Casellas, J; Tarrés, J; Piedrafita, J; Varona, L
2006-10-01
Given that correct assumptions on the baseline survival function are determinant for the validity of further inferences, specific tools to test the fit of a model to real data become essential in proportional hazards models. In this sense, we have proposed a parametric bootstrap to test the fit of survival models. Monte Carlo simulations are used to generate new data sets from the estimates obtained through the assumed models, and then bootstrap intervals can be established for the survival function along the time space studied. Significant fitting deficiencies are revealed when the real survival function is not included within the bootstrap interval. We tested this procedure in a survival data set of Bruna dels Pirineus beef calves, assuming 4 parametric models (exponential, Weibull, exponential time-dependent, Weibull time-dependent) and the Cox's semiparametric model. Fitting deficiencies were not observed for the Cox's model and the exponential time-dependent model, whereas the Weibull time-dependent model suffered from moderate overestimation at different ages. Thus, the exponential time-dependent model appears to be preferable because of its correct fit for survival data of beef calves and its smaller computational and time requirements. Exponential and Weibull models were completely rejected due to the continuous over- and underestimation of the survival probability reported. Results here highlighted the flexibility of parametric models with time-dependent effects, achieving a fit comparable to nonparametric models.
Stanley, Leanne M.; Edwards, Michael C.
2016-01-01
The purpose of this article is to highlight the distinction between the reliability of test scores and the fit of psychometric measurement models, reminding readers why it is important to consider both when evaluating whether test scores are valid for a proposed interpretation and/or use. It is often the case that an investigator judges both the…
Tarrés, J; Fina, M; Piedrafita, J
2010-09-01
The aim of this study was to compare the goodness of fit of the threshold models with homoscedasticity or heteroscedasticity and the grouped data model for the analysis of calving ease in beef cattle by using a parametric bootstrap procedure. Field data included 8,205 records of the Bruna dels Pirineus beef cattle breed in the Pyrenean mountain areas of Catalonia (Spain). The actual distribution was 81.81% of calvings without assistance, 11.02% slightly assisted by the farmer, 5.12% strongly assisted by the farmer, 0.89% assisted by the veterinarian, and 1.16% cesarean, but these percentages were very different in the different herds. This can be explained partially by the different subjective way of scoring of each farmer. Primiparous cows had a greater (P < 0.001) difficulty calving than cows with 5 or more parities (11.74 vs. 4.49% of calvings strongly assisted by the farmer or the veterinarian and 2.8 vs. 0.65% cesarean). Male calves caused greater (P < 0.001) calving difficulty than females (7.71% of male calvings strongly assisted by the farmer or the veterinarian vs. 4.25% of females and 1.83% cesarean in males vs. 0.47% in females). The month and year of calving also had a strong influence on calving ease. These data were analyzed using 3 different models: the threshold models with homoscedasticity or heteroscedasticity and the grouped data model. The bootstrap comparison among models suggested that the threshold models, even allowing for heteroscedasticity, did not fit the herd effects well. In contrast, fitting deficiencies were not observed for the grouped data model in any factor. The variance of direct effect of the calf was estimated using the 3 models, and the heritability estimate ranged from 0.165 for the grouped data model to 0.185 for the hereroscedastic threshold model. This heritability was moderate, but it would justify the inclusion of direct effects of the calf on calving ease in the breeding objective. Overall, results highlighted the
Model fit after pairwise maximum likelihood
Directory of Open Access Journals (Sweden)
M. T. eBarendse
2016-04-01
Full Text Available Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed. As maximizing the likelihood of multivariate response patterns is computationally very intensive, the sum of the log--likelihoods of the bivariate response patterns is maximized instead. Little is yet known about how to assess model fit when the analysis is based on such a pairwise maximum likelihood (PML of two--way contingency tables. We propose new fit criteria for the PML method and conduct a simulation study to evaluate their performance in model selection. With large sample sizes (500 or more, PML performs as well the robust weighted least squares analysis of polychoric correlations.
Classification rates: non‐parametric verses parametric models using ...
African Journals Online (AJOL)
The local likelihood technique was used to model fit the data sets. The same sets of data were modeled using parametric logit and the abilities of the two models to correctly predict the binary outcome compared. The results obtained showed that non‐parametric estimation gives a better prediction rate (classification ratio) for ...
Contrast Gain Control Model Fits Masking Data
Watson, Andrew B.; Solomon, Joshua A.; Null, Cynthia H. (Technical Monitor)
1994-01-01
We studied the fit of a contrast gain control model to data of Foley (JOSA 1994), consisting of thresholds for a Gabor patch masked by gratings of various orientations, or by compounds of two orientations. Our general model includes models of Foley and Teo & Heeger (IEEE 1994). Our specific model used a bank of Gabor filters with octave bandwidths at 8 orientations. Excitatory and inhibitory nonlinearities were power functions with exponents of 2.4 and 2. Inhibitory pooling was broad in orientation, but narrow in spatial frequency and space. Minkowski pooling used an exponent of 4. All of the data for observer KMF were well fit by the model. We have developed a contrast gain control model that fits masking data. Unlike Foley's, our model accepts images as inputs. Unlike Teo & Heeger's, our model did not require multiple channels for different dynamic ranges.
Model fit after pairwise maximum likelihood
Barendse, M. T.; Ligtvoet, R.; Timmerman, M. E.; Oort, F. J.
2016-01-01
Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed. As maximizing the likelihood of multivariate response
Model fit after pairwise maximum likelihood
Barendse, M.T.; Ligtvoet, R.; Timmerman, M.E.; Oort, F.J.
Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed. As maximizing the likelihood of multivariate response
Estimation of retinal vessel caliber using model fitting and random forests
Araújo, Teresa; Mendonça, Ana Maria; Campilho, Aurélio
2017-03-01
Retinal vessel caliber changes are associated with several major diseases, such as diabetes and hypertension. These caliber changes can be evaluated using eye fundus images. However, the clinical assessment is tiresome and prone to errors, motivating the development of automatic methods. An automatic method based on vessel crosssection intensity profile model fitting for the estimation of vessel caliber in retinal images is herein proposed. First, vessels are segmented from the image, vessel centerlines are detected and individual segments are extracted and smoothed. Intensity profiles are extracted perpendicularly to the vessel, and the profile lengths are determined. Then, model fitting is applied to the smoothed profiles. A novel parametric model (DoG-L7) is used, consisting on a Difference-of-Gaussians multiplied by a line which is able to describe profile asymmetry. Finally, the parameters of the best-fit model are used for determining the vessel width through regression using ensembles of bagged regression trees with random sampling of the predictors (random forests). The method is evaluated on the REVIEW public dataset. A precision close to the observers is achieved, outperforming other state-of-the-art methods. The method is robust and reliable for width estimation in images with pathologies and artifacts, with performance independent of the range of diameters.
Correcting Model Fit Criteria for Small Sample Latent Growth Models with Incomplete Data
McNeish, Daniel; Harring, Jeffrey R.
2017-01-01
To date, small sample problems with latent growth models (LGMs) have not received the amount of attention in the literature as related mixed-effect models (MEMs). Although many models can be interchangeably framed as a LGM or a MEM, LGMs uniquely provide criteria to assess global data-model fit. However, previous studies have demonstrated poor…
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
considered. It is shown that adaptive estimation in conditional parametric models can be performed by combining the well known methods of local polynomial regression and recursive least squares with exponential forgetting. The approach used for estimation in conditional parametric models also highlights how...... of a linear model are estimated as functions of some explanatory variable(s). Also, software for handling the estimation is presented. The software runs under S-PLUS and R and contains also a number of tools useful when doing model diagnostics or interpreting the results. Adaptive estimation is also...... networks is included. In this paper, neural networks are used for predicting the electricity production of a wind farm. The results are compared with results obtained using an adaptively estimated ARX-model. Finally, two papers on stochastic differential equations are included. In the first paper, among...
Fan, Xitao; Wang, Lin; Thompson, Bruce
1999-01-01
A Monte Carlo simulation study investigated the effects on 10 structural equation modeling fit indexes of sample size, estimation method, and model specification. Some fit indexes did not appear to be comparable, and it was apparent that estimation method strongly influenced almost all fit indexes examined, especially for misspecified models. (SLD)
Parametric Explosion Spectral Model
Energy Technology Data Exchange (ETDEWEB)
Ford, S R; Walter, W R
2012-01-19
Small underground nuclear explosions need to be confidently detected, identified, and characterized in regions of the world where they have never before occurred. We develop a parametric model of the nuclear explosion seismic source spectrum derived from regional phases that is compatible with earthquake-based geometrical spreading and attenuation. Earthquake spectra are fit with a generalized version of the Brune spectrum, which is a three-parameter model that describes the long-period level, corner-frequency, and spectral slope at high-frequencies. Explosion spectra can be fit with similar spectral models whose parameters are then correlated with near-source geology and containment conditions. We observe a correlation of high gas-porosity (low-strength) with increased spectral slope. The relationship between the parametric equations and the geologic and containment conditions will assist in our physical understanding of the nuclear explosion source.
Automated Model Fit Method for Diesel Engine Control Development
Seykens, X.; Willems, F.P.T.; Kuijpers, B.; Rietjens, C.
2014-01-01
This paper presents an automated fit for a control-oriented physics-based diesel engine combustion model. This method is based on the combination of a dedicated measurement procedure and structured approach to fit the required combustion model parameters. Only a data set is required that is
Rapid world modeling: Fitting range data to geometric primitives
International Nuclear Information System (INIS)
Feddema, J.; Little, C.
1996-01-01
For the past seven years, Sandia National Laboratories has been active in the development of robotic systems to help remediate DOE's waste sites and decommissioned facilities. Some of these facilities have high levels of radioactivity which prevent manual clean-up. Tele-operated and autonomous robotic systems have been envisioned as the only suitable means of removing the radioactive elements. World modeling is defined as the process of creating a numerical geometric model of a real world environment or workspace. This model is often used in robotics to plan robot motions which perform a task while avoiding obstacles. In many applications where the world model does not exist ahead of time, structured lighting, laser range finders, and even acoustical sensors have been used to create three dimensional maps of the environment. These maps consist of thousands of range points which are difficult to handle and interpret. This paper presents a least squares technique for fitting range data to planar and quadric surfaces, including cylinders and ellipsoids. Once fit to these primitive surfaces, the amount of data associated with a surface is greatly reduced up to three orders of magnitude, thus allowing for more rapid handling and analysis of world data
Extended Langmuir model fitting to the filter column adsorption data ...
African Journals Online (AJOL)
Leachate samples collected at different depths of WQD column were analyzed for concentrations of zinc and copper ions using atomic absorption spectrometer. The removal efficiency was around 94% and 92% for zinc and copper respectively using column depth of 1 M at a flow rate of 12 ml/min. The adsorption model ...
Design of spatial experiments: Model fitting and prediction
Energy Technology Data Exchange (ETDEWEB)
Fedorov, V.V.
1996-03-01
The main objective of the paper is to describe and develop model oriented methods and algorithms for the design of spatial experiments. Unlike many other publications in this area, the approach proposed here is essentially based on the ideas of convex design theory.
Reducing uncertainty based on model fitness: Application to a ...
African Journals Online (AJOL)
A weakness of global sensitivity and uncertainty analysis methodologies is the often subjective definition of prior parameter probability distributions, especially ... The reservoir representing the central part of the wetland, where flood waters separate into several independent distributaries, is a keystone area within the model.
The issue of statistical power for overall model fit in evaluating structural equation models
Directory of Open Access Journals (Sweden)
Richard HERMIDA
2015-06-01
Full Text Available Statistical power is an important concept for psychological research. However, examining the power of a structural equation model (SEM is rare in practice. This article provides an accessible review of the concept of statistical power for the Root Mean Square Error of Approximation (RMSEA index of overall model fit in structural equation modeling. By way of example, we examine the current state of power in the literature by reviewing studies in top Industrial-Organizational (I/O Psychology journals using SEMs. Results indicate that in many studies, power is very low, which implies acceptance of invalid models. Additionally, we examined methodological situations which may have an influence on statistical power of SEMs. Results showed that power varies significantly as a function of model type and whether or not the model is the main model for the study. Finally, results indicated that power is significantly related to model fit statistics used in evaluating SEMs. The results from this quantitative review imply that researchers should be more vigilant with respect to power in structural equation modeling. We therefore conclude by offering methodological best practices to increase confidence in the interpretation of structural equation modeling results with respect to statistical power issues.
Lee, Min Jin; Hong, Helen; Chung, Jin Wook
2014-03-01
We propose an automatic vessel segmentation method of vertebral arteries in CT angiography using combined circular and cylindrical model fitting. First, to generate multi-segmented volumes, whole volume is automatically divided into four segments by anatomical properties of bone structures along z-axis of head and neck. To define an optimal volume circumscribing vertebral arteries, anterior-posterior bounding and side boundaries are defined as initial extracted vessel region. Second, the initial vessel candidates are tracked using circular model fitting. Since boundaries of the vertebral arteries are ambiguous in case the arteries pass through the transverse foramen in the cervical vertebra, the circle model is extended along z-axis to cylinder model for considering additional vessel information of neighboring slices. Finally, the boundaries of the vertebral arteries are detected using graph-cut optimization. From the experiments, the proposed method provides accurate results without bone artifacts and eroded vessels in the cervical vertebra.
Efficient occupancy model-fitting for extensive citizen-science data
Morgan, Byron J. T.; Freeman, Stephen N.; Ridout, Martin S.; Brereton, Tom M.; Fox, Richard; Powney, Gary D.; Roy, David B.
2017-01-01
Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species’ range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen
Wey, Andrew; Connett, John; Rudser, Kyle
2015-07-01
For estimating conditional survival functions, non-parametric estimators can be preferred to parametric and semi-parametric estimators due to relaxed assumptions that enable robust estimation. Yet, even when misspecified, parametric and semi-parametric estimators can possess better operating characteristics in small sample sizes due to smaller variance than non-parametric estimators. Fundamentally, this is a bias-variance trade-off situation in that the sample size is not large enough to take advantage of the low bias of non-parametric estimation. Stacked survival models estimate an optimally weighted combination of models that can span parametric, semi-parametric, and non-parametric models by minimizing prediction error. An extensive simulation study demonstrates that stacked survival models consistently perform well across a wide range of scenarios by adaptively balancing the strengths and weaknesses of individual candidate survival models. In addition, stacked survival models perform as well as or better than the model selected through cross-validation. Finally, stacked survival models are applied to a well-known German breast cancer study. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Testing Process Factor Analysis Models Using the Parametric Bootstrap.
Zhang, Guangjian
2018-01-01
Process factor analysis (PFA) is a latent variable model for intensive longitudinal data. It combines P-technique factor analysis and time series analysis. The goodness-of-fit test in PFA is currently unavailable. In the paper, we propose a parametric bootstrap method for assessing model fit in PFA. We illustrate the test with an empirical data set in which 22 participants rated their effects everyday over a period of 90 days. We also explore Type I error and power of the parametric bootstrap test with simulated data.
Shavit Grievink, Liat; Penny, David; Hendy, Michael D; Holland, Barbara R
2010-05-01
Commonly used phylogenetic models assume a homogeneous process through time in all parts of the tree. However, it is known that these models can be too simplistic as they do not account for nonhomogeneous lineage-specific properties. In particular, it is now widely recognized that as constraints on sequences evolve, the proportion and positions of variable sites can vary between lineages causing heterotachy. The extent to which this model misspecification affects tree reconstruction is still unknown. Here, we evaluate the effect of changes in the proportions and positions of variable sites on model fit and tree estimation. We consider 5 current models of nucleotide sequence evolution in a Bayesian Markov chain Monte Carlo framework as well as maximum parsimony (MP). We show that for a tree with 4 lineages where 2 nonsister taxa undergo a change in the proportion of variable sites tree reconstruction under the best-fitting model, which is chosen using a relative test, often results in the wrong tree. In this case, we found that an absolute test of model fit is a better predictor of tree estimation accuracy. We also found further evidence that MP is not immune to heterotachy. In addition, we show that increased sampling of taxa that have undergone a change in proportion and positions of variable sites is critical for accurate tree reconstruction.
PARAMETRIC MODEL OF LUMBAR VERTEBRA
Directory of Open Access Journals (Sweden)
CAPPETTI Nicola
2010-11-01
Full Text Available The present work proposes the realization of a parametric/variational CAD model of a normotype lumbar vertebra, which could be used for improving the effectiveness of actual imaging techniques in informational augmentation of the orthopaedic and traumatological diagnosis. In addition it could be used for ergonomic static and dynamical analysis of the lumbar region and vertebral column.
Parametric Modeling for Fluid Systems
Pizarro, Yaritzmar Rosario; Martinez, Jonathan
2013-01-01
Fluid Systems involves different projects that require parametric modeling, which is a model that maintains consistent relationships between elements as is manipulated. One of these projects is the Neo Liquid Propellant Testbed, which is part of Rocket U. As part of Rocket U (Rocket University), engineers at NASA's Kennedy Space Center in Florida have the opportunity to develop critical flight skills as they design, build and launch high-powered rockets. To build the Neo testbed; hardware from the Space Shuttle Program was repurposed. Modeling for Neo, included: fittings, valves, frames and tubing, between others. These models help in the review process, to make sure regulations are being followed. Another fluid systems project that required modeling is Plant Habitat's TCUI test project. Plant Habitat is a plan to develop a large growth chamber to learn the effects of long-duration microgravity exposure to plants in space. Work for this project included the design and modeling of a duct vent for flow test. Parametric Modeling for these projects was done using Creo Parametric 2.0.
Parametric Models of Periodogram
Indian Academy of Sciences (India)
2016-01-27
Jan 27, 2016 ... http://www.ias.ac.in/article/fulltext/joaa/035/03/0397-0400 ... break or knee frequencies. The extracted information can be used to place constraints on the mass, spin and other properties of the putative central black hole and the region surrounding it through theoretical models involving disk and jet physics.
Interactive Dimensioning of Parametric Models
Kelly, T.
2015-06-22
We propose a solution for the dimensioning of parametric and procedural models. Dimensioning has long been a staple of technical drawings, and we present the first solution for interactive dimensioning: A dimension line positioning system that adapts to the view direction, given behavioral properties. After proposing a set of design principles for interactive dimensioning, we describe our solution consisting of the following major components. First, we describe how an author can specify the desired interactive behavior of a dimension line. Second, we propose a novel algorithm to place dimension lines at interactive speeds. Third, we introduce multiple extensions, including chained dimension lines, controls for different parameter types (e.g. discrete choices, angles), and the use of dimension lines for interactive editing. Our results show the use of dimension lines in an interactive parametric modeling environment for architectural, botanical, and mechanical models.
Alipoor, Mohammad; Maier, Stephan E; Gu, Irene Yu-Hua; Mehnert, Andrew; Kahl, Fredrik
2015-01-01
The monoexponential model is widely used in quantitative biomedical imaging. Notable applications include apparent diffusion coefficient (ADC) imaging and pharmacokinetics. The application of ADC imaging to the detection of malignant tissue has in turn prompted several studies concerning optimal experiment design for monoexponential model fitting. In this paper, we propose a new experiment design method that is based on minimizing the determinant of the covariance matrix of the estimated parameters (D-optimal design). In contrast to previous methods, D-optimal design is independent of the imaged quantities. Applying this method to ADC imaging, we demonstrate its steady performance for the whole range of input variables (imaged parameters, number of measurements, and range of b-values). Using Monte Carlo simulations we show that the D-optimal design outperforms existing experiment design methods in terms of accuracy and precision of the estimated parameters.
Non-Uniqueness of the Geometry of Interplanetary Magnetic Flux Ropes Obtained from Model-Fitting
Marubashi, K.; Cho, K.-S.
2015-12-01
Since the early recognition of the important role of interplanetary magnetic flux ropes (IPFRs) to carry the southward magnetic fields to the Earth, many attempts have been made to determine the structure of the IPFRs by model-fitting analyses to the interplanetary magnetic field variations. This paper describes the results of fitting analyses for three selected solar wind structures in the latter half of 2014. In the fitting analysis a special attention was paid to identification of all the possible models or geometries that can reproduce the observed magnetic field variation. As a result, three or four geometries have been found for each of the three cases. The non-uniqueness of the fitted results include (1) the different geometries naturally stemming from the difference in the models used for fitting, and (2) an unexpected result that either of magnetic field chirality, left-handed and right-handed, can reproduce the observation in some cases. Thus we conclude that the model-fitting cannot always give us a unique geometry of the observed magnetic flux rope. In addition, we have found that the magnetic field chirality of a flux rope cannot be uniquely inferred from the sense of field vector rotation observed in the plane normal to the Earth-Sun line; the sense of rotation changes depending on the direction of the flux rope axis. These findings exert an important impact on the studies aimed at the geometrical relationships between the flux ropes and the magnetic field structures in the solar corona where the flux ropes were produced, such studies being an important step toward predicting geomagnetic storms based on observations of solar eruption phenomena.
Development and design of a late-model fitness test instrument based on LabView
Xie, Ying; Wu, Feiqing
2010-12-01
Undergraduates are pioneers of China's modernization program and undertake the historic mission of rejuvenating our nation in the 21st century, whose physical fitness is vital. A smart fitness test system can well help them understand their fitness and health conditions, thus they can choose more suitable approaches and make practical plans for exercising according to their own situation. following the future trends, a Late-model fitness test Instrument based on LabView has been designed to remedy defects of today's instruments. The system hardware consists of fives types of sensors with their peripheral circuits, an acquisition card of NI USB-6251 and a computer, while the system software, on the basis of LabView, includes modules of user register, data acquisition, data process and display, and data storage. The system, featured by modularization and an open structure, is able to be revised according to actual needs. Tests results have verified the system's stability and reliability.
Levy flights and self-similar exploratory behaviour of termite workers: beyond model fitting.
Directory of Open Access Journals (Sweden)
Octavio Miramontes
Full Text Available Animal movements have been related to optimal foraging strategies where self-similar trajectories are central. Most of the experimental studies done so far have focused mainly on fitting statistical models to data in order to test for movement patterns described by power-laws. Here we show by analyzing over half a million movement displacements that isolated termite workers actually exhibit a range of very interesting dynamical properties--including Lévy flights--in their exploratory behaviour. Going beyond the current trend of statistical model fitting alone, our study analyses anomalous diffusion and structure functions to estimate values of the scaling exponents describing displacement statistics. We evince the fractal nature of the movement patterns and show how the scaling exponents describing termite space exploration intriguingly comply with mathematical relations found in the physics of transport phenomena. By doing this, we rescue a rich variety of physical and biological phenomenology that can be potentially important and meaningful for the study of complex animal behavior and, in particular, for the study of how patterns of exploratory behaviour of individual social insects may impact not only their feeding demands but also nestmate encounter patterns and, hence, their dynamics at the social scale.
Ranger, Jochen; Kuhn, Jörg-Tobias; Szardenings, Carsten
2017-05-01
Cognitive psychometric models embed cognitive process models into a latent trait framework in order to allow for individual differences. Due to their close relationship to the response process the models allow for profound conclusions about the test takers. However, before such a model can be used its fit has to be checked carefully. In this manuscript we give an overview over existing tests of model fit and show their relation to the generalized moment test of Newey (Econometrica, 53, 1985, 1047) and Tauchen (J. Econometrics, 30, 1985, 415). We also present a new test, the Hausman test of misspecification (Hausman, Econometrica, 46, 1978, 1251). The Hausman test consists of a comparison of two estimates of the same item parameters which should be similar if the model holds. The performance of the Hausman test is evaluated in a simulation study. In this study we illustrate its application to two popular models in cognitive psychometrics, the Q-diffusion model and the D-diffusion model (van der Maas, Molenaar, Maris, Kievit, & Boorsboom, Psychol Rev., 118, 2011, 339; Molenaar, Tuerlinckx, & van der Maas, J. Stat. Softw., 66, 2015, 1). We also compare the performance of the test to four alternative tests of model fit, namely the M 2 test (Molenaar et al., J. Stat. Softw., 66, 2015, 1), the moment test (Ranger et al., Br. J. Math. Stat. Psychol., 2016) and the test for binned time (Ranger & Kuhn, Psychol. Test. Asess. , 56, 2014b, 370). The simulation study indicates that the Hausman test is superior to the latter tests. The test closely adheres to the nominal Type I error rate and has higher power in most simulation conditions. © 2017 The British Psychological Society.
Validation of a parametric finite element human femur model.
Klein, Katelyn F; Hu, Jingwen; Reed, Matthew P; Schneider, Lawrence W; Rupp, Jonathan D
2017-05-19
Finite element (FE) models with geometry and material properties that are parametric with subject descriptors, such as age and body shape/size, are being developed to incorporate population variability into crash simulations. However, the validation methods currently being used with these parametric models do not assess whether model predictions are reasonable in the space over which the model is intended to be used. This study presents a parametric model of the femur and applies a unique validation paradigm to this parametric femur model that characterizes whether model predictions reproduce experimentally observed trends. FE models of male and female femurs with geometries that are parametric with age, femur length, and body mass index (BMI) were developed based on existing statistical models that predict femur geometry. These parametric FE femur models were validated by comparing responses from combined loading tests of femoral shafts to simulation results from FE models of the corresponding femoral shafts whose geometry was predicted using the associated age, femur length, and BMI. The effects of subject variables on model responses were also compared with trends in the experimental data set by fitting similarly parameterized statistical models to both the results of the experimental data and the corresponding FE model results and then comparing fitted model coefficients for the experimental and predicted data sets. The average error in impact force at experimental failure for the parametric models was 5%. The coefficients of a statistical model fit to simulation data were within one standard error of the coefficients of a similarly parameterized model of the experimental data except for the age parameter, likely because material properties used in simulations were not varied with specimen age. In simulations to explore the effects of femur length, BMI, and age on impact response, only BMI significantly affected response for both men and women, with increasing
A History of Regression and Related Model-Fitting in the Earth Sciences (1636?-2000)
International Nuclear Information System (INIS)
Howarth, Richard J.
2001-01-01
The (statistical) modeling of the behavior of a dependent variate as a function of one or more predictors provides examples of model-fitting which span the development of the earth sciences from the 17th Century to the present. The historical development of these methods and their subsequent application is reviewed. Bond's predictions (c. 1636 and 1668) of change in the magnetic declination at London may be the earliest attempt to fit such models to geophysical data. Following publication of Newton's theory of gravitation in 1726, analysis of data on the length of a 1 o meridian arc, and the length of a pendulum beating seconds, as a function of sin 2 (latitude), was used to determine the ellipticity of the oblate spheroid defining the Figure of the Earth. The pioneering computational methods of Mayer in 1750, Boscovich in 1755, and Lambert in 1765, and the subsequent independent discoveries of the principle of least squares by Gauss in 1799, Legendre in 1805, and Adrain in 1808, and its later substantiation on the basis of probability theory by Gauss in 1809 were all applied to the analysis of such geodetic and geophysical data. Notable later applications include: the geomagnetic survey of Ireland by Lloyd, Sabine, and Ross in 1836, Gauss's model of the terrestrial magnetic field in 1838, and Airy's 1845 analysis of the residuals from a fit to pendulum lengths, from which he recognized the anomalous character of measurements of gravitational force which had been made on islands. In the early 20th Century applications to geological topics proliferated, but the computational burden effectively held back applications of multivariate analysis. Following World War II, the arrival of digital computers in universities in the 1950s facilitated computation, and fitting linear or polynomial models as a function of geographic coordinates, trend surface analysis, became popular during the 1950-60s. The inception of geostatistics in France at this time by Matheron had its
O'Riordan, J F; Goldstick, T K; Vida, L N; Honig, G R; Ernest, J T
1985-01-01
The ability of nine different models, prominent in the literature, to meaningfully characterize the oxygen-hemoglobin equilibrium curve (OHEC) of normal individuals was examined. Previously reported data (N = 33), obtained using the DCA-1 (Radiometer, Copenhagen), and new data (N = 8), obtained using the Hemox-Analyzer (TCS, Southampton, PA), from blood samples of normal, non-smoking volunteers were used and these devices were found to give statistically similar results. The OHECs were digitized and fitted to the models using least-squares techniques developed in this laboratory. The "goodness-of-fit" was determined by the root-mean-squared (RMS) error, the number of parameters, and the parameter redundancy, i.e., correlation between the parameters. The best RMS error did not necessarily indicate the best model. Most literature models consist of ratios of similar-order polynomials. These showed considerable parameter redundancy which made the curve fitting difficult. The best fits gave RMS errors as low as 0.2% saturation. The Hill model gave a good characterization over the saturation range 20%-98% with RMS errors of about 0.6% saturation. On the other hand, good characterizations over the entire range were given by several other models. The relative advantages and disadvantages of each model have been compared as well as the difficulties in fitting several of the models. No single model is best under all circumstances. The best model depends upon the particular circumstances for which it is to be utilized.
Log-normal frailty models fitted as Poisson generalized linear mixed models.
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.
McNeish, Daniel; Hancock, Gregory R
2018-03-01
Lance, Beck, Fan, and Carter (2016) recently advanced 6 new fit indices and associated cutoff values for assessing data-model fit in the structural portion of traditional latent variable path models. The authors appropriately argued that, although most researchers' theoretical interest rests with the latent structure, they still rely on indices of global model fit that simultaneously assess both the measurement and structural portions of the model. As such, Lance et al. proposed indices intended to assess the structural portion of the model in isolation of the measurement model. Unfortunately, although these strategies separate the assessment of the structure from the fit of the measurement model, they do not isolate the structure's assessment from the quality of the measurement model. That is, even with a perfectly fitting measurement model, poorer quality (i.e., less reliable) measurements will yield a more favorable verdict regarding structural fit, whereas better quality (i.e., more reliable) measurements will yield a less favorable structural assessment. This phenomenon, referred to by Hancock and Mueller (2011) as the reliability paradox, affects not only traditional global fit indices but also those structural indices proposed by Lance et al. as well. Fortunately, as this comment will clarify, indices proposed by Hancock and Mueller help to mitigate this problem and allow the structural portion of the model to be assessed independently of both the fit of the measurement model as well as the quality of indicator variables contained therein. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Hierarchical shrinkage priors and model fitting for high-dimensional generalized linear models.
Yi, Nengjun; Ma, Shuangge
2012-11-26
Abstract Genetic and other scientific studies routinely generate very many predictor variables, which can be naturally grouped, with predictors in the same groups being highly correlated. It is desirable to incorporate the hierarchical structure of the predictor variables into generalized linear models for simultaneous variable selection and coefficient estimation. We propose two prior distributions: hierarchical Cauchy and double-exponential distributions, on coefficients in generalized linear models. The hierarchical priors include both variable-specific and group-specific tuning parameters, thereby not only adopting different shrinkage for different coefficients and different groups but also providing a way to pool the information within groups. We fit generalized linear models with the proposed hierarchical priors by incorporating flexible expectation-maximization (EM) algorithms into the standard iteratively weighted least squares as implemented in the general statistical package R. The methods are illustrated with data from an experiment to identify genetic polymorphisms for survival of mice following infection with Listeria monocytogenes. The performance of the proposed procedures is further assessed via simulation studies. The methods are implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/).
Linear Parametric Model Checking of Timed Automata
DEFF Research Database (Denmark)
Hune, Tohmas Seidelin; Romijn, Judi; Stoelinga, Mariëlle
2001-01-01
We present an extension of the model checker Uppaal capable of synthesize linear parameter constraints for the correctness of parametric timed automata. The symbolic representation of the (parametric) state-space is shown to be correct. A second contribution of this paper is the identication...... of a subclass of parametric timed automata (L/U automata), for which the emptiness problem is decidable, contrary to the full class where it is know to be undecidable. Also we present a number of lemmas enabling the verication eort to be reduced for L/U automata in some cases. We illustrate our approach...
Silva, Mónica A; Jonsen, Ian; Russell, Deborah J F; Prieto, Rui; Thompson, Dave; Baumgartner, Mark F
2014-01-01
Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS) algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs) fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina) tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to "true" GPS locations. Data on 6 fin whales (Balaenoptera physalus) were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM) fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales' behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates.
Analytical modeling of parametrically modulated transmon qubits
Didier, Nicolas; Sete, Eyob A.; da Silva, Marcus P.; Rigetti, Chad
2018-02-01
Building a scalable quantum computer requires developing appropriate models to understand and verify its complex quantum dynamics. We focus on superconducting quantum processors based on transmons for which full numerical simulations are already challenging at the level of qubytes. It is thus highly desirable to develop accurate methods of modeling qubit networks that do not rely solely on numerical computations. Using systematic perturbation theory to large orders in the transmon regime, we derive precise analytic expressions of the transmon parameters. We apply our results to the case of parametrically modulated transmons to study recently implemented, parametrically activated entangling gates.
Parametric model of volumetric scattering
Magarill, Simon; Cassarly, William J.; Jenkins, David R.; Yang, Yang; Yu, Xiaofeng; Liu, Guang
2017-11-01
We develop a method to determine volumetric scattering model parameter values based on measured BSDF characteristics. Example models often use Mie or Gegenbauer particles. The accuracy and flexibility of this approach are illustrated.
Assessing model fit in latent class analysis when asymptotics do not hold
van Kollenburg, Geert H.; Mulder, Joris; Vermunt, Jeroen K.
2015-01-01
The application of latent class (LC) analysis involves evaluating the LC model using goodness-of-fit statistics. To assess the misfit of a specified model, say with the Pearson chi-squared statistic, a p-value can be obtained using an asymptotic reference distribution. However, asymptotic p-values
Directory of Open Access Journals (Sweden)
Bońkowski T.
2017-12-01
Full Text Available This paper is focused on experimental testing and modeling of genuine leather used for a motorcycle personal protective equipment. Simulations of powered two wheelers (PTW accidents are usually performed using human body models (HBM for the injury assessment equipped only with the helmet model. However, the kinematics of the PTW rider during a real accident is disturbed by the stiffness of his suit, which is normally not taken into account during the reconstruction or simulation of the accident scenario. The material model proposed in this paper can be used in numerical simulations of crash scenarios that include the effect of motorcyclist rider garment. The fitting procedure was conducted on 2 sets of samples: 5 uniaxial samples and 5 biaxial samples. The experimental characteristics were used to obtain the set of 25 constitutive material models in terms of Ogden parameters.
Using geometry to improve model fitting and experiment design for glacial isostasy
Kachuck, S. B.; Cathles, L. M.
2017-12-01
As scientists we routinely deal with models, which are geometric objects at their core - the manifestation of a set of parameters as predictions for comparison with observations. When the number of observations exceeds the number of parameters, the model is a hypersurface (the model manifold) in the space of all possible predictions. The object of parameter fitting is to find the parameters corresponding to the point on the model manifold as close to the vector of observations as possible. But the geometry of the model manifold can make this difficult. By curving, ending abruptly (where, for instance, parameters go to zero or infinity), and by stretching and compressing the parameters together in unexpected directions, it can be difficult to design algorithms that efficiently adjust the parameters. Even at the optimal point on the model manifold, parameters might not be individually resolved well enough to be applied to new contexts. In our context of glacial isostatic adjustment, models of sparse surface observations have a broad spread of sensitivity to mixtures of the earth's viscous structure and the surface distribution of ice over the last glacial cycle. This impedes precise statements about crucial geophysical processes, such as the planet's thermal history or the climates that controlled the ice age. We employ geometric methods developed in the field of systems biology to improve the efficiency of fitting (geodesic accelerated Levenberg-Marquardt) and to identify the maximally informative sources of additional data to make better predictions of sea levels and ice configurations (optimal experiment design). We demonstrate this in particular in reconstructions of the Barents Sea Ice Sheet, where we show that only certain kinds of data from the central Barents have the power to distinguish between proposed models.
Model fitting in two dimensions to small angle diffraction patterns from soft tissue
International Nuclear Information System (INIS)
Wilkinson, S J; Rogers, K D; Hall, C J
2006-01-01
In our research programme small angle x-ray scattering (SAXS) is used to provide information on the axial arrangement of collagen molecules as well as data about the state of other components of the extra cellular matrix (ECM) in human tissues. Derivation of parameters to describe and simplify the data is required for much of the SAXS patterns analysis. A method is presented here to achieve function fitting to collagen diffraction peaks along with a representation of the underlying diffuse scatter. A simple model was used which proved reliable in fitting a variety of 2D diffraction patterns. The logarithm of the scatter intensity over the area of the scatter image was taken to reduce the range and improve fitting accuracy. Our model was then used to fit the log data. The model consisted of a radial exponential diffuse scatter component added to a specified number of Gaussian peaks. In 2D the peak model is toroidal, each component being rotated about a common specified centre. Initial search parameters from a 1D averaged sector were supplied to the iterative 2D fitting routine. With the aid of data weighting and basic wavelet filtering, successful and reliable fitting of a specified 2D model to real data is achievable. The process is easily automated. Multiple SAXS patterns can be fitted without operator intervention. As described the model is simple enough to converge rapidly and yet allows image data to be parameterized to a form suitable for extracting the requisite information. The fitting method is flexible enough to be extended to achieve a more comprehensive and complex pattern fitting in two dimensions if this turns out to be necessary. It is our intention to implement orientation distribution functions in the near future by including an angular scaling factor
Cai, Li; Lee, Taehun
2009-01-01
We apply the Supplemented EM algorithm (Meng & Rubin, 1991) to address a chronic problem with the "two-stage" fitting of covariance structure models in the presence of ignorable missing data: the lack of an asymptotically chi-square distributed goodness-of-fit statistic. We show that the Supplemented EM algorithm provides a…
Does Vocational Education Model fit to Fulfil Prisoners’ Needs Based on Gender?
Hayzaki, S. H.; Nurhaeni, I. D. A.
2018-02-01
Men and women have different needs, based on their gender or the socio-cultural construction. The government has issued a policy about accelerating the equivalence of gender since 2012 through responsive planning and budgeting. With the policy, every institution (including the institutions under the ministry of law and human rights) must integrate its gender perspective on planning and budgeting, then it can fulfill the different needs between men and women. One of the programs developed in prisons for prisoners is vocational education and technology for preparing the prisoners’ life after being released from the prison cells. This article was made for evaluating the vocational education and training given to the prisoners. Gender perspective is employed as the analyzing tool. The result was then used as the basis of formulating vocational education model integrating gender perspective. The research was conducted at the Prison of Demak Regency, Indonesia. The method used in the research is qualitative descriptive with data collection techniques using by in-depth interviews, observation and documentation. The data analysis uses statistic description of Harvard’s checklist category model and combined with Moser category model. The result shows that vocational education and training given have not considered the differences between men and women. As a result, the prisoners were still not able to understand their different needs which can cause gender injustice when they come into job market. It is suggested that gender perspective must be included as a teaching material in the vocational education and training.
Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU
Directory of Open Access Journals (Sweden)
Jinwei Wang
2014-01-01
Full Text Available The active appearance model (AAM is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA on the Nvidia’s GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.
Model reduction of parametrized systems
Ohlberger, Mario; Patera, Anthony; Rozza, Gianluigi; Urban, Karsten
2017-01-01
The special volume offers a global guide to new concepts and approaches concerning the following topics: reduced basis methods, proper orthogonal decomposition, proper generalized decomposition, approximation theory related to model reduction, learning theory and compressed sensing, stochastic and high-dimensional problems, system-theoretic methods, nonlinear model reduction, reduction of coupled problems/multiphysics, optimization and optimal control, state estimation and control, reduced order models and domain decomposition methods, Krylov-subspace and interpolatory methods, and applications to real industrial and complex problems. The book represents the state of the art in the development of reduced order methods. It contains contributions from internationally respected experts, guaranteeing a wide range of expertise and topics. Further, it reflects an important effor t, carried out over the last 12 years, to build a growing research community in this field. Though not a textbook, some of the chapters ca...
Temperature dependence of bulk respiration of crop stands. Measurement and model fitting
International Nuclear Information System (INIS)
Tani, Takashi; Arai, Ryuji; Tako, Yasuhiro
2007-01-01
The objective of the present study was to examine whether the temperature dependence of respiration at a crop-stand scale could be directly represented by an Arrhenius function that was widely used for representing the temperature dependence of leaf respiration. We determined temperature dependences of bulk respiration of monospecific stands of rice and soybean within a range of the air temperature from 15 to 30degC using large closed chambers. Measured responses of respiration rates of the two stands were well fitted by the Arrhenius function (R 2 =0.99). In the existing model to assess the local radiological impact of the anthropogenic carbon-14, effects of the physical environmental factors on photosynthesis and respiration of crop stands are not taken into account for the calculation of the net amount of carbon per cultivation area in crops at harvest which is the crucial parameter for the estimation of the activity concentration of carbon-14 in crops. Our result indicates that the Arrhenius function is useful for incorporating the effect of the temperature on respiration of crop stands into the model which is expected to contribute to a more realistic estimate of the activity concentration of carbon-14 in crops. (author)
Energy Technology Data Exchange (ETDEWEB)
Reed, S.L.; et al.
2017-01-17
We present the discovery and spectroscopic confirmation with the ESO NTT and Gemini South telescopes of eight new 6.0 < z < 6.5 quasars with z$_{AB}$ < 21.0. These quasars were photometrically selected without any star-galaxy morphological criteria from 1533 deg$^{2}$ using SED model fitting to photometric data from the Dark Energy Survey (g, r, i, z, Y), the VISTA Hemisphere Survey (J, H, K) and the Wide-Field Infrared Survey Explorer (W1, W2). The photometric data was fitted with a grid of quasar model SEDs with redshift dependent Lyman-{\\alpha} forest absorption and a range of intrinsic reddening as well as a series of low mass cool star models. Candidates were ranked using on a SED-model based $\\chi^{2}$-statistic, which is extendable to other future imaging surveys (e.g. LSST, Euclid). Our spectral confirmation success rate is 100% without the need for follow-up photometric observations as used in other studies of this type. Combined with automatic removal of the main types of non-astrophysical contaminants the method allows large data sets to be processed without human intervention and without being over run by spurious false candidates. We also present a robust parametric redshift estimating technique that gives comparable accuracy to MgII and CO based redshift estimators. We find two z $\\sim$ 6.2 quasars with HII near zone sizes < 3 proper Mpc which could indicate that these quasars may be young with ages < 10$^6$ - 10$^7$ years or lie in over dense regions of the IGM. The z = 6.5 quasar VDESJ0224-4711 has J$_{AB}$ = 19.75 is the second most luminous quasar known with z > 6.5.
Reed, S. L.; McMahon, R. G.; Martini, P.; Banerji, M.; Auger, M.; Hewett, P. C.; Koposov, S. E.; Gibbons, S. L. J.; Gonzalez-Solares, E.; Ostrovski, F.; Tie, S. S.; Abdalla, F. B.; Allam, S.; Benoit-Lévy, A.; Bertin, E.; Brooks, D.; Buckley-Geer, E.; Burke, D. L.; Carnero Rosell, A.; Carrasco Kind, M.; Carretero, J.; da Costa, L. N.; DePoy, D. L.; Desai, S.; Diehl, H. T.; Doel, P.; Evrard, A. E.; Finley, D. A.; Flaugher, B.; Fosalba, P.; Frieman, J.; García-Bellido, J.; Gaztanaga, E.; Goldstein, D. A.; Gruen, D.; Gruendl, R. A.; Gutierrez, G.; James, D. J.; Kuehn, K.; Kuropatkin, N.; Lahav, O.; Lima, M.; Maia, M. A. G.; Marshall, J. L.; Melchior, P.; Miller, C. J.; Miquel, R.; Nord, B.; Ogando, R.; Plazas, A. A.; Romer, A. K.; Sanchez, E.; Scarpine, V.; Schubnell, M.; Sevilla-Noarbe, I.; Smith, R. C.; Sobreira, F.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; Tucker, D. L.; Walker, A. R.; Wester, W.
2017-07-01
We present the discovery and spectroscopic confirmation with the European Southern Observatory New Technology Telescope (NTT) and Gemini South telescopes of eight new, and the rediscovery of two previously known, 6.0 energy distribution (SED) model fitting to photometric data from Dark Energy Survey (g, r, I, z, Y), VISTA Hemisphere Survey (J, H, K) and Wide-field Infrared Survey Explorer (W1, W2). The photometric data were fitted with a grid of quasar model SEDs with redshift-dependent Ly α forest absorption and a range of intrinsic reddening as well as a series of low-mass cool star models. Candidates were ranked using an SED-model-based χ2-statistic, which is extendable to other future imaging surveys (e.g. LSST and Euclid). Our spectral confirmation success rate is 100 per cent without the need for follow-up photometric observations as used in other studies of this type. Combined with automatic removal of the main types of non-astrophysical contaminants, the method allows large data sets to be processed without human intervention and without being overrun by spurious false candidates. We also present a robust parametric redshift estimator that gives comparable accuracy to Mg II and CO-based redshift estimators. We find two z ˜ 6.2 quasars with H II near zone sizes ≤3 proper Mpc that could indicate that these quasars may be young with ages ≲ 106-107 years or lie in over dense regions of the IGM. The z = 6.5 quasar VDES J0224-4711 has JAB = 19.75 and is the second most luminous quasar known with z ≥ 6.5.
Parametric Cost Models for Space Telescopes
Stahl, H. Philip; Henrichs, Todd; Dollinger, Courtney
2010-01-01
Multivariable parametric cost models for space telescopes provide several benefits to designers and space system project managers. They identify major architectural cost drivers and allow high-level design trades. They enable cost-benefit analysis for technology development investment. And, they provide a basis for estimating total project cost. A survey of historical models found that there is no definitive space telescope cost model. In fact, published models vary greatly [1]. Thus, there is a need for parametric space telescopes cost models. An effort is underway to develop single variable [2] and multi-variable [3] parametric space telescope cost models based on the latest available data and applying rigorous analytical techniques. Specific cost estimating relationships (CERs) have been developed which show that aperture diameter is the primary cost driver for large space telescopes; technology development as a function of time reduces cost at the rate of 50% per 17 years; it costs less per square meter of collecting aperture to build a large telescope than a small telescope; and increasing mass reduces cost.
Parametric cost models for space telescopes
Stahl, H. Philip; Henrichs, Todd; Dollinger, Courtnay
2017-11-01
Multivariable parametric cost models for space telescopes provide several benefits to designers and space system project managers. They identify major architectural cost drivers and allow high-level design trades. They enable cost-benefit analysis for technology development investment. And, they provide a basis for estimating total project cost. A survey of historical models found that there is no definitive space telescope cost model. In fact, published models vary greatly [1]. Thus, there is a need for parametric space telescopes cost models. An effort is underway to develop single variable [2] and multi-variable [3] parametric space telescope cost models based on the latest available data and applying rigorous analytical techniques. Specific cost estimating relationships (CERs) have been developed which show that aperture diameter is the primary cost driver for large space telescopes; technology development as a function of time reduces cost at the rate of 50% per 17 years; it costs less per square meter of collecting aperture to build a large telescope than a small telescope; and increasing mass reduces cost.
Hierarchical Winner-Take-All Particle Swarm Optimization Social Network for Neural Model Fitting
Coventry, Brandon S.; Parthasarathy, Aravindakshan; Sommer, Alexandra L.; Bartlett, Edward L.
2016-01-01
Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models. PMID:27726048
Efficient Constrained Local Model Fitting for Non-Rigid Face Alignment.
Lucey, Simon; Wang, Yang; Cox, Mark; Sridharan, Sridha; Cohn, Jeffery F
2009-11-01
Active appearance models (AAMs) have demonstrated great utility when being employed for non-rigid face alignment/tracking. The "simultaneous" algorithm for fitting an AAM achieves good non-rigid face registration performance, but has poor real time performance (2-3 fps). The "project-out" algorithm for fitting an AAM achieves faster than real time performance (> 200 fps) but suffers from poor generic alignment performance. In this paper we introduce an extension to a discriminative method for non-rigid face registration/tracking referred to as a constrained local model (CLM). Our proposed method is able to achieve superior performance to the "simultaneous" AAM algorithm along with real time fitting speeds (35 fps). We improve upon the canonical CLM formulation, to gain this performance, in a number of ways by employing: (i) linear SVMs as patch-experts, (ii) a simplified optimization criteria, and (iii) a composite rather than additive warp update step. Most notably, our simplified optimization criteria for fitting the CLM divides the problem of finding a single complex registration/warp displacement into that of finding N simple warp displacements. From these N simple warp displacements, a single complex warp displacement is estimated using a weighted least-squares constraint. Another major advantage of this simplified optimization lends from its ability to be parallelized, a step which we also theoretically explore in this paper. We refer to our approach for fitting the CLM as the "exhaustive local search" (ELS) algorithm. Experiments were conducted on the CMU Multi-PIE database.
Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting
International Nuclear Information System (INIS)
Ross, James C.; Kindlmann, Gordon L.; Okajima, Yuka; Hatabu, Hiroto; Díaz, Alejandro A.; Silverman, Edwin K.; Washko, George R.; Dy, Jennifer; Estépar, Raúl San José
2013-01-01
Purpose: Performing lobe-based quantitative analysis of the lung in computed tomography (CT) scans can assist in efforts to better characterize complex diseases such as chronic obstructive pulmonary disease (COPD). While airways and vessels can help to indicate the location of lobe boundaries, segmentations of these structures are not always available, so methods to define the lobes in the absence of these structures are desirable. Methods: The authors present a fully automatic lung lobe segmentation algorithm that is effective in volumetric inspiratory and expiratory computed tomography (CT) datasets. The authors rely on ridge surface image features indicating fissure locations and a novel approach to modeling shape variation in the surfaces defining the lobe boundaries. The authors employ a particle system that efficiently samples ridge surfaces in the image domain and provides a set of candidate fissure locations based on the Hessian matrix. Following this, lobe boundary shape models generated from principal component analysis (PCA) are fit to the particles data to discriminate between fissure and nonfissure candidates. The resulting set of particle points are used to fit thin plate spline (TPS) interpolating surfaces to form the final boundaries between the lung lobes. Results: The authors tested algorithm performance on 50 inspiratory and 50 expiratory CT scans taken from the COPDGene study. Results indicate that the authors' algorithm performs comparably to pulmonologist-generated lung lobe segmentations and can produce good results in cases with accessory fissures, incomplete fissures, advanced emphysema, and low dose acquisition protocols. Dice scores indicate that only 29 out of 500 (5.85%) lobes showed Dice scores lower than 0.9. Two different approaches for evaluating lobe boundary surface discrepancies were applied and indicate that algorithm boundary identification is most accurate in the vicinity of fissures detectable on CT. Conclusions: The proposed
Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting
Energy Technology Data Exchange (ETDEWEB)
Ross, James C., E-mail: jross@bwh.harvard.edu [Channing Laboratory, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Surgical Planning Lab, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Laboratory of Mathematics in Imaging, Brigham and Women' s Hospital, Boston, Massachusetts 02126 (United States); Kindlmann, Gordon L. [Computer Science Department and Computation Institute, University of Chicago, Chicago, Illinois 60637 (United States); Okajima, Yuka; Hatabu, Hiroto [Department of Radiology, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Díaz, Alejandro A. [Pulmonary and Critical Care Division, Brigham and Women' s Hospital and Harvard Medical School, Boston, Massachusetts 02215 and Department of Pulmonary Diseases, Pontificia Universidad Católica de Chile, Santiago (Chile); Silverman, Edwin K. [Channing Laboratory, Brigham and Women' s Hospital, Boston, Massachusetts 02215 and Pulmonary and Critical Care Division, Brigham and Women' s Hospital and Harvard Medical School, Boston, Massachusetts 02215 (United States); Washko, George R. [Pulmonary and Critical Care Division, Brigham and Women' s Hospital and Harvard Medical School, Boston, Massachusetts 02215 (United States); Dy, Jennifer [ECE Department, Northeastern University, Boston, Massachusetts 02115 (United States); Estépar, Raúl San José [Department of Radiology, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Surgical Planning Lab, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Laboratory of Mathematics in Imaging, Brigham and Women' s Hospital, Boston, Massachusetts 02126 (United States)
2013-12-15
Purpose: Performing lobe-based quantitative analysis of the lung in computed tomography (CT) scans can assist in efforts to better characterize complex diseases such as chronic obstructive pulmonary disease (COPD). While airways and vessels can help to indicate the location of lobe boundaries, segmentations of these structures are not always available, so methods to define the lobes in the absence of these structures are desirable. Methods: The authors present a fully automatic lung lobe segmentation algorithm that is effective in volumetric inspiratory and expiratory computed tomography (CT) datasets. The authors rely on ridge surface image features indicating fissure locations and a novel approach to modeling shape variation in the surfaces defining the lobe boundaries. The authors employ a particle system that efficiently samples ridge surfaces in the image domain and provides a set of candidate fissure locations based on the Hessian matrix. Following this, lobe boundary shape models generated from principal component analysis (PCA) are fit to the particles data to discriminate between fissure and nonfissure candidates. The resulting set of particle points are used to fit thin plate spline (TPS) interpolating surfaces to form the final boundaries between the lung lobes. Results: The authors tested algorithm performance on 50 inspiratory and 50 expiratory CT scans taken from the COPDGene study. Results indicate that the authors' algorithm performs comparably to pulmonologist-generated lung lobe segmentations and can produce good results in cases with accessory fissures, incomplete fissures, advanced emphysema, and low dose acquisition protocols. Dice scores indicate that only 29 out of 500 (5.85%) lobes showed Dice scores lower than 0.9. Two different approaches for evaluating lobe boundary surface discrepancies were applied and indicate that algorithm boundary identification is most accurate in the vicinity of fissures detectable on CT. Conclusions: The
Open standard CMO for parametric modelling based on semantic web
Bonsma, P.; Bonsma, I.; Zayakova, T.; Van Delft, A.; Sebastian, R.; Böhms, M.
2015-01-01
The Open Standard Concept Modelling Ontology (CMO) with Extensions makes it possible to store parametric modelling semantics and parametric geometry in a Semantic Web environment. The parametric and geometrical part of CMO with Extensions is developed within the EU project Proficient. The nature of
International Nuclear Information System (INIS)
Hameeteman, K; Niessen, W J; Klein, S; Van 't Klooster, R; Selwaness, M; Van der Lugt, A; Witteman, J C M
2013-01-01
We present a method for carotid vessel wall volume quantification from magnetic resonance imaging (MRI). The method combines lumen and outer wall segmentation based on deformable model fitting with a learning-based segmentation correction step. After selecting two initialization points, the vessel wall volume in a region around the bifurcation is automatically determined. The method was trained on eight datasets (16 carotids) from a population-based study in the elderly for which one observer manually annotated both the lumen and outer wall. An evaluation was carried out on a separate set of 19 datasets (38 carotids) from the same study for which two observers made annotations. Wall volume and normalized wall index measurements resulting from the manual annotations were compared to the automatic measurements. Our experiments show that the automatic method performs comparably to the manual measurements. All image data and annotations used in this study together with the measurements are made available through the website http://ergocar.bigr.nl. (paper)
A simple parametric model selection test
Susanne M. Schennach; Daniel Wilhelm
2014-01-01
We propose a simple model selection test for choosing among two parametric likelihoods which can be applied in the most general setting without any assumptions on the relation between the candidate models and the true distribution. That is, both, one or neither is allowed to be correctly speci fied or misspeci fied, they may be nested, non-nested, strictly non-nested or overlapping. Unlike in previous testing approaches, no pre-testing is needed, since in each case, the same test statistic to...
Semi-parametric estimation for ARCH models
Directory of Open Access Journals (Sweden)
Raed Alzghool
2018-03-01
Full Text Available In this paper, we conduct semi-parametric estimation for autoregressive conditional heteroscedasticity (ARCH model with Quasi likelihood (QL and Asymptotic Quasi-likelihood (AQL estimation methods. The QL approach relaxes the distributional assumptions of ARCH processes. The AQL technique is obtained from the QL method when the process conditional variance is unknown. We present an application of the methods to a daily exchange rate series. Keywords: ARCH model, Quasi likelihood (QL, Asymptotic Quasi-likelihood (AQL, Martingale difference, Kernel estimator
Marconi, M.; Molinaro, R.; Ripepi, V.; Cioni, M.-R. L.; Clementini, G.; Moretti, M. I.; Ragosta, F.; de Grijs, R.; Groenewegen, M. A. T.; Ivanov, V. D.
2017-04-01
We present the results of the χ2 minimization model fitting technique applied to optical and near-infrared photometric and radial velocity data for a sample of nine fundamental and three first overtone classical Cepheids in the Small Magellanic Cloud (SMC). The near-infrared photometry (JK filters) was obtained by the European Southern Observatory (ESO) public survey 'VISTA near-infrared Y, J, Ks survey of the Magellanic Clouds system' (VMC). For each pulsator, isoperiodic model sequences have been computed by adopting a non-linear convective hydrodynamical code in order to reproduce the multifilter light and (when available) radial velocity curve amplitudes and morphological details. The inferred individual distances provide an intrinsic mean value for the SMC distance modulus of 19.01 mag and a standard deviation of 0.08 mag, in agreement with the literature. Moreover, the intrinsic masses and luminosities of the best-fitting model show that all these pulsators are brighter than the canonical evolutionary mass-luminosity relation (MLR), suggesting a significant efficiency of core overshooting and/or mass-loss. Assuming that the inferred deviation from the canonical MLR is only due to mass-loss, we derive the expected distribution of percentage mass-loss as a function of both the pulsation period and the canonical stellar mass. Finally, a good agreement is found between the predicted mean radii and current period-radius (PR) relations in the SMC available in the literature. The results of this investigation support the predictive capabilities of the adopted theoretical scenario and pave the way for the application to other extensive data bases at various chemical compositions, including the VMC Large Magellanic Cloud pulsators and Galactic Cepheids with Gaia parallaxes.
Parametric uncertainty in optical image modeling
Potzick, James; Marx, Egon; Davidson, Mark
2006-10-01
Optical photomask feature metrology and wafer exposure process simulation both rely on optical image modeling for accurate results. While it is fair to question the accuracies of the available models, model results also depend on several input parameters describing the object and imaging system. Errors in these parameter values can lead to significant errors in the modeled image. These parameters include wavelength, illumination and objective NA's, magnification, focus, etc. for the optical system, and topography, complex index of refraction n and k, etc. for the object. In this paper each input parameter is varied over a range about its nominal value and the corresponding images simulated. Second order parameter interactions are not explored. Using the scenario of the optical measurement of photomask features, these parametric sensitivities are quantified by calculating the apparent change of the measured linewidth for a small change in the relevant parameter. Then, using reasonable values for the estimated uncertainties of these parameters, the parametric linewidth uncertainties can be calculated and combined to give a lower limit to the linewidth measurement uncertainty for those parameter uncertainties.
Nonscaling parametrization of hadronic spectra and dual parton model
International Nuclear Information System (INIS)
Gaponenko, O.N.
2001-01-01
Using the popular Wdowczyk-Wolfendale parametrization (WW-parametrization) as an example one studies restrictions imposed by a dual parton model for different nonscaling parametrizations of the pulsed hadron spectra in soft hadron-hadron and hadron-nuclear interactions. One derived a new parametrization free from basic drawback of the WW-formulae. In the central range the determined parametrization show agreement with the Wdowczyk-Wolfendale formula, but in contrast to the last-named one it does not result in contradiction with the experiment due to fast reduction of inelastic factor reduction with energy increase [ru
Lepping, R. P.; Wu, C.-C.; Berdichevsky, D. B.; Szabo, A.
2018-04-01
We give the results of parameter fitting of the magnetic clouds (MCs) observed by the Wind spacecraft for the three-year period 2013 to the end of 2015 (called the "Present" period) using the MC model of Lepping, Jones, and Burlaga ( J. Geophys. Res. 95, 11957, 1990). The Present period is almost coincident with the solar maximum of the sunspot number, which has a broad peak starting in about 2012 and extending to almost 2015. There were 49 MCs identified in the Present period. The modeling gives MC quantities such as size, axial attitude, field handedness, axial magnetic-field strength, center time, and closest-approach vector. Derived quantities are also estimated, such as axial magnetic flux, axial current density, and total axial current. Quality estimates are assigned representing excellent, fair/good, and poor. We provide error estimates on the specific fit parameters for the individual MCs, where the poor cases are excluded. Model-fitting results that are based on the Present period are compared to the results of the full Wind mission from 1995 to the end of 2015 (Long-term period), and compared to the results of two other recent studies that encompassed the periods 2007 - 2009 and 2010 - 2012, inclusive. We see that during the Present period, the MCs are, on average, slightly slower, slightly weaker in axial magnetic field (by 8.7%), and larger in diameter (by 6.5%) than those in the Long-term period. However, in most respects, the MCs in the Present period are significantly closer in characteristics to those of the Long-term period than to those of the two recent three-year periods. However, the rate of occurrence of MCs for the Long-term period is 10.3 year^{-1}, whereas this rate for the Present period is 16.3 year^{-1}, similar to that of the period 2010 - 2012. Hence, the MC occurrence rate has increased appreciably in the last six years. MC Type (N-S, S-N, All N, All S, etc.) is assigned to each MC; there is an inordinately large percentage of All S
Testing Parametric versus Semiparametric Modelling in Generalized Linear Models
Härdle, W.K.; Mammen, E.; Müller, M.D.
