previously detected.

Evaluation of parameters of color profile models of LCD and LED screens

NASA Astrophysics Data System (ADS)

Zharinov, I. O.; Zharinov, O. O.

2017-12-01

The purpose of the research relates to the problem of parametric identification of the color profile model of LCD (liquid crystal display) and LED (light emitting diode) screens. The color profile model of a screen is based on the Grassmann’s Law of additive color mixture. Mathematically the problem is to evaluate unknown parameters (numerical coefficients) of the matrix transformation between different color spaces. Several methods of evaluation of these screen profile coefficients were developed. These methods are based either on processing of some colorimetric measurements or on processing of technical documentation data.

Statistical inference involving binomial and negative binomial parameters.

PubMed

García-Pérez, Miguel A; Núñez-Antón, Vicente

2009-05-01

Statistical inference about two binomial parameters implies that they are both estimated by binomial sampling. There are occasions in which one aims at testing the equality of two binomial parameters before and after the occurrence of the first success along a sequence of Bernoulli trials. In these cases, the binomial parameter before the first success is estimated by negative binomial sampling whereas that after the first success is estimated by binomial sampling, and both estimates are related. This paper derives statistical tools to test two hypotheses, namely, that both binomial parameters equal some specified value and that both parameters are equal though unknown. Simulation studies are used to show that in small samples both tests are accurate in keeping the nominal Type-I error rates, and also to determine sample size requirements to detect large, medium, and small effects with adequate power. Additional simulations also show that the tests are sufficiently robust to certain violations of their assumptions.

A comparison between Gauss-Newton and Markov chain Monte Carlo basedmethods for inverting spectral induced polarization data for Cole-Coleparameters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Jinsong; Kemna, Andreas; Hubbard, Susan S.

2008-05-15

We develop a Bayesian model to invert spectral induced polarization (SIP) data for Cole-Cole parameters using Markov chain Monte Carlo (MCMC) sampling methods. We compare the performance of the MCMC based stochastic method with an iterative Gauss-Newton based deterministic method for Cole-Cole parameter estimation through inversion of synthetic and laboratory SIP data. The Gauss-Newton based method can provide an optimal solution for given objective functions under constraints, but the obtained optimal solution generally depends on the choice of initial values and the estimated uncertainty information is often inaccurate or insufficient. In contrast, the MCMC based inversion method provides extensive globalmore » information on unknown parameters, such as the marginal probability distribution functions, from which we can obtain better estimates and tighter uncertainty bounds of the parameters than with the deterministic method. Additionally, the results obtained with the MCMC method are independent of the choice of initial values. Because the MCMC based method does not explicitly offer single optimal solution for given objective functions, the deterministic and stochastic methods can complement each other. For example, the stochastic method can first be used to obtain the means of the unknown parameters by starting from an arbitrary set of initial values and the deterministic method can then be initiated using the means as starting values to obtain the optimal estimates of the Cole-Cole parameters.« less

Adaptive control of Parkinson's state based on a nonlinear computational model with unknown parameters.

PubMed

Su, Fei; Wang, Jiang; Deng, Bin; Wei, Xi-Le; Chen, Ying-Yuan; Liu, Chen; Li, Hui-Yan

2015-02-01

The objective here is to explore the use of adaptive input-output feedback linearization method to achieve an improved deep brain stimulation (DBS) algorithm for closed-loop control of Parkinson's state. The control law is based on a highly nonlinear computational model of Parkinson's disease (PD) with unknown parameters. The restoration of thalamic relay reliability is formulated as the desired outcome of the adaptive control methodology, and the DBS waveform is the control input. The control input is adjusted in real time according to estimates of unknown parameters as well as the feedback signal. Simulation results show that the proposed adaptive control algorithm succeeds in restoring the relay reliability of the thalamus, and at the same time achieves accurate estimation of unknown parameters. Our findings point to the potential value of adaptive control approach that could be used to regulate DBS waveform in more effective treatment of PD.

Dynamic Modeling from Flight Data with Unknown Time Skews

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2016-01-01

A method for estimating dynamic model parameters from flight data with unknown time skews is described and demonstrated. The method combines data reconstruction, nonlinear optimization, and equation-error parameter estimation in the frequency domain to accurately estimate both dynamic model parameters and the relative time skews in the data. Data from a nonlinear F-16 aircraft simulation with realistic noise, instrumentation errors, and arbitrary time skews were used to demonstrate the approach. The approach was further evaluated using flight data from a subscale jet transport aircraft, where the measured data were known to have relative time skews. Comparison of modeling results obtained from time-skewed and time-synchronized data showed that the method accurately estimates both dynamic model parameters and relative time skew parameters from flight data with unknown time skews.

Parameter identifiability of linear dynamical systems

NASA Technical Reports Server (NTRS)

Glover, K.; Willems, J. C.

1974-01-01

It is assumed that the system matrices of a stationary linear dynamical system were parametrized by a set of unknown parameters. The question considered here is, when can such a set of unknown parameters be identified from the observed data? Conditions for the local identifiability of a parametrization are derived in three situations: (1) when input/output observations are made, (2) when there exists an unknown feedback matrix in the system and (3) when the system is assumed to be driven by white noise and only output observations are made. Also a sufficient condition for global identifiability is derived.

Parameter identification of thermophilic anaerobic degradation of valerate.

PubMed

Flotats, Xavier; Ahring, Birgitte K; Angelidaki, Irini

2003-01-01

The considered mathematical model of the decomposition of valerate presents three unknown kinetic parameters, two unknown stoichiometric coefficients, and three unknown initial concentrations for biomass. Applying a structural identifiability study, we concluded that it is necessary to perform simultaneous batch experiments with different initial conditions for estimating these parameters. Four simultaneous batch experiments were conducted at 55 degrees C, characterized by four different initial acetate concentrations. Product inhibition of valerate degradation by acetate was considered. Practical identification was done optimizing the sum of the multiple determination coefficients for all measured state variables and for all experiments simultaneously. The estimated values of kinetic parameters and stoichiometric coefficients were characterized by the parameter correlation matrix, the confidence interval, and the student's t-test at 5% significance level with positive results except for the saturation constant, for which more experiments for improving its identifiability should be conducted. In this article, we discuss kinetic parameter estimation methods.

Discrete-time switching periodic adaptive control for time-varying parameters with unknown periodicity

NASA Astrophysics Data System (ADS)

Yu, Miao; Huang, Deqing; Yang, Wanqiu

2018-06-01

In this paper, we address the problem of unknown periodicity for a class of discrete-time nonlinear parametric systems without assuming any growth conditions on the nonlinearities. The unknown periodicity hides in the parametric uncertainties, which is difficult to estimate with existing techniques. By incorporating a logic-based switching mechanism, we identify the period and bound of unknown parameter simultaneously. Lyapunov-based analysis is given to demonstrate that a finite number of switchings can guarantee the asymptotic tracking for the nonlinear parametric systems. The simulation result also shows the efficacy of the proposed switching periodic adaptive control approach.

GLRT-based array receivers for the detection of a known signal with unknown parameters corrupted by noncircular interferences

NASA Astrophysics Data System (ADS)

Chevalier, Pascal; Oukaci, Abdelkader; Delmas, Jean-Pierre

2011-12-01

The detection of a known signal with unknown parameters in the presence of noise plus interferences (called total noise) whose covariance matrix is unknown is an important problem which has received much attention these last decades for applications such as radar, satellite localization or time acquisition in radio communications. However, most of the available receivers assume a second order (SO) circular (or proper) total noise and become suboptimal in the presence of SO noncircular (or improper) interferences, potentially present in the previous applications. The scarce available receivers which take the potential SO noncircularity of the total noise into account have been developed under the restrictive condition of a known signal with known parameters or under the assumption of a random signal. For this reason, following a generalized likelihood ratio test (GLRT) approach, the purpose of this paper is to introduce and to analyze the performance of different array receivers for the detection of a known signal, with different sets of unknown parameters, corrupted by an unknown noncircular total noise. To simplify the study, we limit the analysis to rectilinear known useful signals for which the baseband signal is real, which concerns many applications.

EuroForMix: An open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts.

PubMed

Bleka, Øyvind; Storvik, Geir; Gill, Peter

2016-03-01

We have released a software named EuroForMix to analyze STR DNA profiles in a user-friendly graphical user interface. The software implements a model to explain the allelic peak height on a continuous scale in order to carry out weight-of-evidence calculations for profiles which could be from a mixture of contributors. Through a properly parameterized model we are able to do inference on mixture proportions, the peak height properties, stutter proportion and degradation. In addition, EuroForMix includes models for allele drop-out, allele drop-in and sub-population structure. EuroForMix supports two inference approaches for likelihood ratio calculations. The first approach uses maximum likelihood estimation of the unknown parameters. The second approach is Bayesian based which requires prior distributions to be specified for the parameters involved. The user may specify any number of known and unknown contributors in the model, however we find that there is a practical computing time limit which restricts the model to a maximum of four unknown contributors. EuroForMix is the first freely open source, continuous model (accommodating peak height, stutter, drop-in, drop-out, population substructure and degradation), to be reported in the literature. It therefore serves an important purpose to act as an unrestricted platform to compare different solutions that are available. The implementation of the continuous model used in the software showed close to identical results to the R-package DNAmixtures, which requires a HUGIN Expert license to be used. An additional feature in EuroForMix is the ability for the user to adapt the Bayesian inference framework by incorporating their own prior information. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Measurement-based perturbation theory and differential equation parameter estimation with applications to satellite gravimetry

NASA Astrophysics Data System (ADS)

Xu, Peiliang

2018-06-01

The numerical integration method has been routinely used by major institutions worldwide, for example, NASA Goddard Space Flight Center and German Research Center for Geosciences (GFZ), to produce global gravitational models from satellite tracking measurements of CHAMP and/or GRACE types. Such Earth's gravitational products have found widest possible multidisciplinary applications in Earth Sciences. The method is essentially implemented by solving the differential equations of the partial derivatives of the orbit of a satellite with respect to the unknown harmonic coefficients under the conditions of zero initial values. From the mathematical and statistical point of view, satellite gravimetry from satellite tracking is essentially the problem of estimating unknown parameters in the Newton's nonlinear differential equations from satellite tracking measurements. We prove that zero initial values for the partial derivatives are incorrect mathematically and not permitted physically. The numerical integration method, as currently implemented and used in mathematics and statistics, chemistry and physics, and satellite gravimetry, is groundless, mathematically and physically. Given the Newton's nonlinear governing differential equations of satellite motion with unknown equation parameters and unknown initial conditions, we develop three methods to derive new local solutions around a nominal reference orbit, which are linked to measurements to estimate the unknown corrections to approximate values of the unknown parameters and the unknown initial conditions. Bearing in mind that satellite orbits can now be tracked almost continuously at unprecedented accuracy, we propose the measurement-based perturbation theory and derive global uniformly convergent solutions to the Newton's nonlinear governing differential equations of satellite motion for the next generation of global gravitational models. Since the solutions are global uniformly convergent, theoretically speaking, they are able to extract smallest possible gravitational signals from modern and future satellite tracking measurements, leading to the production of global high-precision, high-resolution gravitational models. By directly turning the nonlinear differential equations of satellite motion into the nonlinear integral equations, and recognizing the fact that satellite orbits are measured with random errors, we further reformulate the links between satellite tracking measurements and the global uniformly convergent solutions to the Newton's governing differential equations as a condition adjustment model with unknown parameters, or equivalently, the weighted least squares estimation of unknown differential equation parameters with equality constraints, for the reconstruction of global high-precision, high-resolution gravitational models from modern (and future) satellite tracking measurements.

Complete synchronization of uncertain chaotic systems via a single proportional adaptive controller: A comparative study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ahmad, Israr, E-mail: iak-2000plus@yahoo.com; Saaban, Azizan Bin, E-mail: azizan.s@uum.edu.my; Ibrahim, Adyda Binti, E-mail: adyda@uum.edu.my

This paper addresses a comparative computational study on the synchronization quality, cost and converging speed for two pairs of identical chaotic and hyperchaotic systems with unknown time-varying parameters. It is assumed that the unknown time-varying parameters are bounded. Based on the Lyapunov stability theory and using the adaptive control method, a single proportional controller is proposed to achieve the goal of complete synchronizations. Accordingly, appropriate adaptive laws are designed to identify the unknown time-varying parameters. The designed control strategy is easy to implement in practice. Numerical simulations results are provided to verify the effectiveness of the proposed synchronization scheme.

On synchronisation of a class of complex chaotic systems with complex unknown parameters via integral sliding mode control

NASA Astrophysics Data System (ADS)

Tirandaz, Hamed; Karami-Mollaee, Ali

2018-06-01

Chaotic systems demonstrate complex behaviour in their state variables and their parameters, which generate some challenges and consequences. This paper presents a new synchronisation scheme based on integral sliding mode control (ISMC) method on a class of complex chaotic systems with complex unknown parameters. Synchronisation between corresponding states of a class of complex chaotic systems and also convergence of the errors of the system parameters to zero point are studied. The designed feedback control vector and complex unknown parameter vector are analytically achieved based on the Lyapunov stability theory. Moreover, the effectiveness of the proposed methodology is verified by synchronisation of the Chen complex system and the Lorenz complex systems as the leader and the follower chaotic systems, respectively. In conclusion, some numerical simulations related to the synchronisation methodology is given to illustrate the effectiveness of the theoretical discussions.

Temporal gravity field modeling based on least square collocation with short-arc approach

NASA Astrophysics Data System (ADS)

ran, jiangjun; Zhong, Min; Xu, Houze; Liu, Chengshu; Tangdamrongsub, Natthachet

2014-05-01

After the launch of the Gravity Recovery And Climate Experiment (GRACE) in 2002, several research centers have attempted to produce the finest gravity model based on different approaches. In this study, we present an alternative approach to derive the Earth's gravity field, and two main objectives are discussed. Firstly, we seek the optimal method to estimate the accelerometer parameters, and secondly, we intend to recover the monthly gravity model based on least square collocation method. The method has been paid less attention compared to the least square adjustment method because of the massive computational resource's requirement. The positions of twin satellites are treated as pseudo-observations and unknown parameters at the same time. The variance covariance matrices of the pseudo-observations and the unknown parameters are valuable information to improve the accuracy of the estimated gravity solutions. Our analyses showed that introducing a drift parameter as an additional accelerometer parameter, compared to using only a bias parameter, leads to a significant improvement of our estimated monthly gravity field. The gravity errors outside the continents are significantly reduced based on the selected set of the accelerometer parameters. We introduced the improved gravity model namely the second version of Institute of Geodesy and Geophysics, Chinese Academy of Sciences (IGG-CAS 02). The accuracy of IGG-CAS 02 model is comparable to the gravity solutions computed from the Geoforschungszentrum (GFZ), the Center for Space Research (CSR) and the NASA Jet Propulsion Laboratory (JPL). In term of the equivalent water height, the correlation coefficients over the study regions (the Yangtze River valley, the Sahara desert, and the Amazon) among four gravity models are greater than 0.80.

M-MRAC Backstepping for Systems with Unknown Virtual Control Coefficients

NASA Technical Reports Server (NTRS)

Stepanyan, Vahram; Krishnakumar, Kalmanje

2015-01-01

The paper presents an over-parametrization free certainty equivalence state feedback backstepping adaptive control design method for systems of any relative degree with unmatched uncertainties and unknown virtual control coefficients. It uses a fast prediction model to estimate the unknown parameters, which is independent of the control design. It is shown that the system's input and output tracking errors can be systematically decreased by the proper choice of the design parameters. The benefits of the approach are demonstrated in numerical simulations.

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

PubMed

Campbell, D A; Chkrebtii, O

2013-12-01

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

Robust Online Hamiltonian Learning

NASA Astrophysics Data System (ADS)

Granade, Christopher; Ferrie, Christopher; Wiebe, Nathan; Cory, David

2013-05-01

In this talk, we introduce a machine-learning algorithm for the problem of inferring the dynamical parameters of a quantum system, and discuss this algorithm in the example of estimating the precession frequency of a single qubit in a static field. Our algorithm is designed with practicality in mind by including parameters that control trade-offs between the requirements on computational and experimental resources. The algorithm can be implemented online, during experimental data collection, or can be used as a tool for post-processing. Most importantly, our algorithm is capable of learning Hamiltonian parameters even when the parameters change from experiment-to-experiment, and also when additional noise processes are present and unknown. Finally, we discuss the performance of the our algorithm by appeal to the Cramer-Rao bound. This work was financially supported by the Canadian government through NSERC and CERC and by the United States government through DARPA. NW would like to acknowledge funding from USARO-DTO.

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.

A sensitive search for unknown spectral emission lines in the diffuse X-ray background with XMM-Newton

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gewering-Peine, A.; Horns, D.; Schmitt, J.H.M.M., E-mail: alexander.gewering-peine@desy.de, E-mail: dieter.horns@desy.de, E-mail: jschmitt@hs.uni-hamburg.de

The Standard Model of particle physics can be extended to include sterile (right-handed) neutrinos or axions to solve the dark matter problem. Depending upon the mixing angle between active and sterile neutrinos, the latter have the possibility to decay into monoenergetic active neutrinos and photons in the keV-range while axions can couple to two photons. We have used data taken with the X-ray telescope XMM-Newton for the search of line emissions. We used pointings with high exposures and expected dark matter column densities with respect to the dark matter halo of the Milky Way. The posterior predictive p-value analysis hasmore » been applied to locate parameter space regions which favour additional emission lines. In addition, upper limits of the parameter space of the models have been generated such that the preexisting limits have been significantly improved.« less

Bayesian statistics and Monte Carlo methods

NASA Astrophysics Data System (ADS)

Koch, K. R.

2018-03-01

The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is defined as a measure of the plausibility of statements or propositions. Three rules are sufficient to obtain the laws of probability. If the statements refer to the numerical values of variables, the so-called random variables, univariate and multivariate distributions follow. They lead to the point estimation by which unknown quantities, i.e. unknown parameters, are computed from measurements. The unknown parameters are random variables, they are fixed quantities in traditional statistics which is not founded on Bayes' theorem. Bayesian statistics therefore recommends itself for Monte Carlo methods, which generate random variates from given distributions. Monte Carlo methods, of course, can also be applied in traditional statistics. The unknown parameters, are introduced as functions of the measurements, and the Monte Carlo methods give the covariance matrix and the expectation of these functions. A confidence region is derived where the unknown parameters are situated with a given probability. Following a method of traditional statistics, hypotheses are tested by determining whether a value for an unknown parameter lies inside or outside the confidence region. The error propagation of a random vector by the Monte Carlo methods is presented as an application. If the random vector results from a nonlinearly transformed vector, its covariance matrix and its expectation follow from the Monte Carlo estimate. This saves a considerable amount of derivatives to be computed, and errors of the linearization are avoided. The Monte Carlo method is therefore efficient. If the functions of the measurements are given by a sum of two or more random vectors with different multivariate distributions, the resulting distribution is generally not known. TheMonte Carlo methods are then needed to obtain the covariance matrix and the expectation of the sum.

Enhanced Engine Performance During Emergency Operation Using a Model-Based Engine Control Architecture

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Connolly, Joseph W.

2016-01-01

This paper discusses the design and application of model-based engine control (MBEC) for use during emergency operation of the aircraft. The MBEC methodology is applied to the Commercial Modular Aero-Propulsion System Simulation 40k (CMAPSS40k) and features an optimal tuner Kalman Filter (OTKF) to estimate unmeasured engine parameters, which can then be used for control. During an emergency scenario, normally-conservative engine operating limits may be relaxed to increase the performance of the engine and overall survivability of the aircraft; this comes at the cost of additional risk of an engine failure. The MBEC architecture offers the advantage of estimating key engine parameters that are not directly measureable. Estimating the unknown parameters allows for tighter control over these parameters, and on the level of risk the engine will operate at. This will allow the engine to achieve better performance than possible when operating to more conservative limits on a related, measurable parameter.

Enhanced Engine Performance During Emergency Operation Using a Model-Based Engine Control Architecture

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Connolly, Joseph W.

2015-01-01

This paper discusses the design and application of model-based engine control (MBEC) for use during emergency operation of the aircraft. The MBEC methodology is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40,000) and features an optimal tuner Kalman Filter (OTKF) to estimate unmeasured engine parameters, which can then be used for control. During an emergency scenario, normally-conservative engine operating limits may be relaxed to increase the performance of the engine and overall survivability of the aircraft; this comes at the cost of additional risk of an engine failure. The MBEC architecture offers the advantage of estimating key engine parameters that are not directly measureable. Estimating the unknown parameters allows for tighter control over these parameters, and on the level of risk the engine will operate at. This will allow the engine to achieve better performance than possible when operating to more conservative limits on a related, measurable parameter.

On Complete Control and Synchronization of Zhang Chaotic System with Uncertain Parameters using Adaptive Control Method

NASA Astrophysics Data System (ADS)

Tirandaz, Hamed

2018-03-01

Chaos control and synchronization of chaotic systems is seemingly a challenging problem and has got a lot of attention in recent years due to its numerous applications in science and industry. This paper concentrates on the control and synchronization problem of the three-dimensional (3D) Zhang chaotic system. At first, an adaptive control law and a parameter estimation law are achieved for controlling the behavior of the Zhang chaotic system. Then, non-identical synchronization of Zhang chaotic system is provided with considering the Lü chaotic system as the follower system. The synchronization problem and parameters identification are achieved by introducing an adaptive control law and a parameters estimation law. Stability analysis of the proposed method is proved by the Lyapanov stability theorem. In addition, the convergence of the estimated parameters to their truly unknown values are evaluated. Finally, some numerical simulations are carried out to illustrate and to validate the effectiveness of the suggested method.

Demixing-stimulated lane formation in binary complex plasma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Du, C.-R.; Jiang, K.; Suetterlin, K. R.

2011-11-29

Recently lane formation and phase separation have been reported for experiments with binary complex plasmas in the PK3-Plus laboratory onboard the International Space Station (ISS). Positive non-additivity of particle interactions is known to stimulate phase separation (demixing), but its effect on lane formation is unknown. In this work, we used Langevin dynamics (LD) simulation to probe the role of non-additivity interactions on lane formation. The competition between laning and demixing leads to thicker lanes. Analysis based on anisotropic scaling indices reveals a crossover from normal laning mode to a demixing-stimulated laning mode. Extensive numerical simulations enabled us to identify amore » critical value of the non-additivity parameter {Delta} for the crossover.« less

Global 3ν oscillation analysis: Status of unknown parameters and future systematic challenges for ORCA and PINGU

NASA Astrophysics Data System (ADS)

Capozzi, Francesco; Lisi, Eligio; Marrone, Antonio

2016-04-01

Within the standard 3ν oscillation framework, we illustrate the status of currently unknown oscillation parameters: the θ23 octant, the mass hierarchy (normal or inverted), and the possible CP-violating phase δ, as derived by a (preliminary) global analysis of oscillation data available in 2015. We then discuss some challenges that will be faced by future, high-statistics analyses of spectral data, starting with one-dimensional energy spectra in reactor experiments, and concluding with two-dimensional energy-angle spectra in large-volume atmospheric experiments. It is shown that systematic uncertainties in the spectral shapes can noticeably affect the prospective sensitivities to unknown oscillation parameters, in particular to the mass hierarchy.

Market-Based Coordination of Thermostatically Controlled Loads—Part II: Unknown Parameters and Case Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Sen; Zhang, Wei; Lian, Jianming

This two-part paper considers the coordination of a population of Thermostatically Controlled Loads (TCLs) with unknown parameters to achieve group objectives. The problem involves designing the bidding and market clearing strategy to motivate self-interested users to realize efficient energy allocation subject to a peak power constraint. The companion paper (Part I) formulates the problem and proposes a load coordination framework using the mechanism design approach. To address the unknown parameters, Part II of this paper presents a joint state and parameter estimation framework based on the expectation maximization algorithm. The overall framework is then validated using real-world weather data andmore » price data, and is compared with other approaches in terms of aggregated power response. Simulation results indicate that our coordination framework can effectively improve the efficiency of the power grid operations and reduce power congestion at key times.« less

Estimating unknown input parameters when implementing the NGA ground-motion prediction equations in engineering practice

USGS Publications Warehouse

Kaklamanos, James; Baise, Laurie G.; Boore, David M.

2011-01-01

The ground-motion prediction equations (GMPEs) developed as part of the Next Generation Attenuation of Ground Motions (NGA-West) project in 2008 are becoming widely used in seismic hazard analyses. However, these new models are considerably more complicated than previous GMPEs, and they require several more input parameters. When employing the NGA models, users routinely face situations in which some of the required input parameters are unknown. In this paper, we present a framework for estimating the unknown source, path, and site parameters when implementing the NGA models in engineering practice, and we derive geometrically-based equations relating the three distance measures found in the NGA models. Our intent is for the content of this paper not only to make the NGA models more accessible, but also to help with the implementation of other present or future GMPEs.

Identification of linear system models and state estimators for controls

NASA Technical Reports Server (NTRS)

Chen, Chung-Wen

1992-01-01

The following paper is presented in viewgraph format and covers topics including: (1) linear state feedback control system; (2) Kalman filter state estimation; (3) relation between residual and stochastic part of output; (4) obtaining Kalman filter gain; (5) state estimation under unknown system model and unknown noises; and (6) relationship between filter Markov parameters and system Markov parameters.

Sensor fault diagnosis of singular delayed LPV systems with inexact parameters: an uncertain system approach

NASA Astrophysics Data System (ADS)

Hassanabadi, Amir Hossein; Shafiee, Masoud; Puig, Vicenc

2018-01-01

In this paper, sensor fault diagnosis of a singular delayed linear parameter varying (LPV) system is considered. In the considered system, the model matrices are dependent on some parameters which are real-time measurable. The case of inexact parameter measurements is considered which is close to real situations. Fault diagnosis in this system is achieved via fault estimation. For this purpose, an augmented system is created by including sensor faults as additional system states. Then, an unknown input observer (UIO) is designed which estimates both the system states and the faults in the presence of measurement noise, disturbances and uncertainty induced by inexact measured parameters. Error dynamics and the original system constitute an uncertain system due to inconsistencies between real and measured values of the parameters. Then, the robust estimation of the system states and the faults are achieved with H∞ performance and formulated with a set of linear matrix inequalities (LMIs). The designed UIO is also applicable for fault diagnosis of singular delayed LPV systems with unmeasurable scheduling variables. The efficiency of the proposed approach is illustrated with an example.

Parameter-space metric of semicoherent searches for continuous gravitational waves

NASA Astrophysics Data System (ADS)

Pletsch, Holger J.

2010-08-01

Continuous gravitational-wave (CW) signals such as emitted by spinning neutron stars are an important target class for current detectors. However, the enormous computational demand prohibits fully coherent broadband all-sky searches for prior unknown CW sources over wide ranges of parameter space and for yearlong observation times. More efficient hierarchical “semicoherent” search strategies divide the data into segments much shorter than one year, which are analyzed coherently; then detection statistics from different segments are combined incoherently. To optimally perform the incoherent combination, understanding of the underlying parameter-space structure is requisite. This problem is addressed here by using new coordinates on the parameter space, which yield the first analytical parameter-space metric for the incoherent combination step. This semicoherent metric applies to broadband all-sky surveys (also embedding directed searches at fixed sky position) for isolated CW sources. Furthermore, the additional metric resolution attained through the combination of segments is studied. From the search parameters (sky position, frequency, and frequency derivatives), solely the metric resolution in the frequency derivatives is found to significantly increase with the number of segments.

Tune-stabilized, non-scaling, fixed-field, alternating gradient accelerator

DOEpatents

Johnstone, Carol J [Warrenville, IL

2011-02-01

A FFAG is a particle accelerator having turning magnets with a linear field gradient for confinement and a large edge angle to compensate for acceleration. FODO cells contain focus magnets and defocus magnets that are specified by a number of parameters. A set of seven equations, called the FFAG equations relate the parameters to one another. A set of constraints, call the FFAG constraints, constrain the FFAG equations. Selecting a few parameters, such as injection momentum, extraction momentum, and drift distance reduces the number of unknown parameters to seven. Seven equations with seven unknowns can be solved to yield the values for all the parameters and to thereby fully specify a FFAG.

Adaptive control based on retrospective cost optimization

NASA Technical Reports Server (NTRS)

Bernstein, Dennis S. (Inventor); Santillo, Mario A. (Inventor)

2012-01-01

A discrete-time adaptive control law for stabilization, command following, and disturbance rejection that is effective for systems that are unstable, MIMO, and/or nonminimum phase. The adaptive control algorithm includes guidelines concerning the modeling information needed for implementation. This information includes the relative degree, the first nonzero Markov parameter, and the nonminimum-phase zeros. Except when the plant has nonminimum-phase zeros whose absolute value is less than the plant's spectral radius, the required zero information can be approximated by a sufficient number of Markov parameters. No additional information about the poles or zeros need be known. Numerical examples are presented to illustrate the algorithm's effectiveness in handling systems with errors in the required modeling data, unknown latency, sensor noise, and saturation.

Variations on Bayesian Prediction and Inference

DTIC Science & Technology

2016-05-09

inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle

LIBP-Pred: web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteria.

PubMed

González-Díaz, Humberto; Munteanu, Cristian R; Postelnicu, Lucian; Prado-Prado, Francisco; Gestal, Marcos; Pazos, Alejandro

2012-03-01

Lipid-Binding Proteins (LIBPs) or Fatty Acid-Binding Proteins (FABPs) play an important role in many diseases such as different types of cancer, kidney injury, atherosclerosis, diabetes, intestinal ischemia and parasitic infections. Thus, the computational methods that can predict LIBPs based on 3D structure parameters became a goal of major importance for drug-target discovery, vaccine design and biomarker selection. In addition, the Protein Data Bank (PDB) contains 3000+ protein 3D structures with unknown function. This list, as well as new experimental outcomes in proteomics research, is a very interesting source to discover relevant proteins, including LIBPs. However, to the best of our knowledge, there are no general models to predict new LIBPs based on 3D structures. We developed new Quantitative Structure-Activity Relationship (QSAR) models based on 3D electrostatic parameters of 1801 different proteins, including 801 LIBPs. We calculated these electrostatic parameters with the MARCH-INSIDE software and they correspond to the entire protein or to specific protein regions named core, inner, middle, and surface. We used these parameters as inputs to develop a simple Linear Discriminant Analysis (LDA) classifier to discriminate 3D structure of LIBPs from other proteins. We implemented this predictor in the web server named LIBP-Pred, freely available at , along with other important web servers of the Bio-AIMS portal. The users can carry out an automatic retrieval of protein structures from PDB or upload their custom protein structural models from their disk created with LOMETS server. We demonstrated the PDB mining option performing a predictive study of 2000+ proteins with unknown function. Interesting results regarding the discovery of new Cancer Biomarkers in humans or drug targets in parasites have been discussed here in this sense.

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.

A continuous optimization approach for inferring parameters in mathematical models of regulatory networks.

PubMed

Deng, Zhimin; Tian, Tianhai

2014-07-29

The advances of systems biology have raised a large number of sophisticated mathematical models for describing the dynamic property of complex biological systems. One of the major steps in developing mathematical models is to estimate unknown parameters of the model based on experimentally measured quantities. However, experimental conditions limit the amount of data that is available for mathematical modelling. The number of unknown parameters in mathematical models may be larger than the number of observation data. The imbalance between the number of experimental data and number of unknown parameters makes reverse-engineering problems particularly challenging. To address the issue of inadequate experimental data, we propose a continuous optimization approach for making reliable inference of model parameters. This approach first uses a spline interpolation to generate continuous functions of system dynamics as well as the first and second order derivatives of continuous functions. The expanded dataset is the basis to infer unknown model parameters using various continuous optimization criteria, including the error of simulation only, error of both simulation and the first derivative, or error of simulation as well as the first and second derivatives. We use three case studies to demonstrate the accuracy and reliability of the proposed new approach. Compared with the corresponding discrete criteria using experimental data at the measurement time points only, numerical results of the ERK kinase activation module show that the continuous absolute-error criteria using both function and high order derivatives generate estimates with better accuracy. This result is also supported by the second and third case studies for the G1/S transition network and the MAP kinase pathway, respectively. This suggests that the continuous absolute-error criteria lead to more accurate estimates than the corresponding discrete criteria. We also study the robustness property of these three models to examine the reliability of estimates. Simulation results show that the models with estimated parameters using continuous fitness functions have better robustness properties than those using the corresponding discrete fitness functions. The inference studies and robustness analysis suggest that the proposed continuous optimization criteria are effective and robust for estimating unknown parameters in mathematical models.

Synchronization of coupled different chaotic FitzHugh-Nagumo neurons with unknown parameters under communication-direction-dependent coupling.

PubMed

Iqbal, Muhammad; Rehan, Muhammad; Khaliq, Abdul; Saeed-ur-Rehman; Hong, Keum-Shik

2014-01-01

This paper investigates the chaotic behavior and synchronization of two different coupled chaotic FitzHugh-Nagumo (FHN) neurons with unknown parameters under external electrical stimulation (EES). The coupled FHN neurons of different parameters admit unidirectional and bidirectional gap junctions in the medium between them. Dynamical properties, such as the increase in synchronization error as a consequence of the deviation of neuronal parameters for unlike neurons, the effect of difference in coupling strengths caused by the unidirectional gap junctions, and the impact of large time-delay due to separation of neurons, are studied in exploring the behavior of the coupled system. A novel integral-based nonlinear adaptive control scheme, to cope with the infeasibility of the recovery variable, for synchronization of two coupled delayed chaotic FHN neurons of different and unknown parameters under uncertain EES is derived. Further, to guarantee robust synchronization of different neurons against disturbances, the proposed control methodology is modified to achieve the uniformly ultimately bounded synchronization. The parametric estimation errors can be reduced by selecting suitable control parameters. The effectiveness of the proposed control scheme is illustrated via numerical simulations.

Parameter Estimation for GRACE-FO Geometric Ranging Errors

NASA Astrophysics Data System (ADS)

Wegener, H.; Mueller, V.; Darbeheshti, N.; Naeimi, M.; Heinzel, G.

2017-12-01

Onboard GRACE-FO, the novel Laser Ranging Instrument (LRI) serves as a technology demonstrator, but it is a fully functional instrument to provide an additional high-precision measurement of the primary mission observable: the biased range between the two spacecraft. Its (expectedly) two largest error sources are laser frequency noise and tilt-to-length (TTL) coupling. While not much can be done about laser frequency noise, the mechanics of the TTL error are widely understood. They depend, however, on unknown parameters. In order to improve the quality of the ranging data, it is hence essential to accurately estimate these parameters and remove the resulting TTL error from the data.Means to do so will be discussed. In particular, the possibility of using calibration maneuvers, the utility of the attitude information provided by the LRI via Differential Wavefront Sensing (DWS), and the benefit from combining ranging data from LRI with ranging data from the established microwave ranging, will be mentioned.

A nonlocal strain gradient model for dynamic deformation of orthotropic viscoelastic graphene sheets under time harmonic thermal load

NASA Astrophysics Data System (ADS)

Radwan, Ahmed F.; Sobhy, Mohammed

2018-06-01

This work presents a nonlocal strain gradient theory for the dynamic deformation response of a single-layered graphene sheet (SLGS) on a viscoelastic foundation and subjected to a time harmonic thermal load for various boundary conditions. Material of graphene sheets is presumed to be orthotropic and viscoelastic. The viscoelastic foundation is modeled as Kelvin-Voigt's pattern. Based on the two-unknown plate theory, the motion equations are obtained from the dynamic version of the virtual work principle. The nonlocal strain gradient theory is established from Eringen nonlocal and strain gradient theories, therefore, it contains two material scale parameters, which are nonlocal parameter and gradient coefficient. These scale parameters have two different effects on the graphene sheets. The obtained deflection is compared with that predicted in the literature. Additional numerical examples are introduced to illustrate the influences of the two length scale coefficients and other parameters on the dynamic deformation of the viscoelastic graphene sheets.

A novel procedure for detecting and focusing moving objects with SAR based on the Wigner-Ville distribution

NASA Astrophysics Data System (ADS)

Barbarossa, S.; Farina, A.

A novel scheme for detecting moving targets with synthetic aperture radar (SAR) is presented. The proposed approach is based on the use of the Wigner-Ville distribution (WVD) for simultaneously detecting moving targets and estimating their motion kinematic parameters. The estimation plays a key role for focusing the target and correctly locating it with respect to the stationary background. The method has a number of advantages: (i) the detection is efficiently performed on the samples in the time-frequency domain, provided the WVD, without resorting to the use of a bank of filters, each one matched to possible values of the unknown target motion parameters; (ii) the estimation of the target motion parameters can be done on the same time-frequency domain by locating the line where the maximum energy of the WVD is concentrated. A validation of the approach is given by both analytical and simulation means. In addition, the estimation of the target kinematic parameters and the corresponding image focusing are also demonstrated.

An analysis of parameter sensitivities of preference-inspired co-evolutionary algorithms

NASA Astrophysics Data System (ADS)

Wang, Rui; Mansor, Maszatul M.; Purshouse, Robin C.; Fleming, Peter J.

2015-10-01

Many-objective optimisation problems remain challenging for many state-of-the-art multi-objective evolutionary algorithms. Preference-inspired co-evolutionary algorithms (PICEAs) which co-evolve the usual population of candidate solutions with a family of decision-maker preferences during the search have been demonstrated to be effective on such problems. However, it is unknown whether PICEAs are robust with respect to the parameter settings. This study aims to address this question. First, a global sensitivity analysis method - the Sobol' variance decomposition method - is employed to determine the relative importance of the parameters controlling the performance of PICEAs. Experimental results show that the performance of PICEAs is controlled for the most part by the number of function evaluations. Next, we investigate the effect of key parameters identified from the Sobol' test and the genetic operators employed in PICEAs. Experimental results show improved performance of the PICEAs as more preferences are co-evolved. Additionally, some suggestions for genetic operator settings are provided for non-expert users.

Efficient Bayesian experimental design for contaminant source identification

NASA Astrophysics Data System (ADS)

Zhang, J.; Zeng, L.

2013-12-01

In this study, an efficient full Bayesian approach is developed for the optimal sampling well location design and source parameter identification of groundwater contaminants. An information measure, i.e., the relative entropy, is employed to quantify the information gain from indirect concentration measurements in identifying unknown source parameters such as the release time, strength and location. In this approach, the sampling location that gives the maximum relative entropy is selected as the optimal one. Once the sampling location is determined, a Bayesian approach based on Markov Chain Monte Carlo (MCMC) is used to estimate unknown source parameters. In both the design and estimation, the contaminant transport equation is required to be solved many times to evaluate the likelihood. To reduce the computational burden, an interpolation method based on the adaptive sparse grid is utilized to construct a surrogate for the contaminant transport. The approximated likelihood can be evaluated directly from the surrogate, which greatly accelerates the design and estimation process. The accuracy and efficiency of our approach are demonstrated through numerical case studies. Compared with the traditional optimal design, which is based on the Gaussian linear assumption, the method developed in this study can cope with arbitrary nonlinearity. It can be used to assist in groundwater monitor network design and identification of unknown contaminant sources. Contours of the expected information gain. The optimal observing location corresponds to the maximum value. Posterior marginal probability densities of unknown parameters, the thick solid black lines are for the designed location. For comparison, other 7 lines are for randomly chosen locations. The true values are denoted by vertical lines. It is obvious that the unknown parameters are estimated better with the desinged location.

Back analysis of geomechanical parameters in underground engineering using artificial bee colony.

PubMed

Zhu, Changxing; Zhao, Hongbo; Zhao, Ming

2014-01-01

Accurate geomechanical parameters are critical in tunneling excavation, design, and supporting. In this paper, a displacements back analysis based on artificial bee colony (ABC) algorithm is proposed to identify geomechanical parameters from monitored displacements. ABC was used as global optimal algorithm to search the unknown geomechanical parameters for the problem with analytical solution. To the problem without analytical solution, optimal back analysis is time-consuming, and least square support vector machine (LSSVM) was used to build the relationship between unknown geomechanical parameters and displacement and improve the efficiency of back analysis. The proposed method was applied to a tunnel with analytical solution and a tunnel without analytical solution. The results show the proposed method is feasible.

The Dynamic Radio Sky: Future Directions at cm/m-Wavelengths

NASA Astrophysics Data System (ADS)

Bower, Geoffrey C.; Cordes, J.; Croft, S.; Lazio, J.; Lorimer, D.; McLaughlin, M.

2009-01-01

The time domain of the radio wavelength sky has been only sparsely explored. Nevertheless, recent discoveries from limited surveys and serendipitous discoveries indicate that there is much to be found on timescales from nanoseconds to years and at wavelengths from meters to millimeters. These observations have revealed unexpected phenonmena such as rotating radio transients and coherent pulses from brown dwarfs. Additionally, archival studies have revealed an unknown class of radio transients without radio, optical, or high-energy hosts. The current generation of new meter- and centimeter-wave radio telescopes such as the MWA, LWA, PAPER, and ATA will exploit wide fields of view and flexible digital signal processing to systematically explore radio transient parameter space, as well as lay the scientific and technical foundation for the SKA. Known unknowns that will be the target of future transient surveys include orphan gamma-ray burst afterglows, radio supernovae, tidally-disrupted stars, flare stars, and magnetars.

Minimal Unified Resolution to R_{K^{(*)}} and R(D^{(*)}) Anomalies with Lepton Mixing.

PubMed

Choudhury, Debajyoti; Kundu, Anirban; Mandal, Rusa; Sinha, Rahul

2017-10-13

It is a challenging task to explain, in terms of a simple and compelling new physics scenario, the intriguing discrepancies between the standard model expectations and the data for the neutral-current observables R_{K} and R_{K^{*}}, as well as the charged-current observables R(D) and R(D^{*}). We show that this can be achieved in an effective theory with only two unknown parameters. In addition, this class of models predicts some interesting signatures in the context of both B decays as well as high-energy collisions.

Performance characteristics of an adaptive controller based on least-mean-square filters

NASA Technical Reports Server (NTRS)

Mehta, Rajiv S.; Merhav, Shmuel J.

1986-01-01

A closed loop, adaptive control scheme that uses a least mean square filter as the controller model is presented, along with simulation results that demonstrate the excellent robustness of this scheme. It is shown that the scheme adapts very well to unknown plants, even those that are marginally stable, responds appropriately to changes in plant parameters, and is not unduly affected by additive noise. A heuristic argument for the conditions necessary for convergence is presented. Potential applications and extensions of the scheme are also discussed.

Nonlinear adaptive control system design with asymptotically stable parameter estimation error

NASA Astrophysics Data System (ADS)

Mishkov, Rumen; Darmonski, Stanislav

2018-01-01

The paper presents a new general method for nonlinear adaptive system design with asymptotic stability of the parameter estimation error. The advantages of the approach include asymptotic unknown parameter estimation without persistent excitation and capability to directly control the estimates transient response time. The method proposed modifies the basic parameter estimation dynamics designed via a known nonlinear adaptive control approach. The modification is based on the generalised prediction error, a priori constraints with a hierarchical parameter projection algorithm, and the stable data accumulation concepts. The data accumulation principle is the main tool for achieving asymptotic unknown parameter estimation. It relies on the parametric identifiability system property introduced. Necessary and sufficient conditions for exponential stability of the data accumulation dynamics are derived. The approach is applied in a nonlinear adaptive speed tracking vector control of a three-phase induction motor.

Gaussian process model for extrapolation of scattering observables for complex molecules: From benzene to benzonitrile

NASA Astrophysics Data System (ADS)

Cui, Jie; Li, Zhiying; Krems, Roman V.

2015-10-01

We consider a problem of extrapolating the collision properties of a large polyatomic molecule A-H to make predictions of the dynamical properties for another molecule related to A-H by the substitution of the H atom with a small molecular group X, without explicitly computing the potential energy surface for A-X. We assume that the effect of the -H →-X substitution is embodied in a multidimensional function with unknown parameters characterizing the change of the potential energy surface. We propose to apply the Gaussian Process model to determine the dependence of the dynamical observables on the unknown parameters. This can be used to produce an interval of the observable values which corresponds to physical variations of the potential parameters. We show that the Gaussian Process model combined with classical trajectory calculations can be used to obtain the dependence of the cross sections for collisions of C6H5CN with He on the unknown parameters describing the interaction of the He atom with the CN fragment of the molecule. The unknown parameters are then varied within physically reasonable ranges to produce a prediction uncertainty of the cross sections. The results are normalized to the cross sections for He — C6H6 collisions obtained from quantum scattering calculations in order to provide a prediction interval of the thermally averaged cross sections for collisions of C6H5CN with He.

Neutrino oscillations and Non-Standard Interactions

NASA Astrophysics Data System (ADS)

Farzan, Yasaman; Tórtola, Mariam

2018-02-01

Current neutrino experiments are measuring the neutrino mixing parameters with an unprecedented accuracy. The upcoming generation of neutrino experiments will be sensitive to subdominant oscillation effects that can give information on the yet-unknown neutrino parameters: the Dirac CP-violating phase, the mass ordering and the octant of θ_{23}. Determining the exact values of neutrino mass and mixing parameters is crucial to test neutrino models and flavor symmetries designed to predict these neutrino parameters. In the first part of this review, we summarize the current status of the neutrino oscillation parameter determination. We consider the most recent data from all solar experiments and the atmospheric data from Super-Kamiokande, IceCube and ANTARES. We also implement the data from the reactor neutrino experiments KamLAND, Daya Bay, RENO and Double Chooz as well as the long baseline neutrino data from MINOS, T2K and NOvA. If in addition to the standard interactions, neutrinos have subdominant yet-unknown Non-Standard Interactions (NSI) with matter fields, extracting the values of these parameters will suffer from new degeneracies and ambiguities. We review such effects and formulate the conditions on the NSI parameters under which the precision measurement of neutrino oscillation parameters can be distorted. Like standard weak interactions, the non-standard interaction can be categorized into two groups: Charged Current (CC) NSI and Neutral Current (NC) NSI. Our focus will be mainly on neutral current NSI because it is possible to build a class of models that give rise to sizeable NC NSI with discernible effects on neutrino oscillation. These models are based on new U(1) gauge symmetry with a gauge boson of mass ≲ 10 MeV. The UV complete model should be of course electroweak invariant which in general implies that along with neutrinos, charged fermions also acquire new interactions on which there are strong bounds. We enumerate the bounds that already exist on the electroweak symmetric models and demonstrate that it is possible to build viable models avoiding all these bounds. In the end, we review methods to test these models and suggest approaches to break the degeneracies in deriving neutrino mass parameters caused by NSI.

Market-Based Coordination of Thermostatically Controlled Loads—Part I: A Mechanism Design Formulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Sen; Zhang, Wei; Lian, Jianming

This paper focuses on the coordination of a population of Thermostatically Controlled Loads (TCLs) with unknown parameters to achieve group objectives. The problem involves designing the bidding and market clearing strategy to motivate self-interested users to realize efficient energy allocation subject to a peak power constraint. Using the mechanism design approach, we propose a market-based coordination framework, which can effectively incorporate heterogeneous load dynamics, systematically deal with user preferences, account for the unknown load model parameters, and enable the real-world implementation with limited communication resources. This paper is divided into two parts. Part I presents a mathematical formulation of themore » problem and develops a coordination framework using the mechanism design approach. Part II presents a learning scheme to account for the unknown load model parameters, and evaluates the proposed framework through realistic simulations.« less

A dynamical regularization algorithm for solving inverse source problems of elliptic partial differential equations

NASA Astrophysics Data System (ADS)

Zhang, Ye; Gong, Rongfang; Cheng, Xiaoliang; Gulliksson, Mårten

2018-06-01

This study considers the inverse source problem for elliptic partial differential equations with both Dirichlet and Neumann boundary data. The unknown source term is to be determined by additional boundary conditions. Unlike the existing methods found in the literature, which usually employ the first-order in time gradient-like system (such as the steepest descent methods) for numerically solving the regularized optimization problem with a fixed regularization parameter, we propose a novel method with a second-order in time dissipative gradient-like system and a dynamical selected regularization parameter. A damped symplectic scheme is proposed for the numerical solution. Theoretical analysis is given for both the continuous model and the numerical algorithm. Several numerical examples are provided to show the robustness of the proposed algorithm.

A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI.

PubMed

Pisharady, Pramod Kumar; Sotiropoulos, Stamatios N; Sapiro, Guillermo; Lenglet, Christophe

2017-09-01

We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (under-sampled q-space) multi-shell diffusion MRI data. The multi-shell data is represented in a dictionary form using a non-monoexponential decay model of diffusion, based on continuous gamma distribution of diffusivities. The fiber volume fractions with predefined orientations, which are the unknown parameters, form the dictionary weights. These unknown parameters are estimated with a linear un-mixing framework, using a sparse Bayesian learning algorithm. A localized learning of hyperparameters at each voxel and for each possible fiber orientations improves the parameter estimation. Our experiments using synthetic data from the ISBI 2012 HARDI reconstruction challenge and in-vivo data from the Human Connectome Project demonstrate the improvements.

Study the Seasonal Variability of Plankton and Forage Fish in the Gulf of Khambhat Using Npzfd Model

NASA Astrophysics Data System (ADS)

Kumar, V.; Kumar, S.

2016-02-01

Numerical modelling of marine ecology exploits several assumptions and it is indeed quite challenging to include marine ecological phenomena into a mathematical framework with too many unknown parameters. The governing ordinary differential equations represent the interaction of the biological and chemical processes in a marine environment. The key concern in the development of a numerical models are parameterizations based on output, viz., mathematical modelling of ecological system mainly depends on parameters and its variations. Almost, all constituents of each trophic level of marine food web are depended on phytoplankton, which are mostly influenced by initial slope of P-I curve and nutrient stock in the study domain. Whereas, the earlier plankton dynamic models rarely include a compartment of small fish and as an agent in zooplankton mortality, which is most important for the modelling of higher trophic level of marine species. A compartment of forage fish in the modelling of plankton dynamics has been given more realistic mortality rates of plankton, viz., they feed upon phytoplankton and zooplankton. The inclusion of an additional compartment increases complexity of earlier plankton dynamics model as it introduces additional unknown parameters, which has been specified for performing the numerical simulations.As a case study we applied our analysis to explain the aquatic ecology of Gulf of Khambhat (19o 48' N - 22o20' N, 65o E - 72o40' E), west coast of India, which has rich bio-diversity and a high productive area in the form of plankton and forage fish. It has elevated turbidity and varying geography location, viz., one of the regions among world's ocean with high biological productivity.The model presented in this study is able to bring out the essential features of the observed data; that includes the bimodal oscillations in the observed data, monthly mean chlorophyll-a in the SeaWiFs/MODIS Aqua data and in-situ data of plankton. The additional compartment of forage fish and detritus in NPZFD model represents a major structural difference from the earlier NPZ model.

Mechanical Properties of Transcription

NASA Astrophysics Data System (ADS)

Sevier, Stuart A.; Levine, Herbert

2017-06-01

The mechanical properties of transcription have recently been shown to play a central role in gene expression. However, a full physical characterization of this central biological process is lacking. In this Letter, we introduce a simple description of the basic physical elements of transcription where RNA elongation, RNA polymerase rotation, and DNA supercoiling are coupled. The resulting framework describes the relative amount of RNA polymerase rotation and DNA supercoiling that occurs during RNA elongation. Asymptotic behavior is derived and can be used to experimentally extract unknown mechanical parameters of transcription. Mechanical limits to transcription are incorporated through the addition of a DNA supercoiling-dependent RNA polymerase velocity. This addition can lead to transcriptional stalling and resulting implications for gene expression, chromatin structure and genome organization are discussed.

An Integrated Approach for Aircraft Engine Performance Estimation and Fault Diagnostics

NASA Technical Reports Server (NTRS)

imon, Donald L.; Armstrong, Jeffrey B.

2012-01-01

A Kalman filter-based approach for integrated on-line aircraft engine performance estimation and gas path fault diagnostics is presented. This technique is specifically designed for underdetermined estimation problems where there are more unknown system parameters representing deterioration and faults than available sensor measurements. A previously developed methodology is applied to optimally design a Kalman filter to estimate a vector of tuning parameters, appropriately sized to enable estimation. The estimated tuning parameters can then be transformed into a larger vector of health parameters representing system performance deterioration and fault effects. The results of this study show that basing fault isolation decisions solely on the estimated health parameter vector does not provide ideal results. Furthermore, expanding the number of the health parameters to address additional gas path faults causes a decrease in the estimation accuracy of those health parameters representative of turbomachinery performance deterioration. However, improved fault isolation performance is demonstrated through direct analysis of the estimated tuning parameters produced by the Kalman filter. This was found to provide equivalent or superior accuracy compared to the conventional fault isolation approach based on the analysis of sensed engine outputs, while simplifying online implementation requirements. Results from the application of these techniques to an aircraft engine simulation are presented and discussed.

Optimization of Biomathematical Model Predictions for Cognitive Performance Impairment in Individuals: Accounting for Unknown Traits and Uncertain States in Homeostatic and Circadian Processes

PubMed Central

Van Dongen, Hans P. A.; Mott, Christopher G.; Huang, Jen-Kuang; Mollicone, Daniel J.; McKenzie, Frederic D.; Dinges, David F.

2007-01-01

Current biomathematical models of fatigue and performance do not accurately predict cognitive performance for individuals with a priori unknown degrees of trait vulnerability to sleep loss, do not predict performance reliably when initial conditions are uncertain, and do not yield statistically valid estimates of prediction accuracy. These limitations diminish their usefulness for predicting the performance of individuals in operational environments. To overcome these 3 limitations, a novel modeling approach was developed, based on the expansion of a statistical technique called Bayesian forecasting. The expanded Bayesian forecasting procedure was implemented in the two-process model of sleep regulation, which has been used to predict performance on the basis of the combination of a sleep homeostatic process and a circadian process. Employing the two-process model with the Bayesian forecasting procedure to predict performance for individual subjects in the face of unknown traits and uncertain states entailed subject-specific optimization of 3 trait parameters (homeostatic build-up rate, circadian amplitude, and basal performance level) and 2 initial state parameters (initial homeostatic state and circadian phase angle). Prior information about the distribution of the trait parameters in the population at large was extracted from psychomotor vigilance test (PVT) performance measurements in 10 subjects who had participated in a laboratory experiment with 88 h of total sleep deprivation. The PVT performance data of 3 additional subjects in this experiment were set aside beforehand for use in prospective computer simulations. The simulations involved updating the subject-specific model parameters every time the next performance measurement became available, and then predicting performance 24 h ahead. Comparison of the predictions to the subjects' actual data revealed that as more data became available for the individuals at hand, the performance predictions became increasingly more accurate and had progressively smaller 95% confidence intervals, as the model parameters converged efficiently to those that best characterized each individual. Even when more challenging simulations were run (mimicking a change in the initial homeostatic state; simulating the data to be sparse), the predictions were still considerably more accurate than would have been achieved by the two-process model alone. Although the work described here is still limited to periods of consolidated wakefulness with stable circadian rhythms, the results obtained thus far indicate that the Bayesian forecasting procedure can successfully overcome some of the major outstanding challenges for biomathematical prediction of cognitive performance in operational settings. Citation: Van Dongen HPA; Mott CG; Huang JK; Mollicone DJ; McKenzie FD; Dinges DF. Optimization of biomathematical model predictions for cognitive performance impairment in individuals: accounting for unknown traits and uncertain states in homeostatic and circadian processes. SLEEP 2007;30(9):1129-1143. PMID:17910385

A Potential Proxy of the Second Integral of Motion (I2) in a Rotating Barred Potential

NASA Astrophysics Data System (ADS)

Shen, Juntai; Qin, Yujing

2017-06-01

The only analytically known integral of motion in a 2-D rotating barred potential is the Jacobi constant (EJ). In addition to EJ, regular orbits also obey a second integral of motion (I2) whose analytical form is unknown. We show that the time-averaged characteristics of angular momentum in a rotating bar potential resemble the behavior of the analytically-unknown I2. For a given EJ, regular orbits of various families follow a continuous sequence in the space of net angular momentum and its dispersion ("angular momentum space"). In the limiting case where regular orbits of the well-known x1/x4 orbital families dominate the phase space, the orbital sequence can be monotonically traced by a single parameter, namely the ratio of mean angular momentum to its dispersion. This ratio behaves well even in the 3-D case, and thus may be used as a proxy of I2. The potential proxy of I2 may be used as an efficient way to probe the phase space structure, and a convenient new scheme of orbit classification in addition to the frequency mapping technique.

Polarimetric subspace target detector for SAR data based on the Huynen dihedral model

NASA Astrophysics Data System (ADS)

Larson, Victor J.; Novak, Leslie M.

1995-06-01

Two new polarimetric subspace target detectors are developed based on a dihedral signal model for bright peaks within a spatially extended target signature. The first is a coherent dihedral target detector based on the exact Huynen model for a dihedral. The second is a noncoherent dihedral target detector based on the Huynen model with an extra unknown phase term. Expressions for these polarimetric subspace target detectors are developed for both additive Gaussian clutter and more general additive spherically invariant random vector clutter including the K-distribution. For the case of Gaussian clutter with unknown clutter parameters, constant false alarm rate implementations of these polarimetric subspace target detectors are developed. The performance of these dihedral detectors is demonstrated with real millimeter-wave fully polarimetric SAR data. The coherent dihedral detector which is developed with a more accurate description of a dihedral offers no performance advantage over the noncoherent dihedral detector which is computationally more attractive. The dihedral detectors do a better job of separating a set of tactical military targets from natural clutter compared to a detector that assumes no knowledge about the polarimetric structure of the target signal.

Target Capturing Control for Space Robots with Unknown Mass Properties: A Self-Tuning Method Based on Gyros and Cameras.

PubMed

Li, Zhenyu; Wang, Bin; Liu, Hong

2016-08-30

Satellite capturing with free-floating space robots is still a challenging task due to the non-fixed base and unknown mass property issues. In this paper gyro and eye-in-hand camera data are adopted as an alternative choice for solving this problem. For this improved system, a new modeling approach that reduces the complexity of system control and identification is proposed. With the newly developed model, the space robot is equivalent to a ground-fixed manipulator system. Accordingly, a self-tuning control scheme is applied to handle such a control problem including unknown parameters. To determine the controller parameters, an estimator is designed based on the least-squares technique for identifying the unknown mass properties in real time. The proposed method is tested with a credible 3-dimensional ground verification experimental system, and the experimental results confirm the effectiveness of the proposed control scheme.

Target Capturing Control for Space Robots with Unknown Mass Properties: A Self-Tuning Method Based on Gyros and Cameras

PubMed Central

Li, Zhenyu; Wang, Bin; Liu, Hong

2016-01-01

Satellite capturing with free-floating space robots is still a challenging task due to the non-fixed base and unknown mass property issues. In this paper gyro and eye-in-hand camera data are adopted as an alternative choice for solving this problem. For this improved system, a new modeling approach that reduces the complexity of system control and identification is proposed. With the newly developed model, the space robot is equivalent to a ground-fixed manipulator system. Accordingly, a self-tuning control scheme is applied to handle such a control problem including unknown parameters. To determine the controller parameters, an estimator is designed based on the least-squares technique for identifying the unknown mass properties in real time. The proposed method is tested with a credible 3-dimensional ground verification experimental system, and the experimental results confirm the effectiveness of the proposed control scheme. PMID:27589748

Teaching-learning-based Optimization Algorithm for Parameter Identification in the Design of IIR Filters

NASA Astrophysics Data System (ADS)

Singh, R.; Verma, H. K.

2013-12-01

This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Selle, J.E.

A modification was made to the Kaufman method of calculating binary phase diagrams to permit calculation of intra-rare earth diagrams. Atomic volumes for all phases, real or hypothetical, are necessary to determine interaction parameters for calculation of complete diagrams. The procedures used to determine unknown atomic volumes are describes. Also, procedures are described for determining lattice stability parameters for unknown transformations. Results are presented on the calculation of intra-rare earth diagrams between both trivalent and divalent rare earths. 13 refs., 36 figs., 11 tabs.

Assessment of the accuracy of plasma shape reconstruction by the Cauchy condition surface method in JT-60SA

NASA Astrophysics Data System (ADS)

Miyata, Y.; Suzuki, T.; Takechi, M.; Urano, H.; Ide, S.

2015-07-01

For the purpose of stable plasma equilibrium control and detailed analysis, it is essential to reconstruct an accurate plasma boundary on the poloidal cross section in tokamak devices. The Cauchy condition surface (CCS) method is a numerical approach for calculating the spatial distribution of the magnetic flux outside a hypothetical surface and reconstructing the plasma boundary from the magnetic measurements located outside the plasma. The accuracy of the plasma shape reconstruction has been assessed by comparing the CCS method and an equilibrium calculation in JT-60SA with a high elongation and triangularity of plasma shape. The CCS, on which both Dirichlet and Neumann conditions are unknown, is defined as a hypothetical surface located inside the real plasma region. The accuracy of the plasma shape reconstruction is sensitive to the CCS free parameters such as the number of unknown parameters and the shape in JT-60SA. It is found that the optimum number of unknown parameters and the size of the CCS that minimizes errors in the reconstructed plasma shape are in proportion to the plasma size. Furthermore, it is shown that the accuracy of the plasma shape reconstruction is greatly improved using the optimum number of unknown parameters and shape of the CCS, and the reachable reconstruction errors in plasma shape and locations of strike points are within the target ranges in JT-60SA.

Concurrent hyperthermia estimation schemes based on extended Kalman filtering and reduced-order modelling.

PubMed

Potocki, J K; Tharp, H S

1993-01-01

The success of treating cancerous tissue with heat depends on the temperature elevation, the amount of tissue elevated to that temperature, and the length of time that the tissue temperature is elevated. In clinical situations the temperature of most of the treated tissue volume is unknown, because only a small number of temperature sensors can be inserted into the tissue. A state space model based on a finite difference approximation of the bioheat transfer equation (BHTE) is developed for identification purposes. A full-order extended Kalman filter (EKF) is designed to estimate both the unknown blood perfusion parameters and the temperature at unmeasured locations. Two reduced-order estimators are designed as computationally less intensive alternatives to the full-order EKF. Simulation results show that the success of the estimation scheme depends strongly on the number and location of the temperature sensors. Superior results occur when a temperature sensor exists in each unknown blood perfusion zone, and the number of sensors is at least as large as the number of unknown perfusion zones. Unacceptable results occur when there are more unknown perfusion parameters than temperature sensors, or when the sensors are placed in locations that do not sample the unknown perfusion information.

