A new adaptive multiple modelling approach for non-linear and non-stationary systems

NASA Astrophysics Data System (ADS)

Chen, Hao; Gong, Yu; Hong, Xia

2016-07-01

This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.

Frequency Response of Synthetic Vocal Fold Models with Linear and Nonlinear Material Properties

PubMed Central

Shaw, Stephanie M.; Thomson, Scott L.; Dromey, Christopher; Smith, Simeon

2014-01-01

Purpose The purpose of this study was to create synthetic vocal fold models with nonlinear stress-strain properties and to investigate the effect of linear versus nonlinear material properties on fundamental frequency during anterior-posterior stretching. Method Three materially linear and three materially nonlinear models were created and stretched up to 10 mm in 1 mm increments. Phonation onset pressure (Pon) and fundamental frequency (F0) at Pon were recorded for each length. Measurements were repeated as the models were relaxed in 1 mm increments back to their resting lengths, and tensile tests were conducted to determine the stress-strain responses of linear versus nonlinear models. Results Nonlinear models demonstrated a more substantial frequency response than did linear models and a more predictable pattern of F0 increase with respect to increasing length (although range was inconsistent across models). Pon generally increased with increasing vocal fold length for nonlinear models, whereas for linear models, Pon decreased with increasing length. Conclusions Nonlinear synthetic models appear to more accurately represent the human vocal folds than linear models, especially with respect to F0 response. PMID:22271874

Confirming the Lanchestrian linear-logarithmic model of attrition

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

Hartley, D.S. III.

1990-12-01

This paper is the fourth in a series of reports on the breakthrough research in historical validation of attrition in conflict. Significant defense policy decisions, including weapons acquisition and arms reduction, are based in part on models of conflict. Most of these models are driven by their attrition algorithms, usually forms of the Lanchester square and linear laws. None of these algorithms have been validated. The results of this paper confirm the results of earlier papers, using a large database of historical results. The homogeneous linear-logarithmic Lanchestrian attrition model is validated to the extent possible with current initial and finalmore » force size data and is consistent with the Iwo Jima data. A particular differential linear-logarithmic model is described that fits the data very well. A version of Helmbold's victory predicting parameter is also confirmed, with an associated probability function. 37 refs., 73 figs., 68 tabs.« less

Waveform Design for Wireless Power Transfer

NASA Astrophysics Data System (ADS)

Clerckx, Bruno; Bayguzina, Ekaterina

2016-12-01

Far-field Wireless Power Transfer (WPT) has attracted significant attention in recent years. Despite the rapid progress, the emphasis of the research community in the last decade has remained largely concentrated on improving the design of energy harvester (so-called rectenna) and has left aside the effect of transmitter design. In this paper, we study the design of transmit waveform so as to enhance the DC power at the output of the rectenna. We derive a tractable model of the non-linearity of the rectenna and compare with a linear model conventionally used in the literature. We then use those models to design novel multisine waveforms that are adaptive to the channel state information (CSI). Interestingly, while the linear model favours narrowband transmission with all the power allocated to a single frequency, the non-linear model favours a power allocation over multiple frequencies. Through realistic simulations, waveforms designed based on the non-linear model are shown to provide significant gains (in terms of harvested DC power) over those designed based on the linear model and over non-adaptive waveforms. We also compute analytically the theoretical scaling laws of the harvested energy for various waveforms as a function of the number of sinewaves and transmit antennas. Those scaling laws highlight the benefits of CSI knowledge at the transmitter in WPT and of a WPT design based on a non-linear rectenna model over a linear model. Results also motivate the study of a promising architecture relying on large-scale multisine multi-antenna waveforms for WPT. As a final note, results stress the importance of modeling and accounting for the non-linearity of the rectenna in any system design involving wireless power.

Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region

NASA Astrophysics Data System (ADS)

Khan, Muhammad Yousaf; Mittnik, Stefan

2018-01-01

In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.

Numerical solution of non-linear dual-phase-lag bioheat transfer equation within skin tissues.

PubMed

Kumar, Dinesh; Kumar, P; Rai, K N

2017-11-01

This paper deals with numerical modeling and simulation of heat transfer in skin tissues using non-linear dual-phase-lag (DPL) bioheat transfer model under periodic heat flux boundary condition. The blood perfusion is assumed temperature-dependent which results in non-linear DPL bioheat transfer model in order to predict more accurate results. A numerical method of line which is based on finite difference and Runge-Kutta (4,5) schemes, is used to solve the present non-linear problem. Under specific case, the exact solution has been obtained and compared with the present numerical scheme, and we found that those are in good agreement. A comparison based on model selection criterion (AIC) has been made among non-linear DPL models when the variation of blood perfusion rate with temperature is of constant, linear and exponential type with the experimental data and it has been found that non-linear DPL model with exponential variation of blood perfusion rate is closest to the experimental data. In addition, it is found that due to absence of phase-lag phenomena in Pennes bioheat transfer model, it achieves steady state more quickly and always predict higher temperature than thermal and DPL non-linear models. The effect of coefficient of blood perfusion rate, dimensionless heating frequency and Kirchoff number on dimensionless temperature distribution has also been analyzed. The whole analysis is presented in dimensionless form. Copyright © 2017 Elsevier Inc. All rights reserved.

Descriptive Linear modeling of steady-state visual evoked response

NASA Technical Reports Server (NTRS)

Levison, W. H.; Junker, A. M.; Kenner, K.

1986-01-01

A study is being conducted to explore use of the steady state visual-evoke electrocortical response as an indicator of cognitive task loading. Application of linear descriptive modeling to steady state Visual Evoked Response (VER) data is summarized. Two aspects of linear modeling are reviewed: (1) unwrapping the phase-shift portion of the frequency response, and (2) parsimonious characterization of task-loading effects in terms of changes in model parameters. Model-based phase unwrapping appears to be most reliable in applications, such as manual control, where theoretical models are available. Linear descriptive modeling of the VER has not yet been shown to provide consistent and readily interpretable results.

A numerical study of linear and nonlinear kinematic models in fish swimming with the DSD/SST method

NASA Astrophysics Data System (ADS)

Tian, Fang-Bao

2015-03-01

Flow over two fish (modeled by two flexible plates) in tandem arrangement is investigated by solving the incompressible Navier-Stokes equations numerically with the DSD/SST method to understand the differences between the geometrically linear and nonlinear models. In the simulation, the motions of the plates are reconstructed from a vertically flowing soap film tunnel experiment with linear and nonlinear kinematic models. Based on the simulations, the drag, lift, power consumption, vorticity and pressure fields are discussed in detail. It is found that the linear and nonlinear models are able to reasonably predict the forces and power consumption of a single plate in flow. Moreover, if multiple plates are considered, these two models yield totally different results, which implies that the nonlinear model should be used. The results presented in this work provide a guideline for future studies in fish swimming.

Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models

PubMed Central

2011-01-01

Background Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Results Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity. Conclusions Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task. PMID:21867520

Some comparisons of complexity in dictionary-based and linear computational models.

PubMed

Gnecco, Giorgio; Kůrková, Věra; Sanguineti, Marcello

2011-03-01

Neural networks provide a more flexible approximation of functions than traditional linear regression. In the latter, one can only adjust the coefficients in linear combinations of fixed sets of functions, such as orthogonal polynomials or Hermite functions, while for neural networks, one may also adjust the parameters of the functions which are being combined. However, some useful properties of linear approximators (such as uniqueness, homogeneity, and continuity of best approximation operators) are not satisfied by neural networks. Moreover, optimization of parameters in neural networks becomes more difficult than in linear regression. Experimental results suggest that these drawbacks of neural networks are offset by substantially lower model complexity, allowing accuracy of approximation even in high-dimensional cases. We give some theoretical results comparing requirements on model complexity for two types of approximators, the traditional linear ones and so called variable-basis types, which include neural networks, radial, and kernel models. We compare upper bounds on worst-case errors in variable-basis approximation with lower bounds on such errors for any linear approximator. Using methods from nonlinear approximation and integral representations tailored to computational units, we describe some cases where neural networks outperform any linear approximator. Copyright © 2010 Elsevier Ltd. All rights reserved.

Feedback linearization for control of air breathing engines

NASA Technical Reports Server (NTRS)

Phillips, Stephen; Mattern, Duane

1991-01-01

The method of feedback linearization for control of the nonlinear nozzle and compressor components of an air breathing engine is presented. This method overcomes the need for a large number of scheduling variables and operating points to accurately model highly nonlinear plants. Feedback linearization also results in linear closed loop system performance simplifying subsequent control design. Feedback linearization is used for the nonlinear partial engine model and performance is verified through simulation.

Anomaly General Circulation Models.

NASA Astrophysics Data System (ADS)

Navarra, Antonio

The feasibility of the anomaly model is assessed using barotropic and baroclinic models. In the barotropic case, both a stationary and a time-dependent model has been formulated and constructed, whereas only the stationary, linear case is considered in the baroclinic case. Results from the barotropic model indicate that a relation between the stationary solution and the time-averaged non-linear solution exists. The stationary linear baroclinic solution can therefore be considered with some confidence. The linear baroclinic anomaly model poses a formidable mathematical problem because it is necessary to solve a gigantic linear system to obtain the solution. A new method to find solution of large linear system, based on a projection on the Krylov subspace is shown to be successful when applied to the linearized baroclinic anomaly model. The scheme consists of projecting the original linear system on the Krylov subspace, thereby reducing the dimensionality of the matrix to be inverted to obtain the solution. With an appropriate setting of the damping parameters, the iterative Krylov method reaches a solution even using a Krylov subspace ten times smaller than the original space of the problem. This generality allows the treatment of the important problem of linear waves in the atmosphere. A larger class (nonzonally symmetric) of basic states can now be treated for the baroclinic primitive equations. These problem leads to large unsymmetrical linear systems of order 10000 and more which can now be successfully tackled by the Krylov method. The (R7) linear anomaly model is used to investigate extensively the linear response to equatorial and mid-latitude prescribed heating. The results indicate that the solution is deeply affected by the presence of the stationary waves in the basic state. The instability of the asymmetric flows, first pointed out by Simmons et al. (1983), is active also in the baroclinic case. However, the presence of baroclinic processes modifies the dominant response. The most sensitive areas are identified; they correspond to north Japan, the Pole and Greenland regions. A limited set of higher resolution (R15) experiments indicate that this situation is still present and enhanced at higher resolution. The linear anomaly model is also applied to a realistic case. (Abstract shortened with permission of author.).

Deformation-Aware Log-Linear Models

NASA Astrophysics Data System (ADS)

Gass, Tobias; Deselaers, Thomas; Ney, Hermann

In this paper, we present a novel deformation-aware discriminative model for handwritten digit recognition. Unlike previous approaches our model directly considers image deformations and allows discriminative training of all parameters, including those accounting for non-linear transformations of the image. This is achieved by extending a log-linear framework to incorporate a latent deformation variable. The resulting model has an order of magnitude less parameters than competing approaches to handling image deformations. We tune and evaluate our approach on the USPS task and show its generalization capabilities by applying the tuned model to the MNIST task. We gain interesting insights and achieve highly competitive results on both tasks.

Ion radial diffusion in an electrostatic impulse model for stormtime ring current formation

NASA Technical Reports Server (NTRS)

Chen, Margaret W.; Schulz, Michael; Lyons, Larry R.; Gorney, David J.

1992-01-01

Two refinements to the quasi-linear theory of ion radial diffusion are proposed and examined analytically with simulations of particle trajectories. The resonance-broadening correction by Dungey (1965) is applied to the quasi-linear diffusion theory by Faelthammar (1965) for an individual model storm. Quasi-linear theory is then applied to the mean diffusion coefficients resulting from simulations of particle trajectories in 20 model storms. The correction for drift-resonance broadening results in quasi-linear diffusion coefficients with discrepancies from the corresponding simulated values that are reduced by a factor of about 3. Further reductions in the discrepancies are noted following the averaging of the quasi-linear diffusion coefficients, the simulated coefficients, and the resonance-broadened coefficients for the 20 storms. Quasi-linear theory provides good descriptions of particle transport for a single storm but performs even better in conjunction with the present ensemble-averaging.

Parameter estimation of Monod model by the Least-Squares method for microalgae Botryococcus Braunii sp

NASA Astrophysics Data System (ADS)

See, J. J.; Jamaian, S. S.; Salleh, R. M.; Nor, M. E.; Aman, F.

2018-04-01

This research aims to estimate the parameters of Monod model of microalgae Botryococcus Braunii sp growth by the Least-Squares method. Monod equation is a non-linear equation which can be transformed into a linear equation form and it is solved by implementing the Least-Squares linear regression method. Meanwhile, Gauss-Newton method is an alternative method to solve the non-linear Least-Squares problem with the aim to obtain the parameters value of Monod model by minimizing the sum of square error ( SSE). As the result, the parameters of the Monod model for microalgae Botryococcus Braunii sp can be estimated by the Least-Squares method. However, the estimated parameters value obtained by the non-linear Least-Squares method are more accurate compared to the linear Least-Squares method since the SSE of the non-linear Least-Squares method is less than the linear Least-Squares method.

Development of a Linear Stirling Model with Varying Heat Inputs

NASA Technical Reports Server (NTRS)

Regan, Timothy F.; Lewandowski, Edward J.

2007-01-01

The linear model of the Stirling system developed by NASA Glenn Research Center (GRC) has been extended to include a user-specified heat input. Previously developed linear models were limited to the Stirling convertor and electrical load. They represented the thermodynamic cycle with pressure factors that remained constant. The numerical values of the pressure factors were generated by linearizing GRC s non-linear System Dynamic Model (SDM) of the convertor at a chosen operating point. The pressure factors were fixed for that operating point, thus, the model lost accuracy if a transition to a different operating point were simulated. Although the previous linear model was used in developing controllers that manipulated current, voltage, and piston position, it could not be used in the development of control algorithms that regulated hot-end temperature. This basic model was extended to include the thermal dynamics associated with a hot-end temperature that varies over time in response to external changes as well as to changes in the Stirling cycle. The linear model described herein includes not only dynamics of the piston, displacer, gas, and electrical circuit, but also the transient effects of the heater head thermal inertia. The linear version algebraically couples two separate linear dynamic models, one model of the Stirling convertor and one model of the thermal system, through the pressure factors. The thermal system model includes heat flow of heat transfer fluid, insulation loss, and temperature drops from the heat source to the Stirling convertor expansion space. The linear model was compared to a nonlinear model, and performance was very similar. The resulting linear model can be implemented in a variety of computing environments, and is suitable for analysis with classical and state space controls analysis techniques.

Circuit transients due to negative bias arcs-II. [on solar cell power systems in low earth orbit

NASA Technical Reports Server (NTRS)

Metz, R. N.

1986-01-01

Two new models of negative-bias arcing on a solar cell power system in Low Earth Orbit are presented. One is an extended, analytical model and the other is a non-linear, numerical model. The models are based on an earlier analytical model in which the interactions between solar cell interconnects and the space plasma as well as the parameters of the power circuit are approximated linearly. Transient voltages due to arcs struck at the negative thermal of the solar panel are calculated in the time domain. The new models treat, respectively, further linear effects within the solar panel load circuit and non-linear effects associated with the plasma interactions. Results of computer calculations with the models show common-mode voltage transients of the electrically floating solar panel struck by an arc comparable to the early model but load transients that differ substantially from the early model. In particular, load transients of the non-linear model can be more than twice as great as those of the early model and more than twenty times as great as the extended, linear model.

A comparison of linear and nonlinear statistical techniques in performance attribution.

PubMed

Chan, N H; Genovese, C R

2001-01-01

Performance attribution is usually conducted under the linear framework of multifactor models. Although commonly used by practitioners in finance, linear multifactor models are known to be less than satisfactory in many situations. After a brief survey of nonlinear methods, nonlinear statistical techniques are applied to performance attribution of a portfolio constructed from a fixed universe of stocks using factors derived from some commonly used cross sectional linear multifactor models. By rebalancing this portfolio monthly, the cumulative returns for procedures based on standard linear multifactor model and three nonlinear techniques-model selection, additive models, and neural networks-are calculated and compared. It is found that the first two nonlinear techniques, especially in combination, outperform the standard linear model. The results in the neural-network case are inconclusive because of the great variety of possible models. Although these methods are more complicated and may require some tuning, toolboxes are developed and suggestions on calibration are proposed. This paper demonstrates the usefulness of modern nonlinear statistical techniques in performance attribution.

The Aggregation of Single-Case Results Using Hierarchical Linear Models

ERIC Educational Resources Information Center

Van den Noortgate, Wim; Onghena, Patrick

2007-01-01

To investigate the generalizability of the results of single-case experimental studies, evaluating the effect of one or more treatments, in applied research various simultaneous and sequential replication strategies are used. We discuss one approach for aggregating the results for single-cases: the use of hierarchical linear models. This approach…

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

PubMed

Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

2012-01-01

The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

Identifiability Results for Several Classes of Linear Compartment Models.

PubMed

Meshkat, Nicolette; Sullivant, Seth; Eisenberg, Marisa

2015-08-01

Identifiability concerns finding which unknown parameters of a model can be estimated, uniquely or otherwise, from given input-output data. If some subset of the parameters of a model cannot be determined given input-output data, then we say the model is unidentifiable. In this work, we study linear compartment models, which are a class of biological models commonly used in pharmacokinetics, physiology, and ecology. In past work, we used commutative algebra and graph theory to identify a class of linear compartment models that we call identifiable cycle models, which are unidentifiable but have the simplest possible identifiable functions (so-called monomial cycles). Here we show how to modify identifiable cycle models by adding inputs, adding outputs, or removing leaks, in such a way that we obtain an identifiable model. We also prove a constructive result on how to combine identifiable models, each corresponding to strongly connected graphs, into a larger identifiable model. We apply these theoretical results to several real-world biological models from physiology, cell biology, and ecology.

Simplified large African carnivore density estimators from track indices.

PubMed

Winterbach, Christiaan W; Ferreira, Sam M; Funston, Paul J; Somers, Michael J

2016-01-01

The range, population size and trend of large carnivores are important parameters to assess their status globally and to plan conservation strategies. One can use linear models to assess population size and trends of large carnivores from track-based surveys on suitable substrates. The conventional approach of a linear model with intercept may not intercept at zero, but may fit the data better than linear model through the origin. We assess whether a linear regression through the origin is more appropriate than a linear regression with intercept to model large African carnivore densities and track indices. We did simple linear regression with intercept analysis and simple linear regression through the origin and used the confidence interval for ß in the linear model y  =  αx  + ß, Standard Error of Estimate, Mean Squares Residual and Akaike Information Criteria to evaluate the models. The Lion on Clay and Low Density on Sand models with intercept were not significant ( P  > 0.05). The other four models with intercept and the six models thorough origin were all significant ( P  < 0.05). The models using linear regression with intercept all included zero in the confidence interval for ß and the null hypothesis that ß = 0 could not be rejected. All models showed that the linear model through the origin provided a better fit than the linear model with intercept, as indicated by the Standard Error of Estimate and Mean Square Residuals. Akaike Information Criteria showed that linear models through the origin were better and that none of the linear models with intercept had substantial support. Our results showed that linear regression through the origin is justified over the more typical linear regression with intercept for all models we tested. A general model can be used to estimate large carnivore densities from track densities across species and study areas. The formula observed track density = 3.26 × carnivore density can be used to estimate densities of large African carnivores using track counts on sandy substrates in areas where carnivore densities are 0.27 carnivores/100 km 2 or higher. To improve the current models, we need independent data to validate the models and data to test for non-linear relationship between track indices and true density at low densities.

Comparison of all atom, continuum, and linear fitting empirical models for charge screening effect of aqueous medium surrounding a protein molecule

NASA Astrophysics Data System (ADS)

Takahashi, Takuya; Sugiura, Junnnosuke; Nagayama, Kuniaki

2002-05-01

To investigate the role hydration plays in the electrostatic interactions of proteins, the time-averaged electrostatic potential of the B1 domain of protein G in an aqueous solution was calculated with full atomic molecular dynamics simulations that explicitly considers every atom (i.e., an all atom model). This all atom calculated potential was compared with the potential obtained from an electrostatic continuum model calculation. In both cases, the charge-screening effect was fairly well formulated with an effective relative dielectric constant which increased linearly with increasing charge-charge distance. This simulated linear dependence agrees with the experimentally determined linear relation proposed by Pickersgill. Cut-off approximations for Coulomb interactions failed to reproduce this linear relation. Correlation between the all atom model and the continuum models was found to be better than the respective correlation calculated for linear fitting to the two models. This confirms that the continuum model is better at treating the complicated shapes of protein conformations than the simple linear fitting empirical model. We have tried a sigmoid fitting empirical model in addition to the linear one. When weights of all data were treated equally, the sigmoid model, which requires two fitting parameters, fits results of both the all atom and the continuum models less accurately than the linear model which requires only one fitting parameter. When potential values are chosen as weighting factors, the fitting error of the sigmoid model became smaller, and the slope of both linear fitting curves became smaller. This suggests the screening effect of an aqueous medium within a short range, where potential values are relatively large, is smaller than that expected from the linear fitting curve whose slope is almost 4. To investigate the linear increase of the effective relative dielectric constant, the Poisson equation of a low-dielectric sphere in a high-dielectric medium was solved and charges distributed near the molecular surface were indicated as leading to the apparent linearity.

An Application to the Prediction of LOD Change Based on General Regression Neural Network

NASA Astrophysics Data System (ADS)

Zhang, X. H.; Wang, Q. J.; Zhu, J. J.; Zhang, H.

2011-07-01

Traditional prediction of the LOD (length of day) change was based on linear models, such as the least square model and the autoregressive technique, etc. Due to the complex non-linear features of the LOD variation, the performances of the linear model predictors are not fully satisfactory. This paper applies a non-linear neural network - general regression neural network (GRNN) model to forecast the LOD change, and the results are analyzed and compared with those obtained with the back propagation neural network and other models. The comparison shows that the performance of the GRNN model in the prediction of the LOD change is efficient and feasible.

Development of a Linear Stirling System Model with Varying Heat Inputs

NASA Technical Reports Server (NTRS)

Regan, Timothy F.; Lewandowski, Edward J.

2007-01-01

The linear model of the Stirling system developed by NASA Glenn Research Center (GRC) has been extended to include a user-specified heat input. Previously developed linear models were limited to the Stirling convertor and electrical load. They represented the thermodynamic cycle with pressure factors that remained constant. The numerical values of the pressure factors were generated by linearizing GRC's nonlinear System Dynamic Model (SDM) of the convertor at a chosen operating point. The pressure factors were fixed for that operating point, thus, the model lost accuracy if a transition to a different operating point were simulated. Although the previous linear model was used in developing controllers that manipulated current, voltage, and piston position, it could not be used in the development of control algorithms that regulated hot-end temperature. This basic model was extended to include the thermal dynamics associated with a hot-end temperature that varies over time in response to external changes as well as to changes in the Stirling cycle. The linear model described herein includes not only dynamics of the piston, displacer, gas, and electrical circuit, but also the transient effects of the heater head thermal inertia. The linear version algebraically couples two separate linear dynamic models, one model of the Stirling convertor and one model of the thermal system, through the pressure factors. The thermal system model includes heat flow of heat transfer fluid, insulation loss, and temperature drops from the heat source to the Stirling convertor expansion space. The linear model was compared to a nonlinear model, and performance was very similar. The resulting linear model can be implemented in a variety of computing environments, and is suitable for analysis with classical and state space controls analysis techniques.

Constructing an Efficient Self-Tuning Aircraft Engine Model for Control and Health Management Applications

NASA Technical Reports Server (NTRS)

Armstrong, Jeffrey B.; Simon, Donald L.

2012-01-01

Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulations.Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulatns.

Employment of CB models for non-linear dynamic analysis

NASA Technical Reports Server (NTRS)

Klein, M. R. M.; Deloo, P.; Fournier-Sicre, A.

1990-01-01

The non-linear dynamic analysis of large structures is always very time, effort and CPU consuming. Whenever possible the reduction of the size of the mathematical model involved is of main importance to speed up the computational procedures. Such reduction can be performed for the part of the structure which perform linearly. Most of the time, the classical Guyan reduction process is used. For non-linear dynamic process where the non-linearity is present at interfaces between different structures, Craig-Bampton models can provide a very rich information, and allow easy selection of the relevant modes with respect to the phenomenon driving the non-linearity. The paper presents the employment of Craig-Bampton models combined with Newmark direct integration for solving non-linear friction problems appearing at the interface between the Hubble Space Telescope and its solar arrays during in-orbit maneuvers. Theory, implementation in the FEM code ASKA, and practical results are shown.

Sea surface temperature anomalies, planetary waves, and air-sea feedback in the middle latitudes

NASA Technical Reports Server (NTRS)

Frankignoul, C.

1985-01-01

Current analytical models for large-scale air-sea interactions in the middle latitudes are reviewed in terms of known sea-surface temperature (SST) anomalies. The scales and strength of different atmospheric forcing mechanisms are discussed, along with the damping and feedback processes controlling the evolution of the SST. Difficulties with effective SST modeling are described in terms of the techniques and results of case studies, numerical simulations of mixed-layer variability and statistical modeling. The relationship between SST and diabatic heating anomalies is considered and a linear model is developed for the response of the stationary atmosphere to the air-sea feedback. The results obtained with linear wave models are compared with the linear model results. Finally, sample data are presented from experiments with general circulation models into which specific SST anomaly data for the middle latitudes were introduced.

Optimization of isotherm models for pesticide sorption on biopolymer-nanoclay composite by error analysis.

PubMed

Narayanan, Neethu; Gupta, Suman; Gajbhiye, V T; Manjaiah, K M

2017-04-01

A carboxy methyl cellulose-nano organoclay (nano montmorillonite modified with 35-45 wt % dimethyl dialkyl (C 14 -C 18 ) amine (DMDA)) composite was prepared by solution intercalation method. The prepared composite was characterized by infrared spectroscopy (FTIR), X-Ray diffraction spectroscopy (XRD) and scanning electron microscopy (SEM). The composite was utilized for its pesticide sorption efficiency for atrazine, imidacloprid and thiamethoxam. The sorption data was fitted into Langmuir and Freundlich isotherms using linear and non linear methods. The linear regression method suggested best fitting of sorption data into Type II Langmuir and Freundlich isotherms. In order to avoid the bias resulting from linearization, seven different error parameters were also analyzed by non linear regression method. The non linear error analysis suggested that the sorption data fitted well into Langmuir model rather than in Freundlich model. The maximum sorption capacity, Q 0 (μg/g) was given by imidacloprid (2000) followed by thiamethoxam (1667) and atrazine (1429). The study suggests that the degree of determination of linear regression alone cannot be used for comparing the best fitting of Langmuir and Freundlich models and non-linear error analysis needs to be done to avoid inaccurate results. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Analysis of Turkey's PISA 2015 Results Using Two-Level Hierarchical Linear Modelling

ERIC Educational Resources Information Center

Atas, Dogu; Karadag, Özge

2017-01-01

In the field of education, most of the data collected are multi-level structured. Cities, city based schools, school based classes and finally students in the classrooms constitute a hierarchical structure. Hierarchical linear models give more accurate results compared to standard models when the data set has a structure going far as individuals,…

Overshooting thunderstorm cloud top dynamics as approximated by a linear Lagrangian parcel model with analytic exact solutions

NASA Technical Reports Server (NTRS)

Schlesinger, Robert E.

1990-01-01

Results are presented from a linear Lagrangian entraining parcel model of an overshooting thunderstorm cloud top. The model, which is similar to that of Adler and Mack (1986), gives analytic exact solutions for vertical velocity and temperature by representing mixing with Rayleigh damping instead of nonlinearly. Model results are presented for various combinations of stratospheric lapse rate, drag intensity, and mixing strength. The results are compared to those of Adler and Mack.

From Spiking Neuron Models to Linear-Nonlinear Models

PubMed Central

Ostojic, Srdjan; Brunel, Nicolas

2011-01-01

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates. PMID:21283777

From spiking neuron models to linear-nonlinear models.

PubMed

Ostojic, Srdjan; Brunel, Nicolas

2011-01-20

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.

Use of simulation tools to illustrate the effect of data management practices for low and negative plate counts on the estimated parameters of microbial reduction models.

PubMed

Garcés-Vega, Francisco; Marks, Bradley P

2014-08-01

In the last 20 years, the use of microbial reduction models has expanded significantly, including inactivation (linear and nonlinear), survival, and transfer models. However, a major constraint for model development is the impossibility to directly quantify the number of viable microorganisms below the limit of detection (LOD) for a given study. Different approaches have been used to manage this challenge, including ignoring negative plate counts, using statistical estimations, or applying data transformations. Our objective was to illustrate and quantify the effect of negative plate count data management approaches on parameter estimation for microbial reduction models. Because it is impossible to obtain accurate plate counts below the LOD, we performed simulated experiments to generate synthetic data for both log-linear and Weibull-type microbial reductions. We then applied five different, previously reported data management practices and fit log-linear and Weibull models to the resulting data. The results indicated a significant effect (α = 0.05) of the data management practices on the estimated model parameters and performance indicators. For example, when the negative plate counts were replaced by the LOD for log-linear data sets, the slope of the subsequent log-linear model was, on average, 22% smaller than for the original data, the resulting model underpredicted lethality by up to 2.0 log, and the Weibull model was erroneously selected as the most likely correct model for those data. The results demonstrate that it is important to explicitly report LODs and related data management protocols, which can significantly affect model results, interpretation, and utility. Ultimately, we recommend using only the positive plate counts to estimate model parameters for microbial reduction curves and avoiding any data value substitutions or transformations when managing negative plate counts to yield the most accurate model parameters.

Iterative algorithms for large sparse linear systems on parallel computers

NASA Technical Reports Server (NTRS)

Adams, L. M.

1982-01-01

Algorithms for assembling in parallel the sparse system of linear equations that result from finite difference or finite element discretizations of elliptic partial differential equations, such as those that arise in structural engineering are developed. Parallel linear stationary iterative algorithms and parallel preconditioned conjugate gradient algorithms are developed for solving these systems. In addition, a model for comparing parallel algorithms on array architectures is developed and results of this model for the algorithms are given.

Personalized Medicine Enrichment Design for DHA Supplementation Clinical Trial.

PubMed

Lei, Yang; Mayo, Matthew S; Carlson, Susan E; Gajewski, Byron J

2017-03-01

Personalized medicine aims to match patient subpopulation to the most beneficial treatment. The purpose of this study is to design a prospective clinical trial in which we hope to achieve the highest level of confirmation in identifying and making treatment recommendations for subgroups, when the risk levels in the control arm can be ordered. This study was motivated by our goal to identify subgroups in a DHA (docosahexaenoic acid) supplementation trial to reduce preterm birth (gestational age<37 weeks) rate. We performed a meta-analysis to obtain informative prior distributions and simulated operating characteristics to ensure that overall Type I error rate was close to 0.05 in designs with three different models: independent, hierarchical, and dynamic linear models. We performed simulations and sensitivity analysis to examine the subgroup power of models and compared results to a chi-square test. We performed simulations under two hypotheses: a large overall treatment effect and a small overall treatment effect. Within each hypothesis, we designed three different subgroup effects scenarios where resulting subgroup rates are linear, flat, or nonlinear. When the resulting subgroup rates are linear or flat, dynamic linear model appeared to be the most powerful method to identify the subgroups with a treatment effect. It also outperformed other methods when resulting subgroup rates are nonlinear and the overall treatment effect is big. When the resulting subgroup rates are nonlinear and the overall treatment effect is small, hierarchical model and chi-square test did better. Compared to independent and hierarchical models, dynamic linear model tends to be relatively robust and powerful when the control arm has ordinal risk subgroups.

The NASA High Speed ASE Project: Computational Analyses of a Low-Boom Supersonic Configuration

NASA Technical Reports Server (NTRS)

Silva, Walter A.; DeLaGarza, Antonio; Zink, Scott; Bounajem, Elias G.; Johnson, Christopher; Buonanno, Michael; Sanetrik, Mark D.; Yoo, Seung Y.; Kopasakis, George; Christhilf, David M.;

ADME evaluation in drug discovery. 1. Applications of genetic algorithms to the prediction of blood-brain partitioning of a large set of drugs.

PubMed

Hou, Tingjun; Xu, Xiaojie

2002-12-01

In this study, the relationships between the brain-blood concentration ratio of 96 structurally diverse compounds with a large number of structurally derived descriptors were investigated. The linear models were based on molecular descriptors that can be calculated for any compound simply from a knowledge of its molecular structure. The linear correlation coefficients of the models were optimized by genetic algorithms (GAs), and the descriptors used in the linear models were automatically selected from 27 structurally derived descriptors. The GA optimizations resulted in a group of linear models with three or four molecular descriptors with good statistical significance. The change of descriptor use as the evolution proceeds demonstrates that the octane/water partition coefficient and the partial negative solvent-accessible surface area multiplied by the negative charge are crucial to brain-blood barrier permeability. Moreover, we found that the predictions using multiple QSPR models from GA optimization gave quite good results in spite of the diversity of structures, which was better than the predictions using the best single model. The predictions for the two external sets with 37 diverse compounds using multiple QSPR models indicate that the best linear models with four descriptors are sufficiently effective for predictive use. Considering the ease of computation of the descriptors, the linear models may be used as general utilities to screen the blood-brain barrier partitioning of drugs in a high-throughput fashion.

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Effect of non-linearity in predicting doppler waveforms through a novel model

PubMed Central

Gayasen, Aman; Dua, Sunil Kumar; Sengupta, Amit; Nagchoudhuri, D

2003-01-01

Background In pregnancy, the uteroplacental vascular system develops de novo locally in utero and a systemic haemodynamic & bio-rheological alteration accompany it. Any abnormality in the non-linear vascular system is believed to trigger the onset of serious morbid conditions like pre-eclampsia and/or intrauterine growth restriction (IUGR). Exact Aetiopathogenesis is unknown. Advancement in the field of non-invasive doppler image analysis and simulation incorporating non-linearities may unfold the complexities associated with the inaccessible uteroplacental vessels. Earlier modeling approaches approximate it as a linear system. Method We proposed a novel electrical model for the uteroplacental system that uses MOSFETs as non-linear elements in place of traditional linear transmission line (TL) model. The model to simulate doppler FVW's was designed by including the inputs from our non-linear mathematical model. While using the MOSFETs as voltage-controlled switches, a fair degree of controlled-non-linearity has been introduced in the model. Comparative analysis was done between the simulated data and the actual doppler FVW's waveforms. Results & Discussion Normal pregnancy has been successfully modeled and the doppler output waveforms are simulated for different gestation time using the model. It is observed that the dicrotic notch disappears and the S/D ratio decreases as the pregnancy matures. Both these results are established clinical facts. Effects of blood density, viscosity and the arterial wall elasticity on the blood flow velocity profile were also studied. Spectral analysis on the output of the model (blood flow velocity) indicated that the Total Harmonic Distortion (THD) falls during the mid-gestation. Conclusion Total harmonic distortion (THD) is found to be informative in determining the Feto-maternal health. Effects of the blood density, the viscosity and the elasticity changes on the blood FVW are simulated. Future works are expected to concentrate mainly on improving the load with respect to varying non-linear parameters in the model. Heart rate variability, which accounts for the vascular tone, should also be included. We also expect the model to initiate extensive clinical or experimental studies in the near future. PMID:14561227

Modelling daily water temperature from air temperature for the Missouri River.

PubMed

Zhu, Senlin; Nyarko, Emmanuel Karlo; Hadzima-Nyarko, Marijana

2018-01-01

The bio-chemical and physical characteristics of a river are directly affected by water temperature, which thereby affects the overall health of aquatic ecosystems. It is a complex problem to accurately estimate water temperature. Modelling of river water temperature is usually based on a suitable mathematical model and field measurements of various atmospheric factors. In this article, the air-water temperature relationship of the Missouri River is investigated by developing three different machine learning models (Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and Bootstrap Aggregated Decision Trees (BA-DT)). Standard models (linear regression, non-linear regression, and stochastic models) are also developed and compared to machine learning models. Analyzing the three standard models, the stochastic model clearly outperforms the standard linear model and nonlinear model. All the three machine learning models have comparable results and outperform the stochastic model, with GPR having slightly better results for stations No. 2 and 3, while BA-DT has slightly better results for station No. 1. The machine learning models are very effective tools which can be used for the prediction of daily river temperature.

Contact analysis and experimental investigation of a linear ultrasonic motor.

PubMed

Lv, Qibao; Yao, Zhiyuan; Li, Xiang

2017-11-01

The effects of surface roughness are not considered in the traditional motor model which fails to reflect the actual contact mechanism between the stator and slider. An analytical model for calculating the tangential force of linear ultrasonic motor is proposed in this article. The presented model differs from the previous spring contact model, the asperities in contact between stator and slider are considered. The influences of preload and exciting voltage on tangential force in moving direction are analyzed. An experiment is performed to verify the feasibility of this proposed model by comparing the simulation results with the measured data. Moreover, the proposed model and spring model are compared. The results reveal that the proposed model is more accurate than spring model. The discussion is helpful for designing and modeling of linear ultrasonic motors. Copyright © 2017 Elsevier B.V. All rights reserved.

The Elementary Operations of Human Vision Are Not Reducible to Template-Matching

PubMed Central

Neri, Peter

2015-01-01

It is generally acknowledged that biological vision presents nonlinear characteristics, yet linear filtering accounts of visual processing are ubiquitous. The template-matching operation implemented by the linear-nonlinear cascade (linear filter followed by static nonlinearity) is the most widely adopted computational tool in systems neuroscience. This simple model achieves remarkable explanatory power while retaining analytical tractability, potentially extending its reach to a wide range of systems and levels in sensory processing. The extent of its applicability to human behaviour, however, remains unclear. Because sensory stimuli possess multiple attributes (e.g. position, orientation, size), the issue of applicability may be asked by considering each attribute one at a time in relation to a family of linear-nonlinear models, or by considering all attributes collectively in relation to a specified implementation of the linear-nonlinear cascade. We demonstrate that human visual processing can operate under conditions that are indistinguishable from linear-nonlinear transduction with respect to substantially different stimulus attributes of a uniquely specified target signal with associated behavioural task. However, no specific implementation of a linear-nonlinear cascade is able to account for the entire collection of results across attributes; a satisfactory account at this level requires the introduction of a small gain-control circuit, resulting in a model that no longer belongs to the linear-nonlinear family. Our results inform and constrain efforts at obtaining and interpreting comprehensive characterizations of the human sensory process by demonstrating its inescapably nonlinear nature, even under conditions that have been painstakingly fine-tuned to facilitate template-matching behaviour and to produce results that, at some level of inspection, do conform to linear filtering predictions. They also suggest that compliance with linear transduction may be the targeted outcome of carefully crafted nonlinear circuits, rather than default behaviour exhibited by basic components. PMID:26556758

A comparison of linear versus non-linear models of aversive self-awareness, dissociation, and non-suicidal self-injury among young adults.

PubMed

Armey, Michael F; Crowther, Janis H

2008-02-01

Research has identified a significant increase in both the incidence and prevalence of non-suicidal self-injury (NSSI). The present study sought to test both linear and non-linear cusp catastrophe models by using aversive self-awareness, which was operationalized as a composite of aversive self-relevant affect and cognitions, and dissociation as predictors of NSSI. The cusp catastrophe model evidenced a better fit to the data, accounting for 6 times the variance (66%) of a linear model (9%-10%). These results support models of NSSI implicating emotion regulation deficits and experiential avoidance in the occurrence of NSSI and provide preliminary support for the use of cusp catastrophe models to study certain types of low base rate psychopathology such as NSSI. These findings suggest novel approaches to prevention and treatment of NSSI as well.

Women's Endorsement of Models of Sexual Response: Correlates and Predictors.

PubMed

Nowosielski, Krzysztof; Wróbel, Beata; Kowalczyk, Robert

2016-02-01

Few studies have investigated endorsement of female sexual response models, and no single model has been accepted as a normative description of women's sexual response. The aim of the study was to establish how women from a population-based sample endorse current theoretical models of the female sexual response--the linear models and circular model (partial and composite Basson models)--as well as predictors of endorsement. Accordingly, 174 heterosexual women aged 18-55 years were included in a cross-sectional study: 74 women diagnosed with female sexual dysfunction (FSD) based on DSM-5 criteria and 100 non-dysfunctional women. The description of sexual response models was used to divide subjects into four subgroups: linear (Masters-Johnson and Kaplan models), circular (partial Basson model), mixed (linear and circular models in similar proportions, reflective of the composite Basson model), and a different model. Women were asked to choose which of the models best described their pattern of sexual response and how frequently they engaged in each model. Results showed that 28.7% of women endorsed the linear models, 19.5% the partial Basson model, 40.8% the composite Basson model, and 10.9% a different model. Women with FSD endorsed the partial Basson model and a different model more frequently than did non-dysfunctional controls. Individuals who were dissatisfied with a partner as a lover were more likely to endorse a different model. Based on the results, we concluded that the majority of women endorsed a mixed model combining the circular response with the possibility of an innate desire triggering a linear response. Further, relationship difficulties, not FSD, predicted model endorsement.

Non-Condon equilibrium Fermi’s golden rule electronic transition rate constants via the linearized semiclassical method

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

Sun, Xiang; Geva, Eitan

2016-06-28

In this paper, we test the accuracy of the linearized semiclassical (LSC) expression for the equilibrium Fermi’s golden rule rate constant for electronic transitions in the presence of non-Condon effects. We do so by performing a comparison with the exact quantum-mechanical result for a model where the donor and acceptor potential energy surfaces are parabolic and identical except for shifts in the equilibrium energy and geometry, and the coupling between them is linear in the nuclear coordinates. Since non-Condon effects may or may not give rise to conical intersections, both possibilities are examined by considering: (1) A modified Garg-Onuchic-Ambegaokar modelmore » for charge transfer in the condensed phase, where the donor-acceptor coupling is linear in the primary mode coordinate, and for which non-Condon effects do not give rise to a conical intersection; (2) the linear vibronic coupling model for electronic transitions in gas phase molecules, where non-Condon effects give rise to conical intersections. We also present a comprehensive comparison between the linearized semiclassical expression and a progression of more approximate expressions. The comparison is performed over a wide range of frictions and temperatures for model (1) and over a wide range of temperatures for model (2). The linearized semiclassical method is found to reproduce the exact quantum-mechanical result remarkably well for both models over the entire range of parameters under consideration. In contrast, more approximate expressions are observed to deviate considerably from the exact result in some regions of parameter space.« less

Wavelet-linear genetic programming: A new approach for modeling monthly streamflow

NASA Astrophysics Data System (ADS)

Ravansalar, Masoud; Rajaee, Taher; Kisi, Ozgur

2017-06-01

The streamflows are important and effective factors in stream ecosystems and its accurate prediction is an essential and important issue in water resources and environmental engineering systems. A hybrid wavelet-linear genetic programming (WLGP) model, which includes a discrete wavelet transform (DWT) and a linear genetic programming (LGP) to predict the monthly streamflow (Q) in two gauging stations, Pataveh and Shahmokhtar, on the Beshar River at the Yasuj, Iran were used in this study. In the proposed WLGP model, the wavelet analysis was linked to the LGP model where the original time series of streamflow were decomposed into the sub-time series comprising wavelet coefficients. The results were compared with the single LGP, artificial neural network (ANN), a hybrid wavelet-ANN (WANN) and Multi Linear Regression (MLR) models. The comparisons were done by some of the commonly utilized relevant physical statistics. The Nash coefficients (E) were found as 0.877 and 0.817 for the WLGP model, for the Pataveh and Shahmokhtar stations, respectively. The comparison of the results showed that the WLGP model could significantly increase the streamflow prediction accuracy in both stations. Since, the results demonstrate a closer approximation of the peak streamflow values by the WLGP model, this model could be utilized for the simulation of cumulative streamflow data prediction in one month ahead.

Posterior propriety for hierarchical models with log-likelihoods that have norm bounds

DOE PAGES

Michalak, Sarah E.; Morris, Carl N.

2015-07-17

Statisticians often use improper priors to express ignorance or to provide good frequency properties, requiring that posterior propriety be verified. Our paper addresses generalized linear mixed models, GLMMs, when Level I parameters have Normal distributions, with many commonly-used hyperpriors. It provides easy-to-verify sufficient posterior propriety conditions based on dimensions, matrix ranks, and exponentiated norm bounds, ENBs, for the Level I likelihood. Since many familiar likelihoods have ENBs, which is often verifiable via log-concavity and MLE finiteness, our novel use of ENBs permits unification of posterior propriety results and posterior MGF/moment results for many useful Level I distributions, including those commonlymore » used with multilevel generalized linear models, e.g., GLMMs and hierarchical generalized linear models, HGLMs. Furthermore, those who need to verify existence of posterior distributions or of posterior MGFs/moments for a multilevel generalized linear model given a proper or improper multivariate F prior as in Section 1 should find the required results in Sections 1 and 2 and Theorem 3 (GLMMs), Theorem 4 (HGLMs), or Theorem 5 (posterior MGFs/moments).« less

Controlling Flexible Manipulators, an Experimental Investigation. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Hastings, Gordon Greene

1986-01-01

Lightweight, slender manipulators offer faster response and/or greater workspace range for the same size actuators than tradional manipulators. Lightweight construction of manipulator links results in increased structural flexibility. The increase flexibility must be considered in the design of control systems to properly account for the dynamic flexible vibrations and static deflections. Real time control of the flexible manipulator vibrations are experimentally investigated. Models intended for real-time control of distributed parameter system such as flexible manipulators rely on model approximation schemes. An linear model based on the application of Lagrangian dynamics to a rigid body mode and a series of separable flexible modes is examined with respect to model order requirements, and modal candidate selection. Balanced realizations are applied to the linear flexible model to obtain an estimate of appropriate order for a selected model. Describing the flexible deflections as a linear combination of modes results in measurements of beam state, which yield information about several modes. To realize the potential of linear systems theory, knowledge of each state must be available. State estimation is also accomplished by implementation of a Kalman Filter. State feedback control laws are implemented based upon linear quadratic regulator design.

Non-Linear Vibroisolation Pads Design, Numerical FEM Analysis and Introductory Experimental Investigations

NASA Astrophysics Data System (ADS)

Zielnica, J.; Ziółkowski, A.; Cempel, C.

2003-03-01

Design and theoretical and experimental investigation of vibroisolation pads with non-linear static and dynamic responses is the objective of the paper. The analytical investigations are based on non-linear finite element analysis where the load-deflection response is traced against the shape and material properties of the analysed model of the vibroisolation pad. A new model of vibroisolation pad of antisymmetrical type was designed and analysed by the finite element method based on the second-order theory (large displacements and strains) with the assumption of material's non-linearities (Mooney-Rivlin model). Stability loss phenomenon was used in the design of the vibroisolators, and it was proved that it would be possible to design a model of vibroisolator in the form of a continuous pad with non-linear static and dynamic response, typical to vibroisolation purposes. The materials used for the vibroisolator are those of rubber, elastomers, and similar ones. The results of theoretical investigations were examined experimentally. A series of models made of soft rubber were designed for the test purposes. The experimental investigations of the vibroisolation models, under static and dynamic loads, confirmed the results of the FEM analysis.

A simple method for identifying parameter correlations in partially observed linear dynamic models.

PubMed

Li, Pu; Vu, Quoc Dong

2015-12-14

Parameter estimation represents one of the most significant challenges in systems biology. This is because biological models commonly contain a large number of parameters among which there may be functional interrelationships, thus leading to the problem of non-identifiability. Although identifiability analysis has been extensively studied by analytical as well as numerical approaches, systematic methods for remedying practically non-identifiable models have rarely been investigated. We propose a simple method for identifying pairwise correlations and higher order interrelationships of parameters in partially observed linear dynamic models. This is made by derivation of the output sensitivity matrix and analysis of the linear dependencies of its columns. Consequently, analytical relations between the identifiability of the model parameters and the initial conditions as well as the input functions can be achieved. In the case of structural non-identifiability, identifiable combinations can be obtained by solving the resulting homogenous linear equations. In the case of practical non-identifiability, experiment conditions (i.e. initial condition and constant control signals) can be provided which are necessary for remedying the non-identifiability and unique parameter estimation. It is noted that the approach does not consider noisy data. In this way, the practical non-identifiability issue, which is popular for linear biological models, can be remedied. Several linear compartment models including an insulin receptor dynamics model are taken to illustrate the application of the proposed approach. Both structural and practical identifiability of partially observed linear dynamic models can be clarified by the proposed method. The result of this method provides important information for experimental design to remedy the practical non-identifiability if applicable. The derivation of the method is straightforward and thus the algorithm can be easily implemented into a software packet.

Available pressure amplitude of linear compressor based on phasor triangle model

NASA Astrophysics Data System (ADS)

Duan, C. X.; Jiang, X.; Zhi, X. Q.; You, X. K.; Qiu, L. M.

2017-12-01

The linear compressor for cryocoolers possess the advantages of long-life operation, high efficiency, low vibration and compact structure. It is significant to study the match mechanisms between the compressor and the cold finger, which determines the working efficiency of the cryocooler. However, the output characteristics of linear compressor are complicated since it is affected by many interacting parameters. The existing matching methods are simplified and mainly focus on the compressor efficiency and output acoustic power, while neglecting the important output parameter of pressure amplitude. In this study, a phasor triangle model basing on analyzing the forces of the piston is proposed. It can be used to predict not only the output acoustic power, the efficiency, but also the pressure amplitude of the linear compressor. Calculated results agree well with the measurement results of the experiment. By this phasor triangle model, the theoretical maximum output pressure amplitude of the linear compressor can be calculated simply based on a known charging pressure and operating frequency. Compared with the mechanical and electrical model of the linear compressor, the new model can provide an intuitionistic understanding on the match mechanism with faster computational process. The model can also explain the experimental phenomenon of the proportional relationship between the output pressure amplitude and the piston displacement in experiments. By further model analysis, such phenomenon is confirmed as an expression of the unmatched design of the compressor. The phasor triangle model may provide an alternative method for the compressor design and matching with the cold finger.

Nonlinear aeroacoustic characterization of Helmholtz resonators with a local-linear neuro-fuzzy network model

NASA Astrophysics Data System (ADS)

Förner, K.; Polifke, W.

2017-10-01

The nonlinear acoustic behavior of Helmholtz resonators is characterized by a data-based reduced-order model, which is obtained by a combination of high-resolution CFD simulation and system identification. It is shown that even in the nonlinear regime, a linear model is capable of describing the reflection behavior at a particular amplitude with quantitative accuracy. This observation motivates to choose a local-linear model structure for this study, which consists of a network of parallel linear submodels. A so-called fuzzy-neuron layer distributes the input signal over the linear submodels, depending on the root mean square of the particle velocity at the resonator surface. The resulting model structure is referred to as an local-linear neuro-fuzzy network. System identification techniques are used to estimate the free parameters of this model from training data. The training data are generated by CFD simulations of the resonator, with persistent acoustic excitation over a wide range of frequencies and sound pressure levels. The estimated nonlinear, reduced-order models show good agreement with CFD and experimental data over a wide range of amplitudes for several test cases.

Structural Dynamic Analyses And Test Predictions For Spacecraft Structures With Non-Linearities

NASA Astrophysics Data System (ADS)

Vergniaud, Jean-Baptiste; Soula, Laurent; Newerla, Alfred

2012-07-01

The overall objective of the mechanical development and verification process is to ensure that the spacecraft structure is able to sustain the mechanical environments encountered during launch. In general the spacecraft structures are a-priori assumed to behave linear, i.e. the responses to a static load or dynamic excitation, respectively, will increase or decrease proportionally to the amplitude of the load or excitation induced. However, past experiences have shown that various non-linearities might exist in spacecraft structures and the consequences of their dynamic effects can significantly affect the development and verification process. Current processes are mainly adapted to linear spacecraft structure behaviour. No clear rules exist for dealing with major structure non-linearities. They are handled outside the process by individual analysis and margin policy, and analyses after tests to justify the CLA coverage. Non-linearities can primarily affect the current spacecraft development and verification process on two aspects. Prediction of flights loads by launcher/satellite coupled loads analyses (CLA): only linear satellite models are delivered for performing CLA and no well-established rules exist how to properly linearize a model when non- linearities are present. The potential impact of the linearization on the results of the CLA has not yet been properly analyzed. There are thus difficulties to assess that CLA results will cover actual flight levels. Management of satellite verification tests: the CLA results generated with a linear satellite FEM are assumed flight representative. If the internal non- linearities are present in the tested satellite then there might be difficulties to determine which input level must be passed to cover satellite internal loads. The non-linear behaviour can also disturb the shaker control, putting the satellite at risk by potentially imposing too high levels. This paper presents the results of a test campaign performed in the frame of an ESA TRP study [1]. A bread-board including typical non-linearities has been designed, manufactured and tested through a typical spacecraft dynamic test campaign. The study has demonstrate the capabilities to perform non-linear dynamic test predictions on a flight representative spacecraft, the good correlation of test results with respect to Finite Elements Model (FEM) prediction and the possibility to identify modal behaviour and to characterize non-linearities characteristics from test results. As a synthesis for this study, overall guidelines have been derived on the mechanical verification process to improve level of expertise on tests involving spacecraft including non-linearity.

Biomechanically based simulation of brain deformations for intraoperative image correction: coupling of elastic and fluid models

NASA Astrophysics Data System (ADS)

Hagemann, Alexander; Rohr, Karl; Stiehl, H. Siegfried

2000-06-01

In order to improve the accuracy of image-guided neurosurgery, different biomechanical models have been developed to correct preoperative images w.r.t. intraoperative changes like brain shift or tumor resection. All existing biomechanical models simulate different anatomical structures by using either appropriate boundary conditions or by spatially varying material parameter values, while assuming the same physical model for all anatomical structures. In general, this leads to physically implausible results, especially in the case of adjacent elastic and fluid structures. Therefore, we propose a new approach which allows to couple different physical models. In our case, we simulate rigid, elastic, and fluid regions by using the appropriate physical description for each material, namely either the Navier equation or the Stokes equation. To solve the resulting differential equations, we derive a linear matrix system for each region by applying the finite element method (FEM). Thereafter, the linear matrix systems are linked together, ending up with one overall linear matrix system. Our approach has been tested using synthetic as well as tomographic images. It turns out from experiments, that the integrated treatment of rigid, elastic, and fluid regions significantly improves the prediction results in comparison to a pure linear elastic model.

The Linear Bias in the Zeldovich Approximation and a Relation between the Number Density and the Linear Bias of Dark Halos

NASA Astrophysics Data System (ADS)

Fan, Zuhui

2000-01-01

The linear bias of the dark halos from a model under the Zeldovich approximation is derived and compared with the fitting formula of simulation results. While qualitatively similar to the Press-Schechter formula, this model gives a better description for the linear bias around the turnaround point. This advantage, however, may be compromised by the large uncertainty of the actual behavior of the linear bias near the turnaround point. For a broad class of structure formation models in the cold dark matter framework, a general relation exists between the number density and the linear bias of dark halos. This relation can be readily tested by numerical simulations. Thus, instead of laboriously checking these models one by one, numerical simulation studies can falsify a whole category of models. The general validity of this relation is important in identifying key physical processes responsible for the large-scale structure formation in the universe.

Lattice Boltzmann methods for global linear instability analysis

NASA Astrophysics Data System (ADS)

Pérez, José Miguel; Aguilar, Alfonso; Theofilis, Vassilis

2017-12-01

Modal global linear instability analysis is performed using, for the first time ever, the lattice Boltzmann method (LBM) to analyze incompressible flows with two and three inhomogeneous spatial directions. Four linearization models have been implemented in order to recover the linearized Navier-Stokes equations in the incompressible limit. Two of those models employ the single relaxation time and have been proposed previously in the literature as linearization of the collision operator of the lattice Boltzmann equation. Two additional models are derived herein for the first time by linearizing the local equilibrium probability distribution function. Instability analysis results are obtained in three benchmark problems, two in closed geometries and one in open flow, namely the square and cubic lid-driven cavity flow and flow in the wake of the circular cylinder. Comparisons with results delivered by classic spectral element methods verify the accuracy of the proposed new methodologies and point potential limitations particular to the LBM approach. The known issue of appearance of numerical instabilities when the SRT model is used in direct numerical simulations employing the LBM is shown to be reflected in a spurious global eigenmode when the SRT model is used in the instability analysis. Although this mode is absent in the multiple relaxation times model, other spurious instabilities can also arise and are documented herein. Areas of potential improvements in order to make the proposed methodology competitive with established approaches for global instability analysis are discussed.

Modified chloride diffusion model for concrete under the coupling effect of mechanical load and chloride salt environment

NASA Astrophysics Data System (ADS)

Lei, Mingfeng; Lin, Dayong; Liu, Jianwen; Shi, Chenghua; Ma, Jianjun; Yang, Weichao; Yu, Xiaoniu

2018-03-01

For the purpose of investigating lining concrete durability, this study derives a modified chloride diffusion model for concrete based on the odd continuation of boundary conditions and Fourier transform. In order to achieve this, the linear stress distribution on a sectional structure is considered, detailed procedures and methods are presented for model verification and parametric analysis. Simulation results show that the chloride diffusion model can reflect the effects of linear stress distribution of the sectional structure on the chloride diffusivity with reliable accuracy. Along with the natural environmental characteristics of practical engineering structures, reference value ranges of model parameters are provided. Furthermore, a chloride diffusion model is extended for the consideration of multi-factor coupling of linear stress distribution, chloride concentration and diffusion time. Comparison between model simulation and typical current research results shows that the presented model can produce better considerations with a greater universality.

Conditional parametric models for storm sewer runoff

NASA Astrophysics Data System (ADS)

Jonsdottir, H.; Nielsen, H. Aa; Madsen, H.; Eliasson, J.; Palsson, O. P.; Nielsen, M. K.

2007-05-01

The method of conditional parametric modeling is introduced for flow prediction in a sewage system. It is a well-known fact that in hydrological modeling the response (runoff) to input (precipitation) varies depending on soil moisture and several other factors. Consequently, nonlinear input-output models are needed. The model formulation described in this paper is similar to the traditional linear models like final impulse response (FIR) and autoregressive exogenous (ARX) except that the parameters vary as a function of some external variables. The parameter variation is modeled by local lines, using kernels for local linear regression. As such, the method might be referred to as a nearest neighbor method. The results achieved in this study were compared to results from the conventional linear methods, FIR and ARX. The increase in the coefficient of determination is substantial. Furthermore, the new approach conserves the mass balance better. Hence this new approach looks promising for various hydrological models and analysis.

Generation of linear dynamic models from a digital nonlinear simulation

NASA Technical Reports Server (NTRS)

Daniele, C. J.; Krosel, S. M.

1979-01-01

The results and methodology used to derive linear models from a nonlinear simulation are presented. It is shown that averaged positive and negative perturbations in the state variables can reduce numerical errors in finite difference, partial derivative approximations and, in the control inputs, can better approximate the system response in both directions about the operating point. Both explicit and implicit formulations are addressed. Linear models are derived for the F 100 engine, and comparisons of transients are made with the nonlinear simulation. The problem of startup transients in the nonlinear simulation in making these comparisons is addressed. Also, reduction of the linear models is investigated using the modal and normal techniques. Reduced-order models of the F 100 are derived and compared with the full-state models.

Linearized Poststall Aerodynamic and Control Law Models of the X-31A Aircraft and Comparison with Flight Data

NASA Technical Reports Server (NTRS)

Stoliker, Patrick C.; Bosworth, John T.; Georgie, Jennifer

1997-01-01

The X-31A aircraft has a unique configuration that uses thrust-vector vanes and aerodynamic control effectors to provide an operating envelope to a maximum 70 deg angle of attack, an inherently nonlinear portion of the flight envelope. This report presents linearized versions of the X-31A longitudinal and lateral-directional control systems, with aerodynamic models sufficient to evaluate characteristics in the poststall envelope at 30 deg, 45 deg, and 60 deg angle of attack. The models are presented with detail sufficient to allow the reader to reproduce the linear results or perform independent control studies. Comparisons between the responses of the linear models and flight data are presented in the time and frequency domains to demonstrate the strengths and weaknesses of the ability to predict high-angle-of-attack flight dynamics using linear models. The X-31A six-degree-of-freedom simulation contains a program that calculates linear perturbation models throughout the X-31A flight envelope. The models include aerodynamics and flight control system dynamics that are used for stability, controllability, and handling qualities analysis. The models presented in this report demonstrate the ability to provide reasonable linear representations in the poststall flight regime.

Study of Piezoelectric Vibration Energy Harvester with non-linear conditioning circuit using an integrated model

NASA Astrophysics Data System (ADS)

Manzoor, Ali; Rafique, Sajid; Usman Iftikhar, Muhammad; Mahmood Ul Hassan, Khalid; Nasir, Ali

2017-08-01

Piezoelectric vibration energy harvester (PVEH) consists of a cantilever bimorph with piezoelectric layers pasted on its top and bottom, which can harvest power from vibrations and feed to low power wireless sensor nodes through some power conditioning circuit. In this paper, a non-linear conditioning circuit, consisting of a full-bridge rectifier followed by a buck-boost converter, is employed to investigate the issues of electrical side of the energy harvesting system. An integrated mathematical model of complete electromechanical system has been developed. Previously, researchers have studied PVEH with sophisticated piezo-beam models but employed simplistic linear circuits, such as resistor, as electrical load. In contrast, other researchers have worked on more complex non-linear circuits but with over-simplified piezo-beam models. Such models neglect different aspects of the system which result from complex interactions of its electrical and mechanical subsystems. In this work, authors have integrated the distributed parameter-based model of piezo-beam presented in literature with a real world non-linear electrical load. Then, the developed integrated model is employed to analyse the stability of complete energy harvesting system. This work provides a more realistic and useful electromechanical model having realistic non-linear electrical load unlike the simplistic linear circuit elements employed by many researchers.

Evaluation of linearly solvable Markov decision process with dynamic model learning in a mobile robot navigation task.

PubMed

Kinjo, Ken; Uchibe, Eiji; Doya, Kenji

2013-01-01

Linearly solvable Markov Decision Process (LMDP) is a class of optimal control problem in which the Bellman's equation can be converted into a linear equation by an exponential transformation of the state value function (Todorov, 2009b). In an LMDP, the optimal value function and the corresponding control policy are obtained by solving an eigenvalue problem in a discrete state space or an eigenfunction problem in a continuous state using the knowledge of the system dynamics and the action, state, and terminal cost functions. In this study, we evaluate the effectiveness of the LMDP framework in real robot control, in which the dynamics of the body and the environment have to be learned from experience. We first perform a simulation study of a pole swing-up task to evaluate the effect of the accuracy of the learned dynamics model on the derived the action policy. The result shows that a crude linear approximation of the non-linear dynamics can still allow solution of the task, despite with a higher total cost. We then perform real robot experiments of a battery-catching task using our Spring Dog mobile robot platform. The state is given by the position and the size of a battery in its camera view and two neck joint angles. The action is the velocities of two wheels, while the neck joints were controlled by a visual servo controller. We test linear and bilinear dynamic models in tasks with quadratic and Guassian state cost functions. In the quadratic cost task, the LMDP controller derived from a learned linear dynamics model performed equivalently with the optimal linear quadratic regulator (LQR). In the non-quadratic task, the LMDP controller with a linear dynamics model showed the best performance. The results demonstrate the usefulness of the LMDP framework in real robot control even when simple linear models are used for dynamics learning.