1996-01-01
We consider a generalized partially linear model E(Y|X,T) = G{X'b + m(T)} where G is a known function, b is an unknown parameter vector, and m is an unknown function.The paper introduces a test statistic which allows to decide between a parametric and a semiparametric model: (i) m is linear, i.e.
Parametric Testing of Launch Vehicle FDDR Models
Schumann, Johann; Bajwa, Anupa; Berg, Peter; Thirumalainambi, Rajkumar
2011-01-01
For the safe operation of a complex system like a (manned) launch vehicle, real-time information about the state of the system and potential faults is extremely important. The on-board FDDR (Failure Detection, Diagnostics, and Response) system is a software system to detect and identify failures, provide real-time diagnostics, and to initiate fault recovery and mitigation. The ERIS (Evaluation of Rocket Integrated Subsystems) failure simulation is a unified Matlab/Simulink model of the Ares I Launch Vehicle with modular, hierarchical subsystems and components. With this model, the nominal flight performance characteristics can be studied. Additionally, failures can be injected to see their effects on vehicle state and on vehicle behavior. A comprehensive test and analysis of such a complicated model is virtually impossible. In this paper, we will describe, how parametric testing (PT) can be used to support testing and analysis of the ERIS failure simulation. PT uses a combination of Monte Carlo techniques with n-factor combinatorial exploration to generate a small, yet comprehensive set of parameters for the test runs. For the analysis of the high-dimensional simulation data, we are using multivariate clustering to automatically find structure in this high-dimensional data space. Our tools can generate detailed HTML reports that facilitate the analysis.
Reike, Dennis; Schwarz, Wolf
2016-01-01
The time required to determine the larger of 2 digits decreases with their numerical distance, and, for a given distance, increases with their magnitude (Moyer & Landauer, 1967). One detailed quantitative framework to account for these effects is provided by random walk models. These chronometric models describe how number-related noisy…
International Nuclear Information System (INIS)
Smith, D.L.; Guenther, P.T.
1983-11-01
We suggest a procedure for estimating uncertainties in neutron cross sections calculated with a nuclear model descriptive of a specific mass region. It applies standard error propagation techniques, using a model-parameter covariance matrix. Generally, available codes do not generate covariance information in conjunction with their fitting algorithms. Therefore, we resort to estimating a relative covariance matrix a posteriori from a statistical examination of the scatter of elemental parameter values about the regional representation. We numerically demonstrate our method by considering an optical-statistical model analysis of a body of total and elastic scattering data for the light fission-fragment mass region. In this example, strong uncertainty correlations emerge and they conspire to reduce estimated errors to some 50% of those obtained from a naive uncorrelated summation in quadrature. 37 references
Worthington, Thomas A.; Zhang, T.; Logue, Daniel R.; Mittelstet, Aaron R.; Brewer, Shannon K.
2016-01-01
Truncated distributions of pelagophilic fishes have been observed across the Great Plains of North America, with water use and landscape fragmentation implicated as contributing factors. Developing conservation strategies for these species is hindered by the existence of multiple competing flow regime hypotheses related to species persistence. Our primary study objective was to compare the predicted distributions of one pelagophil, the Arkansas River Shiner Notropis girardi, constructed using different flow regime metrics. Further, we investigated different approaches for improving temporal transferability of the species distribution model (SDM). We compared four hypotheses: mean annual flow (a baseline), the 75th percentile of daily flow, the number of zero-flow days, and the number of days above 55th percentile flows, to examine the relative importance of flows during the spawning period. Building on an earlier SDM, we added covariates that quantified wells in each catchment, point source discharges, and non-native species presence to a structured variable framework. We assessed the effects on model transferability and fit by reducing multicollinearity using Spearman’s rank correlations, variance inflation factors, and principal component analysis, as well as altering the regularization coefficient (β) within MaxEnt. The 75th percentile of daily flow was the most important flow metric related to structuring the species distribution. The number of wells and point source discharges were also highly ranked. At the default level of β, model transferability was improved using all methods to reduce collinearity; however, at higher levels of β, the correlation method performed best. Using β = 5 provided the best model transferability, while retaining the majority of variables that contributed 95% to the model. This study provides a workflow for improving model transferability and also presents water-management options that may be considered to improve the
Sakamoto, Toshihiro
2018-04-01
Crop phenological information is a critical variable in evaluating the influence of environmental stress on the final crop yield in spatio-temporal dimensions. Although the MODIS (Moderate Resolution Imaging Spectroradiometer) Land Cover Dynamics product (MCD12Q2) is widely used in place of crop phenological information, the definitions of MCD12Q2-derived phenological events (e.g. green-up date, dormancy date) were not completely consistent with those of crop development stages used in statistical surveys (e.g. emerged date, harvested date). It has been necessary to devise an alternative method focused on detecting continental-scale crop developmental stages using a different approach. Therefore, this study aimed to refine the Shape Model Fitting (SMF) method to improve its applicability to multiple major U.S. crops. The newly-refined SMF methods could estimate the timing of 36 crop-development stages of major U.S. crops, including corn, soybeans, winter wheat, spring wheat, barley, sorghum, rice, and cotton. The newly-developed calibration process did not require any long-term field observation data, and could calibrate crop-specific phenological parameters, which were used as coefficients in estimated equation, by using only freely accessible public data. The calibration of phenological parameters was conducted in two steps. In the first step, the national common phenological parameters, referred to as X0[base], were calibrated by using the statistical data of 2008. The SMF method coupled using X0[base] was named the rSMF[base] method. The second step was a further calibration to gain regionally-adjusted phenological parameters for each state, referred to as X0[local], by using additional statistical data of 2015 and 2016. The rSMF method using the X0[local] was named the rSMF[local] method. This second calibration process improved the estimation accuracy for all tested crops. When applying the rSMF[base] method to the validation data set (2009-2014), the root
Kalicka, Renata; Pietrenko-Dabrowska, Anna
2007-03-01
In the paper MRI measurements are used for assessment of brain tissue perfusion and other features and functions of the brain (cerebral blood flow - CBF, cerebral blood volume - CBV, mean transit time - MTT). Perfusion is an important indicator of tissue viability and functioning as in pathological tissue blood flow, vascular and tissue structure are altered with respect to normal tissue. MRI enables diagnosing diseases at an early stage of their course. The parametric and non-parametric approaches to the identification of MRI models are presented and compared. The non-parametric modeling adopts gamma variate functions. The parametric three-compartmental catenary model, based on the general kinetic model, is also proposed. The parameters of the models are estimated on the basis of experimental data. The goodness of fit of the gamma variate and the three-compartmental models to the data and the accuracy of the parameter estimates are compared. Kalman filtering, smoothing the measurements, was adopted to improve the estimate accuracy of the parametric model. Parametric modeling gives a better fit and better parameter estimates than non-parametric and allows an insight into the functioning of the system. To improve the accuracy optimal experiment design related to the input signal was performed.
International Nuclear Information System (INIS)
Syrmalenios, Panayotis
1973-01-01
This research thesis addresses the issue of safety of fast neutron reactors, and more particularly is a contribution of the study of mechanisms of interaction between molten fuel and sodium. It aims at developing tools of prediction of consequences of three main types of accidents: local fusion of a fuel rod and contact of the fuel with the surrounding sodium, failure of an assembly due to the fusion of several rods and fuel-coolant interaction within the assembly, and fuel-coolant interaction at the level of the reactor core. The author first proposes a bibliographical analysis of experimental and theoretical studies related to this issue of interaction between a hot body and a cold liquid, and of its consequences. Then, he introduces a mathematical model and its resolution method, and reports the use of the associated code (Corfou) for the interpretation of experimental results: expulsion of cold sodium column by expansion of an overheated sodium mass, fusion of a rod by Joule effect, interaction between UO 2 molten by high frequency with liquid sodium. Finally, the author discusses a comparison between the Corfou code and other models which are being currently developed [fr
Probabilistic Reachability for Parametric Markov Models
DEFF Research Database (Denmark)
Hahn, Ernst Moritz; Hermanns, Holger; Zhang, Lijun
2011-01-01
of states (n(log n)).We therefore proceed differently, by tightly intertwining the regular expression computation with its evaluation. This allows us to arrive at an effective method that avoids this blow up in most practical cases. We give a detailed account of the approach, also extending to parametric...
Incident Duration Modeling Using Flexible Parametric Hazard-Based Models
Directory of Open Access Journals (Sweden)
Ruimin Li
2014-01-01
Full Text Available Assessing and prioritizing the duration time and effects of traffic incidents on major roads present significant challenges for road network managers. This study examines the effect of numerous factors associated with various types of incidents on their duration and proposes an incident duration prediction model. Several parametric accelerated failure time hazard-based models were examined, including Weibull, log-logistic, log-normal, and generalized gamma, as well as all models with gamma heterogeneity and flexible parametric hazard-based models with freedom ranging from one to ten, by analyzing a traffic incident dataset obtained from the Incident Reporting and Dispatching System in Beijing in 2008. Results show that different factors significantly affect different incident time phases, whose best distributions were diverse. Given the best hazard-based models of each incident time phase, the prediction result can be reasonable for most incidents. The results of this study can aid traffic incident management agencies not only in implementing strategies that would reduce incident duration, and thus reduce congestion, secondary incidents, and the associated human and economic losses, but also in effectively predicting incident duration time.
Incorporating parametric uncertainty into population viability analysis models
McGowan, Conor P.; Runge, Michael C.; Larson, Michael A.
2011-01-01
Uncertainty in parameter estimates from sampling variation or expert judgment can introduce substantial uncertainty into ecological predictions based on those estimates. However, in standard population viability analyses, one of the most widely used tools for managing plant, fish and wildlife populations, parametric uncertainty is often ignored in or discarded from model projections. We present a method for explicitly incorporating this source of uncertainty into population models to fully account for risk in management and decision contexts. Our method involves a two-step simulation process where parametric uncertainty is incorporated into the replication loop of the model and temporal variance is incorporated into the loop for time steps in the model. Using the piping plover, a federally threatened shorebird in the USA and Canada, as an example, we compare abundance projections and extinction probabilities from simulations that exclude and include parametric uncertainty. Although final abundance was very low for all sets of simulations, estimated extinction risk was much greater for the simulation that incorporated parametric uncertainty in the replication loop. Decisions about species conservation (e.g., listing, delisting, and jeopardy) might differ greatly depending on the treatment of parametric uncertainty in population models.
Bosone, Lucia; Martinez, Frédéric; Kalampalikis, Nikos
2015-04-01
In health-promotional campaigns, positive and negative role models can be deployed to illustrate the benefits or costs of certain behaviors. The main purpose of this article is to investigate why, how, and when exposure to role models strengthens the persuasiveness of a message, according to regulatory fit theory. We argue that exposure to a positive versus a negative model activates individuals' goals toward promotion rather than prevention. By means of two experiments, we demonstrate that high levels of persuasion occur when a message advertising healthy dietary habits offers a regulatory fit between its framing and the described role model. Our data also establish that the effects of such internal regulatory fit by vicarious experience depend on individuals' perceptions of response-efficacy and self-efficacy. Our findings constitute a significant theoretical complement to previous research on regulatory fit and contain valuable practical implications for health-promotional campaigns. © 2015 by the Society for Personality and Social Psychology, Inc.
Housing price prediction: parametric versus semi-parametric spatial hedonic models
Montero, José-María; Mínguez, Román; Fernández-Avilés, Gema
2018-01-01
House price prediction is a hot topic in the economic literature. House price prediction has traditionally been approached using a-spatial linear (or intrinsically linear) hedonic models. It has been shown, however, that spatial effects are inherent in house pricing. This article considers parametric and semi-parametric spatial hedonic model variants that account for spatial autocorrelation, spatial heterogeneity and (smooth and nonparametrically specified) nonlinearities using penalized splines methodology. The models are represented as a mixed model that allow for the estimation of the smoothing parameters along with the other parameters of the model. To assess the out-of-sample performance of the models, the paper uses a database containing the price and characteristics of 10,512 homes in Madrid, Spain (Q1 2010). The results obtained suggest that the nonlinear models accounting for spatial heterogeneity and flexible nonlinear relationships between some of the individual or areal characteristics of the houses and their prices are the best strategies for house price prediction.
Directory of Open Access Journals (Sweden)
Wakhid Slamet Ciptono
2011-02-01
Full Text Available This study purposively is to conduct an empirical analysis of the structural relations among critical factors of quality management practices (QMPs, world-class company practice (WCC, operational excellence practice (OE, and company performance (company non-financial performance or CNFP and company financial performance or CFP in the oil and gas companies operating in Indonesia. The current study additionally examines the relationships between QMPs and CFP through WCC, OE, and CNFP (as partial mediators simultaneously. The study uses data from a survey of 140 strategic business units (SBUs within 49 oil and gas contractor companies in Indonesia. The findings suggest that all six QMPs have positive and significant indirect relationships on CFP through WCC and CNFP. Only four of six QMPs have positive and significant indirect relationships on CFP through OE and CNFP. Hence, WCC, OE, and CNFP play as partial mediators between QMPs and CFP. CNFP has a significant influence on CFP. A major implication of this study is that oil and gas managers need to recognize the structural relations model fit by developing all of the research constructs simultaneously associated with a comprehensive TQM practice. Furthermore, the findings will assist oil and gas companies by improving CNFP, which is very critical to TQM, thereby contributing to a better achievement of CFP. The current study uses the Deming’s principles, Hayes and Wheelwright dimensions of world-class company practice, Chevron Texaco’s operational excellence practice, and the dimensions of company financial and non-financial performances. The paper also provides an insight into the sustainability of TQM implementation model and their effect on company financial performance in oil and gas companies in Indonesia.
SEMIPARAMETRIC VERSUS PARAMETRIC CLASSIFICATION MODELS - AN APPLICATION TO DIRECT MARKETING
BULT, [No Value
In this paper we are concerned with estimation of a classification model using semiparametric and parametric methods. Benefits and limitations of semiparametric models in general, and of Manski's maximum score method in particular, are discussed. The maximum score method yields consistent estimates
Testing the specifications of parametric models using anchoring vignettes
van Soest, A.H.O.; Vonkova, H.
Comparing assessments on a subjective scale across countries or socio-economic groups is often hampered by differences in response scales across groups. Anchoring vignettes help to correct for such differences, either in parametric models (the compound hierarchical ordered probit (CHOPIT) model and
Modeling shell morphology of an epitoniid species with parametric equations
Bernido, Christopher C.; Carpio-Bernido, M. Victoria; Sadudaquil, Jerome A.; Salas, Rochelle I.; Mangyao, Justin Ericson A.; Halasan, Lorenzo C.; Baja, Paz Kenneth S.; Jumawan, Ethel Jade V.
2017-08-01
An epitoniid specimen under the genus Cycloscala is mathematically modeled using parametric equations which allow comparison of growth functions and parameter values with other specimens of the same genus. This mathematical modeling approach may supplement the currently used genetic and microscopy methods in the taxonomic classification of species.
DEVELOPING PARAMETRIC BUILDING MODELS – THE GANDIS USE CASE
Directory of Open Access Journals (Sweden)
W. Thaller
2012-09-01
Full Text Available In the course of a project related to green building design, we have created a group of eight parametric building models that can be manipulated interactively with respect to dimensions, number of ﬂoors, and a few other parameters. We report on the commonalities and differences between the models and the abstractions that we were able to identify.
Directory of Open Access Journals (Sweden)
Erida Gjini
2016-03-01
Full Text Available The efficacy of vaccines is typically estimated prior to implementation, on the basis of randomized controlled trials. This does not preclude, however, subsequent assessment post-licensure, while mass-immunization and nonlinear transmission feedbacks are in place. In this paper we show how cross-sectional prevalence data post-vaccination can be interpreted in terms of pathogen transmission processes and vaccine parameters, using a dynamic epidemiological model. We advocate the use of such frameworks for model-based vaccine evaluation in the field, fitting trajectories of cross-sectional prevalence of pathogen strains before and after intervention. Using SI and SIS models, we illustrate how prevalence ratios in vaccinated and non-vaccinated hosts depend on true vaccine efficacy, the absolute and relative strength of competition between target and non-target strains, the time post follow-up, and transmission intensity. We argue that a mechanistic approach should be added to vaccine efficacy estimation against multi-type pathogens, because it naturally accounts for inter-strain competition and indirect effects, leading to a robust measure of individual protection per contact. Our study calls for systematic attention to epidemiological feedbacks when interpreting population level impact. At a broader level, our parameter estimation procedure provides a promising proof of principle for a generalizable framework to infer vaccine efficacy post-licensure.
A CONTRASTIVE ANALYSIS OF THE FACTORIAL STRUCTURE OF THE PCL-R: WHICH MODEL FITS BEST THE DATA?
Directory of Open Access Journals (Sweden)
Beatriz Pérez
2015-01-01
Full Text Available The aim of this study was to determine which of the factorial solutions proposed for the Hare Psychopathy Checklist-Revised (PCL-R of two, three, four factors, and unidimensional fitted best the data. Two trained and experienced independent raters scored 197 prisoners from the Villabona Penitentiary (Asturias, Spain, age range 21 to 73 years (M = 36.0, SD = 9.7, of whom 60.12% were reoffenders and 73% had committed violent crimes. The results revealed that the two-factor correlational, three-factor hierarchical without testlets, four-factor correlational and hierarchical, and unidimensional models were a poor fit for the data (CFI ≤ .86, and the three-factor model with testlets was a reasonable fit for the data (CFI = .93. The scale resulting from the three-factor hierarchical model with testlets (13 items classified psychopathy significantly higher than the original 20-item scale. The results are discussed in terms of their implications for theoretical models of psychopathy, decision-making, prison classification and intervention, and prevention. Se diseñó un estudio con el objetivo de conocer cuál de las soluciones factoriales propuestas para la Hare Psychopathy Checklist-Revised (PCL-R de dos, tres y cuatro factores y unidimensional era la que presentaba mejor ajuste a los datos. Para ello, dos evaluadores entrenados y con experiencia evaluaron de forma independiente a 197 internos en la prisión Villabona (Asturias, España, con edades comprendidas entre los 21 y los 73 años (M = 36.0, DT = 9.7, de los cuales el 60.12% eran reincidentes y el 73% había cometido delitos violentos. Los resultados mostraron que los modelos unidimensional, correlacional de 2 factores, jerárquico de 3 factores sin testlest y correlacional y jerárquico de 4 factores, presentaban un pobre ajuste con los datos (CFI ≤ .86 y un ajuste razonable del modelo jerárquico de tres factores con testlets (CFI = .93. La escala resultante del modelo de tres factores
DEFF Research Database (Denmark)
Hermund, Anders
2010-01-01
This paper will introduce the PhD research into applied 3d modeling and parametric design outlining the idea of a parametric diagram linked to philosophical and applied examples.......This paper will introduce the PhD research into applied 3d modeling and parametric design outlining the idea of a parametric diagram linked to philosophical and applied examples....
Parametrization of contrails in a comprehensive climate model
Energy Technology Data Exchange (ETDEWEB)
Ponater, M.; Brinkop, S.; Sausen, R.; Schumann, U. [Deutsche Forschungs- und Versuchsanstalt fuer Luft- und Raumfahrt e.V., Oberpfaffenhofen (Germany). Inst. fuer Physik der Atmosphaere
1997-12-31
A contrail parametrization scheme for a general circulation model (GCM) is presented. Guidelines for its development were that it should be based on the thermodynamic theory of contrail formation and that it should be consistent with the cloud parametrization scheme of the GCM. Results of a six-year test integration indicate reasonable results concerning the spatial and temporal development of both contrail coverage and contrail optical properties. Hence, the scheme forms a promising basis for the quantitative estimation of the contrail climatic impact. (author) 9 refs.
Wind Farm parametrization in the mesoscale model WRF
DEFF Research Database (Denmark)
Volker, Patrick; Badger, Jake; Hahmann, Andrea N.
2012-01-01
, but are parametrized as another sub-grid scale process. In order to appropriately capture the wind farm wake recovery and its direction, two properties are important, among others, the total energy extracted by the wind farm and its velocity deficit distribution. In the considered parametrization the individual...... the extracted force is proportional to the turbine area interfacing a grid cell. The sub-grid scale wake expansion is achieved by adding turbulence kinetic energy (proportional to the extracted power) to the flow. The validity of both wind farm parametrizations has been verified against observational data. We...... turbines produce a thrust dependent on the background velocity. For the sub-grid scale velocity deficit, the entrainment from the free atmospheric flow into the wake region, which is responsible for the expansion, is taken into account. Furthermore, since the model horizontal distance is several times...
Monitoring coastal marshes biomass with CASI: a comparison of parametric and non-parametric models
Mo, Y.; Kearney, M.
2017-12-01
Coastal marshes are important carbon sinks that face multiple natural and anthropogenic stresses. Optical remote sensing is a powerful tool for closely monitoring the biomass of coastal marshes. However, application of hyperspectral sensors on assessing the biomass of diverse coastal marsh ecosystems is limited. This study samples spectral and biophysical data from coastal freshwater, intermediate, brackish, and saline marshes in Louisiana, and develops parametric and non-parametric models for using the Compact Airborne Spectrographic Imager (CASI) to retrieve the marshes' biomass. Linear models and random forest models are developed from simulated CASI data (48 bands, 380-1050 nm, bandwidth 14 nm). Linear models are also developed using narrowband vegetation indices computed from all possible band combinations from the blue, red, and near infrared wavelengths. It is found that the linear models derived from the optimal narrowband vegetation indices provide strong predictions for the marshes' Leaf Area Index (LAI; R2 > 0.74 for ARVI), but not for their Aboveground Green Biomass (AGB; R2 > 0.25). The linear models derived from the simulated CASI data strongly predict the marshes' LAI (R2 = 0.93) and AGB (R2 = 0.71) and have 27 and 30 bands/variables in the final models through stepwise regression, respectively. The random forest models derived from the simulated CASI data also strongly predict the marshes' LAI and AGB (R2 = 0.91 and 0.84, respectively), where the most important variables for predicting LAI are near infrared bands at 784 and 756 nm and for predicting ABG are red bands at 684 and 670 nm. In sum, the random forest model is preferable for assessing coastal marsh biomass using CASI data as it offers high R2 for both LAI and AGB. The superior performance of the random forest model is likely to due to that it fully utilizes the full-spectrum data and makes no assumption of the approximate normality of the sampling population. This study offers solutions
Update on Parametric Cost Models for Space Telescopes
Stahl. H. Philip; Henrichs, Todd; Luedtke, Alexander; West, Miranda
2011-01-01
Since the June 2010 Astronomy Conference, an independent review of our cost data base discovered some inaccuracies and inconsistencies which can modify our previously reported results. This paper will review changes to the data base, our confidence in those changes and their effect on various parametric cost models
BIM AND GIS: WHEN PARAMETRIC MODELING MEETS GEOSPATIAL DATA
Directory of Open Access Journals (Sweden)
L. Barazzetti
2017-12-01
Full Text Available Geospatial data have a crucial role in several projects related to infrastructures and land management. GIS software are able to perform advanced geospatial analyses, but they lack several instruments and tools for parametric modelling typically available in BIM. At the same time, BIM software designed for buildings have limited tools to handle geospatial data. As things stand at the moment, BIM and GIS could appear as complementary solutions, notwithstanding research work is currently under development to ensure a better level of interoperability, especially at the scale of the building. On the other hand, the transition from the local (building scale to the infrastructure (where geospatial data cannot be neglected has already demonstrated that parametric modelling integrated with geoinformation is a powerful tool to simplify and speed up some phases of the design workflow. This paper reviews such mixed approaches with both simulated and real examples, demonstrating that integration is already a reality at specific scales, which are not dominated by “pure” GIS or BIM. The paper will also demonstrate that some traditional operations carried out with GIS software are also available in parametric modelling software for BIM, such as transformation between reference systems, DEM generation, feature extraction, and geospatial queries. A real case study is illustrated and discussed to show the advantage of a combined use of both technologies. BIM and GIS integration can generate greater usage of geospatial data in the AECOO (Architecture, Engineering, Construction, Owner and Operator industry, as well as new solutions for parametric modelling with additional geoinformation.
Bim and Gis: when Parametric Modeling Meets Geospatial Data
Barazzetti, L.; Banfi, F.
2017-12-01
Geospatial data have a crucial role in several projects related to infrastructures and land management. GIS software are able to perform advanced geospatial analyses, but they lack several instruments and tools for parametric modelling typically available in BIM. At the same time, BIM software designed for buildings have limited tools to handle geospatial data. As things stand at the moment, BIM and GIS could appear as complementary solutions, notwithstanding research work is currently under development to ensure a better level of interoperability, especially at the scale of the building. On the other hand, the transition from the local (building) scale to the infrastructure (where geospatial data cannot be neglected) has already demonstrated that parametric modelling integrated with geoinformation is a powerful tool to simplify and speed up some phases of the design workflow. This paper reviews such mixed approaches with both simulated and real examples, demonstrating that integration is already a reality at specific scales, which are not dominated by "pure" GIS or BIM. The paper will also demonstrate that some traditional operations carried out with GIS software are also available in parametric modelling software for BIM, such as transformation between reference systems, DEM generation, feature extraction, and geospatial queries. A real case study is illustrated and discussed to show the advantage of a combined use of both technologies. BIM and GIS integration can generate greater usage of geospatial data in the AECOO (Architecture, Engineering, Construction, Owner and Operator) industry, as well as new solutions for parametric modelling with additional geoinformation.
Parametric modeling for damped sinusoids from multiple channels
DEFF Research Database (Denmark)
Zhou, Zhenhua; So, Hing Cheung; Christensen, Mads Græsbøll
2013-01-01
The problem of parametric modeling for noisy damped sinusoidal signals from multiple channels is addressed. Utilizing the shift invariance property of the signal subspace, the number of distinct sinusoidal poles in the multiple channels is first determined. With the estimated number, the distinct...
Fast, Sequence Adaptive Parcellation of Brain MR Using Parametric Models
DEFF Research Database (Denmark)
Puonti, Oula; Iglesias, Juan Eugenio; Van Leemput, Koen
2013-01-01
-of-the-art segmentation performance in both cortical and subcortical structures, while retaining all the benefits of generative parametric models, including high computational speed, automatic adaptiveness to changes in image contrast when different scanner platforms and pulse sequences are used, and the ability...
A Double Parametric Bootstrap Test for Topic Models
Seto, Skyler; Tan, Sarah; Hooker, Giles; Wells, Martin T.
2017-01-01
Non-negative matrix factorization (NMF) is a technique for finding latent representations of data. The method has been applied to corpora to construct topic models. However, NMF has likelihood assumptions which are often violated by real document corpora. We present a double parametric bootstrap test for evaluating the fit of an NMF-based topic model based on the duality of the KL divergence and Poisson maximum likelihood estimation. The test correctly identifies whether a topic model based o...
Parametric Cost and Schedule Modeling for Early Technology Development
2018-04-02
improved sample sizes and initial screening results. This analysis revealed that nonlinear behavior was evident in both TI and SH cost and schedule...MODELING FOR EARLY TECHNOLOGY DEVELOPMENT ix behavior . However, if planned SH and TI levels are known, multivariate models applying both predictor...Cost and Schedule Models,” Journal of Cost Analysis and Parametrics 7, no. 3 (2014): 160–179. THE JOHNS HOPKINS UNIVERSITY APPLIED PHYSICS LABORATORY2
Some comments on the Parametric Fire Model of Eurocode 1
Reitgrüber, Stefan; Pérez-Jimenez, Christian; Di Blasi, Colomba; Franssen, Jean-Marc
2006-01-01
In this paper, the modifications that have been recently introduced in the parametric fire model of Eurocode 1 are presented. The reasons behind these modifications are given. Some Problems that have been discovered in the present formulation are highlighted, namely the fact that the model is not continuous and the fact that the heat release of wood that has been used for the calibration of the model is not consistent anymore with the value that is now recommended in the Eurocode. A proposal ...
Parametric study of a thorium model
International Nuclear Information System (INIS)
Lourenco, M.C.; Lipsztein, J.L.; Szwarcwald, C.L.
2002-01-01
Models for radionuclides distribution in the human body and dosimetry involve assumptions on the biokinetic behavior of the material among compartments representing organs and tissues in the body. One of the most important problem in biokinetic modeling is the assignment of transfer coefficients and biological half-lives to body compartments. In Brazil there are many areas of high natural radioactivity, where the population is chronically exposed to radionuclides of the thorium series. The uncertainties of the thorium biokinetic model are a major cause of uncertainty in the estimates of the committed dose equivalent of the population living in high background areas. The purpose of this study is to discuss the variability in the thorium activities accumulated in the body compartments in relation to the variations in the transfer coefficients and compartments biological half-lives of a thorium-recycling model for continuous exposure. Multiple regression analysis methods were applied to analyze the results. (author)
Parametric modelling of nonstationary platform deck motions
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.
Univariate system identification models are, in general, developed based on teh assumption that offshore dynamic systems are stationary random processes. For nonstationary processes, a method is adopted to transform the time series into wide...
A Parametric Modelling Method for Dexterous Finger Reachable Workspaces
Directory of Open Access Journals (Sweden)
Wenzhen Yang
2016-03-01
Full Text Available The well-known algorithms, such as the graphic method, analytical method or numerical method, have some defects when modelling the dexterous finger workspace, which is a significant kinematical feature of dexterous hands and valuable for grasp planning, motion control and mechanical design. A novel modelling method with convenient and parametric performances is introduced to generate the dexterous-finger reachable workspace. This method constructs the geometric topology of the dexterous-finger reachable workspace, and uses a joint feature recognition algorithm to extract the kinematical parameters of the dexterous finger. Compared with graphic, analytical and numerical methods, this parametric modelling method can automatically and conveniently construct a more vivid workspace's forms and contours of the dexterous finger. The main contribution of this paper is that a workspace-modelling tool with high interactive efficiency is developed for designers to precisely visualize the dexterous-finger reachable workspace, which is valuable for analysing the flexibility of the dexterous finger.
Bayesian non parametric modelling of Higgs pair production
Directory of Open Access Journals (Sweden)
Scarpa Bruno
2017-01-01
Full Text Available Statistical classification models are commonly used to separate a signal from a background. In this talk we face the problem of isolating the signal of Higgs pair production using the decay channel in which each boson decays into a pair of b-quarks. Typically in this context non parametric methods are used, such as Random Forests or different types of boosting tools. We remain in the same non-parametric framework, but we propose to face the problem following a Bayesian approach. A Dirichlet process is used as prior for the random effects in a logit model which is fitted by leveraging the Polya-Gamma data augmentation. Refinements of the model include the insertion in the simple model of P-splines to relate explanatory variables with the response and the use of Bayesian trees (BART to describe the atoms in the Dirichlet process.
Scene Parsing With Integration of Parametric and Non-Parametric Models.
Shuai, Bing; Zuo, Zhen; Wang, Gang; Wang, Bing
2016-05-01
We adopt convolutional neural networks (CNNs) to be our parametric model to learn discriminative features and classifiers for local patch classification. Based on the occurrence frequency distribution of classes, an ensemble of CNNs (CNN-Ensemble) are learned, in which each CNN component focuses on learning different and complementary visual patterns. The local beliefs of pixels are output by CNN-Ensemble. Considering that visually similar pixels are indistinguishable under local context, we leverage the global scene semantics to alleviate the local ambiguity. The global scene constraint is mathematically achieved by adding a global energy term to the labeling energy function, and it is practically estimated in a non-parametric framework. A large margin-based CNN metric learning method is also proposed for better global belief estimation. In the end, the integration of local and global beliefs gives rise to the class likelihood of pixels, based on which maximum marginal inference is performed to generate the label prediction maps. Even without any post-processing, we achieve the state-of-the-art results on the challenging SiftFlow and Barcelona benchmarks.
Parametric Modelling of Potential Evapotranspiration: A Global Survey
Aristoteles Tegos; Nikolaos Malamos; Andreas Efstratiadis; Ioannis Tsoukalas; Alexandros Karanasios; Demetris Koutsoyiannis
2017-01-01
We present and validate a global parametric model of potential evapotranspiration (PET) with two parameters that are estimated through calibration, using as explanatory variables temperature and extraterrestrial radiation. The model is tested over the globe, taking advantage of the Food and Agriculture Organization (FAO CLIMWAT) database that provides monthly averaged values of meteorological inputs at 4300 locations worldwide. A preliminary analysis of these data allows for explaining the ma...
Parametric study for horizontal steam generator modelling
Energy Technology Data Exchange (ETDEWEB)
Ovtcharova, I. [Energoproekt, Sofia (Bulgaria)
1995-12-31
In the presentation some of the calculated results of horizontal steam generator PGV - 440 modelling with RELAP5/Mod3 are described. Two nodalization schemes have been used with different components in the steam dome. A study of parameters variation on the steam generator work and calculated results is made in cases with separator and branch.
Conformally parametrized surfaces associated with CPN-1 sigma models
International Nuclear Information System (INIS)
Grundland, A M; Hereman, W A; Yurdusen, I-dot
2008-01-01
Two-dimensional parametrized surfaces immersed in the su(N) algebra are investigated. The focus is on surfaces parametrized by solutions of the equations for the CP N-1 sigma model. The Lie-point symmetries of the CP N-1 model are computed for arbitrary N. The Weierstrass formula for immersion is determined and an explicit formula for a moving frame on a surface is constructed. This allows us to determine the structural equations and geometrical properties of surfaces in R N 2 -1 . The fundamental forms, Gaussian and mean curvatures, Willmore functional and topological charge of surfaces are given explicitly in terms of any holomorphic solution of the CP 2 model. The approach is illustrated through several examples, including surfaces immersed in low-dimensional su(N) algebras
PARAMETRIC MODELING, CREATIVITY, AND DESIGN: TWO EXPERIENCES WITH ARCHITECTURE’ STUDENTS
Directory of Open Access Journals (Sweden)
Wilson Florio
2012-02-01
Full Text Available The aim of this article is to reflect on the use of the parametric modeling in two didactic experiences. The first experiment involved resources of the Paracloud program and its relation with the Rhinoceros program, that resulted in the production of physical models produced with the aid of the laser cutting. In the second experiment, the students had produced algorithms in the Grasshopper, resulting in families of structures and coverings. The study objects are both the physical models and digital algorithms resultants from this experimentation. For the analysis and synthesis of the results, we adopted four important assumptions: 1. the value of attitudes and environment of work; 2. the importance of experimentation and improvisation; 3. understanding of the design process as a situated act and as a ill-defined problem; 4. the inclusion of creative and critical thought in the disciplines. The results allow us to affirm that the parametric modeling stimulates creativity, therefore allowing combination of different parameters, that result in unexpected discoveries. Keywords: Teach-Learning, Parametric Modeling, Laser Cutter, Grasshopper, Design Process, Creativity.
Study on Semi-Parametric Statistical Model of Safety Monitoring of Cracks in Concrete Dams
Gu, Chongshi; Qin, Dong; Li, Zhanchao; Zheng, Xueqin
2013-01-01
Cracks are one of the hidden dangers in concrete dams. The study on safety monitoring models of concrete dam cracks has always been difficult. Using the parametric statistical model of safety monitoring of cracks in concrete dams, with the help of the semi-parametric statistical theory, and considering the abnormal behaviors of these cracks, the semi-parametric statistical model of safety monitoring of concrete dam cracks is established to overcome the limitation of the parametric model in ex...
Parametric analysis of fire model CFAST
International Nuclear Information System (INIS)
Lee, Y. H.; Yang, J. Y.; Kim, J. H.
2004-01-01
This paper describes the pump room fire of the nuclear power plant using CFAST fire modeling code developed by NIST. It is determined by the constrained or unconstrained fire, Lower Oxygen Limit (LOL), Radiative Fraction (RF), and the times to open doors, which are the input parameters of CAFST. According to the results, pump room fire is ventilation-controlled fire, so it is adequate that the value of LOL is 10% which is also the default value. It is appeared that the RF does not change the temperature of the upper gas layer. But the level of opening of the penetrating area and the times to opening it have an effect on the temperature of the upper layer, so it is determined that the results of it should be carefully analyzed
Parametric modelling of thresholds across scales in wavelet regression
Anestis Antoniadis; Piotr Fryzlewicz
2006-01-01
We propose a parametric wavelet thresholding procedure for estimation in the ‘function plus independent, identically distributed Gaussian noise’ model. To reflect the decreasing sparsity of wavelet coefficients from finer to coarser scales, our thresholds also decrease. They retain the noise-free reconstruction property while being lower than the universal threshold, and are jointly parameterised by a single scalar parameter. We show that our estimator achieves near-optimal risk rates for the...
A Novel Parametric Model For The Human Respiratory System
Directory of Open Access Journals (Sweden)
Clara Mihaela IONESCU
2003-12-01
Full Text Available The purpose of this work is to present some recent results in an ongoing research project between Ghent University and Chess Medical Technology Company Belgium. The overall aim of the project is to provide a fast method for identification of the human respiratory system in order to allow for an instantaneously diagnosis of the patient by the medical staff. A novel parametric model of the human respiratory system as well as the obtained experimental results is presented in this paper. A prototype apparatus developed by the company, based on the forced oscillation technique is used to record experimental data from 4 patients in this paper. Signal processing is based on spectral analysis and is followed by the parametric identification of a non-linear mechanistic model. The parametric model is equivalent to the structure of a simple electrical RLC-circuit, containing a non-linear capacitor. These parameters have a useful and easy-to-interpret physical meaning for the medical staff members.
Maydeu-Olivares, Albert
2005-01-01
Chernyshenko, Stark, Chan, Drasgow, and Williams (2001) investigated the fit of Samejima's logistic graded model and Levine's non-parametric MFS model to the scales of two personality questionnaires and found that the graded model did not fit well. We attribute the poor fit of the graded model to small amounts of multidimensionality present in…
Motion Imitation and Recognition using Parametric Hidden Markov Models
DEFF Research Database (Denmark)
Herzog, Dennis; Ude, Ales; Krüger, Volker
2008-01-01
The recognition and synthesis of parametric movements play an important role in human-robot interaction. To understand the whole purpose of an arm movement of a human agent, both its recognition (e.g., pointing or reaching) as well as its parameterization (i.e., where the agent is pointing at......) are important. Only together they convey the whole meaning of an action. Similarly, to imitate a movement, the robot needs to select the proper action and parameterize it, e.g., by the relative position of the object that needs to be grasped. We propose to utilize parametric hidden Markov models (PHMMs), which...... extend the classical HMMs by introducing a joint parameterization of the observation densities, to simultaneously solve the problems of action recognition, parameterization of the observed actions, and action synthesis. The proposed approach was fully implemented on a humanoid robot HOAP-3. To evaluate...
Parametric Modeling of the Mouse Left Ventricular Myocardial Fiber Structure.
Merchant, Samer S; Gomez, Arnold David; Morgan, James L; Hsu, Edward W
2016-09-01
Magnetic resonance diffusion tensor imaging (DTI) has greatly facilitated detailed quantifications of myocardial structures. However, structural patterns, such as the distinctive transmural rotation of the fibers, remain incompletely described. To investigate the validity and practicality of pattern-based analysis, 3D DTI was performed on 13 fixed mouse hearts and fiber angles in the left ventricle were transformed and fitted to parametric expressions constructed from elementary functions of the prolate spheroidal spatial variables. It was found that, on average, the myocardial fiber helix angle could be represented to 6.5° accuracy by the equivalence of a product of 10th-order polynomials of the radial and longitudinal variables, and 17th-order Fourier series of the circumferential variable. Similarly, the fiber imbrication angle could be described by 10th-order polynomials and 24th-order Fourier series, to 5.6° accuracy. The representations, while relatively concise, did not adversely affect the information commonly derived from DTI datasets including the whole-ventricle mean fiber helix angle transmural span and atlases constructed for the group. The unique ability of parametric models for predicting the 3D myocardial fiber structure from finite number of 2D slices was also demonstrated. These findings strongly support the principle of parametric modeling for characterizing myocardial structures in the mouse and beyond.
A parametric costing model for wave energy technology
International Nuclear Information System (INIS)
1992-01-01
This document describes the philosophy and technical approach to a parametric cost model for offshore wave energy systems. Consideration is given both to existing known devices and other devices yet to be conceptualised. The report is complementary to a spreadsheet based cost estimating model. The latter permits users to derive capital cost estimates using either inherent default data or user provided data, if a particular scheme provides sufficient design definition for more accurate estimation. The model relies on design default data obtained from wave energy device designs and a set of specifically collected cost data. (author)
Spatial variability and parametric uncertainty in performance assessment models
International Nuclear Information System (INIS)
Pensado, Osvaldo; Mancillas, James; Painter, Scott; Tomishima, Yasuo
2011-01-01
The problem of defining an appropriate treatment of distribution functions (which could represent spatial variability or parametric uncertainty) is examined based on a generic performance assessment model for a high-level waste repository. The generic model incorporated source term models available in GoldSim ® , the TDRW code for contaminant transport in sparse fracture networks with a complex fracture-matrix interaction process, and a biosphere dose model known as BDOSE TM . Using the GoldSim framework, several Monte Carlo sampling approaches and transport conceptualizations were evaluated to explore the effect of various treatments of spatial variability and parametric uncertainty on dose estimates. Results from a model employing a representative source and ensemble-averaged pathway properties were compared to results from a model allowing for stochastic variation of transport properties along streamline segments (i.e., explicit representation of spatial variability within a Monte Carlo realization). We concluded that the sampling approach and the definition of an ensemble representative do influence consequence estimates. In the examples analyzed in this paper, approaches considering limited variability of a transport resistance parameter along a streamline increased the frequency of fast pathways resulting in relatively high dose estimates, while those allowing for broad variability along streamlines increased the frequency of 'bottlenecks' reducing dose estimates. On this basis, simplified approaches with limited consideration of variability may suffice for intended uses of the performance assessment model, such as evaluation of site safety. (author)
Process simulation and parametric modeling for strategic project management
Morales, Peter J
2013-01-01
Process Simulation and Parametric Modeling for Strategic Project Management will offer CIOs, CTOs and Software Development Managers, IT Graduate Students an introduction to a set of technologies that will help them understand how to better plan software development projects, manage risk and have better insight into the complexities of the software development process.A novel methodology will be introduced that allows a software development manager to better plan and access risks in the early planning of a project. By providing a better model for early software development estimation and softw
Mace, Andy; Rudolph, David L.; Kachanoski , R. Gary
1998-01-01
The performance of parametric models used to describe soil water retention (SWR) properties and predict unsaturated hydraulic conductivity (K) as a function of volumetric water content (θ) is examined using SWR and K(θ) data for coarse sand and gravel sediments. Six 70 cm long, 10 cm diameter cores of glacial outwash were instrumented at eight depths with porous cup ten-siometers and time domain reflectometry probes to measure soil water pressure head (h) and θ, respectively, for seven unsaturated and one saturated steady-state flow conditions. Forty-two θ(h) and K(θ) relationships were measured from the infiltration tests on the cores. Of the four SWR models compared in the analysis, the van Genuchten (1980) equation with parameters m and n restricted according to the Mualem (m = 1 - 1/n) criterion is best suited to describe the θ(h) relationships. The accuracy of two models that predict K(θ) using parameter values derived from the SWR models was also evaluated. The model developed by van Genuchten (1980) based on the theoretical expression of Mualem (1976) predicted K(θ) more accurately than the van Genuchten (1980) model based on the theory of Burdine (1953). A sensitivity analysis shows that more accurate predictions of K(θ) are achieved using SWR model parameters derived with residual water content (θr) specified according to independent measurements of θ at values of h where θ/h ∼ 0 rather than model-fit θr values. The accuracy of the model K(θ) function improves markedly when at least one value of unsaturated K is used to scale the K(θ) function predicted using the saturated K. The results of this investigation indicate that the hydraulic properties of coarse-grained sediments can be accurately described using the parametric models. In addition, data collection efforts should focus on measuring at least one value of unsaturated hydraulic conductivity and as complete a set of SWR data as possible, particularly in the dry range.
Facial Performance Transfer via Deformable Models and Parametric Correspondence.
Asthana, Akshay; de la Hunty, Miles; Dhall, Abhinav; Goecke, Roland
2012-09-01
The issue of transferring facial performance from one person's face to another's has been an area of interest for the movie industry and the computer graphics community for quite some time. In recent years, deformable face models, such as the Active Appearance Model (AAM), have made it possible to track and synthesize faces in real time. Not surprisingly, deformable face model-based approaches for facial performance transfer have gained tremendous interest in the computer vision and graphics community. In this paper, we focus on the problem of real-time facial performance transfer using the AAM framework. We propose a novel approach of learning the mapping between the parameters of two completely independent AAMs, using them to facilitate the facial performance transfer in a more realistic manner than previous approaches. The main advantage of modeling this parametric correspondence is that it allows a "meaningful" transfer of both the nonrigid shape and texture across faces irrespective of the speakers' gender, shape, and size of the faces, and illumination conditions. We explore linear and nonlinear methods for modeling the parametric correspondence between the AAMs and show that the sparse linear regression method performs the best. Moreover, we show the utility of the proposed framework for a cross-language facial performance transfer that is an area of interest for the movie dubbing industry.
Parametric packet-based audiovisual quality model for IPTV services
Garcia, Marie-Neige
2014-01-01
This volume presents a parametric packet-based audiovisual quality model for Internet Protocol TeleVision (IPTV) services. The model is composed of three quality modules for the respective audio, video and audiovisual components. The audio and video quality modules take as input a parametric description of the audiovisual processing path, and deliver an estimate of the audio and video quality. These outputs are sent to the audiovisual quality module which provides an estimate of the audiovisual quality. Estimates of perceived quality are typically used both in the network planning phase and as part of the quality monitoring. The same audio quality model is used for both these phases, while two variants of the video quality model have been developed for addressing the two application scenarios. The addressed packetization scheme is MPEG2 Transport Stream over Real-time Transport Protocol over Internet Protocol. In the case of quality monitoring, that is the case for which the network is already set-up, the aud...
Parametric System Model for a Stirling Radioisotope Generator
Schmitz, Paul C.
2015-01-01
A Parametric System Model (PSM) was created in order to explore conceptual designs, the impact of component changes and power level on the performance of the Stirling Radioisotope Generator (SRG). Using the General Purpose Heat Source (GPHS approximately 250 Wth) modules as the thermal building block from which a SRG is conceptualized, trade studies are performed to understand the importance of individual component scaling on isotope usage. Mathematical relationships based on heat and power throughput, temperature, mass, and volume were developed for each of the required subsystems. The PSM uses these relationships to perform component- and system-level trades.
Thermodynamic model and parametric analysis of a tubular SOFC module
Campanari, Stefano
Solid oxide fuel cells (SOFCs) have been considered in the last years as one of the most promising technologies for very high-efficiency electric energy generation from natural gas, both with simple fuel cell plants and with integrated gas turbine-fuel cell systems. Among the SOFC technologies, tubular SOFC stacks with internal reforming have emerged as one of the most mature technology, with a serious potential for a future commercialization. In this paper, a thermodynamic model of a tubular SOFC stack, with natural gas feeding, internal reforming of hydrocarbons and internal air preheating is proposed. In the first section of the paper, the model is discussed in detail, analyzing its calculating equations and tracing its logical steps; the model is then calibrated on the available data for a recently demonstrated tubular SOFC prototype plant. In the second section of the paper, it is carried out a detailed parametric analysis of the stack working conditions, as a function of the main operating parameters. The discussion of the results of the thermodynamic and parametric analysis yields interesting considerations about partial load SOFC operation and load regulation, and about system design and integration with gas turbine cycles.
Toward an Empirically-based Parametric Explosion Spectral Model
Ford, S. R.; Walter, W. R.; Ruppert, S.; Matzel, E.; Hauk, T. F.; Gok, R.
2010-12-01
Small underground nuclear explosions need to be confidently detected, identified, and characterized in regions of the world where they have never occurred. We develop a parametric model of the nuclear explosion seismic source spectrum derived from regional phases (Pn, Pg, and Lg) that is compatible with earthquake-based geometrical spreading and attenuation. Earthquake spectra are fit with a generalized version of the Brune spectrum, which is a three-parameter model that describes the long-period level, corner-frequency, and spectral slope at high-frequencies. These parameters are then correlated with near-source geology and containment conditions. There is a correlation of high gas-porosity (low strength) with increased spectral slope. However, there are trade-offs between the slope and corner-frequency, which we try to independently constrain using Mueller-Murphy relations and coda-ratio techniques. The relationship between the parametric equation and the geologic and containment conditions will assist in our physical understanding of the nuclear explosion source, and aid in the prediction of observed local and regional distance seismic amplitudes for event identification and yield determination in regions with incomplete or no prior history of underground nuclear testing.
A parametric model of child body shape in seated postures.
Park, Byoung-Keon D; Ebert, Sheila; Reed, Matthew P
2017-07-04
The shape of the current physical and computational surrogates of children used for restraint system assessments is based largely on standard anthropometric dimensions. These scalar dimensions provide valuable information on the overall size of the individual but do not provide good guidance on shape or posture. This study introduced the development of a parametric model that statistically predicts individual child body shapes in seated postures with a few given parameters. Surface geometry data from a laser scanner of children ages 3 to 11 (n = 135) were standardized by a 2-level fitting method using intermediate templates. The standardized data were analyzed by principal component analysis (PCA) to efficiently describe the body shape variance. Parameters such as stature, body mass index, erect sitting height, and 2 posture variables related to torso recline and lumbar spine flexion were associated with the PCA model using regression. When the original scan data were compared with the predictions of the model using the given subject dimensions, the average root mean square error for the torso was 9.5 mm, and the 95th percentile error was 17.35 mm. For the first time, a statistical model of child body shapes in seated postures is available. This parametric model allows the generation of an infinite number of virtual children spanning a wide range of body sizes and postures. The results have broad applicability in product design and safety analysis. Future work is needed to improve the representation of hands and feet and to extend the age range of the model. The model presented in this article is publicly available online through HumanShape.org.
A Parametric Factor Model of the Term Structure of Mortality
DEFF Research Database (Denmark)
Haldrup, Niels; Rosenskjold, Carsten Paysen T.
The prototypical Lee-Carter mortality model is characterized by a single common time factor that loads differently across age groups. In this paper we propose a factor model for the term structure of mortality where multiple factors are designed to influence the age groups differently via...... parametric loading functions. We identify four different factors: a factor common for all age groups, factors for infant and adult mortality, and a factor for the "accident hump" that primarily affects mortality of relatively young adults and late teenagers. Since the factors are identified via restrictions...... on the loading functions, the factors are not designed to be orthogonal but can be dependent and can possibly cointegrate when the factors have unit roots. We suggest two estimation procedures similar to the estimation of the dynamic Nelson-Siegel term structure model. First, a two-step nonlinear least squares...
An Evaluation of Parametric and Nonparametric Models of Fish Population Response.
Energy Technology Data Exchange (ETDEWEB)
Haas, Timothy C.; Peterson, James T.; Lee, Danny C.
1999-11-01
Predicting the distribution or status of animal populations at large scales often requires the use of broad-scale information describing landforms, climate, vegetation, etc. These data, however, often consist of mixtures of continuous and categorical covariates and nonmultiplicative interactions among covariates, complicating statistical analyses. Using data from the interior Columbia River Basin, USA, we compared four methods for predicting the distribution of seven salmonid taxa using landscape information. Subwatersheds (mean size, 7800 ha) were characterized using a set of 12 covariates describing physiography, vegetation, and current land-use. The techniques included generalized logit modeling, classification trees, a nearest neighbor technique, and a modular neural network. We evaluated model performance using out-of-sample prediction accuracy via leave-one-out cross-validation and introduce a computer-intensive Monte Carlo hypothesis testing approach for examining the statistical significance of landscape covariates with the non-parametric methods. We found the modular neural network and the nearest-neighbor techniques to be the most accurate, but were difficult to summarize in ways that provided ecological insight. The modular neural network also required the most extensive computer resources for model fitting and hypothesis testing. The generalized logit models were readily interpretable, but were the least accurate, possibly due to nonlinear relationships and nonmultiplicative interactions among covariates. Substantial overlap among the statistically significant (P<0.05) covariates for each method suggested that each is capable of detecting similar relationships between responses and covariates. Consequently, we believe that employing one or more methods may provide greater biological insight without sacrificing prediction accuracy.
MTL-Model Checking of One-Clock Parametric Timed Automata is Undecidable
Directory of Open Access Journals (Sweden)
Karin Quaas
2014-03-01
Full Text Available Parametric timed automata extend timed automata (Alur and Dill, 1991 in that they allow the specification of parametric bounds on the clock values. Since their introduction in 1993 by Alur, Henzinger, and Vardi, it is known that the emptiness problem for parametric timed automata with one clock is decidable, whereas it is undecidable if the automaton uses three or more parametric clocks. The problem is open for parametric timed automata with two parametric clocks. Metric temporal logic, MTL for short, is a widely used specification language for real-time systems. MTL-model checking of timed automata is decidable, no matter how many clocks are used in the timed automaton. In this paper, we prove that MTL-model checking for parametric timed automata is undecidable, even if the automaton uses only one clock and one parameter and is deterministic.
Parametric Thermal Soak Model for Earth Entry Vehicles
Agrawal, Parul; Samareh, Jamshid; Doan, Quy D.
2013-01-01
The analysis and design of an Earth Entry Vehicle (EEV) is multidisciplinary in nature, requiring the application many disciplines. An integrated tool called Multi Mission System Analysis for Planetary Entry Descent and Landing or M-SAPE is being developed as part of Entry Vehicle Technology project under In-Space Technology program. Integration of a multidisciplinary problem is a challenging task. Automation of the execution process and data transfer among disciplines can be accomplished to provide significant benefits. Thermal soak analysis and temperature predictions of various interior components of entry vehicle, including the impact foam and payload container are part of the solution that M-SAPE will offer to spacecraft designers. The present paper focuses on the thermal soak analysis of an entry vehicle design based on the Mars Sample Return entry vehicle geometry and discusses a technical approach to develop parametric models for thermal soak analysis that will be integrated into M-SAPE. One of the main objectives is to be able to identify the important parameters and to develop correlation coefficients so that, for a given trajectory, can estimate the peak payload temperature based on relevant trajectory parameters and vehicle geometry. The models are being developed for two primary thermal protection (TPS) materials: 1) carbon phenolic that was used for Galileo and Pioneer Venus probes and, 2) Phenolic Impregnated Carbon Ablator (PICA), TPS material for Mars Science Lab mission. Several representative trajectories were selected from a very large trade space to include in the thermal analysis in order to develop an effective parametric thermal soak model. The selected trajectories covered a wide range of heatload and heatflux combinations. Non-linear, fully transient, thermal finite element simulations were performed for the selected trajectories to generate the temperature histories at the interior of the vehicle. Figure 1 shows the finite element model
Study on Semi-Parametric Statistical Model of Safety Monitoring of Cracks in Concrete Dams
Directory of Open Access Journals (Sweden)
Chongshi Gu
2013-01-01
Full Text Available Cracks are one of the hidden dangers in concrete dams. The study on safety monitoring models of concrete dam cracks has always been difficult. Using the parametric statistical model of safety monitoring of cracks in concrete dams, with the help of the semi-parametric statistical theory, and considering the abnormal behaviors of these cracks, the semi-parametric statistical model of safety monitoring of concrete dam cracks is established to overcome the limitation of the parametric model in expressing the objective model. Previous projects show that the semi-parametric statistical model has a stronger fitting effect and has a better explanation for cracks in concrete dams than the parametric statistical model. However, when used for forecast, the forecast capability of the semi-parametric statistical model is equivalent to that of the parametric statistical model. The modeling of the semi-parametric statistical model is simple, has a reasonable principle, and has a strong practicality, with a good application prospect in the actual project.
Conservative models: parametric entropy vs. temporal entropy in outcomes.
Huang, Lumeng; Ritzi, Robert W; Ramanathan, Ramya
2012-01-01
The geologic architecture in aquifer systems affects the behavior of fluid flow and the dispersion of mass. The spatial distribution and connectivity of higher-permeability facies play an important role. Models that represent this geologic structure have reduced entropy in the spatial distribution of permeability relative to models without structure. The literature shows that the stochastic model with the greatest variance in the distribution of predictions (i.e., the most conservative model) will not simply be the model representing maximum disorder in the permeability field. This principle is further explored using the Shannon entropy as a single metric to quantify and compare model parametric spatial disorder to the temporal distribution of mass residence times in model predictions. The principle is most pronounced when geologic structure manifests as preferential-flow pathways through the system via connected high-permeability sediments. As per percolation theory, at certain volume fractions the full connectivity of the high-permeability sediments will not be represented unless the model is three-dimensional. At these volume fractions, two-dimensional models can profoundly underrepresent the entropy in the real, three-dimensional, aquifer system. Thus to be conservative, stochastic models must be three-dimensional and include geologic structure. © 2011, The Author(s). Ground Water © 2011, National Ground Water Association.
Lumped parametric model of the human ear for sound transmission.
Feng, Bin; Gan, Rong Z
2004-09-01
A lumped parametric model of the human auditoria peripherals consisting of six masses suspended with six springs and ten dashpots was proposed. This model will provide the quantitative basis for the construction of a physical model of the human middle ear. The lumped model parameters were first identified using published anatomical data, and then determined through a parameter optimization process. The transfer function of the middle ear obtained from human temporal bone experiments with laser Doppler interferometers was used for creating the target function during the optimization process. It was found that, among 14 spring and dashpot parameters, there were five parameters which had pronounced effects on the dynamic behaviors of the model. The detailed discussion on the sensitivity of those parameters was provided with appropriate applications for sound transmission in the ear. We expect that the methods for characterizing the lumped model of the human ear and the model parameters will be useful for theoretical modeling of the ear function and construction of the ear physical model.
Parametric overdispersed frailty models for current status data.
Abrams, Steven; Aerts, Marc; Molenberghs, Geert; Hens, Niel
2017-12-01
Frailty models have a prominent place in survival analysis to model univariate and multivariate time-to-event data, often complicated by the presence of different types of censoring. In recent years, frailty modeling gained popularity in infectious disease epidemiology to quantify unobserved heterogeneity using Type I interval-censored serological data or current status data. In a multivariate setting, frailty models prove useful to assess the association between infection times related to multiple distinct infections acquired by the same individual. In addition to dependence among individual infection times, overdispersion can arise when the observed variability in the data exceeds the one implied by the model. In this article, we discuss parametric overdispersed frailty models for time-to-event data under Type I interval-censoring, building upon the work by Molenberghs et al. (2010) and Hens et al. (2009). The proposed methodology is illustrated using bivariate serological data on hepatitis A and B from Flanders, Belgium anno 1993-1994. Furthermore, the relationship between individual heterogeneity and overdispersion at a stratum-specific level is studied through simulations. Although it is important to account for overdispersion, one should be cautious when modeling both individual heterogeneity and overdispersion based on current status data as model selection is hampered by the loss of information due to censoring. © 2017, The International Biometric Society.
Parametric Thermal Models of the Transient Reactor Test Facility (TREAT)
Energy Technology Data Exchange (ETDEWEB)
Bradley K. Heath
2014-03-01
This work supports the restart of transient testing in the United States using the Department of Energy’s Transient Reactor Test Facility at the Idaho National Laboratory. It also supports the Global Threat Reduction Initiative by reducing proliferation risk of high enriched uranium fuel. The work involves the creation of a nuclear fuel assembly model using the fuel performance code known as BISON. The model simulates the thermal behavior of a nuclear fuel assembly during steady state and transient operational modes. Additional models of the same geometry but differing material properties are created to perform parametric studies. The results show that fuel and cladding thermal conductivity have the greatest effect on fuel temperature under the steady state operational mode. Fuel density and fuel specific heat have the greatest effect for transient operational model. When considering a new fuel type it is recommended to use materials that decrease the specific heat of the fuel and the thermal conductivity of the fuel’s cladding in order to deal with higher density fuels that accompany the LEU conversion process. Data on the latest operating conditions of TREAT need to be attained in order to validate BISON’s results. BISON’s models for TREAT (material models, boundary convection models) are modest and need additional work to ensure accuracy and confidence in results.