Distributed robust adaptive control of high order nonlinear multi agent systems.

PubMed

Hashemi, Mahnaz; Shahgholian, Ghazanfar

2018-03-01

In this paper, a robust adaptive neural network based controller is presented for multi agent high order nonlinear systems with unknown nonlinear functions, unknown control gains and unknown actuator failures. At first, Neural Network (NN) is used to approximate the nonlinear uncertainty terms derived from the controller design procedure for the followers. Then, a novel distributed robust adaptive controller is developed by combining the backstepping method and the Dynamic Surface Control (DSC) approach. The proposed controllers are distributed in the sense that the designed controller for each follower agent only requires relative state information between itself and its neighbors. By using the Young's inequality, only few parameters need to be tuned regardless of NN nodes number. Accordingly, the problems of dimensionality curse and explosion of complexity are counteracted, simultaneously. New adaptive laws are designed by choosing the appropriate Lyapunov-Krasovskii functionals. The proposed approach proves the boundedness of all the closed-loop signals in addition to the convergence of the distributed tracking errors to a small neighborhood of the origin. Simulation results indicate that the proposed controller is effective and robust. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Robust adaptive precision motion control of hydraulic actuators with valve dead-zone compensation.

PubMed

Deng, Wenxiang; Yao, Jianyong; Ma, Dawei

2017-09-01

This paper addresses the high performance motion control of hydraulic actuators with parametric uncertainties, unmodeled disturbances and unknown valve dead-zone. By constructing a smooth dead-zone inverse, a robust adaptive controller is proposed via backstepping method, in which adaptive law is synthesized to deal with parametric uncertainties and a continuous nonlinear robust control law to suppress unmodeled disturbances. Since the unknown dead-zone parameters can be estimated by adaptive law and then the effect of dead-zone can be compensated effectively via inverse operation, improved tracking performance can be expected. In addition, the disturbance upper bounds can also be updated online by adaptive laws, which increases the controller operability in practice. The Lyapunov based stability analysis shows that excellent asymptotic output tracking with zero steady-state error can be achieved by the developed controller even in the presence of unmodeled disturbance and unknown valve dead-zone. Finally, the proposed control strategy is experimentally tested on a servovalve controlled hydraulic actuation system subjected to an artificial valve dead-zone. Comparative experimental results are obtained to illustrate the effectiveness of the proposed control scheme. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Direct Adaptive Control of Systems with Actuator Failures: State of the Art and Continuing Challenges

NASA Technical Reports Server (NTRS)

Tao, Gang; Joshi, Suresh M.

2008-01-01

In this paper, the problem of controlling systems with failures and faults is introduced, and an overview of recent work on direct adaptive control for compensation of uncertain actuator failures is presented. Actuator failures may be characterized by some unknown system inputs being stuck at some unknown (fixed or varying) values at unknown time instants, that cannot be influenced by the control signals. The key task of adaptive compensation is to design the control signals in such a manner that the remaining actuators can automatically and seamlessly take over for the failed ones, and achieve desired stability and asymptotic tracking. A certain degree of redundancy is necessary to accomplish failure compensation. The objective of adaptive control design is to effectively use the available actuation redundancy to handle failures without the knowledge of the failure patterns, parameters, and time of occurrence. This is a challenging problem because failures introduce large uncertainties in the dynamic structure of the system, in addition to parametric uncertainties and unknown disturbances. The paper addresses some theoretical issues in adaptive actuator failure compensation: actuator failure modeling, redundant actuation requirements, plant-model matching, error system dynamics, adaptation laws, and stability, tracking, and performance analysis. Adaptive control designs can be shown to effectively handle uncertain actuator failures without explicit failure detection. Some open technical challenges and research problems in this important research area are discussed.

An Adaptive Moving Target Imaging Method for Bistatic Forward-Looking SAR Using Keystone Transform and Optimization NLCS.

PubMed

Li, Zhongyu; Wu, Junjie; Huang, Yulin; Yang, Haiguang; Yang, Jianyu

2017-01-23

Bistatic forward-looking SAR (BFSAR) is a kind of bistatic synthetic aperture radar (SAR) system that can image forward-looking terrain in the flight direction of an aircraft. Until now, BFSAR imaging theories and methods for a stationary scene have been researched thoroughly. However, for moving-target imaging with BFSAR, the non-cooperative movement of the moving target induces some new issues: (I) large and unknown range cell migration (RCM) (including range walk and high-order RCM); (II) the spatial-variances of the Doppler parameters (including the Doppler centroid and high-order Doppler) are not only unknown, but also nonlinear for different point-scatterers. In this paper, we put forward an adaptive moving-target imaging method for BFSAR. First, the large and unknown range walk is corrected by applying keystone transform over the whole received echo, and then, the relationships among the unknown high-order RCM, the nonlinear spatial-variances of the Doppler parameters, and the speed of the mover, are established. After that, using an optimization nonlinear chirp scaling (NLCS) technique, not only can the unknown high-order RCM be accurately corrected, but also the nonlinear spatial-variances of the Doppler parameters can be balanced. At last, a high-order polynomial filter is applied to compress the whole azimuth data of the moving target. Numerical simulations verify the effectiveness of the proposed method.

Adaptive Optimal Control Using Frequency Selective Information of the System Uncertainty With Application to Unmanned Aircraft.

PubMed

Maity, Arnab; Hocht, Leonhard; Heise, Christian; Holzapfel, Florian

2018-01-01

A new efficient adaptive optimal control approach is presented in this paper based on the indirect model reference adaptive control (MRAC) architecture for improvement of adaptation and tracking performance of the uncertain system. The system accounts here for both matched and unmatched unknown uncertainties that can act as plant as well as input effectiveness failures or damages. For adaptation of the unknown parameters of these uncertainties, the frequency selective learning approach is used. Its idea is to compute a filtered expression of the system uncertainty using multiple filters based on online instantaneous information, which is used for augmentation of the update law. It is capable of adjusting a sudden change in system dynamics without depending on high adaptation gains and can satisfy exponential parameter error convergence under certain conditions in the presence of structured matched and unmatched uncertainties as well. Additionally, the controller of the MRAC system is designed using a new optimal control method. This method is a new linear quadratic regulator-based optimal control formulation for both output regulation and command tracking problems. It provides a closed-form control solution. The proposed overall approach is applied in a control of lateral dynamics of an unmanned aircraft problem to show its effectiveness.

Dynamic parameter identification of robot arms with servo-controlled electrical motors

NASA Astrophysics Data System (ADS)

Jiang, Zhao-Hui; Senda, Hiroshi

2005-12-01

This paper addresses the issue of dynamic parameter identification of the robot manipulator with servo-controlled electrical motors. An assumption is made that all kinematical parameters, such as link lengths, are known, and only dynamic parameters containing mass, moment of inertia, and their functions need to be identified. First, we derive dynamics of the robot arm with a linear form of the unknown dynamic parameters by taking dynamic characteristics of the motor and servo unit into consideration. Then, we implement the parameter identification approach to identify the unknown parameters with respect to individual link separately. A pseudo-inverse matrix is used for formulation of the parameter identification. The optimal solution is guaranteed in a sense of least-squares of the mean errors. A Direct Drive (DD) SCARA type industrial robot arm AdeptOne is used as an application example of the parameter identification. Simulations and experiments for both open loop and close loop controls are carried out. Comparison of the results confirms the correctness and usefulness of the parameter identification and the derived dynamic model.

Gaussian process model for extrapolation of scattering observables for complex molecules: From benzene to benzonitrile

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cui, Jie; Krems, Roman V.; Li, Zhiying

2015-10-21

We consider a problem of extrapolating the collision properties of a large polyatomic molecule A–H to make predictions of the dynamical properties for another molecule related to A–H by the substitution of the H atom with a small molecular group X, without explicitly computing the potential energy surface for A–X. We assume that the effect of the −H →−X substitution is embodied in a multidimensional function with unknown parameters characterizing the change of the potential energy surface. We propose to apply the Gaussian Process model to determine the dependence of the dynamical observables on the unknown parameters. This can bemore » used to produce an interval of the observable values which corresponds to physical variations of the potential parameters. We show that the Gaussian Process model combined with classical trajectory calculations can be used to obtain the dependence of the cross sections for collisions of C{sub 6}H{sub 5}CN with He on the unknown parameters describing the interaction of the He atom with the CN fragment of the molecule. The unknown parameters are then varied within physically reasonable ranges to produce a prediction uncertainty of the cross sections. The results are normalized to the cross sections for He — C{sub 6}H{sub 6} collisions obtained from quantum scattering calculations in order to provide a prediction interval of the thermally averaged cross sections for collisions of C{sub 6}H{sub 5}CN with He.« less

An almost-parameter-free harmony search algorithm for groundwater pollution source identification.

PubMed

Jiang, Simin; Zhang, Yali; Wang, Pei; Zheng, Maohui

2013-01-01

The spatiotemporal characterization of unknown sources of groundwater pollution is frequently encountered in environmental problems. This study adopts a simulation-optimization approach that combines a contaminant transport simulation model with a heuristic harmony search algorithm to identify unknown pollution sources. In the proposed methodology, an almost-parameter-free harmony search algorithm is developed. The performance of this methodology is evaluated on an illustrative groundwater pollution source identification problem, and the identified results indicate that the proposed almost-parameter-free harmony search algorithm-based optimization model can give satisfactory estimations, even when the irregular geometry, erroneous monitoring data, and prior information shortage of potential locations are considered.

Atmospherical simulations of the OMEGA/MEX observations

NASA Astrophysics Data System (ADS)

Melchiorri, R.; Drossart, P.; Combes, M.; Encrenaz, T.; Fouchet, T.; Forget, F.; Bibring, J. P.; Ignatiev, N.; Moroz, V.; OMEGA Team

The modelization of the atmospheric contribution in the martian spectrum is an important step for the OMEGA data analysis.A full line by line radiative transfer calculation is made for the gas absorption; the dust opacity component, in a first approximation, is calculated as an optically thin additive component.Due to the large number of parameters needed in the calculations, the building of a huge data base to be interpolated is not envisageable, for each observed OMEGA spectrum with calculation for all the involved parameters (atmospheric pressure, water abundance, CO abundance, dust opacity and geometric angles of observation). The simulation of the observations allows us to fix all the orbital parameters and leave the unknown parameters as the only variables.Starting from the predictions of the current meteorological models of Mars we build a smaller data base corresponding on each observation. We present here a first order simulation, which consists in retrieving atmospheric contribution from the solar reflected component as a multiplicative (for gas absorption) and an additive component (for suspended dust contribution); although a fully consistent approach will require to include surface and atmosphere contributions together in synthetic calculations, this approach is sufficient for retrieving mineralogic information cleaned from atmospheric absorption at first order.First comparison to OMEGA spectra will be presented, with first order retrieval of CO2 pressure, CO and H2O abundance, and dust opacity.

Bayesian model comparison and parameter inference in systems biology using nested sampling.

PubMed

Pullen, Nick; Morris, Richard J

2014-01-01

Inferring parameters for models of biological processes is a current challenge in systems biology, as is the related problem of comparing competing models that explain the data. In this work we apply Skilling's nested sampling to address both of these problems. Nested sampling is a Bayesian method for exploring parameter space that transforms a multi-dimensional integral to a 1D integration over likelihood space. This approach focuses on the computation of the marginal likelihood or evidence. The ratio of evidences of different models leads to the Bayes factor, which can be used for model comparison. We demonstrate how nested sampling can be used to reverse-engineer a system's behaviour whilst accounting for the uncertainty in the results. The effect of missing initial conditions of the variables as well as unknown parameters is investigated. We show how the evidence and the model ranking can change as a function of the available data. Furthermore, the addition of data from extra variables of the system can deliver more information for model comparison than increasing the data from one variable, thus providing a basis for experimental design.

A LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) FOR NONLINEAR SYSTEM IDENTIFICATION

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.; Lofberg, Johan; Brenner, Martin J.

2006-01-01

Identification of parametric nonlinear models involves estimating unknown parameters and detecting its underlying structure. Structure computation is concerned with selecting a subset of parameters to give a parsimonious description of the system which may afford greater insight into the functionality of the system or a simpler controller design. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of nonlinear systems. The LASSO minimises the residual sum of squares by the addition of a 1 penalty term on the parameter vector of the traditional 2 minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to pseudolinear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. The performance of this LASSO structure detection method was evaluated by using it to estimate the structure of a nonlinear polynomial model. Applicability of the method to more complex systems such as those encountered in aerospace applications was shown by identifying a parsimonious system description of the F/A-18 Active Aeroelastic Wing using flight test data.

Procedures and results related to the direct determination of gravity anomalies from satellite and terrestrial gravity data

NASA Technical Reports Server (NTRS)

Rapp, R. H.

1974-01-01

The equations needed for the incorporation of gravity anomalies as unknown parameters in an orbit determination program are described. These equations were implemented in the Geodyn computer program which was used to process optical satellite observations. The arc dependent parameter unknowns, 184 unknown 15 deg and coordinates of 7 tracking stations were considered. Up to 39 arcs (5 to 7 days) involving 10 different satellites, were processed. An anomaly solution from the satellite data and a combination solution with 15 deg terrestrial anomalies were made. The limited data samples indicate that the method works. The 15 deg anomalies from various solutions and the potential coefficients implied by the different solutions are reported.

Monte Carlo sensitivity analysis of unknown parameters in hazardous materials transportation risk assessment.

PubMed

Pet-Armacost, J J; Sepulveda, J; Sakude, M

1999-12-01

The US Department of Transportation was interested in the risks associated with transporting Hydrazine in tanks with and without relief devices. Hydrazine is both highly toxic and flammable, as well as corrosive. Consequently, there was a conflict as to whether a relief device should be used or not. Data were not available on the impact of relief devices on release probabilities or the impact of Hydrazine on the likelihood of fires and explosions. In this paper, a Monte Carlo sensitivity analysis of the unknown parameters was used to assess the risks associated with highway transport of Hydrazine. To help determine whether or not relief devices should be used, fault trees and event trees were used to model the sequences of events that could lead to adverse consequences during transport of Hydrazine. The event probabilities in the event trees were derived as functions of the parameters whose effects were not known. The impacts of these parameters on the risk of toxic exposures, fires, and explosions were analyzed through a Monte Carlo sensitivity analysis and analyzed statistically through an analysis of variance. The analysis allowed the determination of which of the unknown parameters had a significant impact on the risks. It also provided the necessary support to a critical transportation decision even though the values of several key parameters were not known.

Uncertainties in extracted parameters of a Gaussian emission line profile with continuum background.

PubMed

Minin, Serge; Kamalabadi, Farzad

2009-12-20

We derive analytical equations for uncertainties in parameters extracted by nonlinear least-squares fitting of a Gaussian emission function with an unknown continuum background component in the presence of additive white Gaussian noise. The derivation is based on the inversion of the full curvature matrix (equivalent to Fisher information matrix) of the least-squares error, chi(2), in a four-variable fitting parameter space. The derived uncertainty formulas (equivalent to Cramer-Rao error bounds) are found to be in good agreement with the numerically computed uncertainties from a large ensemble of simulated measurements. The derived formulas can be used for estimating minimum achievable errors for a given signal-to-noise ratio and for investigating some aspects of measurement setup trade-offs and optimization. While the intended application is Fabry-Perot spectroscopy for wind and temperature measurements in the upper atmosphere, the derivation is generic and applicable to other spectroscopy problems with a Gaussian line shape.

Using partially labeled data for normal mixture identification with application to class definition

NASA Technical Reports Server (NTRS)

Shahshahani, Behzad M.; Landgrebe, David A.

1992-01-01

The problem of estimating the parameters of a normal mixture density when, in addition to the unlabeled samples, sets of partially labeled samples are available is addressed. The density of the multidimensional feature space is modeled with a normal mixture. It is assumed that the set of components of the mixture can be partitioned into several classes and that training samples are available from each class. Since for any training sample the class of origin is known but the exact component of origin within the corresponding class is unknown, the training samples as considered to be partially labeled. The EM iterative equations are derived for estimating the parameters of the normal mixture in the presence of partially labeled samples. These equations can be used to combine the supervised and nonsupervised learning processes.

Numerical solution to generalized Burgers'-Fisher equation using Exp-function method hybridized with heuristic computation.

PubMed

Malik, Suheel Abdullah; Qureshi, Ijaz Mansoor; Amir, Muhammad; Malik, Aqdas Naveed; Haq, Ihsanul

2015-01-01

In this paper, a new heuristic scheme for the approximate solution of the generalized Burgers'-Fisher equation is proposed. The scheme is based on the hybridization of Exp-function method with nature inspired algorithm. The given nonlinear partial differential equation (NPDE) through substitution is converted into a nonlinear ordinary differential equation (NODE). The travelling wave solution is approximated by the Exp-function method with unknown parameters. The unknown parameters are estimated by transforming the NODE into an equivalent global error minimization problem by using a fitness function. The popular genetic algorithm (GA) is used to solve the minimization problem, and to achieve the unknown parameters. The proposed scheme is successfully implemented to solve the generalized Burgers'-Fisher equation. The comparison of numerical results with the exact solutions, and the solutions obtained using some traditional methods, including adomian decomposition method (ADM), homotopy perturbation method (HPM), and optimal homotopy asymptotic method (OHAM), show that the suggested scheme is fairly accurate and viable for solving such problems.

Numerical Solution to Generalized Burgers'-Fisher Equation Using Exp-Function Method Hybridized with Heuristic Computation

PubMed Central

Malik, Suheel Abdullah; Qureshi, Ijaz Mansoor; Amir, Muhammad; Malik, Aqdas Naveed; Haq, Ihsanul

2015-01-01

In this paper, a new heuristic scheme for the approximate solution of the generalized Burgers'-Fisher equation is proposed. The scheme is based on the hybridization of Exp-function method with nature inspired algorithm. The given nonlinear partial differential equation (NPDE) through substitution is converted into a nonlinear ordinary differential equation (NODE). The travelling wave solution is approximated by the Exp-function method with unknown parameters. The unknown parameters are estimated by transforming the NODE into an equivalent global error minimization problem by using a fitness function. The popular genetic algorithm (GA) is used to solve the minimization problem, and to achieve the unknown parameters. The proposed scheme is successfully implemented to solve the generalized Burgers'-Fisher equation. The comparison of numerical results with the exact solutions, and the solutions obtained using some traditional methods, including adomian decomposition method (ADM), homotopy perturbation method (HPM), and optimal homotopy asymptotic method (OHAM), show that the suggested scheme is fairly accurate and viable for solving such problems. PMID:25811858

Reconstructing high-dimensional two-photon entangled states via compressive sensing

PubMed Central

Tonolini, Francesco; Chan, Susan; Agnew, Megan; Lindsay, Alan; Leach, Jonathan

2014-01-01

Accurately establishing the state of large-scale quantum systems is an important tool in quantum information science; however, the large number of unknown parameters hinders the rapid characterisation of such states, and reconstruction procedures can become prohibitively time-consuming. Compressive sensing, a procedure for solving inverse problems by incorporating prior knowledge about the form of the solution, provides an attractive alternative to the problem of high-dimensional quantum state characterisation. Using a modified version of compressive sensing that incorporates the principles of singular value thresholding, we reconstruct the density matrix of a high-dimensional two-photon entangled system. The dimension of each photon is equal to d = 17, corresponding to a system of 83521 unknown real parameters. Accurate reconstruction is achieved with approximately 2500 measurements, only 3% of the total number of unknown parameters in the state. The algorithm we develop is fast, computationally inexpensive, and applicable to a wide range of quantum states, thus demonstrating compressive sensing as an effective technique for measuring the state of large-scale quantum systems. PMID:25306850

Evolutionary algorithm based heuristic scheme for nonlinear heat transfer equations.

PubMed

Ullah, Azmat; Malik, Suheel Abdullah; Alimgeer, Khurram Saleem

2018-01-01

In this paper, a hybrid heuristic scheme based on two different basis functions i.e. Log Sigmoid and Bernstein Polynomial with unknown parameters is used for solving the nonlinear heat transfer equations efficiently. The proposed technique transforms the given nonlinear ordinary differential equation into an equivalent global error minimization problem. Trial solution for the given nonlinear differential equation is formulated using a fitness function with unknown parameters. The proposed hybrid scheme of Genetic Algorithm (GA) with Interior Point Algorithm (IPA) is opted to solve the minimization problem and to achieve the optimal values of unknown parameters. The effectiveness of the proposed scheme is validated by solving nonlinear heat transfer equations. The results obtained by the proposed scheme are compared and found in sharp agreement with both the exact solution and solution obtained by Haar Wavelet-Quasilinearization technique which witnesses the effectiveness and viability of the suggested scheme. Moreover, the statistical analysis is also conducted for investigating the stability and reliability of the presented scheme.

Inverse modeling with RZWQM2 to predict water quality

USDA-ARS?s Scientific Manuscript database

Agricultural systems models such as RZWQM2 are complex and have numerous parameters that are unknown and difficult to estimate. Inverse modeling provides an objective statistical basis for calibration that involves simultaneous adjustment of model parameters and yields parameter confidence intervals...

Assessment of the accuracy of plasma shape reconstruction by the Cauchy condition surface method in JT-60SA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miyata, Y.; Suzuki, T.; Takechi, M.

2015-07-15

For the purpose of stable plasma equilibrium control and detailed analysis, it is essential to reconstruct an accurate plasma boundary on the poloidal cross section in tokamak devices. The Cauchy condition surface (CCS) method is a numerical approach for calculating the spatial distribution of the magnetic flux outside a hypothetical surface and reconstructing the plasma boundary from the magnetic measurements located outside the plasma. The accuracy of the plasma shape reconstruction has been assessed by comparing the CCS method and an equilibrium calculation in JT-60SA with a high elongation and triangularity of plasma shape. The CCS, on which both Dirichletmore » and Neumann conditions are unknown, is defined as a hypothetical surface located inside the real plasma region. The accuracy of the plasma shape reconstruction is sensitive to the CCS free parameters such as the number of unknown parameters and the shape in JT-60SA. It is found that the optimum number of unknown parameters and the size of the CCS that minimizes errors in the reconstructed plasma shape are in proportion to the plasma size. Furthermore, it is shown that the accuracy of the plasma shape reconstruction is greatly improved using the optimum number of unknown parameters and shape of the CCS, and the reachable reconstruction errors in plasma shape and locations of strike points are within the target ranges in JT-60SA.« less

Furosemide/Fludrocortisone Test and Clinical Parameters to Diagnose Incomplete Distal Renal Tubular Acidosis in Kidney Stone Formers.

PubMed

Dhayat, Nasser A; Gradwell, Michael W; Pathare, Ganesh; Anderegg, Manuel; Schneider, Lisa; Luethi, David; Mattmann, Cedric; Moe, Orson W; Vogt, Bruno; Fuster, Daniel G

2017-09-07

Incomplete distal renal tubular acidosis is a well known cause of calcareous nephrolithiasis but the prevalence is unknown, mostly due to lack of accepted diagnostic tests and criteria. The ammonium chloride test is considered as gold standard for the diagnosis of incomplete distal renal tubular acidosis, but the furosemide/fludrocortisone test was recently proposed as an alternative. Because of the lack of rigorous comparative studies, the validity of the furosemide/fludrocortisone test in stone formers remains unknown. In addition, the performance of conventional, nonprovocative parameters in predicting incomplete distal renal tubular acidosis has not been studied. We conducted a prospective study in an unselected cohort of 170 stone formers that underwent sequential ammonium chloride and furosemide/fludrocortisone testing. Using the ammonium chloride test as gold standard, the prevalence of incomplete distal renal tubular acidosis was 8%. Sensitivity and specificity of the furosemide/fludrocortisone test were 77% and 85%, respectively, yielding a positive predictive value of 30% and a negative predictive value of 98%. Testing of several nonprovocative clinical parameters in the prediction of incomplete distal renal tubular acidosis revealed fasting morning urinary pH and plasma potassium as the most discriminative parameters. The combination of a fasting morning urinary threshold pH <5.3 with a plasma potassium threshold >3.8 mEq/L yielded a negative predictive value of 98% with a sensitivity of 85% and a specificity of 77% for the diagnosis of incomplete distal renal tubular acidosis. The furosemide/fludrocortisone test can be used for incomplete distal renal tubular acidosis screening in stone formers, but an abnormal furosemide/fludrocortisone test result needs confirmation by ammonium chloride testing. Our data furthermore indicate that incomplete distal renal tubular acidosis can reliably be excluded in stone formers by use of nonprovocative clinical parameters. Copyright © 2017 by the American Society of Nephrology.

Adaptive exponential synchronization of complex-valued Cohen-Grossberg neural networks with known and unknown parameters.

PubMed

Hu, Jin; Zeng, Chunna

2017-02-01

The complex-valued Cohen-Grossberg neural network is a special kind of complex-valued neural network. In this paper, the synchronization problem of a class of complex-valued Cohen-Grossberg neural networks with known and unknown parameters is investigated. By using Lyapunov functionals and the adaptive control method based on parameter identification, some adaptive feedback schemes are proposed to achieve synchronization exponentially between the drive and response systems. The results obtained in this paper have extended and improved some previous works on adaptive synchronization of Cohen-Grossberg neural networks. Finally, two numerical examples are given to demonstrate the effectiveness of the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Analysis of multinomial models with unknown index using data augmentation

USGS Publications Warehouse

Royle, J. Andrew; Dorazio, R.M.; Link, W.A.

2007-01-01

Multinomial models with unknown index ('sample size') arise in many practical settings. In practice, Bayesian analysis of such models has proved difficult because the dimension of the parameter space is not fixed, being in some cases a function of the unknown index. We describe a data augmentation approach to the analysis of this class of models that provides for a generic and efficient Bayesian implementation. Under this approach, the data are augmented with all-zero detection histories. The resulting augmented dataset is modeled as a zero-inflated version of the complete-data model where an estimable zero-inflation parameter takes the place of the unknown multinomial index. Interestingly, data augmentation can be justified as being equivalent to imposing a discrete uniform prior on the multinomial index. We provide three examples involving estimating the size of an animal population, estimating the number of diabetes cases in a population using the Rasch model, and the motivating example of estimating the number of species in an animal community with latent probabilities of species occurrence and detection.

CONSTRAINTS FROM ASYMMETRIC HEATING: INVESTIGATING THE EPSILON AURIGAE DISK

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pearson, Richard L. III; Stencel, Robert E., E-mail: richard.pearson@du.edu, E-mail: robert.stencel@du.edu

2015-01-01

Epsilon Aurigae is a long-period eclipsing binary that likely contains an F0Ia star and a circumstellar disk enshrouding a hidden companion, assumed to be a main-sequence B star. High uncertainty in its parallax has kept the evolutionary status of the system in question and, hence, the true nature of each component. This unknown, as well as the absence of solid state spectral features in the infrared, requires an investigation of a wide parameter space by means of both analytic and Monte Carlo radiative transfer (MCRT) methods. The first MCRT models of epsilon Aurigae that include all three system components aremore » presented here. We seek additional system parameter constraints by melding analytic approximations with MCRT outputs (e.g., dust temperatures) on a first-order level. The MCRT models investigate the effects of various parameters on the disk-edge temperatures; these include two distances, three particle size distributions, three compositions, and two disk masses, resulting in 36 independent models. Specifically, the MCRT temperatures permit analytic calculations of effective heating and cooling curves along the disk edge. These are used to calculate representative observed fluxes and corresponding temperatures. This novel application of thermal properties provides the basis for utilization of other binary systems containing disks. We find degeneracies in the model fits for the various parameter sets. However, the results show a preference for a carbon disk with particle size distributions ≥10 μm. Additionally, a linear correlation between the MCRT noon and basal temperatures serves as a tool for effectively eliminating portions of the parameter space.« less

Adaptive backstepping fault-tolerant control for flexible spacecraft with unknown bounded disturbances and actuator failures.

PubMed

Jiang, Ye; Hu, Qinglei; Ma, Guangfu

2010-01-01

In this paper, a robust adaptive fault-tolerant control approach to attitude tracking of flexible spacecraft is proposed for use in situations when there are reaction wheel/actuator failures, persistent bounded disturbances and unknown inertia parameter uncertainties. The controller is designed based on an adaptive backstepping sliding mode control scheme, and a sufficient condition under which this control law can render the system semi-globally input-to-state stable is also provided such that the closed-loop system is robust with respect to any disturbance within a quantifiable restriction on the amplitude, as well as the set of initial conditions, if the control gains are designed appropriately. Moreover, in the design, the control law does not need a fault detection and isolation mechanism even if the failure time instants, patterns and values on actuator failures are also unknown for the designers, as motivated from a practical spacecraft control application. In addition to detailed derivations of the new controller design and a rigorous sketch of all the associated stability and attitude error convergence proofs, illustrative simulation results of an application to flexible spacecraft show that high precise attitude control and vibration suppression are successfully achieved using various scenarios of controlling effective failures. 2009. Published by Elsevier Ltd.

Finite-time sliding surface constrained control for a robot manipulator with an unknown deadzone and disturbance.

PubMed

Ik Han, Seong; Lee, Jangmyung

2016-11-01

This paper presents finite-time sliding mode control (FSMC) with predefined constraints for the tracking error and sliding surface in order to obtain robust positioning of a robot manipulator with input nonlinearity due to an unknown deadzone and external disturbance. An assumed model feedforward FSMC was designed to avoid tedious identification procedures for the manipulator parameters and to obtain a fast response time. Two constraint switching control functions based on the tracking error and finite-time sliding surface were added to the FSMC to guarantee the predefined tracking performance despite the presence of an unknown deadzone and disturbance. The tracking error due to the deadzone and disturbance can be suppressed within the predefined error boundary simply by tuning the gain value of the constraint switching function and without the addition of an extra compensator. Therefore, the designed constraint controller has a simpler structure than conventional transformed error constraint methods and the sliding surface constraint scheme can also indirectly guarantee the tracking error constraint while being more stable than the tracking error constraint control. A simulation and experiment were performed on an articulated robot manipulator to validate the proposed control schemes. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Inverse and forward modeling under uncertainty using MRE-based Bayesian approach

NASA Astrophysics Data System (ADS)

Hou, Z.; Rubin, Y.

2004-12-01

A stochastic inverse approach for subsurface characterization is proposed and applied to shallow vadose zone at a winery field site in north California and to a gas reservoir at the Ormen Lange field site in the North Sea. The approach is formulated in a Bayesian-stochastic framework, whereby the unknown parameters are identified in terms of their statistical moments or their probabilities. Instead of the traditional single-valued estimation /prediction provided by deterministic methods, the approach gives a probability distribution for an unknown parameter. This allows calculating the mean, the mode, and the confidence interval, which is useful for a rational treatment of uncertainty and its consequences. The approach also allows incorporating data of various types and different error levels, including measurements of state variables as well as information such as bounds on or statistical moments of the unknown parameters, which may represent prior information. To obtain minimally subjective prior probabilities required for the Bayesian approach, the principle of Minimum Relative Entropy (MRE) is employed. The approach is tested in field sites for flow parameters identification and soil moisture estimation in the vadose zone and for gas saturation estimation at great depth below the ocean floor. Results indicate the potential of coupling various types of field data within a MRE-based Bayesian formalism for improving the estimation of the parameters of interest.

Mixed linear-non-linear inversion of crustal deformation data: Bayesian inference of model, weighting and regularization parameters

NASA Astrophysics Data System (ADS)

Fukuda, Jun'ichi; Johnson, Kaj M.

2010-06-01

We present a unified theoretical framework and solution method for probabilistic, Bayesian inversions of crustal deformation data. The inversions involve multiple data sets with unknown relative weights, model parameters that are related linearly or non-linearly through theoretic models to observations, prior information on model parameters and regularization priors to stabilize underdetermined problems. To efficiently handle non-linear inversions in which some of the model parameters are linearly related to the observations, this method combines both analytical least-squares solutions and a Monte Carlo sampling technique. In this method, model parameters that are linearly and non-linearly related to observations, relative weights of multiple data sets and relative weights of prior information and regularization priors are determined in a unified Bayesian framework. In this paper, we define the mixed linear-non-linear inverse problem, outline the theoretical basis for the method, provide a step-by-step algorithm for the inversion, validate the inversion method using synthetic data and apply the method to two real data sets. We apply the method to inversions of multiple geodetic data sets with unknown relative data weights for interseismic fault slip and locking depth. We also apply the method to the problem of estimating the spatial distribution of coseismic slip on faults with unknown fault geometry, relative data weights and smoothing regularization weight.

Handling the unknown soil hydraulic parameters in data assimilation for unsaturated flow problems

NASA Astrophysics Data System (ADS)

Lange, Natascha; Erdal, Daniel; Neuweiler, Insa

2017-04-01

Model predictions of flow in the unsaturated zone require the soil hydraulic parameters. However, these parameters cannot be determined easily in applications, in particular if observations are indirect and cover only a small range of possible states. Correlation of parameters or their correlation in the range of states that are observed is a problem, as different parameter combinations may reproduce approximately the same measured water content. In field campaigns this problem can be helped by adding more measurement devices. Often, observation networks are designed to feed models for long term prediction purposes (i.e. for weather forecasting). A popular way of making predictions with such kind of observations are data assimilation methods, like the ensemble Kalman filter (Evensen, 1994). These methods can be used for parameter estimation if the unknown parameters are included in the state vector and updated along with the model states. Given the difficulties related to estimation of the soil hydraulic parameters in general, it is questionable, though, whether these methods can really be used for parameter estimation under natural conditions. Therefore, we investigate the ability of the ensemble Kalman filter to estimate the soil hydraulic parameters. We use synthetic identical twin-experiments to guarantee full knowledge of the model and the true parameters. We use the van Genuchten model to describe the soil water retention and relative permeability functions. This model is unfortunately prone to the above mentioned pseudo-correlations of parameters. Therefore, we also test the simpler Russo Gardner model, which is less affected by that problem, in our experiments. The total number of unknown parameters is varied by considering different layers of soil. Besides, we study the influence of the parameter updates on the water content predictions. We test different iterative filter approaches and compare different observation strategies for parameter identification. Considering heterogeneous soils, we discuss the representativeness of different observation types to be used for the assimilation. G. Evensen. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99(C5):10143-10162, 1994

47. Photocopy of plans. Draftsman unknown, 1916 ADDITION TO CAR ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

47. Photocopy of plans. Draftsman unknown, 1916 ADDITION TO CAR BARN. LONGITUDINAL AND CROSS SECTIONS. VIEWS TO SOUTH AND WEST. - Milwaukee Light, Heat & Traction Company, 8336 West Lapham Street, West Allis, Milwaukee County, WI

Mass properties measurement system dynamics

NASA Technical Reports Server (NTRS)

Doty, Keith L.

1993-01-01

The MPMS mechanism possess two revolute degrees-of-freedom and allows the user to measure the mass, center of gravity, and the inertia tensor of an unknown mass. The dynamics of the Mass Properties Measurement System (MPMS) from the Lagrangian approach to illustrate the dependency of the motion on the unknown parameters.

An apodized Kepler periodogram for separating planetary and stellar activity signals

PubMed Central

Gregory, Philip C.

2016-01-01

A new apodized Keplerian (AK) model is proposed for the analysis of precision radial velocity (RV) data to model both planetary and stellar activity (SA) induced RV signals. A symmetrical Gaussian apodization function with unknown width and centre can distinguish planetary signals from SA signals on the basis of the span of the apodization window. The general model for m AK signals includes a linear regression term between RV and the SA diagnostic log (R′hk), as well as an extra Gaussian noise term with unknown standard deviation. The model parameters are explored using a Bayesian fusion Markov chain Monte Carlo code. A differential version of the generalized Lomb–Scargle periodogram that employs a control diagnostic provides an additional way of distinguishing SA signals and helps guide the choice of new periods. Results are reported for a recent international RV blind challenge which included multiple state-of-the-art simulated data sets supported by a variety of SA diagnostics. In the current implementation, the AK method achieved a reduction in SA noise by a factor of approximately 6. Final parameter estimates for the planetary candidates are derived from fits that include AK signals to model the SA components and simple Keplerians to model the planetary candidates. Preliminary results are also reported for AK models augmented by a moving average component that allows for correlations in the residuals. PMID:27346979

Kron-Branin modelling of ultra-short pulsed signal microelectrode

NASA Astrophysics Data System (ADS)

Xu, Zhifei; Ravelo, Blaise; Liu, Yang; Zhao, Lu; Delaroche, Fabien; Vurpillot, Francois

2018-06-01

An uncommon circuit modelling of microelectrode for ultra-short signal propagation is developed. The proposed model is based on the Tensorial Analysis of Network (TAN) using the Kron-Branin (KB) formalism. The systemic graph topology equivalent to the considered structure problem is established by assuming as unknown variables the branch currents. The TAN mathematical solution is determined after the KB characteristic matrix identification. The TAN can integrate various structure physical parameters. As proof of concept, via hole ended microelectrodes implemented on Kapton substrate were designed, fabricated and tested. The 0.1-MHz-to-6-GHz S-parameter KB model, simulation and measurement are in good agreement. In addition, time-domain analyses with nanosecond duration pulse signals were carried out to predict the microelectrode signal integrity. The modelled microstrip electrode is usually integrated in the atom probe tomography. The proposed unfamiliar KB method is particularly beneficial with respect to the computation speed and adaptability to various structures.

Adaptive control of stochastic linear systems with unknown parameters. M.S. Thesis

NASA Technical Reports Server (NTRS)

Ku, R. T.

1972-01-01

The problem of optimal control of linear discrete-time stochastic dynamical system with unknown and, possibly, stochastically varying parameters is considered on the basis of noisy measurements. It is desired to minimize the expected value of a quadratic cost functional. Since the simultaneous estimation of the state and plant parameters is a nonlinear filtering problem, the extended Kalman filter algorithm is used. Several qualitative and asymptotic properties of the open loop feedback optimal control and the enforced separation scheme are discussed. Simulation results via Monte Carlo method show that, in terms of the performance measure, for stable systems the open loop feedback optimal control system is slightly better than the enforced separation scheme, while for unstable systems the latter scheme is far better.

ATTITUDE FILTERING ON SO(3)

NASA Technical Reports Server (NTRS)

Markley, F. Landis

2005-01-01

A new method is presented for the simultaneous estimation of the attitude of a spacecraft and an N-vector of bias parameters. This method uses a probability distribution function defined on the Cartesian product of SO(3), the group of rotation matrices, and the Euclidean space W N .The Fokker-Planck equation propagates the probability distribution function between measurements, and Bayes s formula incorporates measurement update information. This approach avoids all the issues of singular attitude representations or singular covariance matrices encountered in extended Kalman filters. In addition, the filter has a consistent initialization for a completely unknown initial attitude, owing to the fact that SO(3) is a compact space.

Numerical computations on one-dimensional inverse scattering problems

NASA Technical Reports Server (NTRS)

Dunn, M. H.; Hariharan, S. I.

1983-01-01

An approximate method to determine the index of refraction of a dielectric obstacle is presented. For simplicity one dimensional models of electromagnetic scattering are treated. The governing equations yield a second order boundary value problem, in which the index of refraction appears as a functional parameter. The availability of reflection coefficients yield two additional boundary conditions. The index of refraction by a k-th order spline which can be written as a linear combination of B-splines is approximated. For N distinct reflection coefficients, the resulting N boundary value problems yield a system of N nonlinear equations in N unknowns which are the coefficients of the B-splines.

Experiments of thermomechanical fatigue of SMAs

NASA Astrophysics Data System (ADS)

Lagoudas, Dimitris C.; Miller, David A.

1999-07-01

As SMA wires are gaining in popularity for use as actuators, one constitutive parameter that remain unknown is the thermomechanical fatigue life. Even though the effect of thermal cycles on the transformation characteristics of SMAs has been studied, these teste have not been extended to high number of cycles. In this study, a novel test frame developed to study the thermomechanical fatigue life of SMAs is described. Additionally, a testing protocol is discussed necessary to fully establish the fatigue characteristics of SMAs under various conditions. Initial results of the initial test show a substantial increase in the number of cycles to failure as the applied stress level reduces to approximately 100 MPa.

Distributed Optimization Design of Continuous-Time Multiagent Systems With Unknown-Frequency Disturbances.

PubMed

Wang, Xinghu; Hong, Yiguang; Yi, Peng; Ji, Haibo; Kang, Yu

2017-05-24

In this paper, a distributed optimization problem is studied for continuous-time multiagent systems with unknown-frequency disturbances. A distributed gradient-based control is proposed for the agents to achieve the optimal consensus with estimating unknown frequencies and rejecting the bounded disturbance in the semi-global sense. Based on convex optimization analysis and adaptive internal model approach, the exact optimization solution can be obtained for the multiagent system disturbed by exogenous disturbances with uncertain parameters.

Estimation of Key Parameters of the Coupled Energy and Water Model by Assimilating Land Surface Data

NASA Astrophysics Data System (ADS)

Abdolghafoorian, A.; Farhadi, L.

2017-12-01

Accurate estimation of land surface heat and moisture fluxes, as well as root zone soil moisture, is crucial in various hydrological, meteorological, and agricultural applications. Field measurements of these fluxes are costly and cannot be readily scaled to large areas relevant to weather and climate studies. Therefore, there is a need for techniques to make quantitative estimates of heat and moisture fluxes using land surface state observations that are widely available from remote sensing across a range of scale. In this work, we applies the variational data assimilation approach to estimate land surface fluxes and soil moisture profile from the implicit information contained Land Surface Temperature (LST) and Soil Moisture (SM) (hereafter the VDA model). The VDA model is focused on the estimation of three key parameters: 1- neutral bulk heat transfer coefficient (CHN), 2- evaporative fraction from soil and canopy (EF), and 3- saturated hydraulic conductivity (Ksat). CHN and EF regulate the partitioning of available energy between sensible and latent heat fluxes. Ksat is one of the main parameters used in determining infiltration, runoff, groundwater recharge, and in simulating hydrological processes. In this study, a system of coupled parsimonious energy and water model will constrain the estimation of three unknown parameters in the VDA model. The profile of SM (LST) at multiple depths is estimated using moisture diffusion (heat diffusion) equation. In this study, the uncertainties of retrieved unknown parameters and fluxes are estimated from the inverse of Hesian matrix of cost function which is computed using the Lagrangian methodology. Analysis of uncertainty provides valuable information about the accuracy of estimated parameters and their correlation and guide the formulation of a well-posed estimation problem. The results of proposed algorithm are validated with a series of experiments using a synthetic data set generated by the simultaneous heat and water (SHAW) model. In addition, the feasibility of extending this algorithm to use remote sensing observations that have low temporal resolution is examined by assimilating the limited number of land surface moisture and temperature observations.

Stochastic Inversion of 2D Magnetotelluric Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Jinsong

2010-07-01

The algorithm is developed to invert 2D magnetotelluric (MT) data based on sharp boundary parametrization using a Bayesian framework. Within the algorithm, we consider the locations and the resistivity of regions formed by the interfaces are as unknowns. We use a parallel, adaptive finite-element algorithm to forward simulate frequency-domain MT responses of 2D conductivity structure. Those unknown parameters are spatially correlated and are described by a geostatistical model. The joint posterior probability distribution function is explored by Markov Chain Monte Carlo (MCMC) sampling methods. The developed stochastic model is effective for estimating the interface locations and resistivity. Most importantly, itmore » provides details uncertainty information on each unknown parameter. Hardware requirements: PC, Supercomputer, Multi-platform, Workstation; Software requirements C and Fortan; Operation Systems/version is Linux/Unix or Windows« less

Three-dimensional cinematography with control object of unknown shape.

PubMed

Dapena, J; Harman, E A; Miller, J A

1982-01-01

A technique for reconstruction of three-dimensional (3D) motion which involves a simple filming procedure but allows the deduction of coordinates in large object volumes was developed. Internal camera parameters are calculated from measurements of the film images of two calibrated crosses while external camera parameters are calculated from the film images of points in a control object of unknown shape but at least one known length. The control object, which includes the volume in which the activity is to take place, is formed by a series of poles placed at unknown locations, each carrying two targets. From the internal and external camera parameters, and from locations of the images of point in the films of the two cameras, 3D coordinates of the point can be calculated. Root mean square errors of the three coordinates of points in a large object volume (5m x 5m x 1.5m) were 15 mm, 13 mm, 13 mm and 6 mm, and relative errors in lengths averaged 0.5%, 0.7% and 0.5%, respectively.

Underwater passive acoustic localization of Pacific walruses in the northeastern Chukchi Sea.

PubMed

Rideout, Brendan P; Dosso, Stan E; Hannay, David E

2013-09-01

This paper develops and applies a linearized Bayesian localization algorithm based on acoustic arrival times of marine mammal vocalizations at spatially-separated receivers which provides three-dimensional (3D) location estimates with rigorous uncertainty analysis. To properly account for uncertainty in receiver parameters (3D hydrophone locations and synchronization times) and environmental parameters (water depth and sound-speed correction), these quantities are treated as unknowns constrained by prior estimates and prior uncertainties. Unknown scaling factors on both the prior and arrival-time uncertainties are estimated by minimizing Akaike's Bayesian information criterion (a maximum entropy condition). Maximum a posteriori estimates for sound source locations and times, receiver parameters, and environmental parameters are calculated simultaneously using measurements of arrival times for direct and interface-reflected acoustic paths. Posterior uncertainties for all unknowns incorporate both arrival time and prior uncertainties. Monte Carlo simulation results demonstrate that, for the cases considered here, linearization errors are small and the lack of an accurate sound-speed profile does not cause significant biases in the estimated locations. A sequence of Pacific walrus vocalizations, recorded in the Chukchi Sea northwest of Alaska, is localized using this technique, yielding a track estimate and uncertainties with an estimated speed comparable to normal walrus swim speeds.

Modern control concepts in hydrology. [parameter identification in adaptive stochastic control approach

NASA Technical Reports Server (NTRS)

Duong, N.; Winn, C. B.; Johnson, G. R.

1975-01-01

Two approaches to an identification problem in hydrology are presented, based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time-invariant or time-dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and confirm the results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.

A new chaotic communication scheme based on adaptive synchronization.

PubMed

Xiang-Jun, Wu

2006-12-01

A new chaotic communication scheme using adaptive synchronization technique of two unified chaotic systems is proposed. Different from the existing secure communication methods, the transmitted signal is modulated into the parameter of chaotic systems. The adaptive synchronization technique is used to synchronize two identical chaotic systems embedded in the transmitter and the receiver. It is assumed that the parameter of the receiver system is unknown. Based on the Lyapunov stability theory, an adaptive control law is derived to make the states of two identical unified chaotic systems with unknown system parameters asymptotically synchronized; thus the parameter of the receiver system is identified. Then the recovery of the original information signal in the receiver is successfully achieved on the basis of the estimated parameter. It is noticed that the time required for recovering the information signal and the accuracy of the recovered signal very sensitively depends on the frequency of the information signal. Numerical results have verified the effectiveness of the proposed scheme.

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

NASA Technical Reports Server (NTRS)

Murphy, K. A.

1988-01-01

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

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

NASA Technical Reports Server (NTRS)

Murphy, K. A.

1990-01-01

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

EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations Using an XD Gaussian Mixture Model

NASA Astrophysics Data System (ADS)

Holoien, Thomas W.-S.; Marshall, Philip J.; Wechsler, Risa H.

2017-06-01

We describe two new open-source tools written in Python for performing extreme deconvolution Gaussian mixture modeling (XDGMM) and using a conditioned model to re-sample observed supernova and host galaxy populations. XDGMM is new program that uses Gaussian mixtures to perform density estimation of noisy data using extreme deconvolution (XD) algorithms. Additionally, it has functionality not available in other XD tools. It allows the user to select between the AstroML and Bovy et al. fitting methods and is compatible with scikit-learn machine learning algorithms. Most crucially, it allows the user to condition a model based on the known values of a subset of parameters. This gives the user the ability to produce a tool that can predict unknown parameters based on a model that is conditioned on known values of other parameters. EmpiriciSN is an exemplary application of this functionality, which can be used to fit an XDGMM model to observed supernova/host data sets and predict likely supernova parameters using a model conditioned on observed host properties. It is primarily intended to simulate realistic supernovae for LSST data simulations based on empirical galaxy properties.

EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations Using an XD Gaussian Mixture Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holoien, Thomas W. -S.; Marshall, Philip J.; Wechsler, Risa H.

We describe two new open-source tools written in Python for performing extreme deconvolution Gaussian mixture modeling (XDGMM) and using a conditioned model to re-sample observed supernova and host galaxy populations. XDGMM is new program that uses Gaussian mixtures to perform density estimation of noisy data using extreme deconvolution (XD) algorithms. Additionally, it has functionality not available in other XD tools. It allows the user to select between the AstroML and Bovy et al. fitting methods and is compatible with scikit-learn machine learning algorithms. Most crucially, it allows the user to condition a model based on the known values of amore » subset of parameters. This gives the user the ability to produce a tool that can predict unknown parameters based on a model that is conditioned on known values of other parameters. EmpiriciSN is an exemplary application of this functionality, which can be used to fit an XDGMM model to observed supernova/host data sets and predict likely supernova parameters using a model conditioned on observed host properties. It is primarily intended to simulate realistic supernovae for LSST data simulations based on empirical galaxy properties.« less

Parameter Optimization for Feature and Hit Generation in a General Unknown Screening Method-Proof of Concept Study Using a Design of Experiment Approach for a High Resolution Mass Spectrometry Procedure after Data Independent Acquisition.

PubMed

Elmiger, Marco P; Poetzsch, Michael; Steuer, Andrea E; Kraemer, Thomas

2018-03-06

High resolution mass spectrometry and modern data independent acquisition (DIA) methods enable the creation of general unknown screening (GUS) procedures. However, even when DIA is used, its potential is far from being exploited, because often, the untargeted acquisition is followed by a targeted search. Applying an actual GUS (including untargeted screening) produces an immense amount of data that must be dealt with. An optimization of the parameters regulating the feature detection and hit generation algorithms of the data processing software could significantly reduce the amount of unnecessary data and thereby the workload. Design of experiment (DoE) approaches allow a simultaneous optimization of multiple parameters. In a first step, parameters are evaluated (crucial or noncrucial). Second, crucial parameters are optimized. The aim in this study was to reduce the number of hits, without missing analytes. The obtained parameter settings from the optimization were compared to the standard settings by analyzing a test set of blood samples spiked with 22 relevant analytes as well as 62 authentic forensic cases. The optimization lead to a marked reduction of workload (12.3 to 1.1% and 3.8 to 1.1% hits for the test set and the authentic cases, respectively) while simultaneously increasing the identification rate (68.2 to 86.4% and 68.8 to 88.1%, respectively). This proof of concept study emphasizes the great potential of DoE approaches to master the data overload resulting from modern data independent acquisition methods used for general unknown screening procedures by optimizing software parameters.

Robot-assisted gait training in multiple sclerosis patients: a randomized trial.

PubMed

Schwartz, Isabella; Sajin, Anna; Moreh, Elior; Fisher, Iris; Neeb, Martin; Forest, Adina; Vaknin-Dembinsky, Adi; Karusis, Dimitrios; Meiner, Zeev

2012-06-01

Preservation of locomotor activity in multiple sclerosis (MS) patients is of utmost importance. Robotic-assisted body weight-supported treadmill training is a promising method to improve gait functions in neurologically impaired patients, although its effectiveness in MS patients is still unknown. To compare the effectiveness of robot-assisted gait training (RAGT) with that of conventional walking treatment (CWT) on gait and generalized functions in a group of stable MS patients. A prospective randomized controlled trial of 12 sessions of RAGT or CWT in MS patients of EDSS score 5-7. Primary outcome measures were gait parameters and the secondary outcomes were functional and quality of life parameters. All tests were performed at baseline, 3 and 6 months post-treatment by a blinded rater. Fifteen and 17 patients were randomly allocated to RAGT and CWT, respectively. Both groups were comparable at baseline in all parameters. As compared with baseline, although some gait parameters improved significantly following the treatment at each time point there was no difference between the groups. Both FIM and EDSS scores improved significantly post-treatment with no difference between the groups. At 6 months, most gait and functional parameters had returned to baseline. Robot-assisted gait training is feasible and safe and may be an effective additional therapeutic option in MS patients with severe walking disabilities.

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.

Adaptive estimation of nonlinear parameters of a nonholonomic spherical robot using a modified fuzzy-based speed gradient algorithm

NASA Astrophysics Data System (ADS)

Roozegar, Mehdi; Mahjoob, Mohammad J.; Ayati, Moosa

2017-05-01

This paper deals with adaptive estimation of the unknown parameters and states of a pendulum-driven spherical robot (PDSR), which is a nonlinear in parameters (NLP) chaotic system with parametric uncertainties. Firstly, the mathematical model of the robot is deduced by applying the Newton-Euler methodology for a system of rigid bodies. Then, based on the speed gradient (SG) algorithm, the states and unknown parameters of the robot are estimated online for different step length gains and initial conditions. The estimated parameters are updated adaptively according to the error between estimated and true state values. Since the errors of the estimated states and parameters as well as the convergence rates depend significantly on the value of step length gain, this gain should be chosen optimally. Hence, a heuristic fuzzy logic controller is employed to adjust the gain adaptively. Simulation results indicate that the proposed approach is highly encouraging for identification of this NLP chaotic system even if the initial conditions change and the uncertainties increase; therefore, it is reliable to be implemented on a real robot.

Determination of Unknown Concentrations of Sodium Acetate Using the Method of Standard Addition and Proton NMR: An Experiment for the Undergraduate Analytical Chemistry Laboratory

ERIC Educational Resources Information Center

Rajabzadeh, Massy

2012-01-01

In this experiment, students learn how to find the unknown concentration of sodium acetate using both the graphical treatment of standard addition and the standard addition equation. In the graphical treatment of standard addition, the peak area of the methyl peak in each of the sodium acetate standard solutions is found by integration using…

Characterizing unknown systematics in large scale structure surveys

DOE Office of Scientific and Technical Information (OSTI.GOV)

Agarwal, Nishant; Ho, Shirley; Myers, Adam D.

Photometric large scale structure (LSS) surveys probe the largest volumes in the Universe, but are inevitably limited by systematic uncertainties. Imperfect photometric calibration leads to biases in our measurements of the density fields of LSS tracers such as galaxies and quasars, and as a result in cosmological parameter estimation. Earlier studies have proposed using cross-correlations between different redshift slices or cross-correlations between different surveys to reduce the effects of such systematics. In this paper we develop a method to characterize unknown systematics. We demonstrate that while we do not have sufficient information to correct for unknown systematics in the data,more » we can obtain an estimate of their magnitude. We define a parameter to estimate contamination from unknown systematics using cross-correlations between different redshift slices and propose discarding bins in the angular power spectrum that lie outside a certain contamination tolerance level. We show that this method improves estimates of the bias using simulated data and further apply it to photometric luminous red galaxies in the Sloan Digital Sky Survey as a case study.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kato, Go

We consider the situation where s replicas of a qubit with an unknown state and its orthogonal k replicas are given as an input, and we try to make c clones of the qubit with the unknown state. As a function of s, k, and c, we obtain the optimal fidelity between the qubit with an unknown state and the clone by explicitly giving a completely positive trace-preserving (CPTP) map that represents a cloning machine. We discuss dependency of the fidelity on the values of the parameters s, k, and c.

A dynamical approach in exploring the unknown mass in the Solar system using pulsar timing arrays

NASA Astrophysics Data System (ADS)

Guo, Y. J.; Lee, K. J.; Caballero, R. N.

2018-04-01

The error in the Solar system ephemeris will lead to dipolar correlations in the residuals of pulsar timing array for widely separated pulsars. In this paper, we utilize such correlated signals, and construct a Bayesian data-analysis framework to detect the unknown mass in the Solar system and to measure the orbital parameters. The algorithm is designed to calculate the waveform of the induced pulsar-timing residuals due to the unmodelled objects following the Keplerian orbits in the Solar system. The algorithm incorporates a Bayesian-analysis suit used to simultaneously analyse the pulsar-timing data of multiple pulsars to search for coherent waveforms, evaluate the detection significance of unknown objects, and to measure their parameters. When the object is not detectable, our algorithm can be used to place upper limits on the mass. The algorithm is verified using simulated data sets, and cross-checked with analytical calculations. We also investigate the capability of future pulsar-timing-array experiments in detecting the unknown objects. We expect that the future pulsar-timing data can limit the unknown massive objects in the Solar system to be lighter than 10-11-10-12 M⊙, or measure the mass of Jovian system to a fractional precision of 10-8-10-9.

Unknown loads affect force production capacity in early phases of bench press throws.

PubMed

Hernández Davó, J L; Sabido Solana, R; Sarabia Marínm, J M; Sánchez Martos, Á; Moya Ramón, M

2015-10-01

Explosive strength training aims to improve force generation in early phases of movement due to its importance in sport performance. The present study examined the influence of lack of knowledge about the load lifted in explosive parameters during bench press throws. Thirteen healthy young men (22.8±2.0 years) participated in the study. Participants performed bench press throws with three different loads (30, 50 and 70% of 1 repetition maximum) in two different conditions (known and unknown loads). In unknown condition, loads were changed within sets in each repetition and participants did not know the load, whereas in known condition the load did not change within sets and participants had knowledge about the load lifted. Results of repeated-measures ANOVA revealed that unknown conditions involves higher power in the first 30, 50, 100 and 150 ms with the three loads, higher values of ratio of force development in those first instants, and differences in time to reach maximal rate of force development with 50 and 70% of 1 repetition maximum. This study showed that unknown conditions elicit higher values of explosive parameters in early phases of bench press throws, thereby this kind of methodology could be considered in explosive strength training.

The Kaon B-parameter in mixed action chiral perturbation theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aubin, C.; /Columbia U.; Laiho, Jack

2006-09-01

We calculate the kaon B-parameter, B{sub K}, in chiral perturbation theory for a partially quenched, mixed action theory with Ginsparg-Wilson valence quarks and staggered sea quarks. We find that the resulting expression is similar to that in the continuum, and in fact has only two additional unknown parameters. At one-loop order, taste-symmetry violations in the staggered sea sector only contribute to flavor-disconnected diagrams by generating an {Omicron}(a{sup 2}) shift to the masses of taste-singlet sea-sea mesons. Lattice discretization errors also give rise to an analytic term which shifts the tree-level value of B{sub K} by an amount of {Omicron}(a{sup 2}).more » This term, however, is not strictly due to taste-breaking, and is therefore also present in the expression for B{sub K} for pure G-W lattice fermions. We also present a numerical study of the mixed B{sub K} expression in order to demonstrate that both discretization errors and finite volume effects are small and under control on the MILC improved staggered lattices.« less

Kaon B-parameter in mixed action chiral perturbation theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aubin, C.; Laiho, Jack; Water, Ruth S. van de

2007-02-01

We calculate the kaon B-parameter, B{sub K}, in chiral perturbation theory for a partially quenched, mixed-action theory with Ginsparg-Wilson valence quarks and staggered sea quarks. We find that the resulting expression is similar to that in the continuum, and in fact has only two additional unknown parameters. At 1-loop order, taste-symmetry violations in the staggered sea sector only contribute to flavor-disconnected diagrams by generating an O(a{sup 2}) shift to the masses of taste-singlet sea-sea mesons. Lattice discretization errors also give rise to an analytic term which shifts the tree-level value of B{sub K} by an amount of O(a{sup 2}). Thismore » term, however, is not strictly due to taste breaking, and is therefore also present in the expression for B{sub K} for pure Ginsparg-Wilson lattice fermions. We also present a numerical study of the mixed B{sub K} expression in order to demonstrate that both discretization errors and finite volume effects are small and under control on the MILC improved staggered lattices.« less

Measuring UV Curing Parameters of Commercial Photopolymers used in Additive Manufacturing.

PubMed

Bennett, Joe

2017-12-01

A testing methodology was developed to expose photopolymer resins and measure the cured material to determine two key parameters related to the photopolymerization process: E c (critical energy to initiate polymerization) and D p (penetration depth of curing light). Five commercially available resins were evaluated under exposure from 365 nm and 405 nm light at varying power densities and energies. Three different methods for determining the thickness of the cured resin were evaluated. Caliper measurements, stylus profilometry, and confocal laser scanning microscopy showed similar results for hard materials while caliper measurement of a soft, elastomeric material proved inaccurate. Working curves for the five photopolymers showed unique behavior both within and among the resins as a function of curing light wavelength. E c and D p for the five resins showed variations as large as 10×. Variations of this magnitude, if unknown to the user and not controlled for, will clearly affect printed part quality. This points to the need for a standardized approach for determining and disseminating these, and perhaps, other key parameters.

Pose-free structure from motion using depth from motion constraints.

PubMed

Zhang, Ji; Boutin, Mireille; Aliaga, Daniel G

2011-10-01

Structure from motion (SFM) is the problem of recovering the geometry of a scene from a stream of images taken from unknown viewpoints. One popular approach to estimate the geometry of a scene is to track scene features on several images and reconstruct their position in 3-D. During this process, the unknown camera pose must also be recovered. Unfortunately, recovering the pose can be an ill-conditioned problem which, in turn, can make the SFM problem difficult to solve accurately. We propose an alternative formulation of the SFM problem with fixed internal camera parameters known a priori. In this formulation, obtained by algebraic variable elimination, the external camera pose parameters do not appear. As a result, the problem is better conditioned in addition to involving much fewer variables. Variable elimination is done in three steps. First, we take the standard SFM equations in projective coordinates and eliminate the camera orientations from the equations. We then further eliminate the camera center positions. Finally, we also eliminate all 3-D point positions coordinates, except for their depths with respect to the camera center, thus obtaining a set of simple polynomial equations of degree two and three. We show that, when there are merely a few points and pictures, these "depth-only equations" can be solved in a global fashion using homotopy methods. We also show that, in general, these same equations can be used to formulate a pose-free cost function to refine SFM solutions in a way that is more accurate than by minimizing the total reprojection error, as done when using the bundle adjustment method. The generalization of our approach to the case of varying internal camera parameters is briefly discussed. © 2011 IEEE

Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression.

PubMed

Ding, A Adam; Wu, Hulin

2014-10-01

We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method.

Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression

PubMed Central

Ding, A. Adam; Wu, Hulin

2015-01-01

We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method. PMID:26401093

Groebner Basis Solutions to Satellite Trajectory Control by Pole Placement

NASA Astrophysics Data System (ADS)

Kukelova, Z.; Krsek, P.; Smutny, V.; Pajdla, T.

2013-09-01

Satellites play an important role, e.g., in telecommunication, navigation and weather monitoring. Controlling their trajectories is an important problem. In [1], an approach to the pole placement for the synthesis of a linear controller has been presented. It leads to solving five polynomial equations in nine unknown elements of the state space matrices of a compensator. This is an underconstrained system and therefore four of the unknown elements need to be considered as free parameters and set to some prior values to obtain a system of five equations in five unknowns. In [1], this system was solved for one chosen set of free parameters with the help of Dixon resultants. In this work, we study and present Groebner basis solutions to this problem of computation of a dynamic compensator for the satellite for different combinations of input free parameters. We show that the Groebner basis method for solving systems of polynomial equations leads to very simple solutions for all combinations of free parameters. These solutions require to perform only the Gauss-Jordan elimination of a small matrix and computation of roots of a single variable polynomial. The maximum degree of this polynomial is not greater than six in general but for most combinations of the input free parameters its degree is even lower. [1] B. Palancz. Application of Dixon resultant to satellite trajectory control by pole placement. Journal of Symbolic Computation, Volume 50, March 2013, Pages 79-99, Elsevier.

Method and apparatus for sensor fusion

NASA Technical Reports Server (NTRS)

Krishen, Kumar (Inventor); Shaw, Scott (Inventor); Defigueiredo, Rui J. P. (Inventor)

1991-01-01

Method and apparatus for fusion of data from optical and radar sensors by error minimization procedure is presented. The method was applied to the problem of shape reconstruction of an unknown surface at a distance. The method involves deriving an incomplete surface model from an optical sensor. The unknown characteristics of the surface are represented by some parameter. The correct value of the parameter is computed by iteratively generating theoretical predictions of the radar cross sections (RCS) of the surface, comparing the predicted and the observed values for the RCS, and improving the surface model from results of the comparison. Theoretical RCS may be computed from the surface model in several ways. One RCS prediction technique is the method of moments. The method of moments can be applied to an unknown surface only if some shape information is available from an independent source. The optical image provides the independent information.

Measuring Two Key Parameters of H3 Color Centers in Diamond

NASA Technical Reports Server (NTRS)

Roberts, W. Thomas

2005-01-01

A method of measuring two key parameters of H3 color centers in diamond has been created as part of a continuing effort to develop tunable, continuous-wave, visible lasers that would utilize diamond as the lasing medium. (An H3 color center in a diamond crystal lattice comprises two nitrogen atoms substituted for two carbon atoms bonded to a third carbon atom. H3 color centers can be induced artificially; they also occur naturally. If present in sufficient density, they impart a yellow hue.) The method may also be applicable to the corresponding parameters of other candidate lasing media. One of the parameters is the number density of color centers, which is needed for designing an efficient laser. The other parameter is an optical-absorption cross section, which, as explained below, is needed for determining the number density. The present method represents an improvement over prior methods in which optical-absorption measurements have been used to determine absorption cross sections or number densities. Heretofore, in order to determine a number density from such measurements, it has been necessary to know the applicable absorption cross section; alternatively, to determine the absorption cross section from such measurements, it has been necessary to know the number density. If, as in this case, both the number density and the absorption cross section are initially unknown, then it is impossible to determine either parameter in the absence of additional information.

Assessment of gait parameters and fatigue in MS patients during inpatient rehabilitation: a pilot trial.

PubMed

Sacco, Rosaria; Bussman, Rita; Oesch, Peter; Kesselring, Jürg; Beer, Serafin

2011-05-01

Gait impairment and fatigue are common and disabling problems in multiple sclerosis (MS). Characterisation of abnormal gait in MS patients has been done mainly using observational studies and simple walking tests providing only limited quantitative and no qualitative data, or using intricate and time-consuming assessment procedures. In addition, the correlation of gait impairments with fatigue is largely unknown. The aim of this study was to characterise spatio-temporal gait parameters by a simple and easy-to-use gait analysis system (GAITRite®) in MS patients compared with healthy controls, and to analyse changes and correlation with fatigue during inpatient rehabilitation. Twenty-four MS patients (EDSS <6.5) admitted for inpatient rehabilitation and 19 healthy subjects were evaluated using the GAITRite® Functional Ambulation System. Between-group differences and changes of gait parameters during inpatient rehabilitation were analysed, and correlation with fatigue, using the Wurzburg Fatigue Inventory for Multiple Sclerosis (WEIMuS), was determined. Compared to healthy controls MS patients showed significant impairments in different spatio-temporal gait parameters, which showed a significant improvement during inpatient rehabilitation. Different gait parameters were correlated with fatigue physical score, and change of gait parameters was correlated with improvement of fatigue. Spatio-temporal gait analysis is helpful to assess specific walking impairments in MS patients and subtle changes during rehabilitation. Correlation with fatigue may indicate a possible negative impact of fatigue on rehabilitation outcome.

Determining index of refraction from polarimetric hyperspectral radiance measurements

NASA Astrophysics Data System (ADS)

Martin, Jacob A.; Gross, Kevin C.

2015-09-01

Polarimetric hyperspectral imaging (P-HSI) combines two of the most common remote sensing modalities. This work leverages the combination of these techniques to improve material classification. Classifying and identifying materials requires parameters which are invariant to changing viewing conditions, and most often a material's reflectivity or emissivity is used. Measuring these most often requires assumptions be made about the material and atmospheric conditions. Combining both polarimetric and hyperspectral imaging, we propose a method to remotely estimate the index of refraction of a material. In general, this is an underdetermined problem because both the real and imaginary components of index of refraction are unknown at every spectral point. By modeling the spectral variation of the index of refraction using a few parameters, however, the problem can be made overdetermined. A number of different functions can be used to describe this spectral variation, and some are discussed here. Reducing the number of spectral parameters to fit allows us to add parameters which estimate atmospheric downwelling radiance and transmittance. Additionally, the object temperature is added as a fit parameter. The set of these parameters that best replicate the measured data is then found using a bounded Nelder-Mead simplex search algorithm. Other search algorithms are also examined and discussed. Results show that this technique has promise but also some limitations, which are the subject of ongoing work.

Robust Adaptive Synchronization of Ring Configured Uncertain Chaotic FitzHugh–Nagumo Neurons under Direction-Dependent Coupling

PubMed Central

Iqbal, Muhammad; Rehan, Muhammad; Hong, Keum-Shik

2018-01-01

This paper exploits the dynamical modeling, behavior analysis, and synchronization of a network of four different FitzHugh–Nagumo (FHN) neurons with unknown parameters linked in a ring configuration under direction-dependent coupling. The main purpose is to investigate a robust adaptive control law for the synchronization of uncertain and perturbed neurons, communicating in a medium of bidirectional coupling. The neurons are assumed to be different and interconnected in a ring structure. The strength of the gap junctions is taken to be different for each link in the network, owing to the inter-neuronal coupling medium properties. Robust adaptive control mechanism based on Lyapunov stability analysis is employed and theoretical criteria are derived to realize the synchronization of the network of four FHN neurons in a ring form with unknown parameters under direction-dependent coupling and disturbances. The proposed scheme for synchronization of dissimilar neurons, under external electrical stimuli, coupled in a ring communication topology, having all parameters unknown, and subject to directional coupling medium and perturbations, is addressed for the first time as per our knowledge. To demonstrate the efficacy of the proposed strategy, simulation results are provided. PMID:29535622

A highly precise frequency-based method for estimating the tension of an inclined cable with unknown boundary conditions

NASA Astrophysics Data System (ADS)

Ma, Lin

2017-11-01

This paper develops a method for precisely determining the tension of an inclined cable with unknown boundary conditions. First, the nonlinear motion equation of an inclined cable is derived, and a numerical model of the motion of the cable is proposed using the finite difference method. The proposed numerical model includes the sag-extensibility, flexural stiffness, inclination angle and rotational stiffness at two ends of the cable. Second, the influence of the dynamic parameters of the cable on its frequencies is discussed in detail, and a method for precisely determining the tension of an inclined cable is proposed based on the derivatives of the eigenvalues of the matrices. Finally, a multiparameter identification method is developed that can simultaneously identify multiple parameters, including the rotational stiffness at two ends. This scheme is applicable to inclined cables with varying sag, varying flexural stiffness and unknown boundary conditions. Numerical examples indicate that the method provides good precision. Because the parameters of cables other than tension (e.g., the flexural stiffness and rotational stiffness at the ends) are not accurately known in practical engineering, the multiparameter identification method could further improve the accuracy of cable tension measurements.

Novel Calibration Algorithm for a Three-Axis Strapdown Magnetometer

PubMed Central

Liu, Yan Xia; Li, Xi Sheng; Zhang, Xiao Juan; Feng, Yi Bo

2014-01-01

A complete error calibration model with 12 independent parameters is established by analyzing the three-axis magnetometer error mechanism. The said model conforms to an ellipsoid restriction, the parameters of the ellipsoid equation are estimated, and the ellipsoid coefficient matrix is derived. However, the calibration matrix cannot be determined completely, as there are fewer ellipsoid parameters than calibration model parameters. Mathematically, the calibration matrix derived from the ellipsoid coefficient matrix by a different matrix decomposition method is not unique, and there exists an unknown rotation matrix R between them. This paper puts forward a constant intersection angle method (angles between the geomagnetic field and gravitational field are fixed) to estimate R. The Tikhonov method is adopted to solve the problem that rounding errors or other errors may seriously affect the calculation results of R when the condition number of the matrix is very large. The geomagnetic field vector and heading error are further corrected by R. The constant intersection angle method is convenient and practical, as it is free from any additional calibration procedure or coordinate transformation. In addition, the simulation experiment indicates that the heading error declines from ±1° calibrated by classical ellipsoid fitting to ±0.2° calibrated by a constant intersection angle method, and the signal-to-noise ratio is 50 dB. The actual experiment exhibits that the heading error is further corrected from ±0.8° calibrated by the classical ellipsoid fitting to ±0.3° calibrated by a constant intersection angle method. PMID:24831110

Parameter and state estimation in a Neisseria meningitidis model: A study case of Niger

NASA Astrophysics Data System (ADS)

Bowong, S.; Mountaga, L.; Bah, A.; Tewa, J. J.; Kurths, J.

2016-12-01

Neisseria meningitidis (Nm) is a major cause of bacterial meningitidis outbreaks in Africa and the Middle East. The availability of yearly reported meningitis cases in the African meningitis belt offers the opportunity to analyze the transmission dynamics and the impact of control strategies. In this paper, we propose a method for the estimation of state variables that are not accessible to measurements and an unknown parameter in a Nm model. We suppose that the yearly number of Nm induced mortality and the total population are known inputs, which can be obtained from data, and the yearly number of new Nm cases is the model output. We also suppose that the Nm transmission rate is an unknown parameter. We first show how the recruitment rate into the population can be estimated using real data of the total population and Nm induced mortality. Then, we use an auxiliary system called observer whose solutions converge exponentially to those of the original model. This observer does not use the unknown infection transmission rate but only uses the known inputs and the model output. This allows us to estimate unmeasured state variables such as the number of carriers that play an important role in the transmission of the infection and the total number of infected individuals within a human community. Finally, we also provide a simple method to estimate the unknown Nm transmission rate. In order to validate the estimation results, numerical simulations are conducted using real data of Niger.

Multifunctional and Context-Dependent Control of Vocal Acoustics by Individual Muscles

PubMed Central

Srivastava, Kyle H.; Elemans, Coen P.H.

2015-01-01

The relationship between muscle activity and behavioral output determines how the brain controls and modifies complex skills. In vocal control, ensembles of muscles are used to precisely tune single acoustic parameters such as fundamental frequency and sound amplitude. If individual vocal muscles were dedicated to the control of single parameters, then the brain could control each parameter independently by modulating the appropriate muscle or muscles. Alternatively, if each muscle influenced multiple parameters, a more complex control strategy would be required to selectively modulate a single parameter. Additionally, it is unknown whether the function of single muscles is fixed or varies across different vocal gestures. A fixed relationship would allow the brain to use the same changes in muscle activation to, for example, increase the fundamental frequency of different vocal gestures, whereas a context-dependent scheme would require the brain to calculate different motor modifications in each case. We tested the hypothesis that single muscles control multiple acoustic parameters and that the function of single muscles varies across gestures using three complementary approaches. First, we recorded electromyographic data from vocal muscles in singing Bengalese finches. Second, we electrically perturbed the activity of single muscles during song. Third, we developed an ex vivo technique to analyze the biomechanical and acoustic consequences of single-muscle perturbations. We found that single muscles drive changes in multiple parameters and that the function of single muscles differs across vocal gestures, suggesting that the brain uses a complex, gesture-dependent control scheme to regulate vocal output. PMID:26490859

Real-Time Robust Adaptive Modeling and Scheduling for an Electronic Commerce Server

NASA Astrophysics Data System (ADS)

Du, Bing; Ruan, Chun

With the increasing importance and pervasiveness of Internet services, it is becoming a challenge for the proliferation of electronic commerce services to provide performance guarantees under extreme overload. This paper describes a real-time optimization modeling and scheduling approach for performance guarantee of electronic commerce servers. We show that an electronic commerce server may be simulated as a multi-tank system. A robust adaptive server model is subject to unknown additive load disturbances and uncertain model matching. Overload control techniques are based on adaptive admission control to achieve timing guarantees. We evaluate the performance of the model using a complex simulation that is subjected to varying model parameters and massive overload.

Joint inversion for transponder localization and sound-speed profile temporal variation in high-precision acoustic surveys.

PubMed

Li, Zhao; Dosso, Stan E; Sun, Dajun

2016-07-01

This letter develops a Bayesian inversion for localizing underwater acoustic transponders using a surface ship which compensates for sound-speed profile (SSP) temporal variation during the survey. The method is based on dividing observed acoustic travel-time data into time segments and including depth-independent SSP variations for each segment as additional unknown parameters to approximate the SSP temporal variation. SSP variations are estimated jointly with transponder locations, rather than calculated separately as in existing two-step inversions. Simulation and sea-trial results show this localization/SSP joint inversion performs better than two-step inversion in terms of localization accuracy, agreement with measured SSP variations, and computational efficiency.

Adjoint Methods for Adjusting Three-Dimensional Atmosphere and Surface Properties to Fit Multi-Angle Multi-Pixel Polarimetric Measurements

NASA Technical Reports Server (NTRS)

Martin, William G.; Cairns, Brian; Bal, Guillaume

2014-01-01

This paper derives an efficient procedure for using the three-dimensional (3D) vector radiative transfer equation (VRTE) to adjust atmosphere and surface properties and improve their fit with multi-angle/multi-pixel radiometric and polarimetric measurements of scattered sunlight. The proposed adjoint method uses the 3D VRTE to compute the measurement misfit function and the adjoint 3D VRTE to compute its gradient with respect to all unknown parameters. In the remote sensing problems of interest, the scalar-valued misfit function quantifies agreement with data as a function of atmosphere and surface properties, and its gradient guides the search through this parameter space. Remote sensing of the atmosphere and surface in a three-dimensional region may require thousands of unknown parameters and millions of data points. Many approaches would require calls to the 3D VRTE solver in proportion to the number of unknown parameters or measurements. To avoid this issue of scale, we focus on computing the gradient of the misfit function as an alternative to the Jacobian of the measurement operator. The resulting adjoint method provides a way to adjust 3D atmosphere and surface properties with only two calls to the 3D VRTE solver for each spectral channel, regardless of the number of retrieval parameters, measurement view angles or pixels. This gives a procedure for adjusting atmosphere and surface parameters that will scale to the large problems of 3D remote sensing. For certain types of multi-angle/multi-pixel polarimetric measurements, this encourages the development of a new class of three-dimensional retrieval algorithms with more flexible parametrizations of spatial heterogeneity, less reliance on data screening procedures, and improved coverage in terms of the resolved physical processes in the Earth?s atmosphere.

Parameter Estimation for a Turbulent Buoyant Jet Using Approximate Bayesian Computation

NASA Astrophysics Data System (ADS)

Christopher, Jason D.; Wimer, Nicholas T.; Hayden, Torrey R. S.; Lapointe, Caelan; Grooms, Ian; Rieker, Gregory B.; Hamlington, Peter E.

2016-11-01

Approximate Bayesian Computation (ABC) is a powerful tool that allows sparse experimental or other "truth" data to be used for the prediction of unknown model parameters in numerical simulations of real-world engineering systems. In this presentation, we introduce the ABC approach and then use ABC to predict unknown inflow conditions in simulations of a two-dimensional (2D) turbulent, high-temperature buoyant jet. For this test case, truth data are obtained from a simulation with known boundary conditions and problem parameters. Using spatially-sparse temperature statistics from the 2D buoyant jet truth simulation, we show that the ABC method provides accurate predictions of the true jet inflow temperature. The success of the ABC approach in the present test suggests that ABC is a useful and versatile tool for engineering fluid dynamics research.

Rhelogical constraints on ridge formation on Icy Satellites

NASA Astrophysics Data System (ADS)

Rudolph, M. L.; Manga, M.

2010-12-01

The processes responsible for forming ridges on Europa remain poorly understood. We use a continuum damage mechanics approach to model ridge formation. The main objectives of this contribution are to constrain (1) choice of rheological parameters and (2) maximum ridge size and rate of formation. The key rheological parameters to constrain appear in the evolution equation for a damage variable (D): ˙ {D} = B <<σ >>r}(1-D){-k-α D (p)/(μ ) and in the equation relating damage accumulation to volumetric changes, Jρ 0 = δ (1-D). Similar damage evolution laws have been applied to terrestrial glaciers and to the analysis of rock mechanics experiments. However, it is reasonable to expect that, like viscosity, the rheological constants B, α , and δ depend strongly on temperature, composition, and ice grain size. In order to determine whether the damage model is appropriate for Europa’s ridges, we must find values of the unknown damage parameters that reproduce ridge topography. We perform a suite of numerical experiments to identify the region of parameter space conducive to ridge production and show the sensitivity to changes in each unknown parameter.

Reconstruction of Atmospheric Tracer Releases with Optimal Resolution Features: Concentration Data Assimilation

NASA Astrophysics Data System (ADS)

Singh, Sarvesh Kumar; Turbelin, Gregory; Issartel, Jean-Pierre; Kumar, Pramod; Feiz, Amir Ali

2015-04-01

The fast growing urbanization, industrialization and military developments increase the risk towards the human environment and ecology. This is realized in several past mortality incidents, for instance, Chernobyl nuclear explosion (Ukraine), Bhopal gas leak (India), Fukushima-Daichi radionuclide release (Japan), etc. To reduce the threat and exposure to the hazardous contaminants, a fast and preliminary identification of unknown releases is required by the responsible authorities for the emergency preparedness and air quality analysis. Often, an early detection of such contaminants is pursued by a distributed sensor network. However, identifying the origin and strength of unknown releases following the sensor reported concentrations is a challenging task. This requires an optimal strategy to integrate the measured concentrations with the predictions given by the atmospheric dispersion models. This is an inverse problem. The measured concentrations are insufficient and atmospheric dispersion models suffer from inaccuracy due to the lack of process understanding, turbulence uncertainties, etc. These lead to a loss of information in the reconstruction process and thus, affect the resolution, stability and uniqueness of the retrieved source. An additional well known issue is the numerical artifact arisen at the measurement locations due to the strong concentration gradient and dissipative nature of the concentration. Thus, assimilation techniques are desired which can lead to an optimal retrieval of the unknown releases. In general, this is facilitated within the Bayesian inference and optimization framework with a suitable choice of a priori information, regularization constraints, measurement and background error statistics. An inversion technique is introduced here for an optimal reconstruction of unknown releases using limited concentration measurements. This is based on adjoint representation of the source-receptor relationship and utilization of a weight function which exhibits a priori information about the unknown releases apparent to the monitoring network. The properties of the weight function provide an optimal data resolution and model resolution to the retrieved source estimates. The retrieved source estimates are proved theoretically to be stable against the random measurement errors and their reliability can be interpreted in terms of the distribution of the weight functions. Further, the same framework can be extended for the identification of the point type releases by utilizing the maximum of the retrieved source estimates. The inversion technique has been evaluated with the several diffusion experiments, like, Idaho low wind diffusion experiment (1974), IIT Delhi tracer experiment (1991), European Tracer Experiment (1994), Fusion Field Trials (2007), etc. In case of point release experiments, the source parameters are mostly retrieved close to the true source parameters with least error. Primarily, the proposed technique overcomes two major difficulties incurred in the source reconstruction: (i) The initialization of the source parameters as required by the optimization based techniques. The converged solution depends on their initialization. (ii) The statistical knowledge about the measurement and background errors as required by the Bayesian inference based techniques. These are hypothetically assumed in case of no prior knowledge.

Parameter Estimation for a Pulsating Turbulent Buoyant Jet Using Approximate Bayesian Computation

NASA Astrophysics Data System (ADS)

Christopher, Jason; Wimer, Nicholas; Lapointe, Caelan; Hayden, Torrey; Grooms, Ian; Rieker, Greg; Hamlington, Peter

2017-11-01

Approximate Bayesian Computation (ABC) is a powerful tool that allows sparse experimental or other ``truth'' data to be used for the prediction of unknown parameters, such as flow properties and boundary conditions, in numerical simulations of real-world engineering systems. Here we introduce the ABC approach and then use ABC to predict unknown inflow conditions in simulations of a two-dimensional (2D) turbulent, high-temperature buoyant jet. For this test case, truth data are obtained from a direct numerical simulation (DNS) with known boundary conditions and problem parameters, while the ABC procedure utilizes lower fidelity large eddy simulations. Using spatially-sparse statistics from the 2D buoyant jet DNS, we show that the ABC method provides accurate predictions of true jet inflow parameters. The success of the ABC approach in the present test suggests that ABC is a useful and versatile tool for predicting flow information, such as boundary conditions, that can be difficult to determine experimentally.

Modern control concepts in hydrology

NASA Technical Reports Server (NTRS)

Duong, N.; Johnson, G. R.; Winn, C. B.

1974-01-01

Two approaches to an identification problem in hydrology are presented based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time invariant or time dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and conform with results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second, by using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.

Bootstrap Standard Errors for Maximum Likelihood Ability Estimates When Item Parameters Are Unknown

ERIC Educational Resources Information Center

Patton, Jeffrey M.; Cheng, Ying; Yuan, Ke-Hai; Diao, Qi

2014-01-01

When item parameter estimates are used to estimate the ability parameter in item response models, the standard error (SE) of the ability estimate must be corrected to reflect the error carried over from item calibration. For maximum likelihood (ML) ability estimates, a corrected asymptotic SE is available, but it requires a long test and the…

Rational selection of experimental readout and intervention sites for reducing uncertainties in computational model predictions.

PubMed

Flassig, Robert J; Migal, Iryna; der Zalm, Esther van; Rihko-Struckmann, Liisa; Sundmacher, Kai

2015-01-16

Understanding the dynamics of biological processes can substantially be supported by computational models in the form of nonlinear ordinary differential equations (ODE). Typically, this model class contains many unknown parameters, which are estimated from inadequate and noisy data. Depending on the ODE structure, predictions based on unmeasured states and associated parameters are highly uncertain, even undetermined. For given data, profile likelihood analysis has been proven to be one of the most practically relevant approaches for analyzing the identifiability of an ODE structure, and thus model predictions. In case of highly uncertain or non-identifiable parameters, rational experimental design based on various approaches has shown to significantly reduce parameter uncertainties with minimal amount of effort. In this work we illustrate how to use profile likelihood samples for quantifying the individual contribution of parameter uncertainty to prediction uncertainty. For the uncertainty quantification we introduce the profile likelihood sensitivity (PLS) index. Additionally, for the case of several uncertain parameters, we introduce the PLS entropy to quantify individual contributions to the overall prediction uncertainty. We show how to use these two criteria as an experimental design objective for selecting new, informative readouts in combination with intervention site identification. The characteristics of the proposed multi-criterion objective are illustrated with an in silico example. We further illustrate how an existing practically non-identifiable model for the chlorophyll fluorescence induction in a photosynthetic organism, D. salina, can be rendered identifiable by additional experiments with new readouts. Having data and profile likelihood samples at hand, the here proposed uncertainty quantification based on prediction samples from the profile likelihood provides a simple way for determining individual contributions of parameter uncertainties to uncertainties in model predictions. The uncertainty quantification of specific model predictions allows identifying regions, where model predictions have to be considered with care. Such uncertain regions can be used for a rational experimental design to render initially highly uncertain model predictions into certainty. Finally, our uncertainty quantification directly accounts for parameter interdependencies and parameter sensitivities of the specific prediction.

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

NASA Astrophysics Data System (ADS)

Phanomchoeng, Gridsada

A variety of driver assistance systems such as traction control, electronic stability control (ESC), rollover prevention and lane departure avoidance systems are being developed by automotive manufacturers to reduce driver burden, partially automate normal driving operations, and reduce accidents. The effectiveness of these driver assistance systems can be significant enhanced if the real-time values of several vehicle parameters and state variables, namely tire-road friction coefficient, slip angle, roll angle, and rollover index, can be known. Since there are no inexpensive sensors available to measure these variables, it is necessary to estimate them. However, due to the significant nonlinear dynamics in a vehicle, due to unknown and changing plant parameters, and due to the presence of unknown input disturbances, the design of estimation algorithms for this application is challenging. This dissertation develops a new approach to observer design for nonlinear systems in which the nonlinearity has a globally (or locally) bounded Jacobian. The developed approach utilizes a modified version of the mean value theorem to express the nonlinearity in the estimation error dynamics as a convex combination of known matrices with time varying coefficients. The observer gains are then obtained by solving linear matrix inequalities (LMIs). A number of illustrative examples are presented to show that the developed approach is less conservative and more useful than the standard Lipschitz assumption based nonlinear observer. The developed nonlinear observer is utilized for estimation of slip angle, longitudinal vehicle velocity, and vehicle roll angle. In order to predict and prevent vehicle rollovers in tripped situations, it is necessary to estimate the vertical tire forces in the presence of unknown road disturbance inputs. An approach to estimate unknown disturbance inputs in nonlinear systems using dynamic model inversion and a modified version of the mean value theorem is presented. The developed theory is used to estimate vertical tire forces and predict tripped rollovers in situations involving road bumps, potholes, and lateral unknown force inputs. To estimate the tire-road friction coefficients at each individual tire of the vehicle, algorithms to estimate longitudinal forces and slip ratios at each tire are proposed. Subsequently, tire-road friction coefficients are obtained using recursive least squares parameter estimators that exploit the relationship between longitudinal force and slip ratio at each tire. The developed approaches are evaluated through simulations with industry standard software, CARSIM, with experimental tests on a Volvo XC90 sport utility vehicle and with experimental tests on a 1/8th scaled vehicle. The simulation and experimental results show that the developed approaches can reliably estimate the vehicle parameters and state variables needed for effective ESC and rollover prevention applications.

Bayesian inference in an item response theory model with a generalized student t link function

NASA Astrophysics Data System (ADS)

Azevedo, Caio L. N.; Migon, Helio S.

2012-10-01

In this paper we introduce a new item response theory (IRT) model with a generalized Student t-link function with unknown degrees of freedom (df), named generalized t-link (GtL) IRT model. In this model we consider only the difficulty parameter in the item response function. GtL is an alternative to the two parameter logit and probit models, since the degrees of freedom (df) play a similar role to the discrimination parameter. However, the behavior of the curves of the GtL is different from those of the two parameter models and the usual Student t link, since in GtL the curve obtained from different df's can cross the probit curves in more than one latent trait level. The GtL model has similar proprieties to the generalized linear mixed models, such as the existence of sufficient statistics and easy parameter interpretation. Also, many techniques of parameter estimation, model fit assessment and residual analysis developed for that models can be used for the GtL model. We develop fully Bayesian estimation and model fit assessment tools through a Metropolis-Hastings step within Gibbs sampling algorithm. We consider a prior sensitivity choice concerning the degrees of freedom. The simulation study indicates that the algorithm recovers all parameters properly. In addition, some Bayesian model fit assessment tools are considered. Finally, a real data set is analyzed using our approach and other usual models. The results indicate that our model fits the data better than the two parameter models.

Bayesian Markov Chain Monte Carlo inversion for weak anisotropy parameters and fracture weaknesses using azimuthal elastic impedance

NASA Astrophysics Data System (ADS)

Chen, Huaizhen; Pan, Xinpeng; Ji, Yuxin; Zhang, Guangzhi

2017-08-01

A system of aligned vertical fractures and fine horizontal shale layers combine to form equivalent orthorhombic media. Weak anisotropy parameters and fracture weaknesses play an important role in the description of orthorhombic anisotropy (OA). We propose a novel approach of utilizing seismic reflection amplitudes to estimate weak anisotropy parameters and fracture weaknesses from observed seismic data, based on azimuthal elastic impedance (EI). We first propose perturbation in stiffness matrix in terms of weak anisotropy parameters and fracture weaknesses, and using the perturbation and scattering function, we derive PP-wave reflection coefficient and azimuthal EI for the case of an interface separating two OA media. Then we demonstrate an approach to first use a model constrained damped least-squares algorithm to estimate azimuthal EI from partially incidence-phase-angle-stack seismic reflection data at different azimuths, and then extract weak anisotropy parameters and fracture weaknesses from the estimated azimuthal EI using a Bayesian Markov Chain Monte Carlo inversion method. In addition, a new procedure to construct rock physics effective model is presented to estimate weak anisotropy parameters and fracture weaknesses from well log interpretation results (minerals and their volumes, porosity, saturation, fracture density, etc.). Tests on synthetic and real data indicate that unknown parameters including elastic properties (P- and S-wave impedances and density), weak anisotropy parameters and fracture weaknesses can be estimated stably in the case of seismic data containing a moderate noise, and our approach can make a reasonable estimation of anisotropy in a fractured shale reservoir.

Extrapolation of the dna fragment-size distribution after high-dose irradiation to predict effects at low doses

NASA Technical Reports Server (NTRS)

Ponomarev, A. L.; Cucinotta, F. A.; Sachs, R. K.; Brenner, D. J.; Peterson, L. E.

2001-01-01

The patterns of DSBs induced in the genome are different for sparsely and densely ionizing radiations: In the former case, the patterns are well described by a random-breakage model; in the latter, a more sophisticated tool is needed. We used a Monte Carlo algorithm with a random-walk geometry of chromatin, and a track structure defined by the radial distribution of energy deposition from an incident ion, to fit the PFGE data for fragment-size distribution after high-dose irradiation. These fits determined the unknown parameters of the model, enabling the extrapolation of data for high-dose irradiation to the low doses that are relevant for NASA space radiation research. The randomly-located-clusters formalism was used to speed the simulations. It was shown that only one adjustable parameter, Q, the track efficiency parameter, was necessary to predict DNA fragment sizes for wide ranges of doses. This parameter was determined for a variety of radiations and LETs and was used to predict the DSB patterns at the HPRT locus of the human X chromosome after low-dose irradiation. It was found that high-LET radiation would be more likely than low-LET radiation to induce additional DSBs within the HPRT gene if this gene already contained one DSB.

Extended behavioural modelling of FET and lattice-mismatched HEMT devices

NASA Astrophysics Data System (ADS)

Khawam, Yahya; Albasha, Lutfi

2017-07-01

This study presents an improved large signal model that can be used for high electron mobility transistors (HEMTs) and field effect transistors using measurement-based behavioural modelling techniques. The steps for accurate large and small signal modelling for transistor are also discussed. The proposed DC model is based on the Fager model since it compensates between the number of model's parameters and accuracy. The objective is to increase the accuracy of the drain-source current model with respect to any change in gate or drain voltages. Also, the objective is to extend the improved DC model to account for soft breakdown and kink effect found in some variants of HEMT devices. A hybrid Newton's-Genetic algorithm is used in order to determine the unknown parameters in the developed model. In addition to accurate modelling of a transistor's DC characteristics, the complete large signal model is modelled using multi-bias s-parameter measurements. The way that the complete model is performed is by using a hybrid multi-objective optimisation technique (Non-dominated Sorting Genetic Algorithm II) and local minimum search (multivariable Newton's method) for parasitic elements extraction. Finally, the results of DC modelling and multi-bias s-parameters modelling are presented, and three-device modelling recommendations are discussed.

A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems.

PubMed

Kazemi, Mahdi; Arefi, Mohammad Mehdi

2017-03-01

In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Synchrony suppression in ensembles of coupled oscillators via adaptive vanishing feedback.

PubMed

Montaseri, Ghazal; Yazdanpanah, Mohammad Javad; Pikovsky, Arkady; Rosenblum, Michael

2013-09-01

Synchronization and emergence of a collective mode is a general phenomenon, frequently observed in ensembles of coupled self-sustained oscillators of various natures. In several circumstances, in particular in cases of neurological pathologies, this state of the active medium is undesirable. Destruction of this state by a specially designed stimulation is a challenge of high clinical relevance. Typically, the precise effect of an external action on the ensemble is unknown, since the microscopic description of the oscillators and their interactions are not available. We show that, desynchronization in case of a large degree of uncertainty about important features of the system is nevertheless possible; it can be achieved by virtue of a feedback loop with an additional adaptation of parameters. The adaptation also ensures desynchronization of ensembles with non-stationary, time-varying parameters. We perform the stability analysis of the feedback-controlled system and demonstrate efficient destruction of synchrony for several models, including those of spiking and bursting neurons.

Synchrony suppression in ensembles of coupled oscillators via adaptive vanishing feedback

NASA Astrophysics Data System (ADS)

Montaseri, Ghazal; Javad Yazdanpanah, Mohammad; Pikovsky, Arkady; Rosenblum, Michael

2013-09-01

Synchronization and emergence of a collective mode is a general phenomenon, frequently observed in ensembles of coupled self-sustained oscillators of various natures. In several circumstances, in particular in cases of neurological pathologies, this state of the active medium is undesirable. Destruction of this state by a specially designed stimulation is a challenge of high clinical relevance. Typically, the precise effect of an external action on the ensemble is unknown, since the microscopic description of the oscillators and their interactions are not available. We show that, desynchronization in case of a large degree of uncertainty about important features of the system is nevertheless possible; it can be achieved by virtue of a feedback loop with an additional adaptation of parameters. The adaptation also ensures desynchronization of ensembles with non-stationary, time-varying parameters. We perform the stability analysis of the feedback-controlled system and demonstrate efficient destruction of synchrony for several models, including those of spiking and bursting neurons.

A physiologically based model for tramadol pharmacokinetics in horses.

PubMed

Abbiati, Roberto Andrea; Cagnardi, Petra; Ravasio, Giuliano; Villa, Roberto; Manca, Davide

2017-09-21

This work proposes an application of a minimal complexity physiologically based pharmacokinetic model to predict tramadol concentration vs time profiles in horses. Tramadol is an opioid analgesic also used for veterinary treatments. Researchers and medical doctors can profit from the application of mathematical models as supporting tools to optimize the pharmacological treatment of animal species. The proposed model is based on physiology but adopts the minimal compartmental architecture necessary to describe the experimental data. The model features a system of ordinary differential equations, where most of the model parameters are either assigned or individualized for a given horse, using literature data and correlations. Conversely, residual parameters, whose value is unknown, are regressed exploiting experimental data. The model proved capable of simulating pharmacokinetic profiles with accuracy. In addition, it provides further insights on un-observable tramadol data, as for instance tramadol concentration in the liver or hepatic metabolism and renal excretion extent. Copyright © 2017 Elsevier Ltd. All rights reserved.

A study of parameter identification

NASA Technical Reports Server (NTRS)

Herget, C. J.; Patterson, R. E., III

1978-01-01

A set of definitions for deterministic parameter identification ability were proposed. Deterministic parameter identificability properties are presented based on four system characteristics: direct parameter recoverability, properties of the system transfer function, properties of output distinguishability, and uniqueness properties of a quadratic cost functional. Stochastic parameter identifiability was defined in terms of the existence of an estimation sequence for the unknown parameters which is consistent in probability. Stochastic parameter identifiability properties are presented based on the following characteristics: convergence properties of the maximum likelihood estimate, properties of the joint probability density functions of the observations, and properties of the information matrix.

Exact traveling wave solutions of modified KdV-Zakharov-Kuznetsov equation and viscous Burgers equation.

PubMed

Islam, Md Hamidul; Khan, Kamruzzaman; Akbar, M Ali; Salam, Md Abdus

2014-01-01

Mathematical modeling of many physical systems leads to nonlinear evolution equations because most physical systems are inherently nonlinear in nature. The investigation of traveling wave solutions of nonlinear partial differential equations (NPDEs) plays a significant role in the study of nonlinear physical phenomena. In this article, we construct the traveling wave solutions of modified KDV-ZK equation and viscous Burgers equation by using an enhanced (G '/G) -expansion method. A number of traveling wave solutions in terms of unknown parameters are obtained. Derived traveling wave solutions exhibit solitary waves when special values are given to its unknown parameters. 35C07; 35C08; 35P99.

Vision System for Coarsely Estimating Motion Parameters for Unknown Fast Moving Objects in Space

PubMed Central

Chen, Min; Hashimoto, Koichi

2017-01-01

Motivated by biological interests in analyzing navigation behaviors of flying animals, we attempt to build a system measuring their motion states. To do this, in this paper, we build a vision system to detect unknown fast moving objects within a given space, calculating their motion parameters represented by positions and poses. We proposed a novel method to detect reliable interest points from images of moving objects, which can be hardly detected by general purpose interest point detectors. 3D points reconstructed using these interest points are then grouped and maintained for detected objects, according to a careful schedule, considering appearance and perspective changes. In the estimation step, a method is introduced to adapt the robust estimation procedure used for dense point set to the case for sparse set, reducing the potential risk of greatly biased estimation. Experiments are conducted against real scenes, showing the capability of the system of detecting multiple unknown moving objects and estimating their positions and poses. PMID:29206189

Design and analysis of adaptive Super-Twisting sliding mode control for a microgyroscope.

PubMed

Feng, Zhilin; Fei, Juntao

2018-01-01

This paper proposes a novel adaptive Super-Twisting sliding mode control for a microgyroscope under unknown model uncertainties and external disturbances. In order to improve the convergence rate of reaching the sliding surface and the accuracy of regulating and trajectory tracking, a high order Super-Twisting sliding mode control strategy is employed, which not only can combine the advantages of the traditional sliding mode control with the Super-Twisting sliding mode control, but also guarantee that the designed control system can reach the sliding surface and equilibrium point in a shorter finite time from any initial state and avoid chattering problems. In consideration of unknown parameters of micro gyroscope system, an adaptive algorithm based on Lyapunov stability theory is designed to estimate the unknown parameters and angular velocity of microgyroscope. Finally, the effectiveness of the proposed scheme is demonstrated by simulation results. The comparative study between adaptive Super-Twisting sliding mode control and conventional sliding mode control demonstrate the superiority of the proposed method.

Comparing Different Approaches of Bias Correction for Ability Estimation in IRT Models. Research Report. ETS RR-08-13

ERIC Educational Resources Information Center

Lee, Yi-Hsuan; Zhang, Jinming

2008-01-01

The method of maximum-likelihood is typically applied to item response theory (IRT) models when the ability parameter is estimated while conditioning on the true item parameters. In practice, the item parameters are unknown and need to be estimated first from a calibration sample. Lewis (1985) and Zhang and Lu (2007) proposed the expected response…

AN ONLINE CATALOG OF CATACLYSMIC VARIABLE SPECTRA FROM THE FAR-ULTRAVIOLET SPECTROSCOPIC EXPLORER

DOE Office of Scientific and Technical Information (OSTI.GOV)

Godon, Patrick; Sion, Edward M.; Levay, Karen

2012-12-15

We present an online catalog containing spectra and supporting information for cataclysmic variables that have been observed with the Far-Ultraviolet Spectroscopic Explorer (FUSE). For each object in the catalog we list some of the basic system parameters such as (R.A., decl.), period, inclination, and white dwarf mass, as well as information on the available FUSE spectra: data ID, observation date and time, and exposure time. In addition, we provide parameters needed for the analysis of the FUSE spectra such as the reddening E(B - V), distance, and state (high, low, intermediate) of the system at the time it was observed.more » For some of these spectra we have carried out model fits to the continuum with synthetic stellar and/or disk spectra using the codes TLUSTY and SYNSPEC. We provide the parameters obtained from these model fits; this includes the white dwarf temperature, gravity, projected rotational velocity, and elemental abundances of C, Si, S, and N, together with the disk mass accretion rate, the resulting inclination, and model-derived distance (when unknown). For each object one or more figures are provided (as gif files) with line identification and model fit(s) when available. The FUSE spectra and the synthetic spectra are directly available for download as ASCII tables. References are provided for each object, as well as for the model fits. In this article we present 36 objects, and additional ones will be added to the online catalog in the future. In addition to cataclysmic variables, we also include a few related objects, such as a wind-accreting white dwarf, a pre-cataclysmic variable, and some symbiotics.« less

Nonlinear inversion of resistivity sounding data for 1-D earth models using the Neighbourhood Algorithm

NASA Astrophysics Data System (ADS)

Ojo, A. O.; Xie, Jun; Olorunfemi, M. O.

2018-01-01

To reduce ambiguity related to nonlinearities in the resistivity model-data relationships, an efficient direct-search scheme employing the Neighbourhood Algorithm (NA) was implemented to solve the 1-D resistivity problem. In addition to finding a range of best-fit models which are more likely to be global minimums, this method investigates the entire multi-dimensional model space and provides additional information about the posterior model covariance matrix, marginal probability density function and an ensemble of acceptable models. This provides new insights into how well the model parameters are constrained and make assessing trade-offs between them possible, thus avoiding some common interpretation pitfalls. The efficacy of the newly developed program is tested by inverting both synthetic (noisy and noise-free) data and field data from other authors employing different inversion methods so as to provide a good base for comparative performance. In all cases, the inverted model parameters were in good agreement with the true and recovered model parameters from other methods and remarkably correlate with the available borehole litho-log and known geology for the field dataset. The NA method has proven to be useful whilst a good starting model is not available and the reduced number of unknowns in the 1-D resistivity inverse problem makes it an attractive alternative to the linearized methods. Hence, it is concluded that the newly developed program offers an excellent complementary tool for the global inversion of the layered resistivity structure.

Optimal hemodynamic response model for functional near-infrared spectroscopy

PubMed Central

Kamran, Muhammad A.; Jeong, Myung Yung; Mannan, Malik M. N.

2015-01-01

Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique and measures brain activities by means of near-infrared light of 650–950 nm wavelengths. The cortical hemodynamic response (HR) differs in attributes at different brain regions and on repetition of trials, even if the experimental paradigm is kept exactly the same. Therefore, an HR model that can estimate such variations in the response is the objective of this research. The canonical hemodynamic response function (cHRF) is modeled by two Gamma functions with six unknown parameters (four of them to model the shape and other two to scale and baseline respectively). The HRF model is supposed to be a linear combination of HRF, baseline, and physiological noises (amplitudes and frequencies of physiological noises are supposed to be unknown). An objective function is developed as a square of the residuals with constraints on 12 free parameters. The formulated problem is solved by using an iterative optimization algorithm to estimate the unknown parameters in the model. Inter-subject variations in HRF and physiological noises have been estimated for better cortical functional maps. The accuracy of the algorithm has been verified using 10 real and 15 simulated data sets. Ten healthy subjects participated in the experiment and their HRF for finger-tapping tasks have been estimated and analyzed. The statistical significance of the estimated activity strength parameters has been verified by employing statistical analysis (i.e., t-value > tcritical and p-value < 0.05). PMID:26136668

Optimal hemodynamic response model for functional near-infrared spectroscopy.

PubMed

Kamran, Muhammad A; Jeong, Myung Yung; Mannan, Malik M N

2015-01-01

Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique and measures brain activities by means of near-infrared light of 650-950 nm wavelengths. The cortical hemodynamic response (HR) differs in attributes at different brain regions and on repetition of trials, even if the experimental paradigm is kept exactly the same. Therefore, an HR model that can estimate such variations in the response is the objective of this research. The canonical hemodynamic response function (cHRF) is modeled by two Gamma functions with six unknown parameters (four of them to model the shape and other two to scale and baseline respectively). The HRF model is supposed to be a linear combination of HRF, baseline, and physiological noises (amplitudes and frequencies of physiological noises are supposed to be unknown). An objective function is developed as a square of the residuals with constraints on 12 free parameters. The formulated problem is solved by using an iterative optimization algorithm to estimate the unknown parameters in the model. Inter-subject variations in HRF and physiological noises have been estimated for better cortical functional maps. The accuracy of the algorithm has been verified using 10 real and 15 simulated data sets. Ten healthy subjects participated in the experiment and their HRF for finger-tapping tasks have been estimated and analyzed. The statistical significance of the estimated activity strength parameters has been verified by employing statistical analysis (i.e., t-value > t critical and p-value < 0.05).

A Fast Surrogate-facilitated Data-driven Bayesian Approach to Uncertainty Quantification of a Regional Groundwater Flow Model with Structural Error

NASA Astrophysics Data System (ADS)

Xu, T.; Valocchi, A. J.; Ye, M.; Liang, F.

2016-12-01

Due to simplification and/or misrepresentation of the real aquifer system, numerical groundwater flow and solute transport models are usually subject to model structural error. During model calibration, the hydrogeological parameters may be overly adjusted to compensate for unknown structural error. This may result in biased predictions when models are used to forecast aquifer response to new forcing. In this study, we extend a fully Bayesian method [Xu and Valocchi, 2015] to calibrate a real-world, regional groundwater flow model. The method uses a data-driven error model to describe model structural error and jointly infers model parameters and structural error. In this study, Bayesian inference is facilitated using high performance computing and fast surrogate models. The surrogate models are constructed using machine learning techniques to emulate the response simulated by the computationally expensive groundwater model. We demonstrate in the real-world case study that explicitly accounting for model structural error yields parameter posterior distributions that are substantially different from those derived by the classical Bayesian calibration that does not account for model structural error. In addition, the Bayesian with error model method gives significantly more accurate prediction along with reasonable credible intervals.

Theoretical relationship between elastic wave velocity and electrical resistivity

NASA Astrophysics Data System (ADS)

Lee, Jong-Sub; Yoon, Hyung-Koo

2015-05-01

Elastic wave velocity and electrical resistivity have been commonly applied to estimate stratum structures and obtain subsurface soil design parameters. Both elastic wave velocity and electrical resistivity are related to the void ratio; the objective of this study is therefore to suggest a theoretical relationship between the two physical parameters. Gassmann theory and Archie's equation are applied to propose a new theoretical equation, which relates the compressional wave velocity to shear wave velocity and electrical resistivity. The piezo disk element (PDE) and bender element (BE) are used to measure the compressional and shear wave velocities, respectively. In addition, the electrical resistivity is obtained by using the electrical resistivity probe (ERP). The elastic wave velocity and electrical resistivity are recorded in several types of soils including sand, silty sand, silty clay, silt, and clay-sand mixture. The appropriate input parameters are determined based on the error norm in order to increase the reliability of the proposed relationship. The predicted compressional wave velocities from the shear wave velocity and electrical resistivity are similar to the measured compressional velocities. This study demonstrates that the new theoretical relationship may be effectively used to predict the unknown geophysical property from the measured values.

Simplified adaptive control of an orbiting flexible spacecraft

NASA Astrophysics Data System (ADS)

Maganti, Ganesh B.; Singh, Sahjendra N.

2007-10-01

The paper presents the design of a new simple adaptive system for the rotational maneuver and vibration suppression of an orbiting spacecraft with flexible appendages. A moment generating device located on the central rigid body of the spacecraft is used for the attitude control. It is assumed that the system parameters are unknown and the truncated model of the spacecraft has finite but arbitrary dimension. In addition, only the pitch angle and its derivative are measured and elastic modes are not available for feedback. The control output variable is chosen as the linear combination of the pitch angle and the pitch rate. Exploiting the hyper minimum phase nature of the spacecraft, a simple adaptive control law is derived for the pitch angle control and elastic mode stabilization. The adaptation rule requires only four adjustable parameters and the structure of the control system does not depend on the order of the truncated spacecraft model. For the synthesis of control system, the measured output error and the states of a third-order command generator are used. Simulation results are presented which show that in the closed-loop system adaptive output regulation is accomplished in spite of large parameter uncertainties and disturbance input.

Determination of power system component parameters using nonlinear dead beat estimation method

NASA Astrophysics Data System (ADS)

Kolluru, Lakshmi

Power systems are considered the most complex man-made wonders in existence today. In order to effectively supply the ever increasing demands of the consumers, power systems are required to remain stable at all times. Stability and monitoring of these complex systems are achieved by strategically placed computerized control centers. State and parameter estimation is an integral part of these facilities, as they deal with identifying the unknown states and/or parameters of the systems. Advancements in measurement technologies and the introduction of phasor measurement units (PMU) provide detailed and dynamic information of all measurements. Accurate availability of dynamic measurements provides engineers the opportunity to expand and explore various possibilities in power system dynamic analysis/control. This thesis discusses the development of a parameter determination algorithm for nonlinear power systems, using dynamic data obtained from local measurements. The proposed algorithm was developed by observing the dead beat estimator used in state space estimation of linear systems. The dead beat estimator is considered to be very effective as it is capable of obtaining the required results in a fixed number of steps. The number of steps required is related to the order of the system and the number of parameters to be estimated. The proposed algorithm uses the idea of dead beat estimator and nonlinear finite difference methods to create an algorithm which is user friendly and can determine the parameters fairly accurately and effectively. The proposed algorithm is based on a deterministic approach, which uses dynamic data and mathematical models of power system components to determine the unknown parameters. The effectiveness of the algorithm is tested by implementing it to identify the unknown parameters of a synchronous machine. MATLAB environment is used to create three test cases for dynamic analysis of the system with assumed known parameters. Faults are introduced in the virtual test systems and the dynamic data obtained in each case is analyzed and recorded. Ideally, actual measurements are to be provided to the algorithm. As the measurements are not readily available the data obtained from simulations is fed into the determination algorithm as inputs. The obtained results are then compared to the original (or assumed) values of the parameters. The results obtained suggest that the algorithm is able to determine the parameters of a synchronous machine when crisp data is available.

Discontinuous Spectral Difference Method for Conservation Laws on Unstructured Grids

NASA Technical Reports Server (NTRS)

Liu, Yen; Vinokur, Marcel

2004-01-01

A new, high-order, conservative, and efficient discontinuous spectral finite difference (SD) method for conservation laws on unstructured grids is developed. The concept of discontinuous and high-order local representations to achieve conservation and high accuracy is utilized in a manner similar to the Discontinuous Galerkin (DG) and the Spectral Volume (SV) methods, but while these methods are based on the integrated forms of the equations, the new method is based on the differential form to attain a simpler formulation and higher efficiency. Conventional unstructured finite-difference and finite-volume methods require data reconstruction based on the least-squares formulation using neighboring point or cell data. Since each unknown employs a different stencil, one must repeat the least-squares inversion for every point or cell at each time step, or to store the inversion coefficients. In a high-order, three-dimensional computation, the former would involve impractically large CPU time, while for the latter the memory requirement becomes prohibitive. In addition, the finite-difference method does not satisfy the integral conservation in general. By contrast, the DG and SV methods employ a local, universal reconstruction of a given order of accuracy in each cell in terms of internally defined conservative unknowns. Since the solution is discontinuous across cell boundaries, a Riemann solver is necessary to evaluate boundary flux terms and maintain conservation. In the DG method, a Galerkin finite-element method is employed to update the nodal unknowns within each cell. This requires the inversion of a mass matrix, and the use of quadratures of twice the order of accuracy of the reconstruction to evaluate the surface integrals and additional volume integrals for nonlinear flux functions. In the SV method, the integral conservation law is used to update volume averages over subcells defined by a geometrically similar partition of each grid cell. As the order of accuracy increases, the partitioning for 3D requires the introduction of a large number of parameters, whose optimization to achieve convergence becomes increasingly more difficult. Also, the number of interior facets required to subdivide non-planar faces, and the additional increase in the number of quadrature points for each facet, increases the computational cost greatly.

Rendezvous with connectivity preservation for multi-robot systems with an unknown leader

NASA Astrophysics Data System (ADS)

Dong, Yi

2018-02-01

This paper studies the leader-following rendezvous problem with connectivity preservation for multi-agent systems composed of uncertain multi-robot systems subject to external disturbances and an unknown leader, both of which are generated by a so-called exosystem with parametric uncertainty. By combining internal model design, potential function technique and adaptive control, two distributed control strategies are proposed to maintain the connectivity of the communication network, to achieve the asymptotic tracking of all the followers to the output of the unknown leader system, as well as to reject unknown external disturbances. It is also worth to mention that the uncertain parameters in the multi-robot systems and exosystem are further allowed to belong to unknown and unbounded sets when applying the second fully distributed control law containing a dynamic gain inspired by high-gain adaptive control or self-tuning regulator.

Design of a DNA chip for detection of unknown genetically modified organisms (GMOs).

PubMed

Nesvold, Håvard; Kristoffersen, Anja Bråthen; Holst-Jensen, Arne; Berdal, Knut G

2005-05-01

Unknown genetically modified organisms (GMOs) have not undergone a risk evaluation, and hence might pose a danger to health and environment. There are, today, no methods for detecting unknown GMOs. In this paper we propose a novel method intended as a first step in an approach for detecting unknown genetically modified (GM) material in a single plant. A model is designed where biological and combinatorial reduction rules are applied to a set of DNA chip probes containing all possible sequences of uniform length n, creating probes capable of detecting unknown GMOs. The model is theoretically tested for Arabidopsis thaliana Columbia, and the probabilities for detecting inserts and receiving false positives are assessed for various parameters for this organism. From a theoretical standpoint, the model looks very promising but should be tested further in the laboratory. The model and algorithms will be available upon request to the corresponding author.

Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs.

PubMed

Shi, Wuxi; Luo, Rui; Li, Baoquan

2017-01-01

In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Polarimetric LIDAR with FRI sampling for target characterization

NASA Astrophysics Data System (ADS)

Wijerathna, Erandi; Creusere, Charles D.; Voelz, David; Castorena, Juan

2017-09-01

Polarimetric LIDAR is a significant tool for current remote sensing applications. In addition, measurement of the full waveform of the LIDAR echo provides improved ranging and target discrimination, although, data storage volume in this approach can be problematic. In the work presented here, we investigated the practical issues related to the implementation of a full waveform LIDAR system to identify polarization characteristics of multiple targets within the footprint of the illumination beam. This work was carried out on a laboratory LIDAR testbed that features a flexible arrangement of targets and the ability to change the target polarization characteristics. Targets with different retardance characteristics were illuminated with a linearly polarized laser beam and the return pulse intensities were analyzed by rotating a linear analyzer polarizer in front of a high-speed detector. Additionally, we explored the applicability and the limitations of applying a sparse sampling approach based on Finite Rate of Innovations (FRI) to compress and recover the characteristic parameters of the pulses reflected from the targets. The pulse parameter values extracted by the FRI analysis were accurate and we successfully distinguished the polarimetric characteristics and the range of multiple targets at different depths within the same beam footprint. We also demonstrated the recovery of an unknown target retardance value from the echoes by applying a Mueller matrix system model.

Identifying coagulopathies in the pathophysiology of cold stress syndrome in the Florida manatee Trichechus manatus latirostris.

PubMed

Barratclough, Ashley; Conner, Bobbi J; Brooks, Marjory B; Pontes Stablein, Alyssa; Gerlach, Trevor J; Reep, Roger L; Ball, Ray L; Floyd, Ruth Francis

2017-08-09

Cold stress syndrome (CSS) in the Florida manatee Trichechus manatus latirostris has been defined as morbidity and mortality resulting from prolonged exposure to water temperatures <20°C. The pathophysiology is described as multifactorial, involving nutritional, immunological and metabolic disturbances; however, the exact mechanisms are unknown. We hypothesized that thromboembolic complications contribute to the pathophysiology of CSS in addition to the previously described factors. During the winter of 2014-2015, 10 Florida manatees with clinical signs of CSS were presented to Lowry Park Zoo, Tampa, FL, USA. Thromboelastography (TEG) and coagulation panels were performed at admission. In addition, coagulation panel data from 23 retrospective CSS cases were included in the analyses. There were numerous differences between mean values of TEG and coagulation parameters for healthy manatees and those for CSS cases. Among TEG parameters, reaction time (R), clot formation time (K) and percentage of clot lysed after 30 min (LY30) values were significantly different (p < 0.05) between the 2 groups. CSS cases also had significantly higher mean D-dimer concentration and coagulation factor XI activity, prolonged mean activated partial thromboplastin time (aPTT) and significantly decreased mean antithrombin activity. These combined abnormalities include clinicopathologic criteria of disseminated intravascular coagulation, indicating an increased risk of thromboembolic disease associated with manatee CSS.

Methodological issues regarding power of classical test theory (CTT) and item response theory (IRT)-based approaches for the comparison of patient-reported outcomes in two groups of patients - a simulation study

PubMed Central

2010-01-01

Background Patients-Reported Outcomes (PRO) are increasingly used in clinical and epidemiological research. Two main types of analytical strategies can be found for these data: classical test theory (CTT) based on the observed scores and models coming from Item Response Theory (IRT). However, whether IRT or CTT would be the most appropriate method to analyse PRO data remains unknown. The statistical properties of CTT and IRT, regarding power and corresponding effect sizes, were compared. Methods Two-group cross-sectional studies were simulated for the comparison of PRO data using IRT or CTT-based analysis. For IRT, different scenarios were investigated according to whether items or person parameters were assumed to be known, to a certain extent for item parameters, from good to poor precision, or unknown and therefore had to be estimated. The powers obtained with IRT or CTT were compared and parameters having the strongest impact on them were identified. Results When person parameters were assumed to be unknown and items parameters to be either known or not, the power achieved using IRT or CTT were similar and always lower than the expected power using the well-known sample size formula for normally distributed endpoints. The number of items had a substantial impact on power for both methods. Conclusion Without any missing data, IRT and CTT seem to provide comparable power. The classical sample size formula for CTT seems to be adequate under some conditions but is not appropriate for IRT. In IRT, it seems important to take account of the number of items to obtain an accurate formula. PMID:20338031

Characterizing heterogeneous properties of cerebral aneurysms with unknown stress-free geometry: a precursor to in vivo identification.

PubMed

Zhao, Xuefeng; Raghavan, Madhavan L; Lu, Jia

2011-05-01

Knowledge of elastic properties of cerebral aneurysms is crucial for understanding the biomechanical behavior of the lesion. However, characterizing tissue properties using in vivo motion data presents a tremendous challenge. Aside from the limitation of data accuracy, a pressing issue is that the in vivo motion does not expose the stress-free geometry. This is compounded by the nonlinearity, anisotropy, and heterogeneity of the tissue behavior. This article introduces a method for identifying the heterogeneous properties of aneurysm wall tissue under unknown stress-free configuration. In the proposed approach, an accessible configuration is taken as the reference; the unknown stress-free configuration is represented locally by a metric tensor describing the prestrain from the stress-free configuration to the reference configuration. Material parameters are identified together with the metric tensor pointwisely. The paradigm is tested numerically using a forward-inverse analysis loop. An image-derived sac is considered. The aneurysm tissue is modeled as an eightply laminate whose constitutive behavior is described by an anisotropic hyperelastic strain-energy function containing four material parameters. The parameters are assumed to vary continuously in two assigned patterns to represent two types of material heterogeneity. Nine configurations between the diastolic and systolic pressures are generated by forward quasi-static finite element analyses. These configurations are fed to the inverse analysis to delineate the material parameters and the metric tensor. The recovered and the assigned distributions are in good agreement. A forward verification is conducted by comparing the displacement solutions obtained from the recovered and the assigned material parameters at a different pressure. The nodal displacements are found in excellent agreement.

Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy

NASA Astrophysics Data System (ADS)

He, Shixuan; Xie, Wanyi; Zhang, Wei; Zhang, Liqun; Wang, Yunxia; Liu, Xiaoling; Liu, Yulong; Du, Chunlei

2015-02-01

A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety.

Addition of oral iron to plasma transfusion in human congenital hypotransferrinemia: A 10-year observational follow-up with the effects on hematological parameters and growth.

PubMed

Aslan, Deniz

2018-02-01

Congenital hypotransferrinemia (OMIM 209300) is an extremely rare disorder of inherited iron metabolism. Since its description in 1961, only 16 cases have been reported. The defective gene and molecular defect causing this disorder and clinicolaboratory findings seen in the homozygous and heterozygous states have been documented in both humans and mice. However, due to the lack of follow-up studies of the described cases, the long-term prognosis remains unknown. We present a 10-year observational follow-up of a patient previously diagnosed on a molecular basis who was treated with a unique therapy of plasma transfusion fortified with oral iron, with satisfactory clinicolaboratory responses. © 2017 Wiley Periodicals, Inc.

Development of an ideal observer that incorporates nuisance parameters and processes list-mode data

DOE PAGES

MacGahan, Christopher Jonathan; Kupinski, Matthew Alan; Hilton, Nathan R.; ...

2016-02-01

Observer models were developed to process data in list-mode format in order to perform binary discrimination tasks for use in an arms-control-treaty context. Data used in this study was generated using GEANT4 Monte Carlo simulations for photons using custom models of plutonium inspection objects and a radiation imaging system. We evaluated observer model performance and then presented using the area under the receiver operating characteristic curve. Lastly, we studied the ideal observer under both signal-known-exactly conditions and in the presence of unknowns such as object orientation and absolute count-rate variability; when these additional sources of randomness were present, their incorporationmore » into the observer yielded superior performance.« less

Effects of a chemical weapons incineration plant on red-tailed tropicbirds

USGS Publications Warehouse

Schreiber, E.A.; Doherty, P.F.; Schenk, G.A.