Data mining for the analysis of hippocampal zones in Alzheimer's disease

NASA Astrophysics Data System (ADS)

Ovando Vázquez, Cesaré M.

2012-02-01

In this work, a methodology to classify people with Alzheimer's Disease (AD), Healthy Controls (HC) and people with Mild Cognitive Impairment (MCI) is presented. This methodology consists of an ensemble of Support Vector Machines (SVM) with the hippocampal boxes (HB) as input data, these hippocampal zones are taken from Magnetic Resonance (MRI) and Positron Emission Tomography (PET) images. Two ways of constructing this ensemble are presented, the first consists of linear SVM models and the second of non-linear SVM models. Results demonstrate that the linear models classify HBs more accurately than the non-linear models between HC and MCI and that there are no differences between HC and AD.

SCI Identification (SCIDNT) program user's guide. [maximum likelihood method for linear rotorcraft models

NASA Technical Reports Server (NTRS)

1979-01-01

The computer program Linear SCIDNT which evaluates rotorcraft stability and control coefficients from flight or wind tunnel test data is described. It implements the maximum likelihood method to maximize the likelihood function of the parameters based on measured input/output time histories. Linear SCIDNT may be applied to systems modeled by linear constant-coefficient differential equations. This restriction in scope allows the application of several analytical results which simplify the computation and improve its efficiency over the general nonlinear case.

A class of stochastic optimization problems with one quadratic & several linear objective functions and extended portfolio selection model

NASA Astrophysics Data System (ADS)

Xu, Jiuping; Li, Jun

2002-09-01

In this paper a class of stochastic multiple-objective programming problems with one quadratic, several linear objective functions and linear constraints has been introduced. The former model is transformed into a deterministic multiple-objective nonlinear programming model by means of the introduction of random variables' expectation. The reference direction approach is used to deal with linear objectives and results in a linear parametric optimization formula with a single linear objective function. This objective function is combined with the quadratic function using the weighted sums. The quadratic problem is transformed into a linear (parametric) complementary problem, the basic formula for the proposed approach. The sufficient and necessary conditions for (properly, weakly) efficient solutions and some construction characteristics of (weakly) efficient solution sets are obtained. An interactive algorithm is proposed based on reference direction and weighted sums. Varying the parameter vector on the right-hand side of the model, the DM can freely search the efficient frontier with the model. An extended portfolio selection model is formed when liquidity is considered as another objective to be optimized besides expectation and risk. The interactive approach is illustrated with a practical example.

An approximate generalized linear model with random effects for informative missing data.

PubMed

Follmann, D; Wu, M

1995-03-01

This paper develops a class of models to deal with missing data from longitudinal studies. We assume that separate models for the primary response and missingness (e.g., number of missed visits) are linked by a common random parameter. Such models have been developed in the econometrics (Heckman, 1979, Econometrica 47, 153-161) and biostatistics (Wu and Carroll, 1988, Biometrics 44, 175-188) literature for a Gaussian primary response. We allow the primary response, conditional on the random parameter, to follow a generalized linear model and approximate the generalized linear model by conditioning on the data that describes missingness. The resultant approximation is a mixed generalized linear model with possibly heterogeneous random effects. An example is given to illustrate the approximate approach, and simulations are performed to critique the adequacy of the approximation for repeated binary data.

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

PubMed

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

Reduced order surrogate modelling (ROSM) of high dimensional deterministic simulations

NASA Astrophysics Data System (ADS)

Mitry, Mina

Often, computationally expensive engineering simulations can prohibit the engineering design process. As a result, designers may turn to a less computationally demanding approximate, or surrogate, model to facilitate their design process. However, owing to the the curse of dimensionality, classical surrogate models become too computationally expensive for high dimensional data. To address this limitation of classical methods, we develop linear and non-linear Reduced Order Surrogate Modelling (ROSM) techniques. Two algorithms are presented, which are based on a combination of linear/kernel principal component analysis and radial basis functions. These algorithms are applied to subsonic and transonic aerodynamic data, as well as a model for a chemical spill in a channel. The results of this thesis show that ROSM can provide a significant computational benefit over classical surrogate modelling, sometimes at the expense of a minor loss in accuracy.

Wear-caused deflection evolution of a slide rail, considering linear and non-linear wear models

NASA Astrophysics Data System (ADS)

Kim, Dongwook; Quagliato, Luca; Park, Donghwi; Murugesan, Mohanraj; Kim, Naksoo; Hong, Seokmoo

2017-05-01

The research presented in this paper details an experimental-numerical approach for the quantitative correlation between wear and end-point deflection in a slide rail. Focusing the attention on slide rail utilized in white-goods applications, the aim is to evaluate the number of cycles the slide rail can operate, under different load conditions, before it should be replaced due to unacceptable end-point deflection. In this paper, two formulations are utilized to describe the wear: Archard model for the linear wear and Lemaitre damage model for the nonlinear wear. The linear wear gradually reduces the surface of the slide rail whereas the nonlinear one accounts for the surface element deletion (i.e. due to pitting). To determine the constants to use in the wear models, simple tension test and sliding wear test, by utilizing a designed and developed experiment machine, have been carried out. A full slide rail model simulation has been implemented in ABAQUS including both linear and non-linear wear models and the results have been compared with those of the real rails under different load condition, provided by the rail manufacturer. The comparison between numerically estimated and real rail results proved the reliability of the developed numerical model, limiting the error in a ±10% range. The proposed approach allows predicting the displacement vs cycle curves, parametrized for different loads and, based on a chosen failure criterion, to predict the lifetime of the rail.

Finite element modelling of non-linear magnetic circuits using Cosmic NASTRAN

NASA Technical Reports Server (NTRS)

Sheerer, T. J.

1986-01-01

The general purpose Finite Element Program COSMIC NASTRAN currently has the ability to model magnetic circuits with constant permeablilities. An approach was developed which, through small modifications to the program, allows modelling of non-linear magnetic devices including soft magnetic materials, permanent magnets and coils. Use of the NASTRAN code resulted in output which can be used for subsequent mechanical analysis using a variation of the same computer model. Test problems were found to produce theoretically verifiable results.

Linear summation of outputs in a balanced network model of motor cortex.

PubMed

Capaday, Charles; van Vreeswijk, Carl

2015-01-01

Given the non-linearities of the neural circuitry's elements, we would expect cortical circuits to respond non-linearly when activated. Surprisingly, when two points in the motor cortex are activated simultaneously, the EMG responses are the linear sum of the responses evoked by each of the points activated separately. Additionally, the corticospinal transfer function is close to linear, implying that the synaptic interactions in motor cortex must be effectively linear. To account for this, here we develop a model of motor cortex composed of multiple interconnected points, each comprised of reciprocally connected excitatory and inhibitory neurons. We show how non-linearities in neuronal transfer functions are eschewed by strong synaptic interactions within each point. Consequently, the simultaneous activation of multiple points results in a linear summation of their respective outputs. We also consider the effects of reduction of inhibition at a cortical point when one or more surrounding points are active. The network response in this condition is linear over an approximately two- to three-fold decrease of inhibitory feedback strength. This result supports the idea that focal disinhibition allows linear coupling of motor cortical points to generate movement related muscle activation patterns; albeit with a limitation on gain control. The model also explains why neural activity does not spread as far out as the axonal connectivity allows, whilst also explaining why distant cortical points can be, nonetheless, functionally coupled by focal disinhibition. Finally, we discuss the advantages that linear interactions at the cortical level afford to motor command synthesis.

A phenomenological biological dose model for proton therapy based on linear energy transfer spectra.

PubMed

Rørvik, Eivind; Thörnqvist, Sara; Stokkevåg, Camilla H; Dahle, Tordis J; Fjaera, Lars Fredrik; Ytre-Hauge, Kristian S

2017-06-01

The relative biological effectiveness (RBE) of protons varies with the radiation quality, quantified by the linear energy transfer (LET). Most phenomenological models employ a linear dependency of the dose-averaged LET (LET d ) to calculate the biological dose. However, several experiments have indicated a possible non-linear trend. Our aim was to investigate if biological dose models including non-linear LET dependencies should be considered, by introducing a LET spectrum based dose model. The RBE-LET relationship was investigated by fitting of polynomials from 1st to 5th degree to a database of 85 data points from aerobic in vitro experiments. We included both unweighted and weighted regression, the latter taking into account experimental uncertainties. Statistical testing was performed to decide whether higher degree polynomials provided better fits to the data as compared to lower degrees. The newly developed models were compared to three published LET d based models for a simulated spread out Bragg peak (SOBP) scenario. The statistical analysis of the weighted regression analysis favored a non-linear RBE-LET relationship, with the quartic polynomial found to best represent the experimental data (P = 0.010). The results of the unweighted regression analysis were on the borderline of statistical significance for non-linear functions (P = 0.053), and with the current database a linear dependency could not be rejected. For the SOBP scenario, the weighted non-linear model estimated a similar mean RBE value (1.14) compared to the three established models (1.13-1.17). The unweighted model calculated a considerably higher RBE value (1.22). The analysis indicated that non-linear models could give a better representation of the RBE-LET relationship. However, this is not decisive, as inclusion of the experimental uncertainties in the regression analysis had a significant impact on the determination and ranking of the models. As differences between the models were observed for the SOBP scenario, both non-linear LET spectrum- and linear LET d based models should be further evaluated in clinically realistic scenarios. © 2017 American Association of Physicists in Medicine.

Studies with spike initiators - Linearization by noise allows continuous signal modulation in neural networks

NASA Technical Reports Server (NTRS)

Yu, Xiaolong; Lewis, Edwin R.

1989-01-01

It is shown that noise can be an important element in the translation of neuronal generator potentials (summed inputs) to neuronal spike trains (outputs), creating or expanding a range of amplitudes over which the spike rate is proportional to the generator potential amplitude. Noise converts the basically nonlinear operation of a spike initiator into a nearly linear modulation process. This linearization effect of noise is examined in a simple intuitive model of a static threshold and in a more realistic computer simulation of spike initiator based on the Hodgkin-Huxley (HH) model. The results are qualitatively similar; in each case larger noise amplitude results in a larger range of nearly linear modulation. The computer simulation of the HH model with noise shows linear and nonlinear features that were earlier observed in spike data obtained from the VIIIth nerve of the bullfrog. This suggests that these features can be explained in terms of spike initiator properties, and it also suggests that the HH model may be useful for representing basic spike initiator properties in vertebrates.

Modeling of non-ideal hard permanent magnets with an affine-linear model, illustrated for a bar and a horseshoe magnet

NASA Astrophysics Data System (ADS)

Glane, Sebastian; Reich, Felix A.; Müller, Wolfgang H.

2017-11-01

This study is dedicated to continuum-scale material modeling of isotropic permanent magnets. An affine-linear extension to the commonly used ideal hard model for permanent magnets is proposed, motivated, and detailed. In order to demonstrate the differences between these models, bar and horseshoe magnets are considered. The structure of the boundary value problem for the magnetic field and related solution techniques are discussed. For the ideal model, closed-form analytical solutions were obtained for both geometries. Magnetic fields of the boundary value problems for both models and differently shaped magnets were computed numerically by using the boundary element method. The results show that the character of the magnetic field is strongly influenced by the model that is used. Furthermore, it can be observed that the shape of an affine-linear magnet influences the near-field significantly. Qualitative comparisons with experiments suggest that both the ideal and the affine-linear models are relevant in practice, depending on the magnetic material employed. Mathematically speaking, the ideal magnetic model is a special case of the affine-linear one. Therefore, in applications where knowledge of the near-field is important, the affine-linear model can yield more accurate results—depending on the magnetic material.

Assessing the Tangent Linear Behaviour of Common Tracer Transport Schemes and Their Use in a Linearised Atmospheric General Circulation Model

NASA Technical Reports Server (NTRS)

Holdaway, Daniel; Kent, James

2015-01-01

The linearity of a selection of common advection schemes is tested and examined with a view to their use in the tangent linear and adjoint versions of an atmospheric general circulation model. The schemes are tested within a simple offline one-dimensional periodic domain as well as using a simplified and complete configuration of the linearised version of NASA's Goddard Earth Observing System version 5 (GEOS-5). All schemes which prevent the development of negative values and preserve the shape of the solution are confirmed to have nonlinear behaviour. The piecewise parabolic method (PPM) with certain flux limiters, including that used by default in GEOS-5, is found to support linear growth near the shocks. This property can cause the rapid development of unrealistically large perturbations within the tangent linear and adjoint models. It is shown that these schemes with flux limiters should not be used within the linearised version of a transport scheme. The results from tests using GEOS-5 show that the current default scheme (a version of PPM) is not suitable for the tangent linear and adjoint model, and that using a linear third-order scheme for the linearised model produces better behaviour. Using the third-order scheme for the linearised model improves the correlations between the linear and non-linear perturbation trajectories for cloud liquid water and cloud liquid ice in GEOS-5.

Frequency-domain full-waveform inversion with non-linear descent directions

NASA Astrophysics Data System (ADS)

Geng, Yu; Pan, Wenyong; Innanen, Kristopher A.

2018-05-01

Full-waveform inversion (FWI) is a highly non-linear inverse problem, normally solved iteratively, with each iteration involving an update constructed through linear operations on the residuals. Incorporating a flexible degree of non-linearity within each update may have important consequences for convergence rates, determination of low model wavenumbers and discrimination of parameters. We examine one approach for doing so, wherein higher order scattering terms are included within the sensitivity kernel during the construction of the descent direction, adjusting it away from that of the standard Gauss-Newton approach. These scattering terms are naturally admitted when we construct the sensitivity kernel by varying not the current but the to-be-updated model at each iteration. Linear and/or non-linear inverse scattering methodologies allow these additional sensitivity contributions to be computed from the current data residuals within any given update. We show that in the presence of pre-critical reflection data, the error in a second-order non-linear update to a background of s0 is, in our scheme, proportional to at most (Δs/s0)3 in the actual parameter jump Δs causing the reflection. In contrast, the error in a standard Gauss-Newton FWI update is proportional to (Δs/s0)2. For numerical implementation of more complex cases, we introduce a non-linear frequency-domain scheme, with an inner and an outer loop. A perturbation is determined from the data residuals within the inner loop, and a descent direction based on the resulting non-linear sensitivity kernel is computed in the outer loop. We examine the response of this non-linear FWI using acoustic single-parameter synthetics derived from the Marmousi model. The inverted results vary depending on data frequency ranges and initial models, but we conclude that the non-linear FWI has the capability to generate high-resolution model estimates in both shallow and deep regions, and to converge rapidly, relative to a benchmark FWI approach involving the standard gradient.

Comparison of linear and nonlinear models for coherent hemodynamics spectroscopy (CHS)

NASA Astrophysics Data System (ADS)

Sassaroli, Angelo; Kainerstorfer, Jana; Fantini, Sergio

2015-03-01

A recently proposed linear time-invariant hemodynamic model for coherent hemodynamics spectroscopy1 (CHS) relates the tissue concentrations of oxy- and deoxy-hemoglobin (outputs of the system) to given dynamics of the tissue blood volume, blood flow and rate constant of oxygen diffusion (inputs of the system). This linear model was derived in the limit of "small" perturbations in blood flow velocity. We have extended this model to a more general model (which will be referred to as the nonlinear extension to the original model) that yields the time-dependent changes of oxy and deoxy-hemoglobin concentrations in response to arbitrary dynamic changes in capillary blood flow velocity. The nonlinear extension to the model relies on a general solution of the partial differential equation that governs the spatio-temporal behavior of oxygen saturation of hemoglobin in capillaries and venules on the basis of dynamic (or time resolved) blood transit time. We show preliminary results where the CHS spectra obtained from the linear and nonlinear models are compared to quantify the limits of applicability of the linear model.

Reduced-Size Integer Linear Programming Models for String Selection Problems: Application to the Farthest String Problem.

PubMed

Zörnig, Peter

2015-08-01

We present integer programming models for some variants of the farthest string problem. The number of variables and constraints is substantially less than that of the integer linear programming models known in the literature. Moreover, the solution of the linear programming-relaxation contains only a small proportion of noninteger values, which considerably simplifies the rounding process. Numerical tests have shown excellent results, especially when a small set of long sequences is given.

A note on probabilistic models over strings: the linear algebra approach.

PubMed

Bouchard-Côté, Alexandre

2013-12-01

Probabilistic models over strings have played a key role in developing methods that take into consideration indels as phylogenetically informative events. There is an extensive literature on using automata and transducers on phylogenies to do inference on these probabilistic models, in which an important theoretical question is the complexity of computing the normalization of a class of string-valued graphical models. This question has been investigated using tools from combinatorics, dynamic programming, and graph theory, and has practical applications in Bayesian phylogenetics. In this work, we revisit this theoretical question from a different point of view, based on linear algebra. The main contribution is a set of results based on this linear algebra view that facilitate the analysis and design of inference algorithms on string-valued graphical models. As an illustration, we use this method to give a new elementary proof of a known result on the complexity of inference on the "TKF91" model, a well-known probabilistic model over strings. Compared to previous work, our proving method is easier to extend to other models, since it relies on a novel weak condition, triangular transducers, which is easy to establish in practice. The linear algebra view provides a concise way of describing transducer algorithms and their compositions, opens the possibility of transferring fast linear algebra libraries (for example, based on GPUs), as well as low rank matrix approximation methods, to string-valued inference problems.

Passive dendrites enable single neurons to compute linearly non-separable functions.

PubMed

Cazé, Romain Daniel; Humphries, Mark; Gutkin, Boris

2013-01-01

Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of computing linearly non-separable functions, such as the exclusive OR. Other neuron classes, such as interneurons, may possess only a few independent dendritic sub-units, or only passive dendrites where input summation is purely sub-linear, and where dendritic sub-units are only saturating. To determine if such neurons can also compute linearly non-separable functions, we enumerate, for a given parameter range, the Boolean functions implementable by a binary neuron model with a linear sub-unit and either a single spiking or a saturating dendritic sub-unit. We then analytically generalize these numerical results to an arbitrary number of non-linear sub-units. First, we show that a single non-linear dendritic sub-unit, in addition to the somatic non-linearity, is sufficient to compute linearly non-separable functions. Second, we analytically prove that, with a sufficient number of saturating dendritic sub-units, a neuron can compute all functions computable with purely excitatory inputs. Third, we show that these linearly non-separable functions can be implemented with at least two strategies: one where a dendritic sub-unit is sufficient to trigger a somatic spike; another where somatic spiking requires the cooperation of multiple dendritic sub-units. We formally prove that implementing the latter architecture is possible with both types of dendritic sub-units whereas the former is only possible with spiking dendrites. Finally, we show how linearly non-separable functions can be computed by a generic two-compartment biophysical model and a realistic neuron model of the cerebellar stellate cell interneuron. Taken together our results demonstrate that passive dendrites are sufficient to enable neurons to compute linearly non-separable functions.

Passive Dendrites Enable Single Neurons to Compute Linearly Non-separable Functions

PubMed Central

Cazé, Romain Daniel; Humphries, Mark; Gutkin, Boris

2013-01-01

Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of computing linearly non-separable functions, such as the exclusive OR. Other neuron classes, such as interneurons, may possess only a few independent dendritic sub-units, or only passive dendrites where input summation is purely sub-linear, and where dendritic sub-units are only saturating. To determine if such neurons can also compute linearly non-separable functions, we enumerate, for a given parameter range, the Boolean functions implementable by a binary neuron model with a linear sub-unit and either a single spiking or a saturating dendritic sub-unit. We then analytically generalize these numerical results to an arbitrary number of non-linear sub-units. First, we show that a single non-linear dendritic sub-unit, in addition to the somatic non-linearity, is sufficient to compute linearly non-separable functions. Second, we analytically prove that, with a sufficient number of saturating dendritic sub-units, a neuron can compute all functions computable with purely excitatory inputs. Third, we show that these linearly non-separable functions can be implemented with at least two strategies: one where a dendritic sub-unit is sufficient to trigger a somatic spike; another where somatic spiking requires the cooperation of multiple dendritic sub-units. We formally prove that implementing the latter architecture is possible with both types of dendritic sub-units whereas the former is only possible with spiking dendrites. Finally, we show how linearly non-separable functions can be computed by a generic two-compartment biophysical model and a realistic neuron model of the cerebellar stellate cell interneuron. Taken together our results demonstrate that passive dendrites are sufficient to enable neurons to compute linearly non-separable functions. PMID:23468600

Robust Linear Models for Cis-eQTL Analysis.

PubMed

Rantalainen, Mattias; Lindgren, Cecilia M; Holmes, Christopher C

2015-01-01

Expression Quantitative Trait Loci (eQTL) analysis enables characterisation of functional genetic variation influencing expression levels of individual genes. In outbread populations, including humans, eQTLs are commonly analysed using the conventional linear model, adjusting for relevant covariates, assuming an allelic dosage model and a Gaussian error term. However, gene expression data generally have noise that induces heavy-tailed errors relative to the Gaussian distribution and often include atypical observations, or outliers. Such departures from modelling assumptions can lead to an increased rate of type II errors (false negatives), and to some extent also type I errors (false positives). Careful model checking can reduce the risk of type-I errors but often not type II errors, since it is generally too time-consuming to carefully check all models with a non-significant effect in large-scale and genome-wide studies. Here we propose the application of a robust linear model for eQTL analysis to reduce adverse effects of deviations from the assumption of Gaussian residuals. We present results from a simulation study as well as results from the analysis of real eQTL data sets. Our findings suggest that in many situations robust models have the potential to provide more reliable eQTL results compared to conventional linear models, particularly in respect to reducing type II errors due to non-Gaussian noise. Post-genomic data, such as that generated in genome-wide eQTL studies, are often noisy and frequently contain atypical observations. Robust statistical models have the potential to provide more reliable results and increased statistical power under non-Gaussian conditions. The results presented here suggest that robust models should be considered routinely alongside other commonly used methodologies for eQTL analysis.

Optimal non-linear health insurance.

PubMed

Blomqvist, A

1997-06-01

Most theoretical and empirical work on efficient health insurance has been based on models with linear insurance schedules (a constant co-insurance parameter). In this paper, dynamic optimization techniques are used to analyse the properties of optimal non-linear insurance schedules in a model similar to one originally considered by Spence and Zeckhauser (American Economic Review, 1971, 61, 380-387) and reminiscent of those that have been used in the literature on optimal income taxation. The results of a preliminary numerical example suggest that the welfare losses from the implicit subsidy to employer-financed health insurance under US tax law may be a good deal smaller than previously estimated using linear models.

TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS

PubMed Central

Johndrow, James E.; Bhattacharya, Anirban; Dunson, David B.

2017-01-01

Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. We derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions. PMID:29332971

Estimating Traffic Accidents in Turkey Using Differential Evolution Algorithm

NASA Astrophysics Data System (ADS)

Akgüngör, Ali Payıdar; Korkmaz, Ersin

2017-06-01

Estimating traffic accidents play a vital role to apply road safety procedures. This study proposes Differential Evolution Algorithm (DEA) models to estimate the number of accidents in Turkey. In the model development, population (P) and the number of vehicles (N) are selected as model parameters. Three model forms, linear, exponential and semi-quadratic models, are developed using DEA with the data covering from 2000 to 2014. Developed models are statistically compared to select the best fit model. The results of the DE models show that the linear model form is suitable to estimate the number of accidents. The statistics of this form is better than other forms in terms of performance criteria which are the Mean Absolute Percentage Errors (MAPE) and the Root Mean Square Errors (RMSE). To investigate the performance of linear DE model for future estimations, a ten-year period from 2015 to 2024 is considered. The results obtained from future estimations reveal the suitability of DE method for road safety applications.

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

PubMed Central

Rosenblum, Michael; van der Laan, Mark J.

2010-01-01

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

A study of the linear free energy model for DNA structures using the generalized Hamiltonian formalism

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

Yavari, M., E-mail: yavari@iaukashan.ac.ir

2016-06-15

We generalize the results of Nesterenko [13, 14] and Gogilidze and Surovtsev [15] for DNA structures. Using the generalized Hamiltonian formalism, we investigate solutions of the equilibrium shape equations for the linear free energy model.

Linear dependence between the wavefront gradient and the masked intensity for the point source with a CCD sensor

NASA Astrophysics Data System (ADS)

Yang, Huizhen; Ma, Liang; Wang, Bin

2018-01-01

In contrast to the conventional adaptive optics (AO) system, the wavefront sensorless (WFSless) AO system doesn't need a WFS to measure the wavefront aberrations. It is simpler than the conventional AO in system architecture and can be applied to the complex conditions. The model-based WFSless system has a great potential in real-time correction applications because of its fast convergence. The control algorithm of the model-based WFSless system is based on an important theory result that is the linear relation between the Mean-Square Gradient (MSG) magnitude of the wavefront aberration and the second moment of the masked intensity distribution in the focal plane (also called as Masked Detector Signal-MDS). The linear dependence between MSG and MDS for the point source imaging with a CCD sensor will be discussed from theory and simulation in this paper. The theory relationship between MSG and MDS is given based on our previous work. To verify the linear relation for the point source, we set up an imaging model under atmospheric turbulence. Additionally, the value of MDS will be deviate from that of theory because of the noise of detector and further the deviation will affect the correction effect. The theory results under noise will be obtained through theoretical derivation and then the linear relation between MDS and MDS under noise will be discussed through the imaging model. Results show the linear relation between MDS and MDS under noise is also maintained well, which provides a theoretical support to applications of the model-based WFSless system.

A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.

PubMed

Ferrari, Alberto; Comelli, Mario

2016-12-01

In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.

The halo model in a massive neutrino cosmology

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

Massara, Elena; Villaescusa-Navarro, Francisco; Viel, Matteo, E-mail: emassara@sissa.it, E-mail: villaescusa@oats.inaf.it, E-mail: viel@oats.inaf.it

2014-12-01

We provide a quantitative analysis of the halo model in the context of massive neutrino cosmologies. We discuss all the ingredients necessary to model the non-linear matter and cold dark matter power spectra and compare with the results of N-body simulations that incorporate massive neutrinos. Our neutrino halo model is able to capture the non-linear behavior of matter clustering with a ∼20% accuracy up to very non-linear scales of k = 10 h/Mpc (which would be affected by baryon physics). The largest discrepancies arise in the range k = 0.5 – 1 h/Mpc where the 1-halo and 2-halo terms are comparable and are present also inmore » a massless neutrino cosmology. However, at scales k < 0.2 h/Mpc our neutrino halo model agrees with the results of N-body simulations at the level of 8% for total neutrino masses of < 0.3 eV. We also model the neutrino non-linear density field as a sum of a linear and clustered component and predict the neutrino power spectrum and the cold dark matter-neutrino cross-power spectrum up to k = 1 h/Mpc with ∼30% accuracy. For masses below 0.15 eV the neutrino halo model captures the neutrino induced suppression, casted in terms of matter power ratios between massive and massless scenarios, with a 2% agreement with the results of N-body/neutrino simulations. Finally, we provide a simple application of the halo model: the computation of the clustering of galaxies, in massless and massive neutrinos cosmologies, using a simple Halo Occupation Distribution scheme and our halo model extension.« less

A General Accelerated Degradation Model Based on the Wiener Process.

PubMed

Liu, Le; Li, Xiaoyang; Sun, Fuqiang; Wang, Ning

2016-12-06

Accelerated degradation testing (ADT) is an efficient tool to conduct material service reliability and safety evaluations by analyzing performance degradation data. Traditional stochastic process models are mainly for linear or linearization degradation paths. However, those methods are not applicable for the situations where the degradation processes cannot be linearized. Hence, in this paper, a general ADT model based on the Wiener process is proposed to solve the problem for accelerated degradation data analysis. The general model can consider the unit-to-unit variation and temporal variation of the degradation process, and is suitable for both linear and nonlinear ADT analyses with single or multiple acceleration variables. The statistical inference is given to estimate the unknown parameters in both constant stress and step stress ADT. The simulation example and two real applications demonstrate that the proposed method can yield reliable lifetime evaluation results compared with the existing linear and time-scale transformation Wiener processes in both linear and nonlinear ADT analyses.

A General Accelerated Degradation Model Based on the Wiener Process

PubMed Central

Liu, Le; Li, Xiaoyang; Sun, Fuqiang; Wang, Ning

2016-01-01

Accelerated degradation testing (ADT) is an efficient tool to conduct material service reliability and safety evaluations by analyzing performance degradation data. Traditional stochastic process models are mainly for linear or linearization degradation paths. However, those methods are not applicable for the situations where the degradation processes cannot be linearized. Hence, in this paper, a general ADT model based on the Wiener process is proposed to solve the problem for accelerated degradation data analysis. The general model can consider the unit-to-unit variation and temporal variation of the degradation process, and is suitable for both linear and nonlinear ADT analyses with single or multiple acceleration variables. The statistical inference is given to estimate the unknown parameters in both constant stress and step stress ADT. The simulation example and two real applications demonstrate that the proposed method can yield reliable lifetime evaluation results compared with the existing linear and time-scale transformation Wiener processes in both linear and nonlinear ADT analyses. PMID:28774107

Non-Linear Finite Element Modeling of THUNDER Piezoelectric Actuators

NASA Technical Reports Server (NTRS)

Taleghani, Barmac K.; Campbell, Joel F.

1999-01-01

A NASTRAN non-linear finite element model has been developed for predicting the dome heights of THUNDER (THin Layer UNimorph Ferroelectric DrivER) piezoelectric actuators. To analytically validate the finite element model, a comparison was made with a non-linear plate solution using Von Karmen's approximation. A 500 volt input was used to examine the actuator deformation. The NASTRAN finite element model was also compared with experimental results. Four groups of specimens were fabricated and tested. Four different input voltages, which included 120, 160, 200, and 240 Vp-p with a 0 volts offset, were used for this comparison.

Kullback-Leibler information function and the sequential selection of experiments to discriminate among several linear models. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

A sequential adaptive experimental design procedure for a related problem is studied. It is assumed that a finite set of potential linear models relating certain controlled variables to an observed variable is postulated, and that exactly one of these models is correct. The problem is to sequentially design most informative experiments so that the correct model equation can be determined with as little experimentation as possible. Discussion includes: structure of the linear models; prerequisite distribution theory; entropy functions and the Kullback-Leibler information function; the sequential decision procedure; and computer simulation results. An example of application is given.

The development of global GRAPES 4DVAR

NASA Astrophysics Data System (ADS)

Liu, Yongzhu

2017-04-01

Four-dimensional variation data assimilation (4DVAR) has given a great contribution to the improvement of NWP system over the past twenty years. Therefore, our strategy is to develop an operational global 4D-Var system from the outset. The aim at the paper is to introduce the development of the global GRAPES four-dimensional variation data assimilation (4DVAR) using incremental analysis schemes and to presents results of a comparison between 4DVAR using 6-hour assimilation window and simplified physics during the minimization with three-dimensional variation data assimilation (3DVAR). The dynamical cores of the tangent-linear and adjoint models are developed directly based on the non-hydrostatic forecast model. In addition, the standard correctness checks have been performed. As well as the development adjoint codes, most of our work is focused on improving the computational efficiency since the bulk of the computational cost of 4D-Var is in the integration of the tangent-linear and adjoint models. In terms of tangent-linear model, the wall-clock time is reduced to about 1.2 times as much as one of nonlinear model through the optimizing of the software framework. The significant computational cost savings on adjoint model result from the removing the redundant recompilations of model trajectories. It is encouraging that the wall-clock time of adjoint model is less than 1.5 times as much as one of nonlinear model. The current difficulty is that the numerical scheme used within the linear model is based on strategically on the numeric of the corresponding nonlinear model. Further computational acceleration should be expected from the improvement on nonlinear numerical algorithm. A series of linearized physical parameterization schemes has been developed to improve the representation of perturbed fields in the linear model. It consists of horizontal and vertical diffusion, sub-grid scale orographic gravity wave drag, large-scale condensation and cumulus convection schemes. We also found the straightforward linearization based on the nonlinear physical scheme might lead to significant growing of spurious unstable perturbations. It is essential to simplify the linear physics with respect to the non-linear schemes. The improvement on the perturbed fields in the tangent-linear model is visible with the linear physics included, especially at the low level. GRAPES variation data assimilation system adopts the incremental approach. The work is ongoing to develop a pre-operational 4DVAR suite with 0.25° outer loop resolution and multiple outer-loops configurations. One 4DVAR analysis using 6-hour assimilation windows can be finished within 40-minutes when using the available conventional and satellite data. In summary, it was found that the analysis over the northern, southern hemispheres, tropical region and East Asian area of GRAPES 4DVAR performed better than GRAPES 3DVAR for one month experiments. Moreover, the forecast results show that northern and southern extra-tropical scores for GRAPES 4DVAR are already better than GRAPES 3DVAR, but the tropical performance needs further investigations. Therefore, the subsequent main improvements will aim to enhance its computational efficiency and accuracy in 2017. The global GRAPES 4DVAR is planned for operation in 2018.

Fast intersection detection algorithm for PC-based robot off-line programming

NASA Astrophysics Data System (ADS)

Fedrowitz, Christian H.

1994-11-01

This paper presents a method for fast and reliable collision detection in complex production cells. The algorithm is part of the PC-based robot off-line programming system of the University of Siegen (Ropsus). The method is based on a solid model which is managed by a simplified constructive solid geometry model (CSG-model). The collision detection problem is divided in two steps. In the first step the complexity of the problem is reduced in linear time. In the second step the remaining solids are tested for intersection. For this the Simplex algorithm, which is known from linear optimization, is used. It computes a point which is common to two convex polyhedra. The polyhedra intersect, if such a point exists. Regarding the simplified geometrical model of Ropsus the algorithm runs also in linear time. In conjunction with the first step a resultant collision detection algorithm is found which requires linear time in all. Moreover it computes the resultant intersection polyhedron using the dual transformation.

Identifying the nonlinear mechanical behaviour of micro-speakers from their quasi-linear electrical response

NASA Astrophysics Data System (ADS)

Zilletti, Michele; Marker, Arthur; Elliott, Stephen John; Holland, Keith

2017-05-01

In this study model identification of the nonlinear dynamics of a micro-speaker is carried out by purely electrical measurements, avoiding any explicit vibration measurements. It is shown that a dynamic model of the micro-speaker, which takes into account the nonlinear damping characteristic of the device, can be identified by measuring the response between the voltage input and the current flowing into the coil. An analytical formulation of the quasi-linear model of the micro-speaker is first derived and an optimisation method is then used to identify a polynomial function which describes the mechanical damping behaviour of the micro-speaker. The analytical results of the quasi-linear model are compared with numerical results. This study potentially opens up the possibility of efficiently implementing nonlinear echo cancellers.

Multivariable control of the Space Shuttle Remote Manipulator System using linearization by state feedback

NASA Technical Reports Server (NTRS)

Gettman, Chang-Ching L.; Adams, Neil; Bedrossian, Nazareth; Valavani, Lena

1993-01-01

This paper demonstrates an approach to nonlinear control system design that uses linearization by state feedback to allow faster maneuvering of payloads by the Shuttle Remote Manipulator System (SRMS). A nonlinear feedback law is defined to cancel the nonlinear plant dynamics so that a linear controller can be designed for the SRMS. First a nonlinear design model was generated via SIMULINK. This design model included nonlinear arm dynamics derived from the Lagrangian approach, linearized servo model, and linearized gearbox model. The current SRMS position hold controller was implemented on this system. Next, a trajectory was defined using a rigid body kinematics SRMS tool, KRMS. The maneuver was simulated. Finally, higher bandwidth controllers were developed. Results of the new controllers were compared with the existing SRMS automatic control modes for the Space Station Freedom Mission Build 4 Payload extended on the SRMS.

Linear summation of outputs in a balanced network model of motor cortex

PubMed Central

Capaday, Charles; van Vreeswijk, Carl

2015-01-01

Given the non-linearities of the neural circuitry's elements, we would expect cortical circuits to respond non-linearly when activated. Surprisingly, when two points in the motor cortex are activated simultaneously, the EMG responses are the linear sum of the responses evoked by each of the points activated separately. Additionally, the corticospinal transfer function is close to linear, implying that the synaptic interactions in motor cortex must be effectively linear. To account for this, here we develop a model of motor cortex composed of multiple interconnected points, each comprised of reciprocally connected excitatory and inhibitory neurons. We show how non-linearities in neuronal transfer functions are eschewed by strong synaptic interactions within each point. Consequently, the simultaneous activation of multiple points results in a linear summation of their respective outputs. We also consider the effects of reduction of inhibition at a cortical point when one or more surrounding points are active. The network response in this condition is linear over an approximately two- to three-fold decrease of inhibitory feedback strength. This result supports the idea that focal disinhibition allows linear coupling of motor cortical points to generate movement related muscle activation patterns; albeit with a limitation on gain control. The model also explains why neural activity does not spread as far out as the axonal connectivity allows, whilst also explaining why distant cortical points can be, nonetheless, functionally coupled by focal disinhibition. Finally, we discuss the advantages that linear interactions at the cortical level afford to motor command synthesis. PMID:26097452

Efficient Craig Interpolation for Linear Diophantine (Dis)Equations and Linear Modular Equations

DTIC Science & Technology

2008-02-01

Craig interpolants has enabled the development of powerful hardware and software model checking techniques. Efficient algorithms are known for computing...interpolants in rational and real linear arithmetic. We focus on subsets of integer linear arithmetic. Our main results are polynomial time algorithms ...congruences), and linear diophantine disequations. We show the utility of the proposed interpolation algorithms for discovering modular/divisibility predicates

Assessing Linearity in the Loudness Envelope of the Messa di Voce Singing Exercise Through Acoustic Signal Analysis.

PubMed

Yadav, Manuj; Cabrera, Densil; Kenny, Dianna T

2015-09-01

Messa di voce (MDV) is a singing exercise that involves sustaining a single pitch with a linear change in loudness from silence to maximum intensity (the crescendo part) and back to silence again (the decrescendo part), with time symmetry between the two parts. Previous studies have used the sound pressure level (SPL, in decibels) of a singer's voice to measure loudness, so as to assess the linearity of each part-an approach that has limitations due to loudness and SPL not being linearly related. This article studies the loudness envelope shapes of MDVs, comparing the SPL approach with approaches that are more closely related to human loudness perception. The MDVs were performed by a cohort of tertiary singing students, recorded six times (once per semester) over a period of 3 years. The loudness envelopes were derived for a typical audience listening position, and for listening to one's own singing, using three models: SPL, Stevens' power law-based model, and a computational loudness model. The effects on the envelope shape due to room acoustics (an important effect) and vibrato (minimal effect) were also considered. The results showed that the SPL model yielded a lower proportion of linear crescendi and decrescendi, compared with other models. The Stevens' power law-based model provided results similar to the more complicated computational loudness model. Longitudinally, there was no consistent trend in the shape of the MDV loudness envelope for the cohort although there were some individual singers who exhibited improvements in linearity. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Testing higher-order Lagrangian perturbation theory against numerical simulations. 2: Hierarchical models

NASA Technical Reports Server (NTRS)

Melott, A. L.; Buchert, T.; Weib, A. G.

1995-01-01

We present results showing an improvement of the accuracy of perturbation theory as applied to cosmological structure formation for a useful range of scales. The Lagrangian theory of gravitational instability of Friedmann-Lemaitre cosmogonies is compared with numerical simulations. We study the dynamics of hierarchical models as a second step. In the first step we analyzed the performance of the Lagrangian schemes for pancake models, the difference being that in the latter models the initial power spectrum is truncated. This work probed the quasi-linear and weakly non-linear regimes. We here explore whether the results found for pancake models carry over to hierarchical models which are evolved deeply into the non-linear regime. We smooth the initial data by using a variety of filter types and filter scales in order to determine the optimal performance of the analytical models, as has been done for the 'Zel'dovich-approximation' - hereafter TZA - in previous work. We find that for spectra with negative power-index the second-order scheme performs considerably better than TZA in terms of statistics which probe the dynamics, and slightly better in terms of low-order statistics like the power-spectrum. However, in contrast to the results found for pancake models, where the higher-order schemes get worse than TZA at late non-linear stages and on small scales, we here find that the second-order model is as robust as TZA, retaining the improvement at later stages and on smaller scales. In view of these results we expect that the second-order truncated Lagrangian model is especially useful for the modelling of standard dark matter models such as Hot-, Cold-, and Mixed-Dark-Matter.

Analysis of Nonlinear Dynamics in Linear Compressors Driven by Linear Motors

NASA Astrophysics Data System (ADS)

Chen, Liangyuan

2018-03-01

The analysis of dynamic characteristics of the mechatronics system is of great significance for the linear motor design and control. Steady-state nonlinear response characteristics of a linear compressor are investigated theoretically based on the linearized and nonlinear models. First, the influence factors considering the nonlinear gas force load were analyzed. Then, a simple linearized model was set up to analyze the influence on the stroke and resonance frequency. Finally, the nonlinear model was set up to analyze the effects of piston mass, spring stiffness, driving force as an example of design parameter variation. The simulating results show that the stroke can be obtained by adjusting the excitation amplitude, frequency and other adjustments, the equilibrium position can be adjusted by adjusting the DC input, and to make the more efficient operation, the operating frequency must always equal to the resonance frequency.

Bounded Linear Stability Analysis - A Time Delay Margin Estimation Approach for Adaptive Control

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Ishihara, Abraham K.; Krishnakumar, Kalmanje Srinlvas; Bakhtiari-Nejad, Maryam

2009-01-01

This paper presents a method for estimating time delay margin for model-reference adaptive control of systems with almost linear structured uncertainty. The bounded linear stability analysis method seeks to represent the conventional model-reference adaptive law by a locally bounded linear approximation within a small time window using the comparison lemma. The locally bounded linear approximation of the combined adaptive system is cast in a form of an input-time-delay differential equation over a small time window. The time delay margin of this system represents a local stability measure and is computed analytically by a matrix measure method, which provides a simple analytical technique for estimating an upper bound of time delay margin. Based on simulation results for a scalar model-reference adaptive control system, both the bounded linear stability method and the matrix measure method are seen to provide a reasonably accurate and yet not too conservative time delay margin estimation.

Low dose radiation risks for women surviving the a-bombs in Japan: generalized additive model.

PubMed

Dropkin, Greg

2016-11-24

Analyses of cancer mortality and incidence in Japanese A-bomb survivors have been used to estimate radiation risks, which are generally higher for women. Relative Risk (RR) is usually modelled as a linear function of dose. Extrapolation from data including high doses predicts small risks at low doses. Generalized Additive Models (GAMs) are flexible methods for modelling non-linear behaviour. GAMs are applied to cancer incidence in female low dose subcohorts, using anonymous public data for the 1958 - 1998 Life Span Study, to test for linearity, explore interactions, adjust for the skewed dose distribution, examine significance below 100 mGy, and estimate risks at 10 mGy. For all solid cancer incidence, RR estimated from 0 - 100 mGy and 0 - 20 mGy subcohorts is significantly raised. The response tapers above 150 mGy. At low doses, RR increases with age-at-exposure and decreases with time-since-exposure, the preferred covariate. Using the empirical cumulative distribution of dose improves model fit, and capacity to detect non-linear responses. RR is elevated over wide ranges of covariate values. Results are stable under simulation, or when removing exceptional data cells, or adjusting neutron RBE. Estimates of Excess RR at 10 mGy using the cumulative dose distribution are 10 - 45 times higher than extrapolations from a linear model fitted to the full cohort. Below 100 mGy, quasipoisson models find significant effects for all solid, squamous, uterus, corpus, and thyroid cancers, and for respiratory cancers when age-at-exposure > 35 yrs. Results for the thyroid are compatible with studies of children treated for tinea capitis, and Chernobyl survivors. Results for the uterus are compatible with studies of UK nuclear workers and the Techa River cohort. Non-linear models find large, significant cancer risks for Japanese women exposed to low dose radiation from the atomic bombings. The risks should be reflected in protection standards.

Non-Linear Relationship between Economic Growth and CO2 Emissions in China: An Empirical Study Based on Panel Smooth Transition Regression Models

PubMed Central

Wang, Zheng-Xin; Hao, Peng; Yao, Pei-Yi

2017-01-01

The non-linear relationship between provincial economic growth and carbon emissions is investigated by using panel smooth transition regression (PSTR) models. The research indicates that, on the condition of separately taking Gross Domestic Product per capita (GDPpc), energy structure (Es), and urbanisation level (Ul) as transition variables, three models all reject the null hypothesis of a linear relationship, i.e., a non-linear relationship exists. The results show that the three models all contain only one transition function but different numbers of location parameters. The model taking GDPpc as the transition variable has two location parameters, while the other two models separately considering Es and Ul as the transition variables both contain one location parameter. The three models applied in the study all favourably describe the non-linear relationship between economic growth and CO2 emissions in China. It also can be seen that the conversion rate of the influence of Ul on per capita CO2 emissions is significantly higher than those of GDPpc and Es on per capita CO2 emissions. PMID:29236083

Non-Linear Relationship between Economic Growth and CO₂ Emissions in China: An Empirical Study Based on Panel Smooth Transition Regression Models.

PubMed

Wang, Zheng-Xin; Hao, Peng; Yao, Pei-Yi

2017-12-13

The non-linear relationship between provincial economic growth and carbon emissions is investigated by using panel smooth transition regression (PSTR) models. The research indicates that, on the condition of separately taking Gross Domestic Product per capita (GDPpc), energy structure (Es), and urbanisation level (Ul) as transition variables, three models all reject the null hypothesis of a linear relationship, i.e., a non-linear relationship exists. The results show that the three models all contain only one transition function but different numbers of location parameters. The model taking GDPpc as the transition variable has two location parameters, while the other two models separately considering Es and Ul as the transition variables both contain one location parameter. The three models applied in the study all favourably describe the non-linear relationship between economic growth and CO₂ emissions in China. It also can be seen that the conversion rate of the influence of Ul on per capita CO₂ emissions is significantly higher than those of GDPpc and Es on per capita CO₂ emissions.

A model of the extent and distribution of woody linear features in rural Great Britain.

PubMed

Scholefield, Paul; Morton, Dan; Rowland, Clare; Henrys, Peter; Howard, David; Norton, Lisa

2016-12-01

Hedges and lines of trees (woody linear features) are important boundaries that connect and enclose habitats, buffer the effects of land management, and enhance biodiversity in increasingly impoverished landscapes. Despite their acknowledged importance in the wider countryside, they are usually not considered in models of landscape function due to their linear nature and the difficulties of acquiring relevant data about their character, extent, and location. We present a model which uses national datasets to describe the distribution of woody linear features along boundaries in Great Britain. The method can be applied for other boundary types and in other locations around the world across a range of spatial scales where different types of linear feature can be separated using characteristics such as height or width. Satellite-derived Land Cover Map 2007 (LCM2007) provided the spatial framework for locating linear features and was used to screen out areas unsuitable for their occurrence, that is, offshore, urban, and forest areas. Similarly, Ordnance Survey Land-Form PANORAMA®, a digital terrain model, was used to screen out where they do not occur. The presence of woody linear features on boundaries was modelled using attributes from a canopy height dataset obtained by subtracting a digital terrain map (DTM) from a digital surface model (DSM). The performance of the model was evaluated against existing woody linear feature data in Countryside Survey across a range of scales. The results indicate that, despite some underestimation, this simple approach may provide valuable information on the extents and locations of woody linear features in the countryside at both local and national scales.

Linear regression metamodeling as a tool to summarize and present simulation model results.

PubMed

Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M

2013-10-01

Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.

Non-linear models for the detection of impaired cerebral blood flow autoregulation.

PubMed

Chacón, Max; Jara, José Luis; Miranda, Rodrigo; Katsogridakis, Emmanuel; Panerai, Ronney B

2018-01-01

The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model's derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired.

Guidance for the utility of linear models in meta-analysis of genetic association studies of binary phenotypes.

PubMed

Cook, James P; Mahajan, Anubha; Morris, Andrew P

2017-02-01

Linear mixed models are increasingly used for the analysis of genome-wide association studies (GWAS) of binary phenotypes because they can efficiently and robustly account for population stratification and relatedness through inclusion of random effects for a genetic relationship matrix. However, the utility of linear (mixed) models in the context of meta-analysis of GWAS of binary phenotypes has not been previously explored. In this investigation, we present simulations to compare the performance of linear and logistic regression models under alternative weighting schemes in a fixed-effects meta-analysis framework, considering designs that incorporate variable case-control imbalance, confounding factors and population stratification. Our results demonstrate that linear models can be used for meta-analysis of GWAS of binary phenotypes, without loss of power, even in the presence of extreme case-control imbalance, provided that one of the following schemes is used: (i) effective sample size weighting of Z-scores or (ii) inverse-variance weighting of allelic effect sizes after conversion onto the log-odds scale. Our conclusions thus provide essential recommendations for the development of robust protocols for meta-analysis of binary phenotypes with linear models.

Estimation of reflectance from camera responses by the regularized local linear model.

PubMed

Zhang, Wei-Feng; Tang, Gongguo; Dai, Dao-Qing; Nehorai, Arye

2011-10-01

Because of the limited approximation capability of using fixed basis functions, the performance of reflectance estimation obtained by traditional linear models will not be optimal. We propose an approach based on the regularized local linear model. Our approach performs efficiently and knowledge of the spectral power distribution of the illuminant and the spectral sensitivities of the camera is not needed. Experimental results show that the proposed method performs better than some well-known methods in terms of both reflectance error and colorimetric error. © 2011 Optical Society of America

Linear Modeling and Evaluation of Controls on Flow Response in Western Post-Fire Watersheds

NASA Astrophysics Data System (ADS)

Saxe, S.; Hogue, T. S.; Hay, L.

2015-12-01

This research investigates the impact of wildfires on watershed flow regimes throughout the western United States, specifically focusing on evaluation of fire events within specified subregions and determination of the impact of climate and geophysical variables in post-fire flow response. Fire events were collected through federal and state-level databases and streamflow data were collected from U.S. Geological Survey stream gages. 263 watersheds were identified with at least 10 years of continuous pre-fire daily streamflow records and 5 years of continuous post-fire daily flow records. For each watershed, percent changes in runoff ratio (RO), annual seven day low-flows (7Q2) and annual seven day high-flows (7Q10) were calculated from pre- to post-fire. Numerous independent variables were identified for each watershed and fire event, including topographic, land cover, climate, burn severity, and soils data. The national watersheds were divided into five regions through K-clustering and a lasso linear regression model, applying the Leave-One-Out calibration method, was calculated for each region. Nash-Sutcliffe Efficiency (NSE) was used to determine the accuracy of the resulting models. The regions encompassing the United States along and west of the Rocky Mountains, excluding the coastal watersheds, produced the most accurate linear models. The Pacific coast region models produced poor and inconsistent results, indicating that the regions need to be further subdivided. Presently, RO and HF response variables appear to be more easily modeled than LF. Results of linear regression modeling showed varying importance of watershed and fire event variables, with conflicting correlation between land cover types and soil types by region. The addition of further independent variables and constriction of current variables based on correlation indicators is ongoing and should allow for more accurate linear regression modeling.

The Fermi-Pasta-Ulam Problem and Its Underlying Integrable Dynamics: An Approach Through Lyapunov Exponents

NASA Astrophysics Data System (ADS)

Benettin, G.; Pasquali, S.; Ponno, A.

2018-05-01

FPU models, in dimension one, are perturbations either of the linear model or of the Toda model; perturbations of the linear model include the usual β -model, perturbations of Toda include the usual α +β model. In this paper we explore and compare two families, or hierarchies, of FPU models, closer and closer to either the linear or the Toda model, by computing numerically, for each model, the maximal Lyapunov exponent χ . More precisely, we consider statistically typical trajectories and study the asymptotics of χ for large N (the number of particles) and small ɛ (the specific energy E / N), and find, for all models, asymptotic power laws χ ˜eq Cɛ ^a, C and a depending on the model. The asymptotics turns out to be, in general, rather slow, and producing accurate results requires a great computational effort. We also revisit and extend the analytic computation of χ introduced by Casetti, Livi and Pettini, originally formulated for the β -model. With great evidence the theory extends successfully to all models of the linear hierarchy, but not to models close to Toda.

Accurate electrostatic and van der Waals pull-in prediction for fully clamped nano/micro-beams using linear universal graphs of pull-in instability

NASA Astrophysics Data System (ADS)

Tahani, Masoud; Askari, Amir R.

2014-09-01

In spite of the fact that pull-in instability of electrically actuated nano/micro-beams has been investigated by many researchers to date, no explicit formula has been presented yet which can predict pull-in voltage based on a geometrically non-linear and distributed parameter model. The objective of present paper is to introduce a simple and accurate formula to predict this value for a fully clamped electrostatically actuated nano/micro-beam. To this end, a non-linear Euler-Bernoulli beam model is employed, which accounts for the axial residual stress, geometric non-linearity of mid-plane stretching, distributed electrostatic force and the van der Waals (vdW) attraction. The non-linear boundary value governing equation of equilibrium is non-dimensionalized and solved iteratively through single-term Galerkin based reduced order model (ROM). The solutions are validated thorough direct comparison with experimental and other existing results reported in previous studies. Pull-in instability under electrical and vdW loads are also investigated using universal graphs. Based on the results of these graphs, non-dimensional pull-in and vdW parameters, which are defined in the text, vary linearly versus the other dimensionless parameters of the problem. Using this fact, some linear equations are presented to predict pull-in voltage, the maximum allowable length, the so-called detachment length, and the minimum allowable gap for a nano/micro-system. These linear equations are also reduced to a couple of universal pull-in formulas for systems with small initial gap. The accuracy of the universal pull-in formulas are also validated by comparing its results with available experimental and some previous geometric linear and closed-form findings published in the literature.

On the Validity of the Streaming Model for the Redshift-Space Correlation Function in the Linear Regime

NASA Astrophysics Data System (ADS)

Fisher, Karl B.

1995-08-01

The relation between the galaxy correlation functions in real-space and redshift-space is derived in the linear regime by an appropriate averaging of the joint probability distribution of density and velocity. The derivation recovers the familiar linear theory result on large scales but has the advantage of clearly revealing the dependence of the redshift distortions on the underlying peculiar velocity field; streaming motions give rise to distortions of θ(Ω0.6/b) while variations in the anisotropic velocity dispersion yield terms of order θ(Ω1.2/b2). This probabilistic derivation of the redshift-space correlation function is similar in spirit to the derivation of the commonly used "streaming" model, in which the distortions are given by a convolution of the real-space correlation function with a velocity distribution function. The streaming model is often used to model the redshift-space correlation function on small, highly nonlinear, scales. There have been claims in the literature, however, that the streaming model is not valid in the linear regime. Our analysis confirms this claim, but we show that the streaming model can be made consistent with linear theory provided that the model for the streaming has the functional form predicted by linear theory and that the velocity distribution is chosen to be a Gaussian with the correct linear theory dispersion.

Longitudinal mathematics development of students with learning disabilities and students without disabilities: a comparison of linear, quadratic, and piecewise linear mixed effects models.

PubMed

Kohli, Nidhi; Sullivan, Amanda L; Sadeh, Shanna; Zopluoglu, Cengiz

2015-04-01

Effective instructional planning and intervening rely heavily on accurate understanding of students' growth, but relatively few researchers have examined mathematics achievement trajectories, particularly for students with special needs. We applied linear, quadratic, and piecewise linear mixed-effects models to identify the best-fitting model for mathematics development over elementary and middle school and to ascertain differences in growth trajectories of children with learning disabilities relative to their typically developing peers. The analytic sample of 2150 students was drawn from the Early Childhood Longitudinal Study - Kindergarten Cohort, a nationally representative sample of United States children who entered kindergarten in 1998. We first modeled students' mathematics growth via multiple mixed-effects models to determine the best fitting model of 9-year growth and then compared the trajectories of students with and without learning disabilities. Results indicate that the piecewise linear mixed-effects model captured best the functional form of students' mathematics trajectories. In addition, there were substantial achievement gaps between students with learning disabilities and students with no disabilities, and their trajectories differed such that students without disabilities progressed at a higher rate than their peers who had learning disabilities. The results underscore the need for further research to understand how to appropriately model students' mathematics trajectories and the need for attention to mathematics achievement gaps in policy. Copyright © 2015 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

A comparison of washout filters using a human dynamic orientation model. M.S. Thesis

NASA Technical Reports Server (NTRS)

Riedel, S. A.

1977-01-01

The Ormsby model of human dynamic orientation, a discrete time computer program, was used to provide a vestibular explanation for observed differences between two washout schemes. These washout schemes, a linear washout and a nonlinear washout, were subjectively evaluated. It was found that the linear washout presented false rate cues, causing pilots to rate the simulation fidelity of the linear scheme much lower than the nonlinear scheme. By inputting these motion histories into the Ormsby model, it was shown that the linear filter causes discontinuities in the pilot's perceived angular velocity, resulting in the sensation of an anomalous rate cue. This phenomenon does not occur with the use of the nonlinear filter.

Takagi-Sugeno-Kang fuzzy models of the rainfall-runoff transformation

NASA Astrophysics Data System (ADS)

Jacquin, A. P.; Shamseldin, A. Y.

2009-04-01

Fuzzy inference systems, or fuzzy models, are non-linear models that describe the relation between the inputs and the output of a real system using a set of fuzzy IF-THEN rules. This study deals with the application of Takagi-Sugeno-Kang type fuzzy models to the development of rainfall-runoff models operating on a daily basis, using a system based approach. The models proposed are classified in two types, each intended to account for different kinds of dominant non-linear effects in the rainfall-runoff relationship. Fuzzy models type 1 are intended to incorporate the effect of changes in the prevailing soil moisture content, while fuzzy models type 2 address the phenomenon of seasonality. Each model type consists of five fuzzy models of increasing complexity; the most complex fuzzy model of each model type includes all the model components found in the remaining fuzzy models of the respective type. The models developed are applied to data of six catchments from different geographical locations and sizes. Model performance is evaluated in terms of two measures of goodness of fit, namely the Nash-Sutcliffe criterion and the index of volumetric fit. The results of the fuzzy models are compared with those of the Simple Linear Model, the Linear Perturbation Model and the Nearest Neighbour Linear Perturbation Model, which use similar input information. Overall, the results of this study indicate that Takagi-Sugeno-Kang fuzzy models are a suitable alternative for modelling the rainfall-runoff relationship. However, it is also observed that increasing the complexity of the model structure does not necessarily produce an improvement in the performance of the fuzzy models. The relative importance of the different model components in determining the model performance is evaluated through sensitivity analysis of the model parameters in the accompanying study presented in this meeting. Acknowledgements: We would like to express our gratitude to Prof. Kieran M. O'Connor from the National University of Ireland, Galway, for providing the data used in this study.

Incorporating signal-dependent noise for hyperspectral target detection

NASA Astrophysics Data System (ADS)

Morman, Christopher J.; Meola, Joseph

2015-05-01

The majority of hyperspectral target detection algorithms are developed from statistical data models employing stationary background statistics or white Gaussian noise models. Stationary background models are inaccurate as a result of two separate physical processes. First, varying background classes often exist in the imagery that possess different clutter statistics. Many algorithms can account for this variability through the use of subspaces or clustering techniques. The second physical process, which is often ignored, is a signal-dependent sensor noise term. For photon counting sensors that are often used in hyperspectral imaging systems, sensor noise increases as the measured signal level increases as a result of Poisson random processes. This work investigates the impact of this sensor noise on target detection performance. A linear noise model is developed describing sensor noise variance as a linear function of signal level. The linear noise model is then incorporated for detection of targets using data collected at Wright Patterson Air Force Base.

Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach

NASA Astrophysics Data System (ADS)

Moeeni, Hamid; Bonakdari, Hossein; Ebtehaj, Isa

2017-03-01

Forecasting reservoir inflow is one of the most important components of water resources and hydroelectric systems operation management. Seasonal autoregressive integrated moving average (SARIMA) models have been frequently used for predicting river flow. SARIMA models are linear and do not consider the random component of statistical data. To overcome this shortcoming, monthly inflow is predicted in this study based on a combination of seasonal autoregressive integrated moving average (SARIMA) and gene expression programming (GEP) models, which is a new hybrid method (SARIMA-GEP). To this end, a four-step process is employed. First, the monthly inflow datasets are pre-processed. Second, the datasets are modelled linearly with SARIMA and in the third stage, the non-linearity of residual series caused by linear modelling is evaluated. After confirming the non-linearity, the residuals are modelled in the fourth step using a gene expression programming (GEP) method. The proposed hybrid model is employed to predict the monthly inflow to the Jamishan Dam in west Iran. Thirty years' worth of site measurements of monthly reservoir dam inflow with extreme seasonal variations are used. The results of this hybrid model (SARIMA-GEP) are compared with SARIMA, GEP, artificial neural network (ANN) and SARIMA-ANN models. The results indicate that the SARIMA-GEP model ( R 2=78.8, VAF =78.8, RMSE =0.89, MAPE =43.4, CRM =0.053) outperforms SARIMA and GEP and SARIMA-ANN ( R 2=68.3, VAF =66.4, RMSE =1.12, MAPE =56.6, CRM =0.032) displays better performance than the SARIMA and ANN models. A comparison of the two hybrid models indicates the superiority of SARIMA-GEP over the SARIMA-ANN model.

Genetic parameters of body weight and ascites in broilers: effect of different incidence rates of ascites syndrome.

PubMed

Ahmadpanah, J; Ghavi Hossein-Zadeh, N; Shadparvar, A A; Pakdel, A

2017-02-01

1. The objectives of the current study were to investigate the effect of incidence rate (5%, 10%, 20%, 30% and 50%) of ascites syndrome on the expression of genetic characteristics for body weight at 5 weeks of age (BW5) and AS and to compare different methods of genetic parameter estimation for these traits. 2. Based on stochastic simulation, a population with discrete generations was created in which random mating was used for 10 generations. Two methods of restricted maximum likelihood and Bayesian approach via Gibbs sampling were used for the estimation of genetic parameters. A bivariate model including maternal effects was used. The root mean square error for direct heritabilities was also calculated. 3. The results showed that when incidence rates of ascites increased from 5% to 30%, the heritability of AS increased from 0.013 and 0.005 to 0.110 and 0.162 for linear and threshold models, respectively. 4. Maternal effects were significant for both BW5 and AS. Genetic correlations were decreased by increasing incidence rates of ascites in the population from 0.678 and 0.587 at 5% level of ascites to 0.393 and -0.260 at 50% occurrence for linear and threshold models, respectively. 5. The RMSE of direct heritability from true values for BW5 was greater based on a linear-threshold model compared with the linear model of analysis (0.0092 vs. 0.0015). The RMSE of direct heritability from true values for AS was greater based on a linear-linear model (1.21 vs. 1.14). 6. In order to rank birds for ascites incidence, it is recommended to use a threshold model because it resulted in higher heritability estimates compared with the linear model and that BW5 could be one of the main components of selection goals.

Maximizing the Information and Validity of a Linear Composite in the Factor Analysis Model for Continuous Item Responses

ERIC Educational Resources Information Center

Ferrando, Pere J.

2008-01-01

This paper develops results and procedures for obtaining linear composites of factor scores that maximize: (a) test information, and (b) validity with respect to external variables in the multiple factor analysis (FA) model. I treat FA as a multidimensional item response theory model, and use Ackerman's multidimensional information approach based…

Feedback linearization based control of a variable air volume air conditioning system for cooling applications.