Validation of statistical models for creep rupture by parametric analysis
Energy Technology Data Exchange (ETDEWEB)
Bolton, J., E-mail: john.bolton@uwclub.net [65, Fisher Ave., Rugby, Warks CV22 5HW (United Kingdom)
2012-01-15
Statistical analysis is an efficient method for the optimisation of any candidate mathematical model of creep rupture data, and for the comparative ranking of competing models. However, when a series of candidate models has been examined and the best of the series has been identified, there is no statistical criterion to determine whether a yet more accurate model might be devised. Hence there remains some uncertainty that the best of any series examined is sufficiently accurate to be considered reliable as a basis for extrapolation. This paper proposes that models should be validated primarily by parametric graphical comparison to rupture data and rupture gradient data. It proposes that no mathematical model should be considered reliable for extrapolation unless the visible divergence between model and data is so small as to leave no apparent scope for further reduction. This study is based on the data for a 12% Cr alloy steel used in BS PD6605:1998 to exemplify its recommended statistical analysis procedure. The models considered in this paper include a) a relatively simple model, b) the PD6605 recommended model and c) a more accurate model of somewhat greater complexity. - Highlights: Black-Right-Pointing-Pointer The paper discusses the validation of creep rupture models derived from statistical analysis. Black-Right-Pointing-Pointer It demonstrates that models can be satisfactorily validated by a visual-graphic comparison of models to data. Black-Right-Pointing-Pointer The method proposed utilises test data both as conventional rupture stress and as rupture stress gradient. Black-Right-Pointing-Pointer The approach is shown to be more reliable than a well-established and widely used method (BS PD6605).
Parametric design and analysis framework with integrated dynamic models
DEFF Research Database (Denmark)
Negendahl, Kristoffer
2014-01-01
control with the building designer. Consequence based design is defined by the specific use of integrated dynamic modeling, which includes the parametric capabilities of a scripting tool and building simulation features of a building performance simulation tool. The framework can lead to enhanced......In the wake of uncompromising requirements on building performance and the current emphasis on sustainability, including building energy and indoor environment, designing buildings involves elements of expertise of multiple disciplines. However, building performance analyses, including those...... of building energy and indoor environment, are generally confined to late in the design process. Consequence based design is a framework intended for the early design stage. It involves interdisciplinary expertise that secures validity and quality assurance with a simulationist while sustaining autonomous...
A parametric model of radioisotopic tracer flow through the kidney
International Nuclear Information System (INIS)
Nahorski, Z.
1996-01-01
To assess the state of a kidney from radiological examination a dynamic model of flow of an isotopic tracer through the kidney, of a convolution type, is commonly used. It is a nonparametric model where the output is related to the input by an integral relation. In this paper a new parametric model of tracer flow through the kidney is presented. It takes to much bigger extent into account the physiology of the tracer flow in kidney than previously proposed compartmental models. It consists of the submodels of flow through nephrons which are in the form of ordinary differential equations with pure delays between input and the output flows. The delay is caused by the time of passage of the isotope through nephrons and depends on the nephron length. It is assumed that the distribution of nephrons of different length is exponential. The renal pelvis is described as the one-compartment model. The solutions of the equations are given for the assumed two-exponential decay of the isotope tracer in the blood following the bolus injection of the tracer. The formulae obtained are then used to simulate the influence of changes in the model parameters, representing the flow condition through the kidney, on the shape of the waveforms depicting time evolution of the amount of the isotope being in the kidney
Parametric cost model for solar space power and DIPS systems
International Nuclear Information System (INIS)
Meisl, C.J.
1993-01-01
A detailed cost model has been developed to parametrically determine the program development and production cost of (1) photovoltaic, (2) solar dynamic and (3) dynamic isotope (DIPS) space power systems. The model is applicable in the net electrical power range of 3 to 300 kWe for solar power, and 0.5 to 10 kWe for DIPS. Application of the cost model allows spacecraft or space-based power system architecture and design trade studies or budgetary forecasting and cost benefit analyses. The cost model considers all major power subsystems (i.e., power generation, power conversion, energy storage, thermal management, and power management/distribution/control). It also considers system cost effects such as integration, testing, management, etc. The cost breakdown structure, model assumptions, ground rules, bases, Cost Estimation Relationship (CER) format and rationale are presented, and the application of the cost model to 100-kWe solar space power plants and to a 1.0-kWe DIPS are demonstrated
Parametric Model for Astrophysical Proton-Proton Interactions and Applications
Energy Technology Data Exchange (ETDEWEB)
Karlsson, Niklas [KTH Royal Institute of Technology, Stockholm (Sweden)
2007-01-01
Observations of gamma-rays have been made from celestial sources such as active galaxies, gamma-ray bursts and supernova remnants as well as the Galactic ridge. The study of gamma rays can provide information about production mechanisms and cosmic-ray acceleration. In the high-energy regime, one of the dominant mechanisms for gamma-ray production is the decay of neutral pions produced in interactions of ultra-relativistic cosmic-ray nuclei and interstellar matter. Presented here is a parametric model for calculations of inclusive cross sections and transverse momentum distributions for secondary particles--gamma rays, e^{±}, v_{e}, $\\bar{v}$_{e}, v_{μ} and $\\bar{μ}$_{e}--produced in proton-proton interactions. This parametric model is derived on the proton-proton interaction model proposed by Kamae et al.; it includes the diffraction dissociation process, Feynman-scaling violation and the logarithmically rising inelastic proton-proton cross section. To improve fidelity to experimental data for lower energies, two baryon resonance excitation processes were added; one representing the Δ(1232) and the other multiple resonances with masses around 1600 MeV/c^{2}. The model predicts the power-law spectral index for all secondary particle to be about 0.05 lower in absolute value than that of the incident proton and their inclusive cross sections to be larger than those predicted by previous models based on the Feynman-scaling hypothesis. The applications of the presented model in astrophysics are plentiful. It has been implemented into the Galprop code to calculate the contribution due to pion decays in the Galactic plane. The model has also been used to estimate the cosmic-ray flux in the Large Magellanic Cloud based on HI, CO and gamma-ray observations. The transverse momentum distributions enable calculations when the proton distribution is anisotropic. It is shown that the gamma-ray spectrum and flux due to a
Directory of Open Access Journals (Sweden)
Renata Pires Gonçalves
2012-02-01
. The experiments of type dosage x response are very common in the determination of levels of nutrients in optimal food balance and include the use of regression models to achieve this objective. Nevertheless, the regression analysis routine, generally, uses a priori information about a possible relationship between the response variable. The isotonic regression is a method of estimation by least squares that generates estimates which preserves data ordering. In the theory of isotonic regression this information is essential and it is expected to increase fitting efficiency. The objective of this work was to use an isotonic regression methodology, as an alternative way of analyzing data of Zn deposition in tibia of male birds of Hubbard lineage. We considered the models of plateau response of polynomial quadratic and linear exponential forms. In addition to these models, we also proposed the fitting of a logarithmic model to the data and the efficiency of the methodology was evaluated by Monte Carlo simulations, considering different scenarios for the parametric values. The isotonization of the data yielded an improvement in all the fitting quality parameters evaluated. Among the models used, the logarithmic presented estimates of the parameters more consistent with the values reported in literature.
Parametric Modelling of Potential Evapotranspiration: A Global Survey
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Aristoteles Tegos
2017-10-01
Full Text Available We present and validate a global parametric model of potential evapotranspiration (PET with two parameters that are estimated through calibration, using as explanatory variables temperature and extraterrestrial radiation. The model is tested over the globe, taking advantage of the Food and Agriculture Organization (FAO CLIMWAT database that provides monthly averaged values of meteorological inputs at 4300 locations worldwide. A preliminary analysis of these data allows for explaining the major drivers of PET over the globe and across seasons. The model calibration against the given Penman-Monteith values was carried out through an automatic optimization procedure. For the evaluation of the model, we present global maps of optimized model parameters and associated performance metrics, and also contrast its performance against the well-known Hargreaves-Samani method. Also, we use interpolated values of the optimized parameters to validate the predictive capacity of our model against monthly meteorological time series, at several stations worldwide. The results are very encouraging, since even with the use of abstract climatic information for model calibration and the use of interpolated parameters as local predictors, the model generally ensures reliable PET estimations. Exceptions are mainly attributed to irregular interactions between temperature and extraterrestrial radiation, as well as because the associated processes are influenced by additional drivers, e.g., relative humidity and wind speed. However, the analysis of the residuals shows that the model is consistent in terms of parameters estimation and model validation. The parameter maps allow for the direct use of the model wherever in the world, providing PET estimates in case of missing data, that can be further improved even with a short term acquisition of meteorological data.
Parametric Hidden Markov Models for Recognition and Synthesis of Movements
DEFF Research Database (Denmark)
Herzog, Dennis; Krüger, Volker; Grest, Daniel
2008-01-01
In humanoid robotics, the recognition and synthesis of parametric movements plays an extraordinary role for robot human interaction. Such a parametric movement is a movement of a particular type (semantic), for example, similar pointing movements performed at different table-top positions. For un...
Assessment of parametric uncertainty for groundwater reactive transport modeling,
Shi, Xiaoqing; Ye, Ming; Curtis, Gary P.; Miller, Geoffery L.; Meyer, Philip D.; Kohler, Matthias; Yabusaki, Steve; Wu, Jichun
2014-01-01
The validity of using Gaussian assumptions for model residuals in uncertainty quantification of a groundwater reactive transport model was evaluated in this study. Least squares regression methods explicitly assume Gaussian residuals, and the assumption leads to Gaussian likelihood functions, model parameters, and model predictions. While the Bayesian methods do not explicitly require the Gaussian assumption, Gaussian residuals are widely used. This paper shows that the residuals of the reactive transport model are non-Gaussian, heteroscedastic, and correlated in time; characterizing them requires using a generalized likelihood function such as the formal generalized likelihood function developed by Schoups and Vrugt (2010). For the surface complexation model considered in this study for simulating uranium reactive transport in groundwater, parametric uncertainty is quantified using the least squares regression methods and Bayesian methods with both Gaussian and formal generalized likelihood functions. While the least squares methods and Bayesian methods with Gaussian likelihood function produce similar Gaussian parameter distributions, the parameter distributions of Bayesian uncertainty quantification using the formal generalized likelihood function are non-Gaussian. In addition, predictive performance of formal generalized likelihood function is superior to that of least squares regression and Bayesian methods with Gaussian likelihood function. The Bayesian uncertainty quantification is conducted using the differential evolution adaptive metropolis (DREAM(zs)) algorithm; as a Markov chain Monte Carlo (MCMC) method, it is a robust tool for quantifying uncertainty in groundwater reactive transport models. For the surface complexation model, the regression-based local sensitivity analysis and Morris- and DREAM(ZS)-based global sensitivity analysis yield almost identical ranking of parameter importance. The uncertainty analysis may help select appropriate likelihood
Validating Timed Models of Deployment Components with Parametric Concurrency
Broch Johnsen, Einar; Owe, Olaf; Schlatte, Rudolf; Tapia Tarifa, Silvia Lizeth
Many software systems today are designed without assuming a fixed underlying architecture, and may be adapted for sequential, multicore, or distributed deployment. Examples of such systems are found in, e.g., software product lines, service-oriented computing, information systems, embedded systems, operating systems, and telephony. Models of such systems need to capture and range over relevant deployment scenarios, so it is interesting to lift aspects of low-level deployment concerns to the abstraction level of the modeling language. This paper proposes an abstract model of deployment components for concurrent objects, extending the Creol modeling language. The deployment components are parametric in the amount of concurrency they provide; i.e., they vary in processing resources. We give a formal semantics of deployment components and characterize equivalence between deployment components which differ in concurrent resources in terms of test suites. Our semantics is executable on Maude, which allows simulations and test suites to be applied to a deployment component with different concurrent resources.
Dai, Junyi; Kerestes, Rebecca; Upton, Daniel J; Busemeyer, Jerome R; Stout, Julie C
2015-01-01
The Iowa Gambling Task (IGT) and the Soochow Gambling Task (SGT) are two experience-based risky decision-making tasks for examining decision-making deficits in clinical populations. Several cognitive models, including the expectancy-valence learning (EVL) model and the prospect valence learning (PVL) model, have been developed to disentangle the motivational, cognitive, and response processes underlying the explicit choices in these tasks. The purpose of the current study was to develop an improved model that can fit empirical data better than the EVL and PVL models and, in addition, produce more consistent parameter estimates across the IGT and SGT. Twenty-six opiate users (mean age 34.23; SD 8.79) and 27 control participants (mean age 35; SD 10.44) completed both tasks. Eighteen cognitive models varying in evaluation, updating, and choice rules were fit to individual data and their performances were compared to that of a statistical baseline model to find a best fitting model. The results showed that the model combining the prospect utility function treating gains and losses separately, the decay-reinforcement updating rule, and the trial-independent choice rule performed the best in both tasks. Furthermore, the winning model produced more consistent individual parameter estimates across the two tasks than any of the other models.
Directory of Open Access Journals (Sweden)
Junyi eDai
2015-03-01
Full Text Available The Iowa Gambling Task (IGT and the Soochow Gambling Task (SGT are two experience-based risky decision-making tasks for examining decision-making deficits in clinical populations. Several cognitive models, including the expectancy-valence learning model (EVL and the prospect valence learning model (PVL, have been developed to disentangle the motivational, cognitive, and response processes underlying the explicit choices in these tasks. The purpose of the current study was to develop an improved model that can fit empirical data better than the EVL and PVL models and, in addition, produce more consistent parameter estimates across the IGT and SGT. Twenty-six opiate users (mean age 34.23; SD 8.79 and 27 control participants (mean age 35; SD 10.44 completed both tasks. Eighteen cognitive models varying in evaluation, updating, and choice rules were fit to individual data and their performances were compared to that of a statistical baseline model to find a best fitting model. The results showed that the model combining the prospect utility function treating gains and losses separately, the decay-reinforcement updating rule, and the trial-independent choice rule performed the best in both tasks. Furthermore, the winning model produced more consistent individual parameter estimates across the two tasks than any of the other models.
A Bayesian non-parametric Potts model with application to pre-surgical FMRI data.
Johnson, Timothy D; Liu, Zhuqing; Bartsch, Andreas J; Nichols, Thomas E
2013-08-01
The Potts model has enjoyed much success as a prior model for image segmentation. Given the individual classes in the model, the data are typically modeled as Gaussian random variates or as random variates from some other parametric distribution. In this article, we present a non-parametric Potts model and apply it to a functional magnetic resonance imaging study for the pre-surgical assessment of peritumoral brain activation. In our model, we assume that the Z-score image from a patient can be segmented into activated, deactivated, and null classes, or states. Conditional on the class, or state, the Z-scores are assumed to come from some generic distribution which we model non-parametrically using a mixture of Dirichlet process priors within the Bayesian framework. The posterior distribution of the model parameters is estimated with a Markov chain Monte Carlo algorithm, and Bayesian decision theory is used to make the final classifications. Our Potts prior model includes two parameters, the standard spatial regularization parameter and a parameter that can be interpreted as the a priori probability that each voxel belongs to the null, or background state, conditional on the lack of spatial regularization. We assume that both of these parameters are unknown, and jointly estimate them along with other model parameters. We show through simulation studies that our model performs on par, in terms of posterior expected loss, with parametric Potts models when the parametric model is correctly specified and outperforms parametric models when the parametric model in misspecified.
Parametric Linear Hybrid Automata for Complex Environmental Systems Modeling
Directory of Open Access Journals (Sweden)
Samar Hayat Khan Tareen
2015-07-01
Full Text Available Environmental systems, whether they be weather patterns or predator-prey relationships, are dependent on a number of different variables, each directly or indirectly affecting the system at large. Since not all of these factors are known, these systems take on non-linear dynamics, making it difficult to accurately predict meaningful behavioral trends far into the future. However, such dynamics do not warrant complete ignorance of different efforts to understand and model close approximations of these systems. Towards this end, we have applied a logical modeling approach to model and analyze the behavioral trends and systematic trajectories that these systems exhibit without delving into their quantification. This approach, formalized by René Thomas for discrete logical modeling of Biological Regulatory Networks (BRNs and further extended in our previous studies as parametric biological linear hybrid automata (Bio-LHA, has been previously employed for the analyses of different molecular regulatory interactions occurring across various cells and microbial species. As relationships between different interacting components of a system can be simplified as positive or negative influences, we can employ the Bio-LHA framework to represent different components of the environmental system as positive or negative feedbacks. In the present study, we highlight the benefits of hybrid (discrete/continuous modeling which lead to refinements among the fore-casted behaviors in order to find out which ones are actually possible. We have taken two case studies: an interaction of three microbial species in a freshwater pond, and a more complex atmospheric system, to show the applications of the Bio-LHA methodology for the timed hybrid modeling of environmental systems. Results show that the approach using the Bio-LHA is a viable method for behavioral modeling of complex environmental systems by finding timing constraints while keeping the complexity of the model
A local non-parametric model for trade sign inference
Blazejewski, Adam; Coggins, Richard
2005-03-01
We investigate a regularity in market order submission strategies for 12 stocks with large market capitalization on the Australian Stock Exchange. The regularity is evidenced by a predictable relationship between the trade sign (trade initiator), size of the trade, and the contents of the limit order book before the trade. We demonstrate this predictability by developing an empirical inference model to classify trades into buyer-initiated and seller-initiated. The model employs a local non-parametric method, k-nearest neighbor, which in the past was used successfully for chaotic time series prediction. The k-nearest neighbor with three predictor variables achieves an average out-of-sample classification accuracy of 71.40%, compared to 63.32% for the linear logistic regression with seven predictor variables. The result suggests that a non-linear approach may produce a more parsimonious trade sign inference model with a higher out-of-sample classification accuracy. Furthermore, for most of our stocks the observed regularity in market order submissions seems to have a memory of at least 30 trading days.
Multivariable parametric cost model for space and ground telescopes
Stahl, H. Philip; Henrichs, Todd
2016-09-01
Parametric cost models can be used by designers and project managers to perform relative cost comparisons between major architectural cost drivers and allow high-level design trades; enable cost-benefit analysis for technology development investment; and, provide a basis for estimating total project cost between related concepts. This paper hypothesizes a single model, based on published models and engineering intuition, for both ground and space telescopes: OTA Cost (X) D (1.75 +/- 0.05) λ (-0.5 +/- 0.25) T-0.25 e (-0.04) Y Specific findings include: space telescopes cost 50X to 100X more ground telescopes; diameter is the most important CER; cost is reduced by approximately 50% every 20 years (presumably because of technology advance and process improvements); and, for space telescopes, cost associated with wavelength performance is balanced by cost associated with operating temperature. Finally, duplication only reduces cost for the manufacture of identical systems (i.e. multiple aperture sparse arrays or interferometers). And, while duplication does reduce the cost of manufacturing the mirrors of segmented primary mirror, this cost savings does not appear to manifest itself in the final primary mirror assembly (presumably because the structure for a segmented mirror is more complicated than for a monolithic mirror).
Design and evaluation of a parametric model for cardiac sounds.
Ibarra-Hernández, Roilhi F; Alonso-Arévalo, Miguel A; Cruz-Gutiérrez, Alejandro; Licona-Chávez, Ana L; Villarreal-Reyes, Salvador
2017-10-01
Heart sound analysis plays an important role in the auscultative diagnosis process to detect the presence of cardiovascular diseases. In this paper we propose a novel parametric heart sound model that accurately represents normal and pathological cardiac audio signals, also known as phonocardiograms (PCG). The proposed model considers that the PCG signal is formed by the sum of two parts: one of them is deterministic and the other one is stochastic. The first part contains most of the acoustic energy. This part is modeled by the Matching Pursuit (MP) algorithm, which performs an analysis-synthesis procedure to represent the PCG signal as a linear combination of elementary waveforms. The second part, also called residual, is obtained after subtracting the deterministic signal from the original heart sound recording and can be accurately represented as an autoregressive process using the Linear Predictive Coding (LPC) technique. We evaluate the proposed heart sound model by performing subjective and objective tests using signals corresponding to different pathological cardiac sounds. The results of the objective evaluation show an average Percentage of Root-Mean-Square Difference of approximately 5% between the original heart sound and the reconstructed signal. For the subjective test we conducted a formal methodology for perceptual evaluation of audio quality with the assistance of medical experts. Statistical results of the subjective evaluation show that our model provides a highly accurate approximation of real heart sound signals. We are not aware of any previous heart sound model rigorously evaluated as our proposal. Copyright © 2017 Elsevier Ltd. All rights reserved.
Rabin, Sam S.; Ward, Daniel S.; Malyshev, Sergey L.; Magi, Brian I.; Shevliakova, Elena; Pacala, Stephen W.
2018-03-01
This study describes and evaluates the Fire Including Natural & Agricultural Lands model (FINAL) which, for the first time, explicitly simulates cropland and pasture management fires separately from non-agricultural fires. The non-agricultural fire module uses empirical relationships to simulate burned area in a quasi-mechanistic framework, similar to past fire modeling efforts, but with a novel optimization method that improves the fidelity of simulated fire patterns to new observational estimates of non-agricultural burning. The agricultural fire components are forced with estimates of cropland and pasture fire seasonality and frequency derived from observational land cover and satellite fire datasets. FINAL accurately simulates the amount, distribution, and seasonal timing of burned cropland and pasture over 2001-2009 (global totals: 0.434×106 and 2.02×106 km2 yr-1 modeled, 0.454×106 and 2.04×106 km2 yr-1 observed), but carbon emissions for cropland and pasture fire are overestimated (global totals: 0.295 and 0.706 PgC yr-1 modeled, 0.194 and 0.538 PgC yr-1 observed). The non-agricultural fire module underestimates global burned area (1.91×106 km2 yr-1 modeled, 2.44×106 km2 yr-1 observed) and carbon emissions (1.14 PgC yr-1 modeled, 1.84 PgC yr-1 observed). The spatial pattern of total burned area and carbon emissions is generally well reproduced across much of sub-Saharan Africa, Brazil, Central Asia, and Australia, whereas the boreal zone sees underestimates. FINAL represents an important step in the development of global fire models, and offers a strategy for fire models to consider human-driven fire regimes on cultivated lands. At the regional scale, simulations would benefit from refinements in the parameterizations and improved optimization datasets. We include an in-depth discussion of the lessons learned from using the Levenberg-Marquardt algorithm in an interactive optimization for a dynamic global vegetation model.
Parametrizing growth in dark energy and modified gravity models
Resco, Miguel Aparicio; Maroto, Antonio L.
2018-02-01
It is well known that an extremely accurate parametrization of the growth function of matter density perturbations in Λ CDM cosmology, with errors below 0.25%, is given by f (a )=Ωmγ(a ) with γ ≃0.55 . In this work, we show that a simple modification of this expression also provides a good description of growth in modified gravity theories. We consider the model-independent approach to modified gravity in terms of an effective Newton constant written as μ (a ,k )=Geff/G and show that f (a )=β (a )Ωmγ(a ) provides fits to the numerical solutions with similar accuracy to that of Λ CDM . In the time-independent case with μ =μ (k ), simple analytic expressions for β (μ ) and γ (μ ) are presented. In the time-dependent (but scale-independent) case μ =μ (a ), we show that β (a ) has the same time dependence as μ (a ). As an example, explicit formulas are provided in the Dvali-Gabadadze-Porrati (DGP) model. In the general case, for theories with μ (a ,k ), we obtain a perturbative expansion for β (μ ) around the general relativity case μ =1 which, for f (R ) theories, reaches an accuracy below 1%. Finally, as an example we apply the obtained fitting functions in order to forecast the precision with which future galaxy surveys will be able to measure the μ parameter.
de Villiers, Marelize; Kriticos, Darren J; Veldtman, Ruan
2017-01-01
The European wasp, Vespula germanica (Fabricius) (Hymenoptera: Vespidae), is of Palaearctic origin, being native to Europe, northern Africa and Asia, and introduced into North America, Chile, Argentina, Iceland, Ascension Island, South Africa, Australia and New Zealand. Due to its polyphagous nature and scavenging behaviour, V. germanica threatens agriculture and silviculture, and negatively affects biodiversity, while its aggressive nature and venomous sting pose a health risk to humans. In areas with warmer winters and longer summers, queens and workers can survive the winter months, leading to the build-up of large nests during the following season; thereby increasing the risk posed by this species. To prevent or prepare for such unwanted impacts it is important to know where the wasp may be able to establish, either through natural spread or through introduction as a result of human transport. Distribution data from Argentina and Australia, and seasonal phenology data from Argentina were used to determine the potential distribution of V. germanica using CLIMEX modelling. In contrast to previous models, the influence of irrigation on its distribution was also investigated. Under a natural rainfall scenario, the model showed similarities to previous models. When irrigation is applied, dry stress is alleviated, leading to larger areas modelled climatically suitable compared with previous models, which provided a better fit with the actual distribution of the species. The main areas at risk of invasion by V. germanica include western USA, Mexico, small areas in Central America and in the north-western region of South America, eastern Brazil, western Russia, north-western China, Japan, the Mediterranean coastal regions of North Africa, and parts of southern and eastern Africa.
Directory of Open Access Journals (Sweden)
Marelize de Villiers
Full Text Available The European wasp, Vespula germanica (Fabricius (Hymenoptera: Vespidae, is of Palaearctic origin, being native to Europe, northern Africa and Asia, and introduced into North America, Chile, Argentina, Iceland, Ascension Island, South Africa, Australia and New Zealand. Due to its polyphagous nature and scavenging behaviour, V. germanica threatens agriculture and silviculture, and negatively affects biodiversity, while its aggressive nature and venomous sting pose a health risk to humans. In areas with warmer winters and longer summers, queens and workers can survive the winter months, leading to the build-up of large nests during the following season; thereby increasing the risk posed by this species. To prevent or prepare for such unwanted impacts it is important to know where the wasp may be able to establish, either through natural spread or through introduction as a result of human transport. Distribution data from Argentina and Australia, and seasonal phenology data from Argentina were used to determine the potential distribution of V. germanica using CLIMEX modelling. In contrast to previous models, the influence of irrigation on its distribution was also investigated. Under a natural rainfall scenario, the model showed similarities to previous models. When irrigation is applied, dry stress is alleviated, leading to larger areas modelled climatically suitable compared with previous models, which provided a better fit with the actual distribution of the species. The main areas at risk of invasion by V. germanica include western USA, Mexico, small areas in Central America and in the north-western region of South America, eastern Brazil, western Russia, north-western China, Japan, the Mediterranean coastal regions of North Africa, and parts of southern and eastern Africa.
Wörz, Stefan; Rohr, Karl
2006-02-01
We introduce a new approach for the localization of 3D anatomical point landmarks. This approach is based on 3D parametric intensity models which are directly fitted to 3D images. To efficiently model tip-like, saddle-like, and sphere-like anatomical structures we introduce analytic intensity models based on the Gaussian error function in conjunction with 3D rigid transformations as well as deformations. To select a suitable size of the region-of-interest (ROI) where model fitting is performed, we also propose a new scheme for automatic selection of an optimal 3D ROI size based on the dominant gradient direction. In addition, to achieve a higher level of automation we present an algorithm for automatic initialization of the model parameters. Our approach has been successfully applied to accurately localize anatomical landmarks in 3D synthetic data as well as 3D MR and 3D CT image data. We have also compared the experimental results with the results of a previously proposed 3D differential approach. It turns out that the new approach significantly improves the localization accuracy.
Modeling neuron-glia interactions: from parametric model to neuromorphic hardware.
Ghaderi, Viviane S; Allam, Sushmita L; Ambert, N; Bouteiller, J-M C; Choma, J; Berger, T W
2011-01-01
Recent experimental evidence suggests that glial cells are more than just supporting cells to neurons - they play an active role in signal transmission in the brain. We herein propose to investigate the importance of these mechanisms and model neuron-glia interactions at synapses using three approaches: A parametric model that takes into account the underlying mechanisms of the physiological system, a non-parametric model that extracts its input-output properties, and an ultra-low power, fast processing, neuromorphic hardware model. We use the EONS (Elementary Objects of the Nervous System) platform, a highly elaborate synaptic modeling platform to investigate the influence of astrocytic glutamate transporters on postsynaptic responses in the detailed micro-environment of a tri-partite synapse. The simulation results obtained using EONS are then used to build a non-parametric model that captures the essential features of glutamate dynamics. The structure of the non-parametric model we use is specifically designed for efficient hardware implementation using ultra-low power subthreshold CMOS building blocks. The utilization of the approach described allows us to build large-scale models of neuron/glial interaction and consequently provide useful insights on glial modulation during normal and pathological neural function.
Parametrization of model consistant expectations in the Sidrauski model
Hoogenveen, Victoria; Sterken, Elmer
1996-01-01
This paper discusses a cubic parametrisation of model consistent expectations in a nonlinear dynamic monetary growth model. The so-called Sidrauski model links money, inflation and consumption growth. Iterative least squares combined with simulation is used to address the alleged impact of inflation
Teeling, M.V.M.T.; Turrin, M.; de Ruiter, P.; Turrin, Michela; Peters, Brady; O'Brien, William; Stouffs, Rudi; Dogan, Timur
2017-01-01
This paper presents a parametric approach to an integrated and performance-oriented design, from the conceptual design phase towards materialization. The novelty occurs in the use of parametric models as a way of integrating multidisciplinary design constraints, from daylight optimization to the
Variance in parametric images: direct estimation from parametric projections
International Nuclear Information System (INIS)
Maguire, R.P.; Leenders, K.L.; Spyrou, N.M.
2000-01-01
Recent work has shown that it is possible to apply linear kinetic models to dynamic projection data in PET in order to calculate parameter projections. These can subsequently be back-projected to form parametric images - maps of parameters of physiological interest. Critical to the application of these maps, to test for significant changes between normal and pathophysiology, is an assessment of the statistical uncertainty. In this context, parametric images also include simple integral images from, e.g., [O-15]-water used to calculate statistical parametric maps (SPMs). This paper revisits the concept of parameter projections and presents a more general formulation of the parameter projection derivation as well as a method to estimate parameter variance in projection space, showing which analysis methods (models) can be used. Using simulated pharmacokinetic image data we show that a method based on an analysis in projection space inherently calculates the mathematically rigorous pixel variance. This results in an estimation which is as accurate as either estimating variance in image space during model fitting, or estimation by comparison across sets of parametric images - as might be done between individuals in a group pharmacokinetic PET study. The method based on projections has, however, a higher computational efficiency, and is also shown to be more precise, as reflected in smooth variance distribution images when compared to the other methods. (author)
Reinforcement Toolbox, a Parametric Reinforcement Modelling Tool for Curved Surface Structures
Lauppe, J.; Rolvink, A.; Coenders, J.L.
2013-01-01
This paper presents a computational strategy and parametric modelling toolbox which aim at enhancing the design- and production process of reinforcement in freeform curved surface structures. The computational strategy encompasses the necessary steps of raising an architectural curved surface model
Validation of a Parametric Approach for 3d Fortification Modelling: Application to Scale Models
Jacquot, K.; Chevrier, C.; Halin, G.
2013-02-01
Parametric modelling approach applied to cultural heritage virtual representation is a field of research explored for years since it can address many limitations of digitising tools. For example, essential historical sources for fortification virtual reconstructions like plans-reliefs have several shortcomings when they are scanned. To overcome those problems, knowledge based-modelling can be used: knowledge models based on the analysis of theoretical literature of a specific domain such as bastioned fortification treatises can be the cornerstone of the creation of a parametric library of fortification components. Implemented in Grasshopper, these components are manually adjusted on the data available (i.e. 3D surveys of plans-reliefs or scanned maps). Most of the fortification area is now modelled and the question of accuracy assessment is raised. A specific method is used to evaluate the accuracy of the parametric components. The results of the assessment process will allow us to validate the parametric approach. The automation of the adjustment process can finally be planned. The virtual model of fortification is part of a larger project aimed at valorising and diffusing a very unique cultural heritage item: the collection of plans-reliefs. As such, knowledge models are precious assets when automation and semantic enhancements will be considered.
A non-parametric hidden Markov model for climate state identification
Directory of Open Access Journals (Sweden)
M. F. Lambert
2003-01-01
Full Text Available Hidden Markov models (HMMs can allow for the varying wet and dry cycles in the climate without the need to simulate supplementary climate variables. The fitting of a parametric HMM relies upon assumptions for the state conditional distributions. It is shown that inappropriate assumptions about state conditional distributions can lead to biased estimates of state transition probabilities. An alternative non-parametric model with a hidden state structure that overcomes this problem is described. It is shown that a two-state non-parametric model produces accurate estimates of both transition probabilities and the state conditional distributions. The non-parametric model can be used directly or as a technique for identifying appropriate state conditional distributions to apply when fitting a parametric HMM. The non-parametric model is fitted to data from ten rainfall stations and four streamflow gauging stations at varying distances inland from the Pacific coast of Australia. Evidence for hydrological persistence, though not mathematical persistence, was identified in both rainfall and streamflow records, with the latter showing hidden states with longer sojourn times. Persistence appears to increase with distance from the coast. Keywords: Hidden Markov models, non-parametric, two-state model, climate states, persistence, probability distributions
Fielding, Louis C; Alamin, Todd F; Voronov, Leonard I; Carandang, Gerard; Havey, Robert M; Patwardhan, Avinash G
2013-12-01
Development of a dynamic stabilization system often involves costly and time-consuming design iterations, testing and computational modeling. The aims of this study were (1) develop a simple parametric model of lumbar flexion instability and use this model to identify the appropriate stiffness of a flexion restricting stabilization system (FRSS), and (2) in a cadaveric experiment, validate the predictive value of the parametric model. Literature was surveyed for typical parameters of intact and destabilized spines: stiffness in the high flexibility zone (HFZ) and high stiffness zone, and size of the HFZ. These values were used to construct a bilinear parametric model of flexion kinematics of intact and destabilized lumbar spines. FRSS implantation was modeled by iteratively superimposing constant flexion stiffnesses onto the parametric model. Five cadaveric lumbar spines were tested intact; after L4-L5 destabilization (nucleotomy, midline decompression); and after FRSS implantation. Specimens were loaded in flexion/extension (8 Nm/6 Nm) with 400 N follower load to characterize kinematics for comparison with the parametric model. To accomplish the goal of reducing ROM to intact levels and increasing stiffness to approximately 50 % greater than intact levels, flexion stiffness contributed by the FRSS was determined to be 0.5 Nm/deg using the parametric model. In biomechanical testing, the FRSS restored ROM of the destabilized segment from 146 ± 13 to 105 ± 21 % of intact, and stiffness in the HFZ from 41 ± 7 to 135 ± 38 % of intact. Testing demonstrated excellent predictive value of the parametric model, and that the FRSS attained the desired biomechanical performance developed with the model. A simple parametric model may allow efficient optimization of kinematic design parameters.
Integrating acoustic analysis in the architectural design process using parametric modelling
DEFF Research Database (Denmark)
Peters, Brady
2011-01-01
This paper discusses how parametric modeling techniques can be used to provide architectural designers with a better understanding of the acoustic performance of their designs and provide acoustic engineers with models that can be analyzed using computational acoustic analysis software. Architects...... provide a method by which architects and engineers can work together more efficiently and communicate better. This research is illustrated through the design of an architectural project, a new school in Copenhagen, Denmark by JJW Architects, where parametric modeling techniques have been used in different...... are increasingly using parametric modeling techniques in their design processes to allow the exploration of large numbers of design options using multiple criteria. Parametric modeling software can be performance-driven and sound has the potential to become one of these performance-driven dimensions. This can...
Nonlinear Container Ship Model for the Study of Parametric Roll Resonance
DEFF Research Database (Denmark)
Holden, Christian; Galeazzi, Roberto; Rodríguez, Claudio
2007-01-01
Parametric roll is a critical phenomenon for ships, whose onset may cause roll oscillations up to 40, leading to very dangerous situations and possibly capsizing. Container ships have been shown to be particularly prone to parametric roll resonance when they are sailing in moderate to heavy head...... seas. A Matlab/Simulinkr parametric roll benchmark model for a large container ship has been implemented and validated against a wide set of experimental data. The model is a part of a Matlab/Simulink Toolbox (MSS, 2007). The benchmark implements a 3rd-order nonlinear model where the dynamics of roll...... is strongly coupled with the heave and pitch dynamics. The implemented model has shown good accuracy in predicting the container ship motions, both in the vertical plane and in the transversal one. Parametric roll has been reproduced for all the data sets in which it happened, and the model provides realistic...
Numerical Modelling of Spontaneous Emission in Optical Parametric Amplifiers
DEFF Research Database (Denmark)
Friis, Søren Michael Mørk; Andersen, Ulrik Lund; Rottwitt, Karsten
2013-01-01
Fiber optical parametric processes offer a wide range of applications including phase sensitive as well as phase insensitive amplification, wavelength conversion and signal regeneration. One of the difficult challenges is any of these applications is to predict their associated noise performance...
Kouramas, K.I.
2011-08-01
This work presents a new algorithm for solving the explicit/multi- parametric model predictive control (or mp-MPC) problem for linear, time-invariant discrete-time systems, based on dynamic programming and multi-parametric programming techniques. The algorithm features two key steps: (i) a dynamic programming step, in which the mp-MPC problem is decomposed into a set of smaller subproblems in which only the current control, state variables, and constraints are considered, and (ii) a multi-parametric programming step, in which each subproblem is solved as a convex multi-parametric programming problem, to derive the control variables as an explicit function of the states. The key feature of the proposed method is that it overcomes potential limitations of previous methods for solving multi-parametric programming problems with dynamic programming, such as the need for global optimization for each subproblem of the dynamic programming step. © 2011 Elsevier Ltd. All rights reserved.
A parametric model for the global thermodynamic behavior of fluids in the critical region
International Nuclear Information System (INIS)
Luettmer-Strathmann, J.; Tang, S.; Sengers, J.V.
1992-01-01
The asymptotic thermodynamic behavior of fluids near the critical point is described by scaling laws with universal scaling functions that can be represented by parametric equations. In this paper, we derive a more general parametric model that incorporates the crossover from singular thermodynamic behavior near the critical point to regular classical thermodynamic behavior far away from the critical point. Using ethane as an example, we show that such a parametric crossover model yields an accurate representation of the thermodynamic properties of fluids in a large region around the critical point
Identification of the 1PL Model with Guessing Parameter: Parametric and Semi-Parametric Results
San Martin, Ernesto; Rolin, Jean-Marie; Castro, Luis M.
2013-01-01
In this paper, we study the identification of a particular case of the 3PL model, namely when the discrimination parameters are all constant and equal to 1. We term this model, 1PL-G model. The identification analysis is performed under three different specifications. The first specification considers the abilities as unknown parameters. It is…
Fast and Sequence-Adaptive Whole-Brain Segmentation Using Parametric Bayesian Modeling
DEFF Research Database (Denmark)
Puonti, Oula; Iglesias, Juan Eugenio; Van Leemput, Koen
2016-01-01
the performance of a segmentation algorithm designed to meet these requirements, building upon generative parametric models previously used in tissue classification. The method is tested on four different datasets acquired with different scanners, field strengths and pulse sequences, demonstrating comparable...
APT cost scaling: Preliminary indications from a Parametric Costing Model (PCM)
International Nuclear Information System (INIS)
Krakowski, R.A.
1995-01-01
A Parametric Costing Model has been created and evaluate as a first step in quantitatively understanding important design options for the Accelerator Production of Tritium (APT) concept. This model couples key economic and technical elements of APT in a two-parameter search of beam energy and beam power that minimizes costs within a range of operating constraints. The costing and engineering depth of the Parametric Costing Model is minimal at the present open-quotes entry levelclose quotes, and is intended only to demonstrate a potential for a more-detailed, cost-based integrating design tool. After describing the present basis of the Parametric Costing Model and giving an example of a single parametric scaling run derived therefrom, the impacts of choices related to resistive versus superconducting accelerator structures and cost of electricity versus plant availability (open-quotes load curveclose quotes) are reported. Areas of further development and application are suggested
Parametric Modelling of As-Built Beam Framed Structure in Bim Environment
Yang, X.; Koehl, M.; Grussenmeyer, P.
2017-02-01
A complete documentation and conservation of a historic timber roof requires the integration of geometry modelling, attributional and dynamic information management and results of structural analysis. Recently developed as-built Building Information Modelling (BIM) technique has the potential to provide a uniform platform, which provides possibility to integrate the traditional geometry modelling, parametric elements management and structural analysis together. The main objective of the project presented in this paper is to develop a parametric modelling tool for a timber roof structure whose elements are leaning and crossing beam frame. Since Autodesk Revit, as the typical BIM software, provides the platform for parametric modelling and information management, an API plugin, able to automatically create the parametric beam elements and link them together with strict relationship, was developed. The plugin under development is introduced in the paper, which can obtain the parametric beam model via Autodesk Revit API from total station points and terrestrial laser scanning data. The results show the potential of automatizing the parametric modelling by interactive API development in BIM environment. It also integrates the separate data processing and different platforms into the uniform Revit software.
Parametric model to estimate containment loads following an ex-vessel steam spike
International Nuclear Information System (INIS)
Lopez, R.; Hernandez, J.; Huerta, A.
1998-01-01
This paper describes the use of a relatively simple parametric model to estimate containment loads following an ex-vessel steam spike. The study was motivated because several PSAs have identified containment loads accompanying reactor vessel failures as a major contributor to early containment failure. The paper includes a detailed description of the simple but physically sound parametric model which was adopted to estimate containment loads following a steam spike into the reactor cavity. (author)
Parametric Covariance Model for Horizon-Based Optical Navigation
Hikes, Jacob; Liounis, Andrew J.; Christian, John A.
2016-01-01
This Note presents an entirely parametric version of the covariance for horizon-based optical navigation measurements. The covariance can be written as a function of only the spacecraft position, two sensor design parameters, the illumination direction, the size of the observed planet, the size of the lit arc to be used, and the total number of observed horizon points. As a result, one may now more clearly understand the sensitivity of horizon-based optical navigation performance as a function of these key design parameters, which is insight that was obscured in previous (and nonparametric) versions of the covariance. Finally, the new parametric covariance is shown to agree with both the nonparametric analytic covariance and results from a Monte Carlo analysis.
Parametric level correlations in random-matrix models
International Nuclear Information System (INIS)
Weidenmueller, Hans A
2005-01-01
We show that parametric level correlations in random-matrix theories are closely related to a breaking of the symmetry between the advanced and the retarded Green functions. The form of the parametric level correlation function is the same as for the disordered case considered earlier by Simons and Altshuler and is given by the graded trace of the commutator of the saddle-point solution with the particular matrix that describes the symmetry breaking in the actual case of interest. The strength factor differs from the case of disorder. It is determined solely by the Goldstone mode. It is essentially given by the number of levels that are strongly mixed as the external parameter changes. The factor can easily be estimated in applications
Parametric amplification of metric fluctuations during reheating in two field models
International Nuclear Information System (INIS)
Finelli, F.; Brandenberger, R.
2000-01-01
We study the parametric amplification of super-Hubble-scale scalar metric fluctuations at the end of inflation in some specific two-field models of inflation, a class of which is motivated by hybrid inflation. We demonstrate that there can indeed be a large growth of fluctuations due to parametric resonance and that this effect is not taken into account by the conventional theory of isocurvature perturbations. Scalar field interactions play a crucial role in this analysis. We discuss the conditions under which there can be nontrivial parametric resonance effects on large scales
Augmenting Parametric Optimal Ascent Trajectory Modeling with Graph Theory
Dees, Patrick D.; Zwack, Matthew R.; Edwards, Stephen; Steffens, Michael
2016-01-01
into Conceptual and Pre-Conceptual design, knowledge of the effects originating from changes to the vehicle must be calculated. In order to do this, a model capable of quantitatively describing any vehicle within the entire design space under consideration must be constructed. This model must be based upon analysis of acceptable fidelity, which in this work comes from POST. Design space interrogation can be achieved with surrogate modeling, a parametric, polynomial equation representing a tool. A surrogate model must be informed by data from the tool with enough points to represent the solution space for the chosen number of variables with an acceptable level of error. Therefore, Design Of Experiments (DOE) is used to select points within the design space to maximize information gained on the design space while minimizing number of data points required. To represent a design space with a non-trivial number of variable parameters the number of points required still represent an amount of work which would take an inordinate amount of time via the current paradigm of manual analysis, and so an automated method was developed. The best practices of expert trajectory analysts working within NASA Marshall's Advanced Concepts Office (ACO) were implemented within a tool called multiPOST. These practices include how to use the output data from a previous run of POST to inform the next, determining whether a trajectory solution is feasible from a real-world perspective, and how to handle program execution errors. The tool was then augmented with multiprocessing capability to enable analysis on multiple trajectories simultaneously, allowing throughput to scale with available computational resources. In this update to the previous work the authors discuss issues with the method and solutions.
Teeling, M.V.M.T.; Turrin, M.; de Ruiter, P.; Turrin, Michela; Peters, Brady; O'Brien, William; Stouffs, Rudi; Dogan, Timur
2017-01-01
This paper presents a parametric approach to an integrated and performance-oriented design, from the conceptual design phase towards materialization. The novelty occurs in the use of parametric models as a way of integrating multidisciplinary design constraints, from daylight optimization to the additive manufacturing process. The work focuses on the case of a customized sun-shading system that tailors daylighting effects for a fully glazed façade of the alleged PULSE building.The overall wor...
Current models for the acute toxicity of cationic metals to fish focus on the binding of free metal ions to the gill surface. This binding, and the consequent metal toxicity, can be reduced by metal-complexing ligands...
Parametric Modeling in the CAE Process: Creating a Family of Models
Brown, Christopher J.
2011-01-01
This Presentation meant as an example - Give ideas of approaches to use - The significant benefit of PARAMETRIC geometry based modeling The importance of planning before you build Showcase some NX capabilities - Mesh Controls - Associativity - Divide Face - Offset Surface Reminder - This only had to be done once! - Can be used for any cabinet in that "family" Saves a lot of time if pre-planned Allows re-use in the future
A parametric model order reduction technique for poroelastic finite element models.
Lappano, Ettore; Polanz, Markus; Desmet, Wim; Mundo, Domenico
2017-10-01
This research presents a parametric model order reduction approach for vibro-acoustic problems in the frequency domain of systems containing poroelastic materials (PEM). The method is applied to the Finite Element (FE) discretization of the weak u-p integral formulation based on the Biot-Allard theory and makes use of reduced basis (RB) methods typically employed for parametric problems. The parametric reduction is obtained rewriting the Biot-Allard FE equations for poroelastic materials using an affine representation of the frequency (therefore allowing for RB methods) and projecting the frequency-dependent PEM system on a global reduced order basis generated with the proper orthogonal decomposition instead of standard modal approaches. This has proven to be better suited to describe the nonlinear frequency dependence and the strong coupling introduced by damping. The methodology presented is tested on two three-dimensional systems: in the first experiment, the surface impedance of a PEM layer sample is calculated and compared with results of the literature; in the second, the reduced order model of a multilayer system coupled to an air cavity is assessed and the results are compared to those of the reference FE model.
International Nuclear Information System (INIS)
Gorecki, Paul K.
2013-01-01
The all-island wholesale electricity market, SEM, has to comply with the Target Model by 2016. SEM has worked well for consumers through mitigating market power, facilitating entry and ensuring adequate generation capacity, problems that will persist. But the SEM is a mandatory pool with central dispatch, the Target Model is a self dispatch with bilateral contracts. Minimal change to the SEM in complying with the Target Model is preferable to reinvention of SEM. The latter option might be appropriate when the EU internal electricity market is complete and the all-island market has sufficient interconnection to participate fully in that market. - Highlights: ► The Single Electricity Market (SEM) has worked well for consumers in Ireland. ► The SEM has to conform to the Target Model (TM) by 2016. ► The SEM is mandatory pool/central dispatch; the TM is bilateral contracts/self dispatch. ► Ensuring compliance with TM is best achieved through minimal change to SEM. ► Far reaching change is more appropriate once SEM is fully integrated in the EU electricity market.
Pradip Saud; Thomas B. Lynch; Anup K. C.; James M. Guldin
2016-01-01
The inclusion of quadratic mean diameter (QMD) and relative spacing index (RSI) substantially improved the predictive capacity of heightâdiameter at breast height (d.b.h.) and crown ratio models (CR), respectively. Data were obtained from 208 permanent plots established in western Arkansas and eastern Oklahoma during 1985â1987 and remeasured for the sixth time (2012â...
Simulation of parametric model towards the fixed covariate of right censored lung cancer data
Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Ridwan Olaniran, Oyebayo; Enera Amran, Syahila
2017-09-01
In this study, simulation procedure was applied to measure the fixed covariate of right censored data by using parametric survival model. The scale and shape parameter were modified to differentiate the analysis of parametric regression survival model. Statistically, the biases, mean biases and the coverage probability were used in this analysis. Consequently, different sample sizes were employed to distinguish the impact of parametric regression model towards right censored data with 50, 100, 150 and 200 number of sample. R-statistical software was utilised to develop the coding simulation with right censored data. Besides, the final model of right censored simulation was compared with the right censored lung cancer data in Malaysia. It was found that different values of shape and scale parameter with different sample size, help to improve the simulation strategy for right censored data and Weibull regression survival model is suitable fit towards the simulation of survival of lung cancer patients data in Malaysia.
Koral, Kenneth F.; Avram, Anca M.; Kaminski, Mark S.; Dewaraja, Yuni K.
2012-01-01
Abstract Background For individualized treatment planning in radioimmunotherapy (RIT), correlations must be established between tracer-predicted and therapy-delivered absorbed doses. The focus of this work was to investigate this correlation for tumors. Methods The study analyzed 57 tumors in 19 follicular lymphoma patients treated with I-131 tositumomab and imaged with SPECT/CT multiple times after tracer and therapy administrations. Instead of the typical least-squares fit to a single tumor's measured time-activity data, estimation was accomplished via a biexponential mixed model in which the curves from multiple subjects were jointly estimated. The tumor-absorbed dose estimates were determined by patient-specific Monte Carlo calculation. Results The mixed model gave realistic tumor time-activity fits that showed the expected uptake and clearance phases even with noisy data or missing time points. Correlation between tracer and therapy tumor-residence times (r=0.98; ptracer-predicted and therapy-delivered mean tumor-absorbed doses (r=0.86; ptracer study for tumor dosimetry-based treatment planning in RIT. PMID:22947086
Single-arm phase II trial design under parametric cure models.
Wu, Jianrong
2015-01-01
The current practice of designing single-arm phase II survival trials is limited under the exponential model. Trial design under the exponential model may not be appropriate when a portion of patients are cured. There is no literature available for designing single-arm phase II trials under the parametric cure model. In this paper, a test statistic is proposed, and a sample size formula is derived for designing single-arm phase II trials under a class of parametric cure models. Extensive simulations showed that the proposed test and sample size formula perform very well under different scenarios. Copyright © 2015 John Wiley & Sons, Ltd.
Energy Technology Data Exchange (ETDEWEB)
Baillet, S. (Sylvain); Mosher, J. C. (John C.); Jerbi, K. (Karim); Leahy, R. M. (Richard M.)
2001-01-01
Reliable estimation of the local spatial extent of neural activity is a key to the quantitative analysis of MEG sources across subjects and conditions. In association with an understanding of the temporal dynamics among multiple areas, this would represent a major advance in electrophysiological source imaging. Parametric current dipole approaches to MEG (and EEG) source localization can rapidly generate a physical model of neural current generators using a limited number of parameters. However, physiological interpretation of these models is often difficult, especially in terms of the spatial extent of the true cortical activity. In new approaches using multipolar source models [3, 5], similar problems remain in the analysis of the higher-order source moments as parameters of cortical extent. Image-based approaches to the inverse problem provide a direct estimate of cortical current generators, but computationally expensive nonlinear methods are required to produce focal sources [1,4]. Recent efforts describe how a cortical patch can be grown until a best fit to the data is reached in the least-squares sense [6], but computational considerations necessitate that the growth be seeded in predefined regions of interest. In a previous study [2], a source obtained using a parametric model was remapped onto the cortex by growing a patch of cortical dipoles in the vicinity of the parametric source until the forward MEG or EEG fields of the parametric and cortical sources matched. The source models were dipoles and first-order multipoles. We propose to combine the parametric and imaging methods for MEG source characterization to take advantage of (i) the parsimonious and computationally efficient nature of parametric source localization methods and (ii) the anatomical and physiological consistency of imaging techniques that use relevant a priori information. By performing the cortical remapping imaging step by matching the multipole expansions of the original parametric
Whitening of Background Brain Activity via Parametric Modeling
Directory of Open Access Journals (Sweden)
Nidal Kamel
2007-01-01
Full Text Available Several signal subspace techniques have been recently suggested for the extraction of the visual evoked potential signals from brain background colored noise. The majority of these techniques assume the background noise as white, and for colored noise, it is suggested to be whitened, without further elaboration on how this might be done. In this paper, we investigate the whitening capabilities of two parametric techniques: a direct one based on Levinson solution of Yule-Walker equations, called AR Yule-Walker, and an indirect one based on the least-squares solution of forward-backward linear prediction (FBLP equations, called AR-FBLP. The whitening effect of the two algorithms is investigated with real background electroencephalogram (EEG colored noise and compared in time and frequency domains.
International Nuclear Information System (INIS)
Valdés, José R.; Rodríguez, José M.; Saumell, Javier; Pütz, Thomas
2014-01-01
Highlights: • We develop a methodology for the parametric modelling of flow in hydraulic valves. • We characterize the flow coefficients with a generic function with two parameters. • The parameters are derived from CFD simulations of the generic geometry. • We apply the methodology to two cases from the automotive brake industry. • We validate by comparing with CFD results varying the original dimensions. - Abstract: The main objective of this work is to develop a methodology for the parametric modelling of the flow rate in hydraulic valve systems. This methodology is based on the derivation, from CFD simulations, of the flow coefficient of the critical restrictions as a function of the Reynolds number, using a generalized square root function with two parameters. The methodology is then demonstrated by applying it to two completely different hydraulic systems: a brake master cylinder and an ABS valve. This type of parametric valve models facilitates their implementation in dynamic simulation models of complex hydraulic systems
PRESS-based EFOR algorithm for the dynamic parametrical modeling of nonlinear MDOF systems
Liu, Haopeng; Zhu, Yunpeng; Luo, Zhong; Han, Qingkai
2017-09-01
In response to the identification problem concerning multi-degree of freedom (MDOF) nonlinear systems, this study presents the extended forward orthogonal regression (EFOR) based on predicted residual sums of squares (PRESS) to construct a nonlinear dynamic parametrical model. The proposed parametrical model is based on the non-linear autoregressive with exogenous inputs (NARX) model and aims to explicitly reveal the physical design parameters of the system. The PRESS-based EFOR algorithm is proposed to identify such a model for MDOF systems. By using the algorithm, we built a common-structured model based on the fundamental concept of evaluating its generalization capability through cross-validation. The resulting model aims to prevent over-fitting with poor generalization performance caused by the average error reduction ratio (AERR)-based EFOR algorithm. Then, a functional relationship is established between the coefficients of the terms and the design parameters of the unified model. Moreover, a 5-DOF nonlinear system is taken as a case to illustrate the modeling of the proposed algorithm. Finally, a dynamic parametrical model of a cantilever beam is constructed from experimental data. Results indicate that the dynamic parametrical model of nonlinear systems, which depends on the PRESS-based EFOR, can accurately predict the output response, thus providing a theoretical basis for the optimal design of modeling methods for MDOF nonlinear systems.
Parametrically Guided Generalized Additive Models with Application to Mergers and Acquisitions Data.
Fan, Jianqing; Maity, Arnab; Wang, Yihui; Wu, Yichao
2013-01-01
Generalized nonparametric additive models present a flexible way to evaluate the effects of several covariates on a general outcome of interest via a link function. In this modeling framework, one assumes that the effect of each of the covariates is nonparametric and additive. However, in practice, often there is prior information available about the shape of the regression functions, possibly from pilot studies or exploratory analysis. In this paper, we consider such situations and propose an estimation procedure where the prior information is used as a parametric guide to fit the additive model. Specifically, we first posit a parametric family for each of the regression functions using the prior information (parametric guides). After removing these parametric trends, we then estimate the remainder of the nonparametric functions using a nonparametric generalized additive model, and form the final estimates by adding back the parametric trend. We investigate the asymptotic properties of the estimates and show that when a good guide is chosen, the asymptotic variance of the estimates can be reduced significantly while keeping the asymptotic variance same as the unguided estimator. We observe the performance of our method via a simulation study and demonstrate our method by applying to a real data set on mergers and acquisitions.
Comparison of Parametrization Techniques for an Electrical Circuit Model of Lithium-Sulfur Batteries
DEFF Research Database (Denmark)
Knap, Vaclav; Stroe, Daniel Loan; Teodorescu, Remus
2015-01-01
for various commercial applications, battery performance models are needed. The development of such models represents a challenging task especially for Li-S batteries because this technology during their operation undergo several different chemical reactions, known as polysulfide shuttle. This paper focuses...... on the comparison of different parametrization methods of electrical circuit models (ECMs) for Li-S batteries. These methods are used to parametrize an ECM based on laboratory measurements performed on a Li-S pouch cell. Simulation results of ECMs are presented and compared against measurement values...
Parametric Mass Modeling for Mars Entry, Descent and Landing System Analysis Study
Samareh, Jamshid A.; Komar, D. R.
2011-01-01
This paper provides an overview of the parametric mass models used for the Entry, Descent, and Landing Systems Analysis study conducted by NASA in FY2009-2010. The study examined eight unique exploration class architectures that included elements such as a rigid mid-L/D aeroshell, a lifting hypersonic inflatable decelerator, a drag supersonic inflatable decelerator, a lifting supersonic inflatable decelerator implemented with a skirt, and subsonic/supersonic retro-propulsion. Parametric models used in this study relate the component mass to vehicle dimensions and mission key environmental parameters such as maximum deceleration and total heat load. The use of a parametric mass model allows the simultaneous optimization of trajectory and mass sizing parameters.
A Rapid Model Fitting Tool Suite Project
National Aeronautics and Space Administration — An integral component of many NASA missions involves remote sensing of the environment, both terrestrial and celestial. This is a challenging problem, since...
Modeling and Validation across Scales: Parametrizing the effect of the forested landscape
DEFF Research Database (Denmark)
Dellwik, Ebba; Badger, Merete; Angelou, Nikolas
be transferred into a parametrization of forests in wind models. The presentation covers three scales: the single tree, the forest edges and clearings, and the large-scale forested landscape in which the forest effects are parameterized with a roughness length. Flow modeling results and validation against...
Mis-parametrization subsets for a penalized least squares model selection
Guyon, Xavier; Hardouin, Cécile
2011-01-01
When identifying a model by a penalized minimum contrast procedure, we give a description of the over and under fitting parametrization subsets for a least squares contrast. This allows to determine an accurate sequence of penalization rates ensuring good identification. We present applications for the identification of the covariance for a general time series, and for the variogram identification of a geostatistical model.
Evaluating Portfolio Value-At-Risk Using Semi-Parametric GARCH Models
J.V.K. Rombouts; M.J.C.M. Verbeek (Marno)
2009-01-01
textabstractIn this paper we examine the usefulness of multivariate semi-parametric GARCH models for evaluating the Value-at-Risk (VaR) of a portfolio with arbitrary weights. We specify and estimate several alternative multivariate GARCH models for daily returns on the S&P 500 and Nasdaq indexes.
A non-parametric hierarchical model to discover behavior dynamics from tracks
Kooij, J.F.P.; Englebienne, G.; Gavrila, D.M.
2012-01-01
We present a novel non-parametric Bayesian model to jointly discover the dynamics of low-level actions and high-level behaviors of tracked people in open environments. Our model represents behaviors as Markov chains of actions which capture high-level temporal dynamics. Actions may be shared by
Model and parametric uncertainty in source-based kinematic models of earthquake ground motion
Hartzell, Stephen; Frankel, Arthur; Liu, Pengcheng; Zeng, Yuehua; Rahman, Shariftur
2011-01-01
Four independent ground-motion simulation codes are used to model the strong ground motion for three earthquakes: 1994 Mw 6.7 Northridge, 1989 Mw 6.9 Loma Prieta, and 1999 Mw 7.5 Izmit. These 12 sets of synthetics are used to make estimates of the variability in ground-motion predictions. In addition, ground-motion predictions over a grid of sites are used to estimate parametric uncertainty for changes in rupture velocity. We find that the combined model uncertainty and random variability of the simulations is in the same range as the variability of regional empirical ground-motion data sets. The majority of the standard deviations lie between 0.5 and 0.7 natural-log units for response spectra and 0.5 and 0.8 for Fourier spectra. The estimate of model epistemic uncertainty, based on the different model predictions, lies between 0.2 and 0.4, which is about one-half of the estimates for the standard deviation of the combined model uncertainty and random variability. Parametric uncertainty, based on variation of just the average rupture velocity, is shown to be consistent in amplitude with previous estimates, showing percentage changes in ground motion from 50% to 300% when rupture velocity changes from 2.5 to 2.9 km/s. In addition, there is some evidence that mean biases can be reduced by averaging ground-motion estimates from different methods.