2001-01-01

From 1990 to 2000, the Johnston Atoll Chemical Agent Disposal System (JACADS) incinerated part of the U.S. stockpile of chemical weapons on Johnston Atoll, central Pacific Ocean, which also is a National Wildlife Refuge and home to approximately a half-million breeding seabirds. The effect on wildlife of incineration of these weapons is unknown. Using a multi-strata mark-recapture analysis, we investigated the effects of JACADS on reproductive success, survival, and movement probabilities of red-tailed tropicbirds (Phaethon rubricauda) nesting both downwind and upwind of the incineration site. We found no effect of chemical incineration on these tropicbird demographic parameters over the 8 years of our study. An additional 3 years of monitoring tropicbird demography will take place, post-incineration.

[Activities using websites and social networks: tools and indicators for evaluation].

PubMed

López, María José; Continente, Xavier; Sánchez, Esther; Bartroli, Montse

In the field of health, information and communication technology (ICT) can create a space that, regardless of place or time, enables information to be shared and disseminated quickly. In addition to the usual challenges of evaluating public health activities, other difficulties are present when evaluating activities using ICT, such as lack of previous standards, unknown individual exposure or lack of information on the characteristics of those exposed. The aim of this paper is to describe some tools and indicators that may help to assess the scope, use and parameters related to website positioning on search engines as well as the connected social networks. Copyright © 2017 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

The Use of One-Sample Prediction Intervals for Estimating CO2 Scrubber Canister Durations

DTIC Science & Technology

2012-10-01

Grade and 812 D-Grade Sofnolime.3 Definitions According to Devore,4 A CI (confidence interval) refers to a parameter, or population ... characteristic , whose value is fixed but unknown to us. In contrast, a future value of Y is not a parameter but instead a random variable; for this

Changes of Pain Perception, Autonomic Function, and Endocrine Parameters during Treatment of Anorectic Adolescents

ERIC Educational Resources Information Center

Bar, Karl-Jurgen; Boettger, Silke; Wagner, Gerd; Wilsdorf, Christine; Gerhard, Uwe Jens; Boettger, Michael K.; Blanz, Bernhard; Sauer, Heinrich

2006-01-01

Objectives: The underlying mechanisms of reduced pain perception in anorexia nervosa (AN) are unknown. To gain more insight into the pathology, the authors investigated pain perception, autonomic function, and endocrine parameters before and during successful treatment of adolescent AN patients. Method: Heat pain perception was assessed in 15…

ADMIT: a toolbox for guaranteed model invalidation, estimation and qualitative–quantitative modeling

PubMed Central

Streif, Stefan; Savchenko, Anton; Rumschinski, Philipp; Borchers, Steffen; Findeisen, Rolf

2012-01-01

Summary: Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if–then observations. ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MatLabTM-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity. Availability: ADMIT, tutorials and illustrative examples are available free of charge for non-commercial use at http://ifatwww.et.uni-magdeburg.de/syst/ADMIT/ Contact: stefan.streif@ovgu.de PMID:22451270

ADMIT: a toolbox for guaranteed model invalidation, estimation and qualitative-quantitative modeling.

PubMed

Streif, Stefan; Savchenko, Anton; Rumschinski, Philipp; Borchers, Steffen; Findeisen, Rolf

2012-05-01

Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if-then observations. ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MatLab(TM)-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity. ADMIT, tutorials and illustrative examples are available free of charge for non-commercial use at http://ifatwww.et.uni-magdeburg.de/syst/ADMIT/

Distributed Time-Varying Formation Robust Tracking for General Linear Multiagent Systems With Parameter Uncertainties and External Disturbances.

PubMed

Hua, Yongzhao; Dong, Xiwang; Li, Qingdong; Ren, Zhang

2017-05-18

This paper investigates the time-varying formation robust tracking problems for high-order linear multiagent systems with a leader of unknown control input in the presence of heterogeneous parameter uncertainties and external disturbances. The followers need to accomplish an expected time-varying formation in the state space and track the state trajectory produced by the leader simultaneously. First, a time-varying formation robust tracking protocol with a totally distributed form is proposed utilizing the neighborhood state information. With the adaptive updating mechanism, neither any global knowledge about the communication topology nor the upper bounds of the parameter uncertainties, external disturbances and leader's unknown input are required in the proposed protocol. Then, in order to determine the control parameters, an algorithm with four steps is presented, where feasible conditions for the followers to accomplish the expected time-varying formation tracking are provided. Furthermore, based on the Lyapunov-like analysis theory, it is proved that the formation tracking error can converge to zero asymptotically. Finally, the effectiveness of the theoretical results is verified by simulation examples.

Multiparameter Estimation in Networked Quantum Sensors

NASA Astrophysics Data System (ADS)

Proctor, Timothy J.; Knott, Paul A.; Dunningham, Jacob A.

2018-02-01

We introduce a general model for a network of quantum sensors, and we use this model to consider the following question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. This immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or nonlinear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.

Multisite EPR oximetry from multiple quadrature harmonics.

PubMed

Ahmad, R; Som, S; Johnson, D H; Zweier, J L; Kuppusamy, P; Potter, L C

2012-01-01

Multisite continuous wave (CW) electron paramagnetic resonance (EPR) oximetry using multiple quadrature field modulation harmonics is presented. First, a recently developed digital receiver is used to extract multiple harmonics of field modulated projection data. Second, a forward model is presented that relates the projection data to unknown parameters, including linewidth at each site. Third, a maximum likelihood estimator of unknown parameters is reported using an iterative algorithm capable of jointly processing multiple quadrature harmonics. The data modeling and processing are applicable for parametric lineshapes under nonsaturating conditions. Joint processing of multiple harmonics leads to 2-3-fold acceleration of EPR data acquisition. For demonstration in two spatial dimensions, both simulations and phantom studies on an L-band system are reported. Copyright © 2011 Elsevier Inc. All rights reserved.

Parametric system identification of catamaran for improving controller design

NASA Astrophysics Data System (ADS)

Timpitak, Surasak; Prempraneerach, Pradya; Pengwang, Eakkachai

2018-01-01

This paper presents an estimation of simplified dynamic model for only surge- and yaw- motions of catamaran by using system identification (SI) techniques to determine associated unknown parameters. These methods will enhance the performance of designing processes for the motion control system of Unmanned Surface Vehicle (USV). The simulation results demonstrate an effective way to solve for damping forces and to determine added masses by applying least-square and AutoRegressive Exogenous (ARX) methods. Both methods are then evaluated according to estimated parametric errors from the vehicle’s dynamic model. The ARX method, which yields better estimated accuracy, can then be applied to identify unknown parameters as well as to help improving a controller design of a real unmanned catamaran.

Back-stepping active disturbance rejection control design for integrated missile guidance and control system via reduced-order ESO.

PubMed

Xingling, Shao; Honglun, Wang

2015-07-01

This paper proposes a novel composite integrated guidance and control (IGC) law for missile intercepting against unknown maneuvering target with multiple uncertainties and control constraint. First, by using back-stepping technique, the proposed IGC law design is separated into guidance loop and control loop. The unknown target maneuvers and variations of aerodynamics parameters in guidance and control loop are viewed as uncertainties, which are estimated and compensated by designed model-assisted reduced-order extended state observer (ESO). Second, based on the principle of active disturbance rejection control (ADRC), enhanced feedback linearization (FL) based control law is implemented for the IGC model using the estimates generated by reduced-order ESO. In addition, performance analysis and comparisons between ESO and reduced-order ESO are examined. Nonlinear tracking differentiator is employed to construct the derivative of virtual control command in the control loop. Third, the closed-loop stability for the considered system is established. Finally, the effectiveness of the proposed IGC law in enhanced interception performance such as smooth interception course, improved robustness against multiple uncertainties as well as reduced control consumption during initial phase are demonstrated through simulations. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A Systematic Approach for Model-Based Aircraft Engine Performance Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2010-01-01

A requirement for effective aircraft engine performance estimation is the ability to account for engine degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. This paper presents a linear point design methodology for minimizing the degradation-induced error in model-based aircraft engine performance estimation applications. The technique specifically focuses on the underdetermined estimation problem, where there are more unknown health parameters than available sensor measurements. A condition for Kalman filter-based estimation is that the number of health parameters estimated cannot exceed the number of sensed measurements. In this paper, the estimated health parameter vector will be replaced by a reduced order tuner vector whose dimension is equivalent to the sensed measurement vector. The reduced order tuner vector is systematically selected to minimize the theoretical mean squared estimation error of a maximum a posteriori estimator formulation. This paper derives theoretical estimation errors at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the estimation accuracy achieved through conventional maximum a posteriori and Kalman filter estimation approaches. Maximum a posteriori estimation results demonstrate that reduced order tuning parameter vectors can be found that approximate the accuracy of estimating all health parameters directly. Kalman filter estimation results based on the same reduced order tuning parameter vectors demonstrate that significantly improved estimation accuracy can be achieved over the conventional approach of selecting a subset of health parameters to serve as the tuner vector. However, additional development is necessary to fully extend the methodology to Kalman filter-based estimation applications.

Quantum pattern recognition with multi-neuron interactions

NASA Astrophysics Data System (ADS)

Fard, E. Rezaei; Aghayar, K.; Amniat-Talab, M.

2018-03-01

We present a quantum neural network with multi-neuron interactions for pattern recognition tasks by a combination of extended classic Hopfield network and adiabatic quantum computation. This scheme can be used as an associative memory to retrieve partial patterns with any number of unknown bits. Also, we propose a preprocessing approach to classifying the pattern space S to suppress spurious patterns. The results of pattern clustering show that for pattern association, the number of weights (η ) should equal the numbers of unknown bits in the input pattern ( d). It is also remarkable that associative memory function depends on the location of unknown bits apart from the d and load parameter α.

Rapid rather than gradual weight reduction impairs hemorheological parameters of Taekwondo athletes through reduction in RBC-NOS activation.

PubMed

Yang, Woo Hwi; Heine, Oliver; Pauly, Sebastian; Kim, Pilsang; Bloch, Wilhelm; Mester, Joachim; Grau, Marijke

2015-01-01

Rapid weight reduction is part of the pre-competition routine and has been shown to negatively affect psychological and physiological performance of Taekwondo (TKD) athletes. This is caused by a reduction of the body water and an electrolyte imbalance. So far, it is unknown whether weight reduction also affects hemorheological properties and hemorheology-influencing nitric oxide (NO) signaling, important for oxygen supply to the muscles and organs. For this purpose, ten male TKD athletes reduced their body weight by 5% within four days (rapid weight reduction, RWR). After a recovery phase, athletes reduced body weight by 5% within four weeks (gradual weight reduction, GWR). Each intervention was preceded by two baseline measurements and followed by a simulated competition. Basal blood parameters (red blood cell (RBC) count, hemoglobin concentration, hematocrit, mean corpuscular volume, mean cellular hemoglobin and mean cellular hemoglobin concentration), RBC-NO synthase activation, RBC nitrite as marker for NO synthesis, RBC deformability and aggregation parameters were determined on a total of eight investigation days. Basal blood parameters were not affected by the two interventions. In contrast to GWR, RWR decreased activation of RBC-NO synthase, RBC nitrite, respective NO concentration and RBC deformability. Additionally, RWR increased RBC aggregation and disaggregation threshold. The results point out that a rapid weight reduction negatively affects hemorheological parameters and NO signaling in RBC which might limit performance capacity. Thus, GWR should be preferred to achieve the desired weight prior to a competition to avoid these negative effects.

Inverting multiple suites of thermal indicator data to constrain the heat flow history: A case study from east Kalimantan, Indonesia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mudford, B.S.

1996-12-31

The determination of an appropriate thermal history in an exploration area is of fundamental importance when attempting to understand the evolution of the petroleum system. In this talk we present the results of a single-well modelling study in which bottom hole temperature data, vitrinite reflectance data and three different biomarker ratio datasets were available to constrain the modelling. Previous modelling studies using biomarker ratios have been hampered by the wide variety of published kinetic parameters for biomarker evolution. Generally, these parameters have been determined either from measurements in the laboratory and extrapolation to the geological setting, or from downhole measurementsmore » where the heat flow history is assumed to be known. In the first case serious errors can arise because the heating rate is being extrapolated over many orders of magnitude, while in the second case errors can arise if the assumed heat flow history is incorrect. To circumvent these problems we carried out a parameter optimization in which the heat flow history was treated as an unknown in addition to the biomarker ratio kinetic parameters. This method enabled the heat flow history for the area to be determined together with appropriate kinetic parameters for the three measured biomarker ratios. Within the resolution of the data, the heat flow since the early Miocene has been relatively constant at levels required to yield good agreement between predicted and measured subsurface temperatures.« less

Inverting multiple suites of thermal indicator data to constrain the heat flow history: A case study from east Kalimantan, Indonesia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mudford, B.S.

1996-01-01

The determination of an appropriate thermal history in an exploration area is of fundamental importance when attempting to understand the evolution of the petroleum system. In this talk we present the results of a single-well modelling study in which bottom hole temperature data, vitrinite reflectance data and three different biomarker ratio datasets were available to constrain the modelling. Previous modelling studies using biomarker ratios have been hampered by the wide variety of published kinetic parameters for biomarker evolution. Generally, these parameters have been determined either from measurements in the laboratory and extrapolation to the geological setting, or from downhole measurementsmore » where the heat flow history is assumed to be known. In the first case serious errors can arise because the heating rate is being extrapolated over many orders of magnitude, while in the second case errors can arise if the assumed heat flow history is incorrect. To circumvent these problems we carried out a parameter optimization in which the heat flow history was treated as an unknown in addition to the biomarker ratio kinetic parameters. This method enabled the heat flow history for the area to be determined together with appropriate kinetic parameters for the three measured biomarker ratios. Within the resolution of the data, the heat flow since the early Miocene has been relatively constant at levels required to yield good agreement between predicted and measured subsurface temperatures.« less

Consistent Parameter and Transfer Function Estimation using Context Free Grammars

NASA Astrophysics Data System (ADS)

Klotz, Daniel; Herrnegger, Mathew; Schulz, Karsten

2017-04-01

This contribution presents a method for the inference of transfer functions for rainfall-runoff models. Here, transfer functions are defined as parametrized (functional) relationships between a set of spatial predictors (e.g. elevation, slope or soil texture) and model parameters. They are ultimately used for estimation of consistent, spatially distributed model parameters from a limited amount of lumped global parameters. Additionally, they provide a straightforward method for parameter extrapolation from one set of basins to another and can even be used to derive parameterizations for multi-scale models [see: Samaniego et al., 2010]. Yet, currently an actual knowledge of the transfer functions is often implicitly assumed. As a matter of fact, for most cases these hypothesized transfer functions can rarely be measured and often remain unknown. Therefore, this contribution presents a general method for the concurrent estimation of the structure of transfer functions and their respective (global) parameters. Note, that by consequence an estimation of the distributed parameters of the rainfall-runoff model is also undertaken. The method combines two steps to achieve this. The first generates different possible transfer functions. The second then estimates the respective global transfer function parameters. The structural estimation of the transfer functions is based on the context free grammar concept. Chomsky first introduced context free grammars in linguistics [Chomsky, 1956]. Since then, they have been widely applied in computer science. But, to the knowledge of the authors, they have so far not been used in hydrology. Therefore, the contribution gives an introduction to context free grammars and shows how they can be constructed and used for the structural inference of transfer functions. This is enabled by new methods from evolutionary computation, such as grammatical evolution [O'Neill, 2001], which make it possible to exploit the constructed grammar as a search space for equations. The parametrization of the transfer functions is then achieved through a second optimization routine. The contribution explores different aspects of the described procedure through a set of experiments. These experiments can be divided into three categories: (1) The inference of transfer functions from directly measurable parameters; (2) The estimation of global parameters for given transfer functions from runoff data; and (3) The estimation of sets of completely unknown transfer functions from runoff data. The conducted tests reveal different potentials and limits of the procedure. In concrete it is shown that example (1) and (2) work remarkably well. Example (3) is much more dependent on the setup. In general, it can be said that in that case much more data is needed to derive transfer function estimations, even for simple models and setups. References: - Chomsky, N. (1956): Three Models for the Description of Language. IT IRETr. 2(3), p 113-124 - O'Neil, M. (2001): Grammatical Evolution. IEEE ToEC, Vol.5, No. 4 - Samaniego, L.; Kumar, R.; Attinger, S. (2010): Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. WWR, Vol. 46, W05523, doi:10.1029/2008WR007327

Andreev rectifier: A nonlocal conductance signature of topological phase transitions

NASA Astrophysics Data System (ADS)

Rosdahl, T. Ö.; Vuik, A.; Kjaergaard, M.; Akhmerov, A. R.

2018-01-01

The proximity effect in hybrid superconductor-semiconductor structures, crucial for realizing Majorana edge modes, is complicated to control due to its dependence on many unknown microscopic parameters. In addition, defects can spoil the induced superconductivity locally in the proximitized system, which complicates measuring global properties with a local probe. We show how to use the nonlocal conductance between two spatially separated leads to probe three global properties of a proximitized system: the bulk superconducting gap, the induced gap, and the induced coherence length. Unlike local conductance spectroscopy, nonlocal conductance measurements distinguish between nontopological zero-energy modes localized around potential inhomogeneities, and true Majorana edge modes that emerge in the topological phase. In addition, we find that the nonlocal conductance is an odd function of bias at the topological phase transition, acting as a current rectifier in the low-bias limit. More generally, we identify conditions for crossed Andreev reflection to dominate the nonlocal conductance and show how to design a Cooper pair splitter in the open regime.

Does Oral Coenzyme Q10 Plus NADH Supplementation Improve Fatigue and Biochemical Parameters in Chronic Fatigue Syndrome?

PubMed Central

Cordero, Mario D.; Segundo, María José; Sáez-Francàs, Naia; Calvo, Natalia; Román-Malo, Lourdes; Aliste, Luisa; Fernández de Sevilla, Tomás; Alegre, José

2015-01-01

Abstract Chronic fatigue syndrome (CFS) is a chronic and extremely debilitating illness characterized by prolonged fatigue and multiple symptoms with unknown cause, diagnostic test, or universally effective treatment. Inflammation, oxidative stress, mitochondrial dysfunction, and CoQ10 deficiency have been well documented in CFS. We conducted an 8-week, randomized, double-blind placebo-controlled trial to evaluate the benefits of oral CoQ10 (200 mg/day) plus NADH (20 mg/day) supplementation on fatigue and biochemical parameters in 73 Spanish CFS patients. This study was registered in ClinicalTrials.gov (NCT02063126). A significant improvement of fatigue showing a reduction in fatigue impact scale total score (p<0.05) was reported in treated group versus placebo. In addition, a recovery of the biochemical parameters was also reported. NAD+/NADH (p<0.001), CoQ10 (p<0.05), ATP (p<0.05), and citrate synthase (p<0.05) were significantly higher, and lipoperoxides (p<0.05) were significantly lower in blood mononuclear cells of the treated group. These observations lead to the hypothesis that the oral CoQ10 plus NADH supplementation could confer potential therapeutic benefits on fatigue and biochemical parameters in CFS. Larger sample trials are warranted to confirm these findings. Antioxid. Redox Signal. 22, 679–685. PMID:25386668

A genetic algorithm approach to estimate glacier mass variations from GRACE data

NASA Astrophysics Data System (ADS)

Reimond, Stefan; Klinger, Beate; Krauss, Sandro; Mayer-Gürr, Torsten

2017-04-01

The application of a genetic algorithm (GA) to the inference of glacier mass variations with a point-mass modeling method is described. GRACE K-band ranging data (available since April 2002) processed at the Graz University of Technology serve as input for this study. The reformulation of the point-mass inversion method in terms of an optimization problem is motivated by two reasons: first, an improved choice of the positions of the modeled point-masses (with a particular focus on the depth parameter) is expected to increase the signal-to-noise ratio. Considering these coordinates as additional unknown parameters (besides from the mass change magnitudes) results in a highly non-linear optimization problem. The second reason is that the mass inversion from satellite tracking data is an ill-posed problem, and hence regularization becomes necessary. The main task in this context is the determination of the regularization parameter, which is typically done by means of heuristic selection rules like, e.g., the L-curve criterion. In this study, however, the challenge of selecting a suitable balancing parameter (or even a matrix) is tackled by introducing regularization to the overall optimization problem. Based on this novel approach, estimations of ice-mass changes in various alpine glacier systems (e.g. Svalbard) are presented and compared to existing results and alternative inversion methods.

Multi-objective optimization in quantum parameter estimation

NASA Astrophysics Data System (ADS)

Gong, BeiLi; Cui, Wei

2018-04-01

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

Via generalized function projective synchronization in nonlinear Schrödinger equation for secure communication

NASA Astrophysics Data System (ADS)

Zhao, L. W.; Du, J. G.; Yin, J. L.

2018-05-01

This paper proposes a novel secured communication scheme in a chaotic system by applying generalized function projective synchronization of the nonlinear Schrödinger equation. This phenomenal approach guarantees a secured and convenient communication. Our study applied the Melnikov theorem with an active control strategy to suppress chaos in the system. The transmitted information signal is modulated into the parameter of the nonlinear Schrödinger equation in the transmitter and it is assumed that the parameter of the receiver system is unknown. Based on the Lyapunov stability theory and the adaptive control technique, the controllers are designed to make two identical nonlinear Schrödinger equation with the unknown parameter asymptotically synchronized. The numerical simulation results of our study confirmed the validity, effectiveness and the feasibility of the proposed novel synchronization method and error estimate for a secure communication. The Chaos masking signals of the information communication scheme, further guaranteed a safer and secured information communicated via this approach.

Zero-G experimental validation of a robotics-based inertia identification algorithm

NASA Astrophysics Data System (ADS)

Bruggemann, Jeremy J.; Ferrel, Ivann; Martinez, Gerardo; Xie, Pu; Ma, Ou

2010-04-01

The need to efficiently identify the changing inertial properties of on-orbit spacecraft is becoming more critical as satellite on-orbit services, such as refueling and repairing, become increasingly aggressive and complex. This need stems from the fact that a spacecraft's control system relies on the knowledge of the spacecraft's inertia parameters. However, the inertia parameters may change during flight for reasons such as fuel usage, payload deployment or retrieval, and docking/capturing operations. New Mexico State University's Dynamics, Controls, and Robotics Research Group has proposed a robotics-based method of identifying unknown spacecraft inertia properties1. Previous methods require firing known thrusts then measuring the thrust, and the velocity and acceleration changes. The new method utilizes the concept of momentum conservation, while employing a robotic device powered by renewable energy to excite the state of the satellite. Thus, it requires no fuel usage or force and acceleration measurements. The method has been well studied in theory and demonstrated by simulation. However its experimental validation is challenging because a 6- degree-of-freedom motion in a zero-gravity condition is required. This paper presents an on-going effort to test the inertia identification method onboard the NASA zero-G aircraft. The design and capability of the test unit will be discussed in addition to the flight data. This paper also introduces the design and development of an airbearing based test used to partially validate the method, in addition to the approach used to obtain reference value for the test system's inertia parameters that can be used for comparison with the algorithm results.

A review of the meteorological parameters which affect aerial application

NASA Technical Reports Server (NTRS)

Christensen, L. S.; Frost, W.

1979-01-01

The ambient wind field and temperature gradient were found to be the most important parameters. Investigation results indicated that the majority of meteorological parameters affecting dispersion were interdependent and the exact mechanism by which these factors influence the particle dispersion was largely unknown. The types and approximately ranges of instrumented capabilities for a systematic study of the significant meteorological parameters influencing aerial applications were defined. Current mathematical dispersion models were also briefly reviewed. Unfortunately, a rigorous dispersion model which could be applied to aerial application was not available.

Highly adaptive tests for group differences in brain functional connectivity.

PubMed

Kim, Junghi; Pan, Wei

2015-01-01

Resting-state functional magnetic resonance imaging (rs-fMRI) and other technologies have been offering evidence and insights showing that altered brain functional networks are associated with neurological illnesses such as Alzheimer's disease. Exploring brain networks of clinical populations compared to those of controls would be a key inquiry to reveal underlying neurological processes related to such illnesses. For such a purpose, group-level inference is a necessary first step in order to establish whether there are any genuinely disrupted brain subnetworks. Such an analysis is also challenging due to the high dimensionality of the parameters in a network model and high noise levels in neuroimaging data. We are still in the early stage of method development as highlighted by Varoquaux and Craddock (2013) that "there is currently no unique solution, but a spectrum of related methods and analytical strategies" to learn and compare brain connectivity. In practice the important issue of how to choose several critical parameters in estimating a network, such as what association measure to use and what is the sparsity of the estimated network, has not been carefully addressed, largely because the answers are unknown yet. For example, even though the choice of tuning parameters in model estimation has been extensively discussed in the literature, as to be shown here, an optimal choice of a parameter for network estimation may not be optimal in the current context of hypothesis testing. Arbitrarily choosing or mis-specifying such parameters may lead to extremely low-powered tests. Here we develop highly adaptive tests to detect group differences in brain connectivity while accounting for unknown optimal choices of some tuning parameters. The proposed tests combine statistical evidence against a null hypothesis from multiple sources across a range of plausible tuning parameter values reflecting uncertainty with the unknown truth. These highly adaptive tests are not only easy to use, but also high-powered robustly across various scenarios. The usage and advantages of these novel tests are demonstrated on an Alzheimer's disease dataset and simulated data.

Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy.

PubMed

He, Shixuan; Xie, Wanyi; Zhang, Wei; Zhang, Liqun; Wang, Yunxia; Liu, Xiaoling; Liu, Yulong; Du, Chunlei

2015-02-25

A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety. Copyright © 2014 Elsevier B.V. All rights reserved.

Adaptive Neural Output Feedback Control for Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis and Unknown Control Directions.

PubMed

Yu, Zhaoxu; Li, Shugang; Yu, Zhaosheng; Li, Fangfei

2018-04-01

This paper investigates the problem of output feedback adaptive stabilization for a class of nonstrict-feedback stochastic nonlinear systems with both unknown backlashlike hysteresis and unknown control directions. A new linear state transformation is applied to the original system, and then, control design for the new system becomes feasible. By combining the neural network's (NN's) parameterization, variable separation technique, and Nussbaum gain function method, an input-driven observer-based adaptive NN control scheme, which involves only one parameter to be updated, is developed for such systems. All closed-loop signals are bounded in probability and the error signals remain semiglobally bounded in the fourth moment (or mean square). Finally, the effectiveness and the applicability of the proposed control design are verified by two simulation examples.

A spline-based parameter estimation technique for static models of elastic structures

NASA Technical Reports Server (NTRS)

Dutt, P.; Taasan, S.

1986-01-01

The problem of identifying the spatially varying coefficient of elasticity using an observed solution to the forward problem is considered. Under appropriate conditions this problem can be treated as a first order hyperbolic equation in the unknown coefficient. Some continuous dependence results are developed for this problem and a spline-based technique is proposed for approximating the unknown coefficient, based on these results. The convergence of the numerical scheme is established and error estimates obtained.

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input-output direct feed-through term.

PubMed

Tsai, Jason S-H; Hsu, Wen-Teng; Lin, Long-Guei; Guo, Shu-Mei; Tann, Joseph W

2014-01-01

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input-output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Neural-Network-Based Adaptive Decentralized Fault-Tolerant Control for a Class of Interconnected Nonlinear Systems.

PubMed

Li, Xiao-Jian; Yang, Guang-Hong

2018-01-01

This paper is concerned with the adaptive decentralized fault-tolerant tracking control problem for a class of uncertain interconnected nonlinear systems with unknown strong interconnections. An algebraic graph theory result is introduced to address the considered interconnections. In addition, to achieve the desirable tracking performance, a neural-network-based robust adaptive decentralized fault-tolerant control (FTC) scheme is given to compensate the actuator faults and system uncertainties. Furthermore, via the Lyapunov analysis method, it is proven that all the signals of the resulting closed-loop system are semiglobally bounded, and the tracking errors of each subsystem exponentially converge to a compact set, whose radius is adjustable by choosing different controller design parameters. Finally, the effectiveness and advantages of the proposed FTC approach are illustrated with two simulated examples.

Model-based iterative learning control of Parkinsonian state in thalamic relay neuron

NASA Astrophysics Data System (ADS)

Liu, Chen; Wang, Jiang; Li, Huiyan; Xue, Zhiqin; Deng, Bin; Wei, Xile

2014-09-01

Although the beneficial effects of chronic deep brain stimulation on Parkinson's disease motor symptoms are now largely confirmed, the underlying mechanisms behind deep brain stimulation remain unclear and under debate. Hence, the selection of stimulation parameters is full of challenges. Additionally, due to the complexity of neural system, together with omnipresent noises, the accurate model of thalamic relay neuron is unknown. Thus, the iterative learning control of the thalamic relay neuron's Parkinsonian state based on various variables is presented. Combining the iterative learning control with typical proportional-integral control algorithm, a novel and efficient control strategy is proposed, which does not require any particular knowledge on the detailed physiological characteristics of cortico-basal ganglia-thalamocortical loop and can automatically adjust the stimulation parameters. Simulation results demonstrate the feasibility of the proposed control strategy to restore the fidelity of thalamic relay in the Parkinsonian condition. Furthermore, through changing the important parameter—the maximum ionic conductance densities of low-threshold calcium current, the dominant characteristic of the proposed method which is independent of the accurate model can be further verified.

Novel adaptive neural control design for a constrained flexible air-breathing hypersonic vehicle based on actuator compensation

NASA Astrophysics Data System (ADS)

Bu, Xiangwei; Wu, Xiaoyan; He, Guangjun; Huang, Jiaqi

2016-03-01

This paper investigates the design of a novel adaptive neural controller for the longitudinal dynamics of a flexible air-breathing hypersonic vehicle with control input constraints. To reduce the complexity of controller design, the vehicle dynamics is decomposed into the velocity subsystem and the altitude subsystem, respectively. For each subsystem, only one neural network is utilized to approach the lumped unknown function. By employing a minimal-learning parameter method to estimate the norm of ideal weight vectors rather than their elements, there are only two adaptive parameters required for neural approximation. Thus, the computational burden is lower than the ones derived from neural back-stepping schemes. Specially, to deal with the control input constraints, additional systems are exploited to compensate the actuators. Lyapunov synthesis proves that all the closed-loop signals involved are uniformly ultimately bounded. Finally, simulation results show that the adopted compensation scheme can tackle actuator constraint effectively and moreover velocity and altitude can stably track their reference trajectories even when the physical limitations on control inputs are in effect.

Hydraulics of epiphreatic flow of a karst aquifer

NASA Astrophysics Data System (ADS)

Gabrovšek, Franci; Peric, Borut; Kaufmann, Georg

2018-05-01

The nature of epiphreatic flow remains an important research challenge in karst hydrology. This study focuses on the flood propagation along the epiphreatic system of Reka-Timavo system (Kras/Carso Plateau, Slovenia/Italy). It is based on long-term monitoring of basic physical parameters (pressure/level, temperature, specific electric conductivity) of ground water in six active caves belonging to the flow system. The system vigorously responds to flood events, with stage rising >100 m in some of the caves. Besides presenting the response of the system to flood events of different scales, the work focuses on the interpretation of recorded hydrographs in view of the known distribution and size of conduits and basic hydraulic relations. Furthermore, the hydrographs were used to infer the unknown geometry between the observation points. This way, the main flow restrictors, overflow passages and large epiphreatic storages were identified. The assumptions were tested with a hydraulic model, where the inversion procedure was used for an additional parameter optimisation. Time series of temperature and specific electric conductivity were used to assess the apparent velocities of flow between consecutive points.

Spontaneous switching of frequency-locking by periodic stimulus in oscillators of plasmodium of the true slime mold.

PubMed

Takamatsu, A; Yamamoto, T; Fujii, T

2004-01-01

Microfabrication technique was used to construct a model system with a living cell of plasmodium of the true slime mold, Physarum polycephalum, a living coupled oscillator system. Its parameters can be systematically controlled as in computer simulations, so that results are directly comparable to those of general mathematical models. As the first step, we investigated responses in oscillatory cells, the oscillators of the plasmodium, to periodic stimuli by temperature changes to elucidate characteristics of the cells as nonlinear systems whose internal dynamics are unknown because of their complexity. We observed that the forced oscillator of the plasmodium show 1:1, 2:1, 3:1 frequency locking inside so-called Arnold tongues regions as well as in other nonlinear systems such as chemical systems and other biological systems. In addition, we found spontaneous switching behavior from certain frequency locking states to other states, even under certain fixed parameters. This technique can be applied to more complex systems with multiple elements, such as coupled oscillator systems, and would be useful to investigate complicated phenomena in biological systems such as information processing.

Spectral Accuracy and Sulfur Counting Capabilities of the LTQ-FT-ICR and the LTQ-Orbitrap XL for Small Molecule Analysis

NASA Astrophysics Data System (ADS)

Blake, Samantha L.; Walker, S. Hunter; Muddiman, David C.; Hinks, David; Beck, Keith R.

2011-12-01

Color Index Disperse Yellow 42 (DY42), a high-volume disperse dye for polyester, was used to compare the capabilities of the LTQ-Orbitrap XL and the LTQ-FT-ICR with respect to mass measurement accuracy (MMA), spectral accuracy, and sulfur counting. The results of this research will be used in the construction of a dye database for forensic purposes; the additional spectral information will increase the confidence in the identification of unknown dyes found in fibers at crime scenes. Initial LTQ-Orbitrap XL data showed MMAs greater than 3 ppm and poor spectral accuracy. Modification of several Orbitrap installation parameters (e.g., deflector voltage) resulted in a significant improvement of the data. The LTQ-FT-ICR and LTQ-Orbitrap XL (after installation parameters were modified) exhibited MMA ≤ 3 ppm, good spectral accuracy (χ2 values for the isotopic distribution ≤ 2), and were correctly able to ascertain the number of sulfur atoms in the compound at all resolving powers investigated for AGC targets of 5.00 × 105 and 1.00 × 106.

Multiparameter Estimation in Networked Quantum Sensors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Proctor, Timothy J.; Knott, Paul A.; Dunningham, Jacob A.

We introduce a general model for a network of quantum sensors, and we use this model to consider the question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. Thismore » immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or non-linear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.« less

Multiparameter Estimation in Networked Quantum Sensors

DOE PAGES

Proctor, Timothy J.; Knott, Paul A.; Dunningham, Jacob A.

2018-02-21

We introduce a general model for a network of quantum sensors, and we use this model to consider the question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. Thismore » immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or non-linear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.« less

How far are extraterrestrial life and intelligence after Kepler?

NASA Astrophysics Data System (ADS)

Wandel, Amri

2017-08-01

The Kepler mission has shown that a significant fraction of all stars may have an Earth-size habitable planet. A dramatic support was the recent detection of Proxima Centauri b. Using a Drake-equation like formalism I derive an equation for the abundance of biotic planets as a function of the relatively modest uncertainty in the astronomical data and of the (yet unknown) probability for the evolution of biotic life, Fb. I suggest that Fb may be estimated by future spectral observations of exoplanet biomarkers. It follows that if Fb is not very small, then a biotic planet may be expected within about 10 light years from Earth. Extending this analyses to advanced life, I derive expressions for the distance to putative civilizations in terms of two additional Drake parameters - the probability for evolution of a civilization, Fc, and its average longevity. Assuming "optimistic" values for the Drake parameters, (Fb Fc 1), and a broadcasting duration of a few thousand years, the likely distance to the nearest civilizations detectable by SETI is of the order of a few thousand light years. Finally I calculate the distance and probability of detecting intelligent signals with present and future radio telescopes such as Arecibo and SKA and how it could constrain the Drake parameters.

Model Reduction via Principe Component Analysis and Markov Chain Monte Carlo (MCMC) Methods

NASA Astrophysics Data System (ADS)

Gong, R.; Chen, J.; Hoversten, M. G.; Luo, J.

2011-12-01

Geophysical and hydrogeological inverse problems often include a large number of unknown parameters, ranging from hundreds to millions, depending on parameterization and problems undertaking. This makes inverse estimation and uncertainty quantification very challenging, especially for those problems in two- or three-dimensional spatial domains. Model reduction technique has the potential of mitigating the curse of dimensionality by reducing total numbers of unknowns while describing the complex subsurface systems adequately. In this study, we explore the use of principal component analysis (PCA) and Markov chain Monte Carlo (MCMC) sampling methods for model reduction through the use of synthetic datasets. We compare the performances of three different but closely related model reduction approaches: (1) PCA methods with geometric sampling (referred to as 'Method 1'), (2) PCA methods with MCMC sampling (referred to as 'Method 2'), and (3) PCA methods with MCMC sampling and inclusion of random effects (referred to as 'Method 3'). We consider a simple convolution model with five unknown parameters as our goal is to understand and visualize the advantages and disadvantages of each method by comparing their inversion results with the corresponding analytical solutions. We generated synthetic data with noise added and invert them under two different situations: (1) the noised data and the covariance matrix for PCA analysis are consistent (referred to as the unbiased case), and (2) the noise data and the covariance matrix are inconsistent (referred to as biased case). In the unbiased case, comparison between the analytical solutions and the inversion results show that all three methods provide good estimates of the true values and Method 1 is computationally more efficient. In terms of uncertainty quantification, Method 1 performs poorly because of relatively small number of samples obtained, Method 2 performs best, and Method 3 overestimates uncertainty due to inclusion of random effects. However, in the biased case, only Method 3 correctly estimates all the unknown parameters, and both Methods 1 and 2 provide wrong values for the biased parameters. The synthetic case study demonstrates that if the covariance matrix for PCA analysis is inconsistent with true models, the PCA methods with geometric or MCMC sampling will provide incorrect estimates.

Bearing fault diagnosis under unknown variable speed via gear noise cancellation and rotational order sideband identification

NASA Astrophysics Data System (ADS)

Wang, Tianyang; Liang, Ming; Li, Jianyong; Cheng, Weidong; Li, Chuan

2015-10-01

The interfering vibration signals of a gearbox often represent a challenging issue in rolling bearing fault detection and diagnosis, particularly under unknown variable rotational speed conditions. Though some methods have been proposed to remove the gearbox interfering signals based on their discrete frequency nature, such methods may not work well under unknown variable speed conditions. As such, we propose a new approach to address this issue. The new approach consists of three main steps: (a) adaptive gear interference removal, (b) fault characteristic order (FCO) based fault detection, and (c) rotational-order-sideband (ROS) based fault type identification. For gear interference removal, an enhanced adaptive noise cancellation (ANC) algorithm has been developed in this study. The new ANC algorithm does not require an additional accelerometer to provide reference input. Instead, the reference signal is adaptively constructed from signal maxima and instantaneous dominant meshing multiple (IDMM) trend. Key ANC parameters such as filter length and step size have also been tailored to suit the variable speed conditions, The main advantage of using ROS for fault type diagnosis is that it is insusceptible to confusion caused by the co-existence of bearing and gear rotational frequency peaks in the identification of the bearing fault characteristic frequency in the FCO sub-order region. The effectiveness of the proposed method has been demonstrated using both simulation and experimental data. Our experimental study also indicates that the proposed method is applicable regardless whether the bearing and gear rotational speeds are proportional to each other or not.

Structural damage identification using piezoelectric impedance measurement with sparse inverse analysis

NASA Astrophysics Data System (ADS)

Cao, Pei; Qi, Shuai; Tang, J.

2018-03-01

The impedance/admittance measurements of a piezoelectric transducer bonded to or embedded in a host structure can be used as damage indicator. When a credible model of the healthy structure, such as the finite element model, is available, using the impedance/admittance change information as input, it is possible to identify both the location and severity of damage. The inverse analysis, however, may be under-determined as the number of unknowns in high-frequency analysis is usually large while available input information is limited. The fundamental challenge thus is how to find a small set of solutions that cover the true damage scenario. In this research we cast the damage identification problem into a multi-objective optimization framework to tackle this challenge. With damage locations and severities as unknown variables, one of the objective functions is the difference between impedance-based model prediction in the parametric space and the actual measurements. Considering that damage occurrence generally affects only a small number of elements, we choose the sparsity of the unknown variables as another objective function, deliberately, the l 0 norm. Subsequently, a multi-objective Dividing RECTangles (DIRECT) algorithm is developed to facilitate the inverse analysis where the sparsity is further emphasized by sigmoid transformation. As a deterministic technique, this approach yields results that are repeatable and conclusive. In addition, only one algorithmic parameter, the number of function evaluations, is needed. Numerical and experimental case studies demonstrate that the proposed framework is capable of obtaining high-quality damage identification solutions with limited measurement information.

Noise parameter estimation for poisson corrupted images using variance stabilization transforms.

PubMed

Jin, Xiaodan; Xu, Zhenyu; Hirakawa, Keigo

2014-03-01

Noise is present in all images captured by real-world image sensors. Poisson distribution is said to model the stochastic nature of the photon arrival process and agrees with the distribution of measured pixel values. We propose a method for estimating unknown noise parameters from Poisson corrupted images using properties of variance stabilization. With a significantly lower computational complexity and improved stability, the proposed estimation technique yields noise parameters that are comparable in accuracy to the state-of-art methods.

An easy-to-use tool for the evaluation of leachate production at landfill sites.

PubMed

Grugnaletti, Matteo; Pantini, Sara; Verginelli, Iason; Lombardi, Francesco

2016-09-01

A simulation program for the evaluation of leachate generation at landfill sites is herein presented. The developed tool is based on a water balance model that accounts for all the key processes influencing leachate generation through analytical and empirical equations. After a short description of the tool, different simulations on four Italian landfill sites are shown. The obtained results revealed that when literature values were assumed for the unknown input parameters, the model provided a rough estimation of the leachate production measured in the field. In this case, indeed, the deviations between observed and predicted data appeared, in some cases, significant. Conversely, by performing a preliminary calibration for some of the unknown input parameters (e.g. initial moisture content of wastes, compression index), in nearly all cases the model performances significantly improved. These results although showed the potential capability of a water balance model to estimate the leachate production at landfill sites also highlighted the intrinsic limitation of a deterministic approach to accurately forecast the leachate production over time. Indeed, parameters such as the initial water content of incoming waste and the compression index, that have a great influence on the leachate production, may exhibit temporal variation due to seasonal changing of weather conditions (e.g. rainfall, air humidity) as well as to seasonal variability in the amount and type of specific waste fractions produced (e.g. yard waste, food, plastics) that make their prediction quite complicated. In this sense, we believe that a tool such as the one proposed in this work that requires a limited number of unknown parameters, can be easier handled to quantify the uncertainties. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dynamic identification of axial force and boundary restraints in tie rods and cables with uncertainty quantification using Set Inversion Via Interval Analysis

NASA Astrophysics Data System (ADS)

Kernicky, Timothy; Whelan, Matthew; Al-Shaer, Ehab

2018-06-01

A methodology is developed for the estimation of internal axial force and boundary restraints within in-service, prismatic axial force members of structural systems using interval arithmetic and contractor programming. The determination of the internal axial force and end restraints in tie rods and cables using vibration-based methods has been a long standing problem in the area of structural health monitoring and performance assessment. However, for structural members with low slenderness where the dynamics are significantly affected by the boundary conditions, few existing approaches allow for simultaneous identification of internal axial force and end restraints and none permit for quantifying the uncertainties in the parameter estimates due to measurement uncertainties. This paper proposes a new technique for approaching this challenging inverse problem that leverages the Set Inversion Via Interval Analysis algorithm to solve for the unknown axial forces and end restraints using natural frequency measurements. The framework developed offers the ability to completely enclose the feasible solutions to the parameter identification problem, given specified measurement uncertainties for the natural frequencies. This ability to propagate measurement uncertainty into the parameter space is critical towards quantifying the confidence in the individual parameter estimates to inform decision-making within structural health diagnosis and prognostication applications. The methodology is first verified with simulated data for a case with unknown rotational end restraints and then extended to a case with unknown translational and rotational end restraints. A laboratory experiment is then presented to demonstrate the application of the methodology to an axially loaded rod with progressively increased end restraint at one end.

[Pilot study of domain-specific terminology adaptation for morphological analysis: research on unknown terms in national examination documents of radiological technologists].

PubMed

Tsuji, Shintarou; Nishimoto, Naoki; Ogasawara, Katsuhiko

2008-07-20

Although large medical texts are stored in electronic format, they are seldom reused because of the difficulty of processing narrative texts by computer. Morphological analysis is a key technology for extracting medical terms correctly and automatically. This process parses a sentence into its smallest unit, the morpheme. Phrases consisting of two or more technical terms, however, cause morphological analysis software to fail in parsing the sentence and output unprocessed terms as "unknown words." The purpose of this study was to reduce the number of unknown words in medical narrative text processing. The results of parsing the text with additional dictionaries were compared with the analysis of the number of unknown words in the national examination for radiologists. The ratio of unknown words was reduced 1.0% to 0.36% by adding terminologies of radiological technology, MeSH, and ICD-10 labels. The terminology of radiological technology was the most effective resource, being reduced by 0.62%. This result clearly showed the necessity of additional dictionary selection and trends in unknown words. The potential for this investigation is to make available a large body of clinical information that would otherwise be inaccessible for applications other than manual health care review by personnel.

Use of an anaerobic sequencing batch reactor for parameter estimation in modelling of anaerobic digestion.

PubMed

Batstone, D J; Torrijos, M; Ruiz, C; Schmidt, J E

2004-01-01

The model structure in anaerobic digestion has been clarified following publication of the IWA Anaerobic Digestion Model No. 1 (ADM1). However, parameter values are not well known, and uncertainty and variability in the parameter values given is almost unknown. Additionally, platforms for identification of parameters, namely continuous-flow laboratory digesters, and batch tests suffer from disadvantages such as long run times, and difficulty in defining initial conditions, respectively. Anaerobic sequencing batch reactors (ASBRs) are sequenced into fill-react-settle-decant phases, and offer promising possibilities for estimation of parameters, as they are by nature, dynamic in behaviour, and allow repeatable behaviour to establish initial conditions, and evaluate parameters. In this study, we estimated parameters describing winery wastewater (most COD as ethanol) degradation using data from sequencing operation, and validated these parameters using unsequenced pulses of ethanol and acetate. The model used was the ADM1, with an extension for ethanol degradation. Parameter confidence spaces were found by non-linear, correlated analysis of the two main Monod parameters; maximum uptake rate (k(m)), and half saturation concentration (K(S)). These parameters could be estimated together using only the measured acetate concentration (20 points per cycle). From interpolating the single cycle acetate data to multiple cycles, we estimate that a practical "optimal" identifiability could be achieved after two cycles for the acetate parameters, and three cycles for the ethanol parameters. The parameters found performed well in the short term, and represented the pulses of acetate and ethanol (within 4 days of the winery-fed cycles) very well. The main discrepancy was poor prediction of pH dynamics, which could be due to an unidentified buffer with an overall influence the same as a weak base (possibly CaCO3). Based on this work, ASBR systems are effective for parameter estimation, especially for comparative wastewater characterisation. The main disadvantages are heavy computational requirements for multiple cycles, and difficulty in establishing the correct biomass concentration in the reactor, though the last is also a disadvantage for continuous fixed film reactors, and especially, batch tests.

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

DOE PAGES

Boiteau, Rene M.; Hoyt, David W.; Nicora, Carrie D.; ...

2018-01-17

Here, we introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS 2), and NMR in a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS 2 approach is well suited for discovery ofmore » new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.« less

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boiteau, Rene M.; Hoyt, David W.; Nicora, Carrie D.

Here, we introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS 2), and NMR in a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS 2 approach is well suited for discovery ofmore » new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.« less

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

PubMed Central

Hoyt, David W.; Nicora, Carrie D.; Kinmonth-Schultz, Hannah A.; Ward, Joy K.

2018-01-01

We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS2), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS2 approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases. PMID:29342073

Identifying mechanical property parameters of planetary soil using in-situ data obtained from exploration rovers

NASA Astrophysics Data System (ADS)

Ding, Liang; Gao, Haibo; Liu, Zhen; Deng, Zongquan; Liu, Guangjun

2015-12-01

Identifying the mechanical property parameters of planetary soil based on terramechanics models using in-situ data obtained from autonomous planetary exploration rovers is both an important scientific goal and essential for control strategy optimization and high-fidelity simulations of rovers. However, identifying all the terrain parameters is a challenging task because of the nonlinear and coupling nature of the involved functions. Three parameter identification methods are presented in this paper to serve different purposes based on an improved terramechanics model that takes into account the effects of slip, wheel lugs, etc. Parameter sensitivity and coupling of the equations are analyzed, and the parameters are grouped according to their sensitivity to the normal force, resistance moment and drawbar pull. An iterative identification method using the original integral model is developed first. In order to realize real-time identification, the model is then simplified by linearizing the normal and shearing stresses to derive decoupled closed-form analytical equations. Each equation contains one or two groups of soil parameters, making step-by-step identification of all the unknowns feasible. Experiments were performed using six different types of single-wheels as well as a four-wheeled rover moving on planetary soil simulant. All the unknown model parameters were identified using the measured data and compared with the values obtained by conventional experiments. It is verified that the proposed iterative identification method provides improved accuracy, making it suitable for scientific studies of soil properties, whereas the step-by-step identification methods based on simplified models require less calculation time, making them more suitable for real-time applications. The models have less than 10% margin of error comparing with the measured results when predicting the interaction forces and moments using the corresponding identified parameters.

Autopilot for frequency-modulation atomic force microscopy.

PubMed

Kuchuk, Kfir; Schlesinger, Itai; Sivan, Uri

2015-10-01

One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loops require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.

Autopilot for frequency-modulation atomic force microscopy

NASA Astrophysics Data System (ADS)

Kuchuk, Kfir; Schlesinger, Itai; Sivan, Uri

2015-10-01

One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loops require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.

Autopilot for frequency-modulation atomic force microscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kuchuk, Kfir; Schlesinger, Itai; Sivan, Uri, E-mail: phsivan@tx.technion.ac.il

2015-10-15

One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loopsmore » require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.« less

Bayesian source term determination with unknown covariance of measurements

NASA Astrophysics Data System (ADS)

Belal, Alkomiet; Tichý, Ondřej; Šmídl, Václav

2017-04-01

Determination of a source term of release of a hazardous material into the atmosphere is a very important task for emergency response. We are concerned with the problem of estimation of the source term in the conventional linear inverse problem, y = Mx, where the relationship between the vector of observations y is described using the source-receptor-sensitivity (SRS) matrix M and the unknown source term x. Since the system is typically ill-conditioned, the problem is recast as an optimization problem minR,B(y - Mx)TR-1(y - Mx) + xTB-1x. The first term minimizes the error of the measurements with covariance matrix R, and the second term is a regularization of the source term. There are different types of regularization arising for different choices of matrices R and B, for example, Tikhonov regularization assumes covariance matrix B as the identity matrix multiplied by scalar parameter. In this contribution, we adopt a Bayesian approach to make inference on the unknown source term x as well as unknown R and B. We assume prior on x to be a Gaussian with zero mean and unknown diagonal covariance matrix B. The covariance matrix of the likelihood R is also unknown. We consider two potential choices of the structure of the matrix R. First is the diagonal matrix and the second is a locally correlated structure using information on topology of the measuring network. Since the inference of the model is intractable, iterative variational Bayes algorithm is used for simultaneous estimation of all model parameters. The practical usefulness of our contribution is demonstrated on an application of the resulting algorithm to real data from the European Tracer Experiment (ETEX). This research is supported by EEA/Norwegian Financial Mechanism under project MSMT-28477/2014 Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling (STRADI).

Adaptive neural output-feedback control for nonstrict-feedback time-delay fractional-order systems with output constraints and actuator nonlinearities.

PubMed

Zouari, Farouk; Ibeas, Asier; Boulkroune, Abdesselem; Cao, Jinde; Mehdi Arefi, Mohammad

2018-06-01

This study addresses the issue of the adaptive output tracking control for a category of uncertain nonstrict-feedback delayed incommensurate fractional-order systems in the presence of nonaffine structures, unmeasured pseudo-states, unknown control directions, unknown actuator nonlinearities and output constraints. Firstly, the mean value theorem and the Gaussian error function are introduced to eliminate the difficulties that arise from the nonaffine structures and the unknown actuator nonlinearities, respectively. Secondly, the immeasurable tracking error variables are suitably estimated by constructing a fractional-order linear observer. Thirdly, the neural network, the Razumikhin Lemma, the variable separation approach, and the smooth Nussbaum-type function are used to deal with the uncertain nonlinear dynamics, the unknown time-varying delays, the nonstrict feedback and the unknown control directions, respectively. Fourthly, asymmetric barrier Lyapunov functions are employed to overcome the violation of the output constraints and to tune online the parameters of the adaptive neural controller. Through rigorous analysis, it is proved that the boundedness of all variables in the closed-loop system and the semi global asymptotic tracking are ensured without transgression of the constraints. The principal contributions of this study can be summarized as follows: (1) based on Caputo's definitions and new lemmas, methods concerning the controllability, observability and stability analysis of integer-order systems are extended to fractional-order ones, (2) the output tracking objective for a relatively large class of uncertain systems is achieved with a simple controller and less tuning parameters. Finally, computer-simulation studies from the robotic field are given to demonstrate the effectiveness of the proposed controller. Copyright © 2018 Elsevier Ltd. All rights reserved.

MXLKID: a maximum likelihood parameter identifier. [In LRLTRAN for CDC 7600

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gavel, D.T.

MXLKID (MaXimum LiKelihood IDentifier) is a computer program designed to identify unknown parameters in a nonlinear dynamic system. Using noisy measurement data from the system, the maximum likelihood identifier computes a likelihood function (LF). Identification of system parameters is accomplished by maximizing the LF with respect to the parameters. The main body of this report briefly summarizes the maximum likelihood technique and gives instructions and examples for running the MXLKID program. MXLKID is implemented LRLTRAN on the CDC7600 computer at LLNL. A detailed mathematical description of the algorithm is given in the appendices. 24 figures, 6 tables.

A Multi-Resolution Nonlinear Mapping Technique for Design and Analysis Applications

NASA Technical Reports Server (NTRS)

Phan, Minh Q.

1998-01-01

This report describes a nonlinear mapping technique where the unknown static or dynamic system is approximated by a sum of dimensionally increasing functions (one-dimensional curves, two-dimensional surfaces, etc.). These lower dimensional functions are synthesized from a set of multi-resolution basis functions, where the resolutions specify the level of details at which the nonlinear system is approximated. The basis functions also cause the parameter estimation step to become linear. This feature is taken advantage of to derive a systematic procedure to determine and eliminate basis functions that are less significant for the particular system under identification. The number of unknown parameters that must be estimated is thus reduced and compact models obtained. The lower dimensional functions (identified curves and surfaces) permit a kind of "visualization" into the complexity of the nonlinearity itself.

3D tomographic reconstruction using geometrical models

NASA Astrophysics Data System (ADS)

Battle, Xavier L.; Cunningham, Gregory S.; Hanson, Kenneth M.

1997-04-01

We address the issue of reconstructing an object of constant interior density in the context of 3D tomography where there is prior knowledge about the unknown shape. We explore the direct estimation of the parameters of a chosen geometrical model from a set of radiographic measurements, rather than performing operations (segmentation for example) on a reconstructed volume. The inverse problem is posed in the Bayesian framework. A triangulated surface describes the unknown shape and the reconstruction is computed with a maximum a posteriori (MAP) estimate. The adjoint differentiation technique computes the derivatives needed for the optimization of the model parameters. We demonstrate the usefulness of the approach and emphasize the techniques of designing forward and adjoint codes. We use the system response of the University of Arizona Fast SPECT imager to illustrate this method by reconstructing the shape of a heart phantom.

Global identifiability of linear compartmental models--a computer algebra algorithm.

PubMed

Audoly, S; D'Angiò, L; Saccomani, M P; Cobelli, C

1998-01-01

A priori global identifiability deals with the uniqueness of the solution for the unknown parameters of a model and is, thus, a prerequisite for parameter estimation of biological dynamic models. Global identifiability is however difficult to test, since it requires solving a system of algebraic nonlinear equations which increases both in nonlinearity degree and number of terms and unknowns with increasing model order. In this paper, a computer algebra tool, GLOBI (GLOBal Identifiability) is presented, which combines the topological transfer function method with the Buchberger algorithm, to test global identifiability of linear compartmental models. GLOBI allows for the automatic testing of a priori global identifiability of general structure compartmental models from general multi input-multi output experiments. Examples of usage of GLOBI to analyze a priori global identifiability of some complex biological compartmental models are provided.

Multilevel adaptive control of nonlinear interconnected systems.

PubMed

Motallebzadeh, Farzaneh; Ozgoli, Sadjaad; Momeni, Hamid Reza

2015-01-01

This paper presents an adaptive backstepping-based multilevel approach for the first time to control nonlinear interconnected systems with unknown parameters. The system consists of a nonlinear controller at the first level to neutralize the interaction terms, and some adaptive controllers at the second level, in which the gains are optimally tuned using genetic algorithm. The presented scheme can be used in systems with strong couplings where completely ignoring the interactions leads to problems in performance or stability. In order to test the suitability of the method, two case studies are provided: the uncertain double and triple coupled inverted pendulums connected by springs with unknown parameters. The simulation results show that the method is capable of controlling the system effectively, in both regulation and tracking tasks. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

A Multi-Resolution Nonlinear Mapping Technique for Design and Analysis Application

NASA Technical Reports Server (NTRS)

Phan, Minh Q.

1997-01-01

This report describes a nonlinear mapping technique where the unknown static or dynamic system is approximated by a sum of dimensionally increasing functions (one-dimensional curves, two-dimensional surfaces, etc.). These lower dimensional functions are synthesized from a set of multi-resolution basis functions, where the resolutions specify the level of details at which the nonlinear system is approximated. The basis functions also cause the parameter estimation step to become linear. This feature is taken advantage of to derive a systematic procedure to determine and eliminate basis functions that are less significant for the particular system under identification. The number of unknown parameters that must be estimated is thus reduced and compact models obtained. The lower dimensional functions (identified curves and surfaces) permit a kind of "visualization" into the complexity of the nonlinearity itself.

Fuzzy similarity measures for ultrasound tissue characterization

NASA Astrophysics Data System (ADS)

Emara, Salem M.; Badawi, Ahmed M.; Youssef, Abou-Bakr M.

1995-03-01

Computerized ultrasound tissue characterization has become an objective means for diagnosis of diseases. It is difficult to differentiate diffuse liver diseases, namely cirrhotic and fatty liver from a normal one, by visual inspection from the ultrasound images. The visual criteria for differentiating diffused diseases is rather confusing and highly dependent upon the sonographer's experience. The need for computerized tissue characterization is thus justified to quantitatively assist the sonographer for accurate differentiation and to minimize the degree of risk from erroneous interpretation. In this paper we used the fuzzy similarity measure as an approximate reasoning technique to find the maximum degree of matching between an unknown case defined by a feature vector and a family of prototypes (knowledge base). The feature vector used for the matching process contains 8 quantitative parameters (textural, acoustical, and speckle parameters) extracted from the ultrasound image. The steps done to match an unknown case with the family of prototypes (cirr, fatty, normal) are: Choosing the membership functions for each parameter, then obtaining the fuzzification matrix for the unknown case and the family of prototypes, then by the linguistic evaluation of two fuzzy quantities we obtain the similarity matrix, then by a simple aggregation method and the fuzzy integrals we obtain the degree of similarity. Finally, we find that the similarity measure results are comparable to the neural network classification techniques and it can be used in medical diagnosis to determine the pathology of the liver and to monitor the extent of the disease.

Spin vectors of asteroids 21 Lutetia, 196 Philomela, 250 Bettina, 337 Devosa, and 804 Hispania

NASA Technical Reports Server (NTRS)

Michalowski, Tadeusz

1992-01-01

Such parameters as shape, orientation of spin axis, prograde or retrograde rotation are important for understanding the collisional evolution of asteroids since the primordial epochs of solar system history. These parameters remain unknown for most asteroids and poorly constrained for all but a few. This work presents results for five asteroids: 21, 196, 250, 337, and 804.

Decentralized adaptive neural control for high-order interconnected stochastic nonlinear time-delay systems with unknown system dynamics.

PubMed

Si, Wenjie; Dong, Xunde; Yang, Feifei

2018-03-01

This paper is concerned with the problem of decentralized adaptive backstepping state-feedback control for uncertain high-order large-scale stochastic nonlinear time-delay systems. For the control design of high-order large-scale nonlinear systems, only one adaptive parameter is constructed to overcome the over-parameterization, and neural networks are employed to cope with the difficulties raised by completely unknown system dynamics and stochastic disturbances. And then, the appropriate Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions of high-order large-scale systems for the first time. At last, on the basis of Lyapunov stability theory, the decentralized adaptive neural controller was developed, and it decreases the number of learning parameters. The actual controller can be designed so as to ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges in the small neighborhood of zero. The simulation example is used to further show the validity of the design method. Copyright © 2018 Elsevier Ltd. All rights reserved.

Inverse modelling for real-time estimation of radiological consequences in the early stage of an accidental radioactivity release.

PubMed

Pecha, Petr; Šmídl, Václav

2016-11-01

A stepwise sequential assimilation algorithm is proposed based on an optimisation approach for recursive parameter estimation and tracking of radioactive plume propagation in the early stage of a radiation accident. Predictions of the radiological situation in each time step of the plume propagation are driven by an existing short-term meteorological forecast and the assimilation procedure manipulates the model parameters to match the observations incoming concurrently from the terrain. Mathematically, the task is a typical ill-posed inverse problem of estimating the parameters of the release. The proposed method is designated as a stepwise re-estimation of the source term release dynamics and an improvement of several input model parameters. It results in a more precise determination of the adversely affected areas in the terrain. The nonlinear least-squares regression methodology is applied for estimation of the unknowns. The fast and adequately accurate segmented Gaussian plume model (SGPM) is used in the first stage of direct (forward) modelling. The subsequent inverse procedure infers (re-estimates) the values of important model parameters from the actual observations. Accuracy and sensitivity of the proposed method for real-time forecasting of the accident propagation is studied. First, a twin experiment generating noiseless simulated "artificial" observations is studied to verify the minimisation algorithm. Second, the impact of the measurement noise on the re-estimated source release rate is examined. In addition, the presented method can be used as a proposal for more advanced statistical techniques using, e.g., importance sampling. Copyright © 2016 Elsevier Ltd. All rights reserved.

Analyzing crash frequency in freeway tunnels: A correlated random parameters approach.