PubMed

Thosar, Archana; Patra, Amit; Bhattacharyya, Souvik

2008-07-01

Design of a nonlinear control system for a Variable Air Volume Air Conditioning (VAVAC) plant through feedback linearization is presented in this article. VAVAC systems attempt to reduce building energy consumption while maintaining the primary role of air conditioning. The temperature of the space is maintained at a constant level by establishing a balance between the cooling load generated in the space and the air supply delivered to meet the load. The dynamic model of a VAVAC plant is derived and formulated as a MIMO bilinear system. Feedback linearization is applied for decoupling and linearization of the nonlinear model. Simulation results for a laboratory scale plant are presented to demonstrate the potential of keeping comfort and maintaining energy optimal performance by this methodology. Results obtained with a conventional PI controller and a feedback linearizing controller are compared and the superiority of the proposed approach is clearly established.

Mathematical Modelling in Engineering: An Alternative Way to Teach Linear Algebra

ERIC Educational Resources Information Center

Domínguez-García, S.; García-Planas, M. I.; Taberna, J.

2016-01-01

Technological advances require that basic science courses for engineering, including Linear Algebra, emphasize the development of mathematical strengths associated with modelling and interpretation of results, which are not limited only to calculus abilities. Based on this consideration, we have proposed a project-based learning, giving a dynamic…

A spatial capture-recapture model to estimate fish survival and location from linear continuous monitoring arrays

USGS Publications Warehouse

Raabe, Joshua K.; Gardner, Beth; Hightower, Joseph E.

2013-01-01

We developed a spatial capture–recapture model to evaluate survival and activity centres (i.e., mean locations) of tagged individuals detected along a linear array. Our spatially explicit version of the Cormack–Jolly–Seber model, analyzed using a Bayesian framework, correlates movement between periods and can incorporate environmental or other covariates. We demonstrate the model using 2010 data for anadromous American shad (Alosa sapidissima) tagged with passive integrated transponders (PIT) at a weir near the mouth of a North Carolina river and passively monitored with an upstream array of PIT antennas. The river channel constrained migrations, resulting in linear, one-dimensional encounter histories that included both weir captures and antenna detections. Individual activity centres in a given time period were a function of the individual’s previous estimated location and the river conditions (i.e., gage height). Model results indicate high within-river spawning mortality (mean weekly survival = 0.80) and more extensive movements during elevated river conditions. This model is applicable for any linear array (e.g., rivers, shorelines, and corridors), opening new opportunities to study demographic parameters, movement or migration, and habitat use.

Nonlinear Large Deflection Theory with Modified Aeroelastic Lifting Line Aerodynamics for a High Aspect Ratio Flexible Wing

NASA Technical Reports Server (NTRS)

Nguyen, Nhan; Ting, Eric; Chaparro, Daniel

2017-01-01

This paper investigates the effect of nonlinear large deflection bending on the aerodynamic performance of a high aspect ratio flexible wing. A set of nonlinear static aeroelastic equations are derived for the large bending deflection of a high aspect ratio wing structure. An analysis is conducted to compare the nonlinear bending theory with the linear bending theory. The results show that the nonlinear bending theory is length-preserving whereas the linear bending theory causes a non-physical effect of lengthening the wing structure under the no axial load condition. A modified lifting line theory is developed to compute the lift and drag coefficients of a wing structure undergoing a large bending deflection. The lift and drag coefficients are more accurately estimated by the nonlinear bending theory due to its length-preserving property. The nonlinear bending theory yields lower lift and span efficiency than the linear bending theory. A coupled aerodynamic-nonlinear finite element model is developed to implement the nonlinear bending theory for a Common Research Model (CRM) flexible wing wind tunnel model to be tested in the University of Washington Aeronautical Laboratory (UWAL). The structural stiffness of the model is designed to give about 10% wing tip deflection which is large enough that could cause the nonlinear deflection effect to become significant. The computational results show that the nonlinear bending theory yields slightly less lift than the linear bending theory for this wind tunnel model. As a result, the linear bending theory is deemed adequate for the CRM wind tunnel model.

An object-oriented computational model to study cardiopulmonary hemodynamic interactions in humans.

PubMed

Ngo, Chuong; Dahlmanns, Stephan; Vollmer, Thomas; Misgeld, Berno; Leonhardt, Steffen

2018-06-01

This work introduces an object-oriented computational model to study cardiopulmonary interactions in humans. Modeling was performed in object-oriented programing language Matlab Simscape, where model components are connected with each other through physical connections. Constitutive and phenomenological equations of model elements are implemented based on their non-linear pressure-volume or pressure-flow relationship. The model includes more than 30 physiological compartments, which belong either to the cardiovascular or respiratory system. The model considers non-linear behaviors of veins, pulmonary capillaries, collapsible airways, alveoli, and the chest wall. Model parameters were derisved based on literature values. Model validation was performed by comparing simulation results with clinical and animal data reported in literature. The model is able to provide quantitative values of alveolar, pleural, interstitial, aortic and ventricular pressures, as well as heart and lung volumes during spontaneous breathing and mechanical ventilation. Results of baseline simulation demonstrate the consistency of the assigned parameters. Simulation results during mechanical ventilation with PEEP trials can be directly compared with animal and clinical data given in literature. Object-oriented programming languages can be used to model interconnected systems including model non-linearities. The model provides a useful tool to investigate cardiopulmonary activity during spontaneous breathing and mechanical ventilation. Copyright © 2018 Elsevier B.V. All rights reserved.

A novel methodology for non-linear system identification of battery cells used in non-road hybrid electric vehicles

NASA Astrophysics Data System (ADS)

Unger, Johannes; Hametner, Christoph; Jakubek, Stefan; Quasthoff, Marcus

2014-12-01

An accurate state of charge (SoC) estimation of a traction battery in hybrid electric non-road vehicles, which possess higher dynamics and power densities than on-road vehicles, requires a precise battery cell terminal voltage model. This paper presents a novel methodology for non-linear system identification of battery cells to obtain precise battery models. The methodology comprises the architecture of local model networks (LMN) and optimal model based design of experiments (DoE). Three main novelties are proposed: 1) Optimal model based DoE, which aims to high dynamically excite the battery cells at load ranges frequently used in operation. 2) The integration of corresponding inputs in the LMN to regard the non-linearities SoC, relaxation, hysteresis as well as temperature effects. 3) Enhancements to the local linear model tree (LOLIMOT) construction algorithm, to achieve a physical appropriate interpretation of the LMN. The framework is applicable for different battery cell chemistries and different temperatures, and is real time capable, which is shown on an industrial PC. The accuracy of the obtained non-linear battery model is demonstrated on cells with different chemistries and temperatures. The results show significant improvement due to optimal experiment design and integration of the battery non-linearities within the LMN structure.

Can a minimalist model of wind forced baroclinic Rossby waves produce reasonable results?

NASA Astrophysics Data System (ADS)

Watanabe, Wandrey B.; Polito, Paulo S.; da Silveira, Ilson C. A.

2016-04-01

The linear theory predicts that Rossby waves are the large scale mechanism of adjustment to perturbations of the geophysical fluid. Satellite measurements of sea level anomaly (SLA) provided sturdy evidence of the existence of these waves. Recent studies suggest that the variability in the altimeter records is mostly due to mesoscale nonlinear eddies and challenges the original interpretation of westward propagating features as Rossby waves. The objective of this work is to test whether a classic linear dynamic model is a reasonable explanation for the observed SLA. A linear-reduced gravity non-dispersive Rossby wave model is used to estimate the SLA forced by direct and remote wind stress. Correlations between model results and observations are up to 0.88. The best agreement is in the tropical region of all ocean basins. These correlations decrease towards insignificance in mid-latitudes. The relative contributions of eastern boundary (remote) forcing and local wind forcing in the generation of Rossby waves are also estimated and suggest that the main wave forming mechanism is the remote forcing. Results suggest that linear long baroclinic Rossby wave dynamics explain a significant part of the SLA annual variability at least in the tropical oceans.

Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall a tropical climate.

PubMed

Zeynoddin, Mohammad; Bonakdari, Hossein; Azari, Arash; Ebtehaj, Isa; Gharabaghi, Bahram; Riahi Madavar, Hossein

2018-09-15

A novel hybrid approach is presented that can more accurately predict monthly rainfall in a tropical climate by integrating a linear stochastic model with a powerful non-linear extreme learning machine method. This new hybrid method was then evaluated by considering four general scenarios. In the first scenario, the modeling process is initiated without preprocessing input data as a base case. While in other three scenarios, the one-step and two-step procedures are utilized to make the model predictions more precise. The mentioned scenarios are based on a combination of stationarization techniques (i.e., differencing, seasonal and non-seasonal standardization and spectral analysis), and normality transforms (i.e., Box-Cox, John and Draper, Yeo and Johnson, Johnson, Box-Cox-Mod, log, log standard, and Manly). In scenario 2, which is a one-step scenario, the stationarization methods are employed as preprocessing approaches. In scenario 3 and 4, different combinations of normality transform, and stationarization methods are considered as preprocessing techniques. In total, 61 sub-scenarios are evaluated resulting 11013 models (10785 linear methods, 4 nonlinear models, and 224 hybrid models are evaluated). The uncertainty of the linear, nonlinear and hybrid models are examined by Monte Carlo technique. The best preprocessing technique is the utilization of Johnson normality transform and seasonal standardization (respectively) (R 2  = 0.99; RMSE = 0.6; MAE = 0.38; RMSRE = 0.1, MARE = 0.06, UI = 0.03 &UII = 0.05). The results of uncertainty analysis indicated the good performance of proposed technique (d-factor = 0.27; 95PPU = 83.57). Moreover, the results of the proposed methodology in this study were compared with an evolutionary hybrid of adaptive neuro fuzzy inference system (ANFIS) with firefly algorithm (ANFIS-FFA) demonstrating that the new hybrid methods outperformed ANFIS-FFA method. Copyright © 2018 Elsevier Ltd. All rights reserved.

Delineating chalk sand distribution of Ekofisk formation using probabilistic neural network (PNN) and stepwise regression (SWR): Case study Danish North Sea field

NASA Astrophysics Data System (ADS)

Haris, A.; Nafian, M.; Riyanto, A.

2017-07-01

Danish North Sea Fields consist of several formations (Ekofisk, Tor, and Cromer Knoll) that was started from the age of Paleocene to Miocene. In this study, the integration of seismic and well log data set is carried out to determine the chalk sand distribution in the Danish North Sea field. The integration of seismic and well log data set is performed by using the seismic inversion analysis and seismic multi-attribute. The seismic inversion algorithm, which is used to derive acoustic impedance (AI), is model-based technique. The derived AI is then used as external attributes for the input of multi-attribute analysis. Moreover, the multi-attribute analysis is used to generate the linear and non-linear transformation of among well log properties. In the case of the linear model, selected transformation is conducted by weighting step-wise linear regression (SWR), while for the non-linear model is performed by using probabilistic neural networks (PNN). The estimated porosity, which is resulted by PNN shows better suited to the well log data compared with the results of SWR. This result can be understood since PNN perform non-linear regression so that the relationship between the attribute data and predicted log data can be optimized. The distribution of chalk sand has been successfully identified and characterized by porosity value ranging from 23% up to 30%.

MO-F-16A-02: Simulation of a Medical Linear Accelerator for Teaching Purposes

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

Carlone, M; Lamey, M; Anderson, R

Purpose: Detailed functioning of linear accelerator physics is well known. Less well developed is the basic understanding of how the adjustment of the linear accelerator's electrical components affects the resulting radiation beam. Other than the text by Karzmark, there is very little literature devoted to the practical understanding of linear accelerator functionality targeted at the radiotherapy clinic level. The purpose of this work is to describe a simulation environment for medical linear accelerators with the purpose of teaching linear accelerator physics. Methods: Varian type lineacs were simulated. Klystron saturation and peak output were modelled analytically. The energy gain of anmore » electron beam was modelled using load line expressions. The bending magnet was assumed to be a perfect solenoid whose pass through energy varied linearly with solenoid current. The dose rate calculated at depth in water was assumed to be a simple function of the target's beam current. The flattening filter was modelled as an attenuator with conical shape, and the time-averaged dose rate at a depth in water was determined by calculating kerma. Results: Fifteen analytical models were combined into a single model called SIMAC. Performance was verified systematically by adjusting typical linac control parameters. Increasing klystron pulse voltage increased dose rate to a peak, which then decreased as the beam energy was further increased due to the fixed pass through energy of the bending magnet. Increasing accelerator beam current leads to a higher dose per pulse. However, the energy of the electron beam decreases due to beam loading and so the dose rate eventually maximizes and the decreases as beam current was further increased. Conclusion: SIMAC can realistically simulate the functionality of a linear accelerator. It is expected to have value as a teaching tool for both medical physicists and linear accelerator service personnel.« less

Equilibrium Phase Behavior of the Square-Well Linear Microphase-Forming Model.

PubMed

Zhuang, Yuan; Charbonneau, Patrick

2016-07-07

We have recently developed a simulation approach to calculate the equilibrium phase diagram of particle-based microphase formers. Here, this approach is used to calculate the phase behavior of the square-well linear model for different strengths and ranges of the linear long-range repulsive component. The results are compared with various theoretical predictions for microphase formation. The analysis further allows us to better understand the mechanism for microphase formation in colloidal suspensions.

Time Delay Estimation

DTIC Science & Technology

1976-04-09

of the signal and noise remain HH ***^-^*--~ 53 h, to r(Mc) h2(r» r(we) Figure 3-2 Sy&toetric Impulse Response for Two FIR Linear Phase...Inputs x,y and Outputs x.j. , 15 2-2 Linear System with Impulse Response h("r) 23 2-3 Model of Error Resulting from Linearly Filtering x(t) to...Corrupted with Additive Noise 42 2-6 Model of Directional Signal Corrupted with Additive Noise and Processed .... 45 2-7 Source Driving Two

UCODE_2005 and six other computer codes for universal sensitivity analysis, calibration, and uncertainty evaluation constructed using the JUPITER API

USGS Publications Warehouse

Poeter, Eileen E.; Hill, Mary C.; Banta, Edward R.; Mehl, Steffen; Christensen, Steen

2006-01-01

This report documents the computer codes UCODE_2005 and six post-processors. Together the codes can be used with existing process models to perform sensitivity analysis, data needs assessment, calibration, prediction, and uncertainty analysis. Any process model or set of models can be used; the only requirements are that models have numerical (ASCII or text only) input and output files, that the numbers in these files have sufficient significant digits, that all required models can be run from a single batch file or script, and that simulated values are continuous functions of the parameter values. Process models can include pre-processors and post-processors as well as one or more models related to the processes of interest (physical, chemical, and so on), making UCODE_2005 extremely powerful. An estimated parameter can be a quantity that appears in the input files of the process model(s), or a quantity used in an equation that produces a value that appears in the input files. In the latter situation, the equation is user-defined. UCODE_2005 can compare observations and simulated equivalents. The simulated equivalents can be any simulated value written in the process-model output files or can be calculated from simulated values with user-defined equations. The quantities can be model results, or dependent variables. For example, for ground-water models they can be heads, flows, concentrations, and so on. Prior, or direct, information on estimated parameters also can be considered. Statistics are calculated to quantify the comparison of observations and simulated equivalents, including a weighted least-squares objective function. In addition, data-exchange files are produced that facilitate graphical analysis. UCODE_2005 can be used fruitfully in model calibration through its sensitivity analysis capabilities and its ability to estimate parameter values that result in the best possible fit to the observations. Parameters are estimated using nonlinear regression: a weighted least-squares objective function is minimized with respect to the parameter values using a modified Gauss-Newton method or a double-dogleg technique. Sensitivities needed for the method can be read from files produced by process models that can calculate sensitivities, such as MODFLOW-2000, or can be calculated by UCODE_2005 using a more general, but less accurate, forward- or central-difference perturbation technique. Problems resulting from inaccurate sensitivities and solutions related to the perturbation techniques are discussed in the report. Statistics are calculated and printed for use in (1) diagnosing inadequate data and identifying parameters that probably cannot be estimated; (2) evaluating estimated parameter values; and (3) evaluating how well the model represents the simulated processes. Results from UCODE_2005 and codes RESIDUAL_ANALYSIS and RESIDUAL_ANALYSIS_ADV can be used to evaluate how accurately the model represents the processes it simulates. Results from LINEAR_UNCERTAINTY can be used to quantify the uncertainty of model simulated values if the model is sufficiently linear. Results from MODEL_LINEARITY and MODEL_LINEARITY_ADV can be used to evaluate model linearity and, thereby, the accuracy of the LINEAR_UNCERTAINTY results. UCODE_2005 can also be used to calculate nonlinear confidence and predictions intervals, which quantify the uncertainty of model simulated values when the model is not linear. CORFAC_PLUS can be used to produce factors that allow intervals to account for model intrinsic nonlinearity and small-scale variations in system characteristics that are not explicitly accounted for in the model or the observation weighting. The six post-processing programs are independent of UCODE_2005 and can use the results of other programs that produce the required data-exchange files. UCODE_2005 and the other six codes are intended for use on any computer operating system. The programs con

Transfer matrix method for dynamics modeling and independent modal space vibration control design of linear hybrid multibody system

NASA Astrophysics Data System (ADS)

Rong, Bao; Rui, Xiaoting; Lu, Kun; Tao, Ling; Wang, Guoping; Ni, Xiaojun

2018-05-01

In this paper, an efficient method of dynamics modeling and vibration control design of a linear hybrid multibody system (MS) is studied based on the transfer matrix method. The natural vibration characteristics of a linear hybrid MS are solved by using low-order transfer equations. Then, by constructing the brand-new body dynamics equation, augmented operator and augmented eigenvector, the orthogonality of augmented eigenvector of a linear hybrid MS is satisfied, and its state space model expressed in each independent model space is obtained easily. According to this dynamics model, a robust independent modal space-fuzzy controller is designed for vibration control of a general MS, and the genetic optimization of some critical control parameters of fuzzy tuners is also presented. Two illustrative examples are performed, which results show that this method is computationally efficient and with perfect control performance.

Application of linear logic to simulation

NASA Astrophysics Data System (ADS)

Clarke, Thomas L.

1998-08-01

Linear logic, since its introduction by Girard in 1987 has proven expressive and powerful. Linear logic has provided natural encodings of Turing machines, Petri nets and other computational models. Linear logic is also capable of naturally modeling resource dependent aspects of reasoning. The distinguishing characteristic of linear logic is that it accounts for resources; two instances of the same variable are considered differently from a single instance. Linear logic thus must obey a form of the linear superposition principle. A proportion can be reasoned with only once, unless a special operator is applied. Informally, linear logic distinguishes two kinds of conjunction, two kinds of disjunction, and also introduces a modal storage operator that explicitly indicates propositions that can be reused. This paper discuses the application of linear logic to simulation. A wide variety of logics have been developed; in addition to classical logic, there are fuzzy logics, affine logics, quantum logics, etc. All of these have found application in simulations of one sort or another. The special characteristics of linear logic and its benefits for simulation will be discussed. Of particular interest is a connection that can be made between linear logic and simulated dynamics by using the concept of Lie algebras and Lie groups. Lie groups provide the connection between the exponential modal storage operators of linear logic and the eigen functions of dynamic differential operators. Particularly suggestive are possible relations between complexity result for linear logic and non-computability results for dynamical systems.

Development of non-linear models predicting daily fine particle concentrations using aerosol optical depth retrievals and ground-based measurements at a municipality in the Brazilian Amazon region

NASA Astrophysics Data System (ADS)

Gonçalves, Karen dos Santos; Winkler, Mirko S.; Benchimol-Barbosa, Paulo Roberto; de Hoogh, Kees; Artaxo, Paulo Eduardo; de Souza Hacon, Sandra; Schindler, Christian; Künzli, Nino

2018-07-01

Epidemiological studies generally use particulate matter measurements with diameter less 2.5 μm (PM2.5) from monitoring networks. Satellite aerosol optical depth (AOD) data has considerable potential in predicting PM2.5 concentrations, and thus provides an alternative method for producing knowledge regarding the level of pollution and its health impact in areas where no ground PM2.5 measurements are available. This is the case in the Brazilian Amazon rainforest region where forest fires are frequent sources of high pollution. In this study, we applied a non-linear model for predicting PM2.5 concentration from AOD retrievals using interaction terms between average temperature, relative humidity, sine, cosine of date in a period of 365,25 days and the square of the lagged relative residual. Regression performance statistics were tested comparing the goodness of fit and R2 based on results from linear regression and non-linear regression for six different models. The regression results for non-linear prediction showed the best performance, explaining on average 82% of the daily PM2.5 concentrations when considering the whole period studied. In the context of Amazonia, it was the first study predicting PM2.5 concentrations using the latest high-resolution AOD products also in combination with the testing of a non-linear model performance. Our results permitted a reliable prediction considering the AOD-PM2.5 relationship and set the basis for further investigations on air pollution impacts in the complex context of Brazilian Amazon Region.

Minimal agent based model for financial markets II. Statistical properties of the linear and multiplicative dynamics

NASA Astrophysics Data System (ADS)

Alfi, V.; Cristelli, M.; Pietronero, L.; Zaccaria, A.

2009-02-01

We present a detailed study of the statistical properties of the Agent Based Model introduced in paper I [Eur. Phys. J. B, DOI: 10.1140/epjb/e2009-00028-4] and of its generalization to the multiplicative dynamics. The aim of the model is to consider the minimal elements for the understanding of the origin of the stylized facts and their self-organization. The key elements are fundamentalist agents, chartist agents, herding dynamics and price behavior. The first two elements correspond to the competition between stability and instability tendencies in the market. The herding behavior governs the possibility of the agents to change strategy and it is a crucial element of this class of models. We consider a linear approximation for the price dynamics which permits a simple interpretation of the model dynamics and, for many properties, it is possible to derive analytical results. The generalized non linear dynamics results to be extremely more sensible to the parameter space and much more difficult to analyze and control. The main results for the nature and self-organization of the stylized facts are, however, very similar in the two cases. The main peculiarity of the non linear dynamics is an enhancement of the fluctuations and a more marked evidence of the stylized facts. We will also discuss some modifications of the model to introduce more realistic elements with respect to the real markets.

Non-linear models for the detection of impaired cerebral blood flow autoregulation

PubMed Central

Miranda, Rodrigo; Katsogridakis, Emmanuel

2018-01-01

The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model’s derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired. PMID:29381724

Modeling of salt and pH gradient elution in ion-exchange chromatography.

PubMed

Schmidt, Michael; Hafner, Mathias; Frech, Christian

2014-01-01

The separation of proteins by internally and externally generated pH gradients in chromatofocusing on ion-exchange columns is a well-established analytical method with a large number of applications. In this work, a stoichiometric displacement model was used to describe the retention behavior of lysozyme on SP Sepharose FF and a monoclonal antibody on Fractogel SO3 (S) in linear salt and pH gradient elution. The pH dependence of the binding charge B in the linear gradient elution model is introduced using a protein net charge model, while the pH dependence of the equilibrium constant is based on a thermodynamic approach. The model parameter and pH dependences are calculated from linear salt gradient elutions at different pH values as well as from linear pH gradient elutions at different fixed salt concentrations. The application of the model for the well-characterized protein lysozyme resulted in almost identical model parameters based on either linear salt or pH gradient elution data. For the antibody, only the approach based on linear pH gradients is feasible because of the limited pH range useful for salt gradient elution. The application of the model for the separation of an acid variant of the antibody from the major monomeric form is discussed. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Optimal blood glucose control in diabetes mellitus treatment using dynamic programming based on Ackerman’s linear model

NASA Astrophysics Data System (ADS)

Pradanti, Paskalia; Hartono

2018-03-01

Determination of insulin injection dose in diabetes mellitus treatment can be considered as an optimal control problem. This article is aimed to simulate optimal blood glucose control for patient with diabetes mellitus. The blood glucose regulation of diabetic patient is represented by Ackerman’s Linear Model. This problem is then solved using dynamic programming method. The desired blood glucose level is obtained by minimizing the performance index in Lagrange form. The results show that dynamic programming based on Ackerman’s Linear Model is quite good to solve the problem.

How large are the consequences of covariate imbalance in cluster randomized trials: a simulation study with a continuous outcome and a binary covariate at the cluster level.

PubMed

Moerbeek, Mirjam; van Schie, Sander

2016-07-11

The number of clusters in a cluster randomized trial is often low. It is therefore likely random assignment of clusters to treatment conditions results in covariate imbalance. There are no studies that quantify the consequences of covariate imbalance in cluster randomized trials on parameter and standard error bias and on power to detect treatment effects. The consequences of covariance imbalance in unadjusted and adjusted linear mixed models are investigated by means of a simulation study. The factors in this study are the degree of imbalance, the covariate effect size, the cluster size and the intraclass correlation coefficient. The covariate is binary and measured at the cluster level; the outcome is continuous and measured at the individual level. The results show covariate imbalance results in negligible parameter bias and small standard error bias in adjusted linear mixed models. Ignoring the possibility of covariate imbalance while calculating the sample size at the cluster level may result in a loss in power of at most 25 % in the adjusted linear mixed model. The results are more severe for the unadjusted linear mixed model: parameter biases up to 100 % and standard error biases up to 200 % may be observed. Power levels based on the unadjusted linear mixed model are often too low. The consequences are most severe for large clusters and/or small intraclass correlation coefficients since then the required number of clusters to achieve a desired power level is smallest. The possibility of covariate imbalance should be taken into account while calculating the sample size of a cluster randomized trial. Otherwise more sophisticated methods to randomize clusters to treatments should be used, such as stratification or balance algorithms. All relevant covariates should be carefully identified, be actually measured and included in the statistical model to avoid severe levels of parameter and standard error bias and insufficient power levels.

Modeling of the thermal physical process and study on the reliability of linear energy density for selective laser melting

NASA Astrophysics Data System (ADS)

Xiang, Zhaowei; Yin, Ming; Dong, Guanhua; Mei, Xiaoqin; Yin, Guofu

2018-06-01

A finite element model considering volume shrinkage with powder-to-dense process of powder layer in selective laser melting (SLM) is established. Comparison between models that consider and do not consider volume shrinkage or powder-to-dense process is carried out. Further, parametric analysis of laser power and scan speed is conducted and the reliability of linear energy density as a design parameter is investigated. The results show that the established model is an effective method and has better accuracy allowing for the temperature distribution, and the length and depth of molten pool. The maximum temperature is more sensitive to laser power than scan speed. The maximum heating rate and cooling rate increase with increasing scan speed at constant laser power and increase with increasing laser power at constant scan speed as well. The simulation results and experimental result reveal that linear energy density is not always reliable using as a design parameter in the SLM.

Mathematical modelling and linear stability analysis of laser fusion cutting

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

Hermanns, Torsten; Schulz, Wolfgang; Vossen, Georg

A model for laser fusion cutting is presented and investigated by linear stability analysis in order to study the tendency for dynamic behavior and subsequent ripple formation. The result is a so called stability function that describes the correlation of the setting values of the process and the process’ amount of dynamic behavior.

A Test of a Linear Programming Model as an Optimal Solution to the Problem of Combining Methods of Reading Instruction

ERIC Educational Resources Information Center

Mills, James W.; And Others

1973-01-01

The Study reported here tested an application of the Linear Programming Model at the Reading Clinic of Drew University. Results, while not conclusive, indicate that this approach yields greater gains in speed scores than a traditional approach for this population. (Author)

Non-linear assessment and deficiency of linear relationship for healthcare industry

NASA Astrophysics Data System (ADS)

Nordin, N.; Abdullah, M. M. A. B.; Razak, R. C.

2017-09-01

This paper presents the development of the non-linear service satisfaction model that assumes patients are not necessarily satisfied or dissatisfied with good or poor service delivery. With that, compliment and compliant assessment is considered, simultaneously. Non-linear service satisfaction instrument called Kano-Q and Kano-SS is developed based on Kano model and Theory of Quality Attributes (TQA) to define the unexpected, hidden and unspoken patient satisfaction and dissatisfaction into service quality attribute. A new Kano-Q and Kano-SS algorithm for quality attribute assessment is developed based satisfaction impact theories and found instrumentally fit the reliability and validity test. The results were also validated based on standard Kano model procedure before Kano model and Quality Function Deployment (QFD) is integrated for patient attribute and service attribute prioritization. An algorithm of Kano-QFD matrix operation is developed to compose the prioritized complaint and compliment indexes. Finally, the results of prioritized service attributes are mapped to service delivery category to determine the most prioritized service delivery that need to be improved at the first place by healthcare service provider.

An efficient method for model refinement in diffuse optical tomography

NASA Astrophysics Data System (ADS)

Zirak, A. R.; Khademi, M.

2007-11-01

Diffuse optical tomography (DOT) is a non-linear, ill-posed, boundary value and optimization problem which necessitates regularization. Also, Bayesian methods are suitable owing to measurements data are sparse and correlated. In such problems which are solved with iterative methods, for stabilization and better convergence, the solution space must be small. These constraints subject to extensive and overdetermined system of equations which model retrieving criteria specially total least squares (TLS) must to refine model error. Using TLS is limited to linear systems which is not achievable when applying traditional Bayesian methods. This paper presents an efficient method for model refinement using regularized total least squares (RTLS) for treating on linearized DOT problem, having maximum a posteriori (MAP) estimator and Tikhonov regulator. This is done with combination Bayesian and regularization tools as preconditioner matrices, applying them to equations and then using RTLS to the resulting linear equations. The preconditioning matrixes are guided by patient specific information as well as a priori knowledge gained from the training set. Simulation results illustrate that proposed method improves the image reconstruction performance and localize the abnormally well.

Meshless analysis of shear deformable shells: the linear model

NASA Astrophysics Data System (ADS)

Costa, Jorge C.; Tiago, Carlos M.; Pimenta, Paulo M.

2013-10-01

This work develops a kinematically linear shell model departing from a consistent nonlinear theory. The initial geometry is mapped from a flat reference configuration by a stress-free finite deformation, after which, the actual shell motion takes place. The model maintains the features of a complete stress-resultant theory with Reissner-Mindlin kinematics based on an inextensible director. A hybrid displacement variational formulation is presented, where the domain displacements and kinematic boundary reactions are independently approximated. The resort to a flat reference configuration allows the discretization using 2-D Multiple Fixed Least-Squares (MFLS) on the domain. The consistent definition of stress resultants and consequent plane stress assumption led to a neat formulation for the analysis of shells. The consistent linear approximation, combined with MFLS, made possible efficient computations with a desired continuity degree, leading to smooth results for the displacement, strain and stress fields, as shown by several numerical examples.

Light propagation in linearly perturbed ΛLTB models

NASA Astrophysics Data System (ADS)

Meyer, Sven; Bartelmann, Matthias

2017-11-01

We apply a generic formalism of light propagation to linearly perturbed spherically symmetric dust models including a cosmological constant. For a comoving observer on the central worldline, we derive the equation of geodesic deviation and perform a suitable spherical harmonic decomposition. This allows to map the abstract gauge-invariant perturbation variables to well-known quantities from weak gravitational lensing like convergence or cosmic shear. The resulting set of differential equations can effectively be solved by a Green's function approach leading to line-of-sight integrals sourced by the perturbation variables on the backward lightcone. The resulting spherical harmonic coefficients of the lensing observables are presented and the shear field is decomposed into its E- and B-modes. Results of this work are an essential tool to add information from linear structure formation to the analysis of spherically symmetric dust models with the purpose of testing the Copernican Principle with multiple cosmological probes.

Nonlinear aeroservoelastic analysis of a controlled multiple-actuated-wing model with free-play

NASA Astrophysics Data System (ADS)

Huang, Rui; Hu, Haiyan; Zhao, Yonghui

2013-10-01

In this paper, the effects of structural nonlinearity due to free-play in both leading-edge and trailing-edge outboard control surfaces on the linear flutter control system are analyzed for an aeroelastic model of three-dimensional multiple-actuated-wing. The free-play nonlinearities in the control surfaces are modeled theoretically by using the fictitious mass approach. The nonlinear aeroelastic equations of the presented model can be divided into nine sub-linear modal-based aeroelastic equations according to the different combinations of deflections of the leading-edge and trailing-edge outboard control surfaces. The nonlinear aeroelastic responses can be computed based on these sub-linear aeroelastic systems. To demonstrate the effects of nonlinearity on the linear flutter control system, a single-input and single-output controller and a multi-input and multi-output controller are designed based on the unconstrained optimization techniques. The numerical results indicate that the free-play nonlinearity can lead to either limit cycle oscillations or divergent motions when the linear control system is implemented.

Anomalous diffusion with linear reaction dynamics: from continuous time random walks to fractional reaction-diffusion equations.

PubMed

Henry, B I; Langlands, T A M; Wearne, S L

2006-09-01

We have revisited the problem of anomalously diffusing species, modeled at the mesoscopic level using continuous time random walks, to include linear reaction dynamics. If a constant proportion of walkers are added or removed instantaneously at the start of each step then the long time asymptotic limit yields a fractional reaction-diffusion equation with a fractional order temporal derivative operating on both the standard diffusion term and a linear reaction kinetics term. If the walkers are added or removed at a constant per capita rate during the waiting time between steps then the long time asymptotic limit has a standard linear reaction kinetics term but a fractional order temporal derivative operating on a nonstandard diffusion term. Results from the above two models are compared with a phenomenological model with standard linear reaction kinetics and a fractional order temporal derivative operating on a standard diffusion term. We have also developed further extensions of the CTRW model to include more general reaction dynamics.

Does transport time help explain the high trauma mortality rates in rural areas? New and traditional predictors assessed by new and traditional statistical methods

PubMed Central

Røislien, Jo; Lossius, Hans Morten; Kristiansen, Thomas

2015-01-01

Background Trauma is a leading global cause of death. Trauma mortality rates are higher in rural areas, constituting a challenge for quality and equality in trauma care. The aim of the study was to explore population density and transport time to hospital care as possible predictors of geographical differences in mortality rates, and to what extent choice of statistical method might affect the analytical results and accompanying clinical conclusions. Methods Using data from the Norwegian Cause of Death registry, deaths from external causes 1998–2007 were analysed. Norway consists of 434 municipalities, and municipality population density and travel time to hospital care were entered as predictors of municipality mortality rates in univariate and multiple regression models of increasing model complexity. We fitted linear regression models with continuous and categorised predictors, as well as piecewise linear and generalised additive models (GAMs). Models were compared using Akaike's information criterion (AIC). Results Population density was an independent predictor of trauma mortality rates, while the contribution of transport time to hospital care was highly dependent on choice of statistical model. A multiple GAM or piecewise linear model was superior, and similar, in terms of AIC. However, while transport time was statistically significant in multiple models with piecewise linear or categorised predictors, it was not in GAM or standard linear regression. Conclusions Population density is an independent predictor of trauma mortality rates. The added explanatory value of transport time to hospital care is marginal and model-dependent, highlighting the importance of exploring several statistical models when studying complex associations in observational data. PMID:25972600

[Variable selection methods combined with local linear embedding theory used for optimization of near infrared spectral quantitative models].

PubMed

Hao, Yong; Sun, Xu-Dong; Yang, Qiang

2012-12-01

Variables selection strategy combined with local linear embedding (LLE) was introduced for the analysis of complex samples by using near infrared spectroscopy (NIRS). Three methods include Monte Carlo uninformation variable elimination (MCUVE), successive projections algorithm (SPA) and MCUVE connected with SPA were used for eliminating redundancy spectral variables. Partial least squares regression (PLSR) and LLE-PLSR were used for modeling complex samples. The results shown that MCUVE can both extract effective informative variables and improve the precision of models. Compared with PLSR models, LLE-PLSR models can achieve more accurate analysis results. MCUVE combined with LLE-PLSR is an effective modeling method for NIRS quantitative analysis.

Analysis and simulation of a magnetic bearing suspension system for a laboratory model annular momentum control device

NASA Technical Reports Server (NTRS)

Groom, N. J.; Woolley, C. T.; Joshi, S. M.

1981-01-01

A linear analysis and the results of a nonlinear simulation of a magnetic bearing suspension system which uses permanent magnet flux biasing are presented. The magnetic bearing suspension is part of a 4068 N-m-s (3000 lb-ft-sec) laboratory model annular momentum control device (AMCD). The simulation includes rigid body rim dynamics, linear and nonlinear axial actuators, linear radial actuators, axial and radial rim warp, and power supply and power driver current limits.

Feedback linearizing control of a MIMO power system

NASA Astrophysics Data System (ADS)

Ilyes, Laszlo

Prior research has demonstrated that either the mechanical or electrical subsystem of a synchronous electric generator may be controlled using single-input single-output (SISO) nonlinear feedback linearization. This research suggests a new approach which applies nonlinear feedback linearization to a multi-input multi-output (MIMO) model of the synchronous electric generator connected to an infinite bus load model. In this way, the electrical and mechanical subsystems may be linearized and simultaneously decoupled through the introduction of a pair of auxiliary inputs. This allows well known, linear, SISO control methods to be effectively applied to the resulting systems. The derivation of the feedback linearizing control law is presented in detail, including a discussion on the use of symbolic math processing as a development tool. The linearizing and decoupling properties of the control law are validated through simulation. And finally, the robustness of the control law is demonstrated.

Cole-Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass.

PubMed

Dabros, Michal; Dennewald, Danielle; Currie, David J; Lee, Mark H; Todd, Robert W; Marison, Ian W; von Stockar, Urs

2009-02-01

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole-Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole-Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole-Cole and PLS models, the latter technique giving more satisfactory results.

On Discontinuous Piecewise Linear Models for Memristor Oscillators

NASA Astrophysics Data System (ADS)

Amador, Andrés; Freire, Emilio; Ponce, Enrique; Ros, Javier

2017-06-01

In this paper, we provide for the first time rigorous mathematical results regarding the rich dynamics of piecewise linear memristor oscillators. In particular, for each nonlinear oscillator given in [Itoh & Chua, 2008], we show the existence of an infinite family of invariant manifolds and that the dynamics on such manifolds can be modeled without resorting to discontinuous models. Our approach provides topologically equivalent continuous models with one dimension less but with one extra parameter associated to the initial conditions. It is possible to justify the periodic behavior exhibited by three-dimensional memristor oscillators, by taking advantage of known results for planar continuous piecewise linear systems. The analysis developed not only confirms the numerical results contained in previous works [Messias et al., 2010; Scarabello & Messias, 2014] but also goes much further by showing the existence of closed surfaces in the state space which are foliated by periodic orbits. The important role of initial conditions that justify the infinite number of periodic orbits exhibited by these models, is stressed. The possibility of unsuspected bistable regimes under specific configurations of parameters is also emphasized.

A penalized framework for distributed lag non-linear models.

PubMed

Gasparrini, Antonio; Scheipl, Fabian; Armstrong, Ben; Kenward, Michael G

2017-09-01

Distributed lag non-linear models (DLNMs) are a modelling tool for describing potentially non-linear and delayed dependencies. Here, we illustrate an extension of the DLNM framework through the use of penalized splines within generalized additive models (GAM). This extension offers built-in model selection procedures and the possibility of accommodating assumptions on the shape of the lag structure through specific penalties. In addition, this framework includes, as special cases, simpler models previously proposed for linear relationships (DLMs). Alternative versions of penalized DLNMs are compared with each other and with the standard unpenalized version in a simulation study. Results show that this penalized extension to the DLNM class provides greater flexibility and improved inferential properties. The framework exploits recent theoretical developments of GAMs and is implemented using efficient routines within freely available software. Real-data applications are illustrated through two reproducible examples in time series and survival analysis. © 2017 The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.

Evaluation of confidence intervals for a steady-state leaky aquifer model

USGS Publications Warehouse

Christensen, S.; Cooley, R.L.

1999-01-01

The fact that dependent variables of groundwater models are generally nonlinear functions of model parameters is shown to be a potentially significant factor in calculating accurate confidence intervals for both model parameters and functions of the parameters, such as the values of dependent variables calculated by the model. The Lagrangian method of Vecchia and Cooley [Vecchia, A.V. and Cooley, R.L., Water Resources Research, 1987, 23(7), 1237-1250] was used to calculate nonlinear Scheffe-type confidence intervals for the parameters and the simulated heads of a steady-state groundwater flow model covering 450 km2 of a leaky aquifer. The nonlinear confidence intervals are compared to corresponding linear intervals. As suggested by the significant nonlinearity of the regression model, linear confidence intervals are often not accurate. The commonly made assumption that widths of linear confidence intervals always underestimate the actual (nonlinear) widths was not correct. Results show that nonlinear effects can cause the nonlinear intervals to be asymmetric and either larger or smaller than the linear approximations. Prior information on transmissivities helps reduce the size of the confidence intervals, with the most notable effects occurring for the parameters on which there is prior information and for head values in parameter zones for which there is prior information on the parameters.The fact that dependent variables of groundwater models are generally nonlinear functions of model parameters is shown to be a potentially significant factor in calculating accurate confidence intervals for both model parameters and functions of the parameters, such as the values of dependent variables calculated by the model. The Lagrangian method of Vecchia and Cooley was used to calculate nonlinear Scheffe-type confidence intervals for the parameters and the simulated heads of a steady-state groundwater flow model covering 450 km2 of a leaky aquifer. The nonlinear confidence intervals are compared to corresponding linear intervals. As suggested by the significant nonlinearity of the regression model, linear confidence intervals are often not accurate. The commonly made assumption that widths of linear confidence intervals always underestimate the actual (nonlinear) widths was not correct. Results show that nonlinear effects can cause the nonlinear intervals to be asymmetric and either larger or smaller than the linear approximations. Prior information on transmissivities helps reduce the size of the confidence intervals, with the most notable effects occurring for the parameters on which there is prior information and for head values in parameter zones for which there is prior information on the parameters.

Non-Linear System Identification for Aeroelastic Systems with Application to Experimental Data

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2008-01-01

Representation and identification of a non-linear aeroelastic pitch-plunge system as a model of the NARMAX class is considered. A non-linear difference equation describing this aircraft model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to aeroelastic dynamics and its properties demonstrated via continuous-time simulations of experimental conditions. Simulation results show that (i) the outputs of the NARMAX model match closely those generated using continuous-time methods and (ii) NARMAX identification methods applied to aeroelastic dynamics provide accurate discrete-time parameter estimates. Application of NARMAX identification to experimental pitch-plunge dynamics data gives a high percent fit for cross-validated data.

Closed-loop stability of linear quadratic optimal systems in the presence of modeling errors

NASA Technical Reports Server (NTRS)

Toda, M.; Patel, R.; Sridhar, B.

1976-01-01

The well-known stabilizing property of linear quadratic state feedback design is utilized to evaluate the robustness of a linear quadratic feedback design in the presence of modeling errors. Two general conditions are obtained for allowable modeling errors such that the resulting closed-loop system remains stable. One of these conditions is applied to obtain two more particular conditions which are readily applicable to practical situations where a designer has information on the bounds of modeling errors. Relations are established between the allowable parameter uncertainty and the weighting matrices of the quadratic performance index, thereby enabling the designer to select appropriate weighting matrices to attain a robust feedback design.

A comparison of methods to handle skew distributed cost variables in the analysis of the resource consumption in schizophrenia treatment.

PubMed

Kilian, Reinhold; Matschinger, Herbert; Löeffler, Walter; Roick, Christiane; Angermeyer, Matthias C

2002-03-01

Transformation of the dependent cost variable is often used to solve the problems of heteroscedasticity and skewness in linear ordinary least square regression of health service cost data. However, transformation may cause difficulties in the interpretation of regression coefficients and the retransformation of predicted values. The study compares the advantages and disadvantages of different methods to estimate regression based cost functions using data on the annual costs of schizophrenia treatment. Annual costs of psychiatric service use and clinical and socio-demographic characteristics of the patients were assessed for a sample of 254 patients with a diagnosis of schizophrenia (ICD-10 F 20.0) living in Leipzig. The clinical characteristics of the participants were assessed by means of the BPRS 4.0, the GAF, and the CAN for service needs. Quality of life was measured by WHOQOL-BREF. A linear OLS regression model with non-parametric standard errors, a log-transformed OLS model and a generalized linear model with a log-link and a gamma distribution were used to estimate service costs. For the estimation of robust non-parametric standard errors, the variance estimator by White and a bootstrap estimator based on 2000 replications were employed. Models were evaluated by the comparison of the R2 and the root mean squared error (RMSE). RMSE of the log-transformed OLS model was computed with three different methods of bias-correction. The 95% confidence intervals for the differences between the RMSE were computed by means of bootstrapping. A split-sample-cross-validation procedure was used to forecast the costs for the one half of the sample on the basis of a regression equation computed for the other half of the sample. All three methods showed significant positive influences of psychiatric symptoms and met psychiatric service needs on service costs. Only the log- transformed OLS model showed a significant negative impact of age, and only the GLM shows a significant negative influences of employment status and partnership on costs. All three models provided a R2 of about.31. The Residuals of the linear OLS model revealed significant deviances from normality and homoscedasticity. The residuals of the log-transformed model are normally distributed but still heteroscedastic. The linear OLS model provided the lowest prediction error and the best forecast of the dependent cost variable. The log-transformed model provided the lowest RMSE if the heteroscedastic bias correction was used. The RMSE of the GLM with a log link and a gamma distribution was higher than those of the linear OLS model and the log-transformed OLS model. The difference between the RMSE of the linear OLS model and that of the log-transformed OLS model without bias correction was significant at the 95% level. As result of the cross-validation procedure, the linear OLS model provided the lowest RMSE followed by the log-transformed OLS model with a heteroscedastic bias correction. The GLM showed the weakest model fit again. None of the differences between the RMSE resulting form the cross- validation procedure were found to be significant. The comparison of the fit indices of the different regression models revealed that the linear OLS model provided a better fit than the log-transformed model and the GLM, but the differences between the models RMSE were not significant. Due to the small number of cases in the study the lack of significance does not sufficiently proof that the differences between the RSME for the different models are zero and the superiority of the linear OLS model can not be generalized. The lack of significant differences among the alternative estimators may reflect a lack of sample size adequate to detect important differences among the estimators employed. Further studies with larger case number are necessary to confirm the results. Specification of an adequate regression models requires a careful examination of the characteristics of the data. Estimation of standard errors and confidence intervals by nonparametric methods which are robust against deviations from the normal distribution and the homoscedasticity of residuals are suitable alternatives to the transformation of the skew distributed dependent variable. Further studies with more adequate case numbers are needed to confirm the results.

New Galerkin operational matrices for solving Lane-Emden type equations

NASA Astrophysics Data System (ADS)

Abd-Elhameed, W. M.; Doha, E. H.; Saad, A. S.; Bassuony, M. A.

2016-04-01

Lane-Emden type equations model many phenomena in mathematical physics and astrophysics, such as thermal explosions. This paper is concerned with introducing third and fourth kind Chebyshev-Galerkin operational matrices in order to solve such problems. The principal idea behind the suggested algorithms is based on converting the linear or nonlinear Lane-Emden problem, through the application of suitable spectral methods, into a system of linear or nonlinear equations in the expansion coefficients, which can be efficiently solved. The main advantage of the proposed algorithm in the linear case is that the resulting linear systems are specially structured, and this of course reduces the computational effort required to solve such systems. As an application, we consider the solar model polytrope with n=3 to show that the suggested solutions in this paper are in good agreement with the numerical results.

Prediction of atmospheric degradation data for POPs by gene expression programming.

PubMed

Luan, F; Si, H Z; Liu, H T; Wen, Y Y; Zhang, X Y

2008-01-01

Quantitative structure-activity relationship models for the prediction of the mean and the maximum atmospheric degradation half-life values of persistent organic pollutants were developed based on the linear heuristic method (HM) and non-linear gene expression programming (GEP). Molecular descriptors, calculated from the structures alone, were used to represent the characteristics of the compounds. HM was used both to pre-select the whole descriptor sets and to build the linear model. GEP yielded satisfactory prediction results: the square of the correlation coefficient r(2) was 0.80 and 0.81 for the mean and maximum half-life values of the test set, and the root mean square errors were 0.448 and 0.426, respectively. The results of this work indicate that the GEP is a very promising tool for non-linear approximations.

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

PubMed

Rigatos, Gerasimos G

2016-06-01

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

Generation of High Frequency Response in a Dynamically Loaded, Nonlinear Soil Column

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

Spears, Robert Edward; Coleman, Justin Leigh

2015-08-01

Detailed guidance on linear seismic analysis of soil columns is provided in “Seismic Analysis of Safety-Related Nuclear Structures and Commentary (ASCE 4, 1998),” which is currently under revision. A new Appendix in ASCE 4-2014 (draft) is being added to provide guidance for nonlinear time domain analysis which includes evaluation of soil columns. When performing linear analysis, a given soil column is typically evaluated with a linear, viscous damped constitutive model. When submitted to a sine wave motion, this constitutive model produces a smooth hysteresis loop. For nonlinear analysis, the soil column can be modelled with an appropriate nonlinear hysteretic soilmore » model. For the model in this paper, the stiffness and energy absorption result from a defined post yielding shear stress versus shear strain curve. This curve is input with tabular data points. When submitted to a sine wave motion, this constitutive model produces a hysteresis loop that looks similar in shape to the input tabular data points on the sides with discontinuous, pointed ends. This paper compares linear and nonlinear soil column results. The results show that the nonlinear analysis produces additional high frequency response. The paper provides additional study to establish what portion of the high frequency response is due to numerical noise associated with the tabular input curve and what portion is accurately caused by the pointed ends of the hysteresis loop. Finally, the paper shows how the results are changed when a significant structural mass is added to the top of the soil column.« less

Fourier imaging of non-linear structure formation

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

Brandbyge, Jacob; Hannestad, Steen, E-mail: jacobb@phys.au.dk, E-mail: sth@phys.au.dk

We perform a Fourier space decomposition of the dynamics of non-linear cosmological structure formation in ΛCDM models. From N -body simulations involving only cold dark matter we calculate 3-dimensional non-linear density, velocity divergence and vorticity Fourier realizations, and use these to calculate the fully non-linear mode coupling integrals in the corresponding fluid equations. Our approach allows for a reconstruction of the amount of mode coupling between any two wavenumbers as a function of redshift. With our Fourier decomposition method we identify the transfer of power from larger to smaller scales, the stable clustering regime, the scale where vorticity becomes important,more » and the suppression of the non-linear divergence power spectrum as compared to linear theory. Our results can be used to improve and calibrate semi-analytical structure formation models.« less

Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

PubMed

Azadi, Sama; Karimi-Jashni, Ayoub

2016-02-01

Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verified to predict mean Seasonal Municipal Solid Waste Generation (SMSWG) rate. The accuracy of the proposed models is illustrated through a case study of 20 cities located in Fars Province, Iran. Four performance measures, MAE, MAPE, RMSE and R were used to evaluate the performance of these models. The MLR, as a conventional model, showed poor prediction performance. On the other hand, the results indicated that the ANN model, as a non-linear model, has a higher predictive accuracy when it comes to prediction of the mean SMSWG rate. As a result, in order to develop a more cost-effective strategy for waste management in the future, the ANN model could be used to predict the mean SMSWG rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

Genomic prediction based on data from three layer lines using non-linear regression models.

PubMed

Huang, Heyun; Windig, Jack J; Vereijken, Addie; Calus, Mario P L

2014-11-06

Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. In an attempt to alleviate potential discrepancies between assumptions of linear models and multi-population data, two types of alternative models were used: (1) a multi-trait genomic best linear unbiased prediction (GBLUP) model that modelled trait by line combinations as separate but correlated traits and (2) non-linear models based on kernel learning. These models were compared to conventional linear models for genomic prediction for two lines of brown layer hens (B1 and B2) and one line of white hens (W1). The three lines each had 1004 to 1023 training and 238 to 240 validation animals. Prediction accuracy was evaluated by estimating the correlation between observed phenotypes and predicted breeding values. When the training dataset included only data from the evaluated line, non-linear models yielded at best a similar accuracy as linear models. In some cases, when adding a distantly related line, the linear models showed a slight decrease in performance, while non-linear models generally showed no change in accuracy. When only information from a closely related line was used for training, linear models and non-linear radial basis function (RBF) kernel models performed similarly. The multi-trait GBLUP model took advantage of the estimated genetic correlations between the lines. Combining linear and non-linear models improved the accuracy of multi-line genomic prediction. Linear models and non-linear RBF models performed very similarly for genomic prediction, despite the expectation that non-linear models could deal better with the heterogeneous multi-population data. This heterogeneity of the data can be overcome by modelling trait by line combinations as separate but correlated traits, which avoids the occasional occurrence of large negative accuracies when the evaluated line was not included in the training dataset. Furthermore, when using a multi-line training dataset, non-linear models provided information on the genotype data that was complementary to the linear models, which indicates that the underlying data distributions of the three studied lines were indeed heterogeneous.

Non-linear flow law of rockglacier creep determined from geomorphological observations: A case study from the Murtèl rockglacier (Engadin, SE Switzerland)

NASA Astrophysics Data System (ADS)

Frehner, Marcel; Amschwand, Dominik; Gärtner-Roer, Isabelle

2016-04-01

Rockglaciers consist of unconsolidated rock fragments (silt/sand-rock boulders) with interstitial ice; hence their creep behavior (i.e., rheology) may deviate from the simple and well-known flow-laws for pure ice. Here we constrain the non-linear viscous flow law that governs rockglacier creep based on geomorphological observations. We use the Murtèl rockglacier (upper Engadin valley, SE Switzerland) as a case study, for which high-resolution digital elevation models (DEM), time-lapse borehole deformation data, and geophysical soundings exist that reveal the exterior and interior architecture and dynamics of the landform. Rockglaciers often feature a prominent furrow-and-ridge topography. For the Murtèl rockglacier, Frehner et al. (2015) reproduced the wavelength, amplitude, and distribution of the furrow-and-ridge morphology using a linear viscous (Newtonian) flow model. Arenson et al. (2002) presented borehole deformation data, which highlight the basal shear zone at about 30 m depth and a curved deformation profile above the shear zone. Similarly, the furrow-and-ridge morphology also exhibits a curved geometry in map view. Hence, the surface morphology and the borehole deformation data together describe a curved 3D geometry, which is close to, but not quite parabolic. We use a high-resolution DEM to quantify the curved geometry of the Murtèl furrow-and-ridge morphology. We then calculate theoretical 3D flow geometries using different non-linear viscous flow laws. By comparing them to the measured curved 3D geometry (i.e., both surface morphology and borehole deformation data), we can determine the most adequate flow-law that fits the natural data best. Linear viscous models result in perfectly parabolic flow geometries; non-linear creep leads to localized deformation at the sides and bottom of the rockglacier while the deformation in the interior and top are less intense. In other words, non-linear creep results in non-parabolic flow geometries. Both the linear (power-law exponent, n=1) and strongly non-linear models (n=10) do not match the measured data well. However, the moderately non-linear models (n=2-3) match the data quite well indicating that the creep of the Murtèl rockglacier is governed by a moderately non-linear viscous flow law with a power-law exponent close to the one of pure ice. Our results are crucial for improving existing numerical models of rockglacier flow that currently use simplified (i.e., linear viscous) flow-laws. References: Arenson L., Hoelzle M., and Springman S., 2002: Borehole deformation measurements and internal structure of some rock glaciers in Switzerland, Permafrost and Periglacial Processes 13, 117-135. Frehner M., Ling A.H.M., and Gärtner-Roer I., 2015: Furrow-and-ridge morphology on rockglaciers explained by gravity-driven buckle folding: A case study from the Murtèl rockglacier (Switzerland), Permafrost and Periglacial Processes 26, 57-66.

The development and validation of a numerical integration method for non-linear viscoelastic modeling

PubMed Central

Ramo, Nicole L.; Puttlitz, Christian M.

2018-01-01

Compelling evidence that many biological soft tissues display both strain- and time-dependent behavior has led to the development of fully non-linear viscoelastic modeling techniques to represent the tissue’s mechanical response under dynamic conditions. Since the current stress state of a viscoelastic material is dependent on all previous loading events, numerical analyses are complicated by the requirement of computing and storing the stress at each step throughout the load history. This requirement quickly becomes computationally expensive, and in some cases intractable, for finite element models. Therefore, we have developed a strain-dependent numerical integration approach for capturing non-linear viscoelasticity that enables calculation of the current stress from a strain-dependent history state variable stored from the preceding time step only, which improves both fitting efficiency and computational tractability. This methodology was validated based on its ability to recover non-linear viscoelastic coefficients from simulated stress-relaxation (six strain levels) and dynamic cyclic (three frequencies) experimental stress-strain data. The model successfully fit each data set with average errors in recovered coefficients of 0.3% for stress-relaxation fits and 0.1% for cyclic. The results support the use of the presented methodology to develop linear or non-linear viscoelastic models from stress-relaxation or cyclic experimental data of biological soft tissues. PMID:29293558

Fourier functional analysis for unsteady aerodynamic modeling

NASA Technical Reports Server (NTRS)

Lan, C. Edward; Chin, Suei

1991-01-01

A method based on Fourier analysis is developed to analyze the force and moment data obtained in large amplitude forced oscillation tests at high angles of attack. The aerodynamic models for normal force, lift, drag, and pitching moment coefficients are built up from a set of aerodynamic responses to harmonic motions at different frequencies. Based on the aerodynamic models of harmonic data, the indicial responses are formed. The final expressions for the models involve time integrals of the indicial type advocated by Tobak and Schiff. Results from linear two- and three-dimensional unsteady aerodynamic theories as well as test data for a 70-degree delta wing are used to verify the models. It is shown that the present modeling method is accurate in producing the aerodynamic responses to harmonic motions and the ramp type motions. The model also produces correct trend for a 70-degree delta wing in harmonic motion with different mean angles-of-attack. However, the current model cannot be used to extrapolate data to higher angles-of-attack than that of the harmonic motions which form the aerodynamic model. For linear ramp motions, a special method is used to calculate the corresponding frequency and phase angle at a given time. The calculated results from modeling show a higher lift peak for linear ramp motion than for harmonic ramp motion. The current model also shows reasonably good results for the lift responses at different angles of attack.

Biological effects and equivalent doses in radiotherapy: A software solution

PubMed Central

Voyant, Cyril; Julian, Daniel; Roustit, Rudy; Biffi, Katia; Lantieri, Céline

2013-01-01

Background The limits of TDF (time, dose, and fractionation) and linear quadratic models have been known for a long time. Medical physicists and physicians are required to provide fast and reliable interpretations regarding delivered doses or any future prescriptions relating to treatment changes. Aim We, therefore, propose a calculation interface under the GNU license to be used for equivalent doses, biological doses, and normal tumor complication probability (Lyman model). Materials and methods The methodology used draws from several sources: the linear-quadratic-linear model of Astrahan, the repopulation effects of Dale, and the prediction of multi-fractionated treatments of Thames. Results and conclusions The results are obtained from an algorithm that minimizes an ad-hoc cost function, and then compared to an equivalent dose computed using standard calculators in seven French radiotherapy centers. PMID:24936319

Understanding climate impacts on vegetation using a spatiotemporal non-linear Granger causality framework

NASA Astrophysics Data System (ADS)

Papagiannopoulou, Christina; Decubber, Stijn; Miralles, Diego; Demuzere, Matthias; Dorigo, Wouter; Verhoest, Niko; Waegeman, Willem

2017-04-01

Satellite data provide an abundance of information about crucial climatic and environmental variables. These data - consisting of global records, spanning up to 35 years and having the form of multivariate time series with different spatial and temporal resolutions - enable the study of key climate-vegetation interactions. Although methods which are based on correlations and linear models are typically used for this purpose, their assumptions for linearity about the climate-vegetation relationships are too simplistic. Therefore, we adopt a recently proposed non-linear Granger causality analysis [1], in which we incorporate spatial information, concatenating data from neighboring pixels and training a joint model on the combined data. Experimental results based on global data sets show that considering non-linear relationships leads to a higher explained variance of past vegetation dynamics, compared to simple linear models. Our approach consists of several steps. First, we compile an extensive database [1], which includes multiple data sets for land surface temperature, near-surface air temperature, surface radiation, precipitation, snow water equivalents and surface soil moisture. Based on this database, high-level features are constructed and considered as predictors in our machine-learning framework. These high-level features include (de-trended) seasonal anomalies, lagged variables, past cumulative variables, and extreme indices, all calculated based on the raw climatic data. Second, we apply a spatiotemporal non-linear Granger causality framework - in which the linear predictive model is substituted for a non-linear machine learning algorithm - in order to assess which of these predictor variables Granger-cause vegetation dynamics at each 1° pixel. We use the de-trended anomalies of Normalized Difference Vegetation Index (NDVI) to characterize vegetation, being the target variable of our framework. Experimental results indicate that climate strongly (Granger-)causes vegetation dynamics in most regions globally. More specifically, water availability is the most dominant vegetation driver, being the dominant vegetation driver in 54% of the vegetated surface. Furthermore, our results show that precipitation and soil moisture have prolonged impacts on vegetation in semiarid regions, with up to 10% of additional explained variance on the vegetation dynamics occurring three months later. Finally, hydro-climatic extremes seem to have a remarkable impact on vegetation, since they also explain up to 10% of additional variance of vegetation in certain regions despite their infrequent occurrence. References [1] Papagiannopoulou, C., Miralles, D. G., Verhoest, N. E. C., Dorigo, W. A., and Waegeman, W.: A non-linear Granger causality framework to investigate climate-vegetation dynamics, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-266, in review, 2016.

A Nth-order linear algorithm for extracting diffuse correlation spectroscopy blood flow indices in heterogeneous tissues.

PubMed

Shang, Yu; Yu, Guoqiang

2014-09-29

Conventional semi-infinite analytical solutions of correlation diffusion equation may lead to errors when calculating blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements in tissues with irregular geometries. Very recently, we created an algorithm integrating a N th-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in homogenous tissues with arbitrary geometries for extraction of BFI (i.e., αD B ). The purpose of this study is to extend the capability of the N th-order linear algorithm for extracting BFI in heterogeneous tissues with arbitrary geometries. The previous linear algorithm was modified to extract BFIs in different types of tissues simultaneously through utilizing DCS data at multiple source-detector separations. We compared the proposed linear algorithm with the semi-infinite homogenous solution in a computer model of adult head with heterogeneous tissue layers of scalp, skull, cerebrospinal fluid, and brain. To test the capability of the linear algorithm for extracting relative changes of cerebral blood flow (rCBF) in deep brain, we assigned ten levels of αD B in the brain layer with a step decrement of 10% while maintaining αD B values constant in other layers. Simulation results demonstrate the accuracy (errors < 3%) of high-order ( N  ≥ 5) linear algorithm in extracting BFIs in different tissue layers and rCBF in deep brain. By contrast, the semi-infinite homogenous solution resulted in substantial errors in rCBF (34.5% ≤ errors ≤ 60.2%) and BFIs in different layers. The N th-order linear model simplifies data analysis, thus allowing for online data processing and displaying. Future study will test this linear algorithm in heterogeneous tissues with different levels of blood flow variations and noises.