Directory of Open Access Journals (Sweden)
Jinchao Feng
2018-03-01
Full Text Available We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data. The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.
Feng, Jinchao; Lansford, Joshua; Mironenko, Alexander; Pourkargar, Davood Babaei; Vlachos, Dionisios G.; Katsoulakis, Markos A.
2018-03-01
We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data). The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.
Parametrizing coarse grained models for molecular systems at equilibrium
Kalligiannaki, Evangelia
2016-10-18
Hierarchical coarse graining of atomistic molecular systems at equilibrium has been an intensive research topic over the last few decades. In this work we (a) review theoretical and numerical aspects of different parametrization methods (structural-based, force matching and relative entropy) to derive the effective interaction potential between coarse-grained particles. All methods approximate the many body potential of mean force; resulting, however, in different optimization problems. (b) We also use a reformulation of the force matching method by introducing a generalized force matching condition for the local mean force in the sense that allows the approximation of the potential of mean force under both linear and non-linear coarse graining mappings (E. Kalligiannaki, et al., J. Chem. Phys. 2015). We apply and compare these methods to: (a) a benchmark system of two isolated methane molecules; (b) methane liquid; (c) water; and (d) an alkane fluid. Differences between the effective interactions, derived from the various methods, are found that depend on the actual system under study. The results further reveal the relation of the various methods and the sensitivities that may arise in the implementation of numerical methods used in each case.
International Nuclear Information System (INIS)
Kim, Su Jin; Lee, Jae Sung; Kim, Yu Kyeong; Lee, Dong Soo
2007-01-01
Parametric imaging allows us analysis of the entire brain or body image. Graphical approaches are commonly employed to generate parametric imaging through linear or multilinear regression. However, this linear regression method has limited accuracy due to bias in high level of noise data. Several methods have been proposed to reduce bias for linear regression estimation especially in reversible model. In this study, we focus on generating a net accumulation rate (K i ), which is related to binding parameter in brain receptor study, parametric imaging in an irreversible compartment model using multiple linear analysis. The reliability of a newly developed multiple linear analysis method (MLAIR) was assessed through the Monte Carlo simulation, and we applied it to a [ 11 C]MeNTI PET for opioid receptor
Formation of parametric images using mixed-effects models: a feasibility study.
Huang, Husan-Ming; Shih, Yi-Yu; Lin, Chieh
2016-03-01
Mixed-effects models have been widely used in the analysis of longitudinal data. By presenting the parameters as a combination of fixed effects and random effects, mixed-effects models incorporating both within- and between-subject variations are capable of improving parameter estimation. In this work, we demonstrate the feasibility of using a non-linear mixed-effects (NLME) approach for generating parametric images from medical imaging data of a single study. By assuming that all voxels in the image are independent, we used simulation and animal data to evaluate whether NLME can improve the voxel-wise parameter estimation. For testing purposes, intravoxel incoherent motion (IVIM) diffusion parameters including perfusion fraction, pseudo-diffusion coefficient and true diffusion coefficient were estimated using diffusion-weighted MR images and NLME through fitting the IVIM model. The conventional method of non-linear least squares (NLLS) was used as the standard approach for comparison of the resulted parametric images. In the simulated data, NLME provides more accurate and precise estimates of diffusion parameters compared with NLLS. Similarly, we found that NLME has the ability to improve the signal-to-noise ratio of parametric images obtained from rat brain data. These data have shown that it is feasible to apply NLME in parametric image generation, and the parametric image quality can be accordingly improved with the use of NLME. With the flexibility to be adapted to other models or modalities, NLME may become a useful tool to improve the parametric image quality in the future. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.
Nonlinear Container Ship Model for the Study of Parametric Roll Resonance
Directory of Open Access Journals (Sweden)
Christian Holden
2007-10-01
Full Text Available Parametric roll is a critical phenomenon for ships, whose onset may cause roll oscillations up to +-40 degrees, leading to very dangerous situations and possibly capsizing. Container ships have been shown to be particularly prone to parametric roll resonance when they are sailing in moderate to heavy head seas. A Matlab/Simulink parametric roll benchmark model for a large container ship has been implemented and validated against a wide set of experimental data. The model is a part of a Matlab/Simulink Toolbox (MSS, 2007. The benchmark implements a 3rd-order nonlinear model where the dynamics of roll is strongly coupled with the heave and pitch dynamics. The implemented model has shown good accuracy in predicting the container ship motions, both in the vertical plane and in the transversal one. Parametric roll has been reproduced for all the data sets in which it happened, and the model provides realistic results which are in good agreement with the model tank experiments.
A Parametric Computational Model of the Action Potential of Pacemaker Cells.
Ai, Weiwei; Patel, Nitish D; Roop, Partha S; Malik, Avinash; Andalam, Sidharta; Yip, Eugene; Allen, Nathan; Trew, Mark L
2018-01-01
A flexible, efficient, and verifiable pacemaker cell model is essential to the design of real-time virtual hearts that can be used for closed-loop validation of cardiac devices. A new parametric model of pacemaker action potential is developed to address this need. The action potential phases are modeled using hybrid automaton with one piecewise-linear continuous variable. The model can capture rate-dependent dynamics, such as action potential duration restitution, conduction velocity restitution, and overdrive suppression by incorporating nonlinear update functions. Simulated dynamics of the model compared well with previous models and clinical data. The results show that the parametric model can reproduce the electrophysiological dynamics of a variety of pacemaker cells, such as sinoatrial node, atrioventricular node, and the His-Purkinje system, under varying cardiac conditions. This is an important contribution toward closed-loop validation of cardiac devices using real-time heart models.
Proposal for a parametric conceptual CAD model of a mono-modular inertial fusion reactor
International Nuclear Information System (INIS)
Vezzani, M.; Cerullo, N.; Lanza, S.
2001-01-01
The present work tries to solve the problem of realizing a parametric conceptual CAD model of a modular reactor for future inertial fusion power plants. The choice of a modular structure seems to be a good solution for efficiency and economic requirements. On the other hand, the realization of a parametric-variational CAD model is very useful to optimize nuclear and mechanical parameters and to permit the shift from the conceptual to the final model. First, geometric solutions for a modular reactor are analysed; the most interesting is that of a 20-face regular polyhedron (icosahedron). The subdivision of each face into six equal triangles consents to obtain a mono-modular reactor with 120 modules (called 'ICO120'). This solution should be easy, efficient and cheap. Secondly, the work proposes a conceptual CAD model of the ICO120 reactor in which special attention is put on the parametrization. Starting from such parametric model it will be possible to develop and optimize icosahedral reactors with different features, sizes and performances
A parametric daily precipitation model application in Botswana ...
African Journals Online (AJOL)
Results show that Markov-chain (MC) model can be used to model the persistence behaviour of the transition probability matrix (TPM) of dry and wet day rainfall sequences. With the MC model, the two-parameter gamma distribution is found to be most robust and suitable model to describe the magnitude of rainfall depths in ...
The numerical model for parametric studies of forest haul roads pavements
Directory of Open Access Journals (Sweden)
Lenka Ševelová
2010-01-01
Full Text Available Forest roads pavement structures are considered to be low volume roads. These roads serve as a mean of transport of wood and people. Besides they are currently often used for recreational purpose. The construction of the pavements should be suitable for forest transportation irrespective of their low bearing capacity. These pavement structures are very specific for special unbound materials that are used in their construction. To meet the requirements of the pavement designs and simulation analysis the FEM model in the software ANSYS was created.This paper compares two material models used for the description of the behaviour of unbound materials. The first is linear elastic according to Hook theory (H model and the second one is nonlinear plastic model Drucker-Prager (D–P model. ANSYS software has been used to create flexible model based on the parametrers of variable principle. The flexible model is parametric to realize repeated calculations useful for optimization analysis.
Galindo-Garre, Francisca; Hidalgo, María Dolores; Guilera, Georgina; Pino, Oscar; Rojo, J Emilio; Gómez-Benito, Juana
2015-03-01
The World Health Organization Disability Assessment Schedule II (WHO-DAS II) is a multidimensional instrument developed for measuring disability. It comprises six domains (getting around, self-care, getting along with others, life activities and participation in society). The main purpose of this paper is the evaluation of the psychometric properties for each domain of the WHO-DAS II with parametric and non-parametric Item Response Theory (IRT) models. A secondary objective is to assess whether the WHO-DAS II items within each domain form a hierarchy of invariantly ordered severity indicators of disability. A sample of 352 patients with a schizophrenia spectrum disorder is used in this study. The 36 items WHO-DAS II was administered during the consultation. Partial Credit and Mokken scale models are used to study the psychometric properties of the questionnaire. The psychometric properties of the WHO-DAS II scale are satisfactory for all the domains. However, we identify a few items that do not discriminate satisfactorily between different levels of disability and cannot be invariantly ordered in the scale. In conclusion the WHO-DAS II can be used to assess overall disability in patients with schizophrenia, but some domains are too general to assess functionality in these patients because they contain items that are not applicable to this pathology. Copyright © 2014 John Wiley & Sons, Ltd.
The Parametric Model for PLC Reference Chanells and its Verification in Real PLC Environment
Directory of Open Access Journals (Sweden)
Rastislav Roka
2008-01-01
Full Text Available For the expansion of PLC systems, it is necesssary to have a detailed knowledge of the PLC transmission channel properties. This contribution shortly discusses characteristics of the PLC environment and a classification of PLC transmission channels. A main part is focused on the parametric model for PLC reference channels and its verification in the real PLC environment utilizing experimental measurements.
The Support Reduction Algorithm for Computing Non-Parametric Function Estimates in Mixture Models
GROENEBOOM, PIET; JONGBLOED, GEURT; WELLNER, JON A.
2008-01-01
In this paper, we study an algorithm (which we call the support reduction algorithm) that can be used to compute non-parametric M-estimators in mixture models. The algorithm is compared with natural competitors in the context of convex regression and the ‘Aspect problem’ in quantum physics.
Oude Lansink, A.G.J.M.; Pietola, K.
2005-01-01
This paper applies a semi-parametric approach to estimating a generalised model of investments in heating installations. The results suggest that marginal costs of investments in heating installations increase quickly at small investment levels, whereas the increase slows down at higher investment
A Range-Based Test for the Parametric Form of the Volatility in Diffusion Models
DEFF Research Database (Denmark)
Podolskij, Mark; Ziggel, Daniel
We propose a new test for the parametric form of the volatility function in continuous time diffusion models of the type dXt = a(t;Xt)dt + (t;Xt)dWt. Our approach involves a range-based estimation of the integrated volatility and the integrated quarticity, which are used to construct the test...
A Range-Based Test for the Parametric Form of the Volatility in Diffusion Models
DEFF Research Database (Denmark)
Podolskij, Mark; Ziggel, Daniel
We propose a new test for the parametric form of the volatility function in continuous time diffusion models of the type dXt = a(t,Xt)dt + s(t,Xt)dWt. Our approach involves a range-based estimation of the integrated volatility and the integrated quarticity, which are used to construct the test...
Parametrization of Transfer Matrix: for One-Dimensional Anderson Model with Diagonal Disorder
International Nuclear Information System (INIS)
Kang Kai; Qin Shaojing; Wang Chuilin
2010-01-01
In this paper, we developed a new parametrization method to calculate the localization length in one-dimensional Anderson model with diagonal disorder. This method can avoid the divergence difficulty encountered in the conventional methods, and significantly save computing time as well. (condensed matter: electronic structure, electrical, magnetic, and optical properties)
A comparative study of non-parametric models for identification of ...
African Journals Online (AJOL)
However, the frequency response method using random binary signals was good for unpredicted white noise characteristics and considered the best method for non-parametric system identifica-tion. The autoregressive external input (ARX) model was very useful for system identification, but on applicati-on, few input ...
Visual Literacy and the Integration of Parametric Modeling in the Problem-Based Curriculum
Assenmacher, Matthew Benedict
2013-01-01
This quasi-experimental study investigated the application of visual literacy skills in the form of parametric modeling software in relation to traditional forms of sketching. The study included two groups of high school technical design students. The control and experimental groups involved in the study consisted of two randomly selected groups…
Brown, Gregory G; Thomas, Michael L; Patt, Virginie
Neuropsychology is an applied measurement field with its psychometric work primarily built upon classical test theory (CTT). We describe a series of psychometric models to supplement the use of CTT in neuropsychological research and test development. We introduce increasingly complex psychometric models as measurement algebras, which include model parameters that represent abilities and item properties. Within this framework of parametric model measurement (PMM), neuropsychological assessment involves the estimation of model parameters with ability parameter values assuming the role of test 'scores'. Moreover, the traditional notion of measurement error is replaced by the notion of parameter estimation error, and the definition of reliability becomes linked to notions of item and test information. The more complex PMM approaches incorporate into the assessment of neuropsychological performance formal parametric models of behavior validated in the experimental psychology literature, along with item parameters. These PMM approaches endorse the use of experimental manipulations of model parameters to assess a test's construct representation. Strengths and weaknesses of these models are evaluated by their implications for measurement error conditional upon ability level, sensitivity to sample characteristics, computational challenges to parameter estimation, and construct validity. A family of parametric psychometric models can be used to assess latent processes of interest to neuropsychologists. By modeling latent abilities at the item level, psychometric studies in neuropsychology can investigate construct validity and measurement precision within a single framework and contribute to a unification of statistical methods within the framework of generalized latent variable modeling.
DEFF Research Database (Denmark)
Rohde, John; Toftegaard, Thomas Skjødeberg
2012-01-01
Novel parametric finite-element models are provided for discrete SMD capacitors and inductors in the frequency range 100 MHz to 4 GHz. The aim of the models is to facilitate performance optimization and analysis of RF PCB designs integrating these SMD components with layout geometries such as ant......Novel parametric finite-element models are provided for discrete SMD capacitors and inductors in the frequency range 100 MHz to 4 GHz. The aim of the models is to facilitate performance optimization and analysis of RF PCB designs integrating these SMD components with layout geometries...... such as antennas and PCB traces. The models presented are benchmarked against real-life measurements and conventional circuit models. Furthermore, two example parallel-resonance circuits are designed based on interpolation of the results and validated by measurements in order to demonstrate the accuracy...
Directory of Open Access Journals (Sweden)
Ibsen Chivatá Cárdenas
2008-05-01
Full Text Available This article presents a rainfall model constructed by applying non-parametric modelling and imprecise probabilities; these tools were used because there was not enough homogeneous information in the study area. The area’s hydro-logical information regarding rainfall was scarce and existing hydrological time series were not uniform. A distributed extended rainfall model was constructed from so-called probability boxes (p-boxes, multinomial probability distribu-tion and confidence intervals (a friendly algorithm was constructed for non-parametric modelling by combining the last two tools. This model confirmed the high level of uncertainty involved in local rainfall modelling. Uncertainty en-compassed the whole range (domain of probability values thereby showing the severe limitations on information, leading to the conclusion that a detailed estimation of probability would lead to significant error. Nevertheless, rele-vant information was extracted; it was estimated that maximum daily rainfall threshold (70 mm would be surpassed at least once every three years and the magnitude of uncertainty affecting hydrological parameter estimation. This paper’s conclusions may be of interest to non-parametric modellers and decisions-makers as such modelling and imprecise probability represents an alternative for hydrological variable assessment and maybe an obligatory proce-dure in the future. Its potential lies in treating scarce information and represents a robust modelling strategy for non-seasonal stochastic modelling conditions
Moore, Julia L; Remais, Justin V
2014-03-01
Developmental models that account for the metabolic effect of temperature variability on poikilotherms, such as degree-day models, have been widely used to study organism emergence, range and development, particularly in agricultural and vector-borne disease contexts. Though simple and easy to use, structural and parametric issues can influence the outputs of such models, often substantially. Because the underlying assumptions and limitations of these models have rarely been considered, this paper reviews the structural, parametric, and experimental issues that arise when using degree-day models, including the implications of particular structural or parametric choices, as well as assumptions that underlie commonly used models. Linear and non-linear developmental functions are compared, as are common methods used to incorporate temperature thresholds and calculate daily degree-days. Substantial differences in predicted emergence time arose when using linear versus non-linear developmental functions to model the emergence time in a model organism. The optimal method for calculating degree-days depends upon where key temperature threshold parameters fall relative to the daily minimum and maximum temperatures, as well as the shape of the daily temperature curve. No method is shown to be universally superior, though one commonly used method, the daily average method, consistently provides accurate results. The sensitivity of model projections to these methodological issues highlights the need to make structural and parametric selections based on a careful consideration of the specific biological response of the organism under study, and the specific temperature conditions of the geographic regions of interest. When degree-day model limitations are considered and model assumptions met, the models can be a powerful tool for studying temperature-dependent development.
Flexible parametric modelling of the cause-specific cumulative incidence function.
Lambert, Paul C; Wilkes, Sally R; Crowther, Michael J
2017-04-30
Competing risks arise with time-to-event data when individuals are at risk of more than one type of event and the occurrence of one event precludes the occurrence of all other events. A useful measure with competing risks is the cause-specific cumulative incidence function (CIF), which gives the probability of experiencing a particular event as a function of follow-up time, accounting for the fact that some individuals may have a competing event. When modelling the cause-specific CIF, the most common model is a semi-parametric proportional subhazards model. In this paper, we propose the use of flexible parametric survival models to directly model the cause-specific CIF where the effect of follow-up time is modelled using restricted cubic splines. The models provide smooth estimates of the cause-specific CIF with the important advantage that the approach is easily extended to model time-dependent effects. The models can be fitted using standard survival analysis tools by a combination of data expansion and introducing time-dependent weights. Various link functions are available that allow modelling on different scales and have proportional subhazards, proportional odds and relative absolute risks as particular cases. We conduct a simulation study to evaluate how well the spline functions approximate subhazard functions with complex shapes. The methods are illustrated using data from the European Blood and Marrow Transplantation Registry showing excellent agreement between parametric estimates of the cause-specific CIF and those obtained from a semi-parametric model. We also fit models relaxing the proportional subhazards assumption using alternative link functions and/or including time-dependent effects. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
A generalized Jaynes-Cummings model: The relativistic parametric amplifier and a single trapped ion
Energy Technology Data Exchange (ETDEWEB)
Ojeda-Guillén, D., E-mail: dojedag@ipn.mx [Escuela Superior de Cómputo, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz esq. Av. Miguel Othón de Mendizábal, Col. Lindavista, Delegación Gustavo A. Madero, C.P. 07738 Ciudad de México (Mexico); Mota, R. D. [Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacán, Instituto Politécnico Nacional, Av. Santa Ana No. 1000, Col. San Francisco Culhuacán, Delegación Coyoacán, C.P. 04430 Ciudad de México (Mexico); Granados, V. D. [Escuela Superior de Física y Matemáticas, Instituto Politécnico Nacional, Ed. 9, Unidad Profesional Adolfo López Mateos, Delegación Gustavo A. Madero, C.P. 07738 Ciudad de México (Mexico)
2016-06-15
We introduce a generalization of the Jaynes-Cummings model and study some of its properties. We obtain the energy spectrum and eigenfunctions of this model by using the tilting transformation and the squeezed number states of the one-dimensional harmonic oscillator. As physical applications, we connect this new model to two important and novelty problems: the relativistic parametric amplifier and the quantum simulation of a single trapped ion.
Analysis of survival in breast cancer patients by using different parametric models
Enera Amran, Syahila; Asrul Afendi Abdullah, M.; Kek, Sie Long; Afiqah Muhamad Jamil, Siti
2017-09-01
In biomedical applications or clinical trials, right censoring was often arising when studying the time to event data. In this case, some individuals are still alive at the end of the study or lost to follow up at a certain time. It is an important issue to handle the censoring data in order to prevent any bias information in the analysis. Therefore, this study was carried out to analyze the right censoring data with three different parametric models; exponential model, Weibull model and log-logistic models. Data of breast cancer patients from Hospital Sultan Ismail, Johor Bahru from 30 December 2008 until 15 February 2017 was used in this study to illustrate the right censoring data. Besides, the covariates included in this study are the time of breast cancer infection patients survive t, age of each patients X1 and treatment given to the patients X2 . In order to determine the best parametric models in analysing survival of breast cancer patients, the performance of each model was compare based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood value using statistical software R. When analysing the breast cancer data, all three distributions were shown consistency of data with the line graph of cumulative hazard function resembles a straight line going through the origin. As the result, log-logistic model was the best fitted parametric model compared with exponential and Weibull model since it has the smallest value in AIC and BIC, also the biggest value in log-likelihood.
Change point models for cognitive tests using semi-parametric maximum likelihood.
van den Hout, Ardo; Muniz-Terrera, Graciela; Matthews, Fiona E
2013-01-01
Random-effects change point models are formulated for longitudinal data obtained from cognitive tests. The conditional distribution of the response variable in a change point model is often assumed to be normal even if the response variable is discrete and shows ceiling effects. For the sum score of a cognitive test, the binomial and the beta-binomial distributions are presented as alternatives to the normal distribution. Smooth shapes for the change point models are imposed. Estimation is by marginal maximum likelihood where a parametric population distribution for the random change point is combined with a non-parametric mixing distribution for other random effects. An extension to latent class modelling is possible in case some individuals do not experience a change in cognitive ability. The approach is illustrated using data from a longitudinal study of Swedish octogenarians and nonagenarians that began in 1991. Change point models are applied to investigate cognitive change in the years before death.
Novel Parametric Circuit Modeling for Li-Ion Batteries
Directory of Open Access Journals (Sweden)
Ximing Cheng
2016-07-01
Full Text Available Because of their simplicity and dynamic response, current pulse series are often used to extract parameters for equivalent electrical circuit modeling of Li-ion batteries. These models are then applied for performance simulation, state estimation, and thermal analysis in electric vehicles. However, these methods have two problems: The assumption of linear dependence of the matrix columns and negative parameters estimated from discrete-time equations and least-squares methods. In this paper, continuous-time equations are exploited to construct a linearly independent data matrix and parameterize the circuit model by the combination of non-negative least squares and genetic algorithm, which constrains the model parameters to be positive. Trigonometric functions are then developed to fit the parameter curves. The developed model parameterization methodology was applied and assessed by a standard driving cycle.
Evaluation of wave runup predictions from numerical and parametric models
Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.
2014-01-01
Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.
Numerical model of solar dynamic radiator for parametric analysis
Rhatigan, Jennifer L.
1989-01-01
Growth power requirements for Space Station Freedom will be met through addition of 25 kW solar dynamic (SD) power modules. Extensive thermal and power cycle modeling capabilities have been developed which are powerful tools in Station design and analysis, but which prove cumbersome and costly for simple component preliminary design studies. In order to aid in refining the SD radiator to the mature design stage, a simple and flexible numerical model was developed. The model simulates heat transfer and fluid flow performance of the radiator and calculates area mass and impact survivability for many combinations of flow tube and panel configurations, fluid and material properties, and environmental and cycle variations.
A parametric framework for modelling of bioelectrical signals
Mughal, Yar Muhammad
2016-01-01
This book examines non-invasive, electrical-based methods for disease diagnosis and assessment of heart function. In particular, a formalized signal model is proposed since this offers several advantages over methods that rely on measured data alone. By using a formalized representation, the parameters of the signal model can be easily manipulated and/or modified, thus providing mechanisms that allow researchers to reproduce and control such signals. In addition, having such a formalized signal model makes it possible to develop computer tools that can be used for manipulating and understanding how signal changes result from various heart conditions, as well as for generating input signals for experimenting with and evaluating the performance of e.g. signal extraction methods. The work focuses on bioelectrical information, particularly electrical bio-impedance (EBI). Once the EBI has been measured, the corresponding signals have to be modelled for analysis. This requires a structured approach in order to move...
Bifurcation analysis of parametrically excited bipolar disorder model
Nana, Laurent
2009-02-01
Bipolar II disorder is characterized by alternating hypomanic and major depressive episode. We model the periodic mood variations of a bipolar II patient with a negatively damped harmonic oscillator. The medications administrated to the patient are modeled via a forcing function that is capable of stabilizing the mood variations and of varying their amplitude. We analyze analytically, using perturbation method, the amplitude and stability of limit cycles and check this analysis with numerical simulations.
Study of the long-term values and prices of plutonium; a simplified parametrized model
International Nuclear Information System (INIS)
Gaussens, J.; Paillot, H.
1965-01-01
The authors define the notions of use values and price of plutonium. They give a 'simplified parametrized model' simulating the equilibrium of the offer and the demand in time, concerning the plutonium and the price deriving from the relative scarcity of this metal, taking into account the technical and economic operating parameters of the various reactors confronted. This model is simple enough to allow direct computations and establish clear relations between the various parameters. The use of the linear programmes method allows on the other hand a wide extension of the model. This report includes three main parts: I - General description of the study (without detailed calculations) II - Mathematical development of the simplified parametrized model and application (the basic data and the results of the calculations are given) III - Appendices (giving the detailed computations of part II). (authors) [fr
Scalability of the muscular action in a parametric 3D model of the index finger.
Sancho-Bru, Joaquín L; Vergara, Margarita; Rodríguez-Cervantes, Pablo-Jesús; Giurintano, David J; Pérez-González, Antonio
2008-01-01
A method for scaling the muscle action is proposed and used to achieve a 3D inverse dynamic model of the human finger with all its components scalable. This method is based on scaling the physiological cross-sectional area (PCSA) in a Hill muscle model. Different anthropometric parameters and maximal grip force data have been measured and their correlations have been analyzed and used for scaling the PCSA of each muscle. A linear relationship between the normalized PCSA and the product of the length and breadth of the hand has been finally used for scaling, with a slope of 0.01315 cm(-2), with the length and breadth of the hand expressed in centimeters. The parametric muscle model has been included in a parametric finger model previously developed by the authors, and it has been validated reproducing the results of an experiment in which subjects from different population groups exerted maximal voluntary forces with their index finger in a controlled posture.
Latest astronomical constraints on some non-linear parametric dark energy models
Yang, Weiqiang; Pan, Supriya; Paliathanasis, Andronikos
2018-04-01
We consider non-linear redshift-dependent equation of state parameters as dark energy models in a spatially flat Friedmann-Lemaître-Robertson-Walker universe. To depict the expansion history of the universe in such cosmological scenarios, we take into account the large-scale behaviour of such parametric models and fit them using a set of latest observational data with distinct origin that includes cosmic microwave background radiation, Supernove Type Ia, baryon acoustic oscillations, redshift space distortion, weak gravitational lensing, Hubble parameter measurements from cosmic chronometers, and finally the local Hubble constant from Hubble space telescope. The fitting technique avails the publicly available code Cosmological Monte Carlo (COSMOMC), to extract the cosmological information out of these parametric dark energy models. From our analysis, it follows that those models could describe the late time accelerating phase of the universe, while they are distinguished from the Λ-cosmology.
A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs
Directory of Open Access Journals (Sweden)
Merlijn Blaauw
2017-12-01
Full Text Available We recently presented a new model for singing synthesis based on a modified version of the WaveNet architecture. Instead of modeling raw waveform, we model features produced by a parametric vocoder that separates the influence of pitch and timbre. This allows conveniently modifying pitch to match any target melody, facilitates training on more modest dataset sizes, and significantly reduces training and generation times. Nonetheless, compared to modeling waveform directly, ways of effectively handling higher-dimensional outputs, multiple feature streams and regularization become more important with our approach. In this work, we extend our proposed system to include additional components for predicting F0 and phonetic timings from a musical score with lyrics. These expression-related features are learned together with timbrical features from a single set of natural songs. We compare our method to existing statistical parametric, concatenative, and neural network-based approaches using quantitative metrics as well as listening tests.
Flexible parametric modelling of cause-specific hazards to estimate cumulative incidence functions
2013-01-01
Background Competing risks are a common occurrence in survival analysis. They arise when a patient is at risk of more than one mutually exclusive event, such as death from different causes, and the occurrence of one of these may prevent any other event from ever happening. Methods There are two main approaches to modelling competing risks: the first is to model the cause-specific hazards and transform these to the cumulative incidence function; the second is to model directly on a transformation of the cumulative incidence function. We focus on the first approach in this paper. This paper advocates the use of the flexible parametric survival model in this competing risk framework. Results An illustrative example on the survival of breast cancer patients has shown that the flexible parametric proportional hazards model has almost perfect agreement with the Cox proportional hazards model. However, the large epidemiological data set used here shows clear evidence of non-proportional hazards. The flexible parametric model is able to adequately account for these through the incorporation of time-dependent effects. Conclusion A key advantage of using this approach is that smooth estimates of both the cause-specific hazard rates and the cumulative incidence functions can be obtained. It is also relatively easy to incorporate time-dependent effects which are commonly seen in epidemiological studies. PMID:23384310
Brayton Power Conversion System Parametric Design Modelling for Nuclear Electric Propulsion
Ashe, Thomas L.; Otting, William D.
1993-01-01
The parametrically based closed Brayton cycle (CBC) computer design model was developed for inclusion into the NASA LeRC overall Nuclear Electric Propulsion (NEP) end-to-end systems model. The code is intended to provide greater depth to the NEP system modeling which is required to more accurately predict the impact of specific technology on system performance. The CBC model is parametrically based to allow for conducting detailed optimization studies and to provide for easy integration into an overall optimizer driver routine. The power conversion model includes the modeling of the turbines, alternators, compressors, ducting, and heat exchangers (hot-side heat exchanger and recuperator). The code predicts performance to significant detail. The system characteristics determined include estimates of mass, efficiency, and the characteristic dimensions of the major power conversion system components. These characteristics are parametrically modeled as a function of input parameters such as the aerodynamic configuration (axial or radial), turbine inlet temperature, cycle temperature ratio, power level, lifetime, materials, and redundancy.
International Nuclear Information System (INIS)
Ashe, T.L.; Otting, W.D.
1993-11-01
The parametrically based closed Brayton cycle (CBC) computer design model was developed for inclusion into the NASA LeRC overall Nuclear Electric Propulsion (NEP) end-to-end systems model. The code is intended to provide greater depth to the NEP system modeling which is required to more accurately predict the impact of specific technology on system performance. The CBC model is parametrically based to allow for conducting detailed optimization studies and to provide for easy integration into an overall optimizer driver routine. The power conversion model includes the modeling of the turbines, alternators, compressors, ducting, and heat exchangers (hot-side heat exchanger and recuperator). The code predicts performance to significant detail. The system characteristics determined include estimates of mass, efficiency, and the characteristic dimensions of the major power conversion system components. These characteristics are parametrically modeled as a function of input parameters such as the aerodynamic configuration (axial or radial), turbine inlet temperature, cycle temperature ratio, power level, lifetime, materials, and redundancy
Principles of parametric estimation in modeling language competition.
Zhang, Menghan; Gong, Tao
2013-06-11
It is generally difficult to define reasonable parameters and interpret their values in mathematical models of social phenomena. Rather than directly fitting abstract parameters against empirical data, we should define some concrete parameters to denote the sociocultural factors relevant for particular phenomena, and compute the values of these parameters based upon the corresponding empirical data. Taking the example of modeling studies of language competition, we propose a language diffusion principle and two language inheritance principles to compute two critical parameters, namely the impacts and inheritance rates of competing languages, in our language competition model derived from the Lotka-Volterra competition model in evolutionary biology. These principles assign explicit sociolinguistic meanings to those parameters and calculate their values from the relevant data of population censuses and language surveys. Using four examples of language competition, we illustrate that our language competition model with thus-estimated parameter values can reliably replicate and predict the dynamics of language competition, and it is especially useful in cases lacking direct competition data.
Nikulin, M; Mesbah, M; Limnios, N
2004-01-01
Parametric and semiparametric models are tools with a wide range of applications to reliability, survival analysis, and quality of life. This self-contained volume examines these tools in survey articles written by experts currently working on the development and evaluation of models and methods. While a number of chapters deal with general theory, several explore more specific connections and recent results in "real-world" reliability theory, survival analysis, and related fields.
Parametric Generation of Polygonal Tree Models for Rendering on Tessellation-Enabled Hardware
Nystad, Jørgen
2010-01-01
The main contribution of this thesis is a parametric method for generation of single-mesh polygonal tree models that follow natural rules as indicated by da Vinci in his notebooks. Following these rules allow for a relatively simple scheme of connecting branches to parent branches. Proper branch connection is a requirement for gaining the benefits of subdivision. Techniques for proper texture coordinate generation and subdivision are also explored.The result is a tree model generation scheme ...
First-Order Parametric Model of Reflectance Spectra for Dyed Fabrics
2016-02-19
diffuse reflectance for the purpose of simulating the spectral response of military textiles containing different types and concentrations of near...which provides for both their inverse and direct modeling1. The dyes considered contain spectral features that are of interest to the U.S. Navy for...can be in terms of formulations based on the Beer -Lambert Law or its approximation, which is adopted for the parametric model considered here [8,9
A simple GMM estimator for the semi-parametric mixed proportional hazard model
Bijwaard, G.E.; Ridder, G.; Woutersen, T.
2012-01-01
Ridder and Woutersen (Ridder, G., and T. Woutersen. 2003. “The Singularity of the Efficiency Bound of the Mixed Proportional Hazard Model.” Econometrica 71: 1579–1589) have shown that under a weak condition on the baseline hazard, there exist root-N consistent estimators of the parameters in a semiparametric Mixed Proportional Hazard model with a parametric baseline hazard and unspecified distribution of the unobserved heterogeneity. We extend the linear rank estimator (LRE) of Tsiatis (Tsiat...
Modeling, simulation and parametric optimization of wire EDM ...
African Journals Online (AJOL)
In the present work, quadratic mathematical models have been derived to represent the process behavior of wire electrical discharge machining (WEDM) operation. Experiments have been conducted with six process parameters: discharge current, pulse duration, pulse frequency, wire speed, wire tension and dielectric flow ...
Determining input values for a simple parametric model to estimate ...
African Journals Online (AJOL)
Estimating soil evaporation (Es) is an important part of modelling vineyard evapotranspiration for irrigation purposes. Furthermore, quantification of possible soil texture and trellis effects is essential. Daily Es from six topsoils packed into lysimeters was measured under grapevines on slanting and vertical trellises, ...
Modelling a singly resonant, intracavity ring optical parametric oscillator
DEFF Research Database (Denmark)
Buchhave, Preben; Tidemand-Lichtenberg, Peter; Wei, Hou
2003-01-01
We study theoretically and experimentally the dynamics of a single-frequency, unidirectional ring laser with an intracavity nonlinear singly resonant OPO-crystal in a coupled resonator. We find for a range of operating conditions good agreement between model results and measurements of the laser...
Modeling Sodium Iodide Detector Response Using Parametric Equations
2013-03-22
Equipment 4.2.2.1 3x3 NaI(Tl)Detector A 3x3 cylindrical thallium doped NaI crystal with a flat faced cylindrical geometry allowed the detector...equivalent simplified geometries. For example, can a tree be modeled as a cylindrical column of water ? Developing these generalities will translate into
Framework for the Parametric System Modeling of Space Exploration Architectures
Komar, David R.; Hoffman, Jim; Olds, Aaron D.; Seal, Mike D., II
2008-01-01
This paper presents a methodology for performing architecture definition and assessment prior to, or during, program formulation that utilizes a centralized, integrated architecture modeling framework operated by a small, core team of general space architects. This framework, known as the Exploration Architecture Model for IN-space and Earth-to-orbit (EXAMINE), enables: 1) a significantly larger fraction of an architecture trade space to be assessed in a given study timeframe; and 2) the complex element-to-element and element-to-system relationships to be quantitatively explored earlier in the design process. Discussion of the methodology advantages and disadvantages with respect to the distributed study team approach typically used within NASA to perform architecture studies is presented along with an overview of EXAMINE s functional components and tools. An example Mars transportation system architecture model is used to demonstrate EXAMINE s capabilities in this paper. However, the framework is generally applicable for exploration architecture modeling with destinations to any celestial body in the solar system.
International Nuclear Information System (INIS)
Vu, H.X.; Bezzerides, B.; DuBois, D.F.
1999-01-01
A fully kinetic, reduced-description particle-in-cell (RPIC) model is presented in which deviations from quasineutrality, electron and ion kinetic effects, and nonlinear interactions between low-frequency and high-frequency parametric instabilities are modeled correctly. The model is based on a reduced description where the electromagnetic field is represented by three separate temporal envelopes in order to model parametric instabilities with low-frequency and high-frequency daughter waves. Because temporal envelope approximations are invoked, the simulation can be performed on the electron time scale instead of the time scale of the light waves. The electrons and ions are represented by discrete finite-size particles, permitting electron and ion kinetic effects to be modeled properly. The Poisson equation is utilized to ensure that space-charge effects are included. The RPIC model is fully three dimensional and has been implemented in two dimensions on the Accelerated Strategic Computing Initiative (ASCI) parallel computer at Los Alamos National Laboratory, and the resulting simulation code has been named ASPEN. The authors believe this code is the first particle-in-cell code capable of simulating the interaction between low-frequency and high-frequency parametric instabilities in multiple dimensions. Test simulations of stimulated Raman scattering, stimulated Brillouin scattering, and Langmuir decay instability are presented
CuBe: parametric modeling of 3D foveal shape using cubic Bézier.
Yadav, Sunil Kumar; Motamedi, Seyedamirhosein; Oberwahrenbrock, Timm; Oertel, Frederike Cosima; Polthier, Konrad; Paul, Friedemann; Kadas, Ella Maria; Brandt, Alexander U
2017-09-01
Optical coherence tomography (OCT) allows three-dimensional (3D) imaging of the retina, and is commonly used for assessing pathological changes of fovea and macula in many diseases. Many neuroinflammatory conditions are known to cause modifications to the fovea shape. In this paper, we propose a method for parametric modeling of the foveal shape. Our method exploits invariant features of the macula from OCT data and applies a cubic Bézier polynomial along with a least square optimization to produce a best fit parametric model of the fovea. Additionally, we provide several parameters of the foveal shape based on the proposed 3D parametric modeling. Our quantitative and visual results show that the proposed model is not only able to reconstruct important features from the foveal shape, but also produces less error compared to the state-of-the-art methods. Finally, we apply the model in a comparison of healthy control eyes and eyes from patients with neuroinflammatory central nervous system disorders and optic neuritis, and show that several derived model parameters show significant differences between the two groups.
Parametric linear modeling of circular cMUT membranes in vacuum.
Köymen, Hayrettin; Senlik, Muhammed N; Atalar, Abdullah; Olcum, Selim
2007-06-01
We present a lumped element parametric model for the clamped circular membrane of a capacitive micromachined ultrasonic transducer (cMUT). The model incorporates an electrical port and two sets of acoustic ports, through which the cMUT couples to the medium. The modeling approach is based on matching a lumped element model and the mechanical impedance of the cMUT membrane at the resonance frequencies in vacuum. Very good agreement between finite element simulation results and model impedance is obtained. Equivalent circuit model parameters can be found from material properties and membrane dimensions without a need for finite element simulation.
Form factors of exponential fields for two-parametric family of integrable models
International Nuclear Information System (INIS)
Fateev, V.A.; Lashkevich, M.
2004-01-01
A two-parametric family of integrable models (the SS model) that contains as particular cases several well known integrable quantum field theories is considered. After the quantum group restriction it describes a wide class of integrable perturbed conformal field theories. Exponential fields in the SS model are closely related to the primary fields in these perturbed theories. We use the bosonization approach to derive an integral representation for the form factors of the exponential fields in the SS model. The same representations for the sausage model and the cosine-cosine model are obtained as limiting cases. The results are tested at the special points, where the theory contains free particles
Form factors of exponential fields for two-parametric family of integrable models
Fateev, V. A.; Lashkevich, M.
2004-09-01
A two-parametric family of integrable models (the SS model) that contains as particular cases several well known integrable quantum field theories is considered. After the quantum group restriction it describes a wide class of integrable perturbed conformal field theories. Exponential fields in the SS model are closely related to the primary fields in these perturbed theories. We use the bosonization approach to derive an integral representation for the form factors of the exponential fields in the SS model. The same representations for the sausage model and the cosine-cosine model are obtained as limiting cases. The results are tested at the special points, where the theory contains free particles.
Energy Technology Data Exchange (ETDEWEB)
Dai, Heng [Pacific Northwest National Laboratory, Richland Washington USA; Ye, Ming [Department of Scientific Computing, Florida State University, Tallahassee Florida USA; Walker, Anthony P. [Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge Tennessee USA; Chen, Xingyuan [Pacific Northwest National Laboratory, Richland Washington USA
2017-04-01
Hydrological models are always composed of multiple components that represent processes key to intended model applications. When a process can be simulated by multiple conceptual-mathematical models (process models), model uncertainty in representing the process arises. While global sensitivity analysis methods have been widely used for identifying important processes in hydrologic modeling, the existing methods consider only parametric uncertainty but ignore the model uncertainty for process representation. To address this problem, this study develops a new method to probe multimodel process sensitivity by integrating the model averaging methods into the framework of variance-based global sensitivity analysis, given that the model averaging methods quantify both parametric and model uncertainty. A new process sensitivity index is derived as a metric of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and model parameters. For demonstration, the new index is used to evaluate the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that converting precipitation to recharge, and the geology process is also simulated by two models of different parameterizations of hydraulic conductivity; each process model has its own random parameters. The new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.
Parametric linear hybrid automata for complex environmental systems modeling
Tareen, Samar H. K.; Ahmad, Jamil; Roux, Olivier
2015-01-01
Environmental systems, whether they be weather patterns or predator–prey relationships, are dependent on a number different variables, each directly or indirectly affecting the system at large. Since not all of these factors are known, these systems take on non-linear dynamics, making it difficult to accurately predict meaningful behavioral trends far into the future. However, such dynamics do not warrant complete ignorance of different efforts to understand and model close approximations of ...
Gan, Y.; Liang, X. Z.; Duan, Q.; Xu, J.; Zhao, P.; Hong, Y.
2017-12-01
The uncertainties associated with the parameters of a hydrological model need to be quantified and reduced for it to be useful for operational hydrological forecasting and decision support. An uncertainty quantification framework is presented to facilitate practical assessment and reduction of model parametric uncertainties. A case study, using the distributed hydrological model CREST for daily streamflow simulation during the period 2008-2010 over ten watershed, was used to demonstrate the performance of this new framework. Model behaviors across watersheds were analyzed by a two-stage stepwise sensitivity analysis procedure, using LH-OAT method for screening out insensitive parameters, followed by MARS-based Sobol' sensitivity indices for quantifying each parameter's contribution to the response variance due to its first-order and higher-order effects. Pareto optimal sets of the influential parameters were then found by the adaptive surrogate-based multi-objective optimization procedure, using MARS model for approximating the parameter-response relationship and SCE-UA algorithm for searching the optimal parameter sets of the adaptively updated surrogate model. The final optimal parameter sets were validated against the daily streamflow simulation of the same watersheds during the period 2011-2012. The stepwise sensitivity analysis procedure efficiently reduced the number of parameters that need to be calibrated from twelve to seven, which helps to limit the dimensionality of calibration problem and serves to enhance the efficiency of parameter calibration. The adaptive MARS-based multi-objective calibration exercise provided satisfactory solutions to the reproduction of the observed streamflow for all watersheds. The final optimal solutions showed significant improvement when compared to the default solutions, with about 65-90% reduction in 1-NSE and 60-95% reduction in |RB|. The validation exercise indicated a large improvement in model performance with about 40
Fitting of Parametric Building Models to Oblique Aerial Images
Panday, U. S.; Gerke, M.
2011-09-01
In literature and in photogrammetric workstations many approaches and systems to automatically reconstruct buildings from remote sensing data are described and available. Those building models are being used for instance in city modeling or in cadastre context. If a roof overhang is present, the building walls cannot be estimated correctly from nadir-view aerial images or airborne laser scanning (ALS) data. This leads to inconsistent building outlines, which has a negative influence on visual impression, but more seriously also represents a wrong legal boundary in the cadaster. Oblique aerial images as opposed to nadir-view images reveal greater detail, enabling to see different views of an object taken from different directions. Building walls are visible from oblique images directly and those images are used for automated roof overhang estimation in this research. A fitting algorithm is employed to find roof parameters of simple buildings. It uses a least squares algorithm to fit projected wire frames to their corresponding edge lines extracted from the images. Self-occlusion is detected based on intersection result of viewing ray and the planes formed by the building whereas occlusion from other objects is detected using an ALS point cloud. Overhang and ground height are obtained by sweeping vertical and horizontal planes respectively. Experimental results are verified with high resolution ortho-images, field survey, and ALS data. Planimetric accuracy of 1cm mean and 5cm standard deviation was obtained, while buildings' orientation were accurate to mean of 0.23° and standard deviation of 0.96° with ortho-image. Overhang parameters were aligned to approximately 10cm with field survey. The ground and roof heights were accurate to mean of - 9cm and 8cm with standard deviations of 16cm and 8cm with ALS respectively. The developed approach reconstructs 3D building models well in cases of sufficient texture. More images should be acquired for completeness of
Parametric pattern selection in a reaction-diffusion model.
Directory of Open Access Journals (Sweden)
Michael Stich
Full Text Available We compare spot patterns generated by Turing mechanisms with those generated by replication cascades, in a model one-dimensional reaction-diffusion system. We determine the stability region of spot solutions in parameter space as a function of a natural control parameter (feed-rate where degenerate patterns with different numbers of spots coexist for a fixed feed-rate. While it is possible to generate identical patterns via both mechanisms, we show that replication cascades lead to a wider choice of pattern profiles that can be selected through a tuning of the feed-rate, exploiting hysteresis and directionality effects of the different pattern pathways.
A parametric model for analyzing anticipation in genetically predisposed families
DEFF Research Database (Denmark)
Larsen, Klaus; Petersen, Janne; Bernstein, Inge
2009-01-01
Anticipation, i.e. a decreasing age-at-onset in subsequent generations has been observed in a number of genetically triggered diseases. The impact of anticipation is generally studied in affected parent-child pairs. These analyses are restricted to pairs in which both individuals have been affected....... The suggested model corrects for incomplete observations and considers families rather than affected pairs and thereby allows for studies of large sample sets, facilitates subgroup analyses and provides generation effect estimates.......)/Lynch syndrome family cohort from the national Danish HNPCC register. Age-at-onset was analyzed in 824 individuals from 2-4 generations in 125 families with proved disease-predisposing mutations. A significant effect from anticipation was identified with a mean of 3 years earlier age-at-onset per generation...
A Hybrid Wind-Farm Parametrization for Mesoscale and Climate Models
Pan, Yang; Archer, Cristina L.
2018-04-01
To better understand the potential impact of wind farms on weather and climate at the regional to global scales, a new hybrid wind-farm parametrization is proposed for mesoscale and climate models. The proposed parametrization is a hybrid model because it is not based on physical processes or conservation laws, but on the multiple linear regression of the results of large-eddy simulations (LES) with the geometric properties of the wind-farm layout (e.g., the blockage ratio and blockage distance). The innovative aspect is that each wind turbine is treated individually based on its position in the farm and on the wind direction by predicting the velocity upstream of each turbine. The turbine-induced forces and added turbulence kinetic energy (TKE) are first derived analytically and then implemented in the Weather Research and Forecasting model. Idealized simulations of the offshore Lillgrund wind farm are conducted. The wind-speed deficit and TKE predicted with the hybrid model are in excellent agreement with those from the LES results, while the wind-power production estimated with the hybrid model is within 10% of that observed. Three additional wind farms with larger inter-turbine spacing than at Lillgrund are also considered, and a similar agreement with LES results is found, proving that the hybrid parametrization works well with any wind farm regardless of the spacing between turbines. These results indicate the wind-turbine position, wind direction, and added TKE are essential in accounting for the wind-farm effects on the surroundings, for which the hybrid wind-farm parametrization is a promising tool.
Modeling and parametric analysis of a piezoelectric flexoelectric nanoactuator
Directory of Open Access Journals (Sweden)
Baroudi Sourour
2016-01-01
Full Text Available With the development of nanotechnology, nanoactuators have recently re-stimulated a surge of scientific interests in research communities. One of the interesting transduction mechanisms that showed high efficiency at the nanoscale was flexoelectricity. In fact, the flexoelectric effect in dielectric solids couples polarization and strain gradient, rather than polarization and strain for piezoelectricity, to convert mechanical stimulus into electricity and vice cersa. The objective of the current work is to develop a complete comprehensive electromechanical model of a nanobeam whose for piezoelectrically-actuated nanocantilever sensor in which both the flexoelectricity and piezoelectricity effects will be tzken into consideration. Starting from the enthalpy density function, the Hamilton’s principle is applied to drive the governing coupled equations with appropriate boundary conditions. Then, we investigate the free vibration of the mechanism by formulating the eigenvalue problem associated with the coupled partial differential equations. Using the Galerkin procedure we develop both the static and dynamic of our structure. The results show that a certain aspect ratio flexoelectric effect significantly increases the performance of the nanoactuator.
Bioprocess iterative batch-to-batch optimization based on hybrid parametric/nonparametric models.
Teixeira, Ana P; Clemente, João J; Cunha, António E; Carrondo, Manuel J T; Oliveira, Rui
2006-01-01
This paper presents a novel method for iterative batch-to-batch dynamic optimization of bioprocesses. The relationship between process performance and control inputs is established by means of hybrid grey-box models combining parametric and nonparametric structures. The bioreactor dynamics are defined by material balance equations, whereas the cell population subsystem is represented by an adjustable mixture of nonparametric and parametric models. Thus optimizations are possible without detailed mechanistic knowledge concerning the biological system. A clustering technique is used to supervise the reliability of the nonparametric subsystem during the optimization. Whenever the nonparametric outputs are unreliable, the objective function is penalized. The technique was evaluated with three simulation case studies. The overall results suggest that the convergence to the optimal process performance may be achieved after a small number of batches. The model unreliability risk constraint along with sampling scheduling are crucial to minimize the experimental effort required to attain a given process performance. In general terms, it may be concluded that the proposed method broadens the application of the hybrid parametric/nonparametric modeling technique to "newer" processes with higher potential for optimization.
International Nuclear Information System (INIS)
Lefieux, V.
2007-10-01
Reseau de Transport d'Electricite (RTE), in charge of operating the French electric transportation grid, needs an accurate forecast of the power consumption in order to operate it correctly. The forecasts used everyday result from a model combining a nonlinear parametric regression and a SARIMA model. In order to obtain an adaptive forecasting model, nonparametric forecasting methods have already been tested without real success. In particular, it is known that a nonparametric predictor behaves badly with a great number of explanatory variables, what is commonly called the curse of dimensionality. Recently, semi parametric methods which improve the pure nonparametric approach have been proposed to estimate a regression function. Based on the concept of 'dimension reduction', one those methods (called MAVE : Moving Average -conditional- Variance Estimate) can apply to time series. We study empirically its effectiveness to predict the future values of an autoregressive time series. We then adapt this method, from a practical point of view, to forecast power consumption. We propose a partially linear semi parametric model, based on the MAVE method, which allows to take into account simultaneously the autoregressive aspect of the problem and the exogenous variables. The proposed estimation procedure is practically efficient. (author)
Balaykin, A. V.; Bezsonov, K. A.; Nekhoroshev, M. V.; Shulepov, A. P.
2018-01-01
This paper dwells upon a variance parameterization method. Variance or dimensional parameterization is based on sketching, with various parametric links superimposed on the sketch objects and user-imposed constraints in the form of an equation system that determines the parametric dependencies. This method is fully integrated in a top-down design methodology to enable the creation of multi-variant and flexible fixture assembly models, as all the modeling operations are hierarchically linked in the built tree. In this research the authors consider a parameterization method of machine tooling used for manufacturing parts using multiaxial CNC machining centers in the real manufacturing process. The developed method allows to significantly reduce tooling design time when making changes of a part’s geometric parameters. The method can also reduce time for designing and engineering preproduction, in particular, for development of control programs for CNC equipment and control and measuring machines, automate the release of design and engineering documentation. Variance parameterization helps to optimize construction of parts as well as machine tooling using integrated CAE systems. In the framework of this study, the authors demonstrate a comprehensive approach to parametric modeling of machine tooling in the CAD package used in the real manufacturing process of aircraft engines.
Directory of Open Access Journals (Sweden)
Martin ePyka
2014-09-01
Full Text Available Computational models of neural networks can be based on a variety of different parameters. These parameters include, for example, the 3d shape of neuron layers, the neurons' spatial projection patterns, spiking dynamics and neurotransmitter systems. While many well-developed approaches are available to model, for example, the spiking dynamics, there is a lack of approaches for modeling the anatomical layout of neurons and their projections. We present a new method, called Parametric Anatomical Modeling (PAM, to fill this gap. PAM can be used to derive network connectivities and conduction delays from anatomical data, such as the position and shape of the neuronal layers and the dendritic and axonal projection patterns. Within the PAM framework, several mapping techniques between layers can account for a large variety of connection properties between pre- and post-synaptic neuron layers. PAM is implemented as a Python tool and integrated in the 3d modeling software Blender. We demonstrate on a 3d model of the hippocampal formation how PAM can help reveal complex properties of the synaptic connectivity and conduction delays, properties that might be relevant to uncover the function of the hippocampus. Based on these analyses, two experimentally testable predictions arose: i the number of neurons and the spread of connections is heterogeneously distributed across the main anatomical axes, ii the distribution of connection lengths in CA3-CA1 differ qualitatively from those between DG-CA3 and CA3-CA3. Models created by PAM can also serve as an educational tool to visualize the 3d connectivity of brain regions. The low-dimensional, but yet biologically plausible, parameter space renders PAM suitable to analyse allometric and evolutionary factors in networks and to model the complexity of real networks with comparatively little effort.
Pyka, Martin; Klatt, Sebastian; Cheng, Sen
2014-01-01
Computational models of neural networks can be based on a variety of different parameters. These parameters include, for example, the 3d shape of neuron layers, the neurons' spatial projection patterns, spiking dynamics and neurotransmitter systems. While many well-developed approaches are available to model, for example, the spiking dynamics, there is a lack of approaches for modeling the anatomical layout of neurons and their projections. We present a new method, called Parametric Anatomical Modeling (PAM), to fill this gap. PAM can be used to derive network connectivities and conduction delays from anatomical data, such as the position and shape of the neuronal layers and the dendritic and axonal projection patterns. Within the PAM framework, several mapping techniques between layers can account for a large variety of connection properties between pre- and post-synaptic neuron layers. PAM is implemented as a Python tool and integrated in the 3d modeling software Blender. We demonstrate on a 3d model of the hippocampal formation how PAM can help reveal complex properties of the synaptic connectivity and conduction delays, properties that might be relevant to uncover the function of the hippocampus. Based on these analyses, two experimentally testable predictions arose: (i) the number of neurons and the spread of connections is heterogeneously distributed across the main anatomical axes, (ii) the distribution of connection lengths in CA3-CA1 differ qualitatively from those between DG-CA3 and CA3-CA3. Models created by PAM can also serve as an educational tool to visualize the 3d connectivity of brain regions. The low-dimensional, but yet biologically plausible, parameter space renders PAM suitable to analyse allometric and evolutionary factors in networks and to model the complexity of real networks with comparatively little effort.
Pasta, Salvatore; Gentile, Giovanni; Raffa, Giuseppe M; Scardulla, Francesco; Bellavia, Diego; Luca, Angelo; Pilato, Michele; Scardulla, Cesare
2017-09-01
Bicuspid aortic valve (BAV)-associated ascending aneurysmal aortopathy (namely "bicuspid aortopathy") is a heterogeneous disease making surgeon predictions particularly challenging. Computational flow analysis can be used to evaluate the BAV-related hemodynamic disturbances, which likely lead to aneurysm enlargement and progression. However, the anatomic reconstruction process is time consuming so that predicting hemodynamic and structural evolution by computational modeling is unfeasible in routine clinical practice. The aim of the study was to design and develop a parametric program for three-dimensional (3D) representations of aneurysmal aorta and different BAV phenotypes starting from several measures derived by computed-tomography angiography (CTA). Assuming that wall shear stress (WSS) has an important implication on bicuspid aortopathy, computational flow analyses were then performed to estimate how different would such an important parameter be, if a parametric aortic geometry was used as compared to standard geometric reconstructions obtained by CTA scans. Morphologic parameters here documented can be used to rapidly model the aorta and any phenotypes of BAV. t-test and Bland-Altman plot demonstrated that WSS obtained by flow analysis of parametric aortic geometries was in good agreement with that obtained from the flow analysis of CTA-related geometries. The proposed program offers a rapid and automated tool for 3D anatomic representations of bicuspid aortopathy with promising application in routine clinical practice by reducing the amount of time for anatomic reconstructions. © 2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.
A semi empirical model of the direct methanol fuel cell. Part II. Parametric analysis
Scott, K.; Jackson, C.; Argyropoulos, P.
A parametric analysis of a model equation developed to predict the cell voltage versus current density response of a liquid feed direct methanol fuel cell is presented. The equation is based on a semi-empirical approach in which methanol oxidation and oxygen reduction kinetics are combined with effective mass transport coefficients for the fuel cell electrodes. The model equation is applied to experimental data for a small-scale fuel cell and produces electrochemical parameters generally consistent with those expected for the individual components of the fuel cell MEA. The parameters thus determined are also used in the model to predict the performance of a DMFC with a new membrane electrode assembly.
Modelling of fertilizer drying in a rotary dryer: parametric sensitivity analysis
Directory of Open Access Journals (Sweden)
M. G. Silva
2012-06-01
Full Text Available This study analyzed the influence of the following parameters: overall volumetric heat transfer coefficient, coefficient of heat loss, drying rate, specific heat of the solid and specific heat of dry air on the prediction of a model for the fertilizer drying in rotary dryers. The method of parametric sensitivity using an experimental design was employed in this study. All parameters studied significantly affected the responses of the drying model. In general, the model showed greater sensitivity to the parameters drying rate and overall volumetric heat transfer coefficient.
Oliveira, N. P.; Maciel, L.; Catarino, A. P.; Rocha, A. M.
2017-10-01
This work proposes the creation of models of surfaces using a parametric computer modelling software to obtain three-dimensional structures in weft knitted fabrics produced on single needle system machines. Digital prototyping, another feature of digital modelling software, was also explored in three-dimensional drawings generated using the Rhinoceros software. With this approach, different 3D structures were developed and produced. Physical characterization tests were then performed on the resulting 3D weft knitted structures to assess their ability to promote comfort. From the obtained results, it is apparent that the developed structures have potential for application in different market segments, such as clothing and interior textiles.
Parametric bootstrap methods for testing multiplicative terms in GGE and AMMI models.
Forkman, Johannes; Piepho, Hans-Peter
2014-09-01
The genotype main effects and genotype-by-environment interaction effects (GGE) model and the additive main effects and multiplicative interaction (AMMI) model are two common models for analysis of genotype-by-environment data. These models are frequently used by agronomists, plant breeders, geneticists and statisticians for analysis of multi-environment trials. In such trials, a set of genotypes, for example, crop cultivars, are compared across a range of environments, for example, locations. The GGE and AMMI models use singular value decomposition to partition genotype-by-environment interaction into an ordered sum of multiplicative terms. This article deals with the problem of testing the significance of these multiplicative terms in order to decide how many terms to retain in the final model. We propose parametric bootstrap methods for this problem. Models with fixed main effects, fixed multiplicative terms and random normally distributed errors are considered. Two methods are derived: a full and a simple parametric bootstrap method. These are compared with the alternatives of using approximate F-tests and cross-validation. In a simulation study based on four multi-environment trials, both bootstrap methods performed well with regard to Type I error rate and power. The simple parametric bootstrap method is particularly easy to use, since it only involves repeated sampling of standard normally distributed values. This method is recommended for selecting the number of multiplicative terms in GGE and AMMI models. The proposed methods can also be used for testing components in principal component analysis. © 2014, The International Biometric Society.
Parametric analysis of three dimensional flow models applied to tidal energy sites in Scotland
Rahman, Anas; Venugopal, Vengatesan
2017-04-01
This paper presents a detailed parametric analysis on various input parameters of two different numerical models, namely Telemac3D and Delft3D, used for the simulation of tidal current flow at potential tidal energy sites in the Pentland Firth in Scotland. The motivation behind this work is to investigate the influence of the input parameters on the above 3D models, as the majority of past research has mainly focused on using the 2D depth-averaged flow models for this region. An extended description of the models setup, along with the utilised parameters is provided. The International Hydrographic Organisation (IHO) tidal gauges and Acoustic Doppler and Current Profiler (ADCP) measurements are used in calibrating model output to ensure the robustness of the models. Extensive parametric study on the impact of varying drag coefficients, roughness formulae and turbulence models has been investigated and reported. The results indicate that both Telemac3D and Delft3D models are able to produce excellent comparison against measured data; however, with Delft3D, the model parameters which provided higher correlation with the measured data, are found to be different from those reported in the previous literature, which could be attributed to the choice of boundary conditions and the mesh size.