PubMed

Hou, Qinzhong; Tarko, Andrew P; Meng, Xianghai

2018-02-01

The majority of past road safety studies focused on open road segments while only a few focused on tunnels. Moreover, the past tunnel studies produced some inconsistent results about the safety effects of the traffic patterns, the tunnel design, and the pavement conditions. The effects of these conditions therefore remain unknown, especially for freeway tunnels in China. The study presented in this paper investigated the safety effects of these various factors utilizing a four-year period (2009-2012) of data as well as three models: 1) a random effects negative binomial model (RENB), 2) an uncorrelated random parameters negative binomial model (URPNB), and 3) a correlated random parameters negative binomial model (CRPNB). Of these three, the results showed that the CRPNB model provided better goodness-of-fit and offered more insights into the factors that contribute to tunnel safety. The CRPNB was not only able to allocate the part of the otherwise unobserved heterogeneity to the individual model parameters but also was able to estimate the cross-correlations between these parameters. Furthermore, the study results showed that traffic volume, tunnel length, proportion of heavy trucks, curvature, and pavement rutting were associated with higher frequencies of traffic crashes, while the distance to the tunnel wall, distance to the adjacent tunnel, distress ratio, International Roughness Index (IRI), and friction coefficient were associated with lower crash frequencies. In addition, the effects of the heterogeneity of the proportion of heavy trucks, the curvature, the rutting depth, and the friction coefficient were identified and their inter-correlations were analyzed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Molecular toolbox for the identification of unknown genetically modified organisms.

PubMed

Ruttink, Tom; Demeyer, Rolinde; Van Gulck, Elke; Van Droogenbroeck, Bart; Querci, Maddalena; Taverniers, Isabel; De Loose, Marc

2010-03-01

Competent laboratories monitor genetically modified organisms (GMOs) and products derived thereof in the food and feed chain in the framework of labeling and traceability legislation. In addition, screening is performed to detect the unauthorized presence of GMOs including asynchronously authorized GMOs or GMOs that are not officially registered for commercialization (unknown GMOs). Currently, unauthorized or unknown events are detected by screening blind samples for commonly used transgenic elements, such as p35S or t-nos. If (1) positive detection of such screening elements shows the presence of transgenic material and (2) all known GMOs are tested by event-specific methods but are not detected, then the presence of an unknown GMO is inferred. However, such evidence is indirect because it is based on negative observations and inconclusive because the procedure does not identify the causative event per se. In addition, detection of unknown events is hampered in products that also contain known authorized events. Here, we outline alternative approaches for analytical detection and GMO identification and develop new methods to complement the existing routine screening procedure. We developed a fluorescent anchor-polymerase chain reaction (PCR) method for the identification of the sequences flanking the p35S and t-nos screening elements. Thus, anchor-PCR fingerprinting allows the detection of unique discriminative signals per event. In addition, we established a collection of in silico calculated fingerprints of known events to support interpretation of experimentally generated anchor-PCR GM fingerprints of blind samples. Here, we first describe the molecular characterization of a novel GMO, which expresses recombinant human intrinsic factor in Arabidopsis thaliana. Next, we purposefully treated the novel GMO as a blind sample to simulate how the new methods lead to the molecular identification of a novel unknown event without prior knowledge of its transgene sequence. The results demonstrate that the new methods complement routine screening procedures by providing direct conclusive evidence and may also be useful to resolve masking of unknown events by known events.

Pattern Recognition Algorithm for High-Sensitivity Odorant Detection in Unknown Environments

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2012-01-01

In a realistic odorant detection application environment, the collected sensory data is a mix of unknown chemicals with unknown concentrations and noise. The identification of the odorants among these mixtures is a challenge in data recognition. In addition, deriving their individual concentrations in the mix is also a challenge. A deterministic analytical model was developed to accurately identify odorants and calculate their concentrations in a mixture with noisy data.

Robust Control Design for Uncertain Nonlinear Dynamic Systems

NASA Technical Reports Server (NTRS)

Kenny, Sean P.; Crespo, Luis G.; Andrews, Lindsey; Giesy, Daniel P.

2012-01-01

Robustness to parametric uncertainty is fundamental to successful control system design and as such it has been at the core of many design methods developed over the decades. Despite its prominence, most of the work on robust control design has focused on linear models and uncertainties that are non-probabilistic in nature. Recently, researchers have acknowledged this disparity and have been developing theory to address a broader class of uncertainties. This paper presents an experimental application of robust control design for a hybrid class of probabilistic and non-probabilistic parametric uncertainties. The experimental apparatus is based upon the classic inverted pendulum on a cart. The physical uncertainty is realized by a known additional lumped mass at an unknown location on the pendulum. This unknown location has the effect of substantially altering the nominal frequency and controllability of the nonlinear system, and in the limit has the capability to make the system neutrally stable and uncontrollable. Another uncertainty to be considered is a direct current motor parameter. The control design objective is to design a controller that satisfies stability, tracking error, control power, and transient behavior requirements for the largest range of parametric uncertainties. This paper presents an overview of the theory behind the robust control design methodology and the experimental results.

Wide Field Radio Transient Surveys

NASA Astrophysics Data System (ADS)

Bower, Geoffrey

2011-04-01

The time domain of the radio wavelength sky has been only sparsely explored. Nevertheless, serendipitous discovery and results from limited surveys indicate that there is much to be found on timescales from nanoseconds to years and at wavelengths from meters to millimeters. These observations have revealed unexpected phenomena such as rotating radio transients and coherent pulses from brown dwarfs. Additionally, archival studies have revealed an unknown class of radio transients without radio, optical, or high-energy hosts. The new generation of centimeter-wave radio telescopes such as the Allen Telescope Array (ATA) will exploit wide fields of view and flexible digital signal processing to systematically explore radio transient parameter space, as well as lay the scientific and technical foundation for the Square Kilometer Array. Known unknowns that will be the target of future transient surveys include orphan gamma-ray burst afterglows, radio supernovae, tidally-disrupted stars, flare stars, and magnetars. While probing the variable sky, these surveys will also provide unprecedented information on the static radio sky. I will present results from three large ATA surveys (the Fly's Eye survey, the ATA Twenty CM Survey (ATATS), and the Pi GHz Survey (PiGSS)) and several small ATA transient searches. Finally, I will discuss the landscape and opportunities for future instruments at centimeter wavelengths.

Poroelastic Modeling as a Proof of Concept for Modular Representation of Coupled Geophysical Processes

NASA Astrophysics Data System (ADS)

Walker, R. L., II; Knepley, M.; Aminzadeh, F.

2017-12-01

We seek to use the tools provided by the Portable, Extensible Toolkit for Scientific Computation (PETSc) to represent a multiphysics problem in a form that decouples the element definition from the fully coupled equation through the use of pointwise functions that imitate the strong form of the governing equation. This allows allows individual physical processes to be expressed as independent kernels that may be then coupled with the existing finite element framework, PyLith, and capitalizes upon the flexibility offered by the solver, data management, and time stepping algorithms offered by PETSc. To demonstrate a characteristic example of coupled geophysical simulation devised in this manner, we present a model of a synthetic poroelastic environment, with and without the consideration of inertial effects, with fluid initially represented as a single phase. Matrix displacement and fluid pressure serve as the desired unknowns, with the option for various model parameters represented as dependent variables of the central unknowns. While independent of PyLith, this model also serves to showcase the adaptability of physics kernels for synthetic forward modeling. In addition, we seek to expand the base case to demonstrate the impact of modeling fluid as single phase compressible versus a single incompressible phase. As a goal, we also seek to include multiphase fluid modeling, as well as capillary effects.

Hierarchial mark-recapture models: a framework for inference about demographic processes

USGS Publications Warehouse

Link, W.A.; Barker, R.J.

2004-01-01

The development of sophisticated mark-recapture models over the last four decades has provided fundamental tools for the study of wildlife populations, allowing reliable inference about population sizes and demographic rates based on clearly formulated models for the sampling processes. Mark-recapture models are now routinely described by large numbers of parameters. These large models provide the next challenge to wildlife modelers: the extraction of signal from noise in large collections of parameters. Pattern among parameters can be described by strong, deterministic relations (as in ultrastructural models) but is more flexibly and credibly modeled using weaker, stochastic relations. Trend in survival rates is not likely to be manifest by a sequence of values falling precisely on a given parametric curve; rather, if we could somehow know the true values, we might anticipate a regression relation between parameters and explanatory variables, in which true value equals signal plus noise. Hierarchical models provide a useful framework for inference about collections of related parameters. Instead of regarding parameters as fixed but unknown quantities, we regard them as realizations of stochastic processes governed by hyperparameters. Inference about demographic processes is based on investigation of these hyperparameters. We advocate the Bayesian paradigm as a natural, mathematically and scientifically sound basis for inference about hierarchical models. We describe analysis of capture-recapture data from an open population based on hierarchical extensions of the Cormack-Jolly-Seber model. In addition to recaptures of marked animals, we model first captures of animals and losses on capture, and are thus able to estimate survival probabilities w (i.e., the complement of death or permanent emigration) and per capita growth rates f (i.e., the sum of recruitment and immigration rates). Covariation in these rates, a feature of demographic interest, is explicitly described in the model.

Genomic data assimilation for estimating hybrid functional Petri net from time-course gene expression data.

PubMed

Nagasaki, Masao; Yamaguchi, Rui; Yoshida, Ryo; Imoto, Seiya; Doi, Atsushi; Tamada, Yoshinori; Matsuno, Hiroshi; Miyano, Satoru; Higuchi, Tomoyuki

2006-01-01

We propose an automatic construction method of the hybrid functional Petri net as a simulation model of biological pathways. The problems we consider are how we choose the values of parameters and how we set the network structure. Usually, we tune these unknown factors empirically so that the simulation results are consistent with biological knowledge. Obviously, this approach has the limitation in the size of network of interest. To extend the capability of the simulation model, we propose the use of data assimilation approach that was originally established in the field of geophysical simulation science. We provide genomic data assimilation framework that establishes a link between our simulation model and observed data like microarray gene expression data by using a nonlinear state space model. A key idea of our genomic data assimilation is that the unknown parameters in simulation model are converted as the parameter of the state space model and the estimates are obtained as the maximum a posteriori estimators. In the parameter estimation process, the simulation model is used to generate the system model in the state space model. Such a formulation enables us to handle both the model construction and the parameter tuning within a framework of the Bayesian statistical inferences. In particular, the Bayesian approach provides us a way of controlling overfitting during the parameter estimations that is essential for constructing a reliable biological pathway. We demonstrate the effectiveness of our approach using synthetic data. As a result, parameter estimation using genomic data assimilation works very well and the network structure is suitably selected.

Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Shaohua

This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaosmore » of PMSM and show the effectiveness and robustness of the proposed method.« less

Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain.

PubMed

Luo, Shaohua

2014-09-01

This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.

Identification of vehicle suspension parameters by design optimization

NASA Astrophysics Data System (ADS)

Tey, J. Y.; Ramli, R.; Kheng, C. W.; Chong, S. Y.; Abidin, M. A. Z.

2014-05-01

The design of a vehicle suspension system through simulation requires accurate representation of the design parameters. These parameters are usually difficult to measure or sometimes unavailable. This article proposes an efficient approach to identify the unknown parameters through optimization based on experimental results, where the covariance matrix adaptation-evolutionary strategy (CMA-es) is utilized to improve the simulation and experimental results against the kinematic and compliance tests. This speeds up the design and development cycle by recovering all the unknown data with respect to a set of kinematic measurements through a single optimization process. A case study employing a McPherson strut suspension system is modelled in a multi-body dynamic system. Three kinematic and compliance tests are examined, namely, vertical parallel wheel travel, opposite wheel travel and single wheel travel. The problem is formulated as a multi-objective optimization problem with 40 objectives and 49 design parameters. A hierarchical clustering method based on global sensitivity analysis is used to reduce the number of objectives to 30 by grouping correlated objectives together. Then, a dynamic summation of rank value is used as pseudo-objective functions to reformulate the multi-objective optimization to a single-objective optimization problem. The optimized results show a significant improvement in the correlation between the simulated model and the experimental model. Once accurate representation of the vehicle suspension model is achieved, further analysis, such as ride and handling performances, can be implemented for further optimization.

Performance Analysis for Channel Estimation With 1-Bit ADC and Unknown Quantization Threshold

NASA Astrophysics Data System (ADS)

Stein, Manuel S.; Bar, Shahar; Nossek, Josef A.; Tabrikian, Joseph

2018-05-01

In this work, the problem of signal parameter estimation from measurements acquired by a low-complexity analog-to-digital converter (ADC) with $1$-bit output resolution and an unknown quantization threshold is considered. Single-comparator ADCs are energy-efficient and can be operated at ultra-high sampling rates. For analysis of such systems, a fixed and known quantization threshold is usually assumed. In the symmetric case, i.e., zero hard-limiting offset, it is known that in the low signal-to-noise ratio (SNR) regime the signal processing performance degrades moderately by ${2}/{\\pi}$ ($-1.96$ dB) when comparing to an ideal $\\infty$-bit converter. Due to hardware imperfections, low-complexity $1$-bit ADCs will in practice exhibit an unknown threshold different from zero. Therefore, we study the accuracy which can be obtained with receive data processed by a hard-limiter with unknown quantization level by using asymptotically optimal channel estimation algorithms. To characterize the estimation performance of these nonlinear algorithms, we employ analytic error expressions for different setups while modeling the offset as a nuisance parameter. In the low SNR regime, we establish the necessary condition for a vanishing loss due to missing offset knowledge at the receiver. As an application, we consider the estimation of single-input single-output wireless channels with inter-symbol interference and validate our analysis by comparing the analytic and experimental performance of the studied estimation algorithms. Finally, we comment on the extension to multiple-input multiple-output channel models.

The Routine Fitting of Kinetic Data to Models

PubMed Central

Berman, Mones; Shahn, Ezra; Weiss, Marjory F.

1962-01-01

A mathematical formalism is presented for use with digital computers to permit the routine fitting of data to physical and mathematical models. Given a set of data, the mathematical equations describing a model, initial conditions for an experiment, and initial estimates for the values of model parameters, the computer program automatically proceeds to obtain a least squares fit of the data by an iterative adjustment of the values of the parameters. When the experimental measures are linear combinations of functions, the linear coefficients for a least squares fit may also be calculated. The values of both the parameters of the model and the coefficients for the sum of functions may be unknown independent variables, unknown dependent variables, or known constants. In the case of dependence, only linear dependencies are provided for in routine use. The computer program includes a number of subroutines, each one of which performs a special task. This permits flexibility in choosing various types of solutions and procedures. One subroutine, for example, handles linear differential equations, another, special non-linear functions, etc. The use of analytic or numerical solutions of equations is possible. PMID:13867975

Adaptive Control Based Harvesting Strategy for a Predator-Prey Dynamical System.

PubMed

Sen, Moitri; Simha, Ashutosh; Raha, Soumyendu

2018-04-23

This paper deals with designing a harvesting control strategy for a predator-prey dynamical system, with parametric uncertainties and exogenous disturbances. A feedback control law for the harvesting rate of the predator is formulated such that the population dynamics is asymptotically stabilized at a positive operating point, while maintaining a positive, steady state harvesting rate. The hierarchical block strict feedback structure of the dynamics is exploited in designing a backstepping control law, based on Lyapunov theory. In order to account for unknown parameters, an adaptive control strategy has been proposed in which the control law depends on an adaptive variable which tracks the unknown parameter. Further, a switching component has been incorporated to robustify the control performance against bounded disturbances. Proofs have been provided to show that the proposed adaptive control strategy ensures asymptotic stability of the dynamics at a desired operating point, as well as exact parameter learning in the disturbance-free case and learning with bounded error in the disturbance prone case. The dynamics, with uncertainty in the death rate of the predator, subjected to a bounded disturbance has been simulated with the proposed control strategy.

Efficient Bayesian experimental design for contaminant source identification

NASA Astrophysics Data System (ADS)

Zhang, Jiangjiang; Zeng, Lingzao; Chen, Cheng; Chen, Dingjiang; Wu, Laosheng

2015-01-01

In this study, an efficient full Bayesian approach is developed for the optimal sampling well location design and source parameters identification of groundwater contaminants. An information measure, i.e., the relative entropy, is employed to quantify the information gain from concentration measurements in identifying unknown parameters. In this approach, the sampling locations that give the maximum expected relative entropy are selected as the optimal design. After the sampling locations are determined, a Bayesian approach based on Markov Chain Monte Carlo (MCMC) is used to estimate unknown parameters. In both the design and estimation, the contaminant transport equation is required to be solved many times to evaluate the likelihood. To reduce the computational burden, an interpolation method based on the adaptive sparse grid is utilized to construct a surrogate for the contaminant transport equation. The approximated likelihood can be evaluated directly from the surrogate, which greatly accelerates the design and estimation process. The accuracy and efficiency of our approach are demonstrated through numerical case studies. It is shown that the methods can be used to assist in both single sampling location and monitoring network design for contaminant source identifications in groundwater.

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm.

PubMed

Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam

2014-07-01

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive control of nonlinear uncertain active suspension systems with prescribed performance.

PubMed

Huang, Yingbo; Na, Jing; Wu, Xing; Liu, Xiaoqin; Guo, Yu

2015-01-01

This paper proposes adaptive control designs for vehicle active suspension systems with unknown nonlinear dynamics (e.g., nonlinear spring and piece-wise linear damper dynamics). An adaptive control is first proposed to stabilize the vertical vehicle displacement and thus to improve the ride comfort and to guarantee other suspension requirements (e.g., road holding and suspension space limitation) concerning the vehicle safety and mechanical constraints. An augmented neural network is developed to online compensate for the unknown nonlinearities, and a novel adaptive law is developed to estimate both NN weights and uncertain model parameters (e.g., sprung mass), where the parameter estimation error is used as a leakage term superimposed on the classical adaptations. To further improve the control performance and simplify the parameter tuning, a prescribed performance function (PPF) characterizing the error convergence rate, maximum overshoot and steady-state error is used to propose another adaptive control. The stability for the closed-loop system is proved and particular performance requirements are analyzed. Simulations are included to illustrate the effectiveness of the proposed control schemes. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal experimental designs for the estimation of thermal properties of composite materials

NASA Technical Reports Server (NTRS)

Scott, Elaine P.; Moncman, Deborah A.

1994-01-01

Reliable estimation of thermal properties is extremely important in the utilization of new advanced materials, such as composite materials. The accuracy of these estimates can be increased if the experiments are designed carefully. The objectives of this study are to design optimal experiments to be used in the prediction of these thermal properties and to then utilize these designs in the development of an estimation procedure to determine the effective thermal properties (thermal conductivity and volumetric heat capacity). The experiments were optimized by choosing experimental parameters that maximize the temperature derivatives with respect to all of the unknown thermal properties. This procedure has the effect of minimizing the confidence intervals of the resulting thermal property estimates. Both one-dimensional and two-dimensional experimental designs were optimized. A heat flux boundary condition is required in both analyses for the simultaneous estimation of the thermal properties. For the one-dimensional experiment, the parameters optimized were the heating time of the applied heat flux, the temperature sensor location, and the experimental time. In addition to these parameters, the optimal location of the heat flux was also determined for the two-dimensional experiments. Utilizing the optimal one-dimensional experiment, the effective thermal conductivity perpendicular to the fibers and the effective volumetric heat capacity were then estimated for an IM7-Bismaleimide composite material. The estimation procedure used is based on the minimization of a least squares function which incorporates both calculated and measured temperatures and allows for the parameters to be estimated simultaneously.

Global Assessment of the Impact of Type 2 Diabetes on Sleep through Specific Questionnaires. A Case-Control Study.

PubMed

Lecube, Albert; Sánchez, Enric; Gómez-Peralta, Fernando; Abreu, Cristina; Valls, Joan; Mestre, Olga; Romero, Odile; Martínez, María Dolores; Sampol, Gabriel; Ciudin, Andreea; Hernández, Cristina; Simó, Rafael

2016-01-01

Type 2 diabetes (T2D) is an independent risk factor for sleep breathing disorders. However, it is unknown whether T2D affects daily somnolence and quality of sleep independently of the impairment of polysomnographic parameters. A case-control study including 413 patients with T2D and 413 non-diabetic subjects, matched by age, gender, BMI, and waist and neck circumferences. A polysomnography was performed and daytime sleepiness was evaluated using the Epworth Sleepiness Scale (ESS). In addition, 135 subjects with T2D and 45 controls matched by the same previous parameters were also evaluated through the Pittsburgh Sleep Quality Index (PSQI) to calculate sleep quality. Daytime sleepiness was higher in T2D than in control subjects (p = 0.003), with 23.9% of subjects presenting an excessive daytime sleepiness (ESS>10). Patients with fasting plasma glucose (FPG ≥13.1 mmol/l) were identified as the group with a higher risk associated with an ESS>10 (OR 3.9, 95% CI 1.8-7.9, p = 0.0003). A stepwise regression analyses showed that the presence of T2D, baseline glucose levels and gender but not polysomnographic parameters (i.e apnea-hyoapnea index or sleeping time spent with oxigen saturation lower than 90%) independently predicted the ESS score. In addition, subjects with T2D showed higher sleep disturbances [PSQI: 7.0 (1.0-18.0) vs. 4 (0.0-12.0), p<0.001]. The presence of T2D and high levels of FPG are independent risk factors for daytime sleepiness and adversely affect sleep quality. Prospective studies addressed to demonstrate whether glycemia optimization could improve the sleep quality in T2D patients seem warranted.

Parameter discovery in stochastic biological models using simulated annealing and statistical model checking.

PubMed

Hussain, Faraz; Jha, Sumit K; Jha, Susmit; Langmead, Christopher J

2014-01-01

Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.

Identification of FOPDT and SOPDT process dynamics using closed loop test.

PubMed

Bajarangbali, Raghunath; Majhi, Somanath; Pandey, Saurabh

2014-07-01

In this paper, identification of stable and unstable first order, second order overdamped and underdamped process dynamics with time delay is presented. Relay with hysteresis is used to induce a limit cycle output and using this information, unknown process model parameters are estimated. State space based generalized analytical expressions are derived to achieve accurate results. To show the performance of the proposed method expressions are also derived for systems with a zero. In real time systems, measurement noise is an important issue during identification of process dynamics. A relay with hysteresis reduces the effect of measurement noise, in addition a new multiloop control strategy is proposed to recover the original limit cycle. Simulation results are included to validate the effectiveness of the proposed method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Inference of relativistic electron spectra from measurements of inverse Compton radiation

NASA Astrophysics Data System (ADS)

Craig, I. J. D.; Brown, J. C.

1980-07-01

The inference of relativistic electron spectra from spectral measurement of inverse Compton radiation is discussed for the case where the background photon spectrum is a Planck function. The problem is formulated in terms of an integral transform that relates the measured spectrum to the unknown electron distribution. A general inversion formula is used to provide a quantitative assessment of the information content of the spectral data. It is shown that the observations must generally be augmented by additional information if anything other than a rudimentary two or three parameter model of the source function is to be derived. It is also pointed out that since a similar equation governs the continuum spectra emitted by a distribution of black-body radiators, the analysis is relevant to the problem of stellar population synthesis from galactic spectra.

Continued Analysis of the NIST Neutron Lifetime Measurement Using Ultracold Neutrons

NASA Astrophysics Data System (ADS)

Huffer, Craig; Huffman, P. R.; Schelhammer, K. W.; Dewey, M. S.; Huber, M. G.; Hughes, P. P.; Mumm, H. P.; Thompson, A. K.; Coakley, K.; Yue, A. T.; O'Shaughnessy, C. M.; Yang, L.

2013-10-01

The neutron lifetime is an important parameter for constraining the Standard Model and providing input for Big Bang Nucleosynthesis. The current disagreement in the most recent generation of lifetime experiments suggests unknown or underestimated systematics and motivates the need for alternative measurement methods as well as additional investigations into potential systematics. Our measurement was performed using magnetically trapped Ultracold Neutrons in a 3.1 T Ioffe type trap configuration. The decay rate of the neutron population is recorded in real time by monitoring visible light resulting from beta decay. Data collected in late 2010 and early 2011 is being analyzed and systematic effects are being investigated. An overview of our current work on the analysis, Monte Carlo simulations, and systematic effects will be provided. This work was supported by the NSF and NIST.

Crustal heterogeneity of the moon viewed from the Galileo SSI camera: Lunar sample calibrations and compositional implications

NASA Technical Reports Server (NTRS)

Pieters, Carle M.; Belton, M.; Becker, T.; Carr, M. H.; Chapmann, C.; Fanale, F. P.; Fischer, Erich M.; Gaddis, L.; Greeley, Ronald; Greenberg, R.

1991-01-01

Summaries are given of the spectral calibration, compositional parameters, nearside color, and limb and farside color of the Moon. The farside of the Moon, a large area of lunar crust, is dominated by heavily cratered terrain and basin deposits that represent the products of the first half billion years of crustal evolution. Continuing analysis of the returned lunar samples suggest a magma ocean and/or serial magmatism model for evolution of the primordial lunar crust. However, testing either hypothesis requires compositional information about the crustal stratigraphy and lateral heterogeneity. Resolution of this important planetary science issue is dependent on additional data. New Galileo multispectral images indicate previously unknown local and regional compositional diversity of the farside crust. Future analysis will focus on individual features and a more detailed assessment of crustal stratigraphy and heterogeneity.

Metabolomics and Prostate Cancer

DTIC Science & Technology

2013-05-01

features indicated in pairwise analysis molecular identification revealed Caprolactam, L- Phosphatidic acid , and Peptide (Tyr-Lys-Thr) as possibly...10-8 Unknown – Caprolactam 4 – 1 595.4_153 – 443.3_275 4.2 x 10-8 Unknown – Unknown 4 – 4 597.4_306 - 595.4_153 7.1 x 10-8 L- Phosphatidic acid ...three of the seven additional features implicated including caprolactam, L- Phosphatidic acid and the peptide Tyr-Lys-Thr. Each of these molecules was

Renovation of the fixing and loading factors of the beam by the spectral data of free flexural vibrations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Akhymbek, Meiram Erkanatuly; Yessirkegenov, Nurgissa Amankeldiuly; Sadybekov, Makhmud Abdysametovich

2015-09-18

In the current paper, the problem of bending vibrations of a beam in which the binding on the right end is unknown and not available for visual inspection is studied. The main objective is to study an inverse problem: find additional unknown boundary conditions by additional spectral data, i.e., the conditions of fixing the right end of the rod. In this work, unlike many other works, as such additional conditions we choose the first natural frequencies (eigenvalues) of two new problems corresponding to the problem of bending vibrations of a beam with loads of different weights at the central point.

A method for operative quantitative interpretation of multispectral images of biological tissues

NASA Astrophysics Data System (ADS)

Lisenko, S. A.; Kugeiko, M. M.

2013-10-01

A method for operative retrieval of spatial distributions of biophysical parameters of a biological tissue by using a multispectral image of it has been developed. The method is based on multiple regressions between linearly independent components of the diffuse reflection spectrum of the tissue and unknown parameters. Possibilities of the method are illustrated by an example of determining biophysical parameters of the skin (concentrations of melanin, hemoglobin and bilirubin, blood oxygenation, and scattering coefficient of the tissue). Examples of quantitative interpretation of the experimental data are presented.

A black box optimization approach to parameter estimation in a model for long/short term variations dynamics of commodity prices

NASA Astrophysics Data System (ADS)

De Santis, Alberto; Dellepiane, Umberto; Lucidi, Stefano

2012-11-01

In this paper we investigate the estimation problem for a model of the commodity prices. This model is a stochastic state space dynamical model and the problem unknowns are the state variables and the system parameters. Data are represented by the commodity spot prices, very seldom time series of Futures contracts are available for free. Both the system joint likelihood function (state variables and parameters) and the system marginal likelihood (the state variables are eliminated) function are addressed.

Optical absorption spectra of substitutional Co2+ ions in Mgx Cd1-x Se alloys

NASA Astrophysics Data System (ADS)

Jin, Moon-Seog; Kim, Chang-Dae; Jang, Kiwan; Park, Sang-An; Kim, Duck-Tae; Kim, Hyung-Gon; Kim, Wha-Tek

2006-09-01

Optical absorption spectra of substitutional Co2+ ions in Mgx Cd1-x Se alloys were investigated in the composition region of 0.0 x 0.4 and in the wavelength region of 300 to 2500 nm at 4.8 K and 290 K. We observed several absorption bands in the wavelength regions corresponding to the 4A2(4F) 4T1(4P) transition and the 4A2(4F) 4T1(4F) transition of Co2+ at a tetrahedral Td point symmetry point in the host crystals, as well as unknown absorption bands. The several absorption bands were analyzed in the framework of the crystal-field theory along with the second-order spin-orbit coupling. The unknown absorption bands were assigned as due to phonon-assisted absorption bands. We also investigated the variations of the crystal-field parameter Dq and the Racah parameter B with composition x in the Mgx Cd1-x Se system. The results showed that the crystal-field parameter (Dq ) increases, on the other hand, the Racah parameter (B ) decreases with increasing composition x, which may be connected with an increase in the covalency of the metal-ligand bond with increasing composition x in the Mgx Cd1-x Se system.

Parallel Elemental and Molecular Mass Spectrometry (PEMMS) for Speciation of Metals in Complex Matrices

NASA Astrophysics Data System (ADS)

Derry, L. A.; Sacks, G. L.; Brenna, J. T.

2006-12-01

The mobility, bioavailability, and toxicity of many metals are strongly influenced by their molecular form, or speciation. Many metals (M) in the environment are complexed with organic ligands (L); in some cases such complexed forms comprise virtually all of the dissolved metal. When available, stability constants can be used to model speciation, but only when the identities of species can be known or assumed. In other cases, coupling a separation tool such as HPLC to a metal detection system like ICP-MS can provide information on speciation. But because ICP-MS destroys molecular information the complexing L must be identified by retention time matching of standards. The assumptions inherent in these approaches preclude investigation of unknown compounds. Electrochemical methods can determine conditional stability constants for unknown L but do not provide information on their molecular structure or composition. Molecular MS allows characterization of molecular mass and structure and is a powerful tool for identifying unknown organic compounds. However, sensitivity for M and precision for isotope ratios are often low. We combined HPLC separation with simultaneous parallel detection of metals (M) and ligands (L) by ICP-MS and API-QTOF-MS-MS. The basis of our approach is that the shape of a chromatographic peak is essentially set by interactions with the LC column. The signal of a metal M determined by the ICP-MS in chromatographic mode is fit using an exponentially modified Gaussian function. Peak parameters including retention time, peak width and skew are determined for the M peak. The parallel QTOF signal in full scan mode may show hundreds of peaks in the same time window. The acquired peak library is searched for L peaks that match the parameters determined for the M peak on the ICP-MS. We have found that our system can correctly identify M-L pairs and L in complex samples and generates few false positives. Unknown L can be further interrogated by using the MS-MS product ion scan mode of the QTOF, in which candidate peaks are fragmented in a collision cell after MS1 and the fragments analyzed by MS2. The combination of precise mass determinations, recognition of isotopomers, and product ion scans can often identify unknown M-L in complex natural mixtures without prior knowledge of the sample composition. Data from seleno-compounds illustrate the PEMMS concept. An artificially noisy sample was generated by spiking 30 ppb Se-methionine (SeMet) and 5 ppb Se-methylselenocysteine (SeMSC) with unselenized yeast extract and run by PEMMS. The PEMMS software was able to detect four molecular MS peaks associated with SeMet and two for SeMSC, while filtering out more than 40 co-eluting spectral peaks associated with chemical noise in each sample. Additional data from Cu-thiols produced by marine plankton and soil chelates of Al demonstrate the utility of the PEMMS approach in the study of natural environmental systems.

Image Restoration for Fluorescence Planar Imaging with Diffusion Model

PubMed Central

Gong, Yuzhu; Li, Yang

2017-01-01

Fluorescence planar imaging (FPI) is failure to capture high resolution images of deep fluorochromes due to photon diffusion. This paper presents an image restoration method to deal with this kind of blurring. The scheme of this method is conceived based on a reconstruction method in fluorescence molecular tomography (FMT) with diffusion model. A new unknown parameter is defined through introducing the first mean value theorem for definite integrals. System matrix converting this unknown parameter to the blurry image is constructed with the elements of depth conversion matrices related to a chosen plane named focal plane. Results of phantom and mouse experiments show that the proposed method is capable of reducing the blurring of FPI image caused by photon diffusion when the depth of focal plane is chosen within a proper interval around the true depth of fluorochrome. This method will be helpful to the estimation of the size of deep fluorochrome. PMID:29279843

Adaptive boundary concentration control using Zakai equation

NASA Astrophysics Data System (ADS)

Tenno, R.; Mendelson, A.

2010-06-01

A mean-variance control problem is formulated with respect to a partially observed nonlinear system that includes unknown constant parameters. A physical prototype of the system is the cathode surface reaction in an electrolysis cell, where the controller aim is to keep the boundary concentration of species in the near vicinity of the cathode surface low but not zero. The boundary concentration is a diffusion-controlled process observed through the measured current density and, in practice, controlled through the applied voltage. The former incomplete data control problem is converted to complete data-to the so-called separated control problem whose solution is given by the infinite-dimensional Zakai equation. In this article, the separated control problem is solved numerically using pathwise integration of the Zakai equation. This article demonstrates precise tracking of the target trajectory with a rapid convergence of estimates to unknown parameters, which take place simultaneously with control.

52. Neg. No.none, ca. 1950's, PhotographerUnknown, AERIAL VIEWS OF THE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

52. Neg. No.-none, ca. 1950's, Photographer-Unknown, AERIAL VIEWS OF THE FORD MOTOR COMPANY ASSEMBLY PLANT, SOMETIME AFTER THE ADDITION OF THE NORTHERN WING - Ford Motor Company Long Beach Assembly Plant, Assembly Building, 700 Henry Ford Avenue, Long Beach, Los Angeles County, CA

Optimal estimation of parameters and states in stochastic time-varying systems with time delay

NASA Astrophysics Data System (ADS)

Torkamani, Shahab; Butcher, Eric A.

2013-08-01

In this study estimation of parameters and states in stochastic linear and nonlinear delay differential systems with time-varying coefficients and constant delay is explored. The approach consists of first employing a continuous time approximation to approximate the stochastic delay differential equation with a set of stochastic ordinary differential equations. Then the problem of parameter estimation in the resulting stochastic differential system is represented as an optimal filtering problem using a state augmentation technique. By adapting the extended Kalman-Bucy filter to the resulting system, the unknown parameters of the time-delayed system are estimated from noise-corrupted, possibly incomplete measurements of the states.

SYNTHESIS OF NOVEL ALL-DIELECTRIC GRATING FILTERS USING GENETIC ALGORITHMS

NASA Technical Reports Server (NTRS)

Zuffada, Cinzia; Cwik, Tom; Ditchman, Christopher

1997-01-01

We are concerned with the design of inhomogeneous, all dielectric (lossless) periodic structures which act as filters. Dielectric filters made as stacks of inhomogeneous gratings and layers of materials are being used in optical technology, but are not common at microwave frequencies. The problem is then finding the periodic cell's geometric configuration and permittivity values which correspond to a specified reflectivity/transmittivity response as a function of frequency/illumination angle. This type of design can be thought of as an inverse-source problem, since it entails finding a distribution of sources which produce fields (or quantities derived from them) of given characteristics. Electromagnetic sources (electric and magnetic current densities) in a volume are related to the outside fields by a well known linear integral equation. Additionally, the sources are related to the fields inside the volume by a constitutive equation, involving the material properties. Then, the relationship linking the fields outside the source region to those inside is non-linear, in terms of material properties such as permittivity, permeability and conductivity. The solution of the non-linear inverse problem is cast here as a combination of two linear steps, by explicitly introducing the electromagnetic sources in the computational volume as a set of unknowns in addition to the material unknowns. This allows to solve for material parameters and related electric fields in the source volume which are consistent with Maxwell's equations. Solutions are obtained iteratively by decoupling the two steps. First, we invert for the permittivity only in the minimization of a cost function and second, given the materials, we find the corresponding electric fields through direct solution of the integral equation in the source volume. The sources thus computed are used to generate the far fields and the synthesized triter response. The cost function is obtained by calculating the deviation between the synthesized value of reflectivity/transmittivity and the desired one. Solution geometries for the periodic cell are sought as gratings (ensembles of columns of different heights and widths), or combinations of homogeneous layers of different dielectric materials and gratings. Hence the explicit unknowns of the inversion step are the material permittivities and the relative boundaries separating homogeneous parcels of the periodic cell.

Robust Fault Detection for Switched Fuzzy Systems With Unknown Input.

PubMed

Han, Jian; Zhang, Huaguang; Wang, Yingchun; Sun, Xun

2017-10-03

This paper investigates the fault detection problem for a class of switched nonlinear systems in the T-S fuzzy framework. The unknown input is considered in the systems. A novel fault detection unknown input observer design method is proposed. Based on the proposed observer, the unknown input can be removed from the fault detection residual. The weighted H∞ performance level is considered to ensure the robustness. In addition, the weighted H₋ performance level is introduced, which can increase the sensibility of the proposed detection method. To verify the proposed scheme, a numerical simulation example and an electromechanical system simulation example are provided at the end of this paper.

Comparison of quantitative NMR and IRMS spectrometry for the authentication of "Polish Vodka".

PubMed

Ciepielowski, Grzegorz; Pacholczyk-Sienicka, Barbara; Frączek, Tomasz; Klajman, Kamila; Paneth, Piotr; Albrecht, Łukasz

2018-05-31

The production of "Polish Vodka" is restricted by law to the ethyl alcohol of agricultural origins obtained from rye, wheat, barley, oat, triticale and potatoes grown on the territory of the Republic of Poland. The current labeling system should guarantee that the spirit is authentic and of good quality but not all producers are honest. Unfortunately, the authentic "Polish Vodka" is the most often counterfeited by the addition of cheaper and more accessible maize spirits. These illegal practices significantly reduce costs of the spirit production. Therefore, the determination of the botanical origin of alcohol in Poland is highly relevant. The quantitative 2 H NMR and isotope ratio mass spectrometry (IRMS) were used to investigate the authenticity of 30 samples of Polish spirits. Several isotopic parameters were used to determine the botanical origin of 10 unknown samples. Both approaches lead to the same conclusions regarding the percentage of maize-derived ethanol addition. Applied techniques are a valuable tool in the fight against counterfeiting of products. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Determination of ideal-gas enthalpies of formation for key compounds:

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steele, W.V.; Chirico, R.D.; Nguyen, A.

1991-10-01

The results of a study aimed at improvement of group-contribution methodology for estimation of thermodynamic properties of organic and organosilicon substances are reported. Specific weaknesses where particular group-contribution terms were unknown, or estimated because of lack of experimental data, are addressed by experimental studies of enthalpies of combustion in the condensed phase, vapor-pressure measurements, and differential scanning calorimetric (d.s.c.) heat-capacity measurements. Ideal-gas enthalpies of formation of ({plus minus})-butan-2-ol, tetradecan-1-ol, hexan-1,6-diol, methacrylamide, benzoyl formic acid, naphthalene-2,6-dicarboxylic acid dimethyl ester, and tetraethylsilane are reported. A crystalline-phase enthalpy of formation at 298.15 K was determined for naphthalene-2,6-dicarboxylic acid, which decomposed at 695 Kmore » before melting. The combustion calorimetry of tetraethylsilane used the proven fluorine-additivity methodology. Critical temperature and critical density were determined for tetraethylsilane with differential scanning calorimeter and the critical pressure was derived. Group-additivity parameters useful in the application of group- contribution correlations are derived. 112 refs., 13 figs., 19 tabs.« less

Constrained Allocation Flux Balance Analysis

PubMed Central

Mori, Matteo; Hwa, Terence; Martin, Olivier C.

2016-01-01

New experimental results on bacterial growth inspire a novel top-down approach to study cell metabolism, combining mass balance and proteomic constraints to extend and complement Flux Balance Analysis. We introduce here Constrained Allocation Flux Balance Analysis, CAFBA, in which the biosynthetic costs associated to growth are accounted for in an effective way through a single additional genome-wide constraint. Its roots lie in the experimentally observed pattern of proteome allocation for metabolic functions, allowing to bridge regulation and metabolism in a transparent way under the principle of growth-rate maximization. We provide a simple method to solve CAFBA efficiently and propose an “ensemble averaging” procedure to account for unknown protein costs. Applying this approach to modeling E. coli metabolism, we find that, as the growth rate increases, CAFBA solutions cross over from respiratory, growth-yield maximizing states (preferred at slow growth) to fermentative states with carbon overflow (preferred at fast growth). In addition, CAFBA allows for quantitatively accurate predictions on the rate of acetate excretion and growth yield based on only 3 parameters determined by empirical growth laws. PMID:27355325

Calibration of two complex ecosystem models with different likelihood functions

NASA Astrophysics Data System (ADS)

Hidy, Dóra; Haszpra, László; Pintér, Krisztina; Nagy, Zoltán; Barcza, Zoltán

2014-05-01

The biosphere is a sensitive carbon reservoir. Terrestrial ecosystems were approximately carbon neutral during the past centuries, but they became net carbon sinks due to climate change induced environmental change and associated CO2 fertilization effect of the atmosphere. Model studies and measurements indicate that the biospheric carbon sink can saturate in the future due to ongoing climate change which can act as a positive feedback. Robustness of carbon cycle models is a key issue when trying to choose the appropriate model for decision support. The input parameters of the process-based models are decisive regarding the model output. At the same time there are several input parameters for which accurate values are hard to obtain directly from experiments or no local measurements are available. Due to the uncertainty associated with the unknown model parameters significant bias can be experienced if the model is used to simulate the carbon and nitrogen cycle components of different ecosystems. In order to improve model performance the unknown model parameters has to be estimated. We developed a multi-objective, two-step calibration method based on Bayesian approach in order to estimate the unknown parameters of PaSim and Biome-BGC models. Biome-BGC and PaSim are a widely used biogeochemical models that simulate the storage and flux of water, carbon, and nitrogen between the ecosystem and the atmosphere, and within the components of the terrestrial ecosystems (in this research the developed version of Biome-BGC is used which is referred as BBGC MuSo). Both models were calibrated regardless the simulated processes and type of model parameters. The calibration procedure is based on the comparison of measured data with simulated results via calculating a likelihood function (degree of goodness-of-fit between simulated and measured data). In our research different likelihood function formulations were used in order to examine the effect of the different model goodness metric on calibration. The different likelihoods are different functions of RMSE (root mean squared error) weighted by measurement uncertainty: exponential / linear / quadratic / linear normalized by correlation. As a first calibration step sensitivity analysis was performed in order to select the influential parameters which have strong effect on the output data. In the second calibration step only the sensitive parameters were calibrated (optimal values and confidence intervals were calculated). In case of PaSim more parameters were found responsible for the 95% of the output data variance than is case of BBGC MuSo. Analysis of the results of the optimized models revealed that the exponential likelihood estimation proved to be the most robust (best model simulation with optimized parameter, highest confidence interval increase). The cross-validation of the model simulations can help in constraining the highly uncertain greenhouse gas budget of grasslands.

Inertial parameter identification using contact force information for an unknown object captured by a space manipulator

NASA Astrophysics Data System (ADS)

Chu, Zhongyi; Ma, Ye; Hou, Yueyang; Wang, Fengwen

2017-02-01

This paper presents a novel identification method for the intact inertial parameters of an unknown object in space captured by a manipulator in a space robotic system. With strong dynamic and kinematic coupling existing in the robotic system, the inertial parameter identification of the unknown object is essential for the ideal control strategy based on changes in the attitude and trajectory of the space robot via capturing operations. Conventional studies merely refer to the principle and theory of identification, and an error analysis process of identification is deficient for a practical scenario. To solve this issue, an analysis of the effect of errors on identification is illustrated first, and the accumulation of measurement or estimation errors causing poor identification precision is demonstrated. Meanwhile, a modified identification equation incorporating the contact force, as well as the force/torque of the end-effector, is proposed to weaken the accumulation of errors and improve the identification accuracy. Furthermore, considering a severe disturbance condition caused by various measured noises, the hybrid immune algorithm, Recursive Least Squares and Affine Projection Sign Algorithm (RLS-APSA), is employed to decode the modified identification equation to ensure a stable identification property. Finally, to verify the validity of the proposed identification method, the co-simulation of ADAMS-MATLAB is implemented by multi-degree of freedom models of a space robotic system, and the numerical results show a precise and stable identification performance, which is able to guarantee the execution of aerospace operations and prevent failed control strategies.

Adaptive neural control for a class of nonlinear time-varying delay systems with unknown hysteresis.

PubMed

Liu, Zhi; Lai, Guanyu; Zhang, Yun; Chen, Xin; Chen, Chun Lung Philip

2014-12-01

This paper investigates the fusion of unknown direction hysteresis model with adaptive neural control techniques in face of time-delayed continuous time nonlinear systems without strict-feedback form. Compared with previous works on the hysteresis phenomenon, the direction of the modified Bouc-Wen hysteresis model investigated in the literature is unknown. To reduce the computation burden in adaptation mechanism, an optimized adaptation method is successfully applied to the control design. Based on the Lyapunov-Krasovskii method, two neural-network-based adaptive control algorithms are constructed to guarantee that all the system states and adaptive parameters remain bounded, and the tracking error converges to an adjustable neighborhood of the origin. In final, some numerical examples are provided to validate the effectiveness of the proposed control methods.

Near real-time estimation of ionosphere vertical total electron content from GNSS satellites using B-splines in a Kalman filter

NASA Astrophysics Data System (ADS)

Erdogan, Eren; Schmidt, Michael; Seitz, Florian; Durmaz, Murat

2017-02-01

Although the number of terrestrial global navigation satellite system (GNSS) receivers supported by the International GNSS Service (IGS) is rapidly growing, the worldwide rather inhomogeneously distributed observation sites do not allow the generation of high-resolution global ionosphere products. Conversely, with the regionally enormous increase in highly precise GNSS data, the demands on (near) real-time ionosphere products, necessary in many applications such as navigation, are growing very fast. Consequently, many analysis centers accepted the responsibility of generating such products. In this regard, the primary objective of our work is to develop a near real-time processing framework for the estimation of the vertical total electron content (VTEC) of the ionosphere using proper models that are capable of a global representation adapted to the real data distribution. The global VTEC representation developed in this work is based on a series expansion in terms of compactly supported B-spline functions, which allow for an appropriate handling of the heterogeneous data distribution, including data gaps. The corresponding series coefficients and additional parameters such as differential code biases of the GNSS satellites and receivers constitute the set of unknown parameters. The Kalman filter (KF), as a popular recursive estimator, allows processing of the data immediately after acquisition and paves the way of sequential (near) real-time estimation of the unknown parameters. To exploit the advantages of the chosen data representation and the estimation procedure, the B-spline model is incorporated into the KF under the consideration of necessary constraints. Based on a preprocessing strategy, the developed approach utilizes hourly batches of GPS and GLONASS observations provided by the IGS data centers with a latency of 1 h in its current realization. Two methods for validation of the results are performed, namely the self consistency analysis and a comparison with Jason-2 altimetry data. The highly promising validation results allow the conclusion that under the investigated conditions our derived near real-time product is of the same accuracy level as the so-called final post-processed products provided by the IGS with a latency of several days or even weeks.

Characterizing the SWOT discharge error budget on the Sacramento River, CA

NASA Astrophysics Data System (ADS)

Yoon, Y.; Durand, M. T.; Minear, J. T.; Smith, L.; Merry, C. J.

2013-12-01

The Surface Water and Ocean Topography (SWOT) is an upcoming satellite mission (2020 year) that will provide surface-water elevation and surface-water extent globally. One goal of SWOT is the estimation of river discharge directly from SWOT measurements. SWOT discharge uncertainty is due to two sources. First, SWOT cannot measure channel bathymetry and determine roughness coefficient data necessary for discharge calculations directly; these parameters must be estimated from the measurements or from a priori information. Second, SWOT measurement errors directly impact the discharge estimate accuracy. This study focuses on characterizing parameter and measurement uncertainties for SWOT river discharge estimation. A Bayesian Markov Chain Monte Carlo scheme is used to calculate parameter estimates, given the measurements of river height, slope and width, and mass and momentum constraints. The algorithm is evaluated using simulated both SWOT and AirSWOT (the airborne version of SWOT) observations over seven reaches (about 40 km) of the Sacramento River. The SWOT and AirSWOT observations are simulated by corrupting the ';true' HEC-RAS hydraulic modeling results with the instrument error. This experiment answers how unknown bathymetry and roughness coefficients affect the accuracy of the river discharge algorithm. From the experiment, the discharge error budget is almost completely dominated by unknown bathymetry and roughness; 81% of the variance error is explained by uncertainties in bathymetry and roughness. Second, we show how the errors in water surface, slope, and width observations influence the accuracy of discharge estimates. Indeed, there is a significant sensitivity to water surface, slope, and width errors due to the sensitivity of bathymetry and roughness to measurement errors. Increasing water-surface error above 10 cm leads to a corresponding sharper increase of errors in bathymetry and roughness. Increasing slope error above 1.5 cm/km leads to a significant degradation due to direct error in the discharge estimates. As the width error increases past 20%, the discharge error budget is dominated by the width error. Above two experiments are performed based on AirSWOT scenarios. In addition, we explore the sensitivity of the algorithm to the SWOT scenarios.

Improved mapping of radio sources from VLBI data by least-square fit

NASA Technical Reports Server (NTRS)

Rodemich, E. R.

1985-01-01

A method is described for producing improved mapping of radio sources from Very Long Base Interferometry (VLBI) data. The method described is more direct than existing Fourier methods, is often more accurate, and runs at least as fast. The visibility data is modeled here, as in existing methods, as a function of the unknown brightness distribution and the unknown antenna gains and phases. These unknowns are chosen so that the resulting function values are as near as possible to the observed values. If researchers use the radio mapping source deviation to measure the closeness of this fit to the observed values, they are led to the problem of minimizing a certain function of all the unknown parameters. This minimization problem cannot be solved directly, but it can be attacked by iterative methods which we show converge automatically to the minimum with no user intervention. The resulting brightness distribution will furnish the best fit to the data among all brightness distributions of given resolution.

Adaptive Neural Networks Prescribed Performance Control Design for Switched Interconnected Uncertain Nonlinear Systems.

PubMed

Li, Yongming; Tong, Shaocheng

2017-06-28

In this paper, an adaptive neural networks (NNs)-based decentralized control scheme with the prescribed performance is proposed for uncertain switched nonstrict-feedback interconnected nonlinear systems. It is assumed that nonlinear interconnected terms and nonlinear functions of the concerned systems are unknown, and also the switching signals are unknown and arbitrary. A linear state estimator is constructed to solve the problem of unmeasured states. The NNs are employed to approximate unknown interconnected terms and nonlinear functions. A new output feedback decentralized control scheme is developed by using the adaptive backstepping design technique. The control design problem of nonlinear interconnected switched systems with unknown switching signals can be solved by the proposed scheme, and only a tuning parameter is needed for each subsystem. The proposed scheme can ensure that all variables of the control systems are semi-globally uniformly ultimately bounded and the tracking errors converge to a small residual set with the prescribed performance bound. The effectiveness of the proposed control approach is verified by some simulation results.

On the abundance of extraterrestrial life after the Kepler mission

NASA Astrophysics Data System (ADS)

Wandel, Amri

2015-07-01

The data recently accumulated by the Kepler mission have demonstrated that small planets are quite common and that a significant fraction of all stars may have an Earth-like planet within their habitable zone. These results are combined with a Drake-equation formalism to derive the space density of biotic planets as a function of the relatively modest uncertainty in the astronomical data and of the (yet unknown) probability for the evolution of biotic life, F b. I suggest that F b may be estimated by future spectral observations of exoplanet biomarkers. If F b is in the range 0.001-1, then a biotic planet may be expected within 10-100 light years from Earth. Extending the biotic results to advanced life I derive expressions for the distance to putative civilizations in terms of two additional Drake parameters - the probability for evolution of a civilization, F c, and its average longevity. For instance, assuming optimistic probability values (F b~F c~1) and a broadcasting longevity of a few thousand years, the likely distance to the nearest civilizations detectable by searching for intelligent electromagnetic signals is of the order of a few thousand light years. The probability of detecting intelligent signals with present and future radio telescopes is calculated as a function of the Drake parameters. Finally, I describe how the detection of intelligent signals would constrain the Drake parameters.

The Inverse Problem for Confined Aquifer Flow: Identification and Estimation With Extensions

NASA Astrophysics Data System (ADS)

Loaiciga, Hugo A.; MariñO, Miguel A.

1987-01-01

The contributions of this work are twofold. First, a methodology for estimating the elements of parameter matrices in the governing equation of flow in a confined aquifer is developed. The estimation techniques for the distributed-parameter inverse problem pertain to linear least squares and generalized least squares methods. The linear relationship among the known heads and unknown parameters of the flow equation provides the background for developing criteria for determining the identifiability status of unknown parameters. Under conditions of exact or overidentification it is possible to develop statistically consistent parameter estimators and their asymptotic distributions. The estimation techniques, namely, two-stage least squares and three stage least squares, are applied to a specific groundwater inverse problem and compared between themselves and with an ordinary least squares estimator. The three-stage estimator provides the closer approximation to the actual parameter values, but it also shows relatively large standard errors as compared to the ordinary and two-stage estimators. The estimation techniques provide the parameter matrices required to simulate the unsteady groundwater flow equation. Second, a nonlinear maximum likelihood estimation approach to the inverse problem is presented. The statistical properties of maximum likelihood estimators are derived, and a procedure to construct confidence intervals and do hypothesis testing is given. The relative merits of the linear and maximum likelihood estimators are analyzed. Other topics relevant to the identification and estimation methodologies, i.e., a continuous-time solution to the flow equation, coping with noise-corrupted head measurements, and extension of the developed theory to nonlinear cases are also discussed. A simulation study is used to evaluate the methods developed in this study.

Additive genetic variance and developmental plasticity in growth trajectories in a wild cooperative mammal.

PubMed

Huchard, E; Charmantier, A; English, S; Bateman, A; Nielsen, J F; Clutton-Brock, T

2014-09-01

Individual variation in growth is high in cooperative breeders and may reflect plastic divergence in developmental trajectories leading to breeding vs. helping phenotypes. However, the relative importance of additive genetic variance and developmental plasticity in shaping growth trajectories is largely unknown in cooperative vertebrates. This study exploits weekly sequences of body mass from birth to adulthood to investigate sources of variance in, and covariance between, early and later growth in wild meerkats (Suricata suricatta), a cooperative mongoose. Our results indicate that (i) the correlation between early growth (prior to nutritional independence) and adult mass is positive but weak, and there are frequent changes (compensatory growth) in post-independence growth trajectories; (ii) among parameters describing growth trajectories, those describing growth rate (prior to and at nutritional independence) show undetectable heritability while associated size parameters (mass at nutritional independence and asymptotic mass) are moderately heritable (0.09 ≤ h(2) < 0.3); and (iii) additive genetic effects, rather than early environmental effects, mediate the covariance between early growth and adult mass. These results reveal that meerkat growth trajectories remain plastic throughout development, rather than showing early and irreversible divergence, and that the weak effects of early growth on adult mass, an important determinant of breeding success, are partly genetic. In contrast to most cooperative invertebrates, the acquisition of breeding status is often determined after sexual maturity and strongly impacted by chance in many cooperative vertebrates, who may therefore retain the ability to adjust their morphology to environmental changes and social opportunities arising throughout their development, rather than specializing early. © 2014 The Authors. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Quantification of type I error probabilities for heterogeneity LOD scores.

PubMed

Abreu, Paula C; Hodge, Susan E; Greenberg, David A

2002-02-01

Locus heterogeneity is a major confounding factor in linkage analysis. When no prior knowledge of linkage exists, and one aims to detect linkage and heterogeneity simultaneously, classical distribution theory of log-likelihood ratios does not hold. Despite some theoretical work on this problem, no generally accepted practical guidelines exist. Nor has anyone rigorously examined the combined effect of testing for linkage and heterogeneity and simultaneously maximizing over two genetic models (dominant, recessive). The effect of linkage phase represents another uninvestigated issue. Using computer simulation, we investigated type I error (P value) of the "admixture" heterogeneity LOD (HLOD) score, i.e., the LOD score maximized over both recombination fraction theta and admixture parameter alpha and we compared this with the P values when one maximizes only with respect to theta (i.e., the standard LOD score). We generated datasets of phase-known and -unknown nuclear families, sizes k = 2, 4, and 6 children, under fully penetrant autosomal dominant inheritance. We analyzed these datasets (1) assuming a single genetic model, and maximizing the HLOD over theta and alpha; and (2) maximizing the HLOD additionally over two dominance models (dominant vs. recessive), then subtracting a 0.3 correction. For both (1) and (2), P values increased with family size k; rose less for phase-unknown families than for phase-known ones, with the former approaching the latter as k increased; and did not exceed the one-sided mixture distribution xi = (1/2) chi1(2) + (1/2) chi2(2). Thus, maximizing the HLOD over theta and alpha appears to add considerably less than an additional degree of freedom to the associated chi1(2) distribution. We conclude with practical guidelines for linkage investigators. Copyright 2002 Wiley-Liss, Inc.

Complex Dynamics in a Triopoly Game with Multiple Delays in the Competition of Green Product Level

NASA Astrophysics Data System (ADS)

Si, Fengshan; Ma, Junhai

Research on the output game behavior of oligopoly has greatly advanced in recent years. But many unknowns remain, particularly the influence of consumers’ willingness to buy green products on the oligopoly output game. This paper constructs a triopoly output game model with multiple delays in the competition of green products. The influence of the parameters on the stability and complexity of the system is studied by analyzing the existence and local asymptotic stability of the equilibrium point. It is found that the system loses stability and increases complexity if delay parameters exceed a certain range. In the unstable or chaotic game market, the decisions of oligopoly will be counterproductive. It is also observed that the influence of weight and output adjustment speed on the firm itself is obviously stronger than the influence of other firms. In addition, it is important that weight and output adjustment speed cannot increase indefinitely, otherwise it will bring unnecessary losses to the firm. Finally, chaos control is realized by using the variable feedback control method. The research results of this paper can provide a reference for decision-making for the output of the game of oligopoly.

Coupling Molecular Modeling to the Traditional "IR-ID" Exercise in the Introductory Organic Chemistry Laboratory

ERIC Educational Resources Information Center

Stokes-Huby, Heather; Vitale, Dale E.

2007-01-01

This exercise integrates the infrared unknown identification ("IR-ID") experiment common to most organic laboratory syllabi with computer molecular modeling. In this modification students are still required to identify unknown compounds from their IR spectra, but must additionally match some of the absorptions with computed frequencies they…

Two phase formation of massive elliptical galaxies: study through cross-correlation including spatial effect

NASA Astrophysics Data System (ADS)

Modak, Soumita; Chattopadhyay, Tanuka; Chattopadhyay, Asis Kumar

2017-11-01

Area of study is the formation mechanism of the present-day population of elliptical galaxies, in the context of hierarchical cosmological models accompanied by accretion and minor mergers. The present work investigates the formation and evolution of several components of the nearby massive early-type galaxies (ETGs) through cross-correlation function (CCF), using the spatial parameters right ascension (RA) and declination (DEC), and the intrinsic parameters mass (M_{*}) and size. According to the astrophysical terminology, here these variables, namely mass, size, RA and DEC are termed as parameters, whereas the unknown constants involved in the kernel function are called hyperparameters. Throughout this paper, the parameter size is used to represent the effective radius (Re). Following Huang et al. (2013a), each nearby ETG is divided into three parts on the basis of its Re value. We study the CCF between each of these three components of nearby massive ETGs and the ETGs in the high redshift range, 0.5< z≤ 2.7. It is found that the innermost components of nearby ETGs are highly correlated with ETGs in the redshift range, 2< z≤ 2.7, known as `red nuggets'. The intermediate and the outermost parts have moderate correlations with ETGs in the redshift range, 0.5< z≤ 0.75. The quantitative measures are highly consistent with the two phase formation scenario of nearby massive ETGs, as suggested by various authors, and resolve the conflict raised in a previous work (De et al. 2014) suggesting other possibilities for the formation of the outermost part. A probable cause of this improvement is the inclusion of the spatial effects in addition to the other parameters in the study.

A Bayesian Retrieval of Greenland Ice Sheet Internal Temperature from Ultra-wideband Software-defined Microwave Radiometer (UWBRAD) Measurements

NASA Astrophysics Data System (ADS)

Duan, Y.; Durand, M. T.; Jezek, K. C.; Yardim, C.; Bringer, A.; Aksoy, M.; Johnson, J. T.

2017-12-01

The ultra-wideband software-defined microwave radiometer (UWBRAD) is designed to provide ice sheet internal temperature product via measuring low frequency microwave emission. Twelve channels ranging from 0.5 to 2.0 GHz are covered by the instrument. A Greenland air-borne demonstration was demonstrated in September 2016, provided first demonstration of Ultra-wideband radiometer observations of geophysical scenes, including ice sheets. Another flight is planned for September 2017 for acquiring measurements in central ice sheet. A Bayesian framework is designed to retrieve the ice sheet internal temperature from simulated UWBRAD brightness temperature (Tb) measurements over Greenland flight path with limited prior information of the ground. A 1-D heat-flow model, the Robin Model, was used to model the ice sheet internal temperature profile with ground information. Synthetic UWBRAD Tb observations was generated via the partially coherent radiation transfer model, which utilizes the Robin model temperature profile and an exponential fit of ice density from Borehole measurement as input, and corrupted with noise. The effective surface temperature, geothermal heat flux, the variance of upper layer ice density, and the variance of fine scale density variation at deeper ice sheet were treated as unknown variables within the retrieval framework. Each parameter is defined with its possible range and set to be uniformly distributed. The Markov Chain Monte Carlo (MCMC) approach is applied to make the unknown parameters randomly walk in the parameter space. We investigate whether the variables can be improved over priors using the MCMC approach and contribute to the temperature retrieval theoretically. UWBRAD measurements near camp century from 2016 was also treated with the MCMC to examine the framework with scattering effect. The fine scale density fluctuation is an important parameter. It is the most sensitive yet highly unknown parameter in the estimation framework. Including the fine scale density fluctuation greatly improved the retrieval results. The ice sheet vertical temperature profile, especially the 10m temperature, can be well retrieved via the MCMC process. Future retrieval work will apply the Bayesian approach to UWBRAD airborne measurements.