Non-linear homogenized and heterogeneous FE models for FRCM reinforced masonry walls in diagonal compression

NASA Astrophysics Data System (ADS)

Bertolesi, Elisa; Milani, Gabriele; Poggi, Carlo

2016-12-01

Two FE modeling techniques are presented and critically discussed for the non-linear analysis of tuff masonry panels reinforced with FRCM and subjected to standard diagonal compression tests. The specimens, tested at the University of Naples (Italy), are unreinforced and FRCM retrofitted walls. The extensive characterization of the constituent materials allowed adopting here very sophisticated numerical modeling techniques. In particular, here the results obtained by means of a micro-modeling strategy and homogenization approach are compared. The first modeling technique is a tridimensional heterogeneous micro-modeling where constituent materials (bricks, joints, reinforcing mortar and reinforcing grid) are modeled separately. The second approach is based on a two-step homogenization procedure, previously developed by the authors, where the elementary cell is discretized by means of three-noded plane stress elements and non-linear interfaces. The non-linear structural analyses are performed replacing the homogenized orthotropic continuum with a rigid element and non-linear spring assemblage (RBSM). All the simulations here presented are performed using the commercial software Abaqus. Pros and cons of the two approaches are herein discussed with reference to their reliability in reproducing global force-displacement curves and crack patterns, as well as to the rather different computational effort required by the two strategies.

Model-Based Engine Control Architecture with an Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Connolly, Joseph W.

2016-01-01

This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The non-linear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.

Non-linear modelling and control of semi-active suspensions with variable damping

NASA Astrophysics Data System (ADS)

Chen, Huang; Long, Chen; Yuan, Chao-Chun; Jiang, Hao-Bin

2013-10-01

Electro-hydraulic dampers can provide variable damping force that is modulated by varying the command current; furthermore, they offer advantages such as lower power, rapid response, lower cost, and simple hardware. However, accurate characterisation of non-linear f-v properties in pre-yield and force saturation in post-yield is still required. Meanwhile, traditional linear or quarter vehicle models contain various non-linearities. The development of a multi-body dynamics model is very complex, and therefore, SIMPACK was used with suitable improvements for model development and numerical simulations. A semi-active suspension was built based on a belief-desire-intention (BDI)-agent model framework. Vehicle handling dynamics were analysed, and a co-simulation analysis was conducted in SIMPACK and MATLAB to evaluate the BDI-agent controller. The design effectively improved ride comfort, handling stability, and driving safety. A rapid control prototype was built based on dSPACE to conduct a real vehicle test. The test and simulation results were consistent, which verified the simulation.

MHD Simulations of Plasma Dynamics with Non-Axisymmetric Boundaries

NASA Astrophysics Data System (ADS)

Hansen, Chris; Levesque, Jeffrey; Morgan, Kyle; Jarboe, Thomas

2015-11-01

The arbitrary geometry, 3D extended MHD code PSI-TET is applied to linear and non-linear simulations of MCF plasmas with non-axisymmetric boundaries. Progress and results from simulations on two experiments will be presented: 1) Detailed validation studies of the HIT-SI experiment with self-consistent modeling of plasma dynamics in the helicity injectors. Results will be compared to experimental data and NIMROD simulations that model the effect of the helicity injectors through boundary conditions on an axisymmetric domain. 2) Linear studies of HBT-EP with different wall configurations focusing on toroidal asymmetries in the adjustable conducting wall. HBT-EP studies the effect of active/passive stabilization with an adjustable ferritic wall. Results from linear verification and benchmark studies of ideal mode growth with and without toroidal asymmetries will be presented and compared to DCON predictions. Simulations of detailed experimental geometries are enabled by use of the PSI-TET code, which employs a high order finite element method on unstructured tetrahedral grids that are generated directly from CAD models. Further development of PSI-TET will also be presented including work to support resistive wall regions within extended MHD simulations. Work supported by DoE.

Comparison between Two Linear Supervised Learning Machines' Methods with Principle Component Based Methods for the Spectrofluorimetric Determination of Agomelatine and Its Degradants.

PubMed

Elkhoudary, Mahmoud M; Naguib, Ibrahim A; Abdel Salam, Randa A; Hadad, Ghada M

2017-05-01

Four accurate, sensitive and reliable stability indicating chemometric methods were developed for the quantitative determination of Agomelatine (AGM) whether in pure form or in pharmaceutical formulations. Two supervised learning machines' methods; linear artificial neural networks (PC-linANN) preceded by principle component analysis and linear support vector regression (linSVR), were compared with two principle component based methods; principle component regression (PCR) as well as partial least squares (PLS) for the spectrofluorimetric determination of AGM and its degradants. The results showed the benefits behind using linear learning machines' methods and the inherent merits of their algorithms in handling overlapped noisy spectral data especially during the challenging determination of AGM alkaline and acidic degradants (DG1 and DG2). Relative mean squared error of prediction (RMSEP) for the proposed models in the determination of AGM were 1.68, 1.72, 0.68 and 0.22 for PCR, PLS, SVR and PC-linANN; respectively. The results showed the superiority of supervised learning machines' methods over principle component based methods. Besides, the results suggested that linANN is the method of choice for determination of components in low amounts with similar overlapped spectra and narrow linearity range. Comparison between the proposed chemometric models and a reported HPLC method revealed the comparable performance and quantification power of the proposed models.

Reconstruction of real-space linear matter power spectrum from multipoles of BOSS DR12 results

NASA Astrophysics Data System (ADS)

Lee, Seokcheon

2018-02-01

Recently, the power spectrum (PS) multipoles using the Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 12 (DR12) sample are analyzed [1]. The based model for the analysis is the so-called TNS quasi-linear model and the analysis provides the multipoles up to the hexadecapole [2]. Thus, one might be able to recover the real-space linear matter PS by using the combinations of multipoles to investigate the cosmology [3]. We provide the analytic form of the ratio of quadrupole (hexadecapole) to monopole moments of the quasi-linear PS including the Fingers-of-God (FoG) effect to recover the real-space PS in the linear regime. One expects that observed values of the ratios of multipoles should be consistent with those of the linear theory at large scales. Thus, we compare the ratios of multipoles of the linear theory, including the FoG effect with the measured values. From these, we recover the linear matter power spectra in real-space. These recovered power spectra are consistent with the linear matter power spectra.

Linear separability in superordinate natural language concepts.

PubMed

Ruts, Wim; Storms, Gert; Hampton, James

2004-01-01

Two experiments are reported in which linear separability was investigated in superordinate natural language concept pairs (e.g., toiletry-sewing gear). Representations of the exemplars of semantically related concept pairs were derived in two to five dimensions using multidimensional scaling (MDS) of similarities based on possession of the concept features. Next, category membership, obtained from an exemplar generation study (in Experiment 1) and from a forced-choice classification task (in Experiment 2) was predicted from the coordinates of the MDS representation using log linear analysis. The results showed that all natural kind concept pairs were perfectly linearly separable, whereas artifact concept pairs showed several violations. Clear linear separability of natural language concept pairs is in line with independent cue models. The violations in the artifact pairs, however, yield clear evidence against the independent cue models.

POWERLIB: SAS/IML Software for Computing Power in Multivariate Linear Models

PubMed Central

Johnson, Jacqueline L.; Muller, Keith E.; Slaughter, James C.; Gurka, Matthew J.; Gribbin, Matthew J.; Simpson, Sean L.

2014-01-01

The POWERLIB SAS/IML software provides convenient power calculations for a wide range of multivariate linear models with Gaussian errors. The software includes the Box, Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the “univariate” approach to repeated measures (UNIREP), the Hotelling Lawley Trace, Pillai-Bartlett Trace, and Wilks Lambda tests in “multivariate” approach (MULTIREP), as well as a limited but useful range of mixed models. The familiar univariate linear model with Gaussian errors is an important special case. For estimated covariance, the software provides confidence limits for the resulting estimated power. All power and confidence limits values can be output to a SAS dataset, which can be used to easily produce plots and tables for manuscripts. PMID:25400516

Life cycle cost optimization of biofuel supply chains under uncertainties based on interval linear programming.

PubMed

Ren, Jingzheng; Dong, Liang; Sun, Lu; Goodsite, Michael Evan; Tan, Shiyu; Dong, Lichun

2015-01-01

The aim of this work was to develop a model for optimizing the life cycle cost of biofuel supply chain under uncertainties. Multiple agriculture zones, multiple transportation modes for the transport of grain and biofuel, multiple biofuel plants, and multiple market centers were considered in this model, and the price of the resources, the yield of grain and the market demands were regarded as interval numbers instead of constants. An interval linear programming was developed, and a method for solving interval linear programming was presented. An illustrative case was studied by the proposed model, and the results showed that the proposed model is feasible for designing biofuel supply chain under uncertainties. Copyright © 2015 Elsevier Ltd. All rights reserved.

A dynamic model of the human postural control system

NASA Technical Reports Server (NTRS)

Hill, J. C.

1972-01-01

A digital simulation of the pitch axis dynamics of a stick man of figures is described. Difficulties encountered in linearizing the equations of motion are discussed; the conclusion reached is that a completely linear simulation is of such restricted validity that only a nonlinear simulation is of any practical use. Typical simulation results obtained from the full nonlinear model are presented.

A dynamic model of the human postural control system.

NASA Technical Reports Server (NTRS)

Hill, J. C.

1971-01-01

Description of a digital simulation of the pitch axis dynamics of a stick man. The difficulties encountered in linearizing the equations of motion are discussed; the conclusion reached is that a completely linear simulation is of such restricted validity that only a nonlinear simulation is of any practical use. Typical simulation results obtained from the full nonlinear model are illustrated.

A Method for Modeling the Intrinsic Dynamics of Intraindividual Variability: Recovering the Parameters of Simulated Oscillators in Multi-Wave Panel Data.

ERIC Educational Resources Information Center

Boker, Steven M.; Nesselroade, John R.

2002-01-01

Examined two methods for fitting models of intrinsic dynamics to intraindividual variability data by testing these techniques' behavior in equations through simulation studies. Among the main results is the demonstration that a local linear approximation of derivatives can accurately recover the parameters of a simulated linear oscillator, with…

Testing the Linearity of the Cosmic Origins Spectrograph FUV Channel Thermal Correction

NASA Astrophysics Data System (ADS)

Fix, Mees B.; De Rosa, Gisella; Sahnow, David

2018-05-01

The Far Ultraviolet Cross Delay Line (FUV XDL) detector on the Cosmic Origins Spectrograph (COS) is subject to temperature-dependent distortions. The correction performed by the COS calibration pipeline (CalCOS) assumes that these changes are linear across the detector. In this report we evaluate the accuracy of the linear approximations using data obtained on orbit. Our results show that the thermal distortions are consistent with our current linear model.

[Baseline correction of spectrum for the inversion of chlorophyll-a concentration in the turbidity water].

PubMed

Wei, Yu-Chun; Wang, Guo-Xiang; Cheng, Chun-Mei; Zhang, Jing; Sun, Xiao-Peng

2012-09-01

Suspended particle material is the main factor affecting remote sensing inversion of chlorophyll-a concentration (Chla) in turbidity water. According to the optical property of suspended material in water, the present paper proposed a linear baseline correction method to weaken the suspended particle contribution in the spectrum above turbidity water surface. The linear baseline was defined as the connecting line of reflectance from 450 to 750 nm, and baseline correction is that spectrum reflectance subtracts the baseline. Analysis result of field data in situ of Meiliangwan, Taihu Lake in April, 2011 and March, 2010 shows that spectrum linear baseline correction can improve the inversion precision of Chl a and produce the better model diagnoses. As the data in March, 2010, RMSE of band ratio model built by original spectrum is 4.11 mg x m(-3), and that built by spectrum baseline correction is 3.58 mg x m(-3). Meanwhile, residual distribution and homoscedasticity in the model built by baseline correction spectrum is improved obviously. The model RMSE of April, 2011 shows the similar result. The authors suggest that using linear baseline correction as the spectrum processing method to improve Chla inversion accuracy in turbidity water without algae bloom.

Artificial Neural Network versus Linear Models Forecasting Doha Stock Market

NASA Astrophysics Data System (ADS)

Yousif, Adil; Elfaki, Faiz

2017-12-01

The purpose of this study is to determine the instability of Doha stock market and develop forecasting models. Linear time series models are used and compared with a nonlinear Artificial Neural Network (ANN) namely Multilayer Perceptron (MLP) Technique. It aims to establish the best useful model based on daily and monthly data which are collected from Qatar exchange for the period starting from January 2007 to January 2015. Proposed models are for the general index of Qatar stock exchange and also for the usages in other several sectors. With the help of these models, Doha stock market index and other various sectors were predicted. The study was conducted by using various time series techniques to study and analyze data trend in producing appropriate results. After applying several models, such as: Quadratic trend model, double exponential smoothing model, and ARIMA, it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index. However, ANN model was found to be more accurate than time series models.

Linear models for assessing mechanisms of sperm competition: the trouble with transformations.

PubMed

Eggert, Anne-Katrin; Reinhardt, Klaus; Sakaluk, Scott K

2003-01-01

Although sperm competition is a pervasive selective force shaping the reproductive tactics of males, the mechanisms underlying different patterns of sperm precedence remain obscure. Parker et al. (1990) developed a series of linear models designed to identify two of the more basic mechanisms: sperm lotteries and sperm displacement; the models can be tested experimentally by manipulating the relative numbers of sperm transferred by rival males and determining the paternity of offspring. Here we show that tests of the model derived for sperm lotteries can result in misleading inferences about the underlying mechanism of sperm precedence because the required inverse transformations may lead to a violation of fundamental assumptions of linear regression. We show that this problem can be remedied by reformulating the model using the actual numbers of offspring sired by each male, and log-transforming both sides of the resultant equation. Reassessment of data from a previous study (Sakaluk and Eggert 1996) using the corrected version of the model revealed that we should not have excluded a simple sperm lottery as a possible mechanism of sperm competition in decorated crickets, Gryllodes sigillatus.

Diffusion by one wave and by many waves

NASA Astrophysics Data System (ADS)

Albert, J. M.

2010-03-01

Radiation belt electrons and chorus waves are an outstanding instance of the important role cyclotron resonant wave-particle interactions play in the magnetosphere. Chorus waves are particularly complex, often occurring with large amplitude, narrowband but drifting frequency and fine structure. Nevertheless, modeling their effect on radiation belt electrons with bounce-averaged broadband quasi-linear theory seems to yield reasonable results. It is known that coherent interactions with monochromatic waves can cause particle diffusion, as well as radically different phase bunching and phase trapping behavior. Here the two formulations of diffusion, while conceptually different, are shown to give identical diffusion coefficients, in the narrowband limit of quasi-linear theory. It is further shown that suitably averaging the monochromatic diffusion coefficients over frequency and wave normal angle parameters reproduces the full broadband quasi-linear results. This may account for the rather surprising success of quasi-linear theory in modeling radiation belt electrons undergoing diffusion by chorus waves.

Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models.

PubMed

Wiedermann, Wolfgang; Artner, Richard; von Eye, Alexander

2017-01-01

Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the homoscedasticity assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diagnostics and nine homoscedasticity tests that can be used to make decisions concerning the direction of effects in linear models. Results of a Monte Carlo simulation that demonstrate the adequacy of the approach are presented. An empirical example is provided, and applicability of the methodology in cases of violated assumptions is discussed.

An evaluation of bias in propensity score-adjusted non-linear regression models.

PubMed

Wan, Fei; Mitra, Nandita

2018-03-01

Propensity score methods are commonly used to adjust for observed confounding when estimating the conditional treatment effect in observational studies. One popular method, covariate adjustment of the propensity score in a regression model, has been empirically shown to be biased in non-linear models. However, no compelling underlying theoretical reason has been presented. We propose a new framework to investigate bias and consistency of propensity score-adjusted treatment effects in non-linear models that uses a simple geometric approach to forge a link between the consistency of the propensity score estimator and the collapsibility of non-linear models. Under this framework, we demonstrate that adjustment of the propensity score in an outcome model results in the decomposition of observed covariates into the propensity score and a remainder term. Omission of this remainder term from a non-collapsible regression model leads to biased estimates of the conditional odds ratio and conditional hazard ratio, but not for the conditional rate ratio. We further show, via simulation studies, that the bias in these propensity score-adjusted estimators increases with larger treatment effect size, larger covariate effects, and increasing dissimilarity between the coefficients of the covariates in the treatment model versus the outcome model.

Modeling and simulation of protein elution in linear pH and salt gradients on weak, strong and mixed cation exchange resins applying an extended Donnan ion exchange model.

PubMed

Wittkopp, Felix; Peeck, Lars; Hafner, Mathias; Frech, Christian

2018-04-13

Process development and characterization based on mathematic modeling provides several advantages and has been applied more frequently over the last few years. In this work, a Donnan equilibrium ion exchange (DIX) model is applied for modelling and simulation of ion exchange chromatography of a monoclonal antibody in linear chromatography. Four different cation exchange resin prototypes consisting of weak, strong and mixed ligands are characterized using pH and salt gradient elution experiments applying the extended DIX model. The modelling results are compared with the results using a classic stoichiometric displacement model. The Donnan equilibrium model is able to describe all four prototype resins while the stoichiometric displacement model fails for the weak and mixed weak/strong ligands. Finally, in silico chromatogram simulations of pH and pH/salt dual gradients are performed to verify the results and to show the consistency of the developed model. Copyright © 2018 Elsevier B.V. All rights reserved.

[Quantitative relationship between gas chromatographic retention time and structural parameters of alkylphenols].

PubMed

Ruan, Xiaofang; Zhang, Ruisheng; Yao, Xiaojun; Liu, Mancang; Fan, Botao

2007-03-01

Alkylphenols are a group of permanent pollutants in the environment and could adversely disturb the human endocrine system. It is therefore important to effectively separate and measure the alkylphenols. To guide the chromatographic analysis of these compounds in practice, the development of quantitative relationship between the molecular structure and the retention time of alkylphenols becomes necessary. In this study, topological, constitutional, geometrical, electrostatic and quantum-chemical descriptors of 44 alkylphenols were calculated using a software, CODESSA, and these descriptors were pre-selected using the heuristic method. As a result, three-descriptor linear model (LM) was developed to describe the relationship between the molecular structure and the retention time of alkylphenols. Meanwhile, the non-linear regression model was also developed based on support vector machine (SVM) using the same three descriptors. The correlation coefficient (R(2)) for the LM and SVM was 0.98 and 0. 92, and the corresponding root-mean-square error was 0. 99 and 2. 77, respectively. By comparing the stability and prediction ability of the two models, it was found that the linear model was a better method for describing the quantitative relationship between the retention time of alkylphenols and the molecular structure. The results obtained suggested that the linear model could be applied for the chromatographic analysis of alkylphenols with known molecular structural parameters.

Approximate reduction of linear population models governed by stochastic differential equations: application to multiregional models.

PubMed

Sanz, Luis; Alonso, Juan Antonio

2017-12-01

In this work we develop approximate aggregation techniques in the context of slow-fast linear population models governed by stochastic differential equations and apply the results to the treatment of populations with spatial heterogeneity. Approximate aggregation techniques allow one to transform a complex system involving many coupled variables and in which there are processes with different time scales, by a simpler reduced model with a fewer number of 'global' variables, in such a way that the dynamics of the former can be approximated by that of the latter. In our model we contemplate a linear fast deterministic process together with a linear slow process in which the parameters are affected by additive noise, and give conditions for the solutions corresponding to positive initial conditions to remain positive for all times. By letting the fast process reach equilibrium we build a reduced system with a lesser number of variables, and provide results relating the asymptotic behaviour of the first- and second-order moments of the population vector for the original and the reduced system. The general technique is illustrated by analysing a multiregional stochastic system in which dispersal is deterministic and the rate growth of the populations in each patch is affected by additive noise.

Wind-invariant saltation heights imply linear scaling of aeolian saltation flux with shear stress.

PubMed

Martin, Raleigh L; Kok, Jasper F

2017-06-01

Wind-driven sand transport generates atmospheric dust, forms dunes, and sculpts landscapes. However, it remains unclear how the flux of particles in aeolian saltation-the wind-driven transport of sand in hopping trajectories-scales with wind speed, largely because models do not agree on how particle speeds and trajectories change with wind shear velocity. We present comprehensive measurements, from three new field sites and three published studies, showing that characteristic saltation layer heights remain approximately constant with shear velocity, in agreement with recent wind tunnel studies. These results support the assumption of constant particle speeds in recent models predicting linear scaling of saltation flux with shear stress. In contrast, our results refute widely used older models that assume that particle speed increases with shear velocity, thereby predicting nonlinear 3/2 stress-flux scaling. This conclusion is further supported by direct field measurements of saltation flux versus shear stress. Our results thus argue for adoption of linear saltation flux laws and constant saltation trajectories for modeling saltation-driven aeolian processes on Earth, Mars, and other planetary surfaces.

Wind-invariant saltation heights imply linear scaling of aeolian saltation flux with shear stress

PubMed Central

Martin, Raleigh L.; Kok, Jasper F.

2017-01-01

Wind-driven sand transport generates atmospheric dust, forms dunes, and sculpts landscapes. However, it remains unclear how the flux of particles in aeolian saltation—the wind-driven transport of sand in hopping trajectories—scales with wind speed, largely because models do not agree on how particle speeds and trajectories change with wind shear velocity. We present comprehensive measurements, from three new field sites and three published studies, showing that characteristic saltation layer heights remain approximately constant with shear velocity, in agreement with recent wind tunnel studies. These results support the assumption of constant particle speeds in recent models predicting linear scaling of saltation flux with shear stress. In contrast, our results refute widely used older models that assume that particle speed increases with shear velocity, thereby predicting nonlinear 3/2 stress-flux scaling. This conclusion is further supported by direct field measurements of saltation flux versus shear stress. Our results thus argue for adoption of linear saltation flux laws and constant saltation trajectories for modeling saltation-driven aeolian processes on Earth, Mars, and other planetary surfaces. PMID:28630907

[Equilibrium sorption isotherm for Cu2+ onto Hydrilla verticillata Royle and Myriophyllum spicatum].

PubMed

Yan, Chang-zhou; Zeng, A-yan; Jin, Xiang-can; Wang, Sheng-rui; Xu, Qiu-jin; Zhao, Jing-zhu

2006-06-01

Equilibrium sorption isotherms for Cu2+ onto Hydrilla verticillata Royle and Myriophyllum spicatum were studied. Both methods of linear and non-linear fitting were applied to describe the sorption isotherms, and their applicability were analyzed and compared. The results were: (1) The applicability of simulated equation can't be compared only by R2 and chi2 when equilibrium sorption model was used to quantify and contrast the performance of different biosorbents. Both methods of linear and non-linear fitting can be applied in different fitting equations to describe the equilibrium sorption isotherms respectively in order to obtain the actual and credible fitting results, and the fitting equation best accorded with experimental data can be selected; (2) In this experiment, the Langmuir model is more suitable to describe the sorption isotherm of Cu2+ biosorption by H. verticillata and M. spicatum, and there is greater difference between the experimental data and the calculated value of Freundlich model, especially for the linear form of Freundlich model; (3) The content of crude cellulose in dry matter is one of the main factor affecting the biosorption capacity of a submerged aquatic plant, and -OH and -CONH2 groups of polysaccharides on cell wall maybe are active center of biosorption; (4) According to the coefficients qm of the linear form of Langmuir model, the maximum sorption capacity of Cu2+ was found to be 21.55 mg/g and 10.80mg/g for H. verticillata and M. spicatum, respectively. The maximum specific surface area for H. verticillata for binding Cu2+ was 3.23m2/g, and it was 1.62m2/g for M. spicatum.

Extraction of diffuse correlation spectroscopy flow index by integration of Nth-order linear model with Monte Carlo simulation

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

Shang, Yu; Lin, Yu; Yu, Guoqiang, E-mail: guoqiang.yu@uky.edu

2014-05-12

Conventional semi-infinite solution for extracting blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements may cause errors in estimation of BFI (αD{sub B}) in tissues with small volume and large curvature. We proposed an algorithm integrating Nth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in tissue for the extraction of αD{sub B}. The volume and geometry of the measured tissue were incorporated in the Monte Carlo simulation, which overcome the semi-infinite restrictions. The algorithm was tested using computer simulations on four tissue models with varied volumes/geometries and applied on an in vivo strokemore » model of mouse. Computer simulations shows that the high-order (N ≥ 5) linear algorithm was more accurate in extracting αD{sub B} (errors < ±2%) from the noise-free DCS data than the semi-infinite solution (errors: −5.3% to −18.0%) for different tissue models. Although adding random noises to DCS data resulted in αD{sub B} variations, the mean values of errors in extracting αD{sub B} were similar to those reconstructed from the noise-free DCS data. In addition, the errors in extracting the relative changes of αD{sub B} using both linear algorithm and semi-infinite solution were fairly small (errors < ±2.0%) and did not rely on the tissue volume/geometry. The experimental results from the in vivo stroke mice agreed with those in simulations, demonstrating the robustness of the linear algorithm. DCS with the high-order linear algorithm shows the potential for the inter-subject comparison and longitudinal monitoring of absolute BFI in a variety of tissues/organs with different volumes/geometries.« less

Non-linear dynamic analysis of geared systems, part 2

NASA Technical Reports Server (NTRS)

Singh, Rajendra; Houser, Donald R.; Kahraman, Ahmet

1990-01-01

A good understanding of the steady state dynamic behavior of a geared system is required in order to design reliable and quiet transmissions. This study focuses on a system containing a spur gear pair with backlash and periodically time-varying mesh stiffness, and rolling element bearings with clearance type non-linearities. A dynamic finite element model of the linear time-invariant (LTI) system is developed. Effects of several system parameters, such as torsional and transverse flexibilities of the shafts and prime mover/load inertias, on free and force vibration characteristics are investigated. Several reduced order LTI models are developed and validated by comparing their eigen solution with the finite element model results. Several key system parameters such as mean load and damping ratio are identified and their effects on the non-linear frequency response are evaluated quantitatively. Other fundamental issues such as the dynamic coupling between non-linear modes, dynamic interactions between component non-linearities and time-varying mesh stiffness, and the existence of subharmonic and chaotic solutions including routes to chaos have also been examined in depth.

Genetic parameters for direct and maternal calving ease in Walloon dairy cattle based on linear and threshold models.

PubMed

Vanderick, S; Troch, T; Gillon, A; Glorieux, G; Gengler, N

2014-12-01

Calving ease scores from Holstein dairy cattle in the Walloon Region of Belgium were analysed using univariate linear and threshold animal models. Variance components and derived genetic parameters were estimated from a data set including 33,155 calving records. Included in the models were season, herd and sex of calf × age of dam classes × group of calvings interaction as fixed effects, herd × year of calving, maternal permanent environment and animal direct and maternal additive genetic as random effects. Models were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Direct heritability for calving ease was approximately 8% with linear models and approximately 12% with threshold models. Maternal heritabilities were approximately 2 and 4%, respectively. Genetic correlation between direct and maternal additive effects was found to be not significantly different from zero. Models were compared in terms of goodness of fit and predictive ability. Criteria of comparison such as mean squared error, correlation between observed and predicted calving ease scores as well as between estimated breeding values were estimated from 85,118 calving records. The results provided few differences between linear and threshold models even though correlations between estimated breeding values from subsets of data for sires with progeny from linear model were 17 and 23% greater for direct and maternal genetic effects, respectively, than from threshold model. For the purpose of genetic evaluation for calving ease in Walloon Holstein dairy cattle, the linear animal model without covariance between direct and maternal additive effects was found to be the best choice. © 2014 Blackwell Verlag GmbH.

Linear parameter varying identification of ankle joint intrinsic stiffness during imposed walking movements.

PubMed

Sobhani Tehrani, Ehsan; Jalaleddini, Kian; Kearney, Robert E

2013-01-01

This paper describes a novel model structure and identification method for the time-varying, intrinsic stiffness of human ankle joint during imposed walking (IW) movements. The model structure is based on the superposition of a large signal, linear, time-invariant (LTI) model and a small signal linear-parameter varying (LPV) model. The methodology is based on a two-step algorithm; the LTI model is first estimated using data from an unperturbed IW trial. Then, the LPV model is identified using data from a perturbed IW trial with the output predictions of the LTI model removed from the measured torque. Experimental results demonstrate that the method accurately tracks the continuous-time variation of normal ankle intrinsic stiffness when the joint position changes during the IW movement. Intrinsic stiffness gain decreases from full plantarflexion to near the mid-point of plantarflexion and then increases substantially as the ankle is dosriflexed.

Integrated Control Modeling for Propulsion Systems Using NPSS

NASA Technical Reports Server (NTRS)

Parker, Khary I.; Felder, James L.; Lavelle, Thomas M.; Withrow, Colleen A.; Yu, Albert Y.; Lehmann, William V. A.

2004-01-01

The Numerical Propulsion System Simulation (NPSS), an advanced engineering simulation environment used to design and analyze aircraft engines, has been enhanced by integrating control development tools into it. One of these tools is a generic controller interface that allows NPSS to communicate with control development software environments such as MATLAB and EASY5. The other tool is a linear model generator (LMG) that gives NPSS the ability to generate linear, time-invariant state-space models. Integrating these tools into NPSS enables it to be used for control system development. This paper will discuss the development and integration of these tools into NPSS. In addition, it will show a comparison of transient model results of a generic, dual-spool, military-type engine model that has been implemented in NPSS and Simulink. It will also show the linear model generator s ability to approximate the dynamics of a nonlinear NPSS engine model.

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

Train repathing in emergencies based on fuzzy linear programming.

PubMed

Meng, Xuelei; Cui, Bingmou

2014-01-01

Train pathing is a typical problem which is to assign the train trips on the sets of rail segments, such as rail tracks and links. This paper focuses on the train pathing problem, determining the paths of the train trips in emergencies. We analyze the influencing factors of train pathing, such as transferring cost, running cost, and social adverse effect cost. With the overall consideration of the segment and station capability constraints, we build the fuzzy linear programming model to solve the train pathing problem. We design the fuzzy membership function to describe the fuzzy coefficients. Furthermore, the contraction-expansion factors are introduced to contract or expand the value ranges of the fuzzy coefficients, coping with the uncertainty of the value range of the fuzzy coefficients. We propose a method based on triangular fuzzy coefficient and transfer the train pathing (fuzzy linear programming model) to a determinate linear model to solve the fuzzy linear programming problem. An emergency is supposed based on the real data of the Beijing-Shanghai Railway. The model in this paper was solved and the computation results prove the availability of the model and efficiency of the algorithm.

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

PubMed

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

2016-05-01

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

Mathematical modelling in engineering: an alternative way to teach Linear Algebra

NASA Astrophysics Data System (ADS)

Domínguez-García, S.; García-Planas, M. I.; Taberna, J.

2016-10-01

Technological advances require that basic science courses for engineering, including Linear Algebra, emphasize the development of mathematical strengths associated with modelling and interpretation of results, which are not limited only to calculus abilities. Based on this consideration, we have proposed a project-based learning, giving a dynamic classroom approach in which students modelled real-world problems and turn gain a deeper knowledge of the Linear Algebra subject. Considering that most students are digital natives, we use the e-portfolio as a tool of communication between students and teachers, besides being a good place making the work visible. In this article, we present an overview of the design and implementation of a project-based learning for a Linear Algebra course taught during the 2014-2015 at the 'ETSEIB'of Universitat Politècnica de Catalunya (UPC).

Development and Validation of Linear Alternator Models for the Advanced Stirling Convertor

NASA Technical Reports Server (NTRS)

Metscher, Jonathan F.; Lewandowski, Edward

2014-01-01

Two models of the linear alternator of the Advanced Stirling Convertor (ASC) have been developed using the Sage 1-D modeling software package. The first model relates the piston motion to electric current by means of a motor constant. The second uses electromagnetic model components to model the magnetic circuit of the alternator. The models are tuned and validated using test data and compared against each other. Results show both models can be tuned to achieve results within 7% of ASC test data under normal operating conditions. Using Sage enables the creation of a complete ASC model to be developed and simulations completed quickly compared to more complex multi-dimensional models. These models allow for better insight into overall Stirling convertor performance, aid with Stirling power system modeling, and in the future support NASA mission planning for Stirling-based power systems.

Development and Validation of Linear Alternator Models for the Advanced Stirling Convertor

NASA Technical Reports Server (NTRS)

Metscher, Jonathan F.; Lewandowski, Edward J.

2015-01-01

Two models of the linear alternator of the Advanced Stirling Convertor (ASC) have been developed using the Sage 1-D modeling software package. The first model relates the piston motion to electric current by means of a motor constant. The second uses electromagnetic model components to model the magnetic circuit of the alternator. The models are tuned and validated using test data and also compared against each other. Results show both models can be tuned to achieve results within 7 of ASC test data under normal operating conditions. Using Sage enables the creation of a complete ASC model to be developed and simulations completed quickly compared to more complex multi-dimensional models. These models allow for better insight into overall Stirling convertor performance, aid with Stirling power system modeling, and in the future support NASA mission planning for Stirling-based power systems.

Implementing Nonlinear Buoyancy and Excitation Forces in the WEC-Sim Wave Energy Converter Modeling Tool: Preprint

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

Lawson, M.; Yu, Y. H.; Nelessen, A.

2014-05-01

Wave energy converters (WECs) are commonly designed and analyzed using numerical models that combine multi-body dynamics with hydrodynamic models based on the Cummins Equation and linearized hydrodynamic coefficients. These modeling methods are attractive design tools because they are computationally inexpensive and do not require the use of high performance computing resources necessitated by high-fidelity methods, such as Navier Stokes computational fluid dynamics. Modeling hydrodynamics using linear coefficients assumes that the device undergoes small motions and that the wetted surface area of the devices is approximately constant. WEC devices, however, are typically designed to undergo large motions in order to maximizemore » power extraction, calling into question the validity of assuming that linear hydrodynamic models accurately capture the relevant fluid-structure interactions. In this paper, we study how calculating buoyancy and Froude-Krylov forces from the instantaneous position of a WEC device (referred to as instantaneous buoyancy and Froude-Krylov forces from herein) changes WEC simulation results compared to simulations that use linear hydrodynamic coefficients. First, we describe the WEC-Sim tool used to perform simulations and how the ability to model instantaneous forces was incorporated into WEC-Sim. We then use a simplified one-body WEC device to validate the model and to demonstrate how accounting for these instantaneously calculated forces affects the accuracy of simulation results, such as device motions, hydrodynamic forces, and power generation.« less

Soil amplification with a strong impedance contrast: Boston, Massachusetts

USGS Publications Warehouse

Baise, Laurie G.; Kaklamanos, James; Berry, Bradford M; Thompson, Eric M.

2016-01-01

In this study, we evaluate the effect of strong sediment/bedrock impedance contrasts on soil amplification in Boston, Massachusetts, for typical sites along the Charles and Mystic Rivers. These sites can be characterized by artificial fill overlying marine sediments overlying glacial till and bedrock, where the depth to bedrock ranges from 20 to 80 m. The marine sediments generally consist of organic silts, sand, and Boston Blue Clay. We chose these sites because they represent typical foundation conditions in the city of Boston, and the soil conditions are similar to other high impedance contrast environments. The sediment/bedrock interface in this region results in an impedance ratio on the order of ten, which in turn results in a significant amplification of the ground motion. Using stratigraphic information derived from numerous boreholes across the region paired with geologic and geomorphologic constraints, we develop a depth-to-bedrock model for the greater Boston region. Using shear-wave velocity profiles from 30 locations, we develop average velocity profiles for sites mapped as artificial fill, glaciofluvial deposits, and bedrock. By pairing the depth-to-bedrock model with the surficial geology and the average shear-wave velocity profiles, we can predict soil amplification in Boston. We compare linear and equivalent-linear site response predictions for a soil layer of varying thickness over bedrock, and assess the effects of varying the bedrock shear-wave velocity (VSb) and quality factor (Q). In a moderate seismicity region like Boston, many earthquakes will result in ground motions that can be modeled with linear site response methods. We also assess the effect of bedrock depth on soil amplification for a generic soil profile in artificial fill, using both linear and equivalent-linear site response models. Finally, we assess the accuracy of the model results by comparing the predicted (linear site response) and observed site response at the Northeastern University (NEU) vertical seismometer array during the 2011 M 5.8 Mineral, Virginia, earthquake. Site response at the NEU vertical array results in amplification on the order of 10 times at a period between 0.7-0.8 s. The results from this study provide evidence that the mean short-period and mean intermediate-period amplification used in design codes (i.e., from the Fa and Fv site coefficients) may underpredict soil amplification in strong impedance contrast environments such as Boston.

WE-H-BRA-01: BEST IN PHYSICS (THERAPY): Nano-Dosimetric Kinetic Model for Variable Relative Biological Effectiveness of Proton and Ion Beams

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

Abolfath, R; Bronk, L; Titt, U.

2016-06-15

Purpose: Recent clonogenic cell survival and γH2AX studies suggest proton relative biological effectiveness (RBE) may be a non-linear function of linear energy transfer (LET) in the distal edge of the Bragg peak and beyond. We sought to develop a multiscale model to account for non-linear response phenomena to aid in the optimization of intensity-modulated proton therapy. Methods: The model is based on first-principle simulations of proton track structures, including secondary ions, and an analytical derivation of the dependence on particle LET of the linear-quadratic (LQ) model parameters α and β. The derived formulas are an extension of the microdosimetric kineticmore » (MK) model that captures dissipative track structures and non-Poissonian distribution of DNA damage at the distal edge of the Bragg peak and beyond. Monte Carlo simulations were performed to confirm the non-linear dose-response characteristics arising from the non-Poisson distribution of initial DNA damage. Results: In contrast to low LET segments of the proton depth dose, from the beam entrance to the Bragg peak, strong deviations from non-dissipative track structures and Poisson distribution in the ionization events in the Bragg peak distal edge govern the non-linear cell response and result in the transformation α=(1+c-1 L) α-x+2(c-0 L+c-2 L^2 )(1+c-1 L) β-x and β=(1+c-1 L)^2 β-x. Here L is the charged particle LET, and c-0,c-1, and c-2 are functions of microscopic parameters and can be served as fitting parameters to the cell-survival data. In the low LET limit c-1, and c-2 are negligible hence the linear model proposed and used by Wilkins-Oelfke for the proton treatment planning system can be retrieved. The present model fits well the recent clonogenic survival data measured recently in our group in MDACC. Conclusion: The present hybrid method provides higher accuracy in calculating the RBE-weighted dose in the target and normal tissues.« less

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

Dechant, Lawrence J.

Wave packet analysis provides a connection between linear small disturbance theory and subsequent nonlinear turbulent spot flow behavior. The traditional association between linear stability analysis and nonlinear wave form is developed via the method of stationary phase whereby asymptotic (simplified) mean flow solutions are used to estimate dispersion behavior and stationary phase approximation are used to invert the associated Fourier transform. The resulting process typically requires nonlinear algebraic equations inversions that can be best performed numerically, which partially mitigates the value of the approximation as compared to a more complete, e.g. DNS or linear/nonlinear adjoint methods. To obtain a simpler,more » closed-form analytical result, the complete packet solution is modeled via approximate amplitude (linear convected kinematic wave initial value problem) and local sinusoidal (wave equation) expressions. Significantly, the initial value for the kinematic wave transport expression follows from a separable variable coefficient approximation to the linearized pressure fluctuation Poisson expression. The resulting amplitude solution, while approximate in nature, nonetheless, appears to mimic many of the global features, e.g. transitional flow intermittency and pressure fluctuation magnitude behavior. A low wave number wave packet models also recover meaningful auto-correlation and low frequency spectral behaviors.« less

An hourly PM10 diagnosis model for the Bilbao metropolitan area using a linear regression methodology.

PubMed

González-Aparicio, I; Hidalgo, J; Baklanov, A; Padró, A; Santa-Coloma, O

2013-07-01

There is extensive evidence of the negative impacts on health linked to the rise of the regional background of particulate matter (PM) 10 levels. These levels are often increased over urban areas becoming one of the main air pollution concerns. This is the case on the Bilbao metropolitan area, Spain. This study describes a data-driven model to diagnose PM10 levels in Bilbao at hourly intervals. The model is built with a training period of 7-year historical data covering different urban environments (inland, city centre and coastal sites). The explanatory variables are quantitative-log [NO2], temperature, short-wave incoming radiation, wind speed and direction, specific humidity, hour and vehicle intensity-and qualitative-working days/weekends, season (winter/summer), the hour (from 00 to 23 UTC) and precipitation/no precipitation. Three different linear regression models are compared: simple linear regression; linear regression with interaction terms (INT); and linear regression with interaction terms following the Sawa's Bayesian Information Criteria (INT-BIC). Each type of model is calculated selecting two different periods: the training (it consists of 6 years) and the testing dataset (it consists of 1 year). The results of each type of model show that the INT-BIC-based model (R(2) = 0.42) is the best. Results were R of 0.65, 0.63 and 0.60 for the city centre, inland and coastal sites, respectively, a level of confidence similar to the state-of-the art methodology. The related error calculated for longer time intervals (monthly or seasonal means) diminished significantly (R of 0.75-0.80 for monthly means and R of 0.80 to 0.98 at seasonally means) with respect to shorter periods.

[Study on the early detection of Sclerotinia of Brassica napus based on combinational-stimulated bands].

PubMed

Liu, Fei; Feng, Lei; Lou, Bing-gan; Sun, Guang-ming; Wang, Lian-ping; He, Yong

2010-07-01

The combinational-stimulated bands were used to develop linear and nonlinear calibrations for the early detection of sclerotinia of oilseed rape (Brassica napus L.). Eighty healthy and 100 Sclerotinia leaf samples were scanned, and different preprocessing methods combined with successive projections algorithm (SPA) were applied to develop partial least squares (PLS) discriminant models, multiple linear regression (MLR) and least squares-support vector machine (LS-SVM) models. The results indicated that the optimal full-spectrum PLS model was achieved by direct orthogonal signal correction (DOSC), then De-trending and Raw spectra with correct recognition ratio of 100%, 95.7% and 95.7%, respectively. When using combinational-stimulated bands, the optimal linear models were SPA-MLR (DOSC) and SPA-PLS (DOSC) with correct recognition ratio of 100%. All SPA-LSSVM models using DOSC, De-trending and Raw spectra achieved perfect results with recognition of 100%. The overall results demonstrated that it was feasible to use combinational-stimulated bands for the early detection of Sclerotinia of oilseed rape, and DOSC-SPA was a powerful way for informative wavelength selection. This method supplied a new approach to the early detection and portable monitoring instrument of sclerotinia.

Influence of dynamic inflow on the helicopter vertical response

NASA Technical Reports Server (NTRS)

Chen, Robert T. N.; Hindson, William S.

1986-01-01

A study was conducted to investigate the effects of dynamic inflow on rotor-blade flapping and vertical motion of the helicopter in hover. Linearized versions of two dynamic inflow models, one developed by Carpenter and Fridovich and the other by Pitt and Peters, were incorporated in simplified rotor-body models and were compared for variations in thrust coefficient and the blade Lock number. In addition, a comparison was made between the results of the linear analysis, and the transient and frequency responses measured in flight on the CH-47B variable-stability helicopter. Results indicate that the correlations are good, considering the simplified model used. The linear analysis also shows that dynamic inflow plays a key role in destabilizing the flapping mode. The destabilized flapping mode, along with the inflow mode that the dynamic inflow introduces, results in a large initial overshoot in the vertical acceleration response to an abrupt input in the collective pitch. This overshoot becomes more pronounced as either the thrust coefficient or the blade Lock number is reduced. Compared with Carpenter's inflow model, Pitt's model tends to produce more oscillatory responses because of the less stable flapping mode predicted by it.

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

NASA Astrophysics Data System (ADS)

Auger-Méthé, Marie; Field, Chris; Albertsen, Christoffer M.; Derocher, Andrew E.; Lewis, Mark A.; Jonsen, Ian D.; Mills Flemming, Joanna

2016-05-01

State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results.

Classical Michaelis-Menten and system theory approach to modeling metabolite formation kinetics.

PubMed

Popović, Jovan

2004-01-01

When single doses of drug are administered and kinetics are linear, techniques, which are based on the compartment approach and the linear system theory approach, in modeling the formation of the metabolite from the parent drug are proposed. Unlike the purpose-specific compartment approach, the methodical, conceptual and computational uniformity in modeling various linear biomedical systems is the dominant characteristic of the linear system approach technology. Saturation of the metabolic reaction results in nonlinear kinetics according to the Michaelis-Menten equation. The two compartment open model with Michaelis-Menten elimination kinetics is theorethicaly basic when single doses of drug are administered. To simulate data or to fit real data using this model, one must resort to numerical integration. A biomathematical model for multiple dosage regimen calculations of nonlinear metabolic systems in steady-state and a working example with phenytoin are presented. High correlation between phenytoin steady-state serum levels calculated from individual Km and Vmax values in the 15 adult epileptic outpatients and the observed levels at the third adjustment of phenytoin daily dose (r=0.961, p<0.01) were found.

Modelling of Dictyostelium discoideum movement in a linear gradient of chemoattractant.

PubMed

Eidi, Zahra; Mohammad-Rafiee, Farshid; Khorrami, Mohammad; Gholami, Azam

2017-11-15

Chemotaxis is a ubiquitous biological phenomenon in which cells detect a spatial gradient of chemoattractant, and then move towards the source. Here we present a position-dependent advection-diffusion model that quantitatively describes the statistical features of the chemotactic motion of the social amoeba Dictyostelium discoideum in a linear gradient of cAMP (cyclic adenosine monophosphate). We fit the model to experimental trajectories that are recorded in a microfluidic setup with stationary cAMP gradients and extract the diffusion and drift coefficients in the gradient direction. Our analysis shows that for the majority of gradients, both coefficients decrease over time and become negative as the cells crawl up the gradient. The extracted model parameters also show that besides the expected drift in the direction of the chemoattractant gradient, we observe a nonlinear dependency of the corresponding variance on time, which can be explained by the model. Furthermore, the results of the model show that the non-linear term in the mean squared displacement of the cell trajectories can dominate the linear term on large time scales.

Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications

PubMed Central

Huang, Jian; Zhang, Cun-Hui

2013-01-01

The ℓ1-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weighted ℓ1-penalized estimators for convex loss functions of a general form, including the generalized linear models. We study the estimation, prediction, selection and sparsity properties of the weighted ℓ1-penalized estimator in sparse, high-dimensional settings where the number of predictors p can be much larger than the sample size n. Adaptive Lasso is considered as a special case. A multistage method is developed to approximate concave regularized estimation by applying an adaptive Lasso recursively. We provide prediction and estimation oracle inequalities for single- and multi-stage estimators, a general selection consistency theorem, and an upper bound for the dimension of the Lasso estimator. Important models including the linear regression, logistic regression and log-linear models are used throughout to illustrate the applications of the general results. PMID:24348100

On 3D minimal massive gravity

NASA Astrophysics Data System (ADS)

Alishahiha, Mohsen; Qaemmaqami, Mohammad M.; Naseh, Ali; Shirzad, Ahmad

2014-12-01

We study linearized equations of motion of the newly proposed three dimensional gravity, known as minimal massive gravity, using its metric formulation. By making use of a redefinition of the parameters of the model, we observe that the resulting linearized equations are exactly the same as that of TMG. In particular the model admits logarithmic modes at critical points. We also study several vacuum solutions of the model, specially at a certain limit where the contribution of Chern-Simons term vanishes.

LINEAR - DERIVATION AND DEFINITION OF A LINEAR AIRCRAFT MODEL

NASA Technical Reports Server (NTRS)

Duke, E. L.

1994-01-01

The Derivation and Definition of a Linear Model program, LINEAR, provides the user with a powerful and flexible tool for the linearization of aircraft aerodynamic models. LINEAR was developed to provide a standard, documented, and verified tool to derive linear models for aircraft stability analysis and control law design. Linear system models define the aircraft system in the neighborhood of an analysis point and are determined by the linearization of the nonlinear equations defining vehicle dynamics and sensors. LINEAR numerically determines a linear system model using nonlinear equations of motion and a user supplied linear or nonlinear aerodynamic model. The nonlinear equations of motion used are six-degree-of-freedom equations with stationary atmosphere and flat, nonrotating earth assumptions. LINEAR is capable of extracting both linearized engine effects, such as net thrust, torque, and gyroscopic effects and including these effects in the linear system model. The point at which this linear model is defined is determined either by completely specifying the state and control variables, or by specifying an analysis point on a trajectory and directing the program to determine the control variables and the remaining state variables. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to provide easy selection of state, control, and observation variables to be used in a particular model. Thus, the order of the system model is completely under user control. Further, the program provides the flexibility of allowing alternate formulations of both the state and observation equations. Data describing the aircraft and the test case is input to the program through a terminal or formatted data files. All data can be modified interactively from case to case. The aerodynamic model can be defined in two ways: a set of nondimensional stability and control derivatives for the flight point of interest, or a full non-linear aerodynamic model as used in simulations. LINEAR is written in FORTRAN and has been implemented on a DEC VAX computer operating under VMS with a virtual memory requirement of approximately 296K of 8 bit bytes. Both an interactive and batch version are included. LINEAR was developed in 1988.

Log-normal frailty models fitted as Poisson generalized linear mixed models.

PubMed

Hirsch, Katharina; Wienke, Andreas; Kuss, Oliver

2016-12-01

The equivalence of a survival model with a piecewise constant baseline hazard function and a Poisson regression model has been known since decades. As shown in recent studies, this equivalence carries over to clustered survival data: A frailty model with a log-normal frailty term can be interpreted and estimated as a generalized linear mixed model with a binary response, a Poisson likelihood, and a specific offset. Proceeding this way, statistical theory and software for generalized linear mixed models are readily available for fitting frailty models. This gain in flexibility comes at the small price of (1) having to fix the number of pieces for the baseline hazard in advance and (2) having to "explode" the data set by the number of pieces. In this paper we extend the simulations of former studies by using a more realistic baseline hazard (Gompertz) and by comparing the model under consideration with competing models. Furthermore, the SAS macro %PCFrailty is introduced to apply the Poisson generalized linear mixed approach to frailty models. The simulations show good results for the shared frailty model. Our new %PCFrailty macro provides proper estimates, especially in case of 4 events per piece. The suggested Poisson generalized linear mixed approach for log-normal frailty models based on the %PCFrailty macro provides several advantages in the analysis of clustered survival data with respect to more flexible modelling of fixed and random effects, exact (in the sense of non-approximate) maximum likelihood estimation, and standard errors and different types of confidence intervals for all variance parameters. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Computation of Turbulent Wake Flows in Variable Pressure Gradient

NASA Technical Reports Server (NTRS)

Duquesne, N.; Carlson, J. R.; Rumsey, C. L.; Gatski, T. B.

1999-01-01

Transport aircraft performance is strongly influenced by the effectiveness of high-lift systems. Developing wakes generated by the airfoil elements are subjected to strong pressure gradients and can thicken very rapidly, limiting maximum lift. This paper focuses on the effects of various pressure gradients on developing symmetric wakes and on the ability of a linear eddy viscosity model and a non-linear explicit algebraic stress model to accurately predict their downstream evolution. In order to reduce the uncertainties arising from numerical issues when assessing the performance of turbulence models, three different numerical codes with the same turbulence models are used. Results are compared to available experimental data to assess the accuracy of the computational results.

The relationship between compressive strength and flexural strength of pavement geopolymer grouting material

NASA Astrophysics Data System (ADS)

Zhang, L.; Han, X. X.; Ge, J.; Wang, C. H.

2018-01-01

To determine the relationship between compressive strength and flexural strength of pavement geopolymer grouting material, 20 groups of geopolymer grouting materials were prepared, the compressive strength and flexural strength were determined by mechanical properties test. On the basis of excluding the abnormal values through boxplot, the results show that, the compressive strength test results were normal, but there were two mild outliers in 7days flexural strength test. The compressive strength and flexural strength were linearly fitted by SPSS, six regression models were obtained by linear fitting of compressive strength and flexural strength. The linear relationship between compressive strength and flexural strength can be better expressed by the cubic curve model, and the correlation coefficient was 0.842.

Deflection angle detecting system for the large-angle and high-linearity fast steering mirror using quadrant detector

NASA Astrophysics Data System (ADS)

Ni, Yingxue; Wu, Jiabin; San, Xiaogang; Gao, Shijie; Ding, Shaohang; Wang, Jing; Wang, Tao

2018-02-01

A deflection angle detecting system (DADS) using a quadrant detector (QD) is developed to achieve the large deflection angle and high linearity for the fast steering mirror (FSM). The mathematical model of the DADS is established by analyzing the principle of position detecting and error characteristics of the QD. Based on this mathematical model, the method of optimizing deflection angle and linearity of FSM is demonstrated, which is proved feasible by simulation and experimental results. Finally, a QD-based FSM is designed and tested. The results show that it achieves 0.72% nonlinearity, ±2.0 deg deflection angle, and 1.11-μrad resolution. Therefore, the application of this method will be beneficial to design the FSM.

Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

PubMed Central

Zhao, Xin; Cheung, Leo Wang-Kit

2007-01-01

Background Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily. Furthermore, linear model based algorithms often involve calculating the inverse of a matrix that is possibly singular when the number of potentially important genes is relatively large. This leads to problems of numerical instability. To overcome these limitations, a few non-linear methods have recently been introduced to the area. Many of the existing non-linear methods have a couple of critical problems, the model selection problem and the model parameter tuning problem, that remain unsolved or even untouched. In general, a unified framework that allows model parameters of both linear and non-linear models to be easily tuned is always preferred in real-world applications. Kernel-induced learning methods form a class of approaches that show promising potentials to achieve this goal. Results A hierarchical statistical model named kernel-imbedded Gaussian process (KIGP) is developed under a unified Bayesian framework for binary disease classification problems using microarray gene expression data. In particular, based on a probit regression setting, an adaptive algorithm with a cascading structure is designed to find the appropriate kernel, to discover the potentially significant genes, and to make the optimal class prediction accordingly. A Gibbs sampler is built as the core of the algorithm to make Bayesian inferences. Simulation studies showed that, even without any knowledge of the underlying generative model, the KIGP performed very close to the theoretical Bayesian bound not only in the case with a linear Bayesian classifier but also in the case with a very non-linear Bayesian classifier. This sheds light on its broader usability to microarray data analysis problems, especially to those that linear methods work awkwardly. The KIGP was also applied to four published microarray datasets, and the results showed that the KIGP performed better than or at least as well as any of the referred state-of-the-art methods did in all of these cases. Conclusion Mathematically built on the kernel-induced feature space concept under a Bayesian framework, the KIGP method presented in this paper provides a unified machine learning approach to explore both the linear and the possibly non-linear underlying relationship between the target features of a given binary disease classification problem and the related explanatory gene expression data. More importantly, it incorporates the model parameter tuning into the framework. The model selection problem is addressed in the form of selecting a proper kernel type. The KIGP method also gives Bayesian probabilistic predictions for disease classification. These properties and features are beneficial to most real-world applications. The algorithm is naturally robust in numerical computation. The simulation studies and the published data studies demonstrated that the proposed KIGP performs satisfactorily and consistently. PMID:17328811

Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.

Continuum kinetic methods for analyzing wave physics and distribution function dynamics in the turbulence dissipation challenge

NASA Astrophysics Data System (ADS)

Juno, J.; Hakim, A.; TenBarge, J.; Dorland, W.

2015-12-01

We present for the first time results for the turbulence dissipation challenge, with specific focus on the linear wave portion of the challenge, using a variety of continuum kinetic models: hybrid Vlasov-Maxwell, gyrokinetic, and full Vlasov-Maxwell. As one of the goals of the wave problem as it is outlined is to identify how well various models capture linear physics, we compare our results to linear Vlasov and gyrokinetic theory. Preliminary gyrokinetic results match linear theory extremely well due to the geometry of the problem, which eliminates the dominant nonlinearity. With the non-reduced models, we explore how the subdominant nonlinearities manifest and affect the evolution of the turbulence and the energy budget. We also take advantage of employing continuum methods to study the dynamics of the distribution function, with particular emphasis on the full Vlasov results where a basic collision operator has been implemented. As the community prepares for the next stage of the turbulence dissipation challenge, where we hope to do large 3D simulations to inform the next generation of observational missions such as THOR (Turbulence Heating ObserveR), we argue for the consideration of hybrid Vlasov and full Vlasov as candidate models for these critical simulations. With the use of modern numerical algorithms, we demonstrate the competitiveness of our code with traditional particle-in-cell algorithms, with a clear plan for continued improvements and optimizations to further strengthen the code's viability as an option for the next stage of the challenge.

Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model

PubMed Central

Seaman, Shaun R; White, Ian R; Carpenter, James R

2015-01-01

Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation. Imputation of partially observed covariates is complicated if the substantive model is non-linear (e.g. Cox proportional hazards model), or contains non-linear (e.g. squared) or interaction terms, and standard software implementations of multiple imputation may impute covariates from models that are incompatible with such substantive models. We show how imputation by fully conditional specification, a popular approach for performing multiple imputation, can be modified so that covariates are imputed from models which are compatible with the substantive model. We investigate through simulation the performance of this proposal, and compare it with existing approaches. Simulation results suggest our proposal gives consistent estimates for a range of common substantive models, including models which contain non-linear covariate effects or interactions, provided data are missing at random and the assumed imputation models are correctly specified and mutually compatible. Stata software implementing the approach is freely available. PMID:24525487

Bayesian Correction for Misclassification in Multilevel Count Data Models.

PubMed

Nelson, Tyler; Song, Joon Jin; Chin, Yoo-Mi; Stamey, James D

2018-01-01

Covariate misclassification is well known to yield biased estimates in single level regression models. The impact on hierarchical count models has been less studied. A fully Bayesian approach to modeling both the misclassified covariate and the hierarchical response is proposed. Models with a single diagnostic test and with multiple diagnostic tests are considered. Simulation studies show the ability of the proposed model to appropriately account for the misclassification by reducing bias and improving performance of interval estimators. A real data example further demonstrated the consequences of ignoring the misclassification. Ignoring misclassification yielded a model that indicated there was a significant, positive impact on the number of children of females who observed spousal abuse between their parents. When the misclassification was accounted for, the relationship switched to negative, but not significant. Ignoring misclassification in standard linear and generalized linear models is well known to lead to biased results. We provide an approach to extend misclassification modeling to the important area of hierarchical generalized linear models.

Quantifying the sensitivity of feedstock properties and process conditions on hydrochar yield, carbon content, and energy content.

PubMed

Li, Liang; Wang, Yiying; Xu, Jiting; Flora, Joseph R V; Hoque, Shamia; Berge, Nicole D

2018-08-01

Hydrothermal carbonization (HTC) is a wet, low temperature thermal conversion process that continues to gain attention for the generation of hydrochar. The importance of specific process conditions and feedstock properties on hydrochar characteristics is not well understood. To evaluate this, linear and non-linear models were developed to describe hydrochar characteristics based on data collected from HTC-related literature. A Sobol analysis was subsequently conducted to identify parameters that most influence hydrochar characteristics. Results from this analysis indicate that for each investigated hydrochar property, the model fit and predictive capability associated with the random forest models is superior to both the linear and regression tree models. Based on results from the Sobol analysis, the feedstock properties and process conditions most influential on hydrochar yield, carbon content, and energy content were identified. In addition, a variational process parameter sensitivity analysis was conducted to determine how feedstock property importance changes with process conditions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Cell kill by megavoltage protons with high LET.

PubMed

Kuperman, Vadim Y

2016-07-21

The aim of the current study is to develop a radiobiological model which describes the effect of linear energy transfer (LET) on cell survival and relative biological effectiveness (RBE) of megavoltage protons. By assuming the existence of critical sites within a cell, analytical expression for cell survival S as a function of LET is derived. The obtained results indicate that in cases where dose per fraction is small, [Formula: see text] is a linear-quadratic (LQ) function of dose while both alpha and beta radio-sensitivities are non-linearly dependent on LET. In particular, in the current model alpha increases with increasing LET while beta decreases. Conversely, in the case of large dose per fraction, the LQ dependence of [Formula: see text] on dose is invalid. The proposed radiobiological model predicts cell survival probability and RBE which, in general, deviate from the results obtained by using conventional LQ formalism. The differences between the LQ model and that described in the current study are reflected in the calculated RBE of protons.

Vibration characteristics of the HPOTP (High-Pressure Oxygen Turbopump) of the SSME (Space Shuttle Main Engine)

NASA Technical Reports Server (NTRS)

Childs, D. W.; Moyer, D. S.

1984-01-01

Attention is given to rotor dynamic problems that have been encountered and eliminated in the course of Space Shuttle Main Engine (SSME) development, as well as continuing, subsynchronous problems which are being encountered in the development of a 109-percent power level engine. The basic model for the SSME's High Pressure Oxygen Turbopump (HPOTP) encompasses a structural dynamic model for the rotor and housing, and component models for the liquid and gas seals, turbine clearance excitation forces, and impeller diffuser forces. Linear model results are used to examine the synchronous response and stability characteristics of the HPOTP, with attention to bearing load and stability problems associated with the second critical speed. Differences between linear and nonlinear model results are discussed and explained in terms of simple models. Simulation results indicate that while synchronous bearing loads can be reduced, subsynchronous motion is not eliminated by seal modifications.

Lattice Boltzmann Equation On a 2D Rectangular Grid

NASA Technical Reports Server (NTRS)

Bouzidi, MHamed; DHumieres, Dominique; Lallemand, Pierre; Luo, Li-Shi; Bushnell, Dennis M. (Technical Monitor)

2002-01-01

We construct a multi-relaxation lattice Boltzmann model on a two-dimensional rectangular grid. The model is partly inspired by a previous work of Koelman to construct a lattice BGK model on a two-dimensional rectangular grid. The linearized dispersion equation is analyzed to obtain the constraints on the isotropy of the transport coefficients and Galilean invariance for various wave propagations in the model. The linear stability of the model is also studied. The model is numerically tested for three cases: (a) a vortex moving with a constant velocity on a mesh periodic boundary conditions; (b) Poiseuille flow with an arbitrasy inclined angle with respect to the lattice orientation: and (c) a cylinder &symmetrically placed in a channel. The numerical results of these tests are compared with either analytic solutions or the results obtained by other methods. Satisfactory results are obtained for the numerical simulations.

TWO-STAGE FRAGMENTATION FOR CLUSTER FORMATION: ANALYTICAL MODEL AND OBSERVATIONAL CONSIDERATIONS

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

Bailey, Nicole D.; Basu, Shantanu, E-mail: nwityk@uwo.ca, E-mail: basu@uwo.ca

2012-12-10

Linear analysis of the formation of protostellar cores in planar magnetic interstellar clouds shows that molecular clouds exhibit a preferred length scale for collapse that depends on the mass-to-flux ratio and neutral-ion collision time within the cloud. We extend this linear analysis to the context of clustered star formation. By combining the results of the linear analysis with a realistic ionization profile for the cloud, we find that a molecular cloud may evolve through two fragmentation events in the evolution toward the formation of stars. Our model suggests that the initial fragmentation into clumps occurs for a transcritical cloud onmore » parsec scales while the second fragmentation can occur for transcritical and supercritical cores on subparsec scales. Comparison of our results with several star-forming regions (Perseus, Taurus, Pipe Nebula) shows support for a two-stage fragmentation model.« less

Simplified model to describe the dissociative recombination of linear polyatomic ions of astrophysical interest

NASA Astrophysics Data System (ADS)

Fonseca Dos Santos, Samantha; Douguet, Nicolas; Kokoouline, Viatcheslav; Orel, Ann

2013-05-01

We will present theoretical results on the dissociative recombination (DR) of the linear polyatomic ions HCNH+, HCO+ and N2H+. Besides their astrophysical importance, they also share the characteristic that at low electronic impact energies their DR process happens via the indirect DR mechanism. We apply a general simplified model successfully implemented to treat the DR process of the highly symmetric non-linear molecules H3+, CH3+, H3O+ and NH4+ to calculated cross sections and DR rates for these ions. The model is based on multichannel quantum defect theory and accounts for all the main ingredients of indirect DR. New perspectives on dissociative recombination of HCO+ will also be discussed, including the possible role of HOC+ in storage ring experimental results. This work is supported by the DOE Office of Basic Energy Science and the National Science Foundation, Grant No's PHY-11-60611 and PHY-10-68785.