Parametric analysis of the statistical model of the stick-slip process
Lima, Roberta; Sampaio, Rubens
2017-06-01
In this paper it is performed a parametric analysis of the statistical model of the response of a dry-friction oscillator. The oscillator is a spring-mass system which moves over a base with a rough surface. Due to this roughness, the mass is subject to a dry-frictional force modeled as a Coulomb friction. The system is stochastically excited by an imposed bang-bang base motion. The base velocity is modeled by a Poisson process for which a probabilistic model is fully specified. The excitation induces in the system stochastic stick-slip oscillations. The system response is composed by a random sequence alternating stick and slip-modes. With realizations of the system, a statistical model is constructed for this sequence. In this statistical model, the variables of interest of the sequence are modeled as random variables, as for example, the number of time intervals in which stick or slip occur, the instants at which they begin, and their duration. Samples of the system response are computed by integration of the dynamic equation of the system using independent samples of the base motion. Statistics and histograms of the random variables which characterize the stick-slip process are estimated for the generated samples. The objective of the paper is to analyze how these estimated statistics and histograms vary with the system parameters, i.e., to make a parametric analysis of the statistical model of the stick-slip process.
Dynamic modeling and explicit/multi-parametric MPC control of pressure swing adsorption systems
Khajuria, Harish
2011-01-01
Pressure swing adsorption (PSA) is a flexible, albeit complex gas separation system. Due to its inherent nonlinear nature and discontinuous operation, the design of a model based PSA controller, especially with varying operating conditions, is a challenging task. This work focuses on the design of an explicit/multi-parametric model predictive controller for a PSA system. Based on a system involving four adsorbent beds separating 70% H2, 30% CH4 mixture into high purity hydrogen, the key controller objective is to fast track H2 purity to a set point value of 99.99%. To perform this task, a rigorous and systematic framework is employed. First, a high fidelity detailed dynamic model is built to represent the system\\'s real operation, and understand its dynamic behavior. The model is then used to derive appropriate linear models by applying suitable system identification techniques. For the reduced models, a model predictive control (MPC) step is formulated, where latest developments in multi-parametric programming and control are applied to derive a novel explicit MPC controller. To test the performance of the designed controller, closed loop simulations are performed where the dynamic model is used as the virtual plant. Comparison studies of the derived explicit MPC controller are also performed with conventional PID controllers. © 2010 Elsevier Ltd. All rights reserved.
Driven Bose-Hubbard model with a parametrically modulated harmonic trap
Mann, N.; Bakhtiari, M. Reza; Massel, F.; Pelster, A.; Thorwart, M.
2017-04-01
We investigate a one-dimensional Bose-Hubbard model in a parametrically driven global harmonic trap. The delicate interplay of both the local interaction of the atoms in the lattice and the driving of the global trap allows us to control the dynamical stability of the trapped quantum many-body state. The impact of the atomic interaction on the dynamical stability of the driven quantum many-body state is revealed in the regime of weak interaction by analyzing a discretized Gross-Pitaevskii equation within a Gaussian variational ansatz, yielding a Mathieu equation for the condensate width. The parametric resonance condition is shown to be modified by the atom interaction strength. In particular, the effective eigenfrequency is reduced for growing interaction in the mean-field regime. For a stronger interaction, the impact of the global parametric drive is determined by the numerically exact time-evolving block decimation scheme. When the trapped bosons in the lattice are in a Mott insulating state, the absorption of energy from the driving field is suppressed due to the strongly reduced local compressibility of the quantum many-body state. In particular, we find that the width of the local Mott region shows a breathing dynamics. Finally, we observe that the global modulation also induces an effective time-independent inhomogeneous hopping strength for the atoms.
Parametric sensitivity analysis of an agro-economic model of management of irrigation water
El Ouadi, Ihssan; Ouazar, Driss; El Menyari, Younesse
2015-04-01
The current work aims to build an analysis and decision support tool for policy options concerning the optimal allocation of water resources, while allowing a better reflection on the issue of valuation of water by the agricultural sector in particular. Thus, a model disaggregated by farm type was developed for the rural town of Ait Ben Yacoub located in the east Morocco. This model integrates economic, agronomic and hydraulic data and simulates agricultural gross margin across in this area taking into consideration changes in public policy and climatic conditions, taking into account the competition for collective resources. To identify the model input parameters that influence over the results of the model, a parametric sensitivity analysis is performed by the "One-Factor-At-A-Time" approach within the "Screening Designs" method. Preliminary results of this analysis show that among the 10 parameters analyzed, 6 parameters affect significantly the objective function of the model, it is in order of influence: i) Coefficient of crop yield response to water, ii) Average daily gain in weight of livestock, iii) Exchange of livestock reproduction, iv) maximum yield of crops, v) Supply of irrigation water and vi) precipitation. These 6 parameters register sensitivity indexes ranging between 0.22 and 1.28. Those results show high uncertainties on these parameters that can dramatically skew the results of the model or the need to pay particular attention to their estimates. Keywords: water, agriculture, modeling, optimal allocation, parametric sensitivity analysis, Screening Designs, One-Factor-At-A-Time, agricultural policy, climate change.
A simple parametric model observer for quality assurance in computer tomography
Anton, M.; Khanin, A.; Kretz, T.; Reginatto, M.; Elster, C.
2018-04-01
Model observers are mathematical classifiers that are used for the quality assessment of imaging systems such as computer tomography. The quality of the imaging system is quantified by means of the performance of a selected model observer. For binary classification tasks, the performance of the model observer is defined by the area under its ROC curve (AUC). Typically, the AUC is estimated by applying the model observer to a large set of training and test data. However, the recording of these large data sets is not always practical for routine quality assurance. In this paper we propose as an alternative a parametric model observer that is based on a simple phantom, and we provide a Bayesian estimation of its AUC. It is shown that a limited number of repeatedly recorded images (10–15) is already sufficient to obtain results suitable for the quality assessment of an imaging system. A MATLAB® function is provided for the calculation of the results. The performance of the proposed model observer is compared to that of the established channelized Hotelling observer and the nonprewhitening matched filter for simulated images as well as for images obtained from a low-contrast phantom on an x-ray tomography scanner. The results suggest that the proposed parametric model observer, along with its Bayesian treatment, can provide an efficient, practical alternative for the quality assessment of CT imaging systems.
A simple parametric model observer for quality assurance in computer tomography.
Anton, Mathias; Khanin, Alexander; Kretz, Tobias; Reginatto, Marcel; Elster, Clemens
2018-02-26
Model observers are mathematical classifiers that are used for the quality assessment of imaging systems such as computer tomography. The quality of the imaging system is quantified by means of the performance of a selected model observer. For binary classification tasks, the performance of the model observer is defined by the area under its ROC curve (AUC). Typically, the AUC is estimated by applying the model observer to a large set of training and test data. However, the recording of these large data sets is not always practical for routine quality assurance. In this paper we propose as an alternative a parametric model observer that is based on a simple phantom, and we provide a Bayesian estimation of its AUC. It is shown that a limited number of repeatedly recorded images (10-15) is already sufficient to obtain results suitable for the quality assessment of an imaging system. A MATLAB^{®} function is provided for the calculation of the results. The performance of the proposed model observer is compared to that of the established channelized Hotelling observer (CHO) and the nonprewhitening matched filter (NPW) for simulated images as well as for images obtained from a low-contrast phantom on an x-ray tomography scanner. The results suggest that the proposed parametric model observer, along with its Bayesian treatment, can provide an efficient, practical alternative for the quality assessment of CT imaging systems. © 2018 Institute of Physics and Engineering in Medicine.
Identification of parametric models with a priori knowledge of process properties
Directory of Open Access Journals (Sweden)
Janiszowski Krzysztof B.
2016-12-01
Full Text Available An approach to estimation of a parametric discrete-time model of a process in the case of some a priori knowledge of the investigated process properties is presented. The knowledge of plant properties is introduced in the form of linear bounds, which can be determined for the coefficient vector of the parametric model studied. The approach yields special biased estimation of model coefficients that preserves demanded properties. A formula for estimation of the model coefficients is derived and combined with a recursive scheme determined for minimization of the sum of absolute model errors. The estimation problem of a model with known static gains of inputs is discussed and proper formulas are derived. This approach can overcome the non-identifiability problem which has been observed during estimation based on measurements recorded in industrial closed-loop control systems. The application of the proposed approach to estimation of a model for an industrial plant (a water injector into the steam flow in a power plant is presented and discussed.
X-1 to X-Wings: Developing a Parametric Cost Model
Sterk, Steve; McAtee, Aaron
2015-01-01
In todays cost-constrained environment, NASA needs an X-Plane database and parametric cost model that can quickly provide rough order of magnitude predictions of cost from initial concept to first fight of potential X-Plane aircraft. This paper takes a look at the steps taken in developing such a model and reports the results. The challenges encountered in the collection of historical data and recommendations for future database management are discussed. A step-by-step discussion of the development of Cost Estimating Relationships (CERs) is then covered.
DEFF Research Database (Denmark)
Creixell Mediante, Ester; Jensen, Jakob Søndergaard; Naets, Frank
2018-01-01
performance and modelling them accurately requires a precise description of the strong interaction between the light-weight parts and the internal and surrounding air over a wide frequency range. Parametric optimization of the FE model can be used to reduce the vibroacoustic feedback in a device during...... the design phase; however, it requires solving the model iteratively for multiple frequencies at different parameter values, which becomes highly time consuming when the system is large. Parametric Model Order Reduction (pMOR) techniques aim at reducing the computational cost associated with each analysis......-parameter optimization of a frequency response for a hearing aid model, evaluated at 300 frequencies, where the objective function evaluations become more than one order of magnitude faster than for the full system....
Parametric CAD and Fea Model of a Saddle Tapping Tee
DEFF Research Database (Denmark)
A. Kristensen, Anders Schmidt; Lund Jepsen, Kristian
2007-01-01
is determined from paragraph K302.3.2 in ASME B31.3. A full parametric 3D CAD model of the Saddle Tapping Tee is developed where a number of user-defined parameters are controlled from an Excel spreadsheet allowing parameter studies and technical documentation to be generated effectively. The same Excel spread......-sheet control a full 3D parametric FEA model which is automatically updated from the user-defined parameters set for the CAD-model. As the gasket is subjected to a contact pressure arising from the clamping force acting on the pipe section, a FE contact analysis is carried out. This contact analysis is set up...... required and relevant user-defined parameter on the Saddle Tapping Tee from a single Excel spreadsheet in both the CAD model and the FE model. A full 3D CAD model is effectively generated for dimensions in the range from NPS ½ to NPS 24 and FEA can be performed to provide documentation of the behaviour...
Wang, S.; Jun, Z.
2017-12-01
Climatic characteristics of tropical stratospheric methane have been well researched using various satellite data, and numerical simulations have furtherly conducted using chemical climatic models, while the impact of stratospheric methane oxidation on distribution of water vapor is not paid enough attention in general circulation models. Simulated values of water vapour in the tropical upper stratosphere, and throughout much of the extratropical stratosphere, were too low. Something must be done to remedy this deficiency in order to producing realistic stratospheric water vapor using a general circulation model including the whole stratosphere. Introduction of a simple parametrization of the upper-stratospheric moisture source due to methane oxidation and a sink due to photolysis in the mesosphere was conducted. Numerical simulations and analysis of the influence of stratospheric methane on the prediction of tropical stratospheric moisture and temperature fields were carried out. This study presents the advantages of methane oxidation parametrization in producing a realistic distribution of water vapour in the tropical stratosphere and analyzes the impact of methane chemical process on the general circulation model using two storm cases including a heavy rain in South China and a typhoon caused tropical storm.It is obvious that general circulation model with methane oxidation parametrization succeeds in simulating the water vapor and temperature in stratosphere. The simulating rain center value of contrast experiment is increased up to 10% than that of the control experiment. Introduction of methane oxidation parametrization has modified the distribution of water vapour and then producing a broadly realistic distribution of temperature. Objective weather forecast verifications have been performed using simulating results of one month, which demonstrate somewhat positive effects on the model skill. There is a certain extent impact of methane oxidation
International Nuclear Information System (INIS)
Battaglia, N.; Trac, H.; Cen, R.; Loeb, A.
2013-01-01
We present a new method for modeling inhomogeneous cosmic reionization on large scales. Utilizing high-resolution radiation-hydrodynamic simulations with 2048 3 dark matter particles, 2048 3 gas cells, and 17 billion adaptive rays in a L = 100 Mpc h –1 box, we show that the density and reionization redshift fields are highly correlated on large scales (∼> 1 Mpc h –1 ). This correlation can be statistically represented by a scale-dependent linear bias. We construct a parametric function for the bias, which is then used to filter any large-scale density field to derive the corresponding spatially varying reionization redshift field. The parametric model has three free parameters that can be reduced to one free parameter when we fit the two bias parameters to simulation results. We can differentiate degenerate combinations of the bias parameters by combining results for the global ionization histories and correlation length between ionized regions. Unlike previous semi-analytic models, the evolution of the reionization redshift field in our model is directly compared cell by cell against simulations and performs well in all tests. Our model maps the high-resolution, intermediate-volume radiation-hydrodynamic simulations onto lower-resolution, larger-volume N-body simulations (∼> 2 Gpc h –1 ) in order to make mock observations and theoretical predictions
Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model
Directory of Open Access Journals (Sweden)
Isaac Mugume
2016-01-01
Full Text Available Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE, the mean absolute error (MAE, mean error (ME, skewness, and the bias easy estimate (BES and nonparametric (the sign test, STM methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.
A photometric approach to parametric modelling for optimising multisegmented photodetector rings
International Nuclear Information System (INIS)
Yoon, P S; Siddons, D P
2013-01-01
An analytical (theoretical) method for parametric modelling to optimise fluorescent-type x-ray photodetectors has been developed. The primary purpose of this method is to maximise detector's photon-detection efficiency, thereby enhancing its spatial sensitivity. On the basis of the definition of the solid angle, its sensor-target subsystem was fully parametrised in three dimensions. And afterwards real-valued analytical functions of detector's solid angle were derived, leading to a series of further calculations. As a result of this parametric modelling, a miniaturised ultrasensitive photodetector system was designed with its peak total solid angle as large as 0.70 (steradian) at a practical optimum working distance of 3.0 (mm). Subsequent difference-over-sum calculations yield an enhancement in spatial resolution by a factor of four within its linear band. With the application of this optimisation algorithm embedded in this analytical model, one round of prototyping is sufficient to reach its desired spatial sensitivity, resulting in a drastic reduction of prototyping time and cost. Accordingly, this analytical model with full parametrisation has proved itself to be an indispensable and versatile design tool to utilise in a design phase of such position-sensitive photodetectors. It is therefore envisioned that this photometric approach to modelling photodetectors can be augmented for designing different types of optical instruments in a wide range of scientific disciplines.
Economic policy optimization based on both one stochastic model and the parametric control theory
Ashimov, Abdykappar; Borovskiy, Yuriy; Onalbekov, Mukhit
2016-06-01
A nonlinear dynamic stochastic general equilibrium model with financial frictions is developed to describe two interacting national economies in the environment of the rest of the world. Parameters of nonlinear model are estimated based on its log-linearization by the Bayesian approach. The nonlinear model is verified by retroprognosis, estimation of stability indicators of mappings specified by the model, and estimation the degree of coincidence for results of internal and external shocks' effects on macroeconomic indicators on the basis of the estimated nonlinear model and its log-linearization. On the base of the nonlinear model, the parametric control problems of economic growth and volatility of macroeconomic indicators of Kazakhstan are formulated and solved for two exchange rate regimes (free floating and managed floating exchange rates)
Hybrid Model of Inhomogeneous Solar Wind Plasma Heating by Alfven Wave Spectrum: Parametric Studies
Ofman, L.
2010-01-01
Observations of the solar wind plasma at 0.3 AU and beyond show that a turbulent spectrum of magnetic fluctuations is present. Remote sensing observations of the corona indicate that heavy ions are hotter than protons and their temperature is anisotropic (T(sub perpindicular / T(sub parallel) >> 1). We study the heating and the acceleration of multi-ion plasma in the solar wind by a turbulent spectrum of Alfvenic fluctuations using a 2-D hybrid numerical model. In the hybrid model the protons and heavy ions are treated kinetically as particles, while the electrons are included as neutralizing background fluid. This is the first two-dimensional hybrid parametric study of the solar wind plasma that includes an input turbulent wave spectrum guided by observation with inhomogeneous background density. We also investigate the effects of He++ ion beams in the inhomogeneous background plasma density on the heating of the solar wind plasma. The 2-D hybrid model treats parallel and oblique waves, together with cross-field inhomogeneity, self-consistently. We investigate the parametric dependence of the perpendicular heating, and the temperature anisotropy in the H+-He++ solar wind plasma. It was found that the scaling of the magnetic fluctuations power spectrum steepens in the higher-density regions, and the heating is channeled to these regions from the surrounding lower-density plasma due to wave refraction. The model parameters are applicable to the expected solar wind conditions at about 10 solar radii.
Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models.
Zeng, Ping; Zhou, Xiang
2017-09-06
Using genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling all genetic variants together via polygenic methods. Here, we develop such a polygenic method, which we refer to as the latent Dirichlet process regression model. Dirichlet process regression is non-parametric in nature, relies on the Dirichlet process to flexibly and adaptively model the effect size distribution, and thus enjoys robust prediction performance across a broad spectrum of genetic architectures. We compare Dirichlet process regression with several commonly used prediction methods with simulations. We further apply Dirichlet process regression to predict gene expressions, to conduct PrediXcan based gene set test, to perform genomic selection of four traits in two species, and to predict eight complex traits in a human cohort.Genetic prediction of complex traits with polygenic architecture has wide application from animal breeding to disease prevention. Here, Zeng and Zhou develop a non-parametric genetic prediction method based on latent Dirichlet Process regression models.
Aplication re-engineering, the multi-parametrical hierarchical optimal model
Directory of Open Access Journals (Sweden)
Spiák Ján
2004-09-01
Full Text Available The target of this contribution is to define a new working out way, from re-engineering of production processes coming from the large-dimensional optimalizing problems, with applying the multi-parametrical hierarchical optimal model, builds up from 3 levels ( technology, logistics, economy. The designed working out way comes from generalizing obtained experiences from application the re-engineering in concrete conditions of processes working a processing row material (re-engineering the plant Siderit, Slovmag company and taking in consideration specific conditions of state enterprise experience in Slovak republic.
A non-parametric consistency test of the ΛCDM model with Planck CMB data
Energy Technology Data Exchange (ETDEWEB)
Aghamousa, Amir; Shafieloo, Arman [Korea Astronomy and Space Science Institute, Daejeon 305-348 (Korea, Republic of); Hamann, Jan, E-mail: amir@aghamousa.com, E-mail: jan.hamann@unsw.edu.au, E-mail: shafieloo@kasi.re.kr [School of Physics, The University of New South Wales, Sydney NSW 2052 (Australia)
2017-09-01
Non-parametric reconstruction methods, such as Gaussian process (GP) regression, provide a model-independent way of estimating an underlying function and its uncertainty from noisy data. We demonstrate how GP-reconstruction can be used as a consistency test between a given data set and a specific model by looking for structures in the residuals of the data with respect to the model's best-fit. Applying this formalism to the Planck temperature and polarisation power spectrum measurements, we test their global consistency with the predictions of the base ΛCDM model. Our results do not show any serious inconsistencies, lending further support to the interpretation of the base ΛCDM model as cosmology's gold standard.
Parametric modeling of energy filtering by energy barriers in thermoelectric nanocomposites
Energy Technology Data Exchange (ETDEWEB)
Zianni, Xanthippi, E-mail: xzianni@teiste.gr, E-mail: xzianni@gmail.com [Department of Aircraft Technology, Technological Educational Institution of Sterea Ellada, 34400 Psachna (Greece); Department of Microelectronics, INN, NCSR “Demokritos,” 15310 Athens (Greece); Narducci, Dario [Department of Materials Science, University of Milano Bicocca, 20125 Milano (Italy)
2015-01-21
We present a parametric modeling of the thermoelectric transport coefficients based on a model previously used to interpret experimental measurements on the conductivity, σ, and Seebeck coefficient, S, in highly Boron-doped polycrystalline Si, where a very significant thermoelectric power factor (TPF) enhancement was observed. We have derived analytical formalism for the transport coefficients in the presence of an energy barrier assuming thermionic emission over the barrier for (i) non-degenerate and (ii) degenerate one-band semiconductor. Simple generic parametric equations are found that are in agreement with the exact Boltzmann transport formalism in a wide range of parameters. Moreover, we explore the effect of energy barriers in 1-d composite semiconductors in the presence of two phases: (a) the bulk-like phase and (b) the barrier phase. It is pointed out that significant TPF enhancement can be achieved in the composite structure of two phases with different thermal conductivities. The TPF enhancement is estimated as a function of temperature, the Fermi energy position, the type of scattering, and the barrier height. The derived modeling provides guidance for experiments and device design.
Yang, Xue; Lauzon, Carolyn B; Crainiceanu, Ciprian; Caffo, Brian; Resnick, Susan M; Landman, Bennett A
2012-09-01
Massively univariate regression and inference in the form of statistical parametric mapping have transformed the way in which multi-dimensional imaging data are studied. In functional and structural neuroimaging, the de facto standard "design matrix"-based general linear regression model and its multi-level cousins have enabled investigation of the biological basis of the human brain. With modern study designs, it is possible to acquire multi-modal three-dimensional assessments of the same individuals--e.g., structural, functional and quantitative magnetic resonance imaging, alongside functional and ligand binding maps with positron emission tomography. Largely, current statistical methods in the imaging community assume that the regressors are non-random. For more realistic multi-parametric assessment (e.g., voxel-wise modeling), distributional consideration of all observations is appropriate. Herein, we discuss two unified regression and inference approaches, model II regression and regression calibration, for use in massively univariate inference with imaging data. These methods use the design matrix paradigm and account for both random and non-random imaging regressors. We characterize these methods in simulation and illustrate their use on an empirical dataset. Both methods have been made readily available as a toolbox plug-in for the SPM software. Copyright © 2012 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Pulkit Shamshery
Full Text Available Drip irrigation is a means of distributing the exact amount of water a plant needs by dripping water directly onto the root zone. It can produce up to 90% more crops than rain-fed irrigation, and reduce water consumption by 70% compared to conventional flood irrigation. Drip irrigation may enable millions of poor farmers to rise out of poverty by growing more and higher value crops, while not contributing to overconsumption of water. Achieving this impact will require broadening the engineering knowledge required to design new, low-cost, low-power drip irrigation technology, particularly for poor, off-grid communities in developing countries. For more than 50 years, pressure compensating (PC drip emitters-which can maintain a constant flow rate under variations in pressure, to ensure uniform water distribution on a field-have been designed and optimized empirically. This study presents a parametric model that describes the fluid and solid mechanics that govern the behavior of a common PC emitter architecture, which uses a flexible diaphragm to limit flow. The model was validated by testing nine prototypes with geometric variations, all of which matched predicted performance to within R2 = 0.85. This parametric model will enable irrigation engineers to design new drip emitters with attributes that improve performance and lower cost, which will promote the use of drip irrigation throughout the world.
Shamshery, Pulkit; Wang, Ruo-Qian; Tran, Davis V; Winter V, Amos G
2017-01-01
Drip irrigation is a means of distributing the exact amount of water a plant needs by dripping water directly onto the root zone. It can produce up to 90% more crops than rain-fed irrigation, and reduce water consumption by 70% compared to conventional flood irrigation. Drip irrigation may enable millions of poor farmers to rise out of poverty by growing more and higher value crops, while not contributing to overconsumption of water. Achieving this impact will require broadening the engineering knowledge required to design new, low-cost, low-power drip irrigation technology, particularly for poor, off-grid communities in developing countries. For more than 50 years, pressure compensating (PC) drip emitters-which can maintain a constant flow rate under variations in pressure, to ensure uniform water distribution on a field-have been designed and optimized empirically. This study presents a parametric model that describes the fluid and solid mechanics that govern the behavior of a common PC emitter architecture, which uses a flexible diaphragm to limit flow. The model was validated by testing nine prototypes with geometric variations, all of which matched predicted performance to within R2 = 0.85. This parametric model will enable irrigation engineers to design new drip emitters with attributes that improve performance and lower cost, which will promote the use of drip irrigation throughout the world.
Energy Technology Data Exchange (ETDEWEB)
Marques, T.C.; Cruz Junior, G.; Vinhal, C. [Universidade Federal de Goias (UFG), Goiania, GO (Brazil). Escola de Engenharia Eletrica e de Computacao], Emails: thyago@eeec.ufg.br, gcruz@eeec.ufg.br, vinhal@eeec.ufg.br
2009-07-01
The goal of this paper is to present a methodology to carry out the seasonal stream flow forecasting using database of average monthly inflows of one Brazilian hydroelectric plant located at Grande, Tocantins, Paranaiba, Sao Francisco and Iguacu river's. The model is based on the Adaptive Network Based Fuzzy Inference System (ANFIS), the non-parametric model. The performance of this model was compared with a periodic autoregressive model, the parametric model. The results show that the forecasting errors of the non-parametric model considered are significantly lower than the parametric model. (author)
truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models
Directory of Open Access Journals (Sweden)
Maria Karlsson
2014-05-01
Full Text Available Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and ?nite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.
Wang, Zhen-yu; Yu, Jian-cheng; Zhang, Ai-qun; Wang, Ya-xing; Zhao, Wen-tao
2017-12-01
Combining high precision numerical analysis methods with optimization algorithms to make a systematic exploration of a design space has become an important topic in the modern design methods. During the design process of an underwater glider's flying-wing structure, a surrogate model is introduced to decrease the computation time for a high precision analysis. By these means, the contradiction between precision and efficiency is solved effectively. Based on the parametric geometry modeling, mesh generation and computational fluid dynamics analysis, a surrogate model is constructed by adopting the design of experiment (DOE) theory to solve the multi-objects design optimization problem of the underwater glider. The procedure of a surrogate model construction is presented, and the Gaussian kernel function is specifically discussed. The Particle Swarm Optimization (PSO) algorithm is applied to hydrodynamic design optimization. The hydrodynamic performance of the optimized flying-wing structure underwater glider increases by 9.1%.
Evaluation of parametric models by the prediction error in colorectal cancer survival analysis.
Baghestani, Ahmad Reza; Gohari, Mahmood Reza; Orooji, Arezoo; Pourhoseingholi, Mohamad Amin; Zali, Mohammad Reza
2015-01-01
The aim of this study is to determine the factors influencing predicted survival time for patients with colorectal cancer (CRC) using parametric models and select the best model by predicting error's technique. Survival models are statistical techniques to estimate or predict the overall time up to specific events. Prediction is important in medical science and the accuracy of prediction is determined by a measurement, generally based on loss functions, called prediction error. A total of 600 colorectal cancer patients who admitted to the Cancer Registry Center of Gastroenterology and Liver Disease Research Center, Taleghani Hospital, Tehran, were followed at least for 5 years and have completed selected information for this study. Body Mass Index (BMI), Sex, family history of CRC, tumor site, stage of disease and histology of tumor included in the analysis. The survival time was compared by the Log-rank test and multivariate analysis was carried out using parametric models including Log normal, Weibull and Log logistic regression. For selecting the best model, the prediction error by apparent loss was used. Log rank test showed a better survival for females, BMI more than 25, patients with early stage at diagnosis and patients with colon tumor site. Prediction error by apparent loss was estimated and indicated that Weibull model was the best one for multivariate analysis. BMI and Stage were independent prognostic factors, according to Weibull model. In this study, according to prediction error Weibull regression showed a better fit. Prediction error would be a criterion to select the best model with the ability to make predictions of prognostic factors in survival analysis.
Parametric City Scale Energy Modeling Perspectives on using Termite in city scaled models
DEFF Research Database (Denmark)
Negendahl, Kristoffer; Nielsen, Toke Rammer
Termite is a parametric tool using the Danish building performance simulation engine Be10 written for the Grasshopper3D/Rhino3D environment. The tool Be10 is originally intended for building energy frame calculations and is required by Danish law when constructing new buildings. Termite opens up...
Directory of Open Access Journals (Sweden)
Naoki Kawamura
2017-11-01
Full Text Available It is known that the process of reconstruction of a Positron Emission Tomography (PET image from sinogram data is very sensitive to measurement noises; it is still an important research topic to reconstruct PET images with high signal-to-noise ratios. In this paper, we propose a new reconstruction method for a temporal series of PET images from a temporal series of sinogram data. In the proposed method, PET images are reconstructed by minimizing the Kullback–Leibler divergence between the observed sinogram data and sinogram data derived from a parametric model of PET images. The contributions of the proposition include the following: (1 regions of targets in images are explicitly expressed using a set of spatial bases in order to ignore the noises in the background; (2 a parametric time activity model of PET images is explicitly introduced as a constraint; and (3 an algorithm for solving the optimization problem is clearly described. To demonstrate the advantages of the proposed method, quantitative evaluations are performed using both synthetic and clinical data of human brains.
Developing integrated parametric planning models for budgeting and managing complex projects
Etnyre, Vance A.; Black, Ken U.
1988-01-01
The applicability of integrated parametric models for the budgeting and management of complex projects is investigated. Methods for building a very flexible, interactive prototype for a project planning system, and software resources available for this purpose, are discussed and evaluated. The prototype is required to be sensitive to changing objectives, changing target dates, changing costs relationships, and changing budget constraints. To achieve the integration of costs and project and task durations, parametric cost functions are defined by a process of trapezoidal segmentation, where the total cost for the project is the sum of the various project cost segments, and each project cost segment is the integral of a linearly segmented cost loading function over a specific interval. The cost can thus be expressed algebraically. The prototype was designed using Lotus-123 as the primary software tool. This prototype implements a methodology for interactive project scheduling that provides a model of a system that meets most of the goals for the first phase of the study and some of the goals for the second phase.
Garagnani, S.; Manferdini, A. M.
2013-02-01
Since their introduction, modeling tools aimed to architectural design evolved in today's "digital multi-purpose drawing boards" based on enhanced parametric elements able to originate whole buildings within virtual environments. Semantic splitting and elements topology are features that allow objects to be "intelligent" (i.e. self-aware of what kind of element they are and with whom they can interact), representing this way basics of Building Information Modeling (BIM), a coordinated, consistent and always up to date workflow improved in order to reach higher quality, reliability and cost reductions all over the design process. Even if BIM was originally intended for new architectures, its attitude to store semantic inter-related information can be successfully applied to existing buildings as well, especially if they deserve particular care such as Cultural Heritage sites. BIM engines can easily manage simple parametric geometries, collapsing them to standard primitives connected through hierarchical relationships: however, when components are generated by existing morphologies, for example acquiring point clouds by digital photogrammetry or laser scanning equipment, complex abstractions have to be introduced while remodeling elements by hand, since automatic feature extraction in available software is still not effective. In order to introduce a methodology destined to process point cloud data in a BIM environment with high accuracy, this paper describes some experiences on monumental sites documentation, generated through a plug-in written for Autodesk Revit and codenamed GreenSpider after its capability to layout points in space as if they were nodes of an ideal cobweb.
Schreiner, Samuel S.; Dominguez, Jesus A.; Sibille, Laurent; Hoffman, Jeffrey A.
2015-01-01
We present a parametric sizing model for a Molten Electrolysis Reactor that produces oxygen and molten metals from lunar regolith. The model has a foundation of regolith material properties validated using data from Apollo samples and simulants. A multiphysics simulation of an MRE reactor is developed and leveraged to generate a vast database of reactor performance and design trends. A novel design methodology is created which utilizes this database to parametrically design an MRE reactor that 1) can sustain the required mass of molten regolith, current, and operating temperature to meet the desired oxygen production level, 2) can operate for long durations via joule heated, cold wall operation in which molten regolith does not touch the reactor side walls, 3) can support a range of electrode separations to enable operational flexibility. Mass, power, and performance estimates for an MRE reactor are presented for a range of oxygen production levels. The effects of several design variables are explored, including operating temperature, regolith type/composition, batch time, and the degree of operational flexibility.
Directory of Open Access Journals (Sweden)
K. A. Klyukvin
2017-01-01
Full Text Available The paper deals with the parametrical identification method of differential-difference heat transfer models during determining of lidar temperature condition. The problem is solved for enclosure external flange that is the most thermally influenced device part. During researches carried out in a climatic chamber, discrepancy of the both flange temperature and mounted on it sensor temperature is detected. The need of measuring system thermal inertia compensation for the purpose of error decrease is proved. The algorithm for transient flange temperature determining by forward heat transfer problem solution is formed. The inverse procedure is carried out for the purpose of discrepancy minimizing between true object temperature and measured temperature. Computational experiments are carried out for calculating lidar enclosure flange temperature field under known external heat transfer conditions with the use of special computer program and experimental data. The experiment results enable to conclude about the value of error emerging because of temperature measuring system thermal inertia. We show application feasibility for proposed method of parametrical identification of differential-difference heat transfer model in object for error decrease during the device temperature monitoring and control.
Parametric model of ventilators simulated in OpenFOAM and Elmer
Directory of Open Access Journals (Sweden)
Čibera Václav
2016-01-01
Full Text Available The main goal of presented work was to develop parametric model of a ventilator for CFD and structural analysis. The whole model was designed and scripted in freely available open source programmes in particular in OpenFOAM and Elmer. The main script, which runs or generates other scripts and further control the course of simulation, was written in bash scripting language in Linux environment. Further, the scripts needed for a mesh generation and running of a simulation were prepared using m4 word pre-processor. The use of m4 allowed comfortable set up of the higher amount of scripts. Consequently, the mesh was generated for fluid and solid part of the ventilator within OpenFOAM. Although OpenFOAM offers also a few tools for structural analysis, the mesh of solid parts was transferred into Elmer mesh format with the aim to perform structural analysis in this software. This submitted paper deals namely with part concerning fluid flow through parametrized geometry with different initial conditions. As an example, two simulations were conducted for the same geometric parameters and mesh but for different angular velocity of ventilator rotation.
Parametric model of ventilators simulated in OpenFOAM and Elmer
Čibera, Václav; Matas, Richard; Sedláček, Jan
2016-03-01
The main goal of presented work was to develop parametric model of a ventilator for CFD and structural analysis. The whole model was designed and scripted in freely available open source programmes in particular in OpenFOAM and Elmer. The main script, which runs or generates other scripts and further control the course of simulation, was written in bash scripting language in Linux environment. Further, the scripts needed for a mesh generation and running of a simulation were prepared using m4 word pre-processor. The use of m4 allowed comfortable set up of the higher amount of scripts. Consequently, the mesh was generated for fluid and solid part of the ventilator within OpenFOAM. Although OpenFOAM offers also a few tools for structural analysis, the mesh of solid parts was transferred into Elmer mesh format with the aim to perform structural analysis in this software. This submitted paper deals namely with part concerning fluid flow through parametrized geometry with different initial conditions. As an example, two simulations were conducted for the same geometric parameters and mesh but for different angular velocity of ventilator rotation.
Thermal modeling and parametric studies of a greenhouse fish pond in the Central Himalayan Region
International Nuclear Information System (INIS)
Sarkar, Bikash; Tiwari, G.N.
2006-01-01
This study describes the thermal modeling and its validation of greenhouse fish pond systems. Numerical computations have been performed for a typical day in the month of June, 2005, for the climatic condition of Champawat in the Central Himalayan Region. The energy balance equations have been written considering the effects of conduction, convection, radiation, evaporation and ventilation. The governing equations are numerically solved with Matlab 7.0 software to predict the water temperature. A parametric study has also been performed to find the effects of various parameters, namely the number of air changes per hour, the transmissivity (τ) and the isothermal mass and height of the greenhouse. It is observed that there is no significant effect in the parametric studies on water temperature due to the larger isothermal mass. The model has been validated with experimental data. On an average, the even span passive greenhouse fish pond can increase the inside temperature 4.14 deg. C higher than the temperature of an outdoor pond. Statistical analysis shows that the predicted and experimental values of water temperature exhibited fair agreement with a coefficient of correlation r = 0.90 and root mean square percent deviation e = 1.67%
International Nuclear Information System (INIS)
Luo, Yongqiang; Zhang, Ling; Liu, Zhongbing; Wang, Yingzi; Wu, Jing; Wang, Xiliang
2016-01-01
Highlights: • Dynamic model of thermoelectric radiant panel system is established. • The internal parameters of thermoelectric module are dynamically calculated in simulation. • Both artificial neural networks model and system model are verified through experiment data. • Optimized system structure is obtained through parametric study. - Abstract: Radiant panel system can optimize indoor thermal comfort with lower energy consumption. The thermoelectric radiant panel (TERP) system is a new and effective prototype of radiant system using thermoelectric module (TEM) instead of conventional water pipes, as heat source. The TERP can realize more stable and easier system control as well as lower initial and operative cost. In this study, an improved system dynamic model was established by combining analytical system model and artificial neural networks (ANN) as well as the dynamic calculation functions of internal parameters of TEM. The double integral was used for the calculation of surface average temperature of TERP. The ANN model and system model were in good agreement with experiment data in both cooling and heating mode. In order to optimize the system design structure, parametric study was conducted in terms of the thickness of aluminum panel and insulation, as well as the arrangement of TEMs on the surface of radiant panel. It was found through simulation results that the optimum thickness of aluminum panel and insulation are respectively around 1–2 mm and 40–50 mm. In addition, TEMs should be uniformly installed on the surface of radiant panel and each TEM should stand at the central position of a square-shaped typical region with length around 0.387–0.548 m.
Development of a parametric containment event tree model for a severe BWR accident
Energy Technology Data Exchange (ETDEWEB)
Okkonen, T. [OTO-Consulting Ay, Helsinki (Finland)
1995-04-01
A containment event tree (CET) is built for analysis of severe accidents at the TVO boiling water reactor (BWR) units. Parametric models of severe accident progression and fission product behaviour are developed and integrated in order to construct a compact and self-contained Level 2 PSA model. The model can be easily updated to correspond to new research results. The analyses of the study are limited to severe accidents starting from full-power operation and leading to core melting, and are focused mainly on the use and effects of the dedicated severe accident management (SAM) systems. Severe accident progression from eight plant damage states (PDS), involving different pre-core-damage accident evolution, is examined, but the inclusion of their relative or absolute probabilities, by integration with Level 1, is deferred to integral safety assessments. (33 refs., 5 figs., 7 tabs.).
The Borromini's helicoidal staircase in Barberini Palace: scan laser survey and parametric modeling.
Directory of Open Access Journals (Sweden)
Leonardo Paris
2015-07-01
model, first of all measurable, but also able to disclosing shapes and geometries otherwise hardly perceptible, selecting parts or showing details.A first reading level of the models, obtained according to consolidated procedures of the points-cloud management, has highlighted the formal matrix of the oval with the identification of the centers of the polycentric line, the resulting three-dimensional development of the various helices belonging to cylindrical surface portions adjacent to each other and different radius, the relationship with the slope. Within this formal matrix of the first level it enters the formal matrix of the architectural order into the rhythm of the six pairs of alternate columns and of the balustrades, and in the entablature that develops as a tape into the central space illuminated by a skylight.The modular structure of the scale has also suggested a experimentation by means of parametric modeling techniques to try to trace the ideal model of the Borromini’s helical staircase. The digital parametric model tested here is a new mode of representation than the models already consolidated, either analog or digital. Through a critical selection of some remarkable points belonging to the geometries present, through a statistical and normalized observation of the recurring measures, we have identified a generative algorithm able to be representative of the design intentions of the author. The entire search path followed to reach the definition of parametric generative model – from analysis of the real model to the design of the virtual parametric model; from the development of the algorithm generative until to his check made by comparing the generated model and the model survey - it was found to be a constraint rigorous methodological for a more complete and appropriate knowledge of the work.
A Model for Straight and Helical Solar Jets: II. Parametric Study of the Plasma Beta
Pariat, E.; Dalmasse, K.; DeVore, C. R.; Antiochos, S. K.; Karpen, J. T.
2016-01-01
Context. Jets are dynamic, impulsive, well-collimated plasma events that develop at many different scales and in different layers of the solar atmosphere. Aims. Jets are believed to be induced by magnetic reconnection, a process central to many astrophysical phenomena. Within the solar atmosphere, jet-like events develop in many different environments, e.g. in the vicinity of active regions as well as in coronal holes, and at various scales, from small photospheric spicules to large coronal jets. In all these events, signatures of helical structure and/or twisting/rotating motions are regularly observed. The present study aims to establish that a single model can generally reproduce the observed properties of these jet-like events. Methods. In this study, using our state-of-the-art numerical solver ARMS, we present a parametric study of a numerical tridimensional magnetohydrodynamic (MHD) model of solar jet-like events. Within the MHD paradigm, we study the impact of varying the atmospheric plasma beta on the generation and properties of solar-like jets. Results. The parametric study validates our model of jets for plasma beta ranging from 10(sup 3) to 1, typical of the different layers and magnetic environments of the solar atmosphere. Our model of jets can robustly explain the generation of helical solar jet-like events at various beta less than or equal to 1. We show that the plasma beta modifies the morphology of the helical jet, explaining the different observed shapes of jets at different scales and in different layers of the solar atmosphere. Conclusions. Our results allow us to understand the energisation, triggering, and driving processes of jet-like events. Our model allows us to make predictions of the impulsiveness and energetics of jets as determined by the surrounding environment, as well as the morphological properties of the resulting jets.
Bayesian spatial semi-parametric modeling of HIV variation in Kenya.
Directory of Open Access Journals (Sweden)
Oscar Ngesa
Full Text Available Spatial statistics has seen rapid application in many fields, especially epidemiology and public health. Many studies, nonetheless, make limited use of the geographical location information and also usually assume that the covariates, which are related to the response variable, have linear effects. We develop a Bayesian semi-parametric regression model for HIV prevalence data. Model estimation and inference is based on fully Bayesian approach via Markov Chain Monte Carlo (McMC. The model is applied to HIV prevalence data among men in Kenya, derived from the Kenya AIDS indicator survey, with n = 3,662. Past studies have concluded that HIV infection has a nonlinear association with age. In this study a smooth function based on penalized regression splines is used to estimate this nonlinear effect. Other covariates were assumed to have a linear effect. Spatial references to the counties were modeled as both structured and unstructured spatial effects. We observe that circumcision reduces the risk of HIV infection. The results also indicate that men in the urban areas were more likely to be infected by HIV as compared to their rural counterpart. Men with higher education had the lowest risk of HIV infection. A nonlinear relationship between HIV infection and age was established. Risk of HIV infection increases with age up to the age of 40 then declines with increase in age. Men who had STI in the last 12 months were more likely to be infected with HIV. Also men who had ever used a condom were found to have higher likelihood to be infected by HIV. A significant spatial variation of HIV infection in Kenya was also established. The study shows the practicality and flexibility of Bayesian semi-parametric regression model in analyzing epidemiological data.
Parametric Excitation of Magnetopause Surface Waves: Global Magnetospheric Modeling in SWMF
Ellington, S.
2017-12-01
Magnetopause surface waves are an efficient energy transport modality in the coupling of the solar wind with the magnetosphere. The magnetopause supports several known waves such as the Kelvin-Helmholtz and Kruskal-Schwartzchild modes, which are excited by velocity shear and impulsive solarwind pressure pulses, respectively. Here we have discovered via simulations in SWMF a parametrically excited surface wave in which a slow magnetosonic wave excited by broadband, low amplitude fluctuations in the upstream solarwind number density couples to a circularly polarized shear Alfven wave at half the frequency. By varying the amplitude of the fluctuations and using various constitutive MHD equation sets-ideal, resistive, and anisotropic ion pressure with electron pressure, we verify that the theoretical model of parametric excitation accurately predicts the observed growth rates in each case. We briefly discuss the saturation mechanism of the shear modes as a nonlocal phenomenon mediated by ionospheric conductivity and describe several observations made downstream involving their decay, a process which subsequently couples the surface waves to kink mode waves that then propagate across the entire magnetotail. Lastly, we discuss the implications of these results in the context of observed magnetosphere phenomena and the impact of the numerical design of these simulations therein.
Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices
Directory of Open Access Journals (Sweden)
Antonio Bello
2016-11-01
Full Text Available This paper proposes a new approach to hybrid forecasting methodology, characterized as the statistical recalibration of forecasts from fundamental market price formation models. Such hybrid methods based upon fundamentals are particularly appropriate to medium term forecasting and in this paper the application is to month-ahead, hourly prediction of electricity wholesale prices in Spain. The recalibration methodology is innovative in seeking to perform the recalibration into parametrically defined density functions. The density estimation method selects from a wide diversity of general four-parameter distributions to fit hourly spot prices, in which the first four moments are dynamically estimated as latent functions of the outputs from the fundamental model and several other plausible exogenous drivers. The proposed approach demonstrated its effectiveness against benchmark methods across the full range of percentiles of the price distribution and performed particularly well in the tails.
Boltzmann sampling for an XY model using a non-degenerate optical parametric oscillator network
Takeda, Y.; Tamate, S.; Yamamoto, Y.; Takesue, H.; Inagaki, T.; Utsunomiya, S.
2018-01-01
We present an experimental scheme of implementing multiple spins in a classical XY model using a non-degenerate optical parametric oscillator (NOPO) network. We built an NOPO network to simulate a one-dimensional XY Hamiltonian with 5000 spins and externally controllable effective temperatures. The XY spin variables in our scheme are mapped onto the phases of multiple NOPO pulses in a single ring cavity and interactions between XY spins are implemented by mutual injections between NOPOs. We show the steady-state distribution of optical phases of such NOPO pulses is equivalent to the Boltzmann distribution of the corresponding XY model. Estimated effective temperatures converged to the setting values, and the estimated temperatures and the mean energy exhibited good agreement with the numerical simulations of the Langevin dynamics of NOPO phases.
Tutsoy, Onder; Barkana, Duygun Erol; Tugal, Harun
2018-03-14
In this paper, an adaptive controller is developed for discrete time linear systems that takes into account parametric uncertainty, internal-external non-parametric random uncertainties, and time varying control signal delay. Additionally, the proposed adaptive control is designed in such a way that it is utterly model free. Even though these properties are studied separately in the literature, they are not taken into account all together in adaptive control literature. The Q-function is used to estimate long-term performance of the proposed adaptive controller. Control policy is generated based on the long-term predicted value, and this policy searches an optimal stabilizing control signal for uncertain and unstable systems. The derived control law does not require an initial stabilizing control assumption as in the ones in the recent literature. Learning error, control signal convergence, minimized Q-function, and instantaneous reward are analyzed to demonstrate the stability and effectiveness of the proposed adaptive controller in a simulation environment. Finally, key insights on parameters convergence of the learning and control signals are provided. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Bayesian Semi- and Non-Parametric Models for Longitudinal Data with Multiple Membership Effects in R
Directory of Open Access Journals (Sweden)
Terrance Savitsky
2014-03-01
Full Text Available We introduce growcurves for R that performs analysis of repeated measures multiple membership (MM data. This data structure arises in studies under which an intervention is delivered to each subject through the subjects participation in a set of multiple elements that characterize the intervention. In our motivating study design under which subjects receive a group cognitive behavioral therapy (CBT treatment, an element is a group CBT session and each subject attends multiple sessions that, together, comprise the treatment. The sets of elements, or group CBT sessions, attended by subjects will partly overlap with some of those from other subjects to induce a dependence in their responses. The growcurves package offers two alternative sets of hierarchical models: 1. Separate terms are specified for multivariate subject and MM element random effects, where the subject effects are modeled under a Dirichlet process prior to produce a semi-parametric construction; 2. A single term is employed to model joint subject-by-MM effects. A fully non-parametric dependent Dirichlet process formulation allows exploration of differences in subject responses across different MM elements. This model allows for borrowing information among subjects who express similar longitudinal trajectories for flexible estimation. growcurves deploys estimation functions to perform posterior sampling under a suite of prior options. An accompanying set of plot functions allows the user to readily extract by-subject growth curves. The design approach intends to anticipate inferential goals with tools that fully extract information from repeated measures data. Computational efficiency is achieved by performing the sampling for estimation functions using compiled C++ code.
Paul, Sarbajit; Chang, Junghwan
2017-07-01
This paper presents a design approach for a magnetic sensor module to detect mover position using the proper orthogonal decomposition-dynamic mode decomposition (POD-DMD)-based nonlinear parametric model order reduction (PMOR). The parameterization of the sensor module is achieved by using the multipolar moment matching method. Several geometric variables of the sensor module are considered while developing the parametric study. The operation of the sensor module is based on the principle of the airgap flux density distribution detection by the Hall Effect IC. Therefore, the design objective is to achieve a peak flux density (PFD) greater than 0.1 T and total harmonic distortion (THD) less than 3%. To fulfill the constraint conditions, the specifications for the sensor module is achieved by using POD-DMD based reduced model. The POD-DMD based reduced model provides a platform to analyze the high number of design models very fast, with less computational burden. Finally, with the final specifications, the experimental prototype is designed and tested. Two different modes, 90° and 120° modes respectively are used to obtain the position information of the linear motor mover. The position information thus obtained are compared with that of the linear scale data, used as a reference signal. The position information obtained using the 120° mode has a standard deviation of 0.10 mm from the reference linear scale signal, whereas the 90° mode position signal shows a deviation of 0.23 mm from the reference. The deviation in the output arises due to the mechanical tolerances introduced into the specification during the manufacturing process. This provides a scope for coupling the reliability based design optimization in the design process as a future extension.
Semiparametric modeling: Correcting low-dimensional model error in parametric models
International Nuclear Information System (INIS)
Berry, Tyrus; Harlim, John
2016-01-01
In this paper, a semiparametric modeling approach is introduced as a paradigm for addressing model error arising from unresolved physical phenomena. Our approach compensates for model error by learning an auxiliary dynamical model for the unknown parameters. Practically, the proposed approach consists of the following steps. Given a physics-based model and a noisy data set of historical observations, a Bayesian filtering algorithm is used to extract a time-series of the parameter values. Subsequently, the diffusion forecast algorithm is applied to the retrieved time-series in order to construct the auxiliary model for the time evolving parameters. The semiparametric forecasting algorithm consists of integrating the existing physics-based model with an ensemble of parameters sampled from the probability density function of the diffusion forecast. To specify initial conditions for the diffusion forecast, a Bayesian semiparametric filtering method that extends the Kalman-based filtering framework is introduced. In difficult test examples, which introduce chaotically and stochastically evolving hidden parameters into the Lorenz-96 model, we show that our approach can effectively compensate for model error, with forecasting skill comparable to that of the perfect model.
Parametrization of translational surfaces
Perez-Diaz, Sonia; Shen, Liyong
2014-01-01
The algebraic translational surface is a typical modeling surface in computer aided design and architecture industry. In this paper, we give a necessary and sufficient condition for that algebraic surface having a standard parametric representation and our proof is constructive. If the given algebraic surface is translational, then we can compute a standard parametric representation for the surface.
Quantitative vertebral morphometry based on parametric modeling of vertebral bodies in 3D.
Stern, D; Njagulj, V; Likar, B; Pernuš, F; Vrtovec, T
2013-04-01
Quantitative vertebral morphometry (QVM) was performed by parametric modeling of vertebral bodies in three dimensions (3D). Identification of vertebral fractures in two dimensions is a challenging task due to the projective nature of radiographic images and variability in the vertebral shape. By generating detailed 3D anatomical images, computed tomography (CT) enables accurate measurement of vertebral deformations and fractures. A detailed 3D representation of the vertebral body shape is obtained by automatically aligning a parametric 3D model to vertebral bodies in CT images. The parameters of the 3D model describe clinically meaningful morphometric vertebral body features, and QVM in 3D is performed by comparing the parameters to their statistical values. Thresholds and parameters that best discriminate between normal and fractured vertebral bodies are determined by applying statistical classification analysis. The proposed QVM in 3D was applied to 454 normal and 228 fractured vertebral bodies, yielding classification sensitivity of 92.5% at 7.5% specificity, with corresponding accuracy of 92.5% and precision of 86.1%. The 3D shape parameters that provided the best separation between normal and fractured vertebral bodies were the vertebral body height and the inclination and concavity of both vertebral endplates. The described QVM in 3D is able to efficiently and objectively discriminate between normal and fractured vertebral bodies and identify morphological cases (wedge, (bi)concavity, or crush) and grades (1, 2, or 3) of vertebral body fractures. It may be therefore valuable for diagnosing and predicting vertebral fractures in patients who are at risk of osteoporosis.
Mahachie John, Jestinah M; Van Lishout, François; Gusareva, Elena S; Van Steen, Kristel
2013-04-25
Applying a statistical method implies identifying underlying (model) assumptions and checking their validity in the particular context. One of these contexts is association modeling for epistasis detection. Here, depending on the technique used, violation of model assumptions may result in increased type I error, power loss, or biased parameter estimates. Remedial measures for violated underlying conditions or assumptions include data transformation or selecting a more relaxed modeling or testing strategy. Model-Based Multifactor Dimensionality Reduction (MB-MDR) for epistasis detection relies on association testing between a trait and a factor consisting of multilocus genotype information. For quantitative traits, the framework is essentially Analysis of Variance (ANOVA) that decomposes the variability in the trait amongst the different factors. In this study, we assess through simulations, the cumulative effect of deviations from normality and homoscedasticity on the overall performance of quantitative Model-Based Multifactor Dimensionality Reduction (MB-MDR) to detect 2-locus epistasis signals in the absence of main effects. Our simulation study focuses on pure epistasis models with varying degrees of genetic influence on a quantitative trait. Conditional on a multilocus genotype, we consider quantitative trait distributions that are normal, chi-square or Student's t with constant or non-constant phenotypic variances. All data are analyzed with MB-MDR using the built-in Student's t-test for association, as well as a novel MB-MDR implementation based on Welch's t-test. Traits are either left untransformed or are transformed into new traits via logarithmic, standardization or rank-based transformations, prior to MB-MDR modeling. Our simulation results show that MB-MDR controls type I error and false positive rates irrespective of the association test considered. Empirically-based MB-MDR power estimates for MB-MDR with Welch's t-tests are generally lower than those
Directory of Open Access Journals (Sweden)
Antonella di Luggo
2016-06-01
Full Text Available The application of BIM to architectural heritage and therefore the parameterization of its elements show a certain complexity, because the historical built environment must be subject to systematic readings, in order to detect an information system based on ontologically defined elements, which must be associated with data able to document their material, historical and constructive peculiarities. With reference to a case study, this paper examines some theoretical implications and operational procedures concerning the transition from discrete three-dimensional model of point clouds to a parametric model.
Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach
Directory of Open Access Journals (Sweden)
Oliveira Rui
2010-09-01
Full Text Available Abstract Background This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics. Results The proposed hybrid modelling methodology was evaluated using two case studies. The first of these deals with dynamic modelling of transcriptional factor A in mammalian cells. The protein transport from the cytosol to the nucleus introduced a delay that was accounted for by discrete time series formulation. The second case study focused on a simple network with distributed time delays that demonstrated that the discrete time delay formalism has broad applicability to both discrete and distributed delay problems. Conclusions Significantly better prediction qualities of the novel hybrid model were obtained when compared to dynamical structures without time delays, being the more distinctive the more significant the underlying system delay is. The identification of the system delays by studies of different discrete modelling delays was enabled by the proposed structure. Further, it was shown that the hybrid discrete delay methodology is not limited to discrete delay systems. The proposed method is a powerful tool to identify time delays in ill-defined biochemical networks.
Exemplar-based Parametric Hidden Markov Models for Recognition and Synthesis of Movements
DEFF Research Database (Denmark)
Herzog, Dennis; Krüger, Volker; Grest, Daniel
2007-01-01
A common problem in movement recognition is the recognition of movements of a particular type. E.g. pointing movements are of a particular type but differ in terms of the pointing direction. Arm movements with the goal of reaching out and grasping an object are of a particular type but differ...... with the location of the involved object. In this paper, we present an exemplar-based parametric hidden Markov model (PHMM) that is able to recognize and synthesize movements of a particular type. The PHMM is based on exemplar movements that have to be ``demonstrated'' to the system. Recognition and synthesis...... are carried out through locally linear interpolation of the exemplar movements. Experiments are performed with pointing and grasping movements. Synthesis is done based on the object position as parameterization. In case of the recognition, the coordinates of the grasped or pointed at object are recovered. Our...
An Online Method for Interpolating Linear Parametric Reduced-Order Models
Amsallem, David
2011-01-01
A two-step online method is proposed for interpolating projection-based linear parametric reduced-order models (ROMs) in order to construct a new ROM for a new set of parameter values. The first step of this method transforms each precomputed ROM into a consistent set of generalized coordinates. The second step interpolates the associated linear operators on their appropriate matrix manifold. Real-time performance is achieved by precomputing inner products between the reduced-order bases underlying the precomputed ROMs. The proposed method is illustrated by applications in mechanical and aeronautical engineering. In particular, its robustness is demonstrated by its ability to handle the case where the sampled parameter set values exhibit a mode veering phenomenon. © 2011 Society for Industrial and Applied Mathematics.
DEFF Research Database (Denmark)
Mazzucco, Andrea; Rothuizen, Erasmus; Jørgensen, Jens-Erik
2016-01-01
to the phase change material, mainly occurs after the fueling is completed, resulting in a hydrogen peak temperature higher than 85 C and a lower fueled mass than a gas-cooled system. Such a mass reduction accounts for 12% with respect to the case of a standard tank system fueled at 40 C. A parametric analysis...... that embraces the main thermal properties of the heat-absorbing material as well as the major design parameters is here carried out to determine possible solutions. It is found that the improvement of a single thermal property does not provide any significant benefit and that the most effective strategy......A dynamic fueling model is built to simulate the fueling process of a hydrogen tank with an integrated passive cooling system. The study investigates the possibility of absorbing a part of the heat of compression in the high latent-heat material during melting, with the aim of saving the monetary...
Parametric geometric model and shape optimization of an underwater glider with blended-wing-body
Sun, Chunya; Song, Baowei; Wang, Peng
2015-11-01
Underwater glider, as a new kind of autonomous underwater vehicles, has many merits such as long-range, extended-duration and low costs. The shape of underwater glider is an important factor in determining the hydrodynamic efficiency. In this paper, a high lift to drag ratio configuration, the Blended-Wing-Body (BWB), is used to design a small civilian under water glider. In the parametric geometric model of the BWB underwater glider, the planform is defined with Bezier curve and linear line, and the section is defined with symmetrical airfoil NACA 0012. Computational investigations are carried out to study the hydrodynamic performance of the glider using the commercial Computational Fluid Dynamics (CFD) code Fluent. The Kriging-based genetic algorithm, called Efficient Global Optimization (EGO), is applied to hydrodynamic design optimization. The result demonstrates that the BWB underwater glider has excellent hydrodynamic performance, and the lift to drag ratio of initial design is increased by 7% in the EGO process.
Parametric Packet-Layer Model for Evaluation Audio Quality in Multimedia Streaming Services
Egi, Noritsugu; Hayashi, Takanori; Takahashi, Akira
We propose a parametric packet-layer model for monitoring audio quality in multimedia streaming services such as Internet protocol television (IPTV). This model estimates audio quality of experience (QoE) on the basis of quality degradation due to coding and packet loss of an audio sequence. The input parameters of this model are audio bit rate, sampling rate, frame length, packet-loss frequency, and average burst length. Audio bit rate, packet-loss frequency, and average burst length are calculated from header information in received IP packets. For sampling rate, frame length, and audio codec type, the values or the names used in monitored services are input into this model directly. We performed a subjective listening test to examine the relationships between these input parameters and perceived audio quality. The codec used in this test was the Advanced Audio Codec-Low Complexity (AAC-LC), which is one of the international standards for audio coding. On the basis of the test results, we developed an audio quality evaluation model. The verification results indicate that audio quality estimated by the proposed model has a high correlation with perceived audio quality.