Quantification of the impact of precipitation spatial distribution uncertainty on predictive uncertainty of a snowmelt runoff model

NASA Astrophysics Data System (ADS)

Jacquin, A. P.

2012-04-01

This study is intended to quantify the impact of uncertainty about precipitation spatial distribution on predictive uncertainty of a snowmelt runoff model. This problem is especially relevant in mountain catchments with a sparse precipitation observation network and relative short precipitation records. The model analysed is a conceptual watershed model operating at a monthly time step. The model divides the catchment into five elevation zones, where the fifth zone corresponds to the catchment's glaciers. Precipitation amounts at each elevation zone i are estimated as the product between observed precipitation at a station and a precipitation factor FPi. If other precipitation data are not available, these precipitation factors must be adjusted during the calibration process and are thus seen as parameters of the model. In the case of the fifth zone, glaciers are seen as an inexhaustible source of water that melts when the snow cover is depleted.The catchment case study is Aconcagua River at Chacabuquito, located in the Andean region of Central Chile. The model's predictive uncertainty is measured in terms of the output variance of the mean squared error of the Box-Cox transformed discharge, the relative volumetric error, and the weighted average of snow water equivalent in the elevation zones at the end of the simulation period. Sobol's variance decomposition (SVD) method is used for assessing the impact of precipitation spatial distribution, represented by the precipitation factors FPi, on the models' predictive uncertainty. In the SVD method, the first order effect of a parameter (or group of parameters) indicates the fraction of predictive uncertainty that could be reduced if the true value of this parameter (or group) was known. Similarly, the total effect of a parameter (or group) measures the fraction of predictive uncertainty that would remain if the true value of this parameter (or group) was unknown, but all the remaining model parameters could be fixed. In this study, first order and total effects of the group of precipitation factors FP1- FP4, and the precipitation factor FP5, are calculated separately. First order and total effects of the group FP1- FP4 are much higher than first order and total effects of the factor FP5, which are negligible This situation is due to the fact that the actual value taken by FP5 does not have much influence in the contribution of the glacier zone to the catchment's output discharge, mainly limited by incident solar radiation. In addition to this, first order effects indicate that, in average, nearly 25% of predictive uncertainty could be reduced if the true values of the precipitation factors FPi could be known, but no information was available on the appropriate values for the remaining model parameters. Finally, the total effects of the precipitation factors FP1- FP4 are close to 41% in average, implying that even if the appropriate values for the remaining model parameters could be fixed, predictive uncertainty would be still quite high if the spatial distribution of precipitation remains unknown. Acknowledgements: This research was funded by FONDECYT, Research Project 1110279.

ESTIMATION OF CHEMICAL SPECIFIC PARAMETERS WITHIN PHYSIOLOGICALLY BASED PHARMACOKINETIC/PHARMACODYNAMIC MODELS

EPA Science Inventory

While relationships between chemical structure and observed properties or activities (QSAR - quantitative structure activity relationship) can be used to predict the behavior of unknown chemicals, this method is semiempirical in nature relying on high quality experimental data to...

Optimal critic learning for robot control in time-varying environments.

PubMed

Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng

2015-10-01

In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.

Quantum Hamiltonian identification from measurement time traces.

PubMed

Zhang, Jun; Sarovar, Mohan

2014-08-22

Precise identification of parameters governing quantum processes is a critical task for quantum information and communication technologies. In this Letter, we consider a setting where system evolution is determined by a parametrized Hamiltonian, and the task is to estimate these parameters from temporal records of a restricted set of system observables (time traces). Based on the notion of system realization from linear systems theory, we develop a constructive algorithm that provides estimates of the unknown parameters directly from these time traces. We illustrate the algorithm and its robustness to measurement noise by applying it to a one-dimensional spin chain model with variable couplings.

Bayesian source tracking via focalization and marginalization in an uncertain Mediterranean Sea environment.

PubMed

Dosso, Stan E; Wilmut, Michael J; Nielsen, Peter L

2010-07-01

This paper applies Bayesian source tracking in an uncertain environment to Mediterranean Sea data, and investigates the resulting tracks and track uncertainties as a function of data information content (number of data time-segments, number of frequencies, and signal-to-noise ratio) and of prior information (environmental uncertainties and source-velocity constraints). To track low-level sources, acoustic data recorded for multiple time segments (corresponding to multiple source positions along the track) are inverted simultaneously. Environmental uncertainty is addressed by including unknown water-column and seabed properties as nuisance parameters in an augmented inversion. Two approaches are considered: Focalization-tracking maximizes the posterior probability density (PPD) over the unknown source and environmental parameters. Marginalization-tracking integrates the PPD over environmental parameters to obtain a sequence of joint marginal probability distributions over source coordinates, from which the most-probable track and track uncertainties can be extracted. Both approaches apply track constraints on the maximum allowable vertical and radial source velocity. The two approaches are applied for towed-source acoustic data recorded at a vertical line array at a shallow-water test site in the Mediterranean Sea where previous geoacoustic studies have been carried out.

Scattering of electromagnetic plane wave from a perfect electric conducting strip placed at interface of topological insulator-chiral medium

NASA Astrophysics Data System (ADS)

Shoukat, Sobia; Naqvi, Qaisar A.

2016-12-01

In this manuscript, scattering from a perfect electric conducting strip located at planar interface of topological insulator (TI)-chiral medium is investigated using the Kobayashi Potential method. Longitudinal components of electric and magnetic vector potential in terms of unknown weighting function are considered. Use of related set of boundary conditions yields two algebraic equations and four dual integral equations (DIEs). Integrand of two DIEs are expanded in terms of the characteristic functions with expansion coefficients which must satisfy, simultaneously, the discontinuous property of the Weber-Schafheitlin integrals, required edge and boundary conditions. The resulting expressions are then combined with algebraic equations to express the weighting function in terms of expansion coefficients, these expansion coefficients are then substituted in remaining DIEs. The projection is applied using the Jacobi polynomials. This treatment yields matrix equation for expansion coefficients which is solved numerically. These unknown expansion coefficients are used to find the scattered field. The far zone scattering width is investigated with respect to different parameters of the geometry, i.e, chirality of chiral medium, angle of incidence, size of the strip. Significant effects of different parameters including TI parameter on the scattering width are noted.

Adaptive control of 5 DOF upper-limb exoskeleton robot with improved safety.

PubMed

Kang, Hao-Bo; Wang, Jian-Hui

2013-11-01

This paper studies an adaptive control strategy for a class of 5 DOF upper-limb exoskeleton robot with a special safety consideration. The safety requirement plays a critical role in the clinical treatment when assisting patients with shoulder, elbow and wrist joint movements. With the objective of assuring the tracking performance of the pre-specified operations, the proposed adaptive controller is firstly designed to be robust to the model uncertainties. To further improve the safety and fault-tolerance in the presence of unknown large parameter variances or even actuator faults, the adaptive controller is on-line updated according to the information provided by an adaptive observer without additional sensors. An output tracking performance is well achieved with a tunable error bound. The experimental example also verifies the effectiveness of the proposed control scheme. © 2013 ISA. Published by ISA. All rights reserved.

Effects of Cr 3+ impurity concentration on the crystallography of synthetic emerald crystals

NASA Astrophysics Data System (ADS)

Lee, Pei-Lun; Huang, Eugene; Lee, Jan-Shing; Yu, Shu-Cheng

2011-06-01

Flux method has been adopted for the synthesis of emerald crystals using PbO-V 2O 5 as a flux in order to study the crystallography of the synthetic crystals. In general, the hue of green color of emerald deepens with the addition of Cr 3+. The molar volume of the synthesized crystals was found to increase with the incorporation of Cr 2O 3 dopant. The substitution of Cr 3+ for Al 3+ in the octahedral sites of beryl results in the expansion of a-axis, while c-axis remains nearly unchanged. The maximum Cr 2O 3-content allowed in the crystal lattice of emerald has been found to be about 3.5 wt%. When the doping Cr 2O 3-content exceeds 3.5 wt%, a significant anomaly in lattice parameters starts to take place, accompanying the precipitation of an unknown phase in the emerald matrix.

Extracting Damping Ratio from Dynamic Data and Numerical Solutions

NASA Technical Reports Server (NTRS)

Casiano, M. J.

2016-01-01

There are many ways to extract damping parameters from data or models. This Technical Memorandum provides a quick reference for some of the more common approaches used in dynamics analysis. Described are six methods of extracting damping from data: the half-power method, logarithmic decrement (decay rate) method, an autocorrelation/power spectral density fitting method, a frequency response fitting method, a random decrement fitting method, and a newly developed half-quadratic gain method. Additionally, state-space models and finite element method modeling tools, such as COMSOL Multiphysics (COMSOL), provide a theoretical damping via complex frequency. Each method has its advantages which are briefly noted. There are also likely many other advanced techniques in extracting damping within the operational modal analysis discipline, where an input excitation is unknown; however, these approaches discussed here are objective, direct, and can be implemented in a consistent manner.

Accelerator-based Neutrino Physics at Fermilab

NASA Astrophysics Data System (ADS)

Dukes, Edmond

2008-10-01

The discovery of neutrino mass has excited great interest in elucidating the properties of neutrinos and their role in nature. Experiments around the world take advantage of solar, atmospheric, reactor, and accelerator sources of neutrinos. Accelerator-based sources are particularly convenient since their parameters can be tuned to optimize the measurement in question. At Fermilab an extensive neutrino program includes the MiniBooNE, SciBooNE, and MINOS experiments. Two major new experiments, MINERvA and NOvA, are being constructed, plans for a high-intensity neutrino source to DUSEL are underway, and an R&D effort towards a large liquid argon detector is being pursued. The NOvA experiment intends to search for electron neutrino appearance using a massive surface detector 811 km from Fermilab. In addition to measuring the last unknown mixing angle, theta(13), NOvA has the possibility of seeing matter-antimatter asymmetries in neutrinos and resolving the ordering of the neutrino mass states.

Dynamic Infinite Mixed-Membership Stochastic Blockmodel.

PubMed

Fan, Xuhui; Cao, Longbing; Xu, Richard Yi Da

2015-09-01

Directional and pairwise measurements are often used to model interactions in a social network setting. The mixed-membership stochastic blockmodel (MMSB) was a seminal work in this area, and its ability has been extended. However, models such as MMSB face particular challenges in modeling dynamic networks, for example, with the unknown number of communities. Accordingly, this paper proposes a dynamic infinite mixed-membership stochastic blockmodel, a generalized framework that extends the existing work to potentially infinite communities inside a network in dynamic settings (i.e., networks are observed over time). Additional model parameters are introduced to reflect the degree of persistence among one's memberships at consecutive time stamps. Under this framework, two specific models, namely mixture time variant and mixture time invariant models, are proposed to depict two different time correlation structures. Two effective posterior sampling strategies and their results are presented, respectively, using synthetic and real-world data.

A novel sensitivity-based method for damage detection of structures under unknown periodic excitations

NASA Astrophysics Data System (ADS)

Naseralavi, S. S.; Salajegheh, E.; Fadaee, M. J.; Salajegheh, J.

2014-06-01

This paper presents a technique for damage detection in structures under unknown periodic excitations using the transient displacement response. The method is capable of identifying the damage parameters without finding the input excitations. We first define the concept of displacement space as a linear space in which each point represents displacements of structure under an excitation and initial condition. Roughly speaking, the method is based on the fact that structural displacements under free and forced vibrations are associated with two parallel subspaces in the displacement space. Considering this novel geometrical viewpoint, an equation called kernel parallelization equation (KPE) is derived for damage detection under unknown periodic excitations and a sensitivity-based algorithm for solving KPE is proposed accordingly. The method is evaluated via three case studies under periodic excitations, which confirm the efficiency of the proposed method.

Object-Image Correspondence for Algebraic Curves under Projections

NASA Astrophysics Data System (ADS)

Burdis, Joseph M.; Kogan, Irina A.; Hong, Hoon

2013-03-01

We present a novel algorithm for deciding whether a given planar curve is an image of a given spatial curve, obtained by a central or a parallel projection with unknown parameters. The motivation comes from the problem of establishing a correspondence between an object and an image, taken by a camera with unknown position and parameters. A straightforward approach to this problem consists of setting up a system of conditions on the projection parameters and then checking whether or not this system has a solution. The computational advantage of the algorithm presented here, in comparison to algorithms based on the straightforward approach, lies in a significant reduction of a number of real parameters that need to be eliminated in order to establish existence or non-existence of a projection that maps a given spatial curve to a given planar curve. Our algorithm is based on projection criteria that reduce the projection problem to a certain modification of the equivalence p! roblem of planar curves under affine and projective transformations. To solve the latter problem we make an algebraic adaptation of signature construction that has been used to solve the equivalence problems for smooth curves. We introduce a notion of a classifying set of rational differential invariants and produce explicit formulas for such invariants for the actions of the projective and the affine groups on the plane.

Parameter estimation for stiff deterministic dynamical systems via ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Arnold, Andrea; Calvetti, Daniela; Somersalo, Erkki

2014-10-01

A commonly encountered problem in numerous areas of applications is to estimate the unknown coefficients of a dynamical system from direct or indirect observations at discrete times of some of the components of the state vector. A related problem is to estimate unobserved components of the state. An egregious example of such a problem is provided by metabolic models, in which the numerous model parameters and the concentrations of the metabolites in tissue are to be estimated from concentration data in the blood. A popular method for addressing similar questions in stochastic and turbulent dynamics is the ensemble Kalman filter (EnKF), a particle-based filtering method that generalizes classical Kalman filtering. In this work, we adapt the EnKF algorithm for deterministic systems in which the numerical approximation error is interpreted as a stochastic drift with variance based on classical error estimates of numerical integrators. This approach, which is particularly suitable for stiff systems where the stiffness may depend on the parameters, allows us to effectively exploit the parallel nature of particle methods. Moreover, we demonstrate how spatial prior information about the state vector, which helps the stability of the computed solution, can be incorporated into the filter. The viability of the approach is shown by computed examples, including a metabolic system modeling an ischemic episode in skeletal muscle, with a high number of unknown parameters.

Investigation on Insar Time Series Deformation Model Considering Rheological Parameters for Soft Clay Subgrade Monitoring

NASA Astrophysics Data System (ADS)

Xing, X.; Yuan, Z.; Chen, L. F.; Yu, X. Y.; Xiao, L.

2018-04-01

The stability control is one of the major technical difficulties in the field of highway subgrade construction engineering. Building deformation model is a crucial step for InSAR time series deformation monitoring. Most of the InSAR deformation models for deformation monitoring are pure empirical mathematical models, without considering the physical mechanism of the monitored object. In this study, we take rheology into consideration, inducing rheological parameters into traditional InSAR deformation models. To assess the feasibility and accuracy for our new model, both simulation and real deformation data over Lungui highway (a typical highway built on soft clay subgrade in Guangdong province, China) are investigated with TerraSAR-X satellite imagery. In order to solve the unknows of the non-linear rheological model, three algorithms: Gauss-Newton (GN), Levenberg-Marquarat (LM), and Genetic Algorithm (GA), are utilized and compared to estimate the unknown parameters. Considering both the calculation efficiency and accuracy, GA is chosen as the final choice for the new model in our case study. Preliminary real data experiment is conducted with use of 17 TerraSAR-X Stripmap images (with a 3-m resolution). With the new deformation model and GA aforementioned, the unknown rheological parameters over all the high coherence points are obtained and the LOS deformation (the low-pass component) sequences are generated.

Measurement of additional shear during sludge conditioning and dewatering.

PubMed

Ormeci, Banu; Ahmad, Ayaz

2009-07-01

Optimum polymer dose is influenced both by the polymer demand of the sludge and the shear applied during conditioning. Sludge exposed to additional shear following conditioning will experience a decrease in cake solids concentration for the same polymer dose. Therefore, it is necessary to measure or quantify the additional shear in order to optimize the conditioning and dewatering. There is currently no direct or indirect method to achieve this. The main objective of this study was to develop a method based on torque rheology to measure the amount of shear that a sludge network experiences during conditioning and dewatering. Anaerobically digested sludge samples were exposed to increasing levels of mixing intensities and times, and rheological characteristics of samples were measured using a torque rheometer. Several rheological parameters were evaluated including the peak torque and totalized torque (area under the rheograms). The results of this study show that at the optimum polymer dose, a linear relationship exists between the applied shear and the area under the rheograms, and this relationship can be used to estimate an unknown amount of shear that the sludge was exposed to. The method is useful as a research tool to study the effect of shear on dewatering but also as an optimization tool in a dewatering automation system based on torque rheology.

FRF decoupling of nonlinear systems

NASA Astrophysics Data System (ADS)

Kalaycıoğlu, Taner; Özgüven, H. Nevzat

2018-03-01

Structural decoupling problem, i.e. predicting dynamic behavior of a particular substructure from the knowledge of the dynamics of the coupled structure and the other substructure, has been well investigated for three decades and led to several decoupling methods. In spite of the inherent nonlinearities in a structural system in various forms such as clearances, friction and nonlinear stiffness, all decoupling studies are for linear systems. In this study, decoupling problem for nonlinear systems is addressed for the first time. A method, named as FRF Decoupling Method for Nonlinear Systems (FDM-NS), is proposed for calculating FRFs of a substructure decoupled from a coupled nonlinear structure where nonlinearity can be modeled as a single nonlinear element. Depending on where nonlinear element is, i.e., either in the known or unknown subsystem, or at the connection point, the formulation differs. The method requires relative displacement information between two end points of the nonlinear element, in addition to point and transfer FRFs at some points of the known subsystem. However, it is not necessary to excite the system from the unknown subsystem even when the nonlinear element is in that subsystem. The validation of FDM-NS is demonstrated with two different case studies using nonlinear lumped parameter systems. Finally, a nonlinear experimental test structure is used in order to show the real-life application and accuracy of FDM-NS.

28. Photocopy of photograph dated ca. 1940; photographer unknown; original ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

28. Photocopy of photograph dated ca. 1940; photographer unknown; original filed as MR2.9/SP8/r373 in Audio-Visual Collection of Minnesota Historical Society, St. Paul; WEST SIDE AFTER SECOND-STORY ADDITION; LOOKING EAST - Northwest Airways Hangar & Administration Building, 590 Bayfield Street, St. Paul Downtown Airport (Holman), Saint Paul, Ramsey County, MN

EFFICIENT MODEL-FITTING AND MODEL-COMPARISON FOR HIGH-DIMENSIONAL BAYESIAN GEOSTATISTICAL MODELS. (R826887)

EPA Science Inventory

Geostatistical models are appropriate for spatially distributed data measured at irregularly spaced locations. We propose an efficient Markov chain Monte Carlo (MCMC) algorithm for fitting Bayesian geostatistical models with substantial numbers of unknown parameters to sizable...

Bootstrap Methods: A Very Leisurely Look.

ERIC Educational Resources Information Center

Hinkle, Dennis E.; Winstead, Wayland H.

The Bootstrap method, a computer-intensive statistical method of estimation, is illustrated using a simple and efficient Statistical Analysis System (SAS) routine. The utility of the method for generating unknown parameters, including standard errors for simple statistics, regression coefficients, discriminant function coefficients, and factor…

Gaps along the HIV care continuum: findings among a population seeking sexual health care services in New York City.

PubMed

Pathela, Preeti; Jamison, Kelly; Braunstein, Sarah L; Schillinger, Julia A; Tymejczyk, Olga; Nash, Denis

2018-03-02

Linkage/re-linkage to HIV care for virally unsuppressed persons with new sexually transmitted infections is critical for ending the HIV epidemic. We quantified HIV care continuum gaps, and viral suppression, among HIV-positive patients attending New York City (NYC) sexual health clinics (SHC). 1,649 HIV-positive patients and a 10% sample of 11,954 patients with unknown HIV status on clinic visit date (DOV) were matched against the NYC HIV registry. Using registry diagnosis dates, we categorized matched HIV-positive patients as "new-positives" (newly diagnosed on DOV), "recent-positives (diagnosed ≤90 days before DOV), "prevalent-positives" (diagnosed >90 days before DOV), and "unknown-positives" (previously diagnosed, but status unknown to clinic on DOV). We assessed HIV care continuum outcomes before and after DOV for new-positives, prevalent-positives, and unknown-positives using registry laboratory data. In addition to 1,626 known HIV-positive patients, 5% of the unknown sample (63/1,196) matched to the registry, signifying that about 630 additional HIV-positive patients attended SHCs. Of new-positives, 65% were linked to care after DOV. Of prevalent-positives, 66% were in care on DOV; 43% of the out-of-care were re-linked after DOV. Of unknown-positives, 40% were in care on DOV; 21% of the out-of-care re-linked after DOV. Viral suppression was achieved by: 88% of in-care unknown-positives, 76% in-care prevalent-positives, 50% new-positives, 42% out-of-care prevalent-positives, and 16% out-of-care unknown-positives. Many HIV-positive persons, including those with uncontrolled HIV infection, attend SHCs and potentially contribute to HIV spread. However, HIV status often is not known to staff, resulting in missed linkage/re-linkage to care opportunities. Better outcomes could be facilitated by real-time ascertainment of HIV status and HIV care status.

Indirect adaptive fuzzy fault-tolerant tracking control for MIMO nonlinear systems with actuator and sensor failures.

PubMed

Bounemeur, Abdelhamid; Chemachema, Mohamed; Essounbouli, Najib

2018-05-10

In this paper, an active fuzzy fault tolerant tracking control (AFFTTC) scheme is developed for a class of multi-input multi-output (MIMO) unknown nonlinear systems in the presence of unknown actuator faults, sensor failures and external disturbance. The developed control scheme deals with four kinds of faults for both sensors and actuators. The bias, drift, and loss of accuracy additive faults are considered along with the loss of effectiveness multiplicative fault. A fuzzy adaptive controller based on back-stepping design is developed to deal with actuator failures and unknown system dynamics. However, an additional robust control term is added to deal with sensor faults, approximation errors, and external disturbances. Lyapunov theory is used to prove the stability of the closed loop system. Numerical simulations on a quadrotor are presented to show the effectiveness of the proposed approach. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

MoCha: Molecular Characterization of Unknown Pathways.

PubMed

Lobo, Daniel; Hammelman, Jennifer; Levin, Michael

2016-04-01

Automated methods for the reverse-engineering of complex regulatory networks are paving the way for the inference of mechanistic comprehensive models directly from experimental data. These novel methods can infer not only the relations and parameters of the known molecules defined in their input datasets, but also unknown components and pathways identified as necessary by the automated algorithms. Identifying the molecular nature of these unknown components is a crucial step for making testable predictions and experimentally validating the models, yet no specific and efficient tools exist to aid in this process. To this end, we present here MoCha (Molecular Characterization), a tool optimized for the search of unknown proteins and their pathways from a given set of known interacting proteins. MoCha uses the comprehensive dataset of protein-protein interactions provided by the STRING database, which currently includes more than a billion interactions from over 2,000 organisms. MoCha is highly optimized, performing typical searches within seconds. We demonstrate the use of MoCha with the characterization of unknown components from reverse-engineered models from the literature. MoCha is useful for working on network models by hand or as a downstream step of a model inference engine workflow and represents a valuable and efficient tool for the characterization of unknown pathways using known data from thousands of organisms. MoCha and its source code are freely available online under the GPLv3 license.

Geometrically constrained kinematic global navigation satellite systems positioning: Implementation and performance

NASA Astrophysics Data System (ADS)

Asgari, Jamal; Mohammadloo, Tannaz H.; Amiri-Simkooei, Ali Reza

2015-09-01

GNSS kinematic techniques are capable of providing precise coordinates in extremely short observation time-span. These methods usually determine the coordinates of an unknown station with respect to a reference one. To enhance the precision, accuracy, reliability and integrity of the estimated unknown parameters, GNSS kinematic equations are to be augmented by possible constraints. Such constraints could be derived from the geometric relation of the receiver positions in motion. This contribution presents the formulation of the constrained kinematic global navigation satellite systems positioning. Constraints effectively restrict the definition domain of the unknown parameters from the three-dimensional space to a subspace defined by the equation of motion. To test the concept of the constrained kinematic positioning method, the equation of a circle is employed as a constraint. A device capable of moving on a circle was made and the observations from 11 positions on the circle were analyzed. Relative positioning was conducted by considering the center of the circle as the reference station. The equation of the receiver's motion was rewritten in the ECEF coordinates system. A special attention is drawn onto how a constraint is applied to kinematic positioning. Implementing the constraint in the positioning process provides much more precise results compared to the unconstrained case. This has been verified based on the results obtained from the covariance matrix of the estimated parameters and the empirical results using kinematic positioning samples as well. The theoretical standard deviations of the horizontal components are reduced by a factor ranging from 1.24 to 2.64. The improvement on the empirical standard deviation of the horizontal components ranges from 1.08 to 2.2.

Assessment of source-specific health effects associated with an unknown number of major sources of multiple air pollutants: a unified Bayesian approach.

PubMed

Park, Eun Sug; Hopke, Philip K; Oh, Man-Suk; Symanski, Elaine; Han, Daikwon; Spiegelman, Clifford H

2014-07-01

There has been increasing interest in assessing health effects associated with multiple air pollutants emitted by specific sources. A major difficulty with achieving this goal is that the pollution source profiles are unknown and source-specific exposures cannot be measured directly; rather, they need to be estimated by decomposing ambient measurements of multiple air pollutants. This estimation process, called multivariate receptor modeling, is challenging because of the unknown number of sources and unknown identifiability conditions (model uncertainty). The uncertainty in source-specific exposures (source contributions) as well as uncertainty in the number of major pollution sources and identifiability conditions have been largely ignored in previous studies. A multipollutant approach that can deal with model uncertainty in multivariate receptor models while simultaneously accounting for parameter uncertainty in estimated source-specific exposures in assessment of source-specific health effects is presented in this paper. The methods are applied to daily ambient air measurements of the chemical composition of fine particulate matter ([Formula: see text]), weather data, and counts of cardiovascular deaths from 1995 to 1997 for Phoenix, AZ, USA. Our approach for evaluating source-specific health effects yields not only estimates of source contributions along with their uncertainties and associated health effects estimates but also estimates of model uncertainty (posterior model probabilities) that have been ignored in previous studies. The results from our methods agreed in general with those from the previously conducted workshop/studies on the source apportionment of PM health effects in terms of number of major contributing sources, estimated source profiles, and contributions. However, some of the adverse source-specific health effects identified in the previous studies were not statistically significant in our analysis, which probably resulted because we incorporated parameter uncertainty in estimated source contributions that has been ignored in the previous studies into the estimation of health effects parameters. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A Systematic Approach to Sensor Selection for Aircraft Engine Health Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2009-01-01

A systematic approach for selecting an optimal suite of sensors for on-board aircraft gas turbine engine health estimation is presented. The methodology optimally chooses the engine sensor suite and the model tuning parameter vector to minimize the Kalman filter mean squared estimation error in the engine s health parameters or other unmeasured engine outputs. This technique specifically addresses the underdetermined estimation problem where there are more unknown system health parameters representing degradation than available sensor measurements. This paper presents the theoretical estimation error equations, and describes the optimization approach that is applied to select the sensors and model tuning parameters to minimize these errors. Two different model tuning parameter vector selection approaches are evaluated: the conventional approach of selecting a subset of health parameters to serve as the tuning parameters, and an alternative approach that selects tuning parameters as a linear combination of all health parameters. Results from the application of the technique to an aircraft engine simulation are presented, and compared to those from an alternative sensor selection strategy.

A Non-linear Geodetic Data Inversion Using ABIC for Slip Distribution on a Fault With an Unknown dip Angle

NASA Astrophysics Data System (ADS)

Fukahata, Y.; Wright, T. J.

2006-12-01

We developed a method of geodetic data inversion for slip distribution on a fault with an unknown dip angle. When fault geometry is unknown, the problem of geodetic data inversion is non-linear. A common strategy for obtaining slip distribution is to first determine the fault geometry by minimizing the square misfit under the assumption of a uniform slip on a rectangular fault, and then apply the usual linear inversion technique to estimate a slip distribution on the determined fault. It is not guaranteed, however, that the fault determined under the assumption of a uniform slip gives the best fault geometry for a spatially variable slip distribution. In addition, in obtaining a uniform slip fault model, we have to simultaneously determine the values of the nine mutually dependent parameters, which is a highly non-linear, complicated process. Although the inverse problem is non-linear for cases with unknown fault geometries, the non-linearity of the problems is actually weak, when we can assume the fault surface to be flat. In particular, when a clear fault trace is observed on the EarthOs surface after an earthquake, we can precisely estimate the strike and the location of the fault. In this case only the dip angle has large ambiguity. In geodetic data inversion we usually need to introduce smoothness constraints in order to compromise reciprocal requirements for model resolution and estimation errors in a natural way. Strictly speaking, the inverse problem with smoothness constraints is also non-linear, even if the fault geometry is known. The non-linearity has been dissolved by introducing AkaikeOs Bayesian Information Criterion (ABIC), with which the optimal value of the relative weight of observed data to smoothness constraints is objectively determined. In this study, using ABIC in determining the optimal dip angle, we dissolved the non-linearity of the inverse problem. We applied the method to the InSAR data of the 1995 Dinar, Turkey earthquake and obtained a much shallower dip angle than before.

ON IDENTIFIABILITY OF NONLINEAR ODE MODELS AND APPLICATIONS IN VIRAL DYNAMICS

PubMed Central

MIAO, HONGYU; XIA, XIAOHUA; PERELSON, ALAN S.; WU, HULIN

2011-01-01

Ordinary differential equations (ODE) are a powerful tool for modeling dynamic processes with wide applications in a variety of scientific fields. Over the last 2 decades, ODEs have also emerged as a prevailing tool in various biomedical research fields, especially in infectious disease modeling. In practice, it is important and necessary to determine unknown parameters in ODE models based on experimental data. Identifiability analysis is the first step in determing unknown parameters in ODE models and such analysis techniques for nonlinear ODE models are still under development. In this article, we review identifiability analysis methodologies for nonlinear ODE models developed in the past one to two decades, including structural identifiability analysis, practical identifiability analysis and sensitivity-based identifiability analysis. Some advanced topics and ongoing research are also briefly reviewed. Finally, some examples from modeling viral dynamics of HIV, influenza and hepatitis viruses are given to illustrate how to apply these identifiability analysis methods in practice. PMID:21785515

Distributed adaptive asymptotically consensus tracking control of uncertain Euler-Lagrange systems under directed graph condition.

PubMed

Wang, Wei; Wen, Changyun; Huang, Jiangshuai; Fan, Huijin

2017-11-01

In this paper, a backstepping based distributed adaptive control scheme is proposed for multiple uncertain Euler-Lagrange systems under directed graph condition. The common desired trajectory is allowed totally unknown by part of the subsystems and the linearly parameterized trajectory model assumed in currently available results is no longer needed. To compensate the effects due to unknown trajectory information, a smooth function of consensus errors and certain positive integrable functions are introduced in designing virtual control inputs. Besides, to overcome the difficulty of completely counteracting the coupling terms of distributed consensus errors and parameter estimation errors in the presence of asymmetric Laplacian matrix, extra information transmission of local parameter estimates are introduced among linked subsystem and adaptive gain technique is adopted to generate distributed torque inputs. It is shown that with the proposed distributed adaptive control scheme, global uniform boundedness of all the closed-loop signals and asymptotically output consensus tracking can be achieved. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Backstepping Design of Adaptive Neural Fault-Tolerant Control for MIMO Nonlinear Systems.

PubMed

Gao, Hui; Song, Yongduan; Wen, Changyun

In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.

Mapping land water and energy balance relations through conditional sampling of remote sensing estimates of atmospheric forcing and surface states

NASA Astrophysics Data System (ADS)

Farhadi, Leila; Entekhabi, Dara; Salvucci, Guido

2016-04-01

In this study, we develop and apply a mapping estimation capability for key unknown parameters that link the surface water and energy balance equations. The method is applied to the Gourma region in West Africa. The accuracy of the estimation method at point scale was previously examined using flux tower data. In this study, the capability is scaled to be applicable with remotely sensed data products and hence allow mapping. Parameters of the system are estimated through a process that links atmospheric forcing (precipitation and incident radiation), surface states, and unknown parameters. Based on conditional averaging of land surface temperature and moisture states, respectively, a single objective function is posed that measures moisture and temperature-dependent errors solely in terms of observed forcings and surface states. This objective function is minimized with respect to parameters to identify evapotranspiration and drainage models and estimate water and energy balance flux components. The uncertainty of the estimated parameters (and associated statistical confidence limits) is obtained through the inverse of Hessian of the objective function, which is an approximation of the covariance matrix. This calibration-free method is applied to the mesoscale region of Gourma in West Africa using multiplatform remote sensing data. The retrievals are verified against tower-flux field site data and physiographic characteristics of the region. The focus is to find the functional form of the evaporative fraction dependence on soil moisture, a key closure function for surface and subsurface heat and moisture dynamics, using remote sensing data.

Application of Ensemble Kalman Filter in Power System State Tracking and Sensitivity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Yulan; Huang, Zhenyu; Zhou, Ning

2012-05-01

Ensemble Kalman Filter (EnKF) is proposed to track dynamic states of generators. The algorithm of EnKF and its application to generator state tracking are presented in detail. The accuracy and sensitivity of the method are analyzed with respect to initial state errors, measurement noise, unknown fault locations, time steps and parameter errors. It is demonstrated through simulation studies that even with some errors in the parameters, the developed EnKF can effectively track generator dynamic states using disturbance data.

Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

PubMed

Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

2014-06-01

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

Approximation-Based Adaptive Neural Tracking Control of Nonlinear MIMO Unknown Time-Varying Delay Systems With Full State Constraints.

PubMed

Li, Da-Peng; Li, Dong-Juan; Liu, Yan-Jun; Tong, Shaocheng; Chen, C L Philip

2017-10-01

This paper deals with the tracking control problem for a class of nonlinear multiple input multiple output unknown time-varying delay systems with full state constraints. To overcome the challenges which cause by the appearances of the unknown time-varying delays and full-state constraints simultaneously in the systems, an adaptive control method is presented for such systems for the first time. The appropriate Lyapunov-Krasovskii functions and a separation technique are employed to eliminate the effect of unknown time-varying delays. The barrier Lyapunov functions are employed to prevent the violation of the full state constraints. The singular problems are dealt with by introducing the signal function. Finally, it is proven that the proposed method can both guarantee the good tracking performance of the systems output, all states are remained in the constrained interval and all the closed-loop signals are bounded in the design process based on choosing appropriate design parameters. The practicability of the proposed control technique is demonstrated by a simulation study in this paper.

27. Photocopy of photograph dated ca. 1940; photographer unknown; original ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

27. Photocopy of photograph dated ca. 1940; photographer unknown; original filed as MR2.9/SP8/r374 in Audio-Visual Collection Minnesota Historical Society, St. Paul; THREE-QUARTER VIEW SHOWING WEST SIDE AND SOUTH FRONT AFTER SECOND-STORY ADDITION; LOOKING NORTHEAST - Northwest Airways Hangar & Administration Building, 590 Bayfield Street, St. Paul Downtown Airport (Holman), Saint Paul, Ramsey County, MN

Data Series Subtraction with Unknown and Unmodeled Background Noise

NASA Technical Reports Server (NTRS)

Vitale, Stefano; Congedo, Giuseppe; Dolesi, Rita; Ferroni, Valerio; Hueller, Mauro; Vetrugno, Daniele; Weber, William Joseph; Audley, Heather; Danzmann, Karsten; Diepholz, Ingo;

A Method for Calculating Strain Energy Release Rates in Preliminary Design of Composite Skin/Stringer Debonding Under Multi-Axial Loading

NASA Technical Reports Server (NTRS)

Krueger, Ronald; Minguet, Pierre J.; OBrien, T. Kevin

1999-01-01

Three simple procedures were developed to determine strain energy release rates, G, in composite skin/stringer specimens for various combinations of unaxial and biaxial (in-plane/out-of-plane) loading conditions. These procedures may be used for parametric design studies in such a way that only a few finite element computations will be necessary for a study of many load combinations. The results were compared with mixed mode strain energy release rates calculated directly from nonlinear two-dimensional plane-strain finite element analyses using the virtual crack closure technique. The first procedure involved solving three unknown parameters needed to determine the energy release rates. Good agreement was obtained when the external loads were used in the expression derived. This superposition technique was only applicable if the structure exhibits a linear load/deflection behavior. Consequently, a second technique was derived which was applicable in the case of nonlinear load/deformation behavior. The technique involved calculating six unknown parameters from a set of six simultaneous linear equations with data from six nonlinear analyses to determine the energy release rates. This procedure was not time efficient, and hence, less appealing. A third procedure was developed to calculate mixed mode energy release rates as a function of delamination lengths. This procedure required only one nonlinear finite element analysis of the specimen with a single delamination length to obtain a reference solution for the energy release rates and the scale factors. The delamination was extended in three separate linear models of the local area in the vicinity of the delamination subjected to unit loads to obtain the distribution of G with delamination lengths. This set of sub-problems was Although additional modeling effort is required to create the sub- models, this local technique is efficient for parametric studies.

A Priori Method of Using Photon Activation Analysis to Determine Unknown Trace Element Concentrations in NIST Standards

DOE Office of Scientific and Technical Information (OSTI.GOV)

Green, Jaromy; Sun Zaijing; Wells, Doug

2009-03-10

Photon activation analysis detected elements in two NIST standards that did not have reported concentration values. A method is currently being developed to infer these concentrations by using scaling parameters and the appropriate known quantities within the NIST standard itself. Scaling parameters include: threshold, peak and endpoint energies; photo-nuclear cross sections for specific isotopes; Bremstrahlung spectrum; target thickness; and photon flux. Photo-nuclear cross sections and energies from the unknown elements must also be known. With these quantities, the same integral was performed for both the known and unknown elements resulting in an inference of the concentration of the un-reported elementmore » based on the reported value. Since Rb and Mn were elements that were reported in the standards, and because they had well-identified peaks, they were used as the standards of inference to determine concentrations of the unreported elements of As, I, Nb, Y, and Zr. This method was tested by choosing other known elements within the standards and inferring a value based on the stated procedure. The reported value of Mn in the first NIST standard was 403{+-}15 ppm and the reported value of Ca in the second NIST standard was 87000 ppm (no reported uncertainty). The inferred concentrations were 370{+-}23 ppm and 80200{+-}8700 ppm respectively.« less

Finite-time master-slave synchronization and parameter identification for uncertain Lurie systems.

PubMed

Wang, Tianbo; Zhao, Shouwei; Zhou, Wuneng; Yu, Weiqin

2014-07-01

This paper investigates the finite-time master-slave synchronization and parameter identification problem for uncertain Lurie systems based on the finite-time stability theory and the adaptive control method. The finite-time master-slave synchronization means that the state of a slave system follows with that of a master system in finite time, which is more reasonable than the asymptotical synchronization in applications. The uncertainties include the unknown parameters and noise disturbances. An adaptive controller and update laws which ensures the synchronization and parameter identification to be realized in finite time are constructed. Finally, two numerical examples are given to show the effectiveness of the proposed method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Inference regarding multiple structural changes in linear models with endogenous regressors☆

PubMed Central

Hall, Alastair R.; Han, Sanggohn; Boldea, Otilia

2012-01-01

This paper considers the linear model with endogenous regressors and multiple changes in the parameters at unknown times. It is shown that minimization of a Generalized Method of Moments criterion yields inconsistent estimators of the break fractions, but minimization of the Two Stage Least Squares (2SLS) criterion yields consistent estimators of these parameters. We develop a methodology for estimation and inference of the parameters of the model based on 2SLS. The analysis covers the cases where the reduced form is either stable or unstable. The methodology is illustrated via an application to the New Keynesian Phillips Curve for the US. PMID:23805021

Prediction of Unsteady Aerodynamic Coefficients at High Angles of Attack

NASA Technical Reports Server (NTRS)

Pamadi, Bandu N.; Murphy, Patrick C.; Klein, Vladislav; Brandon, Jay M.

2001-01-01

The nonlinear indicial response method is used to model the unsteady aerodynamic coefficients in the low speed longitudinal oscillatory wind tunnel test data of the 0.1 scale model of the F-16XL aircraft. Exponential functions are used to approximate the deficiency function in the indicial response. Using one set of oscillatory wind tunnel data and parameter identification method, the unknown parameters in the exponential functions are estimated. The genetic algorithm is used as a least square minimizing algorithm. The assumed model structures and parameter estimates are validated by comparing the predictions with other sets of available oscillatory wind tunnel test data.

Two similar cases of elderly women with moderate abdominal pain and pneumoperitoneum of unknown origin: a surgeon's successful conservative management.

PubMed

Vinzens, Fabrizio; Zumstein, Valentin; Bieg, Christian; Ackermann, Christoph

2016-05-26

Patients presenting with abdominal pain and pneumoperitoneum in radiological examination usually require emergency explorative laparoscopy or laparotomy. Pneumoperitoneum mostly associates with gastrointestinal perforation. There are very few cases where surgery can be avoided. We present 2 cases of pneumoperitoneum with unknown origin and successful conservative treatment. Both patients were elderly women presenting to our emergency unit, with moderate abdominal pain. There was neither medical intervention nor trauma in their medical history. Physical examination revealed mild abdominal tenderness, but no clinical sign of peritonitis. Cardiopulmonary examination remained unremarkable. Blood studies showed only slight abnormalities, in particular, inflammation parameters were not significantly increased. Finally, obtained CTs showed free abdominal gas of unknown origin in both cases. We performed conservative management with nil per os, nasogastric tube, total parenteral nutrition and prophylactic antibiotics. After 2 weeks, both were discharged home. 2016 BMJ Publishing Group Ltd.

Probabilistic and deterministic aspects of linear estimation in geodesy. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Dermanis, A.

1976-01-01

Recent advances in observational techniques related to geodetic work (VLBI, laser ranging) make it imperative that more consideration should be given to modeling problems. Uncertainties in the effect of atmospheric refraction, polar motion and precession-nutation parameters, cannot be dispensed with in the context of centimeter level geodesy. Even physical processes that have generally been previously altogether neglected (station motions) must now be taken into consideration. The problem of modeling functions of time or space, or at least their values at observation points (epochs) is explored. When the nature of the function to be modeled is unknown. The need to include a limited number of terms and to a priori decide upon a specific form may result in a representation which fails to sufficiently approximate the unknown function. An alternative approach of increasing application is the modeling of unknown functions as stochastic processes.

Model-free adaptive sliding mode controller design for generalized projective synchronization of the fractional-order chaotic system via radial basis function neural networks

NASA Astrophysics Data System (ADS)

Wang, L. M.

2017-09-01

A novel model-free adaptive sliding mode strategy is proposed for a generalized projective synchronization (GPS) between two entirely unknown fractional-order chaotic systems subject to the external disturbances. To solve the difficulties from the little knowledge about the master-slave system and to overcome the bad effects of the external disturbances on the generalized projective synchronization, the radial basis function neural networks are used to approach the packaged unknown master system and the packaged unknown slave system (including the external disturbances). Consequently, based on the slide mode technology and the neural network theory, a model-free adaptive sliding mode controller is designed to guarantee asymptotic stability of the generalized projective synchronization error. The main contribution of this paper is that a control strategy is provided for the generalized projective synchronization between two entirely unknown fractional-order chaotic systems subject to the unknown external disturbances, and the proposed control strategy only requires that the master system has the same fractional orders as the slave system. Moreover, the proposed method allows us to achieve all kinds of generalized projective chaos synchronizations by turning the user-defined parameters onto the desired values. Simulation results show the effectiveness of the proposed method and the robustness of the controlled system.

Approximation-based adaptive tracking control of pure-feedback nonlinear systems with multiple unknown time-varying delays.

PubMed

Wang, Min; Ge, Shuzhi Sam; Hong, Keum-Shik

2010-11-01

This paper presents adaptive neural tracking control for a class of non-affine pure-feedback systems with multiple unknown state time-varying delays. To overcome the design difficulty from non-affine structure of pure-feedback system, mean value theorem is exploited to deduce affine appearance of state variables x(i) as virtual controls α(i), and of the actual control u. The separation technique is introduced to decompose unknown functions of all time-varying delayed states into a series of continuous functions of each delayed state. The novel Lyapunov-Krasovskii functionals are employed to compensate for the unknown functions of current delayed state, which is effectively free from any restriction on unknown time-delay functions and overcomes the circular construction of controller caused by the neural approximation of a function of u and [Formula: see text] . Novel continuous functions are introduced to overcome the design difficulty deduced from the use of one adaptive parameter. To achieve uniformly ultimate boundedness of all the signals in the closed-loop system and tracking performance, control gains are effectively modified as a dynamic form with a class of even function, which makes stability analysis be carried out at the present of multiple time-varying delays. Simulation studies are provided to demonstrate the effectiveness of the proposed scheme.

Source memory for action in young and older adults: self vs. close or unknown others.

PubMed

Rosa, Nicole M; Gutchess, Angela H

2011-09-01

The present study examines source memory for actions (e.g., placing items in a suitcase). For both young and older adult participants, source memory for actions performed by the self was better than memory for actions performed by either a known (close) or unknown other. In addition, neither young nor older adults were more likely to confuse self with close others than with unknown others. Results suggest an advantage in source memory for actions performed by the self compared to others, possibly associated with sensorimotor cues that are relatively preserved in aging.

An adaptive control scheme for a flexible manipulator

NASA Technical Reports Server (NTRS)

Yang, T. C.; Yang, J. C. S.; Kudva, P.

1987-01-01

The problem of controlling a single link flexible manipulator is considered. A self-tuning adaptive control scheme is proposed which consists of a least squares on-line parameter identification of an equivalent linear model followed by a tuning of the gains of a pole placement controller using the parameter estimates. Since the initial parameter values for this model are assumed unknown, the use of arbitrarily chosen initial parameter estimates in the adaptive controller would result in undesirable transient effects. Hence, the initial stage control is carried out with a PID controller. Once the identified parameters have converged, control is transferred to the adaptive controller. Naturally, the relevant issues in this scheme are tests for parameter convergence and minimization of overshoots during control switch-over. To demonstrate the effectiveness of the proposed scheme, simulation results are presented with an analytical nonlinear dynamic model of a single link flexible manipulator.

One-Dimensional, Two-Phase Flow Modeling Toward Interpreting Motor Slag Expulsion Phenomena

NASA Technical Reports Server (NTRS)

Kibbey, Timothy P.

2012-01-01

Aluminum oxide slag accumulation and expulsion was previously shown to be a player in various solid rocket motor phenomena, including the Space Shuttle's Reusable Solid Rocket Motor (RSRM) pressure perturbation, or "blip," and phantom moment. In the latter case, such un ]commanded side accelerations near the end of burn have also been identified in several other motor systems. However, efforts to estimate the mass expelled during a given event have come up short. Either bulk calculations are performed without enough physics present, or multiphase, multidimensional Computational Fluid Dynamic analyses are performed that give a snapshot in time and space but do not always aid in grasping the general principle. One ]dimensional, two ]phase compressible flow calculations yield an analytical result for nozzle flow under certain assumptions. This can be carried further to relate the bulk motor parameters of pressure, thrust, and mass flow rate under the different exhaust conditions driven by the addition of condensed phase mass flow. An unknown parameter is correlated to airflow testing with water injection where mass flow rates and pressure are known. Comparison is also made to full ]scale static test motor data where thrust and pressure changes are known and similar behavior is shown. The end goal is to be able to include the accumulation and flow of slag in internal ballistics predictions. This will allow better prediction of the tailoff when much slag is ejected and of mass retained versus time, believed to be a contributor to the widely-observed "flight knockdown" parameter.

Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land

NASA Astrophysics Data System (ADS)

Lipponen, Antti; Mielonen, Tero; Pitkänen, Mikko R. A.; Levy, Robert C.; Sawyer, Virginia R.; Romakkaniemi, Sami; Kolehmainen, Ville; Arola, Antti

2018-03-01

We have developed a Bayesian aerosol retrieval (BAR) algorithm for the retrieval of aerosol optical depth (AOD) over land from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the BAR algorithm, we simultaneously retrieve all dark land pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 0.55 µm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (0.47, 0.55, 0.64, and 2.1 µm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BAR significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BAR was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05+15 %) expected error envelope increased from 55 to 76 %. In addition to retrieving the values of AOD, FMF, and surface reflectance, the BAR also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BAR algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.

Maximum likelihood estimation of protein kinetic parameters under weak assumptions from unfolding force spectroscopy experiments

NASA Astrophysics Data System (ADS)

Aioanei, Daniel; Samorì, Bruno; Brucale, Marco

2009-12-01

Single molecule force spectroscopy (SMFS) is extensively used to characterize the mechanical unfolding behavior of individual protein domains under applied force by pulling chimeric polyproteins consisting of identical tandem repeats. Constant velocity unfolding SMFS data can be employed to reconstruct the protein unfolding energy landscape and kinetics. The methods applied so far require the specification of a single stretching force increase function, either theoretically derived or experimentally inferred, which must then be assumed to accurately describe the entirety of the experimental data. The very existence of a suitable optimal force model, even in the context of a single experimental data set, is still questioned. Herein, we propose a maximum likelihood (ML) framework for the estimation of protein kinetic parameters which can accommodate all the established theoretical force increase models. Our framework does not presuppose the existence of a single force characteristic function. Rather, it can be used with a heterogeneous set of functions, each describing the protein behavior in the stretching time range leading to one rupture event. We propose a simple way of constructing such a set of functions via piecewise linear approximation of the SMFS force vs time data and we prove the suitability of the approach both with synthetic data and experimentally. Additionally, when the spontaneous unfolding rate is the only unknown parameter, we find a correction factor that eliminates the bias of the ML estimator while also reducing its variance. Finally, we investigate which of several time-constrained experiment designs leads to better estimators.

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models.

PubMed

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.

Adaptive Parameter Estimation of Person Recognition Model in a Stochastic Human Tracking Process

NASA Astrophysics Data System (ADS)

Nakanishi, W.; Fuse, T.; Ishikawa, T.

2015-05-01

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model, which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions.

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models

PubMed Central

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

Predictive Trip Detection for Nuclear Power Plants

NASA Astrophysics Data System (ADS)

Rankin, Drew J.; Jiang, Jin

2016-08-01

This paper investigates the use of a Kalman filter (KF) to predict, within the shutdown system (SDS) of a nuclear power plant (NPP), whether safety parameter measurements have reached a trip set-point. In addition, least squares (LS) estimation compensates for prediction error due to system-model mismatch. The motivation behind predictive shutdown is to reduce the amount of time between the occurrence of a fault or failure and the time of trip detection, referred to as time-to-trip. These reductions in time-to-trip can ultimately lead to increases in safety and productivity margins. The proposed predictive SDS differs from conventional SDSs in that it compares point-predictions of the measurements, rather than sensor measurements, against trip set-points. The predictive SDS is validated through simulation and experiments for the steam generator water level safety parameter. Performance of the proposed predictive SDS is compared against benchmark conventional SDS with respect to time-to-trip. In addition, this paper analyzes: prediction uncertainty, as well as; the conditions under which it is possible to achieve reduced time-to-trip. Simulation results demonstrate that on average the predictive SDS reduces time-to-trip by an amount of time equal to the length of the prediction horizon and that the distribution of times-to-trip is approximately Gaussian. Experimental results reveal that a reduced time-to-trip can be achieved in a real-world system with unknown system-model mismatch and that the predictive SDS can be implemented with a scan time of under 100ms. Thus, this paper is a proof of concept for KF/LS-based predictive trip detection.

The Information Available to a Moving Observer on Shape with Unknown, Isotropic BRDFs.

PubMed

Chandraker, Manmohan

2016-07-01

Psychophysical studies show motion cues inform about shape even with unknown reflectance. Recent works in computer vision have considered shape recovery for an object of unknown BRDF using light source or object motions. This paper proposes a theory that addresses the remaining problem of determining shape from the (small or differential) motion of the camera, for unknown isotropic BRDFs. Our theory derives a differential stereo relation that relates camera motion to surface depth, which generalizes traditional Lambertian assumptions. Under orthographic projection, we show differential stereo may not determine shape for general BRDFs, but suffices to yield an invariant for several restricted (still unknown) BRDFs exhibited by common materials. For the perspective case, we show that differential stereo yields the surface depth for unknown isotropic BRDF and unknown directional lighting, while additional constraints are obtained with restrictions on the BRDF or lighting. The limits imposed by our theory are intrinsic to the shape recovery problem and independent of choice of reconstruction method. We also illustrate trends shared by theories on shape from differential motion of light source, object or camera, to relate the hardness of surface reconstruction to the complexity of imaging setup.

A 3D interactive method for estimating body segmental parameters in animals: application to the turning and running performance of Tyrannosaurus rex.

PubMed

Hutchinson, John R; Ng-Thow-Hing, Victor; Anderson, Frank C

2007-06-21

We developed a method based on interactive B-spline solids for estimating and visualizing biomechanically important parameters for animal body segments. Although the method is most useful for assessing the importance of unknowns in extinct animals, such as body contours, muscle bulk, or inertial parameters, it is also useful for non-invasive measurement of segmental dimensions in extant animals. Points measured directly from bodies or skeletons are digitized and visualized on a computer, and then a B-spline solid is fitted to enclose these points, allowing quantification of segment dimensions. The method is computationally fast enough so that software implementations can interactively deform the shape of body segments (by warping the solid) or adjust the shape quantitatively (e.g., expanding the solid boundary by some percentage or a specific distance beyond measured skeletal coordinates). As the shape changes, the resulting changes in segment mass, center of mass (CM), and moments of inertia can be recomputed immediately. Volumes of reduced or increased density can be embedded to represent lungs, bones, or other structures within the body. The method was validated by reconstructing an ostrich body from a fleshed and defleshed carcass and comparing the estimated dimensions to empirically measured values from the original carcass. We then used the method to calculate the segmental masses, centers of mass, and moments of inertia for an adult Tyrannosaurus rex, with measurements taken directly from a complete skeleton. We compare these results to other estimates, using the model to compute the sensitivities of unknown parameter values based upon 30 different combinations of trunk, lung and air sac, and hindlimb dimensions. The conclusion that T. rex was not an exceptionally fast runner remains strongly supported by our models-the main area of ambiguity for estimating running ability seems to be estimating fascicle lengths, not body dimensions. Additionally, the craniad position of the CM in all of our models reinforces the notion that T. rex did not stand or move with extremely columnar, elephantine limbs. It required some flexion in the limbs to stand still, but how much flexion depends directly on where its CM is assumed to lie. Finally we used our model to test an unsolved problem in dinosaur biomechanics: how fast a huge biped like T. rex could turn. Depending on the assumptions, our whole body model integrated with a musculoskeletal model estimates that turning 45 degrees on one leg could be achieved slowly, in about 1-2s.

Antimicrobial Activity of Lactic Acid Bacteria in Dairy Products and Gut: Effect on Pathogens

PubMed Central

Rodríguez, Eva; Landete, José María

2015-01-01

The food industry seeks alternatives to satisfy consumer demands of safe foods with a long shelf-life able to maintain the nutritional and organoleptic quality. The application of antimicrobial compounds-producing protective cultures may provide an additional parameter of processing in order to improve the safety and ensure food quality, keeping or enhancing its sensorial characteristics. In addition, strong evidences suggest that certain probiotic strains can confer resistance against infection with enteric pathogens. Several mechanisms have been proposed to support this phenomenon, including antimicrobial compounds secreted by the probiotics, competitive exclusion, or stimulation of the immune system. Recent research has increasingly demonstrated the role of antimicrobial compounds as protective mechanism against intestinal pathogens and therefore certain strains could have an effect on both the food and the gut. In this aspect, the effects of the combination of different strains keep unknown. The development of multistrain probiotic dairy products with good technological properties and with improved characteristics to those shown by the individual strains, able to act not only as protective cultures in foods, but also as probiotics able to exert a protective action against infections, has gained increased interest. PMID:25861634

Dynamic bifurcation and strange nonchaos in a two-frequency parametrically driven nonlinear oscillator

NASA Astrophysics Data System (ADS)

Premraj, D.; Suresh, K.; Palanivel, J.; Thamilmaran, K.

2017-09-01

A periodically forced series LCR circuit with Chua's diode as a nonlinear element exhibits slow passage through Hopf bifurcation. This slow passage leads to a delay in the Hopf bifurcation. The delay in this bifurcation is a unique quantity and it can be predicted using various numerical analysis. We find that when an additional periodic force is added to the system, the delay in bifurcation becomes chaotic which leads to an unpredictability in bifurcation delay. Further, we study the bifurcation of the periodic delay to chaotic delay in the slow passage effect through strange nonchaotic delay. We also report the occurrence of strange nonchaotic dynamics while varying the parameter of the additional force included in the system. We observe that the system exhibits a hitherto unknown dynamical transition to a strange nonchaotic attractor. With the help of Lyapunov exponent, we explain the new transition to strange nonchaotic attractor and its mechanism is studied by making use of rational approximation theory. The birth of SNA has also been confirmed numerically, using Poincaré maps, phase sensitivity exponent, the distribution of finite-time Lyapunov exponents and singular continuous spectrum analysis.

[Subchronic toxicity testing of mold-ripened cheese].

PubMed

Schoch, U; Lüthy, J; Schlatter, C

1984-08-01

The biological effects of known mycotoxins of Penicillium roqueforti or P. camemberti and other still unknown, but potentially toxic metabolites in mould ripened cheese (commercial samples of Blue- and Camembert cheese) were investigated. High amounts of mycelium (equivalents of 100 kg cheese/man and day) were fed to mice in a subchronic feeding trial. The following parameters were determined: development of body weight, organ weights, hematology, blood plasma enzymes. No signs of adverse effects produced by cheese mycotoxins could be detected after 28 days. No still unknown toxic metabolites could be demonstrated. From these results no health hazard from the consumption of mould ripened cheese, even in high amounts, appears to exist.

Simultaneous treatment of unspecified heteroskedastic model error distribution and mismeasured covariates for restricted moment models.

PubMed

Garcia, Tanya P; Ma, Yanyuan

2017-10-01

We develop consistent and efficient estimation of parameters in general regression models with mismeasured covariates. We assume the model error and covariate distributions are unspecified, and the measurement error distribution is a general parametric distribution with unknown variance-covariance. We construct root- n consistent, asymptotically normal and locally efficient estimators using the semiparametric efficient score. We do not estimate any unknown distribution or model error heteroskedasticity. Instead, we form the estimator under possibly incorrect working distribution models for the model error, error-prone covariate, or both. Empirical results demonstrate robustness to different incorrect working models in homoscedastic and heteroskedastic models with error-prone covariates.

Nonlinear robust controller design for multi-robot systems with unknown payloads

NASA Technical Reports Server (NTRS)

Song, Y. D.; Anderson, J. N.; Homaifar, A.; Lai, H. Y.

1992-01-01

This work is concerned with the control problem of a multi-robot system handling a payload with unknown mass properties. Force constraints at the grasp points are considered. Robust control schemes are proposed that cope with the model uncertainty and achieve asymptotic path tracking. To deal with the force constraints, a strategy for optimally sharing the task is suggested. This strategy basically consists of two steps. The first detects the robots that need help and the second arranges that help. It is shown that the overall system is not only robust to uncertain payload parameters, but also satisfies the force constraints.

Automatic range selector

DOEpatents

McNeilly, Clyde E.

1977-01-04

A device is provided for automatically selecting from a plurality of ranges of a scale of values to which a meter may be made responsive, that range which encompasses the value of an unknown parameter. A meter relay indicates whether the unknown is of greater or lesser value than the range to which the meter is then responsive. The rotatable part of a stepping relay is rotated in one direction or the other in response to the indication from the meter relay. Various positions of the rotatable part are associated with particular scales. Switching means are sensitive to the position of the rotatable part to couple the associated range to the meter.

L1 adaptive control of uncertain gear transmission servo systems with deadzone nonlinearity.

PubMed

Zuo, Zongyu; Li, Xiao; Shi, Zhiguang

2015-09-01

This paper deals with the adaptive control problem of Gear Transmission Servo (GTS) systems in the presence of unknown deadzone nonlinearity and viscous friction. A global differential homeomorphism based on a novel differentiable deadzone model is proposed first. Since there exist both matched and unmatched state-dependent unknown nonlinearities, a full-state feedback L1 adaptive controller is constructed to achieve uniformly bounded transient response in addition to steady-state performance. Finally, simulation results are included to show the elimination of limit cycles, in addition to demonstrating the main results in this paper. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Incorporation of prior information on parameters into nonlinear regression groundwater flow models: 1. Theory

USGS Publications Warehouse

Cooley, Richard L.

1982-01-01

Prior information on the parameters of a groundwater flow model can be used to improve parameter estimates obtained from nonlinear regression solution of a modeling problem. Two scales of prior information can be available: (1) prior information having known reliability (that is, bias and random error structure) and (2) prior information consisting of best available estimates of unknown reliability. A regression method that incorporates the second scale of prior information assumes the prior information to be fixed for any particular analysis to produce improved, although biased, parameter estimates. Approximate optimization of two auxiliary parameters of the formulation is used to help minimize the bias, which is almost always much smaller than that resulting from standard ridge regression. It is shown that if both scales of prior information are available, then a combined regression analysis may be made.

Integrated direct/indirect adaptive robust motion trajectory tracking control of pneumatic cylinders

NASA Astrophysics Data System (ADS)

Meng, Deyuan; Tao, Guoliang; Zhu, Xiaocong

2013-09-01

This paper studies the precision motion trajectory tracking control of a pneumatic cylinder driven by a proportional-directional control valve. An integrated direct/indirect adaptive robust controller is proposed. The controller employs a physical model based indirect-type parameter estimation to obtain reliable estimates of unknown model parameters, and utilises a robust control method with dynamic compensation type fast adaptation to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. Due to the use of projection mapping, the robust control law and the parameter adaption algorithm can be designed separately. Since the system model uncertainties are unmatched, the recursive backstepping technology is adopted to design the robust control law. Extensive comparative experimental results are presented to illustrate the effectiveness of the proposed controller and its performance robustness to parameter variations and sudden disturbances.

Parameter estimation in nonlinear distributed systems - Approximation theory and convergence results

NASA Technical Reports Server (NTRS)

Banks, H. T.; Reich, Simeon; Rosen, I. G.