A Three-Dimensional Linearized Unsteady Euler Analysis for Turbomachinery Blade Rows

NASA Technical Reports Server (NTRS)

Montgomery, Matthew D.; Verdon, Joseph M.

1997-01-01

A three-dimensional, linearized, Euler analysis is being developed to provide an efficient unsteady aerodynamic analysis that can be used to predict the aeroelastic and aeroacoustic responses of axial-flow turbo-machinery blading.The field equations and boundary conditions needed to describe nonlinear and linearized inviscid unsteady flows through a blade row operating within a cylindrical annular duct are presented. A numerical model for linearized inviscid unsteady flows, which couples a near-field, implicit, wave-split, finite volume analysis to a far-field eigenanalysis, is also described. The linearized aerodynamic and numerical models have been implemented into a three-dimensional linearized unsteady flow code, called LINFLUX. This code has been applied to selected, benchmark, unsteady, subsonic flows to establish its accuracy and to demonstrate its current capabilities. The unsteady flows considered, have been chosen to allow convenient comparisons between the LINFLUX results and those of well-known, two-dimensional, unsteady flow codes. Detailed numerical results for a helical fan and a three-dimensional version of the 10th Standard Cascade indicate that important progress has been made towards the development of a reliable and useful, three-dimensional, prediction capability that can be used in aeroelastic and aeroacoustic design studies.

Linear control of a boiler-turbine unit: analysis and design.

PubMed

Tan, Wen; Fang, Fang; Tian, Liang; Fu, Caifen; Liu, Jizhen

2008-04-01

Linear control of a boiler-turbine unit is discussed in this paper. Based on the nonlinear model of the unit, this paper analyzes the nonlinearity of the unit, and selects the appropriate operating points so that the linear controller can achieve wide-range performance. Simulation and experimental results at the No. 4 Unit at the Dalate Power Plant show that the linear controller can achieve the desired performance under a specific range of load variations.

Determination of Semivariogram Models to Krige Hourly and Daily Solar Irradiance in Western Nebraska(.

NASA Astrophysics Data System (ADS)

Merino, G. G.; Jones, D.; Stooksbury, D. E.; Hubbard, K. G.

2001-06-01

In this paper, linear and spherical semivariogram models were determined for use in kriging hourly and daily solar irradiation for every season of the year. The data used to generate the models were from 18 weather stations in western Nebraska. The models generated were tested using cross validation. The performance of the spherical and linear semivariogram models were compared with each other and also with the semivariogram models based on the best fit to the sample semivariogram of a particular day or hour. There were no significant differences in the performance of the three models. This result and the comparable errors produced by the models in kriging indicated that the linear and spherical models could be used to perform kriging at any hour and day of the year without deriving an individual semivariogram model for that day or hour.The seasonal mean absolute errors associated with kriging, within the network, when using the spherical or the linear semivariograms models were between 10% and 13% of the mean irradiation for daily irradiation and between 12% and 20% for hourly irradiation. These errors represent an improvement of 1%-2% when compared with replacing data at a given site with the data of the nearest weather station.

[Retrieval of crown closure of moso bamboo forest using unmanned aerial vehicle (UAV) remotely sensed imagery based on geometric-optical model].

PubMed

Wang, Cong; Du, Hua-qiang; Zhou, Guo-mo; Xu, Xiao-jun; Sun, Shao-bo; Gao, Guo-long

2015-05-01

This research focused on the application of remotely sensed imagery from unmanned aerial vehicle (UAV) with high spatial resolution for the estimation of crown closure of moso bamboo forest based on the geometric-optical model, and analyzed the influence of unconstrained and fully constrained linear spectral mixture analysis (SMA) on the accuracy of the estimated results. The results demonstrated that the combination of UAV remotely sensed imagery and geometric-optical model could, to some degrees, achieve the estimation of crown closure. However, the different SMA methods led to significant differentiation in the estimation accuracy. Compared with unconstrained SMA, the fully constrained linear SMA method resulted in higher accuracy of the estimated values, with the coefficient of determination (R2) of 0.63 at 0.01 level, against the measured values acquired during the field survey. Root mean square error (RMSE) of approximate 0.04 was low, indicating that the usage of fully constrained linear SMA could bring about better results in crown closure estimation, which was closer to the actual condition in moso bamboo forest.

A Comprehensive Meta-Analysis of Triple P-Positive Parenting Program Using Hierarchical Linear Modeling: Effectiveness and Moderating Variables

ERIC Educational Resources Information Center

Nowak, Christoph; Heinrichs, Nina

2008-01-01

A meta-analysis encompassing all studies evaluating the impact of the Triple P-Positive Parenting Program on parent and child outcome measures was conducted in an effort to identify variables that moderate the program's effectiveness. Hierarchical linear models (HLM) with three levels of data were employed to analyze effect sizes. The results (N =…

Improving the Power of GWAS and Avoiding Confounding from Population Stratification with PC-Select

PubMed Central

Tucker, George; Price, Alkes L.; Berger, Bonnie

2014-01-01

Using a reduced subset of SNPs in a linear mixed model can improve power for genome-wide association studies, yet this can result in insufficient correction for population stratification. We propose a hybrid approach using principal components that does not inflate statistics in the presence of population stratification and improves power over standard linear mixed models. PMID:24788602

Monte Carlo simulation of star/linear and star/star blends with chemically identical monomers

NASA Astrophysics Data System (ADS)

Theodorakis, P. E.; Avgeropoulos, A.; Freire, J. J.; Kosmas, M.; Vlahos, C.

2007-11-01

The effects of chain size and architectural asymmetry on the miscibility of blends with chemically identical monomers, differing only in their molecular weight and architecture, are studied via Monte Carlo simulation by using the bond fluctuation model. Namely, we consider blends composed of linear/linear, star/linear and star/star chains. We found that linear/linear blends are more miscible than the corresponding star/star mixtures. In star/linear blends, the increase in the volume fraction of the star chains increases the miscibility. For both star/linear and star/star blends, the miscibility decreases with the increase in star functionality. When we increase the molecular weight of linear chains of star/linear mixtures the miscibility decreases. Our findings are compared with recent analytical and experimental results.

Development of a Linearized Unsteady Euler Analysis with Application to Wake/Blade-Row Interactions

NASA Technical Reports Server (NTRS)

Verdon, Joseph M.; Montgomery, Matthew D.; Chuang, H. Andrew

1999-01-01

A three-dimensional, linearized, Euler analysis is being developed to provide a comprehensive and efficient unsteady aerodynamic analysis for predicting the aeroacoustic and aeroelastic responses of axial-flow turbomachinery blading. The mathematical models needed to describe nonlinear and linearized, inviscid, unsteady flows through a blade row operating within a cylindrical annular duct are presented in this report. A numerical model for linearized inviscid unsteady flows, which couples a near-field, implicit, wave-split, finite volume analysis to far-field eigen analyses, is also described. The linearized aerodynamic and numerical models have been implemented into the three-dimensional unsteady flow code, LINFLUX. This code is applied herein to predict unsteady subsonic flows driven by wake or vortical excitations. The intent is to validate the LINFLUX analysis via numerical results for simple benchmark unsteady flows and to demonstrate this analysis via application to a realistic wake/blade-row interaction. Detailed numerical results for a three-dimensional version of the 10th Standard Cascade and a fan exit guide vane indicate that LINFLUX is becoming a reliable and useful unsteady aerodynamic prediction capability that can be applied, in the future, to assess the three-dimensional flow physics important to blade-row, aeroacoustic and aeroelastic responses.

Design and Operation of a 4kW Linear Motor Driven Pulse Tube Cryocooler

NASA Astrophysics Data System (ADS)

Zia, J. H.

2004-06-01

A 4 kW electrical input Linear Motor driven pulse tube cryocooler has successfully been designed, built and tested. The optimum operation frequency is 60 Hz with a design refrigeration of >200 W at 80 K. The design exercise involved modeling and optimization in DeltaE software. Load matching between the cold head and linear motor was achieved by careful sizing of the transfer tube. The cryocooler makes use of a dual orifice inertance network and a single compliance tank for phase optimization and streaming suppression in the pulse tube. The in-line cold head design is modular in structure for convenient change-out and re-assembly of various components. The Regenerator consists of layers of two different grades of wire-mesh. The Linear motor is a clearance seal, dual opposed piston design from CFIC Inc. Initial results have demonstrated the refrigeration target of 200 W by liquefying Nitrogen from an ambient temperature and pressure. Overall Carnot efficiencies of 13% have been achieved and efforts to further improve efficiencies are underway. Linear motor efficiencies up to 84% have been observed. Experimental results have shown satisfactory compliance with model predictions, although the effects of streaming were not part of the model. Refrigeration loss due to streaming was minimal at the design operating conditions of 80 K.

YORP torques with 1D thermal model

NASA Astrophysics Data System (ADS)

Breiter, S.; Bartczak, P.; Czekaj, M.

2010-11-01

A numerical model of the Yarkovsky-O'Keefe-Radzievskii-Paddack (YORP) effect for objects defined in terms of a triangular mesh is described. The algorithm requires that each surface triangle can be handled independently, which implies the use of a 1D thermal model. Insolation of each triangle is determined by an optimized ray-triangle intersection search. Surface temperature is modelled with a spectral approach; imposing a quasi-periodic solution we replace heat conduction equation by the Helmholtz equation. Non-linear boundary conditions are handled by an iterative, fast Fourier transform based solver. The results resolve the question of the YORP effect in rotation rate independence on conductivity within the non-linear 1D thermal model regardless of the accuracy issues and homogeneity assumptions. A seasonal YORP effect in attitude is revealed for objects moving on elliptic orbits when a non-linear thermal model is used.

Dynamics of attitudes and genetic processes.

PubMed

Guastello, Stephen J; Guastello, Denise D

2008-01-01

Relatively new discoveries of a genetic component to attitudes have challenged the traditional viewpoint that attitudes are primarily learned ideas and behaviors. Attitudes that are regarded by respondents as "more important" tend to have greater genetic components to them, and tend to be more closely associated with authoritarianism. Nonlinear theories, nonetheless, have also been introduced to study attitude change. The objective of this study was to determine whether change in authoritarian attitudes across two generations would be more aptly described by a linear or a nonlinear model. Participants were 372 college students, their mothers, and their fathers who completed an attitude questionnaire. Results indicated that the nonlinear model (R2 = .09) was slightly better than the linear model (R2 = .08), but the two models offered very different forecasts for future generations of US society. The linear model projected a gradual and continuing bifurcation between authoritarians and non-authoritarians. The nonlinear model projected a stabilization of authoritarian attitudes.

State variable modeling of the integrated engine and aircraft dynamics

NASA Astrophysics Data System (ADS)

Rotaru, Constantin; Sprinţu, Iuliana

2014-12-01

This study explores the dynamic characteristics of the combined aircraft-engine system, based on the general theory of the state variables for linear and nonlinear systems, with details leading first to the separate formulation of the longitudinal and the lateral directional state variable models, followed by the merging of the aircraft and engine models into a single state variable model. The linearized equations were expressed in a matrix form and the engine dynamics was included in terms of variation of thrust following a deflection of the throttle. The linear model of the shaft dynamics for a two-spool jet engine was derived by extending the one-spool model. The results include the discussion of the thrust effect upon the aircraft response when the thrust force associated with the engine has a sizable moment arm with respect to the aircraft center of gravity for creating a compensating moment.

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

Shang, Yu; Yu, Guoqiang, E-mail: guoqiang.yu@uky.edu

Conventional semi-infinite analytical solutions of correlation diffusion equation may lead to errors when calculating blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements in tissues with irregular geometries. Very recently, we created an algorithm integrating a Nth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in homogenous tissues with arbitrary geometries for extraction of BFI (i.e., αD{sub B}). The purpose of this study is to extend the capability of the Nth-order linear algorithm for extracting BFI in heterogeneous tissues with arbitrary geometries. The previous linear algorithm was modified to extract BFIs in different typesmore » of tissues simultaneously through utilizing DCS data at multiple source-detector separations. We compared the proposed linear algorithm with the semi-infinite homogenous solution in a computer model of adult head with heterogeneous tissue layers of scalp, skull, cerebrospinal fluid, and brain. To test the capability of the linear algorithm for extracting relative changes of cerebral blood flow (rCBF) in deep brain, we assigned ten levels of αD{sub B} in the brain layer with a step decrement of 10% while maintaining αD{sub B} values constant in other layers. Simulation results demonstrate the accuracy (errors < 3%) of high-order (N ≥ 5) linear algorithm in extracting BFIs in different tissue layers and rCBF in deep brain. By contrast, the semi-infinite homogenous solution resulted in substantial errors in rCBF (34.5% ≤ errors ≤ 60.2%) and BFIs in different layers. The Nth-order linear model simplifies data analysis, thus allowing for online data processing and displaying. Future study will test this linear algorithm in heterogeneous tissues with different levels of blood flow variations and noises.« less

Genetic programming over context-free languages with linear constraints for the knapsack problem: first results.

PubMed

Bruhn, Peter; Geyer-Schulz, Andreas

2002-01-01

In this paper, we introduce genetic programming over context-free languages with linear constraints for combinatorial optimization, apply this method to several variants of the multidimensional knapsack problem, and discuss its performance relative to Michalewicz's genetic algorithm with penalty functions. With respect to Michalewicz's approach, we demonstrate that genetic programming over context-free languages with linear constraints improves convergence. A final result is that genetic programming over context-free languages with linear constraints is ideally suited to modeling complementarities between items in a knapsack problem: The more complementarities in the problem, the stronger the performance in comparison to its competitors.

A multichain polymer slip-spring model with fluctuating number of entanglements for linear and nonlinear rheology

DOE PAGES

Ramírez-Hernández, Abelardo; Peters, Brandon L.; Andreev, Marat; ...

2015-12-15

A theoretically informed entangled polymer simulation approach is presented for description of the linear and non-linear rheology of entangled polymer melts. The approach relies on a many-chain representation and introduces the topological effects that arise from the non-crossability of molecules through effective fluctuating interactions, mediated by slip-springs, between neighboring pairs of macromolecules. The total number of slip-springs is not preserved but, instead, it is controlled through a chemical potential that determines the average molecular weight between entanglements. The behavior of the model is discussed in the context of a recent theory for description of homogeneous materials, and its relevance ismore » established by comparing its predictions to experimental linear and non-linear rheology data for a series of well-characterized linear polyisoprene melts. Furthermore, the results are shown to be in quantitative agreement with experiment and suggest that the proposed formalism may also be used to describe the dynamics of inhomogeneous systems, such as composites and copolymers. Importantly, the fundamental connection made here between our many-chain model and the well-established, thermodynamically consistent single-chain mean-field models provides a path to systematic coarse-graining for prediction of polymer rheology in structurally homogeneous and heterogeneous materials.« less

Effects of non-tidal atmospheric loading on a Kalman filter-based terrestrial reference frame

NASA Astrophysics Data System (ADS)

Abbondanza, C.; Altamimi, Z.; Chin, T. M.; Collilieux, X.; Dach, R.; Heflin, M. B.; Gross, R. S.; König, R.; Lemoine, F. G.; MacMillan, D. S.; Parker, J. W.; van Dam, T. M.; Wu, X.

2013-12-01

The International Terrestrial Reference Frame (ITRF) adopts a piece-wise linear model to parameterize regularized station positions and velocities. The space-geodetic (SG) solutions from VLBI, SLR, GPS and DORIS global networks used as input in the ITRF combination process account for tidal loading deformations, but ignore the non-tidal part. As a result, the non-linear signal observed in the time series of SG-derived station positions in part reflects non-tidal loading displacements not introduced in the SG data reduction. In this analysis, the effect of non-tidal atmospheric loading (NTAL) corrections on the TRF is assessed adopting a Remove/Restore approach: (i) Focusing on the a-posteriori approach, the NTAL model derived from the National Center for Environmental Prediction (NCEP) surface pressure is removed from the SINEX files of the SG solutions used as inputs to the TRF determinations. (ii) Adopting a Kalman-filter based approach, a linear TRF is estimated combining the 4 SG solutions free from NTAL displacements. (iii) Linear fits to the NTAL displacements removed at step (i) are restored to the linear reference frame estimated at (ii). The velocity fields of the (standard) linear reference frame in which the NTAL model has not been removed and the one in which the model has been removed/restored are compared and discussed.

Energy-balance climate models

NASA Technical Reports Server (NTRS)

North, G. R.; Cahalan, R. F.; Coakley, J. A., Jr.

1980-01-01

An introductory survey of the global energy balance climate models is presented with an emphasis on analytical results. A sequence of increasingly complicated models involving ice cap and radiative feedback processes are solved and the solutions and parameter sensitivities are studied. The model parameterizations are examined critically in light of many current uncertainties. A simple seasonal model is used to study the effects of changes in orbital elements on the temperature field. A linear stability theorem and a complete nonlinear stability analysis for the models are developed. Analytical solutions are also obtained for the linearized models driven by stochastic forcing elements. In this context the relation between natural fluctuation statistics and climate sensitivity is stressed.

Energy balance climate models

NASA Technical Reports Server (NTRS)

North, G. R.; Cahalan, R. F.; Coakley, J. A., Jr.

1981-01-01

An introductory survey of the global energy balance climate models is presented with an emphasis on analytical results. A sequence of increasingly complicated models involving ice cap and radiative feedback processes are solved, and the solutions and parameter sensitivities are studied. The model parameterizations are examined critically in light of many current uncertainties. A simple seasonal model is used to study the effects of changes in orbital elements on the temperature field. A linear stability theorem and a complete nonlinear stability analysis for the models are developed. Analytical solutions are also obtained for the linearized models driven by stochastic forcing elements. In this context the relation between natural fluctuation statistics and climate sensitivity is stressed.

Modeling the effects of AADT on predicting multiple-vehicle crashes at urban and suburban signalized intersections.

PubMed

Chen, Chen; Xie, Yuanchang

2016-06-01

Annual Average Daily Traffic (AADT) is often considered as a main covariate for predicting crash frequencies at urban and suburban intersections. A linear functional form is typically assumed for the Safety Performance Function (SPF) to describe the relationship between the natural logarithm of expected crash frequency and covariates derived from AADTs. Such a linearity assumption has been questioned by many researchers. This study applies Generalized Additive Models (GAMs) and Piecewise Linear Negative Binomial (PLNB) regression models to fit intersection crash data. Various covariates derived from minor-and major-approach AADTs are considered. Three different dependent variables are modeled, which are total multiple-vehicle crashes, rear-end crashes, and angle crashes. The modeling results suggest that a nonlinear functional form may be more appropriate. Also, the results show that it is important to take into consideration the joint safety effects of multiple covariates. Additionally, it is found that the ratio of minor to major-approach AADT has a varying impact on intersection safety and deserves further investigations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Spike-train spectra and network response functions for non-linear integrate-and-fire neurons.

PubMed

Richardson, Magnus J E

2008-11-01

Reduced models have long been used as a tool for the analysis of the complex activity taking place in neurons and their coupled networks. Recent advances in experimental and theoretical techniques have further demonstrated the usefulness of this approach. Despite the often gross simplification of the underlying biophysical properties, reduced models can still present significant difficulties in their analysis, with the majority of exact and perturbative results available only for the leaky integrate-and-fire model. Here an elementary numerical scheme is demonstrated which can be used to calculate a number of biologically important properties of the general class of non-linear integrate-and-fire models. Exact results for the first-passage-time density and spike-train spectrum are derived, as well as the linear response properties and emergent states of recurrent networks. Given that the exponential integrate-fire model has recently been shown to agree closely with the experimentally measured response of pyramidal cells, the methodology presented here promises to provide a convenient tool to facilitate the analysis of cortical-network dynamics.

An extended car-following model to describe connected traffic dynamics under cyberattacks

NASA Astrophysics Data System (ADS)

Wang, Pengcheng; Yu, Guizhen; Wu, Xinkai; Qin, Hongmao; Wang, Yunpeng

2018-04-01

In this paper, the impacts of the potential cyberattacks on vehicles are modeled through an extended car-following model. To better understand the mechanism of traffic disturbance under cyberattacks, the linear and nonlinear stability analysis are conducted respectively. Particularly, linear stability analysis is performed to obtain different neutral stability conditions with various parameters; and nonlinear stability analysis is carried out by using reductive perturbation method to derive the soliton solution of the modified Korteweg de Vries equation (mKdV) near the critical point, which is used to draw coexisting stability lines. Furthermore, by applying linear and nonlinear stability analysis, traffic flow state can be divided into three states, i.e., stable, metastable and unstable states which are useful to describe shockwave dynamics and driving behaviors under cyberattacks. The theoretical results show that the proposed car-following model is capable of successfully describing the car-following behavior of connected vehicles with cyberattacks. Finally, numerical simulation using real values has confirmed the validity of theoretical analysis. The results further demonstrate our model can be used to help avoid collisions and relieve traffic congestion with cybersecurity threats.

A High-Order, Time Invariant, Linearized Model for Application to HHCIAFCS Interaction Studies

NASA Technical Reports Server (NTRS)

Cheng, Rendy P.; Tischler, Mark B.; Celi, Roberto

2003-01-01

This paper describes a methodology for the extraction of a linear time invariant model from a nonlinear helicopter model, and followed by an examination of the interactions of the Higher Harmonic Control (HHC) and the Automatic Flight Control System (AFCS). This new method includes an embedded harmonic analyzer inside a linear time invariant model, which allows the periodicity of the helicopter response to be captured. The: coupled high-order model provides the needed level of dynamic fidelity to permit an analysis and optimization of the AFCS and HHC loops. Results of this study indicate that the closed-loop HHC system has little influence on the AFCS or on the vehicle handling qualities, which indicates that the AFCS does not need modification to work with the HHC system. The results also show that the vibration response to maneuvers must be considered during the HHC design process, which leads to much higher required HHC loop crossover frequencies. This research also demonstrates that the transient vibration response during maneuvers can be reduced by optimizing the closed-loop higher harmonic control laws using conventional control system analyses.

Integration of system identification and finite element modelling of nonlinear vibrating structures

NASA Astrophysics Data System (ADS)

Cooper, Samson B.; DiMaio, Dario; Ewins, David J.

2018-03-01

The Finite Element Method (FEM), Experimental modal analysis (EMA) and other linear analysis techniques have been established as reliable tools for the dynamic analysis of engineering structures. They are often used to provide solutions to small and large structures and other variety of cases in structural dynamics, even those exhibiting a certain degree of nonlinearity. Unfortunately, when the nonlinear effects are substantial or the accuracy of the predicted response is of vital importance, a linear finite element model will generally prove to be unsatisfactory. As a result, the validated linear FE model requires further enhancement so that it can represent and predict the nonlinear behaviour exhibited by the structure. In this paper, a pragmatic approach to integrating test-based system identification and FE modelling of a nonlinear structure is presented. This integration is based on three different phases: the first phase involves the derivation of an Underlying Linear Model (ULM) of the structure, the second phase includes experiment-based nonlinear identification using measured time series and the third phase covers augmenting the linear FE model and experimental validation of the nonlinear FE model. The proposed case study is demonstrated on a twin cantilever beam assembly coupled with a flexible arch shaped beam. In this case, polynomial-type nonlinearities are identified and validated with force-controlled stepped-sine test data at several excitation levels.

Consistency between hydrological models and field observations: Linking processes at the hillslope scale to hydrological responses at the watershed scale

USGS Publications Warehouse

Clark, M.P.; Rupp, D.E.; Woods, R.A.; Tromp-van, Meerveld; Peters, N.E.; Freer, J.E.

2009-01-01

The purpose of this paper is to identify simple connections between observations of hydrological processes at the hillslope scale and observations of the response of watersheds following rainfall, with a view to building a parsimonious model of catchment processes. The focus is on the well-studied Panola Mountain Research Watershed (PMRW), Georgia, USA. Recession analysis of discharge Q shows that while the relationship between dQ/dt and Q is approximately consistent with a linear reservoir for the hillslope, there is a deviation from linearity that becomes progressively larger with increasing spatial scale. To account for these scale differences conceptual models of streamflow recession are defined at both the hillslope scale and the watershed scale, and an assessment made as to whether models at the hillslope scale can be aggregated to be consistent with models at the watershed scale. Results from this study show that a model with parallel linear reservoirs provides the most plausible explanation (of those tested) for both the linear hillslope response to rainfall and non-linear recession behaviour observed at the watershed outlet. In this model each linear reservoir is associated with a landscape type. The parallel reservoir model is consistent with both geochemical analyses of hydrological flow paths and water balance estimates of bedrock recharge. Overall, this study demonstrates that standard approaches of using recession analysis to identify the functional form of storage-discharge relationships identify model structures that are inconsistent with field evidence, and that recession analysis at multiple spatial scales can provide useful insights into catchment behaviour. Copyright ?? 2008 John Wiley & Sons, Ltd.

Interpreting linear support vector machine models with heat map molecule coloring

PubMed Central

2011-01-01

Background Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. Results We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor. Conclusions In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor. PMID:21439031

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

PubMed

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

2013-07-01

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

The Effects of Q-Matrix Design on Classification Accuracy in the Log-Linear Cognitive Diagnosis Model.

PubMed

Madison, Matthew J; Bradshaw, Laine P

2015-06-01

Diagnostic classification models are psychometric models that aim to classify examinees according to their mastery or non-mastery of specified latent characteristics. These models are well-suited for providing diagnostic feedback on educational assessments because of their practical efficiency and increased reliability when compared with other multidimensional measurement models. A priori specifications of which latent characteristics or attributes are measured by each item are a core element of the diagnostic assessment design. This item-attribute alignment, expressed in a Q-matrix, precedes and supports any inference resulting from the application of the diagnostic classification model. This study investigates the effects of Q-matrix design on classification accuracy for the log-linear cognitive diagnosis model. Results indicate that classification accuracy, reliability, and convergence rates improve when the Q-matrix contains isolated information from each measured attribute.

Linear velocity fields in non-Gaussian models for large-scale structure

NASA Technical Reports Server (NTRS)

Scherrer, Robert J.

1992-01-01

Linear velocity fields in two types of physically motivated non-Gaussian models are examined for large-scale structure: seed models, in which the density field is a convolution of a density profile with a distribution of points, and local non-Gaussian fields, derived from a local nonlinear transformation on a Gaussian field. The distribution of a single component of the velocity is derived for seed models with randomly distributed seeds, and these results are applied to the seeded hot dark matter model and the global texture model with cold dark matter. An expression for the distribution of a single component of the velocity in arbitrary local non-Gaussian models is given, and these results are applied to such fields with chi-squared and lognormal distributions. It is shown that all seed models with randomly distributed seeds and all local non-Guassian models have single-component velocity distributions with positive kurtosis.

The artificial neural network modelling of the piezoelectric actuator vibrations using laser displacement sensor

NASA Astrophysics Data System (ADS)

Paralı, Levent; Sarı, Ali; Kılıç, Ulaş; Şahin, Özge; Pěchoušek, Jiří

2017-09-01

We report an improvement of the artificial neural network (ANN) modelling of a piezoelectric actuator vibration based on the experimental data. The controlled vibrations of an actuator were obtained by utilizing the swept-sine signal excitation. The peak value in the displacement signal response was measured by a laser displacement sensor. The piezoelectric actuator was modelled in both linear and nonlinear operating range. A consistency from 90.3 up to 98.9% of ANN modelled output values and experimental ones was reached. The obtained results clearly demonstrate exact linear relationship between the ANN model and experimental values.

Physics at the e⁺e⁻ linear collider

DOE PAGES

Moortgat-Picka, G.; Kronfeld, A. S.

2015-08-14

A comprehensive review of physics at an e⁺e⁻ linear collider in the energy range of √s = 92 GeV–3 TeV is presented in view of recent and expected LHC results, experiments from low-energy as well as astroparticle physics. The report focuses in particular on Higgs-boson, top-quark and electroweak precision physics, but also discusses several models of beyond the standard model physics such as supersymmetry, little Higgs models and extra gauge bosons. The connection to cosmology has been analysed as well.

Linear viscoelasticity of a single semiflexible polymer with internal friction.

PubMed

Hiraiwa, Tetsuya; Ohta, Takao

2010-07-28

The linear viscoelastic behaviors of single semiflexible chains with internal friction are studied based on the wormlike-chain model. It is shown that the frequency dependence of the complex compliance in the high frequency limit is the same as that of the Voigt model. This asymptotic behavior appears also for the Rouse model with internal friction. We derive the characteristic times for both the high frequency limit and the low frequency limit and compare the results with those obtained by Khatri et al.

Soft tissue modelling through autowaves for surgery simulation.

PubMed

Zhong, Yongmin; Shirinzadeh, Bijan; Alici, Gursel; Smith, Julian

2006-09-01

Modelling of soft tissue deformation is of great importance to virtual reality based surgery simulation. This paper presents a new methodology for simulation of soft tissue deformation by drawing an analogy between autowaves and soft tissue deformation. The potential energy stored in a soft tissue as a result of a deformation caused by an external force is propagated among mass points of the soft tissue by non-linear autowaves. The novelty of the methodology is that (i) autowave techniques are established to describe the potential energy distribution of a deformation for extrapolating internal forces, and (ii) non-linear materials are modelled with non-linear autowaves other than geometric non-linearity. Integration with a haptic device has been achieved to simulate soft tissue deformation with force feedback. The proposed methodology not only deals with large-range deformations, but also accommodates isotropic, anisotropic and inhomogeneous materials by simply changing diffusion coefficients.

Stochastic modeling of mode interactions via linear parabolized stability equations

NASA Astrophysics Data System (ADS)

Ran, Wei; Zare, Armin; Hack, M. J. Philipp; Jovanovic, Mihailo

2017-11-01

Low-complexity approximations of the Navier-Stokes equations have been widely used in the analysis of wall-bounded shear flows. In particular, the parabolized stability equations (PSE) and Floquet theory have been employed to capture the evolution of primary and secondary instabilities in spatially-evolving flows. We augment linear PSE with Floquet analysis to formally treat modal interactions and the evolution of secondary instabilities in the transitional boundary layer via a linear progression. To this end, we leverage Floquet theory by incorporating the primary instability into the base flow and accounting for different harmonics in the flow state. A stochastic forcing is introduced into the resulting linear dynamics to model the effect of nonlinear interactions on the evolution of modes. We examine the H-type transition scenario to demonstrate how our approach can be used to model nonlinear effects and capture the growth of the fundamental and subharmonic modes observed in direct numerical simulations and experiments.

Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data.

PubMed

Montoye, Alexander H K; Begum, Munni; Henning, Zachary; Pfeiffer, Karin A

2017-02-01

This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r  =  0.71-0.88, RMSE: 1.11-1.61 METs; p  >  0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN-correlations: r  =  0.89, RMSE: 1.07-1.08 METs. Linear models-correlations: r  =  0.88, RMSE: 1.10-1.11 METs; p  >  0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN-correlation: r  =  0.88, RMSE: 1.12 METs. Linear models-correlations: r  =  0.86, RMSE: 1.18-1.19 METs; p  <  0.05), and both ANNs had higher correlations and lower RMSE than both linear models for the wrist-worn accelerometers (ANN-correlations: r  =  0.82-0.84, RMSE: 1.26-1.32 METs. Linear models-correlations: r  =  0.71-0.73, RMSE: 1.55-1.61 METs; p  <  0.01). For studies using wrist-worn accelerometers, machine learning models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh-worn accelerometers and may be viable alternative modeling techniques for EE prediction for hip- or thigh-worn accelerometers.

Estimation of suspended-sediment rating curves and mean suspended-sediment loads

USGS Publications Warehouse

Crawford, Charles G.

1991-01-01

A simulation study was done to evaluate: (1) the accuracy and precision of parameter estimates for the bias-corrected, transformed-linear and non-linear models obtained by the method of least squares; (2) the accuracy of mean suspended-sediment loads calculated by the flow-duration, rating-curve method using model parameters obtained by the alternative methods. Parameter estimates obtained by least squares for the bias-corrected, transformed-linear model were considerably more precise than those obtained for the non-linear or weighted non-linear model. The accuracy of parameter estimates obtained for the biascorrected, transformed-linear and weighted non-linear model was similar and was much greater than the accuracy obtained by non-linear least squares. The improved parameter estimates obtained by the biascorrected, transformed-linear or weighted non-linear model yield estimates of mean suspended-sediment load calculated by the flow-duration, rating-curve method that are more accurate and precise than those obtained for the non-linear model.

On Two-Dimensional ARMA Models for Image Analysis.

DTIC Science & Technology

1980-03-24

2-D ARMA models for image analysis . Particular emphasis is placed on restoration of noisy images using 2-D ARMA models. Computer results are...is concluded that the models are very effective linear models for image analysis . (Author)

Hybrid Reynolds-Averaged/Large Eddy Simulation of a Cavity Flameholder; Assessment of Modeling Sensitivities

NASA Technical Reports Server (NTRS)

Baurle, R. A.

2015-01-01

Steady-state and scale-resolving simulations have been performed for flow in and around a model scramjet combustor flameholder. The cases simulated corresponded to those used to examine this flowfield experimentally using particle image velocimetry. A variety of turbulence models were used for the steady-state Reynolds-averaged simulations which included both linear and non-linear eddy viscosity models. The scale-resolving simulations used a hybrid Reynolds-averaged / large eddy simulation strategy that is designed to be a large eddy simulation everywhere except in the inner portion (log layer and below) of the boundary layer. Hence, this formulation can be regarded as a wall-modeled large eddy simulation. This effort was undertaken to formally assess the performance of the hybrid Reynolds-averaged / large eddy simulation modeling approach in a flowfield of interest to the scramjet research community. The numerical errors were quantified for both the steady-state and scale-resolving simulations prior to making any claims of predictive accuracy relative to the measurements. The steady-state Reynolds-averaged results showed a high degree of variability when comparing the predictions obtained from each turbulence model, with the non-linear eddy viscosity model (an explicit algebraic stress model) providing the most accurate prediction of the measured values. The hybrid Reynolds-averaged/large eddy simulation results were carefully scrutinized to ensure that even the coarsest grid had an acceptable level of resolution for large eddy simulation, and that the time-averaged statistics were acceptably accurate. The autocorrelation and its Fourier transform were the primary tools used for this assessment. The statistics extracted from the hybrid simulation strategy proved to be more accurate than the Reynolds-averaged results obtained using the linear eddy viscosity models. However, there was no predictive improvement noted over the results obtained from the explicit Reynolds stress model. Fortunately, the numerical error assessment at most of the axial stations used to compare with measurements clearly indicated that the scale-resolving simulations were improving (i.e. approaching the measured values) as the grid was refined. Hence, unlike a Reynolds-averaged simulation, the hybrid approach provides a mechanism to the end-user for reducing model-form errors.

Fast radiative transfer models for retrieval of cloud properties in the back-scattering region: application to DSCOVR-EPIC sensor

NASA Astrophysics Data System (ADS)

Molina Garcia, Victor; Sasi, Sruthy; Efremenko, Dmitry; Doicu, Adrian; Loyola, Diego

2017-04-01

In this work, the requirements for the retrieval of cloud properties in the back-scattering region are described, and their application to the measurements taken by the Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) is shown. Various radiative transfer models and their linearizations are implemented, and their advantages and issues are analyzed. As radiative transfer calculations in the back-scattering region are computationally time-consuming, several acceleration techniques are also studied. The radiative transfer models analyzed include the exact Discrete Ordinate method with Matrix Exponential (DOME), the Matrix Operator method with Matrix Exponential (MOME), and the approximate asymptotic and equivalent Lambertian cloud models. To reduce the computational cost of the line-by-line (LBL) calculations, the k-distribution method, the Principal Component Analysis (PCA) and a combination of the k-distribution method plus PCA are used. The linearized radiative transfer models for retrieval of cloud properties include the Linearized Discrete Ordinate method with Matrix Exponential (LDOME), the Linearized Matrix Operator method with Matrix Exponential (LMOME) and the Forward-Adjoint Discrete Ordinate method with Matrix Exponential (FADOME). These models were applied to the EPIC oxygen-A band absorption channel at 764 nm. It is shown that the approximate asymptotic and equivalent Lambertian cloud models give inaccurate results, so an offline processor for the retrieval of cloud properties in the back-scattering region requires the use of exact models such as DOME and MOME, which behave similarly. The combination of the k-distribution method plus PCA presents similar accuracy to the LBL calculations, but it is up to 360 times faster, and the relative errors for the computed radiances are less than 1.5% compared to the results when the exact phase function is used. Finally, the linearized models studied show similar behavior, with relative errors less than 1% for the radiance derivatives, but FADOME is 2 times faster than LDOME and 2.5 times faster than LMOME.

Short communication: Genetic variation of saturated fatty acids in Holsteins in the Walloon region of Belgium.

PubMed

Arnould, V M-R; Hammami, H; Soyeurt, H; Gengler, N

2010-09-01

Random regression test-day models using Legendre polynomials are commonly used for the estimation of genetic parameters and genetic evaluation for test-day milk production traits. However, some researchers have reported that these models present some undesirable properties such as the overestimation of variances at the edges of lactation. Describing genetic variation of saturated fatty acids expressed in milk fat might require the testing of different models. Therefore, 3 different functions were used and compared to take into account the lactation curve: (1) Legendre polynomials with the same order as currently applied for genetic model for production traits; 2) linear splines with 10 knots; and 3) linear splines with the same 10 knots reduced to 3 parameters. The criteria used were Akaike's information and Bayesian information criteria, percentage square biases, and log-likelihood function. These criteria indentified Legendre polynomials and linear splines with 10 knots reduced to 3 parameters models as the most useful. Reducing more complex models using eigenvalues seemed appealing because the resulting models are less time demanding and can reduce convergence difficulties, because convergence properties also seemed to be improved. Finally, the results showed that the reduced spline model was very similar to the Legendre polynomials model. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

User's manual for LINEAR, a FORTRAN program to derive linear aircraft models

NASA Technical Reports Server (NTRS)

Duke, Eugene L.; Patterson, Brian P.; Antoniewicz, Robert F.

1987-01-01

This report documents a FORTRAN program that provides a powerful and flexible tool for the linearization of aircraft models. The program LINEAR numerically determines a linear system model using nonlinear equations of motion and a user-supplied nonlinear aerodynamic model. The system model determined by LINEAR consists of matrices for both state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model.

Testing higher-order Lagrangian perturbation theory against numerical simulation. 1: Pancake models

NASA Technical Reports Server (NTRS)

Buchert, T.; Melott, A. L.; Weiss, A. G.

1993-01-01

We present results showing an improvement of the accuracy of perturbation theory as applied to cosmological structure formation for a useful range of quasi-linear scales. The Lagrangian theory of gravitational instability of an Einstein-de Sitter dust cosmogony investigated and solved up to the third order is compared with numerical simulations. In this paper we study the dynamics of pancake models as a first step. In previous work the accuracy of several analytical approximations for the modeling of large-scale structure in the mildly non-linear regime was analyzed in the same way, allowing for direct comparison of the accuracy of various approximations. In particular, the Zel'dovich approximation (hereafter ZA) as a subclass of the first-order Lagrangian perturbation solutions was found to provide an excellent approximation to the density field in the mildly non-linear regime (i.e. up to a linear r.m.s. density contrast of sigma is approximately 2). The performance of ZA in hierarchical clustering models can be greatly improved by truncating the initial power spectrum (smoothing the initial data). We here explore whether this approximation can be further improved with higher-order corrections in the displacement mapping from homogeneity. We study a single pancake model (truncated power-spectrum with power-spectrum with power-index n = -1) using cross-correlation statistics employed in previous work. We found that for all statistical methods used the higher-order corrections improve the results obtained for the first-order solution up to the stage when sigma (linear theory) is approximately 1. While this improvement can be seen for all spatial scales, later stages retain this feature only above a certain scale which is increasing with time. However, third-order is not much improvement over second-order at any stage. The total breakdown of the perturbation approach is observed at the stage, where sigma (linear theory) is approximately 2, which corresponds to the onset of hierarchical clustering. This success is found at a considerable higher non-linearity than is usual for perturbation theory. Whether a truncation of the initial power-spectrum in hierarchical models retains this improvement will be analyzed in a forthcoming work.

Comparison of kinetic model for biogas production from corn cob

NASA Astrophysics Data System (ADS)

Shitophyta, L. M.; Maryudi

2018-04-01

Energy demand increases every day, while the energy source especially fossil energy depletes increasingly. One of the solutions to overcome the energy depletion is to provide renewable energies such as biogas. Biogas can be generated by corn cob and food waste. In this study, biogas production was carried out by solid-state anaerobic digestion. The steps of biogas production were the preparation of feedstock, the solid-state anaerobic digestion, and the measurement of biogas volume. This study was conducted on TS content of 20%, 22%, and 24%. The aim of this research was to compare kinetic models of biogas production from corn cob and food waste as a co-digestion using the linear, exponential equation, and first-kinetic models. The result showed that the exponential equation had a better correlation than the linear equation on the ascending graph of biogas production. On the contrary, the linear equation had a better correlation than the exponential equation on the descending graph of biogas production. The correlation values on the first-kinetic model had the smallest value compared to the linear and exponential models.

Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression.

PubMed

Lemieux, Sébastien

2006-08-25

The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level probe intensities, then deriving significance by comparing these expression levels between conditions. The proposed PL-LM (Probe-Level Linear Model) method implements a linear model applied on the probe-level data to directly estimate the treatment effect. A finite mixture of Gaussian components is then used to identify DEGs using the coefficients estimated by the linear model. This approach can readily be applied to experimental design with or without replication. On a wholly defined dataset, the PL-LM method was able to identify 75% of the differentially expressed genes within 10% of false positives. This accuracy was achieved both using the three replicates per conditions available in the dataset and using only one replicate per condition. The method achieves, on this dataset, a higher accuracy than the best set of tools identified by the authors of the dataset, and does so using only one replicate per condition.

Decreasing the temporal complexity for nonlinear, implicit reduced-order models by forecasting

DOE PAGES

Carlberg, Kevin; Ray, Jaideep; van Bloemen Waanders, Bart

2015-02-14

Implicit numerical integration of nonlinear ODEs requires solving a system of nonlinear algebraic equations at each time step. Each of these systems is often solved by a Newton-like method, which incurs a sequence of linear-system solves. Most model-reduction techniques for nonlinear ODEs exploit knowledge of system's spatial behavior to reduce the computational complexity of each linear-system solve. However, the number of linear-system solves for the reduced-order simulation often remains roughly the same as that for the full-order simulation. We propose exploiting knowledge of the model's temporal behavior to (1) forecast the unknown variable of the reduced-order system of nonlinear equationsmore » at future time steps, and (2) use this forecast as an initial guess for the Newton-like solver during the reduced-order-model simulation. To compute the forecast, we propose using the Gappy POD technique. As a result, the goal is to generate an accurate initial guess so that the Newton solver requires many fewer iterations to converge, thereby decreasing the number of linear-system solves in the reduced-order-model simulation.« less

Assessing variance components in multilevel linear models using approximate Bayes factors: A case study of ethnic disparities in birthweight

PubMed Central

Saville, Benjamin R.; Herring, Amy H.; Kaufman, Jay S.

2013-01-01

Racial/ethnic disparities in birthweight are a large source of differential morbidity and mortality worldwide and have remained largely unexplained in epidemiologic models. We assess the impact of maternal ancestry and census tract residence on infant birth weights in New York City and the modifying effects of race and nativity by incorporating random effects in a multilevel linear model. Evaluating the significance of these predictors involves the test of whether the variances of the random effects are equal to zero. This is problematic because the null hypothesis lies on the boundary of the parameter space. We generalize an approach for assessing random effects in the two-level linear model to a broader class of multilevel linear models by scaling the random effects to the residual variance and introducing parameters that control the relative contribution of the random effects. After integrating over the random effects and variance components, the resulting integrals needed to calculate the Bayes factor can be efficiently approximated with Laplace’s method. PMID:24082430

Point- and line-based transformation models for high resolution satellite image rectification

NASA Astrophysics Data System (ADS)

Abd Elrahman, Ahmed Mohamed Shaker

Rigorous mathematical models with the aid of satellite ephemeris data can present the relationship between the satellite image space and the object space. With government funded satellites, access to calibration and ephemeris data has allowed the development and use of these models. However, for commercial high-resolution satellites, which have been recently launched, these data are withheld from users, and therefore alternative empirical models should be used. In general, the existing empirical models are based on the use of control points and involve linking points in the image space and the corresponding points in the object space. But the lack of control points in some remote areas and the questionable accuracy of the identified discrete conjugate points provide a catalyst for the development of algorithms based on features other than control points. This research, concerned with image rectification and 3D geo-positioning determination using High-Resolution Satellite Imagery (HRSI), has two major objectives. First, the effects of satellite sensor characteristics, number of ground control points (GCPs), and terrain elevation variations on the performance of several point based empirical models are studied. Second, a new mathematical model, using only linear features as control features, or linear features with a minimum number of GCPs, is developed. To meet the first objective, several experiments for different satellites such as Ikonos, QuickBird, and IRS-1D have been conducted using different point based empirical models. Various data sets covering different terrain types are presented and results from representative sets of the experiments are shown and analyzed. The results demonstrate the effectiveness and the superiority of these models under certain conditions. From the results obtained, several alternatives to circumvent the effects of the satellite sensor characteristics, the number of GCPs, and the terrain elevation variations are introduced. To meet the second objective, a new model named the Line Based Transformation Model (LBTM) is developed for HRSI rectification. The model has the flexibility to either solely use linear features or use linear features and a number of control points to define the image transformation parameters. Unlike point features, which must be explicitly defined, linear features have the advantage that they can be implicitly defined by any segment along the line. (Abstract shortened by UMI.)

COED Transactions, Vol. XI, No. 3, March 1979. LSSP: An Educational Aid to the Study of Linear System Dynamics.

ERIC Educational Resources Information Center

Mitchell, Eugene E., Ed.

The study of the dynamics of physical systems is of importance to all engineering students. LSSP, a Linear System Simulation Program, is used to study the behavior of physical phenomena and systems which may be represented to a good degree of approximation by linear models. Emphasis is placed upon the unity resulting from the mathematical…

A Control Model: Interpretation of Fitts' Law

NASA Technical Reports Server (NTRS)

Connelly, E. M.

1984-01-01

The analytical results for several models are given: a first order model where it is assumed that the hand velocity can be directly controlled, and a second order model where it is assumed that the hand acceleration can be directly controlled. Two different types of control-laws are investigated. One is linear function of the hand error and error rate; the other is the time-optimal control law. Results show that the first and second order models with the linear control-law produce a movement time (MT) function with the exact form of the Fitts' Law. The control-law interpretation implies that the effect of target width on MT must be a result of the vertical motion which elevates the hand from the starting point and drops it on the target at the target edge. The time optimal control law did not produce a movement-time formula simular to Fitt's Law.

A Linear Electromagnetic Piston Pump

NASA Astrophysics Data System (ADS)

Hogan, Paul H.

Advancements in mobile hydraulics for human-scale applications have increased demand for a compact hydraulic power supply. Conventional designs couple a rotating electric motor to a hydraulic pump, which increases the package volume and requires several energy conversions. This thesis investigates the use of a free piston as the moving element in a linear motor to eliminate multiple energy conversions and decrease the overall package volume. A coupled model used a quasi-static magnetic equivalent circuit to calculate the motor inductance and the electromagnetic force acting on the piston. The force was an input to a time domain model to evaluate the mechanical and pressure dynamics. The magnetic circuit model was validated with finite element analysis and an experimental prototype linear motor. The coupled model was optimized using a multi-objective genetic algorithm to explore the parameter space and maximize power density and efficiency. An experimental prototype linear pump coupled pistons to an off-the-shelf linear motor to validate the mechanical and pressure dynamics models. The magnetic circuit force calculation agreed within 3% of finite element analysis, and within 8% of experimental data from the unoptimized prototype linear motor. The optimized motor geometry also had good agreement with FEA; at zero piston displacement, the magnetic circuit calculates optimized motor force within 10% of FEA in less than 1/1000 the computational time. This makes it well suited to genetic optimization algorithms. The mechanical model agrees very well with the experimental piston pump position data when tuned for additional unmodeled mechanical friction. Optimized results suggest that an improvement of 400% of the state of the art power density is attainable with as high as 85% net efficiency. This demonstrates that a linear electromagnetic piston pump has potential to serve as a more compact and efficient supply of fluid power for the human scale.

Linearized blade row compression component model. Stability and frequency response analysis of a J85-3 compressor

NASA Technical Reports Server (NTRS)

Tesch, W. A.; Moszee, R. H.; Steenken, W. G.

1976-01-01

NASA developed stability and frequency response analysis techniques were applied to a dynamic blade row compression component stability model to provide a more economic approach to surge line and frequency response determination than that provided by time-dependent methods. This blade row model was linearized and the Jacobian matrix was formed. The clean-inlet-flow stability characteristics of the compressors of two J85-13 engines were predicted by applying the alternate Routh-Hurwitz stability criterion to the Jacobian matrix. The predicted surge line agreed with the clean-inlet-flow surge line predicted by the time-dependent method to a high degree except for one engine at 94% corrected speed. No satisfactory explanation of this discrepancy was found. The frequency response of the linearized system was determined by evaluating its Laplace transfer function. The results of the linearized-frequency-response analysis agree with the time-dependent results when the time-dependent inlet total-pressure and exit-flow function amplitude boundary conditions are less than 1 percent and 3 percent, respectively. The stability analysis technique was extended to a two-sector parallel compressor model with and without interstage crossflow and predictions were carried out for total-pressure distortion extents of 180 deg, 90 deg, 60 deg, and 30 deg.

Comparison of linear and square superposition hardening models for the surface nanoindentation of ion-irradiated materials

NASA Astrophysics Data System (ADS)

Xiao, Xiazi; Yu, Long

2018-05-01

Linear and square superposition hardening models are compared for the surface nanoindentation of ion-irradiated materials. Hardening mechanisms of both dislocations and defects within the plasticity affected region (PAR) are considered. Four sets of experimental data for ion-irradiated materials are adopted to compare with theoretical results of the two hardening models. It is indicated that both models describe experimental data equally well when the PAR is within the irradiated layer; whereas, when the PAR is beyond the irradiated region, the square superposition hardening model performs better. Therefore, the square superposition model is recommended to characterize the hardening behavior of ion-irradiated materials.

A holistic approach to movement education in sport and fitness: a systems based model.

PubMed

Polsgrove, Myles Jay

2012-01-01

The typical model used by movement professionals to enhance performance relies on the notion that a linear increase in load results in steady and progressive gains, whereby, the greater the effort, the greater the gains in performance. Traditional approaches to movement progression typically rely on the proper sequencing of extrinsically based activities to facilitate the individual in reaching performance objectives. However, physical rehabilitation or physical performance rarely progresses in such a linear fashion; instead they tend to evolve non-linearly and rather unpredictably. A dynamic system can be described as an entity that self-organizes into increasingly complex forms. Applying this view to the human body, practitioners could facilitate non-linear performance gains through a systems based programming approach. Utilizing a dynamic systems view, the Holistic Approach to Movement Education (HADME) is a model designed to optimize performance by accounting for non-linear and self-organizing traits associated with human movement. In this model, gains in performance occur through advancing individual perspectives and through optimizing sub-system performance. This inward shift of the focus of performance creates a sharper self-awareness and may lead to more optimal movements. Copyright © 2011 Elsevier Ltd. All rights reserved.

Modeling the stress dependence of Barkhausen phenomena for stress axis linear and noncollinear with applied magnetic field (abstract)

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

Sablik, M.J.; Augustyniak, B.; Chmielewski, M.

1996-04-01

The almost linear dependence of the maximum Barkhausen noise signal amplitude on stress has made it a tool for nondestructive evaluation of residual stress. Recently, a model has been developed to account for the stress dependence of the Barkhausen noise signal. The model uses the development of Alessandro {ital et} {ital al}. who use coupled Langevin equations to derive an expression for the Barkhausen noise power spectrum. The model joins this expression to the magnetomechanical hysteresis model of Sablik {ital et} {ital al}., obtaining both a hysteretic and stress-dependent result for the magnetic-field-dependent Barkhausen noise envelope and obtaining specifically themore » almost linear stress dependence of the Barkhausen noise maximum experimentally. In this paper, we extend the model to derive the angular dependence observed by Kwun of the Barkhausen noise amplitude when stress axis is taken at different angles relative to magnetic field. We also apply the model to the experimental observation that in XC10 French steel, there is an apparent almost linear correlation with stress of hysteresis loss and of the integral of the Barkhausen noise signal over applied field {ital H}. Further, the two quantities, Barkhausen noise integral and hysteresis loss, are linearly correlated with each other. The model shows how that behavior is to be expected for the measured steel because of its sharply rising hysteresis curve. {copyright} {ital 1996 American Institute of Physics.}« less

Trend in Obesity Prevalence in European Adult Cohort Populations during Follow-up since 1996 and Their Predictions to 2015

PubMed Central

von Ruesten, Anne; Steffen, Annika; Floegel, Anna; van der A, Daphne L.; Masala, Giovanna; Tjønneland, Anne; Halkjaer, Jytte; Palli, Domenico; Wareham, Nicholas J.; Loos, Ruth J. F.; Sørensen, Thorkild I. A.; Boeing, Heiner

2011-01-01

Objective To investigate trends in obesity prevalence in recent years and to predict the obesity prevalence in 2015 in European populations. Methods Data of 97 942 participants from seven cohorts involved in the European Prospective Investigation into Cancer and Nutrition (EPIC) study participating in the Diogenes project (named as “Diogenes cohort” in the following) with weight measurements at baseline and follow-up were used to predict future obesity prevalence with logistic linear and non-linear (leveling off) regression models. In addition, linear and leveling off models were fitted to the EPIC-Potsdam dataset with five weight measures during the observation period to find out which of these two models might provide the more realistic prediction. Results During a mean follow-up period of 6 years, the obesity prevalence in the Diogenes cohort increased from 13% to 17%. The linear prediction model predicted an overall obesity prevalence of about 30% in 2015, whereas the leveling off model predicted a prevalence of about 20%. In the EPIC-Potsdam cohort, the shape of obesity trend favors a leveling off model among men (R2 = 0.98), and a linear model among women (R2 = 0.99). Conclusion Our data show an increase in obesity prevalence since the 1990ies, and predictions by 2015 suggests a sizeable further increase in European populations. However, the estimates from the leveling off model were considerably lower. PMID:22102897

On Fitting Generalized Linear Mixed-effects Models for Binary Responses using Different Statistical Packages

PubMed Central

Zhang, Hui; Lu, Naiji; Feng, Changyong; Thurston, Sally W.; Xia, Yinglin; Tu, Xin M.

2011-01-01

Summary The generalized linear mixed-effects model (GLMM) is a popular paradigm to extend models for cross-sectional data to a longitudinal setting. When applied to modeling binary responses, different software packages and even different procedures within a package may give quite different results. In this report, we describe the statistical approaches that underlie these different procedures and discuss their strengths and weaknesses when applied to fit correlated binary responses. We then illustrate these considerations by applying these procedures implemented in some popular software packages to simulated and real study data. Our simulation results indicate a lack of reliability for most of the procedures considered, which carries significant implications for applying such popular software packages in practice. PMID:21671252

Linear and nonlinear 2D finite element analysis of sloshing modes and pressures in rectangular tanks subject to horizontal harmonic motions

NASA Astrophysics Data System (ADS)

Virella, Juan C.; Prato, Carlos A.; Godoy, Luis A.

2008-05-01

The influence of nonlinear wave theory on the sloshing natural periods and their modal pressure distributions are investigated for rectangular tanks under the assumption of two-dimensional behavior. Natural periods and mode shapes are computed and compared for both linear wave theory (LWT) and nonlinear wave theory (NLWT) models, using the finite element package ABAQUS. Linear wave theory is implemented in an acoustic model, whereas a plane strain problem with large displacements is used in NLWT. Pressure distributions acting on the tank walls are obtained for the first three sloshing modes using both linear and nonlinear wave theory. It is found that the nonlinearity does not have significant effects on the natural sloshing periods. For the sloshing pressures on the tank walls, different distributions were found using linear and nonlinear wave theory models. However, in all cases studied, the linear wave theory conservatively estimated the magnitude of the pressure distribution, whereas larger pressures resultant heights were obtained when using the nonlinear theory. It is concluded that the nonlinearity of the surface wave does not have major effects in the pressure distribution on the walls for rectangular tanks.

Compartmental and Data-Based Modeling of Cerebral Hemodynamics: Linear Analysis.

PubMed

Henley, B C; Shin, D C; Zhang, R; Marmarelis, V Z

Compartmental and data-based modeling of cerebral hemodynamics are alternative approaches that utilize distinct model forms and have been employed in the quantitative study of cerebral hemodynamics. This paper examines the relation between a compartmental equivalent-circuit and a data-based input-output model of dynamic cerebral autoregulation (DCA) and CO2-vasomotor reactivity (DVR). The compartmental model is constructed as an equivalent-circuit utilizing putative first principles and previously proposed hypothesis-based models. The linear input-output dynamics of this compartmental model are compared with data-based estimates of the DCA-DVR process. This comparative study indicates that there are some qualitative similarities between the two-input compartmental model and experimental results.

Population response to climate change: linear vs. non-linear modeling approaches.

PubMed

Ellis, Alicia M; Post, Eric

2004-03-31

Research on the ecological consequences of global climate change has elicited a growing interest in the use of time series analysis to investigate population dynamics in a changing climate. Here, we compare linear and non-linear models describing the contribution of climate to the density fluctuations of the population of wolves on Isle Royale, Michigan from 1959 to 1999. The non-linear self excitatory threshold autoregressive (SETAR) model revealed that, due to differences in the strength and nature of density dependence, relatively small and large populations may be differentially affected by future changes in climate. Both linear and non-linear models predict a decrease in the population of wolves with predicted changes in climate. Because specific predictions differed between linear and non-linear models, our study highlights the importance of using non-linear methods that allow the detection of non-linearity in the strength and nature of density dependence. Failure to adopt a non-linear approach to modelling population response to climate change, either exclusively or in addition to linear approaches, may compromise efforts to quantify ecological consequences of future warming.

Carbon dioxide stripping in aquaculture -- part III: model verification

USGS Publications Warehouse

Colt, John; Watten, Barnaby; Pfeiffer, Tim

2012-01-01

Based on conventional mass transfer models developed for oxygen, the use of the non-linear ASCE method, 2-point method, and one parameter linear-regression method were evaluated for carbon dioxide stripping data. For values of KLaCO2 < approximately 1.5/h, the 2-point or ASCE method are a good fit to experimental data, but the fit breaks down at higher values of KLaCO2. How to correct KLaCO2 for gas phase enrichment remains to be determined. The one-parameter linear regression model was used to vary the C*CO2 over the test, but it did not result in a better fit to the experimental data when compared to the ASCE or fixed C*CO2 assumptions.

Effects of source shape on the numerical aperture factor with a geometrical-optics model.

PubMed

Wan, Der-Shen; Schmit, Joanna; Novak, Erik

2004-04-01

We study the effects of an extended light source on the calibration of an interference microscope, also referred to as an optical profiler. Theoretical and experimental numerical aperture (NA) factors for circular and linear light sources along with collimated laser illumination demonstrate that the shape of the light source or effective aperture cone is critical for a correct NA factor calculation. In practice, more-accurate results for the NA factor are obtained when a linear approximation to the filament light source shape is used in a geometric model. We show that previously measured and derived NA factors show some discrepancies because a circular rather than linear approximation to the filament source was used in the modeling.

Symbolic discrete event system specification

NASA Technical Reports Server (NTRS)

Zeigler, Bernard P.; Chi, Sungdo

1992-01-01

Extending discrete event modeling formalisms to facilitate greater symbol manipulation capabilities is important to further their use in intelligent control and design of high autonomy systems. An extension to the DEVS formalism that facilitates symbolic expression of event times by extending the time base from the real numbers to the field of linear polynomials over the reals is defined. A simulation algorithm is developed to generate the branching trajectories resulting from the underlying nondeterminism. To efficiently manage symbolic constraints, a consistency checking algorithm for linear polynomial constraints based on feasibility checking algorithms borrowed from linear programming has been developed. The extended formalism offers a convenient means to conduct multiple, simultaneous explorations of model behaviors. Examples of application are given with concentration on fault model analysis.

A consistent two-mutation model of bone cancer for two data sets of radium-injected beagles.

PubMed

Bijwaard, H; Brugmans, M J P; Leenhouts, H P

2002-09-01

A two-mutation carcinogenesis model has been applied to model osteosarcoma incidence in two data sets of beagles injected with 226Ra. Taking age-specific retention into account, the following results have been obtained: (1) a consistent and well-fitting solution for all age and dose groups, (2) mutation rates that are linearly dependent on dose rate, with an exponential decrease for the second mutation at high dose rates, (3) a linear-quadratic dose-effect relationship, which indicates that care should be taken when extrapolating linearly, (4) highest cumulative incidences for injection at young adult age, and highest risks for injection doses of a few kBq kg(-1) at these ages, and (5) when scaled appropriately, the beagle model compares fairly well with a description for radium dial painters, suggesting that a consistent model description of bone cancer induction in beagles and humans may be possible.

Linear and nonlinear models for predicting fish bioconcentration factors for pesticides.

PubMed

Yuan, Jintao; Xie, Chun; Zhang, Ting; Sun, Jinfang; Yuan, Xuejie; Yu, Shuling; Zhang, Yingbiao; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

2016-08-01

This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prediction of siRNA potency using sparse logistic regression.

PubMed

Hu, Wei; Hu, John

2014-06-01

RNA interference (RNAi) can modulate gene expression at post-transcriptional as well as transcriptional levels. Short interfering RNA (siRNA) serves as a trigger for the RNAi gene inhibition mechanism, and therefore is a crucial intermediate step in RNAi. There have been extensive studies to identify the sequence characteristics of potent siRNAs. One such study built a linear model using LASSO (Least Absolute Shrinkage and Selection Operator) to measure the contribution of each siRNA sequence feature. This model is simple and interpretable, but it requires a large number of nonzero weights. We have introduced a novel technique, sparse logistic regression, to build a linear model using single-position specific nucleotide compositions which has the same prediction accuracy of the linear model based on LASSO. The weights in our new model share the same general trend as those in the previous model, but have only 25 nonzero weights out of a total 84 weights, a 54% reduction compared to the previous model. Contrary to the linear model based on LASSO, our model suggests that only a few positions are influential on the efficacy of the siRNA, which are the 5' and 3' ends and the seed region of siRNA sequences. We also employed sparse logistic regression to build a linear model using dual-position specific nucleotide compositions, a task LASSO is not able to accomplish well due to its high dimensional nature. Our results demonstrate the superiority of sparse logistic regression as a technique for both feature selection and regression over LASSO in the context of siRNA design.