An assessment of key model parametric uncertainties in projections of Greenland Ice Sheet behavior
Directory of Open Access Journals (Sweden)
P. J. Applegate
2012-05-01
Full Text Available Lack of knowledge about the values of ice sheet model input parameters introduces substantial uncertainty into projections of Greenland Ice Sheet contributions to future sea level rise. Computer models of ice sheet behavior provide one of several means of estimating future sea level rise due to mass loss from ice sheets. Such models have many input parameters whose values are not well known. Recent studies have investigated the effects of these parameters on model output, but the range of potential future sea level increases due to model parametric uncertainty has not been characterized. Here, we demonstrate that this range is large, using a 100-member perturbed-physics ensemble with the SICOPOLIS ice sheet model. Each model run is spun up over 125 000 yr using geological forcings and subsequently driven into the future using an asymptotically increasing air temperature anomaly curve. All modeled ice sheets lose mass after 2005 AD. Parameters controlling surface melt dominate the model response to temperature change. After culling the ensemble to include only members that give reasonable ice volumes in 2005 AD, the range of projected sea level rise values in 2100 AD is ~40 % or more of the median. Data on past ice sheet behavior can help reduce this uncertainty, but none of our ensemble members produces a reasonable ice volume change during the mid-Holocene, relative to the present. This problem suggests that the model's exponential relation between temperature and precipitation does not hold during the Holocene, or that the central-Greenland temperature forcing curve used to drive the model is not representative of conditions around the ice margin at this time (among other possibilities. Our simulations also lack certain observed physical processes that may tend to enhance the real ice sheet's response. Regardless, this work has implications for other studies that use ice sheet models to project or hindcast the behavior of the Greenland Ice
Wallis, Thomas S A; Funke, Christina M; Ecker, Alexander S; Gatys, Leon A; Wichmann, Felix A; Bethge, Matthias
2017-10-01
Our visual environment is full of texture-"stuff" like cloth, bark, or gravel as distinct from "things" like dresses, trees, or paths-and humans are adept at perceiving subtle variations in material properties. To investigate image features important for texture perception, we psychophysically compare a recent parametric model of texture appearance (convolutional neural network [CNN] model) that uses the features encoded by a deep CNN (VGG-19) with two other models: the venerable Portilla and Simoncelli model and an extension of the CNN model in which the power spectrum is additionally matched. Observers discriminated model-generated textures from original natural textures in a spatial three-alternative oddity paradigm under two viewing conditions: when test patches were briefly presented to the near-periphery ("parafoveal") and when observers were able to make eye movements to all three patches ("inspection"). Under parafoveal viewing, observers were unable to discriminate 10 of 12 original images from CNN model images, and remarkably, the simpler Portilla and Simoncelli model performed slightly better than the CNN model (11 textures). Under foveal inspection, matching CNN features captured appearance substantially better than the Portilla and Simoncelli model (nine compared to four textures), and including the power spectrum improved appearance matching for two of the three remaining textures. None of the models we test here could produce indiscriminable images for one of the 12 textures under the inspection condition. While deep CNN (VGG-19) features can often be used to synthesize textures that humans cannot discriminate from natural textures, there is currently no uniformly best model for all textures and viewing conditions.
Parametric uncertainties in global model simulations of black carbon column mass concentration
Pearce, Hana; Lee, Lindsay; Reddington, Carly; Carslaw, Ken; Mann, Graham
2016-04-01
Previous studies have deduced that the annual mean direct radiative forcing from black carbon (BC) aerosol may regionally be up to 5 W m-2 larger than expected due to underestimation of global atmospheric BC absorption in models. We have identified the magnitude and important sources of parametric uncertainty in simulations of BC column mass concentration from a global aerosol microphysics model (GLOMAP-Mode). A variance-based uncertainty analysis of 28 parameters has been performed, based on statistical emulators trained on model output from GLOMAP-Mode. This is the largest number of uncertain model parameters to be considered in a BC uncertainty analysis to date and covers primary aerosol emissions, microphysical processes and structural parameters related to the aerosol size distribution. We will present several recommendations for further research to improve the fidelity of simulated BC. In brief, we find that the standard deviation around the simulated mean annual BC column mass concentration varies globally between 2.5 x 10-9 g cm-2 in remote marine regions and 1.25 x 10-6 g cm-2 near emission sources due to parameter uncertainty Between 60 and 90% of the variance over source regions is due to uncertainty associated with primary BC emission fluxes, including biomass burning, fossil fuel and biofuel emissions. While the contributions to BC column uncertainty from microphysical processes, for example those related to dry and wet deposition, are increased over remote regions, we find that emissions still make an important contribution in these areas. It is likely, however, that the importance of structural model error, i.e. differences between models, is greater than parametric uncertainty. We have extended our analysis to emulate vertical BC profiles at several locations in the mid-Pacific Ocean and identify the parameters contributing to uncertainty in the vertical distribution of black carbon at these locations. We will present preliminary comparisons of
Analytic Hierarchy Process (AHP in Ranking Non-Parametric Stochastic Rainfall and Streamflow Models
Directory of Open Access Journals (Sweden)
Masengo Ilunga
2015-08-01
Full Text Available Analytic Hierarchy Process (AHP is used in the selection of categories of non-parametric stochastic models for hydrological data generation and its formulation is based on pairwise comparisons of models. These models or techniques are obtained from a recent study initiated by the Water Research Commission of South Africa (WRC and were compared predominantly based on their capability to extrapolate data beyond the range of historic hydrological data. The different categories of models involved in the selection process were: wavelet (A, reordering (B, K-nearest neighbor (C, kernel density (D and bootstrap (E. In the AHP formulation, criteria for the selection of techniques are: "ability for data to preserve historic characteristics", "ability to generate new hydrological data", "scope of applicability", "presence of negative data generated" and "user friendliness". The pairwise comparisons performed through AHP showed that the overall order of selection (ranking of models was D, C, A, B and C. The weights of these techniques were found to be 27.21%, 24.3 %, 22.15 %, 13.89 % and 11.80 % respectively. Hence, bootstrap category received the highest preference while nearest neighbor received the lowest preference when all selection criteria are taken into consideration.
Lazeroms, Werner M. J.; Jenkins, Adrian; Hilmar Gudmundsson, G.; van de Wal, Roderik S. W.
2018-01-01
Basal melting below ice shelves is a major factor in mass loss from the Antarctic Ice Sheet, which can contribute significantly to possible future sea-level rise. Therefore, it is important to have an adequate description of the basal melt rates for use in ice-dynamical models. Most current ice models use rather simple parametrizations based on the local balance of heat between ice and ocean. In this work, however, we use a recently derived parametrization of the melt rates based on a buoyant meltwater plume travelling upward beneath an ice shelf. This plume parametrization combines a non-linear ocean temperature sensitivity with an inherent geometry dependence, which is mainly described by the grounding-line depth and the local slope of the ice-shelf base. For the first time, this type of parametrization is evaluated on a two-dimensional grid covering the entire Antarctic continent. In order to apply the essentially one-dimensional parametrization to realistic ice-shelf geometries, we present an algorithm that determines effective values for the grounding-line depth and basal slope in any point beneath an ice shelf. Furthermore, since detailed knowledge of temperatures and circulation patterns in the ice-shelf cavities is sparse or absent, we construct an effective ocean temperature field from observational data with the purpose of matching (area-averaged) melt rates from the model with observed present-day melt rates. Our results qualitatively replicate large-scale observed features in basal melt rates around Antarctica, not only in terms of average values, but also in terms of the spatial pattern, with high melt rates typically occurring near the grounding line. The plume parametrization and the effective temperature field presented here are therefore promising tools for future simulations of the Antarctic Ice Sheet requiring a more realistic oceanic forcing.
Ghaffari, Mahsa; Tangen, Kevin; Alaraj, Ali; Du, Xinjian; Charbel, Fady T; Linninger, Andreas A
2017-12-01
In this paper, we present a novel technique for automatic parametric mesh generation of subject-specific cerebral arterial trees. This technique generates high-quality and anatomically accurate computational meshes for fast blood flow simulations extending the scope of 3D vascular modeling to a large portion of cerebral arterial trees. For this purpose, a parametric meshing procedure was developed to automatically decompose the vascular skeleton, extract geometric features and generate hexahedral meshes using a body-fitted coordinate system that optimally follows the vascular network topology. To validate the anatomical accuracy of the reconstructed vasculature, we performed statistical analysis to quantify the alignment between parametric meshes and raw vascular images using receiver operating characteristic curve. Geometric accuracy evaluation showed an agreement with area under the curves value of 0.87 between the constructed mesh and raw MRA data sets. Parametric meshing yielded on-average, 36.6% and 21.7% orthogonal and equiangular skew quality improvement over the unstructured tetrahedral meshes. The parametric meshing and processing pipeline constitutes an automated technique to reconstruct and simulate blood flow throughout a large portion of the cerebral arterial tree down to the level of pial vessels. This study is the first step towards fast large-scale subject-specific hemodynamic analysis for clinical applications. Copyright © 2017 Elsevier Ltd. All rights reserved.
Venkatesan, K.; Ramanujam, R.; Kuppan, P.
2016-04-01
This paper presents a parametric effect, microstructure, micro-hardness and optimization of laser scanning parameters (LSP) on heating experiments during laser assisted machining of Inconel 718 alloy. The laser source used for experiments is a continuous wave Nd:YAG laser with maximum power of 2 kW. The experimental parameters in the present study are cutting speed in the range of 50-100 m/min, feed rate of 0.05-0.1 mm/rev, laser power of 1.25-1.75 kW and approach angle of 60-90°of laser beam axis to tool. The plan of experiments are based on central composite rotatable design L31 (43) orthogonal array. The surface temperature is measured via on-line measurement using infrared pyrometer. Parametric significance on surface temperature is analysed using response surface methodology (RSM), analysis of variance (ANOVA) and 3D surface graphs. The structural change of the material surface is observed using optical microscope and quantitative measurement of heat affected depth that are analysed by Vicker's hardness test. The results indicate that the laser power and approach angle are the most significant parameters to affect the surface temperature. The optimum ranges of laser power and approach angle was identified as 1.25-1.5 kW and 60-65° using overlaid contour plot. The developed second order regression model is found to be in good agreement with experimental values with R2 values of 0.96 and 0.94 respectively for surface temperature and heat affected depth.
SemiMarkov: An R Package for Parametric Estimation in Multi-State Semi-Markov Models
Listwon, Agnieszka; Saint-Pierre, Philippe
2015-01-01
Multi-state models provide a relevant tool for studying the observations of a continuous-time process at arbitrary times. Markov models are often considered even if semi-Markov are better adapted in various situations. Such models are still not frequently applied mainly due to lack of available software. We have developed the R package SemiMarkov to fit homogeneous semi-Markov models to longitudinal data. The package performs maximum likelihood estimation in a parametric framework where the d...
Automating the Simulation of SME Processes through a Discrete Event Parametric Model
Directory of Open Access Journals (Sweden)
Francesco Aggogeri
2015-02-01
Full Text Available At the factory level, the manufacturing system can be described as a group of processes governed by complex weaves of engineering strategies and technologies. Decision- making processes involve a lot of information, driven by managerial strategies, technological implications and layout constraints. Many factors affect decisions, and their combination must be carefully managed to determine the best solutions to optimize performances. In this way, advanced simulation tools could support the decisional process of many SMEs. The accessibility of these tools is limited by knowledge, cost, data availability and development time. These tools should be used to support strategic decisions rather than specific situations. In this paper, a novel approach is proposed that aims to facilitate the simulation of manufacturing processes by fast modelling and evaluation. The idea is to realize a model that is able to be automatically adapted to the user’s specific needs. The model must be characterized by a high degree of flexibility, configurability and adaptability in order to automatically simulate multiple/heterogeneous industrial scenarios. In this way, even a SME can easily access a complex tool, perform thorough analyses and be supported in taking strategic decisions. The parametric DES model is part of a greater software platform developed during COPERNICO EU funded project.
A parametric investigation of hydrogen hcci combustion using a multi-zone model approach
International Nuclear Information System (INIS)
Komninos, N.P.; Hountalas, D.T.; Rakopoulos, C.D.
2007-01-01
The purpose of the present study is to examine the effect of various operating variables of a homogeneous charge compression ignition (HCCI) engine fueled with hydrogen, using a multi-zone model developed by the authors. The multi-zone model consists of zones, which are allotted spatial locations within the combustion chamber. The model takes into account heat transfer between the zones and the combustion chamber walls, providing a spatial temperature distribution during the closed part of the engine cycle, i.e. compression, combustion and expansion. Mass transfer between zones is also accounted for, based on the geometric configuration of the zones, and includes the flow of mass in and out of the crevice regions, represented by the crevice zone. Combustion is incorporated using chemical kinetics based on a chemical reaction mechanism for the oxidation of hydrogen. This chemical reaction mechanism also includes the reactions for nitrogen oxides formation. Using the multi-zone model a parametric investigation is conducted, in order to determine the effect of engine speed, equivalence ratio, compression ratio, inlet pressure and inlet temperature, on the performance, combustion characteristics and emissions of an HCCI engine fueled with hydrogen
van Werkhoven, Kathryn; Wagener, Thorsten; Reed, Patrick; Tang, Yong
2009-08-01
Problem complexity for watershed model calibration is heavily dependent on the number of parameters that can be identified during model calibration. This study investigates the use of global sensitivity analysis as a screening tool to reduce the parametric dimensionality of multi-objective hydrological model calibration problems while maximizing the information extracted from hydrological response data. This study shows that by expanding calibration problem formulations beyond traditional, statistical error metrics to also include metrics that capture indices or signatures of hydrological function, it is possible to reduce the complexity of calibration while maintaining high quality model predictions. The sensitivity-guided calibration is demonstrated using the Sacramento Soil Moisture Accounting (SAC-SMA) conceptual rainfall-runoff model of moderate complexity (i.e., up to 14 freely varying parameters). Using both statistical and hydrological metrics, optimization results demonstrate that parameters controlling at least 20% of the model output variance (through individual effects and interactions) should be included in the calibration process. This threshold generally yields 30-40% reductions in the number of SAC-SMA parameters requiring calibration - setting the others to a priori values - while maintaining high quality predictions. Two parameters are recommended to be calibrated in all cases (percent impervious area and lower zone tension water storage), three parameters are needed in drier watersheds (additional impervious area, riparian zone vegetation, and percent of percolation going to tension storage), and the lower zone parameters are crucial unless the watershed is very dry. Overall, this study demonstrates that a coupled, multi-objective sensitivity and calibration analysis better captures differences between watersheds during model calibration and serves to maximize the value of available watershed response time series. These contributions are
DEFF Research Database (Denmark)
Gaididei, Yu. B.; Christiansen, Peter Leth
2008-01-01
We study a parametrically driven Ginzburg-Landau equation model with nonlinear management. The system is made of laterally coupled long active waveguides placed along a circumference. Stationary solutions of three kinds are found: periodic Ising states and two types of Bloch states, staggered and...
Steinhauer, H. M.
2012-01-01
Engineering graphics has historically been viewed as a challenging course to teach as students struggle to grasp and understand the fundamental concepts and then to master their proper application. The emergence of stable, fast, affordable 3D parametric modeling platforms such as CATIA, Pro-E, and AutoCAD while providing several pedagogical…
Parametric modeling and stagger angle optimization of an axial flow fan
International Nuclear Information System (INIS)
Li, M X; Zhang, C H; Liu, Y; Zheng, S Y
2013-01-01
Axial flow fans are widely used in every field of social production. Improving their efficiency is a sustained and urgent demand of domestic industry. The optimization of stagger angle is an important method to improve fan performance. Parametric modeling and calculation process automation are realized in this paper to improve optimization efficiency. Geometric modeling and mesh division are parameterized based on GAMBIT. Parameter setting and flow field calculation are completed in the batch mode of FLUENT. A control program is developed in Visual C++ to dominate the data exchange of mentioned software. It also extracts calculation results for optimization algorithm module (provided by Matlab) to generate directive optimization control parameters, which as feedback are transferred upwards to modeling module. The center line of the blade airfoil, based on CLARK y profile, is constructed by non-constant circulation and triangle discharge method. Stagger angles of six airfoil sections are optimized, to reduce the influence of inlet shock loss as well as gas leak in blade tip clearance and hub resistance at blade root. Finally an optimal solution is obtained, which meets the total pressure requirement under given conditions and improves total pressure efficiency by about 6%
Splettstoesser, W. R.; Schultz, K. J.; Boxwell, D. A.; Schmitz, F. H.
1984-01-01
Acoustic data taken in the anechoic Deutsch-Niederlaendischer Windkanal (DNW) have documented the blade vortex interaction (BVI) impulsive noise radiated from a 1/7-scale model main rotor of the AH-1 series helicopter. Averaged model scale data were compared with averaged full scale, inflight acoustic data under similar nondimensional test conditions. At low advance ratios (mu = 0.164 to 0.194), the data scale remarkable well in level and waveform shape, and also duplicate the directivity pattern of BVI impulsive noise. At moderate advance ratios (mu = 0.224 to 0.270), the scaling deteriorates, suggesting that the model scale rotor is not adequately simulating the full scale BVI noise; presently, no proved explanation of this discrepancy exists. Carefully performed parametric variations over a complete matrix of testing conditions have shown that all of the four governing nondimensional parameters - tip Mach number at hover, advance ratio, local inflow ratio, and thrust coefficient - are highly sensitive to BVI noise radiation.
Update on Multi-Variable Parametric Cost Models for Ground and Space Telescopes
Stahl, H. Philip; Henrichs, Todd; Luedtke, Alexander; West, Miranda
2012-01-01
Parametric cost models can be used by designers and project managers to perform relative cost comparisons between major architectural cost drivers and allow high-level design trades; enable cost-benefit analysis for technology development investment; and, provide a basis for estimating total project cost between related concepts. This paper reports on recent revisions and improvements to our ground telescope cost model and refinements of our understanding of space telescope cost models. One interesting observation is that while space telescopes are 50X to 100X more expensive than ground telescopes, their respective scaling relationships are similar. Another interesting speculation is that the role of technology development may be different between ground and space telescopes. For ground telescopes, the data indicates that technology development tends to reduce cost by approximately 50% every 20 years. But for space telescopes, there appears to be no such cost reduction because we do not tend to re-fly similar systems. Thus, instead of reducing cost, 20 years of technology development may be required to enable a doubling of space telescope capability. Other findings include: mass should not be used to estimate cost; spacecraft and science instrument costs account for approximately 50% of total mission cost; and, integration and testing accounts for only about 10% of total mission cost.
Holland-Letz, Tim; Gunkel, Nikolas; Amtmann, Eberhard; Kopp-Schneider, Annette
2017-11-27
In toxicology and related areas, interaction effects between two substances are commonly expressed through a combination index [Formula: see text] evaluated separately at different effect levels and mixture ratios. Often, these indices are combined into a graphical representation, the isobologram. Instead of estimating the combination indices at the experimental mixture ratios only, we propose a simple parametric model for estimating the underlying interaction function. We integrate this approach into a joint model where both the parameters of the dose-response functions of the singular substances and the interaction parameters can be estimated simultaneously. As an additional benefit, this concept allows to determine optimal statistical designs for combination studies optimizing the estimation of the interaction function as a whole. From an optimal design perspective, finding the interaction parameters generally corresponds to a [Formula: see text]-optimality resp. [Formula: see text]-optimality design problem, while estimation of all underlying dose response parameters corresponds to a [Formula: see text]-optimality design problem. We show how optimal designs can be obtained in either case as well as how combination designs providing reasonable performance in regard to both criteria can be determined by putting a constraint on the efficiency in regard to one of the criteria and optimizing for the other. As all designs require prior information about model parameter values, which may be unreliable in practice, the effect of misspecifications is investigated as well.
Gersh-Range, Jessica A.; Arnold, William R.; Peck, Mason A.; Stahl, H. Philip
2011-01-01
Since future astrophysics missions require space telescopes with apertures of at least 10 meters, there is a need for on-orbit assembly methods that decouple the size of the primary mirror from the choice of launch vehicle. One option is to connect the segments edgewise using mechanisms analogous to damped springs. To evaluate the feasibility of this approach, a parametric ANSYS model that calculates the mode shapes, natural frequencies, and disturbance response of such a mirror, as well as of the equivalent monolithic mirror, has been developed. This model constructs a mirror using rings of hexagonal segments that are either connected continuously along the edges (to form a monolith) or at discrete locations corresponding to the mechanism locations (to form a segmented mirror). As an example, this paper presents the case of a mirror whose segments are connected edgewise by mechanisms analogous to a set of four collocated single-degree-of-freedom damped springs. The results of a set of parameter studies suggest that such mechanisms can be used to create a 15-m segmented mirror that behaves similarly to a monolith, although fully predicting the segmented mirror performance would require incorporating measured mechanism properties into the model. Keywords: segmented mirror, edgewise connectivity, space telescope
Parametric modeling and stagger angle optimization of an axial flow fan
Li, M. X.; Zhang, C. H.; Liu, Y.; Y Zheng, S.
2013-12-01
Axial flow fans are widely used in every field of social production. Improving their efficiency is a sustained and urgent demand of domestic industry. The optimization of stagger angle is an important method to improve fan performance. Parametric modeling and calculation process automation are realized in this paper to improve optimization efficiency. Geometric modeling and mesh division are parameterized based on GAMBIT. Parameter setting and flow field calculation are completed in the batch mode of FLUENT. A control program is developed in Visual C++ to dominate the data exchange of mentioned software. It also extracts calculation results for optimization algorithm module (provided by Matlab) to generate directive optimization control parameters, which as feedback are transferred upwards to modeling module. The center line of the blade airfoil, based on CLARK y profile, is constructed by non-constant circulation and triangle discharge method. Stagger angles of six airfoil sections are optimized, to reduce the influence of inlet shock loss as well as gas leak in blade tip clearance and hub resistance at blade root. Finally an optimal solution is obtained, which meets the total pressure requirement under given conditions and improves total pressure efficiency by about 6%.
Treatment of tannery effluent by passive uptake-parametric studies and kinetic modeling.
Natarajan, Rajamohan; Manivasagan, Rajasimman
2018-02-01
Galactomyces geotrichum was utilized as a potential biosorbent for the treatment of tannery effluent under controlled environmental conditions. Tannery effluent treatment was studied through parametric experiments to study the effect of effluent pH (3.0-10.0), initial COD (1100-4400 mg/L), and biosorbent dosage (0.3-3.0 g/L).The zeta potential of the biosorbent was determined and found to influence the optimal pH. Increase in effluent COD values resulted in decreased COD removal percentages which attributed to limited availability of surface active sites. The equation relating the COD removal efficiency and biosorbent dose was proposed. Two popular kinetic models, namely pseudo-second order and power function models, were employed to the experimental data. Pseudo-second order model proved to be a good fit with high values of regression coefficient (R 2 > 0.960). Potential application of a fungal biosorption process was explored and the optimal process parameters were identified.
Solar tower power plant using a particle-heated steam generator: Modeling and parametric study
Krüger, Michael; Bartsch, Philipp; Pointner, Harald; Zunft, Stefan
2016-05-01
Within the framework of the project HiTExStor II, a system model for the entire power plant consisting of volumetric air receiver, air-sand heat exchanger, sand storage system, steam generator and water-steam cycle was implemented in software "Ebsilon Professional". As a steam generator, the two technologies fluidized bed cooler and moving bed heat exchangers were considered. Physical models for the non-conventional power plant components as air- sand heat exchanger, fluidized bed coolers and moving bed heat exchanger had to be created and implemented in the simulation environment. Using the simulation model for the power plant, the individual components and subassemblies have been designed and the operating parameters were optimized in extensive parametric studies in terms of the essential degrees of freedom. The annual net electricity output for different systems was determined in annual performance calculations at a selected location (Huelva, Spain) using the optimized values for the studied parameters. The solution with moderate regenerative feed water heating has been found the most advantageous. Furthermore, the system with moving bed heat exchanger prevails over the system with fluidized bed cooler due to a 6 % higher net electricity yield.
Towards a Multi-Variable Parametric Cost Model for Ground and Space Telescopes
Stahl, H. Philip; Henrichs, Todd
2016-01-01
Parametric cost models can be used by designers and project managers to perform relative cost comparisons between major architectural cost drivers and allow high-level design trades; enable cost-benefit analysis for technology development investment; and, provide a basis for estimating total project cost between related concepts. This paper hypothesizes a single model, based on published models and engineering intuition, for both ground and space telescopes: OTA Cost approximately (X) D(exp (1.75 +/- 0.05)) lambda(exp(-0.5 +/- 0.25) T(exp -0.25) e (exp (-0.04)Y). Specific findings include: space telescopes cost 50X to 100X more ground telescopes; diameter is the most important CER; cost is reduced by approximately 50% every 20 years (presumably because of technology advance and process improvements); and, for space telescopes, cost associated with wavelength performance is balanced by cost associated with operating temperature. Finally, duplication only reduces cost for the manufacture of identical systems (i.e. multiple aperture sparse arrays or interferometers). And, while duplication does reduce the cost of manufacturing the mirrors of segmented primary mirror, this cost savings does not appear to manifest itself in the final primary mirror assembly (presumably because the structure for a segmented mirror is more complicated than for a monolithic mirror).
Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI
Energy Technology Data Exchange (ETDEWEB)
Dikaios, Nikolaos; Halligan, Steve; Taylor, Stuart; Atkinson, David; Punwani, Shonit [University College London, Centre for Medical Imaging, London (United Kingdom); University College London Hospital, Departments of Radiology, London (United Kingdom); Alkalbani, Jokha; Sidhu, Harbir Singh; Fujiwara, Taiki [University College London, Centre for Medical Imaging, London (United Kingdom); Abd-Alazeez, Mohamed; Ahmed, Hashim; Emberton, Mark [University College London, Research Department of Urology, London (United Kingdom); Kirkham, Alex; Allen, Clare [University College London Hospital, Departments of Radiology, London (United Kingdom); Freeman, Alex [University College London Hospital, Department of Histopathology, London (United Kingdom)
2014-09-17
We aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI). One hundred and fifty-five patients (training cohort, 70 patients; temporal validation cohort, 85 patients) underwent mp-MRI and transperineal-template-prostate-mapping (TPM) biopsy. Positive cores were classified by cancer definitions: (1) any-cancer; (2) definition-1 [≥Gleason 4 + 3 or ≥ 6 mm cancer core length (CCL)] [high risk significant]; and (3) definition-2 (≥Gleason 3 + 4 or ≥ 4 mm CCL) cancer [intermediate-high risk significant]. For each, logistic-regression mp-MRI models were derived from the training cohort and validated internally and with the temporal cohort. Sensitivity/specificity and the area under the receiver operating characteristic (ROC-AUC) curve were calculated. LR model performance was compared to radiologists' performance. Twenty-eight of 70 patients from the training cohort, and 25/85 patients from the temporal validation cohort had significant cancer on TPM. The ROC-AUC of the LR model for classification of cancer was 0.73/0.67 at internal/temporal validation. The radiologist A/B ROC-AUC was 0.65/0.74 (temporal cohort). For patients scored by radiologists as Prostate Imaging Reporting and Data System (Pi-RADS) score 3, sensitivity/specificity of radiologist A 'best guess' and LR model was 0.14/0.54 and 0.71/0.61, respectively; and radiologist B 'best guess' and LR model was 0.40/0.34 and 0.50/0.76, respectively. LR models can improve classification of Pi-RADS score 3 lesions similar to experienced radiologists. (orig.)
Parametric uncertainty analysis of pulse wave propagation in a model of a human arterial network
Xiu, Dongbin; Sherwin, Spencer J.
2007-10-01
Reduced models of human arterial networks are an efficient approach to analyze quantitative macroscopic features of human arterial flows. The justification for such models typically arise due to the significantly long wavelength associated with the system in comparison to the lengths of arteries in the networks. Although these types of models have been employed extensively and many issues associated with their implementations have been widely researched, the issue of data uncertainty has received comparatively little attention. Similar to many biological systems, a large amount of uncertainty exists in the value of the parameters associated with the models. Clearly reliable assessment of the system behaviour cannot be made unless the effect of such data uncertainty is quantified. In this paper we present a study of parametric data uncertainty in reduced modelling of human arterial networks which is governed by a hyperbolic system. The uncertain parameters are modelled as random variables and the governing equations for the arterial network therefore become stochastic. This type stochastic hyperbolic systems have not been previously systematically studied due to the difficulties introduced by the uncertainty such as a potential change in the mathematical character of the system and imposing boundary conditions. We demonstrate how the application of a high-order stochastic collocation method based on the generalized polynomial chaos expansion, combined with a discontinuous Galerkin spectral/hp element discretization in physical space, can successfully simulate this type of hyperbolic system subject to uncertain inputs with bounds. Building upon a numerical study of propagation of uncertainty and sensitivity in a simplified model with a single bifurcation, a systematical parameter sensitivity analysis is conducted on the wave dynamics in a multiple bifurcating human arterial network. Using the physical understanding of the dynamics of pulse waves in these types of
Kargarian-Marvasti, Sadegh; Rimaz, Shahnaz; Abolghasemi, Jamileh; Heydari, Iraj
2017-01-01
Cox proportional hazard model is the most common method for analyzing the effects of several variables on survival time. However, under certain circumstances, parametric models give more precise estimates to analyze survival data than Cox. The purpose of this study was to investigate the comparative performance of Cox and parametric models in a survival analysis of factors affecting the event time of neuropathy in patients with type 2 diabetes. This study included 371 patients with type 2 diabetes without neuropathy who were registered at Fereydunshahr diabetes clinic. Subjects were followed up for the development of neuropathy between 2006 to March 2016. To investigate the factors influencing the event time of neuropathy, significant variables in univariate model ( P Cox and parametric models ( P Cox and parametric models, ethnicity, high-density lipoprotein and family history of diabetes were identified as predictors of event time of neuropathy ( P Cox and parametric models. According to the results of comparison of survival receiver operating characteristics curves, log-normal model was considered as the most efficient and fitted model.
O'Reilly, Meaghan Anne; Whyne, Cari Marisa
2008-08-01
A comparative analysis of parametric and patient-specific finite element (FE) modeling of spinal motion segments. To develop patient-specific FE models of spinal motion segments using mesh-morphing methods applied to a parametric FE model. To compare strain and displacement patterns in parametric and morphed models for both healthy and metastatically involved vertebrae. Parametric FE models may be limited in their ability to fully represent patient-specific geometries and material property distributions. Generation of multiple patient-specific FE models has been limited because of computational expense. Morphing methods have been successfully used to generate multiple specimen-specific FE models of caudal rat vertebrae. FE models of a healthy and a metastatic T6-T8 spinal motion segment were analyzed with and without patient-specific material properties. Parametric and morphed models were compared using a landmark-based morphing algorithm. Morphing of the parametric FE model and including patient-specific material properties both had a strong impact on magnitudes and patterns of vertebral strain and displacement. Small but important geometric differences can be represented through morphing of parametric FE models. The mesh-morphing algorithm developed provides a rapid method for generating patient-specific FE models of spinal motion segments.
Simulation of Moving Loads in Elastic Multibody Systems With Parametric Model Reduction Techniques
Directory of Open Access Journals (Sweden)
Fischer Michael
2014-08-01
Full Text Available In elastic multibody systems, one considers large nonlinear rigid body motion and small elastic deformations. In a rising number of applications, e.g. automotive engineering, turning and milling processes, the position of acting forces on the elastic body varies. The necessary model order reduction to enable efficient simulations requires the determination of ansatz functions, which depend on the moving force position. For a large number of possible interaction points, the size of the reduced system would increase drastically in the classical Component Mode Synthesis framework. If many nodes are potentially loaded, or the contact area is not known a-priori and only a small number of nodes is loaded simultaneously, the system is described in this contribution with the parameter-dependent force position. This enables the application of parametric model order reduction methods. Here, two techniques based on matrix interpolation are described which transform individually reduced systems and allow the interpolation of the reduced system matrices to determine reduced systems for any force position. The online-offline decomposition and description of the force distribution onto the reduced elastic body are presented in this contribution. The proposed framework enables the simulation of elastic multibody systems with moving loads efficiently because it solely depends on the size of the reduced system. Results in frequency and time domain for the simulation of a thin-walled cylinder with a moving load illustrate the applicability of the proposed method.
Realistic modelling of the seismic input: Site effects and parametric studies
International Nuclear Information System (INIS)
Romanelli, F.; Vaccari, F.; Panza, G.F.
2002-11-01
We illustrate the work done in the framework of a large international cooperation, showing the very recent numerical experiments carried out within the framework of the EC project 'Advanced methods for assessing the seismic vulnerability of existing motorway bridges' (VAB) to assess the importance of non-synchronous seismic excitation of long structures. The definition of the seismic input at the Warth bridge site, i.e. the determination of the seismic ground motion due to an earthquake with a given magnitude and epicentral distance from the site, has been done following a theoretical approach. In order to perform an accurate and realistic estimate of site effects and of differential motion it is necessary to make a parametric study that takes into account the complex combination of the source and propagation parameters, in realistic geological structures. The computation of a wide set of time histories and spectral information, corresponding to possible seismotectonic scenarios for different sources and structural models, allows us the construction of damage scenarios that are out of the reach of stochastic models, at a very low cost/benefit ratio. (author)
International Nuclear Information System (INIS)
Vorontsov, Sergei V.; Jefferies, Stuart M.
2013-01-01
We describe a global parametric model for the observed power spectra of solar oscillations of intermediate and low degree. A physically motivated parameterization is used as a substitute for a direct description of mode excitation and damping as these mechanisms remain poorly understood. The model is targeted at the accurate fitting of power spectra coming from Doppler-velocity measurements and uses an adaptive response function that accounts for both the vertical and horizontal components of the velocity field on the solar surface and for possible instrumental and observational distortions. The model is continuous in frequency, can easily be adapted to intensity measurements, and extends naturally to the analysis of high-frequency pseudomodes (interference peaks at frequencies above the atmospheric acoustic cutoff).
Energy Technology Data Exchange (ETDEWEB)
Vorontsov, Sergei V. [Astronomy Unit, School of Physics and Astronomy, Queen Mary University of London, Mile End Road, London E1 4NS (United Kingdom); Jefferies, Stuart M., E-mail: S.V.Vorontsov@qmul.ac.uk, E-mail: stuartj@ifa.hawaii.edu [Institute for Astronomy, University of Hawaii, 34 Ohia Ku Street, Pukalani, HI 96768 (United States)
2013-11-20
We describe a global parametric model for the observed power spectra of solar oscillations of intermediate and low degree. A physically motivated parameterization is used as a substitute for a direct description of mode excitation and damping as these mechanisms remain poorly understood. The model is targeted at the accurate fitting of power spectra coming from Doppler-velocity measurements and uses an adaptive response function that accounts for both the vertical and horizontal components of the velocity field on the solar surface and for possible instrumental and observational distortions. The model is continuous in frequency, can easily be adapted to intensity measurements, and extends naturally to the analysis of high-frequency pseudomodes (interference peaks at frequencies above the atmospheric acoustic cutoff).
Haque, Md Mazharul; Washington, Simon
2014-01-01
The use of mobile phones while driving is more prevalent among young drivers-a less experienced cohort with elevated crash risk. The objective of this study was to examine and better understand the reaction times of young drivers to a traffic event originating in their peripheral vision whilst engaged in a mobile phone conversation. The CARRS-Q advanced driving simulator was used to test a sample of young drivers on various simulated driving tasks, including an event that originated within the driver's peripheral vision, whereby a pedestrian enters a zebra crossing from a sidewalk. Thirty-two licensed drivers drove the simulator in three phone conditions: baseline (no phone conversation), hands-free and handheld. In addition to driving the simulator each participant completed questionnaires related to driver demographics, driving history, usage of mobile phones while driving, and general mobile phone usage history. The participants were 21-26 years old and split evenly by gender. Drivers' reaction times to a pedestrian in the zebra crossing were modelled using a parametric accelerated failure time (AFT) duration model with a Weibull distribution. Also tested where two different model specifications to account for the structured heterogeneity arising from the repeated measures experimental design. The Weibull AFT model with gamma heterogeneity was found to be the best fitting model and identified four significant variables influencing the reaction times, including phone condition, driver's age, license type (provisional license holder or not), and self-reported frequency of usage of handheld phones while driving. The reaction times of drivers were more than 40% longer in the distracted condition compared to baseline (not distracted). Moreover, the impairment of reaction times due to mobile phone conversations was almost double for provisional compared to open license holders. A reduction in the ability to detect traffic events in the periphery whilst distracted
Migunov, Vladimir V.
2006-01-01
Progressing methods of drawings creating automation is discussed on the basis of so-called modules containing parametric representation of a part of the drawing and the geometrical elements. The stages of evolution of modular technology of automation of engineering are describing alternatives of applying of moduluss for simple association of elements of the drawing without parametric representation with an opportunity of its commenting, for graphic symbols creating in the schemas of automatio...
DEFF Research Database (Denmark)
Niero, Monia; Felice, Francesco, Di; Ren, Jingzheng
2014-01-01
; these correlations can be used to improve the design of new wooden pallets. Methods The conceptual scheme for defining the model is based on ISO14040-44 standards. First of all, the product system was defined identifying the life cycle of a generic wood pallet, as well as its life cycle stages. A list of independent......Purpose This study discusses the use of parameterization within the life cycle inventory (LCI) in the wooden pallet sector, in order to test the effectiveness of LCI parametric models to calculate the environmental impacts of similar products. Starting from a single case study, the objectives...... of this paper are (1) to develop a LCI parametric model adaptable to a range of wooden pallets, (2) to test this model with a reference product (non-reversible pallet with four-way blocks) and (3) to determine numerical correlations between the environmental impacts and the most significant LCI parameters...
International Nuclear Information System (INIS)
Khalvati, Farzad; Wong, Alexander; Haider, Masoom A.
2015-01-01
Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data. In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models. The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy. Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a
Evaluation of treatment response in depression studies using a Bayesian parametric cure rate model.
Santen, Gijs; Danhof, Meindert; Della Pasqua, Oscar
2008-10-01
Efficacy trials with antidepressant drugs often fail to show significant treatment effect even though efficacious treatments are investigated. This failure can, amongst other factors, be attributed to the lack of sensitivity of the statistical method as well as of the endpoints to pharmacological activity. For regulatory purposes the most widely used efficacy endpoint is still the mean change in HAM-D score at the end of the study, despite evidence from literature showing that the HAM-D scale might not be a sensitive tool to assess drug effect and that changes from baseline at the end of treatment may not reflect the extent of response. In the current study, we evaluate the prospect of applying a Bayesian parametric cure rate model (CRM) to analyse antidepressant effect in efficacy trials with paroxetine. The model is based on a survival approach, which allows for a fraction of surviving patients indefinitely after completion of treatment. Data was extracted from GlaxoSmithKline's clinical databases. Response was defined as a 50% change from baseline HAM-D at any assessment time after start of therapy. Survival times were described by a log-normal distribution and drug effect was parameterised as a covariate on the fraction of non-responders. The model was able to fit the data from different studies accurately and results show that response to treatment does not lag for two weeks, as is mythically believed. In conclusion, we demonstrate how parameterisation of a survival model can be used to characterise treatment response in depression trials. The method contrasts with the long-established snapshot on changes from baseline, as it incorporates the time course of response throughout treatment.
Parametric geometric model and shape optimization of an underwater glider with blended-wing-body
Directory of Open Access Journals (Sweden)
Chunya Sun
2015-11-01
Full Text Available Underwater glider, as a new kind of autonomous underwater vehicles, has many merits such as long-range, extended-duration and low costs. The shape of underwater glider is an important factor in determining the hydrodynamic efficiency. In this paper, a high lift to drag ratio configuration, the Blended-Wing-Body (BWB, is used to design a small civilian under water glider. In the parametric geometric model of the BWB underwater glider, the planform is defined with Bezier curve and linear line, and the section is defined with symmetrical airfoil NACA 0012. Computational investigations are carried out to study the hydrodynamic performance of the glider using the commercial Computational Fluid Dynamics (CFD code Fluent. The Kriging-based genetic algorithm, called Efficient Global Optimization (EGO, is applied to hydrodynamic design optimization. The result demonstrates that the BWB underwater glider has excellent hydrodynamic performance, and the lift to drag ratio of initial design is increased by 7% in the EGO process.
Parametric analyses of DEMO Divertor using two dimensional transient thermal hydraulic modelling
Domalapally, Phani; Di Caro, Marco
2017-11-01
Among the options considered for cooling of the Plasma facing components of the DEMO reactor, water cooling is a conservative option because of its high heat removal capability. In this work a two-dimensional transient thermal hydraulic code is developed to support the design of the divertor for the projected DEMO reactor with water as a coolant. The mathematical model accounts for transient 2D heat conduction in the divertor section. Temperature-dependent properties are used for more accurate analysis. Correlations for single phase flow forced convection, partially developed subcooled nucleate boiling, fully developed subcooled nucleate boiling and film boiling are used to calculate the heat transfer coefficients on the channel side considering the swirl flow, wherein different correlations found in the literature are compared against each other. Correlation for the Critical Heat Flux is used to estimate its limit for a given flow conditions. This paper then investigates the results of the parametric analysis performed, whereby flow velocity, diameter of the coolant channel, thickness of the coolant pipe, thickness of the armor material, inlet temperature and operating pressure affect the behavior of the divertor under steady or transient heat fluxes. This code will help in understanding the basic parameterś effect on the behavior of the divertor, to achieve a better design from a thermal hydraulic point of view.
Zhu, Yanjie; Peng, Xi; Wu, Yin; Wu, Ed X; Ying, Leslie; Liu, Xin; Zheng, Hairong; Liang, Dong
2017-02-01
To develop a new model-based method with spatial and parametric constraints (MB-SPC) aimed at accelerating diffusion tensor imaging (DTI) by directly estimating the diffusion tensor from highly undersampled k-space data. The MB-SPC method effectively incorporates the prior information on the joint sparsity of different diffusion-weighted images using an L1-L2 norm and the smoothness of the diffusion tensor using a total variation seminorm. The undersampled k-space datasets were obtained from fully sampled DTI datasets of a simulated phantom and an ex-vivo experimental rat heart with acceleration factors ranging from 2 to 4. The diffusion tensor was directly reconstructed by solving a minimization problem with a nonlinear conjugate gradient descent algorithm. The reconstruction performance was quantitatively assessed using the normalized root mean square error (nRMSE) of the DTI indices. The MB-SPC method achieves acceptable DTI measures at an acceleration factor up to 4. Experimental results demonstrate that the proposed method can estimate the diffusion tensor more accurately than most existing methods operating at higher net acceleration factors. The proposed method can significantly reduce artifact, particularly at higher acceleration factors or lower SNRs. This method can easily be adapted to MR relaxometry parameter mapping and is thus useful in the characterization of biological tissue such as nerves, muscle, and heart tissue. © 2016 American Association of Physicists in Medicine.
Testing of the Trim Tab Parametric Model in NASA Langley's Unitary Plan Wind Tunnel
Murphy, Kelly J.; Watkins, Anthony N.; Korzun, Ashley M.; Edquist, Karl T.
2013-01-01
In support of NASA's Entry, Descent, and Landing technology development efforts, testing of Langley's Trim Tab Parametric Models was conducted in Test Section 2 of NASA Langley's Unitary Plan Wind Tunnel. The objectives of these tests were to generate quantitative aerodynamic data and qualitative surface pressure data for experimental and computational validation and aerodynamic database development. Six component force-and-moment data were measured on 38 unique, blunt body trim tab configurations at Mach numbers of 2.5, 3.5, and 4.5, angles of attack from -4deg to +20deg, and angles of sideslip from 0deg to +8deg. Configuration parameters investigated in this study were forebody shape, tab area, tab cant angle, and tab aspect ratio. Pressure Sensitive Paint was used to provide qualitative surface pressure mapping for a subset of these flow and configuration variables. Over the range of parameters tested, the effects of varying tab area and tab cant angle were found to be much more significant than varying tab aspect ratio relative to key aerodynamic performance requirements. Qualitative surface pressure data supported the integrated aerodynamic data and provided information to aid in future analyses of localized phenomena for trim tab configurations.
Finite element analysis of high aspect ratio wind tunnel wing model: A parametric study
Rosly, N. A.; Harmin, M. Y.
2017-12-01
Procedure for designing the wind tunnel model of a high aspect ratio (HAR) wing containing geometric nonlinearities is described in this paper. The design process begins with identification of basic features of the HAR wing as well as its design constraints. This enables the design space to be narrowed down and consequently, brings ease of convergence towards the design solution. Parametric studies in terms of the spar thickness, the span length and the store diameter are performed using finite element analysis for both undeformed and deformed cases, which respectively demonstrate the linear and nonlinear conditions. Two main criteria are accounted for in the selection of the wing design: the static deflections due to gravitational loading should be within the allowable margin of the size of the wind tunnel test section and the flutter speed of the wing should be much below the maximum speed of the wind tunnel. The findings show that the wing experiences a stiffness hardening effect under the nonlinear static solution and the presence of the store enables significant reduction in linear flutter speed.
A methodology for the customized design of colonic stents based on a parametric model.
Puértolas, S; Navallas, D; Herrera, A; López, E; Millastre, J; Ibarz, E; Gabarre, S; Puértolas, J A; Gracia, L
2017-07-01
The choice of necessary stent properties depends mainly on the length of the stenosis and degree of occlusion. So a stent design with variable radial stiffness along its longitudinal axis would be a good option. The design proposed corresponds to a tube-based stent with closed diamond-shaped cells made from a NiTi alloy. By acting independently on different geometric factors, variable geometries can be obtained with different radial force reactions. A design adjustment according to specific requirements, in order to get a better fit to ill-duct and reduces complications, is possible. A parametric analysis using finite element has been conducted to determine the influence of slot length, number of circumferential slots, tube thickness and shape-factor on stent mechanical behavior, which allow eliminating the need for extensive experimental work and knowing and quantifying the influence of those factors. The results of finite element simulations have been used, by means of least-squares fit techniques, to obtain analytical expressions for the main mechanical characteristics of the stent (Chronic Expansive Radial Force and Radial Compression Resistance) in terms of the different geometrical factors. This allows the stent geometry to be customized without launching an iterative and costly process of modeling and simulation for each case. Copyright © 2017 Elsevier Ltd. All rights reserved.
Fluid flow in porous media using image-based modelling to parametrize Richards' equation
Cooper, L. J.; Daly, K. R.; Hallett, P. D.; Naveed, M.; Koebernick, N.; Bengough, A. G.; George, T. S.; Roose, T.
2017-11-01
The parameters in Richards' equation are usually calculated from experimentally measured values of the soil-water characteristic curve and saturated hydraulic conductivity. The complex pore structures that often occur in porous media complicate such parametrization due to hysteresis between wetting and drying and the effects of tortuosity. Rather than estimate the parameters in Richards' equation from these indirect measurements, image-based modelling is used to investigate the relationship between the pore structure and the parameters. A three-dimensional, X-ray computed tomography image stack of a soil sample with voxel resolution of 6 μm has been used to create a computational mesh. The Cahn-Hilliard-Stokes equations for two-fluid flow, in this case water and air, were applied to this mesh and solved using the finite-element method in COMSOL Multiphysics. The upscaled parameters in Richards' equation are then obtained via homogenization. The effect on the soil-water retention curve due to three different contact angles, 0°, 20° and 60°, was also investigated. The results show that the pore structure affects the properties of the flow on the large scale, and different contact angles can change the parameters for Richards' equation.
Feliciano, Julio Lebron; Kazemi, Amirkhosro; Carbajal, Gerardo; Tutkun, Murat; Bocanegra Evans, Humberto; Curet, Oscar; Castillo, Luciano
2017-11-01
Mangroves are tropical and subtropical trees that aid in protecting coastlines by dissipating the energy carried by tidal flows. These trees attenuate the devastating effects of powerful natural disasters such as hurricanes. Their roots form complex networks extending out of the water's surface and interacting with the tidal flow in estuaries, deltas, and other inter-tidal areas. This study focuses on the parametrization of the hydrodynamics of mangrove root-like geometries and the effect of the mangrove patch porosity and flexural stiffness. A multivariable non-dimensional empirical correlation is proposed to obtain a self-similar solution that describes the hydrodynamics. We introduced an effective-diameter length scale based on the wake signature of the mangrove root models. It was found that in this new dimensionless parameter, based on the Reynolds number and porosity, was able to characterize the drag coefficient. This analysis is complemented with high-resolution PIV experiments performed in a water tank under various flow and porosity conditions. Furthermore, we analyzed the Vortex-Induced Vibrations (VIVs) of the flexible mangrove patch that produce oscillating energy as a potential source for energy harvesting.
Directory of Open Access Journals (Sweden)
M. Tudor
2013-07-01
Full Text Available Meteorological numerical weather prediction (NWP models solve a system of partial differential equations in time and space. Semi-lagrangian advection schemes allow for long time steps. These longer time steps can result in instabilities occurring in the model physics. A system of differential equations in which some solution components decay more rapidly than others is stiff. In this case it is stability rather than accuracy that restricts the time step. The vertical diffusion parametrization can cause fast non-meteorological oscillations around the slowly evolving true solution (fibrillations. These are treated with an anti-fibrillation scheme, but small oscillations remain in operational weather forecasts using ARPÉGE and ALADIN models. In this paper, a simple test is designed to reveal if the formulation of particular a physical parametrization is a stiff problem or potentially numerically unstable in combination with any other part of the model. When the test is applied to a stable scheme, the solution remains stable. However, applying the test to a potentially unstable scheme yields a solution with fibrillations of substantial amplitude. The parametrizations of the NWP model ARPÉGE were tested one by one to see which one may be the source of unstable model behaviour. The test identified the set of equations in the stratiform precipitation scheme (a diagnostic Kessler-type scheme as a stiff problem, particularly the combination of terms arising due to the evaporation of snow.
Wouters, Hendrik; Blahak, Ulrich; Helmert, Jürgen; Raschendorfer, Matthias; Demuzere, Matthias; Fay, Barbara; Trusilova, Kristina; Mironov, Dmitrii; Reinert, Daniel; Lüthi, Daniel; Machulskaya, Ekaterina
2015-04-01
In order to address urban climate at the regional scales, a new efficient urban land-surface parametrization TERRA_URB has been developed and coupled to the atmospheric numerical model COSMO-CLM. Hereby, several new advancements for urban land-surface models are introduced which are crucial for capturing the urban surface-energy balance and its seasonal dependency in the mid-latitudes. This includes a new PDF-based water-storage parametrization for impervious land, the representation of radiative absorption and emission by greenhouse gases in the infra-red spectrum in the urban canopy layer, and the inclusion of heat emission from human activity. TERRA_URB has been applied in offline urban-climate studies during European observation campaigns at Basel (BUBBLE), Toulouse (CAPITOUL), and Singapore, and currently applied in online studies for urban areas in Belgium, Germany, Switzerland, Helsinki, Singapore, and Melbourne. Because of its computational efficiency, high accuracy and its to-the-point conceptual easiness, TERRA_URB has been selected to become the standard urban parametrization of the atmospheric numerical model COSMO(-CLM). This allows for better weather forecasts for temperature and precipitation in cities with COSMO, and an improved assessment of urban outdoor hazards in the context of global climate change and urban expansion with COSMO-CLM. We propose additional extensions to TERRA_URB towards a more robust representation of cities over the world including their structural design. In a first step, COSMO's standard EXTernal PARarameter (EXTPAR) tool is updated for representing the cities into the land cover over the entire globe. Hereby, global datasets in the standard EXTPAR tool are used to retrieve the 'Paved' or 'sealed' surface Fraction (PF) referring to the presence of buildings and streets. Furthermore, new global data sets are incorporated in EXTPAR for describing the Anthropogenic Heat Flux (AHF) due to human activity, and optionally the
Haj-Ali, Rami; Marom, Gil; Ben Zekry, Sagit; Rosenfeld, Moshe; Raanani, Ehud
2012-09-21
The complex three-dimensional (3D) geometry of the native tricuspid aortic valve (AV) is represented by select parametric curves allowing for a general construction and representation of the 3D-AV structure including the cusps, commissures and sinuses. The proposed general mathematical description is performed by using three independent parametric curves, two for the cusp and one for the sinuses. These curves are used to generate different surfaces that form the structure of the AV. Additional dependent curves are also generated and utilized in this process, such as the joint curve between the cusps and the sinuses. The model's feasibility to generate patient-specific parametric geometry is examined against 3D-transesophageal echocardiogram (3D-TEE) measurements from a non-pathological AV. Computational finite-element (FE) mesh can then be easily constructed from these surfaces. Examples are given for constructing several 3D-AV geometries by estimating the needed parameters from echocardiographic measurements. The average distance (error) between the calculated geometry and the 3D-TEE measurements was only 0.78±0.63mm. The proposed general 3D parametric method is very effective in quantitatively representing a wide range of native AV structures, with and without pathology. It can also facilitate a methodical quantitative investigation over the effect of pathology and mechanical loading on these major AV parameters. Copyright © 2012 Elsevier Ltd. All rights reserved.
Refinement of numerical models and parametric study of SOFC stack performance
Burt, Andrew C.
The presence of multiple air and fuel channels per fuel cell and the need to combine many cells in series result in complex steady-state temperature distributions within Solid Oxide Fuel Cell (SOFC) stacks. Flow distribution in these channels, when non-uniform, has a significant effect on cell and stack performance. Large SOFC stacks are very difficult to model using full 3-D CFD codes because of the resource requirements needed to solve for the many scales involved. Studies have shown that implementations based on Reduced Order Methods (ROM), if calibrated appropriately, can provide simulations of stacks consisting of more than 20 cells with reasonable computational effort. A pseudo 2-D SOFC stack model capable of studying co-flow and counter-flow cell geometries was developed by solving multiple 1-D SOFC single cell models in parallel on a Beowulf cluster. In order to study cross-flow geometries a novel Multi-Component Multi-Physics (MCMP) scheme was instantiated to produce a Reduced Order 3-D Fuel Cell Model. A C++ implementation of the MCMP scheme developed in this study utilized geometry, control volume, component, and model structures allowing each physical model to be solved only for those components for which it is relevant. Channel flow dynamics were solved using a 1-D flow model to reduce computational effort. A parametric study was conducted to study the influence of mass flow distribution, radiation, and stack size on fuel cell stack performance. Using the pseudo 2-D planar SOFC stack model with stacks of various sizes from 2 to 40 cells it was shown that, with adiabatic wall conditions, the asymmetry of the individual cell can produce a temperature distribution where high and low temperatures are found in the top and bottom cells, respectively. Heat transfer mechanisms such as radiation were found to affect the reduction of the temperature gradient near the top and bottom cell. Results from the reduced order 3-D fuel cell model showed that greater
Ermida, S. L.; Trigo, I. F.; DaCamara, C.; Ghent, D.
2017-12-01
Land surface temperature (LST) values retrieved from satellite measurements in the thermal infrared (TIR) may be strongly affected by spatial anisotropy. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should represent to the ensemble of directional radiometric temperature of all surface elements within the FOV. Angular effects on LST are here conveniently estimated by means of a parametric model of the surface thermal emission, which describes the angular dependence of LST as a function of viewing and illumination geometry. Two models are consistently analyzed to evaluate their performance of and to assess their respective potential to correct directional effects on LST for a wide range of surface conditions, in terms of tree coverage, vegetation density, surface emissivity. We also propose an optimization of the correction of directional effects through a synergistic use of both models. The models are calibrated using LST data as provided by two sensors: MODIS on-board NASA's TERRA and AQUA; and SEVIRI on-board EUMETSAT's MSG. As shown in our previous feasibility studies the sampling of illumination and view angles has a high impact on the model parameters. This impact may be mitigated when the sampling size is increased by aggregating pixels with similar surface conditions. Here we propose a methodology where land surface is stratified by means of a cluster analysis using information on land cover type, fraction of vegetation cover and topography. The models are then adjusted to LST data corresponding to each cluster. It is shown that the quality of the cluster based models is very close to the pixel based ones. Furthermore, the reduced number of parameters allows improving the model trough the incorporation of a
Lavecchia, C E; Espino, D M; Moerman, K M; Tse, K M; Robinson, D; Lee, P V S; Shepherd, D E T
2018-01-01
Low back pain is a major cause of disability and requires the development of new devices to treat pathologies and improve prognosis following surgery. Understanding the effects of new devices on the biomechanics of the spine is crucial in the development of new effective and functional devices. The aim of this study was to develop a preliminary parametric, scalable and anatomically accurate finite-element model of the lumbar spine allowing for the evaluation of the performance of spinal devices. The principal anatomical surfaces of the lumbar spine were first identified, and then accurately fitted from a previous model supplied by S14 Implants (Bordeaux, France). Finally, the reconstructed model was defined according to 17 parameters which are used to scale the model according to patient dimensions. The developed model, available as a toolbox named the lumbar model generator, enables generating a population of models using subject-specific dimensions obtained from data scans or averaged dimensions evaluated from the correlation analysis. This toolbox allows patient-specific assessment, taking into account individual morphological variation. The models have applications in the design process of new devices, evaluating the biomechanics of the spine and helping clinicians when deciding on treatment strategies. © 2018 The Author(s).
International Nuclear Information System (INIS)
Ma, Peizheng; Wang, Lin-Shu; Guo, Nianhua
2014-01-01
Highlights: • Investigated cooling of thermally homeostatic buildings in 7 U.S. cities by modeling. • Natural energy is harnessed by cooling tower to extract heat for building cooling. • Systematically studied possibility and conditions of using cooling tower in buildings. • Diurnal ambient temperature amplitude is taken into account in cooling tower cooling. • Homeostatic building cooling is possible in locations with large ambient T amplitude. - Abstract: A case is made that while it is important to mitigate dissipative losses associated with heat dissipation and mechanical/electrical resistance for engineering efficiency gain, the “architect” of energy efficiency is the conception of best heat extraction frameworks—which determine the realm of possible efficiency. This precept is applied to building energy efficiency here. Following a proposed process assumption-based design method, which was used for determining the required thermal qualities of building thermal autonomy, this paper continues this line of investigation and applies heat extraction approach investigating the extent of building partial homeostasis and the possibility of full homeostasis by using cooling tower in one summer in seven selected U.S. cities. Cooling tower heat extraction is applied parametrically to hydronically activated radiant-surfaces model-buildings. Instead of sizing equipment as a function of design peak hourly temperature as it is done in heat balance design-approach of selecting HVAC equipment, it is shown that the conditions of using cooling tower depend on both “design-peak” daily-mean temperature and the distribution of diurnal range in hourly temperature (i.e., diurnal temperature amplitude). Our study indicates that homeostatic building with natural cooling (by cooling tower alone) is possible only in locations of special meso-scale climatic condition such as Sacramento, CA. In other locations the use of cooling tower alone can only achieve homeostasis
A Semi-parametric Multivariate Gap-filling Model for Eddy Covariance Latent Heat Flux
Li, M.; Chen, Y.
2010-12-01
Quantitative descriptions of latent heat fluxes are important to study the water and energy exchanges between terrestrial ecosystems and the atmosphere. The eddy covariance approaches have been recognized as the most reliable technique for measuring surface fluxes over time scales ranging from hours to years. However, unfavorable micrometeorological conditions, instrument failures, and applicable measurement limitations may cause inevitable flux gaps in time series data. Development and application of suitable gap-filling techniques are crucial to estimate long term fluxes. In this study, a semi-parametric multivariate gap-filling model was developed to fill latent heat flux gaps for eddy covariance measurements. Our approach combines the advantages of a multivariate statistical analysis (principal component analysis, PCA) and a nonlinear interpolation technique (K-nearest-neighbors, KNN). The PCA method was first used to resolve the multicollinearity relationships among various hydrometeorological factors, such as radiation, soil moisture deficit, LAI, and wind speed. The KNN method was then applied as a nonlinear interpolation tool to estimate the flux gaps as the weighted sum latent heat fluxes with the K-nearest distances in the PCs’ domain. Two years, 2008 and 2009, of eddy covariance and hydrometeorological data from a subtropical mixed evergreen forest (the Lien-Hua-Chih Site) were collected to calibrate and validate the proposed approach with artificial gaps after standard QC/QA procedures. The optimal K values and weighting factors were determined by the maximum likelihood test. The results of gap-filled latent heat fluxes conclude that developed model successful preserving energy balances of daily, monthly, and yearly time scales. Annual amounts of evapotranspiration from this study forest were 747 mm and 708 mm for 2008 and 2009, respectively. Nocturnal evapotranspiration was estimated with filled gaps and results are comparable with other studies
Modeling and parametric study of a 1 kWe HT-PEMFC-based residential micro-CHP system
DEFF Research Database (Denmark)
Arsalis, Alexandros; Nielsen, Mads Pagh; Kær, Søren Knudsen
2011-01-01
A detailed thermodynamic, kinetic and geometric model of a micro-CHP (Combined-Heatand-Power) residential system based on High Temperature-Proton Exchange Membrane Fuel Cell (HT-PEMFC) technology is developed, implemented and validated. HT-PEMFC technology is investigated as a possible candidate...... for fuel cell-based residential micro-CHP systems, since it can operate at higher temperature than Nafion-based fuel cells, and therefore can reach higher cogeneration efficiencies. The proposed system can provide electric power, hot water, and space heating for a typical Danish single-family household....... A complete fuel processing subsystem, with all necessary balance-of-plant components, is modeled and coupled to the fuel cell stack subsystem. The micro-CHP system’s synthesis/ design and operational pattern is analyzed by means of a parametric study. The parametric study is conducted to determine the most...