1988-01-01

An abstract approximation framework and convergence theory is described for Galerkin approximations applied to inverse problems involving nonlinear distributed parameter systems. Parameter estimation problems are considered and formulated as the minimization of a least-squares-like performance index over a compact admissible parameter set subject to state constraints given by an inhomogeneous nonlinear distributed system. The theory applies to systems whose dynamics can be described by either time-independent or nonstationary strongly maximal monotonic operators defined on a reflexive Banach space which is densely and continuously embedded in a Hilbert space. It is demonstrated that if readily verifiable conditions on the system's dependence on the unknown parameters are satisfied, and the usual Galerkin approximation assumption holds, then solutions to the approximating problems exist and approximate a solution to the original infinite-dimensional identification problem.

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.

1991-01-01

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

Type Ia Supernova Intrinsic Magnitude Dispersion and the Fitting of Cosmological Parameters

NASA Astrophysics Data System (ADS)

Kim, A. G.

2011-02-01

I present an analysis for fitting cosmological parameters from a Hubble diagram of a standard candle with unknown intrinsic magnitude dispersion. The dispersion is determined from the data, simultaneously with the cosmological parameters. This contrasts with the strategies used to date. The advantages of the presented analysis are that it is done in a single fit (it is not iterative), it provides a statistically founded and unbiased estimate of the intrinsic dispersion, and its cosmological-parameter uncertainties account for the intrinsic-dispersion uncertainty. Applied to Type Ia supernovae, my strategy provides a statistical measure to test for subtypes and assess the significance of any magnitude corrections applied to the calibrated candle. Parameter bias and differences between likelihood distributions produced by the presented and currently used fitters are negligibly small for existing and projected supernova data sets.

A Report on the Achievements of Subgroup 19 on Activation Cross Sections of the WPEC, OECD-NEA

NASA Astrophysics Data System (ADS)

Plompen, A. J. M.; Smith, D. L.; Semkova, V. M.

2005-05-01

Subgroup 19 on Activation Cross Sections of the Working Party on Evaluation Cooperation of the OECD-NEA has recently concluded its activities. The goal of the subgroup was to generate a large set of new measured activation cross sections relevant to nuclear applications and make these data available to the nuclear science community. In addition, modeling efforts and sensitivity studies were undertaken to evaluate the use of measured data and model calculations for the prediction of unknown cross sections. The latter addresses the potential of model calculations to satisfy issues on the High Priority Request List when no measured data are available. In the course of the activities of the subgroup over ninety reaction channels were studied experimentally. All except the most recent of these data have been compiled into EXFOR format by members of the subgroup and submitted to OECD- NEA. As a result most of these data can now be retrieved online from the data centers. A systematic comparison was made with the new evaluated data files JEF3.0/EAF2003, JENDL3.3, and ENDF/B-VI.8 and with the current status of the global parameter systematics of the model code TALYS-0.57. In addition, a considerable number of locally optimized parameter sets were developed. Both the global and local approaches emphasize the use of consistent physics modelling for all important reaction channels and nuclides involved in the decay. Comparison of the two approaches allows assessment of the effort required when model estimates to a certain accuracy must be made. Parameter sensitivity studies were undertaken to further assess the accuracy requirements on model parameters if target uncertainties for the cross sections have been specified. The value of this approach has demonstrated itself and indicates the need for model codes that fit all available experimental data in order to connect the data covariance with the covariance of the model predictions. The work of the subgroup was a joint effort between IRMM, ANL, FZ-Jülich, INRNE Sofia, NIPNE Bucharest, IEP and Atomki Debrecen, JAERI, and Tohoku University. A comprehensive review of the subgroup activities will be presented.

Some issues related to the novel spectral acceleration method for the fast computation of radiation/scattering from one-dimensional extremely large scale quasi-planar structures

NASA Astrophysics Data System (ADS)

Torrungrueng, Danai; Johnson, Joel T.; Chou, Hsi-Tseng

2002-03-01

The novel spectral acceleration (NSA) algorithm has been shown to produce an $[\\mathcal{O}]$(Ntot) efficient iterative method of moments for the computation of radiation/scattering from both one-dimensional (1-D) and two-dimensional large-scale quasi-planar structures, where Ntot is the total number of unknowns to be solved. This method accelerates the matrix-vector multiplication in an iterative method of moments solution and divides contributions between points into ``strong'' (exact matrix elements) and ``weak'' (NSA algorithm) regions. The NSA method is based on a spectral representation of the electromagnetic Green's function and appropriate contour deformation, resulting in a fast multipole-like formulation in which contributions from large numbers of points to a single point are evaluated simultaneously. In the standard NSA algorithm the NSA parameters are derived on the basis of the assumption that the outermost possible saddle point, φs,max, along the real axis in the complex angular domain is small. For given height variations of quasi-planar structures, this assumption can be satisfied by adjusting the size of the strong region Ls. However, for quasi-planar structures with large height variations, the adjusted size of the strong region is typically large, resulting in significant increases in computational time for the computation of the strong-region contribution and degrading overall efficiency of the NSA algorithm. In addition, for the case of extremely large scale structures, studies based on the physical optics approximation and a flat surface assumption show that the given NSA parameters in the standard NSA algorithm may yield inaccurate results. In this paper, analytical formulas associated with the NSA parameters for an arbitrary value of φs,max are presented, resulting in more flexibility in selecting Ls to compromise between the computation of the contributions of the strong and weak regions. In addition, a ``multilevel'' algorithm, decomposing 1-D extremely large scale quasi-planar structures into more than one weak region and appropriately choosing the NSA parameters for each weak region, is incorporated into the original NSA method to improve its accuracy.

An approximate solution for interlaminar stresses in laminated composites: Applied mechanics program

NASA Technical Reports Server (NTRS)

Rose, Cheryl A.; Herakovich, Carl T.

1992-01-01

An approximate solution for interlaminar stresses in finite width, laminated composites subjected to uniform extensional, and bending loads is presented. The solution is based upon the principle of minimum complementary energy and an assumed, statically admissible stress state, derived by considering local material mismatch effects and global equilibrium requirements. The stresses in each layer are approximated by polynomial functions of the thickness coordinate, multiplied by combinations of exponential functions of the in-plane coordinate, expressed in terms of fourteen unknown decay parameters. Imposing the stationary condition of the laminate complementary energy with respect to the unknown variables yields a system of fourteen non-linear algebraic equations for the parameters. Newton's method is implemented to solve this system. Once the parameters are known, the stresses can be easily determined at any point in the laminate. Results are presented for through-thickness and interlaminar stress distributions for angle-ply, cross-ply (symmetric and unsymmetric laminates), and quasi-isotropic laminates subjected to uniform extension and bending. It is shown that the solution compares well with existing finite element solutions and represents an improved approximate solution for interlaminar stresses, primarily at interfaces where global equilibrium is satisfied by the in-plane stresses, but large local mismatch in properties requires the presence of interlaminar stresses.

Pattern-Based Inverse Modeling for Characterization of Subsurface Flow Models with Complex Geologic Heterogeneity

NASA Astrophysics Data System (ADS)

Golmohammadi, A.; Jafarpour, B.; M Khaninezhad, M. R.

2017-12-01

Calibration of heterogeneous subsurface flow models leads to ill-posed nonlinear inverse problems, where too many unknown parameters are estimated from limited response measurements. When the underlying parameters form complex (non-Gaussian) structured spatial connectivity patterns, classical variogram-based geostatistical techniques cannot describe the underlying connectivity patterns. Modern pattern-based geostatistical methods that incorporate higher-order spatial statistics are more suitable for describing such complex spatial patterns. Moreover, when the underlying unknown parameters are discrete (geologic facies distribution), conventional model calibration techniques that are designed for continuous parameters cannot be applied directly. In this paper, we introduce a novel pattern-based model calibration method to reconstruct discrete and spatially complex facies distributions from dynamic flow response data. To reproduce complex connectivity patterns during model calibration, we impose a feasibility constraint to ensure that the solution follows the expected higher-order spatial statistics. For model calibration, we adopt a regularized least-squares formulation, involving data mismatch, pattern connectivity, and feasibility constraint terms. Using an alternating directions optimization algorithm, the regularized objective function is divided into a continuous model calibration problem, followed by mapping the solution onto the feasible set. The feasibility constraint to honor the expected spatial statistics is implemented using a supervised machine learning algorithm. The two steps of the model calibration formulation are repeated until the convergence criterion is met. Several numerical examples are used to evaluate the performance of the developed method.

A uniform LMI formulation for tuning PID, multi-term fractional-order PID, and Tilt-Integral-Derivative (TID) for integer and fractional-order processes.

PubMed

Merrikh-Bayat, Farshad

2017-05-01

In this paper first the Multi-term Fractional-Order PID (MFOPID) whose transfer function is equal to [Formula: see text] , where k j and α j are unknown and known real parameters respectively, is introduced. Without any loss of generality, a special form of MFOPID with transfer function k p +k i /s+k d1 s+k d2 s μ where k p , k i , k d1 , and k d2 are unknown real and μ is a known positive real parameter, is considered. Similar to PID and TID, MFOPID is also linear in its parameters which makes it possible to study all of them in a same framework. Tuning the parameters of PID, TID, and MFOPID based on loop shaping using Linear Matrix Inequalities (LMIs) is discussed. For this purpose separate LMIs for closed-loop stability (of sufficient type) and adjusting different aspects of the open-loop frequency response are developed. The proposed LMIs for stability are obtained based on the Nyquist stability theorem and can be applied to both integer and fractional-order (not necessarily commensurate) processes which are either stable or have one unstable pole. Numerical simulations show that the performance of the four-variable MFOPID can compete the trivial five-variable FOPID and often excels PID and TID. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

On the predictiveness of single-field inflationary models

NASA Astrophysics Data System (ADS)

Burgess, C. P.; Patil, Subodh P.; Trott, Michael

2014-06-01

We re-examine the predictiveness of single-field inflationary models and discuss how an unknown UV completion can complicate determining inflationary model parameters from observations, even from precision measurements. Besides the usual naturalness issues associated with having a shallow inflationary potential, we describe another issue for inflation, namely, unknown UV physics modifies the running of Standard Model (SM) parameters and thereby introduces uncertainty into the potential inflationary predictions. We illustrate this point using the minimal Higgs Inflationary scenario, which is arguably the most predictive single-field model on the market, because its predictions for A S , r and n s are made using only one new free parameter beyond those measured in particle physics experiments, and run up to the inflationary regime. We find that this issue can already have observable effects. At the same time, this UV-parameter dependence in the Renormalization Group allows Higgs Inflation to occur (in principle) for a slightly larger range of Higgs masses. We comment on the origin of the various UV scales that arise at large field values for the SM Higgs, clarifying cut off scale arguments by further developing the formalism of a non-linear realization of SU L (2) × U(1) in curved space. We discuss the interesting fact that, outside of Higgs Inflation, the effect of a non-minimal coupling to gravity, even in the SM, results in a non-linear EFT for the Higgs sector. Finally, we briefly comment on post BICEP2 attempts to modify the Higgs Inflation scenario.

Modeling companion diagnostics in economic evaluations of targeted oncology therapies: systematic review and methodological checklist.

PubMed

Doble, Brett; Tan, Marcus; Harris, Anthony; Lorgelly, Paula

2015-02-01

The successful use of a targeted therapy is intrinsically linked to the ability of a companion diagnostic to correctly identify patients most likely to benefit from treatment. The aim of this study was to review the characteristics of companion diagnostics that are of importance for inclusion in an economic evaluation. Approaches for including these characteristics in model-based economic evaluations are compared with the intent to describe best practice methods. Five databases and government agency websites were searched to identify model-based economic evaluations comparing a companion diagnostic and subsequent treatment strategy to another alternative treatment strategy with model parameters for the sensitivity and specificity of the companion diagnostic (primary synthesis). Economic evaluations that limited model parameters for the companion diagnostic to only its cost were also identified (secondary synthesis). Quality was assessed using the Quality of Health Economic Studies instrument. 30 studies were included in the review (primary synthesis n = 12; secondary synthesis n = 18). Incremental cost-effectiveness ratios may be lower when the only parameter for the companion diagnostic included in a model is the cost of testing. Incorporating the test's accuracy in addition to its cost may be a more appropriate methodological approach. Altering the prevalence of the genetic biomarker, specific population tested, type of test, test accuracy and timing/sequence of multiple tests can all impact overall model results. The impact of altering a test's threshold for positivity is unknown as it was not addressed in any of the included studies. Additional quality criteria as outlined in our methodological checklist should be considered due to the shortcomings of standard quality assessment tools in differentiating studies that incorporate important test-related characteristics and those that do not. There is a need to refine methods for incorporating the characteristics of companion diagnostics into model-based economic evaluations to ensure consistent and transparent reimbursement decisions are made.

Color-Space Outliers in DPOSS: Quasars and Peculiar Objects

NASA Astrophysics Data System (ADS)

Djorgovski, S. G.; Gal, R. R.; Mahabal, A.; Brunner, R.; Castro, S. M.; Odewahn, S. C.; de Carvalho, R. R.; DPOSS Team

2000-12-01

The processing of DPOSS, a digital version of the POSS-II sky atlas, is now nearly complete. The resulting Palomar--Norris Sky Catalog (PNSC) is expected to contain > 5 x 107 galaxies and > 109 stars, including large numbers of quasars and other unresolved sources. For objects morphologically classified as stellar (i.e., PSF-like), colors and magnitudes provide the only additional source of discriminating information. We investigate the distribution of objects in the parameter space of (g-r) and (r-i) colors as a function of magnitude. Normal stars form a well-defined (temperature) sequence in this parameter space, and we explore the nature of the objects which deviate significantly from this stellar locus. The causes of the deviations include: non-thermal or peculiar spectra, interagalactic absorption (for high-z quasars), presence of strong emission lines in one or more of the bandpasses, or strong variability (because the plates are taken at widely separated epochs). In addition to minor contamination by misclassified compact galaxies, we find the following: (1) Quasars at z > 4; to date, ~ 100 of these objects have been found, and used for a variety of follow-up studies. They are made publicly available immediately after discovery, through http://astro.caltech.edu/ ~george/z4.qsos. (2) Type-2 quasars in the redshift interval z ~ 0.31 - 0.38. (3) Other quasars, starburst and emission-line galaxies, and emission-line stars. (4) Objects with highly peculiar spectra, some or all of which may be rare subtypes of BAL QSOs. (5) Highly variable stars and optical transients, some of which may be GRB ``orphan afterglows''. To date, systematic searches have been made only for (1) and (2); other types of objects were found serendipitously. However, we plan to explore systematically all of the statistically significant outliers in this parameter space. This illustrates the potential of large digital sky surveys for discovery of rare types of objects, both known (e.g., high-z quasars) and as yet unknown.

Comparative effects of cholesteryl ester transfer protein inhibition, statin or ezetimibe on lipid factors: The ACCENTUATE trial.

PubMed

Nicholls, Stephen J; Ray, Kausik K; Ballantyne, Christie M; Beacham, Lauren A; Miller, Debra L; Ruotolo, Giacomo; Nissen, Steven E; Riesmeyer, Jeffrey S

2017-06-01

The optimal approaches to management of patients treated with moderate statin doses on lipid parameters are unknown. The ACCENTUATE study aimed to compare the effects of adding the cholesteryl ester transfer protein inhibitor (CETP) evacetrapib, ezetimibe or increasing statin dose in atorvastatin-treated high-vascular risk patients on lipid parameters. 366 patients with atherosclerotic cardiovascular disease (ASCVD) and/or diabetes were treated with atorvastatin 40 mg/day for 28 days prior to randomization to atorvastatin 40 mg plus evacetrapib 130 mg, atorvastatin 80 mg, atorvastatin 40 mg plus ezetimibe 10 mg or atorvastatin 40 mg plus placebo, daily for 90 days at 64 centers in the United States. Lipid parameters, safety and tolerability were measured. Addition of evacetrapib significantly reduced LDL-C (-33%) compared with ezetimibe (-27%, p=0.045), increasing statin dose (-6%) and statin alone (0%, p<0.001). Evacetrapib also decreased apoB by 23% compared to 19% with ezetimibe (p=0.06) and 7% with increased statin dose (p<0.001), and reduced Lp(a) by 29% (p<0.001 vs. other groups). Evacetrapib increased HDL-C (+125%), apoA-I (+46%), apoC-III (+50%) and apoE (+28%) (p<0.001 vs. other groups). Non-ABCA1-mediated efflux increased by 53% (p<0.001 vs. other groups) with evacetrapib. ABCA1-mediated efflux also increased by 13% with evacetrapib (p<0.001 vs. ezetimibe, p=0.002 vs. increasing statin dose, and p=0.004 vs. statin alone). Addition of evacetrapib to atorvastatin produced an increase in hsCRP compared with ezetimibe (p=0.02). While evacetrapib improved traditional atherogenic and putative protective lipid measures compared with ezetimibe and increasing statin dose in patients with ASCVD and/or diabetes, it also adversely affected novel atherogenic risk factors. These findings may contribute to the lack of clinical benefit observed in the ACCELERATE trial. Copyright © 2017 Elsevier B.V. All rights reserved.

Global Assessment of the Impact of Type 2 Diabetes on Sleep through Specific Questionnaires. A Case-Control Study

PubMed Central

Lecube, Albert; Sánchez, Enric; Gómez-Peralta, Fernando; Abreu, Cristina; Valls, Joan; Mestre, Olga; Romero, Odile; Martínez, María Dolores; Sampol, Gabriel; Ciudin, Andreea; Hernández, Cristina; Simó, Rafael

2016-01-01

Abstract Type 2 diabetes (T2D) is an independent risk factor for sleep breathing disorders. However, it is unknown whether T2D affects daily somnolence and quality of sleep independently of the impairment of polysomnographic parameters. Material and Methods A case-control study including 413 patients with T2D and 413 non-diabetic subjects, matched by age, gender, BMI, and waist and neck circumferences. A polysomnography was performed and daytime sleepiness was evaluated using the Epworth Sleepiness Scale (ESS). In addition, 135 subjects with T2D and 45 controls matched by the same previous parameters were also evaluated through the Pittsburgh Sleep Quality Index (PSQI) to calculate sleep quality. Results Daytime sleepiness was higher in T2D than in control subjects (p = 0.003), with 23.9% of subjects presenting an excessive daytime sleepiness (ESS>10). Patients with fasting plasma glucose (FPG ≥13.1 mmol/l) were identified as the group with a higher risk associated with an ESS>10 (OR 3.9, 95% CI 1.8–7.9, p = 0.0003). A stepwise regression analyses showed that the presence of T2D, baseline glucose levels and gender but not polysomnographic parameters (i.e apnea-hyoapnea index or sleeping time spent with oxigen saturation lower than 90%) independently predicted the ESS score. In addition, subjects with T2D showed higher sleep disturbances [PSQI: 7.0 (1.0–18.0) vs. 4 (0.0–12.0), p<0.001]. Conclusion The presence of T2D and high levels of FPG are independent risk factors for daytime sleepiness and adversely affect sleep quality. Prospective studies addressed to demonstrate whether glycemia optimization could improve the sleep quality in T2D patients seem warranted. PMID:27315083

Search Planning Under Incomplete Information Using Stochastic Optimization and Regression

DTIC Science & Technology

2011-09-01

solve since they involve un- certainty and unknown parameters (see for example Shapiro et al., 2009; Wallace & Ziemba , 2005). One application area is...M16130.2E. 43 Wallace, S. W., & Ziemba , W. T. (2005). Applications of stochastic programming. Philadelphia, PA: Society for Industrial and Applied

A Robust Deconvolution Method based on Transdimensional Hierarchical Bayesian Inference

NASA Astrophysics Data System (ADS)

Kolb, J.; Lekic, V.

2012-12-01

Analysis of P-S and S-P conversions allows us to map receiver side crustal and lithospheric structure. This analysis often involves deconvolution of the parent wave field from the scattered wave field as a means of suppressing source-side complexity. A variety of deconvolution techniques exist including damped spectral division, Wiener filtering, iterative time-domain deconvolution, and the multitaper method. All of these techniques require estimates of noise characteristics as input parameters. We present a deconvolution method based on transdimensional Hierarchical Bayesian inference in which both noise magnitude and noise correlation are used as parameters in calculating the likelihood probability distribution. Because the noise for P-S and S-P conversion analysis in terms of receiver functions is a combination of both background noise - which is relatively easy to characterize - and signal-generated noise - which is much more difficult to quantify - we treat measurement errors as an known quantity, characterized by a probability density function whose mean and variance are model parameters. This transdimensional Hierarchical Bayesian approach has been successfully used previously in the inversion of receiver functions in terms of shear and compressional wave speeds of an unknown number of layers [1]. In our method we used a Markov chain Monte Carlo (MCMC) algorithm to find the receiver function that best fits the data while accurately assessing the noise parameters. In order to parameterize the receiver function we model the receiver function as an unknown number of Gaussians of unknown amplitude and width. The algorithm takes multiple steps before calculating the acceptance probability of a new model, in order to avoid getting trapped in local misfit minima. Using both observed and synthetic data, we show that the MCMC deconvolution method can accurately obtain a receiver function as well as an estimate of the noise parameters given the parent and daughter components. Furthermore, we demonstrate that this new approach is far less susceptible to generating spurious features even at high noise levels. Finally, the method yields not only the most-likely receiver function, but also quantifies its full uncertainty. [1] Bodin, T., M. Sambridge, H. Tkalčić, P. Arroucau, K. Gallagher, and N. Rawlinson (2012), Transdimensional inversion of receiver functions and surface wave dispersion, J. Geophys. Res., 117, B02301

How Far Is Quasar UV/Optical Variability from a Damped Random Walk at Low Frequency?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guo Hengxiao; Wang Junxian; Cai Zhenyi

Studies have shown that UV/optical light curves of quasars can be described using the prevalent damped random walk (DRW) model, also known as the Ornstein–Uhlenbeck process. A white noise power spectral density (PSD) is expected at low frequency in this model; however, a direct observational constraint to the low-frequency PSD slope is difficult due to the limited lengths of the light curves available. Meanwhile, quasars show scatter in their DRW parameters that is too large to be attributed to uncertainties in the measurements and dependence on the variation of known physical factors. In this work we present simulations showing that,more » if the low-frequency PSD deviates from the DRW, the red noise leakage can naturally produce large scatter in the variation parameters measured from simulated light curves. The steeper the low-frequency PSD slope, the larger scatter we expect. Based on observations of SDSS Stripe 82 quasars, we find that the low-frequency PSD slope should be no steeper than −1.3. The actual slope could be flatter, which consequently requires that the quasar variabilities should be influenced by other unknown factors. We speculate that the magnetic field and/or metallicity could be such additional factors.« less

Development and application of a mark-recapture model incorporating predicted sex and transitory behaviour

USGS Publications Warehouse

Conroy, M.J.; Senar, J.C.; Hines, J.E.; Domenech, J.

1999-01-01

We developed an extension of Cormack-Jolly-Seber models to handle a complex mark-recapture problem in which (a) the sex of birds cannot be determined prior to first moult, but can be predicted on the basis of body measurements, and (b) a significant portion of captured birds appear to be transients (i.e. are captured once but leave the area or otherwise become ' untrappable'). We applied this methodology to a data set of 4184 serins (Serinus serinus) trapped in northeastern Spain during 1985-96, in order to investigate age-, sex-, and time-specific variation in survival rates. Using this approach, we were able to successfully incorporate the majority of ringings of serins. Had we eliminated birds not previously captured (as has been advocated to avoid the problem of transience) we would have reduced our sample sizes by >2000 releases. In addition, we were able to include 1610 releases of birds of unknown (but predicted) sex; these data contributed to the precision of our estimates and the power of statistical tests. We discuss problems with data structure, encoding of the algorithms to compute parameter estimates, model selection, identifiability of parameters, and goodness-of-fit, and make recommendations for the design and analysis of future studies facing similar problems.

Development and application of a mark-recapture model incorporating predicted sex and transitory behaviour

USGS Publications Warehouse

Conroy, M.J.; Senar, J.C.; Hines, J.E.; Domenech, J.

1999-01-01

We developed an extension of Cormack-Jolly-Seber models to handle a complex mark-recapture problem in which (a) the sex of birds cannot be determined prior to first moult, but can be predicted on the basis of body measurements, and (b) a significant portion of captured birds appear to be transients (i.e. are captured once but leave the area or otherwise become 'untrappable'). We applied this methodology to a data set of 4184 serins (Serinus serinus) trapped in northeastern Spain during 1985-96, in order to investigate age-, sex-, and time-specific variation in survival rates. Using this approach, we were able to successfully incorporate the majority of ringings of serins. Had we eliminated birds not previously captured (as has been advocated to avoid the problem of transience) we would have reduced our sample sizes by >2000 releases. In addition, we were able to include 1610 releases of birds of unknown (but predicted) sex; these data contributed to the precision of our estimates and the power of statistical tests. We discuss problems with data structure, encoding of the algorithms to compute parameter estimates, model selection, identifiability of parameters, and goodness-of-fit, and make recommendations for the design and analysis of future studies facing similar problems.

Some Physical and Computational Issues in Land Surface Data Assimilation of Satellite Skin Temperatures

NASA Astrophysics Data System (ADS)

Mackaro, Scott M.; McNider, Richard T.; Biazar, Arastoo Pour

2012-03-01

Skin temperatures that reflect the radiating temperature of a surface observed by infrared radiometers are one of the most widely available products from polar orbiting and geostationary satellites and the most commonly used satellite data in land surface assimilation. Past work has indicated that a simple land surface scheme with a few key parameters constrained by observations such as skin temperatures may be preferable to complex land use schemes with many unknown parameters. However, a true radiating skin temperature is sometimes not a prognostic variable in weather forecast models. Additionally, recent research has shown that skin temperatures cannot be directly used in surface similarity forms for inferring fluxes. This paper examines issues encountered in using satellite derived skin temperatures to improve surface flux specifications in weather forecast and air quality models. Attention is given to iterations necessary when attempting to nudge the surface energy budget equation to a desired state. Finally, the issue of mathematical operator splitting is examined in which the surface energy budget calculations are split with the atmospheric vertical diffusion calculations. However, the high level of connectivity between the surface and first atmospheric level means that the operator splitting leads to high frequency oscillations. These oscillations may hinder the assimilation of skin temperature derived moisture fluxes.

Setting Mechanical Properties of High Strength Steels for Rapid Hot Forming Processes

PubMed Central

Löbbe, Christian; Hering, Oliver; Hiegemann, Lars; Tekkaya, A. Erman

2016-01-01

Hot stamping of sheet metal is an established method for the manufacturing of light weight products with tailored properties. However, the generally-applied continuous roller furnace manifests two crucial disadvantages: the overall process time is long and a local setting of mechanical properties is only feasible through special cooling techniques. Hot forming with rapid heating directly before shaping is a new approach, which not only reduces the thermal intervention in the zones of critical formability and requested properties, but also allows the processing of an advantageous microstructure characterized by less grain growth, additional fractions (e.g., retained austenite), and undissolved carbides. Since the austenitization and homogenization process is strongly dependent on the microstructure constitution, the general applicability for the process relevant parameters is unknown. Thus, different austenitization parameters are analyzed for the conventional high strength steels 22MnB5, Docol 1400M, and DP1000 in respect of the mechanical properties. In order to characterize the resulting microstructure, the light optical and scanning electron microscopy, micro and macro hardness measurements, and the X-ray diffraction are conducted subsequent to tensile tests. The investigation proves not only the feasibility to adjust the strength and ductility flexibly, unique microstructures are also observed and the governing mechanisms are clarified. PMID:28773354

Intraovarian markers of follicular and oocyte maturation.

PubMed

Pellicer, A; Diamond, M P; DeCherney, A H; Naftolin, F

1987-08-01

The use of ovulation induction for multiple follicular growth in in vitro fertilization (IVF) has introduced the problem of follicular asynchrony. As a consequence of the asynchrony, the parameters most commonly used by IVF groups to assess follicular and oocyte quality within those follicles are not sufficiently sensitive or specific. Thus, each follicle must be considered separately, and specific markers of follicular and/or oocyte maturation must be sought from within the follicle. In this review we analyze previous reports of potential markers of follicular and oocyte maturation. In regards to the follicular fluid constituents, the level of estradiol in follicular fluid correlates with fertilization and pregnancy in stimulated cycles. Other steroids are only helpful when specific stimulation protocols are used. The level of some follicular proteins such as alpha-1-antitrypsin and fibrinogen also correlates with fertilization and pregnancy outcome. Cyclic AMP levels in follicular fluid are significantly reduced in follicles leading to conception. Regulators of oocyte maturation, such as the Oocyte Maturation Inhibitor (OMI) or the Meiosis Inducing Substance (MIS) have also been correlated with IVF outcome, but their exact structure remains still unknown. In addition, other sophisticated parameters, such as chemotactic activity of human leukocytes, or simple methods, such as the presence of intrafollicular echoes, have also been used as successful markers in predicting IVF outcome.

Cryptosporidiosis susceptibility and risk: a case study.

PubMed

Makri, Anna; Modarres, Reza; Parkin, Rebecca

2004-02-01

Regional estimates of cryptosporidiosis risks from drinking water exposure were developed and validated, accounting for AIDS status and age. We constructed a model with probability distributions and point estimates representing Cryptosporidium in tap water, tap water consumed per day (exposure characterization); dose response, illness given infection, prolonged illness given illness; and three conditional probabilities describing the likelihood of case detection by active surveillance (health effects characterization). The model predictions were combined with population data to derive expected case numbers and incidence rates per 100,000 population, by age and AIDS status, borough specific and for New York City overall in 2000 (risk characterization). They were compared with same-year surveillance data to evaluate predictive ability, assumed to represent true incidence of waterborne cryptosporidiosis. The predicted mean risks, similar to previously published estimates for this region, overpredicted observed incidence-most extensively when accounting for AIDS status. The results suggest that overprediction may be due to conservative parameters applied to both non-AIDS and AIDS populations, and that biological differences for children need to be incorporated. Interpretations are limited by the unknown accuracy of available surveillance data, in addition to variability and uncertainty of model predictions. The model appears sensitive to geographical differences in AIDS prevalence. The use of surveillance data for validation and model parameters pertinent to susceptibility are discussed.

Fitness components and ecological risk of transgenic release: a model using Japanese medaka (Oryzias latipes).

PubMed

Muir, W M; Howard, R D

2001-07-01

Any release of transgenic organisms into nature is a concern because ecological relationships between genetically engineered organisms and other organisms (including their wild-type conspecifics) are unknown. To address this concern, we developed a method to evaluate risk in which we input estimates of fitness parameters from a founder population into a recurrence model to predict changes in transgene frequency after a simulated transgenic release. With this method, we grouped various aspects of an organism's life cycle into six net fitness components: juvenile viability, adult viability, age at sexual maturity, female fecundity, male fertility, and mating advantage. We estimated these components for wild-type and transgenic individuals using the fish, Japanese medaka (Oryzias latipes). We generalized our model's predictions using various combinations of fitness component values in addition to our experimentally derived estimates. Our model predicted that, for a wide range of parameter values, transgenes could spread in populations despite high juvenile viability costs if transgenes also have sufficiently high positive effects on other fitness components. Sensitivity analyses indicated that transgene effects on age at sexual maturity should have the greatest impact on transgene frequency, followed by juvenile viability, mating advantage, female fecundity, and male fertility, with changes in adult viability, resulting in the least impact.

Dendrobium officinale Orchid Extract Prevents Ovariectomy-Induced Osteoporosis in Vivo and Inhibits RANKL-Induced Osteoclast Differentiation in Vitro

PubMed Central

Wang, Qi; Zi, Cheng-Ting; Wang, Jing; Wang, Yu-Na; Huang, Ye-Wei; Fu, Xue-Qi; Wang, Xuan-Jun; Sheng, Jun

2018-01-01

Background: Dendrobium officinale, a traditional Chinese medical herb with high value that is widely used in Asia, possesses many positive effects on human health, including anti-chronic inflammation, anti-obesity, and immune modulation properties; however, whether D. officinale has inhibitory effects on postmenopausal osteoporosis remains unknown. Objective: We investigated the effects of D. officinale extract (DOE) on ovariectomy-induced bone loss in vivo and on osteoclastogenesis in vitro. Methods: In vivo, female rats were divided into a sham-operated (sham) group and five ovariectomized (OVX) subgroups: OVX with vehicle (OVX), OVX with Xian-Ling-Gu-Bao capsule (240 mg/kg body weight/day), and OVX with low-, medium-, and high-dose DOE (150, 300, and 600 mg/kg body weight/day, respectively). Animals in each group were administered their corresponding treatments for 13 weeks. Body weight, serum biochemical parameters, uterine and femoral physical parameters, bone mineral density (BMD), bone biomechanical properties, and bone microarchitecture were obtained. In vitro, the effects of DOE on osteoclastogenesis were examined using RAW264.7 cells. The effects of DOE on osteoclastogenesis and the expression of osteoclast-specific marker genes and proteins were determined. Results: DOE effectively ameliorated serum biochemical parameters, especially alleviated estradiol (E2) deficiency and maintained calcium and phosphorus homeostasis. DOE improved uterine and femoral physical parameters. In addition, DOE improved femoral BMD and biomechanical properties. DOE significantly ameliorated bone microarchitecture. Moreover, DOE inhibited osteoclastogenesis independent of its cytoxicity and suppressed the expression of osteoclast-specific marker genes and proteins. Conclusion: DOE can effectively prevent ovariectomy-induced bone loss in vivo and inhibit osteoclastogenesis in vitro. PMID:29379436

Batch Tests To Determine Activity Distribution and Kinetic Parameters for Acetate Utilization in Expanded-Bed Anaerobic Reactors

PubMed Central

Fox, Peter; Suidan, Makram T.

1990-01-01

Batch tests to measure maximum acetate utilization rates were used to determine the distribution of acetate utilizers in expanded-bed sand and expanded-bed granular activated carbon (GAC) reactors. The reactors were fed a mixture of acetate and 3-ethylphenol, and they contained the same predominant aceticlastic methanogen, Methanothrix sp. Batch tests were performed both on the entire reactor contents and with media removed from the reactors. Results indicated that activity was evenly distributed within the GAC reactors, whereas in the sand reactor a sludge blanket on top of the sand bed contained approximately 50% of the activity. The Monod half-velocity constant (Ks) for the acetate-utilizing methanogens in two expanded-bed GAC reactors was searched for by combining steady-state results with batch test data. All parameters necessary to develop a model with Monod kinetics were experimentally determined except for Ks. However, Ks was a function of the effluent 3-ethylphenol concentration, and batch test results demonstrated that maximum acetate utilization rates were not a function of the effluent 3-ethylphenol concentration. Addition of a competitive inhibition term into the Monod expression predicted the dependence of Ks on the effluent 3-ethylphenol concentration. A two-parameter search determined a Ks of 8.99 mg of acetate per liter and a Ki of 2.41 mg of 3-ethylphenol per liter. Model predictions were in agreement with experimental observations for all effluent 3-ethylphenol concentrations. Batch tests measured the activity for a specific substrate and determined the distribution of activity in the reactor. The use of steady-state data in conjunction with batch test results reduced the number of unknown kinetic parameters and thereby reduced the uncertainty in the results and the assumptions made. PMID:16348175

Batch tests to determine activity distribution and kinetic parameters for acetate utilization in expanded-bed anaerobic reactors.

PubMed

Fox, P; Suidan, M T

1990-04-01

Batch tests to measure maximum acetate utilization rates were used to determine the distribution of acetate utilizers in expanded-bed sand and expanded-bed granular activated carbon (GAC) reactors. The reactors were fed a mixture of acetate and 3-ethylphenol, and they contained the same predominant aceticlastic methanogen, Methanothrix sp. Batch tests were performed both on the entire reactor contents and with media removed from the reactors. Results indicated that activity was evenly distributed within the GAC reactors, whereas in the sand reactor a sludge blanket on top of the sand bed contained approximately 50% of the activity. The Monod half-velocity constant (K(s)) for the acetate-utilizing methanogens in two expanded-bed GAC reactors was searched for by combining steady-state results with batch test data. All parameters necessary to develop a model with Monod kinetics were experimentally determined except for K(s). However, K(s) was a function of the effluent 3-ethylphenol concentration, and batch test results demonstrated that maximum acetate utilization rates were not a function of the effluent 3-ethylphenol concentration. Addition of a competitive inhibition term into the Monod expression predicted the dependence of K(s) on the effluent 3-ethylphenol concentration. A two-parameter search determined a K(s) of 8.99 mg of acetate per liter and a K(i) of 2.41 mg of 3-ethylphenol per liter. Model predictions were in agreement with experimental observations for all effluent 3-ethylphenol concentrations. Batch tests measured the activity for a specific substrate and determined the distribution of activity in the reactor. The use of steady-state data in conjunction with batch test results reduced the number of unknown kinetic parameters and thereby reduced the uncertainty in the results and the assumptions made.

Reconstructing Mammalian Sleep Dynamics with Data Assimilation

PubMed Central

Sedigh-Sarvestani, Madineh; Schiff, Steven J.; Gluckman, Bruce J.

2012-01-01

Data assimilation is a valuable tool in the study of any complex system, where measurements are incomplete, uncertain, or both. It enables the user to take advantage of all available information including experimental measurements and short-term model forecasts of a system. Although data assimilation has been used to study other biological systems, the study of the sleep-wake regulatory network has yet to benefit from this toolset. We present a data assimilation framework based on the unscented Kalman filter (UKF) for combining sparse measurements together with a relatively high-dimensional nonlinear computational model to estimate the state of a model of the sleep-wake regulatory system. We demonstrate with simulation studies that a few noisy variables can be used to accurately reconstruct the remaining hidden variables. We introduce a metric for ranking relative partial observability of computational models, within the UKF framework, that allows us to choose the optimal variables for measurement and also provides a methodology for optimizing framework parameters such as UKF covariance inflation. In addition, we demonstrate a parameter estimation method that allows us to track non-stationary model parameters and accommodate slow dynamics not included in the UKF filter model. Finally, we show that we can even use observed discretized sleep-state, which is not one of the model variables, to reconstruct model state and estimate unknown parameters. Sleep is implicated in many neurological disorders from epilepsy to schizophrenia, but simultaneous observation of the many brain components that regulate this behavior is difficult. We anticipate that this data assimilation framework will enable better understanding of the detailed interactions governing sleep and wake behavior and provide for better, more targeted, therapies. PMID:23209396

Dendrobium officinale Orchid Extract Prevents Ovariectomy-Induced Osteoporosis in Vivo and Inhibits RANKL-Induced Osteoclast Differentiation in Vitro.

PubMed

Wang, Qi; Zi, Cheng-Ting; Wang, Jing; Wang, Yu-Na; Huang, Ye-Wei; Fu, Xue-Qi; Wang, Xuan-Jun; Sheng, Jun

2017-01-01

Background: Dendrobium officinale , a traditional Chinese medical herb with high value that is widely used in Asia, possesses many positive effects on human health, including anti-chronic inflammation, anti-obesity, and immune modulation properties; however, whether D. officinale has inhibitory effects on postmenopausal osteoporosis remains unknown. Objective: We investigated the effects of D. officinale extract (DOE) on ovariectomy-induced bone loss in vivo and on osteoclastogenesis in vitro . Methods: In vivo , female rats were divided into a sham-operated (sham) group and five ovariectomized (OVX) subgroups: OVX with vehicle (OVX), OVX with Xian-Ling-Gu-Bao capsule (240 mg/kg body weight/day), and OVX with low-, medium-, and high-dose DOE (150, 300, and 600 mg/kg body weight/day, respectively). Animals in each group were administered their corresponding treatments for 13 weeks. Body weight, serum biochemical parameters, uterine and femoral physical parameters, bone mineral density (BMD), bone biomechanical properties, and bone microarchitecture were obtained. In vitro , the effects of DOE on osteoclastogenesis were examined using RAW264.7 cells. The effects of DOE on osteoclastogenesis and the expression of osteoclast-specific marker genes and proteins were determined. Results: DOE effectively ameliorated serum biochemical parameters, especially alleviated estradiol (E2) deficiency and maintained calcium and phosphorus homeostasis. DOE improved uterine and femoral physical parameters. In addition, DOE improved femoral BMD and biomechanical properties. DOE significantly ameliorated bone microarchitecture. Moreover, DOE inhibited osteoclastogenesis independent of its cytoxicity and suppressed the expression of osteoclast-specific marker genes and proteins. Conclusion: DOE can effectively prevent ovariectomy-induced bone loss in vivo and inhibit osteoclastogenesis in vitro .

Estimating unknown parameters in haemophilia using expert judgement elicitation.

PubMed

Fischer, K; Lewandowski, D; Janssen, M P

2013-09-01

The increasing attention to healthcare costs and treatment efficiency has led to an increasing demand for quantitative data concerning patient and treatment characteristics in haemophilia. However, most of these data are difficult to obtain. The aim of this study was to use expert judgement elicitation (EJE) to estimate currently unavailable key parameters for treatment models in severe haemophilia A. Using a formal expert elicitation procedure, 19 international experts provided information on (i) natural bleeding frequency according to age and onset of bleeding, (ii) treatment of bleeds, (iii) time needed to control bleeding after starting secondary prophylaxis, (iv) dose requirements for secondary prophylaxis according to onset of bleeding, and (v) life-expectancy. For each parameter experts provided their quantitative estimates (median, P10, P90), which were combined using a graphical method. In addition, information was obtained concerning key decision parameters of haemophilia treatment. There was most agreement between experts regarding bleeding frequencies for patients treated on demand with an average onset of joint bleeding (1.7 years): median 12 joint bleeds per year (95% confidence interval 0.9-36) for patients ≤ 18, and 11 (0.8-61) for adult patients. Less agreement was observed concerning estimated effective dose for secondary prophylaxis in adults: median 2000 IU every other day The majority (63%) of experts expected that a single minor joint bleed could cause irreversible damage, and would accept up to three minor joint bleeds or one trauma related joint bleed annually on prophylaxis. Expert judgement elicitation allowed structured capturing of quantitative expert estimates. It generated novel data to be used in computer modelling, clinical care, and trial design. © 2013 John Wiley & Sons Ltd.

A generic standard additions based method to determine endogenous analyte concentrations by immunoassays to overcome complex biological matrix interference.

PubMed

Pang, Susan; Cowen, Simon

2017-12-13

We describe a novel generic method to derive the unknown endogenous concentrations of analyte within complex biological matrices (e.g. serum or plasma) based upon the relationship between the immunoassay signal response of a biological test sample spiked with known analyte concentrations and the log transformed estimated total concentration. If the estimated total analyte concentration is correct, a portion of the sigmoid on a log-log plot is very close to linear, allowing the unknown endogenous concentration to be estimated using a numerical method. This approach obviates conventional relative quantification using an internal standard curve and need for calibrant diluent, and takes into account the individual matrix interference on the immunoassay by spiking the test sample itself. This technique is based on standard additions for chemical analytes. Unknown endogenous analyte concentrations within even 2-fold diluted human plasma may be determined reliably using as few as four reaction wells.

HIGHLY DIASTEREOSELECTIVE MICHAEL REACTION UNDER SOLVENT-FREE CONDITIONS USING MICROWAVES: CONJUGATE ADDITION OF FLAVANONE TO ITS CHALCONE PRECURSOR

EPA Science Inventory

Microwave-assisted reaction of 2'-hydroxychalcones in the presence of DBU resulted in the formation of hitherto unknown dimers by conjugate addition of the intermediate cyclic ketone to the starting enone.

Benefits from bremsstrahlung distribution evaluation to get unknown information from specimen in SEM and TEM

NASA Astrophysics Data System (ADS)

Eggert, F.; Camus, P. P.; Schleifer, M.; Reinauer, F.

2018-01-01

The energy-dispersive X-ray spectrometer (EDS or EDX) is a commonly used device to characterise the composition of investigated material in scanning and transmission electron microscopes (SEM and TEM). One major benefit compared to wavelength-dispersive X-ray spectrometers (WDS) is that EDS systems collect the entire spectrum simultaneously. Therefore, not only are all emitted characteristic X-ray lines in the spectrum, but also the complete bremsstrahlung distribution is included. It is possible to get information about the specimen even from this radiation, which is usually perceived more as a disturbing background. This is possible by using theoretical model knowledge about bremsstrahlung excitation and absorption in the specimen in comparison to the actual measured spectrum. The core aim of this investigation is to present a method for better bremsstrahlung fitting in unknown geometry cases by variation of the geometry parameters and to utilise this knowledge also for characteristic radiation evaluation. A method is described, which allows the parameterisation of the true X-ray absorption conditions during spectrum acquisition. An ‘effective tilt’ angle parameter is determined by evaluation of the bremsstrahlung shape of the measured SEM spectra. It is useful for bremsstrahlung background approximation, with exact calculations of the absorption edges below the characteristic peaks, required for P/B-ZAF model based quantification methods. It can even be used for ZAF based quantification models as a variable input parameter. The analytical results are then much more reliable for the different absorption effects from irregular specimen surfaces because the unknown absorption dependency is considered. Finally, the method is also applied for evaluation of TEM spectra. In this case, the real physical parameter optimisation is with sample thickness (mass thickness), which is influencing the emitted and measured spectrum due to different absorption with TEM measurements. The effects are in the very low energy part of the spectrum, and are much more visible with most recent windowless TEM detectors. The thickness of the sample can be determined in this way from the measured bremsstrahlung spectrum shape.

Analytical Incorporation of Velocity Parameters into Ice Sheet Elevation Change Rate Computations

NASA Astrophysics Data System (ADS)

Nagarajan, S.; Ahn, Y.; Teegavarapu, R. S. V.

2014-12-01

NASA, ESA and various other agencies have been collecting laser, optical and RADAR altimetry data through various missions to study the elevation changes of the Cryosphere. The laser altimetry collected by various airborne and spaceborne missions provides multi-temporal coverage of Greenland and Antarctica since 1993 to now. Though these missions have increased the data coverage, considering the dynamic nature of the ice surface, it is still sparse both spatially and temporally for accurate elevation change detection studies. The temporal and spatial gaps are usually filled by interpolation techniques. This presentation will demonstrate a method to improve the temporal interpolation. Considering the accuracy, repeat coverage and spatial distribution, the laser scanning data has been widely used to compute elevation change rate of Greenland and Antarctica ice sheets. A major problem with these approaches is non-consideration of ice sheet velocity dynamics into change rate computations. Though the correlation between velocity and elevation change rate have been noticed by Hurkmans et al., 2012, the corrections for velocity changes were applied after computing elevation change rates by assuming linear or higher polynomial relationship. This research will discuss the possibilities of parameterizing ice sheet dynamics as unknowns (dX and dY) in the adjustment mathematical model that computes elevation change (dZ) rates. It is a simultaneous computation of changes in all three directions of the ice surface. Also, the laser points between two time epochs in a crossover area have different distribution and count. Therefore, a registration method that does not require point-to-point correspondence is required to recover the unknown elevation and velocity parameters. This research will experiment the possibilities of registering multi-temporal datasets using volume minimization algorithm, which determines the unknown dX, dY and dZ that minimizes the volume between two or more time-epoch point clouds. In order to make use of other existing data as well as to constrain the adjustment, InSAR velocity will be used as initial values for the parameters dX and dY. The presentation will discuss the results of analytical incorporation of parameters and the volume based registration method for a test site in Greenland.

Surface wave chemical detector using optical radiation

DOEpatents

Thundat, Thomas G.; Warmack, Robert J.

2007-07-17

A surface wave chemical detector comprising at least one surface wave substrate, each of said substrates having a surface wave and at least one measurable surface wave parameter; means for exposing said surface wave substrate to an unknown sample of at least one chemical to be analyzed, said substrate adsorbing said at least one chemical to be sensed if present in said sample; a source of radiation for radiating said surface wave substrate with different wavelengths of said radiation, said surface wave parameter being changed by said adsorbing; and means for recording signals representative of said surface wave parameter of each of said surface wave substrates responsive to said radiation of said different wavelengths, measurable changes of said parameter due to adsorbing said chemical defining a unique signature of a detected chemical.

Optimized tuner selection for engine performance estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L. (Inventor); Garg, Sanjay (Inventor)

2013-01-01

A methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. Theoretical Kalman filter estimation error bias and variance values are derived at steady-state operating conditions, and the tuner selection routine is applied to minimize these values. The new methodology yields an improvement in on-line engine performance estimation accuracy.

Optimal Tuner Selection for Kalman-Filter-Based Aircraft Engine Performance Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2011-01-01

An emerging approach in the field of aircraft engine controls and system health management is the inclusion of real-time, onboard models for the inflight estimation of engine performance variations. This technology, typically based on Kalman-filter concepts, enables the estimation of unmeasured engine performance parameters that can be directly utilized by controls, prognostics, and health-management applications. A challenge that complicates this practice is the fact that an aircraft engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. Through Kalman-filter-based estimation techniques, the level of engine performance degradation can be estimated, given that there are at least as many sensors as health parameters to be estimated. However, in an aircraft engine, the number of sensors available is typically less than the number of health parameters, presenting an under-determined estimation problem. A common approach to address this shortcoming is to estimate a subset of the health parameters, referred to as model tuning parameters. The problem/objective is to optimally select the model tuning parameters to minimize Kalman-filterbased estimation error. A tuner selection technique has been developed that specifically addresses the under-determined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine that seeks to minimize the theoretical mean-squared estimation error of the Kalman filter. This approach can significantly reduce the error in onboard aircraft engine parameter estimation applications such as model-based diagnostic, controls, and life usage calculations. The advantage of the innovation is the significant reduction in estimation errors that it can provide relative to the conventional approach of selecting a subset of health parameters to serve as the model tuning parameter vector. Because this technique needs only to be performed during the system design process, it places no additional computation burden on the onboard Kalman filter implementation. The technique has been developed for aircraft engine onboard estimation applications, as this application typically presents an under-determined estimation problem. However, this generic technique could be applied to other industries using gas turbine engine technology.

Radioassay kit for method of determining methotrexate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Charm, S.E.; Blair, H.E.

1978-07-25

A radioassay system for the determination of methotrexate in biological fluids based on the competitive binding of labeled and unlabeled methotrexate to the enzyme dihydrofolate reductase. Samples of unknown methotrexate level are mixed with I/sup 125/ labeled methotrexate. A portion of the total methotrexate present is bound by the addition of enzyme, and the unbound methotrexate is removed with charcoal. The level of bound I/sup 125/ labeled methotrexate is measured in a gamma counter. To calculate the methotrexate level of the unknown samples, the displacement of bound labeled methotrexate caused by the unknowns is compared to the displacement caused bymore » known methotrexate standards.« less

Orbit Determination and Maneuver Detection Using Event Representation with Thrust-Fourier-Coefficients

NASA Astrophysics Data System (ADS)

Lubey, D.; Ko, H.; Scheeres, D.

The classical orbit determination (OD) method of dealing with unknown maneuvers is to restart the OD process with post-maneuver observations. However, it is also possible to continue the OD process through such unknown maneuvers by representing those unknown maneuvers with an appropriate event representation. It has been shown in previous work (Ko & Scheeres, JGCD 2014) that any maneuver performed by a satellite transitioning between two arbitrary orbital states can be represented as an equivalent maneuver connecting those two states using Thrust-Fourier-Coefficients (TFCs). Event representation using TFCs rigorously provides a unique control law that can generate the desired secular behavior for a given unknown maneuver. This paper presents applications of this representation approach to orbit prediction and maneuver detection problem across unknown maneuvers. The TFCs are appended to a sequential filter as an adjoint state to compensate unknown perturbing accelerations and the modified filter estimates the satellite state and thrust coefficients by processing OD across the time of an unknown maneuver. This modified sequential filter with TFCs is capable of fitting tracking data and maintaining an OD solution in the presence of unknown maneuvers. Also, the modified filter is found effective in detecting a sudden change in TFC values which indicates a maneuver. In order to illustrate that the event representation approach with TFCs is robust and sufficiently general to be easily adjustable, different types of measurement data are processed with the filter in a realistic LEO setting. Further, cases with mis-modeling of non-gravitational force are included in our study to verify the versatility and efficiency of our presented algorithm. Simulation results show that the modified sequential filter with TFCs can detect and estimate the orbit and thrust parameters in the presence of unknown maneuvers with or without measurement data during maneuvers. With no measurement data during maneuvers, the modified filter with TFCs uses an existing pre-maneuver orbit solution to compute a post-maneuver orbit solution by forcing TFCs to compensate for an unknown maneuver. With observation data available during maneuvers, maneuver start time and stop time is determined

Levothyroxine sodium revisited: A wholistic structural elucidation approach of new impurities via HPLC-HRMS/MS, on-line H/D exchange, NMR spectroscopy and chemical synthesis.

PubMed

Ruggenthaler, M; Grass, J; Schuh, W; Huber, C G; Reischl, R J

2017-02-20

The structural elucidation of unknown pharmaceutical impurities plays an important role in the quality control of newly developed and well-established active pharmaceutical ingredients (APIs). The United States Pharmacopeia (USP) monograph for the API Levothyroxine Sodium, a synthetic thyroid hormone, features two high pressure liquid chromatography (HPLC) methods using UV-VIS absorption detection to determine organic impurities in the drug substance. The impurity profile of the first USP method ("Procedure 1") has already been extensively studied, however for the second method ("Procedure 2"), which exhibits a significantly different impurity profile, no wholistic structural elucidation of impurities has been performed yet. Applying minor modifications to the chromatographic parameters of USP "Procedure 2" and using various comprehensive structural elucidation methods such as high resolution tandem mass spectrometry with on-line hydrogen-deuterium (H/D) exchange or two-dimensional nuclear magnetic resonance spectroscopy (NMR) we gained new insights about the complex impurity profile of the synthetic thyroid hormone. This resulted in the characterization of 24 compounds previously unknown to literature and the introduction of two new classes of Levothyroxine Sodium impurities. Five novel compounds were unambiguously identified via isolation or synthesis of reference substances and subsequent NMR spectroscopic investigation. Additionally, Collision-Induced Dissociation (CID)-type fragmentation of identified major impurities as well as neutral loss fragmentation patterns of many characterized impurities were discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

Persistent Surveillance of Transient Events with Unknown Statistics

DTIC Science & Technology

2016-12-18

different bird species by a documentary maker is shown in Fig. 1. Additional examples of scenarios following this setting include robots patrolling the...persistent monitoring application in which a documentary maker would like to monitor three different species of birds appearing in three discrete, species...specific locations. Bird sightings at each location follow a stochastic process with a rate that is initially unknown to the documentary maker and must

Magnetic resonance appearance of monoclonal gammopathies of unknown significance and multiple myeloma. The GRI Study Group.

PubMed

Bellaïche, L; Laredo, J D; Lioté, F; Koeger, A C; Hamze, B; Ziza, J M; Pertuiset, E; Bardin, T; Tubiana, J M

1997-11-01

A prospective multicenter study. To evaluate the use of magnetic resonance imaging, in the differentiation between monoclonal gammopathies of unknown significance and multiple myeloma. Although multiple myeloma has been studied extensively with magnetic resonance imaging, to the authors' knowledge, no study has evaluated the clinical interest of magnetic resonance imaging in the differentiation between monoclonal gammopathies of unknown significance and multiple myeloma. The magnetic resonance examinations of the thoracolumbar spine in 24 patients with newly diagnosed monoclonal gammopathies of unknown significance were compared with those performed in 44 patients with newly diagnosed nontreated multiple myeloma. All findings on magnetic resonance examination performed in patients with monoclonal gammopathies of unknown significance were normal, whereas findings on 38 (86%) of the 44 magnetic resonance examinations performed in patients with multiple myeloma were abnormal. Magnetic resonance imaging can be considered as an additional diagnostic tool in differentiating between monoclonal gammopathies of unknown significance and multiple myeloma, which may be helpful when routine criteria are not sufficient. An abnormal finding on magnetic resonance examination in a patient with monoclonal gammopathies of unknown significance should suggest the diagnosis of multiple myeloma after other causes of marrow signal abnormalities are excluded. Magnetic resonance imaging also may be proposed in the long-term follow-up of monoclonal gammopathies of unknown significance when a new biologic or clinical event suggests the diagnosis of malignant monoclonal gammopathy.

An algebraic aspect of Pareto mixture parameter estimation using censored sample: A Bayesian approach.

PubMed

Saleem, Muhammad; Sharif, Kashif; Fahmi, Aliya

2018-04-27

Applications of Pareto distribution are common in reliability, survival and financial studies. In this paper, A Pareto mixture distribution is considered to model a heterogeneous population comprising of two subgroups. Each of two subgroups is characterized by the same functional form with unknown distinct shape and scale parameters. Bayes estimators have been derived using flat and conjugate priors using squared error loss function. Standard errors have also been derived for the Bayes estimators. An interesting feature of this study is the preparation of components of Fisher Information matrix.

Dynamic calibration of higher eigenmode parameters of a cantilever in atomic force microscopy by using tip–surface interactions

DOE PAGES

Borysov, Stanislav S.; Forchheimer, Daniel; Haviland, David B.

2014-10-29

Here we present a theoretical framework for the dynamic calibration of the higher eigenmode parameters (stiffness and optical lever inverse responsivity) of a cantilever. The method is based on the tip–surface force reconstruction technique and does not require any prior knowledge of the eigenmode shape or the particular form of the tip–surface interaction. The calibration method proposed requires a single-point force measurement by using a multimodal drive and its accuracy is independent of the unknown physical amplitude of a higher eigenmode.

A Modified Cramer-von Mises and Anderson-Darling Test for the Weibull Distribution with Unknown Location and Scale Parameters.

DTIC Science & Technology

1981-12-01

preventing the generation of 16 6 negative location estimators. Because of the invariant pro- perty of the EDF statistics, this transformation will...likelihood. If the parameter estimation method developed by Harter and Moore is used, care must be taken to prevent the location estimators from being...vs A 2 Critical Values, Level-.Ol, n-30 128 , 0 6N m m • w - APPENDIX E Computer Prgrams 129 Program to Calculate the Cramer-von Mises Critical Values

Evaluation of orbits with incomplete knowledge of the mathematical expectancy and the matrix of covariation of errors

NASA Technical Reports Server (NTRS)

Bakhshiyan, B. T.; Nazirov, R. R.; Elyasberg, P. E.

1980-01-01

The problem of selecting the optimal algorithm of filtration and the optimal composition of the measurements is examined assuming that the precise values of the mathematical expectancy and the matrix of covariation of errors are unknown. It is demonstrated that the optimal algorithm of filtration may be utilized for making some parameters more precise (for example, the parameters of the gravitational fields) after preliminary determination of the elements of the orbit by a simpler method of processing (for example, the method of least squares).

Power maximization of a point absorber wave energy converter using improved model predictive control

NASA Astrophysics Data System (ADS)

Milani, Farideh; Moghaddam, Reihaneh Kardehi

2017-08-01

This paper considers controlling and maximizing the absorbed power of wave energy converters for irregular waves. With respect to physical constraints of the system, a model predictive control is applied. Irregular waves' behavior is predicted by Kalman filter method. Owing to the great influence of controller parameters on the absorbed power, these parameters are optimized by imperialist competitive algorithm. The results illustrate the method's efficiency in maximizing the extracted power in the presence of unknown excitation force which should be predicted by Kalman filter.

A method for nonlinear exponential regression analysis

NASA Technical Reports Server (NTRS)

Junkin, B. G.

1971-01-01

A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.

Robust model reference adaptive output feedback tracking for uncertain linear systems with actuator fault based on reinforced dead-zone modification.

PubMed

Bagherpoor, H M; Salmasi, Farzad R

2015-07-01

In this paper, robust model reference adaptive tracking controllers are considered for Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) linear systems containing modeling uncertainties, unknown additive disturbances and actuator fault. Two new lemmas are proposed for both SISO and MIMO, under which dead-zone modification rule is improved such that the tracking error for any reference signal tends to zero in such systems. In the conventional approach, adaption of the controller parameters is ceased inside the dead-zone region which results tracking error, while preserving the system stability. In the proposed scheme, control signal is reinforced with an additive term based on tracking error inside the dead-zone which results in full reference tracking. In addition, no Fault Detection and Diagnosis (FDD) unit is needed in the proposed approach. Closed loop system stability and zero tracking error are proved by considering a suitable Lyapunov functions candidate. It is shown that the proposed control approach can assure that all the signals of the close loop system are bounded in faulty conditions. Finally, validity and performance of the new schemes have been illustrated through numerical simulations of SISO and MIMO systems in the presence of actuator faults, modeling uncertainty and output disturbance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Reason, emotion and decision-making: risk and reward computation with feeling.

PubMed

Quartz, Steven R

2009-05-01

Many models of judgment and decision-making posit distinct cognitive and emotional contributions to decision-making under uncertainty. Cognitive processes typically involve exact computations according to a cost-benefit calculus, whereas emotional processes typically involve approximate, heuristic processes that deliver rapid evaluations without mental effort. However, it remains largely unknown what specific parameters of uncertain decision the brain encodes, the extent to which these parameters correspond to various decision-making frameworks, and their correspondence to emotional and rational processes. Here, I review research suggesting that emotional processes encode in a precise quantitative manner the basic parameters of financial decision theory, indicating a reorientation of emotional and cognitive contributions to risky choice.

PARTICLE FILTERING WITH SEQUENTIAL PARAMETER LEARNING FOR NONLINEAR BOLD fMRI SIGNALS.

PubMed

Xia, Jing; Wang, Michelle Yongmei

Analyzing the blood oxygenation level dependent (BOLD) effect in the functional magnetic resonance imaging (fMRI) is typically based on recent ground-breaking time series analysis techniques. This work represents a significant improvement over existing approaches to system identification using nonlinear hemodynamic models. It is important for three reasons. First, instead of using linearized approximations of the dynamics, we present a nonlinear filtering based on the sequential Monte Carlo method to capture the inherent nonlinearities in the physiological system. Second, we simultaneously estimate the hidden physiological states and the system parameters through particle filtering with sequential parameter learning to fully take advantage of the dynamic information of the BOLD signals. Third, during the unknown static parameter learning, we employ the low-dimensional sufficient statistics for efficiency and avoiding potential degeneration of the parameters. The performance of the proposed method is validated using both the simulated data and real BOLD fMRI data.

On selecting a prior for the precision parameter of Dirichlet process mixture models

USGS Publications Warehouse

Dorazio, R.M.