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

PubMed

Nevill, Alan M; Cooke, Carlton B

2017-05-01

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

On the linear relation between the mean and the standard deviation of a response time distribution.

PubMed

Wagenmakers, Eric-Jan; Brown, Scott

2007-07-01

Although it is generally accepted that the spread of a response time (RT) distribution increases with the mean, the precise nature of this relation remains relatively unexplored. The authors show that in several descriptive RT distributions, the standard deviation increases linearly with the mean. Results from a wide range of tasks from different experimental paradigms support a linear relation between RT mean and RT standard deviation. Both R. Ratcliff's (1978) diffusion model and G. D. Logan's (1988) instance theory of automatization provide explanations for this linear relation. The authors identify and discuss 3 specific boundary conditions for the linear law to hold. The law constrains RT models and supports the use of the coefficient of variation to (a) compare variability while controlling for differences in baseline speed of processing and (b) assess whether changes in performance with practice are due to quantitative speedup or qualitative reorganization. Copyright 2007 APA.

Wave kinetics of random fibre lasers

PubMed Central

Churkin, D V.; Kolokolov, I V.; Podivilov, E V.; Vatnik, I D.; Nikulin, M A.; Vergeles, S S.; Terekhov, I S.; Lebedev, V V.; Falkovich, G.; Babin, S A.; Turitsyn, S K.

2015-01-01

Traditional wave kinetics describes the slow evolution of systems with many degrees of freedom to equilibrium via numerous weak non-linear interactions and fails for very important class of dissipative (active) optical systems with cyclic gain and losses, such as lasers with non-linear intracavity dynamics. Here we introduce a conceptually new class of cyclic wave systems, characterized by non-uniform double-scale dynamics with strong periodic changes of the energy spectrum and slow evolution from cycle to cycle to a statistically steady state. Taking a practically important example—random fibre laser—we show that a model describing such a system is close to integrable non-linear Schrödinger equation and needs a new formalism of wave kinetics, developed here. We derive a non-linear kinetic theory of the laser spectrum, generalizing the seminal linear model of Schawlow and Townes. Experimental results agree with our theory. The work has implications for describing kinetics of cyclical systems beyond photonics. PMID:25645177

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

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

Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari

2009-11-15

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R{sup 2} were used to evaluate performancemore » of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R{sup 2} confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.« less

Mixed models, linear dependency, and identification in age-period-cohort models.

PubMed

O'Brien, Robert M

2017-07-20

This paper examines the identification problem in age-period-cohort models that use either linear or categorically coded ages, periods, and cohorts or combinations of these parameterizations. These models are not identified using the traditional fixed effect regression model approach because of a linear dependency between the ages, periods, and cohorts. However, these models can be identified if the researcher introduces a single just identifying constraint on the model coefficients. The problem with such constraints is that the results can differ substantially depending on the constraint chosen. Somewhat surprisingly, age-period-cohort models that specify one or more of ages and/or periods and/or cohorts as random effects are identified. This is the case without introducing an additional constraint. I label this identification as statistical model identification and show how statistical model identification comes about in mixed models and why which effects are treated as fixed and which are treated as random can substantially change the estimates of the age, period, and cohort effects. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Linear and nonlinear methods in modeling the aqueous solubility of organic compounds.

PubMed

Catana, Cornel; Gao, Hua; Orrenius, Christian; Stouten, Pieter F W

2005-01-01

Solubility data for 930 diverse compounds have been analyzed using linear Partial Least Square (PLS) and nonlinear PLS methods, Continuum Regression (CR), and Neural Networks (NN). 1D and 2D descriptors from MOE package in combination with E-state or ISIS keys have been used. The best model was obtained using linear PLS for a combination between 22 MOE descriptors and 65 ISIS keys. It has a correlation coefficient (r2) of 0.935 and a root-mean-square error (RMSE) of 0.468 log molar solubility (log S(w)). The model validated on a test set of 177 compounds not included in the training set has r2 0.911 and RMSE 0.475 log S(w). The descriptors were ranked according to their importance, and at the top of the list have been found the 22 MOE descriptors. The CR model produced results as good as PLS, and because of the way in which cross-validation has been done it is expected to be a valuable tool in prediction besides PLS model. The statistics obtained using nonlinear methods did not surpass those got with linear ones. The good statistic obtained for linear PLS and CR recommends these models to be used in prediction when it is difficult or impossible to make experimental measurements, for virtual screening, combinatorial library design, and efficient leads optimization.

A comparative study of generalized linear mixed modelling and artificial neural network approach for the joint modelling of survival and incidence of Dengue patients in Sri Lanka

NASA Astrophysics Data System (ADS)

Hapugoda, J. C.; Sooriyarachchi, M. R.

2017-09-01

Survival time of patients with a disease and the incidence of that particular disease (count) is frequently observed in medical studies with the data of a clustered nature. In many cases, though, the survival times and the count can be correlated in a way that, diseases that occur rarely could have shorter survival times or vice versa. Due to this fact, joint modelling of these two variables will provide interesting and certainly improved results than modelling these separately. Authors have previously proposed a methodology using Generalized Linear Mixed Models (GLMM) by joining the Discrete Time Hazard model with the Poisson Regression model to jointly model survival and count model. As Aritificial Neural Network (ANN) has become a most powerful computational tool to model complex non-linear systems, it was proposed to develop a new joint model of survival and count of Dengue patients of Sri Lanka by using that approach. Thus, the objective of this study is to develop a model using ANN approach and compare the results with the previously developed GLMM model. As the response variables are continuous in nature, Generalized Regression Neural Network (GRNN) approach was adopted to model the data. To compare the model fit, measures such as root mean square error (RMSE), absolute mean error (AME) and correlation coefficient (R) were used. The measures indicate the GRNN model fits the data better than the GLMM model.

Dynamics of Permanent-Magnet Biased Active Magnetic Bearings

NASA Technical Reports Server (NTRS)

Fukata, Satoru; Yutani, Kazuyuki

1996-01-01

Active magnetic radial bearings are constructed with a combination of permanent magnets to provide bias forces and electromagnets to generate control forces for the reduction of cost and the operating energy consumption. Ring-shaped permanent magnets with axial magnetization are attached to a shaft and share their magnet stators with the electromagnets. The magnet cores are made of solid iron for simplicity. A simplified magnetic circuit of the combined magnet system is analyzed with linear circuit theory by approximating the characteristics of permanent magnets with a linear relation. A linearized dynamical model of the control force is presented with the first-order approximation of the effects of eddy currents. Frequency responses of the rotor motion to disturbance inputs and the motion for impulsive forces are tested in the non-rotating state. The frequency responses are compared with numerical results. The decay of rotor speed due to magnetic braking is examined. The experimental results and the presented linearized model are similar to those of the all-electromagnetic design.

Non-linear corrections to the time-covariance function derived from a multi-state chemical master equation.

PubMed

Scott, M

2012-08-01

The time-covariance function captures the dynamics of biochemical fluctuations and contains important information about the underlying kinetic rate parameters. Intrinsic fluctuations in biochemical reaction networks are typically modelled using a master equation formalism. In general, the equation cannot be solved exactly and approximation methods are required. For small fluctuations close to equilibrium, a linearisation of the dynamics provides a very good description of the relaxation of the time-covariance function. As the number of molecules in the system decrease, deviations from the linear theory appear. Carrying out a systematic perturbation expansion of the master equation to capture these effects results in formidable algebra; however, symbolic mathematics packages considerably expedite the computation. The authors demonstrate that non-linear effects can reveal features of the underlying dynamics, such as reaction stoichiometry, not available in linearised theory. Furthermore, in models that exhibit noise-induced oscillations, non-linear corrections result in a shift in the base frequency along with the appearance of a secondary harmonic.

Step responses of a torsional system with multiple clearances: Study of vibro-impact phenomenon using experimental and computational methods

NASA Astrophysics Data System (ADS)

Oruganti, Pradeep Sharma; Krak, Michael D.; Singh, Rajendra

2018-01-01

Recently Krak and Singh (2017) proposed a scientific experiment that examined vibro-impacts in a torsional system under a step down excitation and provided preliminary measurements and limited non-linear model studies. A major goal of this article is to extend the prior work with a focus on the examination of vibro-impact phenomena observed under step responses in a torsional system with one, two or three controlled clearances. First, new measurements are made at several locations with a higher sampling frequency. Measured angular accelerations are examined in both time and time-frequency domains. Minimal order non-linear models of the experiment are successfully constructed, using piecewise linear stiffness and Coulomb friction elements; eight cases of the generic system are examined though only three are experimentally studied. Measured and predicted responses for single and dual clearance configurations exhibit double sided impacts and time varying periods suggest softening trends under the step down torque. Non-linear models are experimentally validated by comparing results with new measurements and with those previously reported. Several metrics are utilized to quantify and compare the measured and predicted responses (including peak to peak accelerations). Eigensolutions and step responses of the corresponding linearized models are utilized to better understand the nature of the non-linear dynamic system. Finally, the effect of step amplitude on the non-linear responses is examined for several configurations, and hardening trends are observed in the torsional system with three clearances.

Drug awareness in adolescents attending a mental health service: analysis of longitudinal data.

PubMed

Arnau, Jaume; Bono, Roser; Díaz, Rosa; Goti, Javier

2011-11-01

One of the procedures used most recently with longitudinal data is linear mixed models. In the context of health research the increasing number of studies that now use these models bears witness to the growing interest in this type of analysis. This paper describes the application of linear mixed models to a longitudinal study of a sample of Spanish adolescents attending a mental health service, the aim being to investigate their knowledge about the consumption of alcohol and other drugs. More specifically, the main objective was to compare the efficacy of a motivational interviewing programme with a standard approach to drug awareness. The models used to analyse the overall indicator of drug awareness were as follows: (a) unconditional linear growth curve model; (b) growth model with subject-associated variables; and (c) individual curve model with predictive variables. The results showed that awareness increased over time and that the variable 'schooling years' explained part of the between-subjects variation. The effect of motivational interviewing was also significant.

Product unit neural network models for predicting the growth limits of Listeria monocytogenes.

PubMed

Valero, A; Hervás, C; García-Gimeno, R M; Zurera, G

2007-08-01

A new approach to predict the growth/no growth interface of Listeria monocytogenes as a function of storage temperature, pH, citric acid (CA) and ascorbic acid (AA) is presented. A linear logistic regression procedure was performed and a non-linear model was obtained by adding new variables by means of a Neural Network model based on Product Units (PUNN). The classification efficiency of the training data set and the generalization data of the new Logistic Regression PUNN model (LRPU) were compared with Linear Logistic Regression (LLR) and Polynomial Logistic Regression (PLR) models. 92% of the total cases from the LRPU model were correctly classified, an improvement on the percentage obtained using the PLR model (90%) and significantly higher than the results obtained with the LLR model, 80%. On the other hand predictions of LRPU were closer to data observed which permits to design proper formulations in minimally processed foods. This novel methodology can be applied to predictive microbiology for describing growth/no growth interface of food-borne microorganisms such as L. monocytogenes. The optimal balance is trying to find models with an acceptable interpretation capacity and with good ability to fit the data on the boundaries of variable range. The results obtained conclude that these kinds of models might well be very a valuable tool for mathematical modeling.

Diagnostics for generalized linear hierarchical models in network meta-analysis.

PubMed

Zhao, Hong; Hodges, James S; Carlin, Bradley P

2017-09-01

Network meta-analysis (NMA) combines direct and indirect evidence comparing more than 2 treatments. Inconsistency arises when these 2 information sources differ. Previous work focuses on inconsistency detection, but little has been done on how to proceed after identifying inconsistency. The key issue is whether inconsistency changes an NMA's substantive conclusions. In this paper, we examine such discrepancies from a diagnostic point of view. Our methods seek to detect influential and outlying observations in NMA at a trial-by-arm level. These observations may have a large effect on the parameter estimates in NMA, or they may deviate markedly from other observations. We develop formal diagnostics for a Bayesian hierarchical model to check the effect of deleting any observation. Diagnostics are specified for generalized linear hierarchical NMA models and investigated for both published and simulated datasets. Results from our example dataset using either contrast- or arm-based models and from the simulated datasets indicate that the sources of inconsistency in NMA tend not to be influential, though results from the example dataset suggest that they are likely to be outliers. This mimics a familiar result from linear model theory, in which outliers with low leverage are not influential. Future extensions include incorporating baseline covariates and individual-level patient data. Copyright © 2017 John Wiley & Sons, Ltd.

Inclusion of Linearized Moist Physics in Nasa's Goddard Earth Observing System Data Assimilation Tools

NASA Technical Reports Server (NTRS)

Holdaway, Daniel; Errico, Ronald; Gelaro, Ronaldo; Kim, Jong G.

2013-01-01

Inclusion of moist physics in the linearized version of a weather forecast model is beneficial in terms of variational data assimilation. Further, it improves the capability of important tools, such as adjoint-based observation impacts and sensitivity studies. A linearized version of the relaxed Arakawa-Schubert (RAS) convection scheme has been developed and tested in NASA's Goddard Earth Observing System data assimilation tools. A previous study of the RAS scheme showed it to exhibit reasonable linearity and stability. This motivates the development of a linearization of a near-exact version of the RAS scheme. Linearized large-scale condensation is included through simple conversion of supersaturation into precipitation. The linearization of moist physics is validated against the full nonlinear model for 6- and 24-h intervals, relevant to variational data assimilation and observation impacts, respectively. For a small number of profiles, sudden large growth in the perturbation trajectory is encountered. Efficient filtering of these profiles is achieved by diagnosis of steep gradients in a reduced version of the operator of the tangent linear model. With filtering turned on, the inclusion of linearized moist physics increases the correlation between the nonlinear perturbation trajectory and the linear approximation of the perturbation trajectory. A month-long observation impact experiment is performed and the effect of including moist physics on the impacts is discussed. Impacts from moist-sensitive instruments and channels are increased. The effect of including moist physics is examined for adjoint sensitivity studies. A case study examining an intensifying Northern Hemisphere Atlantic storm is presented. The results show a significant sensitivity with respect to moisture.

SU-E-T-186: Cloud-Based Quality Assurance Application for Linear Accelerator Commissioning

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

Rogers, J

2015-06-15

Purpose: To identify anomalies and safety issues during data collection and modeling for treatment planning systems Methods: A cloud-based quality assurance system (AQUIRE - Automated QUalIty REassurance) has been developed to allow the uploading and analysis of beam data aquired during the treatment planning system commissioning process. In addition to comparing and aggregating measured data, tools have also been developed to extract dose from the treatment planning system for end-to-end testing. A gamma index is perfomed on the data to give a dose difference and distance-to-agreement for validation that a beam model is generating plans consistent with the beam datamore » collection. Results: Over 20 linear accelerators have been commissioning using this platform, and a variety of errors and potential saftey issues have been caught through the validation process. For example, the gamma index of 2% dose, 2mm DTA is quite sufficient to see curves not corrected for effective point of measurement. Also, data imported into the database is analyzed against an aggregate of similar linear accelerators to show data points that are outliers. The resulting curves in the database exhibit a very small standard deviation and imply that a preconfigured beam model based on aggregated linear accelerators will be sufficient in most cases. Conclusion: With the use of this new platform for beam data commissioning, errors in beam data collection and treatment planning system modeling are greatly reduced. With the reduction in errors during acquisition, the resulting beam models are quite similar, suggesting that a common beam model may be possible in the future. Development is ongoing to create routine quality assurance tools to compare back to the beam data acquired during commissioning. I am a medical physicist for Alzyen Medical Physics, and perform commissioning services.« less

Application of the Hyper-Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes.

PubMed

Khazraee, S Hadi; Sáez-Castillo, Antonio Jose; Geedipally, Srinivas Reddy; Lord, Dominique

2015-05-01

The hyper-Poisson distribution can handle both over- and underdispersion, and its generalized linear model formulation allows the dispersion of the distribution to be observation-specific and dependent on model covariates. This study's objective is to examine the potential applicability of a newly proposed generalized linear model framework for the hyper-Poisson distribution in analyzing motor vehicle crash count data. The hyper-Poisson generalized linear model was first fitted to intersection crash data from Toronto, characterized by overdispersion, and then to crash data from railway-highway crossings in Korea, characterized by underdispersion. The results of this study are promising. When fitted to the Toronto data set, the goodness-of-fit measures indicated that the hyper-Poisson model with a variable dispersion parameter provided a statistical fit as good as the traditional negative binomial model. The hyper-Poisson model was also successful in handling the underdispersed data from Korea; the model performed as well as the gamma probability model and the Conway-Maxwell-Poisson model previously developed for the same data set. The advantages of the hyper-Poisson model studied in this article are noteworthy. Unlike the negative binomial model, which has difficulties in handling underdispersed data, the hyper-Poisson model can handle both over- and underdispersed crash data. Although not a major issue for the Conway-Maxwell-Poisson model, the effect of each variable on the expected mean of crashes is easily interpretable in the case of this new model. © 2014 Society for Risk Analysis.

Simple estimation of linear 1+1 D tsunami run-up

NASA Astrophysics Data System (ADS)

Fuentes, M.; Campos, J. A.; Riquelme, S.

2016-12-01

An analytical expression is derived concerning the linear run-up for any given initial wave generated over a sloping bathymetry. Due to the simplicity of the linear formulation, complex transformations are unnecessay, because the shoreline motion is directly obtained in terms of the initial wave. This analytical result not only supports maximum run-up invariance between linear and non-linear theories, but also the time evolution of shoreline motion and velocity. The results exhibit good agreement with the non-linear theory. The present formulation also allows computing the shoreline motion numerically from a customised initial waveform, including non-smooth functions. This is useful for numerical tests, laboratory experiments or realistic cases in which the initial disturbance might be retrieved from seismic data rather than using a theoretical model. It is also shown that the real case studied is consistent with the field observations.

The Use of Sparse Direct Solver in Vector Finite Element Modeling for Calculating Two Dimensional (2-D) Magnetotelluric Responses in Transverse Electric (TE) Mode

NASA Astrophysics Data System (ADS)

Yihaa Roodhiyah, Lisa’; Tjong, Tiffany; Nurhasan; Sutarno, D.

2018-04-01

The late research, linear matrices of vector finite element in two dimensional(2-D) magnetotelluric (MT) responses modeling was solved by non-sparse direct solver in TE mode. Nevertheless, there is some weakness which have to be improved especially accuracy in the low frequency (10-3 Hz-10-5 Hz) which is not achieved yet and high cost computation in dense mesh. In this work, the solver which is used is sparse direct solver instead of non-sparse direct solverto overcome the weaknesses of solving linear matrices of vector finite element metod using non-sparse direct solver. Sparse direct solver will be advantageous in solving linear matrices of vector finite element method because of the matrix properties which is symmetrical and sparse. The validation of sparse direct solver in solving linear matrices of vector finite element has been done for a homogen half-space model and vertical contact model by analytical solution. Thevalidation result of sparse direct solver in solving linear matrices of vector finite element shows that sparse direct solver is more stable than non-sparse direct solver in computing linear problem of vector finite element method especially in low frequency. In the end, the accuracy of 2D MT responses modelling in low frequency (10-3 Hz-10-5 Hz) has been reached out under the efficient allocation memory of array and less computational time consuming.

Computing Linear Mathematical Models Of Aircraft

NASA Technical Reports Server (NTRS)

Duke, Eugene L.; Antoniewicz, Robert F.; Krambeer, Keith D.

1991-01-01

Derivation and Definition of Linear Aircraft Model (LINEAR) computer program provides user with powerful, and flexible, standard, documented, and verified software tool for linearization of mathematical models of aerodynamics of aircraft. Intended for use in software tool to drive linear analysis of stability and design of control laws for aircraft. Capable of both extracting such linearized engine effects as net thrust, torque, and gyroscopic effects, and including these effects in linear model of system. Designed to provide easy selection of state, control, and observation variables used in particular model. Also provides flexibility of allowing alternate formulations of both state and observation equations. Written in FORTRAN.

Estimation of parameters in rational reaction rates of molecular biological systems via weighted least squares

NASA Astrophysics Data System (ADS)

Wu, Fang-Xiang; Mu, Lei; Shi, Zhong-Ke

2010-01-01

The models of gene regulatory networks are often derived from statistical thermodynamics principle or Michaelis-Menten kinetics equation. As a result, the models contain rational reaction rates which are nonlinear in both parameters and states. It is challenging to estimate parameters nonlinear in a model although there have been many traditional nonlinear parameter estimation methods such as Gauss-Newton iteration method and its variants. In this article, we develop a two-step method to estimate the parameters in rational reaction rates of gene regulatory networks via weighted linear least squares. This method takes the special structure of rational reaction rates into consideration. That is, in the rational reaction rates, the numerator and the denominator are linear in parameters. By designing a special weight matrix for the linear least squares, parameters in the numerator and the denominator can be estimated by solving two linear least squares problems. The main advantage of the developed method is that it can produce the analytical solutions to the estimation of parameters in rational reaction rates which originally is nonlinear parameter estimation problem. The developed method is applied to a couple of gene regulatory networks. The simulation results show the superior performance over Gauss-Newton method.

A geometric approach to non-linear correlations with intrinsic scatter

NASA Astrophysics Data System (ADS)

Pihajoki, Pauli

2017-12-01

We propose a new mathematical model for n - k-dimensional non-linear correlations with intrinsic scatter in n-dimensional data. The model is based on Riemannian geometry and is naturally symmetric with respect to the measured variables and invariant under coordinate transformations. We combine the model with a Bayesian approach for estimating the parameters of the correlation relation and the intrinsic scatter. A side benefit of the approach is that censored and truncated data sets and independent, arbitrary measurement errors can be incorporated. We also derive analytic likelihoods for the typical astrophysical use case of linear relations in n-dimensional Euclidean space. We pay particular attention to the case of linear regression in two dimensions and compare our results to existing methods. Finally, we apply our methodology to the well-known MBH-σ correlation between the mass of a supermassive black hole in the centre of a galactic bulge and the corresponding bulge velocity dispersion. The main result of our analysis is that the most likely slope of this correlation is ∼6 for the data sets used, rather than the values in the range of ∼4-5 typically quoted in the literature for these data.

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

PubMed

Suontama, M; van der Werf, J H J; Juga, J; Ojala, M

2012-09-01

Heritability and repeatability and genetic and phenotypic correlations were estimated for trotting race records with linear and generalized linear models using 510,519 records on 17,792 Finnhorses and 513,161 records on 25,536 Standardbred trotters. Heritability and repeatability were estimated for single racing time and earnings traits with linear models, and logarithmic scale was used for racing time and fourth-root scale for earnings to correct for nonnormality. Generalized linear models with a gamma distribution were applied for single racing time and with a multinomial distribution for single earnings traits. In addition, genetic parameters for annual earnings were estimated with linear models on the observed and fourth-root scales. Racing success traits of single placings, winnings, breaking stride, and disqualifications were analyzed using generalized linear models with a binomial distribution. Estimates of heritability were greatest for racing time, which ranged from 0.32 to 0.34. Estimates of heritability were low for single earnings with all distributions, ranging from 0.01 to 0.09. Annual earnings were closer to normal distribution than single earnings. Heritability estimates were moderate for annual earnings on the fourth-root scale, 0.19 for Finnhorses and 0.27 for Standardbred trotters. Heritability estimates for binomial racing success variables ranged from 0.04 to 0.12, being greatest for winnings and least for breaking stride. Genetic correlations among racing traits were high, whereas phenotypic correlations were mainly low to moderate, except correlations between racing time and earnings were high. On the basis of a moderate heritability and moderate to high repeatability for racing time and annual earnings, selection of horses for these traits is effective when based on a few repeated records. Because of high genetic correlations, direct selection for racing time and annual earnings would also result in good genetic response in racing success.

Vertical Distribution of Radiation Stress for Non-linear Shoaling Waves

NASA Astrophysics Data System (ADS)

Webb, B. M.; Slinn, D. N.

2004-12-01

The flux of momentum directed shoreward by an incident wave field, commonly referred to as the radiation stress, plays a significant role in nearshore circulation and, therefore, has a profound impact on the transport of pollutants, biota, and sediment in nearshore systems. Having received much attention since the seminal work of Longuet-Higgins and Stewart in the early 1960's, use of the radiation stress concept continues to be refined and evidence of its utility is widespread in literature pertaining to coastal and ocean science. A number of investigations, both numerical and analytical in nature, have used the concept of the radiation stress to derive appropriate forcing mechanisms that initiate cross-shore and longshore circulation, but typically in a depth-averaged sense due to a lack of information concerning the vertical distribution of the wave stresses. While depth-averaged nearshore circulation models are still widely used today, advancements in technology have permitted the adaptation of three-dimensional (3D) modeling techniques to study flow properties of complex nearshore circulation systems. It has been shown that the resulting circulation in these 3D models is very sensitive to the vertical distribution of the nearshore forcing, which have often been implemented as either depth-uniform or depth-linear distributions. Recently, analytical expressions describing the vertical structure of radiation stress components have appeared in the literature (see Mellor, 2003; Xia et al., 2004) but do not fully describe the magnitude and structure in the region bound by the trough and crest of non-linear, propagating waves. Utilizing a three-dimensional, non-linear, numerical model that resolves the time-dependent free surface, we present mean flow properties resulting from a simulation of Visser's (1984, 1991) laboratory experiment on uniform longshore currents. More specifically, we provide information regarding the vertical distribution of radiation stress components (Sxx and Sxy) resulting from obliquely incident, non-linear shoaling waves. Vertical profiles of the radiation stress components predicted by the numerical model are compared with published analytical solutions, expressions given by linear theory, and observations from an investigation employing second-order cnoidal wave theory.

United States Medical Licensing Examination and American Board of Pediatrics Certification Examination Results: Does the Residency Program Contribute to Trainee Achievement.

PubMed

Welch, Thomas R; Olson, Brad G; Nelsen, Elizabeth; Beck Dallaghan, Gary L; Kennedy, Gloria A; Botash, Ann

2017-09-01

To determine whether training site or prior examinee performance on the US Medical Licensing Examination (USMLE) step 1 and step 2 might predict pass rates on the American Board of Pediatrics (ABP) certifying examination. Data from graduates of pediatric residency programs completing the ABP certifying examination between 2009 and 2013 were obtained. For each, results of the initial ABP certifying examination were obtained, as well as results on National Board of Medical Examiners (NBME) step 1 and step 2 examinations. Hierarchical linear modeling was used to nest first-time ABP results within training programs to isolate program contribution to ABP results while controlling for USMLE step 1 and step 2 scores. Stepwise linear regression was then used to determine which of these examinations was a better predictor of ABP results. A total of 1110 graduates of 15 programs had complete testing results and were subject to analysis. Mean ABP scores for these programs ranged from 186.13 to 214.32. The hierarchical linear model suggested that the interaction of step 1 and 2 scores predicted ABP performance (F[1,1007.70] = 6.44, P = .011). By conducting a multilevel model by training program, both USMLE step examinations predicted first-time ABP results (b = .002, t = 2.54, P = .011). Linear regression analyses indicated that step 2 results were a better predictor of ABP performance than step 1 or a combination of the two USMLE scores. Performance on the USMLE examinations, especially step 2, predicts performance on the ABP certifying examination. The contribution of training site to ABP performance was statistically significant, though contributed modestly to the effect compared with prior USMLE scores. Copyright © 2017 Elsevier Inc. All rights reserved.

Supporting second grade lower secondary school students’ understanding of linear equation system in two variables using ethnomathematics

NASA Astrophysics Data System (ADS)

Nursyahidah, F.; Saputro, B. A.; Rubowo, M. R.

2018-03-01

The aim of this research is to know the students’ understanding of linear equation system in two variables using Ethnomathematics and to acquire learning trajectory of linear equation system in two variables for the second grade of lower secondary school students. This research used methodology of design research that consists of three phases, there are preliminary design, teaching experiment, and retrospective analysis. Subject of this study is 28 second grade students of Sekolah Menengah Pertama (SMP) 37 Semarang. The result of this research shows that the students’ understanding in linear equation system in two variables can be stimulated by using Ethnomathematics in selling buying tradition in Peterongan traditional market in Central Java as a context. All of strategies and model that was applied by students and also their result discussion shows how construction and contribution of students can help them to understand concept of linear equation system in two variables. All the activities that were done by students produce learning trajectory to gain the goal of learning. Each steps of learning trajectory of students have an important role in understanding the concept from informal to the formal level. Learning trajectory using Ethnomathematics that is produced consist of watching video of selling buying activity in Peterongan traditional market to construct linear equation in two variables, determine the solution of linear equation in two variables, construct model of linear equation system in two variables from contextual problem, and solving a contextual problem related to linear equation system in two variables.

Modelling female fertility traits in beef cattle using linear and non-linear models.

PubMed

Naya, H; Peñagaricano, F; Urioste, J I

2017-06-01

Female fertility traits are key components of the profitability of beef cattle production. However, these traits are difficult and expensive to measure, particularly under extensive pastoral conditions, and consequently, fertility records are in general scarce and somehow incomplete. Moreover, fertility traits are usually dominated by the effects of herd-year environment, and it is generally assumed that relatively small margins are kept for genetic improvement. New ways of modelling genetic variation in these traits are needed. Inspired in the methodological developments made by Prof. Daniel Gianola and co-workers, we assayed linear (Gaussian), Poisson, probit (threshold), censored Poisson and censored Gaussian models to three different kinds of endpoints, namely calving success (CS), number of days from first calving (CD) and number of failed oestrus (FE). For models involving FE and CS, non-linear models overperformed their linear counterparts. For models derived from CD, linear versions displayed better adjustment than the non-linear counterparts. Non-linear models showed consistently higher estimates of heritability and repeatability in all cases (h 2  < 0.08 and r < 0.13, for linear models; h 2  > 0.23 and r > 0.24, for non-linear models). While additive and permanent environment effects showed highly favourable correlations between all models (>0.789), consistency in selecting the 10% best sires showed important differences, mainly amongst the considered endpoints (FE, CS and CD). In consequence, endpoints should be considered as modelling different underlying genetic effects, with linear models more appropriate to describe CD and non-linear models better for FE and CS. © 2017 Blackwell Verlag GmbH.

USM3D Unstructured Grid Solutions for CAWAPI at NASA LaRC

NASA Technical Reports Server (NTRS)

Lamar, John E.; Abdol-Hamid, Khaled S.

2007-01-01

In support the Cranked Arrow Wing Aerodynamic Project International (CAWAPI) to improve the Technology Readiness Level of flow solvers by comparing results with measured F-16XL-1 flight data, NASA Langley employed the TetrUSS unstructured grid solver, USM3D, to obtain solutions for all seven flight conditions of interest. A newly available solver version that incorporates a number of turbulence models, including the two-equation linear and non-linear k-epsilon, was used in this study. As a first test, a choice was made to utilize only a single grid resolution with the solver for the simulation of the different flight conditions. Comparisons are presented with three turbulence models in USM3D, flight data for surface pressure, boundary-layer profiles, and skin-friction results, as well as limited predictions from other solvers. A result of these comparisons is that the USM3D solver can be used in an engineering environment to predict flow physics on a complex configuration at flight Reynolds numbers with a two-equation linear k-epsilon turbulence model.

Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Data

USGS Publications Warehouse

Rasmussen, Patrick P.; Gray, John R.; Glysson, G. Douglas; Ziegler, Andrew C.

2009-01-01

In-stream continuous turbidity and streamflow data, calibrated with measured suspended-sediment concentration data, can be used to compute a time series of suspended-sediment concentration and load at a stream site. Development of a simple linear (ordinary least squares) regression model for computing suspended-sediment concentrations from instantaneous turbidity data is the first step in the computation process. If the model standard percentage error (MSPE) of the simple linear regression model meets a minimum criterion, this model should be used to compute a time series of suspended-sediment concentrations. Otherwise, a multiple linear regression model using paired instantaneous turbidity and streamflow data is developed and compared to the simple regression model. If the inclusion of the streamflow variable proves to be statistically significant and the uncertainty associated with the multiple regression model results in an improvement over that for the simple linear model, the turbidity-streamflow multiple linear regression model should be used to compute a suspended-sediment concentration time series. The computed concentration time series is subsequently used with its paired streamflow time series to compute suspended-sediment loads by standard U.S. Geological Survey techniques. Once an acceptable regression model is developed, it can be used to compute suspended-sediment concentration beyond the period of record used in model development with proper ongoing collection and analysis of calibration samples. Regression models to compute suspended-sediment concentrations are generally site specific and should never be considered static, but they represent a set period in a continually dynamic system in which additional data will help verify any change in sediment load, type, and source.

Use of probabilistic weights to enhance linear regression myoelectric control

NASA Astrophysics Data System (ADS)

Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.

2015-12-01

Objective. Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Approach. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts’ law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Main results. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. Significance. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

Reduced-Order Models Based on Linear and Nonlinear Aerodynamic Impulse Responses

NASA Technical Reports Server (NTRS)

Silva, Walter A.

1999-01-01

This paper discusses a method for the identification and application of reduced-order models based on linear and nonlinear aerodynamic impulse responses. The Volterra theory of nonlinear systems and an appropriate kernel identification technique are described. Insight into the nature of kernels is provided by applying the method to the nonlinear Riccati equation in a non-aerodynamic application. The method is then applied to a nonlinear aerodynamic model of RAE 2822 supercritical airfoil undergoing plunge motions using the CFL3D Navier-Stokes flow solver with the Spalart-Allmaras turbulence model. Results demonstrate the computational efficiency of the technique.

Reduced Order Models Based on Linear and Nonlinear Aerodynamic Impulse Responses

NASA Technical Reports Server (NTRS)

Silva, Walter A.

1999-01-01

This paper discusses a method for the identification and application of reduced-order models based on linear and nonlinear aerodynamic impulse responses. The Volterra theory of nonlinear systems and an appropriate kernel identification technique are described. Insight into the nature of kernels is provided by applying the method to the nonlinear Riccati equation in a non-aerodynamic application. The method is then applied to a nonlinear aerodynamic model of an RAE 2822 supercritical airfoil undergoing plunge motions using the CFL3D Navier-Stokes flow solver with the Spalart-Allmaras turbulence model. Results demonstrate the computational efficiency of the technique.

PID-based error signal modeling

NASA Astrophysics Data System (ADS)

Yohannes, Tesfay

1997-10-01

This paper introduces a PID based signal error modeling. The error modeling is based on the betterment process. The resulting iterative learning algorithm is introduced and a detailed proof is provided for both linear and nonlinear systems.

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

Davis, C.G.

Starting with the initial understanding that pulsation in variable stars is caused by the heat engine of Hydrogen and Helium ionization in their atmospheres (A.S. Eddington in Cox 1980) it was soon realized that non-linear effects were responsible for the detailed features on their light and velocity curves. With the advent of the computer we were able to solve the coupled set of hydrodynamics and radiation diffusion equations to model these non-linear features. This paper describes some recent model results for long period (LP) Cepheids in an attempt to get another handle on Cepheid masses. Section II discusses these resultsmore » and Section III considers the implications of these model results on the problem of the Cepheid mass discrepancy.« less

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

PubMed Central

Song, Yong-Ze; Yang, Hong-Lei; Peng, Jun-Huan; Song, Yi-Rong; Sun, Qian; Li, Yuan

2015-01-01

Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi’an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5. PMID:26540446

Dynamic linear models using the Kalman filter for early detection and early warning of malaria outbreaks

NASA Astrophysics Data System (ADS)

Merkord, C. L.; Liu, Y.; DeVos, M.; Wimberly, M. C.

2015-12-01

Malaria early detection and early warning systems are important tools for public health decision makers in regions where malaria transmission is seasonal and varies from year to year with fluctuations in rainfall and temperature. Here we present a new data-driven dynamic linear model based on the Kalman filter with time-varying coefficients that are used to identify malaria outbreaks as they occur (early detection) and predict the location and timing of future outbreaks (early warning). We fit linear models of malaria incidence with trend and Fourier form seasonal components using three years of weekly malaria case data from 30 districts in the Amhara Region of Ethiopia. We identified past outbreaks by comparing the modeled prediction envelopes with observed case data. Preliminary results demonstrated the potential for improved accuracy and timeliness over commonly-used methods in which thresholds are based on simpler summary statistics of historical data. Other benefits of the dynamic linear modeling approach include robustness to missing data and the ability to fit models with relatively few years of training data. To predict future outbreaks, we started with the early detection model for each district and added a regression component based on satellite-derived environmental predictor variables including precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and land surface temperature (LST) and spectral indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). We included lagged environmental predictors in the regression component of the model, with lags chosen based on cross-correlation of the one-step-ahead forecast errors from the first model. Our results suggest that predictions of future malaria outbreaks can be improved by incorporating lagged environmental predictors.

Comparison of the occlusal contact area of virtual models and actual models: a comparative in vitro study on Class I and Class II malocclusion models.

PubMed

Lee, Hyemin; Cha, Jooly; Chun, Youn-Sic; Kim, Minji

2018-06-19

The occlusal registration of virtual models taken by intraoral scanners sometimes shows patterns which seem much different from the patients' occlusion. Therefore, this study aims to evaluate the accuracy of virtual occlusion by comparing virtual occlusal contact area with actual occlusal contact area using a plaster model in vitro. Plaster dental models, 24 sets of Class I models and 20 sets of Class II models, were divided into a Molar, Premolar, and Anterior group. The occlusal contact areas calculated by the Prescale method and the virtual occlusion by scanning method were compared, and the ratio of the molar and incisor area were compared in order to find any particular tendencies. There was no significant difference between the Prescale results and the scanner results in both the molar and premolar groups (p = 0.083 and 0.053, respectively). On the other hand, there was a significant difference between the Prescale and the scanner results in the anterior group with the scanner results presenting overestimation of the occlusal contact points (p < 0.05). In Molars group, the regression analysis shows that the two variables express linear correlation and has a linear equation with a slope of 0.917. R 2 is 0.930. Groups of Premolars and Anteriors had a week linear relationship and greater dispersion. Difference between the actual and virtual occlusion revealed in the anterior portion, where overestimation was observed in the virtual model obtained from the scanning method. Nevertheless, molar and premolar areas showed relatively accurate occlusal contact area in the virtual model.

Linear and nonlinear winter atmospheric responses to extreme phases of low frequency Pacific sea surface temperature variability

NASA Astrophysics Data System (ADS)

Cao, Dandan; Wu, Qigang; Hu, Aixue; Yao, Yonghong; Liu, Shizuo; Schroeder, Steven R.; Yang, Fucheng

2018-02-01

This study examines Northern Hemisphere winter (DJFM) atmospheric responses to opposite strong phases of interdecadal (low frequency, LF) Pacific sea surface temperature (SST) forcing, which resembles El Niño-Southern Oscillation (ENSO) on a longer time scale, in observations and GFDL and CAM4 model simulations. Over the Pacific-North America (PNA) sector, linear observed responses of 500-hPa height (Z500) anomalies resemble the PNA teleconnection pattern, but show a PNA-like nonlinear response because of a westward Z500 shift in the negative (LF-) relative to the positive LF (LF+) phase. Significant extratropical linear responses include a North Atlantic Oscillation (NAO)-like Z500 anomaly, a dipole-like Z500 anomaly over northern Eurasia associated with warming over mid-high latitude Eurasia, and a Southern Annular anomaly pattern associated with warming in southern land areas. Significant nonlinear Z500 responses also include a NAO-like anomaly pattern. Models forced by LF+ and LF- SST anomalies reproduce many aspects of observed linear and nonlinear responses over the Pacific-North America sector, and linear responses over southern land, but not in the North Atlantic-European sector and Eurasia. Both models simulate PNA-like linear responses in the North Pacific-North America region similar to observed, but show larger PNA-like LF+ responses, resulting in a PNA nonlinear response. The nonlinear PNA responses result from both nonlinear western tropical Pacific rainfall changes and extratropical transient eddy feedbacks. With LF tropical Pacific forcing only (LFTP+ and LFTP-, climatological SST elsewhere), CAM4 simulates a significant NAO response to LFTP-, including a linear negative and nonlinear positive NAO response.

Study on magnetic circuit of moving magnet linear compressor

NASA Astrophysics Data System (ADS)

Xia, Ming; Chen, Xiaoping; Chen, Jun

2015-05-01

The moving magnet linear compressors are very popular in the tactical miniature stirling cryocoolers. The magnetic circuit of LFC3600 moving magnet linear compressor, manufactured by Kunming institute of Physics, was studied in this study. Three methods of the analysis theory, numerical calculation and experiment study were applied in the analysis process. The calculated formula of magnetic reluctance and magnetomotive force were given in theoretical analysis model. The magnetic flux density and magnetic flux line were analyzed in numerical analysis model. A testing method was designed to test the magnetic flux density of the linear compressor. When the piston of the motor was in the equilibrium position, the value of the magnetic flux density was at the maximum of 0.27T. The results were almost equal to the ones from numerical analysis.

A frequency domain linearized Navier-Stokes equations approach to acoustic propagation in flow ducts with sharp edges.

PubMed

Kierkegaard, Axel; Boij, Susann; Efraimsson, Gunilla

2010-02-01

Acoustic wave propagation in flow ducts is commonly modeled with time-domain non-linear Navier-Stokes equation methodologies. To reduce computational effort, investigations of a linearized approach in frequency domain are carried out. Calculations of sound wave propagation in a straight duct are presented with an orifice plate and a mean flow present. Results of transmission and reflections at the orifice are presented on a two-port scattering matrix form and are compared to measurements with good agreement. The wave propagation is modeled with a frequency domain linearized Navier-Stokes equation methodology. This methodology is found to be efficient for cases where the acoustic field does not alter the mean flow field, i.e., when whistling does not occur.

Influence of magnetic flutter on tearing growth in linear and nonlinear theory

NASA Astrophysics Data System (ADS)

Kreifels, L.; Hornsby, W. A.; Weikl, A.; Peeters, A. G.

2018-06-01

Recent simulations of tearing modes in turbulent regimes show an unexpected enhancement in the growth rate. In this paper the effect is investigated analytically. The enhancement is linked to the influence of turbulent magnetic flutter, which is modelled by diffusion terms in magnetohydrodynamics (MHD) momentum balance and Ohm’s law. Expressions for the linear growth rate as well as the island width in nonlinear theory for small amplitudes are derived. The results indicate an enhanced linear growth rate and a larger linear layer width compared with resistive MHD. Also the island width in the nonlinear regime grows faster in the diffusive model. These observations correspond well to simulations in which the effect of turbulence on the magnetic island width and tearing mode growth is analyzed.

Log-Linear Modeling of Agreement among Expert Exposure Assessors

PubMed Central

Hunt, Phillip R.; Friesen, Melissa C.; Sama, Susan; Ryan, Louise; Milton, Donald

2015-01-01

Background: Evaluation of expert assessment of exposure depends, in the absence of a validation measurement, upon measures of agreement among the expert raters. Agreement is typically measured using Cohen’s Kappa statistic, however, there are some well-known limitations to this approach. We demonstrate an alternate method that uses log-linear models designed to model agreement. These models contain parameters that distinguish between exact agreement (diagonals of agreement matrix) and non-exact associations (off-diagonals). In addition, they can incorporate covariates to examine whether agreement differs across strata. Methods: We applied these models to evaluate agreement among expert ratings of exposure to sensitizers (none, likely, high) in a study of occupational asthma. Results: Traditional analyses using weighted kappa suggested potential differences in agreement by blue/white collar jobs and office/non-office jobs, but not case/control status. However, the evaluation of the covariates and their interaction terms in log-linear models found no differences in agreement with these covariates and provided evidence that the differences observed using kappa were the result of marginal differences in the distribution of ratings rather than differences in agreement. Differences in agreement were predicted across the exposure scale, with the likely moderately exposed category more difficult for the experts to differentiate from the highly exposed category than from the unexposed category. Conclusions: The log-linear models provided valuable information about patterns of agreement and the structure of the data that were not revealed in analyses using kappa. The models’ lack of dependence on marginal distributions and the ease of evaluating covariates allow reliable detection of observational bias in exposure data. PMID:25748517

Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification.

PubMed

Spinnato, J; Roubaud, M-C; Burle, B; Torrésani, B

2015-06-01

The main goal of this work is to develop a model for multisensor signals, such as magnetoencephalography or electroencephalography (EEG) signals that account for inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI-type experiments. The method involves the linear mixed effects statistical model, wavelet transform, and spatial filtering, and aims at the characterization of localized discriminant features in multisensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e., discriminant) and background noise, using a very simple Gaussian linear mixed model. Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context. The combination of the linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves upon earlier results on similar problems, and the three main ingredients all play an important role.

Improving the Efficiency of Abdominal Aortic Aneurysm Wall Stress Computations

PubMed Central

Zelaya, Jaime E.; Goenezen, Sevan; Dargon, Phong T.; Azarbal, Amir-Farzin; Rugonyi, Sandra

2014-01-01

An abdominal aortic aneurysm is a pathological dilation of the abdominal aorta, which carries a high mortality rate if ruptured. The most commonly used surrogate marker of rupture risk is the maximal transverse diameter of the aneurysm. More recent studies suggest that wall stress from models of patient-specific aneurysm geometries extracted, for instance, from computed tomography images may be a more accurate predictor of rupture risk and an important factor in AAA size progression. However, quantification of wall stress is typically computationally intensive and time-consuming, mainly due to the nonlinear mechanical behavior of the abdominal aortic aneurysm walls. These difficulties have limited the potential of computational models in clinical practice. To facilitate computation of wall stresses, we propose to use a linear approach that ensures equilibrium of wall stresses in the aneurysms. This proposed linear model approach is easy to implement and eliminates the burden of nonlinear computations. To assess the accuracy of our proposed approach to compute wall stresses, results from idealized and patient-specific model simulations were compared to those obtained using conventional approaches and to those of a hypothetical, reference abdominal aortic aneurysm model. For the reference model, wall mechanical properties and the initial unloaded and unstressed configuration were assumed to be known, and the resulting wall stresses were used as reference for comparison. Our proposed linear approach accurately approximates wall stresses for varying model geometries and wall material properties. Our findings suggest that the proposed linear approach could be used as an effective, efficient, easy-to-use clinical tool to estimate patient-specific wall stresses. PMID:25007052

Vestibular coriolis effect differences modeled with three-dimensional linear-angular interactions.

PubMed

Holly, Jan E

2004-01-01

The vestibular coriolis (or "cross-coupling") effect is traditionally explained by cross-coupled angular vectors, which, however, do not explain the differences in perceptual disturbance under different acceleration conditions. For example, during head roll tilt in a rotating chair, the magnitude of perceptual disturbance is affected by a number of factors, including acceleration or deceleration of the chair rotation or a zero-g environment. Therefore, it has been suggested that linear-angular interactions play a role. The present research investigated whether these perceptual differences and others involving linear coriolis accelerations could be explained under one common framework: the laws of motion in three dimensions, which include all linear-angular interactions among all six components of motion (three angular and three linear). The results show that the three-dimensional laws of motion predict the differences in perceptual disturbance. No special properties of the vestibular system or nervous system are required. In addition, simulations were performed with angular, linear, and tilt time constants inserted into the model, giving the same predictions. Three-dimensional graphics were used to highlight the manner in which linear-angular interaction causes perceptual disturbance, and a crucial component is the Stretch Factor, which measures the "unexpected" linear component.

Electromagnetic axial anomaly in a generalized linear sigma model

NASA Astrophysics Data System (ADS)

Fariborz, Amir H.; Jora, Renata

2017-06-01

We construct the electromagnetic anomaly effective term for a generalized linear sigma model with two chiral nonets, one with a quark-antiquark structure, the other one with a four-quark content. We compute in the leading order of this framework the decays into two photons of six pseudoscalars: π0(137 ), π0(1300 ), η (547 ), η (958 ), η (1295 ) and η (1760 ). Our results agree well with the available experimental data.

Compilation of Abstracts of Theses Submitted by Candidates for Degrees: October 1988 to September 1989

DTIC Science & Technology

1989-09-30

to accommodate peripherally non -uniform flow modelling free of experimental uncertainties. It was effects (blockage) in the throughflow code...combines that experimental control functions with a detail in this thesis, and the results of a computer menu-driven, diagnostic subsystem to ensure...equations and design a complete (DSL) for both linear and non -linear models and automatic control system for the three dimensional compared. Cross

Markov and semi-Markov switching linear mixed models used to identify forest tree growth components.

PubMed

Chaubert-Pereira, Florence; Guédon, Yann; Lavergne, Christian; Trottier, Catherine

2010-09-01

Tree growth is assumed to be mainly the result of three components: (i) an endogenous component assumed to be structured as a succession of roughly stationary phases separated by marked change points that are asynchronous among individuals, (ii) a time-varying environmental component assumed to take the form of synchronous fluctuations among individuals, and (iii) an individual component corresponding mainly to the local environment of each tree. To identify and characterize these three components, we propose to use semi-Markov switching linear mixed models, i.e., models that combine linear mixed models in a semi-Markovian manner. The underlying semi-Markov chain represents the succession of growth phases and their lengths (endogenous component) whereas the linear mixed models attached to each state of the underlying semi-Markov chain represent-in the corresponding growth phase-both the influence of time-varying climatic covariates (environmental component) as fixed effects, and interindividual heterogeneity (individual component) as random effects. In this article, we address the estimation of Markov and semi-Markov switching linear mixed models in a general framework. We propose a Monte Carlo expectation-maximization like algorithm whose iterations decompose into three steps: (i) sampling of state sequences given random effects, (ii) prediction of random effects given state sequences, and (iii) maximization. The proposed statistical modeling approach is illustrated by the analysis of successive annual shoots along Corsican pine trunks influenced by climatic covariates. © 2009, The International Biometric Society.

User's manual for interactive LINEAR: A FORTRAN program to derive linear aircraft models

NASA Technical Reports Server (NTRS)

Antoniewicz, Robert F.; Duke, Eugene L.; Patterson, Brian P.

1988-01-01

An interactive FORTRAN program that provides the user with a powerful and flexible tool for the linearization of aircraft aerodynamic models is documented in this report. The program LINEAR numerically determines a linear system model using nonlinear equations of motion and a user-supplied linear or nonlinear aerodynamic model. The nonlinear equations of motion used are six-degree-of-freedom equations with stationary atmosphere and flat, nonrotating earth assumptions. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model.

A model for prediction of color change after tooth bleaching based on CIELAB color space

NASA Astrophysics Data System (ADS)

Herrera, Luis J.; Santana, Janiley; Yebra, Ana; Rivas, María. José; Pulgar, Rosa; Pérez, María. M.

2017-08-01

An experimental study aiming to develop a model based on CIELAB color space for prediction of color change after a tooth bleaching procedure is presented. Multivariate linear regression models were obtained to predict the L*, a*, b* and W* post-bleaching values using the pre-bleaching L*, a*and b*values. Moreover, univariate linear regression models were obtained to predict the variation in chroma (C*), hue angle (h°) and W*. The results demonstrated that is possible to estimate color change when using a carbamide peroxide tooth-bleaching system. The models obtained can be applied in clinic to predict the colour change after bleaching.

Chemical networks with inflows and outflows: a positive linear differential inclusions approach.

PubMed

Angeli, David; De Leenheer, Patrick; Sontag, Eduardo D

2009-01-01

Certain mass-action kinetics models of biochemical reaction networks, although described by nonlinear differential equations, may be partially viewed as state-dependent linear time-varying systems, which in turn may be modeled by convex compact valued positive linear differential inclusions. A result is provided on asymptotic stability of such inclusions, and applied to a ubiquitous biochemical reaction network with inflows and outflows, known as the futile cycle. We also provide a characterization of exponential stability of general homogeneous switched systems which is not only of interest in itself, but also plays a role in the analysis of the futile cycle. 2009 American Institute of Chemical Engineers

The nonlinear dynamics of a spacecraft coupled to the vibration of a contained fluid

NASA Technical Reports Server (NTRS)

Peterson, Lee D.; Crawley, Edward F.; Hansman, R. John

1988-01-01

The dynamics of a linear spacecraft mode coupled to a nonlinear low gravity slosh of a fluid in a cylindrical tank is investigated. Coupled, nonlinear equations of motion for the fluid-spacecraft dynamics are derived through an assumed mode Lagrangian method. Unlike linear fluid slosh models, this nonlinear slosh model retains two fundamental slosh modes and three secondary modes. An approximate perturbation solution of the equations of motion indicates that the nonlinear coupled system response involves fluid-spacecraft modal resonances not predicted by either a linear, or a nonlinear, uncoupled slosh analysis. Experimental results substantiate the analytical predictions.

Revisiting Isotherm Analyses Using R: Comparison of Linear, Non-linear, and Bayesian Techniques

EPA Science Inventory

Extensive adsorption isotherm data exist for an array of chemicals of concern on a variety of engineered and natural sorbents. Several isotherm models exist that can accurately describe these data from which the resultant fitting parameters may subsequently be used in numerical ...

Noise limitations in optical linear algebra processors.

PubMed

Batsell, S G; Jong, T L; Walkup, J F; Krile, T F

1990-05-10

A general statistical noise model is presented for optical linear algebra processors. A statistical analysis which includes device noise, the multiplication process, and the addition operation is undertaken. We focus on those processes which are architecturally independent. Finally, experimental results which verify the analytical predictions are also presented.

Impact of Cross-Axis Structural Dynamics on Validation of Linear Models for Space Launch System

NASA Technical Reports Server (NTRS)

Pei, Jing; Derry, Stephen D.; Zhou Zhiqiang; Newsom, Jerry R.

2014-01-01

A feasibility study was performed to examine the advisability of incorporating a set of Programmed Test Inputs (PTIs) during the Space Launch System (SLS) vehicle flight. The intent of these inputs is to provide validation to the preflight models for control system stability margins, aerodynamics, and structural dynamics. During October 2009, Ares I-X program was successful in carrying out a series of PTI maneuvers which provided a significant amount of valuable data for post-flight analysis. The resulting data comparisons showed excellent agreement with the preflight linear models across the frequency spectrum of interest. However unlike Ares I-X, the structural dynamics associated with the SLS boost phase configuration are far more complex and highly coupled in all three axes. This presents a challenge when implementing this similar system identification technique to SLS. Preliminary simulation results show noticeable mismatches between PTI validation and analytical linear models in the frequency range of the structural dynamics. An alternate approach was examined which demonstrates the potential for better overall characterization of the system frequency response as well as robustness of the control design.

Development and validation of a general purpose linearization program for rigid aircraft models

NASA Technical Reports Server (NTRS)

Duke, E. L.; Antoniewicz, R. F.

1985-01-01

A FORTRAN program that provides the user with a powerful and flexible tool for the linearization of aircraft models is discussed. The program LINEAR numerically determines a linear systems model using nonlinear equations of motion and a user-supplied, nonlinear aerodynamic model. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model. Also, included in the report is a comparison of linear and nonlinear models for a high performance aircraft.

Improving linear accelerator service response with a real- time electronic event reporting system.

PubMed

Hoisak, Jeremy D P; Pawlicki, Todd; Kim, Gwe-Ya; Fletcher, Richard; Moore, Kevin L

2014-09-08

To track linear accelerator performance issues, an online event recording system was developed in-house for use by therapists and physicists to log the details of technical problems arising on our institution's four linear accelerators. In use since October 2010, the system was designed so that all clinical physicists would receive email notification when an event was logged. Starting in October 2012, we initiated a pilot project in collaboration with our linear accelerator vendor to explore a new model of service and support, in which event notifications were also sent electronically directly to dedicated engineers at the vendor's technical help desk, who then initiated a response to technical issues. Previously, technical issues were reported by telephone to the vendor's call center, which then disseminated information and coordinated a response with the Technical Support help desk and local service engineers. The purpose of this work was to investigate the improvements to clinical operations resulting from this new service model. The new and old service models were quantitatively compared by reviewing event logs and the oncology information system database in the nine months prior to and after initiation of the project. Here, we focus on events that resulted in an inoperative linear accelerator ("down" machine). Machine downtime, vendor response time, treatment cancellations, and event resolution were evaluated and compared over two equivalent time periods. In 389 clinical days, there were 119 machine-down events: 59 events before and 60 after introduction of the new model. In the new model, median time to service response decreased from 45 to 8 min, service engineer dispatch time decreased 44%, downtime per event decreased from 45 to 20 min, and treatment cancellations decreased 68%. The decreased vendor response time and reduced number of on-site visits by a service engineer resulted in decreased downtime and decreased patient treatment cancellations.

Correcting for population structure and kinship using the linear mixed model: theory and extensions.

PubMed

Hoffman, Gabriel E

2013-01-01

Population structure and kinship are widespread confounding factors in genome-wide association studies (GWAS). It has been standard practice to include principal components of the genotypes in a regression model in order to account for population structure. More recently, the linear mixed model (LMM) has emerged as a powerful method for simultaneously accounting for population structure and kinship. The statistical theory underlying the differences in empirical performance between modeling principal components as fixed versus random effects has not been thoroughly examined. We undertake an analysis to formalize the relationship between these widely used methods and elucidate the statistical properties of each. Moreover, we introduce a new statistic, effective degrees of freedom, that serves as a metric of model complexity and a novel low rank linear mixed model (LRLMM) to learn the dimensionality of the correction for population structure and kinship, and we assess its performance through simulations. A comparison of the results of LRLMM and a standard LMM analysis applied to GWAS data from the Multi-Ethnic Study of Atherosclerosis (MESA) illustrates how our theoretical results translate into empirical properties of the mixed model. Finally, the analysis demonstrates the ability of the LRLMM to substantially boost the strength of an association for HDL cholesterol in Europeans.

The influence of continuous historical velocity difference information on micro-cooperative driving stability

NASA Astrophysics Data System (ADS)

Yang, Liang-Yi; Sun, Di-Hua; Zhao, Min; Cheng, Sen-Lin; Zhang, Geng; Liu, Hui

2018-03-01

In this paper, a new micro-cooperative driving car-following model is proposed to investigate the effect of continuous historical velocity difference information on traffic stability. The linear stability criterion of the new model is derived with linear stability theory and the results show that the unstable region in the headway-sensitivity space will be shrunk by taking the continuous historical velocity difference information into account. Through nonlinear analysis, the mKdV equation is derived to describe the traffic evolution behavior of the new model near the critical point. Via numerical simulations, the theoretical analysis results are verified and the results indicate that the continuous historical velocity difference information can enhance the stability of traffic flow in the micro-cooperative driving process.

Evidence for self-refraction in a convergence zone: NPE (Nonlinear progressive wave equation) model results

NASA Technical Reports Server (NTRS)

Mcdonald, B. Edward; Plante, Daniel R.

1989-01-01

The nonlinear progressive wave equation (NPE) model was developed by the Naval Ocean Research and Development Activity during 1982 to 1987 to study nonlinear effects in long range oceanic propagation of finite amplitude acoustic waves, including weak shocks. The NPE model was applied to propagation of a generic shock wave (initial condition provided by Sandia Division 1533) in a few illustrative environments. The following consequences of nonlinearity are seen by comparing linear and nonlinear NPE results: (1) a decrease in shock strength versus range (a well-known result of entropy increases at the shock front); (2) an increase in the convergence zone range; and (3) a vertical meandering of the energy path about the corresponding linear ray path. Items (2) and (3) are manifestations of self-refraction.

The effect of choosing three different C factor formulae derived from NDVI on a fully raster-based erosion modelling

NASA Astrophysics Data System (ADS)

Sulistyo, Bambang

2016-11-01

The research was aimed at studying the efect of choosing three different C factor formulae derived from NDVI on a fully raster-based erosion modelling of The USLE using remote sensing data and GIS technique. Methods applied was by analysing all factors affecting erosion such that all data were in the form of raster. Those data were R, K, LS, C and P factors. Monthly R factor was evaluated based on formula developed by Abdurachman. K factor was determined using modified formula used by Ministry of Forestry based on soil samples taken in the field. LS factor was derived from Digital Elevation Model. Three C factors used were all derived from NDVI and developed by Suriyaprasit (non-linear) and by Sulistyo (linear and non-linear). P factor was derived from the combination between slope data and landcover classification interpreted from Landsat 7 ETM+. Another analysis was the creation of map of Bulk Density used to convert erosion unit. To know the model accuracy, model validation was done by applying statistical analysis and by comparing Emodel with Eactual. A threshold value of ≥ 0.80 or ≥ 80% was chosen to justify. The research result showed that all Emodel using three formulae of C factors have coeeficient of correlation value of > 0.8. The results of analysis of variance showed that there was significantly difference between Emodel and Eactual when using C factor formula developed by Suriyaprasit and Sulistyo (non-linear). Among the three formulae, only Emodel using C factor formula developed by Sulistyo (linear) reached the accuracy of 81.13% while the other only 56.02% as developed by Sulistyo (nonlinear) and 4.70% as developed by Suriyaprasit, respectively.

Physics at the [Formula: see text] linear collider.

PubMed

Moortgat-Pick, G; Baer, H; Battaglia, M; Belanger, G; Fujii, K; Kalinowski, J; Heinemeyer, S; Kiyo, Y; Olive, K; Simon, F; Uwer, P; Wackeroth, D; Zerwas, P M; Arbey, A; Asano, M; Bagger, J; Bechtle, P; Bharucha, A; Brau, J; Brümmer, F; Choi, S Y; Denner, A; Desch, K; Dittmaier, S; Ellwanger, U; Englert, C; Freitas, A; Ginzburg, I; Godfrey, S; Greiner, N; Grojean, C; Grünewald, M; Heisig, J; Höcker, A; Kanemura, S; Kawagoe, K; Kogler, R; Krawczyk, M; Kronfeld, A S; Kroseberg, J; Liebler, S; List, J; Mahmoudi, F; Mambrini, Y; Matsumoto, S; Mnich, J; Mönig, K; Mühlleitner, M M; Pöschl, R; Porod, W; Porto, S; Rolbiecki, K; Schmitt, M; Serpico, P; Stanitzki, M; Stål, O; Stefaniak, T; Stöckinger, D; Weiglein, G; Wilson, G W; Zeune, L; Moortgat, F; Xella, S; Bagger, J; Brau, J; Ellis, J; Kawagoe, K; Komamiya, S; Kronfeld, A S; Mnich, J; Peskin, M; Schlatter, D; Wagner, A; Yamamoto, H

A comprehensive review of physics at an [Formula: see text] linear collider in the energy range of [Formula: see text] GeV-3 TeV is presented in view of recent and expected LHC results, experiments from low-energy as well as astroparticle physics. The report focusses in particular on Higgs-boson, top-quark and electroweak precision physics, but also discusses several models of beyond the standard model physics such as supersymmetry, little Higgs models and extra gauge bosons. The connection to cosmology has been analysed as well.

Preliminary results in implementing a model of the world economy on the CYBER 205: A case of large sparse nonsymmetric linear equations

NASA Technical Reports Server (NTRS)

Szyld, D. B.

1984-01-01

A brief description of the Model of the World Economy implemented at the Institute for Economic Analysis is presented, together with our experience in converting the software to vector code. For each time period, the model is reduced to a linear system of over 2000 variables. The matrix of coefficients has a bordered block diagonal structure, and we show how some of the matrix operations can be carried out on all diagonal blocks at once.