Energy Technology Data Exchange (ETDEWEB)
Hourdin, Frederic; Musat, Ionela; Bony, Sandrine; Codron, Francis; Dufresne, Jean-Louis; Fairhead, Laurent; Grandpeix, Jean-Yves; LeVan, Phu; Li, Zhao-Xin; Lott, Francois [CNRS/UPMC, Laboratoire de Meteorologie Dynamique (LMD/IPSL), Paris Cedex 05 (France); Braconnot, Pascale; Friedlingstein, Pierre [Laboratoire des Sciences du Climat et de l' Environnement (LSCE/IPSL), Saclay (France); Filiberti, Marie-Angele [Institut Pierre Simon Laplace (IPSL), Paris (France); Krinner, Gerhard [Laboratoire de Glaciologie et Geophysique de l' Environnement, Grenoble (France)
2006-12-15
The LMDZ4 general circulation model is the atmospheric component of the IPSL-CM4 coupled model which has been used to perform climate change simulations for the 4th IPCC assessment report. The main aspects of the model climatology (forced by observed sea surface temperature) are documented here, as well as the major improvements with respect to the previous versions, which mainly come form the parametrization of tropical convection. A methodology is proposed to help analyse the sensitivity of the tropical Hadley-Walker circulation to the parametrization of cumulus convection and clouds. The tropical circulation is characterized using scalar potentials associated with the horizontal wind and horizontal transport of geopotential (the Laplacian of which is proportional to the total vertical momentum in the atmospheric column). The effect of parametrized physics is analysed in a regime sorted framework using the vertical velocity at 500 hPa as a proxy for large scale vertical motion. Compared to Tiedtke's convection scheme, used in previous versions, the Emanuel's scheme improves the representation of the Hadley-Walker circulation, with a relatively stronger and deeper large scale vertical ascent over tropical continents, and suppresses the marked patterns of concentrated rainfall over oceans. Thanks to the regime sorted analyses, these differences are attributed to intrinsic differences in the vertical distribution of convective heating, and to the lack of self-inhibition by precipitating downdraughts in Tiedtke's parametrization. Both the convection and cloud schemes are shown to control the relative importance of large scale convection over land and ocean, an important point for the behaviour of the coupled model. (orig.)
DEFF Research Database (Denmark)
Niero, Monia; Di Felice, F.; Ren, J.
2014-01-01
LCA methodology is time and resource consuming particularly when it comes to data collection and handling, therefore companies, particularly Small and Medium Enterprises (SMEs), are inclined to use streamlined approaches to shorten the resource-consuming life cycle inventory (LCI) phase. An effec......LCA methodology is time and resource consuming particularly when it comes to data collection and handling, therefore companies, particularly Small and Medium Enterprises (SMEs), are inclined to use streamlined approaches to shorten the resource-consuming life cycle inventory (LCI) phase....... An effective way for speeding up the LCI definition of products with similar characteristics is provided by parametric LCI models. A parametric LCI model uses a defined set of parameters to describe the inventory flows through formulas instead of computed numbers in unit process datasets. We present a case...... of elements constituting the wooden pallet, as well as aspects of the manufacturing process, which are information already available to every company. Apart from applicability, the use of an LCI parametric model has also the advantage of flexibility, since the value of the parameters can be easily modified...
DEFF Research Database (Denmark)
Niero, Monia; Di Felice, F.; Ren, J.
2014-01-01
LCA methodology is time and resource consuming particularly when it comes to data collection and handling, therefore companies, particularly Small and Medium Enterprises (SMEs), are inclined to use streamlined approaches to shorten the resource-consuming life cycle inventory (LCI) phase. An effec......LCA methodology is time and resource consuming particularly when it comes to data collection and handling, therefore companies, particularly Small and Medium Enterprises (SMEs), are inclined to use streamlined approaches to shorten the resource-consuming life cycle inventory (LCI) phase....... An effective way for speeding up the LCI definition of products with similar characteristics is provided by parametric LCI models. A parametric LCI model uses a defined set of parameters to describe the inventory flows through formulas instead of computed numbers in unit process datasets. We present a case...... to consider changes in the design of the wooden pallets. Based on the results of the application of the LCI parametric model to a selection of different wooden pallets, we further determined numerical correlations between the environmental impacts and the most significant inventory parameters, i.e. mass...
Gu, Yongxian
The demand of portable power generation systems for both domestic and military applications has driven the advances of mesoscale internal combustion engine systems. This dissertation was devoted to the gasdynamic modeling and parametric study of the mesoscale internal combustion swing engine/generator systems. First, the system-level thermodynamic modeling for the swing engine/generator systems has been developed. The system performance as well as the potentials of both two- and four-stroke swing engine systems has been investigated based on this model. Then through parameterc studies, the parameters that have significant impacts on the system performance have been identified, among which, the burn time and spark advance time are the critical factors related to combustion process. It is found that the shorter burn time leads to higher system efficiency and power output and the optimal spark advance time is about half of the burn time. Secondly, the turbulent combustion modeling based on levelset method (G-equation) has been implemented into the commercial software FLUENT. Thereafter, the turbulent flame propagation in a generic mesoscale combustion chamber and realistic swing engine chambers has been studied. It is found that, in mesoscale combustion engines, the burn time is dominated by the mean turbulent kinetic energy in the chamber. It is also shown that in a generic mesoscale combustion chamber, the burn time depends on the longest distance between the initial ignition kernel to its walls and by changing the ignition and injection locations, the burn time can be reduced by a factor of two. Furthermore, the studies of turbulent flame propagation in real swing engine chambers show that the combustion can be enhanced through in-chamber turbulence augmentation and with higher engine frequency, the burn time is shorter, which indicates that the in-chamber turbulence can be induced by the motion of moving components as well as the intake gas jet flow. The burn time
An Efficient Bundle Adjustment Model Based on Parallax Parametrization for Environmental Monitoring
Chen, R.; Sun, Y. Y.; Lei, Y.
2017-12-01
With the rapid development of Unmanned Aircraft Systems (UAS), more and more research fields have been successfully equipped with this mature technology, among which is environmental monitoring. One difficult task is how to acquire accurate position of ground object in order to reconstruct the scene more accurate. To handle this problem, we combine bundle adjustment method from Photogrammetry with parallax parametrization from Computer Vision to create a new method call APCP (aerial polar-coordinate photogrammetry). One impressive advantage of this method compared with traditional method is that the 3-dimensional point in space is represented using three angles (elevation angle, azimuth angle and parallax angle) rather than the XYZ value. As the basis for APCP, bundle adjustment could be used to optimize the UAS sensors' pose accurately, reconstruct the 3D models of environment, thus serving as the criterion of accurate position for monitoring. To verity the effectiveness of the proposed method, we test on several UAV dataset obtained by non-metric digital cameras with large attitude angles, and we find that our methods could achieve 1 or 2 times better efficiency with no loss of accuracy than traditional ones. For the classical nonlinear optimization of bundle adjustment model based on the rectangular coordinate, it suffers the problem of being seriously dependent on the initial values, making it unable to converge fast or converge to a stable state. On the contrary, APCP method could deal with quite complex condition of UAS when conducting monitoring as it represent the points in space with angles, including the condition that the sequential images focusing on one object have zero parallax angle. In brief, this paper presents the parameterization of 3D feature points based on APCP, and derives a full bundle adjustment model and the corresponding nonlinear optimization problems based on this method. In addition, we analyze the influence of convergence and
Non-parametric temporal modeling of the hemodynamic response function via a liquid state machine.
Avesani, Paolo; Hazan, Hananel; Koilis, Ester; Manevitz, Larry M; Sona, Diego
2015-10-01
Standard methods for the analysis of functional MRI data strongly rely on prior implicit and explicit hypotheses made to simplify the analysis. In this work the attention is focused on two such commonly accepted hypotheses: (i) the hemodynamic response function (HRF) to be searched in the BOLD signal can be described by a specific parametric model e.g., double-gamma; (ii) the effect of stimuli on the signal is taken to be linearly additive. While these assumptions have been empirically proven to generate high sensitivity for statistical methods, they also limit the identification of relevant voxels to what is already postulated in the signal, thus not allowing the discovery of unknown correlates in the data due to the presence of unexpected hemodynamics. This paper tries to overcome these limitations by proposing a method wherein the HRF is learned directly from data rather than induced from its basic form assumed in advance. This approach produces a set of voxel-wise models of HRF and, as a result, relevant voxels are filterable according to the accuracy of their prediction in a machine learning framework. This approach is instantiated using a temporal architecture based on the paradigm of Reservoir Computing wherein a Liquid State Machine is combined with a decoding Feed-Forward Neural Network. This splits the modeling into two parts: first a representation of the complex temporal reactivity of the hemodynamic response is determined by a universal global "reservoir" which is essentially temporal; second an interpretation of the encoded representation is determined by a standard feed-forward neural network, which is trained by the data. Thus the reservoir models the temporal state of information during and following temporal stimuli in a feed-back system, while the neural network "translates" this data to fit the specific HRF response as given, e.g. by BOLD signal measurements in fMRI. An empirical analysis on synthetic datasets shows that the learning process can
Energy Technology Data Exchange (ETDEWEB)
Andreadis, G.M.; Podias, A.K.M.; Tsiakaras, P.E. [Department of Mechanical and Industrial Engineering, School of Engineering, University of Thessaly, Pedion Areos, 383 34, Volos (Greece)
2009-10-20
In the present work, a model-based parametric analysis of the performance of a direct ethanol polymer electrolyte membrane fuel cell (DE-PEMFC) is conducted with the purpose to investigate the effect of several parameters on the cell's operation. The analysis is based on a previously validated one-dimensional mathematical model that describes the operation of a DE-PEMFC in steady state. More precisely, the effect of several operational and structural parameters on (i) the ethanol crossover rate from the anode to the cathode side of the cell, (ii) the parasitic current generation (mixed potential formation) and (iii) the total cell performance is investigated. According to the model predictions it was found that the increase of the ethanol feed concentration leads to higher ethanol crossover rates, higher parasitic currents and higher mixed potential values resulting in the decrease of the cell's power density. However there is an optimum ethanol feed concentration (approximately 1.0 mol L{sup -1}) for which the cell power density reaches its highest value. The platinum (Pt) loading of the anode and the cathode catalytic layers affects strongly the cell performance. Higher values of Pt loading of the catalytic layers increase the specific reaction surface area resulting in higher cell power densities. An increase of the anode catalyst loading compared to an equal one of the cathode catalyst loading has greater impact on the cell's power density. Another interesting finding is that increasing the diffusion layers' porosity up to a certain extent, improves the cell power density despite the fact that the parasitic current increases. This is explained by the fact that the reactants' concentrations over the catalysts are increased, leading to lower activation overpotential values, which are the main source of the total cell overpotentials. Moreover, the use of a thicker membrane leads to lower ethanol crossover rate, lower parasitic current and
Directory of Open Access Journals (Sweden)
Peyman Kor
2016-12-01
Full Text Available The deposition of asphaltenes on the inner wall of oil wells and pipelines causes flow blockage and significant production loss in these conduits. The major underlying mechanism(s for the deposition of asphaltene particles from the oil stream are still under investigation as an active research topic in the literature. In this work, a new deposition model considering both diffusional and inertial transport of asphaltene toward the tubing surface was developed. Model predictions were compared and verified with two sound experimental data available in the literature to evaluate the model's performance. A parametric study was done using the validated model in order to investigate the effect of the asphaltene particle size, flow velocity and oil viscosity on the magnitude of asphaltene deposition rate. Results of the study revealed that increasing the oil velocity causes more drag force on wall's inner surface; consequently, particles tend to transport away from the surface and the rate of asphaltene deposition is decreased. In addition, the developed model predicts that at low fluid velocity (∼0.7 m/s, the less viscous oil is more prone to asphaltene deposition problem.
The Use of Metaphors as a Parametric Design Teaching Model: A Case Study
Agirbas, Asli
2018-01-01
Teaching methodologies for parametric design are being researched all over the world, since there is a growing demand for computer programming logic and its fabrication process in architectural education. The computer programming courses in architectural education are usually done in a very short period of time, and so students have no chance to…
Mukherjee, Sananda
In recent years, there has been great interest in the potential of green roofs as an alternative roofing option to reduce the energy consumed by individual buildings as well as mitigate large scale urban environmental problems such as the heat island effect. There is a widespread recognition and a growing literature of measured data that suggest green roofs can reduce building energy consumption. This thesis investigates the potential of green roofs in reducing the building energy loads and focuses on how the different parameters of a green roof assembly affect the thermal performance of a building. A green roof assembly is modeled in Design Builder- a 3D graphical design modeling and energy use simulation program (interface) that uses the EnergyPlus simulation engine, and the simulated data set thus obtained is compared to field experiment data to validate the roof assembly model on the basis of how accurately it simulates the behavior of a green roof. Then the software is used to evaluate the thermal performance of several green roof assemblies under three different climate types, looking at the whole building energy consumption. For the purpose of this parametric simulation study, a prototypical single story small office building is considered and one parameter of the green roof is altered for each simulation run in order to understand its effect on building's energy loads. These parameters include different insulation thicknesses, leaf area indices (LAI) and growing medium or soil depth, each of which are tested under the three different climate types. The energy use intensities (EUIs), the peak and annual heating and cooling loads resulting from the use of these green roof assemblies are compared with each other and to a cool roof base case to determine the energy load reductions, if any. The heat flux through the roof is also evaluated and compared. The simulation results are then organized and finally presented as a decision support tool that would
A Parametric Study of Erupting Flux Rope Rotation: Modeling the 'Cartwheel CME' on 9 April 2008
Kliem, B.; Toeroek, T.; Thompson, W. T.
2012-01-01
The rotation of erupting filaments in the solar corona is addressed through a parametric simulation study of unstable, rotating flux ropes in bipolar force-free initial equilibrium. The Lorentz force due to the external shear-field component and the relaxation of tension in the twisted field are the major contributors to the rotation in this model, while reconnection with the ambient field is of minor importance, due to the field's simple structure. In the low-beta corona, the rotation is not guided by the changing orientation of the vertical field component's polarity inversion line with height. The model yields strong initial rotations which saturate in the corona and differ qualitatively from the profile of rotation vs. height obtained in a recent simulation of an eruption without preexisting flux rope. Both major mechanisms writhe the flux rope axis, converting part of the initial twist helicity, and produce rotation profiles which, to a large part, are very similar within a range of shear-twist combinations. A difference lies in the tendency of twist-driven rotation to saturate at lower heights than shear-driven rotation. For parameters characteristic of the source regions of erupting filaments and coronal mass ejections, the shear field is found to be the dominant origin of rotations in the corona and to be required if the rotation reaches angles of order 90 degrees and higher; it dominates even if the twist exceeds the threshold of the helical kink instability. The contributions by shear and twist to the total rotation can be disentangled in the analysis of observations if the rotation and rise profiles are simultaneously compared with model calculations. The resulting twist estimate allows one to judge whether the helical kink instability occurred. This is demonstrated for the erupting prominence in the "Cartwheel CME" on 9 April 2008, which has shown a rotation of approximately 115 deg. up to a height of 1.5 Solar R above the photosphere. Out of a range of
International Nuclear Information System (INIS)
Schröpfer, Gerold; Lorenz, Gunar; Rouvillois, Stéphane; Breit, Stephen
2010-01-01
This paper provides a brief summary of the state-of-the-art of MEMS-specific modeling techniques and describes the validation of new models for a parametric component library. Two recently developed 3D modeling tools are described in more detail. The first one captures a methodology for designing MEMS devices and simulating them together with integrated electronics within a standard electronic design automation (EDA) environment. The MEMS designer can construct the MEMS model directly in a 3D view. The resulting 3D model differs from a typical feature-based 3D CAD modeling tool in that there is an underlying behavioral model and parametric layout associated with each MEMS component. The model of the complete MEMS device that is shared with the standard EDA environment can be fully parameterized with respect to manufacturing- and design-dependent variables. Another recent innovation is a process modeling tool that allows accurate and highly realistic visualization of the step-by-step creation of 3D micro-fabricated devices. The novelty of the tool lies in its use of voxels (3D pixels) rather than conventional 3D CAD techniques to represent the 3D geometry. Case studies for experimental devices are presented showing how the examination of these virtual prototypes can reveal design errors before mask tape out, support process development before actual fabrication and also enable failure analysis after manufacturing.
National Research Council Canada - National Science Library
Bishop, Richard C; Belknap, William; Turner, Charles; Simon, Beverly; Kim, Joseph H
2005-01-01
A parametric investigation on the influence of above water hull form, vertical center of gravity, and bilge keel damping on the roll response of a notional combatant hull form was pursued via model...
Harlander, Niklas; Rosenkranz, Tobias; Hohmann, Volker
2012-08-01
Single channel noise reduction has been well investigated and seems to have reached its limits in terms of speech intelligibility improvement, however, the quality of such schemes can still be advanced. This study tests to what extent novel model-based processing schemes might improve performance in particular for non-stationary noise conditions. Two prototype model-based algorithms, a speech-model-based, and a auditory-model-based algorithm were compared to a state-of-the-art non-parametric minimum statistics algorithm. A speech intelligibility test, preference rating, and listening effort scaling were performed. Additionally, three objective quality measures for the signal, background, and overall distortions were applied. For a better comparison of all algorithms, particular attention was given to the usage of the similar Wiener-based gain rule. The perceptual investigation was performed with fourteen hearing-impaired subjects. The results revealed that the non-parametric algorithm and the auditory model-based algorithm did not affect speech intelligibility, whereas the speech-model-based algorithm slightly decreased intelligibility. In terms of subjective quality, both model-based algorithms perform better than the unprocessed condition and the reference in particular for highly non-stationary noise environments. Data support the hypothesis that model-based algorithms are promising for improving performance in non-stationary noise conditions.
Directory of Open Access Journals (Sweden)
J.M Portugal
2009-12-01
Full Text Available Modelos matemáticos são úteis para estimar o impacto das infestantes nas culturas. Os objetivos deste trabalho foram: elucidar a interação de Solanum nigrum (ervamoira ou maria-pretinha com o tomateiro no sistema de sementeira direta e transplantado; e avaliar o impacto de diferentes densidades de infestação e períodos de tempo de competição na quantidade de frutos do tomateiro. Entre os anos 1991 e 2001, foram conduzidos cinco experimentos em campo na região tomaticultora de Portugal. Os experimentos seguiram o método aditivo, mantendo-se constante a densidade da cultura, enquanto o número de ervas-moiras variou entre 0 e 6 plantas m-2. Outro fator avaliado foi o período de convivência da infestante na cultura, incluindo os períodos do transplante/semeadura e o início da floração até a colheita. Os resultados de perda de produção foram ajustados aos modelos linear e hiperbólico. O comportamento das perdas de produção nas densidades mais baixas de erva-moira é do tipo linear. A erva-moira é uma espécie muito competitiva em relação ao tomateiro tanto quando a cultura é semeada quanto quando é transplantada. As perdas de produção foram maiores quando a cultura foi semeada, em comparação com a transplantada.Mathematical models are useful for estimating the impact of weeds on crops. The objectives of this study were to elucidate the interaction of Solanum nigrum (black nightshade with tomato plants in direct and transplanted sowing and to evaluate the impact of different infestation densities and competition time periods on tomato plant production. Between 1991 and 2001, five field experiments were conducted in the tomato growing region of Portugal using the additive method, keeping crop density constant, while changing the number of weeds from 0 and 6 plants m-2. The weed and crop coexistence period was also evaluated, from transplantation/sowing to the beginning of flowering or until harvest. The tomato yield
Directory of Open Access Journals (Sweden)
MARINA I AVERSA
2011-03-01
Full Text Available Age and growth for the beaked skate was estimated from bands in the vertebral centra of 689 individuals obtained from incidental catches of the Argentine hake (Merluccius hubbsi fishery. Age bias plots and indices of precision indicated that ageing method was precise and unbiased (% CV = 3 % PA = 82.09 %. Edge and marginal increment analysis of the vertebrae support the hypothesis of annual band pair deposition. Three growth models were fitted to length-at-age and the two-phase growth model produced the best fit. This feature has never been described before for D. chilensis and can be related to changes in energy allocation and the shift from juvenile to adult phase. The unrealistic biological estimates of the von Bertalanffy growth model illustrates the importance of fitting alternative models to the data. Female beaked skates reached greater size in length (L∝ as well as in disc width (L∝ = 138.2 cm; DW∝ = 92.46 cm and have lower growth rate (k = 0.08 yr-1 than males (L∝ = 106.7 cm; DW∝ = 74.52 cm; k = 0.121 yr-1. This study provides basic information on age and growth for the beaked skate, D. chilensis, which were previously not available for its south Atlantic range of distribution.La edad y el crecimiento de la raya picuda fue estimado a partir de las bandas en los cuerpos vertebrales de 689 individuos obtenidos de las capturas incidentales de la pesquería de merluza argentina (Merluccius hubbsi. Gráficos de sesgos y el análisis de precisión indicaron que el método utilizado para la determinación de la edad es preciso y no sesgado (% CV = 3 % PA = 82.09 %. El análisis del tipo de borde e incremento marginal vertebral confirmó la hipótesis del depósito anual de un par de bandas. Se ajustaron tres modelos de crecimiento a los datos de largo a la edad y el modelo de dos fases produjo el mejor ajuste. Esta característica nunca antes fue descripta para Dipturus chilensis y podría relacionarse con un cambio en la cuota de
International Nuclear Information System (INIS)
Hernandez-Tenorio, C.; Belyaeva, T.L.; Serkin, V.N.
2007-01-01
The dynamics of nonlinear solitary waves is studied in the framework of the nonlinear Schroedinger equation model with time-dependent harmonic oscillator potential. The model allows one to analyse on general basis a variety of nonlinear phenomena appearing both in Bose-Einstein condensate, condensed matter physics, nonlinear optics, and biophysics. The soliton parametric resonance is investigated by using two complementary methods: the adiabatic perturbation theory and direct numerical experiments. Conditions for reversible and irreversible denaturation of soliton bound states are also considered
Directory of Open Access Journals (Sweden)
I. Foyo-Moreno
2000-11-01
Full Text Available Since the discovery of the ozone depletion in Antarctic and the globally declining trend of stratospheric ozone concentration, public and scientific concern has been raised in the last decades. A very important consequence of this fact is the increased broadband and spectral UV radiation in the environment and the biological effects and heath risks that may take place in the near future. The absence of widespread measurements of this radiometric flux has lead to the development and use of alternative estimation procedures such as the parametric approaches. Parametric models compute the radiant energy using available atmospheric parameters. Some parametric models compute the global solar irradiance at surface level by addition of its direct beam and diffuse components. In the present work, we have developed a comparison between two cloudless sky parametrization schemes. Both methods provide an estimation of the solar spectral irradiance that can be integrated spectrally within the limits of interest. For this test we have used data recorded in a radiometric station located at Granada (37.180°N, 3.580°W, 660 m a.m.s.l., an inland location. The database includes hourly values of the relevant variables covering the years 1994-95. The performance of the models has been tested in relation to their predictive capability of global solar irradiance in the UV range (290–385 nm. After our study, it appears that information concerning the aerosol radiative effects is fundamental in order to obtain a good estimation. The original version of SPCTRAL2 provides estimates of the experimental values with negligible mean bias deviation. This suggests not only the appropriateness of the model but also the convenience of the aerosol features fixed in it to Granada conditions. SMARTS2 model offers increased flexibility concerning the selection of different aerosol models included in the code and provides the best results when the selected models are those
Energy Technology Data Exchange (ETDEWEB)
Boehme, M.
2004-07-01
Continuos improvement of processes and methodologies is one key element to shorten development time, reduce costs, and improve quality, and therefore to answer growing customer demands and global competition. This work describes a new concept of introducing the principles of parametric modeling to the entire product data model in the area of automotive development. Based on the idea, that not only geometric dimensions can be described by parameters, the method of parametric modeling is applied to the complete product model. The concept assumes four major principles: First, the parameters of the product model are handled independently from their proprietary data formats. Secondly, a strictly hierarchical structure is required for the parametric description of the product. The third principle demands an object-based parameterization. Finally the use of parameter-sets for the description of logical units of the product model tree is part of the concept. Those four principles are addressing the following main objectives: Supporting and improving Simultaneous Engineering, achieving data consistency over all development phases, digital approval of product properties, and incorporation of the design intent into the product model. Further improvement of the automotive development process can be achieved with the introduction of parametric product modeling using the principles described in this paper. (orig.) [German] Die Forderung nach kuerzeren Entwicklungszeiten, Reduzierung der Kosten und verbesserter Qualitaet erfordert eine stetige Verbesserung von Prozessen und Methoden in der Produktentwicklung. In dieser Arbeit wird ein neuer Ansatz vorgestellt, der die Methodik des parametrischen Konstruierens auf das gesamte Produktmodell in der Fahrzeugentwicklung anwendet, und somit weitere Potentiale zur Verbesserung des Produktentstehungsprozesses erschliesst. Ausgehend von der Annahme, dass nicht nur geometrische Abmessungen als Parameter beschrieben werden koennen, wird die
Czech Academy of Sciences Publication Activity Database
Svoboda, Jiří; Gamsjäger, E.
2011-01-01
Roč. 102, č. 6 (2011), s. 666-673 ISSN 1862-5282 R&D Projects: GA MŠk(CZ) OC10029 Institutional research plan: CEZ:AV0Z20410507 Keywords : modelling * phase transformation * ediffusion Subject RIV: BJ - Thermodynamics Impact factor: 0.830, year: 2011
Forecasting Exchange Rate Density Using Parametric Models: the Case of Brazil
Directory of Open Access Journals (Sweden)
Benjamin Miranda Tabak
2007-06-01
Full Text Available This paper employs a recently developed parametric technique to obtain density forecasts for the Brazilian exchange rate, using the exchange rate options market. Empirical results suggest that the option market contains useful information about future exchange rate density. These results suggests that density forecasts using options markets may add value for portfolio and risk management, and may be useful for financial regulators to assess financial stability.
Zhang, Kai; Cao, Libo; Wang, Yulong; Hwang, Eunjoo; Reed, Matthew P; Forman, Jason; Hu, Jingwen
2017-10-01
Field data analyses have shown that obesity significantly increases the occupant injury risks in motor vehicle crashes, but the injury assessment tools for people with obesity are largely lacking. The objectives of this study were to use a mesh morphing method to rapidly generate parametric finite element models with a wide range of obesity levels and to evaluate their biofidelity against impact tests using postmortem human subjects (PMHS). Frontal crash tests using three PMHS seated in a vehicle rear seat compartment with body mass index (BMI) from 24 to 40 kg/m 2 were selected. To develop the human models matching the PMHS geometry, statistical models of external body shape, rib cage, pelvis, and femur were applied to predict the target geometry using age, sex, stature, and BMI. A mesh morphing method based on radial basis functions was used to rapidly morph a baseline human model into the target geometry. The model-predicted body excursions and injury measures were compared to the PMHS tests. Comparisons of occupant kinematics and injury measures between the tests and simulations showed reasonable correlations across the wide range of BMI levels. The parametric human models have the capability to account for the obesity effects on the occupant impact responses and injury risks. © 2017 The Obesity Society.
International Nuclear Information System (INIS)
Al Mamon, Abdulla; Das, Sudipta
2015-01-01
In this present work, we try to build up a cosmological model using a non-canonical scalar field within the framework of a spatially flat FRW space-time. In this context, we have considered four different parametrizations of the equation of state parameter of the non-canonical scalar field. Under this scenario, analytical solutions for various cosmological parameters have been found out. It has been found that the deceleration parameter shows a smooth transition from a positive value to some negative value which indicates that the universe was undergoing an early deceleration followed by late time acceleration which is essential for the structure formation of the universe. With these four parametrizations, the future evolution of the models are also discussed. It has been found that one of the models (Generalized Chaplygin gas model, GCG) mimics the concordance ΛCDM in the near future, whereas two other models (CPL and JBP) diverge due to future singularity. Finally, we have studied these theoretical models with the latest datasets from SN Ia + H(z) + BAO/CMB. (orig.)
Beller, S.; Monteiller, V.; Combe, L.; Operto, S.; Nolet, G.
2018-02-01
Full-waveform inversion (FWI) is not yet a mature imaging technology for lithospheric imaging from teleseismic data. Therefore, its promise and pitfalls need to be assessed more accurately according to the specifications of teleseismic experiments. Three important issues are related to (1) the choice of the lithospheric parametrization for optimization and visualization, (2) the initial model and (3) the acquisition design, in particular in terms of receiver spread and sampling. These three issues are investigated with a realistic synthetic example inspired by the CIFALPS experiment in the Western Alps. Isotropic elastic FWI is implemented with an adjoint-state formalism and aims to update three parameter classes by minimization of a classical least-squares difference-based misfit function. Three different subsurface parametrizations, combining density (ρ) with P and S wave speeds (Vp and Vs) , P and S impedances (Ip and Is), or elastic moduli (λ and μ) are first discussed based on their radiation patterns before their assessment by FWI. We conclude that the (ρ, λ, μ) parametrization provides the FWI models that best correlate with the true ones after recombining a posteriori the (ρ, λ, μ) optimization parameters into Ip and Is. Owing to the low frequency content of teleseismic data, 1-D reference global models as PREM provide sufficiently accurate initial models for FWI after smoothing that is necessary to remove the imprint of the layering. Two kinds of station deployments are assessed: coarse areal geometry versus dense linear one. We unambiguously conclude that a coarse areal geometry should be favoured as it dramatically increases the penetration in depth of the imaging as well as the horizontal resolution. This results because the areal geometry significantly increases local wavenumber coverage, through a broader sampling of the scattering and dip angles, compared to a linear deployment.
BEKWAAM, a model fit for reservoir design and management
Benoist, A.P.; Brinkman, A.G.; Diepenbeek, van P.M.J.A.; Waals, J.M.J.
1998-01-01
In the Province of Limburg in the Netherlands a new reservoir will be used for the drinking water production of 20 million m3 per annum from the year 2002. With the use of this reservoir the WML is shifting towards the use of surface water (River Meuse) as primary source instead of ground water.
A Rapid Model Fitting Tool Suite, Phase I
National Aeronautics and Space Administration — Instruments flown on board NASA missions often do not measure quantities of interest to scientists directly, but rather observable quantities. In addition,...
Energy Technology Data Exchange (ETDEWEB)
Chitty, Walter-John [CEA, DSM, IRAMIS, Laboratoire Pierre Suee, F-91191 Gif-sur-Yvette cedex (France); Dillmann, Philippe [CEA, DSM, IRAMIS, Laboratoire Pierre Suee, F-91191 Gif-sur-Yvette cedex (France); Institut de Recherches sur les Archeomateriaux, LMC UMR5060 CNRS (France)], E-mail: philippe.dillmann@cea.fr; L' Hostis, Valerie [CEA, DEN, DPC, SCCME, Laboratoire d' Etude du Comportement des Betons et des Argiles, F-91191 Gif-sur-Yvette (France); Millard, Alain [CEA, DEN, DM2S, SEMT, Laboratoire de Modelisation, Systemes et Simulation, F-91191 Gif-sur-Yvette (France)
2008-11-15
The prediction of long-term behaviour of reinforced concrete structures involved in the nuclear industry requires a phenomenological modelling of the rebars corrosion processes. Previous analytical characterisation of archaeological artefacts allowed to identify a typical layout constituted of four layers (the metal, the dense product layer, the transformed medium and the binder). Additional experiments leaded to identify the long-term corrosion mechanisms. Following these results, this paper proposes an analytical model of long-term corrosion of rebars embedded in concrete. This modelling is considering the kinetic of oxygen diffusion through the system and its consumption at the metal/dense product layer interface as a function of concrete water saturation degree. Corrosion products thicknesses estimated with the model are then compared to corrosion product thicknesses measured on archaeological artefacts. A parametric study is performed and demonstrates that the oxygen diffusivity and the kinetic constant of the cathodic reaction affect in a wide range the model results.
Enabling Parametric Optimal Ascent Trajectory Modeling During Early Phases of Design
Holt, James B.; Dees, Patrick D.; Diaz, Manuel J.
2015-01-01
-modal due to the interaction of various constraints. Additionally, when these obstacles are coupled with The Program to Optimize Simulated Trajectories [1] (POST), an industry standard program to optimize ascent trajectories that is difficult to use, it requires expert trajectory analysts to effectively optimize a vehicle's ascent trajectory. As it has been pointed out, the paradigm of trajectory optimization is still a very manual one because using modern computational resources on POST is still a challenging problem. The nuances and difficulties involved in correctly utilizing, and therefore automating, the program presents a large problem. In order to address these issues, the authors will discuss a methodology that has been developed. The methodology is two-fold: first, a set of heuristics will be introduced and discussed that were captured while working with expert analysts to replicate the current state-of-the-art; secondly, leveraging the power of modern computing to evaluate multiple trajectories simultaneously, and therefore, enable the exploration of the trajectory's design space early during the pre-conceptual and conceptual phases of design. When this methodology is coupled with design of experiments in order to train surrogate models, the authors were able to visualize the trajectory design space, enabling parametric optimal ascent trajectory information to be introduced with other pre-conceptual and conceptual design tools. The potential impact of this methodology's success would be a fully automated POST evaluation suite for the purpose of conceptual and preliminary design trade studies. This will enable engineers to characterize the ascent trajectory's sensitivity to design changes in an arbitrary number of dimensions and for finding settings for trajectory specific variables, which result in optimal performance for a "dialed-in" launch vehicle design. The effort described in this paper was developed for the Advanced Concepts Office [2] at NASA Marshall
Rahmim, Arman; Zhou, Yun; Tang, Jing; Lu, Lijun; Sossi, Vesna; Wong, Dean F.
2012-01-01
Due to high noise levels in the voxel kinetics, development of reliable parametric imaging algorithms remains as one of most active areas in dynamic brain PET imaging, which in the vast majority of cases involves receptor/transporter studies with reversibly binding tracers. As such, the focus of this work has been to develop a novel direct 4D parametric image reconstruction scheme for such tracers. Based on a relative equilibrium (RE) graphical analysis formulation (Zhou et al., 2009b), we developed a closed-form 4D EM algorithm to directly reconstruct distribution volume (DV) parametric images within a plasma input model, as well as DV ratio (DVR) images within a reference tissue model scheme (wherein an initial reconstruction was used to estimate the reference tissue time-activity-curves). A particular challenge with the direct 4D EM formulation is that the intercept parameters in graphical (linearized) analysis of reversible tracers (e.g. Logan or RE analysis) are commonly negative (unlike for irreversible tracers; e.g. using Patlak analysis). Subsequently, we focused our attention on the AB-EM algorithm, derived by Byrne (1998) to allow inclusion of prior information about the lower (A) and upper (B) bounds for image values. We then generalized this algorithm to the 4D EM framework thus allowing negative intercept parameters. Furthermore, our 4D AB-EM algorithm incorporated, and emphasized the use of spatially varying lower bounds to achieve enhanced performance. As validation, the means of parameters estimated from 55 human 11C-raclopride dynamic PET studies were used for extensive simulations using a mathematical brain phantom. Images were reconstructed using conventional indirect as well as proposed direct parametric imaging methods. Noise vs. bias quantitative measurements were performed in various regions of the brain. Direct 4D EM reconstruction resulted in notable qualitative and quantitative accuracy improvements (over 35% noise reduction, with matched
Energy Technology Data Exchange (ETDEWEB)
Cassardo, C. [Alessandria, Univ. di Turin (Italy). Dipt. di Scienze e Tecnologie Avanzate; Carena, E.; Longhetto, A. [Turin Univ. (Italy). Dipt. di Fisica Generale `Amedeo Avogadro`
1998-03-01
The Land Surface Process Model (LSPM) has been improved with respect to the 1. version of 1994. The modifications have involved the parametrizations of the radiation terms and of turbulent heat fluxes. A parametrization of runoff has also been developed, in order to close the hydrologic balance. This 2. version of LSPM has been validated against experimental data gathered at Mottarone (Verbania, Northern Italy) during a field experiment. The results of this validation show that this new version is able to apportionate the energy into sensible and latent heat fluxes. LSPM has also been submitted to a series of sensitivity tests in order to investigate the hydrological part of the model. The physical quantities selected in these sensitivity experiments have been the initial soil moisture content and the rainfall intensity. In each experiment, the model has been forced by using the observations carried out at the synoptic stations of San Pietro Capofiume (Po Valley, Italy). The observed characteristics of soil and vegetation (not involved in the sensitivity tests) have been used as initial and boundary conditions. The results of the simulation show that LSPM can reproduce well the energy, heat and water budgets and their behaviours with varying the selected parameters. A careful analysis of the LSPM output shows also the importance to identify the effective soil type.
PARAMETRIC DRAWINGS VS. AUTOLISP
Directory of Open Access Journals (Sweden)
PRUNĂ Liviu
2015-06-01
Full Text Available In this paper the authors make a critical analysis of the advantages offered by the parametric drawing use by comparison with the AutoLISP computer programs used when it comes about the parametric design. Studying and analysing these two work models the authors have got to some ideas and conclusions which should be considered in the moment in that someone must to decide if it is the case to elaborate a software, using the AutoLISP language, or to establish the base rules that must be followed by the drawing, in the idea to construct outlines or blocks which can be used in the projection process.
PARAMETRIC DRAWINGS VS. AUTOLISP
PRUNĂ Liviu; SLONOVSCHI Andrei
2015-01-01
In this paper the authors make a critical analysis of the advantages offered by the parametric drawing use by comparison with the AutoLISP computer programs used when it comes about the parametric design. Studying and analysing these two work models the authors have got to some ideas and conclusions which should be considered in the moment in that someone must to decide if it is the case to elaborate a software, using the AutoLISP language, or to establish the base rules that must be followed...
Madi, Raneem; Huibert de Rooij, Gerrit; Mielenz, Henrike; Mai, Juliane
2018-02-01
Few parametric expressions for the soil water retention curve are suitable for dry conditions. Furthermore, expressions for the soil hydraulic conductivity curves associated with parametric retention functions can behave unrealistically near saturation. We developed a general criterion for water retention parameterizations that ensures physically plausible conductivity curves. Only 3 of the 18 tested parameterizations met this criterion without restrictions on the parameters of a popular conductivity curve parameterization. A fourth required one parameter to be fixed. We estimated parameters by shuffled complex evolution (SCE) with the objective function tailored to various observation methods used to obtain retention curve data. We fitted the four parameterizations with physically plausible conductivities as well as the most widely used parameterization. The performance of the resulting 12 combinations of retention and conductivity curves was assessed in a numerical study with 751 days of semiarid atmospheric forcing applied to unvegetated, uniform, 1 m freely draining columns for four textures. Choosing different parameterizations had a minor effect on evaporation, but cumulative bottom fluxes varied by up to an order of magnitude between them. This highlights the need for a careful selection of the soil hydraulic parameterization that ideally does not only rely on goodness of fit to static soil water retention data but also on hydraulic conductivity measurements. Parameter fits for 21 soils showed that extrapolations into the dry range of the retention curve often became physically more realistic when the parameterization had a logarithmic dry branch, particularly in fine-textured soils where high residual water contents would otherwise be fitted.
Parametric modal transition systems
DEFF Research Database (Denmark)
Beneš, Nikola; Křetínský, Jan; Larsen, Kim Guldstrand
2011-01-01
in the refinement process like exclusive, conditional and persistent choices. We introduce a new model called parametric modal transition systems (PMTS) together with a general modal refinement notion that overcome many of the limitations and we investigate the computational complexity of modal refinement checking....
Korez, Robert; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž
2014-10-01
Gradual degeneration of intervertebral discs of the lumbar spine is one of the most common causes of low back pain. Although conservative treatment for low back pain may provide relief to most individuals, surgical intervention may be required for individuals with significant continuing symptoms, which is usually performed by replacing the degenerated intervertebral disc with an artificial implant. For designing implants with good bone contact and continuous force distribution, the morphology of the intervertebral disc space and vertebral body endplates is of considerable importance. In this study, we propose a method for parametric modeling of the intervertebral disc space in three dimensions (3D) and show its application to computed tomography (CT) images of the lumbar spine. The initial 3D model of the intervertebral disc space is generated according to the superquadric approach and therefore represented by a truncated elliptical cone, which is initialized by parameters obtained from 3D models of adjacent vertebral bodies. In an optimization procedure, the 3D model of the intervertebral disc space is incrementally deformed by adding parameters that provide a more detailed morphometric description of the observed shape, and aligned to the observed intervertebral disc space in the 3D image. By applying the proposed method to CT images of 20 lumbar spines, the shape and pose of each of the 100 intervertebral disc spaces were represented by a 3D parametric model. The resulting mean (±standard deviation) accuracy of modeling was 1.06±0.98mm in terms of radial Euclidean distance against manually defined ground truth points, with the corresponding success rate of 93% (i.e. 93 out of 100 intervertebral disc spaces were modeled successfully). As the resulting 3D models provide a description of the shape of intervertebral disc spaces in a complete parametric form, morphometric analysis was straightforwardly enabled and allowed the computation of the corresponding
Parametric Optimization of Hospital Design
DEFF Research Database (Denmark)
Holst, Malene Kirstine; Kirkegaard, Poul Henning; Christoffersen, L.D.
2013-01-01
Present paper presents a parametric performancebased design model for optimizing hospital design. The design model operates with geometric input parameters defining the functional requirements of the hospital and input parameters in terms of performance objectives defining the design requirements...
International Nuclear Information System (INIS)
Marchal, Xavier
1992-01-01
In order to use CAD efficiently in the analysis and design of electronic Integrated circuits, adequate modeling of active non-linear devices such as MOSFET transistors must be available to the designer. Many mathematical forms can be given to those models, such as explicit relations, or implicit equations to be solved. A major requirement in developing MOS transistor models for IC simulation is the availability of electrical characteristic curves over a wide range of channel width and length, including the sub-micrometer range. To account in a convenient way for bulk charge influence on I DS = f(V DS , V GS , v BS ) device characteristics, all 3 standard SPICE MOS models use an empirical fitting parameter called the 'charge sharing factor'. Unfortunately, this formulation produces models which only describe correctly either some of the short channel phenomena, or some particular operating conditions (low injection, avalanche effect, etc.). We present here a cellular model (CDM = Charge Distributed Model) implemented in the open modular SPICE-PAC Simulator; this model is derived from the 4-terminal WANG charge controlled MOSFET model, using the charge sheet approximation. The CDM model describes device characteristics in ail operating regions without introducing drain current discontinuities and without requiring a 'charge sharing factor'. A usual problem to be faced by designers when they simulate MOS ICs is to find a reliable source of model parameters. Though most models have a physical basis, some of their parameters cannot be easily estimated from physical considerations. It can also happen that physically determined parameters values do not produce a good fit to measured device characteristics. Thus it is generally necessary to extract model parameters from measured transistor data, to ensure that model equations approximate measured curves accurately enough. Model parameters extraction can be done in 2 different ways, exposed in this
Vignon, Etienne; Hourdin, Frédéric; Genthon, Christophe; Gallée, Hubert; Bazile, Eric; Lefebvre, Marie-Pierre; Madeleine, Jean-Baptiste; Van de Wiel, Bas J. H.
2017-07-01
The parametrization of the atmospheric boundary layer (ABL) is critical over the Antarctic Plateau for climate modelling since it affects the climatological temperature inversion and the negatively buoyant near-surface flow over the ice-sheet. This study challenges state-of-the-art parametrizations used in general circulation models to represent the clear-sky summertime diurnal cycle of the ABL at Dome C, Antarctic Plateau. The Laboratoire de Météorologie Dynamique-Zoom model is run in a 1-D configuration on the fourth Global Energy and Water Cycle Exchanges Project Atmospheric Boundary Layers Study case. Simulations are analyzed and compared to observations, giving insights into the sensitivity of one model that participates to the intercomparison exercise. Snow albedo and thermal inertia are calibrated leading to better surface temperatures. Using the so-called "thermal plume model" improves the momentum mixing in the diurnal ABL. In stable conditions, four turbulence schemes are tested. Best simulations are those in which the turbulence cuts off above 35 m in the middle of the night, highlighting the contribution of the longwave radiation in the ABL heat budget. However, the nocturnal surface layer is not stable enough to distinguish between surface fluxes computed with different stability functions. The absence of subsidence in the forcings and an underestimation of downward longwave radiation are identified to be likely responsible for a cold bias in the nocturnal ABL. Apart from model-specific improvements, the paper clarifies on which are the critical aspects to improve in general circulation models to correctly represent the summertime ABL over the Antarctic Plateau.
Fai, S.; Filippi, M.; Paliaga, S.
2013-07-01
Whether a house of worship or a simple farmhouse, the fabrication of a building reveals both the unspoken cultural aspirations of the builder and the inevitable exigencies of the construction process. In other-words, why buildings are made is intimately and inevitably associated with how buildings are made. Nowhere is this more evident than in vernacular architecture. At the Carleton Immersive Media Studio (CIMS) we are concerned that the de-population of Canada's rural areas, paucity of specialized tradespersons, and increasing complexity of building codes threaten the sustainability of this invaluable cultural resource. For current and future generations, the quantitative and qualitative values of traditional methods of construction are essential for an inclusive cultural memory. More practically, and equally pressing, an operational knowledge of these technologies is essential for the conservation of our built heritage. To address these concerns, CIMS has launched a number of research initiatives over the past five years that explore novel protocols for the documentation and dissemination of knowledge related to traditional methods of construction. Our current project, Cultural Diversity and Material Imagination in Canadian Architecture (CDMICA), made possible through funding from Canada's Social Sciences and Humanities Research Council (SSHRC), explores the potential of building information modelling (BIM) within the context of a web-based environment. In this paper, we discuss our work-to-date on the development of a web-based library of BIM details that is referenced to ''typical'' assemblies culled from 19C and early 20C construction manuals. The parametric potential of these ''typical'' details is further refined by evidence from the documentation of ''specific'' details studied during comprehensive surveys of extant heritage buildings. Here, we consider a BIM of the roof truss assembly of one of the oldest buildings in Canada's national capital - the
Mohseny, Maryam; Amanpour, Farzaneh; Mosavi-Jarrahi, Alireza; Jafari, Hossein; Moradi-Joo, Mohammad; Davoudi Monfared, Esmat
2016-01-01
Breast cancer is one of the most common causes of cancer mortality in Iran. Social determinants of health are among the key factors affecting the pathogenesis of diseases. This cross-sectional study aimed to determine the social determinants of breast cancer survival time with parametric and semi-parametric regression models. It was conducted on male and female patients diagnosed with breast cancer presenting to the Cancer Research Center of Shohada-E-Tajrish Hospital from 2006 to 2010. The Cox proportional hazard model and parametric models including the Weibull, log normal and log-logistic models were applied to determine the social determinants of survival time of breast cancer patients. The Akaike information criterion (AIC) was used to assess the best fit. Statistical analysis was performed with STATA (version 11) software. This study was performed on 797 breast cancer patients, aged 25-93 years with a mean age of 54.7 (±11.9) years. In both semi-parametric and parametric models, the three-year survival was related to level of education and municipal district of residence (P<0.05). The AIC suggested that log normal distribution was the best fit for the three-year survival time of breast cancer patients. Social determinants of health such as level of education and municipal district of residence affect the survival of breast cancer cases. Future studies must focus on the effect of childhood social class on the survival times of cancers, which have hitherto only been paid limited attention.
Cherchye, L.J.H.; de Rock, B.; Vermeulen, F.M.P.
2005-01-01
We non-parametrically test a general collective consumption model with public consumption and externalities inside the household.We further propose a novel approach to model special cases of the general collective model.These special cases include alternative restrictions on the 'sharing rule' that
Realistic modelling of the seismic input Site effects and parametric studies
Romanelli, F; Vaccari, F
2002-01-01
We illustrate the work done in the framework of a large international cooperation, showing the very recent numerical experiments carried out within the framework of the EC project 'Advanced methods for assessing the seismic vulnerability of existing motorway bridges' (VAB) to assess the importance of non-synchronous seismic excitation of long structures. The definition of the seismic input at the Warth bridge site, i.e. the determination of the seismic ground motion due to an earthquake with a given magnitude and epicentral distance from the site, has been done following a theoretical approach. In order to perform an accurate and realistic estimate of site effects and of differential motion it is necessary to make a parametric study that takes into account the complex combination of the source and propagation parameters, in realistic geological structures. The computation of a wide set of time histories and spectral information, corresponding to possible seismotectonic scenarios for different sources and stru...
Directory of Open Access Journals (Sweden)
R. Madi
2018-02-01
Full Text Available Few parametric expressions for the soil water retention curve are suitable for dry conditions. Furthermore, expressions for the soil hydraulic conductivity curves associated with parametric retention functions can behave unrealistically near saturation. We developed a general criterion for water retention parameterizations that ensures physically plausible conductivity curves. Only 3 of the 18 tested parameterizations met this criterion without restrictions on the parameters of a popular conductivity curve parameterization. A fourth required one parameter to be fixed. We estimated parameters by shuffled complex evolution (SCE with the objective function tailored to various observation methods used to obtain retention curve data. We fitted the four parameterizations with physically plausible conductivities as well as the most widely used parameterization. The performance of the resulting 12 combinations of retention and conductivity curves was assessed in a numerical study with 751 days of semiarid atmospheric forcing applied to unvegetated, uniform, 1 m freely draining columns for four textures. Choosing different parameterizations had a minor effect on evaporation, but cumulative bottom fluxes varied by up to an order of magnitude between them. This highlights the need for a careful selection of the soil hydraulic parameterization that ideally does not only rely on goodness of fit to static soil water retention data but also on hydraulic conductivity measurements. Parameter fits for 21 soils showed that extrapolations into the dry range of the retention curve often became physically more realistic when the parameterization had a logarithmic dry branch, particularly in fine-textured soils where high residual water contents would otherwise be fitted.
Dhote, Sharvari; Yang, Zhengbao; Zu, Jean
2018-01-01
This paper presents the modeling and experimental parametric study of a nonlinear multi-frequency broad bandwidth piezoelectric vibration-based energy harvester. The proposed harvester consists of a tri-leg compliant orthoplanar spring (COPS) and multiple masses with piezoelectric plates attached at three different locations. The vibration modes, resonant frequencies, and strain distributions are studied using the finite element analysis. The prototype is manufactured and experimentally investigated to study the effect of single as well as multiple light-weight masses on the bandwidth. The dynamic behavior of the harvester with a mass at the center is modeled numerically and characterized experimentally. The simulation and experimental results are in good agreement. A wide bandwidth with three close nonlinear vibration modes is observed during the experiments when four masses are added to the proposed harvester. The current generator with four masses shows a significant performance improvement with multiple nonlinear peaks under both forward and reverse frequency sweeps.
DEFF Research Database (Denmark)
Niero, Monia; Di Felice, Francesco; Ren, Jingzheng
2014-01-01
; these correlations can be used to improve the design of new wooden pallets.The conceptual scheme for defining the model is based on ISO14040-44 standards. First of all, the product system was defined identifying the life cycle of a generic wood pallet, as well as its life cycle stages. A list of independent......, as the information required for fulfilling the LCI are standard information about the features of the wooden pallet and its manufacturing process. The contribution analysis on the reference product revealed that the most contributing life cycle stages are wood and nails extraction and manufacturing (positive value......This study discusses the use of parameterization within the life cycle inventory (LCI) in the wooden pallet sector, in order to test the effectiveness of LCI parametric models to calculate the environmental impacts of similar products. Starting from a single case study, the objectives of this paper...
Directory of Open Access Journals (Sweden)
Francisco Javier Trujillo
2017-02-01
Full Text Available In this work, the study of the influence of cutting parameters (cutting speed, feed, and depth of cut on the tool wear used in in the dry turning of cylindrical bars of the UNS A97075 (Al-Zn alloy, has been analyzed. In addition, a study of the physicochemical mechanisms of the secondary adhesion wear has been carried out. The behavior of this alloy, from the point of view of tool wear, has been compared to similar aeronautical aluminum alloys, such as the UNS A92024 (Al-Cu alloy and UNS A97050 (Al-Zn alloy. Furthermore, a first approach to the measurement of the 2D surface of the adhered material on the rake face of the tool has been conducted. Finally, a parametric model has been developed from the experimental results. This model allows predicting the intensity of the secondary adhesion wear as a function of the cutting parameters applied.
Self-designing parametric geometries
Sobester, Andras
2015-01-01
The thesis of this paper is that script-based geometry modelling offers the possibility of building `self-designing' intelligence into parametric airframe geometries. We show how sophisticated heuristics (such as optimizers and complex decision structures) can be readily integrated into the parametric geometry model itself using a script-driven modelling architecture. The result is an opportunity for optimization with the scope of conceptual design and the fidelity of preliminary design. Addi...
Degeling, Koen; IJzerman, Maarten J; Koopman, Miriam; Koffijberg, Hendrik
2017-12-15
Parametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive guidance on reflecting parameter uncertainty in the (correlated) parameters of distributions used to represent stochastic uncertainty in patient-level models. This study aims to provide this guidance by proposing appropriate methods and illustrating the impact of this uncertainty on modeling outcomes. Two approaches, 1) using non-parametric bootstrapping and 2) using multivariate Normal distributions, were applied in a simulation and case study. The approaches were compared based on point-estimates and distributions of time-to-event and health economic outcomes. To assess sample size impact on the uncertainty in these outcomes, sample size was varied in the simulation study and subgroup analyses were performed for the case-study. Accounting for parameter uncertainty in distributions that reflect stochastic uncertainty substantially increased the uncertainty surrounding health economic outcomes, illustrated by larger confidence ellipses surrounding the cost-effectiveness point-estimates and different cost-effectiveness acceptability curves. Although both approaches performed similar for larger sample sizes (i.e. n = 500), the second approach was more sensitive to extreme values for small sample sizes (i.e. n = 25), yielding infeasible modeling outcomes. Modelers should be aware that parameter uncertainty in distributions used to describe stochastic uncertainty needs to be reflected in probabilistic sensitivity analysis, as it could substantially impact the total amount of uncertainty surrounding health economic outcomes. If feasible, the bootstrap approach is recommended to account for this uncertainty.
Directory of Open Access Journals (Sweden)
Koen Degeling
2017-12-01
Full Text Available Abstract Background Parametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive guidance on reflecting parameter uncertainty in the (correlated parameters of distributions used to represent stochastic uncertainty in patient-level models. This study aims to provide this guidance by proposing appropriate methods and illustrating the impact of this uncertainty on modeling outcomes. Methods Two approaches, 1 using non-parametric bootstrapping and 2 using multivariate Normal distributions, were applied in a simulation and case study. The approaches were compared based on point-estimates and distributions of time-to-event and health economic outcomes. To assess sample size impact on the uncertainty in these outcomes, sample size was varied in the simulation study and subgroup analyses were performed for the case-study. Results Accounting for parameter uncertainty in distributions that reflect stochastic uncertainty substantially increased the uncertainty surrounding health economic outcomes, illustrated by larger confidence ellipses surrounding the cost-effectiveness point-estimates and different cost-effectiveness acceptability curves. Although both approaches performed similar for larger sample sizes (i.e. n = 500, the second approach was more sensitive to extreme values for small sample sizes (i.e. n = 25, yielding infeasible modeling outcomes. Conclusions Modelers should be aware that parameter uncertainty in distributions used to describe stochastic uncertainty needs to be reflected in probabilistic sensitivity analysis, as it could substantially impact the total amount of uncertainty surrounding health economic outcomes. If feasible, the bootstrap approach is recommended to account for this uncertainty.
Nascimento, Jacinto C; Marques, Jorge S; Lemos, João M
2013-05-01
Many approaches to trajectory analysis, such as clustering or classification, use probabilistic generative models, thus not requiring trajectory alignment/registration. Switched linear dynamical models (e.g., HMMs) have been used in this context, due to their ability to describe different motion regimes. However, these models are not suitable for handling space-dependent dynamics that are more naturally captured by nonlinear models. As is well known, these are more difficult to identify. In this paper, we propose a new way of modeling trajectories, based on a mixture of parametric motion vector fields that depend on a small number of parameters. Switching among these fields follows a probabilistic mechanism, characterized by a field of stochastic matrices. This approach allows representing a wide variety of trajectories and modeling space-dependent behaviors without using global nonlinear dynamical models. Experimental evaluation is conducted in both synthetic and real scenarios. The latter concerning with human trajectory modeling for activity classification, a central task in video surveillance.
Directory of Open Access Journals (Sweden)
Corrado Dimauro
2010-11-01
Full Text Available Test day records for milk yield of 57,390 first lactation Canadian Holsteins were analyzed with a linear model that included the fixed effects of herd-test date and days in milk (DIM interval nested within age and calving season. Residuals from this model were analyzed as a new variable and fitted with a five parameter model, fourth-order Legendre polynomials, with linear, quadratic and cubic spline models with three knots. The fit of the models was rather poor, with about 30-40% of the curves showing an adjusted R-square lower than 0.20 across all models. Results underline a great difficulty in modelling individual deviations around the mean curve for milk yield. However, the Ali and Schaeffer (5 parameter model and the fourth-order Legendre polynomials were able to detect two basic shapes of individual deviations among the mean curve. Quadratic and, especially, cubic spline functions had better fitting performances but a poor predictive ability due to their great flexibility that results in an abrupt change of the estimated curve when data are missing. Parametric and orthogonal polynomials seem to be robust and affordable under this standpoint.
Timoshinova, T. S.; Shmatov, D. P.; Kretinin, A. V.; Drozdov, I. G.
2017-11-01
While formulating a mathematical model of the flow and interaction between oxygen-methane fuel combustion products with tangentially swirled ballast water injected in the end of the combustion chamber in CAE product Fluent, which integrated into the ANSYS Workbench platform, the problem of structural-parametric synthesis is solved for structure optimization of the model. Equations are selected from the catalogue of Fluent physical models. Also optimization helps to find “regime” model parameters that determine the specific implementation of the model inside the synthesized structure. As a result, such solutions which were developed during creation of a numerical algorithm, as the choice of a turbulence model and the state equation, the methods for determining the thermodynamic thermophysical characteristics of combustion products, the choice of the radiation model, the choice of the resistance law for drops, the choice of the expression which allows to evaluate swirling flows lateral force, determination of the turbulent dispersion strength, choice of the mass exchange law, etc. Fields of temperature, pressure, velocity and volume fraction of phases were obtained at different ballast water mass flows. Dependence of wall temperature from mass flow of ballast water is constructed, that allows us to compare results of the experiment and mathematical modeling.
Parametric Verification of Weighted Systems
DEFF Research Database (Denmark)
Christoffersen, Peter; Hansen, Mikkel; Mariegaard, Anders
2015-01-01
This paper addresses the problem of parametric model checking for weighted transition systems. We consider transition systems labelled with linear equations over a set of parameters and we use them to provide semantics for a parametric version of weighted CTL where the until and next operators...... are themselves indexed with linear equations. The parameters change the model-checking problem into a problem of computing a linear system of inequalities that characterizes the parameters that guarantee the satisfiability. To address this problem, we use parametric dependency graphs (PDGs) and we propose...
A Monte Carlo-adjusted goodness-of-fit test for parametric models describing spatial point patterns
Dao, Ngocanh
2014-04-03
Assessing the goodness-of-fit (GOF) for intricate parametric spatial point process models is important for many application fields. When the probability density of the statistic of the GOF test is intractable, a commonly used procedure is the Monte Carlo GOF test. Additionally, if the data comprise a single dataset, a popular version of the test plugs a parameter estimate in the hypothesized parametric model to generate data for theMonte Carlo GOF test. In this case, the test is invalid because the resulting empirical level does not reach the nominal level. In this article, we propose a method consisting of nested Monte Carlo simulations which has the following advantages: the bias of the resulting empirical level of the test is eliminated, hence the empirical levels can always reach the nominal level, and information about inhomogeneity of the data can be provided.We theoretically justify our testing procedure using Taylor expansions and demonstrate that it is correctly sized through various simulation studies. In our first data application, we discover, in agreement with Illian et al., that Phlebocarya filifolia plants near Perth, Australia, can follow a homogeneous Poisson clustered process that provides insight into the propagation mechanism of these plants. In our second data application, we find, in contrast to Diggle, that a pairwise interaction model provides a good fit to the micro-anatomy data of amacrine cells designed for analyzing the developmental growth of immature retina cells in rabbits. This article has supplementary material online. © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
Karabatsos, George
2017-02-01
Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected
Hoteit, I.; Sraj, I.; Zedler, S. E.; Jackson, C. S.; Knio, O. M.