2009-01-01

In hierarchical mixture models the Dirichlet process is used to specify latent patterns of heterogeneity, particularly when the distribution of latent parameters is thought to be clustered (multimodal). The parameters of a Dirichlet process include a precision parameter ?? and a base probability measure G0. In problems where ?? is unknown and must be estimated, inferences about the level of clustering can be sensitive to the choice of prior assumed for ??. In this paper an approach is developed for computing a prior for the precision parameter ?? that can be used in the presence or absence of prior information about the level of clustering. This approach is illustrated in an analysis of counts of stream fishes. The results of this fully Bayesian analysis are compared with an empirical Bayes analysis of the same data and with a Bayesian analysis based on an alternative commonly used prior.

A modified Leslie-Gower predator-prey interaction model and parameter identifiability

NASA Astrophysics Data System (ADS)

Tripathi, Jai Prakash; Meghwani, Suraj S.; Thakur, Manoj; Abbas, Syed

2018-01-01

In this work, bifurcation and a systematic approach for estimation of identifiable parameters of a modified Leslie-Gower predator-prey system with Crowley-Martin functional response and prey refuge is discussed. Global asymptotic stability is discussed by applying fluctuation lemma. The system undergoes into Hopf bifurcation with respect to parameters intrinsic growth rate of predators (s) and prey reserve (m). The stability of Hopf bifurcation is also discussed by calculating Lyapunov number. The sensitivity analysis of the considered model system with respect to all variables is performed which also supports our theoretical study. To estimate the unknown parameter from the data, an optimization procedure (pseudo-random search algorithm) is adopted. System responses and phase plots for estimated parameters are also compared with true noise free data. It is found that the system dynamics with true set of parametric values is similar to the estimated parametric values. Numerical simulations are presented to substantiate the analytical findings.

Chem Lab Simulation #3 and #4.

ERIC Educational Resources Information Center

Pipeline, 1983

1983-01-01

Two copy-protected chemistry simulations (for Apple II) are described. The first demonstrates Hess' law of heat reaction. The second illustrates how heat of vaporization can be used to determine an unknown liquid and shows how to find thermodynamic parameters in an equilibrium reaction. Both are self-instructing and use high-resolution graphics.…

General Anisotropy Identification of Paperboard with Virtual Fields Method

Treesearch

J.M. Considine; F. Pierron; K.T. Turner; D.W. Vahey

2014-01-01

This work extends previous efforts in plate bending of Virtual Fields Method (VFM) parameter identification to include a general 2-D anisotropicmaterial. Such an extension was needed for instances in which material principal directions are unknown or when specimen orientation is not aligned with material principal directions. A new fixture with a multiaxial force...

Interviewing a Silent (Radioactive) Witness through Nuclear Forensic Analysis.

PubMed

Mayer, Klaus; Wallenius, Maria; Varga, Zsolt

2015-12-01

Nuclear forensics is a relatively young discipline in science which aims at providing information on nuclear material of unknown origin. The determination of characteristic parameters through tailored analytical techniques enables establishing linkages to the material's processing history and hence provides hints on its place and date of production and on the intended use.

78 FR 59555 - Endangered and Threatened Wildlife and Plants; Designation of Critical Habitat for the Fluted...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-26

... physical and chemical water quality parameters (such as temperature, dissolved oxygen, pH, and conductivity... unknown. High temperatures can reduce dissolved oxygen concentrations in the water, which slows growth... encystment, increase oxygen consumption, reduce the speed in which they orient themselves in the substrate...

A Spline Regression Model for Latent Variables

ERIC Educational Resources Information Center

Harring, Jeffrey R.

2014-01-01

Spline (or piecewise) regression models have been used in the past to account for patterns in observed data that exhibit distinct phases. The changepoint or knot marking the shift from one phase to the other, in many applications, is an unknown parameter to be estimated. As an extension of this framework, this research considers modeling the…

Estimation in SEM: A Concrete Example

ERIC Educational Resources Information Center

Ferron, John M.; Hess, Melinda R.

2007-01-01

A concrete example is used to illustrate maximum likelihood estimation of a structural equation model with two unknown parameters. The fitting function is found for the example, as are the vector of first-order partial derivatives, the matrix of second-order partial derivatives, and the estimates obtained from each iteration of the Newton-Raphson…

Unitarity and predictiveness in new Higgs inflation

NASA Astrophysics Data System (ADS)

Fumagalli, Jacopo; Mooij, Sander; Postma, Marieke

2018-03-01

In new Higgs inflation the Higgs kinetic terms are non-minimally coupled to the Einstein tensor, allowing the Higgs field to play the role of the inflaton. The new interaction is non-renormalizable, and the model only describes physics below some cutoff scale. Even if the unknown UV physics does not affect the tree level inflaton potential significantly, it may still enter at loop level and modify the running of the Standard Model (SM) parameters. This is analogous to what happens in the original model for Higgs inflation. A key difference, though, is that in new Higgs inflation the inflationary predictions are sensitive to this running. Thus the boundary conditions at the EW scale as well as the unknown UV completion may leave a signature on the inflationary parameters. However, this dependence can be evaded if the kinetic terms of the SM fermions and gauge fields are non-minimally coupled to gravity as well. Our approach to determine the model's UV dependence and the connection between low and high scale physics can be used in any particle physics model of inflation.

Robust inertia-free attitude takeover control of postcapture combined spacecraft with guaranteed prescribed performance.

PubMed

Luo, Jianjun; Wei, Caisheng; Dai, Honghua; Yin, Zeyang; Wei, Xing; Yuan, Jianping

2018-03-01

In this paper, a robust inertia-free attitude takeover control scheme with guaranteed prescribed performance is investigated for postcapture combined spacecraft with consideration of unmeasurable states, unknown inertial property and external disturbance torque. Firstly, to estimate the unavailable angular velocity of combination accurately, a novel finite-time-convergent tracking differentiator is developed with a quite computationally achievable structure free from the unknown nonlinear dynamics of combined spacecraft. Then, a robust inertia-free prescribed performance control scheme is proposed, wherein, the transient and steady-state performance of combined spacecraft is first quantitatively studied by stabilizing the filtered attitude tracking errors. Compared with the existing works, the prominent advantage is that no parameter identifications and no neural or fuzzy nonlinear approximations are needed, which decreases the complexity of robust controller design dramatically. Moreover, the prescribed performance of combined spacecraft is guaranteed a priori without resorting to repeated regulations of the controller parameters. Finally, four illustrative examples are employed to validate the effectiveness of the proposed control scheme and tracking differentiator. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Finite-time tracking control for multiple non-holonomic mobile robots based on visual servoing

NASA Astrophysics Data System (ADS)

Ou, Meiying; Li, Shihua; Wang, Chaoli

2013-12-01

This paper investigates finite-time tracking control problem of multiple non-holonomic mobile robots via visual servoing. It is assumed that the pinhole camera is fixed to the ceiling, and camera parameters are unknown. The desired reference trajectory is represented by a virtual leader whose states are available to only a subset of the followers, and the followers have only interaction. First, the camera-objective visual kinematic model is introduced by utilising the pinhole camera model for each mobile robot. Second, a unified tracking error system between camera-objective visual servoing model and desired reference trajectory is introduced. Third, based on the neighbour rule and by using finite-time control method, continuous distributed cooperative finite-time tracking control laws are designed for each mobile robot with unknown camera parameters, where the communication topology among the multiple mobile robots is assumed to be a directed graph. Rigorous proof shows that the group of mobile robots converges to the desired reference trajectory in finite time. Simulation example illustrates the effectiveness of our method.

Estimation of Complex Permittivity of Composite Multilayer Material at Microwave Frequency Using Waveguide Measurements

NASA Technical Reports Server (NTRS)

Deshpande, Manohar D.; Dudley, Kenneth

2003-01-01

A simple method is presented to estimate the complex dielectric constants of individual layers of a multilayer composite material. Using the MatLab Optimization Tools simple MatLab scripts are written to search for electric properties of individual layers so as to match the measured and calculated S-parameters. A single layer composite material formed by using materials such as Bakelite, Nomex Felt, Fiber Glass, Woven Composite B and G, Nano Material #0, Cork, Garlock, of different thicknesses are tested using the present approach. Assuming the thicknesses of samples unknown, the present approach is shown to work well in estimating the dielectric constants and the thicknesses. A number of two layer composite materials formed by various combinations of above individual materials are tested using the present approach. However, the present approach could not provide estimate values close to their true values when the thicknesses of individual layers were assumed to be unknown. This is attributed to the difficulty in modelling the presence of airgaps between the layers while doing the measurement of S-parameters. A few examples of three layer composites are also presented.

Simple robust control laws for robot manipulators. Part 2: Adaptive case

NASA Technical Reports Server (NTRS)

Bayard, D. S.; Wen, J. T.

1987-01-01

A new class of asymptotically stable adaptive control laws is introduced for application to the robotic manipulator. Unlike most applications of adaptive control theory to robotic manipulators, this analysis addresses the nonlinear dynamics directly without approximation, linearization, or ad hoc assumptions, and utilizes a parameterization based on physical (time-invariant) quantities. This approach is made possible by using energy-like Lyapunov functions which retain the nonlinear character and structure of the dynamics, rather than simple quadratic forms which are ubiquitous to the adaptive control literature, and which have bound the theory tightly to linear systems with unknown parameters. It is a unique feature of these results that the adaptive forms arise by straightforward certainty equivalence adaptation of their nonadaptive counterparts found in the companion to this paper (i.e., by replacing unknown quantities by their estimates) and that this simple approach leads to asymptotically stable closed-loop adaptive systems. Furthermore, it is emphasized that this approach does not require convergence of the parameter estimates (i.e., via persistent excitation), invertibility of the mass matrix estimate, or measurement of the joint accelerations.

Desired Accuracy Estimation of Noise Function from ECG Signal by Fuzzy Approach

PubMed Central

Vahabi, Zahra; Kermani, Saeed

2012-01-01

Unknown noise and artifacts present in medical signals with non-linear fuzzy filter will be estimated and then removed. An adaptive neuro-fuzzy interference system which has a non-linear structure presented for the noise function prediction by before Samples. This paper is about a neuro-fuzzy method to estimate unknown noise of Electrocardiogram signal. Adaptive neural combined with Fuzzy System to construct a fuzzy Predictor. For this system setting parameters such as the number of Membership Functions for each input and output, training epochs, type of MFs for each input and output, learning algorithm and etc. is determined by learning data. At the end simulated experimental results are presented for proper validation. PMID:23717810

Sensitivity of predicted scaling and permeability in Enhanced Geothermal Systems to Thermodynamic Data and Activity Models

NASA Astrophysics Data System (ADS)

Hingerl, Ferdinand F.; Wagner, Thomas; Kulik, Dmitrii A.; Kosakowski, Georg; Driesner, Thomas; Thomsen, Kaj

2010-05-01

A consortium of research groups from ETH Zurich, EPF Lausanne, the Paul Scherrer Institut and the University of Bonn collaborates in a comprehensive program of basic research on key aspects of the Enhanced Geothermal Systems (EGSs). As part of this GEOTHERM project (www.geotherm.ethz.ch), we concentrate on the fundamental investigation of thermodynamic models suitable for describing fluid-rock interactions at geothermal conditions. Predictions of the fluid-rock interaction in EGS still face several major challenges. Slight variations in the input thermodynamic and kinetic parameters may result in significant differences in the predicted mineral solubilities and stable assemblage. Realistic modeling of mineral precipitation in turn has implications onto our understanding of the permeability evolution of the geothermal reservoir, as well as the scaling in technical installations. In order to reasonably model an EGS, thermodynamic databases and activity models must be tailored to geothermal conditions. We therefore implemented in GEMS code the Pitzer formalism, which is the standard model used for computing thermodynamic excess properties of brines at elevated temperatures and pressures. This model, however, depends on a vast amount of interaction parameters, which are to a substantial extend unknown. Furthermore, a high order polynomial temperature interpolation makes extrapolation unreliable if not impossible. As an alternative we additionally implemented the EUNIQUAC activity model. EUNIQUAC requires fewer empirical fit parameters (only binary interaction parameters needed) and uses simpler and more stable temperature and pressure extrapolations. This results in an increase in computation speed, which is of crucial importance when performing coupled long term simulations of geothermal reservoirs. To achieve better performance under geothermal conditions, we are currently partly reformulating EUNIQUAC and refitting the existing parameter set. First results of the Pitzer-EUNIQUAC benchmark applied to relevant aqueous solutions at elevated temperature, pressure and ionic strength will be presented.

Censored data treatment using additional information in intelligent medical systems

NASA Astrophysics Data System (ADS)

Zenkova, Z. N.

2015-11-01

Statistical procedures are a very important and significant part of modern intelligent medical systems. They are used for proceeding, mining and analysis of different types of the data about patients and their diseases; help to make various decisions, regarding the diagnosis, treatment, medication or surgery, etc. In many cases the data can be censored or incomplete. It is a well-known fact that censorship considerably reduces the efficiency of statistical procedures. In this paper the author makes a brief review of the approaches which allow improvement of the procedures using additional information, and describes a modified estimation of an unknown cumulative distribution function involving additional information about a quantile which is known exactly. The additional information is used by applying a projection of a classical estimator to a set of estimators with certain properties. The Kaplan-Meier estimator is considered as an estimator of the unknown cumulative distribution function, the properties of the modified estimator are investigated for a case of a single right censorship by means of simulations.

Optimization of ISOCS Parameters for Quantitative Non-Destructive Analysis of Uranium in Bulk Form

NASA Astrophysics Data System (ADS)

Kutniy, D.; Vanzha, S.; Mikhaylov, V.; Belkin, F.

2011-12-01

Quantitative calculation of the isotopic masses of fissionable U and Pu is important for forensic analysis of nuclear materials. γ-spectrometry is the most commonly applied tool for qualitative detection and analysis of key radionuclides in nuclear materials. Relative isotopic measurement of U and Pu may be obtained from γ-spectra through application of special software such as MGAU (Multi-Group Analysis for Uranium, LLNL) or FRAM (Fixed-Energy Response Function Analysis with Multiple Efficiency, LANL). If the concentration of U/Pu in the matrix is unknown, however, isotopic masses cannot be calculated. At present, active neutron interrogation is the only practical alternative for non-destructive quantification of fissionable isotopes of U and Pu. An active well coincidence counter (AWCC), an alternative for analyses of uranium materials, has the following disadvantages: 1) The detection of small quantities (≤100 g) of 235U is not possible in many models; 2) Representative standards that capture the geometry, density and chemical composition of the analyzed unknown are required for precise analysis; and 3) Specimen size is severely restricted by the size of the measuring chamber. These problems may be addressed using modified γ-spectrometry techniques based on a coaxial HPGe-detector and ISOCS software (In Situ Object Counting System software, Canberra). We present data testing a new gamma-spectrometry method uniting actinide detection with commonly utilized software, modified for application in determining the masses of the fissionable isotopes in unknown samples of nuclear materials. The ISOCS software, widely used in radiation monitoring, calculates the detector efficiency curve in a specified geometry and range of photon energies. In describing the geometry of the source-detector, it is necessary to clearly describe the distance between the source and the detector, the material and the thickness of the walls of the container, as well as material, density and chemical composition of the matrix of the specimen. Obviously, not all parameters can be characterized when measuring samples of unknown composition or uranium in bulk form. Because of this, and especially for uranium materials, the IAEA developed an ISOCS optimization procedure. The target values for the optimization are Μmatrixfixed, the matrix mass determined by weighing with a known mass container, and Εfixed, the 235U enrichment, determined by MGAU. Target values are fitted by varying the matrix density (ρ), and the concentration of uranium in the matrix of the unknown (w). For each (ρi, wi), an efficiency curve is generated, and the masses of uranium isotopes, Μ235Ui and Μ238Ui, determined using spectral activity data and known specific activities for U. Finally, fitted parameters are obtained for Μmatrixi = Μmatrixfixed ± 1σ, Εi = Εfixed ± 1σ, as well as important parameters (ρi, wi, Μ235Ui, Μ238Ui, ΜUi). We examined multiple forms of uranium (powdered, pressed, and scrap UO2 and U3O8) to test this method for its utility in accurately identifying the mass and enrichment of uranium materials, and will present the results of this research.

Automatic alignment for three-dimensional tomographic reconstruction

NASA Astrophysics Data System (ADS)

van Leeuwen, Tristan; Maretzke, Simon; Joost Batenburg, K.

2018-02-01

In tomographic reconstruction, the goal is to reconstruct an unknown object from a collection of line integrals. Given a complete sampling of such line integrals for various angles and directions, explicit inverse formulas exist to reconstruct the object. Given noisy and incomplete measurements, the inverse problem is typically solved through a regularized least-squares approach. A challenge for both approaches is that in practice the exact directions and offsets of the x-rays are only known approximately due to, e.g. calibration errors. Such errors lead to artifacts in the reconstructed image. In the case of sufficient sampling and geometrically simple misalignment, the measurements can be corrected by exploiting so-called consistency conditions. In other cases, such conditions may not apply and we have to solve an additional inverse problem to retrieve the angles and shifts. In this paper we propose a general algorithmic framework for retrieving these parameters in conjunction with an algebraic reconstruction technique. The proposed approach is illustrated by numerical examples for both simulated data and an electron tomography dataset.

Fast estimate of Hartley entropy in image sharpening

NASA Astrophysics Data System (ADS)

Krbcová, Zuzana; Kukal, Jaromír.; Svihlik, Jan; Fliegel, Karel

2016-09-01

Two classes of linear IIR filters: Laplacian of Gaussian (LoG) and Difference of Gaussians (DoG) are frequently used as high pass filters for contextual vision and edge detection. They are also used for image sharpening when linearly combined with the original image. Resulting sharpening filters are radially symmetric in spatial and frequency domains. Our approach is based on the radial approximation of unknown optimal filter, which is designed as a weighted sum of Gaussian filters with various radii. The novel filter is designed for MRI image enhancement where the image intensity represents anatomical structure plus additive noise. We prefer the gradient norm of Hartley entropy of whole image intensity as a measure which has to be maximized for the best sharpening. The entropy estimation procedure is as fast as FFT included in the filter but this estimate is a continuous function of enhanced image intensities. Physically motivated heuristic is used for optimum sharpening filter design by its parameter tuning. Our approach is compared with Wiener filter on MRI images.

User's Manual: Routines for Radiative Heat Transfer and Thermometry

NASA Technical Reports Server (NTRS)

Risch, Timothy K.

2016-01-01

Determining the intensity and spectral distribution of radiation emanating from a heated surface has applications in many areas of science and engineering. Areas of research in which the quantification of spectral radiation is used routinely include thermal radiation heat transfer, infrared signature analysis, and radiation thermometry. In the analysis of radiation, it is helpful to be able to predict the radiative intensity and the spectral distribution of the emitted energy. Presented in this report is a set of routines written in Microsoft Visual Basic for Applications (VBA) (Microsoft Corporation, Redmond, Washington) and incorporating functions specific to Microsoft Excel (Microsoft Corporation, Redmond, Washington) that are useful for predicting the radiative behavior of heated surfaces. These routines include functions for calculating quantities of primary importance to engineers and scientists. In addition, the routines also provide the capability to use such information to determine surface temperatures from spectral intensities and for calculating the sensitivity of the surface temperature measurements to unknowns in the input parameters.

COMPETITIVE ABILITY IN MALE HOUSE MICE (Mus musculus): GENETIC INFLUENCES

PubMed Central

Cunningham, Christopher B.; Ruff, James S.; Chase, Kevin; Potts, Wayne K.; Carrier, David R.

2013-01-01

Conspecifics of many animal species physically compete to gain reproductive resources and thus fitness. Despite the importance of competitive ability across the animal kingdom, specific traits that influence or underpin competitive ability are poorly characterized. Here, we investigate whether there are genetic influences on competitive ability within male house mice. Additionally, we examined if litter demographics (litter size and litter sex ratio) influence competitive ability. We phenotyped two generations for a male s ability to possess a reproductive resource--a prime nesting site--using semi-natural enclosures with mixed sex groupings. We used the animal model coupled with an extensive pedigree to estimate several genetic parameters. Competitive ability was found to be highly heritable, but only displayed a moderate genetic correlation to body mass. Interestingly, litter sex ratio had a weak negative influence on competitive ability. Litter size had no significant influence on competitive ability. Our study also highlights how much remians unknown about the proximal causes of competitive ability. PMID:23291957

Apparent ambiguities in the post-Newtonian expansion for binary systems

NASA Astrophysics Data System (ADS)

Porto, Rafael A.; Rothstein, Ira Z.

2017-07-01

We discuss the source of the apparent ambiguities arising in the calculation of the dynamics of binary black holes within the post-Newtonian framework. Divergences appear in both the near and far zone calculations, and may be of either ultraviolet (UV) or infrared (IR) nature. The effective field theory (EFT) formalism elucidates the origin of the singularities which may introduce apparent ambiguities. In particular, the only (physical) "ambiguity parameters" that necessitate a matching calculation correspond to unknown finite size effects, which first appear at fifth post-Newtonian (5PN) order for nonspinning bodies. We demonstrate that the ambiguities linked to IR divergences in the near zone, that plague the recent derivations of the binding energy at 4PN order, both in the Arnowitt, Deser, and Misner (ADM) and "Fokker-action" approach, can be resolved by implementing the so-called zero-bin subtraction in the EFT framework. The procedure yields ambiguity-free results without the need of additional information beyond the PN expansion.

Stable Local Volatility Calibration Using Kernel Splines

NASA Astrophysics Data System (ADS)

Coleman, Thomas F.; Li, Yuying; Wang, Cheng

2010-09-01

We propose an optimization formulation using L1 norm to ensure accuracy and stability in calibrating a local volatility function for option pricing. Using a regularization parameter, the proposed objective function balances the calibration accuracy with the model complexity. Motivated by the support vector machine learning, the unknown local volatility function is represented by a kernel function generating splines and the model complexity is controlled by minimizing the 1-norm of the kernel coefficient vector. In the context of the support vector regression for function estimation based on a finite set of observations, this corresponds to minimizing the number of support vectors for predictability. We illustrate the ability of the proposed approach to reconstruct the local volatility function in a synthetic market. In addition, based on S&P 500 market index option data, we demonstrate that the calibrated local volatility surface is simple and resembles the observed implied volatility surface in shape. Stability is illustrated by calibrating local volatility functions using market option data from different dates.

Analysis and design of second-order sliding-mode algorithms for quadrotor roll and pitch estimation.

PubMed

Chang, Jing; Cieslak, Jérôme; Dávila, Jorge; Zolghadri, Ali; Zhou, Jun

2017-11-01

The problem addressed in this paper is that of quadrotor roll and pitch estimation without any assumption about the knowledge of perturbation bounds when Inertial Measurement Units (IMU) data or position measurements are available. A Smooth Sliding Mode (SSM) algorithm is first designed to provide reliable estimation under a smooth disturbance assumption. This assumption is next relaxed with the second proposed Adaptive Sliding Mode (ASM) algorithm that deals with disturbances of unknown bounds. In addition, the analysis of the observers are extended to the case where measurements are corrupted by bias and noise. The gains of the proposed algorithms were deduced from the Lyapunov function. Furthermore, some useful guidelines are provided for the selection of the observer turning parameters. The performance of these two approaches is evaluated using a nonlinear simulation model and considering either accelerometer or position measurements. The simulation results demonstrate the benefits of the proposed solutions. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A Cohesive Zone Approach for Fatigue-Driven Delamination Analysis in Composite Materials

NASA Astrophysics Data System (ADS)

Amiri-Rad, Ahmad; Mashayekhi, Mohammad

2017-08-01

A new model for prediction of fatigue-driven delamination in laminated composites is proposed using cohesive interface elements. The presented model provides a link between cohesive elements damage evolution rate and crack growth rate of Paris law. This is beneficial since no additional material parameters are required and the well-known Paris law constants are used. The link between the cohesive zone method and fracture mechanics is achieved without use of effective length which has led to more accurate results. The problem of unknown failure path in calculation of the energy release rate is solved by imposing a condition on the damage model which leads to completely vertical failure path. A global measure of energy release rate is used for the whole cohesive zone which is computationally more efficient compared to previous similar models. The performance of the proposed model is investigated by simulation of well-known delamination tests and comparison against experimental data of the literature.

Elasto-capillarity in insect fibrillar adhesion.

PubMed

Gernay, Sophie; Federle, Walter; Lambert, Pierre; Gilet, Tristan

2016-08-01

The manipulation of microscopic objects is challenging because of high adhesion forces, which render macroscopic gripping strategies unsuitable. Adhesive footpads of climbing insects could reveal principles relevant for micro-grippers, as they are able to attach and detach rapidly during locomotion. However, the underlying mechanisms are still not fully understood. In this work, we characterize the geometry and contact formation of the adhesive setae of dock beetles (Gastrophysa viridula) by interference reflection microscopy. We compare our experimental results to the model of an elastic beam loaded with capillary forces. Fitting the model to experimental data yielded not only estimates for seta adhesion and compliance in agreement with previous direct measurements, but also previously unknown parameters such as the volume of the fluid meniscus and the bending stiffness of the tip. In addition to confirming the primary role of surface tension for insect adhesion, our investigation reveals marked differences in geometry and compliance between the three main kinds of seta tips in leaf beetles. © 2016 The Author(s).

Styrene-Associated Health Outcomes at a Windblade Manufacturing Plant

PubMed Central

McCague, Anna-Binney; Cox-Ganser, Jean M.; Harney, Joshua M.; Alwis, K. Udeni; Blount, Benjamin C.; Cummings, Kristin J.; Edwards, Nicole; Kreiss, Kathleen

2015-01-01

Background Health risks of using styrene to manufacture windblades for the green energy sector are unknown. Methods Using data collected from 355 (73%) current windblade workers and regression analysis, we investigated associations between health outcomes and styrene exposure estimates derived from urinary styrene metabolites. Results The median current styrene exposure was 53.6 mg/g creatinine (interquartile range: 19.5–94.4). Color blindness in men and women (standardized morbidity ratios 2.3 and 16.6, respectively) was not associated with exposure estimates, but was the type previously reported with styrene. Visual contrast sensitivity decreased and chest tightness increased (odds ratio 2.9) with increasing current exposure. Decreases in spirometric parameters and FeNO, and increases in the odds of wheeze and asthma-like symptoms (odds ratios 1.3 and 1.2, respectively) occurred with increasing cumulative exposure. Conclusions Despite styrene exposures below the recommended 400 mg/g creatinine, visual and respiratory effects indicate the need for additional preventative measures in this industry. PMID:26305283

A Bayesian Framework for Coupled Estimation of Key Unknown Parameters of Land Water and Energy Balance Equations

NASA Astrophysics Data System (ADS)

Farhadi, L.; Abdolghafoorian, A.

2015-12-01

The land surface is a key component of climate system. It controls the partitioning of available energy at the surface between sensible and latent heat, and partitioning of available water between evaporation and runoff. Water and energy cycle are intrinsically coupled through evaporation, which represents a heat exchange as latent heat flux. Accurate estimation of fluxes of heat and moisture are of significant importance in many fields such as hydrology, climatology and meteorology. In this study we develop and apply a Bayesian framework for estimating the key unknown parameters of terrestrial water and energy balance equations (i.e. moisture and heat diffusion) and their uncertainty in land surface models. These equations are coupled through flux of evaporation. The estimation system is based on the adjoint method for solving a least-squares optimization problem. The cost function consists of aggregated errors on state (i.e. moisture and temperature) with respect to observation and parameters estimation with respect to prior values over the entire assimilation period. This cost function is minimized with respect to parameters to identify models of sensible heat, latent heat/evaporation and drainage and runoff. Inverse of Hessian of the cost function is an approximation of the posterior uncertainty of parameter estimates. Uncertainty of estimated fluxes is estimated by propagating the uncertainty for linear and nonlinear function of key parameters through the method of First Order Second Moment (FOSM). Uncertainty analysis is used in this method to guide the formulation of a well-posed estimation problem. Accuracy of the method is assessed at point scale using surface energy and water fluxes generated by the Simultaneous Heat and Water (SHAW) model at the selected AmeriFlux stations. This method can be applied to diverse climates and land surface conditions with different spatial scales, using remotely sensed measurements of surface moisture and temperature states

Integrating models to investigate critical phenological overlaps in complex ecological interactions: the mountain pine beetle-fungus symbiosis.

PubMed

Addison, Audrey; Powell, James A; Bentz, Barbara J; Six, Diana L

2015-03-07

The fates of individual species are often tied to synchronization of phenology, however, few methods have been developed for integrating phenological models involving linked species. In this paper, we focus on mountain pine beetle (MPB, Dendroctonus ponderosae) and its two obligate mutualistic fungi, Grosmannia clavigera and Ophiostoma montium. Growth rates of all three partners are driven by temperature, and their idiosyncratic responses affect interactions at important life stage junctures. One critical phase for MPB-fungus symbiosis occurs just before dispersal of teneral (new) adult beetles, when fungi are acquired and transported in specialized structures (mycangia). Before dispersal, fungi must capture sufficient spatial resources within the tree to ensure contact with teneral adults and get packed into mycangia. Mycangial packing occurs at an unknown time during teneral feeding. We adapt thermal models predicting fungal growth and beetle development to predict overlap between the competing fungi and MPB teneral adult feeding windows and emergence. We consider a spectrum of mycangial packing strategies and describe them in terms of explicit functions with unknown parameters. Rates of growth are fixed by laboratory data, the unknown parameters describing various packing strategies, as well as the degree to which mycangial growth is slowed in woody tissues as compared to agar, are determined by maximum likelihood and two years of field observations. At the field location used, the most likely fungus acquisition strategy for MPB was packing mycangia just prior to emergence. Estimated model parameters suggested large differences in the relative growth rates of the two fungi in trees at the study site, with the most likely model estimating that G. clavigera grew approximately twenty-five times faster than O. montium under the bark, which is completely unexpected in comparison with observed fungal growth on agar. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predicting Malignant and Paramalignant Pleural Effusions by Combining Clinical, Radiological and Pleural Fluid Analytical Parameters.

PubMed

Herrera Lara, Susana; Fernández-Fabrellas, Estrella; Juan Samper, Gustavo; Marco Buades, Josefa; Andreu Lapiedra, Rafael; Pinilla Moreno, Amparo; Morales Suárez-Varela, María

2017-10-01

The usefulness of clinical, radiological and pleural fluid analytical parameters for diagnosing malignant and paramalignant pleural effusion is not clearly stated. Hence this study aimed to identify possible predictor variables of diagnosing malignancy in pleural effusion of unknown aetiology. Clinical, radiological and pleural fluid analytical parameters were obtained from consecutive patients who had suffered pleural effusion of unknown aetiology. They were classified into three groups according to their final diagnosis: malignant, paramalignant and benign pleural effusion. The CHAID (Chi-square automatic interaction detector) methodology was used to estimate the implication of the clinical, radiological and analytical variables in daily practice through decision trees. Of 71 patients, malignant (n = 31), paramalignant (n = 15) and benign (n = 25), smoking habit, dyspnoea, weight loss, radiological characteristics (mass, node, adenopathies and pleural thickening) and pleural fluid analytical parameters (pH and glucose) distinguished malignant and paramalignant pleural effusions (all with a p < 0.05). Decision tree 1 classified 77.8% of malignant and paramalignant pleural effusions in step 2. Decision tree 2 classified 83.3% of malignant pleural effusions in step 2, 73.3% of paramalignant pleural effusions and 91.7% of benign ones. The data herein suggest that the identified predictor values applied to tree diagrams, which required no extraordinary measures, have a higher rate of correct identification of malignant, paramalignant and benign effusions when compared to techniques available today and proved most useful for usual clinical practice. Future studies are still needed to further improve the classification of patients.

High-Intensity Interval Training After Stroke: An Opportunity to Promote Functional Recovery, Cardiovascular Health, and Neuroplasticity.

PubMed

Crozier, Jennifer; Roig, Marc; Eng, Janice J; MacKay-Lyons, Marilyn; Fung, Joyce; Ploughman, Michelle; Bailey, Damian M; Sweet, Shane N; Giacomantonio, Nicholas; Thiel, Alexander; Trivino, Michael; Tang, Ada

2018-04-01

Stroke is the leading cause of adult disability. Individuals poststroke possess less than half of the cardiorespiratory fitness (CRF) as their nonstroke counterparts, leading to inactivity, deconditioning, and an increased risk of cardiovascular events. Preserving cardiovascular health is critical to lower stroke risk; however, stroke rehabilitation typically provides limited opportunity for cardiovascular exercise. Optimal cardiovascular training parameters to maximize recovery in stroke survivors also remains unknown. While stroke rehabilitation recommendations suggest the use of moderate-intensity continuous exercise (MICE) to improve CRF, neither is it routinely implemented in clinical practice, nor is the intensity always sufficient to elicit a training effect. High-intensity interval training (HIIT) has emerged as a potentially effective alternative that encompasses brief high-intensity bursts of exercise interspersed with bouts of recovery, aiming to maximize cardiovascular exercise intensity in a time-efficient manner. HIIT may provide an alternative exercise intervention and invoke more pronounced benefits poststroke. To provide an updated review of HIIT poststroke through ( a) synthesizing current evidence; ( b) proposing preliminary considerations of HIIT parameters to optimize benefit; ( c) discussing potential mechanisms underlying changes in function, cardiovascular health, and neuroplasticity following HIIT; and ( d) discussing clinical implications and directions for future research. Preliminary evidence from 10 studies report HIIT-associated improvements in functional, cardiovascular, and neuroplastic outcomes poststroke; however, optimal HIIT parameters remain unknown. Larger randomized controlled trials are necessary to establish ( a) effectiveness, safety, and optimal training parameters within more heterogeneous poststroke populations; (b) potential mechanisms of HIIT-associated improvements; and ( c) adherence and psychosocial outcomes.

Minimal-Approximation-Based Distributed Consensus Tracking of a Class of Uncertain Nonlinear Multiagent Systems With Unknown Control Directions.

PubMed

Choi, Yun Ho; Yoo, Sung Jin

2017-03-28

A minimal-approximation-based distributed adaptive consensus tracking approach is presented for strict-feedback multiagent systems with unknown heterogeneous nonlinearities and control directions under a directed network. Existing approximation-based consensus results for uncertain nonlinear multiagent systems in lower-triangular form have used multiple function approximators in each local controller to approximate unmatched nonlinearities of each follower. Thus, as the follower's order increases, the number of the approximators used in its local controller increases. However, the proposed approach employs only one function approximator to construct the local controller of each follower regardless of the order of the follower. The recursive design methodology using a new error transformation is derived for the proposed minimal-approximation-based design. Furthermore, a bounding lemma on parameters of Nussbaum functions is presented to handle the unknown control direction problem in the minimal-approximation-based distributed consensus tracking framework and the stability of the overall closed-loop system is rigorously analyzed in the Lyapunov sense.

Performance and robustness of optimal fractional fuzzy PID controllers for pitch control of a wind turbine using chaotic optimization algorithms.

PubMed

Asgharnia, Amirhossein; Shahnazi, Reza; Jamali, Ali

2018-05-11

The most studied controller for pitch control of wind turbines is proportional-integral-derivative (PID) controller. However, due to uncertainties in wind turbine modeling and wind speed profiles, the need for more effective controllers is inevitable. On the other hand, the parameters of PID controller usually are unknown and should be selected by the designer which is neither a straightforward task nor optimal. To cope with these drawbacks, in this paper, two advanced controllers called fuzzy PID (FPID) and fractional-order fuzzy PID (FOFPID) are proposed to improve the pitch control performance. Meanwhile, to find the parameters of the controllers the chaotic evolutionary optimization methods are used. Using evolutionary optimization methods not only gives us the unknown parameters of the controllers but also guarantees the optimality based on the chosen objective function. To improve the performance of the evolutionary algorithms chaotic maps are used. All the optimization procedures are applied to the 2-mass model of 5-MW wind turbine model. The proposed optimal controllers are validated using simulator FAST developed by NREL. Simulation results demonstrate that the FOFPID controller can reach to better performance and robustness while guaranteeing fewer fatigue damages in different wind speeds in comparison to FPID, fractional-order PID (FOPID) and gain-scheduling PID (GSPID) controllers. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Development of a mathematical model for mechanical transmission of trypanosomes and other pathogens of cattle transmitted by tabanids.

PubMed

Desquesnes, Marc; Biteau-Coroller, Fabienne; Bouyer, Jérémy; Dia, Mamadou Lamine; Foil, Lane

2009-02-01

Mechanical transmission of pathogens by biting insects is a non-specific phenomenon in which pathogens are transmitted from the blood of an infected host to another host during interrupted feeding of the insects. A large range of pathogens can be mechanically transmitted, e.g. hemoparasites, bacteria and viruses. Some pathogens are almost exclusively mechanically transmitted, while others are also cyclically transmitted. For agents transmitted both cyclically and mechanically (mixed transmission), such as certain African pathogenic trypanosomes, the relative impact of mechanical versus cyclical transmission is essentially unknown. We have developed a mathematical model of pathogen transmission by a defined insect population to evaluate the importance of mechanical transmission. Based on a series of experiments aimed at demonstrating mechanical transmission of African trypanosomes by tabanids, the main parameters of the model were either quantified (host parasitaemia, mean individual insect burden, initial prevalence of infection) or estimated (unknown parameters). This model allows us to simulate the evolution of pathogen prevalence under various predictive circumstances, including control measures and could be used to assess the risk of mechanical transmission under field conditions. If adjustments of parameters are provided, this model could be generalized to other pathogenic agents present in the blood of their hosts (Bovine Leukemia virus, Anaplasma, etc.) or other biting insects such as biting muscids (stomoxyines) and hippoboscids.

Stochastic reduced order models for inverse problems under uncertainty

PubMed Central

Warner, James E.; Aquino, Wilkins; Grigoriu, Mircea D.

2014-01-01

This work presents a novel methodology for solving inverse problems under uncertainty using stochastic reduced order models (SROMs). Given statistical information about an observed state variable in a system, unknown parameters are estimated probabilistically through the solution of a model-constrained, stochastic optimization problem. The point of departure and crux of the proposed framework is the representation of a random quantity using a SROM - a low dimensional, discrete approximation to a continuous random element that permits e cient and non-intrusive stochastic computations. Characterizing the uncertainties with SROMs transforms the stochastic optimization problem into a deterministic one. The non-intrusive nature of SROMs facilitates e cient gradient computations for random vector unknowns and relies entirely on calls to existing deterministic solvers. Furthermore, the method is naturally extended to handle multiple sources of uncertainty in cases where state variable data, system parameters, and boundary conditions are all considered random. The new and widely-applicable SROM framework is formulated for a general stochastic optimization problem in terms of an abstract objective function and constraining model. For demonstration purposes, however, we study its performance in the specific case of inverse identification of random material parameters in elastodynamics. We demonstrate the ability to efficiently recover random shear moduli given material displacement statistics as input data. We also show that the approach remains effective for the case where the loading in the problem is random as well. PMID:25558115

Model misspecification detection by means of multiple generator errors, using the observed potential map.

PubMed

Zhang, Z; Jewett, D L

1994-01-01

Due to model misspecification, currently-used Dipole Source Localization (DSL) methods may contain Multiple-Generator Errors (MulGenErrs) when fitting simultaneously-active dipoles. The size of the MulGenErr is a function of both the model used, and the dipole parameters, including the dipoles' waveforms (time-varying magnitudes). For a given fitting model, by examining the variation of the MulGenErrs (or the fit parameters) under different waveforms for the same generating-dipoles, the accuracy of the fitting model for this set of dipoles can be determined. This method of testing model misspecification can be applied to evoked potential maps even when the parameters of the generating-dipoles are unknown. The dipole parameters fitted in a model should only be accepted if the model can be shown to be sufficiently accurate.

Application of Novel Lateral Tire Force Sensors to Vehicle Parameter Estimation of Electric Vehicles.

PubMed

Nam, Kanghyun

2015-11-11

This article presents methods for estimating lateral vehicle velocity and tire cornering stiffness, which are key parameters in vehicle dynamics control, using lateral tire force measurements. Lateral tire forces acting on each tire are directly measured by load-sensing hub bearings that were invented and further developed by NSK Ltd. For estimating the lateral vehicle velocity, tire force models considering lateral load transfer effects are used, and a recursive least square algorithm is adapted to identify the lateral vehicle velocity as an unknown parameter. Using the estimated lateral vehicle velocity, tire cornering stiffness, which is an important tire parameter dominating the vehicle's cornering responses, is estimated. For the practical implementation, the cornering stiffness estimation algorithm based on a simple bicycle model is developed and discussed. Finally, proposed estimation algorithms were evaluated using experimental test data.

Plasma diagnosis as a tool for the determination of the parameters of electron beam evaporation and sources of ionization

NASA Astrophysics Data System (ADS)

Mukherjee, Jaya; Dileep Kumar, V.; Yadav, S. P.; Barnwal, Tripti A.; Dikshit, Biswaranjan

2016-07-01

The atomic vapor generated by electron beam heating is partially ionized due to atom-atom collisions (Saha ionization) and electron impact ionization, which depend upon the source temperature and area of evaporation as compared to the area of electron beam bombardment on the target. When electron beam evaporation is carried out by inserting the target inside an insulating liner to reduce conductive heat loss, it is expected that the area of evaporation becomes significantly more than the area of electron beam bombardment on the target, resulting in reduced electron impact ionization. To assess this effect and to quantify the parameters of evaporation, such as temperature and area of evaporation, we have carried out experiments using zirconium, tin and aluminum as a target. By measuring the ion content using a Langmuir probe, in addition to measuring the atomic vapor flux at a specific height, and by combining the experimental data with theoretical expressions, we have established a method for simultaneously inferring the source temperature, evaporation area and ion fraction. This assumes significance because the temperature cannot be reliably measured by an optical pyrometer due to the wavelength dependent source emissivity and reflectivity of thin film mirrors. In addition, it also cannot be inferred from only the atomic flux data at a certain height as the area of evaporation is unknown (it can be much more than the area of electron bombardment, especially when the target is placed in a liner). Finally, the reason for the lower observed electron temperatures of the plasma for all the three cases is found to be the energy loss due to electron impact excitation of the atomic vapor during its expansion from the source.

More Sophisticated Fits of the Oribts of Haumea's Interacting Moons

NASA Astrophysics Data System (ADS)

Oldroyd, William Jared; Ragozzine, Darin; Porter, Simon

2018-04-01

Since the discovery of Haumea's moons, it has been a challenge to model the orbits of its moons, Hi’iaka and Namaka. With many precision HST observations, Ragozzine & Brown 2009 succeeded in calculating a three-point mass model which was essential because Keplerian orbits were not a statistically acceptable fit. New data obtained in 2010 could be fit by adding a J2 and spin pole to Haumea, but new data from 2015 was far from the predicted locations, even after an extensive exploration using Bayesian Markov Chain Monte Carlo methods (using emcee). Here we report on continued investigations as to why our model cannot fit the full 10-year baseline of data. We note that by ignoring Haumea and instead examining the relative motion of the two moons in the Hi’iaka centered frame leads to adequate fits for the data. This suggests there are additional parameters connected to Haumea that will be required in a full model. These parameters are potentially related to photocenter-barycenter shifts which could be significant enough to affect the fitting process; these are unlikely to be caused by the newly discovered ring (Ortiz et al. 2017) or by unknown satellites (Burkhart et al. 2016). Additionally, we have developed a new SPIN+N-bodY integrator called SPINNY that self-consistently calculates the interactions between n-quadrupoles and is designed to test the importance of other possible effects (Haumea C22, satellite torques on the spin-pole, Sun, etc.) on our astrometric fits. By correctly determining the orbit of Haumea’s satellites we develop a better understanding of the physical properties of each of the objects with implications for the formation of Haumea, its moons, and its collisional family.

Dual Rate Adaptive Control for an Industrial Heat Supply Process Using Signal Compensation Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chai, Tianyou; Jia, Yao; Wang, Hong

The industrial heat supply process (HSP) is a highly nonlinear cascaded process which uses a steam valve opening as its control input, the steam flow-rate as its inner loop output and the supply water temperature as its outer loop output. The relationship between the heat exchange rate and the model parameters, such as steam density, entropy, and fouling correction factor and heat exchange efficiency are unknown and nonlinear. Moreover, these model parameters vary in line with steam pressure, ambient temperature and the residuals caused by the quality variations of the circulation water. When the steam pressure and the ambient temperaturemore » are of high values and are subjected to frequent external random disturbances, the supply water temperature and the steam flow-rate would interact with each other and fluctuate a lot. This is also true when the process exhibits unknown characteristic variations of the process dynamics caused by the unexpected changes of the heat exchange residuals. As a result, it is difficult to control the supply water temperature and the rates of changes of steam flow-rate well inside their targeted ranges. In this paper, a novel compensation signal based dual rate adaptive controller is developed by representing the unknown variations of dynamics as unmodeled dynamics. In the proposed controller design, such a compensation signal is constructed and added onto the control signal obtained from the linear deterministic model based feedback control design. Such a compensation signal aims at eliminating the unmodeled dynamics and the rate of changes of the currently sample unmodeled dynamics. A successful industrial application is carried out, where it has been shown that both the supply water temperature and the rate of the changes of the steam flow-rate can be controlled well inside their targeted ranges when the process is subjected to unknown variations of its dynamics.« less

Identification of substances migrating from plastic baby bottles using a combination of low-resolution and high-resolution mass spectrometric analysers coupled to gas and liquid chromatography.

PubMed

Onghena, Matthias; Van Hoeck, Els; Van Loco, Joris; Ibáñez, María; Cherta, Laura; Portolés, Tania; Pitarch, Elena; Hernandéz, Félix; Lemière, Filip; Covaci, Adrian

2015-11-01

This work presents a strategy for elucidation of unknown migrants from plastic food contact materials (baby bottles) using a combination of analytical techniques in an untargeted approach. First, gas chromatography (GC) coupled to mass spectrometry (MS) in electron ionisation mode was used to identify migrants through spectral library matching. When no acceptable match was obtained, a second analysis by GC-(electron ionisation) high resolution mass spectrometry time of flight (TOF) was applied to obtain accurate mass fragmentation spectra and isotopic patterns. Databases were then searched to find a possible elemental composition for the unknown compounds. Finally, a GC hybrid quadrupole-TOF-MS with an atmospheric pressure chemical ionisation source was used to obtain the molecular ion or the protonated molecule. Accurate mass data also provided additional information on the fragmentation behaviour as two acquisition functions with different collision energies were available (MS(E) approach). In the low-energy function, limited fragmentation took place, whereas for the high-energy function, fragmentation was enhanced. For less volatile unknowns, ultra-high pressure liquid chromatography-quadrupole-TOF-MS was additionally applied. Using a home-made database containing common migrating compounds and plastic additives, tentative identification was made for several positive findings based on accurate mass of the (de)protonated molecule, product ion fragments and characteristic isotopic ions. Six illustrative examples are shown to demonstrate the modus operandi and the difficulties encountered during identification. The combination of these techniques was proven to be a powerful tool for the elucidation of unknown migrating compounds from plastic baby bottles. Copyright © 2015 John Wiley & Sons, Ltd.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Shi-bing, E-mail: wang-shibing@dlut.edu.cn, E-mail: wangxy@dlut.edu.cn; Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024; Wang, Xing-yuan, E-mail: wang-shibing@dlut.edu.cn, E-mail: wangxy@dlut.edu.cn

With comprehensive consideration of generalized synchronization, combination synchronization and adaptive control, this paper investigates a novel adaptive generalized combination complex synchronization (AGCCS) scheme for different real and complex nonlinear systems with unknown parameters. On the basis of Lyapunov stability theory and adaptive control, an AGCCS controller and parameter update laws are derived to achieve synchronization and parameter identification of two real drive systems and a complex response system, as well as two complex drive systems and a real response system. Two simulation examples, namely, ACGCS for chaotic real Lorenz and Chen systems driving a hyperchaotic complex Lü system, and hyperchaoticmore » complex Lorenz and Chen systems driving a real chaotic Lü system, are presented to verify the feasibility and effectiveness of the proposed scheme.« less

Prediction of binding hot spot residues by using structural and evolutionary parameters.

PubMed

Higa, Roberto Hiroshi; Tozzi, Clésio Luis

2009-07-01

In this work, we present a method for predicting hot spot residues by using a set of structural and evolutionary parameters. Unlike previous studies, we use a set of parameters which do not depend on the structure of the protein in complex, so that the predictor can also be used when the interface region is unknown. Despite the fact that no information concerning proteins in complex is used for prediction, the application of the method to a compiled dataset described in the literature achieved a performance of 60.4%, as measured by F-Measure, corresponding to a recall of 78.1% and a precision of 49.5%. This result is higher than those reported by previous studies using the same data set.

Analysis of aircraft tires via semianalytic finite elements

NASA Technical Reports Server (NTRS)

Noor, Ahmed K.; Kim, Kyun O.; Tanner, John A.

1990-01-01

A computational procedure is presented for the geometrically nonlinear analysis of aircraft tires. The tire was modeled by using a two-dimensional laminated anisotropic shell theory with the effects of variation in material and geometric parameters included. The four key elements of the procedure are: (1) semianalytic finite elements in which the shell variables are represented by Fourier series in the circumferential direction and piecewise polynomials in the meridional direction; (2) a mixed formulation with the fundamental unknowns consisting of strain parameters, stress-resultant parameters, and generalized displacements; (3) multilevel operator splitting to effect successive simplifications, and to uncouple the equations associated with different Fourier harmonics; and (4) multilevel iterative procedures and reduction techniques to generate the response of the shell.

Correlates of Unknown HIV Status Among MSM Participating in the 2014 American Men's Internet Survey (AMIS).

PubMed

Traynor, S M; Brincks, A M; Feaster, D J

2017-08-29

Increasing serostatus awareness is a key HIV prevention strategy. Despite expanded testing efforts, some men who have sex with men (MSM) remain unaware of their HIV status. This study explored demographic characteristics, sexual identity, sexual role, and behavioral factors associated with unknown HIV status among MSM in the United States. Data from 9170 MSM in the 2014 American Men's Internet Survey were analyzed using logistic regression to identify correlates of unknown HIV status. Young age, race, low education, rural residence, and lack of recent healthcare visits were significantly associated with unknown HIV status. In addition, nondisclosure of one's sexual orientation (OR = 3.70, 95% CI 2.99-4.59) and a self-identified sexual role as "bottom" (OR = 1.45, 95% CI 1.24-1.70) were predictors of unknown HIV status. Post-hoc analysis showed HIV-negative MSM not tested in the last year had fewer self-reported risk behaviors than recent testers, suggesting that repeat testing among MSM may be aligned with individual risk.

A nudging data assimilation algorithm for the identification of groundwater pumping

NASA Astrophysics Data System (ADS)

Cheng, Wei-Chen; Kendall, Donald R.; Putti, Mario; Yeh, William W.-G.

2009-08-01

This study develops a nudging data assimilation algorithm for estimating unknown pumping from private wells in an aquifer system using measured data of hydraulic head. The proposed algorithm treats the unknown pumping as an additional sink term in the governing equation of groundwater flow and provides a consistent physical interpretation for pumping rate identification. The algorithm identifies the unknown pumping and, at the same time, reduces the forecast error in hydraulic heads. We apply the proposed algorithm to the Las Posas Groundwater Basin in southern California. We consider the following three pumping scenarios: constant pumping rates, spatially varying pumping rates, and temporally varying pumping rates. We also study the impact of head measurement errors on the proposed algorithm. In the case study we seek to estimate the six unknown pumping rates from private wells using head measurements from four observation wells. The results show an excellent rate of convergence for pumping estimation. The case study demonstrates the applicability, accuracy, and efficiency of the proposed data assimilation algorithm for the identification of unknown pumping in an aquifer system.

A nudging data assimilation algorithm for the identification of groundwater pumping

NASA Astrophysics Data System (ADS)

Cheng, W.; Kendall, D. R.; Putti, M.; Yeh, W. W.

2008-12-01

This study develops a nudging data assimilation algorithm for estimating unknown pumping from private wells in an aquifer system using measurement data of hydraulic head. The proposed algorithm treats the unknown pumping as an additional sink term in the governing equation of groundwater flow and provides a consistently physical interpretation for pumping rate identification. The algorithm identifies unknown pumping and, at the same time, reduces the forecast error in hydraulic heads. We apply the proposed algorithm to the Las Posas Groundwater Basin in southern California. We consider the following three pumping scenarios: constant pumping rate, spatially varying pumping rates, and temporally varying pumping rates. We also study the impact of head measurement errors on the proposed algorithm. In the case study, we seek to estimate the six unknown pumping rates from private wells using head measurements from four observation wells. The results show excellent rate of convergence for pumping estimation. The case study demonstrates the applicability, accuracy, and efficiency of the proposed data assimilation algorithm for the identification of unknown pumping in an aquifer system.

From Atmospheric Neutrinos to the Neutrino Mass Hierarchy

NASA Astrophysics Data System (ADS)

Kappes, A.

2015-08-01

After a brief introduction to neutrino oscillation, the article discusses how proposed detectors like PINGU and ORCA can use atmospheric neutrinos in the GeV range to determine the neutrino mass hierarchy, one of the crucial unknowns in the neutrino sector of particle physics, and what uncertainties on external input parameters have to be taken into account.

The impact of water exchange rate on the health and performance of rainbow trout Oncorhynchus mykiss in recirculation aquaculture systems

USDA-ARS?s Scientific Manuscript database

Fish mortality in recirculating aquaculture systems (RAS) has been observed by the authors to increase when RAS are managed at low makeup water exchange rates with relatively high feed loading. The precise etiology of this elevated mortality was unknown, all typical water quality parameters were wit...

How Many Studies Do You Need? A Primer on Statistical Power for Meta-Analysis

ERIC Educational Resources Information Center

Valentine, Jeffrey C.; Pigott, Therese D.; Rothstein, Hannah R.

2010-01-01

In this article, the authors outline methods for using fixed and random effects power analysis in the context of meta-analysis. Like statistical power analysis for primary studies, power analysis for meta-analysis can be done either prospectively or retrospectively and requires assumptions about parameters that are unknown. The authors provide…

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Quantitative Relationships Involving Additive Differences: Numerical Resilience

ERIC Educational Resources Information Center

Ramful, Ajay; Ho, Siew Yin

2014-01-01

This case study describes the ways in which problems involving additive differences with unknown starting quantities, constrain the problem solver in articulating the inherent quantitative relationship. It gives empirical evidence to show how numerical reasoning takes over as a Grade 6 student instantiates the quantitative relation by resorting to…

The power and robustness of maximum LOD score statistics.

PubMed

Yoo, Y J; Mendell, N R

2008-07-01

The maximum LOD score statistic is extremely powerful for gene mapping when calculated using the correct genetic parameter value. When the mode of genetic transmission is unknown, the maximum of the LOD scores obtained using several genetic parameter values is reported. This latter statistic requires higher critical value than the maximum LOD score statistic calculated from a single genetic parameter value. In this paper, we compare the power of maximum LOD scores based on three fixed sets of genetic parameter values with the power of the LOD score obtained after maximizing over the entire range of genetic parameter values. We simulate family data under nine generating models. For generating models with non-zero phenocopy rates, LOD scores maximized over the entire range of genetic parameters yielded greater power than maximum LOD scores for fixed sets of parameter values with zero phenocopy rates. No maximum LOD score was consistently more powerful than the others for generating models with a zero phenocopy rate. The power loss of the LOD score maximized over the entire range of genetic parameters, relative to the maximum LOD score calculated using the correct genetic parameter value, appeared to be robust to the generating models.

A case-based evaluation of SRD5A1, SRD5A2, AR, and ADRA1A as candidate genes for severity of BPH.

PubMed

Klotsman, M; Weinberg, C R; Davis, K; Binnie, C G; Hartmann, K E

2004-01-01

In men with a clinical diagnosis of benign prostatic hyperplasia (BPH), polytomous logistic regression analysis was conducted to evaluate associations between two silent polymorphisms in SRD5A1 (codon positions 30 and 116), two polymorphisms in SRD5A2 (Val89Leu substitution and C to T transition in intron 1), a trinucleotide (CAG)n repeat in androgen receptor (AR), and an Arg492Cys substitution in ADRA1A and clinical parameters that characterize severity of BPH. Candidate gene selection was based on two mechanistic pathways targeted by pharmacotherapy for BPH: (1) androgen metabolic loci contributing to prostate growth (static obstruction); and (2) factors affecting smooth muscle tone (dynamic obstruction). Polymorphisms in SRD5A2 were not associated with severity of BPH; however, SRD5A1 polymorphisms were associated with severity of BPH. The process(es) in which these silent single-nucleotide polymorphisms (SNPs) influence BPH phenotypes is unknown and additional studies will be needed to assess whether these SNPs have direct functional consequences. The characterization of additional molecular factors that contribute to static and dynamic obstruction may help predict response to pharmacotherapy and serve to identify novel drug targets for the clinical management of BPH.

An overview of juveniles and school violence.

PubMed

Murakami, Saori; Rappaport, Nancy; Penn, Joseph V

2006-09-01

Despite the relative rarity of school shootings, targeted violence, and school-associated violent deaths, any youth who presents with words, gestures, or actions of a threatening or violent nature in a school setting should be assessed and referred for further evaluation by a mental health professional and, if clinically indicated, a forensic evaluator.The request for a juvenile risk assessment for future dangerousness requires careful delineation of role and agency; confidentiality issues; a comprehensive diagnostic evaluation of the youth; and a detailed assessment of the youth's perceived threat or problematic behavior. Various protective and risk factors and consideration of other individual, family, school/peer, and situational factors should also be explored.There is still much information that is unknown when considering school violence or targeted school violence. There is clearly a need for additional research on the identification of at-risk youths, the contributions and significance of various protective and risk factors, the impact of peer relationships, and perceived rejection, socioeconomic status, subtypes of aggression, and developmental stages. Examples of future research direction might include difference by gender, presence of affective or psychotic disorders, substance abuse, emerging characterologic disturbances, and physiologic markers, such as cortisol or serotonin. Additional research regarding best practices and the development of clinical guidelines or practice parameters is also needed.

Application of multiple modelling to hyperthermia estimation: reducing the effects of model mismatch.

PubMed

Potocki, J K; Tharp, H S

1993-01-01

Multiple model estimation is a viable technique for dealing with the spatial perfusion model mismatch associated with hyperthermia dosimetry. Using multiple models, spatial discrimination can be obtained without increasing the number of unknown perfusion zones. Two multiple model estimators based on the extended Kalman filter (EKF) are designed and compared with two EKFs based on single models having greater perfusion zone segmentation. Results given here indicate that multiple modelling is advantageous when the number of thermal sensors is insufficient for convergence of single model estimators having greater perfusion zone segmentation. In situations where sufficient measured outputs exist for greater unknown perfusion parameter estimation, the multiple model estimators and the single model estimators yield equivalent results.

Performance Analysis of Blind Subspace-Based Signature Estimation Algorithms for DS-CDMA Systems with Unknown Correlated Noise

NASA Astrophysics Data System (ADS)

Zarifi, Keyvan; Gershman, Alex B.

2006-12-01

We analyze the performance of two popular blind subspace-based signature waveform estimation techniques proposed by Wang and Poor and Buzzi and Poor for direct-sequence code division multiple-access (DS-CDMA) systems with unknown correlated noise. Using the first-order perturbation theory, analytical expressions for the mean-square error (MSE) of these algorithms are derived. We also obtain simple high SNR approximations of the MSE expressions which explicitly clarify how the performance of these techniques depends on the environmental parameters and how it is related to that of the conventional techniques that are based on the standard white noise assumption. Numerical examples further verify the consistency of the obtained analytical results with simulation results.

Optimum detection of tones transmitted by a spacecraft

NASA Technical Reports Server (NTRS)

Simon, M. K.; Shihabi, M. M.; Moon, T.

1995-01-01

The performance of a scheme proposed for automated routine monitoring of deep-space missions is presented. The scheme uses four different tones (sinusoids) transmitted from the spacecraft (S/C) to a ground station with the positive identification of each of them used to indicate different states of the S/C. Performance is measured in terms of detection probability versus false alarm probability with detection signal-to-noise ratio as a parameter. The cases where the phase of the received tone is unknown and where both the phase and frequency of the received tone are unknown are treated separately. The decision rules proposed for detecting the tones are formulated from average-likelihood ratio and maximum-likelihood ratio tests, the former resulting in optimum receiver structures.

Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies

PubMed Central

Dräger, Andreas; Kronfeld, Marcel; Ziller, Michael J; Supper, Jochen; Planatscher, Hannes; Magnus, Jørgen B; Oldiges, Marco; Kohlbacher, Oliver; Zell, Andreas

2009-01-01

Background To understand the dynamic behavior of cellular systems, mathematical modeling is often necessary and comprises three steps: (1) experimental measurement of participating molecules, (2) assignment of rate laws to each reaction, and (3) parameter calibration with respect to the measurements. In each of these steps the modeler is confronted with a plethora of alternative approaches, e. g., the selection of approximative rate laws in step two as specific equations are often unknown, or the choice of an estimation procedure with its specific settings in step three. This overall process with its numerous choices and the mutual influence between them makes it hard to single out the best modeling approach for a given problem. Results We investigate the modeling process using multiple kinetic equations together with various parameter optimization methods for a well-characterized example network, the biosynthesis of valine and leucine in C. glutamicum. For this purpose, we derive seven dynamic models based on generalized mass action, Michaelis-Menten and convenience kinetics as well as the stochastic Langevin equation. In addition, we introduce two modeling approaches for feedback inhibition to the mass action kinetics. The parameters of each model are estimated using eight optimization strategies. To determine the most promising modeling approaches together with the best optimization algorithms, we carry out a two-step benchmark: (1) coarse-grained comparison of the algorithms on all models and (2) fine-grained tuning of the best optimization algorithms and models. To analyze the space of the best parameters found for each model, we apply clustering, variance, and correlation analysis. Conclusion A mixed model based on the convenience rate law and the Michaelis-Menten equation, in which all reactions are assumed to be reversible, is the most suitable deterministic modeling approach followed by a reversible generalized mass action kinetics model. A Langevin model is advisable to take stochastic effects into account. To estimate the model parameters, three algorithms are particularly useful: For first attempts the settings-free Tribes algorithm yields valuable results. Particle swarm optimization and differential evolution provide significantly better results with appropriate settings. PMID:19144170