Adaptive Failure Compensation for Aircraft Flight Control Using Engine Differentials: Regulation

NASA Technical Reports Server (NTRS)

Yu, Liu; Xidong, Tang; Gang, Tao; Joshi, Suresh M.

2005-01-01

The problem of using engine thrust differentials to compensate for rudder and aileron failures in aircraft flight control is addressed in this paper in a new framework. A nonlinear aircraft model that incorporates engine di erentials in the dynamic equations is employed and linearized to describe the aircraft s longitudinal and lateral motion. In this model two engine thrusts of an aircraft can be adjusted independently so as to provide the control flexibility for rudder or aileron failure compensation. A direct adaptive compensation scheme for asymptotic regulation is developed to handle uncertain actuator failures in the linearized system. A design condition is specified to characterize the system redundancy needed for failure compensation. The adaptive regulation control scheme is applied to the linearized model of a large transport aircraft in which the longitudinal and lateral motions are coupled as the result of using engine thrust differentials. Simulation results are presented to demonstrate the effectiveness of the adaptive compensation scheme.

A Two-Dimensional Linear Bicharacteristic Scheme for Electromagnetics

NASA Technical Reports Server (NTRS)

Beggs, John H.

2002-01-01

The upwind leapfrog or Linear Bicharacteristic Scheme (LBS) has previously been implemented and demonstrated on one-dimensional electromagnetic wave propagation problems. This memorandum extends the Linear Bicharacteristic Scheme for computational electromagnetics to model lossy dielectric and magnetic materials and perfect electrical conductors in two dimensions. This is accomplished by proper implementation of the LBS for homogeneous lossy dielectric and magnetic media and for perfect electrical conductors. Both the Transverse Electric and Transverse Magnetic polarizations are considered. Computational requirements and a Fourier analysis are also discussed. Heterogeneous media are modeled through implementation of surface boundary conditions and no special extrapolations or interpolations at dielectric material boundaries are required. Results are presented for two-dimensional model problems on uniform grids, and the Finite Difference Time Domain (FDTD) algorithm is chosen as a convenient reference algorithm for comparison. The results demonstrate that the two-dimensional explicit LBS is a dissipation-free, second-order accurate algorithm which uses a smaller stencil than the FDTD algorithm, yet it has less phase velocity error.

Linear and Nonlinear Viscoelastic Modeling of Aorta and Carotid Pressure-Area Dynamics under in vivo and ex vivo Conditions

PubMed Central

Valdez-Jasso, Daniela; Bia, Daniel; Zócalo, Yanina; Armentano, Ricardo L.; Haider, Mansoor A.; Olufsen, Mette S.

2013-01-01

A better understanding of the biomechanical properties of the arterial wall provides important insight into arterial vascular biology under normal (healthy) and pathological conditions. This insight has potential to improve tracking of disease progression and to aid in vascular graft design and implementation. In this study, we use linear and nonlinear viscoelastic models to predict biomechanical properties of the thoracic descending aorta and the carotid artery under ex vivo and in vivo conditions in ovine and human arteries. Models analyzed include a four-parameter (linear) Kelvin viscoelastic model and two five-parameter nonlinear viscoelastic models (an arctangent and a sigmoid model) that relate changes in arterial blood pressure to the vessel cross-sectional area (via estimation of vessel strain). These models were developed using the framework of Quasilinear Viscoelasticity (QLV) theory and were validated using measurements from the thoracic descending aorta and the carotid artery obtained from human and ovine arteries. In vivo measurements were obtained from ten ovine aortas and ten human carotid arteries. Ex vivo measurements (from both locations) were made in eleven male Merino sheep. Biomechanical properties were obtained through constrained estimation of model parameters. To further investigate the parameter estimates we computed standard errors and confidence intervals and we used analysis of variance to compare results within and between groups. Overall, our results indicate that optimal model selection depends on the arterial type. Results showed that for the thoracic descending aorta (under both experimental conditions) the best predictions were obtained with the nonlinear sigmoid model, while under healthy physiological pressure loading the carotid arteries nonlinear stiffening with increasing pressure is negligible, and consequently, the linear (Kelvin) viscoelastic model better describes the pressure-area dynamics in this vessel. Results comparing biomechanical properties show that the Kelvin and sigmoid models were able to predict the zero-pressure vessel radius; that under ex vivo conditions vessels are more rigid, and comparatively, that the carotid artery is stiffer than the thoracic descending aorta; and that the viscoelastic gain and relaxation parameters do not differ significantly between vessels or experimental conditions. In conclusion, our study demonstrates that the proposed models can predict pressure-area dynamics and that model parameters can be extracted for further interpretation of biomechanical properties. PMID:21203846

Prediction of Transonic Vortex Flows Using Linear and Nonlinear Turbulent Eddy Viscosity Models

NASA Technical Reports Server (NTRS)

Bartels, Robert E.; Gatski, Thomas B.

2000-01-01

Three-dimensional transonic flow over a delta wing is investigated with a focus on the effect of transition and influence of turbulence stress anisotropies. The performance of linear eddy viscosity models and an explicit algebraic stress model is assessed at the start of vortex flow, and the results compared with experimental data. To assess the effect of transition location, computations that either fix transition or are fully turbulent are performed. To assess the effect of the turbulent stress anisotropy, comparisons are made between predictions from the algebraic stress model and the linear eddy viscosity models. Both transition location and turbulent stress anisotropy significantly affect the 3D flow field. The most significant effect is found to be the modeling of transition location. At a Mach number of 0.90, the computed solution changes character from steady to unsteady depending on transition onset. Accounting for the anisotropies in the turbulent stresses also considerably impacts the flow, most notably in the outboard region of flow separation.

Competing regression models for longitudinal data.

PubMed

Alencar, Airlane P; Singer, Julio M; Rocha, Francisco Marcelo M

2012-03-01

The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a practical example involving pretest-posttest longitudinal data. In particular, we consider log-normal linear mixed models (LNLMM), generalized linear mixed models (GLMM), and models based on generalized estimating equations (GEE). We show how some special features of the data, like a nonconstant coefficient of variation, may be handled in the three approaches and evaluate their performance with respect to the magnitude of standard errors of interpretable and comparable parameters. We also show how different diagnostic tools may be employed to identify outliers and comment on available software. We conclude by noting that the results are similar, but that GEE-based models may be preferable when the goal is to compare the marginal expected responses. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Computational study of nonlinear plasma waves. [plasma simulation model applied to electrostatic waves in collisionless plasma

NASA Technical Reports Server (NTRS)

Matsuda, Y.

1974-01-01

A low-noise plasma simulation model is developed and applied to a series of linear and nonlinear problems associated with electrostatic wave propagation in a one-dimensional, collisionless, Maxwellian plasma, in the absence of magnetic field. It is demonstrated that use of the hybrid simulation model allows economical studies to be carried out in both the linear and nonlinear regimes with better quantitative results, for comparable computing time, than can be obtained by conventional particle simulation models, or direct solution of the Vlasov equation. The characteristics of the hybrid simulation model itself are first investigated, and it is shown to be capable of verifying the theoretical linear dispersion relation at wave energy levels as low as .000001 of the plasma thermal energy. Having established the validity of the hybrid simulation model, it is then used to study the nonlinear dynamics of monochromatic wave, sideband instability due to trapped particles, and satellite growth.

A Comparison of Multivariable Control Design Techniques for a Turbofan Engine Control

NASA Technical Reports Server (NTRS)

Garg, Sanjay; Watts, Stephen R.

1995-01-01

This paper compares two previously published design procedures for two different multivariable control design techniques for application to a linear engine model of a jet engine. The two multivariable control design techniques compared were the Linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR) and the H-Infinity synthesis. The two control design techniques were used with specific previously published design procedures to synthesize controls which would provide equivalent closed loop frequency response for the primary control loops while assuring adequate loop decoupling. The resulting controllers were then reduced in order to minimize the programming and data storage requirements for a typical implementation. The reduced order linear controllers designed by each method were combined with the linear model of an advanced turbofan engine and the system performance was evaluated for the continuous linear system. Included in the performance analysis are the resulting frequency and transient responses as well as actuator usage and rate capability for each design method. The controls were also analyzed for robustness with respect to structured uncertainties in the unmodeled system dynamics. The two controls were then compared for performance capability and hardware implementation issues.

Plasmonic modes in nanowire dimers: A study based on the hydrodynamic Drude model including nonlocal and nonlinear effects

NASA Astrophysics Data System (ADS)

Moeferdt, Matthias; Kiel, Thomas; Sproll, Tobias; Intravaia, Francesco; Busch, Kurt

2018-02-01

A combined analytical and numerical study of the modes in two distinct plasmonic nanowire systems is presented. The computations are based on a discontinuous Galerkin time-domain approach, and a fully nonlinear and nonlocal hydrodynamic Drude model for the metal is utilized. In the linear regime, these computations demonstrate the strong influence of nonlocality on the field distributions as well as on the scattering and absorption spectra. Based on these results, second-harmonic-generation efficiencies are computed over a frequency range that covers all relevant modes of the linear spectra. In order to interpret the physical mechanisms that lead to corresponding field distributions, the associated linear quasielectrostatic problem is solved analytically via conformal transformation techniques. This provides an intuitive classification of the linear excitations of the systems that is then applied to the full Maxwell case. Based on this classification, group theory facilitates the determination of the selection rules for the efficient excitation of modes in both the linear and nonlinear regimes. This leads to significantly enhanced second-harmonic generation via judiciously exploiting the system symmetries. These results regarding the mode structure and second-harmonic generation are of direct relevance to other nanoantenna systems.

Modeling and Simulation of Linear and Nonlinear MEMS Scale Electromagnetic Energy Harvesters for Random Vibration Environments

PubMed Central

Sassani, Farrokh

2014-01-01

The simulation results for electromagnetic energy harvesters (EMEHs) under broad band stationary Gaussian random excitations indicate the importance of both a high transformation factor and a high mechanical quality factor to achieve favourable mean power, mean square load voltage, and output spectral density. The optimum load is different for random vibrations and for sinusoidal vibration. Reducing the total damping ratio under band-limited random excitation yields a higher mean square load voltage. Reduced bandwidth resulting from decreased mechanical damping can be compensated by increasing the electrical damping (transformation factor) leading to a higher mean square load voltage and power. Nonlinear EMEHs with a Duffing spring and with linear plus cubic damping are modeled using the method of statistical linearization. These nonlinear EMEHs exhibit approximately linear behaviour under low levels of broadband stationary Gaussian random vibration; however, at higher levels of such excitation the central (resonant) frequency of the spectral density of the output voltage shifts due to the increased nonlinear stiffness and the bandwidth broadens slightly. Nonlinear EMEHs exhibit lower maximum output voltage and central frequency of the spectral density with nonlinear damping compared to linear damping. Stronger nonlinear damping yields broader bandwidths at stable resonant frequency. PMID:24605063

Fit Point-Wise AB Initio Calculation Potential Energies to a Multi-Dimension Long-Range Model

NASA Astrophysics Data System (ADS)

Zhai, Yu; Li, Hui; Le Roy, Robert J.

2016-06-01

A potential energy surface (PES) is a fundamental tool and source of understanding for theoretical spectroscopy and for dynamical simulations. Making correct assignments for high-resolution rovibrational spectra of floppy polyatomic and van der Waals molecules often relies heavily on predictions generated from a high quality ab initio potential energy surface. Moreover, having an effective analytic model to represent such surfaces can be as important as the ab initio results themselves. For the one-dimensional potentials of diatomic molecules, the most successful such model to date is arguably the ``Morse/Long-Range'' (MLR) function developed by R. J. Le Roy and coworkers. It is very flexible, is everywhere differentiable to all orders. It incorporates correct predicted long-range behaviour, extrapolates sensibly at both large and small distances, and two of its defining parameters are always the physically meaningful well depth {D}_e and equilibrium distance r_e. Extensions of this model, called the Multi-Dimension Morse/Long-Range (MD-MLR) function, linear molecule-linear molecule systems and atom-non-linear molecule system. have been applied successfully to atom-plus-linear molecule, linear molecule-linear molecule and atom-non-linear molecule systems. However, there are several technical challenges faced in modelling the interactions of general molecule-molecule systems, such as the absence of radial minima for some relative alignments, difficulties in fitting short-range potential energies, and challenges in determining relative-orientation dependent long-range coefficients. This talk will illustrate some of these challenges and describe our ongoing work in addressing them. Mol. Phys. 105, 663 (2007); J. Chem. Phys. 131, 204309 (2009); Mol. Phys. 109, 435 (2011). Phys. Chem. Chem. Phys. 10, 4128 (2008); J. Chem. Phys. 130, 144305 (2009) J. Chem. Phys. 132, 214309 (2010) J. Chem. Phys. 140, 214309 (2010)

Dynamic modelling and simulation of linear Fresnel solar field model based on molten salt heat transfer fluid

NASA Astrophysics Data System (ADS)

Hakkarainen, Elina; Tähtinen, Matti

2016-05-01

Demonstrations of direct steam generation (DSG) in linear Fresnel collectors (LFC) have given promising results related to higher steam parameters compared to the current state-of-the-art parabolic trough collector (PTC) technology using oil as heat transfer fluid (HTF). However, DSG technology lacks feasible solution for long-term thermal energy storage (TES) system. This option is important for CSP technology in order to offer dispatchable power. Recently, molten salts have been proposed to be used as HTF and directly as storage medium in both line-focusing solar fields, offering storage capacity of several hours. This direct molten salt (DMS) storage concept has already gained operational experience in solar tower power plant, and it is under demonstration phase both in the case of LFC and PTC systems. Dynamic simulation programs offer a valuable effort for design and optimization of solar power plants. In this work, APROS dynamic simulation program is used to model a DMS linear Fresnel solar field with two-tank TES system, and example simulation results are presented in order to verify the functionality of the model and capability of APROS for CSP modelling and simulation.

A theoretical investigation of symmetry-origin unidirectional energy gradient in light-harvesting dendrimers.

PubMed

Koda, Shin-ichi

2016-03-21

We theoretically investigate a possibility that the symmetry of the repetitively branched structure of light-harvesting dendrimers creates the energy gradient descending toward inner generations (layers of pigment molecules) of the dendrimers. In the first half of this paper, we define a model system using the Frenkel exciton Hamiltonian that focuses only on the topology of dendrimers and numerically show that excitation energy tends to gather at inner generations of the model system at a thermal equilibrium state. This indicates that an energy gradient is formed in the model system. In the last half, we attribute this result to the symmetry of the model system and propose two symmetry-origin mechanisms creating the energy gradient. The present analysis and proposition are based on the theory of the linear chain (LC) decomposition [S. Koda, J. Chem. Phys. 142, 204112 (2015)], which equivalently transforms the model system into a set of one-dimensional systems on the basis of the symmetry of dendrimers. In the picture of the LC decomposition, we find that energy gradient is formed both in each linear chain and among linear chains, and these two mechanisms explain the numerical results well.

A theoretical investigation of symmetry-origin unidirectional energy gradient in light-harvesting dendrimers

NASA Astrophysics Data System (ADS)

Koda, Shin-ichi

2016-03-01

We theoretically investigate a possibility that the symmetry of the repetitively branched structure of light-harvesting dendrimers creates the energy gradient descending toward inner generations (layers of pigment molecules) of the dendrimers. In the first half of this paper, we define a model system using the Frenkel exciton Hamiltonian that focuses only on the topology of dendrimers and numerically show that excitation energy tends to gather at inner generations of the model system at a thermal equilibrium state. This indicates that an energy gradient is formed in the model system. In the last half, we attribute this result to the symmetry of the model system and propose two symmetry-origin mechanisms creating the energy gradient. The present analysis and proposition are based on the theory of the linear chain (LC) decomposition [S. Koda, J. Chem. Phys. 142, 204112 (2015)], which equivalently transforms the model system into a set of one-dimensional systems on the basis of the symmetry of dendrimers. In the picture of the LC decomposition, we find that energy gradient is formed both in each linear chain and among linear chains, and these two mechanisms explain the numerical results well.

Predictive models reduce talent development costs in female gymnastics.

PubMed

Pion, Johan; Hohmann, Andreas; Liu, Tianbiao; Lenoir, Matthieu; Segers, Veerle

2017-04-01

This retrospective study focuses on the comparison of different predictive models based on the results of a talent identification test battery for female gymnasts. We studied to what extent these models have the potential to optimise selection procedures, and at the same time reduce talent development costs in female artistic gymnastics. The dropout rate of 243 female elite gymnasts was investigated, 5 years past talent selection, using linear (discriminant analysis) and non-linear predictive models (Kohonen feature maps and multilayer perceptron). The coaches classified 51.9% of the participants correct. Discriminant analysis improved the correct classification to 71.6% while the non-linear technique of Kohonen feature maps reached 73.7% correctness. Application of the multilayer perceptron even classified 79.8% of the gymnasts correctly. The combination of different predictive models for talent selection can avoid deselection of high-potential female gymnasts. The selection procedure based upon the different statistical analyses results in decrease of 33.3% of cost because the pool of selected athletes can be reduced to 92 instead of 138 gymnasts (as selected by the coaches). Reduction of the costs allows the limited resources to be fully invested in the high-potential athletes.

Modeling of ultrasonic degradation of non-volatile organic compounds by Langmuir-type kinetics.

PubMed

Chiha, Mahdi; Merouani, Slimane; Hamdaoui, Oualid; Baup, Stéphane; Gondrexon, Nicolas; Pétrier, Christian

2010-06-01

Sonochemical degradation of phenol (Ph), 4-isopropylphenol (4-IPP) and Rhodamine B (RhB) in aqueous solutions was investigated for a large range of initial concentrations in order to analyze the reaction kinetics. The initial rates of substrate degradation and H(2)O(2) formation as a function of initial concentrations were determined. The obtained results show that the degradation rate increases with increasing initial substrate concentration up to a plateau and that the sonolytic destruction occurs mainly through reactions with hydroxyl radicals in the interfacial region of cavitation bubbles. The rate of H(2)O(2) formation decreases with increasing substrate concentration and reaches a minimum, followed by almost constant production rate for higher substrate concentrations. Sonolytic degradation data were analyzed by the models of Okitsu et al. [K. Okitsu, K. Iwasaki, Y. Yobiko, H. Bandow, R. Nishimura, Y. Maeda, Sonochemical degradation of azo dyes in aqueous solution: a new heterogeneous kinetics model taking into account the local concentration OH radicals and azo dyes, Ultrason. Sonochem. 12 (2005) 255-262.] and Seprone et al. [N. Serpone, R. Terzian, H. Hidaka, E. Pelizzetti, Ultrasonic induced dehalogenation and oxidation of 2-, 3-, and 4-chlorophenol in air-equilibrated aqueous media. Similarities with irradiated semiconductor particulates, J. Phys. Chem. 98 (1994) 2634-2640.] developed on the basis of a Langmuir-type mechanism. The five linearized forms of the Okitsu et al.'s equation as well as the non-linear curve fitting analysis method were discussed. Results show that it is not appropriate to use the coefficient of determination of the linear regression method for comparing the best-fitting. Among the five linear expressions of the Okitsu et al.'s kinetic model, form-2 expression very well represent the degradation data for Ph and 4-IPP. Non-linear curve fitting analysis method was found to be the more appropriate method to determine the model parameters. An excellent representation of the experimental results of sonolytic destruction of RhB was obtained using the Serpone et al.'s model. The Serpone et al.'s model gives a worse fit for the sonolytic degradation data of Ph and 4-IPP. These results indicate that Ph and 4-IPP undergo degradation predominantly at the bubble/solution interface, whereas RhB undergoes degradation at both bubble/solution interface and in the bulk solution. (c) 2010 Elsevier B.V. All rights reserved.

Equivalent circuit simulation of HPEM-induced transient responses at nonlinear loads

NASA Astrophysics Data System (ADS)

Kotzev, Miroslav; Bi, Xiaotang; Kreitlow, Matthias; Gronwald, Frank

2017-09-01

In this paper the equivalent circuit modeling of a nonlinearly loaded loop antenna and its transient responses to HPEM field excitations are investigated. For the circuit modeling the general strategy to characterize the nonlinearly loaded antenna by a linear and a nonlinear circuit part is pursued. The linear circuit part can be determined by standard methods of antenna theory and numerical field computation. The modeling of the nonlinear circuit part requires realistic circuit models of the nonlinear loads that are given by Schottky diodes. Combining both parts, appropriate circuit models are obtained and analyzed by means of a standard SPICE circuit simulator. It is the main result that in this way full-wave simulation results can be reproduced. Furthermore it is clearly seen that the equivalent circuit modeling offers considerable advantages with respect to computation speed and also leads to improved physical insights regarding the coupling between HPEM field excitation and nonlinearly loaded loop antenna.

Multi-water-bag models of ion temperature gradient instability in cylindrical geometry

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

Coulette, David; Besse, Nicolas

2013-05-15

Ion temperature gradient instabilities play a major role in the understanding of anomalous transport in core fusion plasmas. In the considered cylindrical geometry, ion dynamics is described using a drift-kinetic multi-water-bag model for the parallel velocity dependency of the ion distribution function. In a first stage, global linear stability analysis is performed. From the obtained normal modes, parametric dependencies of the main spectral characteristics of the instability are then examined. Comparison of the multi-water-bag results with a reference continuous Maxwellian case allows us to evaluate the effects of discrete parallel velocity sampling induced by the Multi-Water-Bag model. Differences between themore » global model and local models considered in previous works are discussed. Using results from linear, quasilinear, and nonlinear numerical simulations, an analysis of the first stage saturation dynamics of the instability is proposed, where the divergence between the three models is examined.« less

Solving large-scale fixed cost integer linear programming models for grid-based location problems with heuristic techniques

NASA Astrophysics Data System (ADS)

Noor-E-Alam, Md.; Doucette, John

2015-08-01

Grid-based location problems (GBLPs) can be used to solve location problems in business, engineering, resource exploitation, and even in the field of medical sciences. To solve these decision problems, an integer linear programming (ILP) model is designed and developed to provide the optimal solution for GBLPs considering fixed cost criteria. Preliminary results show that the ILP model is efficient in solving small to moderate-sized problems. However, this ILP model becomes intractable in solving large-scale instances. Therefore, a decomposition heuristic is proposed to solve these large-scale GBLPs, which demonstrates significant reduction of solution runtimes. To benchmark the proposed heuristic, results are compared with the exact solution via ILP. The experimental results show that the proposed method significantly outperforms the exact method in runtime with minimal (and in most cases, no) loss of optimality.

Classification of hyperspectral imagery using MapReduce on a NVIDIA graphics processing unit (Conference Presentation)

NASA Astrophysics Data System (ADS)

Ramirez, Andres; Rahnemoonfar, Maryam

2017-04-01

A hyperspectral image provides multidimensional figure rich in data consisting of hundreds of spectral dimensions. Analyzing the spectral and spatial information of such image with linear and non-linear algorithms will result in high computational time. In order to overcome this problem, this research presents a system using a MapReduce-Graphics Processing Unit (GPU) model that can help analyzing a hyperspectral image through the usage of parallel hardware and a parallel programming model, which will be simpler to handle compared to other low-level parallel programming models. Additionally, Hadoop was used as an open-source version of the MapReduce parallel programming model. This research compared classification accuracy results and timing results between the Hadoop and GPU system and tested it against the following test cases: the CPU and GPU test case, a CPU test case and a test case where no dimensional reduction was applied.

Fair Package Assignment

NASA Astrophysics Data System (ADS)

Lahaie, Sébastien; Parkes, David C.

We consider the problem of fair allocation in the package assignment model, where a set of indivisible items, held by single seller, must be efficiently allocated to agents with quasi-linear utilities. A fair assignment is one that is efficient and envy-free. We consider a model where bidders have superadditive valuations, meaning that items are pure complements. Our central result is that core outcomes are fair and even coalition-fair over this domain, while fair distributions may not even exist for general valuations. Of relevance to auction design, we also establish that the core is equivalent to the set of anonymous-price competitive equilibria, and that superadditive valuations are a maximal domain that guarantees the existence of anonymous-price competitive equilibrium. Our results are analogs of core equivalence results for linear prices in the standard assignment model, and for nonlinear, non-anonymous prices in the package assignment model with general valuations.

Analytical flow duration curves for summer streamflow in Switzerland

NASA Astrophysics Data System (ADS)

Santos, Ana Clara; Portela, Maria Manuela; Rinaldo, Andrea; Schaefli, Bettina

2018-04-01

This paper proposes a systematic assessment of the performance of an analytical modeling framework for streamflow probability distributions for a set of 25 Swiss catchments. These catchments show a wide range of hydroclimatic regimes, including namely snow-influenced streamflows. The model parameters are calculated from a spatially averaged gridded daily precipitation data set and from observed daily discharge time series, both in a forward estimation mode (direct parameter calculation from observed data) and in an inverse estimation mode (maximum likelihood estimation). The performance of the linear and the nonlinear model versions is assessed in terms of reproducing observed flow duration curves and their natural variability. Overall, the nonlinear model version outperforms the linear model for all regimes, but the linear model shows a notable performance increase with catchment elevation. More importantly, the obtained results demonstrate that the analytical model performs well for summer discharge for all analyzed streamflow regimes, ranging from rainfall-driven regimes with summer low flow to snow and glacier regimes with summer high flow. These results suggest that the model's encoding of discharge-generating events based on stochastic soil moisture dynamics is more flexible than previously thought. As shown in this paper, the presence of snowmelt or ice melt is accommodated by a relative increase in the discharge-generating frequency, a key parameter of the model. Explicit quantification of this frequency increase as a function of mean catchment meteorological conditions is left for future research.

A generalized analog implementation of piecewise linear neuron models using CCII building blocks.

PubMed

Soleimani, Hamid; Ahmadi, Arash; Bavandpour, Mohammad; Sharifipoor, Ozra

2014-03-01

This paper presents a set of reconfigurable analog implementations of piecewise linear spiking neuron models using second generation current conveyor (CCII) building blocks. With the same topology and circuit elements, without W/L modification which is impossible after circuit fabrication, these circuits can produce different behaviors, similar to the biological neurons, both for a single neuron as well as a network of neurons just by tuning reference current and voltage sources. The models are investigated, in terms of analog implementation feasibility and costs, targeting large scale hardware implementations. Results show that, in order to gain the best performance, area and accuracy; these models can be compromised. Simulation results are presented for different neuron behaviors with CMOS 350 nm technology. Copyright © 2013 Elsevier Ltd. All rights reserved.

Comparison of Different Attitude Correction Models for ZY-3 Satellite Imagery

NASA Astrophysics Data System (ADS)

Song, Wenping; Liu, Shijie; Tong, Xiaohua; Niu, Changling; Ye, Zhen; Zhang, Han; Jin, Yanmin

2018-04-01

ZY-3 satellite, launched in 2012, is the first civilian high resolution stereo mapping satellite of China. This paper analyzed the positioning errors of ZY-3 satellite imagery and conducted compensation for geo-position accuracy improvement using different correction models, including attitude quaternion correction, attitude angle offset correction, and attitude angle linear correction. The experimental results revealed that there exist systematic errors with ZY-3 attitude observations and the positioning accuracy can be improved after attitude correction with aid of ground controls. There is no significant difference between the results of attitude quaternion correction method and the attitude angle correction method. However, the attitude angle offset correction model produced steady improvement than the linear correction model when limited ground control points are available for single scene.

On fitting generalized linear mixed-effects models for binary responses using different statistical packages.

PubMed

Zhang, Hui; Lu, Naiji; Feng, Changyong; Thurston, Sally W; Xia, Yinglin; Zhu, Liang; Tu, Xin M

2011-09-10

The generalized linear mixed-effects model (GLMM) is a popular paradigm to extend models for cross-sectional data to a longitudinal setting. When applied to modeling binary responses, different software packages and even different procedures within a package may give quite different results. In this report, we describe the statistical approaches that underlie these different procedures and discuss their strengths and weaknesses when applied to fit correlated binary responses. We then illustrate these considerations by applying these procedures implemented in some popular software packages to simulated and real study data. Our simulation results indicate a lack of reliability for most of the procedures considered, which carries significant implications for applying such popular software packages in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Analytical method to estimate waterflood performance

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

Cremonini, A.S.

A method to predict oil production resulting from the injection of immiscible fluids is described. The method is based on two models: one of them considers the vertical and displacement efficiencies, assuming unit areal efficiency and, therefore, a linear flow. It is a layered model without crossflow in which Buckley-Leveret`s displacement theory is used for each layer. The results obtained in the linear model are applied to a streamchannel model similar to the one used by Higgins and Leighton. In this way, areal efficiency is taken into account. The principal innovation is the possibility of applying different relative permeability curvesmore » to each layer. A numerical example in a five-spot pattern which uses relative permeability data obtained from reservoir core samples is presented.« less

Non-susceptible landslide areas in Italy and in the Mediterranean region

NASA Astrophysics Data System (ADS)

Marchesini, I.; Ardizzone, F.; Alvioli, M.; Rossi, M.; Guzzetti, F.

2014-08-01

We used landslide information for 13 study areas in Italy and morphometric information obtained from the 3-arcseconds shuttle radar topography mission digital elevation model (SRTM DEM) to determine areas where landslide susceptibility is expected to be negligible in Italy and in the landmasses surrounding the Mediterranean Sea. The morphometric information consisted of the local terrain slope which was computed in a square 3 × 3-cell moving window, and in the regional relative relief computed in a circular 15 × 15-cell moving window. We tested three different models to classify the "non-susceptible" landslide areas, including a linear model (LNR), a quantile linear model (QLR), and a quantile, non-linear model (QNL). We tested the performance of the three models using independent landslide information presented by the Italian Landslide Inventory (Inventario Fenomeni Franosi in Italia - IFFI). Best results were obtained using the QNL model. The corresponding zonation of non-susceptible landslide areas was intersected in a geographic information system (GIS) with geographical census data for Italy. The result determined that 57.5% of the population of Italy (in 2001) was located in areas where landslide susceptibility is expected to be negligible. We applied the QNL model to the landmasses surrounding the Mediterranean Sea, and we tested the synoptic non-susceptibility zonation using independent landslide information for three study areas in Spain. Results showed that the QNL model was capable of determining where landslide susceptibility is expected to be negligible in the validation areas in Spain. We expect our results to be applicable in similar study areas, facilitating the identification of non-susceptible landslide areas, at the synoptic scale.

Robust distributed model predictive control of linear systems with structured time-varying uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Langwen; Xie, Wei; Wang, Jingcheng

2017-11-01

In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min-max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.

Linear and Nonlinear Elasticity of Networks Made of Comb-like Polymers and Bottle-Brushes

NASA Astrophysics Data System (ADS)

Liang, H.; Dobrynin, A.; Everhart, M.; Daniel, W.; Vatankhah-Varnoosfaderani, M.; Sheiko, S.

We study mechanical properties of networks made of combs and bottle-brushes by computer simulations, theoretical calculations and experimental techniques. The networks are prepared by cross-linking backbones of combs or bottle-brushes with linear chains. This results in ``hybrid'' networks consisting of linear chains and strands of combs or bottle-brushes. In the framework of the phantom network model, the network modulus at small deformations G0 can be represented as a sum of contributions from linear chains, G0 , l, and strands of comb or bottle-brush, G0 , bb. If the length of extended backbone between crosslinks, Rmax, is much longer than the Kuhn length, bk, the modulus scales with the degree of polymerization of the side chains, nsc, and number of monomers between side chains, ng, as G0 , bb (nsc/ng + 1)-1. In the limit when bk becomes of the order of Rmax, the combs and bottle-brushes can be considered as semiflexible chains, resulting in a network modulus to be G0 , bb (nsc/ng + 1)-1(nsc2/2/ng) . In the nonlinear deformation regime, the strain-hardening behavior is described by the nonlinear network deformation model, which predicts that the true stress is a universal function of the structural modulus, G, first strain invariant, I1, and deformation ratio, β. The results of the computer simulations and predictions of the theoretical model are in a good agreement with experimental results. NSF DMR-1409710, DMR-1407645, DMR-1624569, DMR-1436201.

Chaos and insect ecology

Treesearch

Jesse A. Logan; Fred P. Hain

1990-01-01

Recent advances in applied mathematical analysis have uncovered a fascinating and unexpected dynamical richness that underlies behavior of even the simplest non-linear mathematical models. Due to the complexity of solutions to these non-linear equations, a new mathematical term, chaos, has been coined to describe the resulting dynamics. This term captures the notion...

The Overgeneralization of Linear Models among University Students' Mathematical Productions: A Long-Term Study

ERIC Educational Resources Information Center

Esteley, Cristina B.; Villarreal, Monica E.; Alagia, Humberto R.

2010-01-01

Over the past several years, we have been exploring and researching a phenomenon that occurs among undergraduate students that we called extension of linear models to non-linear contexts or overgeneralization of linear models. This phenomenon appears when some students use linear representations in situations that are non-linear. In a first phase,…

Construction of multiple linear regression models using blood biomarkers for selecting against abdominal fat traits in broilers.

PubMed

Dong, J Q; Zhang, X Y; Wang, S Z; Jiang, X F; Zhang, K; Ma, G W; Wu, M Q; Li, H; Zhang, H

2018-01-01

Plasma very low-density lipoprotein (VLDL) can be used to select for low body fat or abdominal fat (AF) in broilers, but its correlation with AF is limited. We investigated whether any other biochemical indicator can be used in combination with VLDL for a better selective effect. Nineteen plasma biochemical indicators were measured in male chickens from the Northeast Agricultural University broiler lines divergently selected for AF content (NEAUHLF) in the fed state at 46 and 48 d of age. The average concentration of every parameter for the 2 d was used for statistical analysis. Levels of these 19 plasma biochemical parameters were compared between the lean and fat lines. The phenotypic correlations between these plasma biochemical indicators and AF traits were analyzed. Then, multiple linear regression models were constructed to select the best model used for selecting against AF content. and the heritabilities of plasma indicators contained in the best models were estimated. The results showed that 11 plasma biochemical indicators (triglycerides, total bile acid, total protein, globulin, albumin/globulin, aspartate transaminase, alanine transaminase, gamma-glutamyl transpeptidase, uric acid, creatinine, and VLDL) differed significantly between the lean and fat lines (P < 0.01), and correlated significantly with AF traits (P < 0.05). The best multiple linear regression models based on albumin/globulin, VLDL, triglycerides, globulin, total bile acid, and uric acid, had higher R2 (0.73) than the model based only on VLDL (0.21). The plasma parameters included in the best models had moderate heritability estimates (0.21 ≤ h2 ≤ 0.43). These results indicate that these multiple linear regression models can be used to select for lean broiler chickens. © 2017 Poultry Science Association Inc.

Finite-time H∞ filtering for non-linear stochastic systems

NASA Astrophysics Data System (ADS)

Hou, Mingzhe; Deng, Zongquan; Duan, Guangren

2016-09-01

This paper describes the robust H∞ filtering analysis and the synthesis of general non-linear stochastic systems with finite settling time. We assume that the system dynamic is modelled by Itô-type stochastic differential equations of which the state and the measurement are corrupted by state-dependent noises and exogenous disturbances. A sufficient condition for non-linear stochastic systems to have the finite-time H∞ performance with gain less than or equal to a prescribed positive number is established in terms of a certain Hamilton-Jacobi inequality. Based on this result, the existence of a finite-time H∞ filter is given for the general non-linear stochastic system by a second-order non-linear partial differential inequality, and the filter can be obtained by solving this inequality. The effectiveness of the obtained result is illustrated by a numerical example.

Embodied linearity of speed control in Drosophila melanogaster.

PubMed

Medici, V; Fry, S N

2012-12-07

Fruitflies regulate flight speed by adjusting their body angle. To understand how low-level posture control serves an overall linear visual speed control strategy, we visually induced free-flight acceleration responses in a wind tunnel and measured the body kinematics using high-speed videography. Subsequently, we reverse engineered the transfer function mapping body pitch angle onto flight speed. A linear model is able to reproduce the behavioural data with good accuracy. Our results show that linearity in speed control is realized already at the level of body posture-mediated speed control and is therefore embodied at the level of the complex aerodynamic mechanisms of body and wings. Together with previous results, this study reveals the existence of a linear hierarchical control strategy, which can provide relevant control principles for biomimetic implementations, such as autonomous flying micro air vehicles.

Embodied linearity of speed control in Drosophila melanogaster

PubMed Central

Medici, V.; Fry, S. N.

2012-01-01

Fruitflies regulate flight speed by adjusting their body angle. To understand how low-level posture control serves an overall linear visual speed control strategy, we visually induced free-flight acceleration responses in a wind tunnel and measured the body kinematics using high-speed videography. Subsequently, we reverse engineered the transfer function mapping body pitch angle onto flight speed. A linear model is able to reproduce the behavioural data with good accuracy. Our results show that linearity in speed control is realized already at the level of body posture-mediated speed control and is therefore embodied at the level of the complex aerodynamic mechanisms of body and wings. Together with previous results, this study reveals the existence of a linear hierarchical control strategy, which can provide relevant control principles for biomimetic implementations, such as autonomous flying micro air vehicles. PMID:22933185

Nonmetallic electronegativity equalization and point-dipole interaction model including exchange interactions for molecular dipole moments and polarizabilities.

PubMed

Smalø, Hans S; Astrand, Per-Olof; Jensen, Lasse

2009-07-28

The electronegativity equalization model (EEM) has been combined with a point-dipole interaction model to obtain a molecular mechanics model consisting of atomic charges, atomic dipole moments, and two-atom relay tensors to describe molecular dipole moments and molecular dipole-dipole polarizabilities. The EEM has been phrased as an atom-atom charge-transfer model allowing for a modification of the charge-transfer terms to avoid that the polarizability approaches infinity for two particles at infinite distance and for long chains. In the present work, these shortcomings have been resolved by adding an energy term for transporting charges through individual atoms. A Gaussian distribution is adopted for the atomic charge distributions, resulting in a damping of the electrostatic interactions at short distances. Assuming that an interatomic exchange term may be described as the overlap between two electronic charge distributions, the EEM has also been extended by a short-range exchange term. The result is a molecular mechanics model where the difference of charge transfer in insulating and metallic systems is modeled regarding the difference in bond length between different types of system. For example, the model is capable of modeling charge transfer in both alkanes and alkenes with alternating double bonds with the same set of carbon parameters only relying on the difference in bond length between carbon sigma- and pi-bonds. Analytical results have been obtained for the polarizability of a long linear chain. These results show that the model is capable of describing the polarizability scaling both linearly and nonlinearly with the size of the system. Similarly, a linear chain with an end atom with a high electronegativity has been analyzed analytically. The dipole moment of this model system can either be independent of the length or increase linearly with the length of the chain. In addition, the model has been parametrized for alkane and alkene chains with data from density functional theory calculations, where the polarizability behaves differently with the chain length. For the molecular dipole moment, the same two systems have been studied with an aldehyde end group. Both the molecular polarizability and the dipole moment are well described as a function of the chain length for both alkane and alkene chains demonstrating the power of the presented model.

Ambient temperature and coronary heart disease mortality in Beijing, China: a time series study

PubMed Central

2012-01-01

Background Many studies have examined the association between ambient temperature and mortality. However, less evidence is available on the temperature effects on coronary heart disease (CHD) mortality, especially in China. In this study, we examined the relationship between ambient temperature and CHD mortality in Beijing, China during 2000 to 2011. In addition, we compared time series and time-stratified case-crossover models for the non-linear effects of temperature. Methods We examined the effects of temperature on CHD mortality using both time series and time-stratified case-crossover models. We also assessed the effects of temperature on CHD mortality by subgroups: gender (female and male) and age (age > =65 and age < 65). We used a distributed lag non-linear model to examine the non-linear effects of temperature on CHD mortality up to 15 lag days. We used Akaike information criterion to assess the model fit for the two designs. Results The time series models had a better model fit than time-stratified case-crossover models. Both designs showed that the relationships between temperature and group-specific CHD mortality were non-linear. Extreme cold and hot temperatures significantly increased the risk of CHD mortality. Hot effects were acute and short-term, while cold effects were delayed by two days and lasted for five days. The old people and women were more sensitive to extreme cold and hot temperatures than young and men. Conclusions This study suggests that time series models performed better than time-stratified case-crossover models according to the model fit, even though they produced similar non-linear effects of temperature on CHD mortality. In addition, our findings indicate that extreme cold and hot temperatures increase the risk of CHD mortality in Beijing, China, particularly for women and old people. PMID:22909034

A Linear Dynamical Systems Approach to Streamflow Reconstruction Reveals History of Regime Shifts in Northern Thailand

NASA Astrophysics Data System (ADS)

Nguyen, Hung T. T.; Galelli, Stefano

2018-03-01

Catchment dynamics is not often modeled in streamflow reconstruction studies; yet, the streamflow generation process depends on both catchment state and climatic inputs. To explicitly account for this interaction, we contribute a linear dynamic model, in which streamflow is a function of both catchment state (i.e., wet/dry) and paleoclimatic proxies. The model is learned using a novel variant of the Expectation-Maximization algorithm, and it is used with a paleo drought record—the Monsoon Asia Drought Atlas—to reconstruct 406 years of streamflow for the Ping River (northern Thailand). Results for the instrumental period show that the dynamic model has higher accuracy than conventional linear regression; all performance scores improve by 45-497%. Furthermore, the reconstructed trajectory of the state variable provides valuable insights about the catchment history—e.g., regime-like behavior—thereby complementing the information contained in the reconstructed streamflow time series. The proposed technique can replace linear regression, since it only requires information on streamflow and climatic proxies (e.g., tree-rings, drought indices); furthermore, it is capable of readily generating stochastic streamflow replicates. With a marginal increase in computational requirements, the dynamic model brings more desirable features and value to streamflow reconstructions.

Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner.

PubMed

Wang, Yubo; Veluvolu, Kalyana C

2017-06-14

It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a new approach to analyze the time-varying characteristics of such signals by employing a simple truncated Fourier series model, namely the band-limited multiple Fourier linear combiner (BMFLC). In contrast to the earlier designs, we first identified the sparsity imposed on the signal model in order to reformulate the model to a sparse linear regression model. The coefficients of the proposed model are then estimated by a convex optimization algorithm. The performance of the proposed method was analyzed with benchmark test signals. An energy ratio metric is employed to quantify the spectral performance and results show that the proposed method Sparse-BMFLC has high mean energy (0.9976) ratio and outperforms existing methods such as short-time Fourier transfrom (STFT), continuous Wavelet transform (CWT) and BMFLC Kalman Smoother. Furthermore, the proposed method provides an overall 6.22% in reconstruction error.

A simple ecohydrological model captures essentials of seasonal leaf dynamics in semi-arid tropical grasslands

NASA Astrophysics Data System (ADS)

Choler, P.; Sea, W.; Briggs, P.; Raupach, M.; Leuning, R.

2009-09-01

Modelling leaf phenology in water-controlled ecosystems remains a difficult task because of high spatial and temporal variability in the interaction of plant growth and soil moisture. Here, we move beyond widely used linear models to examine the performance of low-dimensional, nonlinear ecohydrological models that couple the dynamics of plant cover and soil moisture. The study area encompasses 400 000 km2 of semi-arid perennial tropical grasslands, dominated by C4 grasses, in the Northern Territory and Queensland (Australia). We prepared 8 yr time series (2001-2008) of climatic variables and estimates of fractional vegetation cover derived from MODIS Normalized Difference Vegetation Index (NDVI) for 400 randomly chosen sites, of which 25% were used for model calibration and 75% for model validation. We found that the mean absolute error of linear and nonlinear models did not markedly differ. However, nonlinear models presented key advantages: (1) they exhibited far less systematic error than their linear counterparts; (2) their error magnitude was consistent throughout a precipitation gradient while the performance of linear models deteriorated at the driest sites, and (3) they better captured the sharp transitions in leaf cover that are observed under high seasonality of precipitation. Our results showed that low-dimensional models including feedbacks between soil water balance and plant growth adequately predict leaf dynamics in semi-arid perennial grasslands. Because these models attempt to capture fundamental ecohydrological processes, they should be the favoured approach for prognostic models of phenology.

A simple ecohydrological model captures essentials of seasonal leaf dynamics in semi-arid tropical grasslands

NASA Astrophysics Data System (ADS)

Choler, P.; Sea, W.; Briggs, P.; Raupach, M.; Leuning, R.

2010-03-01

Modelling leaf phenology in water-controlled ecosystems remains a difficult task because of high spatial and temporal variability in the interaction of plant growth and soil moisture. Here, we move beyond widely used linear models to examine the performance of low-dimensional, nonlinear ecohydrological models that couple the dynamics of plant cover and soil moisture. The study area encompasses 400 000 km2 of semi-arid perennial tropical grasslands, dominated by C4 grasses, in the Northern Territory and Queensland (Australia). We prepared 8-year time series (2001-2008) of climatic variables and estimates of fractional vegetation cover derived from MODIS Normalized Difference Vegetation Index (NDVI) for 400 randomly chosen sites, of which 25% were used for model calibration and 75% for model validation. We found that the mean absolute error of linear and nonlinear models did not markedly differ. However, nonlinear models presented key advantages: (1) they exhibited far less systematic error than their linear counterparts; (2) their error magnitude was consistent throughout a precipitation gradient while the performance of linear models deteriorated at the driest sites, and (3) they better captured the sharp transitions in leaf cover that are observed under high seasonality of precipitation. Our results showed that low-dimensional models including feedbacks between soil water balance and plant growth adequately predict leaf dynamics in semi-arid perennial grasslands. Because these models attempt to capture fundamental ecohydrological processes, they should be the favoured approach for prognostic models of phenology.

Investigation of metrics to assess vascular flow modifications for diverter device designs using hydrodynamics and angiographic studies

NASA Astrophysics Data System (ADS)

Ionita, Ciprian N.; Bednarek, Daniel R.; Rudin, Stephen

2012-03-01

Intracranial aneurysm treatment with flow diverters (FD) is a new minimally invasive approach, recently approved for use in human patients. Attempts to correlate the flow reduction observed in angiograms with a parameter related to the FD structure have not been totally successful. To find the proper parameter, we investigated four porous-media flow models. The models describing the relation between the pressure drop and flow velocity that are investigated include the capillary theory linear model (CTLM), the drag force linear model (DFLM), the simple quadratic model (SQM) and the modified quadratic model (MQM). Proportionality parameters are referred to as permeability for the linear models and resistance for the quadratic ones. A two stage experiment was performed. First, we verified flow model validity by placing six different stainless-steel meshes, resembling FD structures, in known flow conditions. The best flow model was used for the second stage, where six different FD's were inserted in aneurysm phantoms and flow modification was estimated using angiographically derived time density curves (TDC). Finally, TDC peak variation was compared with the FD parameter. Model validity experiments indicated errors of: 70% for the linear models, 26% for the SQM and 7% for the MQM. The resistance calculated according to the MQM model correlated well with the contrast flow reduction. Results indicate that resistance calculated according to MQM is appropriate to characterize the FD and could explain the flow modification observed in angiograms.

The role of model dynamics in ensemble Kalman filter performance for chaotic systems

USGS Publications Warehouse

Ng, G.-H.C.; McLaughlin, D.; Entekhabi, D.; Ahanin, A.

2011-01-01

The ensemble Kalman filter (EnKF) is susceptible to losing track of observations, or 'diverging', when applied to large chaotic systems such as atmospheric and ocean models. Past studies have demonstrated the adverse impact of sampling error during the filter's update step. We examine how system dynamics affect EnKF performance, and whether the absence of certain dynamic features in the ensemble may lead to divergence. The EnKF is applied to a simple chaotic model, and ensembles are checked against singular vectors of the tangent linear model, corresponding to short-term growth and Lyapunov vectors, corresponding to long-term growth. Results show that the ensemble strongly aligns itself with the subspace spanned by unstable Lyapunov vectors. Furthermore, the filter avoids divergence only if the full linearized long-term unstable subspace is spanned. However, short-term dynamics also become important as non-linearity in the system increases. Non-linear movement prevents errors in the long-term stable subspace from decaying indefinitely. If these errors then undergo linear intermittent growth, a small ensemble may fail to properly represent all important modes, causing filter divergence. A combination of long and short-term growth dynamics are thus critical to EnKF performance. These findings can help in developing practical robust filters based on model dynamics. ?? 2011 The Authors Tellus A ?? 2011 John Wiley & Sons A/S.

Use of non-linear mixed-effects modelling and regression analysis to predict the number of somatic coliphages by plaque enumeration after 3 hours of incubation.

PubMed

Mendez, Javier; Monleon-Getino, Antonio; Jofre, Juan; Lucena, Francisco

2017-10-01

The present study aimed to establish the kinetics of the appearance of coliphage plaques using the double agar layer titration technique to evaluate the feasibility of using traditional coliphage plaque forming unit (PFU) enumeration as a rapid quantification method. Repeated measurements of the appearance of plaques of coliphages titrated according to ISO 10705-2 at different times were analysed using non-linear mixed-effects regression to determine the most suitable model of their appearance kinetics. Although this model is adequate, to simplify its applicability two linear models were developed to predict the numbers of coliphages reliably, using the PFU counts as determined by the ISO after only 3 hours of incubation. One linear model, when the number of plaques detected was between 4 and 26 PFU after 3 hours, had a linear fit of: (1.48 × Counts 3 h + 1.97); and the other, values >26 PFU, had a fit of (1.18 × Counts 3 h + 2.95). If the number of plaques detected was <4 PFU after 3 hours, we recommend incubation for (18 ± 3) hours. The study indicates that the traditional coliphage plating technique has a reasonable potential to provide results in a single working day without the need to invest in additional laboratory equipment.

Stimulation of a turbofan engine for evaluation of multivariable optimal control concepts. [(computerized simulation)

NASA Technical Reports Server (NTRS)

Seldner, K.

1976-01-01

The development of control systems for jet engines requires a real-time computer simulation. The simulation provides an effective tool for evaluating control concepts and problem areas prior to actual engine testing. The development and use of a real-time simulation of the Pratt and Whitney F100-PW100 turbofan engine is described. The simulation was used in a multi-variable optimal controls research program using linear quadratic regulator theory. The simulation is used to generate linear engine models at selected operating points and evaluate the control algorithm. To reduce the complexity of the design, it is desirable to reduce the order of the linear model. A technique to reduce the order of the model; is discussed. Selected results between high and low order models are compared. The LQR control algorithms can be programmed on digital computer. This computer will control the engine simulation over the desired flight envelope.

A novel heuristic for optimization aggregate production problem: Evidence from flat panel display in Malaysia

NASA Astrophysics Data System (ADS)

Al-Kuhali, K.; Hussain M., I.; Zain Z., M.; Mullenix, P.

2015-05-01

Aim: This paper contribute to the flat panel display industry it terms of aggregate production planning. Methodology: For the minimization cost of total production of LCD manufacturing, a linear programming was applied. The decision variables are general production costs, additional cost incurred for overtime production, additional cost incurred for subcontracting, inventory carrying cost, backorder costs and adjustments for changes incurred within labour levels. Model has been developed considering a manufacturer having several product types, which the maximum types are N, along a total time period of T. Results: Industrial case study based on Malaysia is presented to test and to validate the developed linear programming model for aggregate production planning. Conclusion: The model development is fit under stable environment conditions. Overall it can be recommended to adapt the proven linear programming model to production planning of Malaysian flat panel display industry.

Loading Deformation Characteristic Simulation Study of Engineering Vehicle Refurbished Tire

NASA Astrophysics Data System (ADS)

Qiang, Wang; Xiaojie, Qi; Zhao, Yang; Yunlong, Wang; Guotian, Wang; Degang, Lv

2018-05-01

The paper constructed engineering vehicle refurbished tire computer geometry model, mechanics model, contact model, finite element analysis model, did simulation study on load-deformation property of engineering vehicle refurbished tire by comparing with that of the new and the same type tire, got load-deformation of engineering vehicle refurbished tire under the working condition of static state and ground contact. The analysis result shows that change rules of radial-direction deformation and side-direction deformation of engineering vehicle refurbished tire are close to that of the new tire, radial-direction and side-direction deformation value is a little less than that of the new tire. When air inflation pressure was certain, radial-direction deformation linear rule of engineer vehicle refurbished tire would increase with load adding, however, side-direction deformation showed linear change rule, when air inflation pressure was low; and it would show increase of non-linear change rule, when air inflation pressure was very high.

Statistical methods for launch vehicle guidance, navigation, and control (GN&C) system design and analysis

NASA Astrophysics Data System (ADS)

Rose, Michael Benjamin

A novel trajectory and attitude control and navigation analysis tool for powered ascent is developed. The tool is capable of rapid trade-space analysis and is designed to ultimately reduce turnaround time for launch vehicle design, mission planning, and redesign work. It is streamlined to quickly determine trajectory and attitude control dispersions, propellant dispersions, orbit insertion dispersions, and navigation errors and their sensitivities to sensor errors, actuator execution uncertainties, and random disturbances. The tool is developed by applying both Monte Carlo and linear covariance analysis techniques to a closed-loop, launch vehicle guidance, navigation, and control (GN&C) system. The nonlinear dynamics and flight GN&C software models of a closed-loop, six-degree-of-freedom (6-DOF), Monte Carlo simulation are formulated and developed. The nominal reference trajectory (NRT) for the proposed lunar ascent trajectory is defined and generated. The Monte Carlo truth models and GN&C algorithms are linearized about the NRT, the linear covariance equations are formulated, and the linear covariance simulation is developed. The performance of the launch vehicle GN&C system is evaluated using both Monte Carlo and linear covariance techniques and their trajectory and attitude control dispersion, propellant dispersion, orbit insertion dispersion, and navigation error results are validated and compared. Statistical results from linear covariance analysis are generally within 10% of Monte Carlo results, and in most cases the differences are less than 5%. This is an excellent result given the many complex nonlinearities that are embedded in the ascent GN&C problem. Moreover, the real value of this tool lies in its speed, where the linear covariance simulation is 1036.62 times faster than the Monte Carlo simulation. Although the application and results presented are for a lunar, single-stage-to-orbit (SSTO), ascent vehicle, the tools, techniques, and mathematical formulations that are discussed are applicable to ascent on Earth or other planets as well as other rocket-powered systems such as sounding rockets and ballistic missiles.

Model-based adaptive sliding mode control of the subcritical boiler-turbine system with uncertainties.

PubMed

Tian, Zhen; Yuan, Jingqi; Xu, Liang; Zhang, Xiang; Wang, Jingcheng

2018-05-25

As higher requirements are proposed for the load regulation and efficiency enhancement, the control performance of boiler-turbine systems has become much more important. In this paper, a novel robust control approach is proposed to improve the coordinated control performance for subcritical boiler-turbine units. To capture the key features of the boiler-turbine system, a nonlinear control-oriented model is established and validated with the history operation data of a 300 MW unit. To achieve system linearization and decoupling, an adaptive feedback linearization strategy is proposed, which could asymptotically eliminate the linearization error caused by the model uncertainties. Based on the linearized boiler-turbine system, a second-order sliding mode controller is designed with the super-twisting algorithm. Moreover, the closed-loop system is proved robustly stable with respect to uncertainties and disturbances. Simulation results are presented to illustrate the effectiveness of the proposed control scheme, which achieves excellent tracking performance, strong robustness and chattering reduction. Copyright © 2018. Published by Elsevier Ltd.

Understanding the relationship between duration of untreated psychosis and outcomes: A statistical perspective.

PubMed

Hannigan, Ailish; Bargary, Norma; Kinsella, Anthony; Clarke, Mary

2017-06-14

Although the relationships between duration of untreated psychosis (DUP) and outcomes are often assumed to be linear, few studies have explored the functional form of these relationships. The aim of this study is to demonstrate the potential of recent advances in curve fitting approaches (splines) to explore the form of the relationship between DUP and global assessment of functioning (GAF). Curve fitting approaches were used in models to predict change in GAF at long-term follow-up using DUP for a sample of 83 individuals with schizophrenia. The form of the relationship between DUP and GAF was non-linear. Accounting for non-linearity increased the percentage of variance in GAF explained by the model, resulting in better prediction and understanding of the relationship. The relationship between DUP and outcomes may be complex and model fit may be improved by accounting for the form of the relationship. This should be routinely assessed and new statistical approaches for non-linear relationships exploited, if appropriate. © 2017 John Wiley & Sons Australia, Ltd.

Study of non-linear deformation of vocal folds in simulations of human phonation

NASA Astrophysics Data System (ADS)

Saurabh, Shakti; Bodony, Daniel

2014-11-01

Direct numerical simulation is performed on a two-dimensional compressible, viscous fluid interacting with a non-linear, viscoelastic solid as a model for the generation of the human voice. The vocal fold (VF) tissues are modeled as multi-layered with varying stiffness in each layer and using a finite-strain Standard Linear Solid (SLS) constitutive model implemented in a quadratic finite element code and coupled to a high-order compressible Navier-Stokes solver through a boundary-fitted fluid-solid interface. The large non-linear mesh deformation is handled using an elliptic/poisson smoothening technique. Supra-glottal flow shows asymmetry in the flow, which in turn has a coupling effect on the motion of the VF. The fully compressible simulations gives direct insight into the sound produced as pressure distributions and the vocal fold deformation helps study the unsteady vortical flow resulting from the fluid-structure interaction along the full phonation cycle. Supported by the National Science Foundation (CAREER Award Number 1150439).

An Alternative Derivation of the Energy Levels of the "Particle on a Ring" System

NASA Astrophysics Data System (ADS)

Vincent, Alan

1996-10-01

All acceptable wave functions must be continuous mathematical functions. This criterion limits the acceptable functions for a particle in a linear 1-dimensional box to sine functions. If, however, the linear box is bent round into a ring, acceptable wave functions are those which are continuous at the 'join'. On this model some acceptable linear functions become unacceptable for the ring and some unacceptable cosine functions become acceptable. This approach can be used to produce a straightforward derivation of the energy levels and wave functions of the particle on a ring. These simple wave mechanical systems can be used as models of linear and cyclic delocalised systems such as conjugated hydrocarbons or the benzene ring. The promotion energy of an electron can then be used to calculate the wavelength of absorption of uv light. The simple model gives results of the correct order of magnitude and shows that, as the chain length increases, the uv maximum moves to longer wavelengths, as found experimentally.

Modeling turbidity and flow at daily steps in karst using ARIMA/ARFIMA-GARCH error models

NASA Astrophysics Data System (ADS)

Massei, N.

2013-12-01

Hydrological and physico-chemical variations recorded at karst springs usually reflect highly non-linear processes and the corresponding time series are then very often also highly non-linear. Among others, turbidity, as an important parameter regarding water quality and management, is a very complex response of karst systems to rain events, involving direct transfer of particles from point-source recharge as well as resuspension of particles previously deposited and stored within the system. For those reasons, turbidity modeling has not been well taken in karst hydrological models so far. Most of the time, the modeling approaches would involve stochastic linear models such ARIMA-type models and their derivatives (ARMA, ARMAX, ARIMAX, ARFIMA...). Yet, linear models usually fail to represent well the whole (stochastic) process variability, and their residuals still contain useful information that can be used to either understand the whole variability or to enhance short-term predictability and forecasting. Model residuals are actually not i.i.d., which can be identified by the fact that squared residuals still present clear and significant serial correlation. Indeed, high (low) amplitudes are followed in time by high (low) amplitudes, which can be seen on residuals time series as periods of time during which amplitudes are higher (lower) then the mean amplitude. This is known as the ARCH effet (AutoRegressive Conditional Heteroskedasticity), and the corresponding non-linear process affecting residuals of a linear model can be modeled using ARCH or generalized ARCH (GARCH) non-linear modeling, which approaches are very well known in econometrics. Here we investigated the capability of ARIMA-GARCH error models to represent a ~20-yr daily turbidity time series recorded at a karst spring used for water supply of the city of Le Havre (Upper Normandy, France). ARIMA and ARFIMA models were used to represent the mean behavior of the time series and the residuals clearly appeared to present a pronounced ARCH effect, as confirmed by Ljung-Box and McLeod-Li tests. We then identified and fitted GARCH models to the residuals of ARIMA and ARFIMA models in order to model the conditional variance and volatility of the turbidity time series. The results eventually showed that serial correlation was succesfully removed in the last standardized residuals of the GARCH model, and hence that the ARIMA-GARCH error model appeared consistent for modeling such time series. The approach finally improved short-term (e.g a few steps-ahead) turbidity forecasting.

Forecasting currency circulation data of Bank Indonesia by using hybrid ARIMAX-ANN model

NASA Astrophysics Data System (ADS)

Prayoga, I. Gede Surya Adi; Suhartono, Rahayu, Santi Puteri

2017-05-01

The purpose of this study is to forecast currency inflow and outflow data of Bank Indonesia. Currency circulation in Indonesia is highly influenced by the presence of Eid al-Fitr. One way to forecast the data with Eid al-Fitr effect is using autoregressive integrated moving average with exogenous input (ARIMAX) model. However, ARIMAX is a linear model, which cannot handle nonlinear correlation structures of the data. In the field of forecasting, inaccurate predictions can be considered caused by the existence of nonlinear components that are uncaptured by the model. In this paper, we propose a hybrid model of ARIMAX and artificial neural networks (ANN) that can handle both linear and nonlinear correlation. This method was applied for 46 series of currency inflow and 46 series of currency outflow. The results showed that based on out-of-sample root mean squared error (RMSE), the hybrid models are up to10.26 and 10.65 percent better than ARIMAX for inflow and outflow series, respectively. It means that ANN performs well in modeling nonlinear correlation of the data and can increase the accuracy of linear model.

Credibility analysis of risk classes by generalized linear model

NASA Astrophysics Data System (ADS)

Erdemir, Ovgucan Karadag; Sucu, Meral

2016-06-01

In this paper generalized linear model (GLM) and credibility theory which are frequently used in nonlife insurance pricing are combined for reliability analysis. Using full credibility standard, GLM is associated with limited fluctuation credibility approach. Comparison criteria such as asymptotic variance and credibility probability are used to analyze the credibility of risk classes. An application is performed by using one-year claim frequency data of a Turkish insurance company and results of credible risk classes are interpreted.

Comparison of TOPEX/Poseidon Sea Level and Linear Model Results forced by Various Wind Products for the Tropical Pacific

NASA Technical Reports Server (NTRS)

Hackert, Eric C.; Busalacchi, Antonio J.

1997-01-01

The goal of this paper is to compare TOPEX/Posaidon (T/P) sea level with sea level results from linear ocean model experiments forced by several different wind products for the tropical Pacific. During the period of this study (October 1992 - October 1995), available wind products include satellite winds from the ERS-1 scatterometer product of [HALP 97] and the passive microwave analysis of SSMI winds produced using the variational analysis method (VAM) of [ATLA 91]. In addition, atmospheric GCM winds from the NCEP reanalysis [KALN 96], ECMWF analysis [ECMW94], and the Goddard EOS-1 (GEOS-1) reanalysis experiment [SCHU 93] are available for comparison. The observed ship wind analysis of FSU [STRI 92] is also included in this study. The linear model of [CANE 84] is used as a transfer function to test the quality of each of these wind products for the tropical Pacific. The various wind products are judged by comparing the wind-forced model sea level results against the T/P sea level anomalies. Correlation and RMS difference maps show how well each wind product does in reproducing the T/P sea level signal. These results are summarized in a table showing area average correlations and RMS differences. The large-scale low-frequency temporal signal is reproduced by all of the wind products, However, significant differences exist in both amplitude and phase on regional scales. In general, the model results forced by satellite winds do a better job reproducing the T/P signal (i.e. have a higher average correlation and lower RMS difference) than the results forced by atmospheric model winds.