2016-02-01
We present a Polynomial Chaos (PC)-based Bayesian inference method for quantifying the uncertainties of K-Profile Parametrization (KPP) model in MIT General Circulation Model (MITgcm). The inference of the uncertain parameters is based on a Markov Chain Monte Carlo (MCMC) scheme that utilizes a newly formulated test statistic taking into account the different components representing the structures of turbulent mixing on both daily and seasonal timescales in addition to the data quality, and filters for the effects of parameter perturbations over those due to changes in the wind. To avoid the prohibitive computational cost of integrating the MITgcm model at each MCMC iteration, we build a surrogate model for the test statistic using the PC method. The traditional spectral projection method for finding the PC coefficients suffered from convergence issues due to the internal noise in the model predictions. Instead, a Basis-Pursuit-DeNoising (BPDN) compressed sensing approach was employed that filtered out the noise and determined the PC coefficients of a representative surrogate model. The PC surrogate is then used to evaluate the test statistic in the MCMC step for sampling the posterior of the uncertain parameters. We present results of the posteriors that indicate a good agreement with the default values for two parameters of the KPP model namely the critical bulk and gradient Richardson; while the posteriors of the remaining parameters were hardly informative.
A parametric model to estimate the proportion from true null using a distribution for p-values.
Yu, Chang; Zelterman, Daniel
2017-10-01
Microarray studies generate a large number of p-values from many gene expression comparisons. The estimate of the proportion of the p-values sampled from the null hypothesis draws broad interest. The two-component mixture model is often used to estimate this proportion. If the data are generated under the null hypothesis, the p-values follow the uniform distribution. What is the distribution of p-values when data are sampled from the alternative hypothesis? The distribution is derived for the chi-squared test. Then this distribution is used to estimate the proportion of p-values sampled from the null hypothesis in a parametric framework. Simulation studies are conducted to evaluate its performance in comparison with five recent methods. Even in scenarios with clusters of correlated p-values and a multicomponent mixture or a continuous mixture in the alternative, the new method performs robustly. The methods are demonstrated through an analysis of a real microarray dataset.
International Nuclear Information System (INIS)
Baumjohann, F.; Kroening, J.
1999-01-01
The present paper originates from a contribution to the safety assessment of a reactor pressure vessel (RPV). Investigations evaluating the safety against brittle fracture (exclosure of crack initiation and arrest assessments) are completed by calculations concerning ductile crack extension. Crack geometries including the expected crack extension are generated parametrically by a computer code and are used for further calculations with finite element programs. J-integrals of ductile growing cracks located between two comparative contours are determined by interpolation. The comparative contours are loaded by instationary temperature and pressure fields and are evaluated in advance. Taking the stability condition into consideration, the ductile crack extension is determined by pursuing the equilibrium between loading and crack resistance. The automatic modelling and a mathematical program processing the finite element results evaluate the crack growth of the finite element results very effectively. (orig.)
Bim from Laser SCANS… not Just for Buildings: Nurbs-Based Parametric Modeling of a Medieval Bridge
Barazzetti, L.; Banfi, F.; Brumana, R.; Previtali, M.; Roncoroni, F.
2016-06-01
Building Information Modelling is not limited to buildings. BIM technology includes civil infrastructures such as roads, dams, bridges, communications networks, water and wastewater networks and tunnels. This paper describes a novel methodology for the generation of a detailed BIM of a complex medieval bridge. The use of laser scans and images coupled with the development of algorithms able to handle irregular shapes allowed the creation of advanced parametric objects, which were assembled to obtain an accurate BIM. The lack of existing object libraries required the development of specific families for the different structural elements of the bridge. Finally, some applications aimed at assessing the stability and safety of the bridge are illustrated and discussed. The BIM of the bridge can incorporate this information towards a new "BIMonitoring" concept to preserve the geometric complexity provided by point clouds, obtaining a detailed BIM with object relationships and attributes.
Simard, M.; Liu, K.; Denbina, M. W.; Jensen, D.; Rodriguez, E.; Liao, T. H.; Christensen, A.; Jones, C. E.; Twilley, R.; Lamb, M. P.; Thomas, N. A.
2017-12-01
Our goal is to estimate the fluxes of water and sediments throughout the Wax Lake-Atchafalaya basin. This was achieved by parametrization of a set of 1D (HEC-RAS) and 2D (DELFT3D) hydrology models with state of the art remote sensing measurements of water surface elevation, water surface slope and total suspended sediment (TSS) concentrations. The model implementations are spatially explicit, simulating river currents, lateral flows to distributaries and marshes, and spatial variations of sediment concentrations. Three remote sensing instruments were flown simultaneously to collect data over the Wax Lake-Atchafalaya basin, and along with in situ field data. A Riegl Lidar was used to measure water surface elevation and slope, while the UAVSAR L-band radar collected data in repeat-pass interferometric mode to measure water level change within adjacent marshes and islands. These data were collected several times as the tide rose and fell. AVRIS-NG instruments measured water surface reflectance spectra, used to estimate TSS. Bathymetry was obtained from sonar transects and water level changes were recorded by 19 water level pressure transducers. We used several Acoustic Doppler Current Profiler (ADCP) transects to estimate river discharge. The remotely sensed measurements of water surface slope were small ( 1cm/km) and varied slightly along the channel, especially at the confluence with bayous and the intra-coastal waterway. The slope also underwent significant changes during the tidal cycle. Lateral fluxes to island marshes were mainly observed by UAVSAR close to the distributaries. The extensive remote sensing measurements showed significant disparity with the hydrology model outputs. Observed variations in water surface slopes were unmatched by the model and tidal wave propagation was much faster than gauge measurements. The slope variations were compensated for in the models by tuning local lateral fluxes, bathymetry and riverbed friction. Overall, the simpler 1D
García-Betances, Rebeca I; Cabrera-Umpiérrez, María Fernanda; Ottaviano, Manuel; Pastorino, Matteo; Arredondo, María T
2016-02-22
Despite the speedy evolution of Information and Computer Technology (ICT), and the growing recognition of the importance of the concept of universal design in all domains of daily living, mainstream ICT-based product designers and developers still work without any truly structured tools, guidance or support to effectively adapt their products and services to users' real needs. This paper presents the approach used to define and evaluate parametric cognitive models that describe interaction and usage of ICT by people with aging- and disability-derived functional impairments. A multisensorial training platform was used to train, based on real user measurements in real conditions, the virtual parameterized user models that act as subjects of the test-bed during all stages of simulated disabilities-friendly ICT-based products design. An analytical study was carried out to identify the relevant cognitive functions involved, together with their corresponding parameters as related to aging- and disability-derived functional impairments. Evaluation of the final cognitive virtual user models in a real application has confirmed that the use of these models produce concrete valuable benefits to the design and testing process of accessible ICT-based applications and services. Parameterization of cognitive virtual user models allows incorporating cognitive and perceptual aspects during the design process.
Harding, John E.; Shepherd, Paul
2017-01-01
Parametric modelling software often maintains an explicit history of design development in the form of a graph. However, as the graph increases in complexity it quickly becomes inflexible and unsuitable for exploring a wide design space. By contrast, implicit low-level rule systems can offer wide design exploration due to their lack of structure, but often act as black boxes to human observers with only initial conditions and final designs cognisable. In response to these two extremes, the au...
International Nuclear Information System (INIS)
Das, Shiva K.; Zhou Sumin; Zhang, Junan; Yin, F.-F.; Dewhirst, Mark W.; Marks, Lawrence B.
2007-01-01
Purpose: To develop and test a model to predict for lung radiation-induced Grade 2+ pneumonitis. Methods and Materials: The model was built from a database of 234 lung cancer patients treated with radiotherapy (RT), of whom 43 were diagnosed with pneumonitis. The model augmented the predictive capability of the parametric dose-based Lyman normal tissue complication probability (LNTCP) metric by combining it with weighted nonparametric decision trees that use dose and nondose inputs. The decision trees were sequentially added to the model using a 'boosting' process that enhances the accuracy of prediction. The model's predictive capability was estimated by 10-fold cross-validation. To facilitate dissemination, the cross-validation result was used to extract a simplified approximation to the complicated model architecture created by boosting. Application of the simplified model is demonstrated in two example cases. Results: The area under the model receiver operating characteristics curve for cross-validation was 0.72, a significant improvement over the LNTCP area of 0.63 (p = 0.005). The simplified model used the following variables to output a measure of injury: LNTCP, gender, histologic type, chemotherapy schedule, and treatment schedule. For a given patient RT plan, injury prediction was highest for the combination of pre-RT chemotherapy, once-daily treatment, female gender and lowest for the combination of no pre-RT chemotherapy and nonsquamous cell histologic type. Application of the simplified model to the example cases revealed that injury prediction for a given treatment plan can range from very low to very high, depending on the settings of the nondose variables. Conclusions: Radiation pneumonitis prediction was significantly enhanced by decision trees that added the influence of nondose factors to the LNTCP formulation
Das, Shiva K; Zhou, Sumin; Zhang, Junan; Yin, Fang-Fang; Dewhirst, Mark W; Marks, Lawrence B
2007-07-15
To develop and test a model to predict for lung radiation-induced Grade 2+ pneumonitis. The model was built from a database of 234 lung cancer patients treated with radiotherapy (RT), of whom 43 were diagnosed with pneumonitis. The model augmented the predictive capability of the parametric dose-based Lyman normal tissue complication probability (LNTCP) metric by combining it with weighted nonparametric decision trees that use dose and nondose inputs. The decision trees were sequentially added to the model using a "boosting" process that enhances the accuracy of prediction. The model's predictive capability was estimated by 10-fold cross-validation. To facilitate dissemination, the cross-validation result was used to extract a simplified approximation to the complicated model architecture created by boosting. Application of the simplified model is demonstrated in two example cases. The area under the model receiver operating characteristics curve for cross-validation was 0.72, a significant improvement over the LNTCP area of 0.63 (p = 0.005). The simplified model used the following variables to output a measure of injury: LNTCP, gender, histologic type, chemotherapy schedule, and treatment schedule. For a given patient RT plan, injury prediction was highest for the combination of pre-RT chemotherapy, once-daily treatment, female gender and lowest for the combination of no pre-RT chemotherapy and nonsquamous cell histologic type. Application of the simplified model to the example cases revealed that injury prediction for a given treatment plan can range from very low to very high, depending on the settings of the nondose variables. Radiation pneumonitis prediction was significantly enhanced by decision trees that added the influence of nondose factors to the LNTCP formulation.
Meyer, Swen; Blaschek, Michael; Duttmann, Rainer; Ludwig, Ralf
2016-02-01
According to current climate projections, Mediterranean countries are at high risk for an even pronounced susceptibility to changes in the hydrological budget and extremes. These changes are expected to have severe direct impacts on the management of water resources, agricultural productivity and drinking water supply. Current projections of future hydrological change, based on regional climate model results and subsequent hydrological modeling schemes, are very uncertain and poorly validated. The Rio Mannu di San Sperate Basin, located in Sardinia, Italy, is one test site of the CLIMB project. The Water Simulation Model (WaSiM) was set up to model current and future hydrological conditions. The availability of measured meteorological and hydrological data is poor as it is common for many Mediterranean catchments. In this study we conducted a soil sampling campaign in the Rio Mannu catchment. We tested different deterministic and hybrid geostatistical interpolation methods on soil textures and tested the performance of the applied models. We calculated a new soil texture map based on the best prediction method. The soil model in WaSiM was set up with the improved new soil information. The simulation results were compared to standard soil parametrization. WaSiMs was validated with spatial evapotranspiration rates using the triangle method (Jiang and Islam, 1999). WaSiM was driven with the meteorological forcing taken from 4 different ENSEMBLES climate projections for a reference (1971-2000) and a future (2041-2070) times series. The climate change impact was assessed based on differences between reference and future time series. The simulated results show a reduction of all hydrological quantities in the future in the spring season. Furthermore simulation results reveal an earlier onset of dry conditions in the catchment. We show that a solid soil model setup based on short-term field measurements can improve long-term modeling results, which is especially important
Macromechanical Parametric Amplification
DEFF Research Database (Denmark)
Neumeyer, Stefan
between the two peaks can be altered. The first experimental bistable amplified steady-state responses are also reported. The derived analytical models and experimental setups can readily be extended to investigate other factors. Some of the results are also applicable to the more general field of systems...... for energy harvesting. Using analytical, numerical, and experimental methods, the thesis focuses on superthreshold pumping (above the systems parametric instability threshold), nonlinear effects, frequency response backbones, and frequency detuning effects for parametric amplifiers. Part one of the thesis...... covers superthreshold pumping and nonlinear effects. Superthresh-old pumping produces some useful characteristics. For instance, strong superthreshold pumping yields a high gain even though nonlinear effects tend to reduce it. In addition, a narrower excitation phase range is realized for which...
Parametric analysis of LIBRETTO-4 and 5 in-pile tritium transport model on EcosimPro
Energy Technology Data Exchange (ETDEWEB)
Alcalde, Pablo Martínez, E-mail: pablomiguel.martinez@externos.ciemat.es [Universidad Nacional de Educación a Distancia (UNED), c/Juan del Rosal 12, 28040 Madrid (Spain); Moreno, Carlos; Ibarra, Ángel [CIEMAT, Avda. Complutense 40, 28040 Madrid (Spain)
2014-10-15
Highlights: • Introduction of a new tritium transport model of LIBRETTO-4 and 5 on EcosimPro{sup ®}. • Analysis of model input parameter and variable sensitivities and effects on tritium simulated fluxes. • Demonstrations of high tritium out-flux dependencies on lead-lithium parameters. • Rough fitting achievements proposed by Li17Pb solubility or recombination increase. - Abstract: A new model for LIBRETTO-4/1, 4/2 and 5 experiments have been developed on ECOSIMPro{sup ©} tool to simulate tritium in-pile breeding and transport into two separate purge gas channels with He + 0.1%H{sub 2}. Release from lead lithium eutectic plenum with coupled permeation through an austenitic steel wall on the first and single permeation through EUROFER-97 in the temperature ranges of 300–550 °C can be simulated tuning the transport parameters involved. A parametric study has been performed to reduce the degrees of freedom and to determine the error caused in the simulation due to the uncertainty in experimental input data. The information obtained is essential for the experimental benchmarking. The Tritium Permeation Percentage (TPP) is an output calculated parameter with low variations between 2 and 6% along the whole experimental time easy to compare (730 Full Power Days for LIBRETTO-4 and 520 for 5). Tritium transport parameter ranges verifying this output are defined herein.
Ramezani Tehrani, Fahimeh; Mansournia, Mohammad Ali; Solaymani-Dodaran, Masoud; Steyerberg, Ewout; Azizi, Fereidoun
2016-06-01
This study aimed to improve existing prediction models for age at menopause. We identified all reproductive aged women with regular menstrual cycles who met our eligibility criteria (n = 1,015) in the Tehran Lipid and Glucose Study-an ongoing population-based cohort study initiated in 1998. Participants were examined every 3 years and their reproductive histories were recorded. Blood levels of antimüllerian hormone (AMH) were measured at the time of recruitment. Age at menopause was estimated based on serum concentrations of AMH using flexible parametric survival models. The optimum model was selected according to Akaike Information Criteria and the realness of the range of predicted median menopause age. We followed study participants for a median of 9.8 years during which 277 women reached menopause and found that a spline-based proportional odds model including age-specific AMH percentiles as the covariate performed well in terms of statistical criteria and provided the most clinically relevant and realistic predictions. The range of predicted median age at menopause for this model was 47.1 to 55.9 years. For those who reached menopause, the median of the absolute mean difference between actual and predicted age at menopause was 1.9 years (interquartile range 2.9). The model including the age-specific AMH percentiles as the covariate and using proportional odds as its covariate metrics meets all the statistical criteria for the best model and provides the most clinically relevant and realistic predictions for age at menopause for reproductive-aged women.
Non-parametric Bayesian graph models reveal community structure in resting state fMRI
DEFF Research Database (Denmark)
Andersen, Kasper Winther; Madsen, Kristoffer H.; Siebner, Hartwig Roman
2014-01-01
Modeling of resting state functional magnetic resonance imaging (rs-fMRI) data using network models is of increasing interest. It is often desirable to group nodes into clusters to interpret the communication patterns between nodes. In this study we consider three different nonparametric Bayesian...
Regression Is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.
Vidal, Sherry
Although the concept of the general linear model (GLM) has existed since the 1960s, other univariate analyses such as the t-test and the analysis of variance models have remained popular. The GLM produces an equation that minimizes the mean differences of independent variables as they are related to a dependent variable. From a computer printout…
A Parametric Energy Model for Energy Management of Long Belt Conveyors
Directory of Open Access Journals (Sweden)
Tebello Mathaba
2015-12-01
Full Text Available As electricity prices continue to rise, the increasing need for energy management requires better understanding of models for energy-consuming applications, such as conveyor belts. Conveyor belts are used in a wide range of industries, including power generation, mining and mineral processing. Conveyor technological advances are leading to increasingly long conveyor belts being commissioned. Thus, the energy consumption of each individual belt conveyor unit is becoming increasingly significant. This paper proposes a generic energy model for belt conveyors with long troughed belts. The model has a two-parameter power equation, and it uses a partial differential equation to capture the variable amount of material mass per unit length throughout the belt length. Verification results show that the power consumption calculations of the newly proposed simpler model are consistent with those of a known non-linear model with an error of less than 4%. The online parameter identification set-up of the model is proposed. Simulations indicate that the parameters can be identified successfully from data with up to 15% measurement noise. Results show that the proposed model gives better predictions of the power consumed and material delivered by a long conveyor belt than the steady-state models in the current literature.
Development, Validation and Parametric study of a 3-Year-Old Child Head Finite Element Model
Cui, Shihai; Chen, Yue; Li, Haiyan; Ruan, ShiJie
2015-12-01
Traumatic brain injury caused by drop and traffic accidents is an important reason for children's death and disability. Recently, the computer finite element (FE) head model has been developed to investigate brain injury mechanism and biomechanical responses. Based on CT data of a healthy 3-year-old child head, the FE head model with detailed anatomical structure was developed. The deep brain structures such as white matter, gray matter, cerebral ventricle, hippocampus, were firstly created in this FE model. The FE model was validated by comparing the simulation results with that of cadaver experiments based on reconstructing the child and adult cadaver experiments. In addition, the effects of skull stiffness on the child head dynamic responses were further investigated. All the simulation results confirmed the good biofidelity of the FE model.
Energy Technology Data Exchange (ETDEWEB)
Dikaios, Nikolaos; Halligan, Steve; Taylor, Stuart; Atkinson, David; Punwani, Shonit [University College London, Centre for Medical Imaging, London (United Kingdom); University College London Hospital, Departments of Radiology, London (United Kingdom); Alkalbani, Jokha; Sidhu, Harbir Singh [University College London, Centre for Medical Imaging, London (United Kingdom); Abd-Alazeez, Mohamed; Ahmed, Hashim U.; Emberton, Mark [University College London, Research Department of Urology, Division of Surgery and Interventional Science, London (United Kingdom); Kirkham, Alex [University College London Hospital, Departments of Radiology, London (United Kingdom); Freeman, Alex [University College London Hospital, Department of Histopathology, London (United Kingdom)
2015-09-15
To assess the interchangeability of zone-specific (peripheral-zone (PZ) and transition-zone (TZ)) multiparametric-MRI (mp-MRI) logistic-regression (LR) models for classification of prostate cancer. Two hundred and thirty-one patients (70 TZ training-cohort; 76 PZ training-cohort; 85 TZ temporal validation-cohort) underwent mp-MRI and transperineal-template-prostate-mapping biopsy. PZ and TZ uni/multi-variate mp-MRI LR-models for classification of significant cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any grade with CCL ≥ 4 mm) were derived from the respective cohorts and validated within the same zone by leave-one-out analysis. Inter-zonal performance was tested by applying TZ models to the PZ training-cohort and vice-versa. Classification performance of TZ models for TZ cancer was further assessed in the TZ validation-cohort. ROC area-under-curve (ROC-AUC) analysis was used to compare models. The univariate parameters with the best classification performance were the normalised T2 signal (T2nSI) within the TZ (ROC-AUC = 0.77) and normalized early contrast-enhanced T1 signal (DCE-nSI) within the PZ (ROC-AUC = 0.79). Performance was not significantly improved by bi-variate/tri-variate modelling. PZ models that contained DCE-nSI performed poorly in classification of TZ cancer. The TZ model based solely on maximum-enhancement poorly classified PZ cancer. LR-models dependent on DCE-MRI parameters alone are not interchangeable between prostatic zones; however, models based exclusively on T2 and/or ADC are more robust for inter-zonal application. (orig.)
International Nuclear Information System (INIS)
Mueller, Pablo
2004-01-01
The aim of this work was to develop a model to simulate the evolution of the thermodynamic variables in a nuclear reactor containment with pressure suppression pool under accidental transients.We wanted a program able to give fast results, to facilitate the physical interpretation of the phenomena involved, and to make parametric studies.We did not pretend to get a precise result of a particular case.The program was made to be used as a design tool for the containment and to solve the interactions with the primary cooling system and the other security systems of the reactor, on a conceptual design context.The model consists on energy and mass balances on control volumes with liquid water, steam and a non-condensable gas like air.The dynamics of the system is shown with a base case during a loss of coolant accident.Sensibility and effects of varying some important parameters like volumes and heat and mass transfer coefficients are studied.Finally the results for the CAREM-25 reactor are compared with the codes CORAN, MELCOR 1.8.4 and CONTAIN 2.0 [es
Directory of Open Access Journals (Sweden)
Richard N Henson
2011-08-01
Full Text Available We review recent methodological developments within a Parametric Empirical Bayesian (PEB framework for reconstructing intracranial sources of extracranial electroencephalographic (EEG and magnetoencephalographic (MEG data under linear Gaussian assumptions. The PEB framework offers a natural way to integrate multiple constraints (spatial priors on this inverse problem, such as those derived from different modalities (e.g., from functional magnetic resonance imaging, fMRI or from multiple replications (e.g., subjects. Using variations of the same basic generative model, we illustrate the application of PEB to three cases: 1 symmetric integration (fusion of MEG and EEG; 2 asymmetric integration of MEG or EEG with fMRI, and 3 group-optimisation of spatial priors across subjects. We evaluate these applications on multimodal data acquired from 18 subjects, focusing on energy induced by face perception within a time-frequency window of 100-220ms, 8-18Hz. We show the benefits of multi-modal, multi-subject integration in terms of the model evidence and the reproducibility (over subjects of cortical responses to faces.
Eldeeb, Safaa M; Abdelmoula, Walid M; Shah, Syed M; Fahmy, Ahmed S
2012-01-01
Age-related macular degeneration (AMD) is a major cause of blindness and visual impairment in older adults. The wet form of the disease is characterized by abnormal blood vessels forming a choroidal neovascular membrane (CNV), that result in destruction of normal architecture of the retina. Current evaluation and follow up of wet AMD include subjective evaluation of Fluorescein Angiograms (FA) to determine the activity of the lesion and monitor the progression or regression of the disease. However, this subjective evaluation prevents accurate monitoring of the disease progression or regression in response to a pharmacologic agent. In this work, we present a method that allows objective assessment of the activity of a CNV lesion which can be statistically compared across different patient and time points. The method is based on a hypothesis that the discrepancy in the time-intensity signals among the diseased and normal retinal areas are due to an implicit transfer function whose parameters can be used to characterize the retina. The method begins with parametric modeling of the temporal variation of the lesion and background intensities. Then, the values of the model parameters are used to evaluate the change in the activity of the disease. Preliminary results on five datasets show that the calculated parameters are highly correlated with the Visual Acuity (VA) of the patients.
International Nuclear Information System (INIS)
Ghosal, M.K.; Tiwari, G.N.
2006-01-01
A thermal model has been developed to investigate the potential of using the stored thermal energy of the ground for greenhouse heating and cooling with the help of an earth to air heat exchanger (EAHE) system integrated with the greenhouse located in the premises of IIT, Delhi, India. Experiments were conducted extensively throughout the year 2003, but the developed model was validated against typical clear and sunny days experiments. Parametric studies performed for the EAHE coupled with the greenhouse illustrate the effects of buried pipe length, pipe diameter, mass flow rate of air, depth of ground and types of soil on the greenhouse air temperatures. The temperatures of the greenhouse air, with the experimental parameters of the EAHE, were found to be, on average 7-8 deg. C higher in the winter and 5-6 deg. C lower in the summer than those of the same greenhouse without the EAHE. The greenhouse air temperatures increase in the winter and decrease in the summer with increasing pipe length, decreasing pipe diameter, decreasing mass flow rate of flowing air inside buried pipe and increasing depth of ground up to 4 m. The predicted and measured values of the greenhouse air temperatures that were verified, in terms of root mean square percent deviation and correlation coefficient, exhibited fair agreement
Bennett, Iain; Paracha, Noman; Abrams, Keith; Ray, Joshua
2018-01-01
Rank Preserving Structural Failure Time models are one of the most commonly used statistical methods to adjust for treatment switching in oncology clinical trials. The method is often applied in a decision analytic model without appropriately accounting for additional uncertainty when determining the allocation of health care resources. The aim of the study is to describe novel approaches to adequately account for uncertainty when using a Rank Preserving Structural Failure Time model in a decision analytic model. Using two examples, we tested and compared the performance of the novel Test-based method with the resampling bootstrap method and with the conventional approach of no adjustment. In the first example, we simulated life expectancy using a simple decision analytic model based on a hypothetical oncology trial with treatment switching. In the second example, we applied the adjustment method on published data when no individual patient data were available. Mean estimates of overall and incremental life expectancy were similar across methods. However, the bootstrapped and test-based estimates consistently produced greater estimates of uncertainty compared with the estimate without any adjustment applied. Similar results were observed when using the test based approach on a published data showing that failing to adjust for uncertainty led to smaller confidence intervals. Both the bootstrapping and test-based approaches provide a solution to appropriately incorporate uncertainty, with the benefit that the latter can implemented by researchers in the absence of individual patient data. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.
Royston, Patrick; Sauerbrei, Willi
2016-01-01
In a recent article, Royston (2015, Stata Journal 15: 275-291) introduced the approximate cumulative distribution (acd) transformation of a continuous covariate x as a route toward modeling a sigmoid relationship between x and an outcome variable. In this article, we extend the approach to multivariable modeling by modifying the standard Stata program mfp. The result is a new program, mfpa, that has all the features of mfp plus the ability to fit a new model for user-selected covariates that we call fp1( p 1 , p 2 ). The fp1( p 1 , p 2 ) model comprises the best-fitting combination of a dimension-one fractional polynomial (fp1) function of x and an fp1 function of acd ( x ). We describe a new model-selection algorithm called function-selection procedure with acd transformation, which uses significance testing to attempt to simplify an fp1( p 1 , p 2 ) model to a submodel, an fp1 or linear model in x or in acd ( x ). The function-selection procedure with acd transformation is related in concept to the fsp (fp function-selection procedure), which is an integral part of mfp and which is used to simplify a dimension-two (fp2) function. We describe the mfpa command and give univariable and multivariable examples with real data to demonstrate its use.
Crowther, Michael J; Look, Maxime P; Riley, Richard D
2014-09-28
Multilevel mixed effects survival models are used in the analysis of clustered survival data, such as repeated events, multicenter clinical trials, and individual participant data (IPD) meta-analyses, to investigate heterogeneity in baseline risk and covariate effects. In this paper, we extend parametric frailty models including the exponential, Weibull and Gompertz proportional hazards (PH) models and the log logistic, log normal, and generalized gamma accelerated failure time models to allow any number of normally distributed random effects. Furthermore, we extend the flexible parametric survival model of Royston and Parmar, modeled on the log-cumulative hazard scale using restricted cubic splines, to include random effects while also allowing for non-PH (time-dependent effects). Maximum likelihood is used to estimate the models utilizing adaptive or nonadaptive Gauss-Hermite quadrature. The methods are evaluated through simulation studies representing clinically plausible scenarios of a multicenter trial and IPD meta-analysis, showing good performance of the estimation method. The flexible parametric mixed effects model is illustrated using a dataset of patients with kidney disease and repeated times to infection and an IPD meta-analysis of prognostic factor studies in patients with breast cancer. User-friendly Stata software is provided to implement the methods. Copyright © 2014 John Wiley & Sons, Ltd.
Nerovny, N. A.; Zimin, V. N.
2018-04-01
In this paper, the problem of representing the light pressure force upon the surface of a thin wrinkled film is discussed. The common source of wrinkles is the shear deformation of the membrane sample. The optical model of such a membrane is assumed to be optically orthotropic and an analytic equation for infinitesimal light pressure force is written. A linear regression model in the case of wrinkle geometry, where a surface element can have different optical parameters, is constructed and the Bayesian approach is used to calculate the parameters of this model.
Rights, Jason D; Sterba, Sonya K
2016-11-01
Multilevel data structures are common in the social sciences. Often, such nested data are analysed with multilevel models (MLMs) in which heterogeneity between clusters is modelled by continuously distributed random intercepts and/or slopes. Alternatively, the non-parametric multilevel regression mixture model (NPMM) can accommodate the same nested data structures through discrete latent class variation. The purpose of this article is to delineate analytic relationships between NPMM and MLM parameters that are useful for understanding the indirect interpretation of the NPMM as a non-parametric approximation of the MLM, with relaxed distributional assumptions. We define how seven standard and non-standard MLM specifications can be indirectly approximated by particular NPMM specifications. We provide formulas showing how the NPMM can serve as an approximation of the MLM in terms of intraclass correlation, random coefficient means and (co)variances, heteroscedasticity of residuals at level 1, and heteroscedasticity of residuals at level 2. Further, we discuss how these relationships can be useful in practice. The specific relationships are illustrated with simulated graphical demonstrations, and direct and indirect interpretations of NPMM classes are contrasted. We provide an R function to aid in implementing and visualizing an indirect interpretation of NPMM classes. An empirical example is presented and future directions are discussed. © 2016 The British Psychological Society.
Degeling, Koen; IJzerman, Maarten J; Koopman, Miriam; Koffijberg, Hendrik
2017-01-01
Background Parametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive
Degeling, Koen; Ijzerman, Maarten J.; Koopman, Miriam; Koffijberg, Hendrik
2017-01-01
Background: Parametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive
Parametric modeling of wideband piezoelectric polymer sensors: Design for optoacoustic applications.
Fernández Vidal, A; Ciocci Brazzano, L; Matteo, C L; Sorichetti, P A; González, M G
2017-09-01
In this work, we present a three-dimensional model for the design of wideband piezoelectric polymer sensors which includes the geometry and the properties of the transducer materials. The model uses FFT and numerical integration techniques in an explicit, semi-analytical approach. To validate the model, we made electrical and mechanical measurements on homemade sensors for optoacoustic applications. Each device was implemented using a polyvinylidene fluoride thin film piezoelectric polymer with a thickness of 25 μm. The sensors had detection areas in the range between 0.5 mm 2 and 35 mm 2 and were excited by acoustic pressure pulses of 5 ns (FWHM) from a source with a diameter around 10 μm. The experimental data obtained from the measurements agree well with the model results. We discuss the relative importance of the sensor design parameters for optoacoustic applications and we provide guidelines for the optimization of devices.
Dikaios, Nikolaos; Alkalbani, Jokha; Abd-Alazeez, Mohamed; Sidhu, Harbir Singh; Kirkham, Alex; Ahmed, Hashim U; Emberton, Mark; Freeman, Alex; Halligan, Steve; Taylor, Stuart; Atkinson, David; Punwani, Shonit
2015-09-01
To assess the interchangeability of zone-specific (peripheral-zone (PZ) and transition-zone (TZ)) multiparametric-MRI (mp-MRI) logistic-regression (LR) models for classification of prostate cancer. Two hundred and thirty-one patients (70 TZ training-cohort; 76 PZ training-cohort; 85 TZ temporal validation-cohort) underwent mp-MRI and transperineal-template-prostate-mapping biopsy. PZ and TZ uni/multi-variate mp-MRI LR-models for classification of significant cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any grade with CCL ≥ 4 mm) were derived from the respective cohorts and validated within the same zone by leave-one-out analysis. Inter-zonal performance was tested by applying TZ models to the PZ training-cohort and vice-versa. Classification performance of TZ models for TZ cancer was further assessed in the TZ validation-cohort. ROC area-under-curve (ROC-AUC) analysis was used to compare models. The univariate parameters with the best classification performance were the normalised T2 signal (T2nSI) within the TZ (ROC-AUC = 0.77) and normalized early contrast-enhanced T1 signal (DCE-nSI) within the PZ (ROC-AUC = 0.79). Performance was not significantly improved by bi-variate/tri-variate modelling. PZ models that contained DCE-nSI performed poorly in classification of TZ cancer. The TZ model based solely on maximum-enhancement poorly classified PZ cancer. LR-models dependent on DCE-MRI parameters alone are not interchangable between prostatic zones; however, models based exclusively on T2 and/or ADC are more robust for inter-zonal application. • The ADC and T2-nSI of benign/cancer PZ are higher than benign/cancer TZ. • DCE parameters are significantly different between benign PZ and TZ, but not between cancerous PZ and TZ. • Diagnostic models containing contrast enhancement parameters have reduced performance when applied across zones.
Directory of Open Access Journals (Sweden)
Eloranta Sandra
2012-06-01
Full Text Available Abstract Background Relative survival is commonly used for studying survival of cancer patients as it captures both the direct and indirect contribution of a cancer diagnosis on mortality by comparing the observed survival of the patients to the expected survival in a comparable cancer-free population. However, existing methods do not allow estimation of the impact of isolated conditions (e.g., excess cardiovascular mortality on the total excess mortality. For this purpose we extend flexible parametric survival models for relative survival, which use restricted cubic splines for the baseline cumulative excess hazard and for any time-dependent effects. Methods In the extended model we partition the excess mortality associated with a diagnosis of cancer through estimating a separate baseline excess hazard function for the outcomes under investigation. This is done by incorporating mutually exclusive background mortality rates, stratified by the underlying causes of death reported in the Swedish population, and by introducing cause of death as a time-dependent effect in the extended model. This approach thereby enables modeling of temporal trends in e.g., excess cardiovascular mortality and remaining cancer excess mortality simultaneously. Furthermore, we illustrate how the results from the proposed model can be used to derive crude probabilities of death due to the component parts, i.e., probabilities estimated in the presence of competing causes of death. Results The method is illustrated with examples where the total excess mortality experienced by patients diagnosed with breast cancer is partitioned into excess cardiovascular mortality and remaining cancer excess mortality. Conclusions The proposed method can be used to simultaneously study disease patterns and temporal trends for various causes of cancer-consequent deaths. Such information should be of interest for patients and clinicians as one way of improving prognosis after cancer is
International Nuclear Information System (INIS)
Salimi, Mohsen; Amidpour, Majid
2017-01-01
Highlights: • Integration of small MED unit with gas engine power cycle is studied in this paper. • Modeling, simulation, parametric study and sensitivity analysis were performed. • A thermodynamic model for heat recovery and power generation of the gas engine has been presented. • Annualized Cost of System (ACS) has been employed for economic assessment. • Economic feasibilty dependence of integrated system on natural gas and water prices has been investigated. - Abstract: Due to thermal nature of multi-effect desalination (MED), its integration with a suitable power cycle is highly desirable for waste heat recovery. One of the proper power cycle for proposed integration is internal combustion engine (ICE). The exhaust gas heat of ICE is used to produce motive steam for the required heat for the first effect of MED system. Also, the water jacket heat is utilized in a heat exchanger to pre-heat the seawater. This paper studies a thermodynamic model for a tri-generation system composed of ICE integrated with MED. The ICE thermodynamic model has been used in place of different empirical efficiency relations to estimate performance – load curves reasonably. The entire system performance has been coded in MATLAB, and the results of proposed thermodynamic model for the engine have been verified by manufacturer catalogue. By increasing the engine load from 40% to 100%, the water production of MED unit will increase from 4.38 cubic meters per day to 26.78 cubic meters per day and the tri-generation efficiency from 31% to 56%. Economic analyses of the MED unit integrated with ICE was performed based on Annualized Cost of System method. This integration makes the system more economical. It has been determined that in higher market prices for fresh water (more than 7 US$ per cubic meter), the increase in effects number is more significant to the period of return decrement.
Re-parametrization of a swine model to predict growth performance of broilers
Dukhta, G.; van Milgen, Jacob; Kövér, G.; Halas, V.
2017-01-01
The aim of the study was to investigate whether a pig growth model is suitable to be modified and adapted for broilers. As monogastric animals, pigs and poultry share many similarities in their digestion and metabolism, many structures (body protein and lipid stores) and the nutrient flows of the underlying metabolic pathways are similar among species. For that purpose, the InraPorc model was used as a basis to predict growth performance and body composition at slaughter in broilers. First...
Parametric analysis of a phenomenological model for vortex-induced motions of monocolumn platforms
ROSETTI, Guilherme F.; GONÇALVES, Rodolfo T.; FUJARRA, André L. C.; NISHIMOTO, Kazuo
2011-01-01
Phenomenological models are an important branch in VIV (Vortex-Induced Vibrations) and in VIM (Vortex-Induced Motions) studies to complement the results achieved via CFD (Computational Fluid Dynamics), as the latter tool is not presently a suitable tool for intense use in engineering analysis, due to high computer power requirements. A phenomenological model for evaluating the VIM on monocolumn platforms is presented and its results are compared with experimental ones. The main objective is t...
Demirel, M. C.; Mai, J.; Stisen, S.; Mendiguren González, G.; Koch, J.; Samaniego, L. E.
2016-12-01
Distributed hydrologic models are traditionally calibrated and evaluated against observations of streamflow. Spatially distributed remote sensing observations offer a great opportunity to enhance spatial model calibration schemes. For that it is important to identify the model parameters that can change spatial patterns before the satellite based hydrologic model calibration. Our study is based on two main pillars: first we use spatial sensitivity analysis to identify the key parameters controlling the spatial distribution of actual evapotranspiration (AET). Second, we investigate the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mesoscale Hydrologic Model (mHM). This distributed model is selected as it allows for a change in the spatial distribution of key soil parameters through the calibration of pedo-transfer function parameters and includes options for using fully distributed daily Leaf Area Index (LAI) directly as input. In addition the simulated AET can be estimated at the spatial resolution suitable for comparison to the spatial patterns observed using MODIS data. We introduce a new dynamic scaling function employing remotely sensed vegetation to downscale coarse reference evapotranspiration. In total, 17 parameters of 47 mHM parameters are identified using both sequential screening and Latin hypercube one-at-a-time sampling methods. The spatial patterns are found to be sensitive to the vegetation parameters whereas streamflow dynamics are sensitive to the PTF parameters. The results of multi-objective model calibration show that calibration of mHM against observed streamflow does not reduce the spatial errors in AET while they improve only the streamflow simulations. We will further examine the results of model calibration using only multi spatial objective functions measuring the association between observed AET and simulated AET maps and another case including spatial and streamflow metrics together.
Growth of adult spinal cord in knifefish: Development and parametrization of a distributed model.
Ilieş, Iulian; Sipahi, Rifat; Zupanc, Günther K H
2018-01-21
The study of indeterminate-growing organisms such as teleost fish presents a unique opportunity for improving our understanding of central nervous tissue growth during adulthood. Integrating the existing experimental data associated with this process into a theoretical framework through mathematical or computational modeling provides further research avenues through sensitivity analysis and optimization. While this type of approach has been used extensively in investigations of tumor growth, wound healing, and bone regeneration, the development of nervous tissue has been rarely studied within a modeling framework. To address this gap, the present work introduces a distributed model of spinal cord growth in the knifefish Apteronotus leptorhynchus, an established teleostean model of adult growth in the central nervous system. The proposed model incorporates two mechanisms, cell proliferation by active stem/progenitor cells and cell drift due to population pressure, both of which are subject to global constraints. A coupled reaction-diffusion equation approach was adopted to represent the densities of actively-proliferating and non-proliferating cells along the longitudinal axis of the spinal cord. Computer simulations using this model yielded biologically-feasible growth trajectories. Subsequent comparisons with whole-organism growth curves allowed the estimation of previously-unknown parameters, such as relative growth rates. Copyright © 2017 Elsevier Ltd. All rights reserved.
Smoothed Particle Inference: A Kilo-Parametric Method for X-ray Galaxy Cluster Modeling
Energy Technology Data Exchange (ETDEWEB)
Peterson, John R.; Marshall, P.J.; /KIPAC, Menlo Park; Andersson, K.; /Stockholm U. /SLAC
2005-08-05
We propose an ambitious new method that models the intracluster medium in clusters of galaxies as a set of X-ray emitting smoothed particles of plasma. Each smoothed particle is described by a handful of parameters including temperature, location, size, and elemental abundances. Hundreds to thousands of these particles are used to construct a model cluster of galaxies, with the appropriate complexity estimated from the data quality. This model is then compared iteratively with X-ray data in the form of adaptively binned photon lists via a two-sample likelihood statistic and iterated via Markov Chain Monte Carlo. The complex cluster model is propagated through the X-ray instrument response using direct sampling Monte Carlo methods. Using this approach the method can reproduce many of the features observed in the X-ray emission in a less assumption-dependent way that traditional analyses, and it allows for a more detailed characterization of the density, temperature, and metal abundance structure of clusters. Multi-instrument X-ray analyses and simultaneous X-ray, Sunyaev-Zeldovich (SZ), and lensing analyses are a straight-forward extension of this methodology. Significant challenges still exist in understanding the degeneracy in these models and the statistical noise induced by the complexity of the models.
Directory of Open Access Journals (Sweden)
S. A. Marrero Osorio
2008-09-01
Full Text Available El presente artículo expone una manera de diseñar paramétricamente utilizando los programas de computadora (CAD,CAE, PMS difundidos entre los diseñadores durante los últimos 20 años. La propuesta se basa en modelos matemáticosque consideran el conocimiento sobre la ingeniería del objeto de diseño y lo relacionado con la confección de su modelovirtual tridimensional, planos y otro aspectos; utilizando el Método de los Grafos Dicromáticos para resolver los problemascomputacionales que se presentan en el diseño paramétrico. Se analizan los puntos de vista de diferentes autores en relacióncon el proceso general de diseño y es ubicado dentro del mismo el diseño paramétrico, realizándose una explicación formalque permite arribar a conclusiones interesantes.Palabras claves: Diseño paramétrico, diseño asistido por computadoras (CAD, ingeniería asistida porcomputadoras (CAE, software para el modelado paramétrico (PMS, resolución de problemas._____________________________________________________________________________Abstract:The present article exposes a way to design parametrically applying programs (CAD, CAE, PMS accepted by designers along thelast 20 years. The proposal is based on mathematical models that ponder the knowledge on the engineering of the design object andthe building of its three-dimensional virtual models, blueprints and another aspects; using the dichromatic graph method to solvecomputational problems in parametric design. The points of view of different authors are analyzed in connection with the generalprocess of design, locating parametric design inside it, carrying out a formal explanation which arrives to interesting conclusions.Key words: Parametric design, computer aided design (CAD, computer aided engineering (CAE,parametric modeling software (PMS, problem solving.
Development of a parametric kinematic model of the human hand and a novel robotic exoskeleton.
Burton, T M W; Vaidyanathan, R; Burgess, S C; Turton, A J; Melhuish, C
2011-01-01
This paper reports the integration of a kinematic model of the human hand during cylindrical grasping, with specific focus on the accurate mapping of thumb movement during grasping motions, and a novel, multi-degree-of-freedom assistive exoskeleton mechanism based on this model. The model includes thumb maximum hyper-extension for grasping large objects (~> 50 mm). The exoskeleton includes a novel four-bar mechanism designed to reproduce natural thumb opposition and a novel synchro-motion pulley mechanism for coordinated finger motion. A computer aided design environment is used to allow the exoskeleton to be rapidly customized to the hand dimensions of a specific patient. Trials comparing the kinematic model to observed data of hand movement show the model to be capable of mapping thumb and finger joint flexion angles during grasping motions. Simulations show the exoskeleton to be capable of reproducing the complex motion of the thumb to oppose the fingers during cylindrical and pinch grip motions. © 2011 IEEE
Sensitivity of Population Size Estimation for Violating Parametric Assumptions in Log-linear Models
Directory of Open Access Journals (Sweden)
Gerritse Susanna C.
2015-09-01
Full Text Available An important quality aspect of censuses is the degree of coverage of the population. When administrative registers are available undercoverage can be estimated via capture-recapture methodology. The standard approach uses the log-linear model that relies on the assumption that being in the first register is independent of being in the second register. In models using covariates, this assumption of independence is relaxed into independence conditional on covariates. In this article we describe, in a general setting, how sensitivity analyses can be carried out to assess the robustness of the population size estimate. We make use of log-linear Poisson regression using an offset, to simulate departure from the model. This approach can be extended to the case where we have covariates observed in both registers, and to a model with covariates observed in only one register. The robustness of the population size estimate is a function of implied coverage: as implied coverage is low the robustness is low. We conclude that it is important for researchers to investigate and report the estimated robustness of their population size estimate for quality reasons. Extensions are made to log-linear modeling in case of more than two registers and the multiplier method
Directory of Open Access Journals (Sweden)
S. G. Gocheva-Ilieva
2010-01-01
Full Text Available In order to model the output laser power of a copper bromide laser with wavelengths of 510.6 and 578.2 nm we have applied two regression techniques—multiple linear regression and multivariate adaptive regression splines. The models have been constructed on the basis of PCA factors for historical data. The influence of first- and second-order interactions between predictors has been taken into account. The models are easily interpreted and have good prediction power, which is established from the results of their validation. The comparison of the derived models shows that these based on multivariate adaptive regression splines have an advantage over the others. The obtained results allow for the clarification of relationships between laser generation and the observed laser input variables, for better determining their influence on laser generation, in order to improve the experimental setup and laser production technology. They can be useful for evaluation of known experiments as well as for prediction of future experiments. The developed modeling methodology is also applicable for a wide range of similar laser devices—metal vapor lasers and gas lasers.
Bayesian Non-Parametric Mixtures of GARCH(1,1 Models
Directory of Open Access Journals (Sweden)
John W. Lau
2012-01-01
Full Text Available Traditional GARCH models describe volatility levels that evolve smoothly over time, generated by a single GARCH regime. However, nonstationary time series data may exhibit abrupt changes in volatility, suggesting changes in the underlying GARCH regimes. Further, the number and times of regime changes are not always obvious. This article outlines a nonparametric mixture of GARCH models that is able to estimate the number and time of volatility regime changes by mixing over the Poisson-Kingman process. The process is a generalisation of the Dirichlet process typically used in nonparametric models for time-dependent data provides a richer clustering structure, and its application to time series data is novel. Inference is Bayesian, and a Markov chain Monte Carlo algorithm to explore the posterior distribution is described. The methodology is illustrated on the Standard and Poor's 500 financial index.
Directory of Open Access Journals (Sweden)
A. M. Aibinu
2010-01-01
Full Text Available A new approach for determining the coefficients of a complex-valued autoregressive (CAR and complex-valued autoregressive moving average (CARMA model coefficients using complex-valued neural network (CVNN technique is discussed in this paper. The CAR and complex-valued moving average (CMA coefficients which constitute a CARMA model are computed simultaneously from the adaptive weights and coefficients of the linear activation functions in a two-layered CVNN. The performance of the proposed technique has been evaluated using simulated complex-valued data (CVD with three different types of activation functions. The results show that the proposed method can accurately determine the model coefficients provided that the network is properly trained. Furthermore, application of the developed CVNN-based technique for MRI K-space reconstruction results in images with improve resolution.
Modeling of in-line low-NOx calciners - a parametric study
DEFF Research Database (Denmark)
Iliuta, Ion; Dam-Johansen, Kim; Jensen, Anker
2002-01-01
parameter is the mixing rate of preheated combustion air into the sub-stoichiometric suspension leaving the reducing zone and the most important combustion parameter is the char reactivity. Also, the calcination rate modifies very much the temperature in the calciner, char and limestone conversion......Simulations with a heterogeneous model of an in-line low-NOx calciner, based on non-isothermal diffusion-reaction models for char combustion and limestone calcination combined with a kinetic model for NO formation and reduction, are reported. The analysis shows that the most important hydrodynamic...... and NO emission. Carbon monoxide is a key component for the reduction of NO and reliable data for the kinetics of NO reduction by CO over CaO are very important for the prediction of the NO emission. The internal surface area of char and limestone particles influences the combustion and calcination rates...
Energy Technology Data Exchange (ETDEWEB)
Nordstroem, T.; Kilpinen, P.; Hupa, M. [Aabo Akademi, Turku (Finland). Combustion Chemistry Group
1996-12-31
The objective of this work has been to investigate the soot formation in a blast furnace fired with heavy fuel oil, using detailed kinetic modelling. This work has been concentrated on parameter studies that could explain under which conditions soot is formed and how that formation could be avoided. The parameters investigated were temperature, pressure, stoichiometric ratio, pyrolysis gas composition and reactor model. The calculations were based on a reaction mechanism that consists of 100 species and 446 reactions including polyaromatic hydrocarbons (PAM) up to 7 aromatic rings SULA 2 Research Programme; 4 refs.
Simple Parametric Model for Intensity Calibration of Cassini Composite Infrared Spectrometer Data
Brasunas, J.; Mamoutkine, A.; Gorius, N.
2016-01-01
Accurate intensity calibration of a linear Fourier-transform spectrometer typically requires the unknown science target and the two calibration targets to be acquired under identical conditions. We present a simple model suitable for vector calibration that enables accurate calibration via adjustments of measured spectral amplitudes and phases when these three targets are recorded at different detector or optics temperatures. Our model makes calibration more accurate both by minimizing biases due to changing instrument temperatures that are always present at some level and by decreasing estimate variance through incorporating larger averages of science and calibration interferogram scans.
Parametric Variation for Detailed Model of External Grid in Offshore Wind Power Plants
DEFF Research Database (Denmark)
Myagkov, Vladimir; Petersen, Lennart; Laza, Burutxaga
2014-01-01
The representation of the external grid impedance is a key element in harmonic studies for offshore wind farms. The external grid impedance is here represented by two different approaches: by a simplified impedance model, based on values for short-circuit power and XR-ratio and by locus diagrams ...
Surface soil moisture is an important parameter in hydrology and climate investigations. Current and future satellite missions with L-band passive microwave radiometers can provide valuable information for monitoring the global soil moisture. A factor that can play a significant role in the modeling...
International Nuclear Information System (INIS)
Martin Urreta, J.C.; Bilbao, P.; Cacicedo, J.M.; Ensunza, P.; Megueruela, J.T.
1988-01-01
In this paper the problem of dose fractionation, the expression of this and its mathematical development is studied. Likewise, a comparative study -using an exemple- between the linear quadratic model proposed by Fowler amongst others, and the time-dose fractionation of Orton and Ellis, is made. (Author)
End-point parametrization and guaranteed stability for a model predictive control scheme
Weiland, Siep; Stoorvogel, Antonie Arij; Tiagounov, Andrei A.
2001-01-01
In this paper we consider the closed-loop asymptotic stability of the model predictive control scheme which involves the minimization of a quadratic criterion with a varying weight on the end-point state. In particular, we investigate the stability properties of the (MPC-) controlled system as
Hyperelastic modelling and parametric study of soft tissue embedded lump for MIS applications.
Sokhanvar, S; Dargahi, J; Packirisamy, M
2008-09-01
The existing MIS (minimally invasive surgery) instruments have caused severe restrictions to surgeons' tactile perception. In particular, palpation, which is an important technique in open surgery to assess the softness of the tissue and to detect any hidden lumps, is entirely absent in MIS procedures. Many researchers have developed smart endoscopic graspers to rectify different aspects of this problem. However, the effect of an anatomical feature in general and a lump in particular on the stress distribution on the sensitive surfaces of the smart MIS graspers still needs a lot of attention. This paper investigates the effect of the important parameters of a lump on the stress distribution at the contact surface and subsequently the output of smart endoscopic graspers. Using experimental stress-strain compression test data, the material parameters required for the Mooney-Rivlin model were obtained and used in hyperelastic finite element analysis. The influence of size, depth and stiffness of the lump on the stress distribution at the contact surface are shown and discussed. The results of the non-linear finite element analysis were validated against experiments conducted on elastomeric material replicating soft tissue. The consistency between finite element analysis results and experimental work validates the developed model, which is based on the hyperelastic formulation. The finite element analysis results obtained in this study are particularly useful for the development of an inverse model. The inverse model would extract fundamental information, such as size, depth and stiffness, of any hidden lump, using the outputs of the sensors.
DEFF Research Database (Denmark)
Petersen, Jørgen Holm
2016-01-01
This paper describes a new approach to the estimation in a logistic regression model with two crossed random effects where special interest is in estimating the variance of one of the effects while not making distributional assumptions about the other effect. A composite likelihood is studied...
A simple GMM estimator for the semi-parametric mixed proportional hazard model
Bijwaard, G.E.; Ridder, G.; Woutersen, T.
2013-01-01
Ridder and Woutersen (Ridder, G., and T. Woutersen. 2003. “The Singularity of the Efficiency Bound of the Mixed Proportional Hazard Model.” Econometrica 71: 1579–1589) have shown that under a weak condition on the baseline hazard, there exist root-N consistent estimators of the parameters in a
Caridakis, G; Karpouzis, K; Drosopoulos, A; Kollias, S
2012-12-01
Modeling and recognizing spatiotemporal, as opposed to static input, is a challenging task since it incorporates input dynamics as part of the problem. The vast majority of existing methods tackle the problem as an extension of the static counterpart, using dynamics, such as input derivatives, at feature level and adopting artificial intelligence and machine learning techniques originally designed for solving problems that do not specifically address the temporal aspect. The proposed approach deals with temporal and spatial aspects of the spatiotemporal domain in a discriminative as well as coupling manner. Self Organizing Maps (SOM) model the spatial aspect of the problem and Markov models its temporal counterpart. Incorporation of adjacency, both in training and classification, enhances the overall architecture with robustness and adaptability. The proposed scheme is validated both theoretically, through an error propagation study, and experimentally, on the recognition of individual signs, performed by different, native Greek Sign Language users. Results illustrate the architecture's superiority when compared to Hidden Markov Model techniques and variations both in terms of classification performance and computational cost. Copyright © 2012 Elsevier Ltd. All rights reserved.
Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images.
Stern, Darko; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž
2011-12-07
Accurate and objective evaluation of vertebral deformations is of significant importance in clinical diagnostics and therapy of pathological conditions affecting the spine. Although modern clinical practice is focused on three-dimensional (3D) computed tomography (CT) and magnetic resonance (MR) imaging techniques, the established methods for evaluation of vertebral deformations are limited to measuring deformations in two-dimensional (2D) x-ray images. In this paper, we propose a method for quantitative description of vertebral body deformations by efficient modelling and segmentation of vertebral bodies in 3D. The deformations are evaluated from the parameters of a 3D superquadric model, which is initialized as an elliptical cylinder and then gradually deformed by introducing transformations that yield a more detailed representation of the vertebral body shape. After modelling the vertebral body shape with 25 clinically meaningful parameters and the vertebral body pose with six rigid body parameters, the 3D model is aligned to the observed vertebral body in the 3D image. The performance of the method was evaluated on 75 vertebrae from CT and 75 vertebrae from T(2)-weighted MR spine images, extracted from the thoracolumbar part of normal and pathological spines. The results show that the proposed method can be used for 3D segmentation of vertebral bodies in CT and MR images, as the proposed 3D model is able to describe both normal and pathological vertebral body deformations. The method may therefore be used for initialization of whole vertebra segmentation or for quantitative measurement of vertebral body deformations.
Fjodorova, Natalja; Novič, Marjana
2015-09-03
Engineering optimization is an actual goal in manufacturing and service industries. In the tutorial we represented the concept of traditional parametric estimation models (Factorial Design (FD) and Central Composite Design (CCD)) for searching optimal setting parameters of technological processes. Then the 2D mapping method based on Auto Associative Neural Networks (ANN) (particularly, the Feed Forward Bottle Neck Neural Network (FFBN NN)) was described in comparison with traditional methods. The FFBN NN mapping technique enables visualization of all optimal solutions in considered processes due to the projection of input as well as output parameters in the same coordinates of 2D map. This phenomenon supports the more efficient way of improving the performance of existing systems. Comparison of two methods was performed on the bases of optimization of solder paste printing processes as well as optimization of properties of cheese. Application of both methods enables the double check. This increases the reliability of selected optima or specification limits. Copyright © 2015 Elsevier B.V. All rights reserved.
Goulooze, Sebastiaan C; Välitalo, Pyry A J; Knibbe, Catherijne A J; Krekels, Elke H J
2017-11-27
Repeated time-to-event (RTTE) models are the preferred method to characterize the repeated occurrence of clinical events. Commonly used diagnostics for parametric RTTE models require representative simulations, which may be difficult to generate in situations with dose titration or informative dropout. Here, we present a novel simulation-free diagnostic tool for parametric RTTE models; the kernel-based visual hazard comparison (kbVHC). The kbVHC aims to evaluate whether the mean predicted hazard rate of a parametric RTTE model is an adequate approximation of the true hazard rate. Because the true hazard rate cannot be directly observed, the predicted hazard is compared to a non-parametric kernel estimator of the hazard rate. With the degree of smoothing of the kernel estimator being determined by its bandwidth, the local kernel bandwidth is set to the lowest value that results in a bootstrap coefficient of variation (CV) of the hazard rate that is equal to or lower than a user-defined target value (CV target ). The kbVHC was evaluated in simulated scenarios with different number of subjects, hazard rates, CV target values, and hazard models (Weibull, Gompertz, and circadian-varying hazard). The kbVHC was able to distinguish between Weibull and Gompertz hazard models, even when the hazard rate was relatively low (< 2 events per subject). Additionally, it was more sensitive than the Kaplan-Meier VPC to detect circadian variation of the hazard rate. An additional useful feature of the kernel estimator is that it can be generated prior to model development to explore the shape of the hazard rate function.
International Nuclear Information System (INIS)
Berwald, D.H.; Mendelsohn, S.S.; Myers, T.J.; Paulson, C.C.; Peacock, M.A.; Piaszczyk, CM.; Rathke, J.W.; Piechowiak, E.M.
1996-01-01
Emerging applications for high power rf linacs include fusion materials testing, generation of intense spallation neutrons for neutron physics and materials studies, production of nuclear materials and destruction of nuclear waste. Each requires the selection of an optimal configuration and operating parameters for its accelerator, rf power system and other supporting subsystems. Because of the high cost associated with these facilities, economic considerations become paramount, dictating a full evaluation of the electrical and rf performance, system reliability/availability, and capital, operating, and life cycle costs. The Accelerator Systems Model (ASM), expanded and modified by Northrop Grumman during 1993-96, provides a unique capability for detailed layout and evaluation of a wide variety of normal and superconducting accelerator and rf power configurations. This paper will discuss the current capabilities of ASM, including the available models and data base, and types of trade studies that can be performed for the above applications. (author)
Parametric Modeling of the Safety Effects of NextGen Terminal Maneuvering Area Conflict Scenarios
Rogers, William H.; Waldron, Timothy P.; Stroiney, Steven R.
2011-01-01
The goal of this work was to analytically identify and quantify the issues, challenges, technical hurdles, and pilot-vehicle interface issues associated with conflict detection and resolution (CD&R)in emerging operational concepts for a NextGen terminal aneuvering area, including surface operations. To this end, the work entailed analytical and trade studies focused on modeling the achievable safety benefits of different CD&R strategies and concepts in the current and future airport environment. In addition, crew-vehicle interface and pilot performance enhancements and potential issues were analyzed based on review of envisioned NextGen operations, expected equipage advances, and human factors expertise. The results of perturbation analysis, which quantify the high-level performance impact of changes to key parameters such as median response time and surveillance position error, show that the analytical model developed could be useful in making technology investment decisions.
Quasi-Dimensional Modelling and Parametric Studies of a Heavy-Duty HCCI Engine
Directory of Open Access Journals (Sweden)
Sunil Kumar Pandey
2011-01-01
Full Text Available A quasi-dimensional modelling study is conducted for the first time for a heavy duty, diesel-fuelled, multicylinder engine operating in HCCI mode. This quasidimensional approach involves a zero-dimensional single-zone homogeneous charge compression ignition (HCCI combustion model along with a one-dimensional treatment of the intake and exhaust systems. A skeletal chemical kinetic scheme for n-heptane was used in the simulations. Exhaust gas recirculation (EGR and compression ratio (CR were the two parameters that were altered in order to deal with the challenges of combustion phasing control and operating load range extension. Results from the HCCI mode simulations show good potential when compared to conventional diesel performance with respect to important performance parameters such as peak firing pressure, specific fuel consumption, peak pressure rise, and combustion noise. This study shows that HCCI combustion mode can be employed at part load of 25% varying the EGR rates between 0 and 60%.