A linear and nonlinear study of Mira

NASA Astrophysics Data System (ADS)

Cox, A. N.; Ostlie, D. A.

1993-12-01

Both linear and nonlinear calculations of the 331 day, long period variable star Mira have been undertaken to see what radial pulsation mode is naturally selected. Models are similar to those considered in the linear nonadiabatic stellar pulsation study of Ostlie and Cox (1986). Models are considered with masses near one solar mass, luminosities between 4000 and 5000 solar luminosities, and effective temperatures of approximately 3000 K. These models have fundamental mode periods that closely match the pulsation period of Mira. The equation of state for the stellar material is given by the Stellingwerf (1975ab) procedure, and the opacity is obtained from a fit by Cahn that matches the low temperature molecular absorption data for the poplulation I Ross-Aller 1 mixture calculated from the Los Alamos Astrophysical Opacity Library. For the linear study, the Cox, Brownlee, and Eilers (1966) approximation is used for the linear theory variation of the convection luminosity. For the nonlinear work, the method described by Ostlie (1990) and Cox (1990) is followed. Results showing internal details of the radial fundamental and first overtone modes behavior in linear theory are presented. Preliminary radial fundamental mode nonlinear calculations are discussed. The very tentative conclusion is that neither the fundamental or first overtone mode is excluded from being the actual observed one.

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

PubMed Central

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, p<0.001). Univariate mixture model fits of FDGpre improved R2 from 0.17 to 0.52. Neither baseline FLT PET nor Cu-ATSM PET uptake contributed statistically significant multivariate regression coefficients. Conclusions Spatially resolved regression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

Linear time series modeling of GPS-derived TEC observations over the Indo-Thailand region

NASA Astrophysics Data System (ADS)

Suraj, Puram Sai; Kumar Dabbakuti, J. R. K.; Chowdhary, V. Rajesh; Tripathi, Nitin K.; Ratnam, D. Venkata

2017-12-01

This paper proposes a linear time series model to represent the climatology of the ionosphere and to investigate the characteristics of hourly averaged total electron content (TEC). The GPS-TEC observation data at the Bengaluru international global navigation satellite system (GNSS) service (IGS) station (geographic 13.02°N , 77.57°E ; geomagnetic latitude 4.4°N ) have been utilized for processing the TEC data during an extended period (2009-2016) in the 24{th} solar cycle. Solar flux F10.7p index, geomagnetic Ap index, and periodic oscillation factors have been considered to construct a linear TEC model. It is evident from the results that solar activity effect on TEC is high. It reaches the maximum value (˜ 40 TECU) during the high solar activity (HSA) year (2014) and minimum value (˜ 15 TECU) during the low solar activity (LSA) year (2009). The larger magnitudes of semiannual variations are observed during the HSA periods. The geomagnetic effect on TEC is relatively low, with the highest being ˜ 4 TECU (March 2015). The magnitude of periodic variations can be seen more significantly during HSA periods (2013-2015) and less during LSA periods (2009-2011). The correlation coefficient of 0.89 between the observations and model-based estimations has been found. The RMSE between the observed TEC and model TEC values is 4.0 TECU (linear model) and 4.21 TECU (IRI2016 Model). Further, the linear TEC model has been validated at different latitudes over the northern low-latitude region. The solar component (F10.7p index) value decreases with an increase in latitude. The magnitudes of the periodic component become less significant with the increase in latitude. The influence of geomagnetic component becomes less significant at Lucknow GNSS station (26.76°N, 80.88°E) when compared to other GNSS stations. The hourly averaged TEC values have been considered and ionospheric features are well recovered with linear TEC model.

Advanced analysis technique for the evaluation of linear alternators and linear motors

NASA Technical Reports Server (NTRS)

Holliday, Jeffrey C.

1995-01-01

A method for the mathematical analysis of linear alternator and linear motor devices and designs is described, and an example of its use is included. The technique seeks to surpass other methods of analysis by including more rigorous treatment of phenomena normally omitted or coarsely approximated such as eddy braking, non-linear material properties, and power losses generated within structures surrounding the device. The technique is broadly applicable to linear alternators and linear motors involving iron yoke structures and moving permanent magnets. The technique involves the application of Amperian current equivalents to the modeling of the moving permanent magnet components within a finite element formulation. The resulting steady state and transient mode field solutions can simultaneously account for the moving and static field sources within and around the device.

A polymer, random walk model for the size-distribution of large DNA fragments after high linear energy transfer radiation

NASA Technical Reports Server (NTRS)

Ponomarev, A. L.; Brenner, D.; Hlatky, L. R.; Sachs, R. K.

2000-01-01

DNA double-strand breaks (DSBs) produced by densely ionizing radiation are not located randomly in the genome: recent data indicate DSB clustering along chromosomes. Stochastic DSB clustering at large scales, from > 100 Mbp down to < 0.01 Mbp, is modeled using computer simulations and analytic equations. A random-walk, coarse-grained polymer model for chromatin is combined with a simple track structure model in Monte Carlo software called DNAbreak and is applied to data on alpha-particle irradiation of V-79 cells. The chromatin model neglects molecular details but systematically incorporates an increase in average spatial separation between two DNA loci as the number of base-pairs between the loci increases. Fragment-size distributions obtained using DNAbreak match data on large fragments about as well as distributions previously obtained with a less mechanistic approach. Dose-response relations, linear at small doses of high linear energy transfer (LET) radiation, are obtained. They are found to be non-linear when the dose becomes so large that there is a significant probability of overlapping or close juxtaposition, along one chromosome, for different DSB clusters from different tracks. The non-linearity is more evident for large fragments than for small. The DNAbreak results furnish an example of the RLC (randomly located clusters) analytic formalism, which generalizes the broken-stick fragment-size distribution of the random-breakage model that is often applied to low-LET data.

Moment method analysis of linearly tapered slot antennas

NASA Technical Reports Server (NTRS)

Koeksal, Adnan

1993-01-01

A method of moments (MOM) model for the analysis of the Linearly Tapered Slot Antenna (LTSA) is developed and implemented. The model employs an unequal size rectangular sectioning for conducting parts of the antenna. Piecewise sinusoidal basis functions are used for the expansion of conductor current. The effect of the dielectric is incorporated in the model by using equivalent volume polarization current density and solving the equivalent problem in free-space. The feed section of the antenna including the microstripline is handled rigorously in the MOM model by including slotline short circuit and microstripline currents among the unknowns. Comparison with measurements is made to demonstrate the validity of the model for both the air case and the dielectric case. Validity of the model is also verified by extending the model to handle the analysis of the skew-plate antenna and comparing the results to those of a skew-segmentation modeling results of the same structure and to available data in the literature. Variation of the radiation pattern for the air LTSA with length, height, and taper angle is investigated, and the results are tabulated. Numerical results for the effect of the dielectric thickness and permittivity are presented.

Viscoelastic Properties of Human Tracheal Tissues.

PubMed

Safshekan, Farzaneh; Tafazzoli-Shadpour, Mohammad; Abdouss, Majid; Shadmehr, Mohammad B

2017-01-01

The physiological performance of trachea is highly dependent on its mechanical behavior, and therefore, the mechanical properties of its components. Mechanical characterization of trachea is key to succeed in new treatments such as tissue engineering, which requires the utilization of scaffolds which are mechanically compatible with the native human trachea. In this study, after isolating human trachea samples from brain-dead cases and proper storage, we assessed the viscoelastic properties of tracheal cartilage, smooth muscle, and connective tissue based on stress relaxation tests (at 5% and 10% strains for cartilage and 20%, 30%, and 40% for smooth muscle and connective tissue). After investigation of viscoelastic linearity, constitutive models including Prony series for linear viscoelasticity and quasi-linear viscoelastic, modified superposition, and Schapery models for nonlinear viscoelasticity were fitted to the experimental data to find the best model for each tissue. We also investigated the effect of age on the viscoelastic behavior of tracheal tissues. Based on the results, all three tissues exhibited a (nonsignificant) decrease in relaxation rate with increasing the strain, indicating viscoelastic nonlinearity which was most evident for cartilage and with the least effect for connective tissue. The three-term Prony model was selected for describing the linear viscoelasticity. Among different models, the modified superposition model was best able to capture the relaxation behavior of the three tracheal components. We observed a general (but not significant) stiffening of tracheal cartilage and connective tissue with aging. No change in the stress relaxation percentage with aging was observed. The results of this study may be useful in the design and fabrication of tracheal tissue engineering scaffolds.

Dynamics of electricity market correlations

NASA Astrophysics Data System (ADS)

Alvarez-Ramirez, J.; Escarela-Perez, R.; Espinosa-Perez, G.; Urrea, R.

2009-06-01

Electricity market participants rely on demand and price forecasts to decide their bidding strategies, allocate assets, negotiate bilateral contracts, hedge risks, and plan facility investments. However, forecasting is hampered by the non-linear and stochastic nature of price time series. Diverse modeling strategies, from neural networks to traditional transfer functions, have been explored. These approaches are based on the assumption that price series contain correlations that can be exploited for model-based prediction purposes. While many works have been devoted to the demand and price modeling, a limited number of reports on the nature and dynamics of electricity market correlations are available. This paper uses detrended fluctuation analysis to study correlations in the demand and price time series and takes the Australian market as a case study. The results show the existence of correlations in both demand and prices over three orders of magnitude in time ranging from hours to months. However, the Hurst exponent is not constant over time, and its time evolution was computed over a subsample moving window of 250 observations. The computations, also made for two Canadian markets, show that the correlations present important fluctuations over a seasonal one-year cycle. Interestingly, non-linearities (measured in terms of a multifractality index) and reduced price predictability are found for the June-July periods, while the converse behavior is displayed during the December-January period. In terms of forecasting models, our results suggest that non-linear recursive models should be considered for accurate day-ahead price estimation. On the other hand, linear models seem to suffice for demand forecasting purposes.

Estimation of aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images

NASA Astrophysics Data System (ADS)

Fernández-Manso, O.; Fernández-Manso, A.; Quintano, C.

2014-09-01

Aboveground biomass (AGB) estimation from optical satellite data is usually based on regression models of original or synthetic bands. To overcome the poor relation between AGB and spectral bands due to mixed-pixels when a medium spatial resolution sensor is considered, we propose to base the AGB estimation on fraction images from Linear Spectral Mixture Analysis (LSMA). Our study area is a managed Mediterranean pine woodland (Pinus pinaster Ait.) in central Spain. A total of 1033 circular field plots were used to estimate AGB from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) optical data. We applied Pearson correlation statistics and stepwise multiple regression to identify suitable predictors from the set of variables of original bands, fraction imagery, Normalized Difference Vegetation Index and Tasselled Cap components. Four linear models and one nonlinear model were tested. A linear combination of ASTER band 2 (red, 0.630-0.690 μm), band 8 (short wave infrared 5, 2.295-2.365 μm) and green vegetation fraction (from LSMA) was the best AGB predictor (Radj2=0.632, the root-mean-squared error of estimated AGB was 13.3 Mg ha-1 (or 37.7%), resulting from cross-validation), rather than other combinations of the above cited independent variables. Results indicated that using ASTER fraction images in regression models improves the AGB estimation in Mediterranean pine forests. The spatial distribution of the estimated AGB, based on a multiple linear regression model, may be used as baseline information for forest managers in future studies, such as quantifying the regional carbon budget, fuel accumulation or monitoring of management practices.

Spacecraft nonlinear control

NASA Technical Reports Server (NTRS)

Sheen, Jyh-Jong; Bishop, Robert H.

1992-01-01

The feedback linearization technique is applied to the problem of spacecraft attitude control and momentum management with control moment gyros (CMGs). The feedback linearization consists of a coordinate transformation, which transforms the system to a companion form, and a nonlinear feedback control law to cancel the nonlinear dynamics resulting in a linear equivalent model. Pole placement techniques are then used to place the closed-loop poles. The coordinate transformation proposed here evolves from three output functions of relative degree four, three, and two, respectively. The nonlinear feedback control law is presented. Stability in a neighborhood of a controllable torque equilibrium attitude (TEA) is guaranteed and this fact is demonstrated by the simulation results. An investigation of the nonlinear control law shows that singularities exist in the state space outside the neighborhood of the controllable TEA. The nonlinear control law is simplified by a standard linearization technique and it is shown that the linearized nonlinear controller provides a natural way to select control gains for the multiple-input, multiple-output system. Simulation results using the linearized nonlinear controller show good performance relative to the nonlinear controller in the neighborhood of the TEA.

Linearized self-consistent GW approach satisfying the Ward identity

NASA Astrophysics Data System (ADS)

Kuwahara, Riichi; Ohno, Kaoru

2014-09-01

We propose a linearized self-consistent GW approach satisfying the Ward identity. The vertex function derived from the Ward-Takahashi identity in the limit of q =0 and ω -ω'=0 is included in the self-energy and the polarization function as a consequence of the linearization of the quasiparticle equation. Due to the energy dependence of the self-energy, the Hamiltonian is a non-Hermitian operator and quasiparticle states are nonorthonormal and linearly dependent. However, the linearized quasiparticle states recover orthonormality and fulfill the completeness condition. This approach is very efficient, and the resulting quasiparticle energies are greatly improved compared to the nonlinearized self-consistent GW approach, although its computational cost is not much increased. We show the results for atoms and dimers of Li and Na compared with other approaches. We also propose convenient ways to calculate the Luttinger-Ward functional Φ based on a plasmon-pole model and calculate the total energy for the ground state. As a result, we conclude that the linearization improves overall behaviors in the self-consistent GW approach.

Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models

PubMed Central

Wang, Yifan; Liu, Aiyi; Mills, James L.; Boehnke, Michael; Wilson, Alexander F.; Bailey-Wilson, Joan E.; Xiong, Momiao; Wu, Colin O.; Fan, Ruzong

2015-01-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks’s Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. PMID:25809955

Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.

PubMed

Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong

2015-05-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. © 2015 WILEY PERIODICALS, INC.

Piecewise Linear-Linear Latent Growth Mixture Models with Unknown Knots

ERIC Educational Resources Information Center

Kohli, Nidhi; Harring, Jeffrey R.; Hancock, Gregory R.

2013-01-01

Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear-linear latent growth mixture model (LGMM) for describing segmented change of…

Predicting oropharyngeal tumor volume throughout the course of radiation therapy from pretreatment computed tomography data using general linear models.

PubMed

Yock, Adam D; Rao, Arvind; Dong, Lei; Beadle, Beth M; Garden, Adam S; Kudchadker, Rajat J; Court, Laurence E

2014-05-01

The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: -11.6%-23.8%) and 14.6% (range: -7.3%-27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: -6.8%-40.3%) and 13.1% (range: -1.5%-52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: -11.1%-20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography images and facilitate improved treatment management.

A Numerical Study of Scalable Cardiac Electro-Mechanical Solvers on HPC Architectures

PubMed Central

Colli Franzone, Piero; Pavarino, Luca F.; Scacchi, Simone

2018-01-01

We introduce and study some scalable domain decomposition preconditioners for cardiac electro-mechanical 3D simulations on parallel HPC (High Performance Computing) architectures. The electro-mechanical model of the cardiac tissue is composed of four coupled sub-models: (1) the static finite elasticity equations for the transversely isotropic deformation of the cardiac tissue; (2) the active tension model describing the dynamics of the intracellular calcium, cross-bridge binding and myofilament tension; (3) the anisotropic Bidomain model describing the evolution of the intra- and extra-cellular potentials in the deforming cardiac tissue; and (4) the ionic membrane model describing the dynamics of ionic currents, gating variables, ionic concentrations and stretch-activated channels. This strongly coupled electro-mechanical model is discretized in time with a splitting semi-implicit technique and in space with isoparametric finite elements. The resulting scalable parallel solver is based on Multilevel Additive Schwarz preconditioners for the solution of the Bidomain system and on BDDC preconditioned Newton-Krylov solvers for the non-linear finite elasticity system. The results of several 3D parallel simulations show the scalability of both linear and non-linear solvers and their application to the study of both physiological excitation-contraction cardiac dynamics and re-entrant waves in the presence of different mechano-electrical feedbacks. PMID:29674971

Rheology modification with ring polymers

NASA Astrophysics Data System (ADS)

Vlassopoulos, Dimitris

It is now established that experimental unconcatenated ring polymers can be purified effectively by means of fractionation at the critical condition. For molecular weights well above the entanglement threshold, purified rings relax stress via power-law (with an exponent of about -0.4), sharply departing from their linear counterparts. Experimental results are in harmony with modeling predictions and simulations. Here, we present results from recent interdisciplinary efforts and discuss two challenges: (i) the nonlinear shear rheology of purified ring melts is also very different from that of unlinked chains. Whereas the latter exhibit features that can be explained, to a first approach, in the framework in the tube model, the former behave akin to unentangled chains with finite extensibility and exhibit much small deformation at steady state. (ii) blends of rings and linear polymers exhibit unique features in different regimes: The addition of minute amounts of linear chains drastically affects ring dynamics. This relates to ring purity and the ability of unlinked linear chains to thread rings. With the help of simulations, it is possible to rationalize the observed surprisingly slow viscoelastic relaxation, which is attributed to ring-linear and ring-ring penetrations. On the other hand, adding small amounts of rings to linear polymers of different molecular weights influences their linear and nonlinear rheology in an unprecedented way. The blend viscosity exceeds that of the slower component (linear) in this non-interacting mixture, and its dependencies on composition and molecular weight ratio are examined, whereas the role of molecular architecture is also addressed. Consequently, closing the ends of a linear chain can serve as a powerful means for molecular manipulation of its rheology. This presentation reflects collaborative efforts with S. Costanzo, Z-C. Yan, R. Pasquino, M. Kaliva, S. Kamble, Y. Jeong, P. Lutz, J. Allgaier, T. Chang, D. Talikis, V. Mavrantzas and M. Rubinstein.

Communication: Symmetrical quasi-classical analysis of linear optical spectroscopy

NASA Astrophysics Data System (ADS)

Provazza, Justin; Coker, David F.

2018-05-01

The symmetrical quasi-classical approach for propagation of a many degree of freedom density matrix is explored in the context of computing linear spectra. Calculations on a simple two state model for which exact results are available suggest that the approach gives a qualitative description of peak positions, relative amplitudes, and line broadening. Short time details in the computed dipole autocorrelation function result in exaggerated tails in the spectrum.

A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis.

PubMed

Šiljić Tomić, Aleksandra; Antanasijević, Davor; Ristić, Mirjana; Perić-Grujić, Aleksandra; Pocajt, Viktor

2018-01-01

Accurate prediction of water quality parameters (WQPs) is an important task in the management of water resources. Artificial neural networks (ANNs) are frequently applied for dissolved oxygen (DO) prediction, but often only their interpolation performance is checked. The aims of this research, beside interpolation, were the determination of extrapolation performance of ANN model, which was developed for the prediction of DO content in the Danube River, and the assessment of relationship between the significance of inputs and prediction error in the presence of values which were of out of the range of training. The applied ANN is a polynomial neural network (PNN) which performs embedded selection of most important inputs during learning, and provides a model in the form of linear and non-linear polynomial functions, which can then be used for a detailed analysis of the significance of inputs. Available dataset that contained 1912 monitoring records for 17 water quality parameters was split into a "regular" subset that contains normally distributed and low variability data, and an "extreme" subset that contains monitoring records with outlier values. The results revealed that the non-linear PNN model has good interpolation performance (R 2 =0.82), but it was not robust in extrapolation (R 2 =0.63). The analysis of extrapolation results has shown that the prediction errors are correlated with the significance of inputs. Namely, the out-of-training range values of the inputs with low importance do not affect significantly the PNN model performance, but their influence can be biased by the presence of multi-outlier monitoring records. Subsequently, linear PNN models were successfully applied to study the effect of water quality parameters on DO content. It was observed that DO level is mostly affected by temperature, pH, biological oxygen demand (BOD) and phosphorus concentration, while in extreme conditions the importance of alkalinity and bicarbonates rises over pH and BOD. Copyright © 2017 Elsevier B.V. All rights reserved.

Frequency response of synthetic vocal fold models with linear and nonlinear material properties.

PubMed

Shaw, Stephanie M; Thomson, Scott L; Dromey, Christopher; Smith, Simeon

2012-10-01

The purpose of this study was to create synthetic vocal fold models with nonlinear stress-strain properties and to investigate the effect of linear versus nonlinear material properties on fundamental frequency (F0) during anterior-posterior stretching. Three materially linear and 3 materially nonlinear models were created and stretched up to 10 mm in 1-mm increments. Phonation onset pressure (Pon) and F0 at Pon were recorded for each length. Measurements were repeated as the models were relaxed in 1-mm increments back to their resting lengths, and tensile tests were conducted to determine the stress-strain responses of linear versus nonlinear models. Nonlinear models demonstrated a more substantial frequency response than did linear models and a more predictable pattern of F0 increase with respect to increasing length (although range was inconsistent across models). Pon generally increased with increasing vocal fold length for nonlinear models, whereas for linear models, Pon decreased with increasing length. Nonlinear synthetic models appear to more accurately represent the human vocal folds than do linear models, especially with respect to F0 response.

Linear model correction: A method for transferring a near-infrared multivariate calibration model without standard samples.

PubMed

Liu, Yan; Cai, Wensheng; Shao, Xueguang

2016-12-05

Calibration transfer is essential for practical applications of near infrared (NIR) spectroscopy because the measurements of the spectra may be performed on different instruments and the difference between the instruments must be corrected. For most of calibration transfer methods, standard samples are necessary to construct the transfer model using the spectra of the samples measured on two instruments, named as master and slave instrument, respectively. In this work, a method named as linear model correction (LMC) is proposed for calibration transfer without standard samples. The method is based on the fact that, for the samples with similar physical and chemical properties, the spectra measured on different instruments are linearly correlated. The fact makes the coefficients of the linear models constructed by the spectra measured on different instruments are similar in profile. Therefore, by using the constrained optimization method, the coefficients of the master model can be transferred into that of the slave model with a few spectra measured on slave instrument. Two NIR datasets of corn and plant leaf samples measured with different instruments are used to test the performance of the method. The results show that, for both the datasets, the spectra can be correctly predicted using the transferred partial least squares (PLS) models. Because standard samples are not necessary in the method, it may be more useful in practical uses. Copyright © 2016 Elsevier B.V. All rights reserved.

Non-susceptible landslide areas in Italy and in the Mediterranean region

NASA Astrophysics Data System (ADS)

Marchesini, I.; Ardizzone, F.; Alvioli, M.; Rossi, M.; Guzzetti, F.

2014-04-01

We used landslide information for 13 study areas in Italy and morphometric information obtained from the 3 arc-second SRTM DEM to determine areas where landslide susceptibility is expected to be null or negligible in Italy, and in the landmasses surrounding the Mediterranean Sea. The morphometric information consisted in the local terrain slope computed in a square 3 × 3 cell moving window, and in the regional relative relief computed in a circular 15 × 15 cell moving window. We tested three different models to determine the non-susceptible landslide areas, including a linear model (LR), a quantile linear model (QLR), and a quantile non-linear model (QNL). We tested the performance of the three models using independent landslide information represented by the Italian Landslide Inventory (Inventario Fenomeni Franosi in Italia - IFFI). Best results were obtained using the QNL model. The corresponding zonation of non-susceptible landslide areas was intersected in a GIS with geographical census data for Italy. The result allowed determining that 57.5% of the population of Italy (in 2001) was located in areas where landslide susceptibility is expected to be null or negligible, and that the remaining 42.5% was located in areas where some landslide susceptibility is expected. We applied the QNL model to the landmasses surrounding the Mediterranean Sea, and we tested the synoptic non-susceptibility zonation using independent landslide information for three study areas in Spain. Results proved that the QNL model was capable of determining where landslide susceptibility is expected to be negligible in the Mediterranean area. We expect our results to be applicable in similar study areas, facilitating the identification of non-susceptible and susceptible landslide areas, at the synoptic scale.

Linear solvation energy relationships (LSER) for adsorption of organic compounds by carbon nanotubes.

PubMed

Ersan, Gamze; Apul, Onur G; Karanfil, Tanju

2016-07-01

The objective of this paper was to create a comprehensive database for the adsorption of organic compounds by carbon nanotubes (CNTs) and to use the Linear Solvation Energy Relationship (LSER) technique for developing predictive adsorption models of organic compounds (OCs) by multi-walled carbon nanotubes (MWCNTs) and single-walled carbon nanotubes (SWCNTs). Adsorption data for 123 OCs by MWCNTs and 48 OCs by SWCNTs were compiled from the literature, including some experimental results obtained in our laboratory. The roles of selected OCs properties and CNT types were examined with LSER models. The results showed that the r(2) values of the LSER models displayed small variability for aromatic compounds smaller than 220 g/mol, after which a decreasing trend was observed. The data available for aliphatics was mainly for molecular weights smaller than 250 g/mol, which showed a similar trend to that of aromatics. The r(2) values for the LSER model on the adsorption of aromatic and aliphatic OCs by SWCNTs and MWCNTs were relatively similar indicating the linearity of LSER models did not depend on the CNT types. Among all LSER model descriptors, V term (molecular volume) for aromatic OCs and B term (basicity) for aliphatic OCs were the most predominant descriptors on both type of CNTs. The presence of R term (excess molar refractivity) in LSER model equations resulted in decreases for both V and P (polarizability) parameters without affecting the r(2) values. Overall, the results demonstrate that successful predictive models can be developed for the adsorption of OCs by MWCNTs and SWCNTs with LSER techniques. Copyright © 2016 Elsevier Ltd. All rights reserved.

Extending Linear Models to Non-Linear Contexts: An In-Depth Study about Two University Students' Mathematical Productions

ERIC Educational Resources Information Center

Esteley, Cristina; Villarreal, Monica; Alagia, Humberto

2004-01-01

This research report presents a study of the work of agronomy majors in which an extension of linear models to non-linear contexts can be observed. By linear models we mean the model y=a.x+b, some particular representations of direct proportionality and the diagram for the rule of three. Its presence and persistence in different types of problems…

A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Boucher, Thomas F.; Ozanne, Marie V.; Carmosino, Marco L.; Dyar, M. Darby; Mahadevan, Sridhar; Breves, Elly A.; Lepore, Kate H.; Clegg, Samuel M.

2015-05-01

The ChemCam instrument on the Mars Curiosity rover is generating thousands of LIBS spectra and bringing interest in this technique to public attention. The key to interpreting Mars or any other types of LIBS data are calibrations that relate laboratory standards to unknowns examined in other settings and enable predictions of chemical composition. Here, LIBS spectral data are analyzed using linear regression methods including partial least squares (PLS-1 and PLS-2), principal component regression (PCR), least absolute shrinkage and selection operator (lasso), elastic net, and linear support vector regression (SVR-Lin). These were compared against results from nonlinear regression methods including kernel principal component regression (K-PCR), polynomial kernel support vector regression (SVR-Py) and k-nearest neighbor (kNN) regression to discern the most effective models for interpreting chemical abundances from LIBS spectra of geological samples. The results were evaluated for 100 samples analyzed with 50 laser pulses at each of five locations averaged together. Wilcoxon signed-rank tests were employed to evaluate the statistical significance of differences among the nine models using their predicted residual sum of squares (PRESS) to make comparisons. For MgO, SiO2, Fe2O3, CaO, and MnO, the sparse models outperform all the others except for linear SVR, while for Na2O, K2O, TiO2, and P2O5, the sparse methods produce inferior results, likely because their emission lines in this energy range have lower transition probabilities. The strong performance of the sparse methods in this study suggests that use of dimensionality-reduction techniques as a preprocessing step may improve the performance of the linear models. Nonlinear methods tend to overfit the data and predict less accurately, while the linear methods proved to be more generalizable with better predictive performance. These results are attributed to the high dimensionality of the data (6144 channels) relative to the small number of samples studied. The best-performing models were SVR-Lin for SiO2, MgO, Fe2O3, and Na2O, lasso for Al2O3, elastic net for MnO, and PLS-1 for CaO, TiO2, and K2O. Although these differences in model performance between methods were identified, most of the models produce comparable results when p ≤ 0.05 and all techniques except kNN produced statistically-indistinguishable results. It is likely that a combination of models could be used together to yield a lower total error of prediction, depending on the requirements of the user.

Mathematical model for the contribution of individual organs to non-zero y-intercepts in single and multi-compartment linear models of whole-body energy expenditure.

PubMed

Kaiyala, Karl J

2014-01-01

Mathematical models for the dependence of energy expenditure (EE) on body mass and composition are essential tools in metabolic phenotyping. EE scales over broad ranges of body mass as a non-linear allometric function. When considered within restricted ranges of body mass, however, allometric EE curves exhibit 'local linearity.' Indeed, modern EE analysis makes extensive use of linear models. Such models typically involve one or two body mass compartments (e.g., fat free mass and fat mass). Importantly, linear EE models typically involve a non-zero (usually positive) y-intercept term of uncertain origin, a recurring theme in discussions of EE analysis and a source of confounding in traditional ratio-based EE normalization. Emerging linear model approaches quantify whole-body resting EE (REE) in terms of individual organ masses (e.g., liver, kidneys, heart, brain). Proponents of individual organ REE modeling hypothesize that multi-organ linear models may eliminate non-zero y-intercepts. This could have advantages in adjusting REE for body mass and composition. Studies reveal that individual organ REE is an allometric function of total body mass. I exploit first-order Taylor linearization of individual organ REEs to model the manner in which individual organs contribute to whole-body REE and to the non-zero y-intercept in linear REE models. The model predicts that REE analysis at the individual organ-tissue level will not eliminate intercept terms. I demonstrate that the parameters of a linear EE equation can be transformed into the parameters of the underlying 'latent' allometric equation. This permits estimates of the allometric scaling of EE in a diverse variety of physiological states that are not represented in the allometric EE literature but are well represented by published linear EE analyses.

A performance model for GPUs with caches

DOE PAGES

Dao, Thanh Tuan; Kim, Jungwon; Seo, Sangmin; ...

2014-06-24

To exploit the abundant computational power of the world's fastest supercomputers, an even workload distribution to the typically heterogeneous compute devices is necessary. While relatively accurate performance models exist for conventional CPUs, accurate performance estimation models for modern GPUs do not exist. This paper presents two accurate models for modern GPUs: a sampling-based linear model, and a model based on machine-learning (ML) techniques which improves the accuracy of the linear model and is applicable to modern GPUs with and without caches. We first construct the sampling-based linear model to predict the runtime of an arbitrary OpenCL kernel. Based on anmore » analysis of NVIDIA GPUs' scheduling policies we determine the earliest sampling points that allow an accurate estimation. The linear model cannot capture well the significant effects that memory coalescing or caching as implemented in modern GPUs have on performance. We therefore propose a model based on ML techniques that takes several compiler-generated statistics about the kernel as well as the GPU's hardware performance counters as additional inputs to obtain a more accurate runtime performance estimation for modern GPUs. We demonstrate the effectiveness and broad applicability of the model by applying it to three different NVIDIA GPU architectures and one AMD GPU architecture. On an extensive set of OpenCL benchmarks, on average, the proposed model estimates the runtime performance with less than 7 percent error for a second-generation GTX 280 with no on-chip caches and less than 5 percent for the Fermi-based GTX 580 with hardware caches. On the Kepler-based GTX 680, the linear model has an error of less than 10 percent. On an AMD GPU architecture, Radeon HD 6970, the model estimates with 8 percent of error rates. As a result, the proposed technique outperforms existing models by a factor of 5 to 6 in terms of accuracy.« less

Quantum associative memory with linear and non-linear algorithms for the diagnosis of some tropical diseases.

PubMed

Tchapet Njafa, J-P; Nana Engo, S G

2018-01-01

This paper presents the QAMDiagnos, a model of Quantum Associative Memory (QAM) that can be a helpful tool for medical staff without experience or laboratory facilities, for the diagnosis of four tropical diseases (malaria, typhoid fever, yellow fever and dengue) which have several similar signs and symptoms. The memory can distinguish a single infection from a polyinfection. Our model is a combination of the improved versions of the original linear quantum retrieving algorithm proposed by Ventura and the non-linear quantum search algorithm of Abrams and Lloyd. From the given simulation results, it appears that the efficiency of recognition is good when particular signs and symptoms of a disease are inserted given that the linear algorithm is the main algorithm. The non-linear algorithm helps confirm or correct the diagnosis or give some advice to the medical staff for the treatment. So, our QAMDiagnos that has a friendly graphical user interface for desktop and smart-phone is a sensitive and a low-cost diagnostic tool that enables rapid and accurate diagnosis of four tropical diseases. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modeling Learning in Doubly Multilevel Binary Longitudinal Data Using Generalized Linear Mixed Models: An Application to Measuring and Explaining Word Learning.

PubMed

Cho, Sun-Joo; Goodwin, Amanda P

2016-04-01

When word learning is supported by instruction in experimental studies for adolescents, word knowledge outcomes tend to be collected from complex data structure, such as multiple aspects of word knowledge, multilevel reader data, multilevel item data, longitudinal design, and multiple groups. This study illustrates how generalized linear mixed models can be used to measure and explain word learning for data having such complexity. Results from this application provide deeper understanding of word knowledge than could be attained from simpler models and show that word knowledge is multidimensional and depends on word characteristics and instructional contexts.

Pseudo-second order models for the adsorption of safranin onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth

2007-04-02

Kinetic experiments were carried out for the sorption of safranin onto activated carbon particles. The kinetic data were fitted to pseudo-second order model of Ho, Sobkowsk and Czerwinski, Blanchard et al. and Ritchie by linear and non-linear regression methods. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo-second order models were the same. Non-linear regression analysis showed that both Blanchard et al. and Ho have similar ideas on the pseudo-second order model but with different assumptions. The best fit of experimental data in Ho's pseudo-second order expression by linear and non-linear regression method showed that Ho pseudo-second order model was a better kinetic expression when compared to other pseudo-second order kinetic expressions.

Reanalysis of cancer mortality in Japanese A-bomb survivors exposed to low doses of radiation: bootstrap and simulation methods

PubMed Central

2009-01-01

Background The International Commission on Radiological Protection (ICRP) recommended annual occupational dose limit is 20 mSv. Cancer mortality in Japanese A-bomb survivors exposed to less than 20 mSv external radiation in 1945 was analysed previously, using a latency model with non-linear dose response. Questions were raised regarding statistical inference with this model. Methods Cancers with over 100 deaths in the 0 - 20 mSv subcohort of the 1950-1990 Life Span Study are analysed with Poisson regression models incorporating latency, allowing linear and non-linear dose response. Bootstrap percentile and Bias-corrected accelerated (BCa) methods and simulation of the Likelihood Ratio Test lead to Confidence Intervals for Excess Relative Risk (ERR) and tests against the linear model. Results The linear model shows significant large, positive values of ERR for liver and urinary cancers at latencies from 37 - 43 years. Dose response below 20 mSv is strongly non-linear at the optimal latencies for the stomach (11.89 years), liver (36.9), lung (13.6), leukaemia (23.66), and pancreas (11.86) and across broad latency ranges. Confidence Intervals for ERR are comparable using Bootstrap and Likelihood Ratio Test methods and BCa 95% Confidence Intervals are strictly positive across latency ranges for all 5 cancers. Similar risk estimates for 10 mSv (lagged dose) are obtained from the 0 - 20 mSv and 5 - 500 mSv data for the stomach, liver, lung and leukaemia. Dose response for the latter 3 cancers is significantly non-linear in the 5 - 500 mSv range. Conclusion Liver and urinary cancer mortality risk is significantly raised using a latency model with linear dose response. A non-linear model is strongly superior for the stomach, liver, lung, pancreas and leukaemia. Bootstrap and Likelihood-based confidence intervals are broadly comparable and ERR is strictly positive by bootstrap methods for all 5 cancers. Except for the pancreas, similar estimates of latency and risk from 10 mSv are obtained from the 0 - 20 mSv and 5 - 500 mSv subcohorts. Large and significant cancer risks for Japanese survivors exposed to less than 20 mSv external radiation from the atomic bombs in 1945 cast doubt on the ICRP recommended annual occupational dose limit. PMID:20003238

Prediction uncertainty and data worth assessment for groundwater transport times in an agricultural catchment

NASA Astrophysics Data System (ADS)

Zell, Wesley O.; Culver, Teresa B.; Sanford, Ward E.

2018-06-01

Uncertainties about the age of base-flow discharge can have serious implications for the management of degraded environmental systems where subsurface pathways, and the ongoing release of pollutants that accumulated in the subsurface during past decades, dominate the water quality signal. Numerical groundwater models may be used to estimate groundwater return times and base-flow ages and thus predict the time required for stakeholders to see the results of improved agricultural management practices. However, the uncertainty inherent in the relationship between (i) the observations of atmospherically-derived tracers that are required to calibrate such models and (ii) the predictions of system age that the observations inform have not been investigated. For example, few if any studies have assessed the uncertainty of numerically-simulated system ages or evaluated the uncertainty reductions that may result from the expense of collecting additional subsurface tracer data. In this study we combine numerical flow and transport modeling of atmospherically-derived tracers with prediction uncertainty methods to accomplish four objectives. First, we show the relative importance of head, discharge, and tracer information for characterizing response times in a uniquely data rich catchment that includes 266 age-tracer measurements (SF6, CFCs, and 3H) in addition to long term monitoring of water levels and stream discharge. Second, we calculate uncertainty intervals for model-simulated base-flow ages using both linear and non-linear methods, and find that the prediction sensitivity vector used by linear first-order second-moment methods results in much larger uncertainties than non-linear Monte Carlo methods operating on the same parameter uncertainty. Third, by combining prediction uncertainty analysis with multiple models of the system, we show that data-worth calculations and monitoring network design are sensitive to variations in the amount of water leaving the system via stream discharge and irrigation withdrawals. Finally, we demonstrate a novel model-averaged computation of potential data worth that can account for these uncertainties in model structure.

Nonparametric Model of Smooth Muscle Force Production During Electrical Stimulation.

PubMed

Cole, Marc; Eikenberry, Steffen; Kato, Takahide; Sandler, Roman A; Yamashiro, Stanley M; Marmarelis, Vasilis Z

2017-03-01

A nonparametric model of smooth muscle tension response to electrical stimulation was estimated using the Laguerre expansion technique of nonlinear system kernel estimation. The experimental data consisted of force responses of smooth muscle to energy-matched alternating single pulse and burst current stimuli. The burst stimuli led to at least a 10-fold increase in peak force in smooth muscle from Mytilus edulis, despite the constant energy constraint. A linear model did not fit the data. However, a second-order model fit the data accurately, so the higher-order models were not required to fit the data. Results showed that smooth muscle force response is not linearly related to the stimulation power.

Linear Instability Analysis of non-uniform Bubbly Mixing layer with Two-Fluid model

NASA Astrophysics Data System (ADS)

Sharma, Subash; Chetty, Krishna; Lopez de Bertodano, Martin

We examine the inviscid instability of a non-uniform adiabatic bubbly shear layer with a Two-Fluid model. The Two-Fluid model is made well-posed with the closure relations for interfacial forces. First, a characteristic analysis is carried out to study the well posedness of the model over range of void fraction with interfacial forces for virtual mass, interfacial drag, interfacial pressure. A dispersion analysis then allow us to obtain growth rate and wavelength. Then, the well-posed two-fluid model is solved using CFD to validate the results obtained with the linear stability analysis. The effect of the void fraction and the distribution profile on stability is analyzed.

Dynamical localization of coupled relativistic kicked rotors

NASA Astrophysics Data System (ADS)

Rozenbaum, Efim B.; Galitski, Victor

2017-02-01

A periodically driven rotor is a prototypical model that exhibits a transition to chaos in the classical regime and dynamical localization (related to Anderson localization) in the quantum regime. In a recent work [Phys. Rev. B 94, 085120 (2016), 10.1103/PhysRevB.94.085120], A. C. Keser et al. considered a many-body generalization of coupled quantum kicked rotors, and showed that in the special integrable linear case, dynamical localization survives interactions. By analogy with many-body localization, the phenomenon was dubbed dynamical many-body localization. In the present work, we study nonintegrable models of single and coupled quantum relativistic kicked rotors (QRKRs) that bridge the gap between the conventional quadratic rotors and the integrable linear models. For a single QRKR, we supplement the recent analysis of the angular-momentum-space dynamics with a study of the spin dynamics. Our analysis of two and three coupled QRKRs along with the proved localization in the many-body linear model indicate that dynamical localization exists in few-body systems. Moreover, the relation between QRKR and linear rotor models implies that dynamical many-body localization can exist in generic, nonintegrable many-body systems. And localization can generally result from a complicated interplay between Anderson mechanism and limiting integrability, since the many-body linear model is a high-angular-momentum limit of many-body QRKRs. We also analyze the dynamics of two coupled QRKRs in the highly unusual superballistic regime and find that the resonance conditions are relaxed due to interactions. Finally, we propose experimental realizations of the QRKR model in cold atoms in optical lattices.

Bilinear effect in complex systems

NASA Astrophysics Data System (ADS)

Lam, Lui; Bellavia, David C.; Han, Xiao-Pu; Alston Liu, Chih-Hui; Shu, Chang-Qing; Wei, Zhengjin; Zhou, Tao; Zhu, Jichen

2010-09-01

The distribution of the lifetime of Chinese dynasties (as well as that of the British Isles and Japan) in a linear Zipf plot is found to consist of two straight lines intersecting at a transition point. This two-section piecewise-linear distribution is different from the power law or the stretched exponent distribution, and is called the Bilinear Effect for short. With assumptions mimicking the organization of ancient Chinese regimes, a 3-layer network model is constructed. Numerical results of this model show the bilinear effect, providing a plausible explanation of the historical data. The bilinear effect in two other social systems is presented, indicating that such a piecewise-linear effect is widespread in social systems.

A conformal approach for the analysis of the non-linear stability of radiation cosmologies

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

Luebbe, Christian, E-mail: c.luebbe@ucl.ac.uk; Department of Mathematics, University of Leicester, University Road, LE1 8RH; Valiente Kroon, Juan Antonio, E-mail: j.a.valiente-kroon@qmul.ac.uk

2013-01-15

The conformal Einstein equations for a trace-free (radiation) perfect fluid are derived in terms of the Levi-Civita connection of a conformally rescaled metric. These equations are used to provide a non-linear stability result for de Sitter-like trace-free (radiation) perfect fluid Friedman-Lemaitre-Robertson-Walker cosmological models. The solutions thus obtained exist globally towards the future and are future geodesically complete. - Highlights: Black-Right-Pointing-Pointer We study the Einstein-Euler system in General Relativity using conformal methods. Black-Right-Pointing-Pointer We analyze the structural properties of the associated evolution equations. Black-Right-Pointing-Pointer We establish the non-linear stability of pure radiation cosmological models.

A hybrid linear/nonlinear training algorithm for feedforward neural networks.

PubMed

McLoone, S; Brown, M D; Irwin, G; Lightbody, A

1998-01-01

This paper presents a new hybrid optimization strategy for training feedforward neural networks. The algorithm combines gradient-based optimization of nonlinear weights with singular value decomposition (SVD) computation of linear weights in one integrated routine. It is described for the multilayer perceptron (MLP) and radial basis function (RBF) networks and then extended to the local model network (LMN), a new feedforward structure in which a global nonlinear model is constructed from a set of locally valid submodels. Simulation results are presented demonstrating the superiority of the new hybrid training scheme compared to second-order gradient methods. It is particularly effective for the LMN architecture where the linear to nonlinear parameter ratio is large.

Predicting the nonlinear optical response in the resonant region from the linear characterization: a self-consistent theory for the first-, second-, and third-order (non)linear optical response

NASA Astrophysics Data System (ADS)

Pérez-Moreno, Javier; Clays, Koen; Kuzyk, Mark G.

2010-08-01

We introduce a self-consistent theory for the description of the optical linear and nonlinear response of molecules that is based strictly on the results of the experimental characterization. We show how the Thomas-Kuhn sum-rules can be used to eliminate the dependence of the nonlinear response on parameters that are not directly measurable. Our approach leads to the successful modeling of the dispersion of the nonlinear response of complex molecular structures with different geometries (dipolar and octupolar), and can be used as a guide towards the modeling in terms of fundamental physical parameters.

Modified hyperbolic sine model for titanium dioxide-based memristive thin films

NASA Astrophysics Data System (ADS)

Abu Bakar, Raudah; Syahirah Kamarozaman, Nur; Fazlida Hanim Abdullah, Wan; Herman, Sukreen Hana

2018-03-01

Since the emergence of memristor as the newest fundamental circuit elements, studies on memristor modeling have been evolved. To date, the developed models were based on the linear model, linear ionic drift model using different window functions, tunnelling barrier model and hyperbolic-sine function based model. Although using hyperbolic-sine function model could predict the memristor electrical properties, the model was not well fitted to the experimental data. In order to improve the performance of the hyperbolic-sine function model, the state variable equation was modified. On the one hand, the addition of window function cannot provide an improved fitting. By multiplying the Yakopcic’s state variable model to Chang’s model on the other hand resulted in the closer agreement with the TiO2 thin film experimental data. The percentage error was approximately 2.15%.

Model-Based Engine Control Architecture with an Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Connolly, Joseph W.

2016-01-01

This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The nonlinear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.

Within crown variation in the relationship between foliage biomass and sapwood area in jack pine.

PubMed

Schneider, Robert; Berninger, Frank; Ung, Chhun-Huor; Mäkelä, Annikki; Swift, D Edwin; Zhang, S Y

2011-01-01

The relationship between sapwood area and foliage biomass is the basis for a lot of research on eco-phyisology. In this paper, foliage biomass change between two consecutive whorls is studied, using different variations in the pipe model theory. Linear and non-linear mixed-effect models relating foliage differences to sapwood area increments were tested to take into account whorl location, with the best fit statistics supporting the non-linear formulation. The estimated value of the exponent is 0.5130, which is significantly different from 1, the expected value given by the pipe model theory. When applied to crown stem sapwood taper, the model indicates that foliage biomass distribution influences the foliage biomass to sapwood area at crown base ratio. This result is interpreted as being the consequence of differences in the turnover rates of sapwood and foliage. More importantly, the model explains previously reported trends in jack pine sapwood area at crown base to tree foliage biomass ratio.

Performance of linear and nonlinear texture measures in 2D and 3D for monitoring architectural changes in osteoporosis using computer-generated models of trabecular bone

NASA Astrophysics Data System (ADS)

Boehm, Holger F.; Link, Thomas M.; Monetti, Roberto A.; Mueller, Dirk; Rummeny, Ernst J.; Raeth, Christoph W.

2005-04-01

Osteoporosis is a metabolic bone disease leading to de-mineralization and increased risk of fracture. The two major factors that determine the biomechanical competence of bone are the degree of mineralization and the micro-architectural integrity. Today, modern imaging modalities (high resolution MRI, micro-CT) are capable of depicting structural details of trabecular bone tissue. From the image data, structural properties obtained by quantitative measures are analysed with respect to the presence of osteoporotic fractures of the spine (in-vivo) or correlated with biomechanical strength as derived from destructive testing (in-vitro). Fairly well established are linear structural measures in 2D that are originally adopted from standard histo-morphometry. Recently, non-linear techniques in 2D and 3D based on the scaling index method (SIM), the standard Hough transform (SHT), and the Minkowski Functionals (MF) have been introduced, which show excellent performance in predicting bone strength and fracture risk. However, little is known about the performance of the various parameters with respect to monitoring structural changes due to progression of osteoporosis or as a result of medical treatment. In this contribution, we generate models of trabecular bone with pre-defined structural properties which are exposed to simulated osteoclastic activity. We apply linear and non-linear texture measures to the models and analyse their performance with respect to detecting architectural changes. This study demonstrates, that the texture measures are capable of monitoring structural changes of complex model data. The diagnostic potential varies for the different parameters and is found to depend on the topological composition of the model and initial "bone density". In our models, non-linear texture measures tend to react more sensitively to small structural changes than linear measures. Best performance is observed for the 3rd and 4th Minkowski Functionals and for the scaling index method.

Copula Entropy coupled with Wavelet Neural Network Model for Hydrological Prediction

NASA Astrophysics Data System (ADS)

Wang, Yin; Yue, JiGuang; Liu, ShuGuang; Wang, Li

2018-02-01

Artificial Neural network(ANN) has been widely used in hydrological forecasting. in this paper an attempt has been made to find an alternative method for hydrological prediction by combining Copula Entropy(CE) with Wavelet Neural Network(WNN), CE theory permits to calculate mutual information(MI) to select Input variables which avoids the limitations of the traditional linear correlation(LCC) analysis. Wavelet analysis can provide the exact locality of any changes in the dynamical patterns of the sequence Coupled with ANN Strong non-linear fitting ability. WNN model was able to provide a good fit with the hydrological data. finally, the hybrid model(CE+WNN) have been applied to daily water level of Taihu Lake Basin, and compared with CE ANN, LCC WNN and LCC ANN. Results showed that the hybrid model produced better results in estimating the hydrograph properties than the latter models.

Nonlinear-programming mathematical modeling of coal blending for power plant

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

Tang Longhua; Zhou Junhu; Yao Qiang

At present most of the blending works are guided by experience or linear-programming (LP) which can not reflect the coal complicated characteristics properly. Experimental and theoretical research work shows that most of the coal blend properties can not always be measured as a linear function of the properties of the individual coals in the blend. The authors introduced nonlinear functions or processes (including neural network and fuzzy mathematics), established on the experiments directed by the authors and other researchers, to quantitatively describe the complex coal blend parameters. Finally nonlinear-programming (NLP) mathematical modeling of coal blend is introduced and utilized inmore » the Hangzhou Coal Blending Center. Predictions based on the new method resulted in different results from the ones based on LP modeling. The authors concludes that it is very important to introduce NLP modeling, instead of NL modeling, into the work of coal blending.« less

Comparison of the linear bias models in the light of the Dark Energy Survey

NASA Astrophysics Data System (ADS)

Papageorgiou, A.; Basilakos, S.; Plionis, M.

2018-05-01

The evolution of the linear and scale independent bias, based on the most popular dark matter bias models within the Λ cold dark matter (ΛCDM) cosmology, is confronted to that of the Dark Energy Survey (DES) luminous red galaxies (LRGs). Applying a χ2 minimization procedure between models and data, we find that all the considered linear bias models reproduce well the LRG bias data. The differences among the bias models are absorbed in the predicted mass of the dark-matter halo in which LRGs live and which ranges between ˜6 × 1012 and 1.4 × 1013 h-1 M⊙, for the different bias models. Similar results, reaching however a maximum value of ˜2 × 1013 h-1 M⊙, are found by confronting the SDSS (2SLAQ) Large Red Galaxies clustering with theoretical clustering models, which also include the evolution of bias. This later analysis also provides a value of Ωm = 0.30 ± 0.01, which is in excellent agreement with recent joint analyses of different cosmological probes and the reanalysis of the Planck data.

Non-linear controls influence functions in an aircraft dynamics simulator

NASA Technical Reports Server (NTRS)

Guerreiro, Nelson M.; Hubbard, James E., Jr.; Motter, Mark A.

2006-01-01

In the development and testing of novel structural and controls concepts, such as morphing aircraft wings, appropriate models are needed for proper system characterization. In most instances, available system models do not provide the required additional degrees of freedom for morphing structures but may be modified to some extent to achieve a compatible system. The objective of this study is to apply wind tunnel data collected for an Unmanned Air Vehicle (UAV), that implements trailing edge morphing, to create a non-linear dynamics simulator, using well defined rigid body equations of motion, where the aircraft stability derivatives change with control deflection. An analysis of this wind tunnel data, using data extraction algorithms, was performed to determine the reference aerodynamic force and moment coefficients for the aircraft. Further, non-linear influence functions were obtained for each of the aircraft s control surfaces, including the sixteen trailing edge flap segments. These non-linear controls influence functions are applied to the aircraft dynamics to produce deflection-dependent aircraft stability derivatives in a non-linear dynamics simulator. Time domain analysis of the aircraft motion, trajectory, and state histories can be performed using these nonlinear dynamics and may be visualized using a 3-dimensional aircraft model. Linear system models can be extracted to facilitate frequency domain analysis of the system and for control law development. The results of this study are useful in similar projects where trailing edge morphing is employed and will be instrumental in the University of Maryland s continuing study of active wing load control.

A new analysis of the Fornberg-Whitham equation pertaining to a fractional derivative with Mittag-Leffler-type kernel

NASA Astrophysics Data System (ADS)

Kumar, Devendra; Singh, Jagdev; Baleanu, Dumitru

2018-02-01

The mathematical model of breaking of non-linear dispersive water waves with memory effect is very important in mathematical physics. In the present article, we examine a novel fractional extension of the non-linear Fornberg-Whitham equation occurring in wave breaking. We consider the most recent theory of differentiation involving the non-singular kernel based on the extended Mittag-Leffler-type function to modify the Fornberg-Whitham equation. We examine the existence of the solution of the non-linear Fornberg-Whitham equation of fractional order. Further, we show the uniqueness of the solution. We obtain the numerical solution of the new arbitrary order model of the non-linear Fornberg-Whitham equation with the aid of the Laplace decomposition technique. The numerical outcomes are displayed in the form of graphs and tables. The results indicate that the Laplace decomposition algorithm is a very user-friendly and reliable scheme for handling such type of non-linear problems of fractional order.

New insights into soil temperature time series modeling: linear or nonlinear?

NASA Astrophysics Data System (ADS)

Bonakdari, Hossein; Moeeni, Hamid; Ebtehaj, Isa; Zeynoddin, Mohammad; Mahoammadian, Abdolmajid; Gharabaghi, Bahram

2018-03-01

Soil temperature (ST) is an important dynamic parameter, whose prediction is a major research topic in various fields including agriculture because ST has a critical role in hydrological processes at the soil surface. In this study, a new linear methodology is proposed based on stochastic methods for modeling daily soil temperature (DST). With this approach, the ST series components are determined to carry out modeling and spectral analysis. The results of this process are compared with two linear methods based on seasonal standardization and seasonal differencing in terms of four DST series. The series used in this study were measured at two stations, Champaign and Springfield, at depths of 10 and 20 cm. The results indicate that in all ST series reviewed, the periodic term is the most robust among all components. According to a comparison of the three methods applied to analyze the various series components, it appears that spectral analysis combined with stochastic methods outperformed the seasonal standardization and seasonal differencing methods. In addition to comparing the proposed methodology with linear methods, the ST modeling results were compared with the two nonlinear methods in two forms: considering hydrological variables (HV) as input variables and DST modeling as a time series. In a previous study at the mentioned sites, Kim and Singh Theor Appl Climatol 118:465-479, (2014) applied the popular Multilayer Perceptron (MLP) neural network and Adaptive Neuro-Fuzzy Inference System (ANFIS) nonlinear methods and considered HV as input variables. The comparison results signify that the relative error projected in estimating DST by the proposed methodology was about 6%, while this value with MLP and ANFIS was over 15%. Moreover, MLP and ANFIS models were employed for DST time series modeling. Due to these models' relatively inferior performance to the proposed methodology, two hybrid models were implemented: the weights and membership function of MLP and ANFIS (respectively) were optimized with the particle swarm optimization (PSO) algorithm in conjunction with the wavelet transform and nonlinear methods (Wavelet-MLP & Wavelet-ANFIS). A comparison of the proposed methodology with individual and hybrid nonlinear models in predicting DST time series indicates the lowest Akaike Information Criterion (AIC) index value, which considers model simplicity and accuracy simultaneously at different depths and stations. The methodology presented in this study can thus serve as an excellent alternative to complex nonlinear methods that are normally employed to examine DST.

Process fault detection and nonlinear time series analysis for anomaly detection in safeguards

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

Burr, T.L.; Mullen, M.F.; Wangen, L.E.

In this paper we discuss two advanced techniques, process fault detection and nonlinear time series analysis, and apply them to the analysis of vector-valued and single-valued time-series data. We investigate model-based process fault detection methods for analyzing simulated, multivariate, time-series data from a three-tank system. The model-predictions are compared with simulated measurements of the same variables to form residual vectors that are tested for the presence of faults (possible diversions in safeguards terminology). We evaluate two methods, testing all individual residuals with a univariate z-score and testing all variables simultaneously with the Mahalanobis distance, for their ability to detect lossmore » of material from two different leak scenarios from the three-tank system: a leak without and with replacement of the lost volume. Nonlinear time-series analysis tools were compared with the linear methods popularized by Box and Jenkins. We compare prediction results using three nonlinear and two linear modeling methods on each of six simulated time series: two nonlinear and four linear. The nonlinear methods performed better at predicting the nonlinear time series and did as well as the linear methods at predicting the linear values.« less

Modeling of thermal storage systems in MILP distributed energy resource models

DOE PAGES

Steen, David; Stadler, Michael; Cardoso, Gonçalo; ...

2014-08-04

Thermal energy storage (TES) and distributed generation technologies, such as combined heat and power (CHP) or photovoltaics (PV), can be used to reduce energy costs and decrease CO 2 emissions from buildings by shifting energy consumption to times with less emissions and/or lower energy prices. To determine the feasibility of investing in TES in combination with other distributed energy resources (DER), mixed integer linear programming (MILP) can be used. Such a MILP model is the well-established Distributed Energy Resources Customer Adoption Model (DER-CAM); however, it currently uses only a simplified TES model to guarantee linearity and short run-times. Loss calculationsmore » are based only on the energy contained in the storage. This paper presents a new DER-CAM TES model that allows improved tracking of losses based on ambient and storage temperatures, and compares results with the previous version. A multi-layer TES model is introduced that retains linearity and avoids creating an endogenous optimization problem. The improved model increases the accuracy of the estimated storage losses and enables use of heat pumps for low temperature storage charging. Ultimately,results indicate that the previous model overestimates the attractiveness of TES investments for cases without possibility to invest in heat pumps and underestimates it for some locations when heat pumps are allowed. Despite a variation in optimal technology selection between the two models, the objective function value stays quite stable, illustrating the complexity of optimal DER sizing problems in buildings and microgrids.« less

Representational change and strategy use in children's number line estimation during the first years of primary school

PubMed Central

2012-01-01

Background The objective of this study was to scrutinize number line estimation behaviors displayed by children in mathematics classrooms during the first three years of schooling. We extend existing research by not only mapping potential logarithmic-linear shifts but also provide a new perspective by studying in detail the estimation strategies of individual target digits within a number range familiar to children. Methods Typically developing children (n = 67) from Years 1-3 completed a number-to-position numerical estimation task (0-20 number line). Estimation behaviors were first analyzed via logarithmic and linear regression modeling. Subsequently, using an analysis of variance we compared the estimation accuracy of each digit, thus identifying target digits that were estimated with the assistance of arithmetic strategy. Results Our results further confirm a developmental logarithmic-linear shift when utilizing regression modeling; however, uniquely we have identified that children employ variable strategies when completing numerical estimation, with levels of strategy advancing with development. Conclusion In terms of the existing cognitive research, this strategy factor highlights the limitations of any regression modeling approach, or alternatively, it could underpin the developmental time course of the logarithmic-linear shift. Future studies need to systematically investigate this relationship and also consider the implications for educational practice. PMID:22217191

Computational Modeling of Micro-Crack Induced Attenuation in CFRP Composites

NASA Technical Reports Server (NTRS)

Roberts, R. A.; Leckey, C. A. C.

2012-01-01

A computational study is performed to determine the contribution to ultrasound attenuation in carbon fiber reinforced polymer composite laminates of linear elastic scattering by matrix micro-cracking. Multiple scattering approximations are benchmarked against exact computational approaches. Results support linear scattering as the source of observed increased attenuation in the presence of micro-cracking.

Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.

PubMed

Kamesh, Reddi; Rani, K Yamuna

2016-09-01

A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Modeling the formation of ordered nano-assemblies comprised by dendrimers and linear polyelectrolytes: The role of Coulombic interactions

NASA Astrophysics Data System (ADS)

Eleftheriou, E.; Karatasos, K.

2012-10-01

Models of mixtures of peripherally charged dendrimers with oppositely charged linear polyelectrolytes in the presence of explicit solvent are studied by means of molecular dynamics simulations. Under the influence of varying strength of electrostatic interactions, these systems appear to form dynamically arrested film-like interconnected structures in the polymer-rich phase. Acting like a pseudo-thermodynamic inverse temperature, the increase of the strength of the Coulombic interactions drive the polymeric constituents of the mixture to a gradual dynamic freezing-in. The timescale of the average density fluctuations of the formed complexes initially increases in the weak electrostatic regime reaching a finite limit as the strength of electrostatic interactions grow. Although the models are overall electrically neutral, during this process the dendrimer/linear complexes develop a polar character with an excess charge mainly close to the periphery of the dendrimers. The morphological characteristics of the resulted pattern are found to depend on the size of the polymer chains on account of the distinct conformational features assumed by the complexed linear polyelectrolytes of different length. In addition, the length of the polymer chain appears to affect the dynamics of the counterions, thus affecting the ionic transport properties of the system. It appears, therefore, that the strength of electrostatic interactions together with the length of the linear polyelectrolytes are parameters to which these systems are particularly responsive, offering thus the possibility for a better control of the resulted structure and the electric properties of these soft-colloidal systems.

Developing CORE model-based worksheet with recitation task to facilitate students’ mathematical communication skills in linear algebra course

NASA Astrophysics Data System (ADS)

Risnawati; Khairinnisa, S.; Darwis, A. H.

2018-01-01

The purpose of this study was to develop a CORE model-based worksheet with recitation task that were valid and practical and could facilitate students’ communication skills in Linear Algebra course. This study was conducted in mathematics education department of one public university in Riau, Indonesia. Participants of the study were media and subject matter experts as validators as well as students from mathematics education department. The objects of this study are students’ worksheet and students’ mathematical communication skills. The results of study showed that: (1) based on validation of the experts, the developed students’ worksheet was valid and could be applied for students in Linear Algebra courses; (2) based on the group trial, the practicality percentage was 92.14% in small group and 90.19% in large group, so the worksheet was very practical and could attract students to learn; and (3) based on the post test, the average percentage of ideals was 87.83%. In addition, the results showed that the students’ worksheet was able to facilitate students’ mathematical communication skills in linear algebra course.

Robust H(∞) positional control of 2-DOF robotic arm driven by electro-hydraulic servo system.

PubMed

Guo, Qing; Yu, Tian; Jiang, Dan

2015-11-01

In this paper an H∞ positional feedback controller is developed to improve the robust performance under structural and parametric uncertainty disturbance in electro-hydraulic servo system (EHSS). The robust control model is described as the linear state-space equation by upper linear fractional transformation. According to the solution of H∞ sub-optimal control problem, the robust controller is designed and simplified to lower order linear model which is easily realized in EHSS. The simulation and experimental results can validate the robustness of this proposed method. The comparison result with PI control shows that the robust controller is suitable for this EHSS under the critical condition where the desired system bandwidth is higher and the external load of the hydraulic actuator is closed to its limited capability. